set[str]:\\n url = config.service_endpoint.set(path='/repository/sources',\\n query={'catalog': catalog})\\n\\n def list_source_ids() -> set[str]:\\n response = self._get_url_json(url)\\n return {source['sourceId'] for source in cast(JSONs, response['sources'])}\\n\\n with self._service_account_credentials:\\n self.assertIsSubset(indexed_source_ids, list_source_ids())\\n with self._public_service_account_credentials:\\n public_source_ids = list_source_ids()\\n with self._unregistered_service_account_credentials:\\n self.assertEqual(public_source_ids, list_source_ids())\\n invalid_auth = OAuth2('foo')\\n with self.assertRaises(UnauthorizedError):\\n TDRClient.for_registered_user(invalid_auth)\\n invalid_provider = UserCredentialsProvider(invalid_auth)\\n invalid_client = TDRClient(credentials_provider=invalid_provider)\\n with self._authorization_context(invalid_client):\\n self.assertEqual(401, self._get_url_unchecked(url).status)\\n self.assertEqual(set(), list_source_ids() & managed_access_source_ids)\\n self.assertEqual(public_source_ids, list_source_ids())\\n return public_source_ids\",\n \"def InjectSources(self):\\n # This role is passed onto the injector\\n self._injector.InjectSources(self)\",\n \"def get_all_cards(self, filter='open'):\\n print('Searching Trello cards..\\\\n')\\n done_sources = []\\n for list in self.my_lists:\\n for card in list.list_cards(card_filter=filter):\\n name = card.name.split()[0]\\n done_sources.append(card)\\n return done_sources\",\n \"def num_sources(self):\\n return len(self._sources)\",\n \"def source_list(self):\\n return [g[\\\"name\\\"] for g in self._galleries]\",\n \"def get_country_sources(country_id, **kwargs):\\n\\n instance = Ceic._get_instance()\\n\\n kwargs[\\\"id\\\"] = country_id\\n get_dictionaries_method = instance._dictionary_facade.get_country_sources\\n result = instance._make_request(get_dictionaries_method, **kwargs)\\n\\n return result\",\n \"def test_available_sources(self, field, getAdapters):\\n\\n # create some dummy adapters\\n foo = self.makeSource('foo')\\n bar = self.makeSource('bar')\\n\\n # make zope.interface.getAdapters return our dummy adapters\\n getAdapters.return_value = [('foo', foo), ('bar', bar)]\\n\\n # mock field() to return current source id\\n field.getSourceName.return_value = 'foo'\\n\\n # make widget and set foo as current source\\n widget = self.makeTableWidget()\\n\\n # get available sources\\n sources = widget.availableSources()\\n\\n # test output\\n self.assertEquals(2, len(sources))\\n\\n self.assertEquals('foo', sources[0]['id'])\\n self.assertEquals('foo title', sources[0]['title'])\\n self.assertEquals('foo description', sources[0]['description'])\\n self.assertEquals(True, sources[0]['selected'])\\n\\n self.assertEquals('bar', sources[1]['id'])\\n self.assertEquals('bar title', sources[1]['title'])\\n self.assertEquals('bar description', sources[1]['description'])\\n self.assertEquals(False, sources[1]['selected'])\",\n \"def sources(self):\\n srcs = GroupResolver.sources(self)\\n if srcs:\\n srcs.remove(self.default_sourcename)\\n srcs.insert(0, self.default_sourcename)\\n return srcs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7102343","0.70275754","0.6670911","0.66414875","0.6598273","0.6418739","0.63086843","0.624363","0.61829776","0.61549807","0.6057943","0.60390824","0.60325825","0.6032255","0.6019853","0.600825","0.59725916","0.5939919","0.5932034","0.5928629","0.59208006","0.59208006","0.58937025","0.58883935","0.58721495","0.58129084","0.57543063","0.5752833","0.5746667","0.5714252","0.5682292","0.56802547","0.5640805","0.56283575","0.5560336","0.55543673","0.55420756","0.5530176","0.5517831","0.5515652","0.5477549","0.54740083","0.5462729","0.5441491","0.54213893","0.5401842","0.5346184","0.5345658","0.531279","0.5311659","0.53110456","0.5310579","0.5282943","0.52794945","0.5278112","0.5256985","0.52497256","0.52425736","0.5225705","0.5225606","0.5218487","0.52146184","0.5192731","0.51887566","0.5185113","0.51687217","0.51618695","0.5161249","0.5158584","0.5151695","0.51470685","0.51216733","0.511508","0.5107589","0.5097136","0.50654566","0.50625587","0.5052936","0.5040652","0.49985346","0.49875987","0.4977297","0.49761564","0.49729165","0.49661264","0.4963965","0.49639216","0.49602976","0.49554598","0.4947246","0.49333835","0.49314648","0.49302408","0.49290127","0.48995048","0.4873355","0.4871104","0.4857133","0.48563856","0.4856329"],"string":"[\n \"0.7102343\",\n \"0.70275754\",\n \"0.6670911\",\n \"0.66414875\",\n \"0.6598273\",\n \"0.6418739\",\n \"0.63086843\",\n \"0.624363\",\n \"0.61829776\",\n \"0.61549807\",\n \"0.6057943\",\n \"0.60390824\",\n \"0.60325825\",\n \"0.6032255\",\n \"0.6019853\",\n \"0.600825\",\n \"0.59725916\",\n \"0.5939919\",\n \"0.5932034\",\n \"0.5928629\",\n \"0.59208006\",\n \"0.59208006\",\n \"0.58937025\",\n \"0.58883935\",\n \"0.58721495\",\n \"0.58129084\",\n \"0.57543063\",\n \"0.5752833\",\n \"0.5746667\",\n \"0.5714252\",\n \"0.5682292\",\n \"0.56802547\",\n \"0.5640805\",\n \"0.56283575\",\n \"0.5560336\",\n \"0.55543673\",\n \"0.55420756\",\n \"0.5530176\",\n \"0.5517831\",\n \"0.5515652\",\n \"0.5477549\",\n \"0.54740083\",\n \"0.5462729\",\n \"0.5441491\",\n \"0.54213893\",\n \"0.5401842\",\n \"0.5346184\",\n \"0.5345658\",\n \"0.531279\",\n \"0.5311659\",\n \"0.53110456\",\n \"0.5310579\",\n \"0.5282943\",\n \"0.52794945\",\n \"0.5278112\",\n \"0.5256985\",\n \"0.52497256\",\n \"0.52425736\",\n \"0.5225705\",\n \"0.5225606\",\n \"0.5218487\",\n \"0.52146184\",\n \"0.5192731\",\n \"0.51887566\",\n \"0.5185113\",\n \"0.51687217\",\n \"0.51618695\",\n \"0.5161249\",\n \"0.5158584\",\n \"0.5151695\",\n \"0.51470685\",\n \"0.51216733\",\n \"0.511508\",\n \"0.5107589\",\n \"0.5097136\",\n \"0.50654566\",\n \"0.50625587\",\n \"0.5052936\",\n \"0.5040652\",\n \"0.49985346\",\n \"0.49875987\",\n \"0.4977297\",\n \"0.49761564\",\n \"0.49729165\",\n \"0.49661264\",\n \"0.4963965\",\n \"0.49639216\",\n \"0.49602976\",\n \"0.49554598\",\n \"0.4947246\",\n \"0.49333835\",\n \"0.49314648\",\n \"0.49302408\",\n \"0.49290127\",\n \"0.48995048\",\n \"0.4873355\",\n \"0.4871104\",\n \"0.4857133\",\n \"0.48563856\",\n \"0.4856329\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7821861"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13779,"cells":{"query":{"kind":"string","value":"Returns a list of tweets matching a certain phrase (hashtag, word, etc.)"},"document":{"kind":"string","value":"def search_tweets(q, count=100, result_type=\"recent\"):\n\n return t.search.tweets(q=q, result_type=result_type, count=count)"},"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 filter_by_hashtag(tweets: list, hashtag: str) -> list:\n tweets_with_hashtag = {} # findall(): Kui tekstis on rohkem kui üks regulaaravaldisele vastav alamsõne saab kõikide vastete järjendi moodustada funktsiooniga findall()\n pattern = r\"#\\w+\" # \\w : tähed, numbrid, alakriips, + : 1 või rohkem\n for tweet in tweets: # r\"string\" on \"raw\" tüüpi string, mis tähendab, et kurakaldkriipsud(\"\\\") jäetakse teksti alles.\n find_hashtag = re.findall(pattern, tweet.content) # word:\\w\\w\\w. Regulaaravaldisele vastab täpne sõne \"word:\" ning sellele järgnevad 3 suvalist tähte.\n if find_hashtag:\n tweets_with_hashtag.setdefault(ht, []).append(tweet)\n return tweets_with_hashtag[hashtag]","def search(searchTerm):\n api = twitter.Api()\n tweets = api.GetSearch(searchTerm)\n for tweet in tweets:\n util.safe_print(tweet.GetText())","def searchTweets():\n if 'api' not in globals():\n startTwitterApi(getApiKeys(fileName='apiConf2.txt'))\n #SEARCHING TWEETS CONTAINING THE HASHTAG \"#bitcoin\" USING TWEEPY LIBRARY\n myTweets= []\n #words=list(map(str,words))\n if words:\n myQuery=' OR '.join(words)\n else:\n myQuery = '*'\n if removeRetweets:\n myQuery += ' - filter:retweets'\n kwargs['q']=myQuery\n kwargs['count']=100\n kwargs['tweet_mode']='extended'\n if 'startingDate' in kwargs:\n kwargs['since']=kwargs['startingDate']\n del(kwargs['startingDate'])\n if 'endingDate' in kwargs:\n kwargs['until']=kwargs['endingDate']\n del(kwargs['endingDate'])\n if 'maxTweets' in kwargs:\n del(kwargs['maxTweets'])\n if sortBy=='newest':\n for tweet in tweepy.Cursor(api.search, kwargs).items(maxTweets):\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n else:\n for tweet in tweepy.Cursor(api.search, kwargs).items():\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n return getTopNTweets(myTweets, maxTweets)","def _get_tweets(self):\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n api = tweepy.API(auth)\n search = api.search(self.term, lang='en', count=100)\n\n print(f\"Getting tweets that mention '{self.term}', \"\n f\"this may take a while...\")\n\n save_tweet_text = [tweet._json['text'] for tweet in search]\n while len(save_tweet_text) < 1000:\n try:\n oldest = search[-1].id - 1\n search = api.search(self.term, lang='en', count=100, max_id=oldest)\n new_tweets = [tweet._json['text'] for tweet in search]\n save_tweet_text.extend(new_tweets)\n\n # Turn into a set to remove duplicated tweets, then back to list\n save_tweet_text = list(set(save_tweet_text))\n except IndexError:\n break\n\n print(f\"Done. {len(save_tweet_text)} Tweets received.\")\n return save_tweet_text","def two_word_finder(word1,word2,text):\r\n word1 = word1.lower()\r\n word2 = word2.lower()\r\n text = str(text).lower()\r\n if word1 and word2 in text:\r\n return True #return text to see specific tweets\r\n return False","def get_tweets(hashtag):\n api = twitter.Api(consumer_key=TWITTER_API_CONSUMER_KEY,\n consumer_secret=TWITTER_API_CONSUMER_SECRET,\n access_token_key=TWITTER_API_ACCESS_TOKEN_KEY,\n access_token_secret=TWITTER_API_ACCESS_TOKEN_SECRET)\n\n query = (f\"q=%23{HASHTAG}%20-RT\"\n f\"&result_type=recent&since=2019-01-01&count={NUM_TWEETS}\")\n results = api.GetSearch(raw_query=query)\n\n return [\n format_tweet(tweet.AsDict())\n for tweet in results\n ]","def extract_hashtags(tweet):\n tknzr = TweetTokenizer()\n hashtags = [token.lower() for token in tknzr.tokenize(tweet) if re.match(hashtag_re, token)]\n return hashtags","def get_tweets(twitter, screen_name, num_tweets):\n\n request = robust_request(twitter, 'search/tweets', {'q': screen_name, 'count': num_tweets})\n tweets = [a['text'] for a in request]\n\n return tweets","def get_tweets(search_terms, geocode):\n\turl = 'http://search.twitter.com/search.json'\n\tparams = {\n\t\t'q': search_terms,\n\t\t'geocode': str(geocode['lat']) + ',' + str(geocode['lng']) + ',200mi'\n\t}\n\turl_params = urllib.urlencode(params)\n\tresult = urlfetch.fetch(url=url + '?' + url_params,\n\t\t\t\t\t\t\tmethod=urlfetch.GET)\n\n\tif result.status_code == 200:\n\t\titems = json.loads(result.content)['results']\n\t\t# return [r['text'] for r in items]\n\t\treturn items","def RelevantTweet(Tweet, Termlist, DateRange=None):\n TweetText = Tweet[\"full_text\"].lower() #ignore case. # Changed to full_text now, instead of (previously) truncated.\n\n if Tweet[\"is_quote_status\"] is True: #Append, since we're just matching terms.\n # print(\"Tweet = \", Tweet.keys())\n if \"quoted_status\" in Tweet:\n TweetText = TweetText + \" \" + Tweet[\"quoted_status\"][\"full_text\"].lower()\n\n # From: https://stackoverflow.com/questions/7703865/going-from-twitter-date-to-python-datetime-date\n if DateRange is not None:\n ts = time.strptime(Tweet['created_at'], '%a %b %d %H:%M:%S +0000 %Y')\n\n if ts < time.strptime(DateRange[0], '%b %d %H:%M:%S %Y'):\n return False\n elif ts > time.strptime(DateRange[1], '%b %d %H:%M:%S %Y'):\n return False\n if Termlist is None:\n return True\n else:\n for word in Termlist:\n if word.lower() in TweetText:\n return True\n return False","def search(words):\n newlist = [w for w in words if 'son' in w]\n return newlist","def search_from_terms(api, term, **kwargs):\n tweets=api.GetSearch(term=term)\n return {\"tweets\":tweets}","def searchbrown_phrase(tags):\n l = len(tags)\n brown_tagged_words = brown.tagged_words(categories='news')\n hitwords = []\n for i in range(len(brown_tagged_words)-l+1):\n searchtags = [tag for _,tag in brown_tagged_words[i:i+l]]\n if tags == searchtags:\n hitwords.append(tuple([w.lower()\n for w,_ in brown_tagged_words[i:i+l]]))\n return hitwords","def collect_tweets(redis_client, twitter_client, search_term):\n search = Search(redis_client, twitter_client, search_term)\n search.get_term_state()\n search.parse_term_state()\n search.set_query_string()\n search.set_execution_time()\n search.execute_query()\n search.incr_query_counters()\n search.set_newest_id()\n search.set_oldest_id()\n search.set_scenario()\n search.set_term_state()\n search.store_results()\n search.set_score()\n search.log_state()","def extract_tweets(consumer_key,consumer_secret,access_token,access_token_secret,search_key):\n # Step 1 - Authenticate\n consumer_key= str(consumer_key)\n consumer_secret= str(consumer_secret)\n\n access_token=str(access_token)\n access_token_secret=str(access_token_secret)\n\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_token_secret)\n\n api = tweepy.API(auth)\n\n #Step 3 - Retrieve Tweets\n public_tweets = api.search(search_key)\n tweets_list=[]\n for tweet in public_tweets:\n tweets_list.append(tweet.text)\n return tweets_list","def get_tweets(keyword, max_tweets=200):\n\n # API keys.\n consumer_key = \"kNOG1klRMMUYbsjMuY5TKl4lE\"\n consumer_secret = \"ieghv6WI1qseYly43A0Ra1MPksEw1i5Onma0txfEu5aHantD2v\"\n access_key = \"3291622062-15ssVc0qpJXf2SFXbA7vgfl1Sooz4Ueo2DGPQVz\"\n access_secret = \"9XJuzgGSVLnx93tq6NfRzMT07S6o2lzjmHfjt3VRlkqXn\"\n\n # Initialize tweepy API object and authorize using API key.\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_key, access_secret)\n api = tweepy.API(auth)\n\n \"\"\" Get tweets.\"\"\"\n\n alltweets = []\n for status in tweepy.Cursor(\n api.search,\n q=keyword + \" -RT\", # the -RT flag excludes retweets.\n count=1000,\n result_type=\"recent\",\n include_entities=True,\n monitor_rate_limit=True,\n wait_on_rate_limit=True,\n lang=\"en\",\n ).items():\n\n # get text of the tweet, encoding as utf-8.\n text = str(status.text.encode(\"utf-8\"))\n\n # add to the data structure, alltweets, holding the tweets.\n alltweets.append(text)\n\n # if we've reached max_tweets, break.\n if len(alltweets) >= max_tweets:\n break\n\n return alltweets","def show_search_results():\n\n #Get values from search-box via AJAX\n current_keyword = request.form.get('search').lower()\n print \"**********************\"\n print current_keyword\n print \"**********************\"\n tweets = get_tweets_by_api(term=current_keyword)\n\n result = []\n\n for tweet in tweets:\n # Exclude retweets since they appear as duplicatses to endu ser\n if tweet.retweeted_status is None:\n # Convert tweet text from unicode to text\n tweet_id = tweet.id\n text = unicodedata.normalize('NFKD', tweet.text).encode('ascii', 'ignore')\n # Find URL in text and bind to url\n # url = re.search('((?:http|https)(?::\\\\/{2}[\\\\w]+)(?:[\\\\/|\\\\.]?)(?:[^\\\\s\"]*))', text)\n url = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)\n # Remove URL from text\n text_wo_url = re.sub(r'^https?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE)\n # Handle / Name\n user = unicodedata.normalize('NFKD', tweet.user.screen_name).encode('ascii', 'ignore')\n # Count of favorites\n favorite_count = tweet.favorite_count\n #Return dictionary of hashtags with hashtag as key and number of occurances as value\n if tweet.hashtags:\n # Convert hashtags from unicode to string\n ht_list = []\n for hashtag in tweet.hashtags:\n ht_str = unicodedata.normalize('NFKD', hashtag.text).encode('ascii', 'ignore')\n ht_list.append(ht_str.lower())\n hashtags = Counter(ht_list)\n else:\n hashtags = tweet.hashtags\n # Convert tweet from unicode to datetime\n created_at = tweet.created_at\n # format created_at string to ISO 8610\n created_at_str = time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(created_at, '%a %b %d %H:%M:%S +0000 %Y'))\n # create a moment from the string\n created_at = moment.date(created_at_str, 'YYYY-MM-DD HH:mm:ss')\n result.append({'created_at': created_at_str, 'tweet_text': text_wo_url, 'user': user,\n 'favorite_count': favorite_count, 'hashtags': hashtags,\n 'url': url, 'tweet_id': tweet_id})\n\n print \"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\"\n print result\n print \"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\"\n\n return jsonify(result=result) #, tweets","def trendingTweets():\n api = twitter.Api()\n trending_topics = api.GetTrendsWoeid(PHILA_WOEID)\n for topic in trending_topics:\n topicSearchTerm = topic.name\n trending_tweets = api.GetSearch(topicSearchTerm)\n for tweet in trending_tweets:\n util.safe_print(tweet.GetText())\n # pass","def search_for_phrase(self, phrase):\n uri = uriForPhraseSearch(phrase)\n return json.loads(requests.get(uri).content)","def get_tweets(api, listOfTweets, keyword, numOfTweets=20, date_since='2019-1-1', lang=\"en\"):\n spinner = yaspin()\n spinner.start()\n for tweet in tweepy.Cursor(api.search, q=keyword, lang=lang, since=date_since).items(numOfTweets):\n # Add tweets in this format\n dict_ = {'Screen Name': tweet.user.screen_name,\n 'User Name': tweet.user.name,\n 'Tweet Created At': str(tweet.created_at),\n 'Tweet Text': tweet.text,\n 'Cleaned Tweet Text': func.clean_tweets(tweet.text),\n 'User Location': str(tweet.user.location),\n 'Tweet Coordinates': str(tweet.coordinates),\n 'Retweet Count': str(tweet.retweet_count),\n 'Retweeted': str(tweet.retweeted),\n 'Phone Type': str(tweet.source),\n 'Favorite Count': str(tweet.favorite_count),\n 'Favorited': str(tweet.favorited),\n 'Replied': str(tweet.in_reply_to_status_id_str)\n }\n listOfTweets.append(dict_)\n spinner.stop()\n return listOfTweets","def get_tweets(self, query, count=10):\n # empty list to store parsed tweets\n tweets = []\n\n try:\n # call twitter api to fetch tweets\n fetched_tweets = self.api.search(q=query, count=count)\n\n # parsing tweets one by one\n for tweet in fetched_tweets:\n # empty dictionary to store required params of a tweet\n parsed_tweet = {}\n\n # saving text of tweet\n parsed_tweet['text'] = tweet.text\n # saving sentiment of tweet\n parsed_tweet['sentiment'] = self.get_tweet_sentiment(tweet.text)\n\n # appending parsed tweet to tweets list\n if tweet.retweet_count > 0:\n # if tweet has retweets, ensure that it is appended only once\n if parsed_tweet not in tweets:\n tweets.append(parsed_tweet)\n else:\n tweets.append(parsed_tweet)\n\n # return parsed tweets\n return tweets\n\n except tweepy.TweepError as e:\n # print error (if any)\n print(\"Error : \" + str(e))","def get_tweets_from_search(api, search_string, parameters=\" -filter:retweets\", since=\"2021-08-09\", lang=\"en\", max_tweets=1000):\n\n tweet_list = []\n count = 0\n search = search_string\n params = parameters\n\n for tweet in tweepy.Cursor(api.search, q=search + params,\n count=100,\n tweet_mode=\"extended\",\n lang=lang,\n since=since,\n # until=\"2015-02-01\",\n ).items():\n tweet_list.append(tweet._json[\"full_text\"])\n count += 1\n if count == max_tweets:\n break\n print(count)\n return pd.DataFrame({\"text\": tweet_list})","def getHashtagsAndMentions(tweets):\n hashtags = Counter()\n mentions = Counter()\n plain = Counter()\n\n pattern = re.compile(r\"[^#@\\w'-]+\")\n\n for t in tweets:\n words = pattern.split(t.message)\n for word in words:\n # Ignore null strings caused by split characters at the end of a\n # message and remove standalone hyphens.\n if word and not word.startswith(\"-\"):\n # Increment count for the word in the Counter.\n if word.startswith(\"#\"):\n hashtags.update({word: 1})\n elif word.startswith(\"@\"):\n mentions.update({word: 1})\n else:\n # TODO: apply nltk.corpus.stopwords.words() here,\n # across languages. Consider that the stopwords cut off\n # before apostrophe, therefore check if the word\n # starts with the stopword.\n plain.update({word: 1})\n\n return hashtags, mentions, plain","def filter_tweets(tweets):\n # We keep only tweets by chrisalbon with pictures\n search_tweets = [tw for tw in tweets if tw['username'] == '@chrisalbon' and len(tw['images']) > 0]\n # He made multiple tweets on the same topic, we keep only the most recent tweets\n # We use the indexes of the reversed tweet list and dictionnaries to keep only key \n unique_search_index = sorted(list({t['text'].lower():i for i,t in list(enumerate(search_tweets))[::-1]}.values()))\n unique_search_tweets = [search_tweets[i] for i in unique_search_index]\n\n # Keep non-downloaded tweets\n most_recent_file = sorted([datetime.datetime.fromtimestamp(os.path.getmtime(path)) \n for path in glob.glob(\"./downloaded_pics/*.jpg\")], reverse=True)[0]\n recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > most_recent_file]\n\n # Uncomment for testing new tweets\n # recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > datetime.datetime(2017, 7, 6, 13, 41, 48)]\n return recent_seach_tweets","def filter_tweet_core(tweet):\n if not ('user' in tweet and 'screen_name' in tweet['user'] \\\n and 'text' in tweet):\n return False\n if not filter_tweet_text(tweet['text']):\n return False\n if bannedclients.search(tweet['source']):\n return False\n if bannedterms.search(tweet['text']):\n return False\n if bannedterms.search(tweet['user']['screen_name']):\n return False\n if 'entities' in tweet and bannedterms.search(json.dumps(tweet['entities'])):\n return False\n if not filter_tweet_entropy(tweet['text']):\n return False\n if not filter_tweet_cardspam(tweet):\n return False\n return True","def analyze_hashtag(self, hashtag, count=200):\n tweets = []\n\n for x in xrange(0, int(count / 100)):\n tweets.extend(self.tweet_fetcher.get_tweets(hashtag))\n\n analyzed_tweets = sort_tweets(self.sa.classify(tweets))\n\n self.analyzed_tweets = analyzed_tweets\n\n return analyzed_tweets","def searchbrown_word(tag):\n brown_tagged_words = brown.tagged_words(categories='news')\n hitwords = []\n for i in range(len(brown_tagged_words)):\n if tag == brown_tagged_words[i][1]:\n hitwords.append(brown_tagged_words[i][0].lower())\n return hitwords","def search(self, query_string):\n terms = query_string.lower().split()\n result = set(self.wordDict[terms[0]])\n if len(result) == 0:\n return list()\n else:\n for t in terms[2:]:\n records_containing_t = self.wordDict[t]\n result = result.intersection(records_containing_t)\n return [self.get_record_dict(id).getTuple() for id in result]","def twitter(self):\n\n q = \" OR \".join(self.search_terms) + \" -filter:retweets\"\n results = self.__api.search(q=q, lang='en', count=100)\n\n tweets = []\n\n for res in results:\n\n publishedAt = datetime.strptime(res._json['created_at'], '%a %b %d %H:%M:%S +0000 %Y').strftime(\"%Y-%m-%d\")\n\n if (res._json['in_reply_to_screen_name'] == None and publishedAt == datetime.now().strftime(\"%Y-%m-%d\")):\n tweets.append([res._json['id'],\n res._json['text'],\n res._json['user']['screen_name'],\n publishedAt,\n res._json['user']['followers_count']])\n\n self.list = pd.DataFrame(tweets, columns=['id', 'title', 'user', 'publishedAt', 'followers_count']).nlargest(10,\n 'followers_count')\n\n return","def keep(tweet):\n text = tweet[\"Text\"].lower()\n hashtags = \" \".join(tweet[\"Hashtags\"]).lower()\n for word in keep_words:\n if word in text:\n # print(\"{} in text {}\".format(word, text))\n return True\n if word in hashtags:\n # print(\"{} in hashtags {}\".format(word, hastags))\n return True\n return False","def loadTweets():\n sentences = []\n tweets = Tweet.objects.all()\n for tweet in tweets:\n sentences.append(tweet.text)\n\n return sentences","def crawl(self):\n retrievedTweets = []\n\n count = 1\n \n today = datetime.datetime.now()\n today = today.replace(hour=23, minute=59, second=59, microsecond=999999)\n gap = 1\n yesterday = today - datetime.timedelta(gap) \n nextDay = yesterday + datetime.timedelta(gap)\n \n while True:\n try:\n lst = tweepy.Cursor(self.api.search, lang='en', q=self.keyword, count=50, until=nextDay.date(), result_type='popular').items(50)\n for tweet in lst:\n self.data = [tweet.created_at, tweet.id, tweet.text,\n tweet.user._json['screen_name'], tweet.user._json['name'], \n tweet.favorite_count, tweet.retweet_count, tweet.user.location]\n self.data = tuple(self.data)\n retrievedTweets.append(self.data)\n break\n except tweepy.TweepError as e:\n print(e.reason)\n continue\n except StopIteration: \n break\n\n return retrievedTweets","def get_tweet_texts(tweet_list):\n relevant_tweet_text = ''\n for text in tweet_list:\n relevant_tweet_text += ' ' + text['text']\n\n return relevant_tweet_text","def get_hashtag_tweets(self, hashtag,\n count=settings.TWITTER_DEFAULT_LIMIT):\n url = urljoin(self.base_url, \"/search/tweets.json\")\n response = self.session.get(\n url,\n params={\n \"q\": hashtag,\n \"count\": count,\n \"include_entities\": True\n },\n auth=self.__auth,\n )\n data = response.json()\n if response.ok:\n data = [Tweet(tweet_data) for tweet_data in data['statuses']]\n else:\n if 'error' in data:\n raise TwitterException(data['error'], code=response.status_code)\n elif 'errors' in data:\n error = data['errors'][0]\n raise TwitterException(error['message'], code=response.status_code)\n return data","def keyword_filter(tweet_objects, keywords):\n \n tm = TweetManager()\n\n filtered_list = []\n\n for tweet in tweet_objects:\n # Separate text and hashtags\n tweet_text = text_from_tweet(tweet)\n hashtags = tm.find_hashtags(tweet_text)\n\n content_filtered_text = tm.clean_tweet(tweet_text)\n\n keywords_in_text = tm.find_keywords_in_tweet(content_filtered_text, keywords)\n hashtags_with_keywords = tm.find_hashtags_with_keywords(hashtags, keywords)\n\n if len(keywords_in_text) or len(hashtags_with_keywords):\n filtered_list.append({\n KEY_TEXT_ORIGINAL: tweet_text,\n KEY_TEXT_FILTERED: content_filtered_text,\n KEY_HASHTAGS_WITH_KEYWORDS: hashtags_with_keywords,\n KEY_TEXT_KEYWORDS: keywords_in_text\n })\n\n return filtered_list","def geo_collect_tweets(search_term,latitude,longitude,radius):\n i = None\n tweets = []\n rep = 1\n for n in range(2): #can only search 100 tweets at a time, so run search multiple times\n \tresults = api.GetSearch(term = search_term, \n \t\tcount = 100, \n \t\tresult_type = 'recent', \n \t\tmax_id = i, #start a search from the most recent tweet id, working backwards\n \t\tgeocode =(latitude, longitude, radius))\n for tweet in results:\n tweets.append(tweet.text)\n i = tweet.id - 1 #want it to start at the tweet after the last tweet\n rep += 1\n return list(set(tweets)) #set gets rid of repititve tweets, but need to return a list","def extract_content(tweets):\n result = []\n for t in tweets:\n text = t.text\n result.append(tokenize(text))\n return result","def topictweets(url):\n article = get_article(url)\n keywords = get_keywords(article['text'])\n entities = get_entities(article['text'])\n q = twitter_query(keywords, entities)\n result = search({'q': q, 'count': 100, 'result_type': 'mixed'})\n tweets = screen_name_filter(result.statuses, 'media')\n return tweets","def GetWords(phrase):\n # Remove special characters regex\n # It works faster than the standard \\w+ pattern\n regex = re.compile(r'([^\\d\\`\\~\\!\\@\\#\\$\\%\\^\\&\\*\\(\\)\\+\\=\\[\\{\\]\\}\\|\\\\\\'\\<\\,\\.\\>\\?\\/\\\"\"\\;\\:\\s]+)+',\n re.UNICODE)\n return re.findall(regex,phrase.lower())","def getByHashtags(hashtag):\n\n # set page_limits. The default is 1 \n pages_limit = request.args.get('pages_limit') or 1\n pages_limit = int(pages_limit)\n\n raw_response = get_response(tw_api, 'search/tweets', { 'q': '#' + hashtag, 'count': 100 }, pages_limit)\n list_response = convert_resp2list(raw_response)\n return jsonify(list_response)","def getTweetsByUser(username, maxTweets=1000):\n if 'api' not in globals():\n startTwitterApi(getApiKeys(fileName=\"apiConf2.txt\"))\n myTweets=[]\n if words:\n apiRes = tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items()\n for tweet in apiRes:\n if any(containsWord(tweet._json['full_text'],word) for word in words):\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n else:\n if sortBy=='newest':\n for tweet in tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items(maxTweets):\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n else:\n for tweet in tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items():\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n\n return getTopNTweets(myTweets, maxTweets)","def collect_tweets(search_id, search_term, number_of_tweets):\n\n tweets = []\n for tweet in api_collector.collect(search_term, number_of_tweets):\n tweets.append((tweet.id_str, tweet.created_at, tweet.full_text))\n if len(tweets) == 0:\n search = Search.objects.get(pk=search_id)\n search.empty = True\n search.save()\n notify_searchers.delay(search_id)\n else:\n classify_tweets.delay(search_id, tweets)","def searchByKeyword(self, keyword, until=\"\", since=\"\", count=None, result_type=\"recent\"):\n if count is None:\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\n full_text=True, tweet_mode=\"extended\", lang=\"en\").items()\n else:\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\n full_text=True, tweet_mode=\"extended\", lang=\"en\").items(count)\n\n for status in tweets:\n createdDate = parser.parse(str(status._json[\"created_at\"]).strip())\n createdDate = createdDate.replace(\n tzinfo=pytz.utc) - createdDate.utcoffset()\n status_refined = {\n 'keyword': keyword,\n '_id': status._json[\"id\"],\n 'created_at': createdDate,\n 'tweetText': status._json[\"full_text\"],\n 'hashtags': status._json[\"entities\"][\"hashtags\"],\n 'userLoc': status._json[\"user\"][\"location\"],\n 'tweetGeo': status._json[\"geo\"],\n 'tweetCoordinates': status._json[\"coordinates\"],\n 'tweetPlace': status._json[\"place\"],\n 'retweet': {},\n }\n if hasattr(status, \"retweeted_status\"):\n status_refined['tweetText'] = status._json[\"retweeted_status\"][\"full_text\"]\n status_refined['retweet'] = {\n 'original_retweet_id': status._json[\"retweeted_status\"][\"id\"],\n 'origUserLoc': status._json[\"retweeted_status\"][\"user\"][\"location\"],\n 'origTweetLoc': status._json[\"retweeted_status\"][\"geo\"],\n 'origTweetPlace': status._json[\"retweeted_status\"][\"place\"],\n 'origTweetCoord': status._json[\"retweeted_status\"][\"coordinates\"],\n 'origHashtags': status._json[\"retweeted_status\"][\"entities\"][\"hashtags\"],\n 'retweet_count': status._json[\"retweet_count\"],\n }\n self.tweets.append(status_refined)\n return self.tweets","def get_movies_by_words(words):\n cnx,cur = connect_to_db()\n text = \"'\";\n for word in words:\n text += \"+\"+word+\" \"\n text += \"'\"\n cur.execute(\"SELECT * FROM movies \"\n \"Where match(overview) against(\"+text+\" IN BOOLEAN MODE) LIMIT 100 \")\n lst = cur.fetchall()\n size = len(lst)\n cur.close()\n cnx.close()\n return lst,size","def global_matches(self, text):\n\n matches = []\n n = len(text)\n\n for list in [ self.namespace ]:\n for word in list:\n if word[:n] == text:\n matches.append(word)\n\n return matches","def queryTerm2Twitter(term): \n statusList = api.GetSearch(term, count=100, result_type='recent')\n timeStampOfStatus = [datetime.fromtimestamp(i.created_at_in_seconds) for i in statusList]\n timeStampOfStatus.sort() \n return timeStampOfStatus[0]","def searchphrases(query):\n query_nostopwords = removestopwords(query)\n query_lemmatized = lemmatize(query_nostopwords) #look like\n phraseids = []\n ngramids=[]\n words=query_lemmatized.split()\n query_ngram = \"select id from ngrams where lower(lemmangrams) like lower('%{}%')\".format(query_lemmatized)+\" or lower(lemmangrams) like lower('%{}%')\".format(words[0])\n for word in words[1:]:\n query_ngram=query_ngram+\" or lower(lemmangrams) like lower('%{}%')\".format(word)\n con = it.engine.execute(query_ngram)\n rows_phrase = con.fetchall()\n if rows_phrase:\n ngramids = list(set([str(i[0]) for i in rows_phrase]))\n phraseids.extend(ngramids)\n phraseids = list(set(phraseids))\n results=categorize(phraseids)\n return results","def get_tweets(query, pages=25):\n\n logger = Logger()\n after_part = 'include_available_features=1&include_entities=1&include_new_items_bar=true'\n if query.startswith('#'):\n query = quote(query)\n url = 'https://twitter.com/i/search/timeline?f=tweets&vertical=default&q={}&src=tyah&reset_error_state=false&'.format(query)\n else:\n url = 'https://twitter.com/i/profiles/show/{}/timeline/tweets?'.format(query)\n url += after_part\n \n headers = {\n 'Accept': 'application/json, text/javascript, */*; q=0.01',\n 'Referer': 'https://twitter.com/{}'.format(query),\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/603.3.8 (KHTML, like Gecko) Version/10.1.2 Safari/603.3.8',\n 'X-Twitter-Active-User': 'yes',\n 'X-Requested-With': 'XMLHttpRequest',\n 'Accept-Language': 'en-US'\n }\n\n def gen_tweets(pages):\n logger.add(\"MSG: Sending request to url '{}'...\".format(url))\n r = session.get(url, headers=headers)\n\n logger.add(\"MSG: Parsing result...\".format(url))\n while pages > 0:\n try:\n html = BeautifulSoup(r.json()['items_html'], parser='html', features=\"lxml\")\n except KeyError:\n raise ValueError(\n 'Oops! Either \"{}\" does not exist or is private.'.format(query))\n\n comma = \",\"\n dot = \".\"\n tweets = []\n for tweet in html.select('.stream-item'):\n # 10~11 html elements have `.stream-item` class and also their `data-item-type` is `tweet`\n # but their content doesn't look like a tweet's content\n try:\n text = tweet.select('.tweet-text')[0].get_text()\n except IndexError: # issue #50\n continue\n\n tweet_id = tweet['data-item-id']\n\n time = datetime.fromtimestamp(int(tweet.select('._timestamp')[0]['data-time-ms']) / 1000.0)\n\n interactions = [\n x.get_text()\n for x in tweet.select('.ProfileTweet-actionCount')\n ]\n\n replies = int(\n interactions[0].split(' ')[0].replace(comma, '').replace(dot, '')\n or interactions[3]\n )\n\n retweets = int(\n interactions[1].split(' ')[0].replace(comma, '').replace(dot, '')\n or interactions[4]\n or interactions[5]\n )\n\n likes = int(\n interactions[2].split(' ')[0].replace(comma, '').replace(dot, '')\n or interactions[6]\n or interactions[7]\n )\n\n hashtags = [\n hashtag_node.get_text()\n for hashtag_node in tweet.select('.twitter-hashtag')\n ]\n urls = [\n url_node['data-expanded-url']\n for url_node in tweet.select('a.twitter-timeline-link:not(.u-hidden)')\n ]\n photos = [\n photo_node['data-image-url']\n for photo_node in tweet.select('.AdaptiveMedia-photoContainer')\n ]\n\n is_retweet = False\n if tweet.select('.js-stream-tweet')[0].has_attr('data-retweet-id'):\n is_retweet = True\n\n is_pinned = False\n if tweet.select(\".pinned\"):\n is_pinned = True\n\n videos = []\n video_nodes = tweet.select(\".PlayableMedia-player\")\n for node in video_nodes:\n styles = node['style'].split()\n for style in styles:\n if style.startswith('background'):\n tmp = style.split('/')[-1]\n video_id = tmp[:tmp.index('.jpg')]\n videos.append({'id': video_id})\n\n tweets.append({\n 'tweetId': tweet_id,\n 'isRetweet': is_retweet,\n 'time': time,\n 'text': text,\n 'replies': replies,\n 'retweets': retweets,\n 'likes': likes,\n 'isPinned': is_pinned,\n 'entries': {\n 'hashtags': hashtags, 'urls': urls,\n 'photos': photos, 'videos': videos\n }\n })\n\n\n last_tweet = html.select('.stream-item')[-1]['data-item-id']\n\n for tweet in tweets:\n if tweet:\n tweet['text'] = re.sub(r'\\Shttp', ' http', tweet['text'], 1)\n tweet['text'] = re.sub(r'\\Spic\\.twitter', ' pic.twitter', tweet['text'], 1)\n yield tweet\n\n r = session.get(url, params={'max_position': last_tweet}, headers=headers)\n pages += -1\n yield from gen_tweets(pages)","def get_tweet_list(user_handle):\n client = language.LanguageServiceClient()\n\n tweet_list = twitter.get_tweets(handle=user_handle)\n\n if tweet_list[0] == \"34\":\n return tweet_list\n\n for i in range(len(tweet_list)):\n\n content = tweet_list[i].get(\"text\")\n\n document = types.Document(\n content=content, type=enums.Document.Type.PLAIN_TEXT)\n annotations = client.analyze_sentiment(document=document)\n\n # Print the results\n # print_result(annotations)\n\n score = annotations.document_sentiment.score\n magnitude = annotations.document_sentiment.magnitude\n\n tweet_list[i][\"score\"] = score\n tweet_list[i][\"magnitude\"] = magnitude\n\n # print(tweet_list[i])\n\n return tweet_list","def get_tweets(self):\n keyword = 'covid'\n\n # Load tokens from file\n with open('../data/tokens.json', 'r') as f:\n tokens = json.load(f)\n\n # Stream tweets\n auth = tweepy.OAuthHandler(tokens['consumer_key'], tokens['consumer_secret'])\n auth.set_access_token(tokens['access_token_key'], tokens['access_token_secret'])\n api = tweepy.API(auth)\n\n # listen for tweets\n while True:\n\n # TODO: save file in Cloud Storage\n file_name = date.today().strftime('corpus-%d-%m-%Y.json')\n print(f'Updating {file_name} ...')\n\n StreamListener = StreamListener(\n file_name=file_name, \n max_tweets=1000)\n myStream = tweepy.Stream(\n auth=api.auth, \n listener=StreamListener)\n\n myStream.filter(track=[keyword], languages=['en'])\n \n time.sleep(60)","def get_tweets():\n\n return Tweet.query.all()","def has_keyword(tweet, keywords):\n temp = tweet.lower()\n for keyword in keywords:\n if bool(re.search(f'\\\\b{keyword}\\\\b', temp)):\n return True\n return False","def filter_tweet(tweet):\n if not filter_tweet_core(tweet):\n return False\n if bannedusers.search(tweet['user']['screen_name']) or (\n 'retweeted_status' in tweet and bannedusers.search(tweet['retweeted_status']['user']['screen_name'])):\n return False\n if tweet['user']['screen_name'] == credentials['username']: # Do not match self tweets :-)\n return False\n return True","def query_tweets_once(query, limit=None, num_tweets=0):\n logging.info(\"Querying {}\".format(query))\n query = query.replace(' ', '%20').replace(\"#\", \"%23\").replace(\":\", \"%3A\")\n pos = None\n tweets = []\n try:\n while True:\n new_tweets, pos = query_single_page(\n INIT_URL.format(q=query) if pos is None\n else RELOAD_URL.format(q=query, pos=pos),\n pos is None\n )\n if len(new_tweets) == 0:\n logging.info(\"Got {} tweets for {}.\".format(\n len(tweets), query))\n return tweets\n\n logging.info(\"Got {} tweets ({} new).\".format(\n len(tweets) + num_tweets, len(new_tweets)))\n\n tweets += new_tweets\n\n if limit is not None and len(tweets) + num_tweets >= limit:\n return tweets\n except KeyboardInterrupt:\n logging.info(\"Program interrupted by user. Returning tweets gathered \"\n \"so far...\")\n except BaseException:\n logging.exception(\"An unknown error occurred! Returning tweets \"\n \"gathered so far.\")\n\n return tweets","def get_tweets_by_hashtag_route(hashtag):\n response, code = get_tweets_by_hashtag(\n hashtag, request.args.get('limit', 30))\n return jsonify(response), code","def twitter_search(a, q, x=10000000, t=100, s=None, m=-1):\n\n tweets = list()\n tweet_count = 0\n\n while tweet_count < x:\n try:\n if (m <= 0):\n if (not s):\n new_tweets = a.search(q = q, count = t)\n else:\n new_tweets = a.search(q = q, count = t, since_id = s)\n else:\n if (not s):\n new_tweets = a.search(q = q, count = t, max_id = (m - 1))\n else:\n new_tweets = a.search(q = q, count = t, max_id = (m - 1), since_id = s)\n\n if not new_tweets:\n break\n\n for tweet in new_tweets:\n tweets.append(tweet)\n\n tweet_count += len(new_tweets)\n m = new_tweets[-1].id\n\n except tweepy.TweepError as e:\n error = (-1, \"error:\" + str(e))\n return error\n\n search_results = (tweet_count, tweets)\n\n return search_results","def printHashtagsAndMentions(searchText=None, filterTerms=False, tweetLimit=0):\n tweets = db.Tweet.select()\n if searchText is not None:\n tweets = tweets.filter(db.Tweet.q.message.contains(searchText))\n tweets = tweets.limit(tweetLimit)\n\n hashtags, mentions, plain = getHashtagsAndMentions(tweets)\n\n if searchText and filterTerms:\n hashtags = Counter(\n {k: v for k, v in hashtags.items() if searchText.lower() in k.lower()}\n )\n mentions = Counter(\n {k: v for k, v in mentions.items() if searchText.lower() in k.lower()}\n )\n plain = Counter(\n {k: v for k, v in plain.items() if searchText.lower() in k.lower()}\n )\n\n # Unique word count for each area.\n hashtagWC = len(hashtags)\n mentionWC = len(mentions)\n plainWC = len(plain)\n\n print(\"Summary\")\n print(\"==============\")\n # Count items in the sliced selection since .count() does not work with\n # a limit.\n count = len(list(tweets)) if tweetLimit else tweets.count()\n print(\"{0:7,d} tweets\".format(count))\n print(\"{0:7,d} unique words\".format(hashtagWC + mentionWC + plainWC))\n print(\"{0:7,d} unique hashtags\".format(hashtagWC))\n print(\"{0:7,d} unique mentions\".format(mentionWC))\n print(\"{0:7,d} unique plain words\".format(plainWC))\n print()\n\n print(\"Hashtags\")\n print(\"========\")\n printCounterByCount(hashtags)\n print()\n\n print(\"Mentions\")\n print(\"========\")\n printCounterByCount(mentions)\n\n \"\"\"\n # Removal of stopwords and handling of URIs is needed to make this\n # useful.\n print 'Plain'\n print '========'\n printCounterByCount(plain)\n \"\"\"","def list_tweets():\n tweets = []\n tuples = query_db('''\n select message.*, user.* from message, user\n where message.author_id = user.user_id\n order by message.pub_date desc limit ?''', [PER_PAGE])\n for tuple in tuples:\n tweet = {}\n tweet[\"username\"] = tuple['username']\n tweet[\"email\"] = tuple['email']\n tweet[\"text\"] = tuple['text']\n tweet[\"pub_date\"] = tuple['pub_date']\n tweets.append(tweet)\n return jsonify({'tweets':tweets}),200","def get_tweets(keyword):\n url = 'http://search.twitter.com/search.json?q='\n\n page = urllib.urlopen('%s%s' % (url, keyword))\n blob = page.read()\n jsonblob = json.loads(blob)\n return jsonblob","def tokenize_tweets_by_word(self):\n sent_tok_list = []\n all_tok_list = []\n\n for t in range(len(self.all_tweets) - 1):\n sent_list = sent_tokenize(self.all_tweets[t][0][1]) # all_tweets[tweet][look at tuple][look at text]\n all_tok_list.append([])\n for s in sent_list: # for each sentence\n word_tok_list = word_tokenize(s)\n pos_tag_list = pos_tag(word_tok_list)\n all_tok_list[t].append(pos_tag_list)\n print(pos_tag_list)\n\n return all_tok_list","def query_word(self, word):\n return [r[0] for r in sparqlquery(self.endpoint, self.query % {'word': word})]","def hashtags(self, candidate_list):\n if Tweet.hashtagre is None:\n Tweet.hashtagre = re.compile('|'.join(map(re.escape, candidate_list)))\n return [\n [m.group(0).replace('#', '', 1), m.span()]\n for m in Tweet.hashtagre.finditer(self.text)\n ]","def get_mentions(texts, word):\r\n mentions = {}\r\n for text, label, text_id in texts:\r\n if word in text.lower():\r\n if label not in mentions:\r\n mentions[label] = [text_id]\r\n else:\r\n if text_id not in mentions[label]:\r\n mentions[label].append(text_id)\r\n return mentions","def having(self, wordlist=None, coordinates=None):\n track = \",\".join(wordlist) if wordlist else None\n locations = \",\".join(map(str, coordinates)) if coordinates else None\n\n if track and locations:\n generator = self.stream.statuses.filter(track=track,\n locations=locations)\n elif track:\n generator = self.stream.statuses.filter(track=track)\n elif locations:\n generator = self.stream.statuses.filter(locations=locations)\n else:\n raise ValueError(\"Must provide one of wordlist or coordinates\")\n\n for tweet in generator:\n if locations:\n if 'coordinates' in tweet and tweet['coordinates']:\n yield tweet\n else:\n yield tweet","def search(self, term) -> Iterable:\n # if the term is whitespace, return empty\n if not term.strip():\n yield from ()\n return\n # if the term is latin, let's search in meanings\n if re.match(r'^[a-zA-Z\\s]+$', term.strip()):\n for entry in self.entries:\n for sense in entry[\"senses\"]:\n if any(term in gloss for gloss in sense[\"glosses\"]):\n yield copy.deepcopy(entry)\n # the term is probably kana, let's search in writings and readings\n else:\n for entry in self.entries:\n if any(term in writing for writing in entry[\"writings\"] + entry[\"readings\"]):\n yield copy.deepcopy(entry)","async def get_tweets(self, ctx, username: str, count: int):\n cnt = count\n if count > 25:\n cnt = 25\n\n if username is not None:\n if cnt < 1:\n await self.bot.say(\"I can't do that, silly! Please specify a \\\n number greater than or equal to 1\")\n return\n msg_list = []\n api = self.authenticate()\n try:\n for status in\\\n tw.Cursor(api.user_timeline, id=username).items(cnt):\n if not status.text.startswith(\"@\"):\n msg_list.append(status)\n except tw.TweepError as e:\n await self.bot.say(\"Whoops! Something went wrong here. \\\n The error code is \" + str(e))\n return\n if len(msg_list) > 0:\n await self.tweet_menu(ctx, msg_list, page=0, timeout=30)\n else:\n await self.bot.say(\"No tweets available to display!\")\n else:\n await self.bot.say(\"No username specified!\")\n return","def global_matches(self, text):\n matches = []\n n = len(text)\n for list in [keyword.kwlist,\n __builtin__.__dict__,\n self.namespace]:\n for word in list:\n if word[:n] == text:\n matches.append(word)\n return matches","def twitter_text(text, search_query = False):\r\n tt = TwitterText(text)\r\n if search_query:\r\n tt.text = tt.highlighter.hit_highlight(query = search_query)\r\n tt.text = tt.autolink.auto_link()\r\n return tt.text","def get_tweets(username, amount):\n tweets = []\n twitter = Twython()\n\n finished = False\n page = 1\n while not finished:\n\n if amount <= 200:\n # Make the API call.\n search_results = twitter.getUserTimeline(screen_name=username,\n page=str(page), count=str(amount))\n finished = True\n\n else:\n # Make the API call.\n search_results = twitter.getUserTimeline(screen_name=username,\n page=str(page), count='200')\n amount -= 200\n page += 1\n\n if isinstance(search_results, dict) and search_results['error']:\n raise TwitterAPIException(str(search_results['error']))\n elif not search_results:\n raise TwitterAPIException('User has no tweets.')\n\n for result in search_results:\n tweets.append(result['text']) \n\n return tweets","def original_three_tweets():\n test_tweets = [\n \"is #bigdata finally the answer to end poverty? \\\n @lavanyarathnam http://ow.ly/o8gt3 #analytics\",\n \"interview: xia wang, astrazeneca on #bigdata and the promise of effective \\\n healthcare #kdn http://ow.ly/ot2uj\",\n \"big data is not just for big business. on how #bigdata is being deployed for \\\n small businesses: http://bddy.me/1bzukb3 @cxotodayalerts #smb\"\n ]\n return test_tweets","def search_list(search_term, results=10):\r\n if search_term:\r\n url = SEARCH_URL.format(API_KEY, util.web.quote(search_term.encode('ascii', 'ignore')))\r\n response = util.web.http_get(url=url, json=True, referer='https://tinychat.com')\r\n\r\n if response['json'] is not None:\r\n media_list = []\r\n try:\r\n if 'items' in response['json']:\r\n for i, item in enumerate(response['json']['items']):\r\n if i == results:\r\n return media_list\r\n else:\r\n video_id = item['id']['videoId']\r\n details = video_details(video_id)\r\n if details is not None:\r\n media_info = {\r\n 'type': 'youTube',\r\n 'video_id': video_id,\r\n 'video_time': details['video_time'],\r\n 'video_title': details['video_title']\r\n }\r\n log.debug('Youtube item %s %s' % (i, media_info))\r\n media_list.append(media_info)\r\n except KeyError as ke:\r\n log.error(ke, exc_info=True)\r\n return None","def word_search(self,\r\n term):\r\n\r\n foundset = set()\r\n term = term.strip().lower()\r\n if self.word_dict_contains(term):\r\n foundset = foundset.union(self.get_indexes_for_word(term))\r\n return foundset","def search(search_term):\r\n if search_term:\r\n if 'list' in search_term:\r\n search_term = search_term.split('?list')[0]\r\n\r\n url = SEARCH_URL.format(API_KEY, util.web.quote(search_term.encode('ascii', 'ignore')))\r\n response = util.web.http_get(url=url, json=True, referer='http://tinychat.com')\r\n\r\n if response['json'] is not None:\r\n try:\r\n if 'items' in response['json']:\r\n for item in response['json']['items']:\r\n video_id = item['id']['videoId']\r\n details = video_details(video_id)\r\n if details is not None:\r\n return {\r\n 'type': 'youTube',\r\n 'video_id': video_id,\r\n 'video_time': details['video_time'],\r\n 'video_title': details['video_title']\r\n }\r\n except KeyError as ke:\r\n log.error(ke, exc_info=True)\r\n return None","def search(self, query_string):\n return models.Thread.objects.filter(\n reduce(\n lambda q, f: q & Q(title__icontains=f),\n query_string.split(),\n Q()))","def find_matches(id1, id2, adj_list_ids, adj_list_words):\n\n # combine words from tweets\n stanza_words = adj_list_ids[id1] + adj_list_ids[id2]\n\n # look for other tweets with those words\n pot_ids = set().union(*[adj_list_words[w] for w in stanza_words])\n\n # --- filter down ---\n # remove start tweets\n # ensure tweets contain subset of master_word_set\n pot_ids = pot_ids - {id1, id2}\n pot_ids = [x for x in pot_ids if set(adj_list_ids[x]) <= set(stanza_words)]\n\n # look for valid pairs of potential tweets\n if len(pot_ids) > 1:\n return find_valid_matches(pot_ids, adj_list_ids, stanza_words)\n return []","def process(self, filter_words, count=1):\n user = self.__api.get_user(self.__username)\n\n # print user.screen_name\n # print user.followers_count\n if self.__appMode == 1 and self.__TimeLineMode == 1:\n self.get_timeline(filter_words)\n else:\n if self.__friendMode:\n print(\"Getting all Twitter Friends \\n\")\n for friend in user.friends():\n self.get_tweet(friend.screen_name, filter_words, count)\n else:\n for screen_name in self.__priorityCoin:\n self.get_tweet(screen_name, filter_words, count)\n print('Twitter Data Extraction done!!')","def search(self, word):","def get_tweets(self):\r\n return self.tweets","async def team_search(self, ctx: commands.Context, username: str):\n all_usernames = {team_id: team.username for team_id, team in self.teams.items()\n if team is not None}\n suggestions = []\n log.info(repr(fuzzywuzzy.process.extract(\n username, all_usernames, limit=5)))\n for fuzz_username, rating, fuzz_id in fuzzywuzzy.process.extract(\n username, all_usernames, limit=5):\n if rating < 50:\n break\n fuzz_team = self.teams[fuzz_id]\n suggestions.append(\n f'(ID: **{fuzz_team.team_id}**) **{fuzz_team.display_name[:40]}**'\n f' -- {len(fuzz_team.users)} registered members')\n if suggestions:\n await ctx.send('\\n'.join(suggestions))\n else:\n await ctx.send(f\"Couldn't find any teams whose usernames resembled `{username}`\")","def searchIdiom(i, idiom):\n token = authorize(i)\n endpoint = 'https://api.twitter.com/1.1/search/tweets.json'\n auth = OAuth1(signature_type=\"auth_header\", **token)\n \n params = {}\n params[\"q\"]=\"%23\"+idiom\n params[\"count\"] = tweet_count\n try:\n r = requests.get(endpoint, params=params, auth=auth, timeout=60.0)\n print(r)\n if r.status_code==200:\n\t '''\n print(\"yay\")\n for tweet in r.json()[\"statuses\"]:\n print(tweet[\"user\"][\"screen_name\"])\n #print(tweet[\"entities\"][\"hashtags\"])\n print tweet[\"text\"]\n ''' \n return r.json()[\"statuses\"]\n else:\n if r.status_code==429:\n print('limit exceeded')\n rest_rate_limit(r)\n return searchIdiom(i, idiom)\n else:\n print('critical situation')\n return []\n except ValueError as e:\n print('Error : ', e)\n except Exception as e1:\n print('Error : ', e1)","def get_live_tweets_from_twitter_stream(auth, terms, num_tweets):\n listener = TwitterListener()\n listener._max_tweets = num_tweets\n twitter_stream = Stream(auth, listener)\n twitter_stream.filter(track=terms, languages=['en'])\n listener.store_live_tweets()","def get_tweets(user, num = 200):\n tweets = []\n \n for tweet in user.home_timeline(count = num):\n edited_tweet = tweet.text\n edited_tweet = edited_tweet.encode(encoding='UTF-8', errors='Ignore') \n tweets.append(edited_tweet)\n return tweets","def get_word_frequency(tweets_list):\n tweets_freq_counter = Counter()\n stop_words = utilities.get_stop_words()\n\n for status in tweets_list:\n tweet_texts = [text for text in tokenize(status['text']) if text.lower() not in stop_words]\n tweets_freq_counter.update(tweet_texts)\n return tweets_freq_counter","def get_custom_phrases():\n return [x[0] for x in all_topics if x[2] == \"1\"]","def twitter(self):\n message = \"\"\n count = self.collection.count()\n\n twitter = Twitter(auth = OAuth(self.access_key, self.access_secret, self.consumer_key, self.consumer_secret))\n for keyword in self.twitter_keywords:\n query = twitter.search.tweets(q = keyword)\n for result in query['statuses']:\n try:\n data = {\"id\": count+1, \"source\": \"twitter\", \"timestamp\": datetime.now()}\n data['tweet'] = result['text']\n data['name'] = result[\"user\"][\"screen_name\"]\n data['url'] = \"https://twitter.com/\" + data[\"name\"] + \"/status/\" + str(result['id'])\n data['search_string'] = keyword\n try:\n dataid = self.collection.insert(data)\n except DuplicateKeyError as e:\n continue\n count += 1\n\n # Slack push notification\n length = 82 - len(data['url'])\n message += \"\\nURL: \" + data['url'] + \" search string: \".rjust(length) + keyword\n\n except Exception as e:\n print(e)\n pass\n \n if message:\n print(self.G + \"[+] Twitter\" + self.B + message)\n self.message += \"\\n*Twitter*:\\n```\"\n self.message += message\n self.message += \"\\n```\"\n\n return","def get_suggestions(\n user: 'User',\n hashtag: str,\n valid_user: Callable[['User'], bool],\n since: str,\n max_suggestions: int\n ) -> List[tweepy.models.User]:\n api = get_api(user)\n suggestions = []\n seen = set()\n max_iters = 5000\n\n for tweet in tweepy.Cursor(api.search, q=hashtag, lang=\"en\", since=since).items():\n if tweet.user.screen_name not in seen and valid_user(tweet.user):\n suggestions.append(tweet.user)\n seen.add(tweet.user.screen_name)\n if len(suggestions) >= max_suggestions or len(seen) > max_iters:\n break\n\n return suggestions","def find_tweets(criteria_candidates):\n results = TweetFinder.__get_results(criteria_candidates)\n return results","def search_postman(text):\n result = _search_blog('postman', text)\n _print_results(result)\n return result","def compute_retweet(row):\n return row['full_text'].startswith('RT')","def language_filter(tweet_objects):\n\n filtered_list = []\n\n for tweet in tweet_objects: \n lang = detect(text_from_tweet(tweet))\n if lang == 'en':\n filtered_list.append(tweet)\n\n return filtered_list","def extract_phrases(phrase_dict, csv_reader, word_list):\n count_row = 0\n\n for row in csv_reader:\n phrase = row[3]\n count_row += 1\n\n if phrase not in all_phrases:\n tokens = tokenizer(phrase)\n tokens = list(tokens)\n phrase_dict[phrase] = tokens\n for tok in tokens:\n if tok not in words:\n words.append(tok)\n\n #print(count_row)","def get_search_results(twitter_dict, search_dict):\r\n\r\n search_list = [search_dict['username']] \r\n search_specified_list = []\r\n\r\n for user in search_list:\r\n search_users_list = [user]\r\n \r\n for operation in search_dict['operations']:\r\n search_users_list = search_helper(search_users_list, operation,\\\r\n twitter_dict)\r\n \r\n search_specified_list += search_users_list\r\n \r\n return search_specified_list","def search_by_contains(self, tl):\n print(\"Search by string\")\n string = input(\"Please enter search string: \")\n return tl.findall_contains(string)","def search(self, phrase, content=False, username=None, ct=10, page=0):\r\n page = int(page) + 1\r\n\r\n with WIX.searcher() as search:\r\n fields = ['description', 'extended', 'tags']\r\n\r\n if content:\r\n fields.append('readable')\r\n\r\n parser = qparser.MultifieldParser(fields,\r\n schema=WIX.schema,\r\n group=qparser.OrGroup)\r\n qry = parser.parse(phrase)\r\n\r\n try:\r\n res = search.search_page(qry, page, pagelen=int(ct))\r\n except ValueError, exc:\r\n raise(exc)\r\n\r\n if res:\r\n qry = Bmark.query.filter(\r\n Bmark.bid.in_([r['bid'] for r in res])\r\n )\r\n\r\n if username:\r\n qry = qry.filter(Bmark.username == username)\r\n\r\n qry = qry.options(joinedload('hashed'))\r\n\r\n return qry.all()\r\n else:\r\n return []","def find(self, words):\n for result in self.index.simple_search(self.name, words, field='content'):\n title = result['title']\n score = int(result.score)\n yield score, title","def search(self, term):","def userTweets(username):\n api = twitter.Api()\n user_tweets = api.GetUserTimeline(username)\n for tweet in user_tweets:\n util.safe_print(tweet.GetText())","def tweet_text(tweet):\n return tweet['text']","def get_tweets():\n clean_tweetdb.delay()\n db_tweets = Tweet.objects.all()\n max_id = min([tweet.tweet_id for tweet in db_tweets])\n tweets = api.search(\n q='#python',\n max_id=max_id,\n count=100\n )\n tweets_id = [tweet.id for tweet in tweets]\n tweets_date = [tweet.created_at for tweet in tweets]\n tweets_source = [tweet.source for tweet in tweets]\n tweets_favorite_cnt = [tweet.favorite_count for tweet in tweets]\n tweets_retweet_cnt = [tweet.retweet_count for tweet in tweets]\n tweets_text = [tweet.text for tweet in tweets]\n\n for i, j, k, l, m, n in zip(\n tweets_id,\n tweets_date,\n tweets_source,\n tweets_favorite_cnt,\n tweets_retweet_cnt,\n tweets_text,\n ):\n try:\n Tweet.objects.create(\n tweet_id=i,\n tweet_date=j,\n tweet_source=k,\n tweet_favorite_cnt=l,\n tweet_retweet_cnt=m,\n tweet_text=n,\n )\n except IntegrityError:\n pass","def more_like_text(text, klass):\n back = connections['default'].get_backend()\n\n if hasattr(back, 'conn'):\n query = {'query': {\n 'filtered': {\n 'query': {\n 'fuzzy_like_this': {\n 'like_text': text\n }\n },\n 'filter': {\n 'bool': {\n 'must': {\n 'term': {'django_ct': 'idea.idea'}\n }\n }\n }\n }\n }\n\n }\n results = back.conn.search(query)\n return back._process_results(results)['results']\n else:\n return []"],"string":"[\n \"def filter_by_hashtag(tweets: list, hashtag: str) -> list:\\n tweets_with_hashtag = {} # findall(): Kui tekstis on rohkem kui üks regulaaravaldisele vastav alamsõne saab kõikide vastete järjendi moodustada funktsiooniga findall()\\n pattern = r\\\"#\\\\w+\\\" # \\\\w : tähed, numbrid, alakriips, + : 1 või rohkem\\n for tweet in tweets: # r\\\"string\\\" on \\\"raw\\\" tüüpi string, mis tähendab, et kurakaldkriipsud(\\\"\\\\\\\") jäetakse teksti alles.\\n find_hashtag = re.findall(pattern, tweet.content) # word:\\\\w\\\\w\\\\w. Regulaaravaldisele vastab täpne sõne \\\"word:\\\" ning sellele järgnevad 3 suvalist tähte.\\n if find_hashtag:\\n tweets_with_hashtag.setdefault(ht, []).append(tweet)\\n return tweets_with_hashtag[hashtag]\",\n \"def search(searchTerm):\\n api = twitter.Api()\\n tweets = api.GetSearch(searchTerm)\\n for tweet in tweets:\\n util.safe_print(tweet.GetText())\",\n \"def searchTweets():\\n if 'api' not in globals():\\n startTwitterApi(getApiKeys(fileName='apiConf2.txt'))\\n #SEARCHING TWEETS CONTAINING THE HASHTAG \\\"#bitcoin\\\" USING TWEEPY LIBRARY\\n myTweets= []\\n #words=list(map(str,words))\\n if words:\\n myQuery=' OR '.join(words)\\n else:\\n myQuery = '*'\\n if removeRetweets:\\n myQuery += ' - filter:retweets'\\n kwargs['q']=myQuery\\n kwargs['count']=100\\n kwargs['tweet_mode']='extended'\\n if 'startingDate' in kwargs:\\n kwargs['since']=kwargs['startingDate']\\n del(kwargs['startingDate'])\\n if 'endingDate' in kwargs:\\n kwargs['until']=kwargs['endingDate']\\n del(kwargs['endingDate'])\\n if 'maxTweets' in kwargs:\\n del(kwargs['maxTweets'])\\n if sortBy=='newest':\\n for tweet in tweepy.Cursor(api.search, kwargs).items(maxTweets):\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n else:\\n for tweet in tweepy.Cursor(api.search, kwargs).items():\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n return getTopNTweets(myTweets, maxTweets)\",\n \"def _get_tweets(self):\\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\\n auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\\n api = tweepy.API(auth)\\n search = api.search(self.term, lang='en', count=100)\\n\\n print(f\\\"Getting tweets that mention '{self.term}', \\\"\\n f\\\"this may take a while...\\\")\\n\\n save_tweet_text = [tweet._json['text'] for tweet in search]\\n while len(save_tweet_text) < 1000:\\n try:\\n oldest = search[-1].id - 1\\n search = api.search(self.term, lang='en', count=100, max_id=oldest)\\n new_tweets = [tweet._json['text'] for tweet in search]\\n save_tweet_text.extend(new_tweets)\\n\\n # Turn into a set to remove duplicated tweets, then back to list\\n save_tweet_text = list(set(save_tweet_text))\\n except IndexError:\\n break\\n\\n print(f\\\"Done. {len(save_tweet_text)} Tweets received.\\\")\\n return save_tweet_text\",\n \"def two_word_finder(word1,word2,text):\\r\\n word1 = word1.lower()\\r\\n word2 = word2.lower()\\r\\n text = str(text).lower()\\r\\n if word1 and word2 in text:\\r\\n return True #return text to see specific tweets\\r\\n return False\",\n \"def get_tweets(hashtag):\\n api = twitter.Api(consumer_key=TWITTER_API_CONSUMER_KEY,\\n consumer_secret=TWITTER_API_CONSUMER_SECRET,\\n access_token_key=TWITTER_API_ACCESS_TOKEN_KEY,\\n access_token_secret=TWITTER_API_ACCESS_TOKEN_SECRET)\\n\\n query = (f\\\"q=%23{HASHTAG}%20-RT\\\"\\n f\\\"&result_type=recent&since=2019-01-01&count={NUM_TWEETS}\\\")\\n results = api.GetSearch(raw_query=query)\\n\\n return [\\n format_tweet(tweet.AsDict())\\n for tweet in results\\n ]\",\n \"def extract_hashtags(tweet):\\n tknzr = TweetTokenizer()\\n hashtags = [token.lower() for token in tknzr.tokenize(tweet) if re.match(hashtag_re, token)]\\n return hashtags\",\n \"def get_tweets(twitter, screen_name, num_tweets):\\n\\n request = robust_request(twitter, 'search/tweets', {'q': screen_name, 'count': num_tweets})\\n tweets = [a['text'] for a in request]\\n\\n return tweets\",\n \"def get_tweets(search_terms, geocode):\\n\\turl = 'http://search.twitter.com/search.json'\\n\\tparams = {\\n\\t\\t'q': search_terms,\\n\\t\\t'geocode': str(geocode['lat']) + ',' + str(geocode['lng']) + ',200mi'\\n\\t}\\n\\turl_params = urllib.urlencode(params)\\n\\tresult = urlfetch.fetch(url=url + '?' + url_params,\\n\\t\\t\\t\\t\\t\\t\\tmethod=urlfetch.GET)\\n\\n\\tif result.status_code == 200:\\n\\t\\titems = json.loads(result.content)['results']\\n\\t\\t# return [r['text'] for r in items]\\n\\t\\treturn items\",\n \"def RelevantTweet(Tweet, Termlist, DateRange=None):\\n TweetText = Tweet[\\\"full_text\\\"].lower() #ignore case. # Changed to full_text now, instead of (previously) truncated.\\n\\n if Tweet[\\\"is_quote_status\\\"] is True: #Append, since we're just matching terms.\\n # print(\\\"Tweet = \\\", Tweet.keys())\\n if \\\"quoted_status\\\" in Tweet:\\n TweetText = TweetText + \\\" \\\" + Tweet[\\\"quoted_status\\\"][\\\"full_text\\\"].lower()\\n\\n # From: https://stackoverflow.com/questions/7703865/going-from-twitter-date-to-python-datetime-date\\n if DateRange is not None:\\n ts = time.strptime(Tweet['created_at'], '%a %b %d %H:%M:%S +0000 %Y')\\n\\n if ts < time.strptime(DateRange[0], '%b %d %H:%M:%S %Y'):\\n return False\\n elif ts > time.strptime(DateRange[1], '%b %d %H:%M:%S %Y'):\\n return False\\n if Termlist is None:\\n return True\\n else:\\n for word in Termlist:\\n if word.lower() in TweetText:\\n return True\\n return False\",\n \"def search(words):\\n newlist = [w for w in words if 'son' in w]\\n return newlist\",\n \"def search_from_terms(api, term, **kwargs):\\n tweets=api.GetSearch(term=term)\\n return {\\\"tweets\\\":tweets}\",\n \"def searchbrown_phrase(tags):\\n l = len(tags)\\n brown_tagged_words = brown.tagged_words(categories='news')\\n hitwords = []\\n for i in range(len(brown_tagged_words)-l+1):\\n searchtags = [tag for _,tag in brown_tagged_words[i:i+l]]\\n if tags == searchtags:\\n hitwords.append(tuple([w.lower()\\n for w,_ in brown_tagged_words[i:i+l]]))\\n return hitwords\",\n \"def collect_tweets(redis_client, twitter_client, search_term):\\n search = Search(redis_client, twitter_client, search_term)\\n search.get_term_state()\\n search.parse_term_state()\\n search.set_query_string()\\n search.set_execution_time()\\n search.execute_query()\\n search.incr_query_counters()\\n search.set_newest_id()\\n search.set_oldest_id()\\n search.set_scenario()\\n search.set_term_state()\\n search.store_results()\\n search.set_score()\\n search.log_state()\",\n \"def extract_tweets(consumer_key,consumer_secret,access_token,access_token_secret,search_key):\\n # Step 1 - Authenticate\\n consumer_key= str(consumer_key)\\n consumer_secret= str(consumer_secret)\\n\\n access_token=str(access_token)\\n access_token_secret=str(access_token_secret)\\n\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_token_secret)\\n\\n api = tweepy.API(auth)\\n\\n #Step 3 - Retrieve Tweets\\n public_tweets = api.search(search_key)\\n tweets_list=[]\\n for tweet in public_tweets:\\n tweets_list.append(tweet.text)\\n return tweets_list\",\n \"def get_tweets(keyword, max_tweets=200):\\n\\n # API keys.\\n consumer_key = \\\"kNOG1klRMMUYbsjMuY5TKl4lE\\\"\\n consumer_secret = \\\"ieghv6WI1qseYly43A0Ra1MPksEw1i5Onma0txfEu5aHantD2v\\\"\\n access_key = \\\"3291622062-15ssVc0qpJXf2SFXbA7vgfl1Sooz4Ueo2DGPQVz\\\"\\n access_secret = \\\"9XJuzgGSVLnx93tq6NfRzMT07S6o2lzjmHfjt3VRlkqXn\\\"\\n\\n # Initialize tweepy API object and authorize using API key.\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_key, access_secret)\\n api = tweepy.API(auth)\\n\\n \\\"\\\"\\\" Get tweets.\\\"\\\"\\\"\\n\\n alltweets = []\\n for status in tweepy.Cursor(\\n api.search,\\n q=keyword + \\\" -RT\\\", # the -RT flag excludes retweets.\\n count=1000,\\n result_type=\\\"recent\\\",\\n include_entities=True,\\n monitor_rate_limit=True,\\n wait_on_rate_limit=True,\\n lang=\\\"en\\\",\\n ).items():\\n\\n # get text of the tweet, encoding as utf-8.\\n text = str(status.text.encode(\\\"utf-8\\\"))\\n\\n # add to the data structure, alltweets, holding the tweets.\\n alltweets.append(text)\\n\\n # if we've reached max_tweets, break.\\n if len(alltweets) >= max_tweets:\\n break\\n\\n return alltweets\",\n \"def show_search_results():\\n\\n #Get values from search-box via AJAX\\n current_keyword = request.form.get('search').lower()\\n print \\\"**********************\\\"\\n print current_keyword\\n print \\\"**********************\\\"\\n tweets = get_tweets_by_api(term=current_keyword)\\n\\n result = []\\n\\n for tweet in tweets:\\n # Exclude retweets since they appear as duplicatses to endu ser\\n if tweet.retweeted_status is None:\\n # Convert tweet text from unicode to text\\n tweet_id = tweet.id\\n text = unicodedata.normalize('NFKD', tweet.text).encode('ascii', 'ignore')\\n # Find URL in text and bind to url\\n # url = re.search('((?:http|https)(?::\\\\\\\\/{2}[\\\\\\\\w]+)(?:[\\\\\\\\/|\\\\\\\\.]?)(?:[^\\\\\\\\s\\\"]*))', text)\\n url = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)\\n # Remove URL from text\\n text_wo_url = re.sub(r'^https?:\\\\/\\\\/.*[\\\\r\\\\n]*', '', text, flags=re.MULTILINE)\\n # Handle / Name\\n user = unicodedata.normalize('NFKD', tweet.user.screen_name).encode('ascii', 'ignore')\\n # Count of favorites\\n favorite_count = tweet.favorite_count\\n #Return dictionary of hashtags with hashtag as key and number of occurances as value\\n if tweet.hashtags:\\n # Convert hashtags from unicode to string\\n ht_list = []\\n for hashtag in tweet.hashtags:\\n ht_str = unicodedata.normalize('NFKD', hashtag.text).encode('ascii', 'ignore')\\n ht_list.append(ht_str.lower())\\n hashtags = Counter(ht_list)\\n else:\\n hashtags = tweet.hashtags\\n # Convert tweet from unicode to datetime\\n created_at = tweet.created_at\\n # format created_at string to ISO 8610\\n created_at_str = time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(created_at, '%a %b %d %H:%M:%S +0000 %Y'))\\n # create a moment from the string\\n created_at = moment.date(created_at_str, 'YYYY-MM-DD HH:mm:ss')\\n result.append({'created_at': created_at_str, 'tweet_text': text_wo_url, 'user': user,\\n 'favorite_count': favorite_count, 'hashtags': hashtags,\\n 'url': url, 'tweet_id': tweet_id})\\n\\n print \\\"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\\\"\\n print result\\n print \\\"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\\\"\\n\\n return jsonify(result=result) #, tweets\",\n \"def trendingTweets():\\n api = twitter.Api()\\n trending_topics = api.GetTrendsWoeid(PHILA_WOEID)\\n for topic in trending_topics:\\n topicSearchTerm = topic.name\\n trending_tweets = api.GetSearch(topicSearchTerm)\\n for tweet in trending_tweets:\\n util.safe_print(tweet.GetText())\\n # pass\",\n \"def search_for_phrase(self, phrase):\\n uri = uriForPhraseSearch(phrase)\\n return json.loads(requests.get(uri).content)\",\n \"def get_tweets(api, listOfTweets, keyword, numOfTweets=20, date_since='2019-1-1', lang=\\\"en\\\"):\\n spinner = yaspin()\\n spinner.start()\\n for tweet in tweepy.Cursor(api.search, q=keyword, lang=lang, since=date_since).items(numOfTweets):\\n # Add tweets in this format\\n dict_ = {'Screen Name': tweet.user.screen_name,\\n 'User Name': tweet.user.name,\\n 'Tweet Created At': str(tweet.created_at),\\n 'Tweet Text': tweet.text,\\n 'Cleaned Tweet Text': func.clean_tweets(tweet.text),\\n 'User Location': str(tweet.user.location),\\n 'Tweet Coordinates': str(tweet.coordinates),\\n 'Retweet Count': str(tweet.retweet_count),\\n 'Retweeted': str(tweet.retweeted),\\n 'Phone Type': str(tweet.source),\\n 'Favorite Count': str(tweet.favorite_count),\\n 'Favorited': str(tweet.favorited),\\n 'Replied': str(tweet.in_reply_to_status_id_str)\\n }\\n listOfTweets.append(dict_)\\n spinner.stop()\\n return listOfTweets\",\n \"def get_tweets(self, query, count=10):\\n # empty list to store parsed tweets\\n tweets = []\\n\\n try:\\n # call twitter api to fetch tweets\\n fetched_tweets = self.api.search(q=query, count=count)\\n\\n # parsing tweets one by one\\n for tweet in fetched_tweets:\\n # empty dictionary to store required params of a tweet\\n parsed_tweet = {}\\n\\n # saving text of tweet\\n parsed_tweet['text'] = tweet.text\\n # saving sentiment of tweet\\n parsed_tweet['sentiment'] = self.get_tweet_sentiment(tweet.text)\\n\\n # appending parsed tweet to tweets list\\n if tweet.retweet_count > 0:\\n # if tweet has retweets, ensure that it is appended only once\\n if parsed_tweet not in tweets:\\n tweets.append(parsed_tweet)\\n else:\\n tweets.append(parsed_tweet)\\n\\n # return parsed tweets\\n return tweets\\n\\n except tweepy.TweepError as e:\\n # print error (if any)\\n print(\\\"Error : \\\" + str(e))\",\n \"def get_tweets_from_search(api, search_string, parameters=\\\" -filter:retweets\\\", since=\\\"2021-08-09\\\", lang=\\\"en\\\", max_tweets=1000):\\n\\n tweet_list = []\\n count = 0\\n search = search_string\\n params = parameters\\n\\n for tweet in tweepy.Cursor(api.search, q=search + params,\\n count=100,\\n tweet_mode=\\\"extended\\\",\\n lang=lang,\\n since=since,\\n # until=\\\"2015-02-01\\\",\\n ).items():\\n tweet_list.append(tweet._json[\\\"full_text\\\"])\\n count += 1\\n if count == max_tweets:\\n break\\n print(count)\\n return pd.DataFrame({\\\"text\\\": tweet_list})\",\n \"def getHashtagsAndMentions(tweets):\\n hashtags = Counter()\\n mentions = Counter()\\n plain = Counter()\\n\\n pattern = re.compile(r\\\"[^#@\\\\w'-]+\\\")\\n\\n for t in tweets:\\n words = pattern.split(t.message)\\n for word in words:\\n # Ignore null strings caused by split characters at the end of a\\n # message and remove standalone hyphens.\\n if word and not word.startswith(\\\"-\\\"):\\n # Increment count for the word in the Counter.\\n if word.startswith(\\\"#\\\"):\\n hashtags.update({word: 1})\\n elif word.startswith(\\\"@\\\"):\\n mentions.update({word: 1})\\n else:\\n # TODO: apply nltk.corpus.stopwords.words() here,\\n # across languages. Consider that the stopwords cut off\\n # before apostrophe, therefore check if the word\\n # starts with the stopword.\\n plain.update({word: 1})\\n\\n return hashtags, mentions, plain\",\n \"def filter_tweets(tweets):\\n # We keep only tweets by chrisalbon with pictures\\n search_tweets = [tw for tw in tweets if tw['username'] == '@chrisalbon' and len(tw['images']) > 0]\\n # He made multiple tweets on the same topic, we keep only the most recent tweets\\n # We use the indexes of the reversed tweet list and dictionnaries to keep only key \\n unique_search_index = sorted(list({t['text'].lower():i for i,t in list(enumerate(search_tweets))[::-1]}.values()))\\n unique_search_tweets = [search_tweets[i] for i in unique_search_index]\\n\\n # Keep non-downloaded tweets\\n most_recent_file = sorted([datetime.datetime.fromtimestamp(os.path.getmtime(path)) \\n for path in glob.glob(\\\"./downloaded_pics/*.jpg\\\")], reverse=True)[0]\\n recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > most_recent_file]\\n\\n # Uncomment for testing new tweets\\n # recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > datetime.datetime(2017, 7, 6, 13, 41, 48)]\\n return recent_seach_tweets\",\n \"def filter_tweet_core(tweet):\\n if not ('user' in tweet and 'screen_name' in tweet['user'] \\\\\\n and 'text' in tweet):\\n return False\\n if not filter_tweet_text(tweet['text']):\\n return False\\n if bannedclients.search(tweet['source']):\\n return False\\n if bannedterms.search(tweet['text']):\\n return False\\n if bannedterms.search(tweet['user']['screen_name']):\\n return False\\n if 'entities' in tweet and bannedterms.search(json.dumps(tweet['entities'])):\\n return False\\n if not filter_tweet_entropy(tweet['text']):\\n return False\\n if not filter_tweet_cardspam(tweet):\\n return False\\n return True\",\n \"def analyze_hashtag(self, hashtag, count=200):\\n tweets = []\\n\\n for x in xrange(0, int(count / 100)):\\n tweets.extend(self.tweet_fetcher.get_tweets(hashtag))\\n\\n analyzed_tweets = sort_tweets(self.sa.classify(tweets))\\n\\n self.analyzed_tweets = analyzed_tweets\\n\\n return analyzed_tweets\",\n \"def searchbrown_word(tag):\\n brown_tagged_words = brown.tagged_words(categories='news')\\n hitwords = []\\n for i in range(len(brown_tagged_words)):\\n if tag == brown_tagged_words[i][1]:\\n hitwords.append(brown_tagged_words[i][0].lower())\\n return hitwords\",\n \"def search(self, query_string):\\n terms = query_string.lower().split()\\n result = set(self.wordDict[terms[0]])\\n if len(result) == 0:\\n return list()\\n else:\\n for t in terms[2:]:\\n records_containing_t = self.wordDict[t]\\n result = result.intersection(records_containing_t)\\n return [self.get_record_dict(id).getTuple() for id in result]\",\n \"def twitter(self):\\n\\n q = \\\" OR \\\".join(self.search_terms) + \\\" -filter:retweets\\\"\\n results = self.__api.search(q=q, lang='en', count=100)\\n\\n tweets = []\\n\\n for res in results:\\n\\n publishedAt = datetime.strptime(res._json['created_at'], '%a %b %d %H:%M:%S +0000 %Y').strftime(\\\"%Y-%m-%d\\\")\\n\\n if (res._json['in_reply_to_screen_name'] == None and publishedAt == datetime.now().strftime(\\\"%Y-%m-%d\\\")):\\n tweets.append([res._json['id'],\\n res._json['text'],\\n res._json['user']['screen_name'],\\n publishedAt,\\n res._json['user']['followers_count']])\\n\\n self.list = pd.DataFrame(tweets, columns=['id', 'title', 'user', 'publishedAt', 'followers_count']).nlargest(10,\\n 'followers_count')\\n\\n return\",\n \"def keep(tweet):\\n text = tweet[\\\"Text\\\"].lower()\\n hashtags = \\\" \\\".join(tweet[\\\"Hashtags\\\"]).lower()\\n for word in keep_words:\\n if word in text:\\n # print(\\\"{} in text {}\\\".format(word, text))\\n return True\\n if word in hashtags:\\n # print(\\\"{} in hashtags {}\\\".format(word, hastags))\\n return True\\n return False\",\n \"def loadTweets():\\n sentences = []\\n tweets = Tweet.objects.all()\\n for tweet in tweets:\\n sentences.append(tweet.text)\\n\\n return sentences\",\n \"def crawl(self):\\n retrievedTweets = []\\n\\n count = 1\\n \\n today = datetime.datetime.now()\\n today = today.replace(hour=23, minute=59, second=59, microsecond=999999)\\n gap = 1\\n yesterday = today - datetime.timedelta(gap) \\n nextDay = yesterday + datetime.timedelta(gap)\\n \\n while True:\\n try:\\n lst = tweepy.Cursor(self.api.search, lang='en', q=self.keyword, count=50, until=nextDay.date(), result_type='popular').items(50)\\n for tweet in lst:\\n self.data = [tweet.created_at, tweet.id, tweet.text,\\n tweet.user._json['screen_name'], tweet.user._json['name'], \\n tweet.favorite_count, tweet.retweet_count, tweet.user.location]\\n self.data = tuple(self.data)\\n retrievedTweets.append(self.data)\\n break\\n except tweepy.TweepError as e:\\n print(e.reason)\\n continue\\n except StopIteration: \\n break\\n\\n return retrievedTweets\",\n \"def get_tweet_texts(tweet_list):\\n relevant_tweet_text = ''\\n for text in tweet_list:\\n relevant_tweet_text += ' ' + text['text']\\n\\n return relevant_tweet_text\",\n \"def get_hashtag_tweets(self, hashtag,\\n count=settings.TWITTER_DEFAULT_LIMIT):\\n url = urljoin(self.base_url, \\\"/search/tweets.json\\\")\\n response = self.session.get(\\n url,\\n params={\\n \\\"q\\\": hashtag,\\n \\\"count\\\": count,\\n \\\"include_entities\\\": True\\n },\\n auth=self.__auth,\\n )\\n data = response.json()\\n if response.ok:\\n data = [Tweet(tweet_data) for tweet_data in data['statuses']]\\n else:\\n if 'error' in data:\\n raise TwitterException(data['error'], code=response.status_code)\\n elif 'errors' in data:\\n error = data['errors'][0]\\n raise TwitterException(error['message'], code=response.status_code)\\n return data\",\n \"def keyword_filter(tweet_objects, keywords):\\n \\n tm = TweetManager()\\n\\n filtered_list = []\\n\\n for tweet in tweet_objects:\\n # Separate text and hashtags\\n tweet_text = text_from_tweet(tweet)\\n hashtags = tm.find_hashtags(tweet_text)\\n\\n content_filtered_text = tm.clean_tweet(tweet_text)\\n\\n keywords_in_text = tm.find_keywords_in_tweet(content_filtered_text, keywords)\\n hashtags_with_keywords = tm.find_hashtags_with_keywords(hashtags, keywords)\\n\\n if len(keywords_in_text) or len(hashtags_with_keywords):\\n filtered_list.append({\\n KEY_TEXT_ORIGINAL: tweet_text,\\n KEY_TEXT_FILTERED: content_filtered_text,\\n KEY_HASHTAGS_WITH_KEYWORDS: hashtags_with_keywords,\\n KEY_TEXT_KEYWORDS: keywords_in_text\\n })\\n\\n return filtered_list\",\n \"def geo_collect_tweets(search_term,latitude,longitude,radius):\\n i = None\\n tweets = []\\n rep = 1\\n for n in range(2): #can only search 100 tweets at a time, so run search multiple times\\n \\tresults = api.GetSearch(term = search_term, \\n \\t\\tcount = 100, \\n \\t\\tresult_type = 'recent', \\n \\t\\tmax_id = i, #start a search from the most recent tweet id, working backwards\\n \\t\\tgeocode =(latitude, longitude, radius))\\n for tweet in results:\\n tweets.append(tweet.text)\\n i = tweet.id - 1 #want it to start at the tweet after the last tweet\\n rep += 1\\n return list(set(tweets)) #set gets rid of repititve tweets, but need to return a list\",\n \"def extract_content(tweets):\\n result = []\\n for t in tweets:\\n text = t.text\\n result.append(tokenize(text))\\n return result\",\n \"def topictweets(url):\\n article = get_article(url)\\n keywords = get_keywords(article['text'])\\n entities = get_entities(article['text'])\\n q = twitter_query(keywords, entities)\\n result = search({'q': q, 'count': 100, 'result_type': 'mixed'})\\n tweets = screen_name_filter(result.statuses, 'media')\\n return tweets\",\n \"def GetWords(phrase):\\n # Remove special characters regex\\n # It works faster than the standard \\\\w+ pattern\\n regex = re.compile(r'([^\\\\d\\\\`\\\\~\\\\!\\\\@\\\\#\\\\$\\\\%\\\\^\\\\&\\\\*\\\\(\\\\)\\\\+\\\\=\\\\[\\\\{\\\\]\\\\}\\\\|\\\\\\\\\\\\'\\\\<\\\\,\\\\.\\\\>\\\\?\\\\/\\\\\\\"\\\"\\\\;\\\\:\\\\s]+)+',\\n re.UNICODE)\\n return re.findall(regex,phrase.lower())\",\n \"def getByHashtags(hashtag):\\n\\n # set page_limits. The default is 1 \\n pages_limit = request.args.get('pages_limit') or 1\\n pages_limit = int(pages_limit)\\n\\n raw_response = get_response(tw_api, 'search/tweets', { 'q': '#' + hashtag, 'count': 100 }, pages_limit)\\n list_response = convert_resp2list(raw_response)\\n return jsonify(list_response)\",\n \"def getTweetsByUser(username, maxTweets=1000):\\n if 'api' not in globals():\\n startTwitterApi(getApiKeys(fileName=\\\"apiConf2.txt\\\"))\\n myTweets=[]\\n if words:\\n apiRes = tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items()\\n for tweet in apiRes:\\n if any(containsWord(tweet._json['full_text'],word) for word in words):\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n else:\\n if sortBy=='newest':\\n for tweet in tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items(maxTweets):\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n else:\\n for tweet in tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items():\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n\\n return getTopNTweets(myTweets, maxTweets)\",\n \"def collect_tweets(search_id, search_term, number_of_tweets):\\n\\n tweets = []\\n for tweet in api_collector.collect(search_term, number_of_tweets):\\n tweets.append((tweet.id_str, tweet.created_at, tweet.full_text))\\n if len(tweets) == 0:\\n search = Search.objects.get(pk=search_id)\\n search.empty = True\\n search.save()\\n notify_searchers.delay(search_id)\\n else:\\n classify_tweets.delay(search_id, tweets)\",\n \"def searchByKeyword(self, keyword, until=\\\"\\\", since=\\\"\\\", count=None, result_type=\\\"recent\\\"):\\n if count is None:\\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\\n full_text=True, tweet_mode=\\\"extended\\\", lang=\\\"en\\\").items()\\n else:\\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\\n full_text=True, tweet_mode=\\\"extended\\\", lang=\\\"en\\\").items(count)\\n\\n for status in tweets:\\n createdDate = parser.parse(str(status._json[\\\"created_at\\\"]).strip())\\n createdDate = createdDate.replace(\\n tzinfo=pytz.utc) - createdDate.utcoffset()\\n status_refined = {\\n 'keyword': keyword,\\n '_id': status._json[\\\"id\\\"],\\n 'created_at': createdDate,\\n 'tweetText': status._json[\\\"full_text\\\"],\\n 'hashtags': status._json[\\\"entities\\\"][\\\"hashtags\\\"],\\n 'userLoc': status._json[\\\"user\\\"][\\\"location\\\"],\\n 'tweetGeo': status._json[\\\"geo\\\"],\\n 'tweetCoordinates': status._json[\\\"coordinates\\\"],\\n 'tweetPlace': status._json[\\\"place\\\"],\\n 'retweet': {},\\n }\\n if hasattr(status, \\\"retweeted_status\\\"):\\n status_refined['tweetText'] = status._json[\\\"retweeted_status\\\"][\\\"full_text\\\"]\\n status_refined['retweet'] = {\\n 'original_retweet_id': status._json[\\\"retweeted_status\\\"][\\\"id\\\"],\\n 'origUserLoc': status._json[\\\"retweeted_status\\\"][\\\"user\\\"][\\\"location\\\"],\\n 'origTweetLoc': status._json[\\\"retweeted_status\\\"][\\\"geo\\\"],\\n 'origTweetPlace': status._json[\\\"retweeted_status\\\"][\\\"place\\\"],\\n 'origTweetCoord': status._json[\\\"retweeted_status\\\"][\\\"coordinates\\\"],\\n 'origHashtags': status._json[\\\"retweeted_status\\\"][\\\"entities\\\"][\\\"hashtags\\\"],\\n 'retweet_count': status._json[\\\"retweet_count\\\"],\\n }\\n self.tweets.append(status_refined)\\n return self.tweets\",\n \"def get_movies_by_words(words):\\n cnx,cur = connect_to_db()\\n text = \\\"'\\\";\\n for word in words:\\n text += \\\"+\\\"+word+\\\" \\\"\\n text += \\\"'\\\"\\n cur.execute(\\\"SELECT * FROM movies \\\"\\n \\\"Where match(overview) against(\\\"+text+\\\" IN BOOLEAN MODE) LIMIT 100 \\\")\\n lst = cur.fetchall()\\n size = len(lst)\\n cur.close()\\n cnx.close()\\n return lst,size\",\n \"def global_matches(self, text):\\n\\n matches = []\\n n = len(text)\\n\\n for list in [ self.namespace ]:\\n for word in list:\\n if word[:n] == text:\\n matches.append(word)\\n\\n return matches\",\n \"def queryTerm2Twitter(term): \\n statusList = api.GetSearch(term, count=100, result_type='recent')\\n timeStampOfStatus = [datetime.fromtimestamp(i.created_at_in_seconds) for i in statusList]\\n timeStampOfStatus.sort() \\n return timeStampOfStatus[0]\",\n \"def searchphrases(query):\\n query_nostopwords = removestopwords(query)\\n query_lemmatized = lemmatize(query_nostopwords) #look like\\n phraseids = []\\n ngramids=[]\\n words=query_lemmatized.split()\\n query_ngram = \\\"select id from ngrams where lower(lemmangrams) like lower('%{}%')\\\".format(query_lemmatized)+\\\" or lower(lemmangrams) like lower('%{}%')\\\".format(words[0])\\n for word in words[1:]:\\n query_ngram=query_ngram+\\\" or lower(lemmangrams) like lower('%{}%')\\\".format(word)\\n con = it.engine.execute(query_ngram)\\n rows_phrase = con.fetchall()\\n if rows_phrase:\\n ngramids = list(set([str(i[0]) for i in rows_phrase]))\\n phraseids.extend(ngramids)\\n phraseids = list(set(phraseids))\\n results=categorize(phraseids)\\n return results\",\n \"def get_tweets(query, pages=25):\\n\\n logger = Logger()\\n after_part = 'include_available_features=1&include_entities=1&include_new_items_bar=true'\\n if query.startswith('#'):\\n query = quote(query)\\n url = 'https://twitter.com/i/search/timeline?f=tweets&vertical=default&q={}&src=tyah&reset_error_state=false&'.format(query)\\n else:\\n url = 'https://twitter.com/i/profiles/show/{}/timeline/tweets?'.format(query)\\n url += after_part\\n \\n headers = {\\n 'Accept': 'application/json, text/javascript, */*; q=0.01',\\n 'Referer': 'https://twitter.com/{}'.format(query),\\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/603.3.8 (KHTML, like Gecko) Version/10.1.2 Safari/603.3.8',\\n 'X-Twitter-Active-User': 'yes',\\n 'X-Requested-With': 'XMLHttpRequest',\\n 'Accept-Language': 'en-US'\\n }\\n\\n def gen_tweets(pages):\\n logger.add(\\\"MSG: Sending request to url '{}'...\\\".format(url))\\n r = session.get(url, headers=headers)\\n\\n logger.add(\\\"MSG: Parsing result...\\\".format(url))\\n while pages > 0:\\n try:\\n html = BeautifulSoup(r.json()['items_html'], parser='html', features=\\\"lxml\\\")\\n except KeyError:\\n raise ValueError(\\n 'Oops! Either \\\"{}\\\" does not exist or is private.'.format(query))\\n\\n comma = \\\",\\\"\\n dot = \\\".\\\"\\n tweets = []\\n for tweet in html.select('.stream-item'):\\n # 10~11 html elements have `.stream-item` class and also their `data-item-type` is `tweet`\\n # but their content doesn't look like a tweet's content\\n try:\\n text = tweet.select('.tweet-text')[0].get_text()\\n except IndexError: # issue #50\\n continue\\n\\n tweet_id = tweet['data-item-id']\\n\\n time = datetime.fromtimestamp(int(tweet.select('._timestamp')[0]['data-time-ms']) / 1000.0)\\n\\n interactions = [\\n x.get_text()\\n for x in tweet.select('.ProfileTweet-actionCount')\\n ]\\n\\n replies = int(\\n interactions[0].split(' ')[0].replace(comma, '').replace(dot, '')\\n or interactions[3]\\n )\\n\\n retweets = int(\\n interactions[1].split(' ')[0].replace(comma, '').replace(dot, '')\\n or interactions[4]\\n or interactions[5]\\n )\\n\\n likes = int(\\n interactions[2].split(' ')[0].replace(comma, '').replace(dot, '')\\n or interactions[6]\\n or interactions[7]\\n )\\n\\n hashtags = [\\n hashtag_node.get_text()\\n for hashtag_node in tweet.select('.twitter-hashtag')\\n ]\\n urls = [\\n url_node['data-expanded-url']\\n for url_node in tweet.select('a.twitter-timeline-link:not(.u-hidden)')\\n ]\\n photos = [\\n photo_node['data-image-url']\\n for photo_node in tweet.select('.AdaptiveMedia-photoContainer')\\n ]\\n\\n is_retweet = False\\n if tweet.select('.js-stream-tweet')[0].has_attr('data-retweet-id'):\\n is_retweet = True\\n\\n is_pinned = False\\n if tweet.select(\\\".pinned\\\"):\\n is_pinned = True\\n\\n videos = []\\n video_nodes = tweet.select(\\\".PlayableMedia-player\\\")\\n for node in video_nodes:\\n styles = node['style'].split()\\n for style in styles:\\n if style.startswith('background'):\\n tmp = style.split('/')[-1]\\n video_id = tmp[:tmp.index('.jpg')]\\n videos.append({'id': video_id})\\n\\n tweets.append({\\n 'tweetId': tweet_id,\\n 'isRetweet': is_retweet,\\n 'time': time,\\n 'text': text,\\n 'replies': replies,\\n 'retweets': retweets,\\n 'likes': likes,\\n 'isPinned': is_pinned,\\n 'entries': {\\n 'hashtags': hashtags, 'urls': urls,\\n 'photos': photos, 'videos': videos\\n }\\n })\\n\\n\\n last_tweet = html.select('.stream-item')[-1]['data-item-id']\\n\\n for tweet in tweets:\\n if tweet:\\n tweet['text'] = re.sub(r'\\\\Shttp', ' http', tweet['text'], 1)\\n tweet['text'] = re.sub(r'\\\\Spic\\\\.twitter', ' pic.twitter', tweet['text'], 1)\\n yield tweet\\n\\n r = session.get(url, params={'max_position': last_tweet}, headers=headers)\\n pages += -1\\n yield from gen_tweets(pages)\",\n \"def get_tweet_list(user_handle):\\n client = language.LanguageServiceClient()\\n\\n tweet_list = twitter.get_tweets(handle=user_handle)\\n\\n if tweet_list[0] == \\\"34\\\":\\n return tweet_list\\n\\n for i in range(len(tweet_list)):\\n\\n content = tweet_list[i].get(\\\"text\\\")\\n\\n document = types.Document(\\n content=content, type=enums.Document.Type.PLAIN_TEXT)\\n annotations = client.analyze_sentiment(document=document)\\n\\n # Print the results\\n # print_result(annotations)\\n\\n score = annotations.document_sentiment.score\\n magnitude = annotations.document_sentiment.magnitude\\n\\n tweet_list[i][\\\"score\\\"] = score\\n tweet_list[i][\\\"magnitude\\\"] = magnitude\\n\\n # print(tweet_list[i])\\n\\n return tweet_list\",\n \"def get_tweets(self):\\n keyword = 'covid'\\n\\n # Load tokens from file\\n with open('../data/tokens.json', 'r') as f:\\n tokens = json.load(f)\\n\\n # Stream tweets\\n auth = tweepy.OAuthHandler(tokens['consumer_key'], tokens['consumer_secret'])\\n auth.set_access_token(tokens['access_token_key'], tokens['access_token_secret'])\\n api = tweepy.API(auth)\\n\\n # listen for tweets\\n while True:\\n\\n # TODO: save file in Cloud Storage\\n file_name = date.today().strftime('corpus-%d-%m-%Y.json')\\n print(f'Updating {file_name} ...')\\n\\n StreamListener = StreamListener(\\n file_name=file_name, \\n max_tweets=1000)\\n myStream = tweepy.Stream(\\n auth=api.auth, \\n listener=StreamListener)\\n\\n myStream.filter(track=[keyword], languages=['en'])\\n \\n time.sleep(60)\",\n \"def get_tweets():\\n\\n return Tweet.query.all()\",\n \"def has_keyword(tweet, keywords):\\n temp = tweet.lower()\\n for keyword in keywords:\\n if bool(re.search(f'\\\\\\\\b{keyword}\\\\\\\\b', temp)):\\n return True\\n return False\",\n \"def filter_tweet(tweet):\\n if not filter_tweet_core(tweet):\\n return False\\n if bannedusers.search(tweet['user']['screen_name']) or (\\n 'retweeted_status' in tweet and bannedusers.search(tweet['retweeted_status']['user']['screen_name'])):\\n return False\\n if tweet['user']['screen_name'] == credentials['username']: # Do not match self tweets :-)\\n return False\\n return True\",\n \"def query_tweets_once(query, limit=None, num_tweets=0):\\n logging.info(\\\"Querying {}\\\".format(query))\\n query = query.replace(' ', '%20').replace(\\\"#\\\", \\\"%23\\\").replace(\\\":\\\", \\\"%3A\\\")\\n pos = None\\n tweets = []\\n try:\\n while True:\\n new_tweets, pos = query_single_page(\\n INIT_URL.format(q=query) if pos is None\\n else RELOAD_URL.format(q=query, pos=pos),\\n pos is None\\n )\\n if len(new_tweets) == 0:\\n logging.info(\\\"Got {} tweets for {}.\\\".format(\\n len(tweets), query))\\n return tweets\\n\\n logging.info(\\\"Got {} tweets ({} new).\\\".format(\\n len(tweets) + num_tweets, len(new_tweets)))\\n\\n tweets += new_tweets\\n\\n if limit is not None and len(tweets) + num_tweets >= limit:\\n return tweets\\n except KeyboardInterrupt:\\n logging.info(\\\"Program interrupted by user. Returning tweets gathered \\\"\\n \\\"so far...\\\")\\n except BaseException:\\n logging.exception(\\\"An unknown error occurred! Returning tweets \\\"\\n \\\"gathered so far.\\\")\\n\\n return tweets\",\n \"def get_tweets_by_hashtag_route(hashtag):\\n response, code = get_tweets_by_hashtag(\\n hashtag, request.args.get('limit', 30))\\n return jsonify(response), code\",\n \"def twitter_search(a, q, x=10000000, t=100, s=None, m=-1):\\n\\n tweets = list()\\n tweet_count = 0\\n\\n while tweet_count < x:\\n try:\\n if (m <= 0):\\n if (not s):\\n new_tweets = a.search(q = q, count = t)\\n else:\\n new_tweets = a.search(q = q, count = t, since_id = s)\\n else:\\n if (not s):\\n new_tweets = a.search(q = q, count = t, max_id = (m - 1))\\n else:\\n new_tweets = a.search(q = q, count = t, max_id = (m - 1), since_id = s)\\n\\n if not new_tweets:\\n break\\n\\n for tweet in new_tweets:\\n tweets.append(tweet)\\n\\n tweet_count += len(new_tweets)\\n m = new_tweets[-1].id\\n\\n except tweepy.TweepError as e:\\n error = (-1, \\\"error:\\\" + str(e))\\n return error\\n\\n search_results = (tweet_count, tweets)\\n\\n return search_results\",\n \"def printHashtagsAndMentions(searchText=None, filterTerms=False, tweetLimit=0):\\n tweets = db.Tweet.select()\\n if searchText is not None:\\n tweets = tweets.filter(db.Tweet.q.message.contains(searchText))\\n tweets = tweets.limit(tweetLimit)\\n\\n hashtags, mentions, plain = getHashtagsAndMentions(tweets)\\n\\n if searchText and filterTerms:\\n hashtags = Counter(\\n {k: v for k, v in hashtags.items() if searchText.lower() in k.lower()}\\n )\\n mentions = Counter(\\n {k: v for k, v in mentions.items() if searchText.lower() in k.lower()}\\n )\\n plain = Counter(\\n {k: v for k, v in plain.items() if searchText.lower() in k.lower()}\\n )\\n\\n # Unique word count for each area.\\n hashtagWC = len(hashtags)\\n mentionWC = len(mentions)\\n plainWC = len(plain)\\n\\n print(\\\"Summary\\\")\\n print(\\\"==============\\\")\\n # Count items in the sliced selection since .count() does not work with\\n # a limit.\\n count = len(list(tweets)) if tweetLimit else tweets.count()\\n print(\\\"{0:7,d} tweets\\\".format(count))\\n print(\\\"{0:7,d} unique words\\\".format(hashtagWC + mentionWC + plainWC))\\n print(\\\"{0:7,d} unique hashtags\\\".format(hashtagWC))\\n print(\\\"{0:7,d} unique mentions\\\".format(mentionWC))\\n print(\\\"{0:7,d} unique plain words\\\".format(plainWC))\\n print()\\n\\n print(\\\"Hashtags\\\")\\n print(\\\"========\\\")\\n printCounterByCount(hashtags)\\n print()\\n\\n print(\\\"Mentions\\\")\\n print(\\\"========\\\")\\n printCounterByCount(mentions)\\n\\n \\\"\\\"\\\"\\n # Removal of stopwords and handling of URIs is needed to make this\\n # useful.\\n print 'Plain'\\n print '========'\\n printCounterByCount(plain)\\n \\\"\\\"\\\"\",\n \"def list_tweets():\\n tweets = []\\n tuples = query_db('''\\n select message.*, user.* from message, user\\n where message.author_id = user.user_id\\n order by message.pub_date desc limit ?''', [PER_PAGE])\\n for tuple in tuples:\\n tweet = {}\\n tweet[\\\"username\\\"] = tuple['username']\\n tweet[\\\"email\\\"] = tuple['email']\\n tweet[\\\"text\\\"] = tuple['text']\\n tweet[\\\"pub_date\\\"] = tuple['pub_date']\\n tweets.append(tweet)\\n return jsonify({'tweets':tweets}),200\",\n \"def get_tweets(keyword):\\n url = 'http://search.twitter.com/search.json?q='\\n\\n page = urllib.urlopen('%s%s' % (url, keyword))\\n blob = page.read()\\n jsonblob = json.loads(blob)\\n return jsonblob\",\n \"def tokenize_tweets_by_word(self):\\n sent_tok_list = []\\n all_tok_list = []\\n\\n for t in range(len(self.all_tweets) - 1):\\n sent_list = sent_tokenize(self.all_tweets[t][0][1]) # all_tweets[tweet][look at tuple][look at text]\\n all_tok_list.append([])\\n for s in sent_list: # for each sentence\\n word_tok_list = word_tokenize(s)\\n pos_tag_list = pos_tag(word_tok_list)\\n all_tok_list[t].append(pos_tag_list)\\n print(pos_tag_list)\\n\\n return all_tok_list\",\n \"def query_word(self, word):\\n return [r[0] for r in sparqlquery(self.endpoint, self.query % {'word': word})]\",\n \"def hashtags(self, candidate_list):\\n if Tweet.hashtagre is None:\\n Tweet.hashtagre = re.compile('|'.join(map(re.escape, candidate_list)))\\n return [\\n [m.group(0).replace('#', '', 1), m.span()]\\n for m in Tweet.hashtagre.finditer(self.text)\\n ]\",\n \"def get_mentions(texts, word):\\r\\n mentions = {}\\r\\n for text, label, text_id in texts:\\r\\n if word in text.lower():\\r\\n if label not in mentions:\\r\\n mentions[label] = [text_id]\\r\\n else:\\r\\n if text_id not in mentions[label]:\\r\\n mentions[label].append(text_id)\\r\\n return mentions\",\n \"def having(self, wordlist=None, coordinates=None):\\n track = \\\",\\\".join(wordlist) if wordlist else None\\n locations = \\\",\\\".join(map(str, coordinates)) if coordinates else None\\n\\n if track and locations:\\n generator = self.stream.statuses.filter(track=track,\\n locations=locations)\\n elif track:\\n generator = self.stream.statuses.filter(track=track)\\n elif locations:\\n generator = self.stream.statuses.filter(locations=locations)\\n else:\\n raise ValueError(\\\"Must provide one of wordlist or coordinates\\\")\\n\\n for tweet in generator:\\n if locations:\\n if 'coordinates' in tweet and tweet['coordinates']:\\n yield tweet\\n else:\\n yield tweet\",\n \"def search(self, term) -> Iterable:\\n # if the term is whitespace, return empty\\n if not term.strip():\\n yield from ()\\n return\\n # if the term is latin, let's search in meanings\\n if re.match(r'^[a-zA-Z\\\\s]+$', term.strip()):\\n for entry in self.entries:\\n for sense in entry[\\\"senses\\\"]:\\n if any(term in gloss for gloss in sense[\\\"glosses\\\"]):\\n yield copy.deepcopy(entry)\\n # the term is probably kana, let's search in writings and readings\\n else:\\n for entry in self.entries:\\n if any(term in writing for writing in entry[\\\"writings\\\"] + entry[\\\"readings\\\"]):\\n yield copy.deepcopy(entry)\",\n \"async def get_tweets(self, ctx, username: str, count: int):\\n cnt = count\\n if count > 25:\\n cnt = 25\\n\\n if username is not None:\\n if cnt < 1:\\n await self.bot.say(\\\"I can't do that, silly! Please specify a \\\\\\n number greater than or equal to 1\\\")\\n return\\n msg_list = []\\n api = self.authenticate()\\n try:\\n for status in\\\\\\n tw.Cursor(api.user_timeline, id=username).items(cnt):\\n if not status.text.startswith(\\\"@\\\"):\\n msg_list.append(status)\\n except tw.TweepError as e:\\n await self.bot.say(\\\"Whoops! Something went wrong here. \\\\\\n The error code is \\\" + str(e))\\n return\\n if len(msg_list) > 0:\\n await self.tweet_menu(ctx, msg_list, page=0, timeout=30)\\n else:\\n await self.bot.say(\\\"No tweets available to display!\\\")\\n else:\\n await self.bot.say(\\\"No username specified!\\\")\\n return\",\n \"def global_matches(self, text):\\n matches = []\\n n = len(text)\\n for list in [keyword.kwlist,\\n __builtin__.__dict__,\\n self.namespace]:\\n for word in list:\\n if word[:n] == text:\\n matches.append(word)\\n return matches\",\n \"def twitter_text(text, search_query = False):\\r\\n tt = TwitterText(text)\\r\\n if search_query:\\r\\n tt.text = tt.highlighter.hit_highlight(query = search_query)\\r\\n tt.text = tt.autolink.auto_link()\\r\\n return tt.text\",\n \"def get_tweets(username, amount):\\n tweets = []\\n twitter = Twython()\\n\\n finished = False\\n page = 1\\n while not finished:\\n\\n if amount <= 200:\\n # Make the API call.\\n search_results = twitter.getUserTimeline(screen_name=username,\\n page=str(page), count=str(amount))\\n finished = True\\n\\n else:\\n # Make the API call.\\n search_results = twitter.getUserTimeline(screen_name=username,\\n page=str(page), count='200')\\n amount -= 200\\n page += 1\\n\\n if isinstance(search_results, dict) and search_results['error']:\\n raise TwitterAPIException(str(search_results['error']))\\n elif not search_results:\\n raise TwitterAPIException('User has no tweets.')\\n\\n for result in search_results:\\n tweets.append(result['text']) \\n\\n return tweets\",\n \"def original_three_tweets():\\n test_tweets = [\\n \\\"is #bigdata finally the answer to end poverty? \\\\\\n @lavanyarathnam http://ow.ly/o8gt3 #analytics\\\",\\n \\\"interview: xia wang, astrazeneca on #bigdata and the promise of effective \\\\\\n healthcare #kdn http://ow.ly/ot2uj\\\",\\n \\\"big data is not just for big business. on how #bigdata is being deployed for \\\\\\n small businesses: http://bddy.me/1bzukb3 @cxotodayalerts #smb\\\"\\n ]\\n return test_tweets\",\n \"def search_list(search_term, results=10):\\r\\n if search_term:\\r\\n url = SEARCH_URL.format(API_KEY, util.web.quote(search_term.encode('ascii', 'ignore')))\\r\\n response = util.web.http_get(url=url, json=True, referer='https://tinychat.com')\\r\\n\\r\\n if response['json'] is not None:\\r\\n media_list = []\\r\\n try:\\r\\n if 'items' in response['json']:\\r\\n for i, item in enumerate(response['json']['items']):\\r\\n if i == results:\\r\\n return media_list\\r\\n else:\\r\\n video_id = item['id']['videoId']\\r\\n details = video_details(video_id)\\r\\n if details is not None:\\r\\n media_info = {\\r\\n 'type': 'youTube',\\r\\n 'video_id': video_id,\\r\\n 'video_time': details['video_time'],\\r\\n 'video_title': details['video_title']\\r\\n }\\r\\n log.debug('Youtube item %s %s' % (i, media_info))\\r\\n media_list.append(media_info)\\r\\n except KeyError as ke:\\r\\n log.error(ke, exc_info=True)\\r\\n return None\",\n \"def word_search(self,\\r\\n term):\\r\\n\\r\\n foundset = set()\\r\\n term = term.strip().lower()\\r\\n if self.word_dict_contains(term):\\r\\n foundset = foundset.union(self.get_indexes_for_word(term))\\r\\n return foundset\",\n \"def search(search_term):\\r\\n if search_term:\\r\\n if 'list' in search_term:\\r\\n search_term = search_term.split('?list')[0]\\r\\n\\r\\n url = SEARCH_URL.format(API_KEY, util.web.quote(search_term.encode('ascii', 'ignore')))\\r\\n response = util.web.http_get(url=url, json=True, referer='http://tinychat.com')\\r\\n\\r\\n if response['json'] is not None:\\r\\n try:\\r\\n if 'items' in response['json']:\\r\\n for item in response['json']['items']:\\r\\n video_id = item['id']['videoId']\\r\\n details = video_details(video_id)\\r\\n if details is not None:\\r\\n return {\\r\\n 'type': 'youTube',\\r\\n 'video_id': video_id,\\r\\n 'video_time': details['video_time'],\\r\\n 'video_title': details['video_title']\\r\\n }\\r\\n except KeyError as ke:\\r\\n log.error(ke, exc_info=True)\\r\\n return None\",\n \"def search(self, query_string):\\n return models.Thread.objects.filter(\\n reduce(\\n lambda q, f: q & Q(title__icontains=f),\\n query_string.split(),\\n Q()))\",\n \"def find_matches(id1, id2, adj_list_ids, adj_list_words):\\n\\n # combine words from tweets\\n stanza_words = adj_list_ids[id1] + adj_list_ids[id2]\\n\\n # look for other tweets with those words\\n pot_ids = set().union(*[adj_list_words[w] for w in stanza_words])\\n\\n # --- filter down ---\\n # remove start tweets\\n # ensure tweets contain subset of master_word_set\\n pot_ids = pot_ids - {id1, id2}\\n pot_ids = [x for x in pot_ids if set(adj_list_ids[x]) <= set(stanza_words)]\\n\\n # look for valid pairs of potential tweets\\n if len(pot_ids) > 1:\\n return find_valid_matches(pot_ids, adj_list_ids, stanza_words)\\n return []\",\n \"def process(self, filter_words, count=1):\\n user = self.__api.get_user(self.__username)\\n\\n # print user.screen_name\\n # print user.followers_count\\n if self.__appMode == 1 and self.__TimeLineMode == 1:\\n self.get_timeline(filter_words)\\n else:\\n if self.__friendMode:\\n print(\\\"Getting all Twitter Friends \\\\n\\\")\\n for friend in user.friends():\\n self.get_tweet(friend.screen_name, filter_words, count)\\n else:\\n for screen_name in self.__priorityCoin:\\n self.get_tweet(screen_name, filter_words, count)\\n print('Twitter Data Extraction done!!')\",\n \"def search(self, word):\",\n \"def get_tweets(self):\\r\\n return self.tweets\",\n \"async def team_search(self, ctx: commands.Context, username: str):\\n all_usernames = {team_id: team.username for team_id, team in self.teams.items()\\n if team is not None}\\n suggestions = []\\n log.info(repr(fuzzywuzzy.process.extract(\\n username, all_usernames, limit=5)))\\n for fuzz_username, rating, fuzz_id in fuzzywuzzy.process.extract(\\n username, all_usernames, limit=5):\\n if rating < 50:\\n break\\n fuzz_team = self.teams[fuzz_id]\\n suggestions.append(\\n f'(ID: **{fuzz_team.team_id}**) **{fuzz_team.display_name[:40]}**'\\n f' -- {len(fuzz_team.users)} registered members')\\n if suggestions:\\n await ctx.send('\\\\n'.join(suggestions))\\n else:\\n await ctx.send(f\\\"Couldn't find any teams whose usernames resembled `{username}`\\\")\",\n \"def searchIdiom(i, idiom):\\n token = authorize(i)\\n endpoint = 'https://api.twitter.com/1.1/search/tweets.json'\\n auth = OAuth1(signature_type=\\\"auth_header\\\", **token)\\n \\n params = {}\\n params[\\\"q\\\"]=\\\"%23\\\"+idiom\\n params[\\\"count\\\"] = tweet_count\\n try:\\n r = requests.get(endpoint, params=params, auth=auth, timeout=60.0)\\n print(r)\\n if r.status_code==200:\\n\\t '''\\n print(\\\"yay\\\")\\n for tweet in r.json()[\\\"statuses\\\"]:\\n print(tweet[\\\"user\\\"][\\\"screen_name\\\"])\\n #print(tweet[\\\"entities\\\"][\\\"hashtags\\\"])\\n print tweet[\\\"text\\\"]\\n ''' \\n return r.json()[\\\"statuses\\\"]\\n else:\\n if r.status_code==429:\\n print('limit exceeded')\\n rest_rate_limit(r)\\n return searchIdiom(i, idiom)\\n else:\\n print('critical situation')\\n return []\\n except ValueError as e:\\n print('Error : ', e)\\n except Exception as e1:\\n print('Error : ', e1)\",\n \"def get_live_tweets_from_twitter_stream(auth, terms, num_tweets):\\n listener = TwitterListener()\\n listener._max_tweets = num_tweets\\n twitter_stream = Stream(auth, listener)\\n twitter_stream.filter(track=terms, languages=['en'])\\n listener.store_live_tweets()\",\n \"def get_tweets(user, num = 200):\\n tweets = []\\n \\n for tweet in user.home_timeline(count = num):\\n edited_tweet = tweet.text\\n edited_tweet = edited_tweet.encode(encoding='UTF-8', errors='Ignore') \\n tweets.append(edited_tweet)\\n return tweets\",\n \"def get_word_frequency(tweets_list):\\n tweets_freq_counter = Counter()\\n stop_words = utilities.get_stop_words()\\n\\n for status in tweets_list:\\n tweet_texts = [text for text in tokenize(status['text']) if text.lower() not in stop_words]\\n tweets_freq_counter.update(tweet_texts)\\n return tweets_freq_counter\",\n \"def get_custom_phrases():\\n return [x[0] for x in all_topics if x[2] == \\\"1\\\"]\",\n \"def twitter(self):\\n message = \\\"\\\"\\n count = self.collection.count()\\n\\n twitter = Twitter(auth = OAuth(self.access_key, self.access_secret, self.consumer_key, self.consumer_secret))\\n for keyword in self.twitter_keywords:\\n query = twitter.search.tweets(q = keyword)\\n for result in query['statuses']:\\n try:\\n data = {\\\"id\\\": count+1, \\\"source\\\": \\\"twitter\\\", \\\"timestamp\\\": datetime.now()}\\n data['tweet'] = result['text']\\n data['name'] = result[\\\"user\\\"][\\\"screen_name\\\"]\\n data['url'] = \\\"https://twitter.com/\\\" + data[\\\"name\\\"] + \\\"/status/\\\" + str(result['id'])\\n data['search_string'] = keyword\\n try:\\n dataid = self.collection.insert(data)\\n except DuplicateKeyError as e:\\n continue\\n count += 1\\n\\n # Slack push notification\\n length = 82 - len(data['url'])\\n message += \\\"\\\\nURL: \\\" + data['url'] + \\\" search string: \\\".rjust(length) + keyword\\n\\n except Exception as e:\\n print(e)\\n pass\\n \\n if message:\\n print(self.G + \\\"[+] Twitter\\\" + self.B + message)\\n self.message += \\\"\\\\n*Twitter*:\\\\n```\\\"\\n self.message += message\\n self.message += \\\"\\\\n```\\\"\\n\\n return\",\n \"def get_suggestions(\\n user: 'User',\\n hashtag: str,\\n valid_user: Callable[['User'], bool],\\n since: str,\\n max_suggestions: int\\n ) -> List[tweepy.models.User]:\\n api = get_api(user)\\n suggestions = []\\n seen = set()\\n max_iters = 5000\\n\\n for tweet in tweepy.Cursor(api.search, q=hashtag, lang=\\\"en\\\", since=since).items():\\n if tweet.user.screen_name not in seen and valid_user(tweet.user):\\n suggestions.append(tweet.user)\\n seen.add(tweet.user.screen_name)\\n if len(suggestions) >= max_suggestions or len(seen) > max_iters:\\n break\\n\\n return suggestions\",\n \"def find_tweets(criteria_candidates):\\n results = TweetFinder.__get_results(criteria_candidates)\\n return results\",\n \"def search_postman(text):\\n result = _search_blog('postman', text)\\n _print_results(result)\\n return result\",\n \"def compute_retweet(row):\\n return row['full_text'].startswith('RT')\",\n \"def language_filter(tweet_objects):\\n\\n filtered_list = []\\n\\n for tweet in tweet_objects: \\n lang = detect(text_from_tweet(tweet))\\n if lang == 'en':\\n filtered_list.append(tweet)\\n\\n return filtered_list\",\n \"def extract_phrases(phrase_dict, csv_reader, word_list):\\n count_row = 0\\n\\n for row in csv_reader:\\n phrase = row[3]\\n count_row += 1\\n\\n if phrase not in all_phrases:\\n tokens = tokenizer(phrase)\\n tokens = list(tokens)\\n phrase_dict[phrase] = tokens\\n for tok in tokens:\\n if tok not in words:\\n words.append(tok)\\n\\n #print(count_row)\",\n \"def get_search_results(twitter_dict, search_dict):\\r\\n\\r\\n search_list = [search_dict['username']] \\r\\n search_specified_list = []\\r\\n\\r\\n for user in search_list:\\r\\n search_users_list = [user]\\r\\n \\r\\n for operation in search_dict['operations']:\\r\\n search_users_list = search_helper(search_users_list, operation,\\\\\\r\\n twitter_dict)\\r\\n \\r\\n search_specified_list += search_users_list\\r\\n \\r\\n return search_specified_list\",\n \"def search_by_contains(self, tl):\\n print(\\\"Search by string\\\")\\n string = input(\\\"Please enter search string: \\\")\\n return tl.findall_contains(string)\",\n \"def search(self, phrase, content=False, username=None, ct=10, page=0):\\r\\n page = int(page) + 1\\r\\n\\r\\n with WIX.searcher() as search:\\r\\n fields = ['description', 'extended', 'tags']\\r\\n\\r\\n if content:\\r\\n fields.append('readable')\\r\\n\\r\\n parser = qparser.MultifieldParser(fields,\\r\\n schema=WIX.schema,\\r\\n group=qparser.OrGroup)\\r\\n qry = parser.parse(phrase)\\r\\n\\r\\n try:\\r\\n res = search.search_page(qry, page, pagelen=int(ct))\\r\\n except ValueError, exc:\\r\\n raise(exc)\\r\\n\\r\\n if res:\\r\\n qry = Bmark.query.filter(\\r\\n Bmark.bid.in_([r['bid'] for r in res])\\r\\n )\\r\\n\\r\\n if username:\\r\\n qry = qry.filter(Bmark.username == username)\\r\\n\\r\\n qry = qry.options(joinedload('hashed'))\\r\\n\\r\\n return qry.all()\\r\\n else:\\r\\n return []\",\n \"def find(self, words):\\n for result in self.index.simple_search(self.name, words, field='content'):\\n title = result['title']\\n score = int(result.score)\\n yield score, title\",\n \"def search(self, term):\",\n \"def userTweets(username):\\n api = twitter.Api()\\n user_tweets = api.GetUserTimeline(username)\\n for tweet in user_tweets:\\n util.safe_print(tweet.GetText())\",\n \"def tweet_text(tweet):\\n return tweet['text']\",\n \"def get_tweets():\\n clean_tweetdb.delay()\\n db_tweets = Tweet.objects.all()\\n max_id = min([tweet.tweet_id for tweet in db_tweets])\\n tweets = api.search(\\n q='#python',\\n max_id=max_id,\\n count=100\\n )\\n tweets_id = [tweet.id for tweet in tweets]\\n tweets_date = [tweet.created_at for tweet in tweets]\\n tweets_source = [tweet.source for tweet in tweets]\\n tweets_favorite_cnt = [tweet.favorite_count for tweet in tweets]\\n tweets_retweet_cnt = [tweet.retweet_count for tweet in tweets]\\n tweets_text = [tweet.text for tweet in tweets]\\n\\n for i, j, k, l, m, n in zip(\\n tweets_id,\\n tweets_date,\\n tweets_source,\\n tweets_favorite_cnt,\\n tweets_retweet_cnt,\\n tweets_text,\\n ):\\n try:\\n Tweet.objects.create(\\n tweet_id=i,\\n tweet_date=j,\\n tweet_source=k,\\n tweet_favorite_cnt=l,\\n tweet_retweet_cnt=m,\\n tweet_text=n,\\n )\\n except IntegrityError:\\n pass\",\n \"def more_like_text(text, klass):\\n back = connections['default'].get_backend()\\n\\n if hasattr(back, 'conn'):\\n query = {'query': {\\n 'filtered': {\\n 'query': {\\n 'fuzzy_like_this': {\\n 'like_text': text\\n }\\n },\\n 'filter': {\\n 'bool': {\\n 'must': {\\n 'term': {'django_ct': 'idea.idea'}\\n }\\n }\\n }\\n }\\n }\\n\\n }\\n results = back.conn.search(query)\\n return back._process_results(results)['results']\\n else:\\n return []\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7142697","0.7100457","0.6930307","0.67667294","0.66168964","0.6615334","0.6612648","0.6553347","0.653473","0.6523465","0.6432451","0.6315115","0.6313982","0.6313573","0.6260093","0.62319624","0.6202652","0.61971724","0.61828244","0.6172298","0.61443394","0.6137735","0.60977584","0.60810006","0.60741115","0.60420185","0.6037983","0.602657","0.6013825","0.6004946","0.60015166","0.6000877","0.5995938","0.59657013","0.596101","0.595927","0.5953419","0.59441215","0.5941648","0.58888537","0.5854444","0.58395857","0.5834267","0.58264446","0.5824896","0.5821768","0.58149636","0.5806094","0.5804679","0.5791211","0.5774426","0.5771806","0.5754776","0.575111","0.5746249","0.57440597","0.5740727","0.573682","0.5734253","0.57308626","0.57300484","0.5724067","0.57168114","0.5708115","0.5696067","0.5694714","0.5694293","0.56880075","0.5685244","0.5681466","0.56740415","0.5648421","0.5645423","0.5636741","0.5631309","0.56269383","0.5617324","0.5614101","0.56007206","0.55944127","0.55797195","0.55702204","0.5560724","0.555596","0.5551406","0.55485576","0.55400157","0.5531307","0.55295163","0.5529059","0.55268407","0.552532","0.55198836","0.5518502","0.5517227","0.55128247","0.55031526","0.5499168","0.54920566","0.5485899"],"string":"[\n \"0.7142697\",\n \"0.7100457\",\n \"0.6930307\",\n \"0.67667294\",\n \"0.66168964\",\n \"0.6615334\",\n \"0.6612648\",\n \"0.6553347\",\n \"0.653473\",\n \"0.6523465\",\n \"0.6432451\",\n \"0.6315115\",\n \"0.6313982\",\n \"0.6313573\",\n \"0.6260093\",\n \"0.62319624\",\n \"0.6202652\",\n \"0.61971724\",\n \"0.61828244\",\n \"0.6172298\",\n \"0.61443394\",\n \"0.6137735\",\n \"0.60977584\",\n \"0.60810006\",\n \"0.60741115\",\n \"0.60420185\",\n \"0.6037983\",\n \"0.602657\",\n \"0.6013825\",\n \"0.6004946\",\n \"0.60015166\",\n \"0.6000877\",\n \"0.5995938\",\n \"0.59657013\",\n \"0.596101\",\n \"0.595927\",\n \"0.5953419\",\n \"0.59441215\",\n \"0.5941648\",\n \"0.58888537\",\n \"0.5854444\",\n \"0.58395857\",\n \"0.5834267\",\n \"0.58264446\",\n \"0.5824896\",\n \"0.5821768\",\n \"0.58149636\",\n \"0.5806094\",\n \"0.5804679\",\n \"0.5791211\",\n \"0.5774426\",\n \"0.5771806\",\n \"0.5754776\",\n \"0.575111\",\n \"0.5746249\",\n \"0.57440597\",\n \"0.5740727\",\n \"0.573682\",\n \"0.5734253\",\n \"0.57308626\",\n \"0.57300484\",\n \"0.5724067\",\n \"0.57168114\",\n \"0.5708115\",\n \"0.5696067\",\n \"0.5694714\",\n \"0.5694293\",\n \"0.56880075\",\n \"0.5685244\",\n \"0.5681466\",\n \"0.56740415\",\n \"0.5648421\",\n \"0.5645423\",\n \"0.5636741\",\n \"0.5631309\",\n \"0.56269383\",\n \"0.5617324\",\n \"0.5614101\",\n \"0.56007206\",\n \"0.55944127\",\n \"0.55797195\",\n \"0.55702204\",\n \"0.5560724\",\n \"0.555596\",\n \"0.5551406\",\n \"0.55485576\",\n \"0.55400157\",\n \"0.5531307\",\n \"0.55295163\",\n \"0.5529059\",\n \"0.55268407\",\n \"0.552532\",\n \"0.55198836\",\n \"0.5518502\",\n \"0.5517227\",\n \"0.55128247\",\n \"0.55031526\",\n \"0.5499168\",\n \"0.54920566\",\n \"0.5485899\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64697844"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":13780,"cells":{"query":{"kind":"string","value":"Favorites tweets that match a certain phrase (hashtag, word, etc.)"},"document":{"kind":"string","value":"def auto_fav(q, count=5, result_type=\"recent\"):\n\n result = search_tweets(q, count, result_type)\n\n for tweet in result[\"statuses\"]:\n try:\n # don't favorite your own tweets\n if tweet[\"user\"][\"screen_name\"] == TWITTER_HANDLE:\n continue\n\n result = t.favorites.create(_id=tweet[\"id\"])\n print(\"favorited: %s\" % (result[\"text\"].encode(\"utf-8\")))\n\n # when you have already favorited a tweet, this error is thrown\n except TwitterHTTPError as e:\n print(\"error: %s\" % (str(e)))"},"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 keep(tweet):\n text = tweet[\"Text\"].lower()\n hashtags = \" \".join(tweet[\"Hashtags\"]).lower()\n for word in keep_words:\n if word in text:\n # print(\"{} in text {}\".format(word, text))\n return True\n if word in hashtags:\n # print(\"{} in hashtags {}\".format(word, hastags))\n return True\n return False","def two_word_finder(word1,word2,text):\r\n word1 = word1.lower()\r\n word2 = word2.lower()\r\n text = str(text).lower()\r\n if word1 and word2 in text:\r\n return True #return text to see specific tweets\r\n return False","def filter_by_hashtag(tweets: list, hashtag: str) -> list:\n tweets_with_hashtag = {} # findall(): Kui tekstis on rohkem kui üks regulaaravaldisele vastav alamsõne saab kõikide vastete järjendi moodustada funktsiooniga findall()\n pattern = r\"#\\w+\" # \\w : tähed, numbrid, alakriips, + : 1 või rohkem\n for tweet in tweets: # r\"string\" on \"raw\" tüüpi string, mis tähendab, et kurakaldkriipsud(\"\\\") jäetakse teksti alles.\n find_hashtag = re.findall(pattern, tweet.content) # word:\\w\\w\\w. Regulaaravaldisele vastab täpne sõne \"word:\" ning sellele järgnevad 3 suvalist tähte.\n if find_hashtag:\n tweets_with_hashtag.setdefault(ht, []).append(tweet)\n return tweets_with_hashtag[hashtag]","def searchTweets():\n if 'api' not in globals():\n startTwitterApi(getApiKeys(fileName='apiConf2.txt'))\n #SEARCHING TWEETS CONTAINING THE HASHTAG \"#bitcoin\" USING TWEEPY LIBRARY\n myTweets= []\n #words=list(map(str,words))\n if words:\n myQuery=' OR '.join(words)\n else:\n myQuery = '*'\n if removeRetweets:\n myQuery += ' - filter:retweets'\n kwargs['q']=myQuery\n kwargs['count']=100\n kwargs['tweet_mode']='extended'\n if 'startingDate' in kwargs:\n kwargs['since']=kwargs['startingDate']\n del(kwargs['startingDate'])\n if 'endingDate' in kwargs:\n kwargs['until']=kwargs['endingDate']\n del(kwargs['endingDate'])\n if 'maxTweets' in kwargs:\n del(kwargs['maxTweets'])\n if sortBy=='newest':\n for tweet in tweepy.Cursor(api.search, kwargs).items(maxTweets):\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n else:\n for tweet in tweepy.Cursor(api.search, kwargs).items():\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n return getTopNTweets(myTweets, maxTweets)","def RelevantTweet(Tweet, Termlist, DateRange=None):\n TweetText = Tweet[\"full_text\"].lower() #ignore case. # Changed to full_text now, instead of (previously) truncated.\n\n if Tweet[\"is_quote_status\"] is True: #Append, since we're just matching terms.\n # print(\"Tweet = \", Tweet.keys())\n if \"quoted_status\" in Tweet:\n TweetText = TweetText + \" \" + Tweet[\"quoted_status\"][\"full_text\"].lower()\n\n # From: https://stackoverflow.com/questions/7703865/going-from-twitter-date-to-python-datetime-date\n if DateRange is not None:\n ts = time.strptime(Tweet['created_at'], '%a %b %d %H:%M:%S +0000 %Y')\n\n if ts < time.strptime(DateRange[0], '%b %d %H:%M:%S %Y'):\n return False\n elif ts > time.strptime(DateRange[1], '%b %d %H:%M:%S %Y'):\n return False\n if Termlist is None:\n return True\n else:\n for word in Termlist:\n if word.lower() in TweetText:\n return True\n return False","def show_search_results():\n\n #Get values from search-box via AJAX\n current_keyword = request.form.get('search').lower()\n print \"**********************\"\n print current_keyword\n print \"**********************\"\n tweets = get_tweets_by_api(term=current_keyword)\n\n result = []\n\n for tweet in tweets:\n # Exclude retweets since they appear as duplicatses to endu ser\n if tweet.retweeted_status is None:\n # Convert tweet text from unicode to text\n tweet_id = tweet.id\n text = unicodedata.normalize('NFKD', tweet.text).encode('ascii', 'ignore')\n # Find URL in text and bind to url\n # url = re.search('((?:http|https)(?::\\\\/{2}[\\\\w]+)(?:[\\\\/|\\\\.]?)(?:[^\\\\s\"]*))', text)\n url = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)\n # Remove URL from text\n text_wo_url = re.sub(r'^https?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE)\n # Handle / Name\n user = unicodedata.normalize('NFKD', tweet.user.screen_name).encode('ascii', 'ignore')\n # Count of favorites\n favorite_count = tweet.favorite_count\n #Return dictionary of hashtags with hashtag as key and number of occurances as value\n if tweet.hashtags:\n # Convert hashtags from unicode to string\n ht_list = []\n for hashtag in tweet.hashtags:\n ht_str = unicodedata.normalize('NFKD', hashtag.text).encode('ascii', 'ignore')\n ht_list.append(ht_str.lower())\n hashtags = Counter(ht_list)\n else:\n hashtags = tweet.hashtags\n # Convert tweet from unicode to datetime\n created_at = tweet.created_at\n # format created_at string to ISO 8610\n created_at_str = time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(created_at, '%a %b %d %H:%M:%S +0000 %Y'))\n # create a moment from the string\n created_at = moment.date(created_at_str, 'YYYY-MM-DD HH:mm:ss')\n result.append({'created_at': created_at_str, 'tweet_text': text_wo_url, 'user': user,\n 'favorite_count': favorite_count, 'hashtags': hashtags,\n 'url': url, 'tweet_id': tweet_id})\n\n print \"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\"\n print result\n print \"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\"\n\n return jsonify(result=result) #, tweets","def filter_tweet(tweet):\n if not filter_tweet_core(tweet):\n return False\n if bannedusers.search(tweet['user']['screen_name']) or (\n 'retweeted_status' in tweet and bannedusers.search(tweet['retweeted_status']['user']['screen_name'])):\n return False\n if tweet['user']['screen_name'] == credentials['username']: # Do not match self tweets :-)\n return False\n return True","def filter_tweet_core(tweet):\n if not ('user' in tweet and 'screen_name' in tweet['user'] \\\n and 'text' in tweet):\n return False\n if not filter_tweet_text(tweet['text']):\n return False\n if bannedclients.search(tweet['source']):\n return False\n if bannedterms.search(tweet['text']):\n return False\n if bannedterms.search(tweet['user']['screen_name']):\n return False\n if 'entities' in tweet and bannedterms.search(json.dumps(tweet['entities'])):\n return False\n if not filter_tweet_entropy(tweet['text']):\n return False\n if not filter_tweet_cardspam(tweet):\n return False\n return True","def filter_tweets(tweets):\n # We keep only tweets by chrisalbon with pictures\n search_tweets = [tw for tw in tweets if tw['username'] == '@chrisalbon' and len(tw['images']) > 0]\n # He made multiple tweets on the same topic, we keep only the most recent tweets\n # We use the indexes of the reversed tweet list and dictionnaries to keep only key \n unique_search_index = sorted(list({t['text'].lower():i for i,t in list(enumerate(search_tweets))[::-1]}.values()))\n unique_search_tweets = [search_tweets[i] for i in unique_search_index]\n\n # Keep non-downloaded tweets\n most_recent_file = sorted([datetime.datetime.fromtimestamp(os.path.getmtime(path)) \n for path in glob.glob(\"./downloaded_pics/*.jpg\")], reverse=True)[0]\n recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > most_recent_file]\n\n # Uncomment for testing new tweets\n # recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > datetime.datetime(2017, 7, 6, 13, 41, 48)]\n return recent_seach_tweets","def extract_hashtags(tweet):\n tknzr = TweetTokenizer()\n hashtags = [token.lower() for token in tknzr.tokenize(tweet) if re.match(hashtag_re, token)]\n return hashtags","def like_tweets(pos_tweets):\n\n for tweet in pos_tweets:\n twitter.CreateFavorite(status_id=tweet.id)\n\n return","def like_tweet(tweet_id):\n twitter.create_favorite(id=tweet_id)","def search(searchTerm):\n api = twitter.Api()\n tweets = api.GetSearch(searchTerm)\n for tweet in tweets:\n util.safe_print(tweet.GetText())","def search_tweets(q, count=100, result_type=\"recent\"):\n\n return t.search.tweets(q=q, result_type=result_type, count=count)","def _get_tweets(self):\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n api = tweepy.API(auth)\n search = api.search(self.term, lang='en', count=100)\n\n print(f\"Getting tweets that mention '{self.term}', \"\n f\"this may take a while...\")\n\n save_tweet_text = [tweet._json['text'] for tweet in search]\n while len(save_tweet_text) < 1000:\n try:\n oldest = search[-1].id - 1\n search = api.search(self.term, lang='en', count=100, max_id=oldest)\n new_tweets = [tweet._json['text'] for tweet in search]\n save_tweet_text.extend(new_tweets)\n\n # Turn into a set to remove duplicated tweets, then back to list\n save_tweet_text = list(set(save_tweet_text))\n except IndexError:\n break\n\n print(f\"Done. {len(save_tweet_text)} Tweets received.\")\n return save_tweet_text","def clean_tweet(tweet):\r\n reply_pattern = re.compile(\"^@([a-zA-Z0-9]*) (.*)\")\r\n regexhandler = regex.RegexHandler()\r\n # add mark if tweets starts with a mention (@user)\r\n if reply_pattern.match(tweet.tweet[\"text\"]) is not None:\r\n temp = \"MarkReply \" + tweet.tweet[\"text\"]\r\n else:\r\n temp = tweet.tweet[\"text\"]\r\n # language dependent\r\n\r\n if floodtags.core.statics.StaticData.locations:\r\n for location in floodtags.core.statics.StaticData.locations:\r\n if location in temp:\r\n temp += \" MarkLocation\"\r\n\r\n try:\r\n stemmer = SnowballStemmer(floodtags.core.statics.StaticData.language.lower())\r\n # stem words\r\n temp = \" \".join(\r\n [stemmer.stem(x) if x not in tweet.tweet[\r\n \"keywords\"] and \"MarkReply\" not in x and \"MarkLocation\" not in x else x for x in temp.split()])\r\n except ValueError:\r\n print(\"language not found:\", floodtags.core.statics.StaticData.language)\r\n # pass\r\n\r\n # store language\r\n temp = \"Mark\" + tweet.language + \" \" + temp\r\n\r\n # store keyword\r\n\r\n # replace each website with 'MarkWebsite' to create more similarity\r\n temp = regexhandler.replace(temp, 'MarkWebsite', regex.Expressions.website)\r\n # replace each photo url with 'MarkPhoto' to create more similarity\r\n for i in range(len(tweet.tweet[\"photos\"])):\r\n temp = Vectorizer.rreplace(temp, \"MarkWebsite\", \"MarkPhoto\", 1)\r\n # replace each height with 'MarkHeight' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkHeight\", regex.Expressions.waterheight)\r\n # replace each time with 'MarkTime' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkTime\", regex.Expressions.time)\r\n # replace each date with 'MarkDate' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkDate\", regex.Expressions.date)\r\n # replace each number with 'MarkNumber' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkNumber\", regex.Expressions.number)\r\n temp = re.sub('\\n', ' ', temp)\r\n results = re.findall(\"(^|[^@\\w])@(\\w{1,15})\", temp)\r\n # add mark for each user name\r\n if results is not None:\r\n for i in range(len(results)):\r\n temp += \" MarkUserName\"\r\n results = re.findall(\"#(\\S*)\", temp)\r\n # add mark for each hashtag\r\n if results is not None:\r\n for i in range(len(results)):\r\n temp += \" MarkHashTag\"\r\n # add sender as feature\r\n temp = \"Sender\" + tweet.tweet[\"source\"][\"username\"] + \" \" + temp\r\n # remove unnecessary characters and chance text to lower case\r\n return re.sub('[#\\.,:]', '', temp)","def searchByKeyword(self, keyword, until=\"\", since=\"\", count=None, result_type=\"recent\"):\n if count is None:\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\n full_text=True, tweet_mode=\"extended\", lang=\"en\").items()\n else:\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\n full_text=True, tweet_mode=\"extended\", lang=\"en\").items(count)\n\n for status in tweets:\n createdDate = parser.parse(str(status._json[\"created_at\"]).strip())\n createdDate = createdDate.replace(\n tzinfo=pytz.utc) - createdDate.utcoffset()\n status_refined = {\n 'keyword': keyword,\n '_id': status._json[\"id\"],\n 'created_at': createdDate,\n 'tweetText': status._json[\"full_text\"],\n 'hashtags': status._json[\"entities\"][\"hashtags\"],\n 'userLoc': status._json[\"user\"][\"location\"],\n 'tweetGeo': status._json[\"geo\"],\n 'tweetCoordinates': status._json[\"coordinates\"],\n 'tweetPlace': status._json[\"place\"],\n 'retweet': {},\n }\n if hasattr(status, \"retweeted_status\"):\n status_refined['tweetText'] = status._json[\"retweeted_status\"][\"full_text\"]\n status_refined['retweet'] = {\n 'original_retweet_id': status._json[\"retweeted_status\"][\"id\"],\n 'origUserLoc': status._json[\"retweeted_status\"][\"user\"][\"location\"],\n 'origTweetLoc': status._json[\"retweeted_status\"][\"geo\"],\n 'origTweetPlace': status._json[\"retweeted_status\"][\"place\"],\n 'origTweetCoord': status._json[\"retweeted_status\"][\"coordinates\"],\n 'origHashtags': status._json[\"retweeted_status\"][\"entities\"][\"hashtags\"],\n 'retweet_count': status._json[\"retweet_count\"],\n }\n self.tweets.append(status_refined)\n return self.tweets","def reply_to_tweets():\n last_seen_id = retrieve_last_seen_id(FILE_NAME)\n mentions = api.mentions_timeline(\n last_seen_id,\n tweet_mode='extended')\n\n for mention in reversed(mentions):\n print(str(mention.id) + ' - ' + mention.full_text, flush=True)\n last_seen_id = mention.id\n store_last_seen_id(last_seen_id, FILE_NAME)\n for i in range(len(keywords)):\n if keywords[i] in mention.full_text.lower():\n print(\"responding back to: \" + '@' +\n mention.user.screen_name, flush=True)\n api.update_status('@' + mention.user.screen_name + ' ' +\n salts[i], mention.id)","def has_keyword(tweet, keywords):\n temp = tweet.lower()\n for keyword in keywords:\n if bool(re.search(f'\\\\b{keyword}\\\\b', temp)):\n return True\n return False","def cheer(self, songs):\n if self.favourite_song in songs:\n return \"Whoo!\"","def collect_tweets(redis_client, twitter_client, search_term):\n search = Search(redis_client, twitter_client, search_term)\n search.get_term_state()\n search.parse_term_state()\n search.set_query_string()\n search.set_execution_time()\n search.execute_query()\n search.incr_query_counters()\n search.set_newest_id()\n search.set_oldest_id()\n search.set_scenario()\n search.set_term_state()\n search.store_results()\n search.set_score()\n search.log_state()","def compute_retweet(row):\n return row['full_text'].startswith('RT')","def topictweets(url):\n article = get_article(url)\n keywords = get_keywords(article['text'])\n entities = get_entities(article['text'])\n q = twitter_query(keywords, entities)\n result = search({'q': q, 'count': 100, 'result_type': 'mixed'})\n tweets = screen_name_filter(result.statuses, 'media')\n return tweets","def twitter_text(text, search_query = False):\r\n tt = TwitterText(text)\r\n if search_query:\r\n tt.text = tt.highlighter.hit_highlight(query = search_query)\r\n tt.text = tt.autolink.auto_link()\r\n return tt.text","def original_three_tweets():\n test_tweets = [\n \"is #bigdata finally the answer to end poverty? \\\n @lavanyarathnam http://ow.ly/o8gt3 #analytics\",\n \"interview: xia wang, astrazeneca on #bigdata and the promise of effective \\\n healthcare #kdn http://ow.ly/ot2uj\",\n \"big data is not just for big business. on how #bigdata is being deployed for \\\n small businesses: http://bddy.me/1bzukb3 @cxotodayalerts #smb\"\n ]\n return test_tweets","def like_tweet(self, tag):\n self.bot.get('https://twitter.com/search?q=' + tag + '&src=typed')\n self.__wait(3, 3)\n for i in range(1, 3):\n self.bot.execute_script('window.scrollTo(0,document.body.scrollHeight)')\n self.__wait(2, 3)\n tweets = self.bot.find_elements_by_tag_name('article')\n\n links = []\n for tweet in tweets:\n sub_links = tweet.find_elements_by_tag_name('a')\n links += [sub_link.get_attribute('href')\n for sub_link in sub_links if 'status' in sub_link.get_attribute('href')]\n\n print('Started to like {} tweets'.format(len(links)))\n\n for link in links:\n self.bot.get(link)\n self.__wait(3, 5)\n likes = self.bot.find_elements_by_css_selector('div[data-testid=\"like\"')\n for like in likes:\n like.click()\n self.__wait(3, 5)","def analyze_hashtag(self, hashtag, count=200):\n tweets = []\n\n for x in xrange(0, int(count / 100)):\n tweets.extend(self.tweet_fetcher.get_tweets(hashtag))\n\n analyzed_tweets = sort_tweets(self.sa.classify(tweets))\n\n self.analyzed_tweets = analyzed_tweets\n\n return analyzed_tweets","def keyword_filter(tweet_objects, keywords):\n \n tm = TweetManager()\n\n filtered_list = []\n\n for tweet in tweet_objects:\n # Separate text and hashtags\n tweet_text = text_from_tweet(tweet)\n hashtags = tm.find_hashtags(tweet_text)\n\n content_filtered_text = tm.clean_tweet(tweet_text)\n\n keywords_in_text = tm.find_keywords_in_tweet(content_filtered_text, keywords)\n hashtags_with_keywords = tm.find_hashtags_with_keywords(hashtags, keywords)\n\n if len(keywords_in_text) or len(hashtags_with_keywords):\n filtered_list.append({\n KEY_TEXT_ORIGINAL: tweet_text,\n KEY_TEXT_FILTERED: content_filtered_text,\n KEY_HASHTAGS_WITH_KEYWORDS: hashtags_with_keywords,\n KEY_TEXT_KEYWORDS: keywords_in_text\n })\n\n return filtered_list","def searchbrown_word(tag):\n brown_tagged_words = brown.tagged_words(categories='news')\n hitwords = []\n for i in range(len(brown_tagged_words)):\n if tag == brown_tagged_words[i][1]:\n hitwords.append(brown_tagged_words[i][0].lower())\n return hitwords","def printHashtagsAndMentions(searchText=None, filterTerms=False, tweetLimit=0):\n tweets = db.Tweet.select()\n if searchText is not None:\n tweets = tweets.filter(db.Tweet.q.message.contains(searchText))\n tweets = tweets.limit(tweetLimit)\n\n hashtags, mentions, plain = getHashtagsAndMentions(tweets)\n\n if searchText and filterTerms:\n hashtags = Counter(\n {k: v for k, v in hashtags.items() if searchText.lower() in k.lower()}\n )\n mentions = Counter(\n {k: v for k, v in mentions.items() if searchText.lower() in k.lower()}\n )\n plain = Counter(\n {k: v for k, v in plain.items() if searchText.lower() in k.lower()}\n )\n\n # Unique word count for each area.\n hashtagWC = len(hashtags)\n mentionWC = len(mentions)\n plainWC = len(plain)\n\n print(\"Summary\")\n print(\"==============\")\n # Count items in the sliced selection since .count() does not work with\n # a limit.\n count = len(list(tweets)) if tweetLimit else tweets.count()\n print(\"{0:7,d} tweets\".format(count))\n print(\"{0:7,d} unique words\".format(hashtagWC + mentionWC + plainWC))\n print(\"{0:7,d} unique hashtags\".format(hashtagWC))\n print(\"{0:7,d} unique mentions\".format(mentionWC))\n print(\"{0:7,d} unique plain words\".format(plainWC))\n print()\n\n print(\"Hashtags\")\n print(\"========\")\n printCounterByCount(hashtags)\n print()\n\n print(\"Mentions\")\n print(\"========\")\n printCounterByCount(mentions)\n\n \"\"\"\n # Removal of stopwords and handling of URIs is needed to make this\n # useful.\n print 'Plain'\n print '========'\n printCounterByCount(plain)\n \"\"\"","def extract_hashtags(self):\n most_common_words = get_most_common_words(\n self.get_full_text_from_source())\n queryset = Hashtag.objects.all()\n for word in most_common_words:\n tag = queryset.get_or_create(word=word.lower())[0]\n tag.documents.add(self)","def getHashtagsAndMentions(tweets):\n hashtags = Counter()\n mentions = Counter()\n plain = Counter()\n\n pattern = re.compile(r\"[^#@\\w'-]+\")\n\n for t in tweets:\n words = pattern.split(t.message)\n for word in words:\n # Ignore null strings caused by split characters at the end of a\n # message and remove standalone hyphens.\n if word and not word.startswith(\"-\"):\n # Increment count for the word in the Counter.\n if word.startswith(\"#\"):\n hashtags.update({word: 1})\n elif word.startswith(\"@\"):\n mentions.update({word: 1})\n else:\n # TODO: apply nltk.corpus.stopwords.words() here,\n # across languages. Consider that the stopwords cut off\n # before apostrophe, therefore check if the word\n # starts with the stopword.\n plain.update({word: 1})\n\n return hashtags, mentions, plain","def searchbrown_phrase(tags):\n l = len(tags)\n brown_tagged_words = brown.tagged_words(categories='news')\n hitwords = []\n for i in range(len(brown_tagged_words)-l+1):\n searchtags = [tag for _,tag in brown_tagged_words[i:i+l]]\n if tags == searchtags:\n hitwords.append(tuple([w.lower()\n for w,_ in brown_tagged_words[i:i+l]]))\n return hitwords","def more_like_text(text, klass):\n back = connections['default'].get_backend()\n\n if hasattr(back, 'conn'):\n query = {'query': {\n 'filtered': {\n 'query': {\n 'fuzzy_like_this': {\n 'like_text': text\n }\n },\n 'filter': {\n 'bool': {\n 'must': {\n 'term': {'django_ct': 'idea.idea'}\n }\n }\n }\n }\n }\n\n }\n results = back.conn.search(query)\n return back._process_results(results)['results']\n else:\n return []","def duplicates_filter(tweet_objects):\n\n cache = []\n filtered_list = []\n\n for tweet in tweet_objects:\n t_text = text_from_tweet(tweet)\n \n if t_text not in cache:\n filtered_list.append(tweet)\n cache.append(t_text)\n\n return filtered_list","async def team_search(self, ctx: commands.Context, username: str):\n all_usernames = {team_id: team.username for team_id, team in self.teams.items()\n if team is not None}\n suggestions = []\n log.info(repr(fuzzywuzzy.process.extract(\n username, all_usernames, limit=5)))\n for fuzz_username, rating, fuzz_id in fuzzywuzzy.process.extract(\n username, all_usernames, limit=5):\n if rating < 50:\n break\n fuzz_team = self.teams[fuzz_id]\n suggestions.append(\n f'(ID: **{fuzz_team.team_id}**) **{fuzz_team.display_name[:40]}**'\n f' -- {len(fuzz_team.users)} registered members')\n if suggestions:\n await ctx.send('\\n'.join(suggestions))\n else:\n await ctx.send(f\"Couldn't find any teams whose usernames resembled `{username}`\")","def displayAlsoLike(self):\n\n result = self.client.get(\"/view_favorites\")\n self.assertIn(b\"11925205\", result.data)","def trendingTweets():\n api = twitter.Api()\n trending_topics = api.GetTrendsWoeid(PHILA_WOEID)\n for topic in trending_topics:\n topicSearchTerm = topic.name\n trending_tweets = api.GetSearch(topicSearchTerm)\n for tweet in trending_tweets:\n util.safe_print(tweet.GetText())\n # pass","def query_tweets_once(query, limit=None, num_tweets=0):\n logging.info(\"Querying {}\".format(query))\n query = query.replace(' ', '%20').replace(\"#\", \"%23\").replace(\":\", \"%3A\")\n pos = None\n tweets = []\n try:\n while True:\n new_tweets, pos = query_single_page(\n INIT_URL.format(q=query) if pos is None\n else RELOAD_URL.format(q=query, pos=pos),\n pos is None\n )\n if len(new_tweets) == 0:\n logging.info(\"Got {} tweets for {}.\".format(\n len(tweets), query))\n return tweets\n\n logging.info(\"Got {} tweets ({} new).\".format(\n len(tweets) + num_tweets, len(new_tweets)))\n\n tweets += new_tweets\n\n if limit is not None and len(tweets) + num_tweets >= limit:\n return tweets\n except KeyboardInterrupt:\n logging.info(\"Program interrupted by user. Returning tweets gathered \"\n \"so far...\")\n except BaseException:\n logging.exception(\"An unknown error occurred! Returning tweets \"\n \"gathered so far.\")\n\n return tweets","async def wordfilter_test(self, ctx, *, message):\n found = self.test_sentence(message)\n if found:\n await ctx.send(f\"Message contains `{found}`\")\n else:\n await ctx.send(\"Couldn't detect any filtered words\")","def is_retweet(tweet):\n\n return 'retweeted_status' in tweet","def collect_tweets(search_id, search_term, number_of_tweets):\n\n tweets = []\n for tweet in api_collector.collect(search_term, number_of_tweets):\n tweets.append((tweet.id_str, tweet.created_at, tweet.full_text))\n if len(tweets) == 0:\n search = Search.objects.get(pk=search_id)\n search.empty = True\n search.save()\n notify_searchers.delay(search_id)\n else:\n classify_tweets.delay(search_id, tweets)","def is_likely_pokemon_related(tweet, hashtags):\n # check hash tags\n for tag in hashtags:\n if tag.text in TAGS_WE_CARE_ABOUT:\n return True\n # check tweet content\n for word in TEXT_WE_CARE_ABOUT:\n if word in tweet:\n return True\n return False","def get_tweets(api, listOfTweets, keyword, numOfTweets=20, date_since='2019-1-1', lang=\"en\"):\n spinner = yaspin()\n spinner.start()\n for tweet in tweepy.Cursor(api.search, q=keyword, lang=lang, since=date_since).items(numOfTweets):\n # Add tweets in this format\n dict_ = {'Screen Name': tweet.user.screen_name,\n 'User Name': tweet.user.name,\n 'Tweet Created At': str(tweet.created_at),\n 'Tweet Text': tweet.text,\n 'Cleaned Tweet Text': func.clean_tweets(tweet.text),\n 'User Location': str(tweet.user.location),\n 'Tweet Coordinates': str(tweet.coordinates),\n 'Retweet Count': str(tweet.retweet_count),\n 'Retweeted': str(tweet.retweeted),\n 'Phone Type': str(tweet.source),\n 'Favorite Count': str(tweet.favorite_count),\n 'Favorited': str(tweet.favorited),\n 'Replied': str(tweet.in_reply_to_status_id_str)\n }\n listOfTweets.append(dict_)\n spinner.stop()\n return listOfTweets","def retweet_filter(self, text):\n return not text.lower().startswith('rt')","def search(self, word):","def process(self, filter_words, count=1):\n user = self.__api.get_user(self.__username)\n\n # print user.screen_name\n # print user.followers_count\n if self.__appMode == 1 and self.__TimeLineMode == 1:\n self.get_timeline(filter_words)\n else:\n if self.__friendMode:\n print(\"Getting all Twitter Friends \\n\")\n for friend in user.friends():\n self.get_tweet(friend.screen_name, filter_words, count)\n else:\n for screen_name in self.__priorityCoin:\n self.get_tweet(screen_name, filter_words, count)\n print('Twitter Data Extraction done!!')","def search(self, term):","def search_from_terms(api, term, **kwargs):\n tweets=api.GetSearch(term=term)\n return {\"tweets\":tweets}","def process(self, tweet):\n\n #identify the applicable event keywords for this text\n text = self.cleanup_data(tweet.text)\n tokens = [str(t.lower()).translate(None, string.punctuation) for t in tweet.text.split()]\n applicable_tokens = []\n for phrase in self.match_event_tree.root.keywords:\n if phrase in \" \".join(tokens):\n applicable_tokens.append(phrase)\n\n self.match_event_tree.propogate_tweet(applicable_tokens, tweet)","def analyze_tweets(tweets, model, w2v_model):\n # TODO DO EVERYTHING HERE\n #tweets = [(\"StarWars\", tc.query_tweets(\"StarWars\"))]\n \n #tweets = tc.query_tweets('starwars')\n df = pd.DataFrame(columns=['pos', 'neu', 'neg'])\n if not os.path.isdir('results'):\n os.mkdir('results')\n for topic, topic_tweets in tweets:\n tokenized_tweets = tp.process_raw_tweets(topic_tweets)\n df.loc[topic], dummy = classify_tweets(tokenized_tweets, model, w2v_model)\n vis.word_cloud_from_frequencies(tp.count_tokens(tokenized_tweets), f\"results/{topic}_cloud.png\", width=800, height=400,)\n \n vis.bar_plot_from_dataframe(df, 'results/results.png')\n print(\"\\n\")\n print(df)","def get_tweets(hashtag):\n api = twitter.Api(consumer_key=TWITTER_API_CONSUMER_KEY,\n consumer_secret=TWITTER_API_CONSUMER_SECRET,\n access_token_key=TWITTER_API_ACCESS_TOKEN_KEY,\n access_token_secret=TWITTER_API_ACCESS_TOKEN_SECRET)\n\n query = (f\"q=%23{HASHTAG}%20-RT\"\n f\"&result_type=recent&since=2019-01-01&count={NUM_TWEETS}\")\n results = api.GetSearch(raw_query=query)\n\n return [\n format_tweet(tweet.AsDict())\n for tweet in results\n ]","def twitter_search(a, q, x=10000000, t=100, s=None, m=-1):\n\n tweets = list()\n tweet_count = 0\n\n while tweet_count < x:\n try:\n if (m <= 0):\n if (not s):\n new_tweets = a.search(q = q, count = t)\n else:\n new_tweets = a.search(q = q, count = t, since_id = s)\n else:\n if (not s):\n new_tweets = a.search(q = q, count = t, max_id = (m - 1))\n else:\n new_tweets = a.search(q = q, count = t, max_id = (m - 1), since_id = s)\n\n if not new_tweets:\n break\n\n for tweet in new_tweets:\n tweets.append(tweet)\n\n tweet_count += len(new_tweets)\n m = new_tweets[-1].id\n\n except tweepy.TweepError as e:\n error = (-1, \"error:\" + str(e))\n return error\n\n search_results = (tweet_count, tweets)\n\n return search_results","def having(self, wordlist=None, coordinates=None):\n track = \",\".join(wordlist) if wordlist else None\n locations = \",\".join(map(str, coordinates)) if coordinates else None\n\n if track and locations:\n generator = self.stream.statuses.filter(track=track,\n locations=locations)\n elif track:\n generator = self.stream.statuses.filter(track=track)\n elif locations:\n generator = self.stream.statuses.filter(locations=locations)\n else:\n raise ValueError(\"Must provide one of wordlist or coordinates\")\n\n for tweet in generator:\n if locations:\n if 'coordinates' in tweet and tweet['coordinates']:\n yield tweet\n else:\n yield tweet","def has_retweet(tweet):\n\n pattern = r'^RT'\n return re.match(pattern, tweet)","def tweets_features(tweet):\n tweet = remove_stop_words(tweet)\n return {'TWEET': tweet}","def hashtags(self, candidate_list):\n if Tweet.hashtagre is None:\n Tweet.hashtagre = re.compile('|'.join(map(re.escape, candidate_list)))\n return [\n [m.group(0).replace('#', '', 1), m.span()]\n for m in Tweet.hashtagre.finditer(self.text)\n ]","async def search(self, ctx: \"IceTeaContext\", *, query):\n response_list = await ctx.guild_data.search_tags(query)\n if len(response_list) > 0:\n response_message = \"\\n\".join([tag.title for tag in response_list])\n await ctx.send(f\"Found these tags:\\n{response_message}\")\n else:\n await ctx.send(\"No similar tags found\")","def excludeTwitterTags(tweet):\n\ttwext = tweet['text'].lower()\n\tif tweet['entities']['hashtags']:\n\t\tfor hashtag in tweet['entities']['hashtags']:\n\t\t\ttwext = twext.replace(hashtag['text'].lower(),\"\")\n\t\t\tif tweet['entities']['user_mentions']:\n\t\t\t\tfor user_mention in tweet['entities']['user_mentions']:\n\t\t\t\t\ttwext = twext.replace(user_mention['screen_name'].lower(),\"\")\n\t\t\tif tweet['entities']['urls']:\n\t\t\t\tfor url in tweet['entities']['urls']:\n\t\t\t\t\ttwext = twext.replace(url['url'].lower(),\"\")\n\treturn twext","def search_tweets(request):\n return render(request, 'ede/search.html')","def search_multiple_words(words):\n # YOUR CODE HERE #\n pass # delete this when you write your code","def get_tweets(self):\n keyword = 'covid'\n\n # Load tokens from file\n with open('../data/tokens.json', 'r') as f:\n tokens = json.load(f)\n\n # Stream tweets\n auth = tweepy.OAuthHandler(tokens['consumer_key'], tokens['consumer_secret'])\n auth.set_access_token(tokens['access_token_key'], tokens['access_token_secret'])\n api = tweepy.API(auth)\n\n # listen for tweets\n while True:\n\n # TODO: save file in Cloud Storage\n file_name = date.today().strftime('corpus-%d-%m-%Y.json')\n print(f'Updating {file_name} ...')\n\n StreamListener = StreamListener(\n file_name=file_name, \n max_tweets=1000)\n myStream = tweepy.Stream(\n auth=api.auth, \n listener=StreamListener)\n\n myStream.filter(track=[keyword], languages=['en'])\n \n time.sleep(60)","def expand_tweet(tweet):\r\n return hashtag_regex.sub(lambda hashtag: expand_hashtag(hashtag), tweet)","def get_mentions(texts, word):\r\n mentions = {}\r\n for text, label, text_id in texts:\r\n if word in text.lower():\r\n if label not in mentions:\r\n mentions[label] = [text_id]\r\n else:\r\n if text_id not in mentions[label]:\r\n mentions[label].append(text_id)\r\n return mentions","def search(self, query_string):\n return models.Thread.objects.filter(\n reduce(\n lambda q, f: q & Q(title__icontains=f),\n query_string.split(),\n Q()))","def like_retweet(self):\n \n bot = self.bot \n logging.debug('Get Hashtag')\n hashtag = Twitterbot.getHashtags(self)\n logging.debug('Bot for Like_retweet initalized')\n # fetches the latest tweets with the provided hashtag \n bot.get( \n 'https://twitter.com/search?q=%23'+hashtag+'&src=trend_click&vertical=trends'\n ) \n \n time.sleep(3) \n \n # using set so that only unique links \n # are present and to avoid unnecessary repetition \n links = set() \n \n # obtaining the links of the tweets \n for _ in range(3): \n # executing javascript code \n # to scroll the webpage \n bot.execute_script( \n 'window.scrollTo(0, document.body.scrollHeight)'\n ) \n \n time.sleep(4) \n \n # using list comprehension \n # for adding all the tweets link to the set \n # this particular piece of code might \n # look very complicated but the only reason \n # I opted for list comprehension because is \n # lot faster than traditional loops \n [ \n links.add(elem.get_attribute('href')) \n for elem in bot.find_elements_by_xpath(\"//a[@dir ='auto']\") \n ] \n i = 0\n # traversing through the generated links \n for link in links:\n #Nothing for the Hashtag was found, another run is required\n #if len(links ==6):\n # break\n # Twitterbot.like_retweet(self)\n # opens individual links \n #print(len(links))\n bot.get(link) \n time.sleep(4)\n if i == 3:\n break\n i += 1\n try: \n # retweet button selector \n bot.find_element_by_css_selector( \n '.css-18t94o4[data-testid =\"retweet\"]'\n ).click() \n # initializes action chain \n actions = ActionChains(bot) \n # sends RETURN key to retweet without comment \n actions.send_keys(Keys.RETURN).perform() \n \n # like button selector \n bot.find_element_by_css_selector( \n '.css-18t94o4[data-testid =\"like\"]'\n ).click() \n # adding higher sleep time to avoid \n # getting detected as bot by twitter \n logging.info(f'Liked and retweeted:\"{link}\"')\n time.sleep(10) \n except: \n time.sleep(2) \n \n # fetches the main homepage \n bot.get('https://twitter.com/')","def remove_hashtag(lista_tweets):\n\n novos_tweets = []\n\n for tweet in lista_tweets:\n texto = re.sub(r\"#\\S+\", \"\", tweet)\n novos_tweets.append(texto)\n\n return novos_tweets","def get_tweets(self, query, count=10):\n # empty list to store parsed tweets\n tweets = []\n\n try:\n # call twitter api to fetch tweets\n fetched_tweets = self.api.search(q=query, count=count)\n\n # parsing tweets one by one\n for tweet in fetched_tweets:\n # empty dictionary to store required params of a tweet\n parsed_tweet = {}\n\n # saving text of tweet\n parsed_tweet['text'] = tweet.text\n # saving sentiment of tweet\n parsed_tweet['sentiment'] = self.get_tweet_sentiment(tweet.text)\n\n # appending parsed tweet to tweets list\n if tweet.retweet_count > 0:\n # if tweet has retweets, ensure that it is appended only once\n if parsed_tweet not in tweets:\n tweets.append(parsed_tweet)\n else:\n tweets.append(parsed_tweet)\n\n # return parsed tweets\n return tweets\n\n except tweepy.TweepError as e:\n # print error (if any)\n print(\"Error : \" + str(e))","def searchByKeywordPro(self, query, since=\"\", until=\"\", maxResults=None):\n\n tweetsList = []\n if(not maxResults):\n tweetList, next_token = self.api.search_30_day(\n environment_name=\"developer\", query=query, toDate=until, fromDate=since)\n tweetsList.append(tweetList)\n while(next_token):\n tweetList, next_token = self.api.search_30_day(\n environment_name=\"developer\", query=query, toDate=until, fromDate=since, next=next_token)\n tweetsList.append(tweetList)\n else:\n tweetList, next_token = self.api.search_30_day(\n environment_name=\"developer\", query=query, toDate=until, fromDate=since)\n tweetsList.append(tweetList)\n maxResults -= len(tweetList)\n while(next_token and maxResults > 0):\n tweetList, next_token = self.api.search_30_day(\n environment_name=\"developer\", query=query, toDate=until, fromDate=since, next=next_token)\n tweetsList.append(tweetList)\n maxResults -= len(tweetList)\n for status in tweetsList:\n createdDate = parser.parse(str(status._json[\"created_at\"]).strip())\n createdDate = createdDate.replace(\n tzinfo=pytz.utc) - createdDate.utcoffset()\n status_refined = {\n 'keyword': query,\n '_id': status._json[\"id\"],\n 'created_at': createdDate,\n 'tweetText': status._json[\"text\"],\n 'hashtags': status._json[\"entities\"][\"hashtags\"],\n 'userLoc': status._json[\"user\"][\"location\"],\n 'tweetGeo': status._json[\"geo\"],\n 'tweetCoordinates': status._json[\"coordinates\"],\n 'tweetPlace': status._json[\"place\"],\n 'retweet': {},\n 'quote': {},\n }\n if hasattr(status, \"quoted_status\"):\n if \"extended_tweet\" in status._json[\"quoted_status\"].keys():\n print(\"Taking the expanded tweet\")\n status_refined['tweetText'] = status._json[\"quoted_status\"][\"extended_tweet\"][\"full_text\"]\n else:\n status_refined['tweetText'] = status._json[\"quoted_status\"][\"text\"]\n status_refined['quote'] = {\n 'original_retweet_id': status._json[\"quoted_status\"][\"id\"],\n 'origUserLoc': status._json[\"quoted_status\"][\"user\"][\"location\"],\n 'origTweetLoc': status._json[\"quoted_status\"][\"geo\"],\n 'origTweetPlace': status._json[\"quoted_status\"][\"place\"],\n 'origTweetCoord': status._json[\"quoted_status\"][\"coordinates\"],\n 'origHashtags': status._json[\"quoted_status\"][\"entities\"][\"hashtags\"],\n 'retweet_count': status._json[\"quote_count\"],\n }\n elif hasattr(status, \"retweeted_status\"):\n print(status._json[\"retweeted_status\"])\n if \"extended_tweet\" in status._json[\"retweeted_status\"].keys():\n print(\"Taking the expanded tweet\")\n status_refined['tweetText'] = status._json[\"retweeted_status\"][\"extended_tweet\"][\"full_text\"]\n else:\n status_refined['tweetText'] = status._json[\"retweeted_status\"][\"text\"]\n status_refined['retweet'] = {\n 'original_retweet_id': status._json[\"retweeted_status\"][\"id\"],\n 'origUserLoc': status._json[\"retweeted_status\"][\"user\"][\"location\"],\n 'origTweetLoc': status._json[\"retweeted_status\"][\"geo\"],\n 'origTweetPlace': status._json[\"retweeted_status\"][\"place\"],\n 'origTweetCoord': status._json[\"retweeted_status\"][\"coordinates\"],\n 'origHashtags': status._json[\"retweeted_status\"][\"entities\"][\"hashtags\"],\n 'retweet_count': status._json[\"retweet_count\"],\n }\n elif hasattr(status, \"extended_tweet\"):\n if \"extended_tweet\" in status._json.keys():\n status_refined['tweetText'] = status._json[\"extended_tweet\"][\"full_text\"]\n self.tweets.append(status_refined)\n return self.tweets","def detect_author(user_to_tweets: Dict[str, List[tuple]], tweet_text: str) -> \\\n str:\n acc = []\n \n for keys in user_to_tweets:\n author_hashes = hashtag_seperator(user_to_tweets[keys])\n text_hashes = extract_hashtags(tweet_text)\n if set(text_hashes).issubset(author_hashes):\n acc.append(keys)\n if len(acc) == 1:\n return acc[0]\n return 'unknown'","def search(words):\n newlist = [w for w in words if 'son' in w]\n return newlist","def index_new_tweet(self, id_str, tweet_tokens: list):\n self.tweet_count += 1\n unique_words = set(tweet_tokens)\n unique_word_pairs = set()\n for i in unique_words:\n for j in unique_words - {i}:\n # To us [a, b] = [b, a], and sorting gives us a distinct representation.\n unique_word_pairs.add(tuple(sorted([i, j])))\n for w in unique_words | unique_word_pairs:\n self.index[self.epoch][w] = id_str\n current_freq = self.frequency_map.get(w, 0)\n self.frequency_map[w] = current_freq + 1\n # Get word statistics from hash table\n statistics_present = w in self.stats_map\n if not statistics_present:\n (mu, sigma) = (math.inf, math.inf)\n for h in self.hash_functions:\n c = get_hash(h(), repr(w)) % 2 ** self.bit_count\n if self.buckets[c][\"ewma\"] < mu:\n mu = self.buckets[c][\"ewma\"]\n sigma = self.buckets[c][\"ewmvar\"]\n self.stats_map[w] = (mu, sigma)\n (mu, sigma) = self.stats_map[w]\n # Test for significance threshold\n x = self.frequency_map[w]\n if self._is_frequency_significant(mu, sigma, x):\n self.refinement.append((w, self._get_significance(mu, sigma, x)))\n # if self.refinement:\n # r = self.refinement\n # self.refinement = []\n # return r","def search(request):\n\t\n\t# User's query\n\tquery = request.GET.get('query')\n\n\t# Search for 50 most popular tweets about user's query\n\ttweets = tweepy.Cursor(api.search, q=query, lang=\"en\", tweet_mode='extended', include_entities=True, result_type='popular').items(50)\n\n\t# Search for 20 most relevant news about user's query\n\tall_news = newsapi.get_everything(q=query, language='en', sort_by='relevancy')\n\n\t# Search for 25 hottest subreddits about user's query\n\tsubreddit = reddit.subreddit('all')\n\treddit_news = subreddit.search(query, limit=25, sort='hot')\n\n\tcontext = {\n\t\t\"tweets\": tweets, # most popular tweets\n\t\t\"all_news\": all_news, # most relevant google news\n\t\t\"reddit_news\": reddit_news # hottest subreddits\n\t}\n\n\treturn render(request, 'hashtrend/search.html', context)","async def wordfilter(self, ctx):\n pass","def get_suggestions(\n user: 'User',\n hashtag: str,\n valid_user: Callable[['User'], bool],\n since: str,\n max_suggestions: int\n ) -> List[tweepy.models.User]:\n api = get_api(user)\n suggestions = []\n seen = set()\n max_iters = 5000\n\n for tweet in tweepy.Cursor(api.search, q=hashtag, lang=\"en\", since=since).items():\n if tweet.user.screen_name not in seen and valid_user(tweet.user):\n suggestions.append(tweet.user)\n seen.add(tweet.user.screen_name)\n if len(suggestions) >= max_suggestions or len(seen) > max_iters:\n break\n\n return suggestions","def send_tweet(tweet_text):\n twitter.update_status(status=tweet_text)","def send_tweet(tweet_text):\n twitter.update_status(status=tweet_text)","def send_tweet(tweet_text):\n twitter.update_status(status=tweet_text)","def recoverTweets(authors=[], words=[], removeRetweets=False, sortBy='newest',**kwargs):\n authors = mapToValid(authors)\n words = mapToValid(words)\n\n def getTopNTweets(retrievedTweets, numberOfTweets):\n \"\"\"Sort the retrievedTweets by sortBy specified and returns the top-N Tweets\"\"\"\n if sortBy=='newest':\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['id'], reverse=True)\n elif sortBy=='oldest':\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['id'],reverse=False)\n elif sortBy=='favorite_count':\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['favorite_count'],reverse=True)\n elif sortBy=='retweet_count':\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['retweet_count'],reverse=True)\n else:\n retrievedTweets = random.sample(retrievedTweets, numberOfTweets)\n return retrievedTweets[:numberOfTweets]\n\n def getTweetsByUser(username, maxTweets=1000):\n \"\"\"Returns a list of (json) objects representing the tweets for a specified Twitter username.\n If any words is queried, it will filter out every tweet that doesn't contain any of those words.\"\"\"\n if 'api' not in globals():\n startTwitterApi(getApiKeys(fileName=\"apiConf2.txt\"))\n myTweets=[]\n if words:\n apiRes = tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items()\n for tweet in apiRes:\n if any(containsWord(tweet._json['full_text'],word) for word in words):\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n else:\n if sortBy=='newest':\n for tweet in tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items(maxTweets):\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n else:\n for tweet in tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items():\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n\n return getTopNTweets(myTweets, maxTweets)\n\n def searchTweets():\n \"\"\" returns a list of (json) objects representing the tweets retrieved for a specified query.\n It doesn't work if any authors is specified.\n Then, startingDate and endingDate cannot be older than one week ago because of Twitter restrictions for standardAPI\n :reference: https://developer.twitter.com/en/docs/tweets/search/api-reference/get-search-tweets\n \"\"\"\n if 'api' not in globals():\n startTwitterApi(getApiKeys(fileName='apiConf2.txt'))\n #SEARCHING TWEETS CONTAINING THE HASHTAG \"#bitcoin\" USING TWEEPY LIBRARY\n myTweets= []\n #words=list(map(str,words))\n if words:\n myQuery=' OR '.join(words)\n else:\n myQuery = '*'\n if removeRetweets:\n myQuery += ' - filter:retweets'\n kwargs['q']=myQuery\n kwargs['count']=100\n kwargs['tweet_mode']='extended'\n if 'startingDate' in kwargs:\n kwargs['since']=kwargs['startingDate']\n del(kwargs['startingDate'])\n if 'endingDate' in kwargs:\n kwargs['until']=kwargs['endingDate']\n del(kwargs['endingDate'])\n if 'maxTweets' in kwargs:\n del(kwargs['maxTweets'])\n if sortBy=='newest':\n for tweet in tweepy.Cursor(api.search, kwargs).items(maxTweets):\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n else:\n for tweet in tweepy.Cursor(api.search, kwargs).items():\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n return getTopNTweets(myTweets, maxTweets)\n\n\n def getTwitterscraperTweets():\n \"\"\" returns a list of (json) objects representing the tweets retrieved for the specified inputs.\n It's very useful to avoid restrictions such as number of requests or dates not older than 7 days ago for twitterAPI (and tweepy).\n It will call the recoverTweets.sh script to properly query the API by twitterscraper.\n :reference: https://github.com/taspinar/twitterscraper\n \"\"\"\n import subprocess\n numOfAuthors = len(authors)\n numOfWords = len(words)\n callVars = ['./recoverTweets.sh',str(numOfWords),str(numOfAuthors)]\n callVars.extend([word for word in words]+[author for author in authors])\n if startingDate:\n callVars.extend(['-sd',startingDate])\n if endingDate:\n callVars.extend(['-ed',endingDate])\n #if maxTweets:\n # callVars.extend(['-max',str(maxTweets)])\n callVars.append(\"data/twitterscrapertmp\")\n print(\"Querying twitterAPI by using TwitterScraper... (it may take a long time)\")\n subprocess.call(callVars)\n with open('data/twitterscrapertmp') as json_data:\n tweets = json.load(json_data)\n if removeRetweets:\n tweets = [tweet for tweet in tweets if not isRetweet(tweet)]\n print(\"Query ended. Retrieved: \",len(tweets),\" tweets\")\n #saveTweets(tweets,outputCollection,onFile=True,onDb=True)\n os.remove('data/twitterscrapertmp')\n return tweets\n\n\n if \"maxTweets\" in kwargs:\n maxTweets=kwargs['maxTweets']\n else:\n maxTweets=1000\n\n if len(authors)==0 and len(words)==0:\n return(\"qua\") ###call sample function with maxTweets and (if any) dates\n if 'startingDate' in kwargs or 'endingDate' in kwargs:\n return getTwitterscraperTweets()\n\n if len(authors)!=0:\n tweets, splits, i = [], splitIntegerIntoIntegers(maxTweets,len(authors)), 0\n for author in authors:\n tweets.extend(getTweetsByUser(username=author, maxTweets=splits[i]))\n i+=1\n return tweets\n return getTweets()","def send_tweet(tweet_text):\n twitter.update_status(status = tweet_text)","def search_hashtag(self):\n hashtag = get_random_hashtag()\n self.driver.get(\n '{}/explore/tags/{}'.format(self.base_url, hashtag))\n time.sleep(2)\n\n # mimic a scroll\n scroll_helper(510, self.driver)\n time.sleep(1)\n scroll_helper(600, self.driver)\n time.sleep(1)\n\n # Get a random pic to like\n random_pic = self.driver.find_elements_by_xpath(\n \"//a[contains(@href, '/p/')]\")[randint(5, 40)]\n self.driver.get(random_pic.get_attribute(\"href\"))\n\n # Scroll like button into view and click it\n time.sleep(3)\n scroll_helper(500, self.driver)\n self.like_photo()\n\n # Retrun bot to homepage after clicking like\n time.sleep(0.5)\n self.driver.get(self.base_url)","def test_can_search_hashtags_by_name(self):\n user = make_user()\n headers = make_authentication_headers_for_user(user)\n\n amount_of_hashtags_to_search_for = 5\n\n for i in range(0, amount_of_hashtags_to_search_for):\n hashtag_name = make_hashtag_name()\n hashtag = make_hashtag(name=hashtag_name)\n amount_of_characters_to_query = random.randint(1, len(hashtag_name))\n query = hashtag_name[0:amount_of_characters_to_query]\n final_query = ''\n for character in query:\n final_query = final_query + (character.upper() if fake.boolean() else character.lower())\n\n url = self._get_url()\n response = self.client.get(url, {\n 'query': final_query\n }, **headers)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n parsed_response = json.loads(response.content)\n self.assertEqual(len(parsed_response), 1)\n retrieved_hashtag = parsed_response[0]\n self.assertEqual(retrieved_hashtag['name'], hashtag_name.lower())\n hashtag.delete()","def get_movies_by_words(words):\n cnx,cur = connect_to_db()\n text = \"'\";\n for word in words:\n text += \"+\"+word+\" \"\n text += \"'\"\n cur.execute(\"SELECT * FROM movies \"\n \"Where match(overview) against(\"+text+\" IN BOOLEAN MODE) LIMIT 100 \")\n lst = cur.fetchall()\n size = len(lst)\n cur.close()\n cnx.close()\n return lst,size","def check_for_greeting(sentence):\n for word in sentence.words:\n if word.lower() in greetings:\n return True","def get_tweets(user, num = 200):\n tweets = []\n \n for tweet in user.home_timeline(count = num):\n edited_tweet = tweet.text\n edited_tweet = edited_tweet.encode(encoding='UTF-8', errors='Ignore') \n tweets.append(edited_tweet)\n return tweets","def streamTweets(words = [], authors = [], timeLimit=120, removeRetweets=False, **kwargs):\n if 'stream' not in globals():\n global stream\n if 'api' not in globals():\n startTwitterApi(getApiKeys(fileName=\"apiConf2.txt\"))\n listener = StdOutListener(removeRetweets=removeRetweets)\n auth = api.auth\n stream = tweepy.Stream(auth, listener, tweet_mode='extended')\n else:\n stream.listener.setRemoveRetweets(removeRetweets)\n stream.listener.resetTweets()\n\n words = mapToValid(words)\n authors = mapToValid(authors)\n if not words and not authors:\n words=[\"the\", \"i\", \"to\", \"a\", \"and\", \"'s\", \"is\", \"in\", \"it\", \"you\", \"of\", \"for\", \"on\", \"my\", \"that\", \"e\", \"with\", \"me\", \"do\", \"have\", \"ciao\", \"o\", \"u\", \"cool\", \"good\", \"nice\", \"#\", \"*\", \":\", \";\", \",\", \".\", \"?\", \"-\", \"%\", \"$\", \"€\", \"!\", \"(\", \")\", \"=\", \"'\"]\n\n #myQuery = ' OR '.join(kwargs[\"words\"])\n if authors:\n kwargs[\"follow\"]=[user.id_str for user in list(map(api.get_user,authors))]\n else:\n kwargs[\"track\"]=words\n #if removeRetweets:\n # myQuery += \" -filter:retweets\"\n\n #myQuery += ' from:'\n #myQuery += ' OR from:'.join(kwargs[\"authors\"])\n #print(myQuery)\n import signal\n # Register the signal function handler\n signal.signal(signal.SIGALRM, __streamHandler__)\n # Define a timeout for your function\n signal.alarm(timeLimit)\n try:\n __stream__(stream,**kwargs)\n except Exception:\n print(\"Streaming over after time period of\", timeLimit, \"seconds... Retrieved\", len(stream.listener.getTweets()), \"tweets.\")\n stream.disconnect()\n if authors and words:\n print(\"Filtering out tweets that don't contain the specified words...\")\n myTweets=[]\n for tweet in stream.listener.getTweets():\n if 'full_text' in tweet:\n tweet['text'] = tweet['full_text']\n del (tweet['full_text'])\n if any(containsWord(tweet['text'],word) for word in words):\n myTweets.append(tweet)\n print(\"Done. Retrieved\", len(myTweets), \"tweets written by the authors specified and containing (any of) the words specified.\")\n return myTweets\n return stream.listener.getTweets()","def track(twitter, keywords=[], user_ids=[]):\n\n # Prepare for GET request\n streaming_url = \"https://stream.twitter.com/1.1/statuses/filter.json\"\n\n # Documentation for filter params:\n # https://dev.twitter.com/docs/streaming-apis/parameters\n params = {\"replies\": \"all\"}\n if keywords:\n params[\"track\"] = keywords\n if user_ids:\n params[\"follow\"] = user_ids\n\n # Create Request.get object\n r = twitter.get(url=streaming_url, params=params, stream = True)\n\n # Iterate over the request\n for line in r.iter_lines():\n if line :\n try:\n # TODO \n # Sometimes it returns a \"disconnect\" obj \n # before closing the stream\n tweet = json.loads(line)\n yield tweet\n except ValueError:\n # Couldn't construct a valid tweet\n pass","def get_tweets(twitter, screen_name, num_tweets):\n\n request = robust_request(twitter, 'search/tweets', {'q': screen_name, 'count': num_tweets})\n tweets = [a['text'] for a in request]\n\n return tweets","def tweet(self, tweet, at=None):\n if tweet.strip() == \"\":\n return\n\n num_tweets, tweets = self._divide_tweet(tweet, at)\n if num_tweets > 0:\n # replace @'s with #'s and convert unicode emojis before tweeting\n [self.api.update_status(tw.replace(\"@\", \"#\").encode(\"utf-8\")) for tw in tweets]\n self.log(f\"Tweeted: {' '.join(tweets)}\")\n return tweets[0]","def separate_hastags_mentions_urls(tweet):\n \n text = tweet.lower()\n hashtag_list = re.findall(\"#([a-zA-Z0-9_]{1,50})\", text)\n \n text = re.sub(r'http\\S+', '', text)\n clean_tweet = re.sub(\"@[A-Za-z0-9_]+\",\"\", text)\n clean_tweet = re.sub(\"#[A-Za-z0-9_]+\",\"\", clean_tweet)\n \n return clean_tweet, hashtag_list","def retweet(tweet):\n\n twitter.PostRetweet(tweet.id, trim_user=False)\n\n return","def get_tweets(self, user, count):\n topTweetsList = self.api.user_timeline(screen_name=user, count=count, tweet_mode='extended')\n clnTweets = {}\n for tweet in topTweetsList:\n clnTweets[processTweet(getNonRetweet(tweet))] = ({'like':getFavoriteCount(tweet),'RT':getNumRetweet(tweet),'follower':getNumFollowers(tweet)}) \n\n tweetTxt = [twt for twt in clnTweets.keys()]\n \n if user in self.userTweetsStat:\n self.userTweetsStat[user].append(clnTweets)\n else:\n tmp = []\n tmp.append(clnTweets)\n self.userTweetsStat[user] = tmp\n return tweetTxt, self.userTweetsStat","def extract_important(tweet_objects_list):\n # This section extracts important information such as most common hashtags\n hashtag_dictionary = {}\n for tweet in tweet_objects_list:\n if \"hashtags\" in tweet:\n for individual_hashtag in tweet[\"hashtags\"]:\n if not individual_hashtag[\"text\"].lower() in hashtag_dictionary:\n hashtag_dictionary[individual_hashtag[\"text\"].lower()] = 1\n else:\n hashtag_dictionary[individual_hashtag[\"text\"].lower()] += 1\n frequency = Counter(hashtag_dictionary)\n most_frequent_hashtags = frequency.most_common(50)\n\n user_dictionary = {}\n for tweet in tweet_objects_list:\n if \"user_mentions\" in tweet:\n for individual_user in tweet[\"user_mentions\"]:\n if not individual_user[\"screen_name\"] in user_dictionary:\n user_dictionary[individual_user[\"screen_name\"].lower()] = 1\n else:\n user_dictionary[individual_user[\"screen_name\"].lower()] += 1\n frequency = Counter(user_dictionary)\n most_frequent_users = frequency.most_common(50)\n symbol_dictionary = {}\n for tweet in tweet_objects_list:\n if \"symbols\" in tweet:\n for individual_symbol in tweet[\"symbols\"]:\n if not individual_symbol[\"text\"] in symbol_dictionary:\n symbol_dictionary[individual_symbol[\"text\"]] = 1\n else:\n symbol_dictionary[individual_symbol[\"text\"]] += 1\n frequency = Counter(symbol_dictionary)\n most_frequent_symbols = frequency.most_common(50)\n return most_frequent_hashtags, most_frequent_users, most_frequent_symbols","def get_tweets(keyword, max_tweets=200):\n\n # API keys.\n consumer_key = \"kNOG1klRMMUYbsjMuY5TKl4lE\"\n consumer_secret = \"ieghv6WI1qseYly43A0Ra1MPksEw1i5Onma0txfEu5aHantD2v\"\n access_key = \"3291622062-15ssVc0qpJXf2SFXbA7vgfl1Sooz4Ueo2DGPQVz\"\n access_secret = \"9XJuzgGSVLnx93tq6NfRzMT07S6o2lzjmHfjt3VRlkqXn\"\n\n # Initialize tweepy API object and authorize using API key.\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_key, access_secret)\n api = tweepy.API(auth)\n\n \"\"\" Get tweets.\"\"\"\n\n alltweets = []\n for status in tweepy.Cursor(\n api.search,\n q=keyword + \" -RT\", # the -RT flag excludes retweets.\n count=1000,\n result_type=\"recent\",\n include_entities=True,\n monitor_rate_limit=True,\n wait_on_rate_limit=True,\n lang=\"en\",\n ).items():\n\n # get text of the tweet, encoding as utf-8.\n text = str(status.text.encode(\"utf-8\"))\n\n # add to the data structure, alltweets, holding the tweets.\n alltweets.append(text)\n\n # if we've reached max_tweets, break.\n if len(alltweets) >= max_tweets:\n break\n\n return alltweets","def count_tweets(search_term, location=None):\r\n\r\n if location:\r\n return len(Tweet.objects(Q(keyword_search_term=search_term) & Q(location_address=location)))\r\n else:\r\n return len(Tweet.objects(keyword_search_term=search_term))","def exactSetMatch(self, mention, ignoreSemanticTagList=[]):\n \n dWords = self.importantWords(ignoreSemanticTagList)\n aWords = mention.importantWords(ignoreSemanticTagList) \n\n if len(aWords) == 0:\n # annotated mention consists of \"unimportant\" words.\n # use all words in mention\n dWords = self.allWords()\n aWords = mention.allWords() \n\n if len(dWords) > 0 and dWords == aWords:\n return True\n else:\n return False","def filter(self):\n\t\tparameters = {}\n\n\t\tif self.keywords:\n\t\t\tparameters['track'] = ','.join(self.keywords)\n\n\t\tif self.locations:\n\t\t\tparameters['locations'] = ','.join([','.join([str(latlong) for latlong in loc]) for loc in self.locations])\n\n\t\tif self.usernames:\n\t\t\tparameters['follow'] = ','.join([str(u) for u in self.usernames])\n\n\t\tself.launch('statuses/filter.json', parameters)","def extract_tweets(consumer_key,consumer_secret,access_token,access_token_secret,search_key):\n # Step 1 - Authenticate\n consumer_key= str(consumer_key)\n consumer_secret= str(consumer_secret)\n\n access_token=str(access_token)\n access_token_secret=str(access_token_secret)\n\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_token_secret)\n\n api = tweepy.API(auth)\n\n #Step 3 - Retrieve Tweets\n public_tweets = api.search(search_key)\n tweets_list=[]\n for tweet in public_tweets:\n tweets_list.append(tweet.text)\n return tweets_list","def twitter(self):\n message = \"\"\n count = self.collection.count()\n\n twitter = Twitter(auth = OAuth(self.access_key, self.access_secret, self.consumer_key, self.consumer_secret))\n for keyword in self.twitter_keywords:\n query = twitter.search.tweets(q = keyword)\n for result in query['statuses']:\n try:\n data = {\"id\": count+1, \"source\": \"twitter\", \"timestamp\": datetime.now()}\n data['tweet'] = result['text']\n data['name'] = result[\"user\"][\"screen_name\"]\n data['url'] = \"https://twitter.com/\" + data[\"name\"] + \"/status/\" + str(result['id'])\n data['search_string'] = keyword\n try:\n dataid = self.collection.insert(data)\n except DuplicateKeyError as e:\n continue\n count += 1\n\n # Slack push notification\n length = 82 - len(data['url'])\n message += \"\\nURL: \" + data['url'] + \" search string: \".rjust(length) + keyword\n\n except Exception as e:\n print(e)\n pass\n \n if message:\n print(self.G + \"[+] Twitter\" + self.B + message)\n self.message += \"\\n*Twitter*:\\n```\"\n self.message += message\n self.message += \"\\n```\"\n\n return","def handler(event, context):\n send_tweet(random.choice(potential_tweets))"],"string":"[\n \"def keep(tweet):\\n text = tweet[\\\"Text\\\"].lower()\\n hashtags = \\\" \\\".join(tweet[\\\"Hashtags\\\"]).lower()\\n for word in keep_words:\\n if word in text:\\n # print(\\\"{} in text {}\\\".format(word, text))\\n return True\\n if word in hashtags:\\n # print(\\\"{} in hashtags {}\\\".format(word, hastags))\\n return True\\n return False\",\n \"def two_word_finder(word1,word2,text):\\r\\n word1 = word1.lower()\\r\\n word2 = word2.lower()\\r\\n text = str(text).lower()\\r\\n if word1 and word2 in text:\\r\\n return True #return text to see specific tweets\\r\\n return False\",\n \"def filter_by_hashtag(tweets: list, hashtag: str) -> list:\\n tweets_with_hashtag = {} # findall(): Kui tekstis on rohkem kui üks regulaaravaldisele vastav alamsõne saab kõikide vastete järjendi moodustada funktsiooniga findall()\\n pattern = r\\\"#\\\\w+\\\" # \\\\w : tähed, numbrid, alakriips, + : 1 või rohkem\\n for tweet in tweets: # r\\\"string\\\" on \\\"raw\\\" tüüpi string, mis tähendab, et kurakaldkriipsud(\\\"\\\\\\\") jäetakse teksti alles.\\n find_hashtag = re.findall(pattern, tweet.content) # word:\\\\w\\\\w\\\\w. Regulaaravaldisele vastab täpne sõne \\\"word:\\\" ning sellele järgnevad 3 suvalist tähte.\\n if find_hashtag:\\n tweets_with_hashtag.setdefault(ht, []).append(tweet)\\n return tweets_with_hashtag[hashtag]\",\n \"def searchTweets():\\n if 'api' not in globals():\\n startTwitterApi(getApiKeys(fileName='apiConf2.txt'))\\n #SEARCHING TWEETS CONTAINING THE HASHTAG \\\"#bitcoin\\\" USING TWEEPY LIBRARY\\n myTweets= []\\n #words=list(map(str,words))\\n if words:\\n myQuery=' OR '.join(words)\\n else:\\n myQuery = '*'\\n if removeRetweets:\\n myQuery += ' - filter:retweets'\\n kwargs['q']=myQuery\\n kwargs['count']=100\\n kwargs['tweet_mode']='extended'\\n if 'startingDate' in kwargs:\\n kwargs['since']=kwargs['startingDate']\\n del(kwargs['startingDate'])\\n if 'endingDate' in kwargs:\\n kwargs['until']=kwargs['endingDate']\\n del(kwargs['endingDate'])\\n if 'maxTweets' in kwargs:\\n del(kwargs['maxTweets'])\\n if sortBy=='newest':\\n for tweet in tweepy.Cursor(api.search, kwargs).items(maxTweets):\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n else:\\n for tweet in tweepy.Cursor(api.search, kwargs).items():\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n return getTopNTweets(myTweets, maxTweets)\",\n \"def RelevantTweet(Tweet, Termlist, DateRange=None):\\n TweetText = Tweet[\\\"full_text\\\"].lower() #ignore case. # Changed to full_text now, instead of (previously) truncated.\\n\\n if Tweet[\\\"is_quote_status\\\"] is True: #Append, since we're just matching terms.\\n # print(\\\"Tweet = \\\", Tweet.keys())\\n if \\\"quoted_status\\\" in Tweet:\\n TweetText = TweetText + \\\" \\\" + Tweet[\\\"quoted_status\\\"][\\\"full_text\\\"].lower()\\n\\n # From: https://stackoverflow.com/questions/7703865/going-from-twitter-date-to-python-datetime-date\\n if DateRange is not None:\\n ts = time.strptime(Tweet['created_at'], '%a %b %d %H:%M:%S +0000 %Y')\\n\\n if ts < time.strptime(DateRange[0], '%b %d %H:%M:%S %Y'):\\n return False\\n elif ts > time.strptime(DateRange[1], '%b %d %H:%M:%S %Y'):\\n return False\\n if Termlist is None:\\n return True\\n else:\\n for word in Termlist:\\n if word.lower() in TweetText:\\n return True\\n return False\",\n \"def show_search_results():\\n\\n #Get values from search-box via AJAX\\n current_keyword = request.form.get('search').lower()\\n print \\\"**********************\\\"\\n print current_keyword\\n print \\\"**********************\\\"\\n tweets = get_tweets_by_api(term=current_keyword)\\n\\n result = []\\n\\n for tweet in tweets:\\n # Exclude retweets since they appear as duplicatses to endu ser\\n if tweet.retweeted_status is None:\\n # Convert tweet text from unicode to text\\n tweet_id = tweet.id\\n text = unicodedata.normalize('NFKD', tweet.text).encode('ascii', 'ignore')\\n # Find URL in text and bind to url\\n # url = re.search('((?:http|https)(?::\\\\\\\\/{2}[\\\\\\\\w]+)(?:[\\\\\\\\/|\\\\\\\\.]?)(?:[^\\\\\\\\s\\\"]*))', text)\\n url = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)\\n # Remove URL from text\\n text_wo_url = re.sub(r'^https?:\\\\/\\\\/.*[\\\\r\\\\n]*', '', text, flags=re.MULTILINE)\\n # Handle / Name\\n user = unicodedata.normalize('NFKD', tweet.user.screen_name).encode('ascii', 'ignore')\\n # Count of favorites\\n favorite_count = tweet.favorite_count\\n #Return dictionary of hashtags with hashtag as key and number of occurances as value\\n if tweet.hashtags:\\n # Convert hashtags from unicode to string\\n ht_list = []\\n for hashtag in tweet.hashtags:\\n ht_str = unicodedata.normalize('NFKD', hashtag.text).encode('ascii', 'ignore')\\n ht_list.append(ht_str.lower())\\n hashtags = Counter(ht_list)\\n else:\\n hashtags = tweet.hashtags\\n # Convert tweet from unicode to datetime\\n created_at = tweet.created_at\\n # format created_at string to ISO 8610\\n created_at_str = time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(created_at, '%a %b %d %H:%M:%S +0000 %Y'))\\n # create a moment from the string\\n created_at = moment.date(created_at_str, 'YYYY-MM-DD HH:mm:ss')\\n result.append({'created_at': created_at_str, 'tweet_text': text_wo_url, 'user': user,\\n 'favorite_count': favorite_count, 'hashtags': hashtags,\\n 'url': url, 'tweet_id': tweet_id})\\n\\n print \\\"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\\\"\\n print result\\n print \\\"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\\\"\\n\\n return jsonify(result=result) #, tweets\",\n \"def filter_tweet(tweet):\\n if not filter_tweet_core(tweet):\\n return False\\n if bannedusers.search(tweet['user']['screen_name']) or (\\n 'retweeted_status' in tweet and bannedusers.search(tweet['retweeted_status']['user']['screen_name'])):\\n return False\\n if tweet['user']['screen_name'] == credentials['username']: # Do not match self tweets :-)\\n return False\\n return True\",\n \"def filter_tweet_core(tweet):\\n if not ('user' in tweet and 'screen_name' in tweet['user'] \\\\\\n and 'text' in tweet):\\n return False\\n if not filter_tweet_text(tweet['text']):\\n return False\\n if bannedclients.search(tweet['source']):\\n return False\\n if bannedterms.search(tweet['text']):\\n return False\\n if bannedterms.search(tweet['user']['screen_name']):\\n return False\\n if 'entities' in tweet and bannedterms.search(json.dumps(tweet['entities'])):\\n return False\\n if not filter_tweet_entropy(tweet['text']):\\n return False\\n if not filter_tweet_cardspam(tweet):\\n return False\\n return True\",\n \"def filter_tweets(tweets):\\n # We keep only tweets by chrisalbon with pictures\\n search_tweets = [tw for tw in tweets if tw['username'] == '@chrisalbon' and len(tw['images']) > 0]\\n # He made multiple tweets on the same topic, we keep only the most recent tweets\\n # We use the indexes of the reversed tweet list and dictionnaries to keep only key \\n unique_search_index = sorted(list({t['text'].lower():i for i,t in list(enumerate(search_tweets))[::-1]}.values()))\\n unique_search_tweets = [search_tweets[i] for i in unique_search_index]\\n\\n # Keep non-downloaded tweets\\n most_recent_file = sorted([datetime.datetime.fromtimestamp(os.path.getmtime(path)) \\n for path in glob.glob(\\\"./downloaded_pics/*.jpg\\\")], reverse=True)[0]\\n recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > most_recent_file]\\n\\n # Uncomment for testing new tweets\\n # recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > datetime.datetime(2017, 7, 6, 13, 41, 48)]\\n return recent_seach_tweets\",\n \"def extract_hashtags(tweet):\\n tknzr = TweetTokenizer()\\n hashtags = [token.lower() for token in tknzr.tokenize(tweet) if re.match(hashtag_re, token)]\\n return hashtags\",\n \"def like_tweets(pos_tweets):\\n\\n for tweet in pos_tweets:\\n twitter.CreateFavorite(status_id=tweet.id)\\n\\n return\",\n \"def like_tweet(tweet_id):\\n twitter.create_favorite(id=tweet_id)\",\n \"def search(searchTerm):\\n api = twitter.Api()\\n tweets = api.GetSearch(searchTerm)\\n for tweet in tweets:\\n util.safe_print(tweet.GetText())\",\n \"def search_tweets(q, count=100, result_type=\\\"recent\\\"):\\n\\n return t.search.tweets(q=q, result_type=result_type, count=count)\",\n \"def _get_tweets(self):\\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\\n auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\\n api = tweepy.API(auth)\\n search = api.search(self.term, lang='en', count=100)\\n\\n print(f\\\"Getting tweets that mention '{self.term}', \\\"\\n f\\\"this may take a while...\\\")\\n\\n save_tweet_text = [tweet._json['text'] for tweet in search]\\n while len(save_tweet_text) < 1000:\\n try:\\n oldest = search[-1].id - 1\\n search = api.search(self.term, lang='en', count=100, max_id=oldest)\\n new_tweets = [tweet._json['text'] for tweet in search]\\n save_tweet_text.extend(new_tweets)\\n\\n # Turn into a set to remove duplicated tweets, then back to list\\n save_tweet_text = list(set(save_tweet_text))\\n except IndexError:\\n break\\n\\n print(f\\\"Done. {len(save_tweet_text)} Tweets received.\\\")\\n return save_tweet_text\",\n \"def clean_tweet(tweet):\\r\\n reply_pattern = re.compile(\\\"^@([a-zA-Z0-9]*) (.*)\\\")\\r\\n regexhandler = regex.RegexHandler()\\r\\n # add mark if tweets starts with a mention (@user)\\r\\n if reply_pattern.match(tweet.tweet[\\\"text\\\"]) is not None:\\r\\n temp = \\\"MarkReply \\\" + tweet.tweet[\\\"text\\\"]\\r\\n else:\\r\\n temp = tweet.tweet[\\\"text\\\"]\\r\\n # language dependent\\r\\n\\r\\n if floodtags.core.statics.StaticData.locations:\\r\\n for location in floodtags.core.statics.StaticData.locations:\\r\\n if location in temp:\\r\\n temp += \\\" MarkLocation\\\"\\r\\n\\r\\n try:\\r\\n stemmer = SnowballStemmer(floodtags.core.statics.StaticData.language.lower())\\r\\n # stem words\\r\\n temp = \\\" \\\".join(\\r\\n [stemmer.stem(x) if x not in tweet.tweet[\\r\\n \\\"keywords\\\"] and \\\"MarkReply\\\" not in x and \\\"MarkLocation\\\" not in x else x for x in temp.split()])\\r\\n except ValueError:\\r\\n print(\\\"language not found:\\\", floodtags.core.statics.StaticData.language)\\r\\n # pass\\r\\n\\r\\n # store language\\r\\n temp = \\\"Mark\\\" + tweet.language + \\\" \\\" + temp\\r\\n\\r\\n # store keyword\\r\\n\\r\\n # replace each website with 'MarkWebsite' to create more similarity\\r\\n temp = regexhandler.replace(temp, 'MarkWebsite', regex.Expressions.website)\\r\\n # replace each photo url with 'MarkPhoto' to create more similarity\\r\\n for i in range(len(tweet.tweet[\\\"photos\\\"])):\\r\\n temp = Vectorizer.rreplace(temp, \\\"MarkWebsite\\\", \\\"MarkPhoto\\\", 1)\\r\\n # replace each height with 'MarkHeight' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkHeight\\\", regex.Expressions.waterheight)\\r\\n # replace each time with 'MarkTime' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkTime\\\", regex.Expressions.time)\\r\\n # replace each date with 'MarkDate' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkDate\\\", regex.Expressions.date)\\r\\n # replace each number with 'MarkNumber' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkNumber\\\", regex.Expressions.number)\\r\\n temp = re.sub('\\\\n', ' ', temp)\\r\\n results = re.findall(\\\"(^|[^@\\\\w])@(\\\\w{1,15})\\\", temp)\\r\\n # add mark for each user name\\r\\n if results is not None:\\r\\n for i in range(len(results)):\\r\\n temp += \\\" MarkUserName\\\"\\r\\n results = re.findall(\\\"#(\\\\S*)\\\", temp)\\r\\n # add mark for each hashtag\\r\\n if results is not None:\\r\\n for i in range(len(results)):\\r\\n temp += \\\" MarkHashTag\\\"\\r\\n # add sender as feature\\r\\n temp = \\\"Sender\\\" + tweet.tweet[\\\"source\\\"][\\\"username\\\"] + \\\" \\\" + temp\\r\\n # remove unnecessary characters and chance text to lower case\\r\\n return re.sub('[#\\\\.,:]', '', temp)\",\n \"def searchByKeyword(self, keyword, until=\\\"\\\", since=\\\"\\\", count=None, result_type=\\\"recent\\\"):\\n if count is None:\\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\\n full_text=True, tweet_mode=\\\"extended\\\", lang=\\\"en\\\").items()\\n else:\\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\\n full_text=True, tweet_mode=\\\"extended\\\", lang=\\\"en\\\").items(count)\\n\\n for status in tweets:\\n createdDate = parser.parse(str(status._json[\\\"created_at\\\"]).strip())\\n createdDate = createdDate.replace(\\n tzinfo=pytz.utc) - createdDate.utcoffset()\\n status_refined = {\\n 'keyword': keyword,\\n '_id': status._json[\\\"id\\\"],\\n 'created_at': createdDate,\\n 'tweetText': status._json[\\\"full_text\\\"],\\n 'hashtags': status._json[\\\"entities\\\"][\\\"hashtags\\\"],\\n 'userLoc': status._json[\\\"user\\\"][\\\"location\\\"],\\n 'tweetGeo': status._json[\\\"geo\\\"],\\n 'tweetCoordinates': status._json[\\\"coordinates\\\"],\\n 'tweetPlace': status._json[\\\"place\\\"],\\n 'retweet': {},\\n }\\n if hasattr(status, \\\"retweeted_status\\\"):\\n status_refined['tweetText'] = status._json[\\\"retweeted_status\\\"][\\\"full_text\\\"]\\n status_refined['retweet'] = {\\n 'original_retweet_id': status._json[\\\"retweeted_status\\\"][\\\"id\\\"],\\n 'origUserLoc': status._json[\\\"retweeted_status\\\"][\\\"user\\\"][\\\"location\\\"],\\n 'origTweetLoc': status._json[\\\"retweeted_status\\\"][\\\"geo\\\"],\\n 'origTweetPlace': status._json[\\\"retweeted_status\\\"][\\\"place\\\"],\\n 'origTweetCoord': status._json[\\\"retweeted_status\\\"][\\\"coordinates\\\"],\\n 'origHashtags': status._json[\\\"retweeted_status\\\"][\\\"entities\\\"][\\\"hashtags\\\"],\\n 'retweet_count': status._json[\\\"retweet_count\\\"],\\n }\\n self.tweets.append(status_refined)\\n return self.tweets\",\n \"def reply_to_tweets():\\n last_seen_id = retrieve_last_seen_id(FILE_NAME)\\n mentions = api.mentions_timeline(\\n last_seen_id,\\n tweet_mode='extended')\\n\\n for mention in reversed(mentions):\\n print(str(mention.id) + ' - ' + mention.full_text, flush=True)\\n last_seen_id = mention.id\\n store_last_seen_id(last_seen_id, FILE_NAME)\\n for i in range(len(keywords)):\\n if keywords[i] in mention.full_text.lower():\\n print(\\\"responding back to: \\\" + '@' +\\n mention.user.screen_name, flush=True)\\n api.update_status('@' + mention.user.screen_name + ' ' +\\n salts[i], mention.id)\",\n \"def has_keyword(tweet, keywords):\\n temp = tweet.lower()\\n for keyword in keywords:\\n if bool(re.search(f'\\\\\\\\b{keyword}\\\\\\\\b', temp)):\\n return True\\n return False\",\n \"def cheer(self, songs):\\n if self.favourite_song in songs:\\n return \\\"Whoo!\\\"\",\n \"def collect_tweets(redis_client, twitter_client, search_term):\\n search = Search(redis_client, twitter_client, search_term)\\n search.get_term_state()\\n search.parse_term_state()\\n search.set_query_string()\\n search.set_execution_time()\\n search.execute_query()\\n search.incr_query_counters()\\n search.set_newest_id()\\n search.set_oldest_id()\\n search.set_scenario()\\n search.set_term_state()\\n search.store_results()\\n search.set_score()\\n search.log_state()\",\n \"def compute_retweet(row):\\n return row['full_text'].startswith('RT')\",\n \"def topictweets(url):\\n article = get_article(url)\\n keywords = get_keywords(article['text'])\\n entities = get_entities(article['text'])\\n q = twitter_query(keywords, entities)\\n result = search({'q': q, 'count': 100, 'result_type': 'mixed'})\\n tweets = screen_name_filter(result.statuses, 'media')\\n return tweets\",\n \"def twitter_text(text, search_query = False):\\r\\n tt = TwitterText(text)\\r\\n if search_query:\\r\\n tt.text = tt.highlighter.hit_highlight(query = search_query)\\r\\n tt.text = tt.autolink.auto_link()\\r\\n return tt.text\",\n \"def original_three_tweets():\\n test_tweets = [\\n \\\"is #bigdata finally the answer to end poverty? \\\\\\n @lavanyarathnam http://ow.ly/o8gt3 #analytics\\\",\\n \\\"interview: xia wang, astrazeneca on #bigdata and the promise of effective \\\\\\n healthcare #kdn http://ow.ly/ot2uj\\\",\\n \\\"big data is not just for big business. on how #bigdata is being deployed for \\\\\\n small businesses: http://bddy.me/1bzukb3 @cxotodayalerts #smb\\\"\\n ]\\n return test_tweets\",\n \"def like_tweet(self, tag):\\n self.bot.get('https://twitter.com/search?q=' + tag + '&src=typed')\\n self.__wait(3, 3)\\n for i in range(1, 3):\\n self.bot.execute_script('window.scrollTo(0,document.body.scrollHeight)')\\n self.__wait(2, 3)\\n tweets = self.bot.find_elements_by_tag_name('article')\\n\\n links = []\\n for tweet in tweets:\\n sub_links = tweet.find_elements_by_tag_name('a')\\n links += [sub_link.get_attribute('href')\\n for sub_link in sub_links if 'status' in sub_link.get_attribute('href')]\\n\\n print('Started to like {} tweets'.format(len(links)))\\n\\n for link in links:\\n self.bot.get(link)\\n self.__wait(3, 5)\\n likes = self.bot.find_elements_by_css_selector('div[data-testid=\\\"like\\\"')\\n for like in likes:\\n like.click()\\n self.__wait(3, 5)\",\n \"def analyze_hashtag(self, hashtag, count=200):\\n tweets = []\\n\\n for x in xrange(0, int(count / 100)):\\n tweets.extend(self.tweet_fetcher.get_tweets(hashtag))\\n\\n analyzed_tweets = sort_tweets(self.sa.classify(tweets))\\n\\n self.analyzed_tweets = analyzed_tweets\\n\\n return analyzed_tweets\",\n \"def keyword_filter(tweet_objects, keywords):\\n \\n tm = TweetManager()\\n\\n filtered_list = []\\n\\n for tweet in tweet_objects:\\n # Separate text and hashtags\\n tweet_text = text_from_tweet(tweet)\\n hashtags = tm.find_hashtags(tweet_text)\\n\\n content_filtered_text = tm.clean_tweet(tweet_text)\\n\\n keywords_in_text = tm.find_keywords_in_tweet(content_filtered_text, keywords)\\n hashtags_with_keywords = tm.find_hashtags_with_keywords(hashtags, keywords)\\n\\n if len(keywords_in_text) or len(hashtags_with_keywords):\\n filtered_list.append({\\n KEY_TEXT_ORIGINAL: tweet_text,\\n KEY_TEXT_FILTERED: content_filtered_text,\\n KEY_HASHTAGS_WITH_KEYWORDS: hashtags_with_keywords,\\n KEY_TEXT_KEYWORDS: keywords_in_text\\n })\\n\\n return filtered_list\",\n \"def searchbrown_word(tag):\\n brown_tagged_words = brown.tagged_words(categories='news')\\n hitwords = []\\n for i in range(len(brown_tagged_words)):\\n if tag == brown_tagged_words[i][1]:\\n hitwords.append(brown_tagged_words[i][0].lower())\\n return hitwords\",\n \"def printHashtagsAndMentions(searchText=None, filterTerms=False, tweetLimit=0):\\n tweets = db.Tweet.select()\\n if searchText is not None:\\n tweets = tweets.filter(db.Tweet.q.message.contains(searchText))\\n tweets = tweets.limit(tweetLimit)\\n\\n hashtags, mentions, plain = getHashtagsAndMentions(tweets)\\n\\n if searchText and filterTerms:\\n hashtags = Counter(\\n {k: v for k, v in hashtags.items() if searchText.lower() in k.lower()}\\n )\\n mentions = Counter(\\n {k: v for k, v in mentions.items() if searchText.lower() in k.lower()}\\n )\\n plain = Counter(\\n {k: v for k, v in plain.items() if searchText.lower() in k.lower()}\\n )\\n\\n # Unique word count for each area.\\n hashtagWC = len(hashtags)\\n mentionWC = len(mentions)\\n plainWC = len(plain)\\n\\n print(\\\"Summary\\\")\\n print(\\\"==============\\\")\\n # Count items in the sliced selection since .count() does not work with\\n # a limit.\\n count = len(list(tweets)) if tweetLimit else tweets.count()\\n print(\\\"{0:7,d} tweets\\\".format(count))\\n print(\\\"{0:7,d} unique words\\\".format(hashtagWC + mentionWC + plainWC))\\n print(\\\"{0:7,d} unique hashtags\\\".format(hashtagWC))\\n print(\\\"{0:7,d} unique mentions\\\".format(mentionWC))\\n print(\\\"{0:7,d} unique plain words\\\".format(plainWC))\\n print()\\n\\n print(\\\"Hashtags\\\")\\n print(\\\"========\\\")\\n printCounterByCount(hashtags)\\n print()\\n\\n print(\\\"Mentions\\\")\\n print(\\\"========\\\")\\n printCounterByCount(mentions)\\n\\n \\\"\\\"\\\"\\n # Removal of stopwords and handling of URIs is needed to make this\\n # useful.\\n print 'Plain'\\n print '========'\\n printCounterByCount(plain)\\n \\\"\\\"\\\"\",\n \"def extract_hashtags(self):\\n most_common_words = get_most_common_words(\\n self.get_full_text_from_source())\\n queryset = Hashtag.objects.all()\\n for word in most_common_words:\\n tag = queryset.get_or_create(word=word.lower())[0]\\n tag.documents.add(self)\",\n \"def getHashtagsAndMentions(tweets):\\n hashtags = Counter()\\n mentions = Counter()\\n plain = Counter()\\n\\n pattern = re.compile(r\\\"[^#@\\\\w'-]+\\\")\\n\\n for t in tweets:\\n words = pattern.split(t.message)\\n for word in words:\\n # Ignore null strings caused by split characters at the end of a\\n # message and remove standalone hyphens.\\n if word and not word.startswith(\\\"-\\\"):\\n # Increment count for the word in the Counter.\\n if word.startswith(\\\"#\\\"):\\n hashtags.update({word: 1})\\n elif word.startswith(\\\"@\\\"):\\n mentions.update({word: 1})\\n else:\\n # TODO: apply nltk.corpus.stopwords.words() here,\\n # across languages. Consider that the stopwords cut off\\n # before apostrophe, therefore check if the word\\n # starts with the stopword.\\n plain.update({word: 1})\\n\\n return hashtags, mentions, plain\",\n \"def searchbrown_phrase(tags):\\n l = len(tags)\\n brown_tagged_words = brown.tagged_words(categories='news')\\n hitwords = []\\n for i in range(len(brown_tagged_words)-l+1):\\n searchtags = [tag for _,tag in brown_tagged_words[i:i+l]]\\n if tags == searchtags:\\n hitwords.append(tuple([w.lower()\\n for w,_ in brown_tagged_words[i:i+l]]))\\n return hitwords\",\n \"def more_like_text(text, klass):\\n back = connections['default'].get_backend()\\n\\n if hasattr(back, 'conn'):\\n query = {'query': {\\n 'filtered': {\\n 'query': {\\n 'fuzzy_like_this': {\\n 'like_text': text\\n }\\n },\\n 'filter': {\\n 'bool': {\\n 'must': {\\n 'term': {'django_ct': 'idea.idea'}\\n }\\n }\\n }\\n }\\n }\\n\\n }\\n results = back.conn.search(query)\\n return back._process_results(results)['results']\\n else:\\n return []\",\n \"def duplicates_filter(tweet_objects):\\n\\n cache = []\\n filtered_list = []\\n\\n for tweet in tweet_objects:\\n t_text = text_from_tweet(tweet)\\n \\n if t_text not in cache:\\n filtered_list.append(tweet)\\n cache.append(t_text)\\n\\n return filtered_list\",\n \"async def team_search(self, ctx: commands.Context, username: str):\\n all_usernames = {team_id: team.username for team_id, team in self.teams.items()\\n if team is not None}\\n suggestions = []\\n log.info(repr(fuzzywuzzy.process.extract(\\n username, all_usernames, limit=5)))\\n for fuzz_username, rating, fuzz_id in fuzzywuzzy.process.extract(\\n username, all_usernames, limit=5):\\n if rating < 50:\\n break\\n fuzz_team = self.teams[fuzz_id]\\n suggestions.append(\\n f'(ID: **{fuzz_team.team_id}**) **{fuzz_team.display_name[:40]}**'\\n f' -- {len(fuzz_team.users)} registered members')\\n if suggestions:\\n await ctx.send('\\\\n'.join(suggestions))\\n else:\\n await ctx.send(f\\\"Couldn't find any teams whose usernames resembled `{username}`\\\")\",\n \"def displayAlsoLike(self):\\n\\n result = self.client.get(\\\"/view_favorites\\\")\\n self.assertIn(b\\\"11925205\\\", result.data)\",\n \"def trendingTweets():\\n api = twitter.Api()\\n trending_topics = api.GetTrendsWoeid(PHILA_WOEID)\\n for topic in trending_topics:\\n topicSearchTerm = topic.name\\n trending_tweets = api.GetSearch(topicSearchTerm)\\n for tweet in trending_tweets:\\n util.safe_print(tweet.GetText())\\n # pass\",\n \"def query_tweets_once(query, limit=None, num_tweets=0):\\n logging.info(\\\"Querying {}\\\".format(query))\\n query = query.replace(' ', '%20').replace(\\\"#\\\", \\\"%23\\\").replace(\\\":\\\", \\\"%3A\\\")\\n pos = None\\n tweets = []\\n try:\\n while True:\\n new_tweets, pos = query_single_page(\\n INIT_URL.format(q=query) if pos is None\\n else RELOAD_URL.format(q=query, pos=pos),\\n pos is None\\n )\\n if len(new_tweets) == 0:\\n logging.info(\\\"Got {} tweets for {}.\\\".format(\\n len(tweets), query))\\n return tweets\\n\\n logging.info(\\\"Got {} tweets ({} new).\\\".format(\\n len(tweets) + num_tweets, len(new_tweets)))\\n\\n tweets += new_tweets\\n\\n if limit is not None and len(tweets) + num_tweets >= limit:\\n return tweets\\n except KeyboardInterrupt:\\n logging.info(\\\"Program interrupted by user. Returning tweets gathered \\\"\\n \\\"so far...\\\")\\n except BaseException:\\n logging.exception(\\\"An unknown error occurred! Returning tweets \\\"\\n \\\"gathered so far.\\\")\\n\\n return tweets\",\n \"async def wordfilter_test(self, ctx, *, message):\\n found = self.test_sentence(message)\\n if found:\\n await ctx.send(f\\\"Message contains `{found}`\\\")\\n else:\\n await ctx.send(\\\"Couldn't detect any filtered words\\\")\",\n \"def is_retweet(tweet):\\n\\n return 'retweeted_status' in tweet\",\n \"def collect_tweets(search_id, search_term, number_of_tweets):\\n\\n tweets = []\\n for tweet in api_collector.collect(search_term, number_of_tweets):\\n tweets.append((tweet.id_str, tweet.created_at, tweet.full_text))\\n if len(tweets) == 0:\\n search = Search.objects.get(pk=search_id)\\n search.empty = True\\n search.save()\\n notify_searchers.delay(search_id)\\n else:\\n classify_tweets.delay(search_id, tweets)\",\n \"def is_likely_pokemon_related(tweet, hashtags):\\n # check hash tags\\n for tag in hashtags:\\n if tag.text in TAGS_WE_CARE_ABOUT:\\n return True\\n # check tweet content\\n for word in TEXT_WE_CARE_ABOUT:\\n if word in tweet:\\n return True\\n return False\",\n \"def get_tweets(api, listOfTweets, keyword, numOfTweets=20, date_since='2019-1-1', lang=\\\"en\\\"):\\n spinner = yaspin()\\n spinner.start()\\n for tweet in tweepy.Cursor(api.search, q=keyword, lang=lang, since=date_since).items(numOfTweets):\\n # Add tweets in this format\\n dict_ = {'Screen Name': tweet.user.screen_name,\\n 'User Name': tweet.user.name,\\n 'Tweet Created At': str(tweet.created_at),\\n 'Tweet Text': tweet.text,\\n 'Cleaned Tweet Text': func.clean_tweets(tweet.text),\\n 'User Location': str(tweet.user.location),\\n 'Tweet Coordinates': str(tweet.coordinates),\\n 'Retweet Count': str(tweet.retweet_count),\\n 'Retweeted': str(tweet.retweeted),\\n 'Phone Type': str(tweet.source),\\n 'Favorite Count': str(tweet.favorite_count),\\n 'Favorited': str(tweet.favorited),\\n 'Replied': str(tweet.in_reply_to_status_id_str)\\n }\\n listOfTweets.append(dict_)\\n spinner.stop()\\n return listOfTweets\",\n \"def retweet_filter(self, text):\\n return not text.lower().startswith('rt')\",\n \"def search(self, word):\",\n \"def process(self, filter_words, count=1):\\n user = self.__api.get_user(self.__username)\\n\\n # print user.screen_name\\n # print user.followers_count\\n if self.__appMode == 1 and self.__TimeLineMode == 1:\\n self.get_timeline(filter_words)\\n else:\\n if self.__friendMode:\\n print(\\\"Getting all Twitter Friends \\\\n\\\")\\n for friend in user.friends():\\n self.get_tweet(friend.screen_name, filter_words, count)\\n else:\\n for screen_name in self.__priorityCoin:\\n self.get_tweet(screen_name, filter_words, count)\\n print('Twitter Data Extraction done!!')\",\n \"def search(self, term):\",\n \"def search_from_terms(api, term, **kwargs):\\n tweets=api.GetSearch(term=term)\\n return {\\\"tweets\\\":tweets}\",\n \"def process(self, tweet):\\n\\n #identify the applicable event keywords for this text\\n text = self.cleanup_data(tweet.text)\\n tokens = [str(t.lower()).translate(None, string.punctuation) for t in tweet.text.split()]\\n applicable_tokens = []\\n for phrase in self.match_event_tree.root.keywords:\\n if phrase in \\\" \\\".join(tokens):\\n applicable_tokens.append(phrase)\\n\\n self.match_event_tree.propogate_tweet(applicable_tokens, tweet)\",\n \"def analyze_tweets(tweets, model, w2v_model):\\n # TODO DO EVERYTHING HERE\\n #tweets = [(\\\"StarWars\\\", tc.query_tweets(\\\"StarWars\\\"))]\\n \\n #tweets = tc.query_tweets('starwars')\\n df = pd.DataFrame(columns=['pos', 'neu', 'neg'])\\n if not os.path.isdir('results'):\\n os.mkdir('results')\\n for topic, topic_tweets in tweets:\\n tokenized_tweets = tp.process_raw_tweets(topic_tweets)\\n df.loc[topic], dummy = classify_tweets(tokenized_tweets, model, w2v_model)\\n vis.word_cloud_from_frequencies(tp.count_tokens(tokenized_tweets), f\\\"results/{topic}_cloud.png\\\", width=800, height=400,)\\n \\n vis.bar_plot_from_dataframe(df, 'results/results.png')\\n print(\\\"\\\\n\\\")\\n print(df)\",\n \"def get_tweets(hashtag):\\n api = twitter.Api(consumer_key=TWITTER_API_CONSUMER_KEY,\\n consumer_secret=TWITTER_API_CONSUMER_SECRET,\\n access_token_key=TWITTER_API_ACCESS_TOKEN_KEY,\\n access_token_secret=TWITTER_API_ACCESS_TOKEN_SECRET)\\n\\n query = (f\\\"q=%23{HASHTAG}%20-RT\\\"\\n f\\\"&result_type=recent&since=2019-01-01&count={NUM_TWEETS}\\\")\\n results = api.GetSearch(raw_query=query)\\n\\n return [\\n format_tweet(tweet.AsDict())\\n for tweet in results\\n ]\",\n \"def twitter_search(a, q, x=10000000, t=100, s=None, m=-1):\\n\\n tweets = list()\\n tweet_count = 0\\n\\n while tweet_count < x:\\n try:\\n if (m <= 0):\\n if (not s):\\n new_tweets = a.search(q = q, count = t)\\n else:\\n new_tweets = a.search(q = q, count = t, since_id = s)\\n else:\\n if (not s):\\n new_tweets = a.search(q = q, count = t, max_id = (m - 1))\\n else:\\n new_tweets = a.search(q = q, count = t, max_id = (m - 1), since_id = s)\\n\\n if not new_tweets:\\n break\\n\\n for tweet in new_tweets:\\n tweets.append(tweet)\\n\\n tweet_count += len(new_tweets)\\n m = new_tweets[-1].id\\n\\n except tweepy.TweepError as e:\\n error = (-1, \\\"error:\\\" + str(e))\\n return error\\n\\n search_results = (tweet_count, tweets)\\n\\n return search_results\",\n \"def having(self, wordlist=None, coordinates=None):\\n track = \\\",\\\".join(wordlist) if wordlist else None\\n locations = \\\",\\\".join(map(str, coordinates)) if coordinates else None\\n\\n if track and locations:\\n generator = self.stream.statuses.filter(track=track,\\n locations=locations)\\n elif track:\\n generator = self.stream.statuses.filter(track=track)\\n elif locations:\\n generator = self.stream.statuses.filter(locations=locations)\\n else:\\n raise ValueError(\\\"Must provide one of wordlist or coordinates\\\")\\n\\n for tweet in generator:\\n if locations:\\n if 'coordinates' in tweet and tweet['coordinates']:\\n yield tweet\\n else:\\n yield tweet\",\n \"def has_retweet(tweet):\\n\\n pattern = r'^RT'\\n return re.match(pattern, tweet)\",\n \"def tweets_features(tweet):\\n tweet = remove_stop_words(tweet)\\n return {'TWEET': tweet}\",\n \"def hashtags(self, candidate_list):\\n if Tweet.hashtagre is None:\\n Tweet.hashtagre = re.compile('|'.join(map(re.escape, candidate_list)))\\n return [\\n [m.group(0).replace('#', '', 1), m.span()]\\n for m in Tweet.hashtagre.finditer(self.text)\\n ]\",\n \"async def search(self, ctx: \\\"IceTeaContext\\\", *, query):\\n response_list = await ctx.guild_data.search_tags(query)\\n if len(response_list) > 0:\\n response_message = \\\"\\\\n\\\".join([tag.title for tag in response_list])\\n await ctx.send(f\\\"Found these tags:\\\\n{response_message}\\\")\\n else:\\n await ctx.send(\\\"No similar tags found\\\")\",\n \"def excludeTwitterTags(tweet):\\n\\ttwext = tweet['text'].lower()\\n\\tif tweet['entities']['hashtags']:\\n\\t\\tfor hashtag in tweet['entities']['hashtags']:\\n\\t\\t\\ttwext = twext.replace(hashtag['text'].lower(),\\\"\\\")\\n\\t\\t\\tif tweet['entities']['user_mentions']:\\n\\t\\t\\t\\tfor user_mention in tweet['entities']['user_mentions']:\\n\\t\\t\\t\\t\\ttwext = twext.replace(user_mention['screen_name'].lower(),\\\"\\\")\\n\\t\\t\\tif tweet['entities']['urls']:\\n\\t\\t\\t\\tfor url in tweet['entities']['urls']:\\n\\t\\t\\t\\t\\ttwext = twext.replace(url['url'].lower(),\\\"\\\")\\n\\treturn twext\",\n \"def search_tweets(request):\\n return render(request, 'ede/search.html')\",\n \"def search_multiple_words(words):\\n # YOUR CODE HERE #\\n pass # delete this when you write your code\",\n \"def get_tweets(self):\\n keyword = 'covid'\\n\\n # Load tokens from file\\n with open('../data/tokens.json', 'r') as f:\\n tokens = json.load(f)\\n\\n # Stream tweets\\n auth = tweepy.OAuthHandler(tokens['consumer_key'], tokens['consumer_secret'])\\n auth.set_access_token(tokens['access_token_key'], tokens['access_token_secret'])\\n api = tweepy.API(auth)\\n\\n # listen for tweets\\n while True:\\n\\n # TODO: save file in Cloud Storage\\n file_name = date.today().strftime('corpus-%d-%m-%Y.json')\\n print(f'Updating {file_name} ...')\\n\\n StreamListener = StreamListener(\\n file_name=file_name, \\n max_tweets=1000)\\n myStream = tweepy.Stream(\\n auth=api.auth, \\n listener=StreamListener)\\n\\n myStream.filter(track=[keyword], languages=['en'])\\n \\n time.sleep(60)\",\n \"def expand_tweet(tweet):\\r\\n return hashtag_regex.sub(lambda hashtag: expand_hashtag(hashtag), tweet)\",\n \"def get_mentions(texts, word):\\r\\n mentions = {}\\r\\n for text, label, text_id in texts:\\r\\n if word in text.lower():\\r\\n if label not in mentions:\\r\\n mentions[label] = [text_id]\\r\\n else:\\r\\n if text_id not in mentions[label]:\\r\\n mentions[label].append(text_id)\\r\\n return mentions\",\n \"def search(self, query_string):\\n return models.Thread.objects.filter(\\n reduce(\\n lambda q, f: q & Q(title__icontains=f),\\n query_string.split(),\\n Q()))\",\n \"def like_retweet(self):\\n \\n bot = self.bot \\n logging.debug('Get Hashtag')\\n hashtag = Twitterbot.getHashtags(self)\\n logging.debug('Bot for Like_retweet initalized')\\n # fetches the latest tweets with the provided hashtag \\n bot.get( \\n 'https://twitter.com/search?q=%23'+hashtag+'&src=trend_click&vertical=trends'\\n ) \\n \\n time.sleep(3) \\n \\n # using set so that only unique links \\n # are present and to avoid unnecessary repetition \\n links = set() \\n \\n # obtaining the links of the tweets \\n for _ in range(3): \\n # executing javascript code \\n # to scroll the webpage \\n bot.execute_script( \\n 'window.scrollTo(0, document.body.scrollHeight)'\\n ) \\n \\n time.sleep(4) \\n \\n # using list comprehension \\n # for adding all the tweets link to the set \\n # this particular piece of code might \\n # look very complicated but the only reason \\n # I opted for list comprehension because is \\n # lot faster than traditional loops \\n [ \\n links.add(elem.get_attribute('href')) \\n for elem in bot.find_elements_by_xpath(\\\"//a[@dir ='auto']\\\") \\n ] \\n i = 0\\n # traversing through the generated links \\n for link in links:\\n #Nothing for the Hashtag was found, another run is required\\n #if len(links ==6):\\n # break\\n # Twitterbot.like_retweet(self)\\n # opens individual links \\n #print(len(links))\\n bot.get(link) \\n time.sleep(4)\\n if i == 3:\\n break\\n i += 1\\n try: \\n # retweet button selector \\n bot.find_element_by_css_selector( \\n '.css-18t94o4[data-testid =\\\"retweet\\\"]'\\n ).click() \\n # initializes action chain \\n actions = ActionChains(bot) \\n # sends RETURN key to retweet without comment \\n actions.send_keys(Keys.RETURN).perform() \\n \\n # like button selector \\n bot.find_element_by_css_selector( \\n '.css-18t94o4[data-testid =\\\"like\\\"]'\\n ).click() \\n # adding higher sleep time to avoid \\n # getting detected as bot by twitter \\n logging.info(f'Liked and retweeted:\\\"{link}\\\"')\\n time.sleep(10) \\n except: \\n time.sleep(2) \\n \\n # fetches the main homepage \\n bot.get('https://twitter.com/')\",\n \"def remove_hashtag(lista_tweets):\\n\\n novos_tweets = []\\n\\n for tweet in lista_tweets:\\n texto = re.sub(r\\\"#\\\\S+\\\", \\\"\\\", tweet)\\n novos_tweets.append(texto)\\n\\n return novos_tweets\",\n \"def get_tweets(self, query, count=10):\\n # empty list to store parsed tweets\\n tweets = []\\n\\n try:\\n # call twitter api to fetch tweets\\n fetched_tweets = self.api.search(q=query, count=count)\\n\\n # parsing tweets one by one\\n for tweet in fetched_tweets:\\n # empty dictionary to store required params of a tweet\\n parsed_tweet = {}\\n\\n # saving text of tweet\\n parsed_tweet['text'] = tweet.text\\n # saving sentiment of tweet\\n parsed_tweet['sentiment'] = self.get_tweet_sentiment(tweet.text)\\n\\n # appending parsed tweet to tweets list\\n if tweet.retweet_count > 0:\\n # if tweet has retweets, ensure that it is appended only once\\n if parsed_tweet not in tweets:\\n tweets.append(parsed_tweet)\\n else:\\n tweets.append(parsed_tweet)\\n\\n # return parsed tweets\\n return tweets\\n\\n except tweepy.TweepError as e:\\n # print error (if any)\\n print(\\\"Error : \\\" + str(e))\",\n \"def searchByKeywordPro(self, query, since=\\\"\\\", until=\\\"\\\", maxResults=None):\\n\\n tweetsList = []\\n if(not maxResults):\\n tweetList, next_token = self.api.search_30_day(\\n environment_name=\\\"developer\\\", query=query, toDate=until, fromDate=since)\\n tweetsList.append(tweetList)\\n while(next_token):\\n tweetList, next_token = self.api.search_30_day(\\n environment_name=\\\"developer\\\", query=query, toDate=until, fromDate=since, next=next_token)\\n tweetsList.append(tweetList)\\n else:\\n tweetList, next_token = self.api.search_30_day(\\n environment_name=\\\"developer\\\", query=query, toDate=until, fromDate=since)\\n tweetsList.append(tweetList)\\n maxResults -= len(tweetList)\\n while(next_token and maxResults > 0):\\n tweetList, next_token = self.api.search_30_day(\\n environment_name=\\\"developer\\\", query=query, toDate=until, fromDate=since, next=next_token)\\n tweetsList.append(tweetList)\\n maxResults -= len(tweetList)\\n for status in tweetsList:\\n createdDate = parser.parse(str(status._json[\\\"created_at\\\"]).strip())\\n createdDate = createdDate.replace(\\n tzinfo=pytz.utc) - createdDate.utcoffset()\\n status_refined = {\\n 'keyword': query,\\n '_id': status._json[\\\"id\\\"],\\n 'created_at': createdDate,\\n 'tweetText': status._json[\\\"text\\\"],\\n 'hashtags': status._json[\\\"entities\\\"][\\\"hashtags\\\"],\\n 'userLoc': status._json[\\\"user\\\"][\\\"location\\\"],\\n 'tweetGeo': status._json[\\\"geo\\\"],\\n 'tweetCoordinates': status._json[\\\"coordinates\\\"],\\n 'tweetPlace': status._json[\\\"place\\\"],\\n 'retweet': {},\\n 'quote': {},\\n }\\n if hasattr(status, \\\"quoted_status\\\"):\\n if \\\"extended_tweet\\\" in status._json[\\\"quoted_status\\\"].keys():\\n print(\\\"Taking the expanded tweet\\\")\\n status_refined['tweetText'] = status._json[\\\"quoted_status\\\"][\\\"extended_tweet\\\"][\\\"full_text\\\"]\\n else:\\n status_refined['tweetText'] = status._json[\\\"quoted_status\\\"][\\\"text\\\"]\\n status_refined['quote'] = {\\n 'original_retweet_id': status._json[\\\"quoted_status\\\"][\\\"id\\\"],\\n 'origUserLoc': status._json[\\\"quoted_status\\\"][\\\"user\\\"][\\\"location\\\"],\\n 'origTweetLoc': status._json[\\\"quoted_status\\\"][\\\"geo\\\"],\\n 'origTweetPlace': status._json[\\\"quoted_status\\\"][\\\"place\\\"],\\n 'origTweetCoord': status._json[\\\"quoted_status\\\"][\\\"coordinates\\\"],\\n 'origHashtags': status._json[\\\"quoted_status\\\"][\\\"entities\\\"][\\\"hashtags\\\"],\\n 'retweet_count': status._json[\\\"quote_count\\\"],\\n }\\n elif hasattr(status, \\\"retweeted_status\\\"):\\n print(status._json[\\\"retweeted_status\\\"])\\n if \\\"extended_tweet\\\" in status._json[\\\"retweeted_status\\\"].keys():\\n print(\\\"Taking the expanded tweet\\\")\\n status_refined['tweetText'] = status._json[\\\"retweeted_status\\\"][\\\"extended_tweet\\\"][\\\"full_text\\\"]\\n else:\\n status_refined['tweetText'] = status._json[\\\"retweeted_status\\\"][\\\"text\\\"]\\n status_refined['retweet'] = {\\n 'original_retweet_id': status._json[\\\"retweeted_status\\\"][\\\"id\\\"],\\n 'origUserLoc': status._json[\\\"retweeted_status\\\"][\\\"user\\\"][\\\"location\\\"],\\n 'origTweetLoc': status._json[\\\"retweeted_status\\\"][\\\"geo\\\"],\\n 'origTweetPlace': status._json[\\\"retweeted_status\\\"][\\\"place\\\"],\\n 'origTweetCoord': status._json[\\\"retweeted_status\\\"][\\\"coordinates\\\"],\\n 'origHashtags': status._json[\\\"retweeted_status\\\"][\\\"entities\\\"][\\\"hashtags\\\"],\\n 'retweet_count': status._json[\\\"retweet_count\\\"],\\n }\\n elif hasattr(status, \\\"extended_tweet\\\"):\\n if \\\"extended_tweet\\\" in status._json.keys():\\n status_refined['tweetText'] = status._json[\\\"extended_tweet\\\"][\\\"full_text\\\"]\\n self.tweets.append(status_refined)\\n return self.tweets\",\n \"def detect_author(user_to_tweets: Dict[str, List[tuple]], tweet_text: str) -> \\\\\\n str:\\n acc = []\\n \\n for keys in user_to_tweets:\\n author_hashes = hashtag_seperator(user_to_tweets[keys])\\n text_hashes = extract_hashtags(tweet_text)\\n if set(text_hashes).issubset(author_hashes):\\n acc.append(keys)\\n if len(acc) == 1:\\n return acc[0]\\n return 'unknown'\",\n \"def search(words):\\n newlist = [w for w in words if 'son' in w]\\n return newlist\",\n \"def index_new_tweet(self, id_str, tweet_tokens: list):\\n self.tweet_count += 1\\n unique_words = set(tweet_tokens)\\n unique_word_pairs = set()\\n for i in unique_words:\\n for j in unique_words - {i}:\\n # To us [a, b] = [b, a], and sorting gives us a distinct representation.\\n unique_word_pairs.add(tuple(sorted([i, j])))\\n for w in unique_words | unique_word_pairs:\\n self.index[self.epoch][w] = id_str\\n current_freq = self.frequency_map.get(w, 0)\\n self.frequency_map[w] = current_freq + 1\\n # Get word statistics from hash table\\n statistics_present = w in self.stats_map\\n if not statistics_present:\\n (mu, sigma) = (math.inf, math.inf)\\n for h in self.hash_functions:\\n c = get_hash(h(), repr(w)) % 2 ** self.bit_count\\n if self.buckets[c][\\\"ewma\\\"] < mu:\\n mu = self.buckets[c][\\\"ewma\\\"]\\n sigma = self.buckets[c][\\\"ewmvar\\\"]\\n self.stats_map[w] = (mu, sigma)\\n (mu, sigma) = self.stats_map[w]\\n # Test for significance threshold\\n x = self.frequency_map[w]\\n if self._is_frequency_significant(mu, sigma, x):\\n self.refinement.append((w, self._get_significance(mu, sigma, x)))\\n # if self.refinement:\\n # r = self.refinement\\n # self.refinement = []\\n # return r\",\n \"def search(request):\\n\\t\\n\\t# User's query\\n\\tquery = request.GET.get('query')\\n\\n\\t# Search for 50 most popular tweets about user's query\\n\\ttweets = tweepy.Cursor(api.search, q=query, lang=\\\"en\\\", tweet_mode='extended', include_entities=True, result_type='popular').items(50)\\n\\n\\t# Search for 20 most relevant news about user's query\\n\\tall_news = newsapi.get_everything(q=query, language='en', sort_by='relevancy')\\n\\n\\t# Search for 25 hottest subreddits about user's query\\n\\tsubreddit = reddit.subreddit('all')\\n\\treddit_news = subreddit.search(query, limit=25, sort='hot')\\n\\n\\tcontext = {\\n\\t\\t\\\"tweets\\\": tweets, # most popular tweets\\n\\t\\t\\\"all_news\\\": all_news, # most relevant google news\\n\\t\\t\\\"reddit_news\\\": reddit_news # hottest subreddits\\n\\t}\\n\\n\\treturn render(request, 'hashtrend/search.html', context)\",\n \"async def wordfilter(self, ctx):\\n pass\",\n \"def get_suggestions(\\n user: 'User',\\n hashtag: str,\\n valid_user: Callable[['User'], bool],\\n since: str,\\n max_suggestions: int\\n ) -> List[tweepy.models.User]:\\n api = get_api(user)\\n suggestions = []\\n seen = set()\\n max_iters = 5000\\n\\n for tweet in tweepy.Cursor(api.search, q=hashtag, lang=\\\"en\\\", since=since).items():\\n if tweet.user.screen_name not in seen and valid_user(tweet.user):\\n suggestions.append(tweet.user)\\n seen.add(tweet.user.screen_name)\\n if len(suggestions) >= max_suggestions or len(seen) > max_iters:\\n break\\n\\n return suggestions\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status=tweet_text)\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status=tweet_text)\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status=tweet_text)\",\n \"def recoverTweets(authors=[], words=[], removeRetweets=False, sortBy='newest',**kwargs):\\n authors = mapToValid(authors)\\n words = mapToValid(words)\\n\\n def getTopNTweets(retrievedTweets, numberOfTweets):\\n \\\"\\\"\\\"Sort the retrievedTweets by sortBy specified and returns the top-N Tweets\\\"\\\"\\\"\\n if sortBy=='newest':\\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['id'], reverse=True)\\n elif sortBy=='oldest':\\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['id'],reverse=False)\\n elif sortBy=='favorite_count':\\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['favorite_count'],reverse=True)\\n elif sortBy=='retweet_count':\\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['retweet_count'],reverse=True)\\n else:\\n retrievedTweets = random.sample(retrievedTweets, numberOfTweets)\\n return retrievedTweets[:numberOfTweets]\\n\\n def getTweetsByUser(username, maxTweets=1000):\\n \\\"\\\"\\\"Returns a list of (json) objects representing the tweets for a specified Twitter username.\\n If any words is queried, it will filter out every tweet that doesn't contain any of those words.\\\"\\\"\\\"\\n if 'api' not in globals():\\n startTwitterApi(getApiKeys(fileName=\\\"apiConf2.txt\\\"))\\n myTweets=[]\\n if words:\\n apiRes = tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items()\\n for tweet in apiRes:\\n if any(containsWord(tweet._json['full_text'],word) for word in words):\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n else:\\n if sortBy=='newest':\\n for tweet in tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items(maxTweets):\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n else:\\n for tweet in tweepy.Cursor(api.user_timeline,screen_name=username, count=100, tweet_mode='extended', include_rts=not removeRetweets).items():\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n\\n return getTopNTweets(myTweets, maxTweets)\\n\\n def searchTweets():\\n \\\"\\\"\\\" returns a list of (json) objects representing the tweets retrieved for a specified query.\\n It doesn't work if any authors is specified.\\n Then, startingDate and endingDate cannot be older than one week ago because of Twitter restrictions for standardAPI\\n :reference: https://developer.twitter.com/en/docs/tweets/search/api-reference/get-search-tweets\\n \\\"\\\"\\\"\\n if 'api' not in globals():\\n startTwitterApi(getApiKeys(fileName='apiConf2.txt'))\\n #SEARCHING TWEETS CONTAINING THE HASHTAG \\\"#bitcoin\\\" USING TWEEPY LIBRARY\\n myTweets= []\\n #words=list(map(str,words))\\n if words:\\n myQuery=' OR '.join(words)\\n else:\\n myQuery = '*'\\n if removeRetweets:\\n myQuery += ' - filter:retweets'\\n kwargs['q']=myQuery\\n kwargs['count']=100\\n kwargs['tweet_mode']='extended'\\n if 'startingDate' in kwargs:\\n kwargs['since']=kwargs['startingDate']\\n del(kwargs['startingDate'])\\n if 'endingDate' in kwargs:\\n kwargs['until']=kwargs['endingDate']\\n del(kwargs['endingDate'])\\n if 'maxTweets' in kwargs:\\n del(kwargs['maxTweets'])\\n if sortBy=='newest':\\n for tweet in tweepy.Cursor(api.search, kwargs).items(maxTweets):\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n else:\\n for tweet in tweepy.Cursor(api.search, kwargs).items():\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n return getTopNTweets(myTweets, maxTweets)\\n\\n\\n def getTwitterscraperTweets():\\n \\\"\\\"\\\" returns a list of (json) objects representing the tweets retrieved for the specified inputs.\\n It's very useful to avoid restrictions such as number of requests or dates not older than 7 days ago for twitterAPI (and tweepy).\\n It will call the recoverTweets.sh script to properly query the API by twitterscraper.\\n :reference: https://github.com/taspinar/twitterscraper\\n \\\"\\\"\\\"\\n import subprocess\\n numOfAuthors = len(authors)\\n numOfWords = len(words)\\n callVars = ['./recoverTweets.sh',str(numOfWords),str(numOfAuthors)]\\n callVars.extend([word for word in words]+[author for author in authors])\\n if startingDate:\\n callVars.extend(['-sd',startingDate])\\n if endingDate:\\n callVars.extend(['-ed',endingDate])\\n #if maxTweets:\\n # callVars.extend(['-max',str(maxTweets)])\\n callVars.append(\\\"data/twitterscrapertmp\\\")\\n print(\\\"Querying twitterAPI by using TwitterScraper... (it may take a long time)\\\")\\n subprocess.call(callVars)\\n with open('data/twitterscrapertmp') as json_data:\\n tweets = json.load(json_data)\\n if removeRetweets:\\n tweets = [tweet for tweet in tweets if not isRetweet(tweet)]\\n print(\\\"Query ended. Retrieved: \\\",len(tweets),\\\" tweets\\\")\\n #saveTweets(tweets,outputCollection,onFile=True,onDb=True)\\n os.remove('data/twitterscrapertmp')\\n return tweets\\n\\n\\n if \\\"maxTweets\\\" in kwargs:\\n maxTweets=kwargs['maxTweets']\\n else:\\n maxTweets=1000\\n\\n if len(authors)==0 and len(words)==0:\\n return(\\\"qua\\\") ###call sample function with maxTweets and (if any) dates\\n if 'startingDate' in kwargs or 'endingDate' in kwargs:\\n return getTwitterscraperTweets()\\n\\n if len(authors)!=0:\\n tweets, splits, i = [], splitIntegerIntoIntegers(maxTweets,len(authors)), 0\\n for author in authors:\\n tweets.extend(getTweetsByUser(username=author, maxTweets=splits[i]))\\n i+=1\\n return tweets\\n return getTweets()\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status = tweet_text)\",\n \"def search_hashtag(self):\\n hashtag = get_random_hashtag()\\n self.driver.get(\\n '{}/explore/tags/{}'.format(self.base_url, hashtag))\\n time.sleep(2)\\n\\n # mimic a scroll\\n scroll_helper(510, self.driver)\\n time.sleep(1)\\n scroll_helper(600, self.driver)\\n time.sleep(1)\\n\\n # Get a random pic to like\\n random_pic = self.driver.find_elements_by_xpath(\\n \\\"//a[contains(@href, '/p/')]\\\")[randint(5, 40)]\\n self.driver.get(random_pic.get_attribute(\\\"href\\\"))\\n\\n # Scroll like button into view and click it\\n time.sleep(3)\\n scroll_helper(500, self.driver)\\n self.like_photo()\\n\\n # Retrun bot to homepage after clicking like\\n time.sleep(0.5)\\n self.driver.get(self.base_url)\",\n \"def test_can_search_hashtags_by_name(self):\\n user = make_user()\\n headers = make_authentication_headers_for_user(user)\\n\\n amount_of_hashtags_to_search_for = 5\\n\\n for i in range(0, amount_of_hashtags_to_search_for):\\n hashtag_name = make_hashtag_name()\\n hashtag = make_hashtag(name=hashtag_name)\\n amount_of_characters_to_query = random.randint(1, len(hashtag_name))\\n query = hashtag_name[0:amount_of_characters_to_query]\\n final_query = ''\\n for character in query:\\n final_query = final_query + (character.upper() if fake.boolean() else character.lower())\\n\\n url = self._get_url()\\n response = self.client.get(url, {\\n 'query': final_query\\n }, **headers)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n parsed_response = json.loads(response.content)\\n self.assertEqual(len(parsed_response), 1)\\n retrieved_hashtag = parsed_response[0]\\n self.assertEqual(retrieved_hashtag['name'], hashtag_name.lower())\\n hashtag.delete()\",\n \"def get_movies_by_words(words):\\n cnx,cur = connect_to_db()\\n text = \\\"'\\\";\\n for word in words:\\n text += \\\"+\\\"+word+\\\" \\\"\\n text += \\\"'\\\"\\n cur.execute(\\\"SELECT * FROM movies \\\"\\n \\\"Where match(overview) against(\\\"+text+\\\" IN BOOLEAN MODE) LIMIT 100 \\\")\\n lst = cur.fetchall()\\n size = len(lst)\\n cur.close()\\n cnx.close()\\n return lst,size\",\n \"def check_for_greeting(sentence):\\n for word in sentence.words:\\n if word.lower() in greetings:\\n return True\",\n \"def get_tweets(user, num = 200):\\n tweets = []\\n \\n for tweet in user.home_timeline(count = num):\\n edited_tweet = tweet.text\\n edited_tweet = edited_tweet.encode(encoding='UTF-8', errors='Ignore') \\n tweets.append(edited_tweet)\\n return tweets\",\n \"def streamTweets(words = [], authors = [], timeLimit=120, removeRetweets=False, **kwargs):\\n if 'stream' not in globals():\\n global stream\\n if 'api' not in globals():\\n startTwitterApi(getApiKeys(fileName=\\\"apiConf2.txt\\\"))\\n listener = StdOutListener(removeRetweets=removeRetweets)\\n auth = api.auth\\n stream = tweepy.Stream(auth, listener, tweet_mode='extended')\\n else:\\n stream.listener.setRemoveRetweets(removeRetweets)\\n stream.listener.resetTweets()\\n\\n words = mapToValid(words)\\n authors = mapToValid(authors)\\n if not words and not authors:\\n words=[\\\"the\\\", \\\"i\\\", \\\"to\\\", \\\"a\\\", \\\"and\\\", \\\"'s\\\", \\\"is\\\", \\\"in\\\", \\\"it\\\", \\\"you\\\", \\\"of\\\", \\\"for\\\", \\\"on\\\", \\\"my\\\", \\\"that\\\", \\\"e\\\", \\\"with\\\", \\\"me\\\", \\\"do\\\", \\\"have\\\", \\\"ciao\\\", \\\"o\\\", \\\"u\\\", \\\"cool\\\", \\\"good\\\", \\\"nice\\\", \\\"#\\\", \\\"*\\\", \\\":\\\", \\\";\\\", \\\",\\\", \\\".\\\", \\\"?\\\", \\\"-\\\", \\\"%\\\", \\\"$\\\", \\\"€\\\", \\\"!\\\", \\\"(\\\", \\\")\\\", \\\"=\\\", \\\"'\\\"]\\n\\n #myQuery = ' OR '.join(kwargs[\\\"words\\\"])\\n if authors:\\n kwargs[\\\"follow\\\"]=[user.id_str for user in list(map(api.get_user,authors))]\\n else:\\n kwargs[\\\"track\\\"]=words\\n #if removeRetweets:\\n # myQuery += \\\" -filter:retweets\\\"\\n\\n #myQuery += ' from:'\\n #myQuery += ' OR from:'.join(kwargs[\\\"authors\\\"])\\n #print(myQuery)\\n import signal\\n # Register the signal function handler\\n signal.signal(signal.SIGALRM, __streamHandler__)\\n # Define a timeout for your function\\n signal.alarm(timeLimit)\\n try:\\n __stream__(stream,**kwargs)\\n except Exception:\\n print(\\\"Streaming over after time period of\\\", timeLimit, \\\"seconds... Retrieved\\\", len(stream.listener.getTweets()), \\\"tweets.\\\")\\n stream.disconnect()\\n if authors and words:\\n print(\\\"Filtering out tweets that don't contain the specified words...\\\")\\n myTweets=[]\\n for tweet in stream.listener.getTweets():\\n if 'full_text' in tweet:\\n tweet['text'] = tweet['full_text']\\n del (tweet['full_text'])\\n if any(containsWord(tweet['text'],word) for word in words):\\n myTweets.append(tweet)\\n print(\\\"Done. Retrieved\\\", len(myTweets), \\\"tweets written by the authors specified and containing (any of) the words specified.\\\")\\n return myTweets\\n return stream.listener.getTweets()\",\n \"def track(twitter, keywords=[], user_ids=[]):\\n\\n # Prepare for GET request\\n streaming_url = \\\"https://stream.twitter.com/1.1/statuses/filter.json\\\"\\n\\n # Documentation for filter params:\\n # https://dev.twitter.com/docs/streaming-apis/parameters\\n params = {\\\"replies\\\": \\\"all\\\"}\\n if keywords:\\n params[\\\"track\\\"] = keywords\\n if user_ids:\\n params[\\\"follow\\\"] = user_ids\\n\\n # Create Request.get object\\n r = twitter.get(url=streaming_url, params=params, stream = True)\\n\\n # Iterate over the request\\n for line in r.iter_lines():\\n if line :\\n try:\\n # TODO \\n # Sometimes it returns a \\\"disconnect\\\" obj \\n # before closing the stream\\n tweet = json.loads(line)\\n yield tweet\\n except ValueError:\\n # Couldn't construct a valid tweet\\n pass\",\n \"def get_tweets(twitter, screen_name, num_tweets):\\n\\n request = robust_request(twitter, 'search/tweets', {'q': screen_name, 'count': num_tweets})\\n tweets = [a['text'] for a in request]\\n\\n return tweets\",\n \"def tweet(self, tweet, at=None):\\n if tweet.strip() == \\\"\\\":\\n return\\n\\n num_tweets, tweets = self._divide_tweet(tweet, at)\\n if num_tweets > 0:\\n # replace @'s with #'s and convert unicode emojis before tweeting\\n [self.api.update_status(tw.replace(\\\"@\\\", \\\"#\\\").encode(\\\"utf-8\\\")) for tw in tweets]\\n self.log(f\\\"Tweeted: {' '.join(tweets)}\\\")\\n return tweets[0]\",\n \"def separate_hastags_mentions_urls(tweet):\\n \\n text = tweet.lower()\\n hashtag_list = re.findall(\\\"#([a-zA-Z0-9_]{1,50})\\\", text)\\n \\n text = re.sub(r'http\\\\S+', '', text)\\n clean_tweet = re.sub(\\\"@[A-Za-z0-9_]+\\\",\\\"\\\", text)\\n clean_tweet = re.sub(\\\"#[A-Za-z0-9_]+\\\",\\\"\\\", clean_tweet)\\n \\n return clean_tweet, hashtag_list\",\n \"def retweet(tweet):\\n\\n twitter.PostRetweet(tweet.id, trim_user=False)\\n\\n return\",\n \"def get_tweets(self, user, count):\\n topTweetsList = self.api.user_timeline(screen_name=user, count=count, tweet_mode='extended')\\n clnTweets = {}\\n for tweet in topTweetsList:\\n clnTweets[processTweet(getNonRetweet(tweet))] = ({'like':getFavoriteCount(tweet),'RT':getNumRetweet(tweet),'follower':getNumFollowers(tweet)}) \\n\\n tweetTxt = [twt for twt in clnTweets.keys()]\\n \\n if user in self.userTweetsStat:\\n self.userTweetsStat[user].append(clnTweets)\\n else:\\n tmp = []\\n tmp.append(clnTweets)\\n self.userTweetsStat[user] = tmp\\n return tweetTxt, self.userTweetsStat\",\n \"def extract_important(tweet_objects_list):\\n # This section extracts important information such as most common hashtags\\n hashtag_dictionary = {}\\n for tweet in tweet_objects_list:\\n if \\\"hashtags\\\" in tweet:\\n for individual_hashtag in tweet[\\\"hashtags\\\"]:\\n if not individual_hashtag[\\\"text\\\"].lower() in hashtag_dictionary:\\n hashtag_dictionary[individual_hashtag[\\\"text\\\"].lower()] = 1\\n else:\\n hashtag_dictionary[individual_hashtag[\\\"text\\\"].lower()] += 1\\n frequency = Counter(hashtag_dictionary)\\n most_frequent_hashtags = frequency.most_common(50)\\n\\n user_dictionary = {}\\n for tweet in tweet_objects_list:\\n if \\\"user_mentions\\\" in tweet:\\n for individual_user in tweet[\\\"user_mentions\\\"]:\\n if not individual_user[\\\"screen_name\\\"] in user_dictionary:\\n user_dictionary[individual_user[\\\"screen_name\\\"].lower()] = 1\\n else:\\n user_dictionary[individual_user[\\\"screen_name\\\"].lower()] += 1\\n frequency = Counter(user_dictionary)\\n most_frequent_users = frequency.most_common(50)\\n symbol_dictionary = {}\\n for tweet in tweet_objects_list:\\n if \\\"symbols\\\" in tweet:\\n for individual_symbol in tweet[\\\"symbols\\\"]:\\n if not individual_symbol[\\\"text\\\"] in symbol_dictionary:\\n symbol_dictionary[individual_symbol[\\\"text\\\"]] = 1\\n else:\\n symbol_dictionary[individual_symbol[\\\"text\\\"]] += 1\\n frequency = Counter(symbol_dictionary)\\n most_frequent_symbols = frequency.most_common(50)\\n return most_frequent_hashtags, most_frequent_users, most_frequent_symbols\",\n \"def get_tweets(keyword, max_tweets=200):\\n\\n # API keys.\\n consumer_key = \\\"kNOG1klRMMUYbsjMuY5TKl4lE\\\"\\n consumer_secret = \\\"ieghv6WI1qseYly43A0Ra1MPksEw1i5Onma0txfEu5aHantD2v\\\"\\n access_key = \\\"3291622062-15ssVc0qpJXf2SFXbA7vgfl1Sooz4Ueo2DGPQVz\\\"\\n access_secret = \\\"9XJuzgGSVLnx93tq6NfRzMT07S6o2lzjmHfjt3VRlkqXn\\\"\\n\\n # Initialize tweepy API object and authorize using API key.\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_key, access_secret)\\n api = tweepy.API(auth)\\n\\n \\\"\\\"\\\" Get tweets.\\\"\\\"\\\"\\n\\n alltweets = []\\n for status in tweepy.Cursor(\\n api.search,\\n q=keyword + \\\" -RT\\\", # the -RT flag excludes retweets.\\n count=1000,\\n result_type=\\\"recent\\\",\\n include_entities=True,\\n monitor_rate_limit=True,\\n wait_on_rate_limit=True,\\n lang=\\\"en\\\",\\n ).items():\\n\\n # get text of the tweet, encoding as utf-8.\\n text = str(status.text.encode(\\\"utf-8\\\"))\\n\\n # add to the data structure, alltweets, holding the tweets.\\n alltweets.append(text)\\n\\n # if we've reached max_tweets, break.\\n if len(alltweets) >= max_tweets:\\n break\\n\\n return alltweets\",\n \"def count_tweets(search_term, location=None):\\r\\n\\r\\n if location:\\r\\n return len(Tweet.objects(Q(keyword_search_term=search_term) & Q(location_address=location)))\\r\\n else:\\r\\n return len(Tweet.objects(keyword_search_term=search_term))\",\n \"def exactSetMatch(self, mention, ignoreSemanticTagList=[]):\\n \\n dWords = self.importantWords(ignoreSemanticTagList)\\n aWords = mention.importantWords(ignoreSemanticTagList) \\n\\n if len(aWords) == 0:\\n # annotated mention consists of \\\"unimportant\\\" words.\\n # use all words in mention\\n dWords = self.allWords()\\n aWords = mention.allWords() \\n\\n if len(dWords) > 0 and dWords == aWords:\\n return True\\n else:\\n return False\",\n \"def filter(self):\\n\\t\\tparameters = {}\\n\\n\\t\\tif self.keywords:\\n\\t\\t\\tparameters['track'] = ','.join(self.keywords)\\n\\n\\t\\tif self.locations:\\n\\t\\t\\tparameters['locations'] = ','.join([','.join([str(latlong) for latlong in loc]) for loc in self.locations])\\n\\n\\t\\tif self.usernames:\\n\\t\\t\\tparameters['follow'] = ','.join([str(u) for u in self.usernames])\\n\\n\\t\\tself.launch('statuses/filter.json', parameters)\",\n \"def extract_tweets(consumer_key,consumer_secret,access_token,access_token_secret,search_key):\\n # Step 1 - Authenticate\\n consumer_key= str(consumer_key)\\n consumer_secret= str(consumer_secret)\\n\\n access_token=str(access_token)\\n access_token_secret=str(access_token_secret)\\n\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_token_secret)\\n\\n api = tweepy.API(auth)\\n\\n #Step 3 - Retrieve Tweets\\n public_tweets = api.search(search_key)\\n tweets_list=[]\\n for tweet in public_tweets:\\n tweets_list.append(tweet.text)\\n return tweets_list\",\n \"def twitter(self):\\n message = \\\"\\\"\\n count = self.collection.count()\\n\\n twitter = Twitter(auth = OAuth(self.access_key, self.access_secret, self.consumer_key, self.consumer_secret))\\n for keyword in self.twitter_keywords:\\n query = twitter.search.tweets(q = keyword)\\n for result in query['statuses']:\\n try:\\n data = {\\\"id\\\": count+1, \\\"source\\\": \\\"twitter\\\", \\\"timestamp\\\": datetime.now()}\\n data['tweet'] = result['text']\\n data['name'] = result[\\\"user\\\"][\\\"screen_name\\\"]\\n data['url'] = \\\"https://twitter.com/\\\" + data[\\\"name\\\"] + \\\"/status/\\\" + str(result['id'])\\n data['search_string'] = keyword\\n try:\\n dataid = self.collection.insert(data)\\n except DuplicateKeyError as e:\\n continue\\n count += 1\\n\\n # Slack push notification\\n length = 82 - len(data['url'])\\n message += \\\"\\\\nURL: \\\" + data['url'] + \\\" search string: \\\".rjust(length) + keyword\\n\\n except Exception as e:\\n print(e)\\n pass\\n \\n if message:\\n print(self.G + \\\"[+] Twitter\\\" + self.B + message)\\n self.message += \\\"\\\\n*Twitter*:\\\\n```\\\"\\n self.message += message\\n self.message += \\\"\\\\n```\\\"\\n\\n return\",\n \"def handler(event, context):\\n send_tweet(random.choice(potential_tweets))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69498086","0.6639259","0.6606243","0.64730567","0.6468642","0.6389704","0.62857056","0.62474024","0.6237758","0.6198794","0.61410093","0.6137775","0.6131543","0.6126318","0.60748243","0.60424924","0.6040713","0.5978543","0.59703326","0.5945469","0.5922238","0.5897568","0.5883394","0.5746553","0.5725083","0.570358","0.57031095","0.5699793","0.56968284","0.5695475","0.56732076","0.56514627","0.5651179","0.5645168","0.5619329","0.560277","0.55818784","0.55770594","0.55721664","0.5567262","0.5558622","0.55556","0.5544451","0.55384994","0.5522558","0.5507953","0.5505223","0.5496822","0.5495384","0.547317","0.54678226","0.5461843","0.5434109","0.5428024","0.542412","0.5417226","0.5417125","0.54133415","0.5413057","0.5412437","0.54116297","0.539519","0.5391659","0.5384764","0.53825635","0.5380043","0.53793144","0.5376702","0.5375856","0.5374827","0.5366458","0.53592753","0.53586954","0.5357526","0.53566974","0.53557676","0.53557676","0.53557676","0.5355397","0.534665","0.5339601","0.5334795","0.5334755","0.533471","0.53328013","0.53200716","0.5318184","0.53177434","0.5316065","0.53141385","0.53109646","0.5310772","0.53074765","0.52889746","0.5284989","0.5283313","0.527324","0.5272034","0.52697706","0.5266057"],"string":"[\n \"0.69498086\",\n \"0.6639259\",\n \"0.6606243\",\n \"0.64730567\",\n \"0.6468642\",\n \"0.6389704\",\n \"0.62857056\",\n \"0.62474024\",\n \"0.6237758\",\n \"0.6198794\",\n \"0.61410093\",\n \"0.6137775\",\n \"0.6131543\",\n \"0.6126318\",\n \"0.60748243\",\n \"0.60424924\",\n \"0.6040713\",\n \"0.5978543\",\n \"0.59703326\",\n \"0.5945469\",\n \"0.5922238\",\n \"0.5897568\",\n \"0.5883394\",\n \"0.5746553\",\n \"0.5725083\",\n \"0.570358\",\n \"0.57031095\",\n \"0.5699793\",\n \"0.56968284\",\n \"0.5695475\",\n \"0.56732076\",\n \"0.56514627\",\n \"0.5651179\",\n \"0.5645168\",\n \"0.5619329\",\n \"0.560277\",\n \"0.55818784\",\n \"0.55770594\",\n \"0.55721664\",\n \"0.5567262\",\n \"0.5558622\",\n \"0.55556\",\n \"0.5544451\",\n \"0.55384994\",\n \"0.5522558\",\n \"0.5507953\",\n \"0.5505223\",\n \"0.5496822\",\n \"0.5495384\",\n \"0.547317\",\n \"0.54678226\",\n \"0.5461843\",\n \"0.5434109\",\n \"0.5428024\",\n \"0.542412\",\n \"0.5417226\",\n \"0.5417125\",\n \"0.54133415\",\n \"0.5413057\",\n \"0.5412437\",\n \"0.54116297\",\n \"0.539519\",\n \"0.5391659\",\n \"0.5384764\",\n \"0.53825635\",\n \"0.5380043\",\n \"0.53793144\",\n \"0.5376702\",\n \"0.5375856\",\n \"0.5374827\",\n \"0.5366458\",\n \"0.53592753\",\n \"0.53586954\",\n \"0.5357526\",\n \"0.53566974\",\n \"0.53557676\",\n \"0.53557676\",\n \"0.53557676\",\n \"0.5355397\",\n \"0.534665\",\n \"0.5339601\",\n \"0.5334795\",\n \"0.5334755\",\n \"0.533471\",\n \"0.53328013\",\n \"0.53200716\",\n \"0.5318184\",\n \"0.53177434\",\n \"0.5316065\",\n \"0.53141385\",\n \"0.53109646\",\n \"0.5310772\",\n \"0.53074765\",\n \"0.52889746\",\n \"0.5284989\",\n \"0.5283313\",\n \"0.527324\",\n \"0.5272034\",\n \"0.52697706\",\n \"0.5266057\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6507708"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":13781,"cells":{"query":{"kind":"string","value":"Retweets tweets that match a certain phrase (hashtag, word, etc.)"},"document":{"kind":"string","value":"def auto_rt(q, count=1, result_type=\"recent\"):\n\n result = search_tweets(q, count, result_type)\n\n for tweet in result[\"statuses\"]:\n try:\n # don't retweet your own tweets\n if tweet[\"user\"][\"screen_name\"] == TWITTER_HANDLE:\n continue\n\n result = t.statuses.retweet(id=tweet[\"id\"])\n print(\"retweeted: %s\" % (result[\"text\"].encode(\"utf-8\")))\n\n # when you have already retweeted a tweet, this error is thrown\n except TwitterHTTPError as e:\n print(\"error: %s\" % (str(e)))"},"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 two_word_finder(word1,word2,text):\r\n word1 = word1.lower()\r\n word2 = word2.lower()\r\n text = str(text).lower()\r\n if word1 and word2 in text:\r\n return True #return text to see specific tweets\r\n return False","def searchTweets():\n if 'api' not in globals():\n startTwitterApi(getApiKeys(fileName='apiConf2.txt'))\n #SEARCHING TWEETS CONTAINING THE HASHTAG \"#bitcoin\" USING TWEEPY LIBRARY\n myTweets= []\n #words=list(map(str,words))\n if words:\n myQuery=' OR '.join(words)\n else:\n myQuery = '*'\n if removeRetweets:\n myQuery += ' - filter:retweets'\n kwargs['q']=myQuery\n kwargs['count']=100\n kwargs['tweet_mode']='extended'\n if 'startingDate' in kwargs:\n kwargs['since']=kwargs['startingDate']\n del(kwargs['startingDate'])\n if 'endingDate' in kwargs:\n kwargs['until']=kwargs['endingDate']\n del(kwargs['endingDate'])\n if 'maxTweets' in kwargs:\n del(kwargs['maxTweets'])\n if sortBy=='newest':\n for tweet in tweepy.Cursor(api.search, kwargs).items(maxTweets):\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n else:\n for tweet in tweepy.Cursor(api.search, kwargs).items():\n tweet._json['text']=tweet._json['full_text']\n del (tweet._json['full_text'])\n myTweets.append(tweet._json)\n return getTopNTweets(myTweets, maxTweets)","def search(searchTerm):\n api = twitter.Api()\n tweets = api.GetSearch(searchTerm)\n for tweet in tweets:\n util.safe_print(tweet.GetText())","def RelevantTweet(Tweet, Termlist, DateRange=None):\n TweetText = Tweet[\"full_text\"].lower() #ignore case. # Changed to full_text now, instead of (previously) truncated.\n\n if Tweet[\"is_quote_status\"] is True: #Append, since we're just matching terms.\n # print(\"Tweet = \", Tweet.keys())\n if \"quoted_status\" in Tweet:\n TweetText = TweetText + \" \" + Tweet[\"quoted_status\"][\"full_text\"].lower()\n\n # From: https://stackoverflow.com/questions/7703865/going-from-twitter-date-to-python-datetime-date\n if DateRange is not None:\n ts = time.strptime(Tweet['created_at'], '%a %b %d %H:%M:%S +0000 %Y')\n\n if ts < time.strptime(DateRange[0], '%b %d %H:%M:%S %Y'):\n return False\n elif ts > time.strptime(DateRange[1], '%b %d %H:%M:%S %Y'):\n return False\n if Termlist is None:\n return True\n else:\n for word in Termlist:\n if word.lower() in TweetText:\n return True\n return False","def filter_by_hashtag(tweets: list, hashtag: str) -> list:\n tweets_with_hashtag = {} # findall(): Kui tekstis on rohkem kui üks regulaaravaldisele vastav alamsõne saab kõikide vastete järjendi moodustada funktsiooniga findall()\n pattern = r\"#\\w+\" # \\w : tähed, numbrid, alakriips, + : 1 või rohkem\n for tweet in tweets: # r\"string\" on \"raw\" tüüpi string, mis tähendab, et kurakaldkriipsud(\"\\\") jäetakse teksti alles.\n find_hashtag = re.findall(pattern, tweet.content) # word:\\w\\w\\w. Regulaaravaldisele vastab täpne sõne \"word:\" ning sellele järgnevad 3 suvalist tähte.\n if find_hashtag:\n tweets_with_hashtag.setdefault(ht, []).append(tweet)\n return tweets_with_hashtag[hashtag]","def search_from_terms(api, term, **kwargs):\n tweets=api.GetSearch(term=term)\n return {\"tweets\":tweets}","def search_tweets(q, count=100, result_type=\"recent\"):\n\n return t.search.tweets(q=q, result_type=result_type, count=count)","def searchByKeyword(self, keyword, until=\"\", since=\"\", count=None, result_type=\"recent\"):\n if count is None:\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\n full_text=True, tweet_mode=\"extended\", lang=\"en\").items()\n else:\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\n full_text=True, tweet_mode=\"extended\", lang=\"en\").items(count)\n\n for status in tweets:\n createdDate = parser.parse(str(status._json[\"created_at\"]).strip())\n createdDate = createdDate.replace(\n tzinfo=pytz.utc) - createdDate.utcoffset()\n status_refined = {\n 'keyword': keyword,\n '_id': status._json[\"id\"],\n 'created_at': createdDate,\n 'tweetText': status._json[\"full_text\"],\n 'hashtags': status._json[\"entities\"][\"hashtags\"],\n 'userLoc': status._json[\"user\"][\"location\"],\n 'tweetGeo': status._json[\"geo\"],\n 'tweetCoordinates': status._json[\"coordinates\"],\n 'tweetPlace': status._json[\"place\"],\n 'retweet': {},\n }\n if hasattr(status, \"retweeted_status\"):\n status_refined['tweetText'] = status._json[\"retweeted_status\"][\"full_text\"]\n status_refined['retweet'] = {\n 'original_retweet_id': status._json[\"retweeted_status\"][\"id\"],\n 'origUserLoc': status._json[\"retweeted_status\"][\"user\"][\"location\"],\n 'origTweetLoc': status._json[\"retweeted_status\"][\"geo\"],\n 'origTweetPlace': status._json[\"retweeted_status\"][\"place\"],\n 'origTweetCoord': status._json[\"retweeted_status\"][\"coordinates\"],\n 'origHashtags': status._json[\"retweeted_status\"][\"entities\"][\"hashtags\"],\n 'retweet_count': status._json[\"retweet_count\"],\n }\n self.tweets.append(status_refined)\n return self.tweets","def keep(tweet):\n text = tweet[\"Text\"].lower()\n hashtags = \" \".join(tweet[\"Hashtags\"]).lower()\n for word in keep_words:\n if word in text:\n # print(\"{} in text {}\".format(word, text))\n return True\n if word in hashtags:\n # print(\"{} in hashtags {}\".format(word, hastags))\n return True\n return False","def _get_tweets(self):\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n api = tweepy.API(auth)\n search = api.search(self.term, lang='en', count=100)\n\n print(f\"Getting tweets that mention '{self.term}', \"\n f\"this may take a while...\")\n\n save_tweet_text = [tweet._json['text'] for tweet in search]\n while len(save_tweet_text) < 1000:\n try:\n oldest = search[-1].id - 1\n search = api.search(self.term, lang='en', count=100, max_id=oldest)\n new_tweets = [tweet._json['text'] for tweet in search]\n save_tweet_text.extend(new_tweets)\n\n # Turn into a set to remove duplicated tweets, then back to list\n save_tweet_text = list(set(save_tweet_text))\n except IndexError:\n break\n\n print(f\"Done. {len(save_tweet_text)} Tweets received.\")\n return save_tweet_text","def search_for_phrase(self, phrase):\n uri = uriForPhraseSearch(phrase)\n return json.loads(requests.get(uri).content)","def has_keyword(tweet, keywords):\n temp = tweet.lower()\n for keyword in keywords:\n if bool(re.search(f'\\\\b{keyword}\\\\b', temp)):\n return True\n return False","def filter_tweet_core(tweet):\n if not ('user' in tweet and 'screen_name' in tweet['user'] \\\n and 'text' in tweet):\n return False\n if not filter_tweet_text(tweet['text']):\n return False\n if bannedclients.search(tweet['source']):\n return False\n if bannedterms.search(tweet['text']):\n return False\n if bannedterms.search(tweet['user']['screen_name']):\n return False\n if 'entities' in tweet and bannedterms.search(json.dumps(tweet['entities'])):\n return False\n if not filter_tweet_entropy(tweet['text']):\n return False\n if not filter_tweet_cardspam(tweet):\n return False\n return True","def show_search_results():\n\n #Get values from search-box via AJAX\n current_keyword = request.form.get('search').lower()\n print \"**********************\"\n print current_keyword\n print \"**********************\"\n tweets = get_tweets_by_api(term=current_keyword)\n\n result = []\n\n for tweet in tweets:\n # Exclude retweets since they appear as duplicatses to endu ser\n if tweet.retweeted_status is None:\n # Convert tweet text from unicode to text\n tweet_id = tweet.id\n text = unicodedata.normalize('NFKD', tweet.text).encode('ascii', 'ignore')\n # Find URL in text and bind to url\n # url = re.search('((?:http|https)(?::\\\\/{2}[\\\\w]+)(?:[\\\\/|\\\\.]?)(?:[^\\\\s\"]*))', text)\n url = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)\n # Remove URL from text\n text_wo_url = re.sub(r'^https?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE)\n # Handle / Name\n user = unicodedata.normalize('NFKD', tweet.user.screen_name).encode('ascii', 'ignore')\n # Count of favorites\n favorite_count = tweet.favorite_count\n #Return dictionary of hashtags with hashtag as key and number of occurances as value\n if tweet.hashtags:\n # Convert hashtags from unicode to string\n ht_list = []\n for hashtag in tweet.hashtags:\n ht_str = unicodedata.normalize('NFKD', hashtag.text).encode('ascii', 'ignore')\n ht_list.append(ht_str.lower())\n hashtags = Counter(ht_list)\n else:\n hashtags = tweet.hashtags\n # Convert tweet from unicode to datetime\n created_at = tweet.created_at\n # format created_at string to ISO 8610\n created_at_str = time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(created_at, '%a %b %d %H:%M:%S +0000 %Y'))\n # create a moment from the string\n created_at = moment.date(created_at_str, 'YYYY-MM-DD HH:mm:ss')\n result.append({'created_at': created_at_str, 'tweet_text': text_wo_url, 'user': user,\n 'favorite_count': favorite_count, 'hashtags': hashtags,\n 'url': url, 'tweet_id': tweet_id})\n\n print \"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\"\n print result\n print \"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\"\n\n return jsonify(result=result) #, tweets","def collect_tweets(redis_client, twitter_client, search_term):\n search = Search(redis_client, twitter_client, search_term)\n search.get_term_state()\n search.parse_term_state()\n search.set_query_string()\n search.set_execution_time()\n search.execute_query()\n search.incr_query_counters()\n search.set_newest_id()\n search.set_oldest_id()\n search.set_scenario()\n search.set_term_state()\n search.store_results()\n search.set_score()\n search.log_state()","def search(self, word):","def compute_retweet(row):\n return row['full_text'].startswith('RT')","def get_tweets(hashtag):\n api = twitter.Api(consumer_key=TWITTER_API_CONSUMER_KEY,\n consumer_secret=TWITTER_API_CONSUMER_SECRET,\n access_token_key=TWITTER_API_ACCESS_TOKEN_KEY,\n access_token_secret=TWITTER_API_ACCESS_TOKEN_SECRET)\n\n query = (f\"q=%23{HASHTAG}%20-RT\"\n f\"&result_type=recent&since=2019-01-01&count={NUM_TWEETS}\")\n results = api.GetSearch(raw_query=query)\n\n return [\n format_tweet(tweet.AsDict())\n for tweet in results\n ]","def searchbrown_word(tag):\n brown_tagged_words = brown.tagged_words(categories='news')\n hitwords = []\n for i in range(len(brown_tagged_words)):\n if tag == brown_tagged_words[i][1]:\n hitwords.append(brown_tagged_words[i][0].lower())\n return hitwords","def get_tweets(keyword, max_tweets=200):\n\n # API keys.\n consumer_key = \"kNOG1klRMMUYbsjMuY5TKl4lE\"\n consumer_secret = \"ieghv6WI1qseYly43A0Ra1MPksEw1i5Onma0txfEu5aHantD2v\"\n access_key = \"3291622062-15ssVc0qpJXf2SFXbA7vgfl1Sooz4Ueo2DGPQVz\"\n access_secret = \"9XJuzgGSVLnx93tq6NfRzMT07S6o2lzjmHfjt3VRlkqXn\"\n\n # Initialize tweepy API object and authorize using API key.\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_key, access_secret)\n api = tweepy.API(auth)\n\n \"\"\" Get tweets.\"\"\"\n\n alltweets = []\n for status in tweepy.Cursor(\n api.search,\n q=keyword + \" -RT\", # the -RT flag excludes retweets.\n count=1000,\n result_type=\"recent\",\n include_entities=True,\n monitor_rate_limit=True,\n wait_on_rate_limit=True,\n lang=\"en\",\n ).items():\n\n # get text of the tweet, encoding as utf-8.\n text = str(status.text.encode(\"utf-8\"))\n\n # add to the data structure, alltweets, holding the tweets.\n alltweets.append(text)\n\n # if we've reached max_tweets, break.\n if len(alltweets) >= max_tweets:\n break\n\n return alltweets","def twitter_text(text, search_query = False):\r\n tt = TwitterText(text)\r\n if search_query:\r\n tt.text = tt.highlighter.hit_highlight(query = search_query)\r\n tt.text = tt.autolink.auto_link()\r\n return tt.text","def search(self, term):","def searchbrown_phrase(tags):\n l = len(tags)\n brown_tagged_words = brown.tagged_words(categories='news')\n hitwords = []\n for i in range(len(brown_tagged_words)-l+1):\n searchtags = [tag for _,tag in brown_tagged_words[i:i+l]]\n if tags == searchtags:\n hitwords.append(tuple([w.lower()\n for w,_ in brown_tagged_words[i:i+l]]))\n return hitwords","def get_tweets(search_terms, geocode):\n\turl = 'http://search.twitter.com/search.json'\n\tparams = {\n\t\t'q': search_terms,\n\t\t'geocode': str(geocode['lat']) + ',' + str(geocode['lng']) + ',200mi'\n\t}\n\turl_params = urllib.urlencode(params)\n\tresult = urlfetch.fetch(url=url + '?' + url_params,\n\t\t\t\t\t\t\tmethod=urlfetch.GET)\n\n\tif result.status_code == 200:\n\t\titems = json.loads(result.content)['results']\n\t\t# return [r['text'] for r in items]\n\t\treturn items","def get_tweets_from_search(api, search_string, parameters=\" -filter:retweets\", since=\"2021-08-09\", lang=\"en\", max_tweets=1000):\n\n tweet_list = []\n count = 0\n search = search_string\n params = parameters\n\n for tweet in tweepy.Cursor(api.search, q=search + params,\n count=100,\n tweet_mode=\"extended\",\n lang=lang,\n since=since,\n # until=\"2015-02-01\",\n ).items():\n tweet_list.append(tweet._json[\"full_text\"])\n count += 1\n if count == max_tweets:\n break\n print(count)\n return pd.DataFrame({\"text\": tweet_list})","def search_postman(text):\n result = _search_blog('postman', text)\n _print_results(result)\n return result","def get_tweets(twitter, screen_name, num_tweets):\n\n request = robust_request(twitter, 'search/tweets', {'q': screen_name, 'count': num_tweets})\n tweets = [a['text'] for a in request]\n\n return tweets","def extract_hashtags(tweet):\n tknzr = TweetTokenizer()\n hashtags = [token.lower() for token in tknzr.tokenize(tweet) if re.match(hashtag_re, token)]\n return hashtags","def filter_tweet(tweet):\n if not filter_tweet_core(tweet):\n return False\n if bannedusers.search(tweet['user']['screen_name']) or (\n 'retweeted_status' in tweet and bannedusers.search(tweet['retweeted_status']['user']['screen_name'])):\n return False\n if tweet['user']['screen_name'] == credentials['username']: # Do not match self tweets :-)\n return False\n return True","def search(search_term):\r\n if search_term:\r\n if 'list' in search_term:\r\n search_term = search_term.split('?list')[0]\r\n\r\n url = SEARCH_URL.format(API_KEY, util.web.quote(search_term.encode('ascii', 'ignore')))\r\n response = util.web.http_get(url=url, json=True, referer='http://tinychat.com')\r\n\r\n if response['json'] is not None:\r\n try:\r\n if 'items' in response['json']:\r\n for item in response['json']['items']:\r\n video_id = item['id']['videoId']\r\n details = video_details(video_id)\r\n if details is not None:\r\n return {\r\n 'type': 'youTube',\r\n 'video_id': video_id,\r\n 'video_time': details['video_time'],\r\n 'video_title': details['video_title']\r\n }\r\n except KeyError as ke:\r\n log.error(ke, exc_info=True)\r\n return None","def run_single_keyword(self, keyword_string, language):\n keyword = self.mongo_controller.get_keyword(keyword_string, language, cast=True)\n twitter_results = self.crawler.search(keyword, limit=self.limit_requests)\n return self.__save_tweets(twitter_results)","def queryTerm2Twitter(term): \n statusList = api.GetSearch(term, count=100, result_type='recent')\n timeStampOfStatus = [datetime.fromtimestamp(i.created_at_in_seconds) for i in statusList]\n timeStampOfStatus.sort() \n return timeStampOfStatus[0]","def extract_tweets(consumer_key,consumer_secret,access_token,access_token_secret,search_key):\n # Step 1 - Authenticate\n consumer_key= str(consumer_key)\n consumer_secret= str(consumer_secret)\n\n access_token=str(access_token)\n access_token_secret=str(access_token_secret)\n\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_token_secret)\n\n api = tweepy.API(auth)\n\n #Step 3 - Retrieve Tweets\n public_tweets = api.search(search_key)\n tweets_list=[]\n for tweet in public_tweets:\n tweets_list.append(tweet.text)\n return tweets_list","def reply_to_tweets():\n last_seen_id = retrieve_last_seen_id(FILE_NAME)\n mentions = api.mentions_timeline(\n last_seen_id,\n tweet_mode='extended')\n\n for mention in reversed(mentions):\n print(str(mention.id) + ' - ' + mention.full_text, flush=True)\n last_seen_id = mention.id\n store_last_seen_id(last_seen_id, FILE_NAME)\n for i in range(len(keywords)):\n if keywords[i] in mention.full_text.lower():\n print(\"responding back to: \" + '@' +\n mention.user.screen_name, flush=True)\n api.update_status('@' + mention.user.screen_name + ' ' +\n salts[i], mention.id)","def search(words):\n newlist = [w for w in words if 'son' in w]\n return newlist","def twitterCrawl(self, strInput):\n strVal = self.txtTwitter.get(\"1.0\", 'end')\n if (strVal.strip()):\n self.txtTwitter.delete(\"1.0\", 'end')\n twitResult = twit.search(strInput)\n return twitResult","def trendingTweets():\n api = twitter.Api()\n trending_topics = api.GetTrendsWoeid(PHILA_WOEID)\n for topic in trending_topics:\n topicSearchTerm = topic.name\n trending_tweets = api.GetSearch(topicSearchTerm)\n for tweet in trending_tweets:\n util.safe_print(tweet.GetText())\n # pass","def has_retweet(tweet):\n\n pattern = r'^RT'\n return re.match(pattern, tweet)","def filter_tweets(tweets):\n # We keep only tweets by chrisalbon with pictures\n search_tweets = [tw for tw in tweets if tw['username'] == '@chrisalbon' and len(tw['images']) > 0]\n # He made multiple tweets on the same topic, we keep only the most recent tweets\n # We use the indexes of the reversed tweet list and dictionnaries to keep only key \n unique_search_index = sorted(list({t['text'].lower():i for i,t in list(enumerate(search_tweets))[::-1]}.values()))\n unique_search_tweets = [search_tweets[i] for i in unique_search_index]\n\n # Keep non-downloaded tweets\n most_recent_file = sorted([datetime.datetime.fromtimestamp(os.path.getmtime(path)) \n for path in glob.glob(\"./downloaded_pics/*.jpg\")], reverse=True)[0]\n recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > most_recent_file]\n\n # Uncomment for testing new tweets\n # recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > datetime.datetime(2017, 7, 6, 13, 41, 48)]\n return recent_seach_tweets","def twitter(self):\n message = \"\"\n count = self.collection.count()\n\n twitter = Twitter(auth = OAuth(self.access_key, self.access_secret, self.consumer_key, self.consumer_secret))\n for keyword in self.twitter_keywords:\n query = twitter.search.tweets(q = keyword)\n for result in query['statuses']:\n try:\n data = {\"id\": count+1, \"source\": \"twitter\", \"timestamp\": datetime.now()}\n data['tweet'] = result['text']\n data['name'] = result[\"user\"][\"screen_name\"]\n data['url'] = \"https://twitter.com/\" + data[\"name\"] + \"/status/\" + str(result['id'])\n data['search_string'] = keyword\n try:\n dataid = self.collection.insert(data)\n except DuplicateKeyError as e:\n continue\n count += 1\n\n # Slack push notification\n length = 82 - len(data['url'])\n message += \"\\nURL: \" + data['url'] + \" search string: \".rjust(length) + keyword\n\n except Exception as e:\n print(e)\n pass\n \n if message:\n print(self.G + \"[+] Twitter\" + self.B + message)\n self.message += \"\\n*Twitter*:\\n```\"\n self.message += message\n self.message += \"\\n```\"\n\n return","def searchIdiom(i, idiom):\n token = authorize(i)\n endpoint = 'https://api.twitter.com/1.1/search/tweets.json'\n auth = OAuth1(signature_type=\"auth_header\", **token)\n \n params = {}\n params[\"q\"]=\"%23\"+idiom\n params[\"count\"] = tweet_count\n try:\n r = requests.get(endpoint, params=params, auth=auth, timeout=60.0)\n print(r)\n if r.status_code==200:\n\t '''\n print(\"yay\")\n for tweet in r.json()[\"statuses\"]:\n print(tweet[\"user\"][\"screen_name\"])\n #print(tweet[\"entities\"][\"hashtags\"])\n print tweet[\"text\"]\n ''' \n return r.json()[\"statuses\"]\n else:\n if r.status_code==429:\n print('limit exceeded')\n rest_rate_limit(r)\n return searchIdiom(i, idiom)\n else:\n print('critical situation')\n return []\n except ValueError as e:\n print('Error : ', e)\n except Exception as e1:\n print('Error : ', e1)","def getHashtagsAndMentions(tweets):\n hashtags = Counter()\n mentions = Counter()\n plain = Counter()\n\n pattern = re.compile(r\"[^#@\\w'-]+\")\n\n for t in tweets:\n words = pattern.split(t.message)\n for word in words:\n # Ignore null strings caused by split characters at the end of a\n # message and remove standalone hyphens.\n if word and not word.startswith(\"-\"):\n # Increment count for the word in the Counter.\n if word.startswith(\"#\"):\n hashtags.update({word: 1})\n elif word.startswith(\"@\"):\n mentions.update({word: 1})\n else:\n # TODO: apply nltk.corpus.stopwords.words() here,\n # across languages. Consider that the stopwords cut off\n # before apostrophe, therefore check if the word\n # starts with the stopword.\n plain.update({word: 1})\n\n return hashtags, mentions, plain","def twitter_search(a, q, x=10000000, t=100, s=None, m=-1):\n\n tweets = list()\n tweet_count = 0\n\n while tweet_count < x:\n try:\n if (m <= 0):\n if (not s):\n new_tweets = a.search(q = q, count = t)\n else:\n new_tweets = a.search(q = q, count = t, since_id = s)\n else:\n if (not s):\n new_tweets = a.search(q = q, count = t, max_id = (m - 1))\n else:\n new_tweets = a.search(q = q, count = t, max_id = (m - 1), since_id = s)\n\n if not new_tweets:\n break\n\n for tweet in new_tweets:\n tweets.append(tweet)\n\n tweet_count += len(new_tweets)\n m = new_tweets[-1].id\n\n except tweepy.TweepError as e:\n error = (-1, \"error:\" + str(e))\n return error\n\n search_results = (tweet_count, tweets)\n\n return search_results","def get_tweets(api, listOfTweets, keyword, numOfTweets=20, date_since='2019-1-1', lang=\"en\"):\n spinner = yaspin()\n spinner.start()\n for tweet in tweepy.Cursor(api.search, q=keyword, lang=lang, since=date_since).items(numOfTweets):\n # Add tweets in this format\n dict_ = {'Screen Name': tweet.user.screen_name,\n 'User Name': tweet.user.name,\n 'Tweet Created At': str(tweet.created_at),\n 'Tweet Text': tweet.text,\n 'Cleaned Tweet Text': func.clean_tweets(tweet.text),\n 'User Location': str(tweet.user.location),\n 'Tweet Coordinates': str(tweet.coordinates),\n 'Retweet Count': str(tweet.retweet_count),\n 'Retweeted': str(tweet.retweeted),\n 'Phone Type': str(tweet.source),\n 'Favorite Count': str(tweet.favorite_count),\n 'Favorited': str(tweet.favorited),\n 'Replied': str(tweet.in_reply_to_status_id_str)\n }\n listOfTweets.append(dict_)\n spinner.stop()\n return listOfTweets","def find_search_text(url, search_text):\n try:\n #sock = urllib2.urlopen(url)\n result = urlfetch.fetch(url)\n sock = result.content\n parser = URLParser()\n parser.feed(sock.read())\n sock.close()\n parser.close()\n for texts in parser.text:\n if search_text in texts:\n return True\n return False\n except: # This is to take care of links that are not valid.\n return False","async def search(self, ctx: \"IceTeaContext\", *, query):\n response_list = await ctx.guild_data.search_tags(query)\n if len(response_list) > 0:\n response_message = \"\\n\".join([tag.title for tag in response_list])\n await ctx.send(f\"Found these tags:\\n{response_message}\")\n else:\n await ctx.send(\"No similar tags found\")","def get_tweets_by_hashtag_route(hashtag):\n response, code = get_tweets_by_hashtag(\n hashtag, request.args.get('limit', 30))\n return jsonify(response), code","def get_tweets(keyword):\n url = 'http://search.twitter.com/search.json?q='\n\n page = urllib.urlopen('%s%s' % (url, keyword))\n blob = page.read()\n jsonblob = json.loads(blob)\n return jsonblob","def search(query_string):","def get_tweets(self, query, count=10):\n # empty list to store parsed tweets\n tweets = []\n\n try:\n # call twitter api to fetch tweets\n fetched_tweets = self.api.search(q=query, count=count)\n\n # parsing tweets one by one\n for tweet in fetched_tweets:\n # empty dictionary to store required params of a tweet\n parsed_tweet = {}\n\n # saving text of tweet\n parsed_tweet['text'] = tweet.text\n # saving sentiment of tweet\n parsed_tweet['sentiment'] = self.get_tweet_sentiment(tweet.text)\n\n # appending parsed tweet to tweets list\n if tweet.retweet_count > 0:\n # if tweet has retweets, ensure that it is appended only once\n if parsed_tweet not in tweets:\n tweets.append(parsed_tweet)\n else:\n tweets.append(parsed_tweet)\n\n # return parsed tweets\n return tweets\n\n except tweepy.TweepError as e:\n # print error (if any)\n print(\"Error : \" + str(e))","def tweet_text(tweet):\n return tweet['text']","def searchByKeywordPro(self, query, since=\"\", until=\"\", maxResults=None):\n\n tweetsList = []\n if(not maxResults):\n tweetList, next_token = self.api.search_30_day(\n environment_name=\"developer\", query=query, toDate=until, fromDate=since)\n tweetsList.append(tweetList)\n while(next_token):\n tweetList, next_token = self.api.search_30_day(\n environment_name=\"developer\", query=query, toDate=until, fromDate=since, next=next_token)\n tweetsList.append(tweetList)\n else:\n tweetList, next_token = self.api.search_30_day(\n environment_name=\"developer\", query=query, toDate=until, fromDate=since)\n tweetsList.append(tweetList)\n maxResults -= len(tweetList)\n while(next_token and maxResults > 0):\n tweetList, next_token = self.api.search_30_day(\n environment_name=\"developer\", query=query, toDate=until, fromDate=since, next=next_token)\n tweetsList.append(tweetList)\n maxResults -= len(tweetList)\n for status in tweetsList:\n createdDate = parser.parse(str(status._json[\"created_at\"]).strip())\n createdDate = createdDate.replace(\n tzinfo=pytz.utc) - createdDate.utcoffset()\n status_refined = {\n 'keyword': query,\n '_id': status._json[\"id\"],\n 'created_at': createdDate,\n 'tweetText': status._json[\"text\"],\n 'hashtags': status._json[\"entities\"][\"hashtags\"],\n 'userLoc': status._json[\"user\"][\"location\"],\n 'tweetGeo': status._json[\"geo\"],\n 'tweetCoordinates': status._json[\"coordinates\"],\n 'tweetPlace': status._json[\"place\"],\n 'retweet': {},\n 'quote': {},\n }\n if hasattr(status, \"quoted_status\"):\n if \"extended_tweet\" in status._json[\"quoted_status\"].keys():\n print(\"Taking the expanded tweet\")\n status_refined['tweetText'] = status._json[\"quoted_status\"][\"extended_tweet\"][\"full_text\"]\n else:\n status_refined['tweetText'] = status._json[\"quoted_status\"][\"text\"]\n status_refined['quote'] = {\n 'original_retweet_id': status._json[\"quoted_status\"][\"id\"],\n 'origUserLoc': status._json[\"quoted_status\"][\"user\"][\"location\"],\n 'origTweetLoc': status._json[\"quoted_status\"][\"geo\"],\n 'origTweetPlace': status._json[\"quoted_status\"][\"place\"],\n 'origTweetCoord': status._json[\"quoted_status\"][\"coordinates\"],\n 'origHashtags': status._json[\"quoted_status\"][\"entities\"][\"hashtags\"],\n 'retweet_count': status._json[\"quote_count\"],\n }\n elif hasattr(status, \"retweeted_status\"):\n print(status._json[\"retweeted_status\"])\n if \"extended_tweet\" in status._json[\"retweeted_status\"].keys():\n print(\"Taking the expanded tweet\")\n status_refined['tweetText'] = status._json[\"retweeted_status\"][\"extended_tweet\"][\"full_text\"]\n else:\n status_refined['tweetText'] = status._json[\"retweeted_status\"][\"text\"]\n status_refined['retweet'] = {\n 'original_retweet_id': status._json[\"retweeted_status\"][\"id\"],\n 'origUserLoc': status._json[\"retweeted_status\"][\"user\"][\"location\"],\n 'origTweetLoc': status._json[\"retweeted_status\"][\"geo\"],\n 'origTweetPlace': status._json[\"retweeted_status\"][\"place\"],\n 'origTweetCoord': status._json[\"retweeted_status\"][\"coordinates\"],\n 'origHashtags': status._json[\"retweeted_status\"][\"entities\"][\"hashtags\"],\n 'retweet_count': status._json[\"retweet_count\"],\n }\n elif hasattr(status, \"extended_tweet\"):\n if \"extended_tweet\" in status._json.keys():\n status_refined['tweetText'] = status._json[\"extended_tweet\"][\"full_text\"]\n self.tweets.append(status_refined)\n return self.tweets","def analyze_hashtag(self, hashtag, count=200):\n tweets = []\n\n for x in xrange(0, int(count / 100)):\n tweets.extend(self.tweet_fetcher.get_tweets(hashtag))\n\n analyzed_tweets = sort_tweets(self.sa.classify(tweets))\n\n self.analyzed_tweets = analyzed_tweets\n\n return analyzed_tweets","def getByHashtags(hashtag):\n\n # set page_limits. The default is 1 \n pages_limit = request.args.get('pages_limit') or 1\n pages_limit = int(pages_limit)\n\n raw_response = get_response(tw_api, 'search/tweets', { 'q': '#' + hashtag, 'count': 100 }, pages_limit)\n list_response = convert_resp2list(raw_response)\n return jsonify(list_response)","def topictweets(url):\n article = get_article(url)\n keywords = get_keywords(article['text'])\n entities = get_entities(article['text'])\n q = twitter_query(keywords, entities)\n result = search({'q': q, 'count': 100, 'result_type': 'mixed'})\n tweets = screen_name_filter(result.statuses, 'media')\n return tweets","async def wordfilter_test(self, ctx, *, message):\n found = self.test_sentence(message)\n if found:\n await ctx.send(f\"Message contains `{found}`\")\n else:\n await ctx.send(\"Couldn't detect any filtered words\")","def printHashtagsAndMentions(searchText=None, filterTerms=False, tweetLimit=0):\n tweets = db.Tweet.select()\n if searchText is not None:\n tweets = tweets.filter(db.Tweet.q.message.contains(searchText))\n tweets = tweets.limit(tweetLimit)\n\n hashtags, mentions, plain = getHashtagsAndMentions(tweets)\n\n if searchText and filterTerms:\n hashtags = Counter(\n {k: v for k, v in hashtags.items() if searchText.lower() in k.lower()}\n )\n mentions = Counter(\n {k: v for k, v in mentions.items() if searchText.lower() in k.lower()}\n )\n plain = Counter(\n {k: v for k, v in plain.items() if searchText.lower() in k.lower()}\n )\n\n # Unique word count for each area.\n hashtagWC = len(hashtags)\n mentionWC = len(mentions)\n plainWC = len(plain)\n\n print(\"Summary\")\n print(\"==============\")\n # Count items in the sliced selection since .count() does not work with\n # a limit.\n count = len(list(tweets)) if tweetLimit else tweets.count()\n print(\"{0:7,d} tweets\".format(count))\n print(\"{0:7,d} unique words\".format(hashtagWC + mentionWC + plainWC))\n print(\"{0:7,d} unique hashtags\".format(hashtagWC))\n print(\"{0:7,d} unique mentions\".format(mentionWC))\n print(\"{0:7,d} unique plain words\".format(plainWC))\n print()\n\n print(\"Hashtags\")\n print(\"========\")\n printCounterByCount(hashtags)\n print()\n\n print(\"Mentions\")\n print(\"========\")\n printCounterByCount(mentions)\n\n \"\"\"\n # Removal of stopwords and handling of URIs is needed to make this\n # useful.\n print 'Plain'\n print '========'\n printCounterByCount(plain)\n \"\"\"","def clean_tweet(tweet):\r\n reply_pattern = re.compile(\"^@([a-zA-Z0-9]*) (.*)\")\r\n regexhandler = regex.RegexHandler()\r\n # add mark if tweets starts with a mention (@user)\r\n if reply_pattern.match(tweet.tweet[\"text\"]) is not None:\r\n temp = \"MarkReply \" + tweet.tweet[\"text\"]\r\n else:\r\n temp = tweet.tweet[\"text\"]\r\n # language dependent\r\n\r\n if floodtags.core.statics.StaticData.locations:\r\n for location in floodtags.core.statics.StaticData.locations:\r\n if location in temp:\r\n temp += \" MarkLocation\"\r\n\r\n try:\r\n stemmer = SnowballStemmer(floodtags.core.statics.StaticData.language.lower())\r\n # stem words\r\n temp = \" \".join(\r\n [stemmer.stem(x) if x not in tweet.tweet[\r\n \"keywords\"] and \"MarkReply\" not in x and \"MarkLocation\" not in x else x for x in temp.split()])\r\n except ValueError:\r\n print(\"language not found:\", floodtags.core.statics.StaticData.language)\r\n # pass\r\n\r\n # store language\r\n temp = \"Mark\" + tweet.language + \" \" + temp\r\n\r\n # store keyword\r\n\r\n # replace each website with 'MarkWebsite' to create more similarity\r\n temp = regexhandler.replace(temp, 'MarkWebsite', regex.Expressions.website)\r\n # replace each photo url with 'MarkPhoto' to create more similarity\r\n for i in range(len(tweet.tweet[\"photos\"])):\r\n temp = Vectorizer.rreplace(temp, \"MarkWebsite\", \"MarkPhoto\", 1)\r\n # replace each height with 'MarkHeight' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkHeight\", regex.Expressions.waterheight)\r\n # replace each time with 'MarkTime' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkTime\", regex.Expressions.time)\r\n # replace each date with 'MarkDate' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkDate\", regex.Expressions.date)\r\n # replace each number with 'MarkNumber' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkNumber\", regex.Expressions.number)\r\n temp = re.sub('\\n', ' ', temp)\r\n results = re.findall(\"(^|[^@\\w])@(\\w{1,15})\", temp)\r\n # add mark for each user name\r\n if results is not None:\r\n for i in range(len(results)):\r\n temp += \" MarkUserName\"\r\n results = re.findall(\"#(\\S*)\", temp)\r\n # add mark for each hashtag\r\n if results is not None:\r\n for i in range(len(results)):\r\n temp += \" MarkHashTag\"\r\n # add sender as feature\r\n temp = \"Sender\" + tweet.tweet[\"source\"][\"username\"] + \" \" + temp\r\n # remove unnecessary characters and chance text to lower case\r\n return re.sub('[#\\.,:]', '', temp)","def twitter(self):\n\n q = \" OR \".join(self.search_terms) + \" -filter:retweets\"\n results = self.__api.search(q=q, lang='en', count=100)\n\n tweets = []\n\n for res in results:\n\n publishedAt = datetime.strptime(res._json['created_at'], '%a %b %d %H:%M:%S +0000 %Y').strftime(\"%Y-%m-%d\")\n\n if (res._json['in_reply_to_screen_name'] == None and publishedAt == datetime.now().strftime(\"%Y-%m-%d\")):\n tweets.append([res._json['id'],\n res._json['text'],\n res._json['user']['screen_name'],\n publishedAt,\n res._json['user']['followers_count']])\n\n self.list = pd.DataFrame(tweets, columns=['id', 'title', 'user', 'publishedAt', 'followers_count']).nlargest(10,\n 'followers_count')\n\n return","def search(self, query_string):\n return models.Thread.objects.filter(\n reduce(\n lambda q, f: q & Q(title__icontains=f),\n query_string.split(),\n Q()))","def get_hashtag_tweets(self, hashtag,\n count=settings.TWITTER_DEFAULT_LIMIT):\n url = urljoin(self.base_url, \"/search/tweets.json\")\n response = self.session.get(\n url,\n params={\n \"q\": hashtag,\n \"count\": count,\n \"include_entities\": True\n },\n auth=self.__auth,\n )\n data = response.json()\n if response.ok:\n data = [Tweet(tweet_data) for tweet_data in data['statuses']]\n else:\n if 'error' in data:\n raise TwitterException(data['error'], code=response.status_code)\n elif 'errors' in data:\n error = data['errors'][0]\n raise TwitterException(error['message'], code=response.status_code)\n return data","def tweet(self, message):\n if self.api_key == \"\":\n return False\n ts = TagStripper()\n ts.feed(message)\n params = urllib.urlencode(\n {'api_key': self.api_key,\n 'status': ts.get_collected_data()})\n\n headers = {\"Content-type\": \"application/x-www-form-urlencoded\",\n \"Accept\": \"text/plain\"}\n\n conn = httplib.HTTPConnection(\"api.thingspeak.com:80\")\n\n try:\n conn.request(\"POST\",\n \"/apps/thingtweet/1/statuses/update\",\n params,\n headers)\n\n response = conn.getresponse()\n data = response.read()\n conn.close()\n return True, ts.get_collected_data()\n except httplib.HTTPException as http_exception:\n return False, http_exception.message","def get_mentions(texts, word):\r\n mentions = {}\r\n for text, label, text_id in texts:\r\n if word in text.lower():\r\n if label not in mentions:\r\n mentions[label] = [text_id]\r\n else:\r\n if text_id not in mentions[label]:\r\n mentions[label].append(text_id)\r\n return mentions","def search(request):\n\t\n\t# User's query\n\tquery = request.GET.get('query')\n\n\t# Search for 50 most popular tweets about user's query\n\ttweets = tweepy.Cursor(api.search, q=query, lang=\"en\", tweet_mode='extended', include_entities=True, result_type='popular').items(50)\n\n\t# Search for 20 most relevant news about user's query\n\tall_news = newsapi.get_everything(q=query, language='en', sort_by='relevancy')\n\n\t# Search for 25 hottest subreddits about user's query\n\tsubreddit = reddit.subreddit('all')\n\treddit_news = subreddit.search(query, limit=25, sort='hot')\n\n\tcontext = {\n\t\t\"tweets\": tweets, # most popular tweets\n\t\t\"all_news\": all_news, # most relevant google news\n\t\t\"reddit_news\": reddit_news # hottest subreddits\n\t}\n\n\treturn render(request, 'hashtrend/search.html', context)","def get(self, search_words, language, result_type):\n consumer_key = config.twitter_api_credentials[\"consumer_key\"]\n consumer_secret = config.twitter_api_credentials[\"consumer_secret\"]\n access_token = config.twitter_api_credentials[\"access_token\"]\n access_token_secret = config.twitter_api_credentials[\"access_token_secret\"] \n \n auth = tweepy.OAuthHandler(consumer_key, consumer_secret); \n auth.set_access_token(access_token, access_token_secret);\n api = tweepy.API(auth, wait_on_rate_limit=True) ; \n \n #input_params = ns.payload\n #search_words = input_params['search_words'] \n #language = input_params['language'] # Language code (follows ISO 639-1 standards)\n #until_date = input_params['until_date']\n #result_type = input_params['result_type'] until=until_date\n\n try:\n results = tweepy.Cursor( api.search, q=search_words, lang=language, result_type = result_type).items(10) \n out = { tweet.user.screen_name: { \"followers_count\": tweet.user.followers_count, \\\n \"location\": tweet.user.location ,\"favorite_count\":tweet.favorite_count,\"text\": tweet.text} for tweet in results }\n sorted_keys = {k:v[\"followers_count\"] for (k,v) in out.items()}\n sorted_keys = sorted(sorted_keys, key=sorted_keys.__getitem__, reverse=True)\n out = {k:out[k] for k in sorted_keys}\n return out\n except tweepy.error.TweepError as e:\n return(json.loads(e.response.text)['errors'][0]['message'], 401)","def query_tweets_once(query, limit=None, num_tweets=0):\n logging.info(\"Querying {}\".format(query))\n query = query.replace(' ', '%20').replace(\"#\", \"%23\").replace(\":\", \"%3A\")\n pos = None\n tweets = []\n try:\n while True:\n new_tweets, pos = query_single_page(\n INIT_URL.format(q=query) if pos is None\n else RELOAD_URL.format(q=query, pos=pos),\n pos is None\n )\n if len(new_tweets) == 0:\n logging.info(\"Got {} tweets for {}.\".format(\n len(tweets), query))\n return tweets\n\n logging.info(\"Got {} tweets ({} new).\".format(\n len(tweets) + num_tweets, len(new_tweets)))\n\n tweets += new_tweets\n\n if limit is not None and len(tweets) + num_tweets >= limit:\n return tweets\n except KeyboardInterrupt:\n logging.info(\"Program interrupted by user. Returning tweets gathered \"\n \"so far...\")\n except BaseException:\n logging.exception(\"An unknown error occurred! Returning tweets \"\n \"gathered so far.\")\n\n return tweets","def search_multiple_words(words):\n # YOUR CODE HERE #\n pass # delete this when you write your code","def collect_tweets(search_id, search_term, number_of_tweets):\n\n tweets = []\n for tweet in api_collector.collect(search_term, number_of_tweets):\n tweets.append((tweet.id_str, tweet.created_at, tweet.full_text))\n if len(tweets) == 0:\n search = Search.objects.get(pk=search_id)\n search.empty = True\n search.save()\n notify_searchers.delay(search_id)\n else:\n classify_tweets.delay(search_id, tweets)","def is_retweet(tweet):\n\n return 'retweeted_status' in tweet","def tweet(self, tweet, at=None):\n if tweet.strip() == \"\":\n return\n\n num_tweets, tweets = self._divide_tweet(tweet, at)\n if num_tweets > 0:\n # replace @'s with #'s and convert unicode emojis before tweeting\n [self.api.update_status(tw.replace(\"@\", \"#\").encode(\"utf-8\")) for tw in tweets]\n self.log(f\"Tweeted: {' '.join(tweets)}\")\n return tweets[0]","def test_find_phrase_matches2(self):\n\t\ttest = sentiment.LibraryRun(self.text2, self.lib)\n\t\tobj_ut = test.find_phrase_matches(self.tokens_generator2)[0]\n\t\tself.assertEqual(dict(obj_ut),\n\t\t\t{'not good': [[2, -1, 0], [4, -1, 0]]})","def query_twitter(session, provided_ioc):\n ioc_dicts = []\n\n if provided_ioc.startswith(\"@\"):\n ioc_dicts.append({\"invalid\": \"{} <-- Monitoring users is prohibited!\".format(provided_ioc)})\n return ioc_dicts\n\n encoded_ioc = urllib.quote_plus(provided_ioc)\n search_tweets = session.search(encoded_ioc, rpp=100, lang=\"en\")\n\n for tweet in search_tweets:\n if tweet._json[\"user\"][\"name\"] == provided_ioc.replace(\"#\", \"\") or \\\n tweet._json[\"user\"][\"screen_name\"] == provided_ioc.replace(\"#\", \"\"):\n ioc_dicts.append({\"invalid\": \"{} <-- Monitoring users is prohibited!\".format(provided_ioc)})\n return ioc_dicts\n\n if \"retweeted_status\" in tweet._json.keys():\n if tweet._json[\"retweeted_status\"][\"user\"][\"name\"] == provided_ioc.replace(\"#\", \"\") or \\\n tweet._json[\"retweeted_status\"][\"user\"][\"screen_name\"] == provided_ioc.replace(\"#\", \"\"):\n ioc_dicts.append({\"invalid\": \"{} <-- Monitoring users is prohibited!\".format(provided_ioc)})\n return ioc_dicts\n\n urls = []\n for x in tweet._json[\"entities\"][\"urls\"]:\n if not x[\"expanded_url\"].startswith(\"https://twitter.com/i/web/status/\"):\n urls.append(x[\"expanded_url\"])\n\n hashtags = []\n for x in tweet._json[\"entities\"][\"hashtags\"]:\n hashtags.append(\"#{}\".format(x[\"text\"]))\n\n ioc_dict = {}\n ioc_dict[\"search_term\"] = provided_ioc\n ioc_dict[\"url\"] = \"\\n\".join(urls)\n ioc_dict[\"hashtags\"] = \"\\n\".join(hashtags)\n ioc_dict[\"timestamp\"] = tweet._json[\"created_at\"]\n ioc_dict[\"tweet\"] = tweet._json[\"text\"]\n\n if \"retweeted_status\" in tweet._json.keys():\n ioc_dict[\"timestamp\"] = tweet._json[\"retweeted_status\"][\"created_at\"]\n ioc_dict[\"tweet\"] = tweet._json[\"retweeted_status\"][\"text\"]\n\n ioc_dicts.append(ioc_dict)\n return ioc_dicts","def crawl(self):\n retrievedTweets = []\n\n count = 1\n \n today = datetime.datetime.now()\n today = today.replace(hour=23, minute=59, second=59, microsecond=999999)\n gap = 1\n yesterday = today - datetime.timedelta(gap) \n nextDay = yesterday + datetime.timedelta(gap)\n \n while True:\n try:\n lst = tweepy.Cursor(self.api.search, lang='en', q=self.keyword, count=50, until=nextDay.date(), result_type='popular').items(50)\n for tweet in lst:\n self.data = [tweet.created_at, tweet.id, tweet.text,\n tweet.user._json['screen_name'], tweet.user._json['name'], \n tweet.favorite_count, tweet.retweet_count, tweet.user.location]\n self.data = tuple(self.data)\n retrievedTweets.append(self.data)\n break\n except tweepy.TweepError as e:\n print(e.reason)\n continue\n except StopIteration: \n break\n\n return retrievedTweets","def gettweets(request):\n temp = json.loads(request.body)\n print (temp['hashtags'])\n return Response(tw_fetcher.gethashes(temp['hashtags']), status=status.HTTP_201_CREATED)","def streamTweets(words = [], authors = [], timeLimit=120, removeRetweets=False, **kwargs):\n if 'stream' not in globals():\n global stream\n if 'api' not in globals():\n startTwitterApi(getApiKeys(fileName=\"apiConf2.txt\"))\n listener = StdOutListener(removeRetweets=removeRetweets)\n auth = api.auth\n stream = tweepy.Stream(auth, listener, tweet_mode='extended')\n else:\n stream.listener.setRemoveRetweets(removeRetweets)\n stream.listener.resetTweets()\n\n words = mapToValid(words)\n authors = mapToValid(authors)\n if not words and not authors:\n words=[\"the\", \"i\", \"to\", \"a\", \"and\", \"'s\", \"is\", \"in\", \"it\", \"you\", \"of\", \"for\", \"on\", \"my\", \"that\", \"e\", \"with\", \"me\", \"do\", \"have\", \"ciao\", \"o\", \"u\", \"cool\", \"good\", \"nice\", \"#\", \"*\", \":\", \";\", \",\", \".\", \"?\", \"-\", \"%\", \"$\", \"€\", \"!\", \"(\", \")\", \"=\", \"'\"]\n\n #myQuery = ' OR '.join(kwargs[\"words\"])\n if authors:\n kwargs[\"follow\"]=[user.id_str for user in list(map(api.get_user,authors))]\n else:\n kwargs[\"track\"]=words\n #if removeRetweets:\n # myQuery += \" -filter:retweets\"\n\n #myQuery += ' from:'\n #myQuery += ' OR from:'.join(kwargs[\"authors\"])\n #print(myQuery)\n import signal\n # Register the signal function handler\n signal.signal(signal.SIGALRM, __streamHandler__)\n # Define a timeout for your function\n signal.alarm(timeLimit)\n try:\n __stream__(stream,**kwargs)\n except Exception:\n print(\"Streaming over after time period of\", timeLimit, \"seconds... Retrieved\", len(stream.listener.getTweets()), \"tweets.\")\n stream.disconnect()\n if authors and words:\n print(\"Filtering out tweets that don't contain the specified words...\")\n myTweets=[]\n for tweet in stream.listener.getTweets():\n if 'full_text' in tweet:\n tweet['text'] = tweet['full_text']\n del (tweet['full_text'])\n if any(containsWord(tweet['text'],word) for word in words):\n myTweets.append(tweet)\n print(\"Done. Retrieved\", len(myTweets), \"tweets written by the authors specified and containing (any of) the words specified.\")\n return myTweets\n return stream.listener.getTweets()","def having(self, wordlist=None, coordinates=None):\n track = \",\".join(wordlist) if wordlist else None\n locations = \",\".join(map(str, coordinates)) if coordinates else None\n\n if track and locations:\n generator = self.stream.statuses.filter(track=track,\n locations=locations)\n elif track:\n generator = self.stream.statuses.filter(track=track)\n elif locations:\n generator = self.stream.statuses.filter(locations=locations)\n else:\n raise ValueError(\"Must provide one of wordlist or coordinates\")\n\n for tweet in generator:\n if locations:\n if 'coordinates' in tweet and tweet['coordinates']:\n yield tweet\n else:\n yield tweet","def word_finder(word, text):\r\n word = word.lower()\r\n text = str(text).lower()\r\n match = re.search(word, text)\r\n if match:\r\n return True\r\n return False","def search_from_raw_query(api, raw_query, **kwargs):\n tweets=api.GetSearch(raw_query=raw_query)\n return {\"tweets\":tweets}","def count_tweets(search_term, location=None):\r\n\r\n if location:\r\n return len(Tweet.objects(Q(keyword_search_term=search_term) & Q(location_address=location)))\r\n else:\r\n return len(Tweet.objects(keyword_search_term=search_term))","def find_matches(id1, id2, adj_list_ids, adj_list_words):\n\n # combine words from tweets\n stanza_words = adj_list_ids[id1] + adj_list_ids[id2]\n\n # look for other tweets with those words\n pot_ids = set().union(*[adj_list_words[w] for w in stanza_words])\n\n # --- filter down ---\n # remove start tweets\n # ensure tweets contain subset of master_word_set\n pot_ids = pot_ids - {id1, id2}\n pot_ids = [x for x in pot_ids if set(adj_list_ids[x]) <= set(stanza_words)]\n\n # look for valid pairs of potential tweets\n if len(pot_ids) > 1:\n return find_valid_matches(pot_ids, adj_list_ids, stanza_words)\n return []","def test_find_phrase_matches1(self):\n\t\ttest = sentiment.LibraryRun(self.text1, self.lib)\n\t\tobj_ut = test.find_phrase_matches(self.tokens_generator1)[0]\n\t\tself.assertEqual(dict(obj_ut),\n\t\t\t{'not good': [[2, -1, 0]]})","def process(self, tweet):\n\n #identify the applicable event keywords for this text\n text = self.cleanup_data(tweet.text)\n tokens = [str(t.lower()).translate(None, string.punctuation) for t in tweet.text.split()]\n applicable_tokens = []\n for phrase in self.match_event_tree.root.keywords:\n if phrase in \" \".join(tokens):\n applicable_tokens.append(phrase)\n\n self.match_event_tree.propogate_tweet(applicable_tokens, tweet)","async def team_search(self, ctx: commands.Context, username: str):\n all_usernames = {team_id: team.username for team_id, team in self.teams.items()\n if team is not None}\n suggestions = []\n log.info(repr(fuzzywuzzy.process.extract(\n username, all_usernames, limit=5)))\n for fuzz_username, rating, fuzz_id in fuzzywuzzy.process.extract(\n username, all_usernames, limit=5):\n if rating < 50:\n break\n fuzz_team = self.teams[fuzz_id]\n suggestions.append(\n f'(ID: **{fuzz_team.team_id}**) **{fuzz_team.display_name[:40]}**'\n f' -- {len(fuzz_team.users)} registered members')\n if suggestions:\n await ctx.send('\\n'.join(suggestions))\n else:\n await ctx.send(f\"Couldn't find any teams whose usernames resembled `{username}`\")","def search(self, query_string):\n terms = query_string.lower().split()\n result = set(self.wordDict[terms[0]])\n if len(result) == 0:\n return list()\n else:\n for t in terms[2:]:\n records_containing_t = self.wordDict[t]\n result = result.intersection(records_containing_t)\n return [self.get_record_dict(id).getTuple() for id in result]","def whole_word_matches(self):\n start = '1.0'\n while True:\n start = self.text.search(self.term, start, stopindex=tk.END)\n if not start:\n break\n end = start + ' wordend'\n # whole word includes a space before\n found = self.text.get(start + '-1c', end)\n if found == ' ' + self.term:\n self.text.tag_add('found', start, end)\n start = end","def whole_word_matches(self):\n start = '1.0'\n while True:\n start = self.text.search(self.term, start, stopindex=tk.END)\n if not start:\n break\n end = start + ' wordend'\n # whole word includes a space before\n found = self.text.get(start + '-1c', end)\n if found == ' ' + self.term:\n self.text.tag_add('found', start, end)\n start = end","def searchMovie(api, title, date, count, retweets=False):\n\n since = date\n until = datetime.strptime(since, '%Y-%m-%d') + timedelta(days=1)\n until = until.strftime('%Y-%m-%d')\n\n query = \"\\\"{0}\\\" since:{1} until:{2} -filter:links\".format(processTitle(title), since, until)\n if retweets == False:\n query += \" -filter:retweets\"\n\n rawtweets = tweepy.Cursor(api.search, q=query, result_type=\"recent\", lang=\"en\").items(count)\n results = []\n\n for i, tweet in enumerate(rawtweets):\n try:\n results.append(processTweet(title, tweet, remove_title=True))\n except tweepy.error.TweepError as err:\n print(\"\\nThere was an error processing tweet #{0} for title [{1}]\".format(i, title))\n print(err.messages[0]['code'])\n\n results = pd.DataFrame(results)\n results['title'] = title\n results['tweet_date'] = date\n return results","async def bingsearch(self, *, text):\n settings = loadauth()\n operation = 'websearch'\n if settings['apikey'] == '' or settings['apikey'] == 'blank':\n return await self.bot.say(\"Missing or incorrect API key. Please \" +\n \"contact the owner to add an API key.\")\n apikey = settings['apikey']\n text, limit = self.limitget(text)\n result = self.getfrombing(apikey, text, limit, operation)\n bottext = self.obtainresult(result, operation)\n return await self.bot.say(bottext)","def search(corpus, query, options=SearchOptions()):\n http_response_code_to_status = {\n 200: Status.OK,\n 400: Status.BAD_REQUEST,\n 502: Status.BAD_GATEWAY\n }\n result = SearchResult()\n context = urllibx.urlopen(_make_url(corpus, query, options))\n result.status = http_response_code_to_status[context.getcode()]\n if result.status == Status.OK:\n for line in context.readlines():\n line = line.decode('utf-8')\n phrase = Phrase()\n parts = line.split(\"\\t\")\n for token_with_tag in parts[0].split(\" \"):\n token = Token()\n token.text = token_with_tag[:-2]\n token.tag = int(token_with_tag[-1])\n phrase.tokens.append(token)\n phrase.match_count = int(parts[1])\n phrase.volume_count = int(parts[2])\n phrase.first_year = int(parts[3])\n phrase.last_year = int(parts[4])\n phrase.id = int(parts[5])\n phrase.score = float(parts[6])\n result.phrases.append(phrase)\n context.close()\n return result","def search_by_contains(self, tl):\n print(\"Search by string\")\n string = input(\"Please enter search string: \")\n return tl.findall_contains(string)","def like_tweet(self, tag):\n self.bot.get('https://twitter.com/search?q=' + tag + '&src=typed')\n self.__wait(3, 3)\n for i in range(1, 3):\n self.bot.execute_script('window.scrollTo(0,document.body.scrollHeight)')\n self.__wait(2, 3)\n tweets = self.bot.find_elements_by_tag_name('article')\n\n links = []\n for tweet in tweets:\n sub_links = tweet.find_elements_by_tag_name('a')\n links += [sub_link.get_attribute('href')\n for sub_link in sub_links if 'status' in sub_link.get_attribute('href')]\n\n print('Started to like {} tweets'.format(len(links)))\n\n for link in links:\n self.bot.get(link)\n self.__wait(3, 5)\n likes = self.bot.find_elements_by_css_selector('div[data-testid=\"like\"')\n for like in likes:\n like.click()\n self.__wait(3, 5)","def search_machine(ID,machine):\n\tconsumer_key = machine['consumer_key']\n\tconsumer_secret = machine['consumer_secret']\n\taccess_token = machine['access_token']\n\taccess_secret = machine['access_secret']\n\tauth = OAuthHandler(consumer_key, consumer_secret)\n\tauth.set_access_token(access_token, access_secret)\n\tapi = tweepy.API(auth, wait_on_rate_limit_notify=True)\n\n\t\"\"\"Search for tweets via Twitter Search API.\"\"\"\n\tsinceId = None\n\tmax_id = ID\n\ttweetsCounts = 0\n\tfinshed_job = False\n\twith open (outfile,'w+') as f:\n\t\twhile tweetsCounts < maxTweets:\n\t\t\ttry:\n\t\t\t\tif (max_id <= 0):\n\t\t\t\t\tif (not sinceId):\n\t\t\t\t\t\tnew_tweets = api.search(\n\t\t\t\t\t\t\tq = query,\n\t\t\t\t\t\t\tgeocode = geo,\n\t\t\t\t\t\t\tcount = searchLimits)\n\t\t\t\t\telse:\n\t\t\t\t\t\tnew_tweets = api.search(\n\t\t\t\t\t\t\tq=query,\n\t\t\t\t\t\t\tcount = searchLimits,\n\t\t\t\t\t\t\tgeocode=geo,\n\t\t\t\t\t\t\tsinceId = sinceId)\n\t\t\t\telse:\n\t\t\t\t\tif (not sinceId):\n\t\t\t\t\t\tnew_tweets = api.search(\n\t\t\t\t\t\t\tq=query, \n\t\t\t\t\t\t\tcount=searchLimits,\n\t\t\t\t\t\t\tgeocode = geo,\n\t\t\t\t\t\t\tmax_id=str(max_id - 1))\n\t\t\t\t\telse:\n\t\t\t\t\t\tnew_tweets = api.search(\n\t\t\t\t\t\t\tq=query, \n\t\t\t\t\t\t\tcount=searchLimits,\n\t\t\t\t\t\t\tgeocode = geo,\n\t\t\t\t\t\t\tmax_id=str(max_id - 1),\n\t\t\t\t\t\t\tsince_id=sinceId)\n\t\t\t\tif not new_tweets:\n\t\t\t\t\tprint(\"NO MORE TWEETS\")\n\t\t\t\t\tfinshed_job = True\n\t\t\t\t\tbreak\n\t\t\t\tfor tweet in new_tweets:\n\t\t\t\t\tif tweet.coordinates or tweet.place:\n\t\t\t\t\t\tjson.dump(tweet._json,f,ensure_ascii=False)\n\t\t\t\t\t\tf.write('\\n')\n\t\t\t\t\n\t\t\t\ttweetsCounts += len(new_tweets)\n\t\t\t\t#print(\"Downloaded {0} tweets\".format(tweetsCounts))\n\t\t\t\tmax_id = new_tweets[-1].id\n\t\t\texcept tweepy.RateLimitError as e:\n\t\t\t\tprint(machine['index'],'Time to sleep 15 mins') \n\t\t\t\tAPI_status[machine['index']] = False\n\t\t\t\tif machine['index'] == 0:\n\t\t\t\t\tAPI_status['time'] = time.time() + 901.00\n\t\t\t\treturn finshed_job,max_id\n\t\t\texcept tweepy.TweepError as e:\n\t\t\t\tlogging.error(str(e))\n\t\t\t\tbreak\n\tf.close()\n\treturn finshed_job,max_id","def geo_collect_tweets(search_term,latitude,longitude,radius):\n i = None\n tweets = []\n rep = 1\n for n in range(2): #can only search 100 tweets at a time, so run search multiple times\n \tresults = api.GetSearch(term = search_term, \n \t\tcount = 100, \n \t\tresult_type = 'recent', \n \t\tmax_id = i, #start a search from the most recent tweet id, working backwards\n \t\tgeocode =(latitude, longitude, radius))\n for tweet in results:\n tweets.append(tweet.text)\n i = tweet.id - 1 #want it to start at the tweet after the last tweet\n rep += 1\n return list(set(tweets)) #set gets rid of repititve tweets, but need to return a list","def search_from_geocode(api, geocode, **kwargs):\n tweets=api.GetSearch(geocode=geocode)\n return {\"tweets\":tweets}","def process(self, filter_words, count=1):\n user = self.__api.get_user(self.__username)\n\n # print user.screen_name\n # print user.followers_count\n if self.__appMode == 1 and self.__TimeLineMode == 1:\n self.get_timeline(filter_words)\n else:\n if self.__friendMode:\n print(\"Getting all Twitter Friends \\n\")\n for friend in user.friends():\n self.get_tweet(friend.screen_name, filter_words, count)\n else:\n for screen_name in self.__priorityCoin:\n self.get_tweet(screen_name, filter_words, count)\n print('Twitter Data Extraction done!!')","def search_tweets(request):\n return render(request, 'ede/search.html')","def send_tweet(tweet_text):\n twitter.update_status(status = tweet_text)","def get_tweets(self):\n keyword = 'covid'\n\n # Load tokens from file\n with open('../data/tokens.json', 'r') as f:\n tokens = json.load(f)\n\n # Stream tweets\n auth = tweepy.OAuthHandler(tokens['consumer_key'], tokens['consumer_secret'])\n auth.set_access_token(tokens['access_token_key'], tokens['access_token_secret'])\n api = tweepy.API(auth)\n\n # listen for tweets\n while True:\n\n # TODO: save file in Cloud Storage\n file_name = date.today().strftime('corpus-%d-%m-%Y.json')\n print(f'Updating {file_name} ...')\n\n StreamListener = StreamListener(\n file_name=file_name, \n max_tweets=1000)\n myStream = tweepy.Stream(\n auth=api.auth, \n listener=StreamListener)\n\n myStream.filter(track=[keyword], languages=['en'])\n \n time.sleep(60)","def count_tweets_based_on_words(word, positve_sentiment, negative_sentiment):\n tweet_counter = dict()\n index_db = couch[config.get(\"database\", \"DB_INDEX\")]\n items = index_db.view(view_text_index)\n try:\n tweet_id_holder = items[word].rows[0].value\n except Exception as e:\n return tweet_counter\n\n tweets_all = tweet_database.view(view_tweet_info)\n\n for tweet_id in tweet_id_holder:\n # check redundancy\n if tweet_id not in tweet_index:\n tweet = tweets_all[tweet_id]\n tweet_index.add(tweet_id)\n try:\n # set polarity value\n if negative_sentiment:\n if tweet.rows[0].value[3] < 0:\n tweet_counter = add_state_count(tweet, tweet_counter)\n elif positve_sentiment:\n if tweet.rows[0].value[3] > 0:\n tweet_counter = add_state_count(tweet, tweet_counter)\n else:\n tweet_counter = add_state_count(tweet, tweet_counter)\n except:\n return tweet_counter\n return tweet_counter","def send_tweet(tweet_text):\n twitter.update_status(status=tweet_text)","def send_tweet(tweet_text):\n twitter.update_status(status=tweet_text)"],"string":"[\n \"def two_word_finder(word1,word2,text):\\r\\n word1 = word1.lower()\\r\\n word2 = word2.lower()\\r\\n text = str(text).lower()\\r\\n if word1 and word2 in text:\\r\\n return True #return text to see specific tweets\\r\\n return False\",\n \"def searchTweets():\\n if 'api' not in globals():\\n startTwitterApi(getApiKeys(fileName='apiConf2.txt'))\\n #SEARCHING TWEETS CONTAINING THE HASHTAG \\\"#bitcoin\\\" USING TWEEPY LIBRARY\\n myTweets= []\\n #words=list(map(str,words))\\n if words:\\n myQuery=' OR '.join(words)\\n else:\\n myQuery = '*'\\n if removeRetweets:\\n myQuery += ' - filter:retweets'\\n kwargs['q']=myQuery\\n kwargs['count']=100\\n kwargs['tweet_mode']='extended'\\n if 'startingDate' in kwargs:\\n kwargs['since']=kwargs['startingDate']\\n del(kwargs['startingDate'])\\n if 'endingDate' in kwargs:\\n kwargs['until']=kwargs['endingDate']\\n del(kwargs['endingDate'])\\n if 'maxTweets' in kwargs:\\n del(kwargs['maxTweets'])\\n if sortBy=='newest':\\n for tweet in tweepy.Cursor(api.search, kwargs).items(maxTweets):\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n else:\\n for tweet in tweepy.Cursor(api.search, kwargs).items():\\n tweet._json['text']=tweet._json['full_text']\\n del (tweet._json['full_text'])\\n myTweets.append(tweet._json)\\n return getTopNTweets(myTweets, maxTweets)\",\n \"def search(searchTerm):\\n api = twitter.Api()\\n tweets = api.GetSearch(searchTerm)\\n for tweet in tweets:\\n util.safe_print(tweet.GetText())\",\n \"def RelevantTweet(Tweet, Termlist, DateRange=None):\\n TweetText = Tweet[\\\"full_text\\\"].lower() #ignore case. # Changed to full_text now, instead of (previously) truncated.\\n\\n if Tweet[\\\"is_quote_status\\\"] is True: #Append, since we're just matching terms.\\n # print(\\\"Tweet = \\\", Tweet.keys())\\n if \\\"quoted_status\\\" in Tweet:\\n TweetText = TweetText + \\\" \\\" + Tweet[\\\"quoted_status\\\"][\\\"full_text\\\"].lower()\\n\\n # From: https://stackoverflow.com/questions/7703865/going-from-twitter-date-to-python-datetime-date\\n if DateRange is not None:\\n ts = time.strptime(Tweet['created_at'], '%a %b %d %H:%M:%S +0000 %Y')\\n\\n if ts < time.strptime(DateRange[0], '%b %d %H:%M:%S %Y'):\\n return False\\n elif ts > time.strptime(DateRange[1], '%b %d %H:%M:%S %Y'):\\n return False\\n if Termlist is None:\\n return True\\n else:\\n for word in Termlist:\\n if word.lower() in TweetText:\\n return True\\n return False\",\n \"def filter_by_hashtag(tweets: list, hashtag: str) -> list:\\n tweets_with_hashtag = {} # findall(): Kui tekstis on rohkem kui üks regulaaravaldisele vastav alamsõne saab kõikide vastete järjendi moodustada funktsiooniga findall()\\n pattern = r\\\"#\\\\w+\\\" # \\\\w : tähed, numbrid, alakriips, + : 1 või rohkem\\n for tweet in tweets: # r\\\"string\\\" on \\\"raw\\\" tüüpi string, mis tähendab, et kurakaldkriipsud(\\\"\\\\\\\") jäetakse teksti alles.\\n find_hashtag = re.findall(pattern, tweet.content) # word:\\\\w\\\\w\\\\w. Regulaaravaldisele vastab täpne sõne \\\"word:\\\" ning sellele järgnevad 3 suvalist tähte.\\n if find_hashtag:\\n tweets_with_hashtag.setdefault(ht, []).append(tweet)\\n return tweets_with_hashtag[hashtag]\",\n \"def search_from_terms(api, term, **kwargs):\\n tweets=api.GetSearch(term=term)\\n return {\\\"tweets\\\":tweets}\",\n \"def search_tweets(q, count=100, result_type=\\\"recent\\\"):\\n\\n return t.search.tweets(q=q, result_type=result_type, count=count)\",\n \"def searchByKeyword(self, keyword, until=\\\"\\\", since=\\\"\\\", count=None, result_type=\\\"recent\\\"):\\n if count is None:\\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\\n full_text=True, tweet_mode=\\\"extended\\\", lang=\\\"en\\\").items()\\n else:\\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\\n full_text=True, tweet_mode=\\\"extended\\\", lang=\\\"en\\\").items(count)\\n\\n for status in tweets:\\n createdDate = parser.parse(str(status._json[\\\"created_at\\\"]).strip())\\n createdDate = createdDate.replace(\\n tzinfo=pytz.utc) - createdDate.utcoffset()\\n status_refined = {\\n 'keyword': keyword,\\n '_id': status._json[\\\"id\\\"],\\n 'created_at': createdDate,\\n 'tweetText': status._json[\\\"full_text\\\"],\\n 'hashtags': status._json[\\\"entities\\\"][\\\"hashtags\\\"],\\n 'userLoc': status._json[\\\"user\\\"][\\\"location\\\"],\\n 'tweetGeo': status._json[\\\"geo\\\"],\\n 'tweetCoordinates': status._json[\\\"coordinates\\\"],\\n 'tweetPlace': status._json[\\\"place\\\"],\\n 'retweet': {},\\n }\\n if hasattr(status, \\\"retweeted_status\\\"):\\n status_refined['tweetText'] = status._json[\\\"retweeted_status\\\"][\\\"full_text\\\"]\\n status_refined['retweet'] = {\\n 'original_retweet_id': status._json[\\\"retweeted_status\\\"][\\\"id\\\"],\\n 'origUserLoc': status._json[\\\"retweeted_status\\\"][\\\"user\\\"][\\\"location\\\"],\\n 'origTweetLoc': status._json[\\\"retweeted_status\\\"][\\\"geo\\\"],\\n 'origTweetPlace': status._json[\\\"retweeted_status\\\"][\\\"place\\\"],\\n 'origTweetCoord': status._json[\\\"retweeted_status\\\"][\\\"coordinates\\\"],\\n 'origHashtags': status._json[\\\"retweeted_status\\\"][\\\"entities\\\"][\\\"hashtags\\\"],\\n 'retweet_count': status._json[\\\"retweet_count\\\"],\\n }\\n self.tweets.append(status_refined)\\n return self.tweets\",\n \"def keep(tweet):\\n text = tweet[\\\"Text\\\"].lower()\\n hashtags = \\\" \\\".join(tweet[\\\"Hashtags\\\"]).lower()\\n for word in keep_words:\\n if word in text:\\n # print(\\\"{} in text {}\\\".format(word, text))\\n return True\\n if word in hashtags:\\n # print(\\\"{} in hashtags {}\\\".format(word, hastags))\\n return True\\n return False\",\n \"def _get_tweets(self):\\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\\n auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\\n api = tweepy.API(auth)\\n search = api.search(self.term, lang='en', count=100)\\n\\n print(f\\\"Getting tweets that mention '{self.term}', \\\"\\n f\\\"this may take a while...\\\")\\n\\n save_tweet_text = [tweet._json['text'] for tweet in search]\\n while len(save_tweet_text) < 1000:\\n try:\\n oldest = search[-1].id - 1\\n search = api.search(self.term, lang='en', count=100, max_id=oldest)\\n new_tweets = [tweet._json['text'] for tweet in search]\\n save_tweet_text.extend(new_tweets)\\n\\n # Turn into a set to remove duplicated tweets, then back to list\\n save_tweet_text = list(set(save_tweet_text))\\n except IndexError:\\n break\\n\\n print(f\\\"Done. {len(save_tweet_text)} Tweets received.\\\")\\n return save_tweet_text\",\n \"def search_for_phrase(self, phrase):\\n uri = uriForPhraseSearch(phrase)\\n return json.loads(requests.get(uri).content)\",\n \"def has_keyword(tweet, keywords):\\n temp = tweet.lower()\\n for keyword in keywords:\\n if bool(re.search(f'\\\\\\\\b{keyword}\\\\\\\\b', temp)):\\n return True\\n return False\",\n \"def filter_tweet_core(tweet):\\n if not ('user' in tweet and 'screen_name' in tweet['user'] \\\\\\n and 'text' in tweet):\\n return False\\n if not filter_tweet_text(tweet['text']):\\n return False\\n if bannedclients.search(tweet['source']):\\n return False\\n if bannedterms.search(tweet['text']):\\n return False\\n if bannedterms.search(tweet['user']['screen_name']):\\n return False\\n if 'entities' in tweet and bannedterms.search(json.dumps(tweet['entities'])):\\n return False\\n if not filter_tweet_entropy(tweet['text']):\\n return False\\n if not filter_tweet_cardspam(tweet):\\n return False\\n return True\",\n \"def show_search_results():\\n\\n #Get values from search-box via AJAX\\n current_keyword = request.form.get('search').lower()\\n print \\\"**********************\\\"\\n print current_keyword\\n print \\\"**********************\\\"\\n tweets = get_tweets_by_api(term=current_keyword)\\n\\n result = []\\n\\n for tweet in tweets:\\n # Exclude retweets since they appear as duplicatses to endu ser\\n if tweet.retweeted_status is None:\\n # Convert tweet text from unicode to text\\n tweet_id = tweet.id\\n text = unicodedata.normalize('NFKD', tweet.text).encode('ascii', 'ignore')\\n # Find URL in text and bind to url\\n # url = re.search('((?:http|https)(?::\\\\\\\\/{2}[\\\\\\\\w]+)(?:[\\\\\\\\/|\\\\\\\\.]?)(?:[^\\\\\\\\s\\\"]*))', text)\\n url = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)\\n # Remove URL from text\\n text_wo_url = re.sub(r'^https?:\\\\/\\\\/.*[\\\\r\\\\n]*', '', text, flags=re.MULTILINE)\\n # Handle / Name\\n user = unicodedata.normalize('NFKD', tweet.user.screen_name).encode('ascii', 'ignore')\\n # Count of favorites\\n favorite_count = tweet.favorite_count\\n #Return dictionary of hashtags with hashtag as key and number of occurances as value\\n if tweet.hashtags:\\n # Convert hashtags from unicode to string\\n ht_list = []\\n for hashtag in tweet.hashtags:\\n ht_str = unicodedata.normalize('NFKD', hashtag.text).encode('ascii', 'ignore')\\n ht_list.append(ht_str.lower())\\n hashtags = Counter(ht_list)\\n else:\\n hashtags = tweet.hashtags\\n # Convert tweet from unicode to datetime\\n created_at = tweet.created_at\\n # format created_at string to ISO 8610\\n created_at_str = time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(created_at, '%a %b %d %H:%M:%S +0000 %Y'))\\n # create a moment from the string\\n created_at = moment.date(created_at_str, 'YYYY-MM-DD HH:mm:ss')\\n result.append({'created_at': created_at_str, 'tweet_text': text_wo_url, 'user': user,\\n 'favorite_count': favorite_count, 'hashtags': hashtags,\\n 'url': url, 'tweet_id': tweet_id})\\n\\n print \\\"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\\\"\\n print result\\n print \\\"&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\\\"\\n\\n return jsonify(result=result) #, tweets\",\n \"def collect_tweets(redis_client, twitter_client, search_term):\\n search = Search(redis_client, twitter_client, search_term)\\n search.get_term_state()\\n search.parse_term_state()\\n search.set_query_string()\\n search.set_execution_time()\\n search.execute_query()\\n search.incr_query_counters()\\n search.set_newest_id()\\n search.set_oldest_id()\\n search.set_scenario()\\n search.set_term_state()\\n search.store_results()\\n search.set_score()\\n search.log_state()\",\n \"def search(self, word):\",\n \"def compute_retweet(row):\\n return row['full_text'].startswith('RT')\",\n \"def get_tweets(hashtag):\\n api = twitter.Api(consumer_key=TWITTER_API_CONSUMER_KEY,\\n consumer_secret=TWITTER_API_CONSUMER_SECRET,\\n access_token_key=TWITTER_API_ACCESS_TOKEN_KEY,\\n access_token_secret=TWITTER_API_ACCESS_TOKEN_SECRET)\\n\\n query = (f\\\"q=%23{HASHTAG}%20-RT\\\"\\n f\\\"&result_type=recent&since=2019-01-01&count={NUM_TWEETS}\\\")\\n results = api.GetSearch(raw_query=query)\\n\\n return [\\n format_tweet(tweet.AsDict())\\n for tweet in results\\n ]\",\n \"def searchbrown_word(tag):\\n brown_tagged_words = brown.tagged_words(categories='news')\\n hitwords = []\\n for i in range(len(brown_tagged_words)):\\n if tag == brown_tagged_words[i][1]:\\n hitwords.append(brown_tagged_words[i][0].lower())\\n return hitwords\",\n \"def get_tweets(keyword, max_tweets=200):\\n\\n # API keys.\\n consumer_key = \\\"kNOG1klRMMUYbsjMuY5TKl4lE\\\"\\n consumer_secret = \\\"ieghv6WI1qseYly43A0Ra1MPksEw1i5Onma0txfEu5aHantD2v\\\"\\n access_key = \\\"3291622062-15ssVc0qpJXf2SFXbA7vgfl1Sooz4Ueo2DGPQVz\\\"\\n access_secret = \\\"9XJuzgGSVLnx93tq6NfRzMT07S6o2lzjmHfjt3VRlkqXn\\\"\\n\\n # Initialize tweepy API object and authorize using API key.\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_key, access_secret)\\n api = tweepy.API(auth)\\n\\n \\\"\\\"\\\" Get tweets.\\\"\\\"\\\"\\n\\n alltweets = []\\n for status in tweepy.Cursor(\\n api.search,\\n q=keyword + \\\" -RT\\\", # the -RT flag excludes retweets.\\n count=1000,\\n result_type=\\\"recent\\\",\\n include_entities=True,\\n monitor_rate_limit=True,\\n wait_on_rate_limit=True,\\n lang=\\\"en\\\",\\n ).items():\\n\\n # get text of the tweet, encoding as utf-8.\\n text = str(status.text.encode(\\\"utf-8\\\"))\\n\\n # add to the data structure, alltweets, holding the tweets.\\n alltweets.append(text)\\n\\n # if we've reached max_tweets, break.\\n if len(alltweets) >= max_tweets:\\n break\\n\\n return alltweets\",\n \"def twitter_text(text, search_query = False):\\r\\n tt = TwitterText(text)\\r\\n if search_query:\\r\\n tt.text = tt.highlighter.hit_highlight(query = search_query)\\r\\n tt.text = tt.autolink.auto_link()\\r\\n return tt.text\",\n \"def search(self, term):\",\n \"def searchbrown_phrase(tags):\\n l = len(tags)\\n brown_tagged_words = brown.tagged_words(categories='news')\\n hitwords = []\\n for i in range(len(brown_tagged_words)-l+1):\\n searchtags = [tag for _,tag in brown_tagged_words[i:i+l]]\\n if tags == searchtags:\\n hitwords.append(tuple([w.lower()\\n for w,_ in brown_tagged_words[i:i+l]]))\\n return hitwords\",\n \"def get_tweets(search_terms, geocode):\\n\\turl = 'http://search.twitter.com/search.json'\\n\\tparams = {\\n\\t\\t'q': search_terms,\\n\\t\\t'geocode': str(geocode['lat']) + ',' + str(geocode['lng']) + ',200mi'\\n\\t}\\n\\turl_params = urllib.urlencode(params)\\n\\tresult = urlfetch.fetch(url=url + '?' + url_params,\\n\\t\\t\\t\\t\\t\\t\\tmethod=urlfetch.GET)\\n\\n\\tif result.status_code == 200:\\n\\t\\titems = json.loads(result.content)['results']\\n\\t\\t# return [r['text'] for r in items]\\n\\t\\treturn items\",\n \"def get_tweets_from_search(api, search_string, parameters=\\\" -filter:retweets\\\", since=\\\"2021-08-09\\\", lang=\\\"en\\\", max_tweets=1000):\\n\\n tweet_list = []\\n count = 0\\n search = search_string\\n params = parameters\\n\\n for tweet in tweepy.Cursor(api.search, q=search + params,\\n count=100,\\n tweet_mode=\\\"extended\\\",\\n lang=lang,\\n since=since,\\n # until=\\\"2015-02-01\\\",\\n ).items():\\n tweet_list.append(tweet._json[\\\"full_text\\\"])\\n count += 1\\n if count == max_tweets:\\n break\\n print(count)\\n return pd.DataFrame({\\\"text\\\": tweet_list})\",\n \"def search_postman(text):\\n result = _search_blog('postman', text)\\n _print_results(result)\\n return result\",\n \"def get_tweets(twitter, screen_name, num_tweets):\\n\\n request = robust_request(twitter, 'search/tweets', {'q': screen_name, 'count': num_tweets})\\n tweets = [a['text'] for a in request]\\n\\n return tweets\",\n \"def extract_hashtags(tweet):\\n tknzr = TweetTokenizer()\\n hashtags = [token.lower() for token in tknzr.tokenize(tweet) if re.match(hashtag_re, token)]\\n return hashtags\",\n \"def filter_tweet(tweet):\\n if not filter_tweet_core(tweet):\\n return False\\n if bannedusers.search(tweet['user']['screen_name']) or (\\n 'retweeted_status' in tweet and bannedusers.search(tweet['retweeted_status']['user']['screen_name'])):\\n return False\\n if tweet['user']['screen_name'] == credentials['username']: # Do not match self tweets :-)\\n return False\\n return True\",\n \"def search(search_term):\\r\\n if search_term:\\r\\n if 'list' in search_term:\\r\\n search_term = search_term.split('?list')[0]\\r\\n\\r\\n url = SEARCH_URL.format(API_KEY, util.web.quote(search_term.encode('ascii', 'ignore')))\\r\\n response = util.web.http_get(url=url, json=True, referer='http://tinychat.com')\\r\\n\\r\\n if response['json'] is not None:\\r\\n try:\\r\\n if 'items' in response['json']:\\r\\n for item in response['json']['items']:\\r\\n video_id = item['id']['videoId']\\r\\n details = video_details(video_id)\\r\\n if details is not None:\\r\\n return {\\r\\n 'type': 'youTube',\\r\\n 'video_id': video_id,\\r\\n 'video_time': details['video_time'],\\r\\n 'video_title': details['video_title']\\r\\n }\\r\\n except KeyError as ke:\\r\\n log.error(ke, exc_info=True)\\r\\n return None\",\n \"def run_single_keyword(self, keyword_string, language):\\n keyword = self.mongo_controller.get_keyword(keyword_string, language, cast=True)\\n twitter_results = self.crawler.search(keyword, limit=self.limit_requests)\\n return self.__save_tweets(twitter_results)\",\n \"def queryTerm2Twitter(term): \\n statusList = api.GetSearch(term, count=100, result_type='recent')\\n timeStampOfStatus = [datetime.fromtimestamp(i.created_at_in_seconds) for i in statusList]\\n timeStampOfStatus.sort() \\n return timeStampOfStatus[0]\",\n \"def extract_tweets(consumer_key,consumer_secret,access_token,access_token_secret,search_key):\\n # Step 1 - Authenticate\\n consumer_key= str(consumer_key)\\n consumer_secret= str(consumer_secret)\\n\\n access_token=str(access_token)\\n access_token_secret=str(access_token_secret)\\n\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_token_secret)\\n\\n api = tweepy.API(auth)\\n\\n #Step 3 - Retrieve Tweets\\n public_tweets = api.search(search_key)\\n tweets_list=[]\\n for tweet in public_tweets:\\n tweets_list.append(tweet.text)\\n return tweets_list\",\n \"def reply_to_tweets():\\n last_seen_id = retrieve_last_seen_id(FILE_NAME)\\n mentions = api.mentions_timeline(\\n last_seen_id,\\n tweet_mode='extended')\\n\\n for mention in reversed(mentions):\\n print(str(mention.id) + ' - ' + mention.full_text, flush=True)\\n last_seen_id = mention.id\\n store_last_seen_id(last_seen_id, FILE_NAME)\\n for i in range(len(keywords)):\\n if keywords[i] in mention.full_text.lower():\\n print(\\\"responding back to: \\\" + '@' +\\n mention.user.screen_name, flush=True)\\n api.update_status('@' + mention.user.screen_name + ' ' +\\n salts[i], mention.id)\",\n \"def search(words):\\n newlist = [w for w in words if 'son' in w]\\n return newlist\",\n \"def twitterCrawl(self, strInput):\\n strVal = self.txtTwitter.get(\\\"1.0\\\", 'end')\\n if (strVal.strip()):\\n self.txtTwitter.delete(\\\"1.0\\\", 'end')\\n twitResult = twit.search(strInput)\\n return twitResult\",\n \"def trendingTweets():\\n api = twitter.Api()\\n trending_topics = api.GetTrendsWoeid(PHILA_WOEID)\\n for topic in trending_topics:\\n topicSearchTerm = topic.name\\n trending_tweets = api.GetSearch(topicSearchTerm)\\n for tweet in trending_tweets:\\n util.safe_print(tweet.GetText())\\n # pass\",\n \"def has_retweet(tweet):\\n\\n pattern = r'^RT'\\n return re.match(pattern, tweet)\",\n \"def filter_tweets(tweets):\\n # We keep only tweets by chrisalbon with pictures\\n search_tweets = [tw for tw in tweets if tw['username'] == '@chrisalbon' and len(tw['images']) > 0]\\n # He made multiple tweets on the same topic, we keep only the most recent tweets\\n # We use the indexes of the reversed tweet list and dictionnaries to keep only key \\n unique_search_index = sorted(list({t['text'].lower():i for i,t in list(enumerate(search_tweets))[::-1]}.values()))\\n unique_search_tweets = [search_tweets[i] for i in unique_search_index]\\n\\n # Keep non-downloaded tweets\\n most_recent_file = sorted([datetime.datetime.fromtimestamp(os.path.getmtime(path)) \\n for path in glob.glob(\\\"./downloaded_pics/*.jpg\\\")], reverse=True)[0]\\n recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > most_recent_file]\\n\\n # Uncomment for testing new tweets\\n # recent_seach_tweets = [tw for tw in unique_search_tweets if tw['date'] > datetime.datetime(2017, 7, 6, 13, 41, 48)]\\n return recent_seach_tweets\",\n \"def twitter(self):\\n message = \\\"\\\"\\n count = self.collection.count()\\n\\n twitter = Twitter(auth = OAuth(self.access_key, self.access_secret, self.consumer_key, self.consumer_secret))\\n for keyword in self.twitter_keywords:\\n query = twitter.search.tweets(q = keyword)\\n for result in query['statuses']:\\n try:\\n data = {\\\"id\\\": count+1, \\\"source\\\": \\\"twitter\\\", \\\"timestamp\\\": datetime.now()}\\n data['tweet'] = result['text']\\n data['name'] = result[\\\"user\\\"][\\\"screen_name\\\"]\\n data['url'] = \\\"https://twitter.com/\\\" + data[\\\"name\\\"] + \\\"/status/\\\" + str(result['id'])\\n data['search_string'] = keyword\\n try:\\n dataid = self.collection.insert(data)\\n except DuplicateKeyError as e:\\n continue\\n count += 1\\n\\n # Slack push notification\\n length = 82 - len(data['url'])\\n message += \\\"\\\\nURL: \\\" + data['url'] + \\\" search string: \\\".rjust(length) + keyword\\n\\n except Exception as e:\\n print(e)\\n pass\\n \\n if message:\\n print(self.G + \\\"[+] Twitter\\\" + self.B + message)\\n self.message += \\\"\\\\n*Twitter*:\\\\n```\\\"\\n self.message += message\\n self.message += \\\"\\\\n```\\\"\\n\\n return\",\n \"def searchIdiom(i, idiom):\\n token = authorize(i)\\n endpoint = 'https://api.twitter.com/1.1/search/tweets.json'\\n auth = OAuth1(signature_type=\\\"auth_header\\\", **token)\\n \\n params = {}\\n params[\\\"q\\\"]=\\\"%23\\\"+idiom\\n params[\\\"count\\\"] = tweet_count\\n try:\\n r = requests.get(endpoint, params=params, auth=auth, timeout=60.0)\\n print(r)\\n if r.status_code==200:\\n\\t '''\\n print(\\\"yay\\\")\\n for tweet in r.json()[\\\"statuses\\\"]:\\n print(tweet[\\\"user\\\"][\\\"screen_name\\\"])\\n #print(tweet[\\\"entities\\\"][\\\"hashtags\\\"])\\n print tweet[\\\"text\\\"]\\n ''' \\n return r.json()[\\\"statuses\\\"]\\n else:\\n if r.status_code==429:\\n print('limit exceeded')\\n rest_rate_limit(r)\\n return searchIdiom(i, idiom)\\n else:\\n print('critical situation')\\n return []\\n except ValueError as e:\\n print('Error : ', e)\\n except Exception as e1:\\n print('Error : ', e1)\",\n \"def getHashtagsAndMentions(tweets):\\n hashtags = Counter()\\n mentions = Counter()\\n plain = Counter()\\n\\n pattern = re.compile(r\\\"[^#@\\\\w'-]+\\\")\\n\\n for t in tweets:\\n words = pattern.split(t.message)\\n for word in words:\\n # Ignore null strings caused by split characters at the end of a\\n # message and remove standalone hyphens.\\n if word and not word.startswith(\\\"-\\\"):\\n # Increment count for the word in the Counter.\\n if word.startswith(\\\"#\\\"):\\n hashtags.update({word: 1})\\n elif word.startswith(\\\"@\\\"):\\n mentions.update({word: 1})\\n else:\\n # TODO: apply nltk.corpus.stopwords.words() here,\\n # across languages. Consider that the stopwords cut off\\n # before apostrophe, therefore check if the word\\n # starts with the stopword.\\n plain.update({word: 1})\\n\\n return hashtags, mentions, plain\",\n \"def twitter_search(a, q, x=10000000, t=100, s=None, m=-1):\\n\\n tweets = list()\\n tweet_count = 0\\n\\n while tweet_count < x:\\n try:\\n if (m <= 0):\\n if (not s):\\n new_tweets = a.search(q = q, count = t)\\n else:\\n new_tweets = a.search(q = q, count = t, since_id = s)\\n else:\\n if (not s):\\n new_tweets = a.search(q = q, count = t, max_id = (m - 1))\\n else:\\n new_tweets = a.search(q = q, count = t, max_id = (m - 1), since_id = s)\\n\\n if not new_tweets:\\n break\\n\\n for tweet in new_tweets:\\n tweets.append(tweet)\\n\\n tweet_count += len(new_tweets)\\n m = new_tweets[-1].id\\n\\n except tweepy.TweepError as e:\\n error = (-1, \\\"error:\\\" + str(e))\\n return error\\n\\n search_results = (tweet_count, tweets)\\n\\n return search_results\",\n \"def get_tweets(api, listOfTweets, keyword, numOfTweets=20, date_since='2019-1-1', lang=\\\"en\\\"):\\n spinner = yaspin()\\n spinner.start()\\n for tweet in tweepy.Cursor(api.search, q=keyword, lang=lang, since=date_since).items(numOfTweets):\\n # Add tweets in this format\\n dict_ = {'Screen Name': tweet.user.screen_name,\\n 'User Name': tweet.user.name,\\n 'Tweet Created At': str(tweet.created_at),\\n 'Tweet Text': tweet.text,\\n 'Cleaned Tweet Text': func.clean_tweets(tweet.text),\\n 'User Location': str(tweet.user.location),\\n 'Tweet Coordinates': str(tweet.coordinates),\\n 'Retweet Count': str(tweet.retweet_count),\\n 'Retweeted': str(tweet.retweeted),\\n 'Phone Type': str(tweet.source),\\n 'Favorite Count': str(tweet.favorite_count),\\n 'Favorited': str(tweet.favorited),\\n 'Replied': str(tweet.in_reply_to_status_id_str)\\n }\\n listOfTweets.append(dict_)\\n spinner.stop()\\n return listOfTweets\",\n \"def find_search_text(url, search_text):\\n try:\\n #sock = urllib2.urlopen(url)\\n result = urlfetch.fetch(url)\\n sock = result.content\\n parser = URLParser()\\n parser.feed(sock.read())\\n sock.close()\\n parser.close()\\n for texts in parser.text:\\n if search_text in texts:\\n return True\\n return False\\n except: # This is to take care of links that are not valid.\\n return False\",\n \"async def search(self, ctx: \\\"IceTeaContext\\\", *, query):\\n response_list = await ctx.guild_data.search_tags(query)\\n if len(response_list) > 0:\\n response_message = \\\"\\\\n\\\".join([tag.title for tag in response_list])\\n await ctx.send(f\\\"Found these tags:\\\\n{response_message}\\\")\\n else:\\n await ctx.send(\\\"No similar tags found\\\")\",\n \"def get_tweets_by_hashtag_route(hashtag):\\n response, code = get_tweets_by_hashtag(\\n hashtag, request.args.get('limit', 30))\\n return jsonify(response), code\",\n \"def get_tweets(keyword):\\n url = 'http://search.twitter.com/search.json?q='\\n\\n page = urllib.urlopen('%s%s' % (url, keyword))\\n blob = page.read()\\n jsonblob = json.loads(blob)\\n return jsonblob\",\n \"def search(query_string):\",\n \"def get_tweets(self, query, count=10):\\n # empty list to store parsed tweets\\n tweets = []\\n\\n try:\\n # call twitter api to fetch tweets\\n fetched_tweets = self.api.search(q=query, count=count)\\n\\n # parsing tweets one by one\\n for tweet in fetched_tweets:\\n # empty dictionary to store required params of a tweet\\n parsed_tweet = {}\\n\\n # saving text of tweet\\n parsed_tweet['text'] = tweet.text\\n # saving sentiment of tweet\\n parsed_tweet['sentiment'] = self.get_tweet_sentiment(tweet.text)\\n\\n # appending parsed tweet to tweets list\\n if tweet.retweet_count > 0:\\n # if tweet has retweets, ensure that it is appended only once\\n if parsed_tweet not in tweets:\\n tweets.append(parsed_tweet)\\n else:\\n tweets.append(parsed_tweet)\\n\\n # return parsed tweets\\n return tweets\\n\\n except tweepy.TweepError as e:\\n # print error (if any)\\n print(\\\"Error : \\\" + str(e))\",\n \"def tweet_text(tweet):\\n return tweet['text']\",\n \"def searchByKeywordPro(self, query, since=\\\"\\\", until=\\\"\\\", maxResults=None):\\n\\n tweetsList = []\\n if(not maxResults):\\n tweetList, next_token = self.api.search_30_day(\\n environment_name=\\\"developer\\\", query=query, toDate=until, fromDate=since)\\n tweetsList.append(tweetList)\\n while(next_token):\\n tweetList, next_token = self.api.search_30_day(\\n environment_name=\\\"developer\\\", query=query, toDate=until, fromDate=since, next=next_token)\\n tweetsList.append(tweetList)\\n else:\\n tweetList, next_token = self.api.search_30_day(\\n environment_name=\\\"developer\\\", query=query, toDate=until, fromDate=since)\\n tweetsList.append(tweetList)\\n maxResults -= len(tweetList)\\n while(next_token and maxResults > 0):\\n tweetList, next_token = self.api.search_30_day(\\n environment_name=\\\"developer\\\", query=query, toDate=until, fromDate=since, next=next_token)\\n tweetsList.append(tweetList)\\n maxResults -= len(tweetList)\\n for status in tweetsList:\\n createdDate = parser.parse(str(status._json[\\\"created_at\\\"]).strip())\\n createdDate = createdDate.replace(\\n tzinfo=pytz.utc) - createdDate.utcoffset()\\n status_refined = {\\n 'keyword': query,\\n '_id': status._json[\\\"id\\\"],\\n 'created_at': createdDate,\\n 'tweetText': status._json[\\\"text\\\"],\\n 'hashtags': status._json[\\\"entities\\\"][\\\"hashtags\\\"],\\n 'userLoc': status._json[\\\"user\\\"][\\\"location\\\"],\\n 'tweetGeo': status._json[\\\"geo\\\"],\\n 'tweetCoordinates': status._json[\\\"coordinates\\\"],\\n 'tweetPlace': status._json[\\\"place\\\"],\\n 'retweet': {},\\n 'quote': {},\\n }\\n if hasattr(status, \\\"quoted_status\\\"):\\n if \\\"extended_tweet\\\" in status._json[\\\"quoted_status\\\"].keys():\\n print(\\\"Taking the expanded tweet\\\")\\n status_refined['tweetText'] = status._json[\\\"quoted_status\\\"][\\\"extended_tweet\\\"][\\\"full_text\\\"]\\n else:\\n status_refined['tweetText'] = status._json[\\\"quoted_status\\\"][\\\"text\\\"]\\n status_refined['quote'] = {\\n 'original_retweet_id': status._json[\\\"quoted_status\\\"][\\\"id\\\"],\\n 'origUserLoc': status._json[\\\"quoted_status\\\"][\\\"user\\\"][\\\"location\\\"],\\n 'origTweetLoc': status._json[\\\"quoted_status\\\"][\\\"geo\\\"],\\n 'origTweetPlace': status._json[\\\"quoted_status\\\"][\\\"place\\\"],\\n 'origTweetCoord': status._json[\\\"quoted_status\\\"][\\\"coordinates\\\"],\\n 'origHashtags': status._json[\\\"quoted_status\\\"][\\\"entities\\\"][\\\"hashtags\\\"],\\n 'retweet_count': status._json[\\\"quote_count\\\"],\\n }\\n elif hasattr(status, \\\"retweeted_status\\\"):\\n print(status._json[\\\"retweeted_status\\\"])\\n if \\\"extended_tweet\\\" in status._json[\\\"retweeted_status\\\"].keys():\\n print(\\\"Taking the expanded tweet\\\")\\n status_refined['tweetText'] = status._json[\\\"retweeted_status\\\"][\\\"extended_tweet\\\"][\\\"full_text\\\"]\\n else:\\n status_refined['tweetText'] = status._json[\\\"retweeted_status\\\"][\\\"text\\\"]\\n status_refined['retweet'] = {\\n 'original_retweet_id': status._json[\\\"retweeted_status\\\"][\\\"id\\\"],\\n 'origUserLoc': status._json[\\\"retweeted_status\\\"][\\\"user\\\"][\\\"location\\\"],\\n 'origTweetLoc': status._json[\\\"retweeted_status\\\"][\\\"geo\\\"],\\n 'origTweetPlace': status._json[\\\"retweeted_status\\\"][\\\"place\\\"],\\n 'origTweetCoord': status._json[\\\"retweeted_status\\\"][\\\"coordinates\\\"],\\n 'origHashtags': status._json[\\\"retweeted_status\\\"][\\\"entities\\\"][\\\"hashtags\\\"],\\n 'retweet_count': status._json[\\\"retweet_count\\\"],\\n }\\n elif hasattr(status, \\\"extended_tweet\\\"):\\n if \\\"extended_tweet\\\" in status._json.keys():\\n status_refined['tweetText'] = status._json[\\\"extended_tweet\\\"][\\\"full_text\\\"]\\n self.tweets.append(status_refined)\\n return self.tweets\",\n \"def analyze_hashtag(self, hashtag, count=200):\\n tweets = []\\n\\n for x in xrange(0, int(count / 100)):\\n tweets.extend(self.tweet_fetcher.get_tweets(hashtag))\\n\\n analyzed_tweets = sort_tweets(self.sa.classify(tweets))\\n\\n self.analyzed_tweets = analyzed_tweets\\n\\n return analyzed_tweets\",\n \"def getByHashtags(hashtag):\\n\\n # set page_limits. The default is 1 \\n pages_limit = request.args.get('pages_limit') or 1\\n pages_limit = int(pages_limit)\\n\\n raw_response = get_response(tw_api, 'search/tweets', { 'q': '#' + hashtag, 'count': 100 }, pages_limit)\\n list_response = convert_resp2list(raw_response)\\n return jsonify(list_response)\",\n \"def topictweets(url):\\n article = get_article(url)\\n keywords = get_keywords(article['text'])\\n entities = get_entities(article['text'])\\n q = twitter_query(keywords, entities)\\n result = search({'q': q, 'count': 100, 'result_type': 'mixed'})\\n tweets = screen_name_filter(result.statuses, 'media')\\n return tweets\",\n \"async def wordfilter_test(self, ctx, *, message):\\n found = self.test_sentence(message)\\n if found:\\n await ctx.send(f\\\"Message contains `{found}`\\\")\\n else:\\n await ctx.send(\\\"Couldn't detect any filtered words\\\")\",\n \"def printHashtagsAndMentions(searchText=None, filterTerms=False, tweetLimit=0):\\n tweets = db.Tweet.select()\\n if searchText is not None:\\n tweets = tweets.filter(db.Tweet.q.message.contains(searchText))\\n tweets = tweets.limit(tweetLimit)\\n\\n hashtags, mentions, plain = getHashtagsAndMentions(tweets)\\n\\n if searchText and filterTerms:\\n hashtags = Counter(\\n {k: v for k, v in hashtags.items() if searchText.lower() in k.lower()}\\n )\\n mentions = Counter(\\n {k: v for k, v in mentions.items() if searchText.lower() in k.lower()}\\n )\\n plain = Counter(\\n {k: v for k, v in plain.items() if searchText.lower() in k.lower()}\\n )\\n\\n # Unique word count for each area.\\n hashtagWC = len(hashtags)\\n mentionWC = len(mentions)\\n plainWC = len(plain)\\n\\n print(\\\"Summary\\\")\\n print(\\\"==============\\\")\\n # Count items in the sliced selection since .count() does not work with\\n # a limit.\\n count = len(list(tweets)) if tweetLimit else tweets.count()\\n print(\\\"{0:7,d} tweets\\\".format(count))\\n print(\\\"{0:7,d} unique words\\\".format(hashtagWC + mentionWC + plainWC))\\n print(\\\"{0:7,d} unique hashtags\\\".format(hashtagWC))\\n print(\\\"{0:7,d} unique mentions\\\".format(mentionWC))\\n print(\\\"{0:7,d} unique plain words\\\".format(plainWC))\\n print()\\n\\n print(\\\"Hashtags\\\")\\n print(\\\"========\\\")\\n printCounterByCount(hashtags)\\n print()\\n\\n print(\\\"Mentions\\\")\\n print(\\\"========\\\")\\n printCounterByCount(mentions)\\n\\n \\\"\\\"\\\"\\n # Removal of stopwords and handling of URIs is needed to make this\\n # useful.\\n print 'Plain'\\n print '========'\\n printCounterByCount(plain)\\n \\\"\\\"\\\"\",\n \"def clean_tweet(tweet):\\r\\n reply_pattern = re.compile(\\\"^@([a-zA-Z0-9]*) (.*)\\\")\\r\\n regexhandler = regex.RegexHandler()\\r\\n # add mark if tweets starts with a mention (@user)\\r\\n if reply_pattern.match(tweet.tweet[\\\"text\\\"]) is not None:\\r\\n temp = \\\"MarkReply \\\" + tweet.tweet[\\\"text\\\"]\\r\\n else:\\r\\n temp = tweet.tweet[\\\"text\\\"]\\r\\n # language dependent\\r\\n\\r\\n if floodtags.core.statics.StaticData.locations:\\r\\n for location in floodtags.core.statics.StaticData.locations:\\r\\n if location in temp:\\r\\n temp += \\\" MarkLocation\\\"\\r\\n\\r\\n try:\\r\\n stemmer = SnowballStemmer(floodtags.core.statics.StaticData.language.lower())\\r\\n # stem words\\r\\n temp = \\\" \\\".join(\\r\\n [stemmer.stem(x) if x not in tweet.tweet[\\r\\n \\\"keywords\\\"] and \\\"MarkReply\\\" not in x and \\\"MarkLocation\\\" not in x else x for x in temp.split()])\\r\\n except ValueError:\\r\\n print(\\\"language not found:\\\", floodtags.core.statics.StaticData.language)\\r\\n # pass\\r\\n\\r\\n # store language\\r\\n temp = \\\"Mark\\\" + tweet.language + \\\" \\\" + temp\\r\\n\\r\\n # store keyword\\r\\n\\r\\n # replace each website with 'MarkWebsite' to create more similarity\\r\\n temp = regexhandler.replace(temp, 'MarkWebsite', regex.Expressions.website)\\r\\n # replace each photo url with 'MarkPhoto' to create more similarity\\r\\n for i in range(len(tweet.tweet[\\\"photos\\\"])):\\r\\n temp = Vectorizer.rreplace(temp, \\\"MarkWebsite\\\", \\\"MarkPhoto\\\", 1)\\r\\n # replace each height with 'MarkHeight' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkHeight\\\", regex.Expressions.waterheight)\\r\\n # replace each time with 'MarkTime' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkTime\\\", regex.Expressions.time)\\r\\n # replace each date with 'MarkDate' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkDate\\\", regex.Expressions.date)\\r\\n # replace each number with 'MarkNumber' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkNumber\\\", regex.Expressions.number)\\r\\n temp = re.sub('\\\\n', ' ', temp)\\r\\n results = re.findall(\\\"(^|[^@\\\\w])@(\\\\w{1,15})\\\", temp)\\r\\n # add mark for each user name\\r\\n if results is not None:\\r\\n for i in range(len(results)):\\r\\n temp += \\\" MarkUserName\\\"\\r\\n results = re.findall(\\\"#(\\\\S*)\\\", temp)\\r\\n # add mark for each hashtag\\r\\n if results is not None:\\r\\n for i in range(len(results)):\\r\\n temp += \\\" MarkHashTag\\\"\\r\\n # add sender as feature\\r\\n temp = \\\"Sender\\\" + tweet.tweet[\\\"source\\\"][\\\"username\\\"] + \\\" \\\" + temp\\r\\n # remove unnecessary characters and chance text to lower case\\r\\n return re.sub('[#\\\\.,:]', '', temp)\",\n \"def twitter(self):\\n\\n q = \\\" OR \\\".join(self.search_terms) + \\\" -filter:retweets\\\"\\n results = self.__api.search(q=q, lang='en', count=100)\\n\\n tweets = []\\n\\n for res in results:\\n\\n publishedAt = datetime.strptime(res._json['created_at'], '%a %b %d %H:%M:%S +0000 %Y').strftime(\\\"%Y-%m-%d\\\")\\n\\n if (res._json['in_reply_to_screen_name'] == None and publishedAt == datetime.now().strftime(\\\"%Y-%m-%d\\\")):\\n tweets.append([res._json['id'],\\n res._json['text'],\\n res._json['user']['screen_name'],\\n publishedAt,\\n res._json['user']['followers_count']])\\n\\n self.list = pd.DataFrame(tweets, columns=['id', 'title', 'user', 'publishedAt', 'followers_count']).nlargest(10,\\n 'followers_count')\\n\\n return\",\n \"def search(self, query_string):\\n return models.Thread.objects.filter(\\n reduce(\\n lambda q, f: q & Q(title__icontains=f),\\n query_string.split(),\\n Q()))\",\n \"def get_hashtag_tweets(self, hashtag,\\n count=settings.TWITTER_DEFAULT_LIMIT):\\n url = urljoin(self.base_url, \\\"/search/tweets.json\\\")\\n response = self.session.get(\\n url,\\n params={\\n \\\"q\\\": hashtag,\\n \\\"count\\\": count,\\n \\\"include_entities\\\": True\\n },\\n auth=self.__auth,\\n )\\n data = response.json()\\n if response.ok:\\n data = [Tweet(tweet_data) for tweet_data in data['statuses']]\\n else:\\n if 'error' in data:\\n raise TwitterException(data['error'], code=response.status_code)\\n elif 'errors' in data:\\n error = data['errors'][0]\\n raise TwitterException(error['message'], code=response.status_code)\\n return data\",\n \"def tweet(self, message):\\n if self.api_key == \\\"\\\":\\n return False\\n ts = TagStripper()\\n ts.feed(message)\\n params = urllib.urlencode(\\n {'api_key': self.api_key,\\n 'status': ts.get_collected_data()})\\n\\n headers = {\\\"Content-type\\\": \\\"application/x-www-form-urlencoded\\\",\\n \\\"Accept\\\": \\\"text/plain\\\"}\\n\\n conn = httplib.HTTPConnection(\\\"api.thingspeak.com:80\\\")\\n\\n try:\\n conn.request(\\\"POST\\\",\\n \\\"/apps/thingtweet/1/statuses/update\\\",\\n params,\\n headers)\\n\\n response = conn.getresponse()\\n data = response.read()\\n conn.close()\\n return True, ts.get_collected_data()\\n except httplib.HTTPException as http_exception:\\n return False, http_exception.message\",\n \"def get_mentions(texts, word):\\r\\n mentions = {}\\r\\n for text, label, text_id in texts:\\r\\n if word in text.lower():\\r\\n if label not in mentions:\\r\\n mentions[label] = [text_id]\\r\\n else:\\r\\n if text_id not in mentions[label]:\\r\\n mentions[label].append(text_id)\\r\\n return mentions\",\n \"def search(request):\\n\\t\\n\\t# User's query\\n\\tquery = request.GET.get('query')\\n\\n\\t# Search for 50 most popular tweets about user's query\\n\\ttweets = tweepy.Cursor(api.search, q=query, lang=\\\"en\\\", tweet_mode='extended', include_entities=True, result_type='popular').items(50)\\n\\n\\t# Search for 20 most relevant news about user's query\\n\\tall_news = newsapi.get_everything(q=query, language='en', sort_by='relevancy')\\n\\n\\t# Search for 25 hottest subreddits about user's query\\n\\tsubreddit = reddit.subreddit('all')\\n\\treddit_news = subreddit.search(query, limit=25, sort='hot')\\n\\n\\tcontext = {\\n\\t\\t\\\"tweets\\\": tweets, # most popular tweets\\n\\t\\t\\\"all_news\\\": all_news, # most relevant google news\\n\\t\\t\\\"reddit_news\\\": reddit_news # hottest subreddits\\n\\t}\\n\\n\\treturn render(request, 'hashtrend/search.html', context)\",\n \"def get(self, search_words, language, result_type):\\n consumer_key = config.twitter_api_credentials[\\\"consumer_key\\\"]\\n consumer_secret = config.twitter_api_credentials[\\\"consumer_secret\\\"]\\n access_token = config.twitter_api_credentials[\\\"access_token\\\"]\\n access_token_secret = config.twitter_api_credentials[\\\"access_token_secret\\\"] \\n \\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret); \\n auth.set_access_token(access_token, access_token_secret);\\n api = tweepy.API(auth, wait_on_rate_limit=True) ; \\n \\n #input_params = ns.payload\\n #search_words = input_params['search_words'] \\n #language = input_params['language'] # Language code (follows ISO 639-1 standards)\\n #until_date = input_params['until_date']\\n #result_type = input_params['result_type'] until=until_date\\n\\n try:\\n results = tweepy.Cursor( api.search, q=search_words, lang=language, result_type = result_type).items(10) \\n out = { tweet.user.screen_name: { \\\"followers_count\\\": tweet.user.followers_count, \\\\\\n \\\"location\\\": tweet.user.location ,\\\"favorite_count\\\":tweet.favorite_count,\\\"text\\\": tweet.text} for tweet in results }\\n sorted_keys = {k:v[\\\"followers_count\\\"] for (k,v) in out.items()}\\n sorted_keys = sorted(sorted_keys, key=sorted_keys.__getitem__, reverse=True)\\n out = {k:out[k] for k in sorted_keys}\\n return out\\n except tweepy.error.TweepError as e:\\n return(json.loads(e.response.text)['errors'][0]['message'], 401)\",\n \"def query_tweets_once(query, limit=None, num_tweets=0):\\n logging.info(\\\"Querying {}\\\".format(query))\\n query = query.replace(' ', '%20').replace(\\\"#\\\", \\\"%23\\\").replace(\\\":\\\", \\\"%3A\\\")\\n pos = None\\n tweets = []\\n try:\\n while True:\\n new_tweets, pos = query_single_page(\\n INIT_URL.format(q=query) if pos is None\\n else RELOAD_URL.format(q=query, pos=pos),\\n pos is None\\n )\\n if len(new_tweets) == 0:\\n logging.info(\\\"Got {} tweets for {}.\\\".format(\\n len(tweets), query))\\n return tweets\\n\\n logging.info(\\\"Got {} tweets ({} new).\\\".format(\\n len(tweets) + num_tweets, len(new_tweets)))\\n\\n tweets += new_tweets\\n\\n if limit is not None and len(tweets) + num_tweets >= limit:\\n return tweets\\n except KeyboardInterrupt:\\n logging.info(\\\"Program interrupted by user. Returning tweets gathered \\\"\\n \\\"so far...\\\")\\n except BaseException:\\n logging.exception(\\\"An unknown error occurred! Returning tweets \\\"\\n \\\"gathered so far.\\\")\\n\\n return tweets\",\n \"def search_multiple_words(words):\\n # YOUR CODE HERE #\\n pass # delete this when you write your code\",\n \"def collect_tweets(search_id, search_term, number_of_tweets):\\n\\n tweets = []\\n for tweet in api_collector.collect(search_term, number_of_tweets):\\n tweets.append((tweet.id_str, tweet.created_at, tweet.full_text))\\n if len(tweets) == 0:\\n search = Search.objects.get(pk=search_id)\\n search.empty = True\\n search.save()\\n notify_searchers.delay(search_id)\\n else:\\n classify_tweets.delay(search_id, tweets)\",\n \"def is_retweet(tweet):\\n\\n return 'retweeted_status' in tweet\",\n \"def tweet(self, tweet, at=None):\\n if tweet.strip() == \\\"\\\":\\n return\\n\\n num_tweets, tweets = self._divide_tweet(tweet, at)\\n if num_tweets > 0:\\n # replace @'s with #'s and convert unicode emojis before tweeting\\n [self.api.update_status(tw.replace(\\\"@\\\", \\\"#\\\").encode(\\\"utf-8\\\")) for tw in tweets]\\n self.log(f\\\"Tweeted: {' '.join(tweets)}\\\")\\n return tweets[0]\",\n \"def test_find_phrase_matches2(self):\\n\\t\\ttest = sentiment.LibraryRun(self.text2, self.lib)\\n\\t\\tobj_ut = test.find_phrase_matches(self.tokens_generator2)[0]\\n\\t\\tself.assertEqual(dict(obj_ut),\\n\\t\\t\\t{'not good': [[2, -1, 0], [4, -1, 0]]})\",\n \"def query_twitter(session, provided_ioc):\\n ioc_dicts = []\\n\\n if provided_ioc.startswith(\\\"@\\\"):\\n ioc_dicts.append({\\\"invalid\\\": \\\"{} <-- Monitoring users is prohibited!\\\".format(provided_ioc)})\\n return ioc_dicts\\n\\n encoded_ioc = urllib.quote_plus(provided_ioc)\\n search_tweets = session.search(encoded_ioc, rpp=100, lang=\\\"en\\\")\\n\\n for tweet in search_tweets:\\n if tweet._json[\\\"user\\\"][\\\"name\\\"] == provided_ioc.replace(\\\"#\\\", \\\"\\\") or \\\\\\n tweet._json[\\\"user\\\"][\\\"screen_name\\\"] == provided_ioc.replace(\\\"#\\\", \\\"\\\"):\\n ioc_dicts.append({\\\"invalid\\\": \\\"{} <-- Monitoring users is prohibited!\\\".format(provided_ioc)})\\n return ioc_dicts\\n\\n if \\\"retweeted_status\\\" in tweet._json.keys():\\n if tweet._json[\\\"retweeted_status\\\"][\\\"user\\\"][\\\"name\\\"] == provided_ioc.replace(\\\"#\\\", \\\"\\\") or \\\\\\n tweet._json[\\\"retweeted_status\\\"][\\\"user\\\"][\\\"screen_name\\\"] == provided_ioc.replace(\\\"#\\\", \\\"\\\"):\\n ioc_dicts.append({\\\"invalid\\\": \\\"{} <-- Monitoring users is prohibited!\\\".format(provided_ioc)})\\n return ioc_dicts\\n\\n urls = []\\n for x in tweet._json[\\\"entities\\\"][\\\"urls\\\"]:\\n if not x[\\\"expanded_url\\\"].startswith(\\\"https://twitter.com/i/web/status/\\\"):\\n urls.append(x[\\\"expanded_url\\\"])\\n\\n hashtags = []\\n for x in tweet._json[\\\"entities\\\"][\\\"hashtags\\\"]:\\n hashtags.append(\\\"#{}\\\".format(x[\\\"text\\\"]))\\n\\n ioc_dict = {}\\n ioc_dict[\\\"search_term\\\"] = provided_ioc\\n ioc_dict[\\\"url\\\"] = \\\"\\\\n\\\".join(urls)\\n ioc_dict[\\\"hashtags\\\"] = \\\"\\\\n\\\".join(hashtags)\\n ioc_dict[\\\"timestamp\\\"] = tweet._json[\\\"created_at\\\"]\\n ioc_dict[\\\"tweet\\\"] = tweet._json[\\\"text\\\"]\\n\\n if \\\"retweeted_status\\\" in tweet._json.keys():\\n ioc_dict[\\\"timestamp\\\"] = tweet._json[\\\"retweeted_status\\\"][\\\"created_at\\\"]\\n ioc_dict[\\\"tweet\\\"] = tweet._json[\\\"retweeted_status\\\"][\\\"text\\\"]\\n\\n ioc_dicts.append(ioc_dict)\\n return ioc_dicts\",\n \"def crawl(self):\\n retrievedTweets = []\\n\\n count = 1\\n \\n today = datetime.datetime.now()\\n today = today.replace(hour=23, minute=59, second=59, microsecond=999999)\\n gap = 1\\n yesterday = today - datetime.timedelta(gap) \\n nextDay = yesterday + datetime.timedelta(gap)\\n \\n while True:\\n try:\\n lst = tweepy.Cursor(self.api.search, lang='en', q=self.keyword, count=50, until=nextDay.date(), result_type='popular').items(50)\\n for tweet in lst:\\n self.data = [tweet.created_at, tweet.id, tweet.text,\\n tweet.user._json['screen_name'], tweet.user._json['name'], \\n tweet.favorite_count, tweet.retweet_count, tweet.user.location]\\n self.data = tuple(self.data)\\n retrievedTweets.append(self.data)\\n break\\n except tweepy.TweepError as e:\\n print(e.reason)\\n continue\\n except StopIteration: \\n break\\n\\n return retrievedTweets\",\n \"def gettweets(request):\\n temp = json.loads(request.body)\\n print (temp['hashtags'])\\n return Response(tw_fetcher.gethashes(temp['hashtags']), status=status.HTTP_201_CREATED)\",\n \"def streamTweets(words = [], authors = [], timeLimit=120, removeRetweets=False, **kwargs):\\n if 'stream' not in globals():\\n global stream\\n if 'api' not in globals():\\n startTwitterApi(getApiKeys(fileName=\\\"apiConf2.txt\\\"))\\n listener = StdOutListener(removeRetweets=removeRetweets)\\n auth = api.auth\\n stream = tweepy.Stream(auth, listener, tweet_mode='extended')\\n else:\\n stream.listener.setRemoveRetweets(removeRetweets)\\n stream.listener.resetTweets()\\n\\n words = mapToValid(words)\\n authors = mapToValid(authors)\\n if not words and not authors:\\n words=[\\\"the\\\", \\\"i\\\", \\\"to\\\", \\\"a\\\", \\\"and\\\", \\\"'s\\\", \\\"is\\\", \\\"in\\\", \\\"it\\\", \\\"you\\\", \\\"of\\\", \\\"for\\\", \\\"on\\\", \\\"my\\\", \\\"that\\\", \\\"e\\\", \\\"with\\\", \\\"me\\\", \\\"do\\\", \\\"have\\\", \\\"ciao\\\", \\\"o\\\", \\\"u\\\", \\\"cool\\\", \\\"good\\\", \\\"nice\\\", \\\"#\\\", \\\"*\\\", \\\":\\\", \\\";\\\", \\\",\\\", \\\".\\\", \\\"?\\\", \\\"-\\\", \\\"%\\\", \\\"$\\\", \\\"€\\\", \\\"!\\\", \\\"(\\\", \\\")\\\", \\\"=\\\", \\\"'\\\"]\\n\\n #myQuery = ' OR '.join(kwargs[\\\"words\\\"])\\n if authors:\\n kwargs[\\\"follow\\\"]=[user.id_str for user in list(map(api.get_user,authors))]\\n else:\\n kwargs[\\\"track\\\"]=words\\n #if removeRetweets:\\n # myQuery += \\\" -filter:retweets\\\"\\n\\n #myQuery += ' from:'\\n #myQuery += ' OR from:'.join(kwargs[\\\"authors\\\"])\\n #print(myQuery)\\n import signal\\n # Register the signal function handler\\n signal.signal(signal.SIGALRM, __streamHandler__)\\n # Define a timeout for your function\\n signal.alarm(timeLimit)\\n try:\\n __stream__(stream,**kwargs)\\n except Exception:\\n print(\\\"Streaming over after time period of\\\", timeLimit, \\\"seconds... Retrieved\\\", len(stream.listener.getTweets()), \\\"tweets.\\\")\\n stream.disconnect()\\n if authors and words:\\n print(\\\"Filtering out tweets that don't contain the specified words...\\\")\\n myTweets=[]\\n for tweet in stream.listener.getTweets():\\n if 'full_text' in tweet:\\n tweet['text'] = tweet['full_text']\\n del (tweet['full_text'])\\n if any(containsWord(tweet['text'],word) for word in words):\\n myTweets.append(tweet)\\n print(\\\"Done. Retrieved\\\", len(myTweets), \\\"tweets written by the authors specified and containing (any of) the words specified.\\\")\\n return myTweets\\n return stream.listener.getTweets()\",\n \"def having(self, wordlist=None, coordinates=None):\\n track = \\\",\\\".join(wordlist) if wordlist else None\\n locations = \\\",\\\".join(map(str, coordinates)) if coordinates else None\\n\\n if track and locations:\\n generator = self.stream.statuses.filter(track=track,\\n locations=locations)\\n elif track:\\n generator = self.stream.statuses.filter(track=track)\\n elif locations:\\n generator = self.stream.statuses.filter(locations=locations)\\n else:\\n raise ValueError(\\\"Must provide one of wordlist or coordinates\\\")\\n\\n for tweet in generator:\\n if locations:\\n if 'coordinates' in tweet and tweet['coordinates']:\\n yield tweet\\n else:\\n yield tweet\",\n \"def word_finder(word, text):\\r\\n word = word.lower()\\r\\n text = str(text).lower()\\r\\n match = re.search(word, text)\\r\\n if match:\\r\\n return True\\r\\n return False\",\n \"def search_from_raw_query(api, raw_query, **kwargs):\\n tweets=api.GetSearch(raw_query=raw_query)\\n return {\\\"tweets\\\":tweets}\",\n \"def count_tweets(search_term, location=None):\\r\\n\\r\\n if location:\\r\\n return len(Tweet.objects(Q(keyword_search_term=search_term) & Q(location_address=location)))\\r\\n else:\\r\\n return len(Tweet.objects(keyword_search_term=search_term))\",\n \"def find_matches(id1, id2, adj_list_ids, adj_list_words):\\n\\n # combine words from tweets\\n stanza_words = adj_list_ids[id1] + adj_list_ids[id2]\\n\\n # look for other tweets with those words\\n pot_ids = set().union(*[adj_list_words[w] for w in stanza_words])\\n\\n # --- filter down ---\\n # remove start tweets\\n # ensure tweets contain subset of master_word_set\\n pot_ids = pot_ids - {id1, id2}\\n pot_ids = [x for x in pot_ids if set(adj_list_ids[x]) <= set(stanza_words)]\\n\\n # look for valid pairs of potential tweets\\n if len(pot_ids) > 1:\\n return find_valid_matches(pot_ids, adj_list_ids, stanza_words)\\n return []\",\n \"def test_find_phrase_matches1(self):\\n\\t\\ttest = sentiment.LibraryRun(self.text1, self.lib)\\n\\t\\tobj_ut = test.find_phrase_matches(self.tokens_generator1)[0]\\n\\t\\tself.assertEqual(dict(obj_ut),\\n\\t\\t\\t{'not good': [[2, -1, 0]]})\",\n \"def process(self, tweet):\\n\\n #identify the applicable event keywords for this text\\n text = self.cleanup_data(tweet.text)\\n tokens = [str(t.lower()).translate(None, string.punctuation) for t in tweet.text.split()]\\n applicable_tokens = []\\n for phrase in self.match_event_tree.root.keywords:\\n if phrase in \\\" \\\".join(tokens):\\n applicable_tokens.append(phrase)\\n\\n self.match_event_tree.propogate_tweet(applicable_tokens, tweet)\",\n \"async def team_search(self, ctx: commands.Context, username: str):\\n all_usernames = {team_id: team.username for team_id, team in self.teams.items()\\n if team is not None}\\n suggestions = []\\n log.info(repr(fuzzywuzzy.process.extract(\\n username, all_usernames, limit=5)))\\n for fuzz_username, rating, fuzz_id in fuzzywuzzy.process.extract(\\n username, all_usernames, limit=5):\\n if rating < 50:\\n break\\n fuzz_team = self.teams[fuzz_id]\\n suggestions.append(\\n f'(ID: **{fuzz_team.team_id}**) **{fuzz_team.display_name[:40]}**'\\n f' -- {len(fuzz_team.users)} registered members')\\n if suggestions:\\n await ctx.send('\\\\n'.join(suggestions))\\n else:\\n await ctx.send(f\\\"Couldn't find any teams whose usernames resembled `{username}`\\\")\",\n \"def search(self, query_string):\\n terms = query_string.lower().split()\\n result = set(self.wordDict[terms[0]])\\n if len(result) == 0:\\n return list()\\n else:\\n for t in terms[2:]:\\n records_containing_t = self.wordDict[t]\\n result = result.intersection(records_containing_t)\\n return [self.get_record_dict(id).getTuple() for id in result]\",\n \"def whole_word_matches(self):\\n start = '1.0'\\n while True:\\n start = self.text.search(self.term, start, stopindex=tk.END)\\n if not start:\\n break\\n end = start + ' wordend'\\n # whole word includes a space before\\n found = self.text.get(start + '-1c', end)\\n if found == ' ' + self.term:\\n self.text.tag_add('found', start, end)\\n start = end\",\n \"def whole_word_matches(self):\\n start = '1.0'\\n while True:\\n start = self.text.search(self.term, start, stopindex=tk.END)\\n if not start:\\n break\\n end = start + ' wordend'\\n # whole word includes a space before\\n found = self.text.get(start + '-1c', end)\\n if found == ' ' + self.term:\\n self.text.tag_add('found', start, end)\\n start = end\",\n \"def searchMovie(api, title, date, count, retweets=False):\\n\\n since = date\\n until = datetime.strptime(since, '%Y-%m-%d') + timedelta(days=1)\\n until = until.strftime('%Y-%m-%d')\\n\\n query = \\\"\\\\\\\"{0}\\\\\\\" since:{1} until:{2} -filter:links\\\".format(processTitle(title), since, until)\\n if retweets == False:\\n query += \\\" -filter:retweets\\\"\\n\\n rawtweets = tweepy.Cursor(api.search, q=query, result_type=\\\"recent\\\", lang=\\\"en\\\").items(count)\\n results = []\\n\\n for i, tweet in enumerate(rawtweets):\\n try:\\n results.append(processTweet(title, tweet, remove_title=True))\\n except tweepy.error.TweepError as err:\\n print(\\\"\\\\nThere was an error processing tweet #{0} for title [{1}]\\\".format(i, title))\\n print(err.messages[0]['code'])\\n\\n results = pd.DataFrame(results)\\n results['title'] = title\\n results['tweet_date'] = date\\n return results\",\n \"async def bingsearch(self, *, text):\\n settings = loadauth()\\n operation = 'websearch'\\n if settings['apikey'] == '' or settings['apikey'] == 'blank':\\n return await self.bot.say(\\\"Missing or incorrect API key. Please \\\" +\\n \\\"contact the owner to add an API key.\\\")\\n apikey = settings['apikey']\\n text, limit = self.limitget(text)\\n result = self.getfrombing(apikey, text, limit, operation)\\n bottext = self.obtainresult(result, operation)\\n return await self.bot.say(bottext)\",\n \"def search(corpus, query, options=SearchOptions()):\\n http_response_code_to_status = {\\n 200: Status.OK,\\n 400: Status.BAD_REQUEST,\\n 502: Status.BAD_GATEWAY\\n }\\n result = SearchResult()\\n context = urllibx.urlopen(_make_url(corpus, query, options))\\n result.status = http_response_code_to_status[context.getcode()]\\n if result.status == Status.OK:\\n for line in context.readlines():\\n line = line.decode('utf-8')\\n phrase = Phrase()\\n parts = line.split(\\\"\\\\t\\\")\\n for token_with_tag in parts[0].split(\\\" \\\"):\\n token = Token()\\n token.text = token_with_tag[:-2]\\n token.tag = int(token_with_tag[-1])\\n phrase.tokens.append(token)\\n phrase.match_count = int(parts[1])\\n phrase.volume_count = int(parts[2])\\n phrase.first_year = int(parts[3])\\n phrase.last_year = int(parts[4])\\n phrase.id = int(parts[5])\\n phrase.score = float(parts[6])\\n result.phrases.append(phrase)\\n context.close()\\n return result\",\n \"def search_by_contains(self, tl):\\n print(\\\"Search by string\\\")\\n string = input(\\\"Please enter search string: \\\")\\n return tl.findall_contains(string)\",\n \"def like_tweet(self, tag):\\n self.bot.get('https://twitter.com/search?q=' + tag + '&src=typed')\\n self.__wait(3, 3)\\n for i in range(1, 3):\\n self.bot.execute_script('window.scrollTo(0,document.body.scrollHeight)')\\n self.__wait(2, 3)\\n tweets = self.bot.find_elements_by_tag_name('article')\\n\\n links = []\\n for tweet in tweets:\\n sub_links = tweet.find_elements_by_tag_name('a')\\n links += [sub_link.get_attribute('href')\\n for sub_link in sub_links if 'status' in sub_link.get_attribute('href')]\\n\\n print('Started to like {} tweets'.format(len(links)))\\n\\n for link in links:\\n self.bot.get(link)\\n self.__wait(3, 5)\\n likes = self.bot.find_elements_by_css_selector('div[data-testid=\\\"like\\\"')\\n for like in likes:\\n like.click()\\n self.__wait(3, 5)\",\n \"def search_machine(ID,machine):\\n\\tconsumer_key = machine['consumer_key']\\n\\tconsumer_secret = machine['consumer_secret']\\n\\taccess_token = machine['access_token']\\n\\taccess_secret = machine['access_secret']\\n\\tauth = OAuthHandler(consumer_key, consumer_secret)\\n\\tauth.set_access_token(access_token, access_secret)\\n\\tapi = tweepy.API(auth, wait_on_rate_limit_notify=True)\\n\\n\\t\\\"\\\"\\\"Search for tweets via Twitter Search API.\\\"\\\"\\\"\\n\\tsinceId = None\\n\\tmax_id = ID\\n\\ttweetsCounts = 0\\n\\tfinshed_job = False\\n\\twith open (outfile,'w+') as f:\\n\\t\\twhile tweetsCounts < maxTweets:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tif (max_id <= 0):\\n\\t\\t\\t\\t\\tif (not sinceId):\\n\\t\\t\\t\\t\\t\\tnew_tweets = api.search(\\n\\t\\t\\t\\t\\t\\t\\tq = query,\\n\\t\\t\\t\\t\\t\\t\\tgeocode = geo,\\n\\t\\t\\t\\t\\t\\t\\tcount = searchLimits)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tnew_tweets = api.search(\\n\\t\\t\\t\\t\\t\\t\\tq=query,\\n\\t\\t\\t\\t\\t\\t\\tcount = searchLimits,\\n\\t\\t\\t\\t\\t\\t\\tgeocode=geo,\\n\\t\\t\\t\\t\\t\\t\\tsinceId = sinceId)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif (not sinceId):\\n\\t\\t\\t\\t\\t\\tnew_tweets = api.search(\\n\\t\\t\\t\\t\\t\\t\\tq=query, \\n\\t\\t\\t\\t\\t\\t\\tcount=searchLimits,\\n\\t\\t\\t\\t\\t\\t\\tgeocode = geo,\\n\\t\\t\\t\\t\\t\\t\\tmax_id=str(max_id - 1))\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tnew_tweets = api.search(\\n\\t\\t\\t\\t\\t\\t\\tq=query, \\n\\t\\t\\t\\t\\t\\t\\tcount=searchLimits,\\n\\t\\t\\t\\t\\t\\t\\tgeocode = geo,\\n\\t\\t\\t\\t\\t\\t\\tmax_id=str(max_id - 1),\\n\\t\\t\\t\\t\\t\\t\\tsince_id=sinceId)\\n\\t\\t\\t\\tif not new_tweets:\\n\\t\\t\\t\\t\\tprint(\\\"NO MORE TWEETS\\\")\\n\\t\\t\\t\\t\\tfinshed_job = True\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\tfor tweet in new_tweets:\\n\\t\\t\\t\\t\\tif tweet.coordinates or tweet.place:\\n\\t\\t\\t\\t\\t\\tjson.dump(tweet._json,f,ensure_ascii=False)\\n\\t\\t\\t\\t\\t\\tf.write('\\\\n')\\n\\t\\t\\t\\t\\n\\t\\t\\t\\ttweetsCounts += len(new_tweets)\\n\\t\\t\\t\\t#print(\\\"Downloaded {0} tweets\\\".format(tweetsCounts))\\n\\t\\t\\t\\tmax_id = new_tweets[-1].id\\n\\t\\t\\texcept tweepy.RateLimitError as e:\\n\\t\\t\\t\\tprint(machine['index'],'Time to sleep 15 mins') \\n\\t\\t\\t\\tAPI_status[machine['index']] = False\\n\\t\\t\\t\\tif machine['index'] == 0:\\n\\t\\t\\t\\t\\tAPI_status['time'] = time.time() + 901.00\\n\\t\\t\\t\\treturn finshed_job,max_id\\n\\t\\t\\texcept tweepy.TweepError as e:\\n\\t\\t\\t\\tlogging.error(str(e))\\n\\t\\t\\t\\tbreak\\n\\tf.close()\\n\\treturn finshed_job,max_id\",\n \"def geo_collect_tweets(search_term,latitude,longitude,radius):\\n i = None\\n tweets = []\\n rep = 1\\n for n in range(2): #can only search 100 tweets at a time, so run search multiple times\\n \\tresults = api.GetSearch(term = search_term, \\n \\t\\tcount = 100, \\n \\t\\tresult_type = 'recent', \\n \\t\\tmax_id = i, #start a search from the most recent tweet id, working backwards\\n \\t\\tgeocode =(latitude, longitude, radius))\\n for tweet in results:\\n tweets.append(tweet.text)\\n i = tweet.id - 1 #want it to start at the tweet after the last tweet\\n rep += 1\\n return list(set(tweets)) #set gets rid of repititve tweets, but need to return a list\",\n \"def search_from_geocode(api, geocode, **kwargs):\\n tweets=api.GetSearch(geocode=geocode)\\n return {\\\"tweets\\\":tweets}\",\n \"def process(self, filter_words, count=1):\\n user = self.__api.get_user(self.__username)\\n\\n # print user.screen_name\\n # print user.followers_count\\n if self.__appMode == 1 and self.__TimeLineMode == 1:\\n self.get_timeline(filter_words)\\n else:\\n if self.__friendMode:\\n print(\\\"Getting all Twitter Friends \\\\n\\\")\\n for friend in user.friends():\\n self.get_tweet(friend.screen_name, filter_words, count)\\n else:\\n for screen_name in self.__priorityCoin:\\n self.get_tweet(screen_name, filter_words, count)\\n print('Twitter Data Extraction done!!')\",\n \"def search_tweets(request):\\n return render(request, 'ede/search.html')\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status = tweet_text)\",\n \"def get_tweets(self):\\n keyword = 'covid'\\n\\n # Load tokens from file\\n with open('../data/tokens.json', 'r') as f:\\n tokens = json.load(f)\\n\\n # Stream tweets\\n auth = tweepy.OAuthHandler(tokens['consumer_key'], tokens['consumer_secret'])\\n auth.set_access_token(tokens['access_token_key'], tokens['access_token_secret'])\\n api = tweepy.API(auth)\\n\\n # listen for tweets\\n while True:\\n\\n # TODO: save file in Cloud Storage\\n file_name = date.today().strftime('corpus-%d-%m-%Y.json')\\n print(f'Updating {file_name} ...')\\n\\n StreamListener = StreamListener(\\n file_name=file_name, \\n max_tweets=1000)\\n myStream = tweepy.Stream(\\n auth=api.auth, \\n listener=StreamListener)\\n\\n myStream.filter(track=[keyword], languages=['en'])\\n \\n time.sleep(60)\",\n \"def count_tweets_based_on_words(word, positve_sentiment, negative_sentiment):\\n tweet_counter = dict()\\n index_db = couch[config.get(\\\"database\\\", \\\"DB_INDEX\\\")]\\n items = index_db.view(view_text_index)\\n try:\\n tweet_id_holder = items[word].rows[0].value\\n except Exception as e:\\n return tweet_counter\\n\\n tweets_all = tweet_database.view(view_tweet_info)\\n\\n for tweet_id in tweet_id_holder:\\n # check redundancy\\n if tweet_id not in tweet_index:\\n tweet = tweets_all[tweet_id]\\n tweet_index.add(tweet_id)\\n try:\\n # set polarity value\\n if negative_sentiment:\\n if tweet.rows[0].value[3] < 0:\\n tweet_counter = add_state_count(tweet, tweet_counter)\\n elif positve_sentiment:\\n if tweet.rows[0].value[3] > 0:\\n tweet_counter = add_state_count(tweet, tweet_counter)\\n else:\\n tweet_counter = add_state_count(tweet, tweet_counter)\\n except:\\n return tweet_counter\\n return tweet_counter\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status=tweet_text)\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status=tweet_text)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7105514","0.69805616","0.6976142","0.6756619","0.66342497","0.6593964","0.6430638","0.63529456","0.6340623","0.62740755","0.62429124","0.6205182","0.6183677","0.6167547","0.6147273","0.61250204","0.61221504","0.6089658","0.60785997","0.60453033","0.60328126","0.6029251","0.60261595","0.60087574","0.5991527","0.59838074","0.59719205","0.5946441","0.59409624","0.5901988","0.5882555","0.5872935","0.5870822","0.5865618","0.58497906","0.583118","0.5828577","0.58244383","0.5806111","0.57845414","0.57838005","0.5763564","0.57580787","0.57580644","0.57473165","0.5745074","0.57306445","0.5726625","0.57019067","0.57016426","0.5693697","0.569266","0.56919396","0.5679141","0.5671355","0.56394565","0.5630802","0.5625283","0.5622246","0.5611304","0.5595258","0.55843765","0.5573749","0.55650926","0.55615264","0.5559274","0.55541","0.5553588","0.5543743","0.55372363","0.5533929","0.552938","0.55270606","0.55155295","0.5508383","0.55027395","0.5501353","0.5486683","0.5486337","0.54835033","0.5482105","0.548139","0.5478951","0.5465309","0.5462483","0.5462483","0.54616445","0.5457476","0.5456575","0.5451683","0.5450243","0.5449731","0.5447835","0.5435238","0.5431336","0.542729","0.5425319","0.54235023","0.5423416","0.5422955","0.5422955"],"string":"[\n \"0.7105514\",\n \"0.69805616\",\n \"0.6976142\",\n \"0.6756619\",\n \"0.66342497\",\n \"0.6593964\",\n \"0.6430638\",\n \"0.63529456\",\n \"0.6340623\",\n \"0.62740755\",\n \"0.62429124\",\n \"0.6205182\",\n \"0.6183677\",\n \"0.6167547\",\n \"0.6147273\",\n \"0.61250204\",\n \"0.61221504\",\n \"0.6089658\",\n \"0.60785997\",\n \"0.60453033\",\n \"0.60328126\",\n \"0.6029251\",\n \"0.60261595\",\n \"0.60087574\",\n \"0.5991527\",\n \"0.59838074\",\n \"0.59719205\",\n \"0.5946441\",\n \"0.59409624\",\n \"0.5901988\",\n \"0.5882555\",\n \"0.5872935\",\n \"0.5870822\",\n \"0.5865618\",\n \"0.58497906\",\n \"0.583118\",\n \"0.5828577\",\n \"0.58244383\",\n \"0.5806111\",\n \"0.57845414\",\n \"0.57838005\",\n \"0.5763564\",\n \"0.57580787\",\n \"0.57580644\",\n \"0.57473165\",\n \"0.5745074\",\n \"0.57306445\",\n \"0.5726625\",\n \"0.57019067\",\n \"0.57016426\",\n \"0.5693697\",\n \"0.569266\",\n \"0.56919396\",\n \"0.5679141\",\n \"0.5671355\",\n \"0.56394565\",\n \"0.5630802\",\n \"0.5625283\",\n \"0.5622246\",\n \"0.5611304\",\n \"0.5595258\",\n \"0.55843765\",\n \"0.5573749\",\n \"0.55650926\",\n \"0.55615264\",\n \"0.5559274\",\n \"0.55541\",\n \"0.5553588\",\n \"0.5543743\",\n \"0.55372363\",\n \"0.5533929\",\n \"0.552938\",\n \"0.55270606\",\n \"0.55155295\",\n \"0.5508383\",\n \"0.55027395\",\n \"0.5501353\",\n \"0.5486683\",\n \"0.5486337\",\n \"0.54835033\",\n \"0.5482105\",\n \"0.548139\",\n \"0.5478951\",\n \"0.5465309\",\n \"0.5462483\",\n \"0.5462483\",\n \"0.54616445\",\n \"0.5457476\",\n \"0.5456575\",\n \"0.5451683\",\n \"0.5450243\",\n \"0.5449731\",\n \"0.5447835\",\n \"0.5435238\",\n \"0.5431336\",\n \"0.542729\",\n \"0.5425319\",\n \"0.54235023\",\n \"0.5423416\",\n \"0.5422955\",\n \"0.5422955\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":13782,"cells":{"query":{"kind":"string","value":"Returns list of users the bot has already followed."},"document":{"kind":"string","value":"def get_do_not_follow_list():\n\n # make sure the \"already followed\" file exists\n if not os.path.isfile(ALREADY_FOLLOWED_FILE):\n with open(ALREADY_FOLLOWED_FILE, \"w\") as out_file:\n out_file.write(\"\")\n\n # read in the list of user IDs that the bot has already followed in the\n # past\n do_not_follow = set()\n dnf_list = []\n with open(ALREADY_FOLLOWED_FILE) as in_file:\n for line in in_file:\n dnf_list.append(int(line))\n\n do_not_follow.update(set(dnf_list))\n del dnf_list\n\n return do_not_follow"},"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_follows(self):\n return [c.id for c in self.conf.follows]","def getFollowers():\n followers = []\n next_max_id = True\n while next_max_id:\n # first iteration hack\n if next_max_id is True:\n next_max_id = ''\n\n _ = GetInstagramAnswer.igApi.getUserFollowers(GetInstagramAnswer.igApi.username_id, maxid=next_max_id)\n followers.extend(GetInstagramAnswer.igApi.LastJson.get('users',[]))\n next_max_id = GetInstagramAnswer.igApi.LastJson.get('next_max_id','')\n return \"You have currently \"+str(len(followers))+\" Followers on Instagram.\"","def get_queryset(self, *args, **kwargs):\n following_username = self.kwargs.get(self.look_url_kwarg)\n following_users = FollowUser.objects.filter(\n following_username=following_username)\n\n return following_users","def getFollowers():\n\n cur, user_id = initialise(3)\n cur.execute(\"SELECT following FROM followers WHERE user = (SELECT username FROM users WHERE id = ?)\", [user_id])\n tempFollowers = cur.fetchall()\n followers = []\n for follower in tempFollowers:\n followers.append(follower[0])\n return followers","def _user_following_info(self, uid: int = 0) -> List[_InstagramUser]:\n # If no uid was specified, use the authenticated user's uid\n if uid == 0:\n uid = self.uid\n\n followings: List[Dict[str, Any]] = self.api.getTotalFollowings(uid)\n user_followings = list([_InstagramUser(x) for x in followings])\n return user_followings","def all_followers(twitter_dict, twitter_name): \r\n \r\n following_list = []\r\n for user in twitter_dict:\r\n f_list = twitter_dict[user]['following']\r\n if twitter_name in f_list:\r\n following_list.append(user) \r\n return following_list","def get_followed_profiles(self):\n return self.get_list_of_names(self.FOLLOWED_PROFILES)","def get_to_unfollow(self, to_unfollow, n_unfollow):\n if self.monitored_users:\n current_monitored = \\\n list(filter(lambda x: datetime.fromtimestamp(float(x['followDate'])) + timedelta(days=14)\n < datetime.now() and x['username'] not in self.white_list, self.monitored_users))\n to_unfollow.extend([u['user'] for u in current_monitored])\n\n if len(to_unfollow) < n_unfollow:\n if not self.self_followings:\n self.self_followings = self.get_followings()\n add_followings = [f['pk'] for f in self.self_followings.get('users', [])\n if f['username'] not in self.white_list]\n\n if add_followings:\n if len(add_followings) > n_unfollow - len(to_unfollow):\n to_unfollow.extend([random.choice(add_followings) for _ in range(n_unfollow - len(to_unfollow))])\n else:\n to_unfollow.extend(add_followings)\n else:\n to_unfollow = [random.choice(to_unfollow) for _ in range(n_unfollow)]\n\n return to_unfollow","def signed_up_friends(self):\n friends = self.twitter_oauth.friends\n if not friends:\n return [], []\n return friends, User.query.filter(\n User.username.in_(x.screen_name for x in friends))","def get_followers(user_id):\n return list(Backward.objects.filter(destination_id=user_id) \\\n .values_list('source_id', flat=True))","def followers(self):\n return self.data.get(\"followers\")","def all_followers (twitter_data, username):\n\n # initialize\n followers = []\n\n for key in twitter_data: # go through every username in twitter_data\n if username in twitter_data [key]['following']: # check each 'following'\n followers.append (key)\n\n followers.sort() # sort the list alphabetically for testing purposes\n return followers","def auto_follow_followers():\n\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n not_following_back = followers - following\n\n for user_id in not_following_back:\n try:\n t.friendships.create(user_id=user_id, follow=False)\n except Exception as e:\n print(\"error: %s\" % (str(e)))","def follow_following_followers(self):\n self.logger.log(\"starting follow_following_followers...\")\n follows_accounts = self.following\n random.shuffle(follows_accounts)\n for acc in follows_accounts:\n try:\n try:\n followw = perform_with_ran_delay(self.instagram.get_followers, acc, 150, 15,\n delayed=True)\n accountstofollow = followw[\"accounts\"]\n random.shuffle(accountstofollow)\n if len(accountstofollow) > 10:\n accountstofollow = accountstofollow[:10]\n for ac in accountstofollow:\n if not self.is_user_following(ac.identifier):\n self.add_following(ac.identifier)\n self.logger.log(\"following: {}\".format(ac.username))\n except Exception as e:\n print(e)\n self.logger.log(str(e))\n finally:\n sleep(3)","def resolve_following(self, info):\n user = info.context.user\n follow_request = FollowRequest.objects.filter(follower=user.id, pending=False)\n return [follow.following for follow in follow_request]","def resolve_followers(self, info):\n user = info.context.user\n follow_request = FollowRequest.objects.filter(following=user.id, pending=False)\n return [follow.follower for follow in follow_request]","def get_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\n query = f\"\"\"\n MATCH (:Person {{email: '{email}'}})-[:FOLLOWS]->(follower) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\n \"\"\"\n return tx.run(query)","def user_follow_users(self, ids=None, **kwargs):\n return self._put(\n API.MY_FOLLOWING.value, type=\"user\", ids=\",\".join(ids or []), **kwargs\n )","async def get_new_followers(self, bearer_token:str, user_id:str, after:str) -> list:\n\n headers = {\"Client-Id\": self.TWITCH_PARAMS['client_id'], \"Authorization\": f\"Bearer {bearer_token}\"}\n params = {\"to_id\": user_id, \"first\": 100}\n if after:\n params[\"after\"] = after\n output = []\n while True:\n async with self.bot.session.get(self.TWITCH_USER_FOLLOWS_URL, params=params, headers=headers) as r:\n data = await r.json()\n # self.logger.info(data)\n output.extend(data.get('data', list()))\n if len(data.get('data', list())) < 100:\n break\n params['after'] = data.get('pagination', {}).get('cursor', None)\n return output, data.get('pagination', {}).get('cursor', None)","def getFollowings(user_id, api):\n \n followers = []\n next_max_id = True\n while next_max_id:\n # first iteration hack\n if next_max_id is True:\n next_max_id = ''\n \n _ = api.getUserFollowings(user_id, maxid=next_max_id)\n followers.extend(api.LastJson.get('users', []))\n next_max_id = api.LastJson.get('next_max_id', '')\n return followers","def _user_follower_info(self, uid: int = 0) -> List[_InstagramUser]:\n # If no uid was specified, use the authenticated user's uid\n if uid == 0:\n uid = self.uid\n\n followers: List[Dict[str, Any]] = self.api.getTotalFollowers(uid)\n user_followers = list([_InstagramUser(x) for x in followers])\n return user_followers","def auto_unfollow_nonfollowers():\n\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n # put user IDs here that you want to keep following even if they don't\n # follow you back\n users_keep_following = set([])\n\n not_following_back = following - followers\n\n # make sure the \"already followed\" file exists\n if not os.path.isfile(ALREADY_FOLLOWED_FILE):\n with open(ALREADY_FOLLOWED_FILE, \"w\") as out_file:\n out_file.write(\"\")\n\n # update the \"already followed\" file with users who didn't follow back\n already_followed = set(not_following_back)\n af_list = []\n with open(ALREADY_FOLLOWED_FILE) as in_file:\n for line in in_file:\n af_list.append(int(line))\n\n already_followed.update(set(af_list))\n del af_list\n\n with open(ALREADY_FOLLOWED_FILE, \"w\") as out_file:\n for val in already_followed:\n out_file.write(str(val) + \"\\n\")\n\n for user_id in not_following_back:\n if user_id not in users_keep_following:\n t.friendships.destroy(user_id=user_id)\n print(\"unfollowed %d\" % (user_id))","def follows(self):\r\n return relationships.Follows(self)","def get_user_followers(self, text: str) -> List[str]:\n # Get all follower information\n followers: List[_InstagramUser] = self._user_follower_info()\n # Convert each folllower to just their name\n names: List[str] = list([x.username for x in followers])\n return names","def user_follow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.append(following)\n db.session.commit()\n return {'followed': True}","def getFollowerIDs(self, screen_name):\n follower_ids = []\n for follower_id in tweepy.Cursor(self.api.followers_ids,id=screen_name).items():\n print follower_id\n follower_ids.append(follower_id)\n return follower_ids","def follows(self):\n return relationships.Follows(self)","def get_all_followers(self):\n return get_all_(self.get_followers)","def getUserFollowers(user):\n sleep(5)\n first = user+\"communaute/\"\n driver.get(first)\n sleep(5)\n followers = set()\n followers_page = []\n div_links = []\n\n nb_follower_div = driver.find_element_by_xpath(\"//div[@class='inner-nav-item current ']\").is_displayed()\n if nb_follower_div:\n nb_follower_text = driver.find_element_by_xpath(\"//div[@class='inner-nav-item current ']\").text\n nb_follower = nb_follower_text.split(\"(\",2)[1].split(\")\",2)[0]\n nb_follower = int(nb_follower)\n print(\"NB Followers : \",nb_follower)\n\n pagination = driver.find_elements_by_xpath(\"//a[@class='xXx button button-md item']\")\n page_links = [elem.get_attribute('href') for elem in pagination]\n page_links.insert(0,first)\n\n if nb_follower > 0:\n for num in page_links:\n if page_links.index(num) > 0:\n driver.get(num)\n sleep(5)\n div_links = driver.find_elements_by_xpath(\"//a[@class='xXx']\")\n followers_page = [elem.get_attribute('href') for elem in div_links]\n for link in followers_page:\n if link_patterns in link:\n followers.add(link)\n return followers","def getFollowers(self, user_id, api):\n followers = []\n next_max_id = True\n while next_max_id:\n # first iteration hack\n if next_max_id is True:\n next_max_id = ''\n \n _ = api.getUserFollowers(user_id, maxid=next_max_id)\n followers.extend(api.LastJson.get('users', []))\n next_max_id = api.LastJson.get('next_max_id', '')\n return followers","def users_being_followed_tweets():\n username = request.authorization.username\n tweets = []\n\n user_id = get_user_id(username);\n tuples = query_db('''\n select message.*, user.* from message, user\n where message.author_id = user.user_id and (\n user.user_id = ? or\n user.user_id in (select whom_id from follower\n where who_id = ?))\n order by message.pub_date desc limit ?''',\n [user_id, user_id, PER_PAGE])\n\n for tuple in tuples:\n tweet = {}\n tweet[\"message_id\"] = tuple['message_id']\n tweet[\"author_id\"] = tuple['author_id']\n tweet[\"text\"] = tuple['text']\n tweet[\"pub_date\"] = tuple['pub_date']\n tweet[\"username\"] = tuple['username']\n tweet[\"email\"] = tuple['email']\n tweets.append(tweet)\n\n return jsonify({'tweets': tweets}), 200","def test_get_list_of_following_users_without_auth(self):\n self.authorize_user(self.user)\n with self.settings(\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\n self.register_user(self.user1)\n response = self.client.get(self.following_list_url)\n self.assertEqual(response.content,\n b'{\"following\": []}')\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def find_users_by_rel(self, rel):\n userlist = []\n for r in graph.match(end_node=self.usergroup_node, rel_type=rel):\n userlist.append(User(r.start_node()['username']))\n return userlist","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def is_following(self, you, them):\n if self.filter(from_user=you, to_user=them).count() > 0:\n return True\n return False","def get_posts_of_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\n query = f\"\"\"\n MATCH (:Person {{email: '{email}'}})\n -[:FOLLOWS]->(user:Person)\n -[:POSTED]->(post:Post)\n RETURN DISTINCT {{content:post.content, modified:post.modified, created:post.created, uuid:post.uuid, user_email:user.email}} AS posts\"\"\"\n return tx.run(query)","def get_following(user_id):\n return list(Forward.objects.filter(source_id=user_id).values_list(\n 'destination_id', flat=True))","def bots(self) -> Generator[discord.User, None, None]:\n for user in self.users:\n if user.bot:\n yield user","def followers(self):\r\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\r\n\r\n return request, parsers.parse_json","def followers(self):\r\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\r\n\r\n return request, parsers.parse_json","def followers(self):\r\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\r\n\r\n return request, parsers.parse_json","def followed_by(self):\r\n return relationships.FollowedBy(self)","def userFollowers(nick):\n if (len(nick) != 1):\n print \"Has d'introduir només un nick\"\n return\n i.userFollow(nick[0])","def is_follower(self, you, them):\n if self.filter(from_user=them, to_user=you).count() > 0:\n return True\n return False","def add_untracked_followers(self):\n\n self.log.debug(\"CHECK FOR UNTRACKED FOLLOWERS\")\n followers_ids_api = self.api.followers_ids()\n target = Target.objects.filter(hunter=self.user)\\\n .filter(status=Target.FOLLOWER)\n followers_ids_django = [t.hunted.twitter_id for t in target]\n\n untracked_followers_ids = filter(\n lambda x: unicode(x) not in followers_ids_django,\n followers_ids_api)\n\n untracked_followers, remainder = lookup_users_by_id(self.api,\n untracked_followers_ids)\n for untracked_follower in untracked_followers:\n twitter_account, created = \\\n utils.get_or_create_twitter_account(untracked_follower)\n target, created = Target.objects.get_or_create(\n hunter=self.user, hunted=twitter_account)\n if target.status == Target.PURGATORY:\n # Yay someone we targeted reciprocated follow\n self.follow_reciprocated(target)\n else:\n print target.status\n # Either a totally external follow, an ingrate changed mind,\n # or someone who we chatted became interested and followed\n # Either way the action is the same, follow him\n target.status = Target.FOLLOWER\n target.save()\n self.log.debug(\" => Add follower: %s\" % twitter_account.screen_name)","def generateFollowers(self):\n for f in self._genericGenerator(self.getFollowers):\n yield f","def followed_by(self):\n return relationships.FollowedBy(self)","def follow_followers(self):\n self.logger.log(\"starting follow_followers...\")\n follow = perform_with_ran_delay(self.instagram.get_followers, self.account.identifier, 150, 15, delayed=True)\n for acc in follow[\"accounts\"]:\n try:\n try:\n # print(\"{} follows me, do I follow him ? > {} \".format(acc.username,self.is_user_following(acc.identifier)))\n if not self.is_user_following(acc.identifier):\n if self.add_following(acc.identifier):\n self.logger.log(\"following: {}\".format(acc.username))\n else:\n self.logger.log(\"follow not working at the moment\")\n except Exception as e:\n print(e)\n self.logger.log(str(e))\n continue\n finally:\n sleep(3)","def get_friends(user_id):\n return list(set(get_following(user_id)) &\n set(get_followers(user_id)))","def select_user_following(args):\n is_parameter_exists([\n constants.ID\n ], args)\n\n # Request User\n request_user = args[constants.USER]\n\n # Requested User ID\n requested_user_id = args[constants.ID]\n\n # Page Number\n page_number = 1 if constants.PAGE_NUMBER not in args else int(args[constants.PAGE_NUMBER])\n\n # Check User Id\n if not User.objects.filter(id=requested_user_id).exists():\n raise ApiError(constants.NOT_EXIST_OBJECT)\n\n # Following QuerySet\n queryset = UserFollow.objects.filter(following_user=requested_user_id).values_list('followed_user', flat=True)\n\n # User Ids\n user_ids = get_results_from_queryset(queryset, 10, page_number)\n\n # is_finished\n is_finished = not user_ids.has_next()\n\n # Filter Query\n filter_query = Q(id__in=user_ids)\n\n # Users\n users, _, _ = __get_users(filter_query, request_user, 10)\n\n return users, page_number, is_finished","def main_action(self, follow) -> set:\n dr = self.driver\n to_collect = self.to_collect\n\n prev_users_found = 0\n count = 0\n while count < 3 and prev_users_found < self.total_users:\n user_els = dr.find_elements_by_xpath(\"//a[@title]\")\n\n for i, user in enumerate(user_els):\n f_username = user.get_attribute(\"title\")\n self.users_found.add(f_username)\n\n if follow:\n self.click_follow_users()\n self.check_users_followed()\n\n n_users_found = len(self.users_found)\n logging.info(f\"{n_users_found} {to_collect} found out of {self.total_users} from {self.context.name}\")\n # TODO: better logging progress (enlighten)\n\n if n_users_found == prev_users_found:\n count += 1\n else:\n count = 0\n\n if self.total_users + 2 > 5:\n self.check_modal()\n scroll(dr, down=True, intensity=2)\n prev_users_found = n_users_found\n sleep_random()\n else:\n break\n\n logging.info(f\"All {to_collect} from {self.context.name} collected successfully\")\n return self.users_found","def follow_user(cls, user, following):\r\n pass","def find_new_followers(self):\n api = self.api\n geocode = self.geocode\n queries = self.queries\n hits_per_query = self.hits_per_query\n\n self.log.debug(\"Initialize\")\n self.log.debug(\"[ ********* FIND NEW FOLLOWERS *********** ]\")\n\n if self.strategy == UserProfile.FOLLOW or self.strategy == UserProfile.TWEET:\n # Find statuses that match our interests\n self.log.debug(\"Strategy set to FOLLOW or TWEET\")\n n = hits_per_query\n search_dict = dict()\n search_dict['lang'] = \"en\"\n if not geocode is None:\n search_dict['geocode'] = geocode\n statuses = list()\n self.log.debug(\"Queries:\")\n for q in queries:\n search_dict['q'] = q\n results = [c for c in Cursor(api.search, **search_dict).items(n)]\n self.log.debug(\" => %s: %s hits\" % (q, len(results)))\n statuses.extend(results)\n #self.log.debug(\"Statuses: %s\" % \"\\n\".join([str(s.__dict__) for s in statuses]))\n # Get all the screen names of senders and receivers\n screen_names = ([t.from_user for t in statuses] +\n [t.to_user for t in statuses if t.to_user])\n\n # Convert the strings to Tweepy user objects\n users, remainder = lookup_users_by_screen_name(self.api, screen_names)\n\n elif self.strategy == UserProfile.STEAL:\n users = []\n stolen_from = {}\n for competitor in list(self.competitors):\n self.log.debug(\"[ ********* STEAL %s *********** ]\" % competitor)\n try:\n competitor_friends_ids = self.api.friends_ids(competitor)\n competitor_followers_ids = self.api.followers_ids(competitor)\n\n filter_known_users_to_reduce_api_hits = False\n\n if filter_known_users_to_reduce_api_hits is True:\n new_competitor_friends_ids = [id for id in competitor_friends_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n old_competitor_friends_ids = [id for id in competitor_friends_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n new_competitor_followers_ids = [id for id in competitor_followers_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n old_competitor_followers_ids = [id for id in competitor_followers_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n # print new_competitor_friends_ids\n # print old_competitor_friends_ids\n # print new_competitor_followers_ids\n # print old_competitor_followers_ids\n\n print \"start lookups\"\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, new_competitor_friends_ids)\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, new_competitor_followers_ids)\n print \"end lookups\"\n else:\n # get all the tweepy users\n print \"start lookups\"\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, competitor_friends_ids)\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, competitor_followers_ids)\n print \"end lookups\"\n\n print \"%s has %s friends\" % (competitor, len(new_competitor_friends))\n print \"%s has %s followers\" % (competitor, len(new_competitor_followers))\n\n # holy crap this is so fucked up i'm ashamed that this code is getting written like this!\n\n for u in new_competitor_friends + new_competitor_followers:\n stolen_from.update({u.screen_name.lower(): competitor})\n\n except Exception, e:\n print e\n # didn't get all the users, don't remove the competitor\n # from the competitor list\n pass\n else:\n # got all the competitors friends and followers and converted them\n # to tweepy users.\n users += new_competitor_friends\n users += new_competitor_followers\n # add them to the users list to be processed in the next block (for user in users)\n # then pop the name off the competitors list in the UserProfile\n # fuck it for now i'm going to just cycle the item to the bottom of the competitor list so we can start getting maximal coverage within api constraints by making sure the top person is new every time\n self.competitors.append(self.competitors.pop(0))\n # return # for now\n self.profile.competitors = \"\\r\\n\".join(self.competitors)\n self.profile.save()\n\n # use the profile competitors list\n # for each name in competitors list\n # add all friends\n\n # should filter out garbage users. something like:\n users = [u for u in users if not Target.objects.filter(hunter=self.user, hunted__screen_name=u.screen_name.lower())]\n\n for user in users:\n twitter_account, created = utils.get_or_create_twitter_account(user)\n target, created = Target.objects.get_or_create(\n hunter=self.user, hunted=twitter_account)\n print target.hunted.screen_name, created\n if created:\n try:\n screen_name = user.screen_name.lower()\n match = lambda x: screen_name in \\\n (x.from_user.lower(), x.to_user and x.to_user.lower())\n if not self.strategy == UserProfile.STEAL:\n trigger_tweet = filter(match, statuses)[0].text\n else:\n try:\n trigger_tweet = \"Steal from user: %s\" % stolen_from.get(screen_name.lower(), \"someone. i lost it. sorry.\")\n except Exception, e:\n print \"YUCK. ERRORS.\"\n print \"YUCK. ERRORS.\"\n print e\n print e\n except Exception, e:\n self.log.exception(\"Could not get trigger tweet for %s\" %\n user.screen_name.lower())\n trigger_tweet = \"Error: Couldn't retrieve tweet.\"\n self.log.debug(\"Saved twitter account %s (trigger: %r)\" %\n (twitter_account.screen_name,\n trigger_tweet[:50]))\n target.reason = trigger_tweet\n target.status = Target.ON_DECK\n target.save()\n else:\n pass\n # print \" - Previously followed this dudesicle: %s\" % user.screen_name","def _get_followed(_user_id, _ignore_exceptions=False, _start_index=0, _count=100):\n _following_url = f\"{base_url}/people/{_user_id}/@following?count={_count}\" + \\\n f\"&startIndex={_start_index}\"\n _response = core.get_request_with_retries(_following_url)\n if _response.status_code == 200:\n _following_data = _response.json()\n else:\n if _ignore_exceptions:\n _empty_response = {\"list\": []}\n _following_data = core_utils.convert_dict_to_json(_empty_response)\n else:\n if _response.status_code == 404:\n raise errors.exceptions.UserNotFoundError()\n else:\n raise errors.exceptions.UserQueryError()\n return _following_data","def getTotalFollowers(api, user_id):\n\n followers_ = []\n next_max_id = True\n while next_max_id:\n # first iteration hack\n if next_max_id is True:\n next_max_id = ''\n\n _ = api.getUserFollowers(user_id, maxid=next_max_id)\n followers_.extend(api.LastJson.get('users', []))\n next_max_id = api.LastJson.get('next_max_id', '')\n return followers_","def auto_mute_following():\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n not_muted = following - muted\n\n # put user IDs of people you do not want to mute here\n users_keep_unmuted = set([])\n \n # mute all \n for user_id in not_muted:\n if user_id not in users_keep_unmuted:\n t.mutes.users.create(user_id=user_id)\n print(\"muted %d\" % (user_id))","def users(self) -> List[str]:\n return self._users","def get_followers(api, screen_name, follower_limit=500):\n user_followers = []\n for page in tweepy.Cursor(api.followers_ids, screen_name=screen_name).pages():\n user_followers.extend(page)\n print(\"Got {} followers\".format(str(len(page))))\n if len(user_followers) > follower_limit:\n break\n time.sleep(4)\n user_followers = user_followers[0:follower_limit]\n return user_followers","def follows_target_check(twitter,top_followers_list):\n yes_follow_list = []\n not_follow_list = []\n following_dict = {}\n target = 'HillaryClinton'\n \n for user in top_followers_list:\n params = {'source_id':user, 'target_screen_name':target}\n response = twitter.request('friendships/show', params)\n data = response.json()\n #print(\"DATAAA::\",data)\n if response.status_code == 200:\n #print(\"IN BIGG IFFFFF:::\")\n following_dict = data['relationship']['source']\n #print(\"following_dict::\",following_dict)\n check = following_dict['following']\n #print(\"check::\",check)\n if check:\n #print(\"IN IFFFFF:::\")\n yes_follow_list.append(user)\n \n else:\n #print(\"IN ELSEEEE:::\")\n not_follow_list.append(user)\n \n else:\n print('Got error %s \\nsleeping for 15 minutes.' % response.text)\n sys.stderr.flush()\n time.sleep(61 * 15)\n \n print(\"YES_LIST:::\",yes_follow_list) \n print(\"NO_LIST:::\",not_follow_list) \n return not_follow_list","def getTotalFollowers(api, user_id):\n\n followers = []\n next_max_id = True\n while next_max_id:\n # first iteration hack\n if next_max_id is True:\n next_max_id = ''\n\n _ = api.getUserFollowers(user_id, maxid=next_max_id)\n followers.extend(api.LastJson.get('users', []))\n next_max_id = api.LastJson.get('next_max_id', '')\n return followers","def user_following_gql(self, user_id: int, amount: int = 0) -> list:\n user_id = int(user_id)\n end_cursor = None\n users = []\n variables = {\n \"id\": user_id,\n \"include_reel\": True,\n \"fetch_mutual\": False,\n \"first\": 24\n }\n while True:\n if end_cursor:\n variables[\"after\"] = end_cursor\n data = self.public_graphql_request(\n variables, query_hash=\"e7e2f4da4b02303f74f0841279e52d76\"\n )\n if not data[\"user\"] and not users:\n raise UserNotFound(user_id=user_id, **data)\n page_info = json_value(\n data, \"user\", \"edge_follow\", \"page_info\", default={}\n )\n edges = json_value(\n data, \"user\", \"edge_follow\", \"edges\", default=[]\n )\n for edge in edges:\n users.append(extract_user_short(edge[\"node\"]))\n end_cursor = page_info.get(\"end_cursor\")\n if not page_info.get(\"has_next_page\") or not end_cursor:\n break\n if amount and len(users) >= amount:\n break\n # time.sleep(sleep)\n if amount:\n users = users[:amount]\n return users","def auto_follow_followers_for_user(user_screen_name, count=5):\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers_for_user = set(t.followers.ids(screen_name=user_screen_name)[\"ids\"][:count]);\n do_not_follow = get_do_not_follow_list()\n \n for user_id in followers_for_user:\n try:\n if (user_id not in following and \n user_id not in do_not_follow):\n\n t.friendships.create(user_id=user_id, follow=False)\n print(\"followed %s\" % user_id)\n\n except TwitterHTTPError as e:\n print(\"error: %s\" % (str(e)))","def follow_user(cls, user, following):\n pass","def get_followers(user):\n if user.has_key('followers_list'):\n pass\n else:\n if user.has_key('followers_count'):\n if user['followers_count'] > 4999:\n pages = user['followers_count'] / 5000\n f_list = []\n for page in range(pages):\n try:\n follower_set = api.GetFollowers(user_id=user['id'], cursor=page, count=5000)\n friends_list = []\n for follower in follower_set:\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\n friends_list.append(follower.GetId())\n f_list = friends_list + f_list\n time.sleep(60)\n user['followers_list'] = f_list\n twitter_users.update({'id': user['id']}, user)\n print \"\\n\\nGot %s followers out of %s listed\" % (len(f_list), user['followers_count'])\n except Exception, e:\n print str(e)\n time.sleep(60)\n else:\n try:\n follower_set = api.GetFollowers(user_id=user['id'], count=5000)\n friends_list = []\n for follower in follower_set:\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\n friends_list.append(follower.GetId())\n user['followers_list'] = friends_list\n twitter_users.update({'id': user['id']}, user)\n print \"\\n\\nGot %s followers out of %s listed\" % (len(friends_list), user['followers_count'])\n except Exception, e:\n print str(e)\n time.sleep(60)","def follow(self, user_index, following_index):\n if user_index >= self.num_users or following_index >= self.num_users:\n raise ValueError(\n f\"Number of users is {self.num_users}, but indices \"\n f\"{user_index} and {following_index} were requested.\"\n )\n if self.users_hat[following_index, user_index] == 0:\n self.users_hat[following_index, user_index] = 1\n elif self.is_verbose():\n self.log(f\"User {following_index} was already following user {user_index}\")","def get_followers_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\n query = f\"\"\"\n MATCH (follower)-[:FOLLOWS]->(:Person {{email: '{email}'}}) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\n \"\"\"\n return tx.run(query)","async def get_streams_followed():\n\tuserid, username, display_name, token = get_user(name=config[\"username\"])\n\theaders = {\n\t\t'Client-ID': config['twitch_clientid'],\n\t\t'Authorization': f\"Bearer {token}\",\n\t}\n\treturn [stream async for stream in get_paginated(\"https://api.twitch.tv/helix/streams/followed\", data={\"user_id\": userid}, headers=headers)]","def follow(user, people):\n api = get_api(user)\n current_screen_name = api.VerifyCredentials().GetScreenName()\n\n # don't let a user follow themselves\n screen_names = [person.twitter_screen_name for person in people]\n if current_screen_name in screen_names: screen_names.remove(current_screen_name)\n\n followed = []\n not_followed = []\n\n for screen_name in screen_names:\n try:\n api.CreateFriendship(screen_name=screen_name)\n followed.append(screen_name)\n except twitter.TwitterError:\n not_followed.append(screen_name)\n\n return 'followed %s people' % len(followed)","def followers(self, handles):\n print(handles)\n followers_list = {}\n for handle in handles:\n followers = self.twitter_client.followers_ids(screen_name=handle)\n\n r = []\n for page in self.paginate(followers, 100):\n results = self.twitter_client.lookup_users(user_ids=page)\n for result in results:\n r.append(result.screen_name)\n followers_list[handle] = r\n return followers_list","def doesfollow(user):\n return jsonify({\n 'follows': isFollowed(g.user,user)\n })","def is_following_by_username(self, id):\n return self.followed.filter(followers.c.followed_id == id).count() > 0","async def get_friends(self) -> List[User]:\n me = await self.get_self()\n r = await self.request.request(url=f'https://friends.roblox.com/v1/users/{me.id}/friends', method=\"GET\")\n data = r.json()\n friends = []\n for friend in data['data']:\n friends.append(User(self.request, friend['id'], friend['name']))\n return friends","def get_following_by_user(request):\n response, status_code = get_followings(request)\n if status_code != 200:\n return JsonResponse(response, status=status_code)\n serialize_data = FollowingSerializer(response, many=False).data\n return JsonResponse(serialize_data, status=status_code, safe=False)","def follower(account):\n stm = shared_morphene_instance()\n if mph.rpc is not None:\n mph.rpc.rpcconnect()\n if not account:\n if \"default_account\" in mph.config:\n account = [mph.config[\"default_account\"]]\n for a in account:\n a = Account(a, morphene_instance=stm)\n print(\"\\nFollowers statistics for @%s (please wait...)\" % a.name)\n followers = a.get_followers(False)\n followers.print_summarize_table(tag_type=\"Followers\")","def followed_by(self, user_id):\n\n url = \"https://api.instagram.com/v1/users/{0}/followed-by?access_token={1}\".format(user_id, self.access_token)\n\n request = requests.get(url)\n return request.json()","def user_following(username, max: int = None):\n for user_dict in client.user_relationships(username, max=max, type=\"following\"):\n print(json.dumps(user_dict))","def is_user_already_followed(followed_user_id, user_id):\n result = Follow.objects.filter(followed_user=followed_user_id,\n user=user_id).exists()\n return result","def followers(congressDict, twitterAPI):\n most = twitterAPI.get_user(list(congressDict.items())[0][1]) # Choose an arbitrary starting point from the dictionary and assign it their user details.\n least = most\n for name in congressDict:\n tempAPI = twitterAPI.get_user(congressDict[name]) # Get the user details of each congress members' twitter handle.\n numFollowers = tempAPI._json['followers_count']\n if (numFollowers > most._json['followers_count']): # If the follower count is greater than most, replace the user details with current one.\n most = tempAPI\n elif (numFollowers < least._json['followers_count']): # If the follower count is lower than least, replace the user details with the current one.\n least = tempAPI\n return [most._json[\"name\"], least._json[\"name\"]]","def _users_list(self):\n result = self.slack.api_call(\"users.list\", presence=0)\n\n if not result.get(\"ok\"):\n logging.error(result['error'])\n return None\n\n return result['members']","def follow(self, followerId: int, followeeId: int) -> None:\n self.user_followed[followerId].append(followeeId)","def user_ids(self):\n return list(self.get_users())","def follows():\n query = (db.session.query(db.Series)\n .filter_by(following=True)\n .order_by(db.Series.alias)\n .all())\n output.list([x.alias for x in query])","def get_friends(self):\n edges = DirectedUserToUserEdge.all().filter(\n 'owner_user_id =', self.key().id()).run()\n return db.get([db.Key.from_path('User', edge.friend_user_id) for edge in\n edges])","async def twitch_follower_checker_loop(self):\n\n new_last_timestamp = dt.utcnow()\n new_followers = collections.defaultdict(list) # channel user id: list of new follower usernames\n db = await self.bot.database.get_connection()\n data = await db(\"SELECT * FROM user_settings WHERE twitch_bearer_token IS NOT NULL\")\n\n # Wew let's do it\n for row in data:\n\n # See if we got their data already\n new_follower_list = new_followers.get(row['twitch_user_id'])\n if new_follower_list is None:\n new_follower_list, new_cursor_value = await self.get_new_followers(row['twitch_bearer_token'], row['twitch_user_id'], row['twitch_cursor'])\n if new_cursor_value:\n await db(\"UPDATE user_settings SET twitch_cursor=$1 WHERE twitch_user_id=$2\", new_cursor_value, row['twitch_user_id'])\n new_followers[row['twitch_user_id']] = new_follower_list\n\n # Update the follower timestamps into real timestamps\n # self.logger.info(new_follower_list)\n filtered_new_follower_list = [i for i in new_follower_list if dt.strptime(i['followed_at'], \"%Y-%m-%dT%H:%M:%SZ\") > self.last_twitch_checked]\n # self.logger.info(filtered_new_follower_list)\n\n # Send DM to the user\n if filtered_new_follower_list:\n discord_user = self.bot.get_user(row['user_id']) or await self.bot.fetch_user(row['user_id'])\n new_follower_string = ', '.join([f\"**{i['from_name']}**\" for i in filtered_new_follower_list])\n if len(new_follower_string) >= 1800:\n new_follower_string = \"\"\n try:\n await discord_user.send(f\"You have **{len(filtered_new_follower_list)}** new Twitch follower{'s' if len(filtered_new_follower_list) > 1 else ''}! {new_follower_string}\")\n except discord.HTTPException:\n pass\n\n # Update timestamp\n self.last_twitch_checked = new_last_timestamp\n await db.disconnect()","def follow(request, usertofollow):\n to_follow = Member.objects.get(user__username=usertofollow)\n user = Member.objects.get(user=request.user)\n user.following.add(to_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","def _get_users_list(self):\n return self.users['user_id'].tolist()","def by_follower_id(cls, follower_id, request):\n\t\treturn request.dbsession.query(Follow).filter_by(follower_id=follower_id).all()","def list_users_friends(self):\n user = self.sp.user(self.user)\n return user","def author_following(self):\n\t\tpass","def users(message):\n message.reply(Strings['USERS_FOUND'].format(len(hf.get_users())))","def get_user_list():\n users_tuple = db_session.query(Chat.chatID).all()\n users_list = [user for user, in users_tuple]\n return users_list","def get_new_followers(self):\n sleeper()\n\n if self.api.get_news_inbox():\n news = self.api.last_json\n if news.get('counts', {}).get('relationships') > 0:\n stories = news.get('new_stories', []) + news.get('old_stories', [])\n stories = list(filter(lambda x: x['story_type']==101 and x['args']['timestamp'] > self.last_ts, stories))\n new_followers = []\n for s in stories:\n args = s.get('args', {})\n if args.get('profile_id') and args.get('profile_id') not in new_followers:\n new_followers.append(args.get('profile_id'))\n if args.get('second_profile_id') and args.get('second_profile_id') not in new_followers:\n new_followers.append(args.get('second_profile_id'))\n return new_followers\n else:\n return []","def followed_by_hillary_and_donald(users, twitter):\n ###TODO-- Completed\n for user in users:\n if user['screen_name'] == 'HillaryClinton':\n friends_Hillary = user['friends']\n #print(len(friends_Hillary))\n elif user['screen_name'] == 'realDonaldTrump':\n friends_donald = user['friends']\n #print(len(friends_donald))\n\n common_followed_id = list(set(friends_Hillary) & set(friends_donald))\n\n commn_followed_user = robust_request(twitter,'users/lookup',{'user_id':common_followed_id}).json()\n #print(commn_followed_user[0]['screen_name'])#['screen_name'])\n return commn_followed_user[0]['screen_name']\n #pass","def list( self, mess, args):\n user = self.get_sender_username(mess)\n args = args.replace(' ', '_')\n if user in self.users:\n user_list = 'All these users are subscribed - \\n'\n user_list += '\\n'.join(['%s :: %s' %(u, self.users[u]) for u in sorted(self.users)])\n if self.invited.keys():\n user_list += '\\n The following users are invited - \\n'\n user_list += '\\n'.join(self.invited.keys())\n self.log.info( '%s checks list of users.' % user)\n return user_list","def get_followings(request):\n user_id = request.GET.get(\"user_id\")\n if not user_id:\n return {\"error\": \"User Id should be provided\"}, 400\n following_data = Following.objects.filter(user_profile_id=user_id, is_active=True).first()\n return following_data, 200","def generateFollowings(self):\n for f in self._genericGenerator(self.getFollowings):\n yield f","def test_get_list_of_following_users_with_auth(self):\n with self.settings(\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\n self.authorize_user(self.user)\n self.register_user(self.user1)\n self.client.post(self.follow_url, format='json')\n response = self.client.get(self.following_list_url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)"],"string":"[\n \"def get_follows(self):\\n return [c.id for c in self.conf.follows]\",\n \"def getFollowers():\\n followers = []\\n next_max_id = True\\n while next_max_id:\\n # first iteration hack\\n if next_max_id is True:\\n next_max_id = ''\\n\\n _ = GetInstagramAnswer.igApi.getUserFollowers(GetInstagramAnswer.igApi.username_id, maxid=next_max_id)\\n followers.extend(GetInstagramAnswer.igApi.LastJson.get('users',[]))\\n next_max_id = GetInstagramAnswer.igApi.LastJson.get('next_max_id','')\\n return \\\"You have currently \\\"+str(len(followers))+\\\" Followers on Instagram.\\\"\",\n \"def get_queryset(self, *args, **kwargs):\\n following_username = self.kwargs.get(self.look_url_kwarg)\\n following_users = FollowUser.objects.filter(\\n following_username=following_username)\\n\\n return following_users\",\n \"def getFollowers():\\n\\n cur, user_id = initialise(3)\\n cur.execute(\\\"SELECT following FROM followers WHERE user = (SELECT username FROM users WHERE id = ?)\\\", [user_id])\\n tempFollowers = cur.fetchall()\\n followers = []\\n for follower in tempFollowers:\\n followers.append(follower[0])\\n return followers\",\n \"def _user_following_info(self, uid: int = 0) -> List[_InstagramUser]:\\n # If no uid was specified, use the authenticated user's uid\\n if uid == 0:\\n uid = self.uid\\n\\n followings: List[Dict[str, Any]] = self.api.getTotalFollowings(uid)\\n user_followings = list([_InstagramUser(x) for x in followings])\\n return user_followings\",\n \"def all_followers(twitter_dict, twitter_name): \\r\\n \\r\\n following_list = []\\r\\n for user in twitter_dict:\\r\\n f_list = twitter_dict[user]['following']\\r\\n if twitter_name in f_list:\\r\\n following_list.append(user) \\r\\n return following_list\",\n \"def get_followed_profiles(self):\\n return self.get_list_of_names(self.FOLLOWED_PROFILES)\",\n \"def get_to_unfollow(self, to_unfollow, n_unfollow):\\n if self.monitored_users:\\n current_monitored = \\\\\\n list(filter(lambda x: datetime.fromtimestamp(float(x['followDate'])) + timedelta(days=14)\\n < datetime.now() and x['username'] not in self.white_list, self.monitored_users))\\n to_unfollow.extend([u['user'] for u in current_monitored])\\n\\n if len(to_unfollow) < n_unfollow:\\n if not self.self_followings:\\n self.self_followings = self.get_followings()\\n add_followings = [f['pk'] for f in self.self_followings.get('users', [])\\n if f['username'] not in self.white_list]\\n\\n if add_followings:\\n if len(add_followings) > n_unfollow - len(to_unfollow):\\n to_unfollow.extend([random.choice(add_followings) for _ in range(n_unfollow - len(to_unfollow))])\\n else:\\n to_unfollow.extend(add_followings)\\n else:\\n to_unfollow = [random.choice(to_unfollow) for _ in range(n_unfollow)]\\n\\n return to_unfollow\",\n \"def signed_up_friends(self):\\n friends = self.twitter_oauth.friends\\n if not friends:\\n return [], []\\n return friends, User.query.filter(\\n User.username.in_(x.screen_name for x in friends))\",\n \"def get_followers(user_id):\\n return list(Backward.objects.filter(destination_id=user_id) \\\\\\n .values_list('source_id', flat=True))\",\n \"def followers(self):\\n return self.data.get(\\\"followers\\\")\",\n \"def all_followers (twitter_data, username):\\n\\n # initialize\\n followers = []\\n\\n for key in twitter_data: # go through every username in twitter_data\\n if username in twitter_data [key]['following']: # check each 'following'\\n followers.append (key)\\n\\n followers.sort() # sort the list alphabetically for testing purposes\\n return followers\",\n \"def auto_follow_followers():\\n\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n not_following_back = followers - following\\n\\n for user_id in not_following_back:\\n try:\\n t.friendships.create(user_id=user_id, follow=False)\\n except Exception as e:\\n print(\\\"error: %s\\\" % (str(e)))\",\n \"def follow_following_followers(self):\\n self.logger.log(\\\"starting follow_following_followers...\\\")\\n follows_accounts = self.following\\n random.shuffle(follows_accounts)\\n for acc in follows_accounts:\\n try:\\n try:\\n followw = perform_with_ran_delay(self.instagram.get_followers, acc, 150, 15,\\n delayed=True)\\n accountstofollow = followw[\\\"accounts\\\"]\\n random.shuffle(accountstofollow)\\n if len(accountstofollow) > 10:\\n accountstofollow = accountstofollow[:10]\\n for ac in accountstofollow:\\n if not self.is_user_following(ac.identifier):\\n self.add_following(ac.identifier)\\n self.logger.log(\\\"following: {}\\\".format(ac.username))\\n except Exception as e:\\n print(e)\\n self.logger.log(str(e))\\n finally:\\n sleep(3)\",\n \"def resolve_following(self, info):\\n user = info.context.user\\n follow_request = FollowRequest.objects.filter(follower=user.id, pending=False)\\n return [follow.following for follow in follow_request]\",\n \"def resolve_followers(self, info):\\n user = info.context.user\\n follow_request = FollowRequest.objects.filter(following=user.id, pending=False)\\n return [follow.follower for follow in follow_request]\",\n \"def get_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\\n query = f\\\"\\\"\\\"\\n MATCH (:Person {{email: '{email}'}})-[:FOLLOWS]->(follower) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\\n \\\"\\\"\\\"\\n return tx.run(query)\",\n \"def user_follow_users(self, ids=None, **kwargs):\\n return self._put(\\n API.MY_FOLLOWING.value, type=\\\"user\\\", ids=\\\",\\\".join(ids or []), **kwargs\\n )\",\n \"async def get_new_followers(self, bearer_token:str, user_id:str, after:str) -> list:\\n\\n headers = {\\\"Client-Id\\\": self.TWITCH_PARAMS['client_id'], \\\"Authorization\\\": f\\\"Bearer {bearer_token}\\\"}\\n params = {\\\"to_id\\\": user_id, \\\"first\\\": 100}\\n if after:\\n params[\\\"after\\\"] = after\\n output = []\\n while True:\\n async with self.bot.session.get(self.TWITCH_USER_FOLLOWS_URL, params=params, headers=headers) as r:\\n data = await r.json()\\n # self.logger.info(data)\\n output.extend(data.get('data', list()))\\n if len(data.get('data', list())) < 100:\\n break\\n params['after'] = data.get('pagination', {}).get('cursor', None)\\n return output, data.get('pagination', {}).get('cursor', None)\",\n \"def getFollowings(user_id, api):\\n \\n followers = []\\n next_max_id = True\\n while next_max_id:\\n # first iteration hack\\n if next_max_id is True:\\n next_max_id = ''\\n \\n _ = api.getUserFollowings(user_id, maxid=next_max_id)\\n followers.extend(api.LastJson.get('users', []))\\n next_max_id = api.LastJson.get('next_max_id', '')\\n return followers\",\n \"def _user_follower_info(self, uid: int = 0) -> List[_InstagramUser]:\\n # If no uid was specified, use the authenticated user's uid\\n if uid == 0:\\n uid = self.uid\\n\\n followers: List[Dict[str, Any]] = self.api.getTotalFollowers(uid)\\n user_followers = list([_InstagramUser(x) for x in followers])\\n return user_followers\",\n \"def auto_unfollow_nonfollowers():\\n\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n # put user IDs here that you want to keep following even if they don't\\n # follow you back\\n users_keep_following = set([])\\n\\n not_following_back = following - followers\\n\\n # make sure the \\\"already followed\\\" file exists\\n if not os.path.isfile(ALREADY_FOLLOWED_FILE):\\n with open(ALREADY_FOLLOWED_FILE, \\\"w\\\") as out_file:\\n out_file.write(\\\"\\\")\\n\\n # update the \\\"already followed\\\" file with users who didn't follow back\\n already_followed = set(not_following_back)\\n af_list = []\\n with open(ALREADY_FOLLOWED_FILE) as in_file:\\n for line in in_file:\\n af_list.append(int(line))\\n\\n already_followed.update(set(af_list))\\n del af_list\\n\\n with open(ALREADY_FOLLOWED_FILE, \\\"w\\\") as out_file:\\n for val in already_followed:\\n out_file.write(str(val) + \\\"\\\\n\\\")\\n\\n for user_id in not_following_back:\\n if user_id not in users_keep_following:\\n t.friendships.destroy(user_id=user_id)\\n print(\\\"unfollowed %d\\\" % (user_id))\",\n \"def follows(self):\\r\\n return relationships.Follows(self)\",\n \"def get_user_followers(self, text: str) -> List[str]:\\n # Get all follower information\\n followers: List[_InstagramUser] = self._user_follower_info()\\n # Convert each folllower to just their name\\n names: List[str] = list([x.username for x in followers])\\n return names\",\n \"def user_follow():\\n data = request.get_json(force=True)\\n follower = User.query.get(data['follower'])\\n following = User.query.get(data['following'])\\n follower.followcheck.append(following)\\n db.session.commit()\\n return {'followed': True}\",\n \"def getFollowerIDs(self, screen_name):\\n follower_ids = []\\n for follower_id in tweepy.Cursor(self.api.followers_ids,id=screen_name).items():\\n print follower_id\\n follower_ids.append(follower_id)\\n return follower_ids\",\n \"def follows(self):\\n return relationships.Follows(self)\",\n \"def get_all_followers(self):\\n return get_all_(self.get_followers)\",\n \"def getUserFollowers(user):\\n sleep(5)\\n first = user+\\\"communaute/\\\"\\n driver.get(first)\\n sleep(5)\\n followers = set()\\n followers_page = []\\n div_links = []\\n\\n nb_follower_div = driver.find_element_by_xpath(\\\"//div[@class='inner-nav-item current ']\\\").is_displayed()\\n if nb_follower_div:\\n nb_follower_text = driver.find_element_by_xpath(\\\"//div[@class='inner-nav-item current ']\\\").text\\n nb_follower = nb_follower_text.split(\\\"(\\\",2)[1].split(\\\")\\\",2)[0]\\n nb_follower = int(nb_follower)\\n print(\\\"NB Followers : \\\",nb_follower)\\n\\n pagination = driver.find_elements_by_xpath(\\\"//a[@class='xXx button button-md item']\\\")\\n page_links = [elem.get_attribute('href') for elem in pagination]\\n page_links.insert(0,first)\\n\\n if nb_follower > 0:\\n for num in page_links:\\n if page_links.index(num) > 0:\\n driver.get(num)\\n sleep(5)\\n div_links = driver.find_elements_by_xpath(\\\"//a[@class='xXx']\\\")\\n followers_page = [elem.get_attribute('href') for elem in div_links]\\n for link in followers_page:\\n if link_patterns in link:\\n followers.add(link)\\n return followers\",\n \"def getFollowers(self, user_id, api):\\n followers = []\\n next_max_id = True\\n while next_max_id:\\n # first iteration hack\\n if next_max_id is True:\\n next_max_id = ''\\n \\n _ = api.getUserFollowers(user_id, maxid=next_max_id)\\n followers.extend(api.LastJson.get('users', []))\\n next_max_id = api.LastJson.get('next_max_id', '')\\n return followers\",\n \"def users_being_followed_tweets():\\n username = request.authorization.username\\n tweets = []\\n\\n user_id = get_user_id(username);\\n tuples = query_db('''\\n select message.*, user.* from message, user\\n where message.author_id = user.user_id and (\\n user.user_id = ? or\\n user.user_id in (select whom_id from follower\\n where who_id = ?))\\n order by message.pub_date desc limit ?''',\\n [user_id, user_id, PER_PAGE])\\n\\n for tuple in tuples:\\n tweet = {}\\n tweet[\\\"message_id\\\"] = tuple['message_id']\\n tweet[\\\"author_id\\\"] = tuple['author_id']\\n tweet[\\\"text\\\"] = tuple['text']\\n tweet[\\\"pub_date\\\"] = tuple['pub_date']\\n tweet[\\\"username\\\"] = tuple['username']\\n tweet[\\\"email\\\"] = tuple['email']\\n tweets.append(tweet)\\n\\n return jsonify({'tweets': tweets}), 200\",\n \"def test_get_list_of_following_users_without_auth(self):\\n self.authorize_user(self.user)\\n with self.settings(\\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\\n self.register_user(self.user1)\\n response = self.client.get(self.following_list_url)\\n self.assertEqual(response.content,\\n b'{\\\"following\\\": []}')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def find_users_by_rel(self, rel):\\n userlist = []\\n for r in graph.match(end_node=self.usergroup_node, rel_type=rel):\\n userlist.append(User(r.start_node()['username']))\\n return userlist\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def is_following(self, you, them):\\n if self.filter(from_user=you, to_user=them).count() > 0:\\n return True\\n return False\",\n \"def get_posts_of_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\\n query = f\\\"\\\"\\\"\\n MATCH (:Person {{email: '{email}'}})\\n -[:FOLLOWS]->(user:Person)\\n -[:POSTED]->(post:Post)\\n RETURN DISTINCT {{content:post.content, modified:post.modified, created:post.created, uuid:post.uuid, user_email:user.email}} AS posts\\\"\\\"\\\"\\n return tx.run(query)\",\n \"def get_following(user_id):\\n return list(Forward.objects.filter(source_id=user_id).values_list(\\n 'destination_id', flat=True))\",\n \"def bots(self) -> Generator[discord.User, None, None]:\\n for user in self.users:\\n if user.bot:\\n yield user\",\n \"def followers(self):\\r\\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def followers(self):\\r\\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def followers(self):\\r\\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def followed_by(self):\\r\\n return relationships.FollowedBy(self)\",\n \"def userFollowers(nick):\\n if (len(nick) != 1):\\n print \\\"Has d'introduir només un nick\\\"\\n return\\n i.userFollow(nick[0])\",\n \"def is_follower(self, you, them):\\n if self.filter(from_user=them, to_user=you).count() > 0:\\n return True\\n return False\",\n \"def add_untracked_followers(self):\\n\\n self.log.debug(\\\"CHECK FOR UNTRACKED FOLLOWERS\\\")\\n followers_ids_api = self.api.followers_ids()\\n target = Target.objects.filter(hunter=self.user)\\\\\\n .filter(status=Target.FOLLOWER)\\n followers_ids_django = [t.hunted.twitter_id for t in target]\\n\\n untracked_followers_ids = filter(\\n lambda x: unicode(x) not in followers_ids_django,\\n followers_ids_api)\\n\\n untracked_followers, remainder = lookup_users_by_id(self.api,\\n untracked_followers_ids)\\n for untracked_follower in untracked_followers:\\n twitter_account, created = \\\\\\n utils.get_or_create_twitter_account(untracked_follower)\\n target, created = Target.objects.get_or_create(\\n hunter=self.user, hunted=twitter_account)\\n if target.status == Target.PURGATORY:\\n # Yay someone we targeted reciprocated follow\\n self.follow_reciprocated(target)\\n else:\\n print target.status\\n # Either a totally external follow, an ingrate changed mind,\\n # or someone who we chatted became interested and followed\\n # Either way the action is the same, follow him\\n target.status = Target.FOLLOWER\\n target.save()\\n self.log.debug(\\\" => Add follower: %s\\\" % twitter_account.screen_name)\",\n \"def generateFollowers(self):\\n for f in self._genericGenerator(self.getFollowers):\\n yield f\",\n \"def followed_by(self):\\n return relationships.FollowedBy(self)\",\n \"def follow_followers(self):\\n self.logger.log(\\\"starting follow_followers...\\\")\\n follow = perform_with_ran_delay(self.instagram.get_followers, self.account.identifier, 150, 15, delayed=True)\\n for acc in follow[\\\"accounts\\\"]:\\n try:\\n try:\\n # print(\\\"{} follows me, do I follow him ? > {} \\\".format(acc.username,self.is_user_following(acc.identifier)))\\n if not self.is_user_following(acc.identifier):\\n if self.add_following(acc.identifier):\\n self.logger.log(\\\"following: {}\\\".format(acc.username))\\n else:\\n self.logger.log(\\\"follow not working at the moment\\\")\\n except Exception as e:\\n print(e)\\n self.logger.log(str(e))\\n continue\\n finally:\\n sleep(3)\",\n \"def get_friends(user_id):\\n return list(set(get_following(user_id)) &\\n set(get_followers(user_id)))\",\n \"def select_user_following(args):\\n is_parameter_exists([\\n constants.ID\\n ], args)\\n\\n # Request User\\n request_user = args[constants.USER]\\n\\n # Requested User ID\\n requested_user_id = args[constants.ID]\\n\\n # Page Number\\n page_number = 1 if constants.PAGE_NUMBER not in args else int(args[constants.PAGE_NUMBER])\\n\\n # Check User Id\\n if not User.objects.filter(id=requested_user_id).exists():\\n raise ApiError(constants.NOT_EXIST_OBJECT)\\n\\n # Following QuerySet\\n queryset = UserFollow.objects.filter(following_user=requested_user_id).values_list('followed_user', flat=True)\\n\\n # User Ids\\n user_ids = get_results_from_queryset(queryset, 10, page_number)\\n\\n # is_finished\\n is_finished = not user_ids.has_next()\\n\\n # Filter Query\\n filter_query = Q(id__in=user_ids)\\n\\n # Users\\n users, _, _ = __get_users(filter_query, request_user, 10)\\n\\n return users, page_number, is_finished\",\n \"def main_action(self, follow) -> set:\\n dr = self.driver\\n to_collect = self.to_collect\\n\\n prev_users_found = 0\\n count = 0\\n while count < 3 and prev_users_found < self.total_users:\\n user_els = dr.find_elements_by_xpath(\\\"//a[@title]\\\")\\n\\n for i, user in enumerate(user_els):\\n f_username = user.get_attribute(\\\"title\\\")\\n self.users_found.add(f_username)\\n\\n if follow:\\n self.click_follow_users()\\n self.check_users_followed()\\n\\n n_users_found = len(self.users_found)\\n logging.info(f\\\"{n_users_found} {to_collect} found out of {self.total_users} from {self.context.name}\\\")\\n # TODO: better logging progress (enlighten)\\n\\n if n_users_found == prev_users_found:\\n count += 1\\n else:\\n count = 0\\n\\n if self.total_users + 2 > 5:\\n self.check_modal()\\n scroll(dr, down=True, intensity=2)\\n prev_users_found = n_users_found\\n sleep_random()\\n else:\\n break\\n\\n logging.info(f\\\"All {to_collect} from {self.context.name} collected successfully\\\")\\n return self.users_found\",\n \"def follow_user(cls, user, following):\\r\\n pass\",\n \"def find_new_followers(self):\\n api = self.api\\n geocode = self.geocode\\n queries = self.queries\\n hits_per_query = self.hits_per_query\\n\\n self.log.debug(\\\"Initialize\\\")\\n self.log.debug(\\\"[ ********* FIND NEW FOLLOWERS *********** ]\\\")\\n\\n if self.strategy == UserProfile.FOLLOW or self.strategy == UserProfile.TWEET:\\n # Find statuses that match our interests\\n self.log.debug(\\\"Strategy set to FOLLOW or TWEET\\\")\\n n = hits_per_query\\n search_dict = dict()\\n search_dict['lang'] = \\\"en\\\"\\n if not geocode is None:\\n search_dict['geocode'] = geocode\\n statuses = list()\\n self.log.debug(\\\"Queries:\\\")\\n for q in queries:\\n search_dict['q'] = q\\n results = [c for c in Cursor(api.search, **search_dict).items(n)]\\n self.log.debug(\\\" => %s: %s hits\\\" % (q, len(results)))\\n statuses.extend(results)\\n #self.log.debug(\\\"Statuses: %s\\\" % \\\"\\\\n\\\".join([str(s.__dict__) for s in statuses]))\\n # Get all the screen names of senders and receivers\\n screen_names = ([t.from_user for t in statuses] +\\n [t.to_user for t in statuses if t.to_user])\\n\\n # Convert the strings to Tweepy user objects\\n users, remainder = lookup_users_by_screen_name(self.api, screen_names)\\n\\n elif self.strategy == UserProfile.STEAL:\\n users = []\\n stolen_from = {}\\n for competitor in list(self.competitors):\\n self.log.debug(\\\"[ ********* STEAL %s *********** ]\\\" % competitor)\\n try:\\n competitor_friends_ids = self.api.friends_ids(competitor)\\n competitor_followers_ids = self.api.followers_ids(competitor)\\n\\n filter_known_users_to_reduce_api_hits = False\\n\\n if filter_known_users_to_reduce_api_hits is True:\\n new_competitor_friends_ids = [id for id in competitor_friends_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n old_competitor_friends_ids = [id for id in competitor_friends_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n new_competitor_followers_ids = [id for id in competitor_followers_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n old_competitor_followers_ids = [id for id in competitor_followers_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n # print new_competitor_friends_ids\\n # print old_competitor_friends_ids\\n # print new_competitor_followers_ids\\n # print old_competitor_followers_ids\\n\\n print \\\"start lookups\\\"\\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, new_competitor_friends_ids)\\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, new_competitor_followers_ids)\\n print \\\"end lookups\\\"\\n else:\\n # get all the tweepy users\\n print \\\"start lookups\\\"\\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, competitor_friends_ids)\\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, competitor_followers_ids)\\n print \\\"end lookups\\\"\\n\\n print \\\"%s has %s friends\\\" % (competitor, len(new_competitor_friends))\\n print \\\"%s has %s followers\\\" % (competitor, len(new_competitor_followers))\\n\\n # holy crap this is so fucked up i'm ashamed that this code is getting written like this!\\n\\n for u in new_competitor_friends + new_competitor_followers:\\n stolen_from.update({u.screen_name.lower(): competitor})\\n\\n except Exception, e:\\n print e\\n # didn't get all the users, don't remove the competitor\\n # from the competitor list\\n pass\\n else:\\n # got all the competitors friends and followers and converted them\\n # to tweepy users.\\n users += new_competitor_friends\\n users += new_competitor_followers\\n # add them to the users list to be processed in the next block (for user in users)\\n # then pop the name off the competitors list in the UserProfile\\n # fuck it for now i'm going to just cycle the item to the bottom of the competitor list so we can start getting maximal coverage within api constraints by making sure the top person is new every time\\n self.competitors.append(self.competitors.pop(0))\\n # return # for now\\n self.profile.competitors = \\\"\\\\r\\\\n\\\".join(self.competitors)\\n self.profile.save()\\n\\n # use the profile competitors list\\n # for each name in competitors list\\n # add all friends\\n\\n # should filter out garbage users. something like:\\n users = [u for u in users if not Target.objects.filter(hunter=self.user, hunted__screen_name=u.screen_name.lower())]\\n\\n for user in users:\\n twitter_account, created = utils.get_or_create_twitter_account(user)\\n target, created = Target.objects.get_or_create(\\n hunter=self.user, hunted=twitter_account)\\n print target.hunted.screen_name, created\\n if created:\\n try:\\n screen_name = user.screen_name.lower()\\n match = lambda x: screen_name in \\\\\\n (x.from_user.lower(), x.to_user and x.to_user.lower())\\n if not self.strategy == UserProfile.STEAL:\\n trigger_tweet = filter(match, statuses)[0].text\\n else:\\n try:\\n trigger_tweet = \\\"Steal from user: %s\\\" % stolen_from.get(screen_name.lower(), \\\"someone. i lost it. sorry.\\\")\\n except Exception, e:\\n print \\\"YUCK. ERRORS.\\\"\\n print \\\"YUCK. ERRORS.\\\"\\n print e\\n print e\\n except Exception, e:\\n self.log.exception(\\\"Could not get trigger tweet for %s\\\" %\\n user.screen_name.lower())\\n trigger_tweet = \\\"Error: Couldn't retrieve tweet.\\\"\\n self.log.debug(\\\"Saved twitter account %s (trigger: %r)\\\" %\\n (twitter_account.screen_name,\\n trigger_tweet[:50]))\\n target.reason = trigger_tweet\\n target.status = Target.ON_DECK\\n target.save()\\n else:\\n pass\\n # print \\\" - Previously followed this dudesicle: %s\\\" % user.screen_name\",\n \"def _get_followed(_user_id, _ignore_exceptions=False, _start_index=0, _count=100):\\n _following_url = f\\\"{base_url}/people/{_user_id}/@following?count={_count}\\\" + \\\\\\n f\\\"&startIndex={_start_index}\\\"\\n _response = core.get_request_with_retries(_following_url)\\n if _response.status_code == 200:\\n _following_data = _response.json()\\n else:\\n if _ignore_exceptions:\\n _empty_response = {\\\"list\\\": []}\\n _following_data = core_utils.convert_dict_to_json(_empty_response)\\n else:\\n if _response.status_code == 404:\\n raise errors.exceptions.UserNotFoundError()\\n else:\\n raise errors.exceptions.UserQueryError()\\n return _following_data\",\n \"def getTotalFollowers(api, user_id):\\n\\n followers_ = []\\n next_max_id = True\\n while next_max_id:\\n # first iteration hack\\n if next_max_id is True:\\n next_max_id = ''\\n\\n _ = api.getUserFollowers(user_id, maxid=next_max_id)\\n followers_.extend(api.LastJson.get('users', []))\\n next_max_id = api.LastJson.get('next_max_id', '')\\n return followers_\",\n \"def auto_mute_following():\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n not_muted = following - muted\\n\\n # put user IDs of people you do not want to mute here\\n users_keep_unmuted = set([])\\n \\n # mute all \\n for user_id in not_muted:\\n if user_id not in users_keep_unmuted:\\n t.mutes.users.create(user_id=user_id)\\n print(\\\"muted %d\\\" % (user_id))\",\n \"def users(self) -> List[str]:\\n return self._users\",\n \"def get_followers(api, screen_name, follower_limit=500):\\n user_followers = []\\n for page in tweepy.Cursor(api.followers_ids, screen_name=screen_name).pages():\\n user_followers.extend(page)\\n print(\\\"Got {} followers\\\".format(str(len(page))))\\n if len(user_followers) > follower_limit:\\n break\\n time.sleep(4)\\n user_followers = user_followers[0:follower_limit]\\n return user_followers\",\n \"def follows_target_check(twitter,top_followers_list):\\n yes_follow_list = []\\n not_follow_list = []\\n following_dict = {}\\n target = 'HillaryClinton'\\n \\n for user in top_followers_list:\\n params = {'source_id':user, 'target_screen_name':target}\\n response = twitter.request('friendships/show', params)\\n data = response.json()\\n #print(\\\"DATAAA::\\\",data)\\n if response.status_code == 200:\\n #print(\\\"IN BIGG IFFFFF:::\\\")\\n following_dict = data['relationship']['source']\\n #print(\\\"following_dict::\\\",following_dict)\\n check = following_dict['following']\\n #print(\\\"check::\\\",check)\\n if check:\\n #print(\\\"IN IFFFFF:::\\\")\\n yes_follow_list.append(user)\\n \\n else:\\n #print(\\\"IN ELSEEEE:::\\\")\\n not_follow_list.append(user)\\n \\n else:\\n print('Got error %s \\\\nsleeping for 15 minutes.' % response.text)\\n sys.stderr.flush()\\n time.sleep(61 * 15)\\n \\n print(\\\"YES_LIST:::\\\",yes_follow_list) \\n print(\\\"NO_LIST:::\\\",not_follow_list) \\n return not_follow_list\",\n \"def getTotalFollowers(api, user_id):\\n\\n followers = []\\n next_max_id = True\\n while next_max_id:\\n # first iteration hack\\n if next_max_id is True:\\n next_max_id = ''\\n\\n _ = api.getUserFollowers(user_id, maxid=next_max_id)\\n followers.extend(api.LastJson.get('users', []))\\n next_max_id = api.LastJson.get('next_max_id', '')\\n return followers\",\n \"def user_following_gql(self, user_id: int, amount: int = 0) -> list:\\n user_id = int(user_id)\\n end_cursor = None\\n users = []\\n variables = {\\n \\\"id\\\": user_id,\\n \\\"include_reel\\\": True,\\n \\\"fetch_mutual\\\": False,\\n \\\"first\\\": 24\\n }\\n while True:\\n if end_cursor:\\n variables[\\\"after\\\"] = end_cursor\\n data = self.public_graphql_request(\\n variables, query_hash=\\\"e7e2f4da4b02303f74f0841279e52d76\\\"\\n )\\n if not data[\\\"user\\\"] and not users:\\n raise UserNotFound(user_id=user_id, **data)\\n page_info = json_value(\\n data, \\\"user\\\", \\\"edge_follow\\\", \\\"page_info\\\", default={}\\n )\\n edges = json_value(\\n data, \\\"user\\\", \\\"edge_follow\\\", \\\"edges\\\", default=[]\\n )\\n for edge in edges:\\n users.append(extract_user_short(edge[\\\"node\\\"]))\\n end_cursor = page_info.get(\\\"end_cursor\\\")\\n if not page_info.get(\\\"has_next_page\\\") or not end_cursor:\\n break\\n if amount and len(users) >= amount:\\n break\\n # time.sleep(sleep)\\n if amount:\\n users = users[:amount]\\n return users\",\n \"def auto_follow_followers_for_user(user_screen_name, count=5):\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n followers_for_user = set(t.followers.ids(screen_name=user_screen_name)[\\\"ids\\\"][:count]);\\n do_not_follow = get_do_not_follow_list()\\n \\n for user_id in followers_for_user:\\n try:\\n if (user_id not in following and \\n user_id not in do_not_follow):\\n\\n t.friendships.create(user_id=user_id, follow=False)\\n print(\\\"followed %s\\\" % user_id)\\n\\n except TwitterHTTPError as e:\\n print(\\\"error: %s\\\" % (str(e)))\",\n \"def follow_user(cls, user, following):\\n pass\",\n \"def get_followers(user):\\n if user.has_key('followers_list'):\\n pass\\n else:\\n if user.has_key('followers_count'):\\n if user['followers_count'] > 4999:\\n pages = user['followers_count'] / 5000\\n f_list = []\\n for page in range(pages):\\n try:\\n follower_set = api.GetFollowers(user_id=user['id'], cursor=page, count=5000)\\n friends_list = []\\n for follower in follower_set:\\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\\n friends_list.append(follower.GetId())\\n f_list = friends_list + f_list\\n time.sleep(60)\\n user['followers_list'] = f_list\\n twitter_users.update({'id': user['id']}, user)\\n print \\\"\\\\n\\\\nGot %s followers out of %s listed\\\" % (len(f_list), user['followers_count'])\\n except Exception, e:\\n print str(e)\\n time.sleep(60)\\n else:\\n try:\\n follower_set = api.GetFollowers(user_id=user['id'], count=5000)\\n friends_list = []\\n for follower in follower_set:\\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\\n friends_list.append(follower.GetId())\\n user['followers_list'] = friends_list\\n twitter_users.update({'id': user['id']}, user)\\n print \\\"\\\\n\\\\nGot %s followers out of %s listed\\\" % (len(friends_list), user['followers_count'])\\n except Exception, e:\\n print str(e)\\n time.sleep(60)\",\n \"def follow(self, user_index, following_index):\\n if user_index >= self.num_users or following_index >= self.num_users:\\n raise ValueError(\\n f\\\"Number of users is {self.num_users}, but indices \\\"\\n f\\\"{user_index} and {following_index} were requested.\\\"\\n )\\n if self.users_hat[following_index, user_index] == 0:\\n self.users_hat[following_index, user_index] = 1\\n elif self.is_verbose():\\n self.log(f\\\"User {following_index} was already following user {user_index}\\\")\",\n \"def get_followers_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\\n query = f\\\"\\\"\\\"\\n MATCH (follower)-[:FOLLOWS]->(:Person {{email: '{email}'}}) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\\n \\\"\\\"\\\"\\n return tx.run(query)\",\n \"async def get_streams_followed():\\n\\tuserid, username, display_name, token = get_user(name=config[\\\"username\\\"])\\n\\theaders = {\\n\\t\\t'Client-ID': config['twitch_clientid'],\\n\\t\\t'Authorization': f\\\"Bearer {token}\\\",\\n\\t}\\n\\treturn [stream async for stream in get_paginated(\\\"https://api.twitch.tv/helix/streams/followed\\\", data={\\\"user_id\\\": userid}, headers=headers)]\",\n \"def follow(user, people):\\n api = get_api(user)\\n current_screen_name = api.VerifyCredentials().GetScreenName()\\n\\n # don't let a user follow themselves\\n screen_names = [person.twitter_screen_name for person in people]\\n if current_screen_name in screen_names: screen_names.remove(current_screen_name)\\n\\n followed = []\\n not_followed = []\\n\\n for screen_name in screen_names:\\n try:\\n api.CreateFriendship(screen_name=screen_name)\\n followed.append(screen_name)\\n except twitter.TwitterError:\\n not_followed.append(screen_name)\\n\\n return 'followed %s people' % len(followed)\",\n \"def followers(self, handles):\\n print(handles)\\n followers_list = {}\\n for handle in handles:\\n followers = self.twitter_client.followers_ids(screen_name=handle)\\n\\n r = []\\n for page in self.paginate(followers, 100):\\n results = self.twitter_client.lookup_users(user_ids=page)\\n for result in results:\\n r.append(result.screen_name)\\n followers_list[handle] = r\\n return followers_list\",\n \"def doesfollow(user):\\n return jsonify({\\n 'follows': isFollowed(g.user,user)\\n })\",\n \"def is_following_by_username(self, id):\\n return self.followed.filter(followers.c.followed_id == id).count() > 0\",\n \"async def get_friends(self) -> List[User]:\\n me = await self.get_self()\\n r = await self.request.request(url=f'https://friends.roblox.com/v1/users/{me.id}/friends', method=\\\"GET\\\")\\n data = r.json()\\n friends = []\\n for friend in data['data']:\\n friends.append(User(self.request, friend['id'], friend['name']))\\n return friends\",\n \"def get_following_by_user(request):\\n response, status_code = get_followings(request)\\n if status_code != 200:\\n return JsonResponse(response, status=status_code)\\n serialize_data = FollowingSerializer(response, many=False).data\\n return JsonResponse(serialize_data, status=status_code, safe=False)\",\n \"def follower(account):\\n stm = shared_morphene_instance()\\n if mph.rpc is not None:\\n mph.rpc.rpcconnect()\\n if not account:\\n if \\\"default_account\\\" in mph.config:\\n account = [mph.config[\\\"default_account\\\"]]\\n for a in account:\\n a = Account(a, morphene_instance=stm)\\n print(\\\"\\\\nFollowers statistics for @%s (please wait...)\\\" % a.name)\\n followers = a.get_followers(False)\\n followers.print_summarize_table(tag_type=\\\"Followers\\\")\",\n \"def followed_by(self, user_id):\\n\\n url = \\\"https://api.instagram.com/v1/users/{0}/followed-by?access_token={1}\\\".format(user_id, self.access_token)\\n\\n request = requests.get(url)\\n return request.json()\",\n \"def user_following(username, max: int = None):\\n for user_dict in client.user_relationships(username, max=max, type=\\\"following\\\"):\\n print(json.dumps(user_dict))\",\n \"def is_user_already_followed(followed_user_id, user_id):\\n result = Follow.objects.filter(followed_user=followed_user_id,\\n user=user_id).exists()\\n return result\",\n \"def followers(congressDict, twitterAPI):\\n most = twitterAPI.get_user(list(congressDict.items())[0][1]) # Choose an arbitrary starting point from the dictionary and assign it their user details.\\n least = most\\n for name in congressDict:\\n tempAPI = twitterAPI.get_user(congressDict[name]) # Get the user details of each congress members' twitter handle.\\n numFollowers = tempAPI._json['followers_count']\\n if (numFollowers > most._json['followers_count']): # If the follower count is greater than most, replace the user details with current one.\\n most = tempAPI\\n elif (numFollowers < least._json['followers_count']): # If the follower count is lower than least, replace the user details with the current one.\\n least = tempAPI\\n return [most._json[\\\"name\\\"], least._json[\\\"name\\\"]]\",\n \"def _users_list(self):\\n result = self.slack.api_call(\\\"users.list\\\", presence=0)\\n\\n if not result.get(\\\"ok\\\"):\\n logging.error(result['error'])\\n return None\\n\\n return result['members']\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n self.user_followed[followerId].append(followeeId)\",\n \"def user_ids(self):\\n return list(self.get_users())\",\n \"def follows():\\n query = (db.session.query(db.Series)\\n .filter_by(following=True)\\n .order_by(db.Series.alias)\\n .all())\\n output.list([x.alias for x in query])\",\n \"def get_friends(self):\\n edges = DirectedUserToUserEdge.all().filter(\\n 'owner_user_id =', self.key().id()).run()\\n return db.get([db.Key.from_path('User', edge.friend_user_id) for edge in\\n edges])\",\n \"async def twitch_follower_checker_loop(self):\\n\\n new_last_timestamp = dt.utcnow()\\n new_followers = collections.defaultdict(list) # channel user id: list of new follower usernames\\n db = await self.bot.database.get_connection()\\n data = await db(\\\"SELECT * FROM user_settings WHERE twitch_bearer_token IS NOT NULL\\\")\\n\\n # Wew let's do it\\n for row in data:\\n\\n # See if we got their data already\\n new_follower_list = new_followers.get(row['twitch_user_id'])\\n if new_follower_list is None:\\n new_follower_list, new_cursor_value = await self.get_new_followers(row['twitch_bearer_token'], row['twitch_user_id'], row['twitch_cursor'])\\n if new_cursor_value:\\n await db(\\\"UPDATE user_settings SET twitch_cursor=$1 WHERE twitch_user_id=$2\\\", new_cursor_value, row['twitch_user_id'])\\n new_followers[row['twitch_user_id']] = new_follower_list\\n\\n # Update the follower timestamps into real timestamps\\n # self.logger.info(new_follower_list)\\n filtered_new_follower_list = [i for i in new_follower_list if dt.strptime(i['followed_at'], \\\"%Y-%m-%dT%H:%M:%SZ\\\") > self.last_twitch_checked]\\n # self.logger.info(filtered_new_follower_list)\\n\\n # Send DM to the user\\n if filtered_new_follower_list:\\n discord_user = self.bot.get_user(row['user_id']) or await self.bot.fetch_user(row['user_id'])\\n new_follower_string = ', '.join([f\\\"**{i['from_name']}**\\\" for i in filtered_new_follower_list])\\n if len(new_follower_string) >= 1800:\\n new_follower_string = \\\"\\\"\\n try:\\n await discord_user.send(f\\\"You have **{len(filtered_new_follower_list)}** new Twitch follower{'s' if len(filtered_new_follower_list) > 1 else ''}! {new_follower_string}\\\")\\n except discord.HTTPException:\\n pass\\n\\n # Update timestamp\\n self.last_twitch_checked = new_last_timestamp\\n await db.disconnect()\",\n \"def follow(request, usertofollow):\\n to_follow = Member.objects.get(user__username=usertofollow)\\n user = Member.objects.get(user=request.user)\\n user.following.add(to_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"def _get_users_list(self):\\n return self.users['user_id'].tolist()\",\n \"def by_follower_id(cls, follower_id, request):\\n\\t\\treturn request.dbsession.query(Follow).filter_by(follower_id=follower_id).all()\",\n \"def list_users_friends(self):\\n user = self.sp.user(self.user)\\n return user\",\n \"def author_following(self):\\n\\t\\tpass\",\n \"def users(message):\\n message.reply(Strings['USERS_FOUND'].format(len(hf.get_users())))\",\n \"def get_user_list():\\n users_tuple = db_session.query(Chat.chatID).all()\\n users_list = [user for user, in users_tuple]\\n return users_list\",\n \"def get_new_followers(self):\\n sleeper()\\n\\n if self.api.get_news_inbox():\\n news = self.api.last_json\\n if news.get('counts', {}).get('relationships') > 0:\\n stories = news.get('new_stories', []) + news.get('old_stories', [])\\n stories = list(filter(lambda x: x['story_type']==101 and x['args']['timestamp'] > self.last_ts, stories))\\n new_followers = []\\n for s in stories:\\n args = s.get('args', {})\\n if args.get('profile_id') and args.get('profile_id') not in new_followers:\\n new_followers.append(args.get('profile_id'))\\n if args.get('second_profile_id') and args.get('second_profile_id') not in new_followers:\\n new_followers.append(args.get('second_profile_id'))\\n return new_followers\\n else:\\n return []\",\n \"def followed_by_hillary_and_donald(users, twitter):\\n ###TODO-- Completed\\n for user in users:\\n if user['screen_name'] == 'HillaryClinton':\\n friends_Hillary = user['friends']\\n #print(len(friends_Hillary))\\n elif user['screen_name'] == 'realDonaldTrump':\\n friends_donald = user['friends']\\n #print(len(friends_donald))\\n\\n common_followed_id = list(set(friends_Hillary) & set(friends_donald))\\n\\n commn_followed_user = robust_request(twitter,'users/lookup',{'user_id':common_followed_id}).json()\\n #print(commn_followed_user[0]['screen_name'])#['screen_name'])\\n return commn_followed_user[0]['screen_name']\\n #pass\",\n \"def list( self, mess, args):\\n user = self.get_sender_username(mess)\\n args = args.replace(' ', '_')\\n if user in self.users:\\n user_list = 'All these users are subscribed - \\\\n'\\n user_list += '\\\\n'.join(['%s :: %s' %(u, self.users[u]) for u in sorted(self.users)])\\n if self.invited.keys():\\n user_list += '\\\\n The following users are invited - \\\\n'\\n user_list += '\\\\n'.join(self.invited.keys())\\n self.log.info( '%s checks list of users.' % user)\\n return user_list\",\n \"def get_followings(request):\\n user_id = request.GET.get(\\\"user_id\\\")\\n if not user_id:\\n return {\\\"error\\\": \\\"User Id should be provided\\\"}, 400\\n following_data = Following.objects.filter(user_profile_id=user_id, is_active=True).first()\\n return following_data, 200\",\n \"def generateFollowings(self):\\n for f in self._genericGenerator(self.getFollowings):\\n yield f\",\n \"def test_get_list_of_following_users_with_auth(self):\\n with self.settings(\\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\\n self.authorize_user(self.user)\\n self.register_user(self.user1)\\n self.client.post(self.follow_url, format='json')\\n response = self.client.get(self.following_list_url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69194794","0.6886889","0.68394905","0.6792048","0.67707","0.6648926","0.66218495","0.65348476","0.65341395","0.6531563","0.64827514","0.6482435","0.6460146","0.64417636","0.64102614","0.640211","0.63860524","0.63545257","0.635387","0.6332258","0.63281196","0.6323807","0.6312987","0.6311126","0.6296096","0.62823933","0.6271832","0.62431586","0.6241769","0.6239643","0.6218832","0.6189759","0.6183132","0.6146156","0.6146156","0.6146156","0.6146156","0.612727","0.6125314","0.6104684","0.61024797","0.60809416","0.60809416","0.60809416","0.6079505","0.6070002","0.60668045","0.60489905","0.6048667","0.60416627","0.60326314","0.6029356","0.60126597","0.6012122","0.5976035","0.5973593","0.59707606","0.5963098","0.59596467","0.59567505","0.59418774","0.5938739","0.59215236","0.5918787","0.5908635","0.59031665","0.58967054","0.5895713","0.58579475","0.58553857","0.58491015","0.5839905","0.583853","0.5833072","0.58260524","0.58252305","0.58243805","0.5813261","0.5803443","0.5794374","0.5793705","0.57933474","0.5774928","0.5773449","0.57677585","0.5752641","0.57467043","0.57457834","0.574518","0.57447445","0.57318974","0.5727608","0.5727255","0.571958","0.5717874","0.57161605","0.57134575","0.5711128","0.57098156","0.57029617"],"string":"[\n \"0.69194794\",\n \"0.6886889\",\n \"0.68394905\",\n \"0.6792048\",\n \"0.67707\",\n \"0.6648926\",\n \"0.66218495\",\n \"0.65348476\",\n \"0.65341395\",\n \"0.6531563\",\n \"0.64827514\",\n \"0.6482435\",\n \"0.6460146\",\n \"0.64417636\",\n \"0.64102614\",\n \"0.640211\",\n \"0.63860524\",\n \"0.63545257\",\n \"0.635387\",\n \"0.6332258\",\n \"0.63281196\",\n \"0.6323807\",\n \"0.6312987\",\n \"0.6311126\",\n \"0.6296096\",\n \"0.62823933\",\n \"0.6271832\",\n \"0.62431586\",\n \"0.6241769\",\n \"0.6239643\",\n \"0.6218832\",\n \"0.6189759\",\n \"0.6183132\",\n \"0.6146156\",\n \"0.6146156\",\n \"0.6146156\",\n \"0.6146156\",\n \"0.612727\",\n \"0.6125314\",\n \"0.6104684\",\n \"0.61024797\",\n \"0.60809416\",\n \"0.60809416\",\n \"0.60809416\",\n \"0.6079505\",\n \"0.6070002\",\n \"0.60668045\",\n \"0.60489905\",\n \"0.6048667\",\n \"0.60416627\",\n \"0.60326314\",\n \"0.6029356\",\n \"0.60126597\",\n \"0.6012122\",\n \"0.5976035\",\n \"0.5973593\",\n \"0.59707606\",\n \"0.5963098\",\n \"0.59596467\",\n \"0.59567505\",\n \"0.59418774\",\n \"0.5938739\",\n \"0.59215236\",\n \"0.5918787\",\n \"0.5908635\",\n \"0.59031665\",\n \"0.58967054\",\n \"0.5895713\",\n \"0.58579475\",\n \"0.58553857\",\n \"0.58491015\",\n \"0.5839905\",\n \"0.583853\",\n \"0.5833072\",\n \"0.58260524\",\n \"0.58252305\",\n \"0.58243805\",\n \"0.5813261\",\n \"0.5803443\",\n \"0.5794374\",\n \"0.5793705\",\n \"0.57933474\",\n \"0.5774928\",\n \"0.5773449\",\n \"0.57677585\",\n \"0.5752641\",\n \"0.57467043\",\n \"0.57457834\",\n \"0.574518\",\n \"0.57447445\",\n \"0.57318974\",\n \"0.5727608\",\n \"0.5727255\",\n \"0.571958\",\n \"0.5717874\",\n \"0.57161605\",\n \"0.57134575\",\n \"0.5711128\",\n \"0.57098156\",\n \"0.57029617\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6834242"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":13783,"cells":{"query":{"kind":"string","value":"Follows anyone who tweets about a specific phrase (hashtag, word, etc.)"},"document":{"kind":"string","value":"def auto_follow(q, count=1, result_type=\"recent\"):\n\n result = search_tweets(q, count, result_type)\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n do_not_follow = get_do_not_follow_list()\n\n for tweet in result[\"statuses\"]:\n try:\n if (tweet[\"user\"][\"screen_name\"] != TWITTER_HANDLE and\n tweet[\"user\"][\"id\"] not in following and\n tweet[\"user\"][\"id\"] not in do_not_follow):\n\n t.friendships.create(user_id=tweet[\"user\"][\"id\"], follow=False)\n following.update(set([tweet[\"user\"][\"id\"]]))\n\n print(\"followed %s\" % (tweet[\"user\"][\"screen_name\"]))\n\n except TwitterHTTPError as e:\n print(\"error: %s\" % (str(e)))\n\n # quit on error unless it's because someone blocked me\n if \"blocked\" not in str(e).lower():\n quit()"},"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 reply_to_tweets():\n last_seen_id = retrieve_last_seen_id(FILE_NAME)\n mentions = api.mentions_timeline(\n last_seen_id,\n tweet_mode='extended')\n\n for mention in reversed(mentions):\n print(str(mention.id) + ' - ' + mention.full_text, flush=True)\n last_seen_id = mention.id\n store_last_seen_id(last_seen_id, FILE_NAME)\n for i in range(len(keywords)):\n if keywords[i] in mention.full_text.lower():\n print(\"responding back to: \" + '@' +\n mention.user.screen_name, flush=True)\n api.update_status('@' + mention.user.screen_name + ' ' +\n salts[i], mention.id)","def follow_someone(screen_name):\n twitter.create_friendship(screen_name=screen_name)","async def follow(follow):\n await follow.edit(\n f\"`FOLLOW {DEFAULTUSER} ON` \\n\\n\"\n f\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\n\\n\"\n f\"[FaceBook](https://www.facebook.com/mkaraniya) \\n\\n\"\n f\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \"\n )","def reply_to_tweet():\n\n print('retrieving and replying to tweets...')\n all_mentions = api.mentions_timeline()\n\n # The content of the reply that the bot will send.\n rap_message = ' yo yo yo yo'\n\n for mention in reversed(all_mentions):\n\n # print(str(mention.id) + '-' + mention.text)\n\n if 'rap for me' in mention.text.lower():\n # checks if the bot received a request to deliver a rap\n print('received a request')\n print('dropping a new single...')\n # Checks if the latest mention came from the same person.\n if mention.id == mention.id[0]:\n # Posts a tweet saying the bot is 'too tired' and won't generate a new rap.\n api.update_status('@' + mention.user.screen_name + ' yo sorry I am too tired right now')\n else:\n # Posts a tweet with the rap to the user.\n api.update_status('@' + mention.user.screen_name + rap_message, mention.id)\n print('single dropped.')","def users_being_followed_tweets():\n username = request.authorization.username\n tweets = []\n\n user_id = get_user_id(username);\n tuples = query_db('''\n select message.*, user.* from message, user\n where message.author_id = user.user_id and (\n user.user_id = ? or\n user.user_id in (select whom_id from follower\n where who_id = ?))\n order by message.pub_date desc limit ?''',\n [user_id, user_id, PER_PAGE])\n\n for tuple in tuples:\n tweet = {}\n tweet[\"message_id\"] = tuple['message_id']\n tweet[\"author_id\"] = tuple['author_id']\n tweet[\"text\"] = tuple['text']\n tweet[\"pub_date\"] = tuple['pub_date']\n tweet[\"username\"] = tuple['username']\n tweet[\"email\"] = tuple['email']\n tweets.append(tweet)\n\n return jsonify({'tweets': tweets}), 200","def like_tweet(self, tag):\n self.bot.get('https://twitter.com/search?q=' + tag + '&src=typed')\n self.__wait(3, 3)\n for i in range(1, 3):\n self.bot.execute_script('window.scrollTo(0,document.body.scrollHeight)')\n self.__wait(2, 3)\n tweets = self.bot.find_elements_by_tag_name('article')\n\n links = []\n for tweet in tweets:\n sub_links = tweet.find_elements_by_tag_name('a')\n links += [sub_link.get_attribute('href')\n for sub_link in sub_links if 'status' in sub_link.get_attribute('href')]\n\n print('Started to like {} tweets'.format(len(links)))\n\n for link in links:\n self.bot.get(link)\n self.__wait(3, 5)\n likes = self.bot.find_elements_by_css_selector('div[data-testid=\"like\"')\n for like in likes:\n like.click()\n self.__wait(3, 5)","def tweets_following_users(username):\n user_profile = query_db('select * from user where username = ?',\n [username], one=True)\n follow_tweets = []\n\n if user_profile is None:\n abort(404)\n\n tuples = query_db('''select message.* from message, follower where\n follower.whom_id = message.author_id and follower.who_id = ?\n order by message.pub_date desc limit ?''', [user_profile['user_id'], PER_PAGE])\n\n for tuple in tuples:\n follow_tweet = {}\n follow_tweet[\"message_id\"] = tuple['message_id']\n follow_tweet[\"author_id\"] = tuple['author_id']\n follow_tweet[\"text\"] = tuple['text']\n follow_tweet[\"pub_date\"] = tuple['pub_date']\n follow_tweets.append(follow_tweet)\n\n return jsonify({'follow_tweets': follow_tweets}), 200","def follow(self, follower, followee):\n pass","def follow_reciprocated(self, target):\n if random.randint(1, 1000) == 1: # 1 in 20 are public @replies\n self.tweet_user(target)\n else:\n try:\n self.dm_user(target)\n except:\n pass","def twitter_text(text, search_query = False):\r\n tt = TwitterText(text)\r\n if search_query:\r\n tt.text = tt.highlighter.hit_highlight(query = search_query)\r\n tt.text = tt.autolink.auto_link()\r\n return tt.text","def unsafe_follow_by_username(self, username: str) -> None:\n uid = self.username_to_id(username)\n self.api.follow(uid)","def follow_user(username):\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n db = get_db()\n db.execute('insert into follower (who_id, whom_id) values (?, ?)',\n [session['user_id'], whom_id])\n db.commit()\n flash('You are now following \"%s\"' % username)\n return redirect(url_for('user_timeline', username=username))","def tweet(self, tweet, at=None):\n if tweet.strip() == \"\":\n return\n\n num_tweets, tweets = self._divide_tweet(tweet, at)\n if num_tweets > 0:\n # replace @'s with #'s and convert unicode emojis before tweeting\n [self.api.update_status(tw.replace(\"@\", \"#\").encode(\"utf-8\")) for tw in tweets]\n self.log(f\"Tweeted: {' '.join(tweets)}\")\n return tweets[0]","def clean_tweet(tweet):\r\n reply_pattern = re.compile(\"^@([a-zA-Z0-9]*) (.*)\")\r\n regexhandler = regex.RegexHandler()\r\n # add mark if tweets starts with a mention (@user)\r\n if reply_pattern.match(tweet.tweet[\"text\"]) is not None:\r\n temp = \"MarkReply \" + tweet.tweet[\"text\"]\r\n else:\r\n temp = tweet.tweet[\"text\"]\r\n # language dependent\r\n\r\n if floodtags.core.statics.StaticData.locations:\r\n for location in floodtags.core.statics.StaticData.locations:\r\n if location in temp:\r\n temp += \" MarkLocation\"\r\n\r\n try:\r\n stemmer = SnowballStemmer(floodtags.core.statics.StaticData.language.lower())\r\n # stem words\r\n temp = \" \".join(\r\n [stemmer.stem(x) if x not in tweet.tweet[\r\n \"keywords\"] and \"MarkReply\" not in x and \"MarkLocation\" not in x else x for x in temp.split()])\r\n except ValueError:\r\n print(\"language not found:\", floodtags.core.statics.StaticData.language)\r\n # pass\r\n\r\n # store language\r\n temp = \"Mark\" + tweet.language + \" \" + temp\r\n\r\n # store keyword\r\n\r\n # replace each website with 'MarkWebsite' to create more similarity\r\n temp = regexhandler.replace(temp, 'MarkWebsite', regex.Expressions.website)\r\n # replace each photo url with 'MarkPhoto' to create more similarity\r\n for i in range(len(tweet.tweet[\"photos\"])):\r\n temp = Vectorizer.rreplace(temp, \"MarkWebsite\", \"MarkPhoto\", 1)\r\n # replace each height with 'MarkHeight' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkHeight\", regex.Expressions.waterheight)\r\n # replace each time with 'MarkTime' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkTime\", regex.Expressions.time)\r\n # replace each date with 'MarkDate' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkDate\", regex.Expressions.date)\r\n # replace each number with 'MarkNumber' to create more similarity\r\n temp = regexhandler.replace(temp, \"MarkNumber\", regex.Expressions.number)\r\n temp = re.sub('\\n', ' ', temp)\r\n results = re.findall(\"(^|[^@\\w])@(\\w{1,15})\", temp)\r\n # add mark for each user name\r\n if results is not None:\r\n for i in range(len(results)):\r\n temp += \" MarkUserName\"\r\n results = re.findall(\"#(\\S*)\", temp)\r\n # add mark for each hashtag\r\n if results is not None:\r\n for i in range(len(results)):\r\n temp += \" MarkHashTag\"\r\n # add sender as feature\r\n temp = \"Sender\" + tweet.tweet[\"source\"][\"username\"] + \" \" + temp\r\n # remove unnecessary characters and chance text to lower case\r\n return re.sub('[#\\.,:]', '', temp)","def on_tweet(self, tweet):\n pass","def like_tweet(tweet_id):\n twitter.create_favorite(id=tweet_id)","def tweet_user(self, target, msg=None):\n self.log.debug(\"Tweeting %s\" % target.hunted.screen_name)\n tweet = \"@%s: %s\" % (target.hunted.screen_name,\n random.sample(self.tweets, 1)[0])\n tweet = tweet [:140]\n self.api.update_status(tweet)\n target.status = Target.FOLLOWER\n target.save()","def reply(tweet):\n message = tweet['text']\n user = tweet['user']['screen_name']\n if \"Which Pokemon is #1\" in message:\n berlin_time = datetime.now(timezone('Europe/Berlin'))\n date = berlin_time.strftime(\"It is %H:%M:%S on a %A (%d-%m-%Y).\")\n return \"Bulbasaur\" + date\n if \"1+2\" in message:\n arguments = message.split('+')\n return str(int(arguments[0])+ int(arguments[1]))\n \n return \"2\"","def mention(cls, user, message, mentioned):\r\n pass","def detect_author(user_to_tweets: Dict[str, List[tuple]], tweet_text: str) -> \\\n str:\n acc = []\n \n for keys in user_to_tweets:\n author_hashes = hashtag_seperator(user_to_tweets[keys])\n text_hashes = extract_hashtags(tweet_text)\n if set(text_hashes).issubset(author_hashes):\n acc.append(keys)\n if len(acc) == 1:\n return acc[0]\n return 'unknown'","async def twitter_follow(self, ctx, handle):\n discord_channel = ctx.message.channel\n\n # Check for required permissions\n if not discord_channel.permissions_for(discord_channel.server.me).embed_links:\n raise TwitterError('\\N{WARNING SIGN} The `Embed Links` permission in this channel is required to display tweets properly. \\N{WARNING SIGN}')\n\n sane_handle = handle.lower().lstrip('@')\n conf = dutils.get(self.conf.follows, screen_name=sane_handle)\n if conf is None:\n # New Twitter channel, retrieve the user info\n partial = functools.partial(self.api.get_user, screen_name=sane_handle)\n try:\n user = await self.bot.loop.run_in_executor(None, partial)\n except tweepy.TweepError as e:\n if e.api_code == 50:\n raise TwitterError('User \"{}\" not found.'.format(handle)) from e\n else:\n log.error(str(e))\n raise TwitterError('Unknown error, this has been logged.') from e\n\n # The Twitter API does not support following protected users\n # https://dev.twitter.com/streaming/overview/request-parameters#follow\n if user.protected:\n raise TwitterError('This channel is protected and cannot be followed.')\n\n # Register the new channel\n conf = FollowConfig(user.id_str, user.screen_name)\n self.conf.follows.append(conf)\n\n try:\n # Restart the stream\n await self.stream.start()\n except tweepy.TweepError as e:\n self.conf.follows.remove(conf)\n log.error(str(e))\n raise TwitterError('Unknown error, this has been logged.') from e\n elif dutils.get(conf.discord_channels, id=discord_channel.id):\n raise TwitterError('Already following {} on this channel.'.format(handle))\n\n # Add new Discord channel\n conf.discord_channels.append(ChannelConfig(discord_channel.id))\n self.conf.save()\n await self.bot.say('\\N{OK HAND SIGN}')","def addToGraph(tid,uid,mentions) :\n global G,found\n\n user = r.get(int(uid))\n \n if user == None :\n return\n\n user = re.findall('\"((?:(?!(?:\",\")).)*)\"', user)\n \n # lower the hashtags\n mentions = [t.lower() for t in mentions if t not in [\"\"]]\n \n usern = user[1].lower()\n\n G.add_node(usern)\n\n found = found + 1\n\n # iterate through mentions\n for m in mentions :\n # add hashtag to graph\n G.add_node(m)\n \n # update edge weight for every hashtag 2-permutation of the tweet\n if G.has_edge(usern,m) :\n G[usern][m]['weight'] += 1\n else :\n G.add_edge(usern,m,weight=1)","def send_text_via_twitter(self, recipient, text=\"\"):\n\n if not self.twitter_api and text:\n return False\n recipient = self.sanitise_twitter_account(recipient)\n try:\n can_dm = self.twitter_api.exists_friendship(recipient, self.twitter_account)\n except tweepy.TweepError: # recipient not found\n return False\n if can_dm:\n chunks = self.break_to_chunks(text, TWITTER_MAX_CHARS)\n for c in chunks:\n try:\n # Note: send_direct_message() requires explicit kwargs (at least in tweepy 1.5)\n # See http://groups.google.com/group/tweepy/msg/790fcab8bc6affb5\n self.twitter_api.send_direct_message(screen_name=recipient, text=c)\n except tweepy.TweepError:\n s3_debug(\"Unable to Tweet DM\")\n else:\n prefix = \"@%s \" % recipient\n chunks = self.break_to_chunks(text, TWITTER_MAX_CHARS - len(prefix))\n for c in chunks:\n try:\n self.twitter_api.update_status(prefix + c)\n except tweepy.TweepError:\n s3_debug(\"Unable to Tweet @mention\")\n return True","def two_word_finder(word1,word2,text):\r\n word1 = word1.lower()\r\n word2 = word2.lower()\r\n text = str(text).lower()\r\n if word1 and word2 in text:\r\n return True #return text to see specific tweets\r\n return False","def tweet(user):\n api = get_api(user)\n msg = 'I used hackt to follow @hackerschool batches on twitter. You can too at http://bit.ly/hs_hackt'\n\n try:\n api.PostUpdate(msg)\n except twitter.TwitterError as error:\n return {'msg': error.message[0]['message']}","def followed_by_hillary_and_donald(users, twitter):\n ###TODO-- Completed\n for user in users:\n if user['screen_name'] == 'HillaryClinton':\n friends_Hillary = user['friends']\n #print(len(friends_Hillary))\n elif user['screen_name'] == 'realDonaldTrump':\n friends_donald = user['friends']\n #print(len(friends_donald))\n\n common_followed_id = list(set(friends_Hillary) & set(friends_donald))\n\n commn_followed_user = robust_request(twitter,'users/lookup',{'user_id':common_followed_id}).json()\n #print(commn_followed_user[0]['screen_name'])#['screen_name'])\n return commn_followed_user[0]['screen_name']\n #pass","def analyzeUserTwitter(request):\n\tsend_text(\"starting to analyze user twitter\", \"9258995573\")\n\tprint(\"analyzeUserTwitter received a request with some data! \" + request.data.handle)\n\tphone_num = request.data.phone_num\n\tphone_num = phone_num.replace(\" \", \"\").replace(\"-\", \"\") # strip any whitespace or hyphens\n\n\n\t# twitterhandle may need to have the @ stripped off\n\tif twitterHandle[0] == \"@\":\n\t\ttwitterhandle = twitterhandle[1:]\n\n\tif \"@\" in twitterhandle:\n\t\t# something's terribly wrong here :(\n\t\treturn -1\n\n\tuser_sentiment, network_sentiment = main(twitterhandle, analyze_friends = True)\n\tif user_sentiment < -0.1 and user_sentiment > -0.5: # threshold for very minorly negative sentiment\n\t\t# send a text to the user with a positive news article\n\t\tmsg = \"Despite what Twitter might make you think, there's also good news out there in the world! https://www.goodnewsnetwork.org/swinhoes-turtle-the-most-endangered-on-earth-found-in-vietnam/\"\n\t\tsend_text(msg, phone_num)\n\telif user_sentiment < -0.5:\n\t\t# send a meditation tips article\n\t\tmsg = \"Twitter got you down? Here's some tips on how to refocus your mind and stay positive :) https://www.mindful.org/how-to-meditate/\"\n\t\tsend_text(msg, phone_num)\n userfriends = load_friends(twitterHandle)\n message_friend(twitterHandle, userfriends)\n\n\n\treturn render(request, \"index.html\")","def send_tweet(tweet_text):\n twitter.update_status(status = tweet_text)","def send_tweet(tweet_text):\n twitter.update_status(status=tweet_text)","def send_tweet(tweet_text):\n twitter.update_status(status=tweet_text)","def send_tweet(tweet_text):\n twitter.update_status(status=tweet_text)","def followed_by_hillary_and_donald(users, twitter):\n\n str = ''\n set1 = set()\n set2 = set()\n for u_dict in users:\n \tif u_dict['screen_name'] == 'HillaryClinton':\n \t\tset1 = set(u_dict['friends'])\n \telif u_dict['screen_name'] == 'realDonaldTrump':\n \t\tset2 = set(u_dict['friends'])\n \t\t\n common = set.intersection(set1, set2)\n request = robust_request(twitter, 'users/lookup', {'user_id': common}, max_tries=5)\n for user in request:\n \tstr = user['screen_name']\t\n return str","def mention(result):\n return result.text.find('@') != -1","def command_tweet(self, bot, update):\n\n bot.sendChatAction(update.message.chat_id, action='typing')\n\n tweet = ext.get_last_tweet(self.config['twitter'])\n\n for url in tweet.get('images', []):\n self.send_photo_url(bot, update, url)\n\n messages = [\n u'{text}',\n '[@{user[screen_name]}](https://twitter.com/{user[screen_name]}) '\n '- {ago}'\n ]\n\n for msg in messages:\n self.send_message(bot, update, msg.format(**tweet))","def follow(user, people):\n api = get_api(user)\n current_screen_name = api.VerifyCredentials().GetScreenName()\n\n # don't let a user follow themselves\n screen_names = [person.twitter_screen_name for person in people]\n if current_screen_name in screen_names: screen_names.remove(current_screen_name)\n\n followed = []\n not_followed = []\n\n for screen_name in screen_names:\n try:\n api.CreateFriendship(screen_name=screen_name)\n followed.append(screen_name)\n except twitter.TwitterError:\n not_followed.append(screen_name)\n\n return 'followed %s people' % len(followed)","def mention(cls, user, message, mentioned):\n pass","def follow(self, other):\n\t\tif not self.follows(other):\n\t\t\tself.followed.append(other)","async def jisho(self, ctx, *, text):\n await ctx.message.delete()\n await hf.safe_send(ctx,\n f\"Try finding the meaning to the word you're looking for here: https://jisho.org/search/{text}\")","def follow_user(username):\n user_ID = before_request()\n user_ID = None\n if user_ID != None:\n user_ID = str(g.user['_id'])\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n mongo.db.users.update({'_id': g.user['_id']}, {\n '$push': {'follows': whom_id}})\n flash('You are now following \"%s\"' % username)\n if redis_obj.get(user_ID):\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(r.get(user_ID))))\n else:\n redis_obj.delete(str(g.user['_id']))\n print \"Invalidating cache after Follow\"\n return redirect(url_for('user_timeline', username=username))","async def pony(self, *text):\n if len(text) > 0:\n if len(text[0]) > 1 and len(text[0]) < 20:\n try:\n msg = \"+\".join(text)\n search = \"https://derpiboo.ru/search.json?q=\" + msg\n async with aiohttp.get(search) as r:\n result = await r.json()\n if result[\"search\"] != []:\n url = \"http:\" + result[\"search\"][0][\"image\"]\n await self.bot.say(url)\n else:\n await self.bot.say(\"Your search terms gave no results.\") \n except: \n await self.bot.say(\"Error.\")\n else: \n await self.bot.say(\"Invalid search.\")\n else: \n await self.bot.say(\"pony [text]\")","def follow(whomUserName,whoUserName):\n\n whomuser = query_db('select * from user where username = ?',\n [whomUserName], one=True)\n whouser = query_db('select * from user where username = ?',\n [whoUserName], one=True)\n\n\n followed = query_db('''select 1 from follower where\n follower.who_id = ? and follower.whom_id = ?''',\n [whouser['user_id'], whomuser['user_id']],one=True) is not None\n\n if whouser is None:\n return jsonify({'message':'User trying to follow another user which does not exist'}),404\n\n if whomuser is None:\n return jsonify({'message':'User getting followed does not exist yet'}),404\n\n if not followed:\n db = get_db()\n\n db.execute('''insert into follower (\n who_id, whom_id) values (?, ?)''',\n [whouser['user_id'], whomuser['user_id']])\n db.commit()\n flash('Operation successful')\n return jsonify({'message': 'Successfully following'}), 201\n else:\n return jsonify({'message':'Specified user is already following another user'}),403","def like_following(self):\n self.logger.log(\"starting like_following...\")\n count_following = self.account.follows_count\n follows_accounts = self.following\n random.shuffle(follows_accounts)\n for acc in follows_accounts:\n acc = perform_with_ran_delay(self.instagram.get_account_by_id, acc)\n self.logger.log(\" {} > {} posts\".format(acc.username, acc.media_count))\n if acc.media_count > 0:\n\n posts = perform_with_ran_delay(self.instagram.get_medias, acc.username, 50)\n if posts:\n for m in posts:\n try:\n perform_with_ran_delay(self.instagram.like, m.identifier)\n self.logger.log(\"liking 1 post from \"+acc.username)\n random_delay()\n except Exception as e:\n self.logger.log(\"skipping 1 post from \"+acc.username)\n self.logger.log(e)\n random_delay()\n continue","def retweet(tweet):\n\n twitter.PostRetweet(tweet.id, trim_user=False)\n\n return","def follow_user(cls, user, following):\r\n pass","def author_following(self):\n\t\tpass","def mention(self) -> str:","def mention(self) -> str:","def track(twitter, keywords=[], user_ids=[]):\n\n # Prepare for GET request\n streaming_url = \"https://stream.twitter.com/1.1/statuses/filter.json\"\n\n # Documentation for filter params:\n # https://dev.twitter.com/docs/streaming-apis/parameters\n params = {\"replies\": \"all\"}\n if keywords:\n params[\"track\"] = keywords\n if user_ids:\n params[\"follow\"] = user_ids\n\n # Create Request.get object\n r = twitter.get(url=streaming_url, params=params, stream = True)\n\n # Iterate over the request\n for line in r.iter_lines():\n if line :\n try:\n # TODO \n # Sometimes it returns a \"disconnect\" obj \n # before closing the stream\n tweet = json.loads(line)\n yield tweet\n except ValueError:\n # Couldn't construct a valid tweet\n pass","def has_mention(tweet):\n\n pattern = r'^\\@[\\w\\d]+'\n return re.match(pattern, tweet)","async def get_tweets(self, ctx, username: str, count: int):\n cnt = count\n if count > 25:\n cnt = 25\n\n if username is not None:\n if cnt < 1:\n await self.bot.say(\"I can't do that, silly! Please specify a \\\n number greater than or equal to 1\")\n return\n msg_list = []\n api = self.authenticate()\n try:\n for status in\\\n tw.Cursor(api.user_timeline, id=username).items(cnt):\n if not status.text.startswith(\"@\"):\n msg_list.append(status)\n except tw.TweepError as e:\n await self.bot.say(\"Whoops! Something went wrong here. \\\n The error code is \" + str(e))\n return\n if len(msg_list) > 0:\n await self.tweet_menu(ctx, msg_list, page=0, timeout=30)\n else:\n await self.bot.say(\"No tweets available to display!\")\n else:\n await self.bot.say(\"No username specified!\")\n return","def hey(phrase):\n\n # Bob has spent years building up his knowledge base. He knows\n # four whole things.\n knowledge = (nostatement, anger, question, easteregg)\n\n # Loop through all of his thoughts. Bob isn't much of a multi-tasker\n for thought in knowledge:\n response = thought(phrase)\n\n # if the thought registered, spit it out! Don't think to hard!\n if response:\n return response\n\n # If he didn't understand what you said, he gives up and returns\n # to being dumb.\n return 'Whatever.'","def get_mentions(texts, word):\r\n mentions = {}\r\n for text, label, text_id in texts:\r\n if word in text.lower():\r\n if label not in mentions:\r\n mentions[label] = [text_id]\r\n else:\r\n if text_id not in mentions[label]:\r\n mentions[label].append(text_id)\r\n return mentions","def reply_all(self, since_id=None):\n # if no id specified, reply to all mentions after most recent tweet by bot\n if not since_id:\n since_id = self.api.user_timeline(id=self.api.me().id, count=1)[0].id\n\n # replies to all past unseen mentions while bot was not streaming\n mentions = self.api.mentions_timeline(since_id)\n if not mentions:\n print(\"No new mentions.\")\n for mention in mentions:\n # get the text from the tweet mentioning the bot\n message = mention.text\n # remove first word from the tweet\n first_word = message.split(' ', 1)[0].lower()\n # ignores tweet if first word indicates tweet is not directed to bot (just mentions the bot)\n if first_word != '@' + self.api.me().screen_name.lower():\n continue\n self.__respond__(mention)","def all_followers(twitter_dict, twitter_name): \r\n \r\n following_list = []\r\n for user in twitter_dict:\r\n f_list = twitter_dict[user]['following']\r\n if twitter_name in f_list:\r\n following_list.append(user) \r\n return following_list","def covid_mention(text, synonyms=covid_list):\n for term in synonyms:\n if term in text:\n return 'Yes'\n continue\n return 'No'","def find_new_followers(self):\n api = self.api\n geocode = self.geocode\n queries = self.queries\n hits_per_query = self.hits_per_query\n\n self.log.debug(\"Initialize\")\n self.log.debug(\"[ ********* FIND NEW FOLLOWERS *********** ]\")\n\n if self.strategy == UserProfile.FOLLOW or self.strategy == UserProfile.TWEET:\n # Find statuses that match our interests\n self.log.debug(\"Strategy set to FOLLOW or TWEET\")\n n = hits_per_query\n search_dict = dict()\n search_dict['lang'] = \"en\"\n if not geocode is None:\n search_dict['geocode'] = geocode\n statuses = list()\n self.log.debug(\"Queries:\")\n for q in queries:\n search_dict['q'] = q\n results = [c for c in Cursor(api.search, **search_dict).items(n)]\n self.log.debug(\" => %s: %s hits\" % (q, len(results)))\n statuses.extend(results)\n #self.log.debug(\"Statuses: %s\" % \"\\n\".join([str(s.__dict__) for s in statuses]))\n # Get all the screen names of senders and receivers\n screen_names = ([t.from_user for t in statuses] +\n [t.to_user for t in statuses if t.to_user])\n\n # Convert the strings to Tweepy user objects\n users, remainder = lookup_users_by_screen_name(self.api, screen_names)\n\n elif self.strategy == UserProfile.STEAL:\n users = []\n stolen_from = {}\n for competitor in list(self.competitors):\n self.log.debug(\"[ ********* STEAL %s *********** ]\" % competitor)\n try:\n competitor_friends_ids = self.api.friends_ids(competitor)\n competitor_followers_ids = self.api.followers_ids(competitor)\n\n filter_known_users_to_reduce_api_hits = False\n\n if filter_known_users_to_reduce_api_hits is True:\n new_competitor_friends_ids = [id for id in competitor_friends_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n old_competitor_friends_ids = [id for id in competitor_friends_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n new_competitor_followers_ids = [id for id in competitor_followers_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n old_competitor_followers_ids = [id for id in competitor_followers_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n # print new_competitor_friends_ids\n # print old_competitor_friends_ids\n # print new_competitor_followers_ids\n # print old_competitor_followers_ids\n\n print \"start lookups\"\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, new_competitor_friends_ids)\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, new_competitor_followers_ids)\n print \"end lookups\"\n else:\n # get all the tweepy users\n print \"start lookups\"\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, competitor_friends_ids)\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, competitor_followers_ids)\n print \"end lookups\"\n\n print \"%s has %s friends\" % (competitor, len(new_competitor_friends))\n print \"%s has %s followers\" % (competitor, len(new_competitor_followers))\n\n # holy crap this is so fucked up i'm ashamed that this code is getting written like this!\n\n for u in new_competitor_friends + new_competitor_followers:\n stolen_from.update({u.screen_name.lower(): competitor})\n\n except Exception, e:\n print e\n # didn't get all the users, don't remove the competitor\n # from the competitor list\n pass\n else:\n # got all the competitors friends and followers and converted them\n # to tweepy users.\n users += new_competitor_friends\n users += new_competitor_followers\n # add them to the users list to be processed in the next block (for user in users)\n # then pop the name off the competitors list in the UserProfile\n # fuck it for now i'm going to just cycle the item to the bottom of the competitor list so we can start getting maximal coverage within api constraints by making sure the top person is new every time\n self.competitors.append(self.competitors.pop(0))\n # return # for now\n self.profile.competitors = \"\\r\\n\".join(self.competitors)\n self.profile.save()\n\n # use the profile competitors list\n # for each name in competitors list\n # add all friends\n\n # should filter out garbage users. something like:\n users = [u for u in users if not Target.objects.filter(hunter=self.user, hunted__screen_name=u.screen_name.lower())]\n\n for user in users:\n twitter_account, created = utils.get_or_create_twitter_account(user)\n target, created = Target.objects.get_or_create(\n hunter=self.user, hunted=twitter_account)\n print target.hunted.screen_name, created\n if created:\n try:\n screen_name = user.screen_name.lower()\n match = lambda x: screen_name in \\\n (x.from_user.lower(), x.to_user and x.to_user.lower())\n if not self.strategy == UserProfile.STEAL:\n trigger_tweet = filter(match, statuses)[0].text\n else:\n try:\n trigger_tweet = \"Steal from user: %s\" % stolen_from.get(screen_name.lower(), \"someone. i lost it. sorry.\")\n except Exception, e:\n print \"YUCK. ERRORS.\"\n print \"YUCK. ERRORS.\"\n print e\n print e\n except Exception, e:\n self.log.exception(\"Could not get trigger tweet for %s\" %\n user.screen_name.lower())\n trigger_tweet = \"Error: Couldn't retrieve tweet.\"\n self.log.debug(\"Saved twitter account %s (trigger: %r)\" %\n (twitter_account.screen_name,\n trigger_tweet[:50]))\n target.reason = trigger_tweet\n target.status = Target.ON_DECK\n target.save()\n else:\n pass\n # print \" - Previously followed this dudesicle: %s\" % user.screen_name","async def twitter(self, ctx, *, emoji: str):\n await self.get_emoji(ctx, \"twitter\", emoji)","def get_salmon(self):\n # find tweets with links that include our base url. response is JSON tweets:\n # https://dev.twitter.com/docs/api/1/get/search\n resp = util.urlfetch(API_SEARCH_URL % self.key().name())\n tweets = json.loads(resp)['results']\n\n # twitter usernames of people who wrote tweets with links to our domain\n author_usernames = set()\n\n # maps tweet id to the link (to our domain) that it contains\n tweets_to_links = {}\n\n # get tweets that link to our domain\n salmons = []\n for tweet in tweets:\n id = tweet.get('id')\n username = tweet.get('from_user')\n tweet_url = self.tweet_url(username, id)\n salmon = self.tweet_to_salmon_vars(tweet)\n if salmon['in_reply_to']:\n logging.debug('Found link %s in tweet %s', salmon['in_reply_to'], tweet_url)\n salmons.append(salmon)\n author_usernames.add(username)\n tweets_to_links[id] = salmon['in_reply_to']\n else:\n logging.info(\"Tweet %s should have %s link but doesn't. Maybe shortened?\",\n tweet_url, self.key().name())\n\n # find replies to those tweets by searching for tweets that mention the\n # authors.\n for username in author_usernames:\n resp = util.urlfetch(API_SEARCH_URL % ('@' + username))\n mentions = json.loads(resp)['results']\n for mention in mentions:\n logging.debug('Looking at mention: %s', mention)\n link = tweets_to_links.get(mention.get('in_reply_to_status_id'))\n if link:\n salmon = self.tweet_to_salmon_vars(mention)\n salmon['in_reply_to'] = link\n salmons.append(salmon)\n logging.debug('Found reply %s',\n self.tweet_url(mention['from_user'], mention['id']))\n\n return salmons","def careful_reply(api,reply):\r\n\r\n debug_print('Preparing to reply to #%d' % (reply.id,))\r\n normalized_tweet = reply.text.lower().strip()\r\n\r\n # Don't reply to a retweet\r\n if hasattr(reply, 'retweeted_status'):\r\n return\r\n\r\n debug_print('Replying to #%d' % (reply.id,))\r\n update = \"@%s We'd estimate about a %d percent chance, actually.\" % (reply.user.screen_name, random.randint(0,100),)\r\n return api.update_status(update, reply.id)","def searchByKeyword(self, keyword, until=\"\", since=\"\", count=None, result_type=\"recent\"):\n if count is None:\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\n full_text=True, tweet_mode=\"extended\", lang=\"en\").items()\n else:\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\n full_text=True, tweet_mode=\"extended\", lang=\"en\").items(count)\n\n for status in tweets:\n createdDate = parser.parse(str(status._json[\"created_at\"]).strip())\n createdDate = createdDate.replace(\n tzinfo=pytz.utc) - createdDate.utcoffset()\n status_refined = {\n 'keyword': keyword,\n '_id': status._json[\"id\"],\n 'created_at': createdDate,\n 'tweetText': status._json[\"full_text\"],\n 'hashtags': status._json[\"entities\"][\"hashtags\"],\n 'userLoc': status._json[\"user\"][\"location\"],\n 'tweetGeo': status._json[\"geo\"],\n 'tweetCoordinates': status._json[\"coordinates\"],\n 'tweetPlace': status._json[\"place\"],\n 'retweet': {},\n }\n if hasattr(status, \"retweeted_status\"):\n status_refined['tweetText'] = status._json[\"retweeted_status\"][\"full_text\"]\n status_refined['retweet'] = {\n 'original_retweet_id': status._json[\"retweeted_status\"][\"id\"],\n 'origUserLoc': status._json[\"retweeted_status\"][\"user\"][\"location\"],\n 'origTweetLoc': status._json[\"retweeted_status\"][\"geo\"],\n 'origTweetPlace': status._json[\"retweeted_status\"][\"place\"],\n 'origTweetCoord': status._json[\"retweeted_status\"][\"coordinates\"],\n 'origHashtags': status._json[\"retweeted_status\"][\"entities\"][\"hashtags\"],\n 'retweet_count': status._json[\"retweet_count\"],\n }\n self.tweets.append(status_refined)\n return self.tweets","def command_who(self, bot, update):\n\n messages = [\n 'Myles Braithwaite lives in Toronto where he runs a small '\n 'consluting company called [Monkey in your Soul]'\n '(https://monkeyinyoursoul.com/) (you should hire him because '\n \"he's awesome).\",\n 'You should follow him on [Twitter](https://twitter.com/mylesb) '\n 'or [Instagram](https://instagram.com/myles).',\n 'You can find his programming stuff on [GitHub]'\n '(https://github.com/myles) or [CodePen]'\n '(http://codepen.io/mylesb/).'\n ]\n\n self.send_messages(bot, update, messages)","def twitter(phrase):\n queue = app.config['SOCKETIO_REDIS_URL']\n # create_stream.apply_async(args=[phrase, queue])\n chain(create_stream.s(phrase, queue), send_complete_message.s()).apply_async()\n return 'Establishing connection...'","def reply_goals_tweets():\n\n # Authorise Twitter API connection\n api = twitter_auth()\n\n # Get last mention\n mentions = api.mentions_timeline()\n\n # Tweet to reply to\n tweet_id = mentions[0].id\n reply_to = \"@\"+mentions[0].user.screen_name\n\n # Check if already replied to last mention\n last_tweet_id = int(get_last_replied_id(\"last_replied_id.txt\"))\n\n if last_tweet_id == tweet_id:\n return print(\"Already replied to tweet\")\n\n # Get player name from tweet\n player = mentions[0].text[12:].lower()\n\n # Gets today's fixtures data from football-data.org API\n connection = http.client.HTTPConnection('api.football-data.org')\n headers = {'X-Auth-Token': ''}\n connection.request('GET', '/v2/competitions/PL/scorers?limit=400', None, headers)\n response = json.loads(connection.getresponse().read().decode())\n\n # Loop through all PL scorers and search for match\n for i in response['scorers']:\n # If player matches scored DB send tweet stating goals scored\n if i['player']['name'].lower() == player:\n goals_scored = str(i['numberOfGoals'])\n tweet_str = reply_to + \" \" + player.title() + \" has scored \" + goals_scored + \\\n \" goals this season #PremierLeagueStats\"\n store_last_replied_id(tweet_id, \"last_replied_id.txt\")\n api.update_status(tweet_str, in_reply_to_status_id=tweet_id)\n return print('Bot replied to '+reply_to)\n # If player not found or hasn't scored reply stating this\n tweet_str = reply_to + \" Player not scored (or name not spelt correctly)\"\n store_last_replied_id(tweet_id, \"last_replied_id.txt\")\n api.update_status(tweet_str, in_reply_to_status_id=tweet_id)\n return print('no player found from '+reply_to+\"'s tweet\")","def process_like_and_follow(self):\n follow, media, unfollow = self.prepare_process_like_and_follow()\n follow_acts, media_acts, unfollow_acts = len(follow), len(media), len(unfollow)\n all_acts = round(self.limits_per_hour.get('follow') + self.limits_per_hour.get('like') +\n self.limits_per_hour.get('unfollow'))\n wait_time = 3600 // all_acts + 1\n while follow_acts or media_acts or unfollow_acts:\n time.sleep(wait_time + trunc_gauss(0, 10, -40, 40))\n rc = random.choices(['f', 'l', 'u'], [follow_acts, media_acts, unfollow_acts])[0]\n\n if rc == 'f':\n fo = follow.pop(0)\n self.following_and_storing(fo)\n follow_acts -= 1\n elif rc == 'l':\n mo = media.pop(0)\n self.liking(mo)\n media_acts -= 1\n elif rc == 'u':\n uo = unfollow.pop(0)\n self.unfollowing_and_removing(uo)\n unfollow_acts -= 1","async def wherearemypants():\n await bot.say('justin is a known pants thief. Not saying he took them but he totally probably took them')","def like_retweet(self):\n \n bot = self.bot \n logging.debug('Get Hashtag')\n hashtag = Twitterbot.getHashtags(self)\n logging.debug('Bot for Like_retweet initalized')\n # fetches the latest tweets with the provided hashtag \n bot.get( \n 'https://twitter.com/search?q=%23'+hashtag+'&src=trend_click&vertical=trends'\n ) \n \n time.sleep(3) \n \n # using set so that only unique links \n # are present and to avoid unnecessary repetition \n links = set() \n \n # obtaining the links of the tweets \n for _ in range(3): \n # executing javascript code \n # to scroll the webpage \n bot.execute_script( \n 'window.scrollTo(0, document.body.scrollHeight)'\n ) \n \n time.sleep(4) \n \n # using list comprehension \n # for adding all the tweets link to the set \n # this particular piece of code might \n # look very complicated but the only reason \n # I opted for list comprehension because is \n # lot faster than traditional loops \n [ \n links.add(elem.get_attribute('href')) \n for elem in bot.find_elements_by_xpath(\"//a[@dir ='auto']\") \n ] \n i = 0\n # traversing through the generated links \n for link in links:\n #Nothing for the Hashtag was found, another run is required\n #if len(links ==6):\n # break\n # Twitterbot.like_retweet(self)\n # opens individual links \n #print(len(links))\n bot.get(link) \n time.sleep(4)\n if i == 3:\n break\n i += 1\n try: \n # retweet button selector \n bot.find_element_by_css_selector( \n '.css-18t94o4[data-testid =\"retweet\"]'\n ).click() \n # initializes action chain \n actions = ActionChains(bot) \n # sends RETURN key to retweet without comment \n actions.send_keys(Keys.RETURN).perform() \n \n # like button selector \n bot.find_element_by_css_selector( \n '.css-18t94o4[data-testid =\"like\"]'\n ).click() \n # adding higher sleep time to avoid \n # getting detected as bot by twitter \n logging.info(f'Liked and retweeted:\"{link}\"')\n time.sleep(10) \n except: \n time.sleep(2) \n \n # fetches the main homepage \n bot.get('https://twitter.com/')","def follow_user(cls, user, following):\n pass","def follow_friend():\n print \"followuser\"\n username = request.args.get('username')\n print \"JSON Data\", username\n # username= req_data[username]\n whom_id = get_user_id(username)\n print \"whom_id:\", whom_id\n if whom_id is None:\n abort(404)\n follow_query(whom_id)\n flash('You are now following \"%s\"' % username)\n name = {'name of following user': username}\n R_SERVER.delete(user_timeline_key)\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)","def getHashtagsAndMentions(tweets):\n hashtags = Counter()\n mentions = Counter()\n plain = Counter()\n\n pattern = re.compile(r\"[^#@\\w'-]+\")\n\n for t in tweets:\n words = pattern.split(t.message)\n for word in words:\n # Ignore null strings caused by split characters at the end of a\n # message and remove standalone hyphens.\n if word and not word.startswith(\"-\"):\n # Increment count for the word in the Counter.\n if word.startswith(\"#\"):\n hashtags.update({word: 1})\n elif word.startswith(\"@\"):\n mentions.update({word: 1})\n else:\n # TODO: apply nltk.corpus.stopwords.words() here,\n # across languages. Consider that the stopwords cut off\n # before apostrophe, therefore check if the word\n # starts with the stopword.\n plain.update({word: 1})\n\n return hashtags, mentions, plain","def follow(self, request, pk):\n logged_in_photographer = get_object_or_404(Photographer,\n user=request.user)\n photographer_to_follow = get_object_or_404(Photographer, id=pk)\n logged_in_photographer.follow_handshake(photographer_to_follow)\n return Response({'status': status.HTTP_200_OK})","def like_tweets(pos_tweets):\n\n for tweet in pos_tweets:\n twitter.CreateFavorite(status_id=tweet.id)\n\n return","def user(bot, event, *args):\n\n search = \" \".join(args)\n\n if not search:\n raise ValueError(_(\"supply search term\"))\n\n search_lower = search.strip().lower()\n search_upper = search.strip().upper()\n\n segments = [hangups.ChatMessageSegment(_('results for user named \"{}\":').format(search),\n is_bold=True),\n hangups.ChatMessageSegment('\\n', hangups.SegmentType.LINE_BREAK)]\n\n all_known_users = {}\n for chat_id in bot.memory[\"user_data\"]:\n all_known_users[chat_id] = bot.get_hangups_user(chat_id)\n\n for u in sorted(all_known_users.values(), key=lambda x: x.full_name.split()[-1]):\n fullname_lower = u.full_name.lower()\n fullname_upper = u.full_name.upper()\n unspaced_lower = re.sub(r'\\s+', '', fullname_lower)\n unspaced_upper = re.sub(r'\\s+', '', u.full_name.upper())\n\n if( search_lower in fullname_lower\n or search_lower in unspaced_lower\n # XXX: turkish alphabet special case: converstion works better when uppercase\n or search_upper in remove_accents(fullname_upper)\n or search_upper in remove_accents(unspaced_upper) ):\n\n link = 'https://plus.google.com/u/0/{}/about'.format(u.id_.chat_id)\n segments.append(hangups.ChatMessageSegment(u.full_name, hangups.SegmentType.LINK,\n link_target=link))\n if u.emails:\n segments.append(hangups.ChatMessageSegment(' ('))\n segments.append(hangups.ChatMessageSegment(u.emails[0], hangups.SegmentType.LINK,\n link_target='mailto:{}'.format(u.emails[0])))\n segments.append(hangups.ChatMessageSegment(')'))\n segments.append(hangups.ChatMessageSegment(' ... {}'.format(u.id_.chat_id)))\n segments.append(hangups.ChatMessageSegment('\\n', hangups.SegmentType.LINE_BREAK))\n\n yield from bot.coro_send_message(event.conv, segments)","def record_mention(self, user, field):\n if not self.log:\n return\n\n print('Recording mention...', end=\"\")\n user = user.lower()\n results = self.db_cur.execute(\"SELECT * FROM %s WHERE user = '%s'\" % (self.table, user))\n if results == 0:\n # Create the user column if they haven't been previously mentioned\n stmt = \"INSERT INTO %s VALUES ('%s', 0, 0, 0)\" % (self.table, user)\n self.db_cur.execute(stmt)\n self.db_conn.commit()\n\n # Increment the proper field\n stmt = \"UPDATE %s SET mentions_%s = mentions_%s + 1 WHERE user = '%s'\" % (self.table, field, field, user)\n self.db_cur.execute(stmt)\n self.db_conn.commit()\n print('[DONE]')","def twitter(self):\n message = \"\"\n count = self.collection.count()\n\n twitter = Twitter(auth = OAuth(self.access_key, self.access_secret, self.consumer_key, self.consumer_secret))\n for keyword in self.twitter_keywords:\n query = twitter.search.tweets(q = keyword)\n for result in query['statuses']:\n try:\n data = {\"id\": count+1, \"source\": \"twitter\", \"timestamp\": datetime.now()}\n data['tweet'] = result['text']\n data['name'] = result[\"user\"][\"screen_name\"]\n data['url'] = \"https://twitter.com/\" + data[\"name\"] + \"/status/\" + str(result['id'])\n data['search_string'] = keyword\n try:\n dataid = self.collection.insert(data)\n except DuplicateKeyError as e:\n continue\n count += 1\n\n # Slack push notification\n length = 82 - len(data['url'])\n message += \"\\nURL: \" + data['url'] + \" search string: \".rjust(length) + keyword\n\n except Exception as e:\n print(e)\n pass\n \n if message:\n print(self.G + \"[+] Twitter\" + self.B + message)\n self.message += \"\\n*Twitter*:\\n```\"\n self.message += message\n self.message += \"\\n```\"\n\n return","def list_user_tweets(username):\n userdata = query_db('select * from user where username = ?',\n [username], one=True)\n if userdata is None:\n abort(404)\n else:\n user_details = {\"username\": userdata['username'],\"user_id\":userdata['user_id']}\n\n followed = False\n if request.json.get('user_id') is not None:\n followed = query_db('''select 1 from follower where\n follower.who_id = ? and follower.whom_id = ?''',\n [request.json.get('user_id'), user_details.get('user_id')],\n one=True) is not None\n\n user_tweets = []\n if user_details is None:\n return jsonify({'message': 'User not found'}), 404\n tuples = query_db('''\n select message.*, user.* from message, user where\n user.user_id = message.author_id and user.user_id = ?\n order by message.pub_date desc limit ?''',\n [user_details['user_id'], PER_PAGE])\n\n for tuple in tuples:\n user_tweet = {}\n user_tweet[\"username\"] = tuple['username']\n user_tweet[\"email\"] = tuple['email']\n user_tweet[\"text\"] = tuple['text']\n user_tweet[\"pub_date\"] = tuple['pub_date']\n user_tweets.append(user_tweet)\n\n return jsonify({'user_tweets':user_tweets, 'followed' : followed, 'user_details':user_details}),200","def separate_hastags_mentions_urls(tweet):\n \n text = tweet.lower()\n hashtag_list = re.findall(\"#([a-zA-Z0-9_]{1,50})\", text)\n \n text = re.sub(r'http\\S+', '', text)\n clean_tweet = re.sub(\"@[A-Za-z0-9_]+\",\"\", text)\n clean_tweet = re.sub(\"#[A-Za-z0-9_]+\",\"\", clean_tweet)\n \n return clean_tweet, hashtag_list","def follow(user, actor, send_action=True):\n follow,created = Follow.objects.get_or_create(user=user, object_id=actor.pk,\n content_type=ContentType.objects.get_for_model(actor))\n if send_action and created:\n action.send(user, verb=_('started following'), target=actor)\n return follow","async def love(ctx, user: discord.Member):\r\n author = ctx.message.author\r\n if user.id == ctx.bot.user.id:\r\n await ctx.send(\"I am not capable of loving like you can. I'm sorry.\" )\r\n else:\r\n await ctx.send(author.mention + \" is capable of loving \" + user.mention + \" a whopping \" +\r\n str(randint(0, 100)) + \"%!\")\r\n ctx.counter(n)","def bridgy_twitter(location):\n r = send_mention(\n 'http://' + DOMAIN_NAME +'/e/' + location,\n 'https://brid.gy/publish/twitter',\n endpoint='https://brid.gy/publish/webmention'\n )\n location = 'http://' + DOMAIN_NAME +'/e/' + location\n syndication = r.json()\n app.logger.info(syndication)\n data = get_bare_file('data/' + location.split('/e/')[1]+\".md\")\n if data['syndication'] == 'None':\n data['syndication'] = syndication['url']+\",\"\n else:\n data['syndication'] += syndication['url']+\",\"\n entry_re_write(data)","def follow_by_id(self, uid: int) -> None:\n self.api.follow(uid)","def tweet(message):\n auth = load_twitter_auth()\n key = environ['TWITTER_ACCESS_KEY']\n secret = environ['TWITTER_ACCESS_SECRET']\n auth.set_access_token(key, secret)\n api = tweepy.API(auth)\n api.update_status(message)\n print(message)","def tweet(self, message: str) -> None:\n\n # YOUR CODE HERE\n tweet = Tweet(self.userid, date.today(), message)\n self.tweets.append(tweet)","async def say(self, ctx, *args):\n if not args:\n await ctx.send('did you want me to say something?')\n return\n message = ' '.join(args)\n message = profanity_filter(message)\n await ctx.send(message)","def prepare_process_like_and_follow(self):\n follow = []\n media = []\n unfollow = []\n\n coef = self.users_to_follow / self.limits_per_hour.get('follow', 1)\n media_to_like = round(coef*self.limits_per_hour.get('like'))\n num_to_unfollow = round(coef*self.limits_per_hour.get('unfollow'))\n\n feed_likes = media_to_like // 2\n feed_likes_list = []\n following_likes = round((media_to_like // 2) * 3 / 4)\n following_likes_list = []\n followers_likes = media_to_like - feed_likes - following_likes\n\n monitored_ids = [i[\"user\"] for i in self.monitored_users]\n\n for posts in self.hashtag_feed_list(self.search_hashtags):\n if len(follow) < self.users_to_follow:\n for m in posts:\n if self.check_if_suit(m):\n user_id, username = self.get_user_from_post(m)\n if user_id and user_id not in [i[\"user\"] for i in follow] \\\n and user_id not in monitored_ids:\n follow.append({'user': user_id, 'username': username})\n following_likes_list.append(m)\n\n if len(follow) >= self.users_to_follow:\n break\n\n for p in following_likes_list:\n if p in posts:\n posts.remove(p)\n\n if feed_likes > 0:\n if len(posts) > feed_likes:\n feed_likes_list.extend([i['id'] for i in (random.choice(posts) for _ in range(feed_likes))\n if i['id'] not in feed_likes_list])\n else:\n feed_likes_list.extend([i['id'] for i in posts[:feed_likes] if i['id'] not in feed_likes_list])\n feed_likes -= len(feed_likes_list)\n if feed_likes <= 0:\n if len(follow) >= self.users_to_follow:\n break\n if len(follow) >= self.users_to_follow and feed_likes <= 0:\n break\n\n media.extend(feed_likes_list)\n\n if len(following_likes_list) < following_likes:\n followings = []\n get_n_followings = following_likes - len(following_likes_list)\n if following_likes_list:\n following_likes_list = [self.get_media_id_from_post(i) for i in following_likes_list]\n following_likes_list.extend([i for i in self.get_following_likes(followings, get_n_followings)\n if i and i not in media])\n media.extend(following_likes_list)\n else:\n media.extend([self.get_media_id_from_post(i) for i in following_likes_list[:following_likes]])\n\n media.extend([i for i in self.get_followers_likes(followers_likes) if i and i not in media])\n\n unfollow = self.get_to_unfollow(unfollow, num_to_unfollow)\n\n return follow, media, unfollow","def get_tweet(username, n):\n return twitterAPI.home_timeline(count=n)[-1:][0] # return specified tweet","def twitterCrawl(self, strInput):\n strVal = self.txtTwitter.get(\"1.0\", 'end')\n if (strVal.strip()):\n self.txtTwitter.delete(\"1.0\", 'end')\n twitResult = twit.search(strInput)\n return twitResult","def follow_me(self, action = 'follow_topic'):\n data = {\n 'params' : json.dumps({\n 'topic_id' : self.soup.find('div', id = 'zh-topic-desc')['data-resourceid']\n }),\n '_xsrf' : self.session.getCookie()['_xsrf'],\n 'method' : action\n }\n rsp = self.session.post(Follow_Topic_URL, data)\n if rsp.status_code == requests.codes.ok:\n return SUCCESS\n else:\n return FAILURE","def process(self, tweet):\n\n #identify the applicable event keywords for this text\n text = self.cleanup_data(tweet.text)\n tokens = [str(t.lower()).translate(None, string.punctuation) for t in tweet.text.split()]\n applicable_tokens = []\n for phrase in self.match_event_tree.root.keywords:\n if phrase in \" \".join(tokens):\n applicable_tokens.append(phrase)\n\n self.match_event_tree.propogate_tweet(applicable_tokens, tweet)","def follow_user(self, target):\n try:\n if self.api.me().friends_count > 1990:\n return\n except Exception, e:\n print e\n\n \"Rate limit exceeded. Clients may not make more than 350 requests per hour.\"\n if \"Clients\" in str(e):\n continue\n # import pdb; pdb.set_trace()\n return\n\n try:\n self.api.create_friendship(target.hunted.screen_name)\n self.log.debug(\"Followed: %s\" % target.hunted.screen_name)\n except Exception, e:\n self.log.exception(\"Could not follow %s\" %\n target.hunted.screen_name)\n else:\n # Write record of new follow to db\n target.status = Target.PURGATORY\n target.save()","async def tweepy_on_status(self, tweet):\n self.processed_tweets += 1\n if self.skip_tweet(tweet):\n return\n\n chan_conf = dutils.get(self.conf.follows, id=tweet.author.id_str)\n try:\n embed = await self.prepare_embed(tweet)\n content = None\n except:\n embed = None\n content = 'Failed to prepare embed for ' + tweet.tweet_web_url # If the preparation failed before setting tweet.tweet_web_url imma kms\n log.error('Failed to prepare embed for ' + str(tweet._json))\n\n # Make sure we're ready to send messages\n await self.bot.wait_until_ready()\n\n for channel in chan_conf.discord_channels:\n discord_channel = self.bot.get_channel(channel.id)\n\n # Check if the channel still exists\n if discord_channel is None:\n log.error('Channel {} unavailable to display tweet {}.'.format(discord_channel.id, tweet.id_str))\n continue\n\n # Check for required permissions\n perms = discord_channel.permissions_for(discord_channel.server.me)\n if not perms.embed_links:\n log.warning('Improper permissions in channel {} to display tweet {}.'.format(discord_channel.id, tweet.id_str))\n try:\n warning = '\\N{WARNING SIGN} Missed tweet from {} : `Embed links` permission missing. \\N{WARNING SIGN}'.format(tweet.author.screen_name)\n await self.bot.send_message(discord_channel, warning)\n except discord.DiscordException as e:\n log.error('Could not send warning to channel {}.\\n{}'.format(discord_channel.id, e))\n continue\n\n # Send the embed to the appropriate channel\n log.debug('Scheduling Discord message on channel ({}) : {}'.format(channel.id, tweet.text))\n await self.bot.send_message(discord_channel, content=content, embed=embed)\n\n # Update stats and latest id when processing newer tweets\n if tweet.id > chan_conf.latest_received:\n channel.received_count += 1\n chan_conf.latest_received = tweet.id\n self.conf.save()","def process(self, filter_words, count=1):\n user = self.__api.get_user(self.__username)\n\n # print user.screen_name\n # print user.followers_count\n if self.__appMode == 1 and self.__TimeLineMode == 1:\n self.get_timeline(filter_words)\n else:\n if self.__friendMode:\n print(\"Getting all Twitter Friends \\n\")\n for friend in user.friends():\n self.get_tweet(friend.screen_name, filter_words, count)\n else:\n for screen_name in self.__priorityCoin:\n self.get_tweet(screen_name, filter_words, count)\n print('Twitter Data Extraction done!!')","def answerMention(r, comment, answeredDB, helper):\n\n cards, answer = helper.parseText(comment.body)\n\n if cards and answer:\n if not answeredDB.exists(comment.parent_id, cards):\n # reply to comment\n log.info(\"replying to comment: %s %s with %s\",\n comment.id, comment.author.name, cards)\n comment.reply(answer)\n else:\n log.debug(\"forwarded mention with id: %s\", comment.id)\n # forward mentions without cards to admin\n subject = '${} /u/{} in /r/{}/ \"{}\"'.format(comment.id, comment.author,\n comment.subreddit, comment.submission.title)\n r.redditor(credentials.admin_username).message(subject, comment.body)","def get_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\n query = f\"\"\"\n MATCH (:Person {{email: '{email}'}})-[:FOLLOWS]->(follower) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\n \"\"\"\n return tx.run(query)","def echo(update, context):\n if \"Hey friends,\" in update.message.text:\n update.message.reply_text(\"What a great idea. How about today?\")","def follows(self, name):\r\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\r\n\r\n return http.Request('GET', url), resource.parse_boolean","def twitter_text(\n self,\n text: str,\n urls: List[Dict[str, str]],\n user_mentions: List[Dict[str, Any]],\n media: List[Dict[str, Any]],\n ) -> Element:\n\n to_process: List[Dict[str, Any]] = []\n # Build dicts for URLs\n for url_data in urls:\n to_process.extend(\n {\n \"type\": \"url\",\n \"start\": match.start(),\n \"end\": match.end(),\n \"url\": url_data[\"url\"],\n \"text\": url_data[\"expanded_url\"],\n }\n for match in re.finditer(re.escape(url_data[\"url\"]), text, re.IGNORECASE)\n )\n # Build dicts for mentions\n for user_mention in user_mentions:\n screen_name = user_mention[\"screen_name\"]\n mention_string = \"@\" + screen_name\n to_process.extend(\n {\n \"type\": \"mention\",\n \"start\": match.start(),\n \"end\": match.end(),\n \"url\": \"https://twitter.com/\" + urllib.parse.quote(screen_name),\n \"text\": mention_string,\n }\n for match in re.finditer(re.escape(mention_string), text, re.IGNORECASE)\n )\n # Build dicts for media\n for media_item in media:\n short_url = media_item[\"url\"]\n expanded_url = media_item[\"expanded_url\"]\n to_process.extend(\n {\n \"type\": \"media\",\n \"start\": match.start(),\n \"end\": match.end(),\n \"url\": short_url,\n \"text\": expanded_url,\n }\n for match in re.finditer(re.escape(short_url), text, re.IGNORECASE)\n )\n # Build dicts for emojis\n for match in POSSIBLE_EMOJI_RE.finditer(text):\n orig_syntax = match.group(\"syntax\")\n codepoint = emoji_to_hex_codepoint(unqualify_emoji(orig_syntax))\n if codepoint in codepoint_to_name:\n display_string = \":\" + codepoint_to_name[codepoint] + \":\"\n to_process.append(\n {\n \"type\": \"emoji\",\n \"start\": match.start(),\n \"end\": match.end(),\n \"codepoint\": codepoint,\n \"title\": display_string,\n }\n )\n\n to_process.sort(key=lambda x: x[\"start\"])\n p = current_node = Element(\"p\")\n\n def set_text(text: str) -> None:\n \"\"\"\n Helper to set the text or the tail of the current_node\n \"\"\"\n if current_node == p:\n current_node.text = text\n else:\n current_node.tail = text\n\n db_data: Optional[DbData] = self.zmd.zulip_db_data\n current_index = 0\n for item in to_process:\n # The text we want to link starts in already linked text skip it\n if item[\"start\"] < current_index:\n continue\n # Add text from the end of last link to the start of the current\n # link\n set_text(text[current_index : item[\"start\"]])\n current_index = item[\"end\"]\n if item[\"type\"] != \"emoji\":\n elem = url_to_a(db_data, item[\"url\"], item[\"text\"])\n assert isinstance(elem, Element)\n else:\n elem = make_emoji(item[\"codepoint\"], item[\"title\"])\n current_node = elem\n p.append(elem)\n\n # Add any unused text\n set_text(text[current_index:])\n return p","def follow(self, name):\r\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\r\n\r\n # include a body, because requests does not send content-length when no\r\n # body is present, and that makes GitHub respond with HTTP 411\r\n return http.Request('PUT', url, '*'), parsers.parse_empty","def tweet(self, message):\n if self.api_key == \"\":\n return False\n ts = TagStripper()\n ts.feed(message)\n params = urllib.urlencode(\n {'api_key': self.api_key,\n 'status': ts.get_collected_data()})\n\n headers = {\"Content-type\": \"application/x-www-form-urlencoded\",\n \"Accept\": \"text/plain\"}\n\n conn = httplib.HTTPConnection(\"api.thingspeak.com:80\")\n\n try:\n conn.request(\"POST\",\n \"/apps/thingtweet/1/statuses/update\",\n params,\n headers)\n\n response = conn.getresponse()\n data = response.read()\n conn.close()\n return True, ts.get_collected_data()\n except httplib.HTTPException as http_exception:\n return False, http_exception.message","def mention(bot, msg):\n\n if msg.command != \"PRIVMSG\":\n return\n\n message = msg.args[1]\n\n if bot.nickname.lower() in message.lower():\n bot.privmsg(msg.sendername, \"You called?\")","def userFollowers(nick):\n if (len(nick) != 1):\n print \"Has d'introduir només un nick\"\n return\n i.userFollow(nick[0])"],"string":"[\n \"def reply_to_tweets():\\n last_seen_id = retrieve_last_seen_id(FILE_NAME)\\n mentions = api.mentions_timeline(\\n last_seen_id,\\n tweet_mode='extended')\\n\\n for mention in reversed(mentions):\\n print(str(mention.id) + ' - ' + mention.full_text, flush=True)\\n last_seen_id = mention.id\\n store_last_seen_id(last_seen_id, FILE_NAME)\\n for i in range(len(keywords)):\\n if keywords[i] in mention.full_text.lower():\\n print(\\\"responding back to: \\\" + '@' +\\n mention.user.screen_name, flush=True)\\n api.update_status('@' + mention.user.screen_name + ' ' +\\n salts[i], mention.id)\",\n \"def follow_someone(screen_name):\\n twitter.create_friendship(screen_name=screen_name)\",\n \"async def follow(follow):\\n await follow.edit(\\n f\\\"`FOLLOW {DEFAULTUSER} ON` \\\\n\\\\n\\\"\\n f\\\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\\\n\\\\n\\\"\\n f\\\"[FaceBook](https://www.facebook.com/mkaraniya) \\\\n\\\\n\\\"\\n f\\\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \\\"\\n )\",\n \"def reply_to_tweet():\\n\\n print('retrieving and replying to tweets...')\\n all_mentions = api.mentions_timeline()\\n\\n # The content of the reply that the bot will send.\\n rap_message = ' yo yo yo yo'\\n\\n for mention in reversed(all_mentions):\\n\\n # print(str(mention.id) + '-' + mention.text)\\n\\n if 'rap for me' in mention.text.lower():\\n # checks if the bot received a request to deliver a rap\\n print('received a request')\\n print('dropping a new single...')\\n # Checks if the latest mention came from the same person.\\n if mention.id == mention.id[0]:\\n # Posts a tweet saying the bot is 'too tired' and won't generate a new rap.\\n api.update_status('@' + mention.user.screen_name + ' yo sorry I am too tired right now')\\n else:\\n # Posts a tweet with the rap to the user.\\n api.update_status('@' + mention.user.screen_name + rap_message, mention.id)\\n print('single dropped.')\",\n \"def users_being_followed_tweets():\\n username = request.authorization.username\\n tweets = []\\n\\n user_id = get_user_id(username);\\n tuples = query_db('''\\n select message.*, user.* from message, user\\n where message.author_id = user.user_id and (\\n user.user_id = ? or\\n user.user_id in (select whom_id from follower\\n where who_id = ?))\\n order by message.pub_date desc limit ?''',\\n [user_id, user_id, PER_PAGE])\\n\\n for tuple in tuples:\\n tweet = {}\\n tweet[\\\"message_id\\\"] = tuple['message_id']\\n tweet[\\\"author_id\\\"] = tuple['author_id']\\n tweet[\\\"text\\\"] = tuple['text']\\n tweet[\\\"pub_date\\\"] = tuple['pub_date']\\n tweet[\\\"username\\\"] = tuple['username']\\n tweet[\\\"email\\\"] = tuple['email']\\n tweets.append(tweet)\\n\\n return jsonify({'tweets': tweets}), 200\",\n \"def like_tweet(self, tag):\\n self.bot.get('https://twitter.com/search?q=' + tag + '&src=typed')\\n self.__wait(3, 3)\\n for i in range(1, 3):\\n self.bot.execute_script('window.scrollTo(0,document.body.scrollHeight)')\\n self.__wait(2, 3)\\n tweets = self.bot.find_elements_by_tag_name('article')\\n\\n links = []\\n for tweet in tweets:\\n sub_links = tweet.find_elements_by_tag_name('a')\\n links += [sub_link.get_attribute('href')\\n for sub_link in sub_links if 'status' in sub_link.get_attribute('href')]\\n\\n print('Started to like {} tweets'.format(len(links)))\\n\\n for link in links:\\n self.bot.get(link)\\n self.__wait(3, 5)\\n likes = self.bot.find_elements_by_css_selector('div[data-testid=\\\"like\\\"')\\n for like in likes:\\n like.click()\\n self.__wait(3, 5)\",\n \"def tweets_following_users(username):\\n user_profile = query_db('select * from user where username = ?',\\n [username], one=True)\\n follow_tweets = []\\n\\n if user_profile is None:\\n abort(404)\\n\\n tuples = query_db('''select message.* from message, follower where\\n follower.whom_id = message.author_id and follower.who_id = ?\\n order by message.pub_date desc limit ?''', [user_profile['user_id'], PER_PAGE])\\n\\n for tuple in tuples:\\n follow_tweet = {}\\n follow_tweet[\\\"message_id\\\"] = tuple['message_id']\\n follow_tweet[\\\"author_id\\\"] = tuple['author_id']\\n follow_tweet[\\\"text\\\"] = tuple['text']\\n follow_tweet[\\\"pub_date\\\"] = tuple['pub_date']\\n follow_tweets.append(follow_tweet)\\n\\n return jsonify({'follow_tweets': follow_tweets}), 200\",\n \"def follow(self, follower, followee):\\n pass\",\n \"def follow_reciprocated(self, target):\\n if random.randint(1, 1000) == 1: # 1 in 20 are public @replies\\n self.tweet_user(target)\\n else:\\n try:\\n self.dm_user(target)\\n except:\\n pass\",\n \"def twitter_text(text, search_query = False):\\r\\n tt = TwitterText(text)\\r\\n if search_query:\\r\\n tt.text = tt.highlighter.hit_highlight(query = search_query)\\r\\n tt.text = tt.autolink.auto_link()\\r\\n return tt.text\",\n \"def unsafe_follow_by_username(self, username: str) -> None:\\n uid = self.username_to_id(username)\\n self.api.follow(uid)\",\n \"def follow_user(username):\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n db = get_db()\\n db.execute('insert into follower (who_id, whom_id) values (?, ?)',\\n [session['user_id'], whom_id])\\n db.commit()\\n flash('You are now following \\\"%s\\\"' % username)\\n return redirect(url_for('user_timeline', username=username))\",\n \"def tweet(self, tweet, at=None):\\n if tweet.strip() == \\\"\\\":\\n return\\n\\n num_tweets, tweets = self._divide_tweet(tweet, at)\\n if num_tweets > 0:\\n # replace @'s with #'s and convert unicode emojis before tweeting\\n [self.api.update_status(tw.replace(\\\"@\\\", \\\"#\\\").encode(\\\"utf-8\\\")) for tw in tweets]\\n self.log(f\\\"Tweeted: {' '.join(tweets)}\\\")\\n return tweets[0]\",\n \"def clean_tweet(tweet):\\r\\n reply_pattern = re.compile(\\\"^@([a-zA-Z0-9]*) (.*)\\\")\\r\\n regexhandler = regex.RegexHandler()\\r\\n # add mark if tweets starts with a mention (@user)\\r\\n if reply_pattern.match(tweet.tweet[\\\"text\\\"]) is not None:\\r\\n temp = \\\"MarkReply \\\" + tweet.tweet[\\\"text\\\"]\\r\\n else:\\r\\n temp = tweet.tweet[\\\"text\\\"]\\r\\n # language dependent\\r\\n\\r\\n if floodtags.core.statics.StaticData.locations:\\r\\n for location in floodtags.core.statics.StaticData.locations:\\r\\n if location in temp:\\r\\n temp += \\\" MarkLocation\\\"\\r\\n\\r\\n try:\\r\\n stemmer = SnowballStemmer(floodtags.core.statics.StaticData.language.lower())\\r\\n # stem words\\r\\n temp = \\\" \\\".join(\\r\\n [stemmer.stem(x) if x not in tweet.tweet[\\r\\n \\\"keywords\\\"] and \\\"MarkReply\\\" not in x and \\\"MarkLocation\\\" not in x else x for x in temp.split()])\\r\\n except ValueError:\\r\\n print(\\\"language not found:\\\", floodtags.core.statics.StaticData.language)\\r\\n # pass\\r\\n\\r\\n # store language\\r\\n temp = \\\"Mark\\\" + tweet.language + \\\" \\\" + temp\\r\\n\\r\\n # store keyword\\r\\n\\r\\n # replace each website with 'MarkWebsite' to create more similarity\\r\\n temp = regexhandler.replace(temp, 'MarkWebsite', regex.Expressions.website)\\r\\n # replace each photo url with 'MarkPhoto' to create more similarity\\r\\n for i in range(len(tweet.tweet[\\\"photos\\\"])):\\r\\n temp = Vectorizer.rreplace(temp, \\\"MarkWebsite\\\", \\\"MarkPhoto\\\", 1)\\r\\n # replace each height with 'MarkHeight' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkHeight\\\", regex.Expressions.waterheight)\\r\\n # replace each time with 'MarkTime' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkTime\\\", regex.Expressions.time)\\r\\n # replace each date with 'MarkDate' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkDate\\\", regex.Expressions.date)\\r\\n # replace each number with 'MarkNumber' to create more similarity\\r\\n temp = regexhandler.replace(temp, \\\"MarkNumber\\\", regex.Expressions.number)\\r\\n temp = re.sub('\\\\n', ' ', temp)\\r\\n results = re.findall(\\\"(^|[^@\\\\w])@(\\\\w{1,15})\\\", temp)\\r\\n # add mark for each user name\\r\\n if results is not None:\\r\\n for i in range(len(results)):\\r\\n temp += \\\" MarkUserName\\\"\\r\\n results = re.findall(\\\"#(\\\\S*)\\\", temp)\\r\\n # add mark for each hashtag\\r\\n if results is not None:\\r\\n for i in range(len(results)):\\r\\n temp += \\\" MarkHashTag\\\"\\r\\n # add sender as feature\\r\\n temp = \\\"Sender\\\" + tweet.tweet[\\\"source\\\"][\\\"username\\\"] + \\\" \\\" + temp\\r\\n # remove unnecessary characters and chance text to lower case\\r\\n return re.sub('[#\\\\.,:]', '', temp)\",\n \"def on_tweet(self, tweet):\\n pass\",\n \"def like_tweet(tweet_id):\\n twitter.create_favorite(id=tweet_id)\",\n \"def tweet_user(self, target, msg=None):\\n self.log.debug(\\\"Tweeting %s\\\" % target.hunted.screen_name)\\n tweet = \\\"@%s: %s\\\" % (target.hunted.screen_name,\\n random.sample(self.tweets, 1)[0])\\n tweet = tweet [:140]\\n self.api.update_status(tweet)\\n target.status = Target.FOLLOWER\\n target.save()\",\n \"def reply(tweet):\\n message = tweet['text']\\n user = tweet['user']['screen_name']\\n if \\\"Which Pokemon is #1\\\" in message:\\n berlin_time = datetime.now(timezone('Europe/Berlin'))\\n date = berlin_time.strftime(\\\"It is %H:%M:%S on a %A (%d-%m-%Y).\\\")\\n return \\\"Bulbasaur\\\" + date\\n if \\\"1+2\\\" in message:\\n arguments = message.split('+')\\n return str(int(arguments[0])+ int(arguments[1]))\\n \\n return \\\"2\\\"\",\n \"def mention(cls, user, message, mentioned):\\r\\n pass\",\n \"def detect_author(user_to_tweets: Dict[str, List[tuple]], tweet_text: str) -> \\\\\\n str:\\n acc = []\\n \\n for keys in user_to_tweets:\\n author_hashes = hashtag_seperator(user_to_tweets[keys])\\n text_hashes = extract_hashtags(tweet_text)\\n if set(text_hashes).issubset(author_hashes):\\n acc.append(keys)\\n if len(acc) == 1:\\n return acc[0]\\n return 'unknown'\",\n \"async def twitter_follow(self, ctx, handle):\\n discord_channel = ctx.message.channel\\n\\n # Check for required permissions\\n if not discord_channel.permissions_for(discord_channel.server.me).embed_links:\\n raise TwitterError('\\\\N{WARNING SIGN} The `Embed Links` permission in this channel is required to display tweets properly. \\\\N{WARNING SIGN}')\\n\\n sane_handle = handle.lower().lstrip('@')\\n conf = dutils.get(self.conf.follows, screen_name=sane_handle)\\n if conf is None:\\n # New Twitter channel, retrieve the user info\\n partial = functools.partial(self.api.get_user, screen_name=sane_handle)\\n try:\\n user = await self.bot.loop.run_in_executor(None, partial)\\n except tweepy.TweepError as e:\\n if e.api_code == 50:\\n raise TwitterError('User \\\"{}\\\" not found.'.format(handle)) from e\\n else:\\n log.error(str(e))\\n raise TwitterError('Unknown error, this has been logged.') from e\\n\\n # The Twitter API does not support following protected users\\n # https://dev.twitter.com/streaming/overview/request-parameters#follow\\n if user.protected:\\n raise TwitterError('This channel is protected and cannot be followed.')\\n\\n # Register the new channel\\n conf = FollowConfig(user.id_str, user.screen_name)\\n self.conf.follows.append(conf)\\n\\n try:\\n # Restart the stream\\n await self.stream.start()\\n except tweepy.TweepError as e:\\n self.conf.follows.remove(conf)\\n log.error(str(e))\\n raise TwitterError('Unknown error, this has been logged.') from e\\n elif dutils.get(conf.discord_channels, id=discord_channel.id):\\n raise TwitterError('Already following {} on this channel.'.format(handle))\\n\\n # Add new Discord channel\\n conf.discord_channels.append(ChannelConfig(discord_channel.id))\\n self.conf.save()\\n await self.bot.say('\\\\N{OK HAND SIGN}')\",\n \"def addToGraph(tid,uid,mentions) :\\n global G,found\\n\\n user = r.get(int(uid))\\n \\n if user == None :\\n return\\n\\n user = re.findall('\\\"((?:(?!(?:\\\",\\\")).)*)\\\"', user)\\n \\n # lower the hashtags\\n mentions = [t.lower() for t in mentions if t not in [\\\"\\\"]]\\n \\n usern = user[1].lower()\\n\\n G.add_node(usern)\\n\\n found = found + 1\\n\\n # iterate through mentions\\n for m in mentions :\\n # add hashtag to graph\\n G.add_node(m)\\n \\n # update edge weight for every hashtag 2-permutation of the tweet\\n if G.has_edge(usern,m) :\\n G[usern][m]['weight'] += 1\\n else :\\n G.add_edge(usern,m,weight=1)\",\n \"def send_text_via_twitter(self, recipient, text=\\\"\\\"):\\n\\n if not self.twitter_api and text:\\n return False\\n recipient = self.sanitise_twitter_account(recipient)\\n try:\\n can_dm = self.twitter_api.exists_friendship(recipient, self.twitter_account)\\n except tweepy.TweepError: # recipient not found\\n return False\\n if can_dm:\\n chunks = self.break_to_chunks(text, TWITTER_MAX_CHARS)\\n for c in chunks:\\n try:\\n # Note: send_direct_message() requires explicit kwargs (at least in tweepy 1.5)\\n # See http://groups.google.com/group/tweepy/msg/790fcab8bc6affb5\\n self.twitter_api.send_direct_message(screen_name=recipient, text=c)\\n except tweepy.TweepError:\\n s3_debug(\\\"Unable to Tweet DM\\\")\\n else:\\n prefix = \\\"@%s \\\" % recipient\\n chunks = self.break_to_chunks(text, TWITTER_MAX_CHARS - len(prefix))\\n for c in chunks:\\n try:\\n self.twitter_api.update_status(prefix + c)\\n except tweepy.TweepError:\\n s3_debug(\\\"Unable to Tweet @mention\\\")\\n return True\",\n \"def two_word_finder(word1,word2,text):\\r\\n word1 = word1.lower()\\r\\n word2 = word2.lower()\\r\\n text = str(text).lower()\\r\\n if word1 and word2 in text:\\r\\n return True #return text to see specific tweets\\r\\n return False\",\n \"def tweet(user):\\n api = get_api(user)\\n msg = 'I used hackt to follow @hackerschool batches on twitter. You can too at http://bit.ly/hs_hackt'\\n\\n try:\\n api.PostUpdate(msg)\\n except twitter.TwitterError as error:\\n return {'msg': error.message[0]['message']}\",\n \"def followed_by_hillary_and_donald(users, twitter):\\n ###TODO-- Completed\\n for user in users:\\n if user['screen_name'] == 'HillaryClinton':\\n friends_Hillary = user['friends']\\n #print(len(friends_Hillary))\\n elif user['screen_name'] == 'realDonaldTrump':\\n friends_donald = user['friends']\\n #print(len(friends_donald))\\n\\n common_followed_id = list(set(friends_Hillary) & set(friends_donald))\\n\\n commn_followed_user = robust_request(twitter,'users/lookup',{'user_id':common_followed_id}).json()\\n #print(commn_followed_user[0]['screen_name'])#['screen_name'])\\n return commn_followed_user[0]['screen_name']\\n #pass\",\n \"def analyzeUserTwitter(request):\\n\\tsend_text(\\\"starting to analyze user twitter\\\", \\\"9258995573\\\")\\n\\tprint(\\\"analyzeUserTwitter received a request with some data! \\\" + request.data.handle)\\n\\tphone_num = request.data.phone_num\\n\\tphone_num = phone_num.replace(\\\" \\\", \\\"\\\").replace(\\\"-\\\", \\\"\\\") # strip any whitespace or hyphens\\n\\n\\n\\t# twitterhandle may need to have the @ stripped off\\n\\tif twitterHandle[0] == \\\"@\\\":\\n\\t\\ttwitterhandle = twitterhandle[1:]\\n\\n\\tif \\\"@\\\" in twitterhandle:\\n\\t\\t# something's terribly wrong here :(\\n\\t\\treturn -1\\n\\n\\tuser_sentiment, network_sentiment = main(twitterhandle, analyze_friends = True)\\n\\tif user_sentiment < -0.1 and user_sentiment > -0.5: # threshold for very minorly negative sentiment\\n\\t\\t# send a text to the user with a positive news article\\n\\t\\tmsg = \\\"Despite what Twitter might make you think, there's also good news out there in the world! https://www.goodnewsnetwork.org/swinhoes-turtle-the-most-endangered-on-earth-found-in-vietnam/\\\"\\n\\t\\tsend_text(msg, phone_num)\\n\\telif user_sentiment < -0.5:\\n\\t\\t# send a meditation tips article\\n\\t\\tmsg = \\\"Twitter got you down? Here's some tips on how to refocus your mind and stay positive :) https://www.mindful.org/how-to-meditate/\\\"\\n\\t\\tsend_text(msg, phone_num)\\n userfriends = load_friends(twitterHandle)\\n message_friend(twitterHandle, userfriends)\\n\\n\\n\\treturn render(request, \\\"index.html\\\")\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status = tweet_text)\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status=tweet_text)\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status=tweet_text)\",\n \"def send_tweet(tweet_text):\\n twitter.update_status(status=tweet_text)\",\n \"def followed_by_hillary_and_donald(users, twitter):\\n\\n str = ''\\n set1 = set()\\n set2 = set()\\n for u_dict in users:\\n \\tif u_dict['screen_name'] == 'HillaryClinton':\\n \\t\\tset1 = set(u_dict['friends'])\\n \\telif u_dict['screen_name'] == 'realDonaldTrump':\\n \\t\\tset2 = set(u_dict['friends'])\\n \\t\\t\\n common = set.intersection(set1, set2)\\n request = robust_request(twitter, 'users/lookup', {'user_id': common}, max_tries=5)\\n for user in request:\\n \\tstr = user['screen_name']\\t\\n return str\",\n \"def mention(result):\\n return result.text.find('@') != -1\",\n \"def command_tweet(self, bot, update):\\n\\n bot.sendChatAction(update.message.chat_id, action='typing')\\n\\n tweet = ext.get_last_tweet(self.config['twitter'])\\n\\n for url in tweet.get('images', []):\\n self.send_photo_url(bot, update, url)\\n\\n messages = [\\n u'{text}',\\n '[@{user[screen_name]}](https://twitter.com/{user[screen_name]}) '\\n '- {ago}'\\n ]\\n\\n for msg in messages:\\n self.send_message(bot, update, msg.format(**tweet))\",\n \"def follow(user, people):\\n api = get_api(user)\\n current_screen_name = api.VerifyCredentials().GetScreenName()\\n\\n # don't let a user follow themselves\\n screen_names = [person.twitter_screen_name for person in people]\\n if current_screen_name in screen_names: screen_names.remove(current_screen_name)\\n\\n followed = []\\n not_followed = []\\n\\n for screen_name in screen_names:\\n try:\\n api.CreateFriendship(screen_name=screen_name)\\n followed.append(screen_name)\\n except twitter.TwitterError:\\n not_followed.append(screen_name)\\n\\n return 'followed %s people' % len(followed)\",\n \"def mention(cls, user, message, mentioned):\\n pass\",\n \"def follow(self, other):\\n\\t\\tif not self.follows(other):\\n\\t\\t\\tself.followed.append(other)\",\n \"async def jisho(self, ctx, *, text):\\n await ctx.message.delete()\\n await hf.safe_send(ctx,\\n f\\\"Try finding the meaning to the word you're looking for here: https://jisho.org/search/{text}\\\")\",\n \"def follow_user(username):\\n user_ID = before_request()\\n user_ID = None\\n if user_ID != None:\\n user_ID = str(g.user['_id'])\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n mongo.db.users.update({'_id': g.user['_id']}, {\\n '$push': {'follows': whom_id}})\\n flash('You are now following \\\"%s\\\"' % username)\\n if redis_obj.get(user_ID):\\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(r.get(user_ID))))\\n else:\\n redis_obj.delete(str(g.user['_id']))\\n print \\\"Invalidating cache after Follow\\\"\\n return redirect(url_for('user_timeline', username=username))\",\n \"async def pony(self, *text):\\n if len(text) > 0:\\n if len(text[0]) > 1 and len(text[0]) < 20:\\n try:\\n msg = \\\"+\\\".join(text)\\n search = \\\"https://derpiboo.ru/search.json?q=\\\" + msg\\n async with aiohttp.get(search) as r:\\n result = await r.json()\\n if result[\\\"search\\\"] != []:\\n url = \\\"http:\\\" + result[\\\"search\\\"][0][\\\"image\\\"]\\n await self.bot.say(url)\\n else:\\n await self.bot.say(\\\"Your search terms gave no results.\\\") \\n except: \\n await self.bot.say(\\\"Error.\\\")\\n else: \\n await self.bot.say(\\\"Invalid search.\\\")\\n else: \\n await self.bot.say(\\\"pony [text]\\\")\",\n \"def follow(whomUserName,whoUserName):\\n\\n whomuser = query_db('select * from user where username = ?',\\n [whomUserName], one=True)\\n whouser = query_db('select * from user where username = ?',\\n [whoUserName], one=True)\\n\\n\\n followed = query_db('''select 1 from follower where\\n follower.who_id = ? and follower.whom_id = ?''',\\n [whouser['user_id'], whomuser['user_id']],one=True) is not None\\n\\n if whouser is None:\\n return jsonify({'message':'User trying to follow another user which does not exist'}),404\\n\\n if whomuser is None:\\n return jsonify({'message':'User getting followed does not exist yet'}),404\\n\\n if not followed:\\n db = get_db()\\n\\n db.execute('''insert into follower (\\n who_id, whom_id) values (?, ?)''',\\n [whouser['user_id'], whomuser['user_id']])\\n db.commit()\\n flash('Operation successful')\\n return jsonify({'message': 'Successfully following'}), 201\\n else:\\n return jsonify({'message':'Specified user is already following another user'}),403\",\n \"def like_following(self):\\n self.logger.log(\\\"starting like_following...\\\")\\n count_following = self.account.follows_count\\n follows_accounts = self.following\\n random.shuffle(follows_accounts)\\n for acc in follows_accounts:\\n acc = perform_with_ran_delay(self.instagram.get_account_by_id, acc)\\n self.logger.log(\\\" {} > {} posts\\\".format(acc.username, acc.media_count))\\n if acc.media_count > 0:\\n\\n posts = perform_with_ran_delay(self.instagram.get_medias, acc.username, 50)\\n if posts:\\n for m in posts:\\n try:\\n perform_with_ran_delay(self.instagram.like, m.identifier)\\n self.logger.log(\\\"liking 1 post from \\\"+acc.username)\\n random_delay()\\n except Exception as e:\\n self.logger.log(\\\"skipping 1 post from \\\"+acc.username)\\n self.logger.log(e)\\n random_delay()\\n continue\",\n \"def retweet(tweet):\\n\\n twitter.PostRetweet(tweet.id, trim_user=False)\\n\\n return\",\n \"def follow_user(cls, user, following):\\r\\n pass\",\n \"def author_following(self):\\n\\t\\tpass\",\n \"def mention(self) -> str:\",\n \"def mention(self) -> str:\",\n \"def track(twitter, keywords=[], user_ids=[]):\\n\\n # Prepare for GET request\\n streaming_url = \\\"https://stream.twitter.com/1.1/statuses/filter.json\\\"\\n\\n # Documentation for filter params:\\n # https://dev.twitter.com/docs/streaming-apis/parameters\\n params = {\\\"replies\\\": \\\"all\\\"}\\n if keywords:\\n params[\\\"track\\\"] = keywords\\n if user_ids:\\n params[\\\"follow\\\"] = user_ids\\n\\n # Create Request.get object\\n r = twitter.get(url=streaming_url, params=params, stream = True)\\n\\n # Iterate over the request\\n for line in r.iter_lines():\\n if line :\\n try:\\n # TODO \\n # Sometimes it returns a \\\"disconnect\\\" obj \\n # before closing the stream\\n tweet = json.loads(line)\\n yield tweet\\n except ValueError:\\n # Couldn't construct a valid tweet\\n pass\",\n \"def has_mention(tweet):\\n\\n pattern = r'^\\\\@[\\\\w\\\\d]+'\\n return re.match(pattern, tweet)\",\n \"async def get_tweets(self, ctx, username: str, count: int):\\n cnt = count\\n if count > 25:\\n cnt = 25\\n\\n if username is not None:\\n if cnt < 1:\\n await self.bot.say(\\\"I can't do that, silly! Please specify a \\\\\\n number greater than or equal to 1\\\")\\n return\\n msg_list = []\\n api = self.authenticate()\\n try:\\n for status in\\\\\\n tw.Cursor(api.user_timeline, id=username).items(cnt):\\n if not status.text.startswith(\\\"@\\\"):\\n msg_list.append(status)\\n except tw.TweepError as e:\\n await self.bot.say(\\\"Whoops! Something went wrong here. \\\\\\n The error code is \\\" + str(e))\\n return\\n if len(msg_list) > 0:\\n await self.tweet_menu(ctx, msg_list, page=0, timeout=30)\\n else:\\n await self.bot.say(\\\"No tweets available to display!\\\")\\n else:\\n await self.bot.say(\\\"No username specified!\\\")\\n return\",\n \"def hey(phrase):\\n\\n # Bob has spent years building up his knowledge base. He knows\\n # four whole things.\\n knowledge = (nostatement, anger, question, easteregg)\\n\\n # Loop through all of his thoughts. Bob isn't much of a multi-tasker\\n for thought in knowledge:\\n response = thought(phrase)\\n\\n # if the thought registered, spit it out! Don't think to hard!\\n if response:\\n return response\\n\\n # If he didn't understand what you said, he gives up and returns\\n # to being dumb.\\n return 'Whatever.'\",\n \"def get_mentions(texts, word):\\r\\n mentions = {}\\r\\n for text, label, text_id in texts:\\r\\n if word in text.lower():\\r\\n if label not in mentions:\\r\\n mentions[label] = [text_id]\\r\\n else:\\r\\n if text_id not in mentions[label]:\\r\\n mentions[label].append(text_id)\\r\\n return mentions\",\n \"def reply_all(self, since_id=None):\\n # if no id specified, reply to all mentions after most recent tweet by bot\\n if not since_id:\\n since_id = self.api.user_timeline(id=self.api.me().id, count=1)[0].id\\n\\n # replies to all past unseen mentions while bot was not streaming\\n mentions = self.api.mentions_timeline(since_id)\\n if not mentions:\\n print(\\\"No new mentions.\\\")\\n for mention in mentions:\\n # get the text from the tweet mentioning the bot\\n message = mention.text\\n # remove first word from the tweet\\n first_word = message.split(' ', 1)[0].lower()\\n # ignores tweet if first word indicates tweet is not directed to bot (just mentions the bot)\\n if first_word != '@' + self.api.me().screen_name.lower():\\n continue\\n self.__respond__(mention)\",\n \"def all_followers(twitter_dict, twitter_name): \\r\\n \\r\\n following_list = []\\r\\n for user in twitter_dict:\\r\\n f_list = twitter_dict[user]['following']\\r\\n if twitter_name in f_list:\\r\\n following_list.append(user) \\r\\n return following_list\",\n \"def covid_mention(text, synonyms=covid_list):\\n for term in synonyms:\\n if term in text:\\n return 'Yes'\\n continue\\n return 'No'\",\n \"def find_new_followers(self):\\n api = self.api\\n geocode = self.geocode\\n queries = self.queries\\n hits_per_query = self.hits_per_query\\n\\n self.log.debug(\\\"Initialize\\\")\\n self.log.debug(\\\"[ ********* FIND NEW FOLLOWERS *********** ]\\\")\\n\\n if self.strategy == UserProfile.FOLLOW or self.strategy == UserProfile.TWEET:\\n # Find statuses that match our interests\\n self.log.debug(\\\"Strategy set to FOLLOW or TWEET\\\")\\n n = hits_per_query\\n search_dict = dict()\\n search_dict['lang'] = \\\"en\\\"\\n if not geocode is None:\\n search_dict['geocode'] = geocode\\n statuses = list()\\n self.log.debug(\\\"Queries:\\\")\\n for q in queries:\\n search_dict['q'] = q\\n results = [c for c in Cursor(api.search, **search_dict).items(n)]\\n self.log.debug(\\\" => %s: %s hits\\\" % (q, len(results)))\\n statuses.extend(results)\\n #self.log.debug(\\\"Statuses: %s\\\" % \\\"\\\\n\\\".join([str(s.__dict__) for s in statuses]))\\n # Get all the screen names of senders and receivers\\n screen_names = ([t.from_user for t in statuses] +\\n [t.to_user for t in statuses if t.to_user])\\n\\n # Convert the strings to Tweepy user objects\\n users, remainder = lookup_users_by_screen_name(self.api, screen_names)\\n\\n elif self.strategy == UserProfile.STEAL:\\n users = []\\n stolen_from = {}\\n for competitor in list(self.competitors):\\n self.log.debug(\\\"[ ********* STEAL %s *********** ]\\\" % competitor)\\n try:\\n competitor_friends_ids = self.api.friends_ids(competitor)\\n competitor_followers_ids = self.api.followers_ids(competitor)\\n\\n filter_known_users_to_reduce_api_hits = False\\n\\n if filter_known_users_to_reduce_api_hits is True:\\n new_competitor_friends_ids = [id for id in competitor_friends_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n old_competitor_friends_ids = [id for id in competitor_friends_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n new_competitor_followers_ids = [id for id in competitor_followers_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n old_competitor_followers_ids = [id for id in competitor_followers_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n # print new_competitor_friends_ids\\n # print old_competitor_friends_ids\\n # print new_competitor_followers_ids\\n # print old_competitor_followers_ids\\n\\n print \\\"start lookups\\\"\\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, new_competitor_friends_ids)\\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, new_competitor_followers_ids)\\n print \\\"end lookups\\\"\\n else:\\n # get all the tweepy users\\n print \\\"start lookups\\\"\\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, competitor_friends_ids)\\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, competitor_followers_ids)\\n print \\\"end lookups\\\"\\n\\n print \\\"%s has %s friends\\\" % (competitor, len(new_competitor_friends))\\n print \\\"%s has %s followers\\\" % (competitor, len(new_competitor_followers))\\n\\n # holy crap this is so fucked up i'm ashamed that this code is getting written like this!\\n\\n for u in new_competitor_friends + new_competitor_followers:\\n stolen_from.update({u.screen_name.lower(): competitor})\\n\\n except Exception, e:\\n print e\\n # didn't get all the users, don't remove the competitor\\n # from the competitor list\\n pass\\n else:\\n # got all the competitors friends and followers and converted them\\n # to tweepy users.\\n users += new_competitor_friends\\n users += new_competitor_followers\\n # add them to the users list to be processed in the next block (for user in users)\\n # then pop the name off the competitors list in the UserProfile\\n # fuck it for now i'm going to just cycle the item to the bottom of the competitor list so we can start getting maximal coverage within api constraints by making sure the top person is new every time\\n self.competitors.append(self.competitors.pop(0))\\n # return # for now\\n self.profile.competitors = \\\"\\\\r\\\\n\\\".join(self.competitors)\\n self.profile.save()\\n\\n # use the profile competitors list\\n # for each name in competitors list\\n # add all friends\\n\\n # should filter out garbage users. something like:\\n users = [u for u in users if not Target.objects.filter(hunter=self.user, hunted__screen_name=u.screen_name.lower())]\\n\\n for user in users:\\n twitter_account, created = utils.get_or_create_twitter_account(user)\\n target, created = Target.objects.get_or_create(\\n hunter=self.user, hunted=twitter_account)\\n print target.hunted.screen_name, created\\n if created:\\n try:\\n screen_name = user.screen_name.lower()\\n match = lambda x: screen_name in \\\\\\n (x.from_user.lower(), x.to_user and x.to_user.lower())\\n if not self.strategy == UserProfile.STEAL:\\n trigger_tweet = filter(match, statuses)[0].text\\n else:\\n try:\\n trigger_tweet = \\\"Steal from user: %s\\\" % stolen_from.get(screen_name.lower(), \\\"someone. i lost it. sorry.\\\")\\n except Exception, e:\\n print \\\"YUCK. ERRORS.\\\"\\n print \\\"YUCK. ERRORS.\\\"\\n print e\\n print e\\n except Exception, e:\\n self.log.exception(\\\"Could not get trigger tweet for %s\\\" %\\n user.screen_name.lower())\\n trigger_tweet = \\\"Error: Couldn't retrieve tweet.\\\"\\n self.log.debug(\\\"Saved twitter account %s (trigger: %r)\\\" %\\n (twitter_account.screen_name,\\n trigger_tweet[:50]))\\n target.reason = trigger_tweet\\n target.status = Target.ON_DECK\\n target.save()\\n else:\\n pass\\n # print \\\" - Previously followed this dudesicle: %s\\\" % user.screen_name\",\n \"async def twitter(self, ctx, *, emoji: str):\\n await self.get_emoji(ctx, \\\"twitter\\\", emoji)\",\n \"def get_salmon(self):\\n # find tweets with links that include our base url. response is JSON tweets:\\n # https://dev.twitter.com/docs/api/1/get/search\\n resp = util.urlfetch(API_SEARCH_URL % self.key().name())\\n tweets = json.loads(resp)['results']\\n\\n # twitter usernames of people who wrote tweets with links to our domain\\n author_usernames = set()\\n\\n # maps tweet id to the link (to our domain) that it contains\\n tweets_to_links = {}\\n\\n # get tweets that link to our domain\\n salmons = []\\n for tweet in tweets:\\n id = tweet.get('id')\\n username = tweet.get('from_user')\\n tweet_url = self.tweet_url(username, id)\\n salmon = self.tweet_to_salmon_vars(tweet)\\n if salmon['in_reply_to']:\\n logging.debug('Found link %s in tweet %s', salmon['in_reply_to'], tweet_url)\\n salmons.append(salmon)\\n author_usernames.add(username)\\n tweets_to_links[id] = salmon['in_reply_to']\\n else:\\n logging.info(\\\"Tweet %s should have %s link but doesn't. Maybe shortened?\\\",\\n tweet_url, self.key().name())\\n\\n # find replies to those tweets by searching for tweets that mention the\\n # authors.\\n for username in author_usernames:\\n resp = util.urlfetch(API_SEARCH_URL % ('@' + username))\\n mentions = json.loads(resp)['results']\\n for mention in mentions:\\n logging.debug('Looking at mention: %s', mention)\\n link = tweets_to_links.get(mention.get('in_reply_to_status_id'))\\n if link:\\n salmon = self.tweet_to_salmon_vars(mention)\\n salmon['in_reply_to'] = link\\n salmons.append(salmon)\\n logging.debug('Found reply %s',\\n self.tweet_url(mention['from_user'], mention['id']))\\n\\n return salmons\",\n \"def careful_reply(api,reply):\\r\\n\\r\\n debug_print('Preparing to reply to #%d' % (reply.id,))\\r\\n normalized_tweet = reply.text.lower().strip()\\r\\n\\r\\n # Don't reply to a retweet\\r\\n if hasattr(reply, 'retweeted_status'):\\r\\n return\\r\\n\\r\\n debug_print('Replying to #%d' % (reply.id,))\\r\\n update = \\\"@%s We'd estimate about a %d percent chance, actually.\\\" % (reply.user.screen_name, random.randint(0,100),)\\r\\n return api.update_status(update, reply.id)\",\n \"def searchByKeyword(self, keyword, until=\\\"\\\", since=\\\"\\\", count=None, result_type=\\\"recent\\\"):\\n if count is None:\\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\\n full_text=True, tweet_mode=\\\"extended\\\", lang=\\\"en\\\").items()\\n else:\\n tweets = tweepy.Cursor(self.api.search, q=keyword, until=until, since=since, result_type=result_type,\\n full_text=True, tweet_mode=\\\"extended\\\", lang=\\\"en\\\").items(count)\\n\\n for status in tweets:\\n createdDate = parser.parse(str(status._json[\\\"created_at\\\"]).strip())\\n createdDate = createdDate.replace(\\n tzinfo=pytz.utc) - createdDate.utcoffset()\\n status_refined = {\\n 'keyword': keyword,\\n '_id': status._json[\\\"id\\\"],\\n 'created_at': createdDate,\\n 'tweetText': status._json[\\\"full_text\\\"],\\n 'hashtags': status._json[\\\"entities\\\"][\\\"hashtags\\\"],\\n 'userLoc': status._json[\\\"user\\\"][\\\"location\\\"],\\n 'tweetGeo': status._json[\\\"geo\\\"],\\n 'tweetCoordinates': status._json[\\\"coordinates\\\"],\\n 'tweetPlace': status._json[\\\"place\\\"],\\n 'retweet': {},\\n }\\n if hasattr(status, \\\"retweeted_status\\\"):\\n status_refined['tweetText'] = status._json[\\\"retweeted_status\\\"][\\\"full_text\\\"]\\n status_refined['retweet'] = {\\n 'original_retweet_id': status._json[\\\"retweeted_status\\\"][\\\"id\\\"],\\n 'origUserLoc': status._json[\\\"retweeted_status\\\"][\\\"user\\\"][\\\"location\\\"],\\n 'origTweetLoc': status._json[\\\"retweeted_status\\\"][\\\"geo\\\"],\\n 'origTweetPlace': status._json[\\\"retweeted_status\\\"][\\\"place\\\"],\\n 'origTweetCoord': status._json[\\\"retweeted_status\\\"][\\\"coordinates\\\"],\\n 'origHashtags': status._json[\\\"retweeted_status\\\"][\\\"entities\\\"][\\\"hashtags\\\"],\\n 'retweet_count': status._json[\\\"retweet_count\\\"],\\n }\\n self.tweets.append(status_refined)\\n return self.tweets\",\n \"def command_who(self, bot, update):\\n\\n messages = [\\n 'Myles Braithwaite lives in Toronto where he runs a small '\\n 'consluting company called [Monkey in your Soul]'\\n '(https://monkeyinyoursoul.com/) (you should hire him because '\\n \\\"he's awesome).\\\",\\n 'You should follow him on [Twitter](https://twitter.com/mylesb) '\\n 'or [Instagram](https://instagram.com/myles).',\\n 'You can find his programming stuff on [GitHub]'\\n '(https://github.com/myles) or [CodePen]'\\n '(http://codepen.io/mylesb/).'\\n ]\\n\\n self.send_messages(bot, update, messages)\",\n \"def twitter(phrase):\\n queue = app.config['SOCKETIO_REDIS_URL']\\n # create_stream.apply_async(args=[phrase, queue])\\n chain(create_stream.s(phrase, queue), send_complete_message.s()).apply_async()\\n return 'Establishing connection...'\",\n \"def reply_goals_tweets():\\n\\n # Authorise Twitter API connection\\n api = twitter_auth()\\n\\n # Get last mention\\n mentions = api.mentions_timeline()\\n\\n # Tweet to reply to\\n tweet_id = mentions[0].id\\n reply_to = \\\"@\\\"+mentions[0].user.screen_name\\n\\n # Check if already replied to last mention\\n last_tweet_id = int(get_last_replied_id(\\\"last_replied_id.txt\\\"))\\n\\n if last_tweet_id == tweet_id:\\n return print(\\\"Already replied to tweet\\\")\\n\\n # Get player name from tweet\\n player = mentions[0].text[12:].lower()\\n\\n # Gets today's fixtures data from football-data.org API\\n connection = http.client.HTTPConnection('api.football-data.org')\\n headers = {'X-Auth-Token': ''}\\n connection.request('GET', '/v2/competitions/PL/scorers?limit=400', None, headers)\\n response = json.loads(connection.getresponse().read().decode())\\n\\n # Loop through all PL scorers and search for match\\n for i in response['scorers']:\\n # If player matches scored DB send tweet stating goals scored\\n if i['player']['name'].lower() == player:\\n goals_scored = str(i['numberOfGoals'])\\n tweet_str = reply_to + \\\" \\\" + player.title() + \\\" has scored \\\" + goals_scored + \\\\\\n \\\" goals this season #PremierLeagueStats\\\"\\n store_last_replied_id(tweet_id, \\\"last_replied_id.txt\\\")\\n api.update_status(tweet_str, in_reply_to_status_id=tweet_id)\\n return print('Bot replied to '+reply_to)\\n # If player not found or hasn't scored reply stating this\\n tweet_str = reply_to + \\\" Player not scored (or name not spelt correctly)\\\"\\n store_last_replied_id(tweet_id, \\\"last_replied_id.txt\\\")\\n api.update_status(tweet_str, in_reply_to_status_id=tweet_id)\\n return print('no player found from '+reply_to+\\\"'s tweet\\\")\",\n \"def process_like_and_follow(self):\\n follow, media, unfollow = self.prepare_process_like_and_follow()\\n follow_acts, media_acts, unfollow_acts = len(follow), len(media), len(unfollow)\\n all_acts = round(self.limits_per_hour.get('follow') + self.limits_per_hour.get('like') +\\n self.limits_per_hour.get('unfollow'))\\n wait_time = 3600 // all_acts + 1\\n while follow_acts or media_acts or unfollow_acts:\\n time.sleep(wait_time + trunc_gauss(0, 10, -40, 40))\\n rc = random.choices(['f', 'l', 'u'], [follow_acts, media_acts, unfollow_acts])[0]\\n\\n if rc == 'f':\\n fo = follow.pop(0)\\n self.following_and_storing(fo)\\n follow_acts -= 1\\n elif rc == 'l':\\n mo = media.pop(0)\\n self.liking(mo)\\n media_acts -= 1\\n elif rc == 'u':\\n uo = unfollow.pop(0)\\n self.unfollowing_and_removing(uo)\\n unfollow_acts -= 1\",\n \"async def wherearemypants():\\n await bot.say('justin is a known pants thief. Not saying he took them but he totally probably took them')\",\n \"def like_retweet(self):\\n \\n bot = self.bot \\n logging.debug('Get Hashtag')\\n hashtag = Twitterbot.getHashtags(self)\\n logging.debug('Bot for Like_retweet initalized')\\n # fetches the latest tweets with the provided hashtag \\n bot.get( \\n 'https://twitter.com/search?q=%23'+hashtag+'&src=trend_click&vertical=trends'\\n ) \\n \\n time.sleep(3) \\n \\n # using set so that only unique links \\n # are present and to avoid unnecessary repetition \\n links = set() \\n \\n # obtaining the links of the tweets \\n for _ in range(3): \\n # executing javascript code \\n # to scroll the webpage \\n bot.execute_script( \\n 'window.scrollTo(0, document.body.scrollHeight)'\\n ) \\n \\n time.sleep(4) \\n \\n # using list comprehension \\n # for adding all the tweets link to the set \\n # this particular piece of code might \\n # look very complicated but the only reason \\n # I opted for list comprehension because is \\n # lot faster than traditional loops \\n [ \\n links.add(elem.get_attribute('href')) \\n for elem in bot.find_elements_by_xpath(\\\"//a[@dir ='auto']\\\") \\n ] \\n i = 0\\n # traversing through the generated links \\n for link in links:\\n #Nothing for the Hashtag was found, another run is required\\n #if len(links ==6):\\n # break\\n # Twitterbot.like_retweet(self)\\n # opens individual links \\n #print(len(links))\\n bot.get(link) \\n time.sleep(4)\\n if i == 3:\\n break\\n i += 1\\n try: \\n # retweet button selector \\n bot.find_element_by_css_selector( \\n '.css-18t94o4[data-testid =\\\"retweet\\\"]'\\n ).click() \\n # initializes action chain \\n actions = ActionChains(bot) \\n # sends RETURN key to retweet without comment \\n actions.send_keys(Keys.RETURN).perform() \\n \\n # like button selector \\n bot.find_element_by_css_selector( \\n '.css-18t94o4[data-testid =\\\"like\\\"]'\\n ).click() \\n # adding higher sleep time to avoid \\n # getting detected as bot by twitter \\n logging.info(f'Liked and retweeted:\\\"{link}\\\"')\\n time.sleep(10) \\n except: \\n time.sleep(2) \\n \\n # fetches the main homepage \\n bot.get('https://twitter.com/')\",\n \"def follow_user(cls, user, following):\\n pass\",\n \"def follow_friend():\\n print \\\"followuser\\\"\\n username = request.args.get('username')\\n print \\\"JSON Data\\\", username\\n # username= req_data[username]\\n whom_id = get_user_id(username)\\n print \\\"whom_id:\\\", whom_id\\n if whom_id is None:\\n abort(404)\\n follow_query(whom_id)\\n flash('You are now following \\\"%s\\\"' % username)\\n name = {'name of following user': username}\\n R_SERVER.delete(user_timeline_key)\\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)\",\n \"def getHashtagsAndMentions(tweets):\\n hashtags = Counter()\\n mentions = Counter()\\n plain = Counter()\\n\\n pattern = re.compile(r\\\"[^#@\\\\w'-]+\\\")\\n\\n for t in tweets:\\n words = pattern.split(t.message)\\n for word in words:\\n # Ignore null strings caused by split characters at the end of a\\n # message and remove standalone hyphens.\\n if word and not word.startswith(\\\"-\\\"):\\n # Increment count for the word in the Counter.\\n if word.startswith(\\\"#\\\"):\\n hashtags.update({word: 1})\\n elif word.startswith(\\\"@\\\"):\\n mentions.update({word: 1})\\n else:\\n # TODO: apply nltk.corpus.stopwords.words() here,\\n # across languages. Consider that the stopwords cut off\\n # before apostrophe, therefore check if the word\\n # starts with the stopword.\\n plain.update({word: 1})\\n\\n return hashtags, mentions, plain\",\n \"def follow(self, request, pk):\\n logged_in_photographer = get_object_or_404(Photographer,\\n user=request.user)\\n photographer_to_follow = get_object_or_404(Photographer, id=pk)\\n logged_in_photographer.follow_handshake(photographer_to_follow)\\n return Response({'status': status.HTTP_200_OK})\",\n \"def like_tweets(pos_tweets):\\n\\n for tweet in pos_tweets:\\n twitter.CreateFavorite(status_id=tweet.id)\\n\\n return\",\n \"def user(bot, event, *args):\\n\\n search = \\\" \\\".join(args)\\n\\n if not search:\\n raise ValueError(_(\\\"supply search term\\\"))\\n\\n search_lower = search.strip().lower()\\n search_upper = search.strip().upper()\\n\\n segments = [hangups.ChatMessageSegment(_('results for user named \\\"{}\\\":').format(search),\\n is_bold=True),\\n hangups.ChatMessageSegment('\\\\n', hangups.SegmentType.LINE_BREAK)]\\n\\n all_known_users = {}\\n for chat_id in bot.memory[\\\"user_data\\\"]:\\n all_known_users[chat_id] = bot.get_hangups_user(chat_id)\\n\\n for u in sorted(all_known_users.values(), key=lambda x: x.full_name.split()[-1]):\\n fullname_lower = u.full_name.lower()\\n fullname_upper = u.full_name.upper()\\n unspaced_lower = re.sub(r'\\\\s+', '', fullname_lower)\\n unspaced_upper = re.sub(r'\\\\s+', '', u.full_name.upper())\\n\\n if( search_lower in fullname_lower\\n or search_lower in unspaced_lower\\n # XXX: turkish alphabet special case: converstion works better when uppercase\\n or search_upper in remove_accents(fullname_upper)\\n or search_upper in remove_accents(unspaced_upper) ):\\n\\n link = 'https://plus.google.com/u/0/{}/about'.format(u.id_.chat_id)\\n segments.append(hangups.ChatMessageSegment(u.full_name, hangups.SegmentType.LINK,\\n link_target=link))\\n if u.emails:\\n segments.append(hangups.ChatMessageSegment(' ('))\\n segments.append(hangups.ChatMessageSegment(u.emails[0], hangups.SegmentType.LINK,\\n link_target='mailto:{}'.format(u.emails[0])))\\n segments.append(hangups.ChatMessageSegment(')'))\\n segments.append(hangups.ChatMessageSegment(' ... {}'.format(u.id_.chat_id)))\\n segments.append(hangups.ChatMessageSegment('\\\\n', hangups.SegmentType.LINE_BREAK))\\n\\n yield from bot.coro_send_message(event.conv, segments)\",\n \"def record_mention(self, user, field):\\n if not self.log:\\n return\\n\\n print('Recording mention...', end=\\\"\\\")\\n user = user.lower()\\n results = self.db_cur.execute(\\\"SELECT * FROM %s WHERE user = '%s'\\\" % (self.table, user))\\n if results == 0:\\n # Create the user column if they haven't been previously mentioned\\n stmt = \\\"INSERT INTO %s VALUES ('%s', 0, 0, 0)\\\" % (self.table, user)\\n self.db_cur.execute(stmt)\\n self.db_conn.commit()\\n\\n # Increment the proper field\\n stmt = \\\"UPDATE %s SET mentions_%s = mentions_%s + 1 WHERE user = '%s'\\\" % (self.table, field, field, user)\\n self.db_cur.execute(stmt)\\n self.db_conn.commit()\\n print('[DONE]')\",\n \"def twitter(self):\\n message = \\\"\\\"\\n count = self.collection.count()\\n\\n twitter = Twitter(auth = OAuth(self.access_key, self.access_secret, self.consumer_key, self.consumer_secret))\\n for keyword in self.twitter_keywords:\\n query = twitter.search.tweets(q = keyword)\\n for result in query['statuses']:\\n try:\\n data = {\\\"id\\\": count+1, \\\"source\\\": \\\"twitter\\\", \\\"timestamp\\\": datetime.now()}\\n data['tweet'] = result['text']\\n data['name'] = result[\\\"user\\\"][\\\"screen_name\\\"]\\n data['url'] = \\\"https://twitter.com/\\\" + data[\\\"name\\\"] + \\\"/status/\\\" + str(result['id'])\\n data['search_string'] = keyword\\n try:\\n dataid = self.collection.insert(data)\\n except DuplicateKeyError as e:\\n continue\\n count += 1\\n\\n # Slack push notification\\n length = 82 - len(data['url'])\\n message += \\\"\\\\nURL: \\\" + data['url'] + \\\" search string: \\\".rjust(length) + keyword\\n\\n except Exception as e:\\n print(e)\\n pass\\n \\n if message:\\n print(self.G + \\\"[+] Twitter\\\" + self.B + message)\\n self.message += \\\"\\\\n*Twitter*:\\\\n```\\\"\\n self.message += message\\n self.message += \\\"\\\\n```\\\"\\n\\n return\",\n \"def list_user_tweets(username):\\n userdata = query_db('select * from user where username = ?',\\n [username], one=True)\\n if userdata is None:\\n abort(404)\\n else:\\n user_details = {\\\"username\\\": userdata['username'],\\\"user_id\\\":userdata['user_id']}\\n\\n followed = False\\n if request.json.get('user_id') is not None:\\n followed = query_db('''select 1 from follower where\\n follower.who_id = ? and follower.whom_id = ?''',\\n [request.json.get('user_id'), user_details.get('user_id')],\\n one=True) is not None\\n\\n user_tweets = []\\n if user_details is None:\\n return jsonify({'message': 'User not found'}), 404\\n tuples = query_db('''\\n select message.*, user.* from message, user where\\n user.user_id = message.author_id and user.user_id = ?\\n order by message.pub_date desc limit ?''',\\n [user_details['user_id'], PER_PAGE])\\n\\n for tuple in tuples:\\n user_tweet = {}\\n user_tweet[\\\"username\\\"] = tuple['username']\\n user_tweet[\\\"email\\\"] = tuple['email']\\n user_tweet[\\\"text\\\"] = tuple['text']\\n user_tweet[\\\"pub_date\\\"] = tuple['pub_date']\\n user_tweets.append(user_tweet)\\n\\n return jsonify({'user_tweets':user_tweets, 'followed' : followed, 'user_details':user_details}),200\",\n \"def separate_hastags_mentions_urls(tweet):\\n \\n text = tweet.lower()\\n hashtag_list = re.findall(\\\"#([a-zA-Z0-9_]{1,50})\\\", text)\\n \\n text = re.sub(r'http\\\\S+', '', text)\\n clean_tweet = re.sub(\\\"@[A-Za-z0-9_]+\\\",\\\"\\\", text)\\n clean_tweet = re.sub(\\\"#[A-Za-z0-9_]+\\\",\\\"\\\", clean_tweet)\\n \\n return clean_tweet, hashtag_list\",\n \"def follow(user, actor, send_action=True):\\n follow,created = Follow.objects.get_or_create(user=user, object_id=actor.pk,\\n content_type=ContentType.objects.get_for_model(actor))\\n if send_action and created:\\n action.send(user, verb=_('started following'), target=actor)\\n return follow\",\n \"async def love(ctx, user: discord.Member):\\r\\n author = ctx.message.author\\r\\n if user.id == ctx.bot.user.id:\\r\\n await ctx.send(\\\"I am not capable of loving like you can. I'm sorry.\\\" )\\r\\n else:\\r\\n await ctx.send(author.mention + \\\" is capable of loving \\\" + user.mention + \\\" a whopping \\\" +\\r\\n str(randint(0, 100)) + \\\"%!\\\")\\r\\n ctx.counter(n)\",\n \"def bridgy_twitter(location):\\n r = send_mention(\\n 'http://' + DOMAIN_NAME +'/e/' + location,\\n 'https://brid.gy/publish/twitter',\\n endpoint='https://brid.gy/publish/webmention'\\n )\\n location = 'http://' + DOMAIN_NAME +'/e/' + location\\n syndication = r.json()\\n app.logger.info(syndication)\\n data = get_bare_file('data/' + location.split('/e/')[1]+\\\".md\\\")\\n if data['syndication'] == 'None':\\n data['syndication'] = syndication['url']+\\\",\\\"\\n else:\\n data['syndication'] += syndication['url']+\\\",\\\"\\n entry_re_write(data)\",\n \"def follow_by_id(self, uid: int) -> None:\\n self.api.follow(uid)\",\n \"def tweet(message):\\n auth = load_twitter_auth()\\n key = environ['TWITTER_ACCESS_KEY']\\n secret = environ['TWITTER_ACCESS_SECRET']\\n auth.set_access_token(key, secret)\\n api = tweepy.API(auth)\\n api.update_status(message)\\n print(message)\",\n \"def tweet(self, message: str) -> None:\\n\\n # YOUR CODE HERE\\n tweet = Tweet(self.userid, date.today(), message)\\n self.tweets.append(tweet)\",\n \"async def say(self, ctx, *args):\\n if not args:\\n await ctx.send('did you want me to say something?')\\n return\\n message = ' '.join(args)\\n message = profanity_filter(message)\\n await ctx.send(message)\",\n \"def prepare_process_like_and_follow(self):\\n follow = []\\n media = []\\n unfollow = []\\n\\n coef = self.users_to_follow / self.limits_per_hour.get('follow', 1)\\n media_to_like = round(coef*self.limits_per_hour.get('like'))\\n num_to_unfollow = round(coef*self.limits_per_hour.get('unfollow'))\\n\\n feed_likes = media_to_like // 2\\n feed_likes_list = []\\n following_likes = round((media_to_like // 2) * 3 / 4)\\n following_likes_list = []\\n followers_likes = media_to_like - feed_likes - following_likes\\n\\n monitored_ids = [i[\\\"user\\\"] for i in self.monitored_users]\\n\\n for posts in self.hashtag_feed_list(self.search_hashtags):\\n if len(follow) < self.users_to_follow:\\n for m in posts:\\n if self.check_if_suit(m):\\n user_id, username = self.get_user_from_post(m)\\n if user_id and user_id not in [i[\\\"user\\\"] for i in follow] \\\\\\n and user_id not in monitored_ids:\\n follow.append({'user': user_id, 'username': username})\\n following_likes_list.append(m)\\n\\n if len(follow) >= self.users_to_follow:\\n break\\n\\n for p in following_likes_list:\\n if p in posts:\\n posts.remove(p)\\n\\n if feed_likes > 0:\\n if len(posts) > feed_likes:\\n feed_likes_list.extend([i['id'] for i in (random.choice(posts) for _ in range(feed_likes))\\n if i['id'] not in feed_likes_list])\\n else:\\n feed_likes_list.extend([i['id'] for i in posts[:feed_likes] if i['id'] not in feed_likes_list])\\n feed_likes -= len(feed_likes_list)\\n if feed_likes <= 0:\\n if len(follow) >= self.users_to_follow:\\n break\\n if len(follow) >= self.users_to_follow and feed_likes <= 0:\\n break\\n\\n media.extend(feed_likes_list)\\n\\n if len(following_likes_list) < following_likes:\\n followings = []\\n get_n_followings = following_likes - len(following_likes_list)\\n if following_likes_list:\\n following_likes_list = [self.get_media_id_from_post(i) for i in following_likes_list]\\n following_likes_list.extend([i for i in self.get_following_likes(followings, get_n_followings)\\n if i and i not in media])\\n media.extend(following_likes_list)\\n else:\\n media.extend([self.get_media_id_from_post(i) for i in following_likes_list[:following_likes]])\\n\\n media.extend([i for i in self.get_followers_likes(followers_likes) if i and i not in media])\\n\\n unfollow = self.get_to_unfollow(unfollow, num_to_unfollow)\\n\\n return follow, media, unfollow\",\n \"def get_tweet(username, n):\\n return twitterAPI.home_timeline(count=n)[-1:][0] # return specified tweet\",\n \"def twitterCrawl(self, strInput):\\n strVal = self.txtTwitter.get(\\\"1.0\\\", 'end')\\n if (strVal.strip()):\\n self.txtTwitter.delete(\\\"1.0\\\", 'end')\\n twitResult = twit.search(strInput)\\n return twitResult\",\n \"def follow_me(self, action = 'follow_topic'):\\n data = {\\n 'params' : json.dumps({\\n 'topic_id' : self.soup.find('div', id = 'zh-topic-desc')['data-resourceid']\\n }),\\n '_xsrf' : self.session.getCookie()['_xsrf'],\\n 'method' : action\\n }\\n rsp = self.session.post(Follow_Topic_URL, data)\\n if rsp.status_code == requests.codes.ok:\\n return SUCCESS\\n else:\\n return FAILURE\",\n \"def process(self, tweet):\\n\\n #identify the applicable event keywords for this text\\n text = self.cleanup_data(tweet.text)\\n tokens = [str(t.lower()).translate(None, string.punctuation) for t in tweet.text.split()]\\n applicable_tokens = []\\n for phrase in self.match_event_tree.root.keywords:\\n if phrase in \\\" \\\".join(tokens):\\n applicable_tokens.append(phrase)\\n\\n self.match_event_tree.propogate_tweet(applicable_tokens, tweet)\",\n \"def follow_user(self, target):\\n try:\\n if self.api.me().friends_count > 1990:\\n return\\n except Exception, e:\\n print e\\n\\n \\\"Rate limit exceeded. Clients may not make more than 350 requests per hour.\\\"\\n if \\\"Clients\\\" in str(e):\\n continue\\n # import pdb; pdb.set_trace()\\n return\\n\\n try:\\n self.api.create_friendship(target.hunted.screen_name)\\n self.log.debug(\\\"Followed: %s\\\" % target.hunted.screen_name)\\n except Exception, e:\\n self.log.exception(\\\"Could not follow %s\\\" %\\n target.hunted.screen_name)\\n else:\\n # Write record of new follow to db\\n target.status = Target.PURGATORY\\n target.save()\",\n \"async def tweepy_on_status(self, tweet):\\n self.processed_tweets += 1\\n if self.skip_tweet(tweet):\\n return\\n\\n chan_conf = dutils.get(self.conf.follows, id=tweet.author.id_str)\\n try:\\n embed = await self.prepare_embed(tweet)\\n content = None\\n except:\\n embed = None\\n content = 'Failed to prepare embed for ' + tweet.tweet_web_url # If the preparation failed before setting tweet.tweet_web_url imma kms\\n log.error('Failed to prepare embed for ' + str(tweet._json))\\n\\n # Make sure we're ready to send messages\\n await self.bot.wait_until_ready()\\n\\n for channel in chan_conf.discord_channels:\\n discord_channel = self.bot.get_channel(channel.id)\\n\\n # Check if the channel still exists\\n if discord_channel is None:\\n log.error('Channel {} unavailable to display tweet {}.'.format(discord_channel.id, tweet.id_str))\\n continue\\n\\n # Check for required permissions\\n perms = discord_channel.permissions_for(discord_channel.server.me)\\n if not perms.embed_links:\\n log.warning('Improper permissions in channel {} to display tweet {}.'.format(discord_channel.id, tweet.id_str))\\n try:\\n warning = '\\\\N{WARNING SIGN} Missed tweet from {} : `Embed links` permission missing. \\\\N{WARNING SIGN}'.format(tweet.author.screen_name)\\n await self.bot.send_message(discord_channel, warning)\\n except discord.DiscordException as e:\\n log.error('Could not send warning to channel {}.\\\\n{}'.format(discord_channel.id, e))\\n continue\\n\\n # Send the embed to the appropriate channel\\n log.debug('Scheduling Discord message on channel ({}) : {}'.format(channel.id, tweet.text))\\n await self.bot.send_message(discord_channel, content=content, embed=embed)\\n\\n # Update stats and latest id when processing newer tweets\\n if tweet.id > chan_conf.latest_received:\\n channel.received_count += 1\\n chan_conf.latest_received = tweet.id\\n self.conf.save()\",\n \"def process(self, filter_words, count=1):\\n user = self.__api.get_user(self.__username)\\n\\n # print user.screen_name\\n # print user.followers_count\\n if self.__appMode == 1 and self.__TimeLineMode == 1:\\n self.get_timeline(filter_words)\\n else:\\n if self.__friendMode:\\n print(\\\"Getting all Twitter Friends \\\\n\\\")\\n for friend in user.friends():\\n self.get_tweet(friend.screen_name, filter_words, count)\\n else:\\n for screen_name in self.__priorityCoin:\\n self.get_tweet(screen_name, filter_words, count)\\n print('Twitter Data Extraction done!!')\",\n \"def answerMention(r, comment, answeredDB, helper):\\n\\n cards, answer = helper.parseText(comment.body)\\n\\n if cards and answer:\\n if not answeredDB.exists(comment.parent_id, cards):\\n # reply to comment\\n log.info(\\\"replying to comment: %s %s with %s\\\",\\n comment.id, comment.author.name, cards)\\n comment.reply(answer)\\n else:\\n log.debug(\\\"forwarded mention with id: %s\\\", comment.id)\\n # forward mentions without cards to admin\\n subject = '${} /u/{} in /r/{}/ \\\"{}\\\"'.format(comment.id, comment.author,\\n comment.subreddit, comment.submission.title)\\n r.redditor(credentials.admin_username).message(subject, comment.body)\",\n \"def get_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\\n query = f\\\"\\\"\\\"\\n MATCH (:Person {{email: '{email}'}})-[:FOLLOWS]->(follower) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\\n \\\"\\\"\\\"\\n return tx.run(query)\",\n \"def echo(update, context):\\n if \\\"Hey friends,\\\" in update.message.text:\\n update.message.reply_text(\\\"What a great idea. How about today?\\\")\",\n \"def follows(self, name):\\r\\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\\r\\n\\r\\n return http.Request('GET', url), resource.parse_boolean\",\n \"def twitter_text(\\n self,\\n text: str,\\n urls: List[Dict[str, str]],\\n user_mentions: List[Dict[str, Any]],\\n media: List[Dict[str, Any]],\\n ) -> Element:\\n\\n to_process: List[Dict[str, Any]] = []\\n # Build dicts for URLs\\n for url_data in urls:\\n to_process.extend(\\n {\\n \\\"type\\\": \\\"url\\\",\\n \\\"start\\\": match.start(),\\n \\\"end\\\": match.end(),\\n \\\"url\\\": url_data[\\\"url\\\"],\\n \\\"text\\\": url_data[\\\"expanded_url\\\"],\\n }\\n for match in re.finditer(re.escape(url_data[\\\"url\\\"]), text, re.IGNORECASE)\\n )\\n # Build dicts for mentions\\n for user_mention in user_mentions:\\n screen_name = user_mention[\\\"screen_name\\\"]\\n mention_string = \\\"@\\\" + screen_name\\n to_process.extend(\\n {\\n \\\"type\\\": \\\"mention\\\",\\n \\\"start\\\": match.start(),\\n \\\"end\\\": match.end(),\\n \\\"url\\\": \\\"https://twitter.com/\\\" + urllib.parse.quote(screen_name),\\n \\\"text\\\": mention_string,\\n }\\n for match in re.finditer(re.escape(mention_string), text, re.IGNORECASE)\\n )\\n # Build dicts for media\\n for media_item in media:\\n short_url = media_item[\\\"url\\\"]\\n expanded_url = media_item[\\\"expanded_url\\\"]\\n to_process.extend(\\n {\\n \\\"type\\\": \\\"media\\\",\\n \\\"start\\\": match.start(),\\n \\\"end\\\": match.end(),\\n \\\"url\\\": short_url,\\n \\\"text\\\": expanded_url,\\n }\\n for match in re.finditer(re.escape(short_url), text, re.IGNORECASE)\\n )\\n # Build dicts for emojis\\n for match in POSSIBLE_EMOJI_RE.finditer(text):\\n orig_syntax = match.group(\\\"syntax\\\")\\n codepoint = emoji_to_hex_codepoint(unqualify_emoji(orig_syntax))\\n if codepoint in codepoint_to_name:\\n display_string = \\\":\\\" + codepoint_to_name[codepoint] + \\\":\\\"\\n to_process.append(\\n {\\n \\\"type\\\": \\\"emoji\\\",\\n \\\"start\\\": match.start(),\\n \\\"end\\\": match.end(),\\n \\\"codepoint\\\": codepoint,\\n \\\"title\\\": display_string,\\n }\\n )\\n\\n to_process.sort(key=lambda x: x[\\\"start\\\"])\\n p = current_node = Element(\\\"p\\\")\\n\\n def set_text(text: str) -> None:\\n \\\"\\\"\\\"\\n Helper to set the text or the tail of the current_node\\n \\\"\\\"\\\"\\n if current_node == p:\\n current_node.text = text\\n else:\\n current_node.tail = text\\n\\n db_data: Optional[DbData] = self.zmd.zulip_db_data\\n current_index = 0\\n for item in to_process:\\n # The text we want to link starts in already linked text skip it\\n if item[\\\"start\\\"] < current_index:\\n continue\\n # Add text from the end of last link to the start of the current\\n # link\\n set_text(text[current_index : item[\\\"start\\\"]])\\n current_index = item[\\\"end\\\"]\\n if item[\\\"type\\\"] != \\\"emoji\\\":\\n elem = url_to_a(db_data, item[\\\"url\\\"], item[\\\"text\\\"])\\n assert isinstance(elem, Element)\\n else:\\n elem = make_emoji(item[\\\"codepoint\\\"], item[\\\"title\\\"])\\n current_node = elem\\n p.append(elem)\\n\\n # Add any unused text\\n set_text(text[current_index:])\\n return p\",\n \"def follow(self, name):\\r\\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\\r\\n\\r\\n # include a body, because requests does not send content-length when no\\r\\n # body is present, and that makes GitHub respond with HTTP 411\\r\\n return http.Request('PUT', url, '*'), parsers.parse_empty\",\n \"def tweet(self, message):\\n if self.api_key == \\\"\\\":\\n return False\\n ts = TagStripper()\\n ts.feed(message)\\n params = urllib.urlencode(\\n {'api_key': self.api_key,\\n 'status': ts.get_collected_data()})\\n\\n headers = {\\\"Content-type\\\": \\\"application/x-www-form-urlencoded\\\",\\n \\\"Accept\\\": \\\"text/plain\\\"}\\n\\n conn = httplib.HTTPConnection(\\\"api.thingspeak.com:80\\\")\\n\\n try:\\n conn.request(\\\"POST\\\",\\n \\\"/apps/thingtweet/1/statuses/update\\\",\\n params,\\n headers)\\n\\n response = conn.getresponse()\\n data = response.read()\\n conn.close()\\n return True, ts.get_collected_data()\\n except httplib.HTTPException as http_exception:\\n return False, http_exception.message\",\n \"def mention(bot, msg):\\n\\n if msg.command != \\\"PRIVMSG\\\":\\n return\\n\\n message = msg.args[1]\\n\\n if bot.nickname.lower() in message.lower():\\n bot.privmsg(msg.sendername, \\\"You called?\\\")\",\n \"def userFollowers(nick):\\n if (len(nick) != 1):\\n print \\\"Has d'introduir només un nick\\\"\\n return\\n i.userFollow(nick[0])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7187562","0.6514674","0.63038933","0.62657815","0.6236046","0.62126845","0.61821735","0.6114621","0.6068988","0.6059423","0.6033002","0.5977076","0.59490186","0.5944211","0.5915049","0.5908332","0.59074265","0.59028906","0.58801764","0.58654356","0.58603084","0.58239067","0.58221817","0.58216506","0.5818802","0.5748235","0.5737494","0.57338774","0.573194","0.573194","0.573194","0.5722351","0.57204175","0.5707261","0.5663157","0.56568706","0.5651439","0.56485426","0.56457305","0.56340265","0.562476","0.5617878","0.5617341","0.56165814","0.56153065","0.5611889","0.5611889","0.5610774","0.55786884","0.55297464","0.5524142","0.5518153","0.54942256","0.5482725","0.5481306","0.5474292","0.54718524","0.5470879","0.5466828","0.5466615","0.5460777","0.5455144","0.54466504","0.54389435","0.54204595","0.54158276","0.54092413","0.5408886","0.54082507","0.5406578","0.53962743","0.53957415","0.53904045","0.5375855","0.53733104","0.53729725","0.537173","0.53680575","0.5367669","0.5338049","0.5308742","0.53074574","0.5307","0.5297292","0.5296492","0.52955186","0.52933943","0.52864724","0.5282084","0.5274035","0.5273705","0.52715725","0.52632457","0.5258625","0.52575463","0.5256097","0.5252747","0.5246235","0.5245806","0.5242068"],"string":"[\n \"0.7187562\",\n \"0.6514674\",\n \"0.63038933\",\n \"0.62657815\",\n \"0.6236046\",\n \"0.62126845\",\n \"0.61821735\",\n \"0.6114621\",\n \"0.6068988\",\n \"0.6059423\",\n \"0.6033002\",\n \"0.5977076\",\n \"0.59490186\",\n \"0.5944211\",\n \"0.5915049\",\n \"0.5908332\",\n \"0.59074265\",\n \"0.59028906\",\n \"0.58801764\",\n \"0.58654356\",\n \"0.58603084\",\n \"0.58239067\",\n \"0.58221817\",\n \"0.58216506\",\n \"0.5818802\",\n \"0.5748235\",\n \"0.5737494\",\n \"0.57338774\",\n \"0.573194\",\n \"0.573194\",\n \"0.573194\",\n \"0.5722351\",\n \"0.57204175\",\n \"0.5707261\",\n \"0.5663157\",\n \"0.56568706\",\n \"0.5651439\",\n \"0.56485426\",\n \"0.56457305\",\n \"0.56340265\",\n \"0.562476\",\n \"0.5617878\",\n \"0.5617341\",\n \"0.56165814\",\n \"0.56153065\",\n \"0.5611889\",\n \"0.5611889\",\n \"0.5610774\",\n \"0.55786884\",\n \"0.55297464\",\n \"0.5524142\",\n \"0.5518153\",\n \"0.54942256\",\n \"0.5482725\",\n \"0.5481306\",\n \"0.5474292\",\n \"0.54718524\",\n \"0.5470879\",\n \"0.5466828\",\n \"0.5466615\",\n \"0.5460777\",\n \"0.5455144\",\n \"0.54466504\",\n \"0.54389435\",\n \"0.54204595\",\n \"0.54158276\",\n \"0.54092413\",\n \"0.5408886\",\n \"0.54082507\",\n \"0.5406578\",\n \"0.53962743\",\n \"0.53957415\",\n \"0.53904045\",\n \"0.5375855\",\n \"0.53733104\",\n \"0.53729725\",\n \"0.537173\",\n \"0.53680575\",\n \"0.5367669\",\n \"0.5338049\",\n \"0.5308742\",\n \"0.53074574\",\n \"0.5307\",\n \"0.5297292\",\n \"0.5296492\",\n \"0.52955186\",\n \"0.52933943\",\n \"0.52864724\",\n \"0.5282084\",\n \"0.5274035\",\n \"0.5273705\",\n \"0.52715725\",\n \"0.52632457\",\n \"0.5258625\",\n \"0.52575463\",\n \"0.5256097\",\n \"0.5252747\",\n \"0.5246235\",\n \"0.5245806\",\n \"0.5242068\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62884736"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":13784,"cells":{"query":{"kind":"string","value":"Follows the followers of a user"},"document":{"kind":"string","value":"def auto_follow_followers_for_user(user_screen_name, count=5):\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers_for_user = set(t.followers.ids(screen_name=user_screen_name)[\"ids\"][:count]);\n do_not_follow = get_do_not_follow_list()\n \n for user_id in followers_for_user:\n try:\n if (user_id not in following and \n user_id not in do_not_follow):\n\n t.friendships.create(user_id=user_id, follow=False)\n print(\"followed %s\" % user_id)\n\n except TwitterHTTPError as e:\n print(\"error: %s\" % (str(e)))"},"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 follow_user(cls, user, following):\r\n pass","def follow_user(cls, user, following):\n pass","def follow(self, follower, followee):\n pass","def follow_followers(self):\n self.logger.log(\"starting follow_followers...\")\n follow = perform_with_ran_delay(self.instagram.get_followers, self.account.identifier, 150, 15, delayed=True)\n for acc in follow[\"accounts\"]:\n try:\n try:\n # print(\"{} follows me, do I follow him ? > {} \".format(acc.username,self.is_user_following(acc.identifier)))\n if not self.is_user_following(acc.identifier):\n if self.add_following(acc.identifier):\n self.logger.log(\"following: {}\".format(acc.username))\n else:\n self.logger.log(\"follow not working at the moment\")\n except Exception as e:\n print(e)\n self.logger.log(str(e))\n continue\n finally:\n sleep(3)","def follow_following_followers(self):\n self.logger.log(\"starting follow_following_followers...\")\n follows_accounts = self.following\n random.shuffle(follows_accounts)\n for acc in follows_accounts:\n try:\n try:\n followw = perform_with_ran_delay(self.instagram.get_followers, acc, 150, 15,\n delayed=True)\n accountstofollow = followw[\"accounts\"]\n random.shuffle(accountstofollow)\n if len(accountstofollow) > 10:\n accountstofollow = accountstofollow[:10]\n for ac in accountstofollow:\n if not self.is_user_following(ac.identifier):\n self.add_following(ac.identifier)\n self.logger.log(\"following: {}\".format(ac.username))\n except Exception as e:\n print(e)\n self.logger.log(str(e))\n finally:\n sleep(3)","def user_follow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.append(following)\n db.session.commit()\n return {'followed': True}","def follow(self, user):\n if not self.is_following(user):\n self.followed.append(user)\n return self","def follow(self, user):\n if not self.is_following(user):\n self.followed.append(user)\n return self","def follow(self, user):\n if not self.is_following(user):\n f = Follow(follower=self, followed=user)\n db.session.add(f)","def user_follow_users(self, ids=None, **kwargs):\n return self._put(\n API.MY_FOLLOWING.value, type=\"user\", ids=\",\".join(ids or []), **kwargs\n )","def follow(self, followerId: int, followeeId: int) -> None:\n self.user_followed[followerId].append(followeeId)","def auto_follow_followers():\n\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n not_following_back = followers - following\n\n for user_id in not_following_back:\n try:\n t.friendships.create(user_id=user_id, follow=False)\n except Exception as e:\n print(\"error: %s\" % (str(e)))","def follow(self, user_index, following_index):\n if user_index >= self.num_users or following_index >= self.num_users:\n raise ValueError(\n f\"Number of users is {self.num_users}, but indices \"\n f\"{user_index} and {following_index} were requested.\"\n )\n if self.users_hat[following_index, user_index] == 0:\n self.users_hat[following_index, user_index] = 1\n elif self.is_verbose():\n self.log(f\"User {following_index} was already following user {user_index}\")","def follow(request, usertofollow):\n to_follow = Member.objects.get(user__username=usertofollow)\n user = Member.objects.get(user=request.user)\n user.following.add(to_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","def follow(self, followerId, followeeId):\n if followerId in self.follows:\n self.follows[followerId].add(followeeId)\n else:\n self.follows[followerId] = set([followeeId])","def follow(self, followerId, followeeId):\r\n if followerId != followeeId:\r\n self.follows[followerId].add(followeeId)","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def follow(user, people):\n api = get_api(user)\n current_screen_name = api.VerifyCredentials().GetScreenName()\n\n # don't let a user follow themselves\n screen_names = [person.twitter_screen_name for person in people]\n if current_screen_name in screen_names: screen_names.remove(current_screen_name)\n\n followed = []\n not_followed = []\n\n for screen_name in screen_names:\n try:\n api.CreateFriendship(screen_name=screen_name)\n followed.append(screen_name)\n except twitter.TwitterError:\n not_followed.append(screen_name)\n\n return 'followed %s people' % len(followed)","def _user_follower_info(self, uid: int = 0) -> List[_InstagramUser]:\n # If no uid was specified, use the authenticated user's uid\n if uid == 0:\n uid = self.uid\n\n followers: List[Dict[str, Any]] = self.api.getTotalFollowers(uid)\n user_followers = list([_InstagramUser(x) for x in followers])\n return user_followers","def follow(self, followerId, followeeId):\n\n # 把 followeeId append到他的 follow 属性中\n if followerId == followeeId: # 不能自己关注自己\n return\n # 实例化一个user(followerID)\n follower = UserInfo()\n follower.user_id = followerId \n follower.follows.append(followeeId) \n self.user_pool[followerId] = follower","def follow_user(username):\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n db = get_db()\n db.execute('insert into follower (who_id, whom_id) values (?, ?)',\n [session['user_id'], whom_id])\n db.commit()\n flash('You are now following \"%s\"' % username)\n return redirect(url_for('user_timeline', username=username))","def follow_user(self, user):\n self.nav_user(user)\n follow_button = self.driver.find_element_by_xpath(\n \"//button[contains(text(), 'Follow')]\")\n follow_button.click()\n time.sleep(1)\n self.driver.get(self.base_url)","def userFollowers(nick):\n if (len(nick) != 1):\n print \"Has d'introduir només un nick\"\n return\n i.userFollow(nick[0])","def follow_user(username):\n user_ID = before_request()\n user_ID = None\n if user_ID != None:\n user_ID = str(g.user['_id'])\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n mongo.db.users.update({'_id': g.user['_id']}, {\n '$push': {'follows': whom_id}})\n flash('You are now following \"%s\"' % username)\n if redis_obj.get(user_ID):\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(r.get(user_ID))))\n else:\n redis_obj.delete(str(g.user['_id']))\n print \"Invalidating cache after Follow\"\n return redirect(url_for('user_timeline', username=username))","def followers(self):\r\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\r\n\r\n return request, parsers.parse_json","def followers(self):\r\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\r\n\r\n return request, parsers.parse_json","def followers(self):\r\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\r\n\r\n return request, parsers.parse_json","def _user_following_info(self, uid: int = 0) -> List[_InstagramUser]:\n # If no uid was specified, use the authenticated user's uid\n if uid == 0:\n uid = self.uid\n\n followings: List[Dict[str, Any]] = self.api.getTotalFollowings(uid)\n user_followings = list([_InstagramUser(x) for x in followings])\n return user_followings","def follows(self, user_id):\n\n url = \"https://api.instagram.com/v1/users/{0}/follows?access_token={1}\".format(user_id, self.access_token)\n\n request = requests.get(url)\n return request.json()","def follow(self, followerId: int, followeeId: int) -> None:\n if followerId == followeeId: return\n self.users[followerId].add(followeeId)","def follow(self, followerId: int, followeeId: int) -> None:\n if followerId == followeeId:\n return\n if followerId not in self.users.keys():\n self.users[followerId] = user()\n if followeeId not in self.users.keys():\n self.users[followeeId] = user()\n self.users[followerId].followees[followeeId] = self.users[followeeId]","def post(self, request, username):\n\n # Retrieve the user from the user table if the user exists\n try:\n user_details = User.objects.get(username=username)\n current_user = request.user\n # If a user is trying to follow themselves then stop the request\n if user_details.profile.id == current_user.profile.id:\n return Response(\n {\"errors\": FOLLOW_USER_MSGS['CANNOT_FOLLOW_SELF']},\n status=status.HTTP_400_BAD_REQUEST\n )\n # Otherwise follow the author the current user has indicated\n current_user.profile.follows.add(user_details.profile)\n\n # notify user of new follower\n send_notifications(request,\n notification_type=\"user_followed\",\n instance=current_user,\n recipients=[user_details])\n\n # Get the following & followers username list\n # And the following & followers count for the current user\n user_following_data = get_user_following_data(current_user)\n return Response(\n {\n \"message\": FOLLOW_USER_MSGS['USER_FOLLOW_SUCCESSFUL'],\n \"following\": user_following_data[\"following\"],\n \"followers\": user_following_data[\"followers\"],\n \"followingCount\": user_following_data[\"followingCount\"],\n \"followersCount\": user_following_data[\"followersCount\"]\n },\n status=status.HTTP_201_CREATED\n )\n except User.DoesNotExist:\n return Response(\n {\"errors\": FOLLOW_USER_MSGS['USER_NOT_FOUND']},\n status=status.HTTP_404_NOT_FOUND\n )","def users_followers(user_id):\n\n user = User.query.get_or_404(user_id)\n return render_template('users/followers.html', user=user)","def follow(self, followerId: int, followeeId: int) -> None:\n if followeeId not in self.followList.get(followerId, [followerId]):\n self.followList[followerId] = self.followList.get(followerId, [followerId]) + [followeeId]\n # print(self.followList)","def resolve_followers(self, info):\n user = info.context.user\n follow_request = FollowRequest.objects.filter(following=user.id, pending=False)\n return [follow.follower for follow in follow_request]","def follow_by_id(self, uid: int) -> None:\n self.api.follow(uid)","def follower_followee_count(user_id):\n profile = instaloader.Profile.from_username(context(), user_id)\n return {'follower_cnt': profile.followers,\n 'followee_cnt': profile.followees}","def follower(account):\n stm = shared_morphene_instance()\n if mph.rpc is not None:\n mph.rpc.rpcconnect()\n if not account:\n if \"default_account\" in mph.config:\n account = [mph.config[\"default_account\"]]\n for a in account:\n a = Account(a, morphene_instance=stm)\n print(\"\\nFollowers statistics for @%s (please wait...)\" % a.name)\n followers = a.get_followers(False)\n followers.print_summarize_table(tag_type=\"Followers\")","def followed_by(self, user_id):\n\n url = \"https://api.instagram.com/v1/users/{0}/followed-by?access_token={1}\".format(user_id, self.access_token)\n\n request = requests.get(url)\n return request.json()","def get_followers_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\n query = f\"\"\"\n MATCH (follower)-[:FOLLOWS]->(:Person {{email: '{email}'}}) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\n \"\"\"\n return tx.run(query)","def follow(self, followerId, followeeId):\n if followerId not in self.follow_map:\n self.follow_map[followerId] = set()\n \n self.follow_map[followerId].add(followeeId)","def follows(self):\r\n return relationships.Follows(self)","def followUser(following):\n\n cur, user_id, con = initialise(3, True)\n cur.execute(\"INSERT INTO followers (user, following) VALUES ((SELECT username FROM users WHERE id = ?), ?)\", (user_id, following))\n finish(con)","def user_followers(username, max: int = None):\n for user_dict in client.user_relationships(username, max=max, type=\"followers\"):\n print(json.dumps(user_dict))","def follows(self):\n return relationships.Follows(self)","def user_follow_artists(self, ids=None, **kwargs):\n return self._put(\n API.MY_FOLLOWING.value, type=\"artist\", ids=\",\".join(ids or []), **kwargs\n )","def user_follow(self, user_id: int) -> bool:\n assert self.user_id, \"Login required\"\n user_id = int(user_id)\n if user_id in self._users_following.get(self.user_id, []):\n self.logger.debug(\"User %s already followed\", user_id)\n return False\n data = self.with_action_data({\"user_id\": user_id})\n result = self.private_request(f\"friendships/create/{user_id}/\", data)\n if self.user_id in self._users_following:\n self._users_following.pop(self.user_id) # reset\n return result[\"friendship_status\"][\"following\"] is True","def get_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\n query = f\"\"\"\n MATCH (:Person {{email: '{email}'}})-[:FOLLOWS]->(follower) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\n \"\"\"\n return tx.run(query)","def getFollowers():\n followers = []\n next_max_id = True\n while next_max_id:\n # first iteration hack\n if next_max_id is True:\n next_max_id = ''\n\n _ = GetInstagramAnswer.igApi.getUserFollowers(GetInstagramAnswer.igApi.username_id, maxid=next_max_id)\n followers.extend(GetInstagramAnswer.igApi.LastJson.get('users',[]))\n next_max_id = GetInstagramAnswer.igApi.LastJson.get('next_max_id','')\n return \"You have currently \"+str(len(followers))+\" Followers on Instagram.\"","def follow(self, followerId: int, followeeId: int) -> None:\n self.follow_map[followerId].add(followeeId)","async def follow(follow):\n await follow.edit(\n f\"`FOLLOW {DEFAULTUSER} ON` \\n\\n\"\n f\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\n\\n\"\n f\"[FaceBook](https://www.facebook.com/mkaraniya) \\n\\n\"\n f\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \"\n )","async def get_new_followers(self, bearer_token:str, user_id:str, after:str) -> list:\n\n headers = {\"Client-Id\": self.TWITCH_PARAMS['client_id'], \"Authorization\": f\"Bearer {bearer_token}\"}\n params = {\"to_id\": user_id, \"first\": 100}\n if after:\n params[\"after\"] = after\n output = []\n while True:\n async with self.bot.session.get(self.TWITCH_USER_FOLLOWS_URL, params=params, headers=headers) as r:\n data = await r.json()\n # self.logger.info(data)\n output.extend(data.get('data', list()))\n if len(data.get('data', list())) < 100:\n break\n params['after'] = data.get('pagination', {}).get('cursor', None)\n return output, data.get('pagination', {}).get('cursor', None)","def follow(self, followerId: int, followeeId: int) -> None:\n if followeeId not in self.users:\n self._create_user(followeeId)\n if followerId not in self.users:\n self._create_user(followerId)\n self.users[followerId].add(followeeId)","def follow(self, followerId: int, followeeId: int) -> None:\n self.followees[followerId].add(followeeId)","def follow(self, followerId: int, followeeId: int) -> None:\n # Time Complexity: O(1)\n if followerId != followeeId:\n if followerId not in self.followees:\n self.followees[followerId] = set()\n\n self.followees[followerId].add(followeeId)","async def follow(self, TargetId: int):\n data = {\n 'targetUserId': TargetId\n }\n e = await self.request.request(url=f'https://friends.roblox.com/v1/users/{TargetId}/follow', method='post',\n data=data)\n return e","def is_follower(self, you, them):\n if self.filter(from_user=them, to_user=you).count() > 0:\n return True\n return False","def users_being_followed_tweets():\n username = request.authorization.username\n tweets = []\n\n user_id = get_user_id(username);\n tuples = query_db('''\n select message.*, user.* from message, user\n where message.author_id = user.user_id and (\n user.user_id = ? or\n user.user_id in (select whom_id from follower\n where who_id = ?))\n order by message.pub_date desc limit ?''',\n [user_id, user_id, PER_PAGE])\n\n for tuple in tuples:\n tweet = {}\n tweet[\"message_id\"] = tuple['message_id']\n tweet[\"author_id\"] = tuple['author_id']\n tweet[\"text\"] = tuple['text']\n tweet[\"pub_date\"] = tuple['pub_date']\n tweet[\"username\"] = tuple['username']\n tweet[\"email\"] = tuple['email']\n tweets.append(tweet)\n\n return jsonify({'tweets': tweets}), 200","def get_followers(user):\n if user.has_key('followers_list'):\n pass\n else:\n if user.has_key('followers_count'):\n if user['followers_count'] > 4999:\n pages = user['followers_count'] / 5000\n f_list = []\n for page in range(pages):\n try:\n follower_set = api.GetFollowers(user_id=user['id'], cursor=page, count=5000)\n friends_list = []\n for follower in follower_set:\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\n friends_list.append(follower.GetId())\n f_list = friends_list + f_list\n time.sleep(60)\n user['followers_list'] = f_list\n twitter_users.update({'id': user['id']}, user)\n print \"\\n\\nGot %s followers out of %s listed\" % (len(f_list), user['followers_count'])\n except Exception, e:\n print str(e)\n time.sleep(60)\n else:\n try:\n follower_set = api.GetFollowers(user_id=user['id'], count=5000)\n friends_list = []\n for follower in follower_set:\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\n friends_list.append(follower.GetId())\n user['followers_list'] = friends_list\n twitter_users.update({'id': user['id']}, user)\n print \"\\n\\nGot %s followers out of %s listed\" % (len(friends_list), user['followers_count'])\n except Exception, e:\n print str(e)\n time.sleep(60)","def users_followers(user_id):\n\n if not g.user:\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\")\n\n user = User.query.get_or_404(user_id)\n users_blocking = [block.user_blocking_id for block in Blocks.query.all() if block.user_being_blocked_id == g.user.id]\n likes = [message for message in user.likes if message.user_id not in users_blocking]\n return render_template('users/followers.html', user=user, likes=likes)","def followers(self, offset=0):\n url = '/question/%d/followers' % self.id\n data = {\n 'offset': offset,\n 'start': 0,\n '_xsrf': req._xsrf\n }\n r = req.post(url, data=data)\n return parser.followers(r['msg'][1])","def follows(self):\r\n request = http.Request('GET', '{0}/follows/'.format(self.get_url()))\r\n\r\n return request, parsers.parse_json","def get_follows_route(request):\n\n db_conn = request['db_conn']\n current_user = get_current_user(request)\n user_id = request['params'].get('user_id')\n if user_id:\n user = get_user({'id': user_id}, db_conn)\n if not user:\n return abort(404)\n if (user != current_user and\n user['settings']['view_follows'] != 'public'):\n return abort(403)\n else:\n user = current_user\n if not user:\n return abort(401)\n params = dict(**request['params'])\n params['user_id'] = user['id']\n follows = list_follows(params, db_conn)\n return 200, {\n 'follows': [deliver_follow(follow, access='private')\n for follow in follows]\n }","def generateFollowers(self):\n for f in self._genericGenerator(self.getFollowers):\n yield f","def follow(self, followerId: 'int', followeeId: 'int') -> 'None':\n self.followees[followerId].add(followeeId)","def followers(self):\n return self.data.get(\"followers\")","def get_user_followers(self, user_id, count = 30, page = 1):\n uri = 'users/' + user_id + '/followers'\n options = { 'per_page': count, 'page': page}\n return self.make_request(uri, options)","def doesfollow(user):\n return jsonify({\n 'follows': isFollowed(g.user,user)\n })","def get_followers(user_id):\n return list(Backward.objects.filter(destination_id=user_id) \\\n .values_list('source_id', flat=True))","def follow(self, context, callback):\n followers_list = self.followers.setdefault(context, [])\n if callback not in followers_list:\n followers_list.append(callback)","def followed_by(self):\r\n return relationships.FollowedBy(self)","def author_following(self):\n\t\tpass","def follow(whomUserName,whoUserName):\n\n whomuser = query_db('select * from user where username = ?',\n [whomUserName], one=True)\n whouser = query_db('select * from user where username = ?',\n [whoUserName], one=True)\n\n\n followed = query_db('''select 1 from follower where\n follower.who_id = ? and follower.whom_id = ?''',\n [whouser['user_id'], whomuser['user_id']],one=True) is not None\n\n if whouser is None:\n return jsonify({'message':'User trying to follow another user which does not exist'}),404\n\n if whomuser is None:\n return jsonify({'message':'User getting followed does not exist yet'}),404\n\n if not followed:\n db = get_db()\n\n db.execute('''insert into follower (\n who_id, whom_id) values (?, ?)''',\n [whouser['user_id'], whomuser['user_id']])\n db.commit()\n flash('Operation successful')\n return jsonify({'message': 'Successfully following'}), 201\n else:\n return jsonify({'message':'Specified user is already following another user'}),403","def add_following(self, user_id):\n sleep(360) # too much follows => function ban\n self.following.append(user_id)\n return perform_with_ran_delay(self.instagram.follow, user_id)","def resolve_following(self, info):\n user = info.context.user\n follow_request = FollowRequest.objects.filter(follower=user.id, pending=False)\n return [follow.following for follow in follow_request]","def post(self, request, username):\n user_exists = User.objects.filter(username=username).exists()\n if not user_exists:\n return Response(\n {'error': 'user with that name was not found'},\n status.HTTP_404_NOT_FOUND)\n # we check if the user is already followed\n followed_user = User.objects.get(username=username)\n already_followed = Follow.is_user_already_followed(\n followed_user_id=followed_user.id,\n user_id=self.request.user.id\n )\n if already_followed:\n return Response({'error': 'user already followed'},\n status.HTTP_400_BAD_REQUEST)\n if followed_user.id == self.request.user.id:\n return Response({'error': \"you cannot follow yourself.\"},\n status.HTTP_400_BAD_REQUEST)\n data = {\n \"followed_user\": followed_user.id,\n \"user\": self.request.user.id}\n serializer = FollowSerializer(data=data)\n serializer.is_valid(raise_exception=True)\n serializer.save()\n\n return Response({'message': 'user followed successfully'},\n status.HTTP_201_CREATED)","def follow_friend():\n print \"followuser\"\n username = request.args.get('username')\n print \"JSON Data\", username\n # username= req_data[username]\n whom_id = get_user_id(username)\n print \"whom_id:\", whom_id\n if whom_id is None:\n abort(404)\n follow_query(whom_id)\n flash('You are now following \"%s\"' % username)\n name = {'name of following user': username}\n R_SERVER.delete(user_timeline_key)\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)","def follow(self, other):\n\t\tif not self.follows(other):\n\t\t\tself.followed.append(other)","def set_follow(self, follow):\n self.follow = follow","def is_following(self, user):\n return self.followed.filter(followers.c.followed_id == user.id).count() > 0","def is_following(self, user):\n return self.followed.filter(followers.c.followed_id == user.id).count() > 0","def add_follow(follow_id):\n\n want_to_follow_user = User.query.get_or_404(follow_id)\n if want_to_follow_user.private:\n # =========== NEED TO IMPLEMENT ====================\n # send them a request to follow\n want_to_follow_user.from_users.append(g.user) \n db.session.commit()\n flash(\"Your request has been sent\", \"success\")\n return redirect(f\"/users/{g.user.id}/following\")\n\n g.user.following.append(want_to_follow_user)\n db.session.commit()\n\n return redirect(f\"/users/{g.user.id}/following\")","def current_user_followed_artists(self, limit=20, after=None, **kwargs):\n return self._get(\n API.MY_FOLLOWING.value, type=\"artist\", limit=limit, after=after, **kwargs\n )","def _get_followed(_user_id, _ignore_exceptions=False, _start_index=0, _count=100):\n _following_url = f\"{base_url}/people/{_user_id}/@following?count={_count}\" + \\\n f\"&startIndex={_start_index}\"\n _response = core.get_request_with_retries(_following_url)\n if _response.status_code == 200:\n _following_data = _response.json()\n else:\n if _ignore_exceptions:\n _empty_response = {\"list\": []}\n _following_data = core_utils.convert_dict_to_json(_empty_response)\n else:\n if _response.status_code == 404:\n raise errors.exceptions.UserNotFoundError()\n else:\n raise errors.exceptions.UserQueryError()\n return _following_data","def test_followers(self):\n\n follow1 = Follows(\n user_being_followed_id = self.testuser.id,\n user_following_id = self.testuser2.id\n )\n follow2 = Follows(\n user_being_followed_id = self.testuser3.id,\n user_following_id = self.testuser.id\n )\n\n db.session.add_all((follow1, follow2))\n db.session.commit()\n\n with self.client as c:\n with c.session_transaction() as session:\n session[CURR_USER_KEY] = self.testuser.id\n \n response = c.get(f\"/users/{self.testuser.id}/followers\")\n data = str(response.data)\n\n self.assertEqual(response.status_code, 200)\n self.assertIn(\"@bob\", data)\n self.assertNotIn(\"@carl\", data)\n self.assertNotIn(\"@alvin\", data)","def find_new_followers(self):\n api = self.api\n geocode = self.geocode\n queries = self.queries\n hits_per_query = self.hits_per_query\n\n self.log.debug(\"Initialize\")\n self.log.debug(\"[ ********* FIND NEW FOLLOWERS *********** ]\")\n\n if self.strategy == UserProfile.FOLLOW or self.strategy == UserProfile.TWEET:\n # Find statuses that match our interests\n self.log.debug(\"Strategy set to FOLLOW or TWEET\")\n n = hits_per_query\n search_dict = dict()\n search_dict['lang'] = \"en\"\n if not geocode is None:\n search_dict['geocode'] = geocode\n statuses = list()\n self.log.debug(\"Queries:\")\n for q in queries:\n search_dict['q'] = q\n results = [c for c in Cursor(api.search, **search_dict).items(n)]\n self.log.debug(\" => %s: %s hits\" % (q, len(results)))\n statuses.extend(results)\n #self.log.debug(\"Statuses: %s\" % \"\\n\".join([str(s.__dict__) for s in statuses]))\n # Get all the screen names of senders and receivers\n screen_names = ([t.from_user for t in statuses] +\n [t.to_user for t in statuses if t.to_user])\n\n # Convert the strings to Tweepy user objects\n users, remainder = lookup_users_by_screen_name(self.api, screen_names)\n\n elif self.strategy == UserProfile.STEAL:\n users = []\n stolen_from = {}\n for competitor in list(self.competitors):\n self.log.debug(\"[ ********* STEAL %s *********** ]\" % competitor)\n try:\n competitor_friends_ids = self.api.friends_ids(competitor)\n competitor_followers_ids = self.api.followers_ids(competitor)\n\n filter_known_users_to_reduce_api_hits = False\n\n if filter_known_users_to_reduce_api_hits is True:\n new_competitor_friends_ids = [id for id in competitor_friends_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n old_competitor_friends_ids = [id for id in competitor_friends_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n new_competitor_followers_ids = [id for id in competitor_followers_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n old_competitor_followers_ids = [id for id in competitor_followers_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n # print new_competitor_friends_ids\n # print old_competitor_friends_ids\n # print new_competitor_followers_ids\n # print old_competitor_followers_ids\n\n print \"start lookups\"\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, new_competitor_friends_ids)\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, new_competitor_followers_ids)\n print \"end lookups\"\n else:\n # get all the tweepy users\n print \"start lookups\"\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, competitor_friends_ids)\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, competitor_followers_ids)\n print \"end lookups\"\n\n print \"%s has %s friends\" % (competitor, len(new_competitor_friends))\n print \"%s has %s followers\" % (competitor, len(new_competitor_followers))\n\n # holy crap this is so fucked up i'm ashamed that this code is getting written like this!\n\n for u in new_competitor_friends + new_competitor_followers:\n stolen_from.update({u.screen_name.lower(): competitor})\n\n except Exception, e:\n print e\n # didn't get all the users, don't remove the competitor\n # from the competitor list\n pass\n else:\n # got all the competitors friends and followers and converted them\n # to tweepy users.\n users += new_competitor_friends\n users += new_competitor_followers\n # add them to the users list to be processed in the next block (for user in users)\n # then pop the name off the competitors list in the UserProfile\n # fuck it for now i'm going to just cycle the item to the bottom of the competitor list so we can start getting maximal coverage within api constraints by making sure the top person is new every time\n self.competitors.append(self.competitors.pop(0))\n # return # for now\n self.profile.competitors = \"\\r\\n\".join(self.competitors)\n self.profile.save()\n\n # use the profile competitors list\n # for each name in competitors list\n # add all friends\n\n # should filter out garbage users. something like:\n users = [u for u in users if not Target.objects.filter(hunter=self.user, hunted__screen_name=u.screen_name.lower())]\n\n for user in users:\n twitter_account, created = utils.get_or_create_twitter_account(user)\n target, created = Target.objects.get_or_create(\n hunter=self.user, hunted=twitter_account)\n print target.hunted.screen_name, created\n if created:\n try:\n screen_name = user.screen_name.lower()\n match = lambda x: screen_name in \\\n (x.from_user.lower(), x.to_user and x.to_user.lower())\n if not self.strategy == UserProfile.STEAL:\n trigger_tweet = filter(match, statuses)[0].text\n else:\n try:\n trigger_tweet = \"Steal from user: %s\" % stolen_from.get(screen_name.lower(), \"someone. i lost it. sorry.\")\n except Exception, e:\n print \"YUCK. ERRORS.\"\n print \"YUCK. ERRORS.\"\n print e\n print e\n except Exception, e:\n self.log.exception(\"Could not get trigger tweet for %s\" %\n user.screen_name.lower())\n trigger_tweet = \"Error: Couldn't retrieve tweet.\"\n self.log.debug(\"Saved twitter account %s (trigger: %r)\" %\n (twitter_account.screen_name,\n trigger_tweet[:50]))\n target.reason = trigger_tweet\n target.status = Target.ON_DECK\n target.save()\n else:\n pass\n # print \" - Previously followed this dudesicle: %s\" % user.screen_name","def followed_by(self):\n return relationships.FollowedBy(self)","def getFollowers():\n\n cur, user_id = initialise(3)\n cur.execute(\"SELECT following FROM followers WHERE user = (SELECT username FROM users WHERE id = ?)\", [user_id])\n tempFollowers = cur.fetchall()\n followers = []\n for follower in tempFollowers:\n followers.append(follower[0])\n return followers","def follow(username):\n follow_form = FollowForm()\n unfollow_form = UnfollowForm()\n\n if follow_form.validate_on_submit():\n try:\n current_user.follow(username)\n flash('Followed {}'.format(username))\n except ValueError as excep:\n flash(str(excep))\n return render_template('user_history.html',\n username=username,\n follow_form=follow_form,\n unfollow_form=unfollow_form)\n\n return render_template('user_history.html',\n username=username,\n follow_form=follow_form,\n unfollow_form=unfollow_form)","def unsafe_follow_by_username(self, username: str) -> None:\n uid = self.username_to_id(username)\n self.api.follow(uid)","def get(self, request):\n # Retrieve the user from the request if they have been authenticated\n current_user = request.user\n # Get the following & followers username list\n # And the following & followers count for the current user\n user_following_data = get_user_following_data(current_user)\n # Return the follower details for the current user\n return Response(\n {\n \"message\": FOLLOW_USER_MSGS['MY_FOLLOWERS_SUCCESSFUL'],\n \"following\": user_following_data[\"following\"],\n \"followers\": user_following_data[\"followers\"],\n \"followingCount\": user_following_data[\"followingCount\"],\n \"followersCount\": user_following_data[\"followersCount\"]\n },\n status=status.HTTP_200_OK\n )","def getFollowers(self, user_id, api):\n followers = []\n next_max_id = True\n while next_max_id:\n # first iteration hack\n if next_max_id is True:\n next_max_id = ''\n \n _ = api.getUserFollowers(user_id, maxid=next_max_id)\n followers.extend(api.LastJson.get('users', []))\n next_max_id = api.LastJson.get('next_max_id', '')\n return followers","def followers(self, handles):\n print(handles)\n followers_list = {}\n for handle in handles:\n followers = self.twitter_client.followers_ids(screen_name=handle)\n\n r = []\n for page in self.paginate(followers, 100):\n results = self.twitter_client.lookup_users(user_ids=page)\n for result in results:\n r.append(result.screen_name)\n followers_list[handle] = r\n return followers_list","def user_following(username, max: int = None):\n for user_dict in client.user_relationships(username, max=max, type=\"following\"):\n print(json.dumps(user_dict))","def get_followers(self):\n rsp = self.session.get(self.url + \"/followers\")\n soup = self.getSoup(rsp.content)\n followers = soup.find_all('div', class_ = 'zm-person-item')\n if not followers:\n return\n i, follower = 0, None\n for follower in followers:\n i += 1\n yield follower.find('a', recursive = False)['href']\n while not i % Page_Items_Num:\n data = {\n 'offset' : i,\n 'start' : follower['id'].split('-')[-1],\n '_xsrf' : self.session.getCookie()['_xsrf']\n }\n rsp = self.session.post(self.url + \"/followers\", data = data)\n if rsp.json()['r'] == 0:\n followers = self.getSoup(rsp.json()['msg'][1]).find_all('div', class_ = 'zm-person-item')\n for follower in followers:\n i += 1\n yield follower.find('a', recursive = False)['href']\n else:\n return","def update_insta_followers_info(self):\n\n cur_following = self.GSpread.sheet_to_df('kontstats',\n 'INSTA_FOLLOWERS')\n new_following = self.Instagram.get_followers_df()\n\n cur_followers = set(cur_following.username.values)\n new_followers = set(new_following.username.values)\n\n who_left = list(cur_followers.difference(new_followers))\n who_joined = list(new_followers.difference(cur_followers))\n\n if len(who_left) > 0:\n self.GSpread.write_raw_log('INSTAGRAM',\n '',\n 'FOLLOWERS_LEFT',\n ', '.join(who_left))\n\n if len(who_joined) > 0:\n self.GSpread.write_raw_log('INSTAGRAM',\n '',\n 'FOLLOWERS_JOINED',\n ', '.join(who_joined))\n\n if (len(who_left) > 0) or (len(who_joined) > 0):\n self.GSpread.df_to_sheet('kontstats',\n 'INSTA_FOLLOWERS',\n new_following)","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followerId not in self.users.keys() or followeeId not in self.users.keys():\n return\n if followeeId not in self.users[followerId].followees.keys():\n return\n self.users[followerId].followees.pop(followeeId)\n\n\n\n # Your Twitter object will be instantiated and called as such:\n # obj = Twitter()\n # obj.postTweet(userId,tweetId)\n # param_2 = obj.getNewsFeed(userId)\n # obj.follow(followerId,followeeId)\n # obj.unfollow(followerId,followeeId)"],"string":"[\n \"def follow_user(cls, user, following):\\r\\n pass\",\n \"def follow_user(cls, user, following):\\n pass\",\n \"def follow(self, follower, followee):\\n pass\",\n \"def follow_followers(self):\\n self.logger.log(\\\"starting follow_followers...\\\")\\n follow = perform_with_ran_delay(self.instagram.get_followers, self.account.identifier, 150, 15, delayed=True)\\n for acc in follow[\\\"accounts\\\"]:\\n try:\\n try:\\n # print(\\\"{} follows me, do I follow him ? > {} \\\".format(acc.username,self.is_user_following(acc.identifier)))\\n if not self.is_user_following(acc.identifier):\\n if self.add_following(acc.identifier):\\n self.logger.log(\\\"following: {}\\\".format(acc.username))\\n else:\\n self.logger.log(\\\"follow not working at the moment\\\")\\n except Exception as e:\\n print(e)\\n self.logger.log(str(e))\\n continue\\n finally:\\n sleep(3)\",\n \"def follow_following_followers(self):\\n self.logger.log(\\\"starting follow_following_followers...\\\")\\n follows_accounts = self.following\\n random.shuffle(follows_accounts)\\n for acc in follows_accounts:\\n try:\\n try:\\n followw = perform_with_ran_delay(self.instagram.get_followers, acc, 150, 15,\\n delayed=True)\\n accountstofollow = followw[\\\"accounts\\\"]\\n random.shuffle(accountstofollow)\\n if len(accountstofollow) > 10:\\n accountstofollow = accountstofollow[:10]\\n for ac in accountstofollow:\\n if not self.is_user_following(ac.identifier):\\n self.add_following(ac.identifier)\\n self.logger.log(\\\"following: {}\\\".format(ac.username))\\n except Exception as e:\\n print(e)\\n self.logger.log(str(e))\\n finally:\\n sleep(3)\",\n \"def user_follow():\\n data = request.get_json(force=True)\\n follower = User.query.get(data['follower'])\\n following = User.query.get(data['following'])\\n follower.followcheck.append(following)\\n db.session.commit()\\n return {'followed': True}\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n self.followed.append(user)\\n return self\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n self.followed.append(user)\\n return self\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n f = Follow(follower=self, followed=user)\\n db.session.add(f)\",\n \"def user_follow_users(self, ids=None, **kwargs):\\n return self._put(\\n API.MY_FOLLOWING.value, type=\\\"user\\\", ids=\\\",\\\".join(ids or []), **kwargs\\n )\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n self.user_followed[followerId].append(followeeId)\",\n \"def auto_follow_followers():\\n\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n not_following_back = followers - following\\n\\n for user_id in not_following_back:\\n try:\\n t.friendships.create(user_id=user_id, follow=False)\\n except Exception as e:\\n print(\\\"error: %s\\\" % (str(e)))\",\n \"def follow(self, user_index, following_index):\\n if user_index >= self.num_users or following_index >= self.num_users:\\n raise ValueError(\\n f\\\"Number of users is {self.num_users}, but indices \\\"\\n f\\\"{user_index} and {following_index} were requested.\\\"\\n )\\n if self.users_hat[following_index, user_index] == 0:\\n self.users_hat[following_index, user_index] = 1\\n elif self.is_verbose():\\n self.log(f\\\"User {following_index} was already following user {user_index}\\\")\",\n \"def follow(request, usertofollow):\\n to_follow = Member.objects.get(user__username=usertofollow)\\n user = Member.objects.get(user=request.user)\\n user.following.add(to_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"def follow(self, followerId, followeeId):\\n if followerId in self.follows:\\n self.follows[followerId].add(followeeId)\\n else:\\n self.follows[followerId] = set([followeeId])\",\n \"def follow(self, followerId, followeeId):\\r\\n if followerId != followeeId:\\r\\n self.follows[followerId].add(followeeId)\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def follow(user, people):\\n api = get_api(user)\\n current_screen_name = api.VerifyCredentials().GetScreenName()\\n\\n # don't let a user follow themselves\\n screen_names = [person.twitter_screen_name for person in people]\\n if current_screen_name in screen_names: screen_names.remove(current_screen_name)\\n\\n followed = []\\n not_followed = []\\n\\n for screen_name in screen_names:\\n try:\\n api.CreateFriendship(screen_name=screen_name)\\n followed.append(screen_name)\\n except twitter.TwitterError:\\n not_followed.append(screen_name)\\n\\n return 'followed %s people' % len(followed)\",\n \"def _user_follower_info(self, uid: int = 0) -> List[_InstagramUser]:\\n # If no uid was specified, use the authenticated user's uid\\n if uid == 0:\\n uid = self.uid\\n\\n followers: List[Dict[str, Any]] = self.api.getTotalFollowers(uid)\\n user_followers = list([_InstagramUser(x) for x in followers])\\n return user_followers\",\n \"def follow(self, followerId, followeeId):\\n\\n # 把 followeeId append到他的 follow 属性中\\n if followerId == followeeId: # 不能自己关注自己\\n return\\n # 实例化一个user(followerID)\\n follower = UserInfo()\\n follower.user_id = followerId \\n follower.follows.append(followeeId) \\n self.user_pool[followerId] = follower\",\n \"def follow_user(username):\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n db = get_db()\\n db.execute('insert into follower (who_id, whom_id) values (?, ?)',\\n [session['user_id'], whom_id])\\n db.commit()\\n flash('You are now following \\\"%s\\\"' % username)\\n return redirect(url_for('user_timeline', username=username))\",\n \"def follow_user(self, user):\\n self.nav_user(user)\\n follow_button = self.driver.find_element_by_xpath(\\n \\\"//button[contains(text(), 'Follow')]\\\")\\n follow_button.click()\\n time.sleep(1)\\n self.driver.get(self.base_url)\",\n \"def userFollowers(nick):\\n if (len(nick) != 1):\\n print \\\"Has d'introduir només un nick\\\"\\n return\\n i.userFollow(nick[0])\",\n \"def follow_user(username):\\n user_ID = before_request()\\n user_ID = None\\n if user_ID != None:\\n user_ID = str(g.user['_id'])\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n mongo.db.users.update({'_id': g.user['_id']}, {\\n '$push': {'follows': whom_id}})\\n flash('You are now following \\\"%s\\\"' % username)\\n if redis_obj.get(user_ID):\\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(r.get(user_ID))))\\n else:\\n redis_obj.delete(str(g.user['_id']))\\n print \\\"Invalidating cache after Follow\\\"\\n return redirect(url_for('user_timeline', username=username))\",\n \"def followers(self):\\r\\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def followers(self):\\r\\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def followers(self):\\r\\n request = http.Request('GET', '{0}/followers/'.format(self.get_url()))\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def _user_following_info(self, uid: int = 0) -> List[_InstagramUser]:\\n # If no uid was specified, use the authenticated user's uid\\n if uid == 0:\\n uid = self.uid\\n\\n followings: List[Dict[str, Any]] = self.api.getTotalFollowings(uid)\\n user_followings = list([_InstagramUser(x) for x in followings])\\n return user_followings\",\n \"def follows(self, user_id):\\n\\n url = \\\"https://api.instagram.com/v1/users/{0}/follows?access_token={1}\\\".format(user_id, self.access_token)\\n\\n request = requests.get(url)\\n return request.json()\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followerId == followeeId: return\\n self.users[followerId].add(followeeId)\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followerId == followeeId:\\n return\\n if followerId not in self.users.keys():\\n self.users[followerId] = user()\\n if followeeId not in self.users.keys():\\n self.users[followeeId] = user()\\n self.users[followerId].followees[followeeId] = self.users[followeeId]\",\n \"def post(self, request, username):\\n\\n # Retrieve the user from the user table if the user exists\\n try:\\n user_details = User.objects.get(username=username)\\n current_user = request.user\\n # If a user is trying to follow themselves then stop the request\\n if user_details.profile.id == current_user.profile.id:\\n return Response(\\n {\\\"errors\\\": FOLLOW_USER_MSGS['CANNOT_FOLLOW_SELF']},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n # Otherwise follow the author the current user has indicated\\n current_user.profile.follows.add(user_details.profile)\\n\\n # notify user of new follower\\n send_notifications(request,\\n notification_type=\\\"user_followed\\\",\\n instance=current_user,\\n recipients=[user_details])\\n\\n # Get the following & followers username list\\n # And the following & followers count for the current user\\n user_following_data = get_user_following_data(current_user)\\n return Response(\\n {\\n \\\"message\\\": FOLLOW_USER_MSGS['USER_FOLLOW_SUCCESSFUL'],\\n \\\"following\\\": user_following_data[\\\"following\\\"],\\n \\\"followers\\\": user_following_data[\\\"followers\\\"],\\n \\\"followingCount\\\": user_following_data[\\\"followingCount\\\"],\\n \\\"followersCount\\\": user_following_data[\\\"followersCount\\\"]\\n },\\n status=status.HTTP_201_CREATED\\n )\\n except User.DoesNotExist:\\n return Response(\\n {\\\"errors\\\": FOLLOW_USER_MSGS['USER_NOT_FOUND']},\\n status=status.HTTP_404_NOT_FOUND\\n )\",\n \"def users_followers(user_id):\\n\\n user = User.query.get_or_404(user_id)\\n return render_template('users/followers.html', user=user)\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followeeId not in self.followList.get(followerId, [followerId]):\\n self.followList[followerId] = self.followList.get(followerId, [followerId]) + [followeeId]\\n # print(self.followList)\",\n \"def resolve_followers(self, info):\\n user = info.context.user\\n follow_request = FollowRequest.objects.filter(following=user.id, pending=False)\\n return [follow.follower for follow in follow_request]\",\n \"def follow_by_id(self, uid: int) -> None:\\n self.api.follow(uid)\",\n \"def follower_followee_count(user_id):\\n profile = instaloader.Profile.from_username(context(), user_id)\\n return {'follower_cnt': profile.followers,\\n 'followee_cnt': profile.followees}\",\n \"def follower(account):\\n stm = shared_morphene_instance()\\n if mph.rpc is not None:\\n mph.rpc.rpcconnect()\\n if not account:\\n if \\\"default_account\\\" in mph.config:\\n account = [mph.config[\\\"default_account\\\"]]\\n for a in account:\\n a = Account(a, morphene_instance=stm)\\n print(\\\"\\\\nFollowers statistics for @%s (please wait...)\\\" % a.name)\\n followers = a.get_followers(False)\\n followers.print_summarize_table(tag_type=\\\"Followers\\\")\",\n \"def followed_by(self, user_id):\\n\\n url = \\\"https://api.instagram.com/v1/users/{0}/followed-by?access_token={1}\\\".format(user_id, self.access_token)\\n\\n request = requests.get(url)\\n return request.json()\",\n \"def get_followers_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\\n query = f\\\"\\\"\\\"\\n MATCH (follower)-[:FOLLOWS]->(:Person {{email: '{email}'}}) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\\n \\\"\\\"\\\"\\n return tx.run(query)\",\n \"def follow(self, followerId, followeeId):\\n if followerId not in self.follow_map:\\n self.follow_map[followerId] = set()\\n \\n self.follow_map[followerId].add(followeeId)\",\n \"def follows(self):\\r\\n return relationships.Follows(self)\",\n \"def followUser(following):\\n\\n cur, user_id, con = initialise(3, True)\\n cur.execute(\\\"INSERT INTO followers (user, following) VALUES ((SELECT username FROM users WHERE id = ?), ?)\\\", (user_id, following))\\n finish(con)\",\n \"def user_followers(username, max: int = None):\\n for user_dict in client.user_relationships(username, max=max, type=\\\"followers\\\"):\\n print(json.dumps(user_dict))\",\n \"def follows(self):\\n return relationships.Follows(self)\",\n \"def user_follow_artists(self, ids=None, **kwargs):\\n return self._put(\\n API.MY_FOLLOWING.value, type=\\\"artist\\\", ids=\\\",\\\".join(ids or []), **kwargs\\n )\",\n \"def user_follow(self, user_id: int) -> bool:\\n assert self.user_id, \\\"Login required\\\"\\n user_id = int(user_id)\\n if user_id in self._users_following.get(self.user_id, []):\\n self.logger.debug(\\\"User %s already followed\\\", user_id)\\n return False\\n data = self.with_action_data({\\\"user_id\\\": user_id})\\n result = self.private_request(f\\\"friendships/create/{user_id}/\\\", data)\\n if self.user_id in self._users_following:\\n self._users_following.pop(self.user_id) # reset\\n return result[\\\"friendship_status\\\"][\\\"following\\\"] is True\",\n \"def get_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\\n query = f\\\"\\\"\\\"\\n MATCH (:Person {{email: '{email}'}})-[:FOLLOWS]->(follower) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\\n \\\"\\\"\\\"\\n return tx.run(query)\",\n \"def getFollowers():\\n followers = []\\n next_max_id = True\\n while next_max_id:\\n # first iteration hack\\n if next_max_id is True:\\n next_max_id = ''\\n\\n _ = GetInstagramAnswer.igApi.getUserFollowers(GetInstagramAnswer.igApi.username_id, maxid=next_max_id)\\n followers.extend(GetInstagramAnswer.igApi.LastJson.get('users',[]))\\n next_max_id = GetInstagramAnswer.igApi.LastJson.get('next_max_id','')\\n return \\\"You have currently \\\"+str(len(followers))+\\\" Followers on Instagram.\\\"\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n self.follow_map[followerId].add(followeeId)\",\n \"async def follow(follow):\\n await follow.edit(\\n f\\\"`FOLLOW {DEFAULTUSER} ON` \\\\n\\\\n\\\"\\n f\\\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\\\n\\\\n\\\"\\n f\\\"[FaceBook](https://www.facebook.com/mkaraniya) \\\\n\\\\n\\\"\\n f\\\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \\\"\\n )\",\n \"async def get_new_followers(self, bearer_token:str, user_id:str, after:str) -> list:\\n\\n headers = {\\\"Client-Id\\\": self.TWITCH_PARAMS['client_id'], \\\"Authorization\\\": f\\\"Bearer {bearer_token}\\\"}\\n params = {\\\"to_id\\\": user_id, \\\"first\\\": 100}\\n if after:\\n params[\\\"after\\\"] = after\\n output = []\\n while True:\\n async with self.bot.session.get(self.TWITCH_USER_FOLLOWS_URL, params=params, headers=headers) as r:\\n data = await r.json()\\n # self.logger.info(data)\\n output.extend(data.get('data', list()))\\n if len(data.get('data', list())) < 100:\\n break\\n params['after'] = data.get('pagination', {}).get('cursor', None)\\n return output, data.get('pagination', {}).get('cursor', None)\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followeeId not in self.users:\\n self._create_user(followeeId)\\n if followerId not in self.users:\\n self._create_user(followerId)\\n self.users[followerId].add(followeeId)\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n self.followees[followerId].add(followeeId)\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n # Time Complexity: O(1)\\n if followerId != followeeId:\\n if followerId not in self.followees:\\n self.followees[followerId] = set()\\n\\n self.followees[followerId].add(followeeId)\",\n \"async def follow(self, TargetId: int):\\n data = {\\n 'targetUserId': TargetId\\n }\\n e = await self.request.request(url=f'https://friends.roblox.com/v1/users/{TargetId}/follow', method='post',\\n data=data)\\n return e\",\n \"def is_follower(self, you, them):\\n if self.filter(from_user=them, to_user=you).count() > 0:\\n return True\\n return False\",\n \"def users_being_followed_tweets():\\n username = request.authorization.username\\n tweets = []\\n\\n user_id = get_user_id(username);\\n tuples = query_db('''\\n select message.*, user.* from message, user\\n where message.author_id = user.user_id and (\\n user.user_id = ? or\\n user.user_id in (select whom_id from follower\\n where who_id = ?))\\n order by message.pub_date desc limit ?''',\\n [user_id, user_id, PER_PAGE])\\n\\n for tuple in tuples:\\n tweet = {}\\n tweet[\\\"message_id\\\"] = tuple['message_id']\\n tweet[\\\"author_id\\\"] = tuple['author_id']\\n tweet[\\\"text\\\"] = tuple['text']\\n tweet[\\\"pub_date\\\"] = tuple['pub_date']\\n tweet[\\\"username\\\"] = tuple['username']\\n tweet[\\\"email\\\"] = tuple['email']\\n tweets.append(tweet)\\n\\n return jsonify({'tweets': tweets}), 200\",\n \"def get_followers(user):\\n if user.has_key('followers_list'):\\n pass\\n else:\\n if user.has_key('followers_count'):\\n if user['followers_count'] > 4999:\\n pages = user['followers_count'] / 5000\\n f_list = []\\n for page in range(pages):\\n try:\\n follower_set = api.GetFollowers(user_id=user['id'], cursor=page, count=5000)\\n friends_list = []\\n for follower in follower_set:\\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\\n friends_list.append(follower.GetId())\\n f_list = friends_list + f_list\\n time.sleep(60)\\n user['followers_list'] = f_list\\n twitter_users.update({'id': user['id']}, user)\\n print \\\"\\\\n\\\\nGot %s followers out of %s listed\\\" % (len(f_list), user['followers_count'])\\n except Exception, e:\\n print str(e)\\n time.sleep(60)\\n else:\\n try:\\n follower_set = api.GetFollowers(user_id=user['id'], count=5000)\\n friends_list = []\\n for follower in follower_set:\\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\\n friends_list.append(follower.GetId())\\n user['followers_list'] = friends_list\\n twitter_users.update({'id': user['id']}, user)\\n print \\\"\\\\n\\\\nGot %s followers out of %s listed\\\" % (len(friends_list), user['followers_count'])\\n except Exception, e:\\n print str(e)\\n time.sleep(60)\",\n \"def users_followers(user_id):\\n\\n if not g.user:\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n\\n user = User.query.get_or_404(user_id)\\n users_blocking = [block.user_blocking_id for block in Blocks.query.all() if block.user_being_blocked_id == g.user.id]\\n likes = [message for message in user.likes if message.user_id not in users_blocking]\\n return render_template('users/followers.html', user=user, likes=likes)\",\n \"def followers(self, offset=0):\\n url = '/question/%d/followers' % self.id\\n data = {\\n 'offset': offset,\\n 'start': 0,\\n '_xsrf': req._xsrf\\n }\\n r = req.post(url, data=data)\\n return parser.followers(r['msg'][1])\",\n \"def follows(self):\\r\\n request = http.Request('GET', '{0}/follows/'.format(self.get_url()))\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get_follows_route(request):\\n\\n db_conn = request['db_conn']\\n current_user = get_current_user(request)\\n user_id = request['params'].get('user_id')\\n if user_id:\\n user = get_user({'id': user_id}, db_conn)\\n if not user:\\n return abort(404)\\n if (user != current_user and\\n user['settings']['view_follows'] != 'public'):\\n return abort(403)\\n else:\\n user = current_user\\n if not user:\\n return abort(401)\\n params = dict(**request['params'])\\n params['user_id'] = user['id']\\n follows = list_follows(params, db_conn)\\n return 200, {\\n 'follows': [deliver_follow(follow, access='private')\\n for follow in follows]\\n }\",\n \"def generateFollowers(self):\\n for f in self._genericGenerator(self.getFollowers):\\n yield f\",\n \"def follow(self, followerId: 'int', followeeId: 'int') -> 'None':\\n self.followees[followerId].add(followeeId)\",\n \"def followers(self):\\n return self.data.get(\\\"followers\\\")\",\n \"def get_user_followers(self, user_id, count = 30, page = 1):\\n uri = 'users/' + user_id + '/followers'\\n options = { 'per_page': count, 'page': page}\\n return self.make_request(uri, options)\",\n \"def doesfollow(user):\\n return jsonify({\\n 'follows': isFollowed(g.user,user)\\n })\",\n \"def get_followers(user_id):\\n return list(Backward.objects.filter(destination_id=user_id) \\\\\\n .values_list('source_id', flat=True))\",\n \"def follow(self, context, callback):\\n followers_list = self.followers.setdefault(context, [])\\n if callback not in followers_list:\\n followers_list.append(callback)\",\n \"def followed_by(self):\\r\\n return relationships.FollowedBy(self)\",\n \"def author_following(self):\\n\\t\\tpass\",\n \"def follow(whomUserName,whoUserName):\\n\\n whomuser = query_db('select * from user where username = ?',\\n [whomUserName], one=True)\\n whouser = query_db('select * from user where username = ?',\\n [whoUserName], one=True)\\n\\n\\n followed = query_db('''select 1 from follower where\\n follower.who_id = ? and follower.whom_id = ?''',\\n [whouser['user_id'], whomuser['user_id']],one=True) is not None\\n\\n if whouser is None:\\n return jsonify({'message':'User trying to follow another user which does not exist'}),404\\n\\n if whomuser is None:\\n return jsonify({'message':'User getting followed does not exist yet'}),404\\n\\n if not followed:\\n db = get_db()\\n\\n db.execute('''insert into follower (\\n who_id, whom_id) values (?, ?)''',\\n [whouser['user_id'], whomuser['user_id']])\\n db.commit()\\n flash('Operation successful')\\n return jsonify({'message': 'Successfully following'}), 201\\n else:\\n return jsonify({'message':'Specified user is already following another user'}),403\",\n \"def add_following(self, user_id):\\n sleep(360) # too much follows => function ban\\n self.following.append(user_id)\\n return perform_with_ran_delay(self.instagram.follow, user_id)\",\n \"def resolve_following(self, info):\\n user = info.context.user\\n follow_request = FollowRequest.objects.filter(follower=user.id, pending=False)\\n return [follow.following for follow in follow_request]\",\n \"def post(self, request, username):\\n user_exists = User.objects.filter(username=username).exists()\\n if not user_exists:\\n return Response(\\n {'error': 'user with that name was not found'},\\n status.HTTP_404_NOT_FOUND)\\n # we check if the user is already followed\\n followed_user = User.objects.get(username=username)\\n already_followed = Follow.is_user_already_followed(\\n followed_user_id=followed_user.id,\\n user_id=self.request.user.id\\n )\\n if already_followed:\\n return Response({'error': 'user already followed'},\\n status.HTTP_400_BAD_REQUEST)\\n if followed_user.id == self.request.user.id:\\n return Response({'error': \\\"you cannot follow yourself.\\\"},\\n status.HTTP_400_BAD_REQUEST)\\n data = {\\n \\\"followed_user\\\": followed_user.id,\\n \\\"user\\\": self.request.user.id}\\n serializer = FollowSerializer(data=data)\\n serializer.is_valid(raise_exception=True)\\n serializer.save()\\n\\n return Response({'message': 'user followed successfully'},\\n status.HTTP_201_CREATED)\",\n \"def follow_friend():\\n print \\\"followuser\\\"\\n username = request.args.get('username')\\n print \\\"JSON Data\\\", username\\n # username= req_data[username]\\n whom_id = get_user_id(username)\\n print \\\"whom_id:\\\", whom_id\\n if whom_id is None:\\n abort(404)\\n follow_query(whom_id)\\n flash('You are now following \\\"%s\\\"' % username)\\n name = {'name of following user': username}\\n R_SERVER.delete(user_timeline_key)\\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)\",\n \"def follow(self, other):\\n\\t\\tif not self.follows(other):\\n\\t\\t\\tself.followed.append(other)\",\n \"def set_follow(self, follow):\\n self.follow = follow\",\n \"def is_following(self, user):\\n return self.followed.filter(followers.c.followed_id == user.id).count() > 0\",\n \"def is_following(self, user):\\n return self.followed.filter(followers.c.followed_id == user.id).count() > 0\",\n \"def add_follow(follow_id):\\n\\n want_to_follow_user = User.query.get_or_404(follow_id)\\n if want_to_follow_user.private:\\n # =========== NEED TO IMPLEMENT ====================\\n # send them a request to follow\\n want_to_follow_user.from_users.append(g.user) \\n db.session.commit()\\n flash(\\\"Your request has been sent\\\", \\\"success\\\")\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\\n\\n g.user.following.append(want_to_follow_user)\\n db.session.commit()\\n\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\",\n \"def current_user_followed_artists(self, limit=20, after=None, **kwargs):\\n return self._get(\\n API.MY_FOLLOWING.value, type=\\\"artist\\\", limit=limit, after=after, **kwargs\\n )\",\n \"def _get_followed(_user_id, _ignore_exceptions=False, _start_index=0, _count=100):\\n _following_url = f\\\"{base_url}/people/{_user_id}/@following?count={_count}\\\" + \\\\\\n f\\\"&startIndex={_start_index}\\\"\\n _response = core.get_request_with_retries(_following_url)\\n if _response.status_code == 200:\\n _following_data = _response.json()\\n else:\\n if _ignore_exceptions:\\n _empty_response = {\\\"list\\\": []}\\n _following_data = core_utils.convert_dict_to_json(_empty_response)\\n else:\\n if _response.status_code == 404:\\n raise errors.exceptions.UserNotFoundError()\\n else:\\n raise errors.exceptions.UserQueryError()\\n return _following_data\",\n \"def test_followers(self):\\n\\n follow1 = Follows(\\n user_being_followed_id = self.testuser.id,\\n user_following_id = self.testuser2.id\\n )\\n follow2 = Follows(\\n user_being_followed_id = self.testuser3.id,\\n user_following_id = self.testuser.id\\n )\\n\\n db.session.add_all((follow1, follow2))\\n db.session.commit()\\n\\n with self.client as c:\\n with c.session_transaction() as session:\\n session[CURR_USER_KEY] = self.testuser.id\\n \\n response = c.get(f\\\"/users/{self.testuser.id}/followers\\\")\\n data = str(response.data)\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertIn(\\\"@bob\\\", data)\\n self.assertNotIn(\\\"@carl\\\", data)\\n self.assertNotIn(\\\"@alvin\\\", data)\",\n \"def find_new_followers(self):\\n api = self.api\\n geocode = self.geocode\\n queries = self.queries\\n hits_per_query = self.hits_per_query\\n\\n self.log.debug(\\\"Initialize\\\")\\n self.log.debug(\\\"[ ********* FIND NEW FOLLOWERS *********** ]\\\")\\n\\n if self.strategy == UserProfile.FOLLOW or self.strategy == UserProfile.TWEET:\\n # Find statuses that match our interests\\n self.log.debug(\\\"Strategy set to FOLLOW or TWEET\\\")\\n n = hits_per_query\\n search_dict = dict()\\n search_dict['lang'] = \\\"en\\\"\\n if not geocode is None:\\n search_dict['geocode'] = geocode\\n statuses = list()\\n self.log.debug(\\\"Queries:\\\")\\n for q in queries:\\n search_dict['q'] = q\\n results = [c for c in Cursor(api.search, **search_dict).items(n)]\\n self.log.debug(\\\" => %s: %s hits\\\" % (q, len(results)))\\n statuses.extend(results)\\n #self.log.debug(\\\"Statuses: %s\\\" % \\\"\\\\n\\\".join([str(s.__dict__) for s in statuses]))\\n # Get all the screen names of senders and receivers\\n screen_names = ([t.from_user for t in statuses] +\\n [t.to_user for t in statuses if t.to_user])\\n\\n # Convert the strings to Tweepy user objects\\n users, remainder = lookup_users_by_screen_name(self.api, screen_names)\\n\\n elif self.strategy == UserProfile.STEAL:\\n users = []\\n stolen_from = {}\\n for competitor in list(self.competitors):\\n self.log.debug(\\\"[ ********* STEAL %s *********** ]\\\" % competitor)\\n try:\\n competitor_friends_ids = self.api.friends_ids(competitor)\\n competitor_followers_ids = self.api.followers_ids(competitor)\\n\\n filter_known_users_to_reduce_api_hits = False\\n\\n if filter_known_users_to_reduce_api_hits is True:\\n new_competitor_friends_ids = [id for id in competitor_friends_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n old_competitor_friends_ids = [id for id in competitor_friends_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n new_competitor_followers_ids = [id for id in competitor_followers_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n old_competitor_followers_ids = [id for id in competitor_followers_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n # print new_competitor_friends_ids\\n # print old_competitor_friends_ids\\n # print new_competitor_followers_ids\\n # print old_competitor_followers_ids\\n\\n print \\\"start lookups\\\"\\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, new_competitor_friends_ids)\\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, new_competitor_followers_ids)\\n print \\\"end lookups\\\"\\n else:\\n # get all the tweepy users\\n print \\\"start lookups\\\"\\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, competitor_friends_ids)\\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, competitor_followers_ids)\\n print \\\"end lookups\\\"\\n\\n print \\\"%s has %s friends\\\" % (competitor, len(new_competitor_friends))\\n print \\\"%s has %s followers\\\" % (competitor, len(new_competitor_followers))\\n\\n # holy crap this is so fucked up i'm ashamed that this code is getting written like this!\\n\\n for u in new_competitor_friends + new_competitor_followers:\\n stolen_from.update({u.screen_name.lower(): competitor})\\n\\n except Exception, e:\\n print e\\n # didn't get all the users, don't remove the competitor\\n # from the competitor list\\n pass\\n else:\\n # got all the competitors friends and followers and converted them\\n # to tweepy users.\\n users += new_competitor_friends\\n users += new_competitor_followers\\n # add them to the users list to be processed in the next block (for user in users)\\n # then pop the name off the competitors list in the UserProfile\\n # fuck it for now i'm going to just cycle the item to the bottom of the competitor list so we can start getting maximal coverage within api constraints by making sure the top person is new every time\\n self.competitors.append(self.competitors.pop(0))\\n # return # for now\\n self.profile.competitors = \\\"\\\\r\\\\n\\\".join(self.competitors)\\n self.profile.save()\\n\\n # use the profile competitors list\\n # for each name in competitors list\\n # add all friends\\n\\n # should filter out garbage users. something like:\\n users = [u for u in users if not Target.objects.filter(hunter=self.user, hunted__screen_name=u.screen_name.lower())]\\n\\n for user in users:\\n twitter_account, created = utils.get_or_create_twitter_account(user)\\n target, created = Target.objects.get_or_create(\\n hunter=self.user, hunted=twitter_account)\\n print target.hunted.screen_name, created\\n if created:\\n try:\\n screen_name = user.screen_name.lower()\\n match = lambda x: screen_name in \\\\\\n (x.from_user.lower(), x.to_user and x.to_user.lower())\\n if not self.strategy == UserProfile.STEAL:\\n trigger_tweet = filter(match, statuses)[0].text\\n else:\\n try:\\n trigger_tweet = \\\"Steal from user: %s\\\" % stolen_from.get(screen_name.lower(), \\\"someone. i lost it. sorry.\\\")\\n except Exception, e:\\n print \\\"YUCK. ERRORS.\\\"\\n print \\\"YUCK. ERRORS.\\\"\\n print e\\n print e\\n except Exception, e:\\n self.log.exception(\\\"Could not get trigger tweet for %s\\\" %\\n user.screen_name.lower())\\n trigger_tweet = \\\"Error: Couldn't retrieve tweet.\\\"\\n self.log.debug(\\\"Saved twitter account %s (trigger: %r)\\\" %\\n (twitter_account.screen_name,\\n trigger_tweet[:50]))\\n target.reason = trigger_tweet\\n target.status = Target.ON_DECK\\n target.save()\\n else:\\n pass\\n # print \\\" - Previously followed this dudesicle: %s\\\" % user.screen_name\",\n \"def followed_by(self):\\n return relationships.FollowedBy(self)\",\n \"def getFollowers():\\n\\n cur, user_id = initialise(3)\\n cur.execute(\\\"SELECT following FROM followers WHERE user = (SELECT username FROM users WHERE id = ?)\\\", [user_id])\\n tempFollowers = cur.fetchall()\\n followers = []\\n for follower in tempFollowers:\\n followers.append(follower[0])\\n return followers\",\n \"def follow(username):\\n follow_form = FollowForm()\\n unfollow_form = UnfollowForm()\\n\\n if follow_form.validate_on_submit():\\n try:\\n current_user.follow(username)\\n flash('Followed {}'.format(username))\\n except ValueError as excep:\\n flash(str(excep))\\n return render_template('user_history.html',\\n username=username,\\n follow_form=follow_form,\\n unfollow_form=unfollow_form)\\n\\n return render_template('user_history.html',\\n username=username,\\n follow_form=follow_form,\\n unfollow_form=unfollow_form)\",\n \"def unsafe_follow_by_username(self, username: str) -> None:\\n uid = self.username_to_id(username)\\n self.api.follow(uid)\",\n \"def get(self, request):\\n # Retrieve the user from the request if they have been authenticated\\n current_user = request.user\\n # Get the following & followers username list\\n # And the following & followers count for the current user\\n user_following_data = get_user_following_data(current_user)\\n # Return the follower details for the current user\\n return Response(\\n {\\n \\\"message\\\": FOLLOW_USER_MSGS['MY_FOLLOWERS_SUCCESSFUL'],\\n \\\"following\\\": user_following_data[\\\"following\\\"],\\n \\\"followers\\\": user_following_data[\\\"followers\\\"],\\n \\\"followingCount\\\": user_following_data[\\\"followingCount\\\"],\\n \\\"followersCount\\\": user_following_data[\\\"followersCount\\\"]\\n },\\n status=status.HTTP_200_OK\\n )\",\n \"def getFollowers(self, user_id, api):\\n followers = []\\n next_max_id = True\\n while next_max_id:\\n # first iteration hack\\n if next_max_id is True:\\n next_max_id = ''\\n \\n _ = api.getUserFollowers(user_id, maxid=next_max_id)\\n followers.extend(api.LastJson.get('users', []))\\n next_max_id = api.LastJson.get('next_max_id', '')\\n return followers\",\n \"def followers(self, handles):\\n print(handles)\\n followers_list = {}\\n for handle in handles:\\n followers = self.twitter_client.followers_ids(screen_name=handle)\\n\\n r = []\\n for page in self.paginate(followers, 100):\\n results = self.twitter_client.lookup_users(user_ids=page)\\n for result in results:\\n r.append(result.screen_name)\\n followers_list[handle] = r\\n return followers_list\",\n \"def user_following(username, max: int = None):\\n for user_dict in client.user_relationships(username, max=max, type=\\\"following\\\"):\\n print(json.dumps(user_dict))\",\n \"def get_followers(self):\\n rsp = self.session.get(self.url + \\\"/followers\\\")\\n soup = self.getSoup(rsp.content)\\n followers = soup.find_all('div', class_ = 'zm-person-item')\\n if not followers:\\n return\\n i, follower = 0, None\\n for follower in followers:\\n i += 1\\n yield follower.find('a', recursive = False)['href']\\n while not i % Page_Items_Num:\\n data = {\\n 'offset' : i,\\n 'start' : follower['id'].split('-')[-1],\\n '_xsrf' : self.session.getCookie()['_xsrf']\\n }\\n rsp = self.session.post(self.url + \\\"/followers\\\", data = data)\\n if rsp.json()['r'] == 0:\\n followers = self.getSoup(rsp.json()['msg'][1]).find_all('div', class_ = 'zm-person-item')\\n for follower in followers:\\n i += 1\\n yield follower.find('a', recursive = False)['href']\\n else:\\n return\",\n \"def update_insta_followers_info(self):\\n\\n cur_following = self.GSpread.sheet_to_df('kontstats',\\n 'INSTA_FOLLOWERS')\\n new_following = self.Instagram.get_followers_df()\\n\\n cur_followers = set(cur_following.username.values)\\n new_followers = set(new_following.username.values)\\n\\n who_left = list(cur_followers.difference(new_followers))\\n who_joined = list(new_followers.difference(cur_followers))\\n\\n if len(who_left) > 0:\\n self.GSpread.write_raw_log('INSTAGRAM',\\n '',\\n 'FOLLOWERS_LEFT',\\n ', '.join(who_left))\\n\\n if len(who_joined) > 0:\\n self.GSpread.write_raw_log('INSTAGRAM',\\n '',\\n 'FOLLOWERS_JOINED',\\n ', '.join(who_joined))\\n\\n if (len(who_left) > 0) or (len(who_joined) > 0):\\n self.GSpread.df_to_sheet('kontstats',\\n 'INSTA_FOLLOWERS',\\n new_following)\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followerId not in self.users.keys() or followeeId not in self.users.keys():\\n return\\n if followeeId not in self.users[followerId].followees.keys():\\n return\\n self.users[followerId].followees.pop(followeeId)\\n\\n\\n\\n # Your Twitter object will be instantiated and called as such:\\n # obj = Twitter()\\n # obj.postTweet(userId,tweetId)\\n # param_2 = obj.getNewsFeed(userId)\\n # obj.follow(followerId,followeeId)\\n # obj.unfollow(followerId,followeeId)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.84074855","0.8322308","0.80839336","0.79454315","0.7784225","0.7753276","0.7748424","0.7748424","0.7657068","0.758209","0.75808793","0.75617784","0.75339836","0.7445854","0.7444906","0.7435006","0.7433624","0.7433624","0.7433624","0.7433624","0.7409177","0.7391407","0.7372572","0.734416","0.73267305","0.73075706","0.7306904","0.7287193","0.7287193","0.7287193","0.7283112","0.7268656","0.7248598","0.7236045","0.7234185","0.72304565","0.72216123","0.7200374","0.7175392","0.7156406","0.71455896","0.7131473","0.7105297","0.7102847","0.70958513","0.7091075","0.7079893","0.7069183","0.7062061","0.7039643","0.7019955","0.7018622","0.7017914","0.7004814","0.6992392","0.6984488","0.6969375","0.6916935","0.6898047","0.6871245","0.6867958","0.6846715","0.683936","0.68262976","0.6821063","0.6816983","0.68120605","0.68057805","0.678342","0.67749286","0.6770209","0.67671067","0.6764807","0.6758127","0.67564374","0.67535836","0.67504525","0.674742","0.67433447","0.67375773","0.67373323","0.6734578","0.67331237","0.67331237","0.67304003","0.672611","0.6726072","0.6721306","0.6716529","0.6710106","0.67045575","0.67013425","0.6698607","0.66980857","0.6694198","0.6687596","0.6687254","0.66865855","0.66835076","0.66749185"],"string":"[\n \"0.84074855\",\n \"0.8322308\",\n \"0.80839336\",\n \"0.79454315\",\n \"0.7784225\",\n \"0.7753276\",\n \"0.7748424\",\n \"0.7748424\",\n \"0.7657068\",\n \"0.758209\",\n \"0.75808793\",\n \"0.75617784\",\n \"0.75339836\",\n \"0.7445854\",\n \"0.7444906\",\n \"0.7435006\",\n \"0.7433624\",\n \"0.7433624\",\n \"0.7433624\",\n \"0.7433624\",\n \"0.7409177\",\n \"0.7391407\",\n \"0.7372572\",\n \"0.734416\",\n \"0.73267305\",\n \"0.73075706\",\n \"0.7306904\",\n \"0.7287193\",\n \"0.7287193\",\n \"0.7287193\",\n \"0.7283112\",\n \"0.7268656\",\n \"0.7248598\",\n \"0.7236045\",\n \"0.7234185\",\n \"0.72304565\",\n \"0.72216123\",\n \"0.7200374\",\n \"0.7175392\",\n \"0.7156406\",\n \"0.71455896\",\n \"0.7131473\",\n \"0.7105297\",\n \"0.7102847\",\n \"0.70958513\",\n \"0.7091075\",\n \"0.7079893\",\n \"0.7069183\",\n \"0.7062061\",\n \"0.7039643\",\n \"0.7019955\",\n \"0.7018622\",\n \"0.7017914\",\n \"0.7004814\",\n \"0.6992392\",\n \"0.6984488\",\n \"0.6969375\",\n \"0.6916935\",\n \"0.6898047\",\n \"0.6871245\",\n \"0.6867958\",\n \"0.6846715\",\n \"0.683936\",\n \"0.68262976\",\n \"0.6821063\",\n \"0.6816983\",\n \"0.68120605\",\n \"0.68057805\",\n \"0.678342\",\n \"0.67749286\",\n \"0.6770209\",\n \"0.67671067\",\n \"0.6764807\",\n \"0.6758127\",\n \"0.67564374\",\n \"0.67535836\",\n \"0.67504525\",\n \"0.674742\",\n \"0.67433447\",\n \"0.67375773\",\n \"0.67373323\",\n \"0.6734578\",\n \"0.67331237\",\n \"0.67331237\",\n \"0.67304003\",\n \"0.672611\",\n \"0.6726072\",\n \"0.6721306\",\n \"0.6716529\",\n \"0.6710106\",\n \"0.67045575\",\n \"0.67013425\",\n \"0.6698607\",\n \"0.66980857\",\n \"0.6694198\",\n \"0.6687596\",\n \"0.6687254\",\n \"0.66865855\",\n \"0.66835076\",\n \"0.66749185\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70788556"},"document_rank":{"kind":"string","value":"47"}}},{"rowIdx":13785,"cells":{"query":{"kind":"string","value":"Follows back everyone who's followed you"},"document":{"kind":"string","value":"def auto_follow_followers():\n\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n not_following_back = followers - following\n\n for user_id in not_following_back:\n try:\n t.friendships.create(user_id=user_id, follow=False)\n except Exception as e:\n print(\"error: %s\" % (str(e)))"},"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 follow(self, follower, followee):\n pass","def follow_user(cls, user, following):\r\n pass","def author_following(self):\n\t\tpass","def user_follow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.append(following)\n db.session.commit()\n return {'followed': True}","def follow_user(cls, user, following):\n pass","def userFollowers(nick):\n if (len(nick) != 1):\n print \"Has d'introduir només un nick\"\n return\n i.userFollow(nick[0])","def follow(request, usertofollow):\n to_follow = Member.objects.get(user__username=usertofollow)\n user = Member.objects.get(user=request.user)\n user.following.add(to_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","def follow_following_followers(self):\n self.logger.log(\"starting follow_following_followers...\")\n follows_accounts = self.following\n random.shuffle(follows_accounts)\n for acc in follows_accounts:\n try:\n try:\n followw = perform_with_ran_delay(self.instagram.get_followers, acc, 150, 15,\n delayed=True)\n accountstofollow = followw[\"accounts\"]\n random.shuffle(accountstofollow)\n if len(accountstofollow) > 10:\n accountstofollow = accountstofollow[:10]\n for ac in accountstofollow:\n if not self.is_user_following(ac.identifier):\n self.add_following(ac.identifier)\n self.logger.log(\"following: {}\".format(ac.username))\n except Exception as e:\n print(e)\n self.logger.log(str(e))\n finally:\n sleep(3)","def follow(self, followerId, followeeId):\n if followerId in self.follows:\n self.follows[followerId].add(followeeId)\n else:\n self.follows[followerId] = set([followeeId])","def follow(self, followerId, followeeId):\r\n if followerId != followeeId:\r\n self.follows[followerId].add(followeeId)","def follow(self, other):\n\t\tif not self.follows(other):\n\t\t\tself.followed.append(other)","async def follow(follow):\n await follow.edit(\n f\"`FOLLOW {DEFAULTUSER} ON` \\n\\n\"\n f\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\n\\n\"\n f\"[FaceBook](https://www.facebook.com/mkaraniya) \\n\\n\"\n f\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \"\n )","def follow_followers(self):\n self.logger.log(\"starting follow_followers...\")\n follow = perform_with_ran_delay(self.instagram.get_followers, self.account.identifier, 150, 15, delayed=True)\n for acc in follow[\"accounts\"]:\n try:\n try:\n # print(\"{} follows me, do I follow him ? > {} \".format(acc.username,self.is_user_following(acc.identifier)))\n if not self.is_user_following(acc.identifier):\n if self.add_following(acc.identifier):\n self.logger.log(\"following: {}\".format(acc.username))\n else:\n self.logger.log(\"follow not working at the moment\")\n except Exception as e:\n print(e)\n self.logger.log(str(e))\n continue\n finally:\n sleep(3)","def follows(self):\r\n return relationships.Follows(self)","def follow(self, user):\n if not self.is_following(user):\n self.followed.append(user)\n return self","def follow(self, user):\n if not self.is_following(user):\n self.followed.append(user)\n return self","def follow(self, followerId: int, followeeId: int) -> None:\n if followeeId not in self.followList.get(followerId, [followerId]):\n self.followList[followerId] = self.followList.get(followerId, [followerId]) + [followeeId]\n # print(self.followList)","def add_follow(follow_id):\n\n want_to_follow_user = User.query.get_or_404(follow_id)\n if want_to_follow_user.private:\n # =========== NEED TO IMPLEMENT ====================\n # send them a request to follow\n want_to_follow_user.from_users.append(g.user) \n db.session.commit()\n flash(\"Your request has been sent\", \"success\")\n return redirect(f\"/users/{g.user.id}/following\")\n\n g.user.following.append(want_to_follow_user)\n db.session.commit()\n\n return redirect(f\"/users/{g.user.id}/following\")","def follow(user, people):\n api = get_api(user)\n current_screen_name = api.VerifyCredentials().GetScreenName()\n\n # don't let a user follow themselves\n screen_names = [person.twitter_screen_name for person in people]\n if current_screen_name in screen_names: screen_names.remove(current_screen_name)\n\n followed = []\n not_followed = []\n\n for screen_name in screen_names:\n try:\n api.CreateFriendship(screen_name=screen_name)\n followed.append(screen_name)\n except twitter.TwitterError:\n not_followed.append(screen_name)\n\n return 'followed %s people' % len(followed)","def followed_by(self):\r\n return relationships.FollowedBy(self)","def follow(self, followerId: int, followeeId: int) -> None:\n self.user_followed[followerId].append(followeeId)","def get_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\n query = f\"\"\"\n MATCH (:Person {{email: '{email}'}})-[:FOLLOWS]->(follower) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\n \"\"\"\n return tx.run(query)","def follow(self, followerId, followeeId):\n if followerId not in self.follow_map:\n self.follow_map[followerId] = set()\n \n self.follow_map[followerId].add(followeeId)","def follows(self):\n return relationships.Follows(self)","def followed_by(self):\n return relationships.FollowedBy(self)","def is_follower(self, you, them):\n if self.filter(from_user=them, to_user=you).count() > 0:\n return True\n return False","def unsafe_follow_by_username(self, username: str) -> None:\n uid = self.username_to_id(username)\n self.api.follow(uid)","def follow_user(username):\n user_ID = before_request()\n user_ID = None\n if user_ID != None:\n user_ID = str(g.user['_id'])\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n mongo.db.users.update({'_id': g.user['_id']}, {\n '$push': {'follows': whom_id}})\n flash('You are now following \"%s\"' % username)\n if redis_obj.get(user_ID):\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(r.get(user_ID))))\n else:\n redis_obj.delete(str(g.user['_id']))\n print \"Invalidating cache after Follow\"\n return redirect(url_for('user_timeline', username=username))","def follow(self, user_index, following_index):\n if user_index >= self.num_users or following_index >= self.num_users:\n raise ValueError(\n f\"Number of users is {self.num_users}, but indices \"\n f\"{user_index} and {following_index} were requested.\"\n )\n if self.users_hat[following_index, user_index] == 0:\n self.users_hat[following_index, user_index] = 1\n elif self.is_verbose():\n self.log(f\"User {following_index} was already following user {user_index}\")","def followUser(following):\n\n cur, user_id, con = initialise(3, True)\n cur.execute(\"INSERT INTO followers (user, following) VALUES ((SELECT username FROM users WHERE id = ?), ?)\", (user_id, following))\n finish(con)","def doesfollow(user):\n return jsonify({\n 'follows': isFollowed(g.user,user)\n })","def getFollowers():\n\n cur, user_id = initialise(3)\n cur.execute(\"SELECT following FROM followers WHERE user = (SELECT username FROM users WHERE id = ?)\", [user_id])\n tempFollowers = cur.fetchall()\n followers = []\n for follower in tempFollowers:\n followers.append(follower[0])\n return followers","def auto_unfollow_nonfollowers():\n\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n # put user IDs here that you want to keep following even if they don't\n # follow you back\n users_keep_following = set([])\n\n not_following_back = following - followers\n\n # make sure the \"already followed\" file exists\n if not os.path.isfile(ALREADY_FOLLOWED_FILE):\n with open(ALREADY_FOLLOWED_FILE, \"w\") as out_file:\n out_file.write(\"\")\n\n # update the \"already followed\" file with users who didn't follow back\n already_followed = set(not_following_back)\n af_list = []\n with open(ALREADY_FOLLOWED_FILE) as in_file:\n for line in in_file:\n af_list.append(int(line))\n\n already_followed.update(set(af_list))\n del af_list\n\n with open(ALREADY_FOLLOWED_FILE, \"w\") as out_file:\n for val in already_followed:\n out_file.write(str(val) + \"\\n\")\n\n for user_id in not_following_back:\n if user_id not in users_keep_following:\n t.friendships.destroy(user_id=user_id)\n print(\"unfollowed %d\" % (user_id))","def follows(self, other):\n\t\treturn self.followed.filter(followers.c.followed_by == other.id).count() > 0","def get_follow(self):\n return self.follow","def follow(self, followerId: int, followeeId: int) -> None:\n # Time Complexity: O(1)\n if followerId != followeeId:\n if followerId not in self.followees:\n self.followees[followerId] = set()\n\n self.followees[followerId].add(followeeId)","def follow_someone(screen_name):\n twitter.create_friendship(screen_name=screen_name)","def follow(self, user):\n if not self.is_following(user):\n f = Follow(follower=self, followed=user)\n db.session.add(f)","def follow(whomUserName,whoUserName):\n\n whomuser = query_db('select * from user where username = ?',\n [whomUserName], one=True)\n whouser = query_db('select * from user where username = ?',\n [whoUserName], one=True)\n\n\n followed = query_db('''select 1 from follower where\n follower.who_id = ? and follower.whom_id = ?''',\n [whouser['user_id'], whomuser['user_id']],one=True) is not None\n\n if whouser is None:\n return jsonify({'message':'User trying to follow another user which does not exist'}),404\n\n if whomuser is None:\n return jsonify({'message':'User getting followed does not exist yet'}),404\n\n if not followed:\n db = get_db()\n\n db.execute('''insert into follower (\n who_id, whom_id) values (?, ?)''',\n [whouser['user_id'], whomuser['user_id']])\n db.commit()\n flash('Operation successful')\n return jsonify({'message': 'Successfully following'}), 201\n else:\n return jsonify({'message':'Specified user is already following another user'}),403","def follower(account):\n stm = shared_morphene_instance()\n if mph.rpc is not None:\n mph.rpc.rpcconnect()\n if not account:\n if \"default_account\" in mph.config:\n account = [mph.config[\"default_account\"]]\n for a in account:\n a = Account(a, morphene_instance=stm)\n print(\"\\nFollowers statistics for @%s (please wait...)\" % a.name)\n followers = a.get_followers(False)\n followers.print_summarize_table(tag_type=\"Followers\")","def follow_user(username):\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n db = get_db()\n db.execute('insert into follower (who_id, whom_id) values (?, ?)',\n [session['user_id'], whom_id])\n db.commit()\n flash('You are now following \"%s\"' % username)\n return redirect(url_for('user_timeline', username=username))","def get_follows(self):\n return [c.id for c in self.conf.follows]","def follow(self, followerId: int, followeeId: int) -> None:\n if followerId == followeeId: return\n self.users[followerId].add(followeeId)","def is_following(self, you, them):\n if self.filter(from_user=you, to_user=them).count() > 0:\n return True\n return False","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followerId not in self.users.keys() or followeeId not in self.users.keys():\n return\n if followeeId not in self.users[followerId].followees.keys():\n return\n self.users[followerId].followees.pop(followeeId)\n\n\n\n # Your Twitter object will be instantiated and called as such:\n # obj = Twitter()\n # obj.postTweet(userId,tweetId)\n # param_2 = obj.getNewsFeed(userId)\n # obj.follow(followerId,followeeId)\n # obj.unfollow(followerId,followeeId)","def resolve_following(self, info):\n user = info.context.user\n follow_request = FollowRequest.objects.filter(follower=user.id, pending=False)\n return [follow.following for follow in follow_request]","def follow_by_id(self, uid: int) -> None:\n self.api.follow(uid)","def set_follow(self, follow):\n self.follow = follow","def follow(self, followerId: int, followeeId: int) -> None:\n if followerId == followeeId:\n return\n if followerId not in self.users.keys():\n self.users[followerId] = user()\n if followeeId not in self.users.keys():\n self.users[followeeId] = user()\n self.users[followerId].followees[followeeId] = self.users[followeeId]","def accept_follow_request(request):\n username = request.POST[\"user\"]\n requester = get_user_from_username(request.user, username)\n\n try:\n follow_request = models.UserFollowRequest.objects.get(\n user_subject=requester, user_object=request.user\n )\n except models.UserFollowRequest.DoesNotExist:\n # Request already dealt with.\n return redirect(request.user.local_path)\n follow_request.accept()\n\n return redirect(request.user.local_path)","def follow_closely(api_, follow_username):\n big_list = True\n max_id = ''\n following = []\n\n while big_list:\n api_.getSelfUsersFollowing(maxid=max_id)\n followers_ = api_.LastJson\n for f in followers_['users']:\n following.append(f)\n big_list = followers_['big_list']\n if not big_list:\n break\n # this key only exists if there is more pages\n max_id = followers_['next_max_id']\n\n for f in following:\n if f['username'] == follow_username:\n return True, f","def get_followers_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\n query = f\"\"\"\n MATCH (follower)-[:FOLLOWS]->(:Person {{email: '{email}'}}) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\n \"\"\"\n return tx.run(query)","def add_follow(follow_id):\n followed_user = User.query.get_or_404(follow_id)\n if not g.user or g.user.id == follow_id or followed_user.is_blocking(g.user):\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\")\n\n g.user.following.append(followed_user)\n db.session.commit()\n\n return redirect(f\"/users/{g.user.id}/following\")","def getFollowers():\n followers = []\n next_max_id = True\n while next_max_id:\n # first iteration hack\n if next_max_id is True:\n next_max_id = ''\n\n _ = GetInstagramAnswer.igApi.getUserFollowers(GetInstagramAnswer.igApi.username_id, maxid=next_max_id)\n followers.extend(GetInstagramAnswer.igApi.LastJson.get('users',[]))\n next_max_id = GetInstagramAnswer.igApi.LastJson.get('next_max_id','')\n return \"You have currently \"+str(len(followers))+\" Followers on Instagram.\"","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followerId != followeeId and followeeId in self.followList.get(followerId, []):\n self.followList[followerId].remove(followeeId)\n # print(self.followList)","def following(account):\n stm = shared_morphene_instance()\n if mph.rpc is not None:\n mph.rpc.rpcconnect()\n if not account:\n if \"default_account\" in mph.config:\n account = [mph.config[\"default_account\"]]\n for a in account:\n a = Account(a, morphene_instance=stm)\n print(\"\\nFollowing statistics for @%s (please wait...)\" % a.name)\n following = a.get_following(False)\n following.print_summarize_table(tag_type=\"Following\")","def resolve_followers(self, info):\n user = info.context.user\n follow_request = FollowRequest.objects.filter(following=user.id, pending=False)\n return [follow.follower for follow in follow_request]","def user_unfollow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.remove(following)\n db.session.commit()\n return {'unfollowed': True}","def follow(self, followerId: int, followeeId: int) -> None:\n self.follow_map[followerId].add(followeeId)","def follow_friend():\n print \"followuser\"\n username = request.args.get('username')\n print \"JSON Data\", username\n # username= req_data[username]\n whom_id = get_user_id(username)\n print \"whom_id:\", whom_id\n if whom_id is None:\n abort(404)\n follow_query(whom_id)\n flash('You are now following \"%s\"' % username)\n name = {'name of following user': username}\n R_SERVER.delete(user_timeline_key)\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)","def follow_user(self, target):\n try:\n if self.api.me().friends_count > 1990:\n return\n except Exception, e:\n print e\n\n \"Rate limit exceeded. Clients may not make more than 350 requests per hour.\"\n if \"Clients\" in str(e):\n continue\n # import pdb; pdb.set_trace()\n return\n\n try:\n self.api.create_friendship(target.hunted.screen_name)\n self.log.debug(\"Followed: %s\" % target.hunted.screen_name)\n except Exception, e:\n self.log.exception(\"Could not follow %s\" %\n target.hunted.screen_name)\n else:\n # Write record of new follow to db\n target.status = Target.PURGATORY\n target.save()","def unfollowUser(following):\n \n cur, user_id, con = initialise(3, True)\n cur.execute(\"DELETE FROM followers WHERE user = (SELECT username FROM users WHERE id = ?) AND following = ?\", (user_id, following))\n finish(con)","def was_followed(sender, instance, created, **kwargs):\n\n sendr = User.objects.get(id=instance.user_id)\n followed = User.objects.get(id=instance.followed_user_id)\n if created:\n notify.send(sender=sendr, recipient=followed, verb='followed',\n description=\"{} followed you.\".format(sendr.username))","def get_posts_of_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\n query = f\"\"\"\n MATCH (:Person {{email: '{email}'}})\n -[:FOLLOWS]->(user:Person)\n -[:POSTED]->(post:Post)\n RETURN DISTINCT {{content:post.content, modified:post.modified, created:post.created, uuid:post.uuid, user_email:user.email}} AS posts\"\"\"\n return tx.run(query)","def get_follows_route(request):\n\n db_conn = request['db_conn']\n current_user = get_current_user(request)\n user_id = request['params'].get('user_id')\n if user_id:\n user = get_user({'id': user_id}, db_conn)\n if not user:\n return abort(404)\n if (user != current_user and\n user['settings']['view_follows'] != 'public'):\n return abort(403)\n else:\n user = current_user\n if not user:\n return abort(401)\n params = dict(**request['params'])\n params['user_id'] = user['id']\n follows = list_follows(params, db_conn)\n return 200, {\n 'follows': [deliver_follow(follow, access='private')\n for follow in follows]\n }","def user_follow_users(self, ids=None, **kwargs):\n return self._put(\n API.MY_FOLLOWING.value, type=\"user\", ids=\",\".join(ids or []), **kwargs\n )","def user_following(username, max: int = None):\n for user_dict in client.user_relationships(username, max=max, type=\"following\"):\n print(json.dumps(user_dict))","def follows(self):\r\n request = http.Request('GET', '{0}/follows/'.format(self.get_url()))\r\n\r\n return request, parsers.parse_json","def users_being_followed_tweets():\n username = request.authorization.username\n tweets = []\n\n user_id = get_user_id(username);\n tuples = query_db('''\n select message.*, user.* from message, user\n where message.author_id = user.user_id and (\n user.user_id = ? or\n user.user_id in (select whom_id from follower\n where who_id = ?))\n order by message.pub_date desc limit ?''',\n [user_id, user_id, PER_PAGE])\n\n for tuple in tuples:\n tweet = {}\n tweet[\"message_id\"] = tuple['message_id']\n tweet[\"author_id\"] = tuple['author_id']\n tweet[\"text\"] = tuple['text']\n tweet[\"pub_date\"] = tuple['pub_date']\n tweet[\"username\"] = tuple['username']\n tweet[\"email\"] = tuple['email']\n tweets.append(tweet)\n\n return jsonify({'tweets': tweets}), 200","def auto_follow(q, count=1, result_type=\"recent\"):\n\n result = search_tweets(q, count, result_type)\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n do_not_follow = get_do_not_follow_list()\n\n for tweet in result[\"statuses\"]:\n try:\n if (tweet[\"user\"][\"screen_name\"] != TWITTER_HANDLE and\n tweet[\"user\"][\"id\"] not in following and\n tweet[\"user\"][\"id\"] not in do_not_follow):\n\n t.friendships.create(user_id=tweet[\"user\"][\"id\"], follow=False)\n following.update(set([tweet[\"user\"][\"id\"]]))\n\n print(\"followed %s\" % (tweet[\"user\"][\"screen_name\"]))\n\n except TwitterHTTPError as e:\n print(\"error: %s\" % (str(e)))\n\n # quit on error unless it's because someone blocked me\n if \"blocked\" not in str(e).lower():\n quit()","def follow(self, followerId, followeeId):\n\n # 把 followeeId append到他的 follow 属性中\n if followerId == followeeId: # 不能自己关注自己\n return\n # 实例化一个user(followerID)\n follower = UserInfo()\n follower.user_id = followerId \n follower.follows.append(followeeId) \n self.user_pool[followerId] = follower","def all_followers(twitter_dict, twitter_name): \r\n \r\n following_list = []\r\n for user in twitter_dict:\r\n f_list = twitter_dict[user]['following']\r\n if twitter_name in f_list:\r\n following_list.append(user) \r\n return following_list","def follow(username):\n follow_form = FollowForm()\n unfollow_form = UnfollowForm()\n\n if follow_form.validate_on_submit():\n try:\n current_user.follow(username)\n flash('Followed {}'.format(username))\n except ValueError as excep:\n flash(str(excep))\n return render_template('user_history.html',\n username=username,\n follow_form=follow_form,\n unfollow_form=unfollow_form)\n\n return render_template('user_history.html',\n username=username,\n follow_form=follow_form,\n unfollow_form=unfollow_form)","async def follow(self, TargetId: int):\n data = {\n 'targetUserId': TargetId\n }\n e = await self.request.request(url=f'https://friends.roblox.com/v1/users/{TargetId}/follow', method='post',\n data=data)\n return e","def follow_user(self, user):\n self.nav_user(user)\n follow_button = self.driver.find_element_by_xpath(\n \"//button[contains(text(), 'Follow')]\")\n follow_button.click()\n time.sleep(1)\n self.driver.get(self.base_url)","def followed_by_hillary_and_donald(users, twitter):\n ###TODO-- Completed\n for user in users:\n if user['screen_name'] == 'HillaryClinton':\n friends_Hillary = user['friends']\n #print(len(friends_Hillary))\n elif user['screen_name'] == 'realDonaldTrump':\n friends_donald = user['friends']\n #print(len(friends_donald))\n\n common_followed_id = list(set(friends_Hillary) & set(friends_donald))\n\n commn_followed_user = robust_request(twitter,'users/lookup',{'user_id':common_followed_id}).json()\n #print(commn_followed_user[0]['screen_name'])#['screen_name'])\n return commn_followed_user[0]['screen_name']\n #pass","def auto_follow_followers_for_user(user_screen_name, count=5):\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers_for_user = set(t.followers.ids(screen_name=user_screen_name)[\"ids\"][:count]);\n do_not_follow = get_do_not_follow_list()\n \n for user_id in followers_for_user:\n try:\n if (user_id not in following and \n user_id not in do_not_follow):\n\n t.friendships.create(user_id=user_id, follow=False)\n print(\"followed %s\" % user_id)\n\n except TwitterHTTPError as e:\n print(\"error: %s\" % (str(e)))","def follow(self, followerId: int, followeeId: int) -> None:\n self.followees[followerId].add(followeeId)","def unfollow(self, followerId, followeeId):\n if followerId in self.follows:\n if followeeId in self.follows[followerId]:\n self.follows[followerId].remove(followeeId)","def follow(self, followerId: 'int', followeeId: 'int') -> 'None':\n self.followees[followerId].add(followeeId)","def test_is_followed_by(self):\n\n self.u1.followers.append(self.u2)\n db.session.commit()\n\n self.assertTrue(self.u1.is_followed_by(self.u2))\n self.assertFalse(self.u2.is_followed_by(self.u1))","def follow_reciprocated(self, target):\n if random.randint(1, 1000) == 1: # 1 in 20 are public @replies\n self.tweet_user(target)\n else:\n try:\n self.dm_user(target)\n except:\n pass","def find_new_followers(self):\n api = self.api\n geocode = self.geocode\n queries = self.queries\n hits_per_query = self.hits_per_query\n\n self.log.debug(\"Initialize\")\n self.log.debug(\"[ ********* FIND NEW FOLLOWERS *********** ]\")\n\n if self.strategy == UserProfile.FOLLOW or self.strategy == UserProfile.TWEET:\n # Find statuses that match our interests\n self.log.debug(\"Strategy set to FOLLOW or TWEET\")\n n = hits_per_query\n search_dict = dict()\n search_dict['lang'] = \"en\"\n if not geocode is None:\n search_dict['geocode'] = geocode\n statuses = list()\n self.log.debug(\"Queries:\")\n for q in queries:\n search_dict['q'] = q\n results = [c for c in Cursor(api.search, **search_dict).items(n)]\n self.log.debug(\" => %s: %s hits\" % (q, len(results)))\n statuses.extend(results)\n #self.log.debug(\"Statuses: %s\" % \"\\n\".join([str(s.__dict__) for s in statuses]))\n # Get all the screen names of senders and receivers\n screen_names = ([t.from_user for t in statuses] +\n [t.to_user for t in statuses if t.to_user])\n\n # Convert the strings to Tweepy user objects\n users, remainder = lookup_users_by_screen_name(self.api, screen_names)\n\n elif self.strategy == UserProfile.STEAL:\n users = []\n stolen_from = {}\n for competitor in list(self.competitors):\n self.log.debug(\"[ ********* STEAL %s *********** ]\" % competitor)\n try:\n competitor_friends_ids = self.api.friends_ids(competitor)\n competitor_followers_ids = self.api.followers_ids(competitor)\n\n filter_known_users_to_reduce_api_hits = False\n\n if filter_known_users_to_reduce_api_hits is True:\n new_competitor_friends_ids = [id for id in competitor_friends_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n old_competitor_friends_ids = [id for id in competitor_friends_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n new_competitor_followers_ids = [id for id in competitor_followers_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n old_competitor_followers_ids = [id for id in competitor_followers_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\n # print new_competitor_friends_ids\n # print old_competitor_friends_ids\n # print new_competitor_followers_ids\n # print old_competitor_followers_ids\n\n print \"start lookups\"\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, new_competitor_friends_ids)\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, new_competitor_followers_ids)\n print \"end lookups\"\n else:\n # get all the tweepy users\n print \"start lookups\"\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, competitor_friends_ids)\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, competitor_followers_ids)\n print \"end lookups\"\n\n print \"%s has %s friends\" % (competitor, len(new_competitor_friends))\n print \"%s has %s followers\" % (competitor, len(new_competitor_followers))\n\n # holy crap this is so fucked up i'm ashamed that this code is getting written like this!\n\n for u in new_competitor_friends + new_competitor_followers:\n stolen_from.update({u.screen_name.lower(): competitor})\n\n except Exception, e:\n print e\n # didn't get all the users, don't remove the competitor\n # from the competitor list\n pass\n else:\n # got all the competitors friends and followers and converted them\n # to tweepy users.\n users += new_competitor_friends\n users += new_competitor_followers\n # add them to the users list to be processed in the next block (for user in users)\n # then pop the name off the competitors list in the UserProfile\n # fuck it for now i'm going to just cycle the item to the bottom of the competitor list so we can start getting maximal coverage within api constraints by making sure the top person is new every time\n self.competitors.append(self.competitors.pop(0))\n # return # for now\n self.profile.competitors = \"\\r\\n\".join(self.competitors)\n self.profile.save()\n\n # use the profile competitors list\n # for each name in competitors list\n # add all friends\n\n # should filter out garbage users. something like:\n users = [u for u in users if not Target.objects.filter(hunter=self.user, hunted__screen_name=u.screen_name.lower())]\n\n for user in users:\n twitter_account, created = utils.get_or_create_twitter_account(user)\n target, created = Target.objects.get_or_create(\n hunter=self.user, hunted=twitter_account)\n print target.hunted.screen_name, created\n if created:\n try:\n screen_name = user.screen_name.lower()\n match = lambda x: screen_name in \\\n (x.from_user.lower(), x.to_user and x.to_user.lower())\n if not self.strategy == UserProfile.STEAL:\n trigger_tweet = filter(match, statuses)[0].text\n else:\n try:\n trigger_tweet = \"Steal from user: %s\" % stolen_from.get(screen_name.lower(), \"someone. i lost it. sorry.\")\n except Exception, e:\n print \"YUCK. ERRORS.\"\n print \"YUCK. ERRORS.\"\n print e\n print e\n except Exception, e:\n self.log.exception(\"Could not get trigger tweet for %s\" %\n user.screen_name.lower())\n trigger_tweet = \"Error: Couldn't retrieve tweet.\"\n self.log.debug(\"Saved twitter account %s (trigger: %r)\" %\n (twitter_account.screen_name,\n trigger_tweet[:50]))\n target.reason = trigger_tweet\n target.status = Target.ON_DECK\n target.save()\n else:\n pass\n # print \" - Previously followed this dudesicle: %s\" % user.screen_name","def follow(request):\n user_id = request.POST.get('user_id')\n action = request.POST.get('action')\n if user_id and action:\n try:\n user = User.objects.get(id=user_id)\n if request.user == user:\n return JsonResponse({'status':0,'msg':'You can\\'t follow yourself'})\n if action == 'follow':\n if request.user.is_following(user):\n return JsonResponse({'status':0,'msg':'already following'})\n else:\n request.user.follow(user)\n return JsonResponse({'status':1,'msg':'followed'})\n else:\n if request.user.is_following(user):\n request.user.unfollow(user)\n return JsonResponse({'status':1,'msg':'un-followed'})\n else:\n return JsonResponse({'status':0,'msg':'not following'})\n except User.DoesNotExist:\n return JsonResponse({'status':0,'msg':'user not found'})\n return JsonResponse({'status':0})","def user_follow(self, user_id: int) -> bool:\n assert self.user_id, \"Login required\"\n user_id = int(user_id)\n if user_id in self._users_following.get(self.user_id, []):\n self.logger.debug(\"User %s already followed\", user_id)\n return False\n data = self.with_action_data({\"user_id\": user_id})\n result = self.private_request(f\"friendships/create/{user_id}/\", data)\n if self.user_id in self._users_following:\n self._users_following.pop(self.user_id) # reset\n return result[\"friendship_status\"][\"following\"] is True","def like_following(self):\n self.logger.log(\"starting like_following...\")\n count_following = self.account.follows_count\n follows_accounts = self.following\n random.shuffle(follows_accounts)\n for acc in follows_accounts:\n acc = perform_with_ran_delay(self.instagram.get_account_by_id, acc)\n self.logger.log(\" {} > {} posts\".format(acc.username, acc.media_count))\n if acc.media_count > 0:\n\n posts = perform_with_ran_delay(self.instagram.get_medias, acc.username, 50)\n if posts:\n for m in posts:\n try:\n perform_with_ran_delay(self.instagram.like, m.identifier)\n self.logger.log(\"liking 1 post from \"+acc.username)\n random_delay()\n except Exception as e:\n self.logger.log(\"skipping 1 post from \"+acc.username)\n self.logger.log(e)\n random_delay()\n continue","def unfollow(self, followerId, followeeId):\r\n self.follows[followerId].discard(followeeId)","def followers(congressDict, twitterAPI):\n most = twitterAPI.get_user(list(congressDict.items())[0][1]) # Choose an arbitrary starting point from the dictionary and assign it their user details.\n least = most\n for name in congressDict:\n tempAPI = twitterAPI.get_user(congressDict[name]) # Get the user details of each congress members' twitter handle.\n numFollowers = tempAPI._json['followers_count']\n if (numFollowers > most._json['followers_count']): # If the follower count is greater than most, replace the user details with current one.\n most = tempAPI\n elif (numFollowers < least._json['followers_count']): # If the follower count is lower than least, replace the user details with the current one.\n least = tempAPI\n return [most._json[\"name\"], least._json[\"name\"]]","def add_following(self, user_id):\n sleep(360) # too much follows => function ban\n self.following.append(user_id)\n return perform_with_ran_delay(self.instagram.follow, user_id)","def user_follow_artists(self, ids=None, **kwargs):\n return self._put(\n API.MY_FOLLOWING.value, type=\"artist\", ids=\",\".join(ids or []), **kwargs\n )","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def followers(self):\r\n url = '{0}/followers'.format(self.get_url())\r\n return http.Request('GET', url), parsers.parse_json","def generateFollowers(self):\n for f in self._genericGenerator(self.getFollowers):\n yield f","def unfollow(self, followerId: int, followeeId: int) -> None:\n following = self.user_followed[followerId]\n if followeeId in following:\n following.remove(followeeId)\n self.user_followed[followerId] = following","def approve_follow(made_request_id, approver_id):\n\n if not g.user.id == approver_id:\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\"), 403\n\n wanted_to_follow_user = User.query.get_or_404(made_request_id)\n g.user.followers.append(wanted_to_follow_user)\n g.user.from_users.remove(wanted_to_follow_user)\n db.session.commit()\n\n flash(f\"Follow request from {wanted_to_follow_user.username} approved.\", \"success\")\n return redirect(f\"/users/{g.user.id}/followers\")","def follow(request):\n username = request.POST[\"user\"]\n to_follow = get_user_from_username(request.user, username)\n clear_cache(request.user, to_follow)\n\n follow_request, created = models.UserFollowRequest.objects.get_or_create(\n user_subject=request.user,\n user_object=to_follow,\n )\n\n if not created:\n # this request probably failed to connect with the remote\n # that means we should save to trigger a re-broadcast\n follow_request.save()\n\n if is_api_request(request):\n return HttpResponse()\n return redirect(to_follow.local_path)","def compute_follow(self):\n compute_follow_sets(self)","def follows(self, name):\r\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\r\n\r\n return http.Request('GET', url), resource.parse_boolean"],"string":"[\n \"def follow(self, follower, followee):\\n pass\",\n \"def follow_user(cls, user, following):\\r\\n pass\",\n \"def author_following(self):\\n\\t\\tpass\",\n \"def user_follow():\\n data = request.get_json(force=True)\\n follower = User.query.get(data['follower'])\\n following = User.query.get(data['following'])\\n follower.followcheck.append(following)\\n db.session.commit()\\n return {'followed': True}\",\n \"def follow_user(cls, user, following):\\n pass\",\n \"def userFollowers(nick):\\n if (len(nick) != 1):\\n print \\\"Has d'introduir només un nick\\\"\\n return\\n i.userFollow(nick[0])\",\n \"def follow(request, usertofollow):\\n to_follow = Member.objects.get(user__username=usertofollow)\\n user = Member.objects.get(user=request.user)\\n user.following.add(to_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"def follow_following_followers(self):\\n self.logger.log(\\\"starting follow_following_followers...\\\")\\n follows_accounts = self.following\\n random.shuffle(follows_accounts)\\n for acc in follows_accounts:\\n try:\\n try:\\n followw = perform_with_ran_delay(self.instagram.get_followers, acc, 150, 15,\\n delayed=True)\\n accountstofollow = followw[\\\"accounts\\\"]\\n random.shuffle(accountstofollow)\\n if len(accountstofollow) > 10:\\n accountstofollow = accountstofollow[:10]\\n for ac in accountstofollow:\\n if not self.is_user_following(ac.identifier):\\n self.add_following(ac.identifier)\\n self.logger.log(\\\"following: {}\\\".format(ac.username))\\n except Exception as e:\\n print(e)\\n self.logger.log(str(e))\\n finally:\\n sleep(3)\",\n \"def follow(self, followerId, followeeId):\\n if followerId in self.follows:\\n self.follows[followerId].add(followeeId)\\n else:\\n self.follows[followerId] = set([followeeId])\",\n \"def follow(self, followerId, followeeId):\\r\\n if followerId != followeeId:\\r\\n self.follows[followerId].add(followeeId)\",\n \"def follow(self, other):\\n\\t\\tif not self.follows(other):\\n\\t\\t\\tself.followed.append(other)\",\n \"async def follow(follow):\\n await follow.edit(\\n f\\\"`FOLLOW {DEFAULTUSER} ON` \\\\n\\\\n\\\"\\n f\\\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\\\n\\\\n\\\"\\n f\\\"[FaceBook](https://www.facebook.com/mkaraniya) \\\\n\\\\n\\\"\\n f\\\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \\\"\\n )\",\n \"def follow_followers(self):\\n self.logger.log(\\\"starting follow_followers...\\\")\\n follow = perform_with_ran_delay(self.instagram.get_followers, self.account.identifier, 150, 15, delayed=True)\\n for acc in follow[\\\"accounts\\\"]:\\n try:\\n try:\\n # print(\\\"{} follows me, do I follow him ? > {} \\\".format(acc.username,self.is_user_following(acc.identifier)))\\n if not self.is_user_following(acc.identifier):\\n if self.add_following(acc.identifier):\\n self.logger.log(\\\"following: {}\\\".format(acc.username))\\n else:\\n self.logger.log(\\\"follow not working at the moment\\\")\\n except Exception as e:\\n print(e)\\n self.logger.log(str(e))\\n continue\\n finally:\\n sleep(3)\",\n \"def follows(self):\\r\\n return relationships.Follows(self)\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n self.followed.append(user)\\n return self\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n self.followed.append(user)\\n return self\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followeeId not in self.followList.get(followerId, [followerId]):\\n self.followList[followerId] = self.followList.get(followerId, [followerId]) + [followeeId]\\n # print(self.followList)\",\n \"def add_follow(follow_id):\\n\\n want_to_follow_user = User.query.get_or_404(follow_id)\\n if want_to_follow_user.private:\\n # =========== NEED TO IMPLEMENT ====================\\n # send them a request to follow\\n want_to_follow_user.from_users.append(g.user) \\n db.session.commit()\\n flash(\\\"Your request has been sent\\\", \\\"success\\\")\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\\n\\n g.user.following.append(want_to_follow_user)\\n db.session.commit()\\n\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\",\n \"def follow(user, people):\\n api = get_api(user)\\n current_screen_name = api.VerifyCredentials().GetScreenName()\\n\\n # don't let a user follow themselves\\n screen_names = [person.twitter_screen_name for person in people]\\n if current_screen_name in screen_names: screen_names.remove(current_screen_name)\\n\\n followed = []\\n not_followed = []\\n\\n for screen_name in screen_names:\\n try:\\n api.CreateFriendship(screen_name=screen_name)\\n followed.append(screen_name)\\n except twitter.TwitterError:\\n not_followed.append(screen_name)\\n\\n return 'followed %s people' % len(followed)\",\n \"def followed_by(self):\\r\\n return relationships.FollowedBy(self)\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n self.user_followed[followerId].append(followeeId)\",\n \"def get_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\\n query = f\\\"\\\"\\\"\\n MATCH (:Person {{email: '{email}'}})-[:FOLLOWS]->(follower) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\\n \\\"\\\"\\\"\\n return tx.run(query)\",\n \"def follow(self, followerId, followeeId):\\n if followerId not in self.follow_map:\\n self.follow_map[followerId] = set()\\n \\n self.follow_map[followerId].add(followeeId)\",\n \"def follows(self):\\n return relationships.Follows(self)\",\n \"def followed_by(self):\\n return relationships.FollowedBy(self)\",\n \"def is_follower(self, you, them):\\n if self.filter(from_user=them, to_user=you).count() > 0:\\n return True\\n return False\",\n \"def unsafe_follow_by_username(self, username: str) -> None:\\n uid = self.username_to_id(username)\\n self.api.follow(uid)\",\n \"def follow_user(username):\\n user_ID = before_request()\\n user_ID = None\\n if user_ID != None:\\n user_ID = str(g.user['_id'])\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n mongo.db.users.update({'_id': g.user['_id']}, {\\n '$push': {'follows': whom_id}})\\n flash('You are now following \\\"%s\\\"' % username)\\n if redis_obj.get(user_ID):\\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(r.get(user_ID))))\\n else:\\n redis_obj.delete(str(g.user['_id']))\\n print \\\"Invalidating cache after Follow\\\"\\n return redirect(url_for('user_timeline', username=username))\",\n \"def follow(self, user_index, following_index):\\n if user_index >= self.num_users or following_index >= self.num_users:\\n raise ValueError(\\n f\\\"Number of users is {self.num_users}, but indices \\\"\\n f\\\"{user_index} and {following_index} were requested.\\\"\\n )\\n if self.users_hat[following_index, user_index] == 0:\\n self.users_hat[following_index, user_index] = 1\\n elif self.is_verbose():\\n self.log(f\\\"User {following_index} was already following user {user_index}\\\")\",\n \"def followUser(following):\\n\\n cur, user_id, con = initialise(3, True)\\n cur.execute(\\\"INSERT INTO followers (user, following) VALUES ((SELECT username FROM users WHERE id = ?), ?)\\\", (user_id, following))\\n finish(con)\",\n \"def doesfollow(user):\\n return jsonify({\\n 'follows': isFollowed(g.user,user)\\n })\",\n \"def getFollowers():\\n\\n cur, user_id = initialise(3)\\n cur.execute(\\\"SELECT following FROM followers WHERE user = (SELECT username FROM users WHERE id = ?)\\\", [user_id])\\n tempFollowers = cur.fetchall()\\n followers = []\\n for follower in tempFollowers:\\n followers.append(follower[0])\\n return followers\",\n \"def auto_unfollow_nonfollowers():\\n\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n # put user IDs here that you want to keep following even if they don't\\n # follow you back\\n users_keep_following = set([])\\n\\n not_following_back = following - followers\\n\\n # make sure the \\\"already followed\\\" file exists\\n if not os.path.isfile(ALREADY_FOLLOWED_FILE):\\n with open(ALREADY_FOLLOWED_FILE, \\\"w\\\") as out_file:\\n out_file.write(\\\"\\\")\\n\\n # update the \\\"already followed\\\" file with users who didn't follow back\\n already_followed = set(not_following_back)\\n af_list = []\\n with open(ALREADY_FOLLOWED_FILE) as in_file:\\n for line in in_file:\\n af_list.append(int(line))\\n\\n already_followed.update(set(af_list))\\n del af_list\\n\\n with open(ALREADY_FOLLOWED_FILE, \\\"w\\\") as out_file:\\n for val in already_followed:\\n out_file.write(str(val) + \\\"\\\\n\\\")\\n\\n for user_id in not_following_back:\\n if user_id not in users_keep_following:\\n t.friendships.destroy(user_id=user_id)\\n print(\\\"unfollowed %d\\\" % (user_id))\",\n \"def follows(self, other):\\n\\t\\treturn self.followed.filter(followers.c.followed_by == other.id).count() > 0\",\n \"def get_follow(self):\\n return self.follow\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n # Time Complexity: O(1)\\n if followerId != followeeId:\\n if followerId not in self.followees:\\n self.followees[followerId] = set()\\n\\n self.followees[followerId].add(followeeId)\",\n \"def follow_someone(screen_name):\\n twitter.create_friendship(screen_name=screen_name)\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n f = Follow(follower=self, followed=user)\\n db.session.add(f)\",\n \"def follow(whomUserName,whoUserName):\\n\\n whomuser = query_db('select * from user where username = ?',\\n [whomUserName], one=True)\\n whouser = query_db('select * from user where username = ?',\\n [whoUserName], one=True)\\n\\n\\n followed = query_db('''select 1 from follower where\\n follower.who_id = ? and follower.whom_id = ?''',\\n [whouser['user_id'], whomuser['user_id']],one=True) is not None\\n\\n if whouser is None:\\n return jsonify({'message':'User trying to follow another user which does not exist'}),404\\n\\n if whomuser is None:\\n return jsonify({'message':'User getting followed does not exist yet'}),404\\n\\n if not followed:\\n db = get_db()\\n\\n db.execute('''insert into follower (\\n who_id, whom_id) values (?, ?)''',\\n [whouser['user_id'], whomuser['user_id']])\\n db.commit()\\n flash('Operation successful')\\n return jsonify({'message': 'Successfully following'}), 201\\n else:\\n return jsonify({'message':'Specified user is already following another user'}),403\",\n \"def follower(account):\\n stm = shared_morphene_instance()\\n if mph.rpc is not None:\\n mph.rpc.rpcconnect()\\n if not account:\\n if \\\"default_account\\\" in mph.config:\\n account = [mph.config[\\\"default_account\\\"]]\\n for a in account:\\n a = Account(a, morphene_instance=stm)\\n print(\\\"\\\\nFollowers statistics for @%s (please wait...)\\\" % a.name)\\n followers = a.get_followers(False)\\n followers.print_summarize_table(tag_type=\\\"Followers\\\")\",\n \"def follow_user(username):\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n db = get_db()\\n db.execute('insert into follower (who_id, whom_id) values (?, ?)',\\n [session['user_id'], whom_id])\\n db.commit()\\n flash('You are now following \\\"%s\\\"' % username)\\n return redirect(url_for('user_timeline', username=username))\",\n \"def get_follows(self):\\n return [c.id for c in self.conf.follows]\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followerId == followeeId: return\\n self.users[followerId].add(followeeId)\",\n \"def is_following(self, you, them):\\n if self.filter(from_user=you, to_user=them).count() > 0:\\n return True\\n return False\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followerId not in self.users.keys() or followeeId not in self.users.keys():\\n return\\n if followeeId not in self.users[followerId].followees.keys():\\n return\\n self.users[followerId].followees.pop(followeeId)\\n\\n\\n\\n # Your Twitter object will be instantiated and called as such:\\n # obj = Twitter()\\n # obj.postTweet(userId,tweetId)\\n # param_2 = obj.getNewsFeed(userId)\\n # obj.follow(followerId,followeeId)\\n # obj.unfollow(followerId,followeeId)\",\n \"def resolve_following(self, info):\\n user = info.context.user\\n follow_request = FollowRequest.objects.filter(follower=user.id, pending=False)\\n return [follow.following for follow in follow_request]\",\n \"def follow_by_id(self, uid: int) -> None:\\n self.api.follow(uid)\",\n \"def set_follow(self, follow):\\n self.follow = follow\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followerId == followeeId:\\n return\\n if followerId not in self.users.keys():\\n self.users[followerId] = user()\\n if followeeId not in self.users.keys():\\n self.users[followeeId] = user()\\n self.users[followerId].followees[followeeId] = self.users[followeeId]\",\n \"def accept_follow_request(request):\\n username = request.POST[\\\"user\\\"]\\n requester = get_user_from_username(request.user, username)\\n\\n try:\\n follow_request = models.UserFollowRequest.objects.get(\\n user_subject=requester, user_object=request.user\\n )\\n except models.UserFollowRequest.DoesNotExist:\\n # Request already dealt with.\\n return redirect(request.user.local_path)\\n follow_request.accept()\\n\\n return redirect(request.user.local_path)\",\n \"def follow_closely(api_, follow_username):\\n big_list = True\\n max_id = ''\\n following = []\\n\\n while big_list:\\n api_.getSelfUsersFollowing(maxid=max_id)\\n followers_ = api_.LastJson\\n for f in followers_['users']:\\n following.append(f)\\n big_list = followers_['big_list']\\n if not big_list:\\n break\\n # this key only exists if there is more pages\\n max_id = followers_['next_max_id']\\n\\n for f in following:\\n if f['username'] == follow_username:\\n return True, f\",\n \"def get_followers_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\\n query = f\\\"\\\"\\\"\\n MATCH (follower)-[:FOLLOWS]->(:Person {{email: '{email}'}}) RETURN follower.full_name AS full_name, follower.email AS email, follower.profile_image AS profile_image\\n \\\"\\\"\\\"\\n return tx.run(query)\",\n \"def add_follow(follow_id):\\n followed_user = User.query.get_or_404(follow_id)\\n if not g.user or g.user.id == follow_id or followed_user.is_blocking(g.user):\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n\\n g.user.following.append(followed_user)\\n db.session.commit()\\n\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\",\n \"def getFollowers():\\n followers = []\\n next_max_id = True\\n while next_max_id:\\n # first iteration hack\\n if next_max_id is True:\\n next_max_id = ''\\n\\n _ = GetInstagramAnswer.igApi.getUserFollowers(GetInstagramAnswer.igApi.username_id, maxid=next_max_id)\\n followers.extend(GetInstagramAnswer.igApi.LastJson.get('users',[]))\\n next_max_id = GetInstagramAnswer.igApi.LastJson.get('next_max_id','')\\n return \\\"You have currently \\\"+str(len(followers))+\\\" Followers on Instagram.\\\"\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followerId != followeeId and followeeId in self.followList.get(followerId, []):\\n self.followList[followerId].remove(followeeId)\\n # print(self.followList)\",\n \"def following(account):\\n stm = shared_morphene_instance()\\n if mph.rpc is not None:\\n mph.rpc.rpcconnect()\\n if not account:\\n if \\\"default_account\\\" in mph.config:\\n account = [mph.config[\\\"default_account\\\"]]\\n for a in account:\\n a = Account(a, morphene_instance=stm)\\n print(\\\"\\\\nFollowing statistics for @%s (please wait...)\\\" % a.name)\\n following = a.get_following(False)\\n following.print_summarize_table(tag_type=\\\"Following\\\")\",\n \"def resolve_followers(self, info):\\n user = info.context.user\\n follow_request = FollowRequest.objects.filter(following=user.id, pending=False)\\n return [follow.follower for follow in follow_request]\",\n \"def user_unfollow():\\n data = request.get_json(force=True)\\n follower = User.query.get(data['follower'])\\n following = User.query.get(data['following'])\\n follower.followcheck.remove(following)\\n db.session.commit()\\n return {'unfollowed': True}\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n self.follow_map[followerId].add(followeeId)\",\n \"def follow_friend():\\n print \\\"followuser\\\"\\n username = request.args.get('username')\\n print \\\"JSON Data\\\", username\\n # username= req_data[username]\\n whom_id = get_user_id(username)\\n print \\\"whom_id:\\\", whom_id\\n if whom_id is None:\\n abort(404)\\n follow_query(whom_id)\\n flash('You are now following \\\"%s\\\"' % username)\\n name = {'name of following user': username}\\n R_SERVER.delete(user_timeline_key)\\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)\",\n \"def follow_user(self, target):\\n try:\\n if self.api.me().friends_count > 1990:\\n return\\n except Exception, e:\\n print e\\n\\n \\\"Rate limit exceeded. Clients may not make more than 350 requests per hour.\\\"\\n if \\\"Clients\\\" in str(e):\\n continue\\n # import pdb; pdb.set_trace()\\n return\\n\\n try:\\n self.api.create_friendship(target.hunted.screen_name)\\n self.log.debug(\\\"Followed: %s\\\" % target.hunted.screen_name)\\n except Exception, e:\\n self.log.exception(\\\"Could not follow %s\\\" %\\n target.hunted.screen_name)\\n else:\\n # Write record of new follow to db\\n target.status = Target.PURGATORY\\n target.save()\",\n \"def unfollowUser(following):\\n \\n cur, user_id, con = initialise(3, True)\\n cur.execute(\\\"DELETE FROM followers WHERE user = (SELECT username FROM users WHERE id = ?) AND following = ?\\\", (user_id, following))\\n finish(con)\",\n \"def was_followed(sender, instance, created, **kwargs):\\n\\n sendr = User.objects.get(id=instance.user_id)\\n followed = User.objects.get(id=instance.followed_user_id)\\n if created:\\n notify.send(sender=sendr, recipient=followed, verb='followed',\\n description=\\\"{} followed you.\\\".format(sendr.username))\",\n \"def get_posts_of_followings_of_a_user(tx: Transaction, email: str) -> BoltStatementResult:\\n query = f\\\"\\\"\\\"\\n MATCH (:Person {{email: '{email}'}})\\n -[:FOLLOWS]->(user:Person)\\n -[:POSTED]->(post:Post)\\n RETURN DISTINCT {{content:post.content, modified:post.modified, created:post.created, uuid:post.uuid, user_email:user.email}} AS posts\\\"\\\"\\\"\\n return tx.run(query)\",\n \"def get_follows_route(request):\\n\\n db_conn = request['db_conn']\\n current_user = get_current_user(request)\\n user_id = request['params'].get('user_id')\\n if user_id:\\n user = get_user({'id': user_id}, db_conn)\\n if not user:\\n return abort(404)\\n if (user != current_user and\\n user['settings']['view_follows'] != 'public'):\\n return abort(403)\\n else:\\n user = current_user\\n if not user:\\n return abort(401)\\n params = dict(**request['params'])\\n params['user_id'] = user['id']\\n follows = list_follows(params, db_conn)\\n return 200, {\\n 'follows': [deliver_follow(follow, access='private')\\n for follow in follows]\\n }\",\n \"def user_follow_users(self, ids=None, **kwargs):\\n return self._put(\\n API.MY_FOLLOWING.value, type=\\\"user\\\", ids=\\\",\\\".join(ids or []), **kwargs\\n )\",\n \"def user_following(username, max: int = None):\\n for user_dict in client.user_relationships(username, max=max, type=\\\"following\\\"):\\n print(json.dumps(user_dict))\",\n \"def follows(self):\\r\\n request = http.Request('GET', '{0}/follows/'.format(self.get_url()))\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def users_being_followed_tweets():\\n username = request.authorization.username\\n tweets = []\\n\\n user_id = get_user_id(username);\\n tuples = query_db('''\\n select message.*, user.* from message, user\\n where message.author_id = user.user_id and (\\n user.user_id = ? or\\n user.user_id in (select whom_id from follower\\n where who_id = ?))\\n order by message.pub_date desc limit ?''',\\n [user_id, user_id, PER_PAGE])\\n\\n for tuple in tuples:\\n tweet = {}\\n tweet[\\\"message_id\\\"] = tuple['message_id']\\n tweet[\\\"author_id\\\"] = tuple['author_id']\\n tweet[\\\"text\\\"] = tuple['text']\\n tweet[\\\"pub_date\\\"] = tuple['pub_date']\\n tweet[\\\"username\\\"] = tuple['username']\\n tweet[\\\"email\\\"] = tuple['email']\\n tweets.append(tweet)\\n\\n return jsonify({'tweets': tweets}), 200\",\n \"def auto_follow(q, count=1, result_type=\\\"recent\\\"):\\n\\n result = search_tweets(q, count, result_type)\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n do_not_follow = get_do_not_follow_list()\\n\\n for tweet in result[\\\"statuses\\\"]:\\n try:\\n if (tweet[\\\"user\\\"][\\\"screen_name\\\"] != TWITTER_HANDLE and\\n tweet[\\\"user\\\"][\\\"id\\\"] not in following and\\n tweet[\\\"user\\\"][\\\"id\\\"] not in do_not_follow):\\n\\n t.friendships.create(user_id=tweet[\\\"user\\\"][\\\"id\\\"], follow=False)\\n following.update(set([tweet[\\\"user\\\"][\\\"id\\\"]]))\\n\\n print(\\\"followed %s\\\" % (tweet[\\\"user\\\"][\\\"screen_name\\\"]))\\n\\n except TwitterHTTPError as e:\\n print(\\\"error: %s\\\" % (str(e)))\\n\\n # quit on error unless it's because someone blocked me\\n if \\\"blocked\\\" not in str(e).lower():\\n quit()\",\n \"def follow(self, followerId, followeeId):\\n\\n # 把 followeeId append到他的 follow 属性中\\n if followerId == followeeId: # 不能自己关注自己\\n return\\n # 实例化一个user(followerID)\\n follower = UserInfo()\\n follower.user_id = followerId \\n follower.follows.append(followeeId) \\n self.user_pool[followerId] = follower\",\n \"def all_followers(twitter_dict, twitter_name): \\r\\n \\r\\n following_list = []\\r\\n for user in twitter_dict:\\r\\n f_list = twitter_dict[user]['following']\\r\\n if twitter_name in f_list:\\r\\n following_list.append(user) \\r\\n return following_list\",\n \"def follow(username):\\n follow_form = FollowForm()\\n unfollow_form = UnfollowForm()\\n\\n if follow_form.validate_on_submit():\\n try:\\n current_user.follow(username)\\n flash('Followed {}'.format(username))\\n except ValueError as excep:\\n flash(str(excep))\\n return render_template('user_history.html',\\n username=username,\\n follow_form=follow_form,\\n unfollow_form=unfollow_form)\\n\\n return render_template('user_history.html',\\n username=username,\\n follow_form=follow_form,\\n unfollow_form=unfollow_form)\",\n \"async def follow(self, TargetId: int):\\n data = {\\n 'targetUserId': TargetId\\n }\\n e = await self.request.request(url=f'https://friends.roblox.com/v1/users/{TargetId}/follow', method='post',\\n data=data)\\n return e\",\n \"def follow_user(self, user):\\n self.nav_user(user)\\n follow_button = self.driver.find_element_by_xpath(\\n \\\"//button[contains(text(), 'Follow')]\\\")\\n follow_button.click()\\n time.sleep(1)\\n self.driver.get(self.base_url)\",\n \"def followed_by_hillary_and_donald(users, twitter):\\n ###TODO-- Completed\\n for user in users:\\n if user['screen_name'] == 'HillaryClinton':\\n friends_Hillary = user['friends']\\n #print(len(friends_Hillary))\\n elif user['screen_name'] == 'realDonaldTrump':\\n friends_donald = user['friends']\\n #print(len(friends_donald))\\n\\n common_followed_id = list(set(friends_Hillary) & set(friends_donald))\\n\\n commn_followed_user = robust_request(twitter,'users/lookup',{'user_id':common_followed_id}).json()\\n #print(commn_followed_user[0]['screen_name'])#['screen_name'])\\n return commn_followed_user[0]['screen_name']\\n #pass\",\n \"def auto_follow_followers_for_user(user_screen_name, count=5):\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n followers_for_user = set(t.followers.ids(screen_name=user_screen_name)[\\\"ids\\\"][:count]);\\n do_not_follow = get_do_not_follow_list()\\n \\n for user_id in followers_for_user:\\n try:\\n if (user_id not in following and \\n user_id not in do_not_follow):\\n\\n t.friendships.create(user_id=user_id, follow=False)\\n print(\\\"followed %s\\\" % user_id)\\n\\n except TwitterHTTPError as e:\\n print(\\\"error: %s\\\" % (str(e)))\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n self.followees[followerId].add(followeeId)\",\n \"def unfollow(self, followerId, followeeId):\\n if followerId in self.follows:\\n if followeeId in self.follows[followerId]:\\n self.follows[followerId].remove(followeeId)\",\n \"def follow(self, followerId: 'int', followeeId: 'int') -> 'None':\\n self.followees[followerId].add(followeeId)\",\n \"def test_is_followed_by(self):\\n\\n self.u1.followers.append(self.u2)\\n db.session.commit()\\n\\n self.assertTrue(self.u1.is_followed_by(self.u2))\\n self.assertFalse(self.u2.is_followed_by(self.u1))\",\n \"def follow_reciprocated(self, target):\\n if random.randint(1, 1000) == 1: # 1 in 20 are public @replies\\n self.tweet_user(target)\\n else:\\n try:\\n self.dm_user(target)\\n except:\\n pass\",\n \"def find_new_followers(self):\\n api = self.api\\n geocode = self.geocode\\n queries = self.queries\\n hits_per_query = self.hits_per_query\\n\\n self.log.debug(\\\"Initialize\\\")\\n self.log.debug(\\\"[ ********* FIND NEW FOLLOWERS *********** ]\\\")\\n\\n if self.strategy == UserProfile.FOLLOW or self.strategy == UserProfile.TWEET:\\n # Find statuses that match our interests\\n self.log.debug(\\\"Strategy set to FOLLOW or TWEET\\\")\\n n = hits_per_query\\n search_dict = dict()\\n search_dict['lang'] = \\\"en\\\"\\n if not geocode is None:\\n search_dict['geocode'] = geocode\\n statuses = list()\\n self.log.debug(\\\"Queries:\\\")\\n for q in queries:\\n search_dict['q'] = q\\n results = [c for c in Cursor(api.search, **search_dict).items(n)]\\n self.log.debug(\\\" => %s: %s hits\\\" % (q, len(results)))\\n statuses.extend(results)\\n #self.log.debug(\\\"Statuses: %s\\\" % \\\"\\\\n\\\".join([str(s.__dict__) for s in statuses]))\\n # Get all the screen names of senders and receivers\\n screen_names = ([t.from_user for t in statuses] +\\n [t.to_user for t in statuses if t.to_user])\\n\\n # Convert the strings to Tweepy user objects\\n users, remainder = lookup_users_by_screen_name(self.api, screen_names)\\n\\n elif self.strategy == UserProfile.STEAL:\\n users = []\\n stolen_from = {}\\n for competitor in list(self.competitors):\\n self.log.debug(\\\"[ ********* STEAL %s *********** ]\\\" % competitor)\\n try:\\n competitor_friends_ids = self.api.friends_ids(competitor)\\n competitor_followers_ids = self.api.followers_ids(competitor)\\n\\n filter_known_users_to_reduce_api_hits = False\\n\\n if filter_known_users_to_reduce_api_hits is True:\\n new_competitor_friends_ids = [id for id in competitor_friends_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n old_competitor_friends_ids = [id for id in competitor_friends_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n new_competitor_followers_ids = [id for id in competitor_followers_ids if not len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n old_competitor_followers_ids = [id for id in competitor_followers_ids if len(TwitterAccount.objects.filter(twitter_id=id)) > 0 ]\\n # print new_competitor_friends_ids\\n # print old_competitor_friends_ids\\n # print new_competitor_followers_ids\\n # print old_competitor_followers_ids\\n\\n print \\\"start lookups\\\"\\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, new_competitor_friends_ids)\\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, new_competitor_followers_ids)\\n print \\\"end lookups\\\"\\n else:\\n # get all the tweepy users\\n print \\\"start lookups\\\"\\n new_competitor_friends, remaining_friends = utils.lookup_users_by_id(self.api, competitor_friends_ids)\\n new_competitor_followers, remaining_followers = utils.lookup_users_by_id(self.api, competitor_followers_ids)\\n print \\\"end lookups\\\"\\n\\n print \\\"%s has %s friends\\\" % (competitor, len(new_competitor_friends))\\n print \\\"%s has %s followers\\\" % (competitor, len(new_competitor_followers))\\n\\n # holy crap this is so fucked up i'm ashamed that this code is getting written like this!\\n\\n for u in new_competitor_friends + new_competitor_followers:\\n stolen_from.update({u.screen_name.lower(): competitor})\\n\\n except Exception, e:\\n print e\\n # didn't get all the users, don't remove the competitor\\n # from the competitor list\\n pass\\n else:\\n # got all the competitors friends and followers and converted them\\n # to tweepy users.\\n users += new_competitor_friends\\n users += new_competitor_followers\\n # add them to the users list to be processed in the next block (for user in users)\\n # then pop the name off the competitors list in the UserProfile\\n # fuck it for now i'm going to just cycle the item to the bottom of the competitor list so we can start getting maximal coverage within api constraints by making sure the top person is new every time\\n self.competitors.append(self.competitors.pop(0))\\n # return # for now\\n self.profile.competitors = \\\"\\\\r\\\\n\\\".join(self.competitors)\\n self.profile.save()\\n\\n # use the profile competitors list\\n # for each name in competitors list\\n # add all friends\\n\\n # should filter out garbage users. something like:\\n users = [u for u in users if not Target.objects.filter(hunter=self.user, hunted__screen_name=u.screen_name.lower())]\\n\\n for user in users:\\n twitter_account, created = utils.get_or_create_twitter_account(user)\\n target, created = Target.objects.get_or_create(\\n hunter=self.user, hunted=twitter_account)\\n print target.hunted.screen_name, created\\n if created:\\n try:\\n screen_name = user.screen_name.lower()\\n match = lambda x: screen_name in \\\\\\n (x.from_user.lower(), x.to_user and x.to_user.lower())\\n if not self.strategy == UserProfile.STEAL:\\n trigger_tweet = filter(match, statuses)[0].text\\n else:\\n try:\\n trigger_tweet = \\\"Steal from user: %s\\\" % stolen_from.get(screen_name.lower(), \\\"someone. i lost it. sorry.\\\")\\n except Exception, e:\\n print \\\"YUCK. ERRORS.\\\"\\n print \\\"YUCK. ERRORS.\\\"\\n print e\\n print e\\n except Exception, e:\\n self.log.exception(\\\"Could not get trigger tweet for %s\\\" %\\n user.screen_name.lower())\\n trigger_tweet = \\\"Error: Couldn't retrieve tweet.\\\"\\n self.log.debug(\\\"Saved twitter account %s (trigger: %r)\\\" %\\n (twitter_account.screen_name,\\n trigger_tweet[:50]))\\n target.reason = trigger_tweet\\n target.status = Target.ON_DECK\\n target.save()\\n else:\\n pass\\n # print \\\" - Previously followed this dudesicle: %s\\\" % user.screen_name\",\n \"def follow(request):\\n user_id = request.POST.get('user_id')\\n action = request.POST.get('action')\\n if user_id and action:\\n try:\\n user = User.objects.get(id=user_id)\\n if request.user == user:\\n return JsonResponse({'status':0,'msg':'You can\\\\'t follow yourself'})\\n if action == 'follow':\\n if request.user.is_following(user):\\n return JsonResponse({'status':0,'msg':'already following'})\\n else:\\n request.user.follow(user)\\n return JsonResponse({'status':1,'msg':'followed'})\\n else:\\n if request.user.is_following(user):\\n request.user.unfollow(user)\\n return JsonResponse({'status':1,'msg':'un-followed'})\\n else:\\n return JsonResponse({'status':0,'msg':'not following'})\\n except User.DoesNotExist:\\n return JsonResponse({'status':0,'msg':'user not found'})\\n return JsonResponse({'status':0})\",\n \"def user_follow(self, user_id: int) -> bool:\\n assert self.user_id, \\\"Login required\\\"\\n user_id = int(user_id)\\n if user_id in self._users_following.get(self.user_id, []):\\n self.logger.debug(\\\"User %s already followed\\\", user_id)\\n return False\\n data = self.with_action_data({\\\"user_id\\\": user_id})\\n result = self.private_request(f\\\"friendships/create/{user_id}/\\\", data)\\n if self.user_id in self._users_following:\\n self._users_following.pop(self.user_id) # reset\\n return result[\\\"friendship_status\\\"][\\\"following\\\"] is True\",\n \"def like_following(self):\\n self.logger.log(\\\"starting like_following...\\\")\\n count_following = self.account.follows_count\\n follows_accounts = self.following\\n random.shuffle(follows_accounts)\\n for acc in follows_accounts:\\n acc = perform_with_ran_delay(self.instagram.get_account_by_id, acc)\\n self.logger.log(\\\" {} > {} posts\\\".format(acc.username, acc.media_count))\\n if acc.media_count > 0:\\n\\n posts = perform_with_ran_delay(self.instagram.get_medias, acc.username, 50)\\n if posts:\\n for m in posts:\\n try:\\n perform_with_ran_delay(self.instagram.like, m.identifier)\\n self.logger.log(\\\"liking 1 post from \\\"+acc.username)\\n random_delay()\\n except Exception as e:\\n self.logger.log(\\\"skipping 1 post from \\\"+acc.username)\\n self.logger.log(e)\\n random_delay()\\n continue\",\n \"def unfollow(self, followerId, followeeId):\\r\\n self.follows[followerId].discard(followeeId)\",\n \"def followers(congressDict, twitterAPI):\\n most = twitterAPI.get_user(list(congressDict.items())[0][1]) # Choose an arbitrary starting point from the dictionary and assign it their user details.\\n least = most\\n for name in congressDict:\\n tempAPI = twitterAPI.get_user(congressDict[name]) # Get the user details of each congress members' twitter handle.\\n numFollowers = tempAPI._json['followers_count']\\n if (numFollowers > most._json['followers_count']): # If the follower count is greater than most, replace the user details with current one.\\n most = tempAPI\\n elif (numFollowers < least._json['followers_count']): # If the follower count is lower than least, replace the user details with the current one.\\n least = tempAPI\\n return [most._json[\\\"name\\\"], least._json[\\\"name\\\"]]\",\n \"def add_following(self, user_id):\\n sleep(360) # too much follows => function ban\\n self.following.append(user_id)\\n return perform_with_ran_delay(self.instagram.follow, user_id)\",\n \"def user_follow_artists(self, ids=None, **kwargs):\\n return self._put(\\n API.MY_FOLLOWING.value, type=\\\"artist\\\", ids=\\\",\\\".join(ids or []), **kwargs\\n )\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def followers(self):\\r\\n url = '{0}/followers'.format(self.get_url())\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def generateFollowers(self):\\n for f in self._genericGenerator(self.getFollowers):\\n yield f\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n following = self.user_followed[followerId]\\n if followeeId in following:\\n following.remove(followeeId)\\n self.user_followed[followerId] = following\",\n \"def approve_follow(made_request_id, approver_id):\\n\\n if not g.user.id == approver_id:\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\"), 403\\n\\n wanted_to_follow_user = User.query.get_or_404(made_request_id)\\n g.user.followers.append(wanted_to_follow_user)\\n g.user.from_users.remove(wanted_to_follow_user)\\n db.session.commit()\\n\\n flash(f\\\"Follow request from {wanted_to_follow_user.username} approved.\\\", \\\"success\\\")\\n return redirect(f\\\"/users/{g.user.id}/followers\\\")\",\n \"def follow(request):\\n username = request.POST[\\\"user\\\"]\\n to_follow = get_user_from_username(request.user, username)\\n clear_cache(request.user, to_follow)\\n\\n follow_request, created = models.UserFollowRequest.objects.get_or_create(\\n user_subject=request.user,\\n user_object=to_follow,\\n )\\n\\n if not created:\\n # this request probably failed to connect with the remote\\n # that means we should save to trigger a re-broadcast\\n follow_request.save()\\n\\n if is_api_request(request):\\n return HttpResponse()\\n return redirect(to_follow.local_path)\",\n \"def compute_follow(self):\\n compute_follow_sets(self)\",\n \"def follows(self, name):\\r\\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\\r\\n\\r\\n return http.Request('GET', url), resource.parse_boolean\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7804903","0.7517027","0.7427908","0.7388741","0.732171","0.7260585","0.72381175","0.7165132","0.7156749","0.712556","0.7086118","0.7050882","0.69780487","0.6961415","0.6956244","0.6956244","0.6944516","0.69404787","0.6928964","0.69156355","0.68406665","0.6831351","0.68225664","0.6794572","0.67710507","0.6759474","0.6754096","0.6718852","0.6710419","0.66899765","0.668939","0.6661727","0.6660523","0.6645243","0.662305","0.66168284","0.66157764","0.6604358","0.659799","0.6582059","0.65810895","0.65785116","0.6577327","0.65754634","0.65750235","0.65524155","0.65285414","0.6525011","0.6506556","0.649035","0.648414","0.64584905","0.64531225","0.644245","0.6430373","0.64213526","0.6421026","0.6419466","0.64123446","0.64122236","0.6406057","0.64053494","0.6405175","0.6400019","0.6396878","0.6386675","0.63863826","0.6385326","0.63780075","0.6362489","0.63548625","0.6336771","0.63322324","0.63284284","0.6325474","0.6302625","0.6296965","0.6289261","0.6284187","0.62813175","0.6271859","0.62696767","0.62644863","0.62574404","0.62553614","0.6255195","0.62534994","0.6252713","0.62443393","0.62409586","0.62264276","0.62264276","0.62264276","0.62264276","0.6222325","0.62157506","0.6201947","0.6196158","0.6194386","0.6192496"],"string":"[\n \"0.7804903\",\n \"0.7517027\",\n \"0.7427908\",\n \"0.7388741\",\n \"0.732171\",\n \"0.7260585\",\n \"0.72381175\",\n \"0.7165132\",\n \"0.7156749\",\n \"0.712556\",\n \"0.7086118\",\n \"0.7050882\",\n \"0.69780487\",\n \"0.6961415\",\n \"0.6956244\",\n \"0.6956244\",\n \"0.6944516\",\n \"0.69404787\",\n \"0.6928964\",\n \"0.69156355\",\n \"0.68406665\",\n \"0.6831351\",\n \"0.68225664\",\n \"0.6794572\",\n \"0.67710507\",\n \"0.6759474\",\n \"0.6754096\",\n \"0.6718852\",\n \"0.6710419\",\n \"0.66899765\",\n \"0.668939\",\n \"0.6661727\",\n \"0.6660523\",\n \"0.6645243\",\n \"0.662305\",\n \"0.66168284\",\n \"0.66157764\",\n \"0.6604358\",\n \"0.659799\",\n \"0.6582059\",\n \"0.65810895\",\n \"0.65785116\",\n \"0.6577327\",\n \"0.65754634\",\n \"0.65750235\",\n \"0.65524155\",\n \"0.65285414\",\n \"0.6525011\",\n \"0.6506556\",\n \"0.649035\",\n \"0.648414\",\n \"0.64584905\",\n \"0.64531225\",\n \"0.644245\",\n \"0.6430373\",\n \"0.64213526\",\n \"0.6421026\",\n \"0.6419466\",\n \"0.64123446\",\n \"0.64122236\",\n \"0.6406057\",\n \"0.64053494\",\n \"0.6405175\",\n \"0.6400019\",\n \"0.6396878\",\n \"0.6386675\",\n \"0.63863826\",\n \"0.6385326\",\n \"0.63780075\",\n \"0.6362489\",\n \"0.63548625\",\n \"0.6336771\",\n \"0.63322324\",\n \"0.63284284\",\n \"0.6325474\",\n \"0.6302625\",\n \"0.6296965\",\n \"0.6289261\",\n \"0.6284187\",\n \"0.62813175\",\n \"0.6271859\",\n \"0.62696767\",\n \"0.62644863\",\n \"0.62574404\",\n \"0.62553614\",\n \"0.6255195\",\n \"0.62534994\",\n \"0.6252713\",\n \"0.62443393\",\n \"0.62409586\",\n \"0.62264276\",\n \"0.62264276\",\n \"0.62264276\",\n \"0.62264276\",\n \"0.6222325\",\n \"0.62157506\",\n \"0.6201947\",\n \"0.6196158\",\n \"0.6194386\",\n \"0.6192496\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7198094"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":13786,"cells":{"query":{"kind":"string","value":"Unfollows everyone who hasn't followed you back"},"document":{"kind":"string","value":"def auto_unfollow_nonfollowers():\n\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n # put user IDs here that you want to keep following even if they don't\n # follow you back\n users_keep_following = set([])\n\n not_following_back = following - followers\n\n # make sure the \"already followed\" file exists\n if not os.path.isfile(ALREADY_FOLLOWED_FILE):\n with open(ALREADY_FOLLOWED_FILE, \"w\") as out_file:\n out_file.write(\"\")\n\n # update the \"already followed\" file with users who didn't follow back\n already_followed = set(not_following_back)\n af_list = []\n with open(ALREADY_FOLLOWED_FILE) as in_file:\n for line in in_file:\n af_list.append(int(line))\n\n already_followed.update(set(af_list))\n del af_list\n\n with open(ALREADY_FOLLOWED_FILE, \"w\") as out_file:\n for val in already_followed:\n out_file.write(str(val) + \"\\n\")\n\n for user_id in not_following_back:\n if user_id not in users_keep_following:\n t.friendships.destroy(user_id=user_id)\n print(\"unfollowed %d\" % (user_id))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def user_unfollow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.remove(following)\n db.session.commit()\n return {'unfollowed': True}","def unfollow(self, other):\n\t\tif self.follows(other):\n\t\t\tself.followed.remove(other)","def unfollow(self, followerId, followeeId):\r\n self.follows[followerId].discard(followeeId)","def unfollowUser(following):\n \n cur, user_id, con = initialise(3, True)\n cur.execute(\"DELETE FROM followers WHERE user = (SELECT username FROM users WHERE id = ?) AND following = ?\", (user_id, following))\n finish(con)","def unfollow(self, user):\n f = self.followed.filter_by(followed_id=user.id).first()\n if f:\n db.session.delete(f)","def unfollow(self, user):\n if self.is_following(user):\n self.followed.remove(user)\n return self","def unfollow_me(self):\n return self.follow_me('unfollow_topic')","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followerId not in self.users.keys() or followeeId not in self.users.keys():\n return\n if followeeId not in self.users[followerId].followees.keys():\n return\n self.users[followerId].followees.pop(followeeId)\n\n\n\n # Your Twitter object will be instantiated and called as such:\n # obj = Twitter()\n # obj.postTweet(userId,tweetId)\n # param_2 = obj.getNewsFeed(userId)\n # obj.follow(followerId,followeeId)\n # obj.unfollow(followerId,followeeId)","def unfollow(request, usertostopfollow):\n stop_follow = Member.objects.get(user__username=usertostopfollow)\n user = Member.objects.get(user=request.user)\n user.following.remove(stop_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","def unfollow(self, followerId, followeeId):\n if followerId in self.follows:\n if followeeId in self.follows[followerId]:\n self.follows[followerId].remove(followeeId)","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followerId != followeeId and followeeId in self.followList.get(followerId, []):\n self.followList[followerId].remove(followeeId)\n # print(self.followList)","def unfollow_profile(self):\n self.find_clickable_element(self.ISFOLLOWED_BTN).click()","def unfollow(self, followerId: int, followeeId: int) -> None:\n self.users[followerId].discard(followeeId)","def unfollow(self,id):\n # DELETE /followings/$id\n debugMain('unfollow')\n resource = '/followings/%s'%id\n requestUrl = self.apiRootUrls[0] + resource\n debugRequest('unfollowing: %s'%requestUrl)\n r = self.session.delete(requestUrl)\n \n debugDetail('request headers:')\n debugJson(r.request.headers)\n debugDetail()\n debugDetail(' -- -- -- --')\n debugDetail()\n debugDetail('response headers:')\n debugJson(r.headers)\n debugDetail()\n \n if r.status_code is not 200:\n debugError('failed to unfollow.')\n debugDetail()\n return False\n return True","def unfollow(self, name):\r\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\r\n\r\n return http.Request('DELETE', url), parsers.parse_empty","def unfollow_user(username):\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n db = get_db()\n db.execute('delete from follower where who_id=? and whom_id=?',\n [session['user_id'], whom_id])\n db.commit()\n flash('You are no longer following \"%s\"' % username)\n return redirect(url_for('user_timeline', username=username))","def unfollow(alias):\n s = db.Series.alias_lookup(alias)\n s.following = False\n db.session.commit()\n output.series('Removing follow for {}'.format(s.name))","def unfollow_user(request, course_id, followed_user_id):\r\n user = cc.User.from_django_user(request.user)\r\n followed_user = cc.User.find(followed_user_id)\r\n user.unfollow(followed_user)\r\n return JsonResponse({})","def unfollow(self, followerId, followeeId):\n if followerId in self.follow_map and followeeId in self.follow_map[followerId]:\n self.follow_map[followerId].remove(followeeId)","def unfollow(self, user_index, following_index):\n if user_index >= self.num_users or following_index >= self.num_users:\n raise ValueError(\n f\"Number of users is {self.num_users}, but indices \"\n f\"{user_index} and {following_index} were requested.\"\n )\n if self.users_hat[following_index, user_index] == 1:\n self.users_hat[following_index, user_index] = 0\n elif self.is_verbose():\n self.log(f\"User {following_index} was not following user {user_index}\")","def unfollow(self, followerId: int, followeeId: int) -> None:\n following = self.user_followed[followerId]\n if followeeId in following:\n following.remove(followeeId)\n self.user_followed[followerId] = following","def unfollow(self, followerId: int, followeeId: int) -> None:\n self.followees[followerId].discard(followeeId)","def unfollow_friend(username):\n\n if not g.user:\n print \"401\"\n abort(401)\n whom_id = get_user_id(username)\n print whom_id\n if whom_id is None:\n abort(404)\n unfollow_query(whom_id)\n flash('You are no longer following \"%s\"' % username)\n name = {'name of unfollowing user': username}\n ############### REDIS cache invalidate #####################\n R_SERVER.delete(user_timeline_key)\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)","def unfollow(user, actor, send_action=False):\n Follow.objects.filter(user = user, object_id = actor.pk,\n content_type = ContentType.objects.get_for_model(actor)).delete()\n if send_action:\n action.send(user, verb=_('stopped following'), target=actor)","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followeeId in self.follow_map[followerId]:\n self.follow_map[followerId].remove(followeeId)","def new_unfollow(self, user_id, user_name):\n url_unfollow = self.url_unfollow % (user_id)\n try:\n unfollow = self._send_post_request(url_unfollow)\n if unfollow.status_code == 200:\n self.unfollow_counter += 1\n log_string = \"Unfollow: %s #%i.\" % (user_name,\n self.unfollow_counter)\n self.log.debug(log_string)\n return unfollow\n except:\n self.log.debug(\"Exept on unfollow!\")\n return False","def unfollow_user(username):\n user_ID = before_request()\n user_ID = None\n if user_ID != None:\n user_ID = str(g.user['_id'])\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n mongo.db.users.update({'_id': g.user['_id']}, {\n '$pull': {'follows': whom_id}})\n flash('You are no longer following \"%s\"' % username)\n if redis_obj.get(user_ID):\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(redis_obj.get(user_ID))))\n else:\n redis_obj.delete(session['user_id'])\n print \"Invalidating cache after Unfollow\"\n return redirect(url_for('user_timeline', username=username))","def unfollow(self, followerId: 'int', followeeId: 'int') -> 'None':\n self.followees[followerId].discard(followeeId)","async def twitter_unfollow(self, ctx, handle):\n sane_handle = handle.lower().lstrip('@')\n conf = dutils.get(self.conf.follows, screen_name=sane_handle)\n chan_conf = dutils.get(conf.discord_channels, id=ctx.message.channel.id) if conf is not None else None\n\n if chan_conf is None:\n raise TwitterError('Not following {} on this channel.'.format(handle))\n\n # Remove the Discord channel from the Twitter channel conf\n conf.discord_channels.remove(chan_conf)\n if not conf.discord_channels:\n # If there are no more Discord channel to feed, unfollow the Twitter channel\n self.conf.follows.remove(conf)\n del conf\n\n # Update the tweepy stream\n if len(self.conf.follows) > 0:\n await self.stream.start()\n else:\n self.stream.stop()\n\n self.conf.save()\n\n await self.bot.say('\\N{OK HAND SIGN}')","def unfollow(self, followerId: int, followeeId: int) -> None:\n # Time Complexity: O(1)\n if followerId in self.followees and followeeId in self.followees[followerId]:\n self.followees[followerId].remove(followeeId)","async def unfollow(self, TargetId: int):\n data = {\n 'targetUserId': TargetId\n }\n e = await self.request.request(url=f'https://friends.roblox.com/v1/users/{TargetId}/unfollow', method='post',\n data=data)\n return e","def unfollow(self, followerId, star):\n if followerId in self.followstar and star in self.followstar[followerId]:\n self.followstar[followerId].remove(star)","def unfollow_route(request, follow_id):\n\n db_conn = request['db_conn']\n current_user = get_current_user(request)\n if not current_user:\n return abort(401)\n follow = get_follow({'id': follow_id}, db_conn)\n if not follow:\n return abort(404)\n if follow['user_id'] != current_user['id']:\n return abort(403)\n errors = delete_follow(follow['id'], db_conn)\n if errors:\n return 400, {\n 'errors': errors,\n 'ref': 'iGmpx8UwoFcKNmSKq9Aocy1a'\n }\n return 200, {}","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followeeId not in self.users:\n self._create_user(followeeId)\n if followerId not in self.users:\n self._create_user(followerId)\n if followerId != followeeId and followeeId in self.users[followerId]:\n self.users[followerId].remove(followeeId)","def test_un_following_existing_user(self):\n response = self.client.delete(\n reverse(\n 'follow',\n kwargs={'username': self.followed['user'].get('username')}\n )\n )\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)","def unfollow(self, followerId, followeeId):\n # 在user_pool 中查询这个用户 follower\n if self.user_pool[followerId]:\n # 如果在用户的关注列表中才删除\n if followeeId in self.user_pool[followerId].follows:\n self.user_pool[followerId].follows.remove(followeeId)","def unsubscribe_user_follow(self, uuid: UUID) -> bool:\n return self._generic_unsubscribe('/users/follows', uuid)","def stop_following(follow_id):\n\n followed_user = User.query.get(follow_id)\n g.user.following.remove(followed_user)\n db.session.commit()\n\n return redirect(f\"/users/{g.user.id}/following\")","def user_unfollow(self, user_id: int) -> bool:\n assert self.user_id, \"Login required\"\n user_id = int(user_id)\n data = self.with_action_data({\"user_id\": user_id})\n result = self.private_request(f\"friendships/destroy/{user_id}/\", data)\n if self.user_id in self._users_following:\n self._users_following[self.user_id].pop(user_id, None)\n return result[\"friendship_status\"][\"following\"] is False","def unfollow(username):\n follow_form = FollowForm()\n unfollow_form = UnfollowForm()\n\n if unfollow_form.validate_on_submit():\n try:\n current_user.unfollow(username)\n flash('Unfollowed {}'.format(username))\n except ValueError as excep:\n flash(str(excep))\n return render_template('user_history.html',\n username=username,\n follow_form=follow_form,\n unfollow_form=unfollow_form)\n\n return render_template('user_history.html',\n username=username,\n follow_form=follow_form,\n unfollow_form=unfollow_form)","def test_unfollow_user_without_auth(self):\n with self.settings(\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\n self.register_user(self.user1)\n self.client.post(self.follow_url, format='json')\n response = self.client.delete(self.unfollow_url,\n data=self.followed_user)\n self.assertEqual(response.data['detail'],\n \"Authentication credentials were not provided.\")\n\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)","def btn_unfollow_clicked(self, widget, data=None):\n print \"unfollow clicked\"","def stop_following(follow_id):\n\n if not g.user:\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\")\n\n followed_user = User.query.get_or_404(follow_id)\n g.user.following.remove(followed_user)\n db.session.commit()\n\n return redirect(f\"/users/{g.user.id}/following\")","def test_unfollowing_yourself(self):\n response = self.client.delete(\n reverse(\n 'follow',\n kwargs={'username': self.follower['user'].get('username')}\n )\n )\n self.assertEqual(response.status_code, status.HTTP_406_NOT_ACCEPTABLE)","def unfollow(source_id, destination_id):\n Forward.objects.filter(source_id=source_id,\n destination_id=destination_id).delete()\n Backward.objects.filter(destination_id=destination_id,\n source_id=source_id).delete()","def user_playlist_unfollow(self, user, playlist_id, **kwargs):\n return self._delete(\n \"users/%s/playlists/%s/followers\" % (user, playlist_id), **kwargs\n )","def test_unfollowing_non_existing_user(self):\n response = self.client.delete(\n reverse(\n 'follow',\n kwargs={'username': 'NotThere'}\n )\n )\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)","def test_unfollow_user_successfully(self):\n self.authorize_user(self.user)\n with self.settings(\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\n self.register_user(self.user1)\n self.client.post(self.follow_url, format='json')\n response = self.client.delete(self.unfollow_url,\n data=self.followed_user)\n self.assertEqual(response.content,\n b'{\"message\":\"user unfollowed\"}')\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def unfollow_group(request, pk):\n group = get_object_or_404(Group, id=pk)\n\n # Check user is not member of the group\n if not group.members.filter(id=request.user.id).exists():\n actions.unfollow(request.user, group, send_action=False)\n request.user.userprofile.follow_groups.remove(group)\n messages.warning(\n request,\n 'Successed, you are not following this Group anymore.')\n # the group members can choose not follow the group anymore, but still\n # been the member\n else:\n actions.unfollow(request.user, group, send_action=False)\n messages.warning(\n request,\n 'Successed, you are not following this Group anymore. But you are still the one of the members of this group.')\n\n return redirect('groups:groups-detail', pk)","def unfollow_thread(request, course_id, thread_id):\r\n user = cc.User.from_django_user(request.user)\r\n thread = cc.Thread.find(thread_id)\r\n user.unfollow(thread)\r\n return JsonResponse({})","def on_deleted_follow(sender, instance: models_actstream.Follow, **kwargs):\n content_type = ContentType.objects.get_for_id(instance.content_type_id)\n log.debug(\"Unfollowing %s %s\" % (content_type.name, instance.object_id))\n dillo.tasks.feeds.repopulate_timeline_content(\n instance.content_type_id, instance.object_id, instance.user_id, 'unfollow'\n )","def removeFollower(self,id):\n # DELETE /followers/$id\n pass","def delete(self, request, username):\n\n # Retrieve the user from the user table if the user exists\n try:\n user_to_unfollow = User.objects.get(username=username)\n current_user = request.user\n # If a user is trying to unfollow themselves then stop the request\n if user_to_unfollow.profile.id == current_user.profile.id:\n return Response(\n {\"errors\": FOLLOW_USER_MSGS['CANNOT_UNFOLLOW_SELF']},\n status=status.HTTP_400_BAD_REQUEST\n )\n # Check if the user to be unfollowed\n # is in the current users following list\n try:\n profile_id = user_to_unfollow.profile.id\n user_being_followed = CustomFollows.objects.get(\n to_profile_id=profile_id,\n from_profile_id=current_user.profile.id\n )\n # If not tell the user the request can't happen\n # Because they don't follow the user\n except Exception as e:\n return Response(\n {\n \"errors\": FOLLOW_USER_MSGS['USER_UNFOLLOWED_ALREADY']\n },\n status=status.HTTP_400_BAD_REQUEST\n )\n # Otherwise unfollow the user as requested\n current_user.profile.follows.remove(user_to_unfollow.profile)\n # Get the following & followers username list\n # And the following & followers count for the current user\n user_following_data = get_user_following_data(current_user)\n return Response(\n {\n \"message\": FOLLOW_USER_MSGS['USER_UNFOLLOW_SUCCESSFUL'],\n \"following\": user_following_data[\"following\"],\n \"followers\": user_following_data[\"followers\"],\n \"followingCount\": user_following_data[\"followingCount\"],\n \"followersCount\": user_following_data[\"followersCount\"]\n },\n status=status.HTTP_200_OK\n )\n # End request if we cannot find the user we want to unfollow.\n except User.DoesNotExist:\n return Response(\n {\"errors\": FOLLOW_USER_MSGS['USER_NOT_FOUND']},\n status=status.HTTP_404_NOT_FOUND\n )","def test_unfollowing_existing_user_not_authenticated(self):\n self.client.credentials()\n response = self.client.delete(\n reverse(\n 'follow',\n kwargs={'username': self.followed['user'].get('username')}\n )\n )\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)","def get_to_unfollow(self, to_unfollow, n_unfollow):\n if self.monitored_users:\n current_monitored = \\\n list(filter(lambda x: datetime.fromtimestamp(float(x['followDate'])) + timedelta(days=14)\n < datetime.now() and x['username'] not in self.white_list, self.monitored_users))\n to_unfollow.extend([u['user'] for u in current_monitored])\n\n if len(to_unfollow) < n_unfollow:\n if not self.self_followings:\n self.self_followings = self.get_followings()\n add_followings = [f['pk'] for f in self.self_followings.get('users', [])\n if f['username'] not in self.white_list]\n\n if add_followings:\n if len(add_followings) > n_unfollow - len(to_unfollow):\n to_unfollow.extend([random.choice(add_followings) for _ in range(n_unfollow - len(to_unfollow))])\n else:\n to_unfollow.extend(add_followings)\n else:\n to_unfollow = [random.choice(to_unfollow) for _ in range(n_unfollow)]\n\n return to_unfollow","def prune_losers(self):\n self.log.debug(\"PRUNE LOSERS\")\n # check to see if people i followed follow me back\n cutoff_time = (datetime.now()\n - timedelta(hours=self.reciprocation_window))\n ingrates = Target.objects.filter(\n hunter=self.user, status=Target.PURGATORY,\n modified__lt=cutoff_time) # They didn't follow back in time\n\n for ingrate in ingrates:\n ingrate.status = Target.INGRATE\n ingrate.save()\n self.log.debug(\" => Unfollowed %s\" % ingrate.hunted.screen_name)\n try:\n self.api.destroy_friendship(ingrate.hunted)\n except Exception, e:\n print e\n return\n finally:\n pass\n #self.contact(ingrate)","def test_remove_followers(self):\n pass","def _is_unfollow_request(environ, result):\n r = Request(environ)\n if r.params.get(\"action\") == \"unfollow\":\n return True\n else:\n return False","def unfollow_commentable(request, course_id, commentable_id):\r\n user = cc.User.from_django_user(request.user)\r\n commentable = cc.Commentable.find(commentable_id)\r\n user.unfollow(commentable)\r\n return JsonResponse({})","def reject_follow(made_request_id, approver_id):\n\n if not g.user.id == approver_id:\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\"), 403\n\n wanted_to_follow_user = User.query.get_or_404(made_request_id)\n g.user.from_users.remove(wanted_to_follow_user)\n db.session.commit()\n flash(f\"Follow request from {wanted_to_follow_user.username} rejected.\", \"success\")\n\n return redirect(f\"/users/{g.user.id}\")","def reset():\n\n Follower.clear()","def test_undo_follow_request(self):\n self.local_user.manually_approves_followers = True\n self.local_user.save(\n broadcast=False, update_fields=[\"manually_approves_followers\"]\n )\n with patch(\"bookwyrm.models.activitypub_mixin.broadcast_task.apply_async\"):\n request = models.UserFollowRequest.objects.create(\n user_subject=self.remote_user, user_object=self.local_user\n )\n self.assertTrue(self.local_user.follower_requests.exists())\n\n activity = {\n \"type\": \"Undo\",\n \"id\": \"bleh\",\n \"to\": [\"https://www.w3.org/ns/activitystreams#Public\"],\n \"cc\": [\"https://example.com/user/mouse/followers\"],\n \"actor\": self.remote_user.remote_id,\n \"@context\": \"https://www.w3.org/ns/activitystreams\",\n \"object\": {\n \"@context\": \"https://www.w3.org/ns/activitystreams\",\n \"id\": request.remote_id,\n \"type\": \"Follow\",\n \"actor\": \"https://example.com/users/rat\",\n \"object\": \"https://example.com/user/mouse\",\n },\n }\n\n views.inbox.activity_task(activity)\n\n self.assertFalse(self.local_user.follower_requests.exists())","def delete(self, request, *args, **kwargs):\n\n following_username = token_payload(request)\n\n followed_username = self.kwargs.get(self.look_url_kwarg)\n try:\n followed_user = User.objects.get(username=followed_username)\n followed_user_id = followed_user.id\n\n is_following = FollowUser.objects.filter(\n following_username=following_username)\n for user in is_following:\n if user.following_username == following_username and user.followed_username == followed_username:\n returned_user = FollowUser.objects.get(pk=user.id)\n returned_user.delete()\n\n followed_user_profile = Profile.objects.get(\n profile_user=followed_user_id)\n serializer_profile = self.serializer_classprofile(\n followed_user_profile)\n return Response({\n \"user\": following_username,\n \"unfollowing\": serializer_profile.data\n })\n return Response({\n \"message\": 'User has been unfollowed or You are unfollowing a user you were not orignally following'\n }, status=status.HTTP_400_BAD_REQUEST)\n except:\n return Response({\n \"message\": 'User does not exist'\n }, status=status.HTTP_400_BAD_REQUEST)","def test_unauthorized_followers(self):\n\n follow = Follows(\n user_being_followed_id = self.testuser.id,\n user_following_id = self.testuser2.id\n )\n\n db.session.add(follow)\n db.session.commit()\n\n with self.client as c:\n response = c.get(f\"/users/{self.testuser.id}/followers\", follow_redirects=True)\n data = str(response.data)\n\n self.assertEqual(response.status_code, 200)\n self.assertNotIn(\"@bob\", data)\n self.assertIn(\"Access unauthorized\", data)","def test_unauthorized_following(self):\n\n follow = Follows(\n user_being_followed_id = self.testuser2.id,\n user_following_id = self.testuser.id\n )\n\n db.session.add(follow)\n db.session.commit()\n\n with self.client as c:\n response = c.get(f\"/users/{self.testuser.id}/following\", follow_redirects=True)\n data = str(response.data)\n\n self.assertEqual(response.status_code, 200)\n self.assertNotIn(\"@bob\", data)\n self.assertIn(\"Access unauthorized\", data)","def unfollowing_and_removing(self, user_id):\n if self.unfollowing(user_id):\n ind = [i for i, j in enumerate(self.monitored_users) if j.get('user', '') == user_id]\n if ind:\n self.monitored_users.remove(self.monitored_users[ind[0]])","def unfollow_user(self:'InstaClient', user:str, nav_to_user=True, check_user=True):\n profile = self.get_profile(user)\n if not profile:\n raise InvalidUserError(user)\n\n LOGGER.debug('INSTACLIENT: User <{}> is valid'.format(user))\n self._nav_user(user, check_user=False)\n \n if profile.requested_by_viewer:\n requested_btn = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.REQUESTED_BTN)))\n self._press_button(requested_btn)\n confirm_unfollow = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.CONFIRM_UNFOLLOW_BTN)))\n self._press_button(confirm_unfollow)\n LOGGER.debug(f'Cancelled Follow Request for user <{user}>')\n \n elif self._check_existence(EC.presence_of_element_located((By.XPATH, Paths.UNFOLLOW_BTN))):\n unfollow_btn = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.UNFOLLOW_BTN)))\n self._press_button(unfollow_btn)\n time.sleep(1)\n confirm_unfollow = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.CONFIRM_UNFOLLOW_BTN)))\n self._press_button(confirm_unfollow)\n LOGGER.debug('INSTACLIENT: Unfollowed user <{}>'.format(user))","def unsafe_follow_by_username(self, username: str) -> None:\n uid = self.username_to_id(username)\n self.api.follow(uid)","def follow(self, follower, followee):\n pass","def add_untracked_followers(self):\n\n self.log.debug(\"CHECK FOR UNTRACKED FOLLOWERS\")\n followers_ids_api = self.api.followers_ids()\n target = Target.objects.filter(hunter=self.user)\\\n .filter(status=Target.FOLLOWER)\n followers_ids_django = [t.hunted.twitter_id for t in target]\n\n untracked_followers_ids = filter(\n lambda x: unicode(x) not in followers_ids_django,\n followers_ids_api)\n\n untracked_followers, remainder = lookup_users_by_id(self.api,\n untracked_followers_ids)\n for untracked_follower in untracked_followers:\n twitter_account, created = \\\n utils.get_or_create_twitter_account(untracked_follower)\n target, created = Target.objects.get_or_create(\n hunter=self.user, hunted=twitter_account)\n if target.status == Target.PURGATORY:\n # Yay someone we targeted reciprocated follow\n self.follow_reciprocated(target)\n else:\n print target.status\n # Either a totally external follow, an ingrate changed mind,\n # or someone who we chatted became interested and followed\n # Either way the action is the same, follow him\n target.status = Target.FOLLOWER\n target.save()\n self.log.debug(\" => Add follower: %s\" % twitter_account.screen_name)","def test_stream_stale_follows(self):\n self.user2.delete()\n self.assertNotIn('Two', str(user_stream(self.user1)))","def unfriend(self, remove):\n remover_friends_list = self # person terminating the friendship \n \n # remove friend from remover friend list\n remover_friends_list.remove_friend(removee)\n\n #remove friend from removee friend list\n friends_list = FriendList.objects.get(user=removee)\n friend_list.remove_friend(self.user)","def auto_follow_followers():\n\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n not_following_back = followers - following\n\n for user_id in not_following_back:\n try:\n t.friendships.create(user_id=user_id, follow=False)\n except Exception as e:\n print(\"error: %s\" % (str(e)))","def delete_follow_request(request):\n username = request.POST[\"user\"]\n requester = get_user_from_username(request.user, username)\n\n follow_request = get_object_or_404(\n models.UserFollowRequest, user_subject=requester, user_object=request.user\n )\n follow_request.raise_not_deletable(request.user)\n\n follow_request.delete()\n return redirect(f\"/user/{request.user.localname}\")","def unfriend(self, removee):\n remover_friends_list = self # person terminating the friendship\n # Remove friend from remover friend list\n remover_friends_list.remove_friend(removee)\n # Remove friend from removee's friend list\n friends_list = FriendList.objects.get(user=removee)\n friends_list.remove_friend(self.user)","def auto_mute_following():\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n not_muted = following - muted\n\n # put user IDs of people you do not want to mute here\n users_keep_unmuted = set([])\n \n # mute all \n for user_id in not_muted:\n if user_id not in users_keep_unmuted:\n t.mutes.users.create(user_id=user_id)\n print(\"muted %d\" % (user_id))","def follow(self, other):\n\t\tif not self.follows(other):\n\t\t\tself.followed.append(other)","def follow_following_followers(self):\n self.logger.log(\"starting follow_following_followers...\")\n follows_accounts = self.following\n random.shuffle(follows_accounts)\n for acc in follows_accounts:\n try:\n try:\n followw = perform_with_ran_delay(self.instagram.get_followers, acc, 150, 15,\n delayed=True)\n accountstofollow = followw[\"accounts\"]\n random.shuffle(accountstofollow)\n if len(accountstofollow) > 10:\n accountstofollow = accountstofollow[:10]\n for ac in accountstofollow:\n if not self.is_user_following(ac.identifier):\n self.add_following(ac.identifier)\n self.logger.log(\"following: {}\".format(ac.username))\n except Exception as e:\n print(e)\n self.logger.log(str(e))\n finally:\n sleep(3)","def unblock(self):\n data = {'container': self._reddit.user.me().fullname,\n 'name': str(self), 'type': 'enemy'}\n url = API_PATH['unfriend'].format(subreddit='all')\n # PRAW5 REMOVE (return statement)\n return self._reddit.post(url, data=data)","def test_follow_reject(self):\n with patch(\"bookwyrm.models.activitypub_mixin.broadcast_task.apply_async\"):\n rel = models.UserFollowRequest.objects.create(\n user_subject=self.local_user, user_object=self.remote_user\n )\n activity = {\n \"@context\": \"https://www.w3.org/ns/activitystreams\",\n \"id\": \"https://example.com/users/rat/follows/123#accepts\",\n \"type\": \"Reject\",\n \"actor\": \"https://example.com/users/rat\",\n \"object\": {\n \"id\": rel.remote_id,\n \"type\": \"Follow\",\n \"actor\": \"https://example.com/user/mouse\",\n \"object\": \"https://example.com/users/rat\",\n },\n }\n\n self.assertEqual(models.UserFollowRequest.objects.count(), 1)\n\n views.inbox.activity_task(activity)\n\n # request should be deleted\n self.assertFalse(models.UserFollowRequest.objects.exists())\n self.assertFalse(self.remote_user.followers.exists())","def delete(self, request, username):\n followed_user_exists = User.objects.filter(username=username).exists()\n if not followed_user_exists:\n return Response({'error': 'user not found'},\n status.HTTP_404_NOT_FOUND)\n followed_user = User.objects.get(username=username)\n user_exists = Follow.is_user_already_followed(\n followed_user_id=followed_user.id,\n user_id=request.user.id\n )\n if user_exists:\n instance = Follow.objects.filter(\n user=self.request.user.id, followed_user=followed_user.id\n )\n instance.delete()\n return Response({'message': 'user unfollowed'},\n status.HTTP_200_OK)\n return Response({'message': 'user not in followers'},\n status.HTTP_404_NOT_FOUND)","def test_followers_following_list_unauthorized(self):\n\n self.u2.following.append(self.u)\n db.session.commit()\n\n with self.client as client:\n response = client.get(\"/users/2/following\")\n\n self.assertEqual(response.location, \"http://localhost/\")\n self.assertIn('Access unauthorized.', get_flashed_messages())\n\n response2 = client.get(\"/users/2/followers\")\n\n self.assertEqual(response2.location, \"http://localhost/\")\n self.assertIn('Access unauthorized.', get_flashed_messages())","def follow_user(cls, user, following):\r\n pass","def author_following(self):\n\t\tpass","def unfriend(self, removee):\n\t\tremover_friend_list = self # person terminating the friendship\n\n\t\t# Remove friend from friend request\n\t\tremover_friend_list.remove_friend(removee)\n\n\t\t# Remove friend from the removeee friend list\n\t\tfriends_list = FriendList.objects.get(user=removee)\n\t\tfriends_list.remove_friend(self.user)","def untether(self) -> None:\n if self.msg.sender != self.owner:\n revert(f'Only the owner can call the untether method.')\n pass","def follow(self, followerId, followeeId):\n if followerId in self.follows:\n self.follows[followerId].add(followeeId)\n else:\n self.follows[followerId] = set([followeeId])","def test_following_non_existing_user(self):\n response = self.client.post(\n reverse(\n 'follow',\n kwargs={'username': 'NotThere'}\n )\n )\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)","async def tourney_ping_off(ctx):\n\n user = find_user_in_db(ctx.author.id)\n if user is None:\n await ctx.send(\n f\"{ctx.author.mention} you are not even being notified.\\n If you want to be notified use `{prefix}pingme`.\")\n return\n\n delete_user_from_db(ctx.author.id)\n\n await ctx.send(f\"{ctx.author.mention} you won't be notified ever again unless you type `{prefix}pingme` again.\")\n return","def add_untracked_friends(self):\n\n self.log.debug(\"CHECK FOR UNTRACKED FRIENDS\")\n friends_ids_api = self.api.friends_ids()\n targets = Target.objects.filter(hunter=self.user)\\\n .exclude(status__in=Target.ON_DECK)\n friends_ids_django = [t.hunted.twitter_id for t in targets]\n untracked_friends_ids = \\\n filter(lambda x: unicode(x) not in friends_ids_django,\n friends_ids_api)\n\n untracked_friends, remainder = lookup_users_by_id(self.api,\n untracked_friends_ids)\n for untracked_friend in untracked_friends:\n \"\"\"These could be people who don't follow us, but we want to follow,\n for example to keep up with news of their company\"\"\"\n twitter_account, created = utils.get_or_create_twitter_account(\n untracked_friend)\n target, created = Target.objects.get_or_create(\n hunter=self.user, hunted=twitter_account)\n if created:\n target.reason = \"External add.\"\n target.status = Target.FOLLOWER\n target.save()\n self.log.debug(\" => add friend: %s\" % twitter_account.screen_name)\n else:\n self.log.debug(\" => we're following, but no reciprocation: %s\" % twitter_account.screen_name)","def follow_user(cls, user, following):\n pass","def test_follow_without_auth(self):\n response = self.client.post(self.follow_url, format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)","def test_following_existing_user_not_authenticated(self):\n self.client.credentials()\n response = self.client.post(\n reverse(\n 'follow',\n kwargs={'username': self.followed['user'].get('username')}\n )\n )\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)","def follow(self, user):\n if not self.is_following(user):\n self.followed.append(user)\n return self","def follow(self, user):\n if not self.is_following(user):\n self.followed.append(user)\n return self","def follow(request, usertofollow):\n to_follow = Member.objects.get(user__username=usertofollow)\n user = Member.objects.get(user=request.user)\n user.following.add(to_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","def follow(self, followerId, followeeId):\r\n if followerId != followeeId:\r\n self.follows[followerId].add(followeeId)","async def unfollow(\n self,\n *,\n index: str,\n error_trace: t.Optional[bool] = None,\n filter_path: t.Optional[\n t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]\n ] = None,\n human: t.Optional[bool] = None,\n pretty: t.Optional[bool] = None,\n ) -> ObjectApiResponse[t.Any]:\n if index in SKIP_IN_PATH:\n raise ValueError(\"Empty value passed for parameter 'index'\")\n __path = f\"/{_quote(index)}/_ccr/unfollow\"\n __query: t.Dict[str, t.Any] = {}\n if error_trace is not None:\n __query[\"error_trace\"] = error_trace\n if filter_path is not None:\n __query[\"filter_path\"] = filter_path\n if human is not None:\n __query[\"human\"] = human\n if pretty is not None:\n __query[\"pretty\"] = pretty\n __headers = {\"accept\": \"application/json\"}\n return await self.perform_request( # type: ignore[return-value]\n \"POST\", __path, params=__query, headers=__headers\n )","def set_follow(self, follow):\n self.follow = follow","def honeypot_unpeer(self,honeypotids):\n req = {\"type\":\"unpeer\",\n \"from\":self.network.mc_id,\n \"to\":honeypotids}\n expect_dict = {\"type\":\"unpeered\"}\n msg_list = self.send_receive(req,honeypotids,expect_dict)\n answer = []\n for msg in msg_list:\n answer.append(msg[\"from\"])\n return answer"],"string":"[\n \"def user_unfollow():\\n data = request.get_json(force=True)\\n follower = User.query.get(data['follower'])\\n following = User.query.get(data['following'])\\n follower.followcheck.remove(following)\\n db.session.commit()\\n return {'unfollowed': True}\",\n \"def unfollow(self, other):\\n\\t\\tif self.follows(other):\\n\\t\\t\\tself.followed.remove(other)\",\n \"def unfollow(self, followerId, followeeId):\\r\\n self.follows[followerId].discard(followeeId)\",\n \"def unfollowUser(following):\\n \\n cur, user_id, con = initialise(3, True)\\n cur.execute(\\\"DELETE FROM followers WHERE user = (SELECT username FROM users WHERE id = ?) AND following = ?\\\", (user_id, following))\\n finish(con)\",\n \"def unfollow(self, user):\\n f = self.followed.filter_by(followed_id=user.id).first()\\n if f:\\n db.session.delete(f)\",\n \"def unfollow(self, user):\\n if self.is_following(user):\\n self.followed.remove(user)\\n return self\",\n \"def unfollow_me(self):\\n return self.follow_me('unfollow_topic')\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followerId not in self.users.keys() or followeeId not in self.users.keys():\\n return\\n if followeeId not in self.users[followerId].followees.keys():\\n return\\n self.users[followerId].followees.pop(followeeId)\\n\\n\\n\\n # Your Twitter object will be instantiated and called as such:\\n # obj = Twitter()\\n # obj.postTweet(userId,tweetId)\\n # param_2 = obj.getNewsFeed(userId)\\n # obj.follow(followerId,followeeId)\\n # obj.unfollow(followerId,followeeId)\",\n \"def unfollow(request, usertostopfollow):\\n stop_follow = Member.objects.get(user__username=usertostopfollow)\\n user = Member.objects.get(user=request.user)\\n user.following.remove(stop_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"def unfollow(self, followerId, followeeId):\\n if followerId in self.follows:\\n if followeeId in self.follows[followerId]:\\n self.follows[followerId].remove(followeeId)\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followerId != followeeId and followeeId in self.followList.get(followerId, []):\\n self.followList[followerId].remove(followeeId)\\n # print(self.followList)\",\n \"def unfollow_profile(self):\\n self.find_clickable_element(self.ISFOLLOWED_BTN).click()\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n self.users[followerId].discard(followeeId)\",\n \"def unfollow(self,id):\\n # DELETE /followings/$id\\n debugMain('unfollow')\\n resource = '/followings/%s'%id\\n requestUrl = self.apiRootUrls[0] + resource\\n debugRequest('unfollowing: %s'%requestUrl)\\n r = self.session.delete(requestUrl)\\n \\n debugDetail('request headers:')\\n debugJson(r.request.headers)\\n debugDetail()\\n debugDetail(' -- -- -- --')\\n debugDetail()\\n debugDetail('response headers:')\\n debugJson(r.headers)\\n debugDetail()\\n \\n if r.status_code is not 200:\\n debugError('failed to unfollow.')\\n debugDetail()\\n return False\\n return True\",\n \"def unfollow(self, name):\\r\\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\\r\\n\\r\\n return http.Request('DELETE', url), parsers.parse_empty\",\n \"def unfollow_user(username):\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n db = get_db()\\n db.execute('delete from follower where who_id=? and whom_id=?',\\n [session['user_id'], whom_id])\\n db.commit()\\n flash('You are no longer following \\\"%s\\\"' % username)\\n return redirect(url_for('user_timeline', username=username))\",\n \"def unfollow(alias):\\n s = db.Series.alias_lookup(alias)\\n s.following = False\\n db.session.commit()\\n output.series('Removing follow for {}'.format(s.name))\",\n \"def unfollow_user(request, course_id, followed_user_id):\\r\\n user = cc.User.from_django_user(request.user)\\r\\n followed_user = cc.User.find(followed_user_id)\\r\\n user.unfollow(followed_user)\\r\\n return JsonResponse({})\",\n \"def unfollow(self, followerId, followeeId):\\n if followerId in self.follow_map and followeeId in self.follow_map[followerId]:\\n self.follow_map[followerId].remove(followeeId)\",\n \"def unfollow(self, user_index, following_index):\\n if user_index >= self.num_users or following_index >= self.num_users:\\n raise ValueError(\\n f\\\"Number of users is {self.num_users}, but indices \\\"\\n f\\\"{user_index} and {following_index} were requested.\\\"\\n )\\n if self.users_hat[following_index, user_index] == 1:\\n self.users_hat[following_index, user_index] = 0\\n elif self.is_verbose():\\n self.log(f\\\"User {following_index} was not following user {user_index}\\\")\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n following = self.user_followed[followerId]\\n if followeeId in following:\\n following.remove(followeeId)\\n self.user_followed[followerId] = following\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n self.followees[followerId].discard(followeeId)\",\n \"def unfollow_friend(username):\\n\\n if not g.user:\\n print \\\"401\\\"\\n abort(401)\\n whom_id = get_user_id(username)\\n print whom_id\\n if whom_id is None:\\n abort(404)\\n unfollow_query(whom_id)\\n flash('You are no longer following \\\"%s\\\"' % username)\\n name = {'name of unfollowing user': username}\\n ############### REDIS cache invalidate #####################\\n R_SERVER.delete(user_timeline_key)\\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)\",\n \"def unfollow(user, actor, send_action=False):\\n Follow.objects.filter(user = user, object_id = actor.pk,\\n content_type = ContentType.objects.get_for_model(actor)).delete()\\n if send_action:\\n action.send(user, verb=_('stopped following'), target=actor)\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followeeId in self.follow_map[followerId]:\\n self.follow_map[followerId].remove(followeeId)\",\n \"def new_unfollow(self, user_id, user_name):\\n url_unfollow = self.url_unfollow % (user_id)\\n try:\\n unfollow = self._send_post_request(url_unfollow)\\n if unfollow.status_code == 200:\\n self.unfollow_counter += 1\\n log_string = \\\"Unfollow: %s #%i.\\\" % (user_name,\\n self.unfollow_counter)\\n self.log.debug(log_string)\\n return unfollow\\n except:\\n self.log.debug(\\\"Exept on unfollow!\\\")\\n return False\",\n \"def unfollow_user(username):\\n user_ID = before_request()\\n user_ID = None\\n if user_ID != None:\\n user_ID = str(g.user['_id'])\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n mongo.db.users.update({'_id': g.user['_id']}, {\\n '$pull': {'follows': whom_id}})\\n flash('You are no longer following \\\"%s\\\"' % username)\\n if redis_obj.get(user_ID):\\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(redis_obj.get(user_ID))))\\n else:\\n redis_obj.delete(session['user_id'])\\n print \\\"Invalidating cache after Unfollow\\\"\\n return redirect(url_for('user_timeline', username=username))\",\n \"def unfollow(self, followerId: 'int', followeeId: 'int') -> 'None':\\n self.followees[followerId].discard(followeeId)\",\n \"async def twitter_unfollow(self, ctx, handle):\\n sane_handle = handle.lower().lstrip('@')\\n conf = dutils.get(self.conf.follows, screen_name=sane_handle)\\n chan_conf = dutils.get(conf.discord_channels, id=ctx.message.channel.id) if conf is not None else None\\n\\n if chan_conf is None:\\n raise TwitterError('Not following {} on this channel.'.format(handle))\\n\\n # Remove the Discord channel from the Twitter channel conf\\n conf.discord_channels.remove(chan_conf)\\n if not conf.discord_channels:\\n # If there are no more Discord channel to feed, unfollow the Twitter channel\\n self.conf.follows.remove(conf)\\n del conf\\n\\n # Update the tweepy stream\\n if len(self.conf.follows) > 0:\\n await self.stream.start()\\n else:\\n self.stream.stop()\\n\\n self.conf.save()\\n\\n await self.bot.say('\\\\N{OK HAND SIGN}')\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n # Time Complexity: O(1)\\n if followerId in self.followees and followeeId in self.followees[followerId]:\\n self.followees[followerId].remove(followeeId)\",\n \"async def unfollow(self, TargetId: int):\\n data = {\\n 'targetUserId': TargetId\\n }\\n e = await self.request.request(url=f'https://friends.roblox.com/v1/users/{TargetId}/unfollow', method='post',\\n data=data)\\n return e\",\n \"def unfollow(self, followerId, star):\\n if followerId in self.followstar and star in self.followstar[followerId]:\\n self.followstar[followerId].remove(star)\",\n \"def unfollow_route(request, follow_id):\\n\\n db_conn = request['db_conn']\\n current_user = get_current_user(request)\\n if not current_user:\\n return abort(401)\\n follow = get_follow({'id': follow_id}, db_conn)\\n if not follow:\\n return abort(404)\\n if follow['user_id'] != current_user['id']:\\n return abort(403)\\n errors = delete_follow(follow['id'], db_conn)\\n if errors:\\n return 400, {\\n 'errors': errors,\\n 'ref': 'iGmpx8UwoFcKNmSKq9Aocy1a'\\n }\\n return 200, {}\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followeeId not in self.users:\\n self._create_user(followeeId)\\n if followerId not in self.users:\\n self._create_user(followerId)\\n if followerId != followeeId and followeeId in self.users[followerId]:\\n self.users[followerId].remove(followeeId)\",\n \"def test_un_following_existing_user(self):\\n response = self.client.delete(\\n reverse(\\n 'follow',\\n kwargs={'username': self.followed['user'].get('username')}\\n )\\n )\\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\",\n \"def unfollow(self, followerId, followeeId):\\n # 在user_pool 中查询这个用户 follower\\n if self.user_pool[followerId]:\\n # 如果在用户的关注列表中才删除\\n if followeeId in self.user_pool[followerId].follows:\\n self.user_pool[followerId].follows.remove(followeeId)\",\n \"def unsubscribe_user_follow(self, uuid: UUID) -> bool:\\n return self._generic_unsubscribe('/users/follows', uuid)\",\n \"def stop_following(follow_id):\\n\\n followed_user = User.query.get(follow_id)\\n g.user.following.remove(followed_user)\\n db.session.commit()\\n\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\",\n \"def user_unfollow(self, user_id: int) -> bool:\\n assert self.user_id, \\\"Login required\\\"\\n user_id = int(user_id)\\n data = self.with_action_data({\\\"user_id\\\": user_id})\\n result = self.private_request(f\\\"friendships/destroy/{user_id}/\\\", data)\\n if self.user_id in self._users_following:\\n self._users_following[self.user_id].pop(user_id, None)\\n return result[\\\"friendship_status\\\"][\\\"following\\\"] is False\",\n \"def unfollow(username):\\n follow_form = FollowForm()\\n unfollow_form = UnfollowForm()\\n\\n if unfollow_form.validate_on_submit():\\n try:\\n current_user.unfollow(username)\\n flash('Unfollowed {}'.format(username))\\n except ValueError as excep:\\n flash(str(excep))\\n return render_template('user_history.html',\\n username=username,\\n follow_form=follow_form,\\n unfollow_form=unfollow_form)\\n\\n return render_template('user_history.html',\\n username=username,\\n follow_form=follow_form,\\n unfollow_form=unfollow_form)\",\n \"def test_unfollow_user_without_auth(self):\\n with self.settings(\\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\\n self.register_user(self.user1)\\n self.client.post(self.follow_url, format='json')\\n response = self.client.delete(self.unfollow_url,\\n data=self.followed_user)\\n self.assertEqual(response.data['detail'],\\n \\\"Authentication credentials were not provided.\\\")\\n\\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\",\n \"def btn_unfollow_clicked(self, widget, data=None):\\n print \\\"unfollow clicked\\\"\",\n \"def stop_following(follow_id):\\n\\n if not g.user:\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n\\n followed_user = User.query.get_or_404(follow_id)\\n g.user.following.remove(followed_user)\\n db.session.commit()\\n\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\",\n \"def test_unfollowing_yourself(self):\\n response = self.client.delete(\\n reverse(\\n 'follow',\\n kwargs={'username': self.follower['user'].get('username')}\\n )\\n )\\n self.assertEqual(response.status_code, status.HTTP_406_NOT_ACCEPTABLE)\",\n \"def unfollow(source_id, destination_id):\\n Forward.objects.filter(source_id=source_id,\\n destination_id=destination_id).delete()\\n Backward.objects.filter(destination_id=destination_id,\\n source_id=source_id).delete()\",\n \"def user_playlist_unfollow(self, user, playlist_id, **kwargs):\\n return self._delete(\\n \\\"users/%s/playlists/%s/followers\\\" % (user, playlist_id), **kwargs\\n )\",\n \"def test_unfollowing_non_existing_user(self):\\n response = self.client.delete(\\n reverse(\\n 'follow',\\n kwargs={'username': 'NotThere'}\\n )\\n )\\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\",\n \"def test_unfollow_user_successfully(self):\\n self.authorize_user(self.user)\\n with self.settings(\\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\\n self.register_user(self.user1)\\n self.client.post(self.follow_url, format='json')\\n response = self.client.delete(self.unfollow_url,\\n data=self.followed_user)\\n self.assertEqual(response.content,\\n b'{\\\"message\\\":\\\"user unfollowed\\\"}')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def unfollow_group(request, pk):\\n group = get_object_or_404(Group, id=pk)\\n\\n # Check user is not member of the group\\n if not group.members.filter(id=request.user.id).exists():\\n actions.unfollow(request.user, group, send_action=False)\\n request.user.userprofile.follow_groups.remove(group)\\n messages.warning(\\n request,\\n 'Successed, you are not following this Group anymore.')\\n # the group members can choose not follow the group anymore, but still\\n # been the member\\n else:\\n actions.unfollow(request.user, group, send_action=False)\\n messages.warning(\\n request,\\n 'Successed, you are not following this Group anymore. But you are still the one of the members of this group.')\\n\\n return redirect('groups:groups-detail', pk)\",\n \"def unfollow_thread(request, course_id, thread_id):\\r\\n user = cc.User.from_django_user(request.user)\\r\\n thread = cc.Thread.find(thread_id)\\r\\n user.unfollow(thread)\\r\\n return JsonResponse({})\",\n \"def on_deleted_follow(sender, instance: models_actstream.Follow, **kwargs):\\n content_type = ContentType.objects.get_for_id(instance.content_type_id)\\n log.debug(\\\"Unfollowing %s %s\\\" % (content_type.name, instance.object_id))\\n dillo.tasks.feeds.repopulate_timeline_content(\\n instance.content_type_id, instance.object_id, instance.user_id, 'unfollow'\\n )\",\n \"def removeFollower(self,id):\\n # DELETE /followers/$id\\n pass\",\n \"def delete(self, request, username):\\n\\n # Retrieve the user from the user table if the user exists\\n try:\\n user_to_unfollow = User.objects.get(username=username)\\n current_user = request.user\\n # If a user is trying to unfollow themselves then stop the request\\n if user_to_unfollow.profile.id == current_user.profile.id:\\n return Response(\\n {\\\"errors\\\": FOLLOW_USER_MSGS['CANNOT_UNFOLLOW_SELF']},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n # Check if the user to be unfollowed\\n # is in the current users following list\\n try:\\n profile_id = user_to_unfollow.profile.id\\n user_being_followed = CustomFollows.objects.get(\\n to_profile_id=profile_id,\\n from_profile_id=current_user.profile.id\\n )\\n # If not tell the user the request can't happen\\n # Because they don't follow the user\\n except Exception as e:\\n return Response(\\n {\\n \\\"errors\\\": FOLLOW_USER_MSGS['USER_UNFOLLOWED_ALREADY']\\n },\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n # Otherwise unfollow the user as requested\\n current_user.profile.follows.remove(user_to_unfollow.profile)\\n # Get the following & followers username list\\n # And the following & followers count for the current user\\n user_following_data = get_user_following_data(current_user)\\n return Response(\\n {\\n \\\"message\\\": FOLLOW_USER_MSGS['USER_UNFOLLOW_SUCCESSFUL'],\\n \\\"following\\\": user_following_data[\\\"following\\\"],\\n \\\"followers\\\": user_following_data[\\\"followers\\\"],\\n \\\"followingCount\\\": user_following_data[\\\"followingCount\\\"],\\n \\\"followersCount\\\": user_following_data[\\\"followersCount\\\"]\\n },\\n status=status.HTTP_200_OK\\n )\\n # End request if we cannot find the user we want to unfollow.\\n except User.DoesNotExist:\\n return Response(\\n {\\\"errors\\\": FOLLOW_USER_MSGS['USER_NOT_FOUND']},\\n status=status.HTTP_404_NOT_FOUND\\n )\",\n \"def test_unfollowing_existing_user_not_authenticated(self):\\n self.client.credentials()\\n response = self.client.delete(\\n reverse(\\n 'follow',\\n kwargs={'username': self.followed['user'].get('username')}\\n )\\n )\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def get_to_unfollow(self, to_unfollow, n_unfollow):\\n if self.monitored_users:\\n current_monitored = \\\\\\n list(filter(lambda x: datetime.fromtimestamp(float(x['followDate'])) + timedelta(days=14)\\n < datetime.now() and x['username'] not in self.white_list, self.monitored_users))\\n to_unfollow.extend([u['user'] for u in current_monitored])\\n\\n if len(to_unfollow) < n_unfollow:\\n if not self.self_followings:\\n self.self_followings = self.get_followings()\\n add_followings = [f['pk'] for f in self.self_followings.get('users', [])\\n if f['username'] not in self.white_list]\\n\\n if add_followings:\\n if len(add_followings) > n_unfollow - len(to_unfollow):\\n to_unfollow.extend([random.choice(add_followings) for _ in range(n_unfollow - len(to_unfollow))])\\n else:\\n to_unfollow.extend(add_followings)\\n else:\\n to_unfollow = [random.choice(to_unfollow) for _ in range(n_unfollow)]\\n\\n return to_unfollow\",\n \"def prune_losers(self):\\n self.log.debug(\\\"PRUNE LOSERS\\\")\\n # check to see if people i followed follow me back\\n cutoff_time = (datetime.now()\\n - timedelta(hours=self.reciprocation_window))\\n ingrates = Target.objects.filter(\\n hunter=self.user, status=Target.PURGATORY,\\n modified__lt=cutoff_time) # They didn't follow back in time\\n\\n for ingrate in ingrates:\\n ingrate.status = Target.INGRATE\\n ingrate.save()\\n self.log.debug(\\\" => Unfollowed %s\\\" % ingrate.hunted.screen_name)\\n try:\\n self.api.destroy_friendship(ingrate.hunted)\\n except Exception, e:\\n print e\\n return\\n finally:\\n pass\\n #self.contact(ingrate)\",\n \"def test_remove_followers(self):\\n pass\",\n \"def _is_unfollow_request(environ, result):\\n r = Request(environ)\\n if r.params.get(\\\"action\\\") == \\\"unfollow\\\":\\n return True\\n else:\\n return False\",\n \"def unfollow_commentable(request, course_id, commentable_id):\\r\\n user = cc.User.from_django_user(request.user)\\r\\n commentable = cc.Commentable.find(commentable_id)\\r\\n user.unfollow(commentable)\\r\\n return JsonResponse({})\",\n \"def reject_follow(made_request_id, approver_id):\\n\\n if not g.user.id == approver_id:\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\"), 403\\n\\n wanted_to_follow_user = User.query.get_or_404(made_request_id)\\n g.user.from_users.remove(wanted_to_follow_user)\\n db.session.commit()\\n flash(f\\\"Follow request from {wanted_to_follow_user.username} rejected.\\\", \\\"success\\\")\\n\\n return redirect(f\\\"/users/{g.user.id}\\\")\",\n \"def reset():\\n\\n Follower.clear()\",\n \"def test_undo_follow_request(self):\\n self.local_user.manually_approves_followers = True\\n self.local_user.save(\\n broadcast=False, update_fields=[\\\"manually_approves_followers\\\"]\\n )\\n with patch(\\\"bookwyrm.models.activitypub_mixin.broadcast_task.apply_async\\\"):\\n request = models.UserFollowRequest.objects.create(\\n user_subject=self.remote_user, user_object=self.local_user\\n )\\n self.assertTrue(self.local_user.follower_requests.exists())\\n\\n activity = {\\n \\\"type\\\": \\\"Undo\\\",\\n \\\"id\\\": \\\"bleh\\\",\\n \\\"to\\\": [\\\"https://www.w3.org/ns/activitystreams#Public\\\"],\\n \\\"cc\\\": [\\\"https://example.com/user/mouse/followers\\\"],\\n \\\"actor\\\": self.remote_user.remote_id,\\n \\\"@context\\\": \\\"https://www.w3.org/ns/activitystreams\\\",\\n \\\"object\\\": {\\n \\\"@context\\\": \\\"https://www.w3.org/ns/activitystreams\\\",\\n \\\"id\\\": request.remote_id,\\n \\\"type\\\": \\\"Follow\\\",\\n \\\"actor\\\": \\\"https://example.com/users/rat\\\",\\n \\\"object\\\": \\\"https://example.com/user/mouse\\\",\\n },\\n }\\n\\n views.inbox.activity_task(activity)\\n\\n self.assertFalse(self.local_user.follower_requests.exists())\",\n \"def delete(self, request, *args, **kwargs):\\n\\n following_username = token_payload(request)\\n\\n followed_username = self.kwargs.get(self.look_url_kwarg)\\n try:\\n followed_user = User.objects.get(username=followed_username)\\n followed_user_id = followed_user.id\\n\\n is_following = FollowUser.objects.filter(\\n following_username=following_username)\\n for user in is_following:\\n if user.following_username == following_username and user.followed_username == followed_username:\\n returned_user = FollowUser.objects.get(pk=user.id)\\n returned_user.delete()\\n\\n followed_user_profile = Profile.objects.get(\\n profile_user=followed_user_id)\\n serializer_profile = self.serializer_classprofile(\\n followed_user_profile)\\n return Response({\\n \\\"user\\\": following_username,\\n \\\"unfollowing\\\": serializer_profile.data\\n })\\n return Response({\\n \\\"message\\\": 'User has been unfollowed or You are unfollowing a user you were not orignally following'\\n }, status=status.HTTP_400_BAD_REQUEST)\\n except:\\n return Response({\\n \\\"message\\\": 'User does not exist'\\n }, status=status.HTTP_400_BAD_REQUEST)\",\n \"def test_unauthorized_followers(self):\\n\\n follow = Follows(\\n user_being_followed_id = self.testuser.id,\\n user_following_id = self.testuser2.id\\n )\\n\\n db.session.add(follow)\\n db.session.commit()\\n\\n with self.client as c:\\n response = c.get(f\\\"/users/{self.testuser.id}/followers\\\", follow_redirects=True)\\n data = str(response.data)\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertNotIn(\\\"@bob\\\", data)\\n self.assertIn(\\\"Access unauthorized\\\", data)\",\n \"def test_unauthorized_following(self):\\n\\n follow = Follows(\\n user_being_followed_id = self.testuser2.id,\\n user_following_id = self.testuser.id\\n )\\n\\n db.session.add(follow)\\n db.session.commit()\\n\\n with self.client as c:\\n response = c.get(f\\\"/users/{self.testuser.id}/following\\\", follow_redirects=True)\\n data = str(response.data)\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertNotIn(\\\"@bob\\\", data)\\n self.assertIn(\\\"Access unauthorized\\\", data)\",\n \"def unfollowing_and_removing(self, user_id):\\n if self.unfollowing(user_id):\\n ind = [i for i, j in enumerate(self.monitored_users) if j.get('user', '') == user_id]\\n if ind:\\n self.monitored_users.remove(self.monitored_users[ind[0]])\",\n \"def unfollow_user(self:'InstaClient', user:str, nav_to_user=True, check_user=True):\\n profile = self.get_profile(user)\\n if not profile:\\n raise InvalidUserError(user)\\n\\n LOGGER.debug('INSTACLIENT: User <{}> is valid'.format(user))\\n self._nav_user(user, check_user=False)\\n \\n if profile.requested_by_viewer:\\n requested_btn = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.REQUESTED_BTN)))\\n self._press_button(requested_btn)\\n confirm_unfollow = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.CONFIRM_UNFOLLOW_BTN)))\\n self._press_button(confirm_unfollow)\\n LOGGER.debug(f'Cancelled Follow Request for user <{user}>')\\n \\n elif self._check_existence(EC.presence_of_element_located((By.XPATH, Paths.UNFOLLOW_BTN))):\\n unfollow_btn = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.UNFOLLOW_BTN)))\\n self._press_button(unfollow_btn)\\n time.sleep(1)\\n confirm_unfollow = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.CONFIRM_UNFOLLOW_BTN)))\\n self._press_button(confirm_unfollow)\\n LOGGER.debug('INSTACLIENT: Unfollowed user <{}>'.format(user))\",\n \"def unsafe_follow_by_username(self, username: str) -> None:\\n uid = self.username_to_id(username)\\n self.api.follow(uid)\",\n \"def follow(self, follower, followee):\\n pass\",\n \"def add_untracked_followers(self):\\n\\n self.log.debug(\\\"CHECK FOR UNTRACKED FOLLOWERS\\\")\\n followers_ids_api = self.api.followers_ids()\\n target = Target.objects.filter(hunter=self.user)\\\\\\n .filter(status=Target.FOLLOWER)\\n followers_ids_django = [t.hunted.twitter_id for t in target]\\n\\n untracked_followers_ids = filter(\\n lambda x: unicode(x) not in followers_ids_django,\\n followers_ids_api)\\n\\n untracked_followers, remainder = lookup_users_by_id(self.api,\\n untracked_followers_ids)\\n for untracked_follower in untracked_followers:\\n twitter_account, created = \\\\\\n utils.get_or_create_twitter_account(untracked_follower)\\n target, created = Target.objects.get_or_create(\\n hunter=self.user, hunted=twitter_account)\\n if target.status == Target.PURGATORY:\\n # Yay someone we targeted reciprocated follow\\n self.follow_reciprocated(target)\\n else:\\n print target.status\\n # Either a totally external follow, an ingrate changed mind,\\n # or someone who we chatted became interested and followed\\n # Either way the action is the same, follow him\\n target.status = Target.FOLLOWER\\n target.save()\\n self.log.debug(\\\" => Add follower: %s\\\" % twitter_account.screen_name)\",\n \"def test_stream_stale_follows(self):\\n self.user2.delete()\\n self.assertNotIn('Two', str(user_stream(self.user1)))\",\n \"def unfriend(self, remove):\\n remover_friends_list = self # person terminating the friendship \\n \\n # remove friend from remover friend list\\n remover_friends_list.remove_friend(removee)\\n\\n #remove friend from removee friend list\\n friends_list = FriendList.objects.get(user=removee)\\n friend_list.remove_friend(self.user)\",\n \"def auto_follow_followers():\\n\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n not_following_back = followers - following\\n\\n for user_id in not_following_back:\\n try:\\n t.friendships.create(user_id=user_id, follow=False)\\n except Exception as e:\\n print(\\\"error: %s\\\" % (str(e)))\",\n \"def delete_follow_request(request):\\n username = request.POST[\\\"user\\\"]\\n requester = get_user_from_username(request.user, username)\\n\\n follow_request = get_object_or_404(\\n models.UserFollowRequest, user_subject=requester, user_object=request.user\\n )\\n follow_request.raise_not_deletable(request.user)\\n\\n follow_request.delete()\\n return redirect(f\\\"/user/{request.user.localname}\\\")\",\n \"def unfriend(self, removee):\\n remover_friends_list = self # person terminating the friendship\\n # Remove friend from remover friend list\\n remover_friends_list.remove_friend(removee)\\n # Remove friend from removee's friend list\\n friends_list = FriendList.objects.get(user=removee)\\n friends_list.remove_friend(self.user)\",\n \"def auto_mute_following():\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n not_muted = following - muted\\n\\n # put user IDs of people you do not want to mute here\\n users_keep_unmuted = set([])\\n \\n # mute all \\n for user_id in not_muted:\\n if user_id not in users_keep_unmuted:\\n t.mutes.users.create(user_id=user_id)\\n print(\\\"muted %d\\\" % (user_id))\",\n \"def follow(self, other):\\n\\t\\tif not self.follows(other):\\n\\t\\t\\tself.followed.append(other)\",\n \"def follow_following_followers(self):\\n self.logger.log(\\\"starting follow_following_followers...\\\")\\n follows_accounts = self.following\\n random.shuffle(follows_accounts)\\n for acc in follows_accounts:\\n try:\\n try:\\n followw = perform_with_ran_delay(self.instagram.get_followers, acc, 150, 15,\\n delayed=True)\\n accountstofollow = followw[\\\"accounts\\\"]\\n random.shuffle(accountstofollow)\\n if len(accountstofollow) > 10:\\n accountstofollow = accountstofollow[:10]\\n for ac in accountstofollow:\\n if not self.is_user_following(ac.identifier):\\n self.add_following(ac.identifier)\\n self.logger.log(\\\"following: {}\\\".format(ac.username))\\n except Exception as e:\\n print(e)\\n self.logger.log(str(e))\\n finally:\\n sleep(3)\",\n \"def unblock(self):\\n data = {'container': self._reddit.user.me().fullname,\\n 'name': str(self), 'type': 'enemy'}\\n url = API_PATH['unfriend'].format(subreddit='all')\\n # PRAW5 REMOVE (return statement)\\n return self._reddit.post(url, data=data)\",\n \"def test_follow_reject(self):\\n with patch(\\\"bookwyrm.models.activitypub_mixin.broadcast_task.apply_async\\\"):\\n rel = models.UserFollowRequest.objects.create(\\n user_subject=self.local_user, user_object=self.remote_user\\n )\\n activity = {\\n \\\"@context\\\": \\\"https://www.w3.org/ns/activitystreams\\\",\\n \\\"id\\\": \\\"https://example.com/users/rat/follows/123#accepts\\\",\\n \\\"type\\\": \\\"Reject\\\",\\n \\\"actor\\\": \\\"https://example.com/users/rat\\\",\\n \\\"object\\\": {\\n \\\"id\\\": rel.remote_id,\\n \\\"type\\\": \\\"Follow\\\",\\n \\\"actor\\\": \\\"https://example.com/user/mouse\\\",\\n \\\"object\\\": \\\"https://example.com/users/rat\\\",\\n },\\n }\\n\\n self.assertEqual(models.UserFollowRequest.objects.count(), 1)\\n\\n views.inbox.activity_task(activity)\\n\\n # request should be deleted\\n self.assertFalse(models.UserFollowRequest.objects.exists())\\n self.assertFalse(self.remote_user.followers.exists())\",\n \"def delete(self, request, username):\\n followed_user_exists = User.objects.filter(username=username).exists()\\n if not followed_user_exists:\\n return Response({'error': 'user not found'},\\n status.HTTP_404_NOT_FOUND)\\n followed_user = User.objects.get(username=username)\\n user_exists = Follow.is_user_already_followed(\\n followed_user_id=followed_user.id,\\n user_id=request.user.id\\n )\\n if user_exists:\\n instance = Follow.objects.filter(\\n user=self.request.user.id, followed_user=followed_user.id\\n )\\n instance.delete()\\n return Response({'message': 'user unfollowed'},\\n status.HTTP_200_OK)\\n return Response({'message': 'user not in followers'},\\n status.HTTP_404_NOT_FOUND)\",\n \"def test_followers_following_list_unauthorized(self):\\n\\n self.u2.following.append(self.u)\\n db.session.commit()\\n\\n with self.client as client:\\n response = client.get(\\\"/users/2/following\\\")\\n\\n self.assertEqual(response.location, \\\"http://localhost/\\\")\\n self.assertIn('Access unauthorized.', get_flashed_messages())\\n\\n response2 = client.get(\\\"/users/2/followers\\\")\\n\\n self.assertEqual(response2.location, \\\"http://localhost/\\\")\\n self.assertIn('Access unauthorized.', get_flashed_messages())\",\n \"def follow_user(cls, user, following):\\r\\n pass\",\n \"def author_following(self):\\n\\t\\tpass\",\n \"def unfriend(self, removee):\\n\\t\\tremover_friend_list = self # person terminating the friendship\\n\\n\\t\\t# Remove friend from friend request\\n\\t\\tremover_friend_list.remove_friend(removee)\\n\\n\\t\\t# Remove friend from the removeee friend list\\n\\t\\tfriends_list = FriendList.objects.get(user=removee)\\n\\t\\tfriends_list.remove_friend(self.user)\",\n \"def untether(self) -> None:\\n if self.msg.sender != self.owner:\\n revert(f'Only the owner can call the untether method.')\\n pass\",\n \"def follow(self, followerId, followeeId):\\n if followerId in self.follows:\\n self.follows[followerId].add(followeeId)\\n else:\\n self.follows[followerId] = set([followeeId])\",\n \"def test_following_non_existing_user(self):\\n response = self.client.post(\\n reverse(\\n 'follow',\\n kwargs={'username': 'NotThere'}\\n )\\n )\\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\",\n \"async def tourney_ping_off(ctx):\\n\\n user = find_user_in_db(ctx.author.id)\\n if user is None:\\n await ctx.send(\\n f\\\"{ctx.author.mention} you are not even being notified.\\\\n If you want to be notified use `{prefix}pingme`.\\\")\\n return\\n\\n delete_user_from_db(ctx.author.id)\\n\\n await ctx.send(f\\\"{ctx.author.mention} you won't be notified ever again unless you type `{prefix}pingme` again.\\\")\\n return\",\n \"def add_untracked_friends(self):\\n\\n self.log.debug(\\\"CHECK FOR UNTRACKED FRIENDS\\\")\\n friends_ids_api = self.api.friends_ids()\\n targets = Target.objects.filter(hunter=self.user)\\\\\\n .exclude(status__in=Target.ON_DECK)\\n friends_ids_django = [t.hunted.twitter_id for t in targets]\\n untracked_friends_ids = \\\\\\n filter(lambda x: unicode(x) not in friends_ids_django,\\n friends_ids_api)\\n\\n untracked_friends, remainder = lookup_users_by_id(self.api,\\n untracked_friends_ids)\\n for untracked_friend in untracked_friends:\\n \\\"\\\"\\\"These could be people who don't follow us, but we want to follow,\\n for example to keep up with news of their company\\\"\\\"\\\"\\n twitter_account, created = utils.get_or_create_twitter_account(\\n untracked_friend)\\n target, created = Target.objects.get_or_create(\\n hunter=self.user, hunted=twitter_account)\\n if created:\\n target.reason = \\\"External add.\\\"\\n target.status = Target.FOLLOWER\\n target.save()\\n self.log.debug(\\\" => add friend: %s\\\" % twitter_account.screen_name)\\n else:\\n self.log.debug(\\\" => we're following, but no reciprocation: %s\\\" % twitter_account.screen_name)\",\n \"def follow_user(cls, user, following):\\n pass\",\n \"def test_follow_without_auth(self):\\n response = self.client.post(self.follow_url, format='json')\\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\",\n \"def test_following_existing_user_not_authenticated(self):\\n self.client.credentials()\\n response = self.client.post(\\n reverse(\\n 'follow',\\n kwargs={'username': self.followed['user'].get('username')}\\n )\\n )\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n self.followed.append(user)\\n return self\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n self.followed.append(user)\\n return self\",\n \"def follow(request, usertofollow):\\n to_follow = Member.objects.get(user__username=usertofollow)\\n user = Member.objects.get(user=request.user)\\n user.following.add(to_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"def follow(self, followerId, followeeId):\\r\\n if followerId != followeeId:\\r\\n self.follows[followerId].add(followeeId)\",\n \"async def unfollow(\\n self,\\n *,\\n index: str,\\n error_trace: t.Optional[bool] = None,\\n filter_path: t.Optional[\\n t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]\\n ] = None,\\n human: t.Optional[bool] = None,\\n pretty: t.Optional[bool] = None,\\n ) -> ObjectApiResponse[t.Any]:\\n if index in SKIP_IN_PATH:\\n raise ValueError(\\\"Empty value passed for parameter 'index'\\\")\\n __path = f\\\"/{_quote(index)}/_ccr/unfollow\\\"\\n __query: t.Dict[str, t.Any] = {}\\n if error_trace is not None:\\n __query[\\\"error_trace\\\"] = error_trace\\n if filter_path is not None:\\n __query[\\\"filter_path\\\"] = filter_path\\n if human is not None:\\n __query[\\\"human\\\"] = human\\n if pretty is not None:\\n __query[\\\"pretty\\\"] = pretty\\n __headers = {\\\"accept\\\": \\\"application/json\\\"}\\n return await self.perform_request( # type: ignore[return-value]\\n \\\"POST\\\", __path, params=__query, headers=__headers\\n )\",\n \"def set_follow(self, follow):\\n self.follow = follow\",\n \"def honeypot_unpeer(self,honeypotids):\\n req = {\\\"type\\\":\\\"unpeer\\\",\\n \\\"from\\\":self.network.mc_id,\\n \\\"to\\\":honeypotids}\\n expect_dict = {\\\"type\\\":\\\"unpeered\\\"}\\n msg_list = self.send_receive(req,honeypotids,expect_dict)\\n answer = []\\n for msg in msg_list:\\n answer.append(msg[\\\"from\\\"])\\n return answer\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82762796","0.8009202","0.79544","0.79389596","0.78107613","0.7790903","0.7778263","0.7770787","0.7754866","0.7753583","0.77530307","0.77448803","0.7620163","0.76050144","0.7601129","0.7597027","0.75560814","0.7501844","0.7492976","0.7484456","0.74831533","0.746153","0.74574614","0.74523157","0.74469477","0.7372194","0.7345147","0.731679","0.7157376","0.7148874","0.71486354","0.7034158","0.70210683","0.70151585","0.7003955","0.69669724","0.69291687","0.6925338","0.69199955","0.6906866","0.6843641","0.67511666","0.6725504","0.6700476","0.6698697","0.6697351","0.66783804","0.65381676","0.65291554","0.6507101","0.6495755","0.64785457","0.6472524","0.6442243","0.6421347","0.6417555","0.6408649","0.6401108","0.64004916","0.6369057","0.6334912","0.62283707","0.62078875","0.61761135","0.6174603","0.61734176","0.6133153","0.61313206","0.60988134","0.608161","0.6078668","0.60743284","0.6068927","0.6017741","0.5978583","0.59557235","0.5894001","0.58745414","0.58489054","0.58085996","0.5784483","0.57427347","0.5727344","0.5721424","0.5715359","0.5703434","0.56788546","0.5609686","0.5606416","0.55862266","0.5571672","0.5564131","0.5559407","0.55361307","0.55361307","0.5531532","0.55172855","0.5508574","0.5492287","0.5488193"],"string":"[\n \"0.82762796\",\n \"0.8009202\",\n \"0.79544\",\n \"0.79389596\",\n \"0.78107613\",\n \"0.7790903\",\n \"0.7778263\",\n \"0.7770787\",\n \"0.7754866\",\n \"0.7753583\",\n \"0.77530307\",\n \"0.77448803\",\n \"0.7620163\",\n \"0.76050144\",\n \"0.7601129\",\n \"0.7597027\",\n \"0.75560814\",\n \"0.7501844\",\n \"0.7492976\",\n \"0.7484456\",\n \"0.74831533\",\n \"0.746153\",\n \"0.74574614\",\n \"0.74523157\",\n \"0.74469477\",\n \"0.7372194\",\n \"0.7345147\",\n \"0.731679\",\n \"0.7157376\",\n \"0.7148874\",\n \"0.71486354\",\n \"0.7034158\",\n \"0.70210683\",\n \"0.70151585\",\n \"0.7003955\",\n \"0.69669724\",\n \"0.69291687\",\n \"0.6925338\",\n \"0.69199955\",\n \"0.6906866\",\n \"0.6843641\",\n \"0.67511666\",\n \"0.6725504\",\n \"0.6700476\",\n \"0.6698697\",\n \"0.6697351\",\n \"0.66783804\",\n \"0.65381676\",\n \"0.65291554\",\n \"0.6507101\",\n \"0.6495755\",\n \"0.64785457\",\n \"0.6472524\",\n \"0.6442243\",\n \"0.6421347\",\n \"0.6417555\",\n \"0.6408649\",\n \"0.6401108\",\n \"0.64004916\",\n \"0.6369057\",\n \"0.6334912\",\n \"0.62283707\",\n \"0.62078875\",\n \"0.61761135\",\n \"0.6174603\",\n \"0.61734176\",\n \"0.6133153\",\n \"0.61313206\",\n \"0.60988134\",\n \"0.608161\",\n \"0.6078668\",\n \"0.60743284\",\n \"0.6068927\",\n \"0.6017741\",\n \"0.5978583\",\n \"0.59557235\",\n \"0.5894001\",\n \"0.58745414\",\n \"0.58489054\",\n \"0.58085996\",\n \"0.5784483\",\n \"0.57427347\",\n \"0.5727344\",\n \"0.5721424\",\n \"0.5715359\",\n \"0.5703434\",\n \"0.56788546\",\n \"0.5609686\",\n \"0.5606416\",\n \"0.55862266\",\n \"0.5571672\",\n \"0.5564131\",\n \"0.5559407\",\n \"0.55361307\",\n \"0.55361307\",\n \"0.5531532\",\n \"0.55172855\",\n \"0.5508574\",\n \"0.5492287\",\n \"0.5488193\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7816306"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":13787,"cells":{"query":{"kind":"string","value":"Mutes everyone that you are following"},"document":{"kind":"string","value":"def auto_mute_following():\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n not_muted = following - muted\n\n # put user IDs of people you do not want to mute here\n users_keep_unmuted = set([])\n \n # mute all \n for user_id in not_muted:\n if user_id not in users_keep_unmuted:\n t.mutes.users.create(user_id=user_id)\n print(\"muted %d\" % (user_id))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def follow_reciprocated(self, target):\n if random.randint(1, 1000) == 1: # 1 in 20 are public @replies\n self.tweet_user(target)\n else:\n try:\n self.dm_user(target)\n except:\n pass","def author_following(self):\n\t\tpass","def follow_user(cls, user, following):\r\n pass","def follow_user(cls, user, following):\n pass","def follow_user(self, target):\n try:\n if self.api.me().friends_count > 1990:\n return\n except Exception, e:\n print e\n\n \"Rate limit exceeded. Clients may not make more than 350 requests per hour.\"\n if \"Clients\" in str(e):\n continue\n # import pdb; pdb.set_trace()\n return\n\n try:\n self.api.create_friendship(target.hunted.screen_name)\n self.log.debug(\"Followed: %s\" % target.hunted.screen_name)\n except Exception, e:\n self.log.exception(\"Could not follow %s\" %\n target.hunted.screen_name)\n else:\n # Write record of new follow to db\n target.status = Target.PURGATORY\n target.save()","def follow(self, follower, followee):\n pass","def follow(self, user_index, following_index):\n if user_index >= self.num_users or following_index >= self.num_users:\n raise ValueError(\n f\"Number of users is {self.num_users}, but indices \"\n f\"{user_index} and {following_index} were requested.\"\n )\n if self.users_hat[following_index, user_index] == 0:\n self.users_hat[following_index, user_index] = 1\n elif self.is_verbose():\n self.log(f\"User {following_index} was already following user {user_index}\")","def follow(request, usertofollow):\n to_follow = Member.objects.get(user__username=usertofollow)\n user = Member.objects.get(user=request.user)\n user.following.add(to_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","async def love(ctx, user: discord.Member):\r\n author = ctx.message.author\r\n if user.id == ctx.bot.user.id:\r\n await ctx.send(\"I am not capable of loving like you can. I'm sorry.\" )\r\n else:\r\n await ctx.send(author.mention + \" is capable of loving \" + user.mention + \" a whopping \" +\r\n str(randint(0, 100)) + \"%!\")\r\n ctx.counter(n)","def following_changed(sender, action, instance, *args, **kwargs):\n\n # m2mchanged.connect specified in apps.py\n\n following = instance.following.all()\n creator = instance.user\n\n if creator in following:\n raise ValidationError (\"can't like own post\")","def get_everyone_granted(self):","def follow(self, other):\n\t\tif not self.follows(other):\n\t\t\tself.followed.append(other)","def expireme(message):\n users = hf.get_users()\n requester = message._get_user_id()\n for user in users:\n if user[\"id\"] == requester:\n name = user[\"name\"]\n break\n\n hf.expire_user(name)","async def selfmute(ctx, *args):\n user = ctx.message.author\n if await is_staff(ctx):\n return await ctx.send(\"Staff members can't self mute.\")\n time = \" \".join(args)\n await _mute(ctx, user, time, self=True)","async def stealthtorment(self, ctx, *, member = None, times : int = None):\r\n\r\n\t\tchannel = ctx.message.channel\r\n\t\tauthor = ctx.message.author\r\n\t\tserver = ctx.message.guild\r\n\t\tmessage = ctx.message\r\n\r\n\t\t# Only allow owner\r\n\t\tisOwner = self.settings.isOwner(ctx.author)\r\n\t\tif isOwner == None:\r\n\t\t\treturn\r\n\t\telif isOwner == False:\r\n\t\t\treturn\r\n\t\t\t\t\r\n\t\tusage = 'Usage: `{}torment [role/member] [times]`'.format(ctx.prefix)\r\n\r\n\t\tisRole = False\r\n\r\n\t\tif member == None:\r\n\t\t\tawait ctx.channel.send(usage)\r\n\t\t\treturn\r\n\t\t\t\t\r\n\t\t# Check for formatting issues\r\n\t\tif times == None:\r\n\t\t\t# Either xp wasn't set - or it's the last section\r\n\t\t\tif type(member) is str:\r\n\t\t\t\t# It' a string - the hope continues\r\n\t\t\t\troleCheck = DisplayName.checkRoleForInt(member, server)\r\n\t\t\t\tif roleCheck and roleCheck[\"Role\"]:\r\n\t\t\t\t\tisRole = True\r\n\t\t\t\t\tmember = roleCheck[\"Role\"]\r\n\t\t\t\t\ttimes = roleCheck[\"Int\"]\r\n\t\t\t\telse:\r\n\t\t\t\t\t# Role is invalid - check for member instead\r\n\t\t\t\t\tnameCheck = DisplayName.checkNameForInt(member, server)\r\n\t\t\t\t\tif not nameCheck:\r\n\t\t\t\t\t\tawait ctx.channel.send(usage)\r\n\t\t\t\t\t\treturn\r\n\t\t\t\t\tif not nameCheck[\"Member\"]:\r\n\t\t\t\t\t\tmsg = 'I couldn\\'t find that user or role on the server.'.format(member)\r\n\t\t\t\t\t\tawait ctx.channel.send(msg)\r\n\t\t\t\t\t\treturn\r\n\t\t\t\t\tmember = nameCheck[\"Member\"]\r\n\t\t\t\t\ttimes = nameCheck[\"Int\"]\r\n\t\t\t\t\t\r\n\t\t# Set the torment flag\r\n\t\tself.toTorment = True\r\n\r\n\t\tif times == None:\r\n\t\t\t# Still no times - roll back to default\r\n\t\t\ttimes = 25\r\n\t\t\t\r\n\t\tif times > 100:\r\n\t\t\ttimes = 100\r\n\t\t\t\r\n\t\tif times == 0:\r\n\t\t\tawait ctx.channel.send('Oooooh - I bet they feel *sooooo* tormented...')\r\n\t\t\treturn\r\n\t\t\r\n\t\tif times < 0:\r\n\t\t\tawait ctx.channel.send('I just uh... *un-tormented* them. Yeah.')\r\n\t\t\treturn\r\n\r\n\t\t# Delete original torment message\r\n\t\tawait message.delete()\r\n\t\t\r\n\t\tfor i in range(0, times):\r\n\t\t\t# Do this over time\r\n\t\t\ttry:\r\n\t\t\t\tif member.name == \"@everyone\" and type(member) is discord.Role:\r\n\t\t\t\t\ttmessage = await ctx.channel.send(\"{}\".format(member.name),allowed_mentions=discord.AllowedMentions.all())\r\n\t\t\t\telse:\r\n\t\t\t\t\ttmessage = await ctx.channel.send('{}'.format(member.mention),allowed_mentions=discord.AllowedMentions.all())\r\n\t\t\t\tawait tmessage.delete()\r\n\t\t\texcept Exception:\r\n\t\t\t\tpass\r\n\t\t\tfor j in range(0, self.waitBetween):\r\n\t\t\t\t# Wait for 1 second, then check if we should cancel - then wait some more\r\n\t\t\t\tawait asyncio.sleep(1)\r\n\t\t\t\tif not self.toTorment:\r\n\t\t\t\t\treturn","async def follow(follow):\n await follow.edit(\n f\"`FOLLOW {DEFAULTUSER} ON` \\n\\n\"\n f\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\n\\n\"\n f\"[FaceBook](https://www.facebook.com/mkaraniya) \\n\\n\"\n f\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \"\n )","def is_following(self, you, them):\n if self.filter(from_user=you, to_user=them).count() > 0:\n return True\n return False","async def treatme(self, ctx):\n await ctx.send(await self.cure_user(ctx, ctx.author))","async def pending(self, ctx):\r\n if ctx.guild.id == 445092370006933505:\r\n data = self.config.guild(ctx.guild)\r\n lst = await data.get_raw('neededlist')\r\n description = \"\"\r\n coach = await data.coachid()\r\n coach_role = ctx.guild.get_role(coach)\r\n x = ctx.author.top_role\r\n if x >= coach_role:\r\n for member in lst:\r\n userobj = ctx.guild.get_member(int(member))\r\n description += (str(userobj.mention) + '\\n')\r\n embed = discord.Embed(color=0xFFFF00, title='Coaching Needed by following people', description=description)\r\n embed.set_footer(text=credit)\r\n await ctx.send(embed=embed)\r\n await ctx.send('Type \"{0}coaching done @<player name>\" if the player has been coached or type \"{0}coaching info <@playername>\" to view the details submitted by the user'.format(ctx.prefix))\r\n \r\n else:\r\n await ctx.send(\"You are not allowed to do that\")\r\n\r\n else:\r\n await ctx.send(\"This command only works in the Legend eSports server, join us at: https://discord.gg/GGuCXDn\")","def add_follow(follow_id):\n\n want_to_follow_user = User.query.get_or_404(follow_id)\n if want_to_follow_user.private:\n # =========== NEED TO IMPLEMENT ====================\n # send them a request to follow\n want_to_follow_user.from_users.append(g.user) \n db.session.commit()\n flash(\"Your request has been sent\", \"success\")\n return redirect(f\"/users/{g.user.id}/following\")\n\n g.user.following.append(want_to_follow_user)\n db.session.commit()\n\n return redirect(f\"/users/{g.user.id}/following\")","def follow_someone(screen_name):\n twitter.create_friendship(screen_name=screen_name)","def user_appears(self, user):\n pass","def tweet_user(self, target, msg=None):\n self.log.debug(\"Tweeting %s\" % target.hunted.screen_name)\n tweet = \"@%s: %s\" % (target.hunted.screen_name,\n random.sample(self.tweets, 1)[0])\n tweet = tweet [:140]\n self.api.update_status(tweet)\n target.status = Target.FOLLOWER\n target.save()","def follow(self, followerId, followeeId):\r\n if followerId != followeeId:\r\n self.follows[followerId].add(followeeId)","def whisper(self,name):\n\n self.sendCommand(\"global /join\",name+self.userName+\" private\")\n self.master.after(300,self.sendCommand,name+self.userName+\" /invite\",name)","def command_who(self, bot, update):\n\n messages = [\n 'Myles Braithwaite lives in Toronto where he runs a small '\n 'consluting company called [Monkey in your Soul]'\n '(https://monkeyinyoursoul.com/) (you should hire him because '\n \"he's awesome).\",\n 'You should follow him on [Twitter](https://twitter.com/mylesb) '\n 'or [Instagram](https://instagram.com/myles).',\n 'You can find his programming stuff on [GitHub]'\n '(https://github.com/myles) or [CodePen]'\n '(http://codepen.io/mylesb/).'\n ]\n\n self.send_messages(bot, update, messages)","def following(account):\n stm = shared_morphene_instance()\n if mph.rpc is not None:\n mph.rpc.rpcconnect()\n if not account:\n if \"default_account\" in mph.config:\n account = [mph.config[\"default_account\"]]\n for a in account:\n a = Account(a, morphene_instance=stm)\n print(\"\\nFollowing statistics for @%s (please wait...)\" % a.name)\n following = a.get_following(False)\n following.print_summarize_table(tag_type=\"Following\")","def promoteUser(self):\n\t\t#ensure they're supposed to be here and haven't been here before\n\t\tif self.goodEventsCount >= 3 and not self.verified:\n\t\t\tself.verifiedUser=True\n\t\t\tself.put()\n\t\t\tmessage = mail.EmailMessage(\n\t\t\t\t\tsender=\"Friends with Food Admin <admin@friendswfood.appspot.com>\",\n subject=\"Your account has been verified!\")\n\n\t\t\tmessage.to = self.id.email()\n\t\t\tmessage.cc = \"friendswfood@case.edu\"\n\t\t\tmessage.body = \"\"\"\n\t\t\tDear %s:\n\n\t\t\tYour account on Friends with Food has been verified! Because you've \n\t\t\tshown us so many good events, we've upgraded your account. Now, you'll \n\t\t\tget notified of free food on campus ASAP! You'll also be able to verify\n\t\t\tevents so that everyone knows they're legit.\n\t\t\t\n\t\t\t*With great power comes great responsibility*\n\t\t\t\n\t\t\tThanks,\n\t\t\t\n\t\t\tThe Friends with Food Team\n\t\t\t\"\"\" % self.id.nickname()\n\t\t\tmessage.send()","def leader(self):\n pass","def leader(self):\n pass","async def mute(self, ctx):\n author = ctx.message.author\n channel = author.voice.channel\n members = channel.members\n for member in members:\n user = ctx.guild.get_member(member.id)\n await user.edit(mute=True)\n\n embed = await embeds.generate_embed(ctx, author, members,\n description=\":white_check_mark: Successfully muted the following users:\",\n title=channel.name)\n await ctx.send(embed=embed)","async def plaguebearer(self, ctx):\n currency = await bank.get_currency_name(ctx.guild)\n await self.config.user(ctx.author).gameRole.set(\"Plaguebearer\")\n await self.notify_user(ctx=ctx, user=ctx.author, notificationType=\"plaguebearer\")\n await ctx.send(f\"{ctx.author} has spent 10,000 {currency} and become a Plaguebearer.\")","def userFollowers(nick):\n if (len(nick) != 1):\n print \"Has d'introduir només un nick\"\n return\n i.userFollow(nick[0])","async def mute(self, ctx: Context, members: commands.Greedy[discord.Member], reason=\"no reason\"):\n\n role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n member_display = []\n\n for i, member in enumerate(members):\n if role in member.roles:\n await ctx.send(f\"guild member `{member.display_name}` is already muted\", delete_after=8)\n del members[i]\n\n if role is None:\n permissions = discord.Permissions()\n permissions.change_nickname = True\n permissions.send_messages = False\n permissions.read_message_history = True\n role = await ctx.guild.create_role(name=\"Muted\", permissions=permissions)\n\n await self.set_perms(ctx.guild, role)\n\n for member in members:\n\n if await self.hiearchy_check(ctx, member):\n continue\n\n member_display.append(str(member))\n await member.add_roles(role, reason=reason)\n\n member_display = \", \".join(member_display)\n\n if not member_display:\n member_display = \"no one\"\n\n await ctx.send(f\"> {ctx.author.name} muted {member_display}\")","def idme(bot, update):\n update.message.reply_text(\"Your ID is: \" + str(update.message.from_user.id))","async def plaguedoctor(self, ctx):\n currency = await bank.get_currency_name(ctx.guild)\n await self.config.user(ctx.author).gameRole.set(\"Doctor\")\n await self.notify_user(ctx=ctx, user=ctx.author, notificationType=\"doctor\")\n await ctx.send(f\"{ctx.author} has spent 10,000 {currency} and become a Doctor.\")","def follow(self, followerId: int, followeeId: int) -> None:\n if followeeId not in self.followList.get(followerId, [followerId]):\n self.followList[followerId] = self.followList.get(followerId, [followerId]) + [followeeId]\n # print(self.followList)","def follow_user(username):\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n db = get_db()\n db.execute('insert into follower (who_id, whom_id) values (?, ?)',\n [session['user_id'], whom_id])\n db.commit()\n flash('You are now following \"%s\"' % username)\n return redirect(url_for('user_timeline', username=username))","def user_follow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.append(following)\n db.session.commit()\n return {'followed': True}","def followUser(following):\n\n cur, user_id, con = initialise(3, True)\n cur.execute(\"INSERT INTO followers (user, following) VALUES ((SELECT username FROM users WHERE id = ?), ?)\", (user_id, following))\n finish(con)","async def _sync_total(self, ctx: Context):\n\n guild = ctx.guild\n\n player_id = await self.config.guild(guild).player_id()\n player_role = discord.utils.get(guild.roles, id=player_id)\n\n signed = len([\n user.id for user in guild.members\n if player_role in user.roles\n ])\n\n await self.config.guild(guild).signed.set(signed)\n\n await ctx.send(_(\"Synced total number of signed-up players.\"))","def follow(self, followerId: int, followeeId: int) -> None:\n if followerId == followeeId:\n return\n if followerId not in self.users.keys():\n self.users[followerId] = user()\n if followeeId not in self.users.keys():\n self.users[followeeId] = user()\n self.users[followerId].followees[followeeId] = self.users[followeeId]","def signedOn(self):\n log.msg(\"Signed on\")\n if self.nickname != self.factory.nickname:\n log.msg('Name taken, new is ''\"{}\".'.format(self.nickname))\n self.join(self.factory.channel)","async def on_member_update(before, after):\r\n if Counter(before.roles) == Counter(after.roles):\r\n return\r\n await check_member_rules(after)","async def torment(self, ctx, *, member = None, times : int = None):\r\n\r\n\t\tchannel = ctx.message.channel\r\n\t\tauthor = ctx.message.author\r\n\t\tserver = ctx.message.guild\r\n\t\tmessage = ctx.message\r\n\r\n\t\t# Only allow owner to change server stats\r\n\t\tisOwner = self.settings.isOwner(ctx.author)\r\n\t\tif isOwner == None:\r\n\t\t\treturn\r\n\t\telif isOwner == False:\r\n\t\t\treturn\r\n\t\t\t\t\r\n\t\tusage = 'Usage: `{}torment [role/member] [times]`'.format(ctx.prefix)\r\n\r\n\t\tisRole = False\r\n\r\n\t\tif member == None:\r\n\t\t\tawait ctx.channel.send(usage)\r\n\t\t\treturn\r\n\t\t\t\t\r\n\t\t# Check for formatting issues\r\n\t\tif times == None:\r\n\t\t\t# Either xp wasn't set - or it's the last section\r\n\t\t\tif type(member) is str:\r\n\t\t\t\t# It' a string - the hope continues\r\n\t\t\t\troleCheck = DisplayName.checkRoleForInt(member, server)\r\n\t\t\t\tif roleCheck and roleCheck[\"Role\"]:\r\n\t\t\t\t\tisRole = True\r\n\t\t\t\t\tmember = roleCheck[\"Role\"]\r\n\t\t\t\t\ttimes = roleCheck[\"Int\"]\r\n\t\t\t\telse:\r\n\t\t\t\t\t# Role is invalid - check for member instead\r\n\t\t\t\t\tnameCheck = DisplayName.checkNameForInt(member, server)\r\n\t\t\t\t\tif not nameCheck:\r\n\t\t\t\t\t\tawait ctx.channel.send(usage)\r\n\t\t\t\t\t\treturn\r\n\t\t\t\t\tif not nameCheck[\"Member\"]:\r\n\t\t\t\t\t\tmsg = 'I couldn\\'t find that user or role on the server.'.format(member)\r\n\t\t\t\t\t\tawait ctx.channel.send(msg)\r\n\t\t\t\t\t\treturn\r\n\t\t\t\t\tmember = nameCheck[\"Member\"]\r\n\t\t\t\t\ttimes = nameCheck[\"Int\"]\r\n\t\t\t\t\t\r\n\t\t# Set the torment flag\r\n\t\tself.toTorment = True\r\n\r\n\t\tif times == None:\r\n\t\t\t# Still no times - roll back to default\r\n\t\t\ttimes = 25\r\n\t\t\t\r\n\t\tif times > 100:\r\n\t\t\ttimes = 100\r\n\t\t\t\r\n\t\tif times == 0:\r\n\t\t\tawait ctx.channel.send('Oooooh - I bet they feel *sooooo* tormented...')\r\n\t\t\treturn\r\n\t\t\r\n\t\tif times < 0:\r\n\t\t\tawait ctx.channel.send('I just uh... *un-tormented* them. Yeah.')\r\n\t\t\treturn\r\n\t\t\r\n\t\t# Delete original torment message\r\n\t\tawait message.delete()\r\n\r\n\t\tfor i in range(0, times):\r\n\t\t\t# Do this over time\r\n\t\t\ttry:\r\n\t\t\t\tif member.name == \"@everyone\" and type(member) is discord.Role:\r\n\t\t\t\t\tawait channel.send(\"{}\".format(member.name),allowed_mentions=discord.AllowedMentions.all())\r\n\t\t\t\telse:\r\n\t\t\t\t\tawait channel.send('{}'.format(member.mention),allowed_mentions=discord.AllowedMentions.all())\r\n\t\t\texcept Exception:\r\n\t\t\t\tpass\r\n\t\t\tfor j in range(0, self.waitBetween):\r\n\t\t\t\t# Wait for 1 second, then check if we should cancel - then wait some more\r\n\t\t\t\tawait asyncio.sleep(1)\r\n\t\t\t\tif not self.toTorment:\r\n\t\t\t\t\treturn","async def randping(self, ctx):\r\n while True:\r\n memb = random.choice(ctx.guild.members)\r\n if not memb.bot:\r\n break\r\n memb = memb.mention\r\n await ctx.send(memb)","def follow(user, people):\n api = get_api(user)\n current_screen_name = api.VerifyCredentials().GetScreenName()\n\n # don't let a user follow themselves\n screen_names = [person.twitter_screen_name for person in people]\n if current_screen_name in screen_names: screen_names.remove(current_screen_name)\n\n followed = []\n not_followed = []\n\n for screen_name in screen_names:\n try:\n api.CreateFriendship(screen_name=screen_name)\n followed.append(screen_name)\n except twitter.TwitterError:\n not_followed.append(screen_name)\n\n return 'followed %s people' % len(followed)","def follow_user(username):\n user_ID = before_request()\n user_ID = None\n if user_ID != None:\n user_ID = str(g.user['_id'])\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n mongo.db.users.update({'_id': g.user['_id']}, {\n '$push': {'follows': whom_id}})\n flash('You are now following \"%s\"' % username)\n if redis_obj.get(user_ID):\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(r.get(user_ID))))\n else:\n redis_obj.delete(str(g.user['_id']))\n print \"Invalidating cache after Follow\"\n return redirect(url_for('user_timeline', username=username))","def follow(self, followerId: int, followeeId: int) -> None:\n if followerId == followeeId: return\n self.users[followerId].add(followeeId)","def save_model(self, request, obj, form, change):\n From = User.objects.get(id=obj.From.id)\n To = User.objects.get(id=obj.To.id)\n From.following_numIn()\n To.followed_numIn()\n obj.save()","async def mute(self, ctx, member: discord.Member, *, time:TimeConverter = None):\r\n\r\n if member.top_role >= ctx.author.top_role:\r\n return await ctx.send(\"you can't mute that person\")\r\n\r\n role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\r\n await member.add_roles(role)\r\n await ctx.reply((f\"Muted {member} for {time}s\" if time else f\"Muted {member}\"))\r\n\r\n if time:\r\n await asyncio.sleep(time)\r\n await member.remove_roles(role)","def follow(self, followerId: int, followeeId: int) -> None:\n self.user_followed[followerId].append(followeeId)","def whoami(bot, update):\n\n id = update.effective_message.chat.id\n bot.send_message(id, \"Tämän chätin ID on {}\".format(id))","def auto_follow_followers():\n\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n not_following_back = followers - following\n\n for user_id in not_following_back:\n try:\n t.friendships.create(user_id=user_id, follow=False)\n except Exception as e:\n print(\"error: %s\" % (str(e)))","async def moot(self, ctx, \n\t\ttarget: discord.Member, \n\t\t*, reason: str = \"No reason given.\"\n\t):\n\n\t\tself.check_perms(ctx.author, target)\n\t\tawait ctx.success(f\"{target} (`{target.id}`) has been mooted for:\\n{reason}\")","def follow(self, followerId, followeeId):\n if followerId in self.follows:\n self.follows[followerId].add(followeeId)\n else:\n self.follows[followerId] = set([followeeId])","def follow_project(cls, user, project):\r\n pass","def _RecordVisitTime(self, mr, now=None):\n now = now or int(time.time())\n if not settings.read_only and mr.auth.user_id:\n user_pb = mr.auth.user_pb\n if (user_pb.last_visit_timestamp <\n now - framework_constants.VISIT_RESOLUTION):\n user_pb.last_visit_timestamp = now\n self.services.user.UpdateUser(mr.cnxn, user_pb.user_id, user_pb)","async def twitch_follower_checker_loop(self):\n\n new_last_timestamp = dt.utcnow()\n new_followers = collections.defaultdict(list) # channel user id: list of new follower usernames\n db = await self.bot.database.get_connection()\n data = await db(\"SELECT * FROM user_settings WHERE twitch_bearer_token IS NOT NULL\")\n\n # Wew let's do it\n for row in data:\n\n # See if we got their data already\n new_follower_list = new_followers.get(row['twitch_user_id'])\n if new_follower_list is None:\n new_follower_list, new_cursor_value = await self.get_new_followers(row['twitch_bearer_token'], row['twitch_user_id'], row['twitch_cursor'])\n if new_cursor_value:\n await db(\"UPDATE user_settings SET twitch_cursor=$1 WHERE twitch_user_id=$2\", new_cursor_value, row['twitch_user_id'])\n new_followers[row['twitch_user_id']] = new_follower_list\n\n # Update the follower timestamps into real timestamps\n # self.logger.info(new_follower_list)\n filtered_new_follower_list = [i for i in new_follower_list if dt.strptime(i['followed_at'], \"%Y-%m-%dT%H:%M:%SZ\") > self.last_twitch_checked]\n # self.logger.info(filtered_new_follower_list)\n\n # Send DM to the user\n if filtered_new_follower_list:\n discord_user = self.bot.get_user(row['user_id']) or await self.bot.fetch_user(row['user_id'])\n new_follower_string = ', '.join([f\"**{i['from_name']}**\" for i in filtered_new_follower_list])\n if len(new_follower_string) >= 1800:\n new_follower_string = \"\"\n try:\n await discord_user.send(f\"You have **{len(filtered_new_follower_list)}** new Twitch follower{'s' if len(filtered_new_follower_list) > 1 else ''}! {new_follower_string}\")\n except discord.HTTPException:\n pass\n\n # Update timestamp\n self.last_twitch_checked = new_last_timestamp\n await db.disconnect()","async def stealthservertorment(self, ctx, *, member = None, times : int = None):\r\n\r\n\t\tchannel = ctx.message.channel\r\n\t\tauthor = ctx.message.author\r\n\t\tserver = ctx.message.guild\r\n\t\tmessage = ctx.message\r\n\r\n\t\t# Only allow owner\r\n\t\tisOwner = self.settings.isOwner(ctx.author)\r\n\t\tif isOwner == None:\r\n\t\t\treturn\r\n\t\telif isOwner == False:\r\n\t\t\treturn\r\n\t\t\t\t\r\n\t\tusage = 'Usage: `{}torment [role/member] [times]`'.format(ctx.prefix)\r\n\r\n\t\tisRole = False\r\n\r\n\t\tif member == None:\r\n\t\t\tawait ctx.channel.send(usage)\r\n\t\t\treturn\r\n\t\t\t\t\r\n\t\t# Check for formatting issues\r\n\t\tif times == None:\r\n\t\t\t# Either xp wasn't set - or it's the last section\r\n\t\t\tif type(member) is str:\r\n\t\t\t\t# It' a string - the hope continues\r\n\t\t\t\troleCheck = DisplayName.checkRoleForInt(member, server)\r\n\t\t\t\tif roleCheck and roleCheck[\"Role\"]:\r\n\t\t\t\t\tisRole = True\r\n\t\t\t\t\tmember = roleCheck[\"Role\"]\r\n\t\t\t\t\ttimes = roleCheck[\"Int\"]\r\n\t\t\t\telse:\r\n\t\t\t\t\t# Role is invalid - check for member instead\r\n\t\t\t\t\tnameCheck = DisplayName.checkNameForInt(member, server)\r\n\t\t\t\t\tif not nameCheck:\r\n\t\t\t\t\t\tawait ctx.channel.send(usage)\r\n\t\t\t\t\t\treturn\r\n\t\t\t\t\tif not nameCheck[\"Member\"]:\r\n\t\t\t\t\t\tmsg = 'I couldn\\'t find that user or role on the server.'.format(member)\r\n\t\t\t\t\t\tawait ctx.channel.send(msg)\r\n\t\t\t\t\t\treturn\r\n\t\t\t\t\tmember = nameCheck[\"Member\"]\r\n\t\t\t\t\ttimes = nameCheck[\"Int\"]\r\n\t\t\t\t\t\r\n\t\t# Set the torment flag\r\n\t\tself.toTorment = True\r\n\r\n\t\tif times == None:\r\n\t\t\t# Still no times - roll back to default\r\n\t\t\ttimes = 25\r\n\t\t\t\r\n\t\tif times > 100:\r\n\t\t\ttimes = 100\r\n\t\t\t\r\n\t\tif times == 0:\r\n\t\t\tawait ctx.channel.send('Oooooh - I bet they feel *sooooo* tormented...')\r\n\t\t\treturn\r\n\t\t\r\n\t\tif times < 0:\r\n\t\t\tawait ctx.channel.send('I just uh... *un-tormented* them. Yeah.')\r\n\t\t\treturn\r\n\r\n\t\t# Delete original torment message\r\n\t\tawait message.delete()\r\n\t\t\r\n\t\tfor i in range(0, times):\r\n\t\t\t# Do this over time\r\n\t\t\tfor channel in server.channels:\r\n\t\t\t\t# Get user's permissions\r\n\t\t\t\tif type(member) is discord.Role or channel.permissions_for(member).read_messages and type(channel) is discord.TextChannel:\r\n\t\t\t\t\t# Only ping where they can read\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\tif member.name == \"@everyone\" and type(member) is discord.Role:\r\n\t\t\t\t\t\t\ttmessage = await channel.send(\"{}\".format(member.name),allowed_mentions=discord.AllowedMentions.all())\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\ttmessage = await channel.send('{}'.format(member.mention),allowed_mentions=discord.AllowedMentions.all())\r\n\t\t\t\t\t\tawait tmessage.delete()\r\n\t\t\t\t\texcept Exception:\r\n\t\t\t\t\t\tpass\r\n\t\t\tfor j in range(0, self.waitBetween):\r\n\t\t\t\t# Wait for 1 second, then check if we should cancel - then wait some more\r\n\t\t\t\tawait asyncio.sleep(1)\r\n\t\t\t\tif not self.toTorment:\r\n\t\t\t\t\treturn","def follow_following_followers(self):\n self.logger.log(\"starting follow_following_followers...\")\n follows_accounts = self.following\n random.shuffle(follows_accounts)\n for acc in follows_accounts:\n try:\n try:\n followw = perform_with_ran_delay(self.instagram.get_followers, acc, 150, 15,\n delayed=True)\n accountstofollow = followw[\"accounts\"]\n random.shuffle(accountstofollow)\n if len(accountstofollow) > 10:\n accountstofollow = accountstofollow[:10]\n for ac in accountstofollow:\n if not self.is_user_following(ac.identifier):\n self.add_following(ac.identifier)\n self.logger.log(\"following: {}\".format(ac.username))\n except Exception as e:\n print(e)\n self.logger.log(str(e))\n finally:\n sleep(3)","async def _mute(ctx, user:discord.Member, time: str, self: bool):\n if user.id in PI_BOT_IDS:\n return await ctx.send(\"Hey! You can't mute me!!\")\n if time == None:\n return await ctx.send(\"You need to specify a length that this used will be muted. Examples are: `1 day`, `2 months, 1 day`, or `indef` (aka, forever).\")\n role = None\n if self:\n role = discord.utils.get(user.guild.roles, name=ROLE_SELFMUTE)\n else:\n role = discord.utils.get(user.guild.roles, name=ROLE_MUTED)\n parsed = \"indef\"\n if time != \"indef\":\n parsed = dateparser.parse(time, settings={\"PREFER_DATES_FROM\": \"future\"})\n if parsed == None:\n return await ctx.send(\"Sorry, but I don't understand that length of time.\")\n CRON_LIST.append({\"date\": parsed, \"do\": f\"unmute {user.id}\"})\n await user.add_roles(role)\n eastern = pytz.timezone(\"US/Eastern\")\n await ctx.send(f\"Successfully muted {user.mention} until `{str(eastern.localize(parsed))} EST`.\")","def follow_followers(self):\n self.logger.log(\"starting follow_followers...\")\n follow = perform_with_ran_delay(self.instagram.get_followers, self.account.identifier, 150, 15, delayed=True)\n for acc in follow[\"accounts\"]:\n try:\n try:\n # print(\"{} follows me, do I follow him ? > {} \".format(acc.username,self.is_user_following(acc.identifier)))\n if not self.is_user_following(acc.identifier):\n if self.add_following(acc.identifier):\n self.logger.log(\"following: {}\".format(acc.username))\n else:\n self.logger.log(\"follow not working at the moment\")\n except Exception as e:\n print(e)\n self.logger.log(str(e))\n continue\n finally:\n sleep(3)","def follow(self, followerId, followeeId):\n\n # 把 followeeId append到他的 follow 属性中\n if followerId == followeeId: # 不能自己关注自己\n return\n # 实例化一个user(followerID)\n follower = UserInfo()\n follower.user_id = followerId \n follower.follows.append(followeeId) \n self.user_pool[followerId] = follower","def give_all(self, player):\n self.transfer(self, player, self.account.balance)\n return '%s spins \\'gimel\\' and gets everything.' % (player,)","def hola(update, context):\n user = update.message.from_user\n update.message.reply_text('Hola! ' + user.first_name + ' :). Tu apellido es: ' + user.last_name + ', ¿cuál es tu descendencia? ')","async def team_whoami(self, ctx: commands.Context):\n team_id = await self.config.user(ctx.author).team_id()\n\n if team_id == -1:\n await ctx.send('You are not affiliated with any team.')\n return\n if team_id not in self.teams:\n await ctx.send('You are affiliated with a team whose ID I do not'\n f'recognize. ({team_id})')\n return\n await ctx.send('You are affiliated with Team **%s**.' % (\n self.teams[team_id].display_name),)","async def coach(self, ctx):\r\n if ctx.guild.id == 445092370006933505:\r\n user = ctx.author\r\n dm_channel = user.dm_channel\r\n guild_data = self.config.guild(ctx.guild)\r\n coach_id = await guild_data.coachid()\r\n coach = ctx.guild.get_role(int(coach_id))\r\n channel_id = await self.config.guild(ctx.guild).coachchannel()\r\n channel = ctx.guild.get_channel(int(channel_id))\r\n if dm_channel is None:\r\n dm_channel = await user.create_dm()\r\n lst = await guild_data.get_raw(\"neededlist\")\r\n player_data = self.config.member(ctx.author)\r\n\r\n def check(m):\r\n return m.channel == dm_channel and m.author == user\r\n\r\n try:\r\n if user.id in lst:\r\n await ctx.send(\"You already have a coaching request pending please stay patient or contact our staff if its been over 48 hrs since your coaching request\")\r\n else:\r\n await ctx.send(\"Please check your DM's...\")\r\n await user.send(\"Please tell us your In game name?, Type 'stop' to stop the process\")\r\n ign = await self.bot.wait_for('message', timeout=60, check=check)\r\n ign_use = ign.content\r\n new_ign = ign.content.lower()\r\n if new_ign == \"stop\":\r\n raise UserEnd\r\n await user.send(\"Please tell us your Player Tag?, Type 'stop' to stop the process\")\r\n tag = await self.bot.wait_for('message', timeout=60, check=check)\r\n tag_use = tag.content\r\n new_tag = tag.content.lower()\r\n if new_tag == \"stop\":\r\n raise UserEnd\r\n await user.send(\"What time do you prefer for coaching? (Times in UTC only), Type 'stop' to stop the process\")\r\n time = await self.bot.wait_for('message', timeout=60, check=check)\r\n time_use = time.content\r\n np = time.content.lower()\r\n if np == \"stop\":\r\n raise UserEnd\r\n await user.send(\"What archatypes do you prefer to play?\")\r\n deck = await self.bot.wait_for('message', timeout=60, check=check)\r\n new_deck = deck.content.lower() # I know I could have made a function to check this but my brain is not working\r\n deck_use = deck.content\r\n if new_deck == \"stop\":\r\n raise UserEnd\r\n\r\n await user.send(\"You will be contacted by one of our coaches please stay patient.\")\r\n await channel.send(\"{} New coaching request from {}\".format(coach.mention, user.mention))\r\n await self.emb(ctx, \"Discord Name\", \"In Game Name\", \"Player Tag\", \"Preferred Time\", \"Deck Type\", user.mention, ign_use, tag_use, time_use, deck_use)\r\n lst.append(user.id)\r\n await self.config.guild(ctx.guild).neededlist.set(lst)\r\n await player_data.ign.set(ign_use)\r\n await player_data.tag.set(tag_use)\r\n await player_data.time.set(time_use)\r\n await player_data.deck_type.set(deck_use)\r\n\r\n except asyncio.exceptions.TimeoutError:\r\n await user.send(\"Timeout...\") # not sure where to send these messages\r\n return\r\n except UserEnd:\r\n await user.send(\"Stopped!\") # not sure where to send these messages\r\n return\r\n else:\r\n await ctx.send(\"This command only works in the Legend eSports server, join us at: https://discord.gg/GGuCXDn\")","def __pass_time(self):\n self.hunger += 1\n self.boredom += 1","async def _balance(ctx, user : discord.Member):\n await balance(bot, ctx.message.author, user)","async def bait(self, ctx:commands.Context):\r\n\r\n bait = ''\r\n member_bait = await self.config.member(ctx.message.author).bait()\r\n for i in member_bait.keys():\r\n if not member_bait[i] == 0:\r\n bait += f'{i}{\" \" * (25 - len(i))}{member_bait[i]}\\n'\r\n await ctx.send(f'You have:\\n```{bait[:-1]}```')","def test_follow_manually_approved(self):\n activity = {\n \"@context\": \"https://www.w3.org/ns/activitystreams\",\n \"id\": \"https://example.com/users/rat/follows/123\",\n \"type\": \"Follow\",\n \"actor\": \"https://example.com/users/rat\",\n \"object\": \"https://example.com/user/mouse\",\n }\n\n self.local_user.manually_approves_followers = True\n self.local_user.save(\n broadcast=False, update_fields=[\"manually_approves_followers\"]\n )\n\n with patch(\"bookwyrm.models.activitypub_mixin.broadcast_task.apply_async\"):\n views.inbox.activity_task(activity)\n\n # notification created\n notification = models.Notification.objects.get()\n self.assertEqual(notification.user, self.local_user)\n self.assertEqual(notification.notification_type, \"FOLLOW_REQUEST\")\n\n # the request should exist\n request = models.UserFollowRequest.objects.get()\n self.assertEqual(request.user_subject, self.remote_user)\n self.assertEqual(request.user_object, self.local_user)\n\n # the follow relationship should not exist\n follow = models.UserFollows.objects.all()\n self.assertEqual(list(follow), [])","async def on_member_join(member):\n if boterate.has_member(member):\n boterate.update_member(member)\n else:\n boterate.insert_user(member)","def add_following(self, user_id):\n sleep(360) # too much follows => function ban\n self.following.append(user_id)\n return perform_with_ran_delay(self.instagram.follow, user_id)","def update_user():","async def muterole(self, ctx, *, role: discord.Role):\n await queries.update_setting(ctx, \"guild_settings\", \"mute_role_id\", role.id)\n await util.send_success(ctx, f\"Muting someone now gives them the role {role.mention}\")","async def on_member_update(old, updated):\n if old.nick != updated.nick:\n boterate.update_member(updated)","def follow_project(cls, user, project):\n pass","def do_the_thing(self):\n # r = rospy.Rate(2)\n if self.current_state == \"wait\":\n self.wait()\n elif self.current_state == \"follow\":\n self.follow()\n\n self.pub.publish(self.twist)\n\n # r.sleep()","async def update(self, ctx):\n if is_support_guild(ctx.guild.id):\n await ctx.send('Sorry, this discord does not allow update, saveid, '\n 'leaderboard, and series commands so as not to overload me. '\n 'Try `!careerstats` or `!yearlystats` with your customer ID to test '\n 'or go to #invite-link to bring the bot to your discord for all functionality')\n return\n await ctx.send(f'Updating user: {ctx.author.name}, this may take a minute')\n log.info(f'Updating user: {ctx.author.name}')\n await self.updater.update_member(ctx)","async def game(self):\n self.time_remaining = randint(\n int(pow(14 * len(self.participants), 0.8)),\n int(pow(30 * len(self.participants), 0.8))\n )\n\n member = choice(self.participants)\n Timer(1, self.timer).start()\n reply = True\n pass_to = []\n notify = randint(2, int(self.time_remaining / 2))\n\n while self.time_remaining > 0:\n if not pass_to:\n pass_from = list(self.participants)\n pass_from.pop(pass_from.index(member))\n pass_to = [choice(pass_from)]\n pass_from.pop(pass_from.index(pass_to[0]))\n pass_to.append(choice(pass_from))\n\n if reply is not None:\n await client.send_message(self.channel, \"{} :bomb: got the bomb! Pass it to either {} or {}!\".format(\n member.mention, pass_to[0].mention, pass_to[1].mention))\n\n def check(m):\n if len(m.mentions) > 0:\n if m.mentions[0] in pass_to:\n return True\n\n return False\n\n wait = (self.time_remaining - notify) if (self.time_remaining >= notify) else self.time_remaining\n reply = await client.wait_for_message(timeout=wait, channel=self.channel, author=member,\n check=check)\n\n if reply:\n member = reply.mentions[0]\n pass_to = []\n if self.member.permissions_in(self.channel).manage_messages:\n asyncio.ensure_future(client.delete_message(reply))\n elif self.time_remaining == notify:\n asyncio.ensure_future(client.send_message(self.channel, \":bomb: :fire: **IT'S GONNA BLOW!**\"))\n self.time_remaining -= 1\n\n await client.send_message(self.channel, \"{0.mention} :fire: :boom: :boom: :fire:\".format(member))\n await client.send_message(self.channel, \"**GAME OVER**\")","def unfollow(request, usertostopfollow):\n stop_follow = Member.objects.get(user__username=usertostopfollow)\n user = Member.objects.get(user=request.user)\n user.following.remove(stop_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","def test_is_following(self):\n\n self.u1.following.append(self.u2)\n db.session.commit()\n\n self.assertTrue(self.u1.is_following(self.u2))\n self.assertFalse(self.u2.is_following(self.u1))","async def tod_status(self, ctx, *args):\n n = len(self.players)\n if n > 0:\n if n == 1:\n s = \"person\"\n else:\n s = \"people\"\n message = f\"A Truth or Dare game is currently taking place with {n} {s}!\"\n else:\n message = \"No Truth or Dare game is currently taking place.\"\n await ctx.send(message)","def updateBuddy(self,username,online,evilness,signontime,idletime,userclass,away):\n print \"status changed for\",username","def like_following(self):\n self.logger.log(\"starting like_following...\")\n count_following = self.account.follows_count\n follows_accounts = self.following\n random.shuffle(follows_accounts)\n for acc in follows_accounts:\n acc = perform_with_ran_delay(self.instagram.get_account_by_id, acc)\n self.logger.log(\" {} > {} posts\".format(acc.username, acc.media_count))\n if acc.media_count > 0:\n\n posts = perform_with_ran_delay(self.instagram.get_medias, acc.username, 50)\n if posts:\n for m in posts:\n try:\n perform_with_ran_delay(self.instagram.like, m.identifier)\n self.logger.log(\"liking 1 post from \"+acc.username)\n random_delay()\n except Exception as e:\n self.logger.log(\"skipping 1 post from \"+acc.username)\n self.logger.log(e)\n random_delay()\n continue","async def credits(self, ctx):\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\n description='\\n:ok_hand: :laughing:\\n :telephone_receiver::shirt::call_me:\\n :jeans: :fire:',\n colour=0xf20006)\n last_message = await self.bot.say(embed=embed)\n await self.bot.add_reaction(last_message, self.emojiUnicode['succes'])","def on_me_joined(self, raw_msg, **kwargs):","def status(message):\n message.reply('User_id: ' +\n str(message._client.users[message._get_user_id()]))","def status(message):\n message.reply('User_id: ' +\n str(message._client.users[message._get_user_id()]))","def _apply_rate_limit(self):\n update_time = time()\n user_name = self.bot.user.full_name\n if user_name in self.tokens.keys():\n last_change = self.tokens[user_name][0]\n # Add 1 token for every 30 seconds from the last change\n added_tokens = int((update_time - last_change) / 30)\n self.tokens[user_name][1] += added_tokens\n # Max at 5 self.tokens\n if self.tokens[user_name][1] > 5:\n self.tokens[user_name][1] = 5\n else:\n # Initialize the users token pair (last change, # of self.tokens)\n self.tokens[user_name] = [update_time, 5] # Start with 5 self.tokens\n if self.tokens[user_name][1] <= 0:\n return False\n self.tokens[user_name][1] -= 1\n return True","def loan(self):","def follow(request, base62_id, stat_type = 1):\n import datetime\n \n key = base62.to_decimal(base62_id)\n link = get_object_or_404(Link, pk = key)\n if request.user.is_anonymous():\n user = User.objects.get(username=\"admin\")\n else:\n user = request.user\n \n if str(stat_type) == \"v\":\n stat_type = 2 \n else:\n stat_type = 1\n \n stat = Stat(\n link = link,\n http_host = request.META.get(\"HTTP_HOST\",\"\"), \n http_referer = request.META.get(\"HTTP_REFERER\",\"\"), \n http_user_agent = request.META.get(\"HTTP_USER_AGENT\",\"\"),\n remote_addr = request.META.get(\"REMOTE_ADDR\",\"\"), \n remote_host = request.META.get(\"REMOTE_HOST\",\"\"),\n stat_type = stat_type\n )\n stat.user = user\n stat.save()\n if stat_type == 1:\n link.clicks = link.clicks + 1\n else:\n link.views = link.views + 1\n link.save()\n if stat_type == 2:\n return HttpResponse(content=\"\", status=304)\n \n return HttpResponsePermanentRedirect(link.url)","async def invite(ctx):\r\n await ctx.send(\"https://discordapp.com/oauth2/authorize?client_id=457903893079392256&scope=bot&permissions=2146958591\")\r\n ctx.counter(n)","def update_user():\n #TODO user update \n pass","async def newusers(self, ctx, *, count=5):\n\n count = min(max(count, 5), 25)\n\n now = datetime.now(timezone.utc)\n e = disnake.Embed()\n\n for idx, member in enumerate(\n sorted(ctx.guild.members, key=lambda m: m.joined_at, reverse=True)\n ):\n if idx >= count:\n break\n\n value = \"Joined {0} ago\\nCreated {1} ago\".format(\n pretty_timedelta(now - member.joined_at),\n pretty_timedelta(now - member.created_at),\n )\n e.add_field(name=po(member), value=value, inline=False)\n\n await ctx.send(embed=e)","def is_follower(self, you, them):\n if self.filter(from_user=them, to_user=you).count() > 0:\n return True\n return False","def follow_user(self, user):\n self.nav_user(user)\n follow_button = self.driver.find_element_by_xpath(\n \"//button[contains(text(), 'Follow')]\")\n follow_button.click()\n time.sleep(1)\n self.driver.get(self.base_url)","async def _pay_money(ctx, user : discord.Member, amount : int):\n if amount<0:\n await bot.reply(\"You can't pay someone a negative amount!\")\n elif user==ctx.message.author:\n await bot.reply(\"You can't pay yourself!\")\n else:\n await transfer(bot, ctx.message.author, user, amount)","def test_followers_following_list_unauthorized(self):\n\n self.u2.following.append(self.u)\n db.session.commit()\n\n with self.client as client:\n response = client.get(\"/users/2/following\")\n\n self.assertEqual(response.location, \"http://localhost/\")\n self.assertIn('Access unauthorized.', get_flashed_messages())\n\n response2 = client.get(\"/users/2/followers\")\n\n self.assertEqual(response2.location, \"http://localhost/\")\n self.assertIn('Access unauthorized.', get_flashed_messages())"],"string":"[\n \"def follow_reciprocated(self, target):\\n if random.randint(1, 1000) == 1: # 1 in 20 are public @replies\\n self.tweet_user(target)\\n else:\\n try:\\n self.dm_user(target)\\n except:\\n pass\",\n \"def author_following(self):\\n\\t\\tpass\",\n \"def follow_user(cls, user, following):\\r\\n pass\",\n \"def follow_user(cls, user, following):\\n pass\",\n \"def follow_user(self, target):\\n try:\\n if self.api.me().friends_count > 1990:\\n return\\n except Exception, e:\\n print e\\n\\n \\\"Rate limit exceeded. Clients may not make more than 350 requests per hour.\\\"\\n if \\\"Clients\\\" in str(e):\\n continue\\n # import pdb; pdb.set_trace()\\n return\\n\\n try:\\n self.api.create_friendship(target.hunted.screen_name)\\n self.log.debug(\\\"Followed: %s\\\" % target.hunted.screen_name)\\n except Exception, e:\\n self.log.exception(\\\"Could not follow %s\\\" %\\n target.hunted.screen_name)\\n else:\\n # Write record of new follow to db\\n target.status = Target.PURGATORY\\n target.save()\",\n \"def follow(self, follower, followee):\\n pass\",\n \"def follow(self, user_index, following_index):\\n if user_index >= self.num_users or following_index >= self.num_users:\\n raise ValueError(\\n f\\\"Number of users is {self.num_users}, but indices \\\"\\n f\\\"{user_index} and {following_index} were requested.\\\"\\n )\\n if self.users_hat[following_index, user_index] == 0:\\n self.users_hat[following_index, user_index] = 1\\n elif self.is_verbose():\\n self.log(f\\\"User {following_index} was already following user {user_index}\\\")\",\n \"def follow(request, usertofollow):\\n to_follow = Member.objects.get(user__username=usertofollow)\\n user = Member.objects.get(user=request.user)\\n user.following.add(to_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"async def love(ctx, user: discord.Member):\\r\\n author = ctx.message.author\\r\\n if user.id == ctx.bot.user.id:\\r\\n await ctx.send(\\\"I am not capable of loving like you can. I'm sorry.\\\" )\\r\\n else:\\r\\n await ctx.send(author.mention + \\\" is capable of loving \\\" + user.mention + \\\" a whopping \\\" +\\r\\n str(randint(0, 100)) + \\\"%!\\\")\\r\\n ctx.counter(n)\",\n \"def following_changed(sender, action, instance, *args, **kwargs):\\n\\n # m2mchanged.connect specified in apps.py\\n\\n following = instance.following.all()\\n creator = instance.user\\n\\n if creator in following:\\n raise ValidationError (\\\"can't like own post\\\")\",\n \"def get_everyone_granted(self):\",\n \"def follow(self, other):\\n\\t\\tif not self.follows(other):\\n\\t\\t\\tself.followed.append(other)\",\n \"def expireme(message):\\n users = hf.get_users()\\n requester = message._get_user_id()\\n for user in users:\\n if user[\\\"id\\\"] == requester:\\n name = user[\\\"name\\\"]\\n break\\n\\n hf.expire_user(name)\",\n \"async def selfmute(ctx, *args):\\n user = ctx.message.author\\n if await is_staff(ctx):\\n return await ctx.send(\\\"Staff members can't self mute.\\\")\\n time = \\\" \\\".join(args)\\n await _mute(ctx, user, time, self=True)\",\n \"async def stealthtorment(self, ctx, *, member = None, times : int = None):\\r\\n\\r\\n\\t\\tchannel = ctx.message.channel\\r\\n\\t\\tauthor = ctx.message.author\\r\\n\\t\\tserver = ctx.message.guild\\r\\n\\t\\tmessage = ctx.message\\r\\n\\r\\n\\t\\t# Only allow owner\\r\\n\\t\\tisOwner = self.settings.isOwner(ctx.author)\\r\\n\\t\\tif isOwner == None:\\r\\n\\t\\t\\treturn\\r\\n\\t\\telif isOwner == False:\\r\\n\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\r\\n\\t\\tusage = 'Usage: `{}torment [role/member] [times]`'.format(ctx.prefix)\\r\\n\\r\\n\\t\\tisRole = False\\r\\n\\r\\n\\t\\tif member == None:\\r\\n\\t\\t\\tawait ctx.channel.send(usage)\\r\\n\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t# Check for formatting issues\\r\\n\\t\\tif times == None:\\r\\n\\t\\t\\t# Either xp wasn't set - or it's the last section\\r\\n\\t\\t\\tif type(member) is str:\\r\\n\\t\\t\\t\\t# It' a string - the hope continues\\r\\n\\t\\t\\t\\troleCheck = DisplayName.checkRoleForInt(member, server)\\r\\n\\t\\t\\t\\tif roleCheck and roleCheck[\\\"Role\\\"]:\\r\\n\\t\\t\\t\\t\\tisRole = True\\r\\n\\t\\t\\t\\t\\tmember = roleCheck[\\\"Role\\\"]\\r\\n\\t\\t\\t\\t\\ttimes = roleCheck[\\\"Int\\\"]\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t# Role is invalid - check for member instead\\r\\n\\t\\t\\t\\t\\tnameCheck = DisplayName.checkNameForInt(member, server)\\r\\n\\t\\t\\t\\t\\tif not nameCheck:\\r\\n\\t\\t\\t\\t\\t\\tawait ctx.channel.send(usage)\\r\\n\\t\\t\\t\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\tif not nameCheck[\\\"Member\\\"]:\\r\\n\\t\\t\\t\\t\\t\\tmsg = 'I couldn\\\\'t find that user or role on the server.'.format(member)\\r\\n\\t\\t\\t\\t\\t\\tawait ctx.channel.send(msg)\\r\\n\\t\\t\\t\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\tmember = nameCheck[\\\"Member\\\"]\\r\\n\\t\\t\\t\\t\\ttimes = nameCheck[\\\"Int\\\"]\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t# Set the torment flag\\r\\n\\t\\tself.toTorment = True\\r\\n\\r\\n\\t\\tif times == None:\\r\\n\\t\\t\\t# Still no times - roll back to default\\r\\n\\t\\t\\ttimes = 25\\r\\n\\t\\t\\t\\r\\n\\t\\tif times > 100:\\r\\n\\t\\t\\ttimes = 100\\r\\n\\t\\t\\t\\r\\n\\t\\tif times == 0:\\r\\n\\t\\t\\tawait ctx.channel.send('Oooooh - I bet they feel *sooooo* tormented...')\\r\\n\\t\\t\\treturn\\r\\n\\t\\t\\r\\n\\t\\tif times < 0:\\r\\n\\t\\t\\tawait ctx.channel.send('I just uh... *un-tormented* them. Yeah.')\\r\\n\\t\\t\\treturn\\r\\n\\r\\n\\t\\t# Delete original torment message\\r\\n\\t\\tawait message.delete()\\r\\n\\t\\t\\r\\n\\t\\tfor i in range(0, times):\\r\\n\\t\\t\\t# Do this over time\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tif member.name == \\\"@everyone\\\" and type(member) is discord.Role:\\r\\n\\t\\t\\t\\t\\ttmessage = await ctx.channel.send(\\\"{}\\\".format(member.name),allowed_mentions=discord.AllowedMentions.all())\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\ttmessage = await ctx.channel.send('{}'.format(member.mention),allowed_mentions=discord.AllowedMentions.all())\\r\\n\\t\\t\\t\\tawait tmessage.delete()\\r\\n\\t\\t\\texcept Exception:\\r\\n\\t\\t\\t\\tpass\\r\\n\\t\\t\\tfor j in range(0, self.waitBetween):\\r\\n\\t\\t\\t\\t# Wait for 1 second, then check if we should cancel - then wait some more\\r\\n\\t\\t\\t\\tawait asyncio.sleep(1)\\r\\n\\t\\t\\t\\tif not self.toTorment:\\r\\n\\t\\t\\t\\t\\treturn\",\n \"async def follow(follow):\\n await follow.edit(\\n f\\\"`FOLLOW {DEFAULTUSER} ON` \\\\n\\\\n\\\"\\n f\\\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\\\n\\\\n\\\"\\n f\\\"[FaceBook](https://www.facebook.com/mkaraniya) \\\\n\\\\n\\\"\\n f\\\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \\\"\\n )\",\n \"def is_following(self, you, them):\\n if self.filter(from_user=you, to_user=them).count() > 0:\\n return True\\n return False\",\n \"async def treatme(self, ctx):\\n await ctx.send(await self.cure_user(ctx, ctx.author))\",\n \"async def pending(self, ctx):\\r\\n if ctx.guild.id == 445092370006933505:\\r\\n data = self.config.guild(ctx.guild)\\r\\n lst = await data.get_raw('neededlist')\\r\\n description = \\\"\\\"\\r\\n coach = await data.coachid()\\r\\n coach_role = ctx.guild.get_role(coach)\\r\\n x = ctx.author.top_role\\r\\n if x >= coach_role:\\r\\n for member in lst:\\r\\n userobj = ctx.guild.get_member(int(member))\\r\\n description += (str(userobj.mention) + '\\\\n')\\r\\n embed = discord.Embed(color=0xFFFF00, title='Coaching Needed by following people', description=description)\\r\\n embed.set_footer(text=credit)\\r\\n await ctx.send(embed=embed)\\r\\n await ctx.send('Type \\\"{0}coaching done @<player name>\\\" if the player has been coached or type \\\"{0}coaching info <@playername>\\\" to view the details submitted by the user'.format(ctx.prefix))\\r\\n \\r\\n else:\\r\\n await ctx.send(\\\"You are not allowed to do that\\\")\\r\\n\\r\\n else:\\r\\n await ctx.send(\\\"This command only works in the Legend eSports server, join us at: https://discord.gg/GGuCXDn\\\")\",\n \"def add_follow(follow_id):\\n\\n want_to_follow_user = User.query.get_or_404(follow_id)\\n if want_to_follow_user.private:\\n # =========== NEED TO IMPLEMENT ====================\\n # send them a request to follow\\n want_to_follow_user.from_users.append(g.user) \\n db.session.commit()\\n flash(\\\"Your request has been sent\\\", \\\"success\\\")\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\\n\\n g.user.following.append(want_to_follow_user)\\n db.session.commit()\\n\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\",\n \"def follow_someone(screen_name):\\n twitter.create_friendship(screen_name=screen_name)\",\n \"def user_appears(self, user):\\n pass\",\n \"def tweet_user(self, target, msg=None):\\n self.log.debug(\\\"Tweeting %s\\\" % target.hunted.screen_name)\\n tweet = \\\"@%s: %s\\\" % (target.hunted.screen_name,\\n random.sample(self.tweets, 1)[0])\\n tweet = tweet [:140]\\n self.api.update_status(tweet)\\n target.status = Target.FOLLOWER\\n target.save()\",\n \"def follow(self, followerId, followeeId):\\r\\n if followerId != followeeId:\\r\\n self.follows[followerId].add(followeeId)\",\n \"def whisper(self,name):\\n\\n self.sendCommand(\\\"global /join\\\",name+self.userName+\\\" private\\\")\\n self.master.after(300,self.sendCommand,name+self.userName+\\\" /invite\\\",name)\",\n \"def command_who(self, bot, update):\\n\\n messages = [\\n 'Myles Braithwaite lives in Toronto where he runs a small '\\n 'consluting company called [Monkey in your Soul]'\\n '(https://monkeyinyoursoul.com/) (you should hire him because '\\n \\\"he's awesome).\\\",\\n 'You should follow him on [Twitter](https://twitter.com/mylesb) '\\n 'or [Instagram](https://instagram.com/myles).',\\n 'You can find his programming stuff on [GitHub]'\\n '(https://github.com/myles) or [CodePen]'\\n '(http://codepen.io/mylesb/).'\\n ]\\n\\n self.send_messages(bot, update, messages)\",\n \"def following(account):\\n stm = shared_morphene_instance()\\n if mph.rpc is not None:\\n mph.rpc.rpcconnect()\\n if not account:\\n if \\\"default_account\\\" in mph.config:\\n account = [mph.config[\\\"default_account\\\"]]\\n for a in account:\\n a = Account(a, morphene_instance=stm)\\n print(\\\"\\\\nFollowing statistics for @%s (please wait...)\\\" % a.name)\\n following = a.get_following(False)\\n following.print_summarize_table(tag_type=\\\"Following\\\")\",\n \"def promoteUser(self):\\n\\t\\t#ensure they're supposed to be here and haven't been here before\\n\\t\\tif self.goodEventsCount >= 3 and not self.verified:\\n\\t\\t\\tself.verifiedUser=True\\n\\t\\t\\tself.put()\\n\\t\\t\\tmessage = mail.EmailMessage(\\n\\t\\t\\t\\t\\tsender=\\\"Friends with Food Admin <admin@friendswfood.appspot.com>\\\",\\n subject=\\\"Your account has been verified!\\\")\\n\\n\\t\\t\\tmessage.to = self.id.email()\\n\\t\\t\\tmessage.cc = \\\"friendswfood@case.edu\\\"\\n\\t\\t\\tmessage.body = \\\"\\\"\\\"\\n\\t\\t\\tDear %s:\\n\\n\\t\\t\\tYour account on Friends with Food has been verified! Because you've \\n\\t\\t\\tshown us so many good events, we've upgraded your account. Now, you'll \\n\\t\\t\\tget notified of free food on campus ASAP! You'll also be able to verify\\n\\t\\t\\tevents so that everyone knows they're legit.\\n\\t\\t\\t\\n\\t\\t\\t*With great power comes great responsibility*\\n\\t\\t\\t\\n\\t\\t\\tThanks,\\n\\t\\t\\t\\n\\t\\t\\tThe Friends with Food Team\\n\\t\\t\\t\\\"\\\"\\\" % self.id.nickname()\\n\\t\\t\\tmessage.send()\",\n \"def leader(self):\\n pass\",\n \"def leader(self):\\n pass\",\n \"async def mute(self, ctx):\\n author = ctx.message.author\\n channel = author.voice.channel\\n members = channel.members\\n for member in members:\\n user = ctx.guild.get_member(member.id)\\n await user.edit(mute=True)\\n\\n embed = await embeds.generate_embed(ctx, author, members,\\n description=\\\":white_check_mark: Successfully muted the following users:\\\",\\n title=channel.name)\\n await ctx.send(embed=embed)\",\n \"async def plaguebearer(self, ctx):\\n currency = await bank.get_currency_name(ctx.guild)\\n await self.config.user(ctx.author).gameRole.set(\\\"Plaguebearer\\\")\\n await self.notify_user(ctx=ctx, user=ctx.author, notificationType=\\\"plaguebearer\\\")\\n await ctx.send(f\\\"{ctx.author} has spent 10,000 {currency} and become a Plaguebearer.\\\")\",\n \"def userFollowers(nick):\\n if (len(nick) != 1):\\n print \\\"Has d'introduir només un nick\\\"\\n return\\n i.userFollow(nick[0])\",\n \"async def mute(self, ctx: Context, members: commands.Greedy[discord.Member], reason=\\\"no reason\\\"):\\n\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n member_display = []\\n\\n for i, member in enumerate(members):\\n if role in member.roles:\\n await ctx.send(f\\\"guild member `{member.display_name}` is already muted\\\", delete_after=8)\\n del members[i]\\n\\n if role is None:\\n permissions = discord.Permissions()\\n permissions.change_nickname = True\\n permissions.send_messages = False\\n permissions.read_message_history = True\\n role = await ctx.guild.create_role(name=\\\"Muted\\\", permissions=permissions)\\n\\n await self.set_perms(ctx.guild, role)\\n\\n for member in members:\\n\\n if await self.hiearchy_check(ctx, member):\\n continue\\n\\n member_display.append(str(member))\\n await member.add_roles(role, reason=reason)\\n\\n member_display = \\\", \\\".join(member_display)\\n\\n if not member_display:\\n member_display = \\\"no one\\\"\\n\\n await ctx.send(f\\\"> {ctx.author.name} muted {member_display}\\\")\",\n \"def idme(bot, update):\\n update.message.reply_text(\\\"Your ID is: \\\" + str(update.message.from_user.id))\",\n \"async def plaguedoctor(self, ctx):\\n currency = await bank.get_currency_name(ctx.guild)\\n await self.config.user(ctx.author).gameRole.set(\\\"Doctor\\\")\\n await self.notify_user(ctx=ctx, user=ctx.author, notificationType=\\\"doctor\\\")\\n await ctx.send(f\\\"{ctx.author} has spent 10,000 {currency} and become a Doctor.\\\")\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followeeId not in self.followList.get(followerId, [followerId]):\\n self.followList[followerId] = self.followList.get(followerId, [followerId]) + [followeeId]\\n # print(self.followList)\",\n \"def follow_user(username):\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n db = get_db()\\n db.execute('insert into follower (who_id, whom_id) values (?, ?)',\\n [session['user_id'], whom_id])\\n db.commit()\\n flash('You are now following \\\"%s\\\"' % username)\\n return redirect(url_for('user_timeline', username=username))\",\n \"def user_follow():\\n data = request.get_json(force=True)\\n follower = User.query.get(data['follower'])\\n following = User.query.get(data['following'])\\n follower.followcheck.append(following)\\n db.session.commit()\\n return {'followed': True}\",\n \"def followUser(following):\\n\\n cur, user_id, con = initialise(3, True)\\n cur.execute(\\\"INSERT INTO followers (user, following) VALUES ((SELECT username FROM users WHERE id = ?), ?)\\\", (user_id, following))\\n finish(con)\",\n \"async def _sync_total(self, ctx: Context):\\n\\n guild = ctx.guild\\n\\n player_id = await self.config.guild(guild).player_id()\\n player_role = discord.utils.get(guild.roles, id=player_id)\\n\\n signed = len([\\n user.id for user in guild.members\\n if player_role in user.roles\\n ])\\n\\n await self.config.guild(guild).signed.set(signed)\\n\\n await ctx.send(_(\\\"Synced total number of signed-up players.\\\"))\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followerId == followeeId:\\n return\\n if followerId not in self.users.keys():\\n self.users[followerId] = user()\\n if followeeId not in self.users.keys():\\n self.users[followeeId] = user()\\n self.users[followerId].followees[followeeId] = self.users[followeeId]\",\n \"def signedOn(self):\\n log.msg(\\\"Signed on\\\")\\n if self.nickname != self.factory.nickname:\\n log.msg('Name taken, new is ''\\\"{}\\\".'.format(self.nickname))\\n self.join(self.factory.channel)\",\n \"async def on_member_update(before, after):\\r\\n if Counter(before.roles) == Counter(after.roles):\\r\\n return\\r\\n await check_member_rules(after)\",\n \"async def torment(self, ctx, *, member = None, times : int = None):\\r\\n\\r\\n\\t\\tchannel = ctx.message.channel\\r\\n\\t\\tauthor = ctx.message.author\\r\\n\\t\\tserver = ctx.message.guild\\r\\n\\t\\tmessage = ctx.message\\r\\n\\r\\n\\t\\t# Only allow owner to change server stats\\r\\n\\t\\tisOwner = self.settings.isOwner(ctx.author)\\r\\n\\t\\tif isOwner == None:\\r\\n\\t\\t\\treturn\\r\\n\\t\\telif isOwner == False:\\r\\n\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\r\\n\\t\\tusage = 'Usage: `{}torment [role/member] [times]`'.format(ctx.prefix)\\r\\n\\r\\n\\t\\tisRole = False\\r\\n\\r\\n\\t\\tif member == None:\\r\\n\\t\\t\\tawait ctx.channel.send(usage)\\r\\n\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t# Check for formatting issues\\r\\n\\t\\tif times == None:\\r\\n\\t\\t\\t# Either xp wasn't set - or it's the last section\\r\\n\\t\\t\\tif type(member) is str:\\r\\n\\t\\t\\t\\t# It' a string - the hope continues\\r\\n\\t\\t\\t\\troleCheck = DisplayName.checkRoleForInt(member, server)\\r\\n\\t\\t\\t\\tif roleCheck and roleCheck[\\\"Role\\\"]:\\r\\n\\t\\t\\t\\t\\tisRole = True\\r\\n\\t\\t\\t\\t\\tmember = roleCheck[\\\"Role\\\"]\\r\\n\\t\\t\\t\\t\\ttimes = roleCheck[\\\"Int\\\"]\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t# Role is invalid - check for member instead\\r\\n\\t\\t\\t\\t\\tnameCheck = DisplayName.checkNameForInt(member, server)\\r\\n\\t\\t\\t\\t\\tif not nameCheck:\\r\\n\\t\\t\\t\\t\\t\\tawait ctx.channel.send(usage)\\r\\n\\t\\t\\t\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\tif not nameCheck[\\\"Member\\\"]:\\r\\n\\t\\t\\t\\t\\t\\tmsg = 'I couldn\\\\'t find that user or role on the server.'.format(member)\\r\\n\\t\\t\\t\\t\\t\\tawait ctx.channel.send(msg)\\r\\n\\t\\t\\t\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\tmember = nameCheck[\\\"Member\\\"]\\r\\n\\t\\t\\t\\t\\ttimes = nameCheck[\\\"Int\\\"]\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t# Set the torment flag\\r\\n\\t\\tself.toTorment = True\\r\\n\\r\\n\\t\\tif times == None:\\r\\n\\t\\t\\t# Still no times - roll back to default\\r\\n\\t\\t\\ttimes = 25\\r\\n\\t\\t\\t\\r\\n\\t\\tif times > 100:\\r\\n\\t\\t\\ttimes = 100\\r\\n\\t\\t\\t\\r\\n\\t\\tif times == 0:\\r\\n\\t\\t\\tawait ctx.channel.send('Oooooh - I bet they feel *sooooo* tormented...')\\r\\n\\t\\t\\treturn\\r\\n\\t\\t\\r\\n\\t\\tif times < 0:\\r\\n\\t\\t\\tawait ctx.channel.send('I just uh... *un-tormented* them. Yeah.')\\r\\n\\t\\t\\treturn\\r\\n\\t\\t\\r\\n\\t\\t# Delete original torment message\\r\\n\\t\\tawait message.delete()\\r\\n\\r\\n\\t\\tfor i in range(0, times):\\r\\n\\t\\t\\t# Do this over time\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tif member.name == \\\"@everyone\\\" and type(member) is discord.Role:\\r\\n\\t\\t\\t\\t\\tawait channel.send(\\\"{}\\\".format(member.name),allowed_mentions=discord.AllowedMentions.all())\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tawait channel.send('{}'.format(member.mention),allowed_mentions=discord.AllowedMentions.all())\\r\\n\\t\\t\\texcept Exception:\\r\\n\\t\\t\\t\\tpass\\r\\n\\t\\t\\tfor j in range(0, self.waitBetween):\\r\\n\\t\\t\\t\\t# Wait for 1 second, then check if we should cancel - then wait some more\\r\\n\\t\\t\\t\\tawait asyncio.sleep(1)\\r\\n\\t\\t\\t\\tif not self.toTorment:\\r\\n\\t\\t\\t\\t\\treturn\",\n \"async def randping(self, ctx):\\r\\n while True:\\r\\n memb = random.choice(ctx.guild.members)\\r\\n if not memb.bot:\\r\\n break\\r\\n memb = memb.mention\\r\\n await ctx.send(memb)\",\n \"def follow(user, people):\\n api = get_api(user)\\n current_screen_name = api.VerifyCredentials().GetScreenName()\\n\\n # don't let a user follow themselves\\n screen_names = [person.twitter_screen_name for person in people]\\n if current_screen_name in screen_names: screen_names.remove(current_screen_name)\\n\\n followed = []\\n not_followed = []\\n\\n for screen_name in screen_names:\\n try:\\n api.CreateFriendship(screen_name=screen_name)\\n followed.append(screen_name)\\n except twitter.TwitterError:\\n not_followed.append(screen_name)\\n\\n return 'followed %s people' % len(followed)\",\n \"def follow_user(username):\\n user_ID = before_request()\\n user_ID = None\\n if user_ID != None:\\n user_ID = str(g.user['_id'])\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n mongo.db.users.update({'_id': g.user['_id']}, {\\n '$push': {'follows': whom_id}})\\n flash('You are now following \\\"%s\\\"' % username)\\n if redis_obj.get(user_ID):\\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(r.get(user_ID))))\\n else:\\n redis_obj.delete(str(g.user['_id']))\\n print \\\"Invalidating cache after Follow\\\"\\n return redirect(url_for('user_timeline', username=username))\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followerId == followeeId: return\\n self.users[followerId].add(followeeId)\",\n \"def save_model(self, request, obj, form, change):\\n From = User.objects.get(id=obj.From.id)\\n To = User.objects.get(id=obj.To.id)\\n From.following_numIn()\\n To.followed_numIn()\\n obj.save()\",\n \"async def mute(self, ctx, member: discord.Member, *, time:TimeConverter = None):\\r\\n\\r\\n if member.top_role >= ctx.author.top_role:\\r\\n return await ctx.send(\\\"you can't mute that person\\\")\\r\\n\\r\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\r\\n await member.add_roles(role)\\r\\n await ctx.reply((f\\\"Muted {member} for {time}s\\\" if time else f\\\"Muted {member}\\\"))\\r\\n\\r\\n if time:\\r\\n await asyncio.sleep(time)\\r\\n await member.remove_roles(role)\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n self.user_followed[followerId].append(followeeId)\",\n \"def whoami(bot, update):\\n\\n id = update.effective_message.chat.id\\n bot.send_message(id, \\\"Tämän chätin ID on {}\\\".format(id))\",\n \"def auto_follow_followers():\\n\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n not_following_back = followers - following\\n\\n for user_id in not_following_back:\\n try:\\n t.friendships.create(user_id=user_id, follow=False)\\n except Exception as e:\\n print(\\\"error: %s\\\" % (str(e)))\",\n \"async def moot(self, ctx, \\n\\t\\ttarget: discord.Member, \\n\\t\\t*, reason: str = \\\"No reason given.\\\"\\n\\t):\\n\\n\\t\\tself.check_perms(ctx.author, target)\\n\\t\\tawait ctx.success(f\\\"{target} (`{target.id}`) has been mooted for:\\\\n{reason}\\\")\",\n \"def follow(self, followerId, followeeId):\\n if followerId in self.follows:\\n self.follows[followerId].add(followeeId)\\n else:\\n self.follows[followerId] = set([followeeId])\",\n \"def follow_project(cls, user, project):\\r\\n pass\",\n \"def _RecordVisitTime(self, mr, now=None):\\n now = now or int(time.time())\\n if not settings.read_only and mr.auth.user_id:\\n user_pb = mr.auth.user_pb\\n if (user_pb.last_visit_timestamp <\\n now - framework_constants.VISIT_RESOLUTION):\\n user_pb.last_visit_timestamp = now\\n self.services.user.UpdateUser(mr.cnxn, user_pb.user_id, user_pb)\",\n \"async def twitch_follower_checker_loop(self):\\n\\n new_last_timestamp = dt.utcnow()\\n new_followers = collections.defaultdict(list) # channel user id: list of new follower usernames\\n db = await self.bot.database.get_connection()\\n data = await db(\\\"SELECT * FROM user_settings WHERE twitch_bearer_token IS NOT NULL\\\")\\n\\n # Wew let's do it\\n for row in data:\\n\\n # See if we got their data already\\n new_follower_list = new_followers.get(row['twitch_user_id'])\\n if new_follower_list is None:\\n new_follower_list, new_cursor_value = await self.get_new_followers(row['twitch_bearer_token'], row['twitch_user_id'], row['twitch_cursor'])\\n if new_cursor_value:\\n await db(\\\"UPDATE user_settings SET twitch_cursor=$1 WHERE twitch_user_id=$2\\\", new_cursor_value, row['twitch_user_id'])\\n new_followers[row['twitch_user_id']] = new_follower_list\\n\\n # Update the follower timestamps into real timestamps\\n # self.logger.info(new_follower_list)\\n filtered_new_follower_list = [i for i in new_follower_list if dt.strptime(i['followed_at'], \\\"%Y-%m-%dT%H:%M:%SZ\\\") > self.last_twitch_checked]\\n # self.logger.info(filtered_new_follower_list)\\n\\n # Send DM to the user\\n if filtered_new_follower_list:\\n discord_user = self.bot.get_user(row['user_id']) or await self.bot.fetch_user(row['user_id'])\\n new_follower_string = ', '.join([f\\\"**{i['from_name']}**\\\" for i in filtered_new_follower_list])\\n if len(new_follower_string) >= 1800:\\n new_follower_string = \\\"\\\"\\n try:\\n await discord_user.send(f\\\"You have **{len(filtered_new_follower_list)}** new Twitch follower{'s' if len(filtered_new_follower_list) > 1 else ''}! {new_follower_string}\\\")\\n except discord.HTTPException:\\n pass\\n\\n # Update timestamp\\n self.last_twitch_checked = new_last_timestamp\\n await db.disconnect()\",\n \"async def stealthservertorment(self, ctx, *, member = None, times : int = None):\\r\\n\\r\\n\\t\\tchannel = ctx.message.channel\\r\\n\\t\\tauthor = ctx.message.author\\r\\n\\t\\tserver = ctx.message.guild\\r\\n\\t\\tmessage = ctx.message\\r\\n\\r\\n\\t\\t# Only allow owner\\r\\n\\t\\tisOwner = self.settings.isOwner(ctx.author)\\r\\n\\t\\tif isOwner == None:\\r\\n\\t\\t\\treturn\\r\\n\\t\\telif isOwner == False:\\r\\n\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\r\\n\\t\\tusage = 'Usage: `{}torment [role/member] [times]`'.format(ctx.prefix)\\r\\n\\r\\n\\t\\tisRole = False\\r\\n\\r\\n\\t\\tif member == None:\\r\\n\\t\\t\\tawait ctx.channel.send(usage)\\r\\n\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t# Check for formatting issues\\r\\n\\t\\tif times == None:\\r\\n\\t\\t\\t# Either xp wasn't set - or it's the last section\\r\\n\\t\\t\\tif type(member) is str:\\r\\n\\t\\t\\t\\t# It' a string - the hope continues\\r\\n\\t\\t\\t\\troleCheck = DisplayName.checkRoleForInt(member, server)\\r\\n\\t\\t\\t\\tif roleCheck and roleCheck[\\\"Role\\\"]:\\r\\n\\t\\t\\t\\t\\tisRole = True\\r\\n\\t\\t\\t\\t\\tmember = roleCheck[\\\"Role\\\"]\\r\\n\\t\\t\\t\\t\\ttimes = roleCheck[\\\"Int\\\"]\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t# Role is invalid - check for member instead\\r\\n\\t\\t\\t\\t\\tnameCheck = DisplayName.checkNameForInt(member, server)\\r\\n\\t\\t\\t\\t\\tif not nameCheck:\\r\\n\\t\\t\\t\\t\\t\\tawait ctx.channel.send(usage)\\r\\n\\t\\t\\t\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\tif not nameCheck[\\\"Member\\\"]:\\r\\n\\t\\t\\t\\t\\t\\tmsg = 'I couldn\\\\'t find that user or role on the server.'.format(member)\\r\\n\\t\\t\\t\\t\\t\\tawait ctx.channel.send(msg)\\r\\n\\t\\t\\t\\t\\t\\treturn\\r\\n\\t\\t\\t\\t\\tmember = nameCheck[\\\"Member\\\"]\\r\\n\\t\\t\\t\\t\\ttimes = nameCheck[\\\"Int\\\"]\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t# Set the torment flag\\r\\n\\t\\tself.toTorment = True\\r\\n\\r\\n\\t\\tif times == None:\\r\\n\\t\\t\\t# Still no times - roll back to default\\r\\n\\t\\t\\ttimes = 25\\r\\n\\t\\t\\t\\r\\n\\t\\tif times > 100:\\r\\n\\t\\t\\ttimes = 100\\r\\n\\t\\t\\t\\r\\n\\t\\tif times == 0:\\r\\n\\t\\t\\tawait ctx.channel.send('Oooooh - I bet they feel *sooooo* tormented...')\\r\\n\\t\\t\\treturn\\r\\n\\t\\t\\r\\n\\t\\tif times < 0:\\r\\n\\t\\t\\tawait ctx.channel.send('I just uh... *un-tormented* them. Yeah.')\\r\\n\\t\\t\\treturn\\r\\n\\r\\n\\t\\t# Delete original torment message\\r\\n\\t\\tawait message.delete()\\r\\n\\t\\t\\r\\n\\t\\tfor i in range(0, times):\\r\\n\\t\\t\\t# Do this over time\\r\\n\\t\\t\\tfor channel in server.channels:\\r\\n\\t\\t\\t\\t# Get user's permissions\\r\\n\\t\\t\\t\\tif type(member) is discord.Role or channel.permissions_for(member).read_messages and type(channel) is discord.TextChannel:\\r\\n\\t\\t\\t\\t\\t# Only ping where they can read\\r\\n\\t\\t\\t\\t\\ttry:\\r\\n\\t\\t\\t\\t\\t\\tif member.name == \\\"@everyone\\\" and type(member) is discord.Role:\\r\\n\\t\\t\\t\\t\\t\\t\\ttmessage = await channel.send(\\\"{}\\\".format(member.name),allowed_mentions=discord.AllowedMentions.all())\\r\\n\\t\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\t\\ttmessage = await channel.send('{}'.format(member.mention),allowed_mentions=discord.AllowedMentions.all())\\r\\n\\t\\t\\t\\t\\t\\tawait tmessage.delete()\\r\\n\\t\\t\\t\\t\\texcept Exception:\\r\\n\\t\\t\\t\\t\\t\\tpass\\r\\n\\t\\t\\tfor j in range(0, self.waitBetween):\\r\\n\\t\\t\\t\\t# Wait for 1 second, then check if we should cancel - then wait some more\\r\\n\\t\\t\\t\\tawait asyncio.sleep(1)\\r\\n\\t\\t\\t\\tif not self.toTorment:\\r\\n\\t\\t\\t\\t\\treturn\",\n \"def follow_following_followers(self):\\n self.logger.log(\\\"starting follow_following_followers...\\\")\\n follows_accounts = self.following\\n random.shuffle(follows_accounts)\\n for acc in follows_accounts:\\n try:\\n try:\\n followw = perform_with_ran_delay(self.instagram.get_followers, acc, 150, 15,\\n delayed=True)\\n accountstofollow = followw[\\\"accounts\\\"]\\n random.shuffle(accountstofollow)\\n if len(accountstofollow) > 10:\\n accountstofollow = accountstofollow[:10]\\n for ac in accountstofollow:\\n if not self.is_user_following(ac.identifier):\\n self.add_following(ac.identifier)\\n self.logger.log(\\\"following: {}\\\".format(ac.username))\\n except Exception as e:\\n print(e)\\n self.logger.log(str(e))\\n finally:\\n sleep(3)\",\n \"async def _mute(ctx, user:discord.Member, time: str, self: bool):\\n if user.id in PI_BOT_IDS:\\n return await ctx.send(\\\"Hey! You can't mute me!!\\\")\\n if time == None:\\n return await ctx.send(\\\"You need to specify a length that this used will be muted. Examples are: `1 day`, `2 months, 1 day`, or `indef` (aka, forever).\\\")\\n role = None\\n if self:\\n role = discord.utils.get(user.guild.roles, name=ROLE_SELFMUTE)\\n else:\\n role = discord.utils.get(user.guild.roles, name=ROLE_MUTED)\\n parsed = \\\"indef\\\"\\n if time != \\\"indef\\\":\\n parsed = dateparser.parse(time, settings={\\\"PREFER_DATES_FROM\\\": \\\"future\\\"})\\n if parsed == None:\\n return await ctx.send(\\\"Sorry, but I don't understand that length of time.\\\")\\n CRON_LIST.append({\\\"date\\\": parsed, \\\"do\\\": f\\\"unmute {user.id}\\\"})\\n await user.add_roles(role)\\n eastern = pytz.timezone(\\\"US/Eastern\\\")\\n await ctx.send(f\\\"Successfully muted {user.mention} until `{str(eastern.localize(parsed))} EST`.\\\")\",\n \"def follow_followers(self):\\n self.logger.log(\\\"starting follow_followers...\\\")\\n follow = perform_with_ran_delay(self.instagram.get_followers, self.account.identifier, 150, 15, delayed=True)\\n for acc in follow[\\\"accounts\\\"]:\\n try:\\n try:\\n # print(\\\"{} follows me, do I follow him ? > {} \\\".format(acc.username,self.is_user_following(acc.identifier)))\\n if not self.is_user_following(acc.identifier):\\n if self.add_following(acc.identifier):\\n self.logger.log(\\\"following: {}\\\".format(acc.username))\\n else:\\n self.logger.log(\\\"follow not working at the moment\\\")\\n except Exception as e:\\n print(e)\\n self.logger.log(str(e))\\n continue\\n finally:\\n sleep(3)\",\n \"def follow(self, followerId, followeeId):\\n\\n # 把 followeeId append到他的 follow 属性中\\n if followerId == followeeId: # 不能自己关注自己\\n return\\n # 实例化一个user(followerID)\\n follower = UserInfo()\\n follower.user_id = followerId \\n follower.follows.append(followeeId) \\n self.user_pool[followerId] = follower\",\n \"def give_all(self, player):\\n self.transfer(self, player, self.account.balance)\\n return '%s spins \\\\'gimel\\\\' and gets everything.' % (player,)\",\n \"def hola(update, context):\\n user = update.message.from_user\\n update.message.reply_text('Hola! ' + user.first_name + ' :). Tu apellido es: ' + user.last_name + ', ¿cuál es tu descendencia? ')\",\n \"async def team_whoami(self, ctx: commands.Context):\\n team_id = await self.config.user(ctx.author).team_id()\\n\\n if team_id == -1:\\n await ctx.send('You are not affiliated with any team.')\\n return\\n if team_id not in self.teams:\\n await ctx.send('You are affiliated with a team whose ID I do not'\\n f'recognize. ({team_id})')\\n return\\n await ctx.send('You are affiliated with Team **%s**.' % (\\n self.teams[team_id].display_name),)\",\n \"async def coach(self, ctx):\\r\\n if ctx.guild.id == 445092370006933505:\\r\\n user = ctx.author\\r\\n dm_channel = user.dm_channel\\r\\n guild_data = self.config.guild(ctx.guild)\\r\\n coach_id = await guild_data.coachid()\\r\\n coach = ctx.guild.get_role(int(coach_id))\\r\\n channel_id = await self.config.guild(ctx.guild).coachchannel()\\r\\n channel = ctx.guild.get_channel(int(channel_id))\\r\\n if dm_channel is None:\\r\\n dm_channel = await user.create_dm()\\r\\n lst = await guild_data.get_raw(\\\"neededlist\\\")\\r\\n player_data = self.config.member(ctx.author)\\r\\n\\r\\n def check(m):\\r\\n return m.channel == dm_channel and m.author == user\\r\\n\\r\\n try:\\r\\n if user.id in lst:\\r\\n await ctx.send(\\\"You already have a coaching request pending please stay patient or contact our staff if its been over 48 hrs since your coaching request\\\")\\r\\n else:\\r\\n await ctx.send(\\\"Please check your DM's...\\\")\\r\\n await user.send(\\\"Please tell us your In game name?, Type 'stop' to stop the process\\\")\\r\\n ign = await self.bot.wait_for('message', timeout=60, check=check)\\r\\n ign_use = ign.content\\r\\n new_ign = ign.content.lower()\\r\\n if new_ign == \\\"stop\\\":\\r\\n raise UserEnd\\r\\n await user.send(\\\"Please tell us your Player Tag?, Type 'stop' to stop the process\\\")\\r\\n tag = await self.bot.wait_for('message', timeout=60, check=check)\\r\\n tag_use = tag.content\\r\\n new_tag = tag.content.lower()\\r\\n if new_tag == \\\"stop\\\":\\r\\n raise UserEnd\\r\\n await user.send(\\\"What time do you prefer for coaching? (Times in UTC only), Type 'stop' to stop the process\\\")\\r\\n time = await self.bot.wait_for('message', timeout=60, check=check)\\r\\n time_use = time.content\\r\\n np = time.content.lower()\\r\\n if np == \\\"stop\\\":\\r\\n raise UserEnd\\r\\n await user.send(\\\"What archatypes do you prefer to play?\\\")\\r\\n deck = await self.bot.wait_for('message', timeout=60, check=check)\\r\\n new_deck = deck.content.lower() # I know I could have made a function to check this but my brain is not working\\r\\n deck_use = deck.content\\r\\n if new_deck == \\\"stop\\\":\\r\\n raise UserEnd\\r\\n\\r\\n await user.send(\\\"You will be contacted by one of our coaches please stay patient.\\\")\\r\\n await channel.send(\\\"{} New coaching request from {}\\\".format(coach.mention, user.mention))\\r\\n await self.emb(ctx, \\\"Discord Name\\\", \\\"In Game Name\\\", \\\"Player Tag\\\", \\\"Preferred Time\\\", \\\"Deck Type\\\", user.mention, ign_use, tag_use, time_use, deck_use)\\r\\n lst.append(user.id)\\r\\n await self.config.guild(ctx.guild).neededlist.set(lst)\\r\\n await player_data.ign.set(ign_use)\\r\\n await player_data.tag.set(tag_use)\\r\\n await player_data.time.set(time_use)\\r\\n await player_data.deck_type.set(deck_use)\\r\\n\\r\\n except asyncio.exceptions.TimeoutError:\\r\\n await user.send(\\\"Timeout...\\\") # not sure where to send these messages\\r\\n return\\r\\n except UserEnd:\\r\\n await user.send(\\\"Stopped!\\\") # not sure where to send these messages\\r\\n return\\r\\n else:\\r\\n await ctx.send(\\\"This command only works in the Legend eSports server, join us at: https://discord.gg/GGuCXDn\\\")\",\n \"def __pass_time(self):\\n self.hunger += 1\\n self.boredom += 1\",\n \"async def _balance(ctx, user : discord.Member):\\n await balance(bot, ctx.message.author, user)\",\n \"async def bait(self, ctx:commands.Context):\\r\\n\\r\\n bait = ''\\r\\n member_bait = await self.config.member(ctx.message.author).bait()\\r\\n for i in member_bait.keys():\\r\\n if not member_bait[i] == 0:\\r\\n bait += f'{i}{\\\" \\\" * (25 - len(i))}{member_bait[i]}\\\\n'\\r\\n await ctx.send(f'You have:\\\\n```{bait[:-1]}```')\",\n \"def test_follow_manually_approved(self):\\n activity = {\\n \\\"@context\\\": \\\"https://www.w3.org/ns/activitystreams\\\",\\n \\\"id\\\": \\\"https://example.com/users/rat/follows/123\\\",\\n \\\"type\\\": \\\"Follow\\\",\\n \\\"actor\\\": \\\"https://example.com/users/rat\\\",\\n \\\"object\\\": \\\"https://example.com/user/mouse\\\",\\n }\\n\\n self.local_user.manually_approves_followers = True\\n self.local_user.save(\\n broadcast=False, update_fields=[\\\"manually_approves_followers\\\"]\\n )\\n\\n with patch(\\\"bookwyrm.models.activitypub_mixin.broadcast_task.apply_async\\\"):\\n views.inbox.activity_task(activity)\\n\\n # notification created\\n notification = models.Notification.objects.get()\\n self.assertEqual(notification.user, self.local_user)\\n self.assertEqual(notification.notification_type, \\\"FOLLOW_REQUEST\\\")\\n\\n # the request should exist\\n request = models.UserFollowRequest.objects.get()\\n self.assertEqual(request.user_subject, self.remote_user)\\n self.assertEqual(request.user_object, self.local_user)\\n\\n # the follow relationship should not exist\\n follow = models.UserFollows.objects.all()\\n self.assertEqual(list(follow), [])\",\n \"async def on_member_join(member):\\n if boterate.has_member(member):\\n boterate.update_member(member)\\n else:\\n boterate.insert_user(member)\",\n \"def add_following(self, user_id):\\n sleep(360) # too much follows => function ban\\n self.following.append(user_id)\\n return perform_with_ran_delay(self.instagram.follow, user_id)\",\n \"def update_user():\",\n \"async def muterole(self, ctx, *, role: discord.Role):\\n await queries.update_setting(ctx, \\\"guild_settings\\\", \\\"mute_role_id\\\", role.id)\\n await util.send_success(ctx, f\\\"Muting someone now gives them the role {role.mention}\\\")\",\n \"async def on_member_update(old, updated):\\n if old.nick != updated.nick:\\n boterate.update_member(updated)\",\n \"def follow_project(cls, user, project):\\n pass\",\n \"def do_the_thing(self):\\n # r = rospy.Rate(2)\\n if self.current_state == \\\"wait\\\":\\n self.wait()\\n elif self.current_state == \\\"follow\\\":\\n self.follow()\\n\\n self.pub.publish(self.twist)\\n\\n # r.sleep()\",\n \"async def update(self, ctx):\\n if is_support_guild(ctx.guild.id):\\n await ctx.send('Sorry, this discord does not allow update, saveid, '\\n 'leaderboard, and series commands so as not to overload me. '\\n 'Try `!careerstats` or `!yearlystats` with your customer ID to test '\\n 'or go to #invite-link to bring the bot to your discord for all functionality')\\n return\\n await ctx.send(f'Updating user: {ctx.author.name}, this may take a minute')\\n log.info(f'Updating user: {ctx.author.name}')\\n await self.updater.update_member(ctx)\",\n \"async def game(self):\\n self.time_remaining = randint(\\n int(pow(14 * len(self.participants), 0.8)),\\n int(pow(30 * len(self.participants), 0.8))\\n )\\n\\n member = choice(self.participants)\\n Timer(1, self.timer).start()\\n reply = True\\n pass_to = []\\n notify = randint(2, int(self.time_remaining / 2))\\n\\n while self.time_remaining > 0:\\n if not pass_to:\\n pass_from = list(self.participants)\\n pass_from.pop(pass_from.index(member))\\n pass_to = [choice(pass_from)]\\n pass_from.pop(pass_from.index(pass_to[0]))\\n pass_to.append(choice(pass_from))\\n\\n if reply is not None:\\n await client.send_message(self.channel, \\\"{} :bomb: got the bomb! Pass it to either {} or {}!\\\".format(\\n member.mention, pass_to[0].mention, pass_to[1].mention))\\n\\n def check(m):\\n if len(m.mentions) > 0:\\n if m.mentions[0] in pass_to:\\n return True\\n\\n return False\\n\\n wait = (self.time_remaining - notify) if (self.time_remaining >= notify) else self.time_remaining\\n reply = await client.wait_for_message(timeout=wait, channel=self.channel, author=member,\\n check=check)\\n\\n if reply:\\n member = reply.mentions[0]\\n pass_to = []\\n if self.member.permissions_in(self.channel).manage_messages:\\n asyncio.ensure_future(client.delete_message(reply))\\n elif self.time_remaining == notify:\\n asyncio.ensure_future(client.send_message(self.channel, \\\":bomb: :fire: **IT'S GONNA BLOW!**\\\"))\\n self.time_remaining -= 1\\n\\n await client.send_message(self.channel, \\\"{0.mention} :fire: :boom: :boom: :fire:\\\".format(member))\\n await client.send_message(self.channel, \\\"**GAME OVER**\\\")\",\n \"def unfollow(request, usertostopfollow):\\n stop_follow = Member.objects.get(user__username=usertostopfollow)\\n user = Member.objects.get(user=request.user)\\n user.following.remove(stop_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"def test_is_following(self):\\n\\n self.u1.following.append(self.u2)\\n db.session.commit()\\n\\n self.assertTrue(self.u1.is_following(self.u2))\\n self.assertFalse(self.u2.is_following(self.u1))\",\n \"async def tod_status(self, ctx, *args):\\n n = len(self.players)\\n if n > 0:\\n if n == 1:\\n s = \\\"person\\\"\\n else:\\n s = \\\"people\\\"\\n message = f\\\"A Truth or Dare game is currently taking place with {n} {s}!\\\"\\n else:\\n message = \\\"No Truth or Dare game is currently taking place.\\\"\\n await ctx.send(message)\",\n \"def updateBuddy(self,username,online,evilness,signontime,idletime,userclass,away):\\n print \\\"status changed for\\\",username\",\n \"def like_following(self):\\n self.logger.log(\\\"starting like_following...\\\")\\n count_following = self.account.follows_count\\n follows_accounts = self.following\\n random.shuffle(follows_accounts)\\n for acc in follows_accounts:\\n acc = perform_with_ran_delay(self.instagram.get_account_by_id, acc)\\n self.logger.log(\\\" {} > {} posts\\\".format(acc.username, acc.media_count))\\n if acc.media_count > 0:\\n\\n posts = perform_with_ran_delay(self.instagram.get_medias, acc.username, 50)\\n if posts:\\n for m in posts:\\n try:\\n perform_with_ran_delay(self.instagram.like, m.identifier)\\n self.logger.log(\\\"liking 1 post from \\\"+acc.username)\\n random_delay()\\n except Exception as e:\\n self.logger.log(\\\"skipping 1 post from \\\"+acc.username)\\n self.logger.log(e)\\n random_delay()\\n continue\",\n \"async def credits(self, ctx):\\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\\n description='\\\\n:ok_hand: :laughing:\\\\n :telephone_receiver::shirt::call_me:\\\\n :jeans: :fire:',\\n colour=0xf20006)\\n last_message = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(last_message, self.emojiUnicode['succes'])\",\n \"def on_me_joined(self, raw_msg, **kwargs):\",\n \"def status(message):\\n message.reply('User_id: ' +\\n str(message._client.users[message._get_user_id()]))\",\n \"def status(message):\\n message.reply('User_id: ' +\\n str(message._client.users[message._get_user_id()]))\",\n \"def _apply_rate_limit(self):\\n update_time = time()\\n user_name = self.bot.user.full_name\\n if user_name in self.tokens.keys():\\n last_change = self.tokens[user_name][0]\\n # Add 1 token for every 30 seconds from the last change\\n added_tokens = int((update_time - last_change) / 30)\\n self.tokens[user_name][1] += added_tokens\\n # Max at 5 self.tokens\\n if self.tokens[user_name][1] > 5:\\n self.tokens[user_name][1] = 5\\n else:\\n # Initialize the users token pair (last change, # of self.tokens)\\n self.tokens[user_name] = [update_time, 5] # Start with 5 self.tokens\\n if self.tokens[user_name][1] <= 0:\\n return False\\n self.tokens[user_name][1] -= 1\\n return True\",\n \"def loan(self):\",\n \"def follow(request, base62_id, stat_type = 1):\\n import datetime\\n \\n key = base62.to_decimal(base62_id)\\n link = get_object_or_404(Link, pk = key)\\n if request.user.is_anonymous():\\n user = User.objects.get(username=\\\"admin\\\")\\n else:\\n user = request.user\\n \\n if str(stat_type) == \\\"v\\\":\\n stat_type = 2 \\n else:\\n stat_type = 1\\n \\n stat = Stat(\\n link = link,\\n http_host = request.META.get(\\\"HTTP_HOST\\\",\\\"\\\"), \\n http_referer = request.META.get(\\\"HTTP_REFERER\\\",\\\"\\\"), \\n http_user_agent = request.META.get(\\\"HTTP_USER_AGENT\\\",\\\"\\\"),\\n remote_addr = request.META.get(\\\"REMOTE_ADDR\\\",\\\"\\\"), \\n remote_host = request.META.get(\\\"REMOTE_HOST\\\",\\\"\\\"),\\n stat_type = stat_type\\n )\\n stat.user = user\\n stat.save()\\n if stat_type == 1:\\n link.clicks = link.clicks + 1\\n else:\\n link.views = link.views + 1\\n link.save()\\n if stat_type == 2:\\n return HttpResponse(content=\\\"\\\", status=304)\\n \\n return HttpResponsePermanentRedirect(link.url)\",\n \"async def invite(ctx):\\r\\n await ctx.send(\\\"https://discordapp.com/oauth2/authorize?client_id=457903893079392256&scope=bot&permissions=2146958591\\\")\\r\\n ctx.counter(n)\",\n \"def update_user():\\n #TODO user update \\n pass\",\n \"async def newusers(self, ctx, *, count=5):\\n\\n count = min(max(count, 5), 25)\\n\\n now = datetime.now(timezone.utc)\\n e = disnake.Embed()\\n\\n for idx, member in enumerate(\\n sorted(ctx.guild.members, key=lambda m: m.joined_at, reverse=True)\\n ):\\n if idx >= count:\\n break\\n\\n value = \\\"Joined {0} ago\\\\nCreated {1} ago\\\".format(\\n pretty_timedelta(now - member.joined_at),\\n pretty_timedelta(now - member.created_at),\\n )\\n e.add_field(name=po(member), value=value, inline=False)\\n\\n await ctx.send(embed=e)\",\n \"def is_follower(self, you, them):\\n if self.filter(from_user=them, to_user=you).count() > 0:\\n return True\\n return False\",\n \"def follow_user(self, user):\\n self.nav_user(user)\\n follow_button = self.driver.find_element_by_xpath(\\n \\\"//button[contains(text(), 'Follow')]\\\")\\n follow_button.click()\\n time.sleep(1)\\n self.driver.get(self.base_url)\",\n \"async def _pay_money(ctx, user : discord.Member, amount : int):\\n if amount<0:\\n await bot.reply(\\\"You can't pay someone a negative amount!\\\")\\n elif user==ctx.message.author:\\n await bot.reply(\\\"You can't pay yourself!\\\")\\n else:\\n await transfer(bot, ctx.message.author, user, amount)\",\n \"def test_followers_following_list_unauthorized(self):\\n\\n self.u2.following.append(self.u)\\n db.session.commit()\\n\\n with self.client as client:\\n response = client.get(\\\"/users/2/following\\\")\\n\\n self.assertEqual(response.location, \\\"http://localhost/\\\")\\n self.assertIn('Access unauthorized.', get_flashed_messages())\\n\\n response2 = client.get(\\\"/users/2/followers\\\")\\n\\n self.assertEqual(response2.location, \\\"http://localhost/\\\")\\n self.assertIn('Access unauthorized.', get_flashed_messages())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6377603","0.6330849","0.6264719","0.6097981","0.60914946","0.60311484","0.57444423","0.5744262","0.57255733","0.5723841","0.56434613","0.5592666","0.554689","0.55407053","0.552391","0.55216295","0.55041254","0.54987043","0.5481601","0.54815555","0.5457507","0.5447923","0.54252946","0.54232013","0.5399918","0.53827333","0.5379372","0.536289","0.535908","0.535908","0.5347736","0.5330982","0.53247505","0.5320701","0.5312673","0.53079575","0.53023535","0.5293631","0.5291236","0.52903384","0.52875906","0.5286809","0.5284725","0.5284608","0.5283283","0.5275625","0.52717745","0.52664685","0.52560145","0.52545923","0.52474403","0.5246488","0.52397466","0.5219636","0.52030814","0.5196209","0.51960415","0.5195266","0.5183465","0.5179062","0.51622427","0.5160093","0.5159797","0.51443106","0.51247203","0.51198137","0.5119318","0.51098317","0.51027924","0.5095903","0.5088356","0.5087504","0.5084595","0.5081895","0.50801617","0.5070322","0.5057029","0.5056534","0.505296","0.5048608","0.50473213","0.5046152","0.50450605","0.50434846","0.5043319","0.5040069","0.50390387","0.5034782","0.50331414","0.50331414","0.503016","0.5027742","0.50261986","0.50197065","0.50196683","0.50176495","0.50140613","0.50129503","0.50077355","0.5006368"],"string":"[\n \"0.6377603\",\n \"0.6330849\",\n \"0.6264719\",\n \"0.6097981\",\n \"0.60914946\",\n \"0.60311484\",\n \"0.57444423\",\n \"0.5744262\",\n \"0.57255733\",\n \"0.5723841\",\n \"0.56434613\",\n \"0.5592666\",\n \"0.554689\",\n \"0.55407053\",\n \"0.552391\",\n \"0.55216295\",\n \"0.55041254\",\n \"0.54987043\",\n \"0.5481601\",\n \"0.54815555\",\n \"0.5457507\",\n \"0.5447923\",\n \"0.54252946\",\n \"0.54232013\",\n \"0.5399918\",\n \"0.53827333\",\n \"0.5379372\",\n \"0.536289\",\n \"0.535908\",\n \"0.535908\",\n \"0.5347736\",\n \"0.5330982\",\n \"0.53247505\",\n \"0.5320701\",\n \"0.5312673\",\n \"0.53079575\",\n \"0.53023535\",\n \"0.5293631\",\n \"0.5291236\",\n \"0.52903384\",\n \"0.52875906\",\n \"0.5286809\",\n \"0.5284725\",\n \"0.5284608\",\n \"0.5283283\",\n \"0.5275625\",\n \"0.52717745\",\n \"0.52664685\",\n \"0.52560145\",\n \"0.52545923\",\n \"0.52474403\",\n \"0.5246488\",\n \"0.52397466\",\n \"0.5219636\",\n \"0.52030814\",\n \"0.5196209\",\n \"0.51960415\",\n \"0.5195266\",\n \"0.5183465\",\n \"0.5179062\",\n \"0.51622427\",\n \"0.5160093\",\n \"0.5159797\",\n \"0.51443106\",\n \"0.51247203\",\n \"0.51198137\",\n \"0.5119318\",\n \"0.51098317\",\n \"0.51027924\",\n \"0.5095903\",\n \"0.5088356\",\n \"0.5087504\",\n \"0.5084595\",\n \"0.5081895\",\n \"0.50801617\",\n \"0.5070322\",\n \"0.5057029\",\n \"0.5056534\",\n \"0.505296\",\n \"0.5048608\",\n \"0.50473213\",\n \"0.5046152\",\n \"0.50450605\",\n \"0.50434846\",\n \"0.5043319\",\n \"0.5040069\",\n \"0.50390387\",\n \"0.5034782\",\n \"0.50331414\",\n \"0.50331414\",\n \"0.503016\",\n \"0.5027742\",\n \"0.50261986\",\n \"0.50197065\",\n \"0.50196683\",\n \"0.50176495\",\n \"0.50140613\",\n \"0.50129503\",\n \"0.50077355\",\n \"0.5006368\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68102974"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13788,"cells":{"query":{"kind":"string","value":"Unmutes everyone that you have muted"},"document":{"kind":"string","value":"def auto_unmute():\n muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n # put user IDs of people you want to remain muted here\n users_keep_muted = set([])\n \n # mute all \n for user_id in muted:\n if user_id not in users_keep_muted:\n t.mutes.users.destroy(user_id=user_id)\n print(\"unmuted %d\" % (user_id))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["async def unmute(self, ctx, user: Redeemed):\n if member == None or member == ctx.message.author:\n await ctx.send(\"You cannot unmute yourself!\")\n return \n await user.remove_roles(discord.utils.get(ctx.guild.roles, name=\"Muted\"))\n await ctx.send(f\"{user.mention} has been unmuted\")","async def unmute(self, ctx, member: discord.Member):\n for channel in ctx.guild.text_channels:\n permissions = channel.permissions_for(member)\n\n if permissions.read_messages:\n # This removes the PermissionOverwrite on the channel, it\n # does not grant send_messages=True\n await channel.set_permissions(member, overwrite=None)","async def unmute(self, ctx, user: discord.Member, *, reason: str = None):\r\n server = ctx.message.guild\r\n channel = ctx.message.channel\r\n author = ctx.message.author\r\n action = \"Unmute\"\r\n role = discord.utils.get(server.roles, name=\"Muted - Sensei\")\r\n if not role:\r\n await ctx.send(\"No-one is muted in this server :no_entry:\")\r\n return\r\n if role not in user.roles:\r\n await ctx.send(\"**{}** is not muted :no_entry:\".format(user))\r\n return\r\n try:\r\n await user.remove_roles(role)\r\n except:\r\n await ctx.send(\"I cannot remove the mute role from the user :no_entry:\")\r\n return\r\n await ctx.send(f\"**{user}** has been unmuted {self.bot.get_emoji(470063310386233344)}\")\r\n try:\r\n await self._log(author, server, action, reason, user)\r\n except:\r\n pass\r\n self.d[str(server.id)][str(user.id)][\"toggle\"] = False\r\n self.d[str(server.id)][str(user.id)][\"time\"] = None\r\n self.d[str(server.id)][str(user.id)][\"amount\"] = None\r\n dataIO.save_json(self.file, self.d)\r\n try:\r\n s = discord.Embed(title=\"You have been unmuted early in {}\".format(server.name), colour=000000,\r\n timestamp=datetime.datetime.utcnow())\r\n s.add_field(name=\"Moderator\", value=\"{} ({})\".format(author, str(author.id)))\r\n await user.send(embed=s)\r\n except:\r\n pass","async def unmute(self, ctx: Context, members: commands.Greedy[discord.Member], *, reason: str = None):\n\n role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n member_display = []\n\n for member in members:\n if role not in member.roles:\n await ctx.send(f\"guild member `{member.display_name}` is already unmuted\")\n\n else:\n\n if await self.hiearchy_check(ctx, member):\n continue\n\n member_display.append(str(member))\n await member.remove_roles(role, reason=reason)\n\n member_display = \", \".join(member_display)\n\n if not member_display:\n member_display = \"no one\"\n\n await ctx.send(f\"> {ctx.author.name} unmuted {member_display}\")","async def unmute(self, ctx,\n\t\ttarget: discord.Member\n\t):\n\n\t\tself.check_perms(ctx.author, target)\n\n\t\thandler = await Handler.new(self.bot, ctx.guild)\n\t\tawait handler.unmute(ctx.author, target)\n\n\t\tawait ctx.success(f\"{target} (`{target.id}`) has been unmuted.\")","async def tradingunmute(self, ctx, target: discord.Member, *, reason=None):\n\n action = TradingUnmute(\n target=target,\n user=ctx.author,\n reason=reason,\n guild_id=ctx.guild.id,\n )\n await action.execute(ctx)\n await action.notify()\n await ctx.send(f\"Unmuted **{target}** in trading channels.\")","async def mute(self, ctx):\n author = ctx.message.author\n channel = author.voice.channel\n members = channel.members\n for member in members:\n user = ctx.guild.get_member(member.id)\n await user.edit(mute=True)\n\n embed = await embeds.generate_embed(ctx, author, members,\n description=\":white_check_mark: Successfully muted the following users:\",\n title=channel.name)\n await ctx.send(embed=embed)","async def remove_mute(id: int) -> None:\n\n guild = BOT_GLOBAL.get_guild(BOT_GLOBAL.settings.guild_id)\n if guild is not None:\n mute_role = BOT_GLOBAL.settings.guild().role_mute\n mute_role = guild.get_role(mute_role)\n if mute_role is not None:\n user = guild.get_member(id)\n if user is not None:\n await user.remove_roles(mute_role)\n case = Case(\n _id=BOT_GLOBAL.settings.guild().case_id,\n _type=\"UNMUTE\",\n mod_id=BOT_GLOBAL.user.id,\n mod_tag=str(BOT_GLOBAL.user),\n reason=\"Temporary mute expired.\",\n )\n await BOT_GLOBAL.settings.inc_caseid()\n await BOT_GLOBAL.settings.add_case(user.id, case)\n\n u = await BOT_GLOBAL.settings.user(id=user.id)\n u.is_muted = False\n u.save()\n\n log = await prepare_unmute_log(BOT_GLOBAL.user, user, case)\n\n log.remove_author()\n log.set_thumbnail(url=user.avatar_url)\n\n public_chan = guild.get_channel(\n BOT_GLOBAL.settings.guild().channel_public)\n \n dmed = True\n try:\n await user.send(embed=log)\n except Exception:\n dmed = False\n \n await public_chan.send(user.mention if not dmed else \"\", embed=log)\n\n else:\n case = Case(\n _id=BOT_GLOBAL.settings.guild().case_id,\n _type=\"UNMUTE\",\n mod_id=BOT_GLOBAL.user.id,\n mod_tag=str(BOT_GLOBAL.user),\n reason=\"Temporary mute expired.\",\n )\n await BOT_GLOBAL.settings.inc_caseid()\n await BOT_GLOBAL.settings.add_case(id, case)\n\n u = await BOT_GLOBAL.settings.user(id=id)\n u.is_muted = False\n u.save()","async def voice_unmute(self, ctx, member: discord.Member, *, reason: typing.Optional[str]):\n if member.voice and member.voice.mute:\n await member.edit(mute=False, reason=reason[:512])\n await ctx.send(f\"User {member.mention} successfully unmuted from voice\")\n return\n if member.voice and not member.voice.mute:\n await ctx.send(\"User is not muted\")\n return\n self.to_unmute.append(member.id)\n await self.add_to_unmutes(member.id)\n await ctx.send(f\"User {member.mention} added to users that will be unmuted\")","async def unmute(self, ctx, target: discord.Member, *, reason=None):\n\n action = Unmute(\n target=target,\n user=ctx.author,\n reason=reason,\n guild_id=ctx.guild.id,\n )\n await action.execute(ctx)\n await action.notify()\n await ctx.send(f\"Unmuted **{target}**.\")","def disable_mute(self):\n self.mute = False","def handle_mic_unmute(_):\n loop.unmute()","def mute(self, msg, args):\n if self.mute:\n self.mute=False\n return \"Yay, I can make noise again!\"\n else:\n self.mute=True\n return \"OK, I'll shut up now!\"","def unmute(self, nick, chan, arg):\n if not arg:\n \tbot.msg(chan, get_doc())\n self.state.unmute(arg)\n self.msg(chan, \"%s: You are now allowed to use this bot\" % (arg))","async def mute(self, *args, **kwargs):\n self.muted = not self.muted # toogle\n if self.muted:\n self.just_muted = True\n return \"I've been muted :(\"\n return \"I'm back! :D\"","def mute():\n request_command(tv_command=TVCommand.mute)","async def unmute(self, ctx, user: discord.Member = None):\n try:\n if not user:\n return await ctx.send(f\"> **<@{ctx.author.id}>, Please specify a user to unmute.**\")\n if user.id == ctx.author.id:\n return await ctx.send(f\"> **<@{ctx.author.id}>, You cannot unmute yourself.**\")\n mute_role = await self.get_mute_role(ctx)\n muted = await self.check_if_muted(user.id, mute_role)\n if not mute_role:\n return await ctx.send(\n \">**This user was not muted by me as the mute role could not be found. In order for me to create a \"\n \"custom mute role, I need to mute someone first.**\")\n if muted:\n await user.remove_roles(mute_role,\n reason=f\"UnMuting User - Requested by {ctx.author.display_name} ({user.id})\")\n return await ctx.send(f\"> **<@{user.id}> has been unmuted.**\")\n else:\n return await ctx.send(f\"> **<@{user.id}> is not muted.**\")\n except Exception as e:\n log.console(e)\n return await ctx.send(f\"> **I am missing permissions to unmute {user.display_name}. {e}**\")","async def mute(self, ctx: Context, members: commands.Greedy[discord.Member], reason=\"no reason\"):\n\n role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n member_display = []\n\n for i, member in enumerate(members):\n if role in member.roles:\n await ctx.send(f\"guild member `{member.display_name}` is already muted\", delete_after=8)\n del members[i]\n\n if role is None:\n permissions = discord.Permissions()\n permissions.change_nickname = True\n permissions.send_messages = False\n permissions.read_message_history = True\n role = await ctx.guild.create_role(name=\"Muted\", permissions=permissions)\n\n await self.set_perms(ctx.guild, role)\n\n for member in members:\n\n if await self.hiearchy_check(ctx, member):\n continue\n\n member_display.append(str(member))\n await member.add_roles(role, reason=reason)\n\n member_display = \", \".join(member_display)\n\n if not member_display:\n member_display = \"no one\"\n\n await ctx.send(f\"> {ctx.author.name} muted {member_display}\")","def set_unmute_finding(finding_path: str) -> None:\n from google.cloud import securitycenter\n\n client = securitycenter.SecurityCenterClient()\n\n request = securitycenter.SetMuteRequest()\n request.name = finding_path\n request.mute = securitycenter.Finding.Mute.UNMUTED\n\n finding = client.set_mute(request)\n print(f\"Mute value for the finding: {finding.mute.name}\")","async def unshush(self, ctx):\n author = ctx.message.author\n channel = author.voice.channel\n members = channel.members\n for member in members:\n user = ctx.guild.get_member(member.id)\n await user.edit(mute=False, deafen=False)\n\n embed = await embeds.generate_embed(ctx, author, members,\n description=\":white_check_mark: Successfully unshushed the following users:\",\n title=channel.name)\n await ctx.send(embed=embed)","async def mute(self, ctx, member: discord.Member, *, time:TimeConverter = None):\r\n\r\n if member.top_role >= ctx.author.top_role:\r\n return await ctx.send(\"you can't mute that person\")\r\n\r\n role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\r\n await member.add_roles(role)\r\n await ctx.reply((f\"Muted {member} for {time}s\" if time else f\"Muted {member}\"))\r\n\r\n if time:\r\n await asyncio.sleep(time)\r\n await member.remove_roles(role)","async def mute(self, ctx, member: discord.Member, time='15m'):\n guild_permissions = member.guild_permissions\n wait_time = parse_time(time).total_seconds()\n # Because sometimes members have nicknames with markdown\n escaped_name = escape_markdown(member.display_name)\n\n if guild_permissions.kick_members:\n # do not mute someone who has permissions to kick members\n await ctx.send(f'Cannot mute {escaped_name} due to roles.')\n\n elif member.bot:\n # do not mute bots\n await ctx.send(f'Cannot mute {escaped_name} (is a bot).')\n\n else:\n overwrite = discord.PermissionOverwrite(\n add_reactions=False,\n send_messages=False,\n )\n\n log_str = (f'{ctx.author.display_name} has muted '\n f'member {member} (<@{member.id}>) for {time}.')\n logger.info(log_str)\n\n for channel in ctx.guild.text_channels:\n permissions = channel.permissions_for(member)\n\n if permissions.read_messages:\n await channel.set_permissions(member, overwrite=overwrite)\n\n await asyncio.sleep(wait_time)\n await ctx.invoke(self.unmute, member)","async def async_turn_off(self):\n await self.async_mute_volume(True)","async def mute(self, ctx, member : discord.Member, *, reason : str):\r\n mutedRole = discord.utils.get(ctx.guild.roles, name = \"Muted\")\r\n if not mutedRole:\r\n channels = 0\r\n mutedRole = await ctx.guild.create_role(name=\"Muted\")\r\n for channel in ctx.guild.text_channels:\r\n await channel.set_permissions(mutedRole, send_messages=False)\r\n channels += 1 \r\n await ctx.send(f\"Successfully applied overwrites for {channels} channels\")\r\n await member.add_roles(mutedRole)\r\n embed = discord.Embed(title=\"Muted\", description = f\"You have been muted in **{ctx.guild.name}** by **{ctx.author}** **indefinetly** for reason **{reason}**\", colour = ctx.author.color, timestamp = datetime.datetime.now())\r\n await member.send(embed=embed)","def toggle_mute(cls) -> bool:\n raise NotImplementedError","async def removeroleall(self, ctx, role: discord.Role):\n muted_role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n punished_role = discord.utils.get(ctx.guild.roles, name=\"Punished\")\n\n if role > ctx.author.top_role:\n return await ctx.send(\n embed=discord.Embed(\n title=\"You don't have permission to remove this role\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.darker_grey(),\n )\n )\n\n if role == muted_role or role == punished_role:\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Can not remove *{role}* role using this command.\",\n description=\"For more information run ```.help removeroleall```\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.red(),\n )\n )\n\n for i in ctx.guild.members:\n if not i.bot:\n await i.remove_roles(role)\n\n await ctx.send(\n embed=discord.Embed(\n title=f\"*{role}* has been removed from **{len(ctx.guild.members)}** members!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","async def selfmute(ctx, *args):\n user = ctx.message.author\n if await is_staff(ctx):\n return await ctx.send(\"Staff members can't self mute.\")\n time = \" \".join(args)\n await _mute(ctx, user, time, self=True)","def unmute_callback(id: int) -> None:\n\n BOT_GLOBAL.loop.create_task(remove_mute(id))","def mute_track(self, track, muted):\n pass","def mute(self, nick, chan, arg):\n if not arg:\n \treturn bot.msg(chan, get_doc())\n self.state.mute(arg)\n self.msg(chan, \"%s: You are temporarily prohibited from using this bot\" % (arg))","def auto_mute_following():\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n not_muted = following - muted\n\n # put user IDs of people you do not want to mute here\n users_keep_unmuted = set([])\n \n # mute all \n for user_id in not_muted:\n if user_id not in users_keep_unmuted:\n t.mutes.users.create(user_id=user_id)\n print(\"muted %d\" % (user_id))","async def _mute(ctx, user:discord.Member, time: str, self: bool):\n if user.id in PI_BOT_IDS:\n return await ctx.send(\"Hey! You can't mute me!!\")\n if time == None:\n return await ctx.send(\"You need to specify a length that this used will be muted. Examples are: `1 day`, `2 months, 1 day`, or `indef` (aka, forever).\")\n role = None\n if self:\n role = discord.utils.get(user.guild.roles, name=ROLE_SELFMUTE)\n else:\n role = discord.utils.get(user.guild.roles, name=ROLE_MUTED)\n parsed = \"indef\"\n if time != \"indef\":\n parsed = dateparser.parse(time, settings={\"PREFER_DATES_FROM\": \"future\"})\n if parsed == None:\n return await ctx.send(\"Sorry, but I don't understand that length of time.\")\n CRON_LIST.append({\"date\": parsed, \"do\": f\"unmute {user.id}\"})\n await user.add_roles(role)\n eastern = pytz.timezone(\"US/Eastern\")\n await ctx.send(f\"Successfully muted {user.mention} until `{str(eastern.localize(parsed))} EST`.\")","def mute_members(_) -> int:\n return 1 << 22","def mute_members(_) -> int:\n return 1 << 22","def unmute(guild, channel):\n\tlogger.info('Unmuting channel {}::{}...', guild.name, channel.name)\n\tif str(guild.id) in Settings.muted_channels:\n\t\tif str(channel.id) in Settings.muted_channels[str(guild.id)]:\n\t\t\tSettings.muted_channels[str(guild.id)].remove(str(channel.id))","async def massremove(\n self,\n ctx,\n role: discord.Role,\n member: commands.Greedy[discord.Member],\n ):\n\n role = discord.utils.get(ctx.guild.roles, id=role.id)\n\n muted_role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n punished_role = discord.utils.get(ctx.guild.roles, name=\"Punished\")\n\n if role > ctx.author.top_role:\n return await ctx.send(\n embed=discord.Embed(\n title=\"You don't have permission to remove this role\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.darker_grey(),\n )\n )\n\n if role == muted_role or role == punished_role:\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Can not remove *{role}* role using this command.\",\n description=\"For more information run ```.help massremove```\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.red(),\n )\n )\n\n for i in member:\n if role not in i.roles:\n await ctx.channel.send(\n embed=discord.Embed(\n title=f\"*{i}* doesn't have *{role}* Role!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.greyple(),\n )\n )\n\n await i.remove_roles(role)\n\n await ctx.send(\n embed=discord.Embed(\n title=f\"*{role}* has been removed from **{len(member)}** members!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","async def toggle_mute(self, group_id: int) -> None:\n await self._api.call('group', 'toggle_mute', gid=group_id)","async def async_set_muted(self, mute):\n self._muted = mute","def mute(self, status=None):\n if status is not None:\n st = status\n else:\n st = not status.volume_muted\n\n self._socket_client.receiver_controller.set_volume_muted(st)","async def unlock(ctx):\n member = ctx.message.author\n channel = ctx.message.channel\n\n if (channel.category.name in [\"beta\", \"staff\", \"Pi-Bot\"]):\n return await ctx.send(\"This command is not suitable for this channel because of its category.\")\n\n if (channel.category.name == CATEGORY_SO or channel.category.name == CATEGORY_GENERAL):\n await ctx.send(\"Synced permissions with channel category.\")\n return await channel.edit(sync_permissions=True)\n\n member_role = discord.utils.get(member.guild.roles, name=ROLE_MR)\n if (channel.category.name != CATEGORY_STATES):\n await ctx.channel.set_permissions(member_role, add_reactions=True, send_messages=True, read_messages=True)\n else:\n await ctx.channel.set_permissions(member_role, add_reactions=True, send_messages=True)\n\n wiki_role = discord.utils.get(member.guild.roles, name=ROLE_WM)\n gm_role = discord.utils.get(member.guild.roles, name=ROLE_GM)\n aRole = discord.utils.get(member.guild.roles, name=ROLE_AD)\n bRole = discord.utils.get(member.guild.roles, name=ROLE_BT)\n await ctx.channel.set_permissions(wiki_role, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.channel.set_permissions(gm_role, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.channel.set_permissions(aRole, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.channel.set_permissions(bRole, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.send(\"Unlocked the channel to Member access. Please check if permissions need to be synced.\")","def unblock(self):\n data = {'container': self._reddit.user.me().fullname,\n 'name': str(self), 'type': 'enemy'}\n url = API_PATH['unfriend'].format(subreddit='all')\n # PRAW5 REMOVE (return statement)\n return self._reddit.post(url, data=data)","async def test_unsilence_role(self):\n test_cases = (\n (MockTextChannel(), self.cog.bot.get_guild(Guild.id).default_role),\n (MockVoiceChannel(), self.cog.bot.get_guild(Guild.id).get_role(Roles.voice_verified))\n )\n\n for channel, role in test_cases:\n with self.subTest(channel=channel, role=role):\n await self.cog._unsilence_wrapper(channel, MockContext())\n channel.overwrites_for.assert_called_with(role)","def mute(self, status=None):\n if status is None:\n status = not self.status.volume_muted\n\n self._socket_client.receiver_controller.set_volume_muted(status)","def muted(self, muted):\n if muted is None:\n raise ValueError(\"Invalid value for `muted`, must not be `None`\") # noqa: E501\n\n self._muted = muted","async def async_toggle(self):\n await self.async_mute_volume(not self._muted)","async def mute(self, ctx,\n\t\ttarget: discord.Member,\n\t\t*, reason: str = \"No reason given.\"\n\t):\n\n\t\tself.check_perms(ctx.author, target)\n\t\t\n\t\thandler = await Handler.new(self.bot, ctx.guild)\n\t\tawait handler.mute(ctx.author, target, reason)\n\t\t\n\t\tawait ctx.success(f\"{target} (`{target.id}`) has been muted for:\\n{reason}\")","async def mute(\n self, ctx, target: discord.Member, *, reason: Union[ModerationUserFriendlyTime, str]\n ):\n\n if target.guild_permissions.kick_members:\n return await ctx.send(\"You can't punish that person!\")\n\n if isinstance(reason, time.UserFriendlyTime):\n expires_at = reason.dt\n reason = reason.arg\n else:\n expires_at = None\n\n action = Mute(\n target=target,\n user=ctx.author,\n reason=reason,\n guild_id=ctx.guild.id,\n created_at=ctx.message.created_at,\n expires_at=expires_at,\n )\n await action.execute(ctx)\n await action.notify()\n if action.duration is None:\n await ctx.send(f\"Muted **{target}**.\")\n else:\n await ctx.send(f\"Muted **{target}** for **{time.human_timedelta(action.duration)}**.\")","async def clrreact(ctx, msg: discord.Message, *args: discord.Member):\n users = args\n if (not users):\n await msg.clear_reactions()\n await ctx.send(\"Cleared all reactions on message.\")\n else:\n for u in users:\n for r in msg.reactions:\n await r.remove(u)\n await ctx.send(f\"Cleared reactions on message from {len(users)} user(s).\")","async def team_unignore(self, ctx: commands.Context):\n await self.config.user(ctx.author).do_not_message.set(False)\n await ctx.send('Okay, I\\'ll include you back in team-wide DMs.')","async def mute(self, ctx, user: discord.Member, time_and_unit=None, *, reason: str = None):\r\n server = ctx.message.guild\r\n channel = ctx.message.channel\r\n author = ctx.message.author\r\n if channel.permissions_for(user).administrator:\r\n await ctx.send(\"That user has administrator perms, why would I even try :no_entry:\")\r\n return\r\n if user.top_role.position >= author.top_role.position:\r\n if author == server.owner:\r\n pass\r\n else:\r\n await ctx.send(\"You can not mute someone higher than your own role :no_entry:\")\r\n return\r\n if not time_and_unit:\r\n time2 = 600\r\n time = \"10\"\r\n unit = \"minutes\"\r\n else:\r\n try:\r\n unit = time_and_unit[len(time_and_unit) - 1:len(time_and_unit)]\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n try:\r\n time = time_and_unit[0:len(time_and_unit) - 1]\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n if unit == \"s\":\r\n try:\r\n time2 = int(time)\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n if time == \"1\":\r\n unit = \"second\"\r\n else:\r\n unit = \"seconds\"\r\n elif unit == \"m\":\r\n try:\r\n time2 = int(time) * 60\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n if time == \"1\":\r\n unit = \"minute\"\r\n else:\r\n unit = \"minutes\"\r\n elif unit == \"h\":\r\n try:\r\n time2 = int(time) * 3600\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n if time == \"1\":\r\n unit = \"hour\"\r\n else:\r\n unit = \"hours\"\r\n elif unit == \"d\":\r\n try:\r\n time2 = int(time) * 86400\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n if time == \"1\":\r\n unit = \"day\"\r\n else:\r\n unit = \"days\"\r\n else:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n action = \"Mute ({} {})\".format(time, unit)\r\n if str(server.id) not in self.d:\r\n self.d[str(server.id)] = {}\r\n dataIO.save_json(self.file, self.d)\r\n if str(user.id) not in self.d[str(server.id)]:\r\n self.d[str(server.id)][str(user.id)] = {}\r\n dataIO.save_json(self.file, self.d)\r\n if \"toggle\" not in self.d[str(server.id)][str(user.id)]:\r\n self.d[str(server.id)][str(user.id)][\"toggle\"] = False\r\n dataIO.save_json(self.file, self.d)\r\n if \"time\" not in self.d[str(server.id)][str(user.id)]:\r\n self.d[str(server.id)][str(user.id)][\"time\"] = None\r\n dataIO.save_json(self.file, self.d)\r\n if \"amount\" not in self.d[str(server.id)][str(user.id)]:\r\n self.d[str(server.id)][str(user.id)][\"amount\"] = None\r\n dataIO.save_json(self.file, self.d)\r\n role = discord.utils.get(server.roles, name=\"Muted - Sensei\")\r\n overwrite = discord.PermissionOverwrite()\r\n overwrite.send_messages = False\r\n perms = discord.PermissionOverwrite()\r\n perms.speak = False\r\n if not role:\r\n role = await server.create_role(name=\"Muted - Sensei\")\r\n for channels in ctx.guild.text_channels:\r\n await channels.set_permissions(role, overwrite=overwrite)\r\n for channels in ctx.guild.voice_channels:\r\n await channels.set_permissions(role, overwrite=perms)\r\n if role in user.roles:\r\n await ctx.send(\"**{}** is already muted :no_entry:\".format(user))\r\n return\r\n try:\r\n await user.add_roles(role)\r\n except:\r\n await ctx.send(\"I cannot add the mute role to the user :no_entry:\")\r\n return\r\n await ctx.send(f\"**{user}** has been muted for {time} {unit} {self.bot.get_emoji(470063310386233344)}\")\r\n try:\r\n await self._log(author, server, action, reason, user)\r\n except:\r\n pass\r\n self.d[str(server.id)][str(user.id)][\"toggle\"] = True\r\n self.d[str(server.id)][str(user.id)][\"amount\"] = time2\r\n self.d[str(server.id)][str(user.id)][\"time\"] = ctx.message.created_at.timestamp()\r\n dataIO.save_json(self.file, self.d)\r\n try:\r\n s = discord.Embed(title=\"You have been muted in {} :speak_no_evil:\".format(server.name), colour=0xfff90d,\r\n timestamp=__import__('datetime').datetime.utcnow())\r\n s.add_field(name=\"Moderator\", value=\"{} ({})\".format(author, str(author.id)), inline=False)\r\n s.add_field(name=\"Time\", value=\"{} {}\".format(time, unit), inline=False)\r\n if reason:\r\n s.add_field(name=\"Reason\", value=reason, inline=False)\r\n await user.send(embed=s)\r\n except:\r\n pass","def mute2(individual):\n mutatePt=random.randint(0,len(individual)-1)\n if mutatePt==0:\n individual[mutatePt]=random.uniform(0.0, 0.02)\n elif mutatePt>=2 and mutatePt<=4:\n individual[mutatePt]=random.uniform(0.0, 0.005)\n elif mutatePt==5:\n individual[mutatePt]=random.uniform(0.0, 0.07)\n \n return individual,","def is_muted(self):\n return self.muting_handler.is_muted()","async def _clear_heist(self, ctx, user: discord.Member):\r\n author = ctx.message.author\r\n await self.thief.member_clear(user)\r\n await ctx.send(\"```{} administratively cleared {}```\".format(author.name, user.name))","async def moderation(self, ctx):\n\n new_value = await self.toggle_dm_setting(ctx.author.id, \"ban_kick_mute\")\n\n if new_value:\n message = \":white_check_mark: You will now receive DMs when you get muted, kicked or banned by me.\"\n else:\n message = \":white_check_mark: You will no longer receive DMs when you get muted, kicked or banned.\"\n\n await ctx.send(message)","async def mutedlist(self, ctx):\r\n server = ctx.message.guild\r\n msg = \"\"\r\n i = 0\r\n try:\r\n for userid in self.d[str(server.id)]:\r\n if self.d[str(server.id)][userid][\"toggle\"] == True:\r\n i = i + 1\r\n except:\r\n await ctx.send(\"No one is muted in this server :no_entry:\")\r\n return\r\n if i == 0:\r\n await ctx.send(\"No one is muted in this server :no_entry:\")\r\n return\r\n for userid in self.d[str(server.id)]:\r\n if self.d[str(server.id)][userid][\"time\"] == None or self.d[str(server.id)][userid][\"time\"] - ctx.message.created_at.timestamp() + self.d[str(server.id)][userid][\"amount\"] <= 0:\r\n time = \"Infinite\"\r\n else:\r\n m, s = divmod(self.d[str(server.id)][userid][\"time\"] - ctx.message.created_at.timestamp() +\r\n self.d[str(server.id)][userid][\"amount\"], 60)\r\n h, m = divmod(m, 60)\r\n d, h = divmod(h, 24)\r\n if d == 0:\r\n time = \"%d hours %d minutes %d seconds\" % (h, m, s)\r\n if h == 0 and d == 0:\r\n time = \"%d minutes %d seconds\" % (m, s)\r\n elif h == 0 and m == 0:\r\n time = \"%d seconds\" % (s)\r\n else:\r\n time = \"%d days %d hours %d minutes %d seconds\" % (d, h, m, s)\r\n if self.d[str(server.id)][userid][\"toggle\"] == True:\r\n user = discord.utils.get(server.members, id=int(userid))\r\n if user:\r\n msg += \"{} - {} (Till mute ends)\\n\".format(user, time)\r\n if not msg:\r\n await ctx.send(\"No one is muted in this server :no_entry:\")\r\n return\r\n s = discord.Embed(description=msg, colour=0xfff90d, timestamp=datetime.datetime.utcnow())\r\n s.set_author(name=\"Mute List for {}\".format(server), icon_url=server.icon_url)\r\n await ctx.send(embed=s)","def is_muted(self):\n # type: () -> bool\n return self._is_muted","async def tempmute(self, ctx,\n\t\ttarget: discord.Member,\n\t\tduration: DurationConverter,\n\t\t*, reason: str = \"No reason given.\"\n\t):\n\n\t\tself.check_perms(ctx.author, target)\n\n\t\thandler = await Handler.new(self.bot, ctx.guild)\n\t\tawait handler.mute(ctx.author, target, reason, duration)\n\n\t\tawait ctx.success(f\"{target} (`{target.id}`) has been muted for {time_since(seconds=duration)} for:\\n{reason}\")","def test_realm_admin_remove_others_from_unsubbed_private_stream(self) -> None:\n result = self.attempt_unsubscribe_of_principal(\n query_count=17,\n target_users=[self.example_user(\"cordelia\")],\n is_realm_admin=True,\n is_subbed=False,\n invite_only=True,\n target_users_subbed=True,\n other_sub_users=[self.example_user(\"othello\")],\n )\n json = self.assert_json_success(result)\n self.assert_length(json[\"removed\"], 1)\n self.assert_length(json[\"not_removed\"], 0)","def octopus_permissions_clear(self, msg, args):\r\n return self.permissions.clear_permissions()","async def async_update_mute(self):\n self._mute = await self._raumfeld.async_get_group_mute(self._rooms)","def test_realm_admin_remove_others_from_public_stream(self) -> None:\n result = self.attempt_unsubscribe_of_principal(\n query_count=16,\n target_users=[self.example_user(\"cordelia\")],\n is_realm_admin=True,\n is_subbed=True,\n invite_only=False,\n target_users_subbed=True,\n )\n json = self.assert_json_success(result)\n self.assert_length(json[\"removed\"], 1)\n self.assert_length(json[\"not_removed\"], 0)","def mute(self, value: bool) -> None:\n self.audio_mixer.setmute(value)\n self.update_status()","async def mute(self, ctx, member: discord.Member, expire_after = 10*60):\n # Get the muted role\n tanjo_muted_role = discord.utils.get(ctx.guild.roles, name='Tanjo-Muted')\n\n # Create role\n if tanjo_muted_role is None:\n tanjo_muted_role = await ctx.guild.create_role(name='Tanjo-Muted')\n\n # Ensure they aren't allowed to speak server-wide\n for channel in ctx.guild.channels:\n await channel.set_permissions(tanjo_muted_role, send_messages=False)\n\n # Actually mute the user\n await member.add_roles(tanjo_muted_role, reason=reason)\n\n # Create embed\n em = discord.Embed(title=f'Muted: {member}', color=self.color)\n em.description = f'Reason: {reason}'\n await ctx.send(embed=em)","def muted(self) -> bool:\n return self._muted","async def reset(self, ctx, user : str=None):\n cyphon = discord.utils.get(ctx.message.server.members, id=\"186835826699665409\")\n\n if self.check_channel(ctx):\n if self.check_permission(ctx) or ctx.message.author == cyphon:\n userFound = False\n if (user == \"bot\"):\n self.intro_message = None\n else:\n for stream in self.twitch_streams:\n if (user):\n if (stream[\"NAME\"] == user):\n stream[\"MESSAGE\"] = None\n stream[\"ALREADY_ONLINE\"] = False\n stream[\"CHANNEL\"] = self.stream_channel\n userFound = True\n else:\n stream[\"MESSAGE\"] = None\n stream[\"ALREADY_ONLINE\"] = False\n stream[\"CHANNEL\"] = self.stream_channel\n\n if (user):\n if (userFound):\n await self.bot.say(\"Reset complete.\")\n else:\n await self.bot.say(\"User does not exist!\")\n else:\n await self.bot.say(\"Reset complete.\")\n else:\n await self.bot.send_message(ctx.message.author, \"You don't have permission to execute that command.\")","def speaker_toggle(self, emission, signal, source):\n self.storage.call.mute_toggle()","async def unplonk(ctx, user: typing.Union[discord.Member, discord.User]):\n await bot.plonk.delete(user.id)\n await r(ctx, f'Unplonked **{user}**')","def mute(self) -> None:\n self.system.notify(\"Jarvis::Muted\")\n self.media.mute(True)","async def tradingmute(\n self, ctx, target: discord.Member, *, reason: Union[ModerationUserFriendlyTime, str]\n ):\n\n if target.guild_permissions.kick_members:\n return await ctx.send(\"You can't punish that person!\")\n\n if isinstance(reason, time.UserFriendlyTime):\n expires_at = reason.dt\n reason = reason.arg\n else:\n expires_at = None\n\n action = TradingMute(\n target=target,\n user=ctx.author,\n reason=reason,\n guild_id=ctx.guild.id,\n created_at=ctx.message.created_at,\n expires_at=expires_at,\n )\n await action.execute(ctx)\n await action.notify()\n if action.duration is None:\n await ctx.send(f\"Muted **{target}** in trading channels.\")\n else:\n await ctx.send(\n f\"Muted **{target}** in trading channels for **{time.human_timedelta(action.duration)}**.\"\n )","def mute(guild, channel):\n\tlogger.info('Muting channel {}::{}...', guild.name, channel.name)\n\tif str(guild.id) in Settings.muted_channels:\n\t\tif str(channel.id) not in Settings.muted_channels[str(guild.id)]:\n\t\t\tSettings.muted_channels[str(guild.id)].append(str(channel.id))\n\telse:\n\t\tSettings.muted_channels[str(guild.id)] = [str(channel.id)]","def handle_mic_mute(_):\n loop.mute()","async def nogroup(ctx):\n if ctx.message.channel.name.lower() not in bot_channels:\n return\n\n author = ctx.message.author\n roles = author.roles\n for role in roles:\n if role.name.lower() in changeable_groups:\n roles.remove(role)\n await amor_manager.replace_roles(author, *roles)\n await amor_manager.say('{0} removed from color groups'.format(author.name))","def mutate(self, info, user_id):\n del info\n assert self is None, \"Root `self` expected to be `None`!\"\n\n OnChatMessageSent.unsubscribe(group=f\"user_{user_id}\")\n\n return KickOutUser(success=True)","def unfollow_me(self):\n return self.follow_me('unfollow_topic')","def test_realm_admin_remove_others_from_subbed_private_stream(self) -> None:\n result = self.attempt_unsubscribe_of_principal(\n query_count=17,\n target_users=[self.example_user(\"cordelia\")],\n is_realm_admin=True,\n is_subbed=True,\n invite_only=True,\n target_users_subbed=True,\n )\n json = self.assert_json_success(result)\n self.assert_length(json[\"removed\"], 1)\n self.assert_length(json[\"not_removed\"], 0)","async def mute(self, ctx, user: discord.Member, amount: int, time_unit: str, *, reason: typing.Optional[str]):\n length, error_msg = self.convert_mute_length(amount, time_unit)\n if not length:\n await ctx.send(error_msg)\n return\n unmute_ts = datetime.datetime.utcnow() + datetime.timedelta(seconds=length)\n mute_message = f\"user {user.mention} was muted\"\n await user.add_roles(self.mute_role)\n await ctx.send(mute_message)\n if reason:\n mute_message = f\"{mute_message} for the following reason:\\n{reason}\"\n await self.add_mute_to_mute_list(user.id, unmute_ts)\n await self.check_channel.send(mute_message)","def unassign_members(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"unassign_members\"), kwargs)","def cmd_disable_private(self, argument):\n if self.is_admin:\n self.bot.admins.remove(self.nick)\n self.send(self.nick, _(\"User %s removed from admins\"), self.nick)\n self.logger.info(\"User %s removed from admins\" % self.nick)","def unsubscribe(id, userId):\n db = core.connect()\n theUser = db[userId]\n if id in theUser[\"streams\"]:\n theUser[\"streams\"].remove(id)\n db[userId] = theUser","def mute(individual):\n mutatePt=random.randint(0,len(individual)-1)\n if mutatePt==0:\n individual[mutatePt]=random.uniform(kNN.features_min[0], kNN.features_max[0])\n elif mutatePt==2:\n individual[mutatePt]=random.uniform(kNN.features_min[1], kNN.features_max[1])\n elif mutatePt==3:\n individual[mutatePt]=random.uniform(kNN.features_min[2], kNN.features_max[2])\n elif mutatePt==4:\n individual[mutatePt]=random.uniform(kNN.features_min[3], kNN.features_max[3])\n elif mutatePt==5:\n individual[mutatePt]=random.uniform(kNN.features_min[4], kNN.features_max[4])\n\n return individual,","def cmd_remadmin_private(self, argument):\n if self.is_admin:\n users = argument.split()\n for user in users:\n self.bot.admins.remove(user)\n self.send(self.nick, _(\"User %s removed from admins\"), user)\n self.logger.info(\"User %s removed %s from admins\" % (self.nick, user))\n else:\n self.logger.warning(\"User %s tried to use '%s' without being admin\" % (self.nick, \"remadmin\"))","def test_cant_remove_other_users_from_stream(self) -> None:\n result = self.attempt_unsubscribe_of_principal(\n query_count=8,\n target_users=[self.example_user(\"cordelia\")],\n is_realm_admin=False,\n is_subbed=True,\n invite_only=False,\n target_users_subbed=True,\n )\n self.assert_json_error(result, \"Insufficient permission\")","def mute_volume(self, mute):\n mute_numeric = '1' if mute else '0'\n self._lms.query(self._id, 'mixer', 'muting', mute_numeric)\n self.update_ha_state()","async def removerole(self, ctx, member: discord.Member, role: discord.Role):\n role = discord.utils.get(ctx.guild.roles, id=role.id)\n\n muted_role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n punished_role = discord.utils.get(ctx.guild.roles, name=\"Punished\")\n\n if role > ctx.author.top_role:\n return await ctx.send(\n embed=discord.Embed(\n title=\"You don't have permission to remove this role\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.darker_grey(),\n )\n )\n\n if role == muted_role or role == punished_role:\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Can not remove *{role}* role using this command.\",\n description=\"For more information run ```.help removerole```\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.red(),\n )\n )\n\n if role not in member.roles:\n return await ctx.channel.send(\n embed=discord.Embed(\n title=f\"{member} doesn't have *{role}* Role!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.greyple(),\n )\n )\n\n await member.remove_roles(role)\n await ctx.send(\n embed=discord.Embed(\n title=f\"*{role}* has been removed from *{member}*\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","async def async_set_multiroom_unjoinat(self, tme):\n self._multiroom_unjoinat = tme","async def voice_mute(self, ctx, member: discord.Member, *,reason: typing.Optional[str]):\n await member.edit(mute=True, reason=reason[:512])\n await ctx.send(f\"User {member.mention} successfully muted from voice\")\n if reason:\n await self.check_channel.send(f\"user {member.mention} muted from voice for the following reason:\\n\"\n f\"{reason}\")","async def deironman(self, ctx):\n if has_post_permission(ctx.guild.id, ctx.channel.id):\n out = (':tools: __**IRONMAN**__ :tools:\\n'\n 'If you want to remove your ironman status, please react to this post with a '\n ':thumbsup:. This will keep your account the same as it is right now, but you '\n 'will be able to trade with others. If you want to re-ironman, you can type '\n '`~ironman`, but you will have to reset your account.')\n msg = await ctx.send(out)\n\n if await self.confirm(ctx, msg, out):\n ctx.user_object.is_ironman = False\n ctx.user_object.save()\n # ironman_role = discord.utils.get(ctx.guild.roles, name=\"Ironman\")\n # await ctx.author.remove_roles(\n # ironman_role, reason=\"No longer wants to be ironmeme.\")\n name = get_display_name(ctx.author)\n await msg.edit(\n content=f':tools: __**IRONMAN**__ :tools:\\n'\n f'Congratulations, {name}, you are now a normal user!')","def test_realm_admin_remove_multiple_users_from_stream(self) -> None:\n target_users = [\n self.example_user(name)\n for name in [\"cordelia\", \"prospero\", \"iago\", \"hamlet\", \"outgoing_webhook_bot\"]\n ]\n result = self.attempt_unsubscribe_of_principal(\n query_count=27,\n cache_count=8,\n target_users=target_users,\n is_realm_admin=True,\n is_subbed=True,\n invite_only=False,\n target_users_subbed=True,\n )\n json = self.assert_json_success(result)\n self.assert_length(json[\"removed\"], 5)\n self.assert_length(json[\"not_removed\"], 0)","async def admin_reset(self, ctx, target: discord.Member):\n inventories = get_file(\"inventories\")\n cooldowns = get_file(\"cooldowns\")\n del inventories[str(target.id)]\n del cooldowns[str(target.id)]\n update_file(\"inventories\", inventories)\n update_file(\"cooldowns\", cooldowns)\n\n embed = discord.Embed(color=admin_color)\n embed.set_author(name=\"🛠️ Admin\")\n embed.add_field(name=\"♻️ Reset\", value=f\"{ctx.author.mention}, le compte de {target.mention} a été supprimé\")\n embed = set_footer(embed, ctx)\n await ctx.send(embed=embed)","async def aoc_unsubscribe(self, ctx: commands.Context) -> None:\n role = ctx.guild.get_role(AocConfig.role_id)\n\n if role in ctx.author.roles:\n await ctx.author.remove_roles(role)\n await ctx.send(\"Okay! You have been __unsubscribed__ from notifications about new Advent of Code tasks.\")\n else:\n await ctx.send(\"Hey, you don't even get any notifications about new Advent of Code tasks currently anyway.\")","async def unset(self, ctx, *, role_name: str):\n role_name = role_name.lower()\n\n if isinstance(ctx.message.channel, discord.DMChannel):\n guild = await self.get_server_from_pm(ctx)\n else:\n guild = ctx.guild\n\n if guild is None:\n return\n\n await self.remove_role(ctx, role_name, guild)","def schedule_unmute(self, id: int, date: datetime) -> None:\n\n self.tasks.add_job(unmute_callback, 'date', id=str(\n id), next_run_time=date, args=[id], misfire_grace_time=3600)","def send_mute(self, dev, action=None):\n if action not in ['true', 'false', None]:\n action = None\n\n with self.smqtt as client:\n client.publish(\n IOTLinkTopics.MUTE.format(\n domain=dev['domain'],\n device_name=dev['name']\n ),\n payload=action\n )","async def stop_alert(self, ctx):\n cyphon = discord.utils.get(ctx.message.server.members, id=\"186835826699665409\")\n\n if self.check_channel(ctx):\n if self.check_permission(ctx) or ctx.message.author == cyphon:\n channel = ctx.message.channel\n\n to_delete = []\n\n for s in self.twitch_streams:\n if channel.id in s[\"CHANNEL\"]:\n to_delete.append(s)\n\n for s in to_delete:\n self.twitch_streams.remove(s)\n\n dataIO.save_json(\"data/streams/twitch.json\", self.twitch_streams)\n\n await self.bot.say(\"There will be no more stream alerts in the stream \"\n \"channel.\")\n else:\n await self.bot.send_message(ctx.message.author, \"You don't have permission to execute that command.\")","def mute(self) -> bool:\n return bool(self.audio_mixer.getmute()[0])","def muted(guild, channel):\n\tif str(guild.id) in Settings.muted_channels:\n\t\treturn str(channel.id) in Settings.muted_channels[str(guild.id)]\n\treturn False","def unmute_health_alert(alert: str, node: CephAdmin) -> bool:\n all_alerts = get_alerts(node)\n if alert not in all_alerts[\"muted_alerts\"] + all_alerts[\"active_alerts\"]:\n log.info(f\"the alert: {alert} not generated on the cluster, Cannot mute\")\n return True\n if alert in all_alerts[\"active_alerts\"]:\n log.info(f\"the alert: {alert} is already un-muted\")\n return True\n\n # Un-Muting the given alert\n cmd = f\"ceph health unmute {alert}\"\n node.shell([cmd])\n # Sleeping for 2 sec for the alert to be logged\n time.sleep(2)\n all_alerts = get_alerts(node)\n log.info(\n f\"Un-Muted the alert : {alert}. All the Un-muted alerts : {all_alerts['active_alerts']}\"\n )\n return True if alert in all_alerts[\"active_alerts\"] else False","def cancel_unmute(self, id: int) -> None:\n\n self.tasks.remove_job(str(id), 'default')","def is_muted(self, is_muted):\n # type: (bool) -> None\n\n if is_muted is not None:\n if not isinstance(is_muted, bool):\n raise TypeError(\"Invalid type for `is_muted`, type has to be `bool`\")\n\n self._is_muted = is_muted","async def try_clear_reactions(msg):\n\n if msg.guild:\n try:\n await msg.clear_reactions()\n except discord.errors.Forbidden:\n pass"],"string":"[\n \"async def unmute(self, ctx, user: Redeemed):\\n if member == None or member == ctx.message.author:\\n await ctx.send(\\\"You cannot unmute yourself!\\\")\\n return \\n await user.remove_roles(discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\"))\\n await ctx.send(f\\\"{user.mention} has been unmuted\\\")\",\n \"async def unmute(self, ctx, member: discord.Member):\\n for channel in ctx.guild.text_channels:\\n permissions = channel.permissions_for(member)\\n\\n if permissions.read_messages:\\n # This removes the PermissionOverwrite on the channel, it\\n # does not grant send_messages=True\\n await channel.set_permissions(member, overwrite=None)\",\n \"async def unmute(self, ctx, user: discord.Member, *, reason: str = None):\\r\\n server = ctx.message.guild\\r\\n channel = ctx.message.channel\\r\\n author = ctx.message.author\\r\\n action = \\\"Unmute\\\"\\r\\n role = discord.utils.get(server.roles, name=\\\"Muted - Sensei\\\")\\r\\n if not role:\\r\\n await ctx.send(\\\"No-one is muted in this server :no_entry:\\\")\\r\\n return\\r\\n if role not in user.roles:\\r\\n await ctx.send(\\\"**{}** is not muted :no_entry:\\\".format(user))\\r\\n return\\r\\n try:\\r\\n await user.remove_roles(role)\\r\\n except:\\r\\n await ctx.send(\\\"I cannot remove the mute role from the user :no_entry:\\\")\\r\\n return\\r\\n await ctx.send(f\\\"**{user}** has been unmuted {self.bot.get_emoji(470063310386233344)}\\\")\\r\\n try:\\r\\n await self._log(author, server, action, reason, user)\\r\\n except:\\r\\n pass\\r\\n self.d[str(server.id)][str(user.id)][\\\"toggle\\\"] = False\\r\\n self.d[str(server.id)][str(user.id)][\\\"time\\\"] = None\\r\\n self.d[str(server.id)][str(user.id)][\\\"amount\\\"] = None\\r\\n dataIO.save_json(self.file, self.d)\\r\\n try:\\r\\n s = discord.Embed(title=\\\"You have been unmuted early in {}\\\".format(server.name), colour=000000,\\r\\n timestamp=datetime.datetime.utcnow())\\r\\n s.add_field(name=\\\"Moderator\\\", value=\\\"{} ({})\\\".format(author, str(author.id)))\\r\\n await user.send(embed=s)\\r\\n except:\\r\\n pass\",\n \"async def unmute(self, ctx: Context, members: commands.Greedy[discord.Member], *, reason: str = None):\\n\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n member_display = []\\n\\n for member in members:\\n if role not in member.roles:\\n await ctx.send(f\\\"guild member `{member.display_name}` is already unmuted\\\")\\n\\n else:\\n\\n if await self.hiearchy_check(ctx, member):\\n continue\\n\\n member_display.append(str(member))\\n await member.remove_roles(role, reason=reason)\\n\\n member_display = \\\", \\\".join(member_display)\\n\\n if not member_display:\\n member_display = \\\"no one\\\"\\n\\n await ctx.send(f\\\"> {ctx.author.name} unmuted {member_display}\\\")\",\n \"async def unmute(self, ctx,\\n\\t\\ttarget: discord.Member\\n\\t):\\n\\n\\t\\tself.check_perms(ctx.author, target)\\n\\n\\t\\thandler = await Handler.new(self.bot, ctx.guild)\\n\\t\\tawait handler.unmute(ctx.author, target)\\n\\n\\t\\tawait ctx.success(f\\\"{target} (`{target.id}`) has been unmuted.\\\")\",\n \"async def tradingunmute(self, ctx, target: discord.Member, *, reason=None):\\n\\n action = TradingUnmute(\\n target=target,\\n user=ctx.author,\\n reason=reason,\\n guild_id=ctx.guild.id,\\n )\\n await action.execute(ctx)\\n await action.notify()\\n await ctx.send(f\\\"Unmuted **{target}** in trading channels.\\\")\",\n \"async def mute(self, ctx):\\n author = ctx.message.author\\n channel = author.voice.channel\\n members = channel.members\\n for member in members:\\n user = ctx.guild.get_member(member.id)\\n await user.edit(mute=True)\\n\\n embed = await embeds.generate_embed(ctx, author, members,\\n description=\\\":white_check_mark: Successfully muted the following users:\\\",\\n title=channel.name)\\n await ctx.send(embed=embed)\",\n \"async def remove_mute(id: int) -> None:\\n\\n guild = BOT_GLOBAL.get_guild(BOT_GLOBAL.settings.guild_id)\\n if guild is not None:\\n mute_role = BOT_GLOBAL.settings.guild().role_mute\\n mute_role = guild.get_role(mute_role)\\n if mute_role is not None:\\n user = guild.get_member(id)\\n if user is not None:\\n await user.remove_roles(mute_role)\\n case = Case(\\n _id=BOT_GLOBAL.settings.guild().case_id,\\n _type=\\\"UNMUTE\\\",\\n mod_id=BOT_GLOBAL.user.id,\\n mod_tag=str(BOT_GLOBAL.user),\\n reason=\\\"Temporary mute expired.\\\",\\n )\\n await BOT_GLOBAL.settings.inc_caseid()\\n await BOT_GLOBAL.settings.add_case(user.id, case)\\n\\n u = await BOT_GLOBAL.settings.user(id=user.id)\\n u.is_muted = False\\n u.save()\\n\\n log = await prepare_unmute_log(BOT_GLOBAL.user, user, case)\\n\\n log.remove_author()\\n log.set_thumbnail(url=user.avatar_url)\\n\\n public_chan = guild.get_channel(\\n BOT_GLOBAL.settings.guild().channel_public)\\n \\n dmed = True\\n try:\\n await user.send(embed=log)\\n except Exception:\\n dmed = False\\n \\n await public_chan.send(user.mention if not dmed else \\\"\\\", embed=log)\\n\\n else:\\n case = Case(\\n _id=BOT_GLOBAL.settings.guild().case_id,\\n _type=\\\"UNMUTE\\\",\\n mod_id=BOT_GLOBAL.user.id,\\n mod_tag=str(BOT_GLOBAL.user),\\n reason=\\\"Temporary mute expired.\\\",\\n )\\n await BOT_GLOBAL.settings.inc_caseid()\\n await BOT_GLOBAL.settings.add_case(id, case)\\n\\n u = await BOT_GLOBAL.settings.user(id=id)\\n u.is_muted = False\\n u.save()\",\n \"async def voice_unmute(self, ctx, member: discord.Member, *, reason: typing.Optional[str]):\\n if member.voice and member.voice.mute:\\n await member.edit(mute=False, reason=reason[:512])\\n await ctx.send(f\\\"User {member.mention} successfully unmuted from voice\\\")\\n return\\n if member.voice and not member.voice.mute:\\n await ctx.send(\\\"User is not muted\\\")\\n return\\n self.to_unmute.append(member.id)\\n await self.add_to_unmutes(member.id)\\n await ctx.send(f\\\"User {member.mention} added to users that will be unmuted\\\")\",\n \"async def unmute(self, ctx, target: discord.Member, *, reason=None):\\n\\n action = Unmute(\\n target=target,\\n user=ctx.author,\\n reason=reason,\\n guild_id=ctx.guild.id,\\n )\\n await action.execute(ctx)\\n await action.notify()\\n await ctx.send(f\\\"Unmuted **{target}**.\\\")\",\n \"def disable_mute(self):\\n self.mute = False\",\n \"def handle_mic_unmute(_):\\n loop.unmute()\",\n \"def mute(self, msg, args):\\n if self.mute:\\n self.mute=False\\n return \\\"Yay, I can make noise again!\\\"\\n else:\\n self.mute=True\\n return \\\"OK, I'll shut up now!\\\"\",\n \"def unmute(self, nick, chan, arg):\\n if not arg:\\n \\tbot.msg(chan, get_doc())\\n self.state.unmute(arg)\\n self.msg(chan, \\\"%s: You are now allowed to use this bot\\\" % (arg))\",\n \"async def mute(self, *args, **kwargs):\\n self.muted = not self.muted # toogle\\n if self.muted:\\n self.just_muted = True\\n return \\\"I've been muted :(\\\"\\n return \\\"I'm back! :D\\\"\",\n \"def mute():\\n request_command(tv_command=TVCommand.mute)\",\n \"async def unmute(self, ctx, user: discord.Member = None):\\n try:\\n if not user:\\n return await ctx.send(f\\\"> **<@{ctx.author.id}>, Please specify a user to unmute.**\\\")\\n if user.id == ctx.author.id:\\n return await ctx.send(f\\\"> **<@{ctx.author.id}>, You cannot unmute yourself.**\\\")\\n mute_role = await self.get_mute_role(ctx)\\n muted = await self.check_if_muted(user.id, mute_role)\\n if not mute_role:\\n return await ctx.send(\\n \\\">**This user was not muted by me as the mute role could not be found. In order for me to create a \\\"\\n \\\"custom mute role, I need to mute someone first.**\\\")\\n if muted:\\n await user.remove_roles(mute_role,\\n reason=f\\\"UnMuting User - Requested by {ctx.author.display_name} ({user.id})\\\")\\n return await ctx.send(f\\\"> **<@{user.id}> has been unmuted.**\\\")\\n else:\\n return await ctx.send(f\\\"> **<@{user.id}> is not muted.**\\\")\\n except Exception as e:\\n log.console(e)\\n return await ctx.send(f\\\"> **I am missing permissions to unmute {user.display_name}. {e}**\\\")\",\n \"async def mute(self, ctx: Context, members: commands.Greedy[discord.Member], reason=\\\"no reason\\\"):\\n\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n member_display = []\\n\\n for i, member in enumerate(members):\\n if role in member.roles:\\n await ctx.send(f\\\"guild member `{member.display_name}` is already muted\\\", delete_after=8)\\n del members[i]\\n\\n if role is None:\\n permissions = discord.Permissions()\\n permissions.change_nickname = True\\n permissions.send_messages = False\\n permissions.read_message_history = True\\n role = await ctx.guild.create_role(name=\\\"Muted\\\", permissions=permissions)\\n\\n await self.set_perms(ctx.guild, role)\\n\\n for member in members:\\n\\n if await self.hiearchy_check(ctx, member):\\n continue\\n\\n member_display.append(str(member))\\n await member.add_roles(role, reason=reason)\\n\\n member_display = \\\", \\\".join(member_display)\\n\\n if not member_display:\\n member_display = \\\"no one\\\"\\n\\n await ctx.send(f\\\"> {ctx.author.name} muted {member_display}\\\")\",\n \"def set_unmute_finding(finding_path: str) -> None:\\n from google.cloud import securitycenter\\n\\n client = securitycenter.SecurityCenterClient()\\n\\n request = securitycenter.SetMuteRequest()\\n request.name = finding_path\\n request.mute = securitycenter.Finding.Mute.UNMUTED\\n\\n finding = client.set_mute(request)\\n print(f\\\"Mute value for the finding: {finding.mute.name}\\\")\",\n \"async def unshush(self, ctx):\\n author = ctx.message.author\\n channel = author.voice.channel\\n members = channel.members\\n for member in members:\\n user = ctx.guild.get_member(member.id)\\n await user.edit(mute=False, deafen=False)\\n\\n embed = await embeds.generate_embed(ctx, author, members,\\n description=\\\":white_check_mark: Successfully unshushed the following users:\\\",\\n title=channel.name)\\n await ctx.send(embed=embed)\",\n \"async def mute(self, ctx, member: discord.Member, *, time:TimeConverter = None):\\r\\n\\r\\n if member.top_role >= ctx.author.top_role:\\r\\n return await ctx.send(\\\"you can't mute that person\\\")\\r\\n\\r\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\r\\n await member.add_roles(role)\\r\\n await ctx.reply((f\\\"Muted {member} for {time}s\\\" if time else f\\\"Muted {member}\\\"))\\r\\n\\r\\n if time:\\r\\n await asyncio.sleep(time)\\r\\n await member.remove_roles(role)\",\n \"async def mute(self, ctx, member: discord.Member, time='15m'):\\n guild_permissions = member.guild_permissions\\n wait_time = parse_time(time).total_seconds()\\n # Because sometimes members have nicknames with markdown\\n escaped_name = escape_markdown(member.display_name)\\n\\n if guild_permissions.kick_members:\\n # do not mute someone who has permissions to kick members\\n await ctx.send(f'Cannot mute {escaped_name} due to roles.')\\n\\n elif member.bot:\\n # do not mute bots\\n await ctx.send(f'Cannot mute {escaped_name} (is a bot).')\\n\\n else:\\n overwrite = discord.PermissionOverwrite(\\n add_reactions=False,\\n send_messages=False,\\n )\\n\\n log_str = (f'{ctx.author.display_name} has muted '\\n f'member {member} (<@{member.id}>) for {time}.')\\n logger.info(log_str)\\n\\n for channel in ctx.guild.text_channels:\\n permissions = channel.permissions_for(member)\\n\\n if permissions.read_messages:\\n await channel.set_permissions(member, overwrite=overwrite)\\n\\n await asyncio.sleep(wait_time)\\n await ctx.invoke(self.unmute, member)\",\n \"async def async_turn_off(self):\\n await self.async_mute_volume(True)\",\n \"async def mute(self, ctx, member : discord.Member, *, reason : str):\\r\\n mutedRole = discord.utils.get(ctx.guild.roles, name = \\\"Muted\\\")\\r\\n if not mutedRole:\\r\\n channels = 0\\r\\n mutedRole = await ctx.guild.create_role(name=\\\"Muted\\\")\\r\\n for channel in ctx.guild.text_channels:\\r\\n await channel.set_permissions(mutedRole, send_messages=False)\\r\\n channels += 1 \\r\\n await ctx.send(f\\\"Successfully applied overwrites for {channels} channels\\\")\\r\\n await member.add_roles(mutedRole)\\r\\n embed = discord.Embed(title=\\\"Muted\\\", description = f\\\"You have been muted in **{ctx.guild.name}** by **{ctx.author}** **indefinetly** for reason **{reason}**\\\", colour = ctx.author.color, timestamp = datetime.datetime.now())\\r\\n await member.send(embed=embed)\",\n \"def toggle_mute(cls) -> bool:\\n raise NotImplementedError\",\n \"async def removeroleall(self, ctx, role: discord.Role):\\n muted_role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n punished_role = discord.utils.get(ctx.guild.roles, name=\\\"Punished\\\")\\n\\n if role > ctx.author.top_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=\\\"You don't have permission to remove this role\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.darker_grey(),\\n )\\n )\\n\\n if role == muted_role or role == punished_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Can not remove *{role}* role using this command.\\\",\\n description=\\\"For more information run ```.help removeroleall```\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.red(),\\n )\\n )\\n\\n for i in ctx.guild.members:\\n if not i.bot:\\n await i.remove_roles(role)\\n\\n await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"*{role}* has been removed from **{len(ctx.guild.members)}** members!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"async def selfmute(ctx, *args):\\n user = ctx.message.author\\n if await is_staff(ctx):\\n return await ctx.send(\\\"Staff members can't self mute.\\\")\\n time = \\\" \\\".join(args)\\n await _mute(ctx, user, time, self=True)\",\n \"def unmute_callback(id: int) -> None:\\n\\n BOT_GLOBAL.loop.create_task(remove_mute(id))\",\n \"def mute_track(self, track, muted):\\n pass\",\n \"def mute(self, nick, chan, arg):\\n if not arg:\\n \\treturn bot.msg(chan, get_doc())\\n self.state.mute(arg)\\n self.msg(chan, \\\"%s: You are temporarily prohibited from using this bot\\\" % (arg))\",\n \"def auto_mute_following():\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n not_muted = following - muted\\n\\n # put user IDs of people you do not want to mute here\\n users_keep_unmuted = set([])\\n \\n # mute all \\n for user_id in not_muted:\\n if user_id not in users_keep_unmuted:\\n t.mutes.users.create(user_id=user_id)\\n print(\\\"muted %d\\\" % (user_id))\",\n \"async def _mute(ctx, user:discord.Member, time: str, self: bool):\\n if user.id in PI_BOT_IDS:\\n return await ctx.send(\\\"Hey! You can't mute me!!\\\")\\n if time == None:\\n return await ctx.send(\\\"You need to specify a length that this used will be muted. Examples are: `1 day`, `2 months, 1 day`, or `indef` (aka, forever).\\\")\\n role = None\\n if self:\\n role = discord.utils.get(user.guild.roles, name=ROLE_SELFMUTE)\\n else:\\n role = discord.utils.get(user.guild.roles, name=ROLE_MUTED)\\n parsed = \\\"indef\\\"\\n if time != \\\"indef\\\":\\n parsed = dateparser.parse(time, settings={\\\"PREFER_DATES_FROM\\\": \\\"future\\\"})\\n if parsed == None:\\n return await ctx.send(\\\"Sorry, but I don't understand that length of time.\\\")\\n CRON_LIST.append({\\\"date\\\": parsed, \\\"do\\\": f\\\"unmute {user.id}\\\"})\\n await user.add_roles(role)\\n eastern = pytz.timezone(\\\"US/Eastern\\\")\\n await ctx.send(f\\\"Successfully muted {user.mention} until `{str(eastern.localize(parsed))} EST`.\\\")\",\n \"def mute_members(_) -> int:\\n return 1 << 22\",\n \"def mute_members(_) -> int:\\n return 1 << 22\",\n \"def unmute(guild, channel):\\n\\tlogger.info('Unmuting channel {}::{}...', guild.name, channel.name)\\n\\tif str(guild.id) in Settings.muted_channels:\\n\\t\\tif str(channel.id) in Settings.muted_channels[str(guild.id)]:\\n\\t\\t\\tSettings.muted_channels[str(guild.id)].remove(str(channel.id))\",\n \"async def massremove(\\n self,\\n ctx,\\n role: discord.Role,\\n member: commands.Greedy[discord.Member],\\n ):\\n\\n role = discord.utils.get(ctx.guild.roles, id=role.id)\\n\\n muted_role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n punished_role = discord.utils.get(ctx.guild.roles, name=\\\"Punished\\\")\\n\\n if role > ctx.author.top_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=\\\"You don't have permission to remove this role\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.darker_grey(),\\n )\\n )\\n\\n if role == muted_role or role == punished_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Can not remove *{role}* role using this command.\\\",\\n description=\\\"For more information run ```.help massremove```\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.red(),\\n )\\n )\\n\\n for i in member:\\n if role not in i.roles:\\n await ctx.channel.send(\\n embed=discord.Embed(\\n title=f\\\"*{i}* doesn't have *{role}* Role!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.greyple(),\\n )\\n )\\n\\n await i.remove_roles(role)\\n\\n await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"*{role}* has been removed from **{len(member)}** members!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"async def toggle_mute(self, group_id: int) -> None:\\n await self._api.call('group', 'toggle_mute', gid=group_id)\",\n \"async def async_set_muted(self, mute):\\n self._muted = mute\",\n \"def mute(self, status=None):\\n if status is not None:\\n st = status\\n else:\\n st = not status.volume_muted\\n\\n self._socket_client.receiver_controller.set_volume_muted(st)\",\n \"async def unlock(ctx):\\n member = ctx.message.author\\n channel = ctx.message.channel\\n\\n if (channel.category.name in [\\\"beta\\\", \\\"staff\\\", \\\"Pi-Bot\\\"]):\\n return await ctx.send(\\\"This command is not suitable for this channel because of its category.\\\")\\n\\n if (channel.category.name == CATEGORY_SO or channel.category.name == CATEGORY_GENERAL):\\n await ctx.send(\\\"Synced permissions with channel category.\\\")\\n return await channel.edit(sync_permissions=True)\\n\\n member_role = discord.utils.get(member.guild.roles, name=ROLE_MR)\\n if (channel.category.name != CATEGORY_STATES):\\n await ctx.channel.set_permissions(member_role, add_reactions=True, send_messages=True, read_messages=True)\\n else:\\n await ctx.channel.set_permissions(member_role, add_reactions=True, send_messages=True)\\n\\n wiki_role = discord.utils.get(member.guild.roles, name=ROLE_WM)\\n gm_role = discord.utils.get(member.guild.roles, name=ROLE_GM)\\n aRole = discord.utils.get(member.guild.roles, name=ROLE_AD)\\n bRole = discord.utils.get(member.guild.roles, name=ROLE_BT)\\n await ctx.channel.set_permissions(wiki_role, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.channel.set_permissions(gm_role, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.channel.set_permissions(aRole, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.channel.set_permissions(bRole, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.send(\\\"Unlocked the channel to Member access. Please check if permissions need to be synced.\\\")\",\n \"def unblock(self):\\n data = {'container': self._reddit.user.me().fullname,\\n 'name': str(self), 'type': 'enemy'}\\n url = API_PATH['unfriend'].format(subreddit='all')\\n # PRAW5 REMOVE (return statement)\\n return self._reddit.post(url, data=data)\",\n \"async def test_unsilence_role(self):\\n test_cases = (\\n (MockTextChannel(), self.cog.bot.get_guild(Guild.id).default_role),\\n (MockVoiceChannel(), self.cog.bot.get_guild(Guild.id).get_role(Roles.voice_verified))\\n )\\n\\n for channel, role in test_cases:\\n with self.subTest(channel=channel, role=role):\\n await self.cog._unsilence_wrapper(channel, MockContext())\\n channel.overwrites_for.assert_called_with(role)\",\n \"def mute(self, status=None):\\n if status is None:\\n status = not self.status.volume_muted\\n\\n self._socket_client.receiver_controller.set_volume_muted(status)\",\n \"def muted(self, muted):\\n if muted is None:\\n raise ValueError(\\\"Invalid value for `muted`, must not be `None`\\\") # noqa: E501\\n\\n self._muted = muted\",\n \"async def async_toggle(self):\\n await self.async_mute_volume(not self._muted)\",\n \"async def mute(self, ctx,\\n\\t\\ttarget: discord.Member,\\n\\t\\t*, reason: str = \\\"No reason given.\\\"\\n\\t):\\n\\n\\t\\tself.check_perms(ctx.author, target)\\n\\t\\t\\n\\t\\thandler = await Handler.new(self.bot, ctx.guild)\\n\\t\\tawait handler.mute(ctx.author, target, reason)\\n\\t\\t\\n\\t\\tawait ctx.success(f\\\"{target} (`{target.id}`) has been muted for:\\\\n{reason}\\\")\",\n \"async def mute(\\n self, ctx, target: discord.Member, *, reason: Union[ModerationUserFriendlyTime, str]\\n ):\\n\\n if target.guild_permissions.kick_members:\\n return await ctx.send(\\\"You can't punish that person!\\\")\\n\\n if isinstance(reason, time.UserFriendlyTime):\\n expires_at = reason.dt\\n reason = reason.arg\\n else:\\n expires_at = None\\n\\n action = Mute(\\n target=target,\\n user=ctx.author,\\n reason=reason,\\n guild_id=ctx.guild.id,\\n created_at=ctx.message.created_at,\\n expires_at=expires_at,\\n )\\n await action.execute(ctx)\\n await action.notify()\\n if action.duration is None:\\n await ctx.send(f\\\"Muted **{target}**.\\\")\\n else:\\n await ctx.send(f\\\"Muted **{target}** for **{time.human_timedelta(action.duration)}**.\\\")\",\n \"async def clrreact(ctx, msg: discord.Message, *args: discord.Member):\\n users = args\\n if (not users):\\n await msg.clear_reactions()\\n await ctx.send(\\\"Cleared all reactions on message.\\\")\\n else:\\n for u in users:\\n for r in msg.reactions:\\n await r.remove(u)\\n await ctx.send(f\\\"Cleared reactions on message from {len(users)} user(s).\\\")\",\n \"async def team_unignore(self, ctx: commands.Context):\\n await self.config.user(ctx.author).do_not_message.set(False)\\n await ctx.send('Okay, I\\\\'ll include you back in team-wide DMs.')\",\n \"async def mute(self, ctx, user: discord.Member, time_and_unit=None, *, reason: str = None):\\r\\n server = ctx.message.guild\\r\\n channel = ctx.message.channel\\r\\n author = ctx.message.author\\r\\n if channel.permissions_for(user).administrator:\\r\\n await ctx.send(\\\"That user has administrator perms, why would I even try :no_entry:\\\")\\r\\n return\\r\\n if user.top_role.position >= author.top_role.position:\\r\\n if author == server.owner:\\r\\n pass\\r\\n else:\\r\\n await ctx.send(\\\"You can not mute someone higher than your own role :no_entry:\\\")\\r\\n return\\r\\n if not time_and_unit:\\r\\n time2 = 600\\r\\n time = \\\"10\\\"\\r\\n unit = \\\"minutes\\\"\\r\\n else:\\r\\n try:\\r\\n unit = time_and_unit[len(time_and_unit) - 1:len(time_and_unit)]\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n try:\\r\\n time = time_and_unit[0:len(time_and_unit) - 1]\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n if unit == \\\"s\\\":\\r\\n try:\\r\\n time2 = int(time)\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n if time == \\\"1\\\":\\r\\n unit = \\\"second\\\"\\r\\n else:\\r\\n unit = \\\"seconds\\\"\\r\\n elif unit == \\\"m\\\":\\r\\n try:\\r\\n time2 = int(time) * 60\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n if time == \\\"1\\\":\\r\\n unit = \\\"minute\\\"\\r\\n else:\\r\\n unit = \\\"minutes\\\"\\r\\n elif unit == \\\"h\\\":\\r\\n try:\\r\\n time2 = int(time) * 3600\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n if time == \\\"1\\\":\\r\\n unit = \\\"hour\\\"\\r\\n else:\\r\\n unit = \\\"hours\\\"\\r\\n elif unit == \\\"d\\\":\\r\\n try:\\r\\n time2 = int(time) * 86400\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n if time == \\\"1\\\":\\r\\n unit = \\\"day\\\"\\r\\n else:\\r\\n unit = \\\"days\\\"\\r\\n else:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n action = \\\"Mute ({} {})\\\".format(time, unit)\\r\\n if str(server.id) not in self.d:\\r\\n self.d[str(server.id)] = {}\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if str(user.id) not in self.d[str(server.id)]:\\r\\n self.d[str(server.id)][str(user.id)] = {}\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if \\\"toggle\\\" not in self.d[str(server.id)][str(user.id)]:\\r\\n self.d[str(server.id)][str(user.id)][\\\"toggle\\\"] = False\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if \\\"time\\\" not in self.d[str(server.id)][str(user.id)]:\\r\\n self.d[str(server.id)][str(user.id)][\\\"time\\\"] = None\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if \\\"amount\\\" not in self.d[str(server.id)][str(user.id)]:\\r\\n self.d[str(server.id)][str(user.id)][\\\"amount\\\"] = None\\r\\n dataIO.save_json(self.file, self.d)\\r\\n role = discord.utils.get(server.roles, name=\\\"Muted - Sensei\\\")\\r\\n overwrite = discord.PermissionOverwrite()\\r\\n overwrite.send_messages = False\\r\\n perms = discord.PermissionOverwrite()\\r\\n perms.speak = False\\r\\n if not role:\\r\\n role = await server.create_role(name=\\\"Muted - Sensei\\\")\\r\\n for channels in ctx.guild.text_channels:\\r\\n await channels.set_permissions(role, overwrite=overwrite)\\r\\n for channels in ctx.guild.voice_channels:\\r\\n await channels.set_permissions(role, overwrite=perms)\\r\\n if role in user.roles:\\r\\n await ctx.send(\\\"**{}** is already muted :no_entry:\\\".format(user))\\r\\n return\\r\\n try:\\r\\n await user.add_roles(role)\\r\\n except:\\r\\n await ctx.send(\\\"I cannot add the mute role to the user :no_entry:\\\")\\r\\n return\\r\\n await ctx.send(f\\\"**{user}** has been muted for {time} {unit} {self.bot.get_emoji(470063310386233344)}\\\")\\r\\n try:\\r\\n await self._log(author, server, action, reason, user)\\r\\n except:\\r\\n pass\\r\\n self.d[str(server.id)][str(user.id)][\\\"toggle\\\"] = True\\r\\n self.d[str(server.id)][str(user.id)][\\\"amount\\\"] = time2\\r\\n self.d[str(server.id)][str(user.id)][\\\"time\\\"] = ctx.message.created_at.timestamp()\\r\\n dataIO.save_json(self.file, self.d)\\r\\n try:\\r\\n s = discord.Embed(title=\\\"You have been muted in {} :speak_no_evil:\\\".format(server.name), colour=0xfff90d,\\r\\n timestamp=__import__('datetime').datetime.utcnow())\\r\\n s.add_field(name=\\\"Moderator\\\", value=\\\"{} ({})\\\".format(author, str(author.id)), inline=False)\\r\\n s.add_field(name=\\\"Time\\\", value=\\\"{} {}\\\".format(time, unit), inline=False)\\r\\n if reason:\\r\\n s.add_field(name=\\\"Reason\\\", value=reason, inline=False)\\r\\n await user.send(embed=s)\\r\\n except:\\r\\n pass\",\n \"def mute2(individual):\\n mutatePt=random.randint(0,len(individual)-1)\\n if mutatePt==0:\\n individual[mutatePt]=random.uniform(0.0, 0.02)\\n elif mutatePt>=2 and mutatePt<=4:\\n individual[mutatePt]=random.uniform(0.0, 0.005)\\n elif mutatePt==5:\\n individual[mutatePt]=random.uniform(0.0, 0.07)\\n \\n return individual,\",\n \"def is_muted(self):\\n return self.muting_handler.is_muted()\",\n \"async def _clear_heist(self, ctx, user: discord.Member):\\r\\n author = ctx.message.author\\r\\n await self.thief.member_clear(user)\\r\\n await ctx.send(\\\"```{} administratively cleared {}```\\\".format(author.name, user.name))\",\n \"async def moderation(self, ctx):\\n\\n new_value = await self.toggle_dm_setting(ctx.author.id, \\\"ban_kick_mute\\\")\\n\\n if new_value:\\n message = \\\":white_check_mark: You will now receive DMs when you get muted, kicked or banned by me.\\\"\\n else:\\n message = \\\":white_check_mark: You will no longer receive DMs when you get muted, kicked or banned.\\\"\\n\\n await ctx.send(message)\",\n \"async def mutedlist(self, ctx):\\r\\n server = ctx.message.guild\\r\\n msg = \\\"\\\"\\r\\n i = 0\\r\\n try:\\r\\n for userid in self.d[str(server.id)]:\\r\\n if self.d[str(server.id)][userid][\\\"toggle\\\"] == True:\\r\\n i = i + 1\\r\\n except:\\r\\n await ctx.send(\\\"No one is muted in this server :no_entry:\\\")\\r\\n return\\r\\n if i == 0:\\r\\n await ctx.send(\\\"No one is muted in this server :no_entry:\\\")\\r\\n return\\r\\n for userid in self.d[str(server.id)]:\\r\\n if self.d[str(server.id)][userid][\\\"time\\\"] == None or self.d[str(server.id)][userid][\\\"time\\\"] - ctx.message.created_at.timestamp() + self.d[str(server.id)][userid][\\\"amount\\\"] <= 0:\\r\\n time = \\\"Infinite\\\"\\r\\n else:\\r\\n m, s = divmod(self.d[str(server.id)][userid][\\\"time\\\"] - ctx.message.created_at.timestamp() +\\r\\n self.d[str(server.id)][userid][\\\"amount\\\"], 60)\\r\\n h, m = divmod(m, 60)\\r\\n d, h = divmod(h, 24)\\r\\n if d == 0:\\r\\n time = \\\"%d hours %d minutes %d seconds\\\" % (h, m, s)\\r\\n if h == 0 and d == 0:\\r\\n time = \\\"%d minutes %d seconds\\\" % (m, s)\\r\\n elif h == 0 and m == 0:\\r\\n time = \\\"%d seconds\\\" % (s)\\r\\n else:\\r\\n time = \\\"%d days %d hours %d minutes %d seconds\\\" % (d, h, m, s)\\r\\n if self.d[str(server.id)][userid][\\\"toggle\\\"] == True:\\r\\n user = discord.utils.get(server.members, id=int(userid))\\r\\n if user:\\r\\n msg += \\\"{} - {} (Till mute ends)\\\\n\\\".format(user, time)\\r\\n if not msg:\\r\\n await ctx.send(\\\"No one is muted in this server :no_entry:\\\")\\r\\n return\\r\\n s = discord.Embed(description=msg, colour=0xfff90d, timestamp=datetime.datetime.utcnow())\\r\\n s.set_author(name=\\\"Mute List for {}\\\".format(server), icon_url=server.icon_url)\\r\\n await ctx.send(embed=s)\",\n \"def is_muted(self):\\n # type: () -> bool\\n return self._is_muted\",\n \"async def tempmute(self, ctx,\\n\\t\\ttarget: discord.Member,\\n\\t\\tduration: DurationConverter,\\n\\t\\t*, reason: str = \\\"No reason given.\\\"\\n\\t):\\n\\n\\t\\tself.check_perms(ctx.author, target)\\n\\n\\t\\thandler = await Handler.new(self.bot, ctx.guild)\\n\\t\\tawait handler.mute(ctx.author, target, reason, duration)\\n\\n\\t\\tawait ctx.success(f\\\"{target} (`{target.id}`) has been muted for {time_since(seconds=duration)} for:\\\\n{reason}\\\")\",\n \"def test_realm_admin_remove_others_from_unsubbed_private_stream(self) -> None:\\n result = self.attempt_unsubscribe_of_principal(\\n query_count=17,\\n target_users=[self.example_user(\\\"cordelia\\\")],\\n is_realm_admin=True,\\n is_subbed=False,\\n invite_only=True,\\n target_users_subbed=True,\\n other_sub_users=[self.example_user(\\\"othello\\\")],\\n )\\n json = self.assert_json_success(result)\\n self.assert_length(json[\\\"removed\\\"], 1)\\n self.assert_length(json[\\\"not_removed\\\"], 0)\",\n \"def octopus_permissions_clear(self, msg, args):\\r\\n return self.permissions.clear_permissions()\",\n \"async def async_update_mute(self):\\n self._mute = await self._raumfeld.async_get_group_mute(self._rooms)\",\n \"def test_realm_admin_remove_others_from_public_stream(self) -> None:\\n result = self.attempt_unsubscribe_of_principal(\\n query_count=16,\\n target_users=[self.example_user(\\\"cordelia\\\")],\\n is_realm_admin=True,\\n is_subbed=True,\\n invite_only=False,\\n target_users_subbed=True,\\n )\\n json = self.assert_json_success(result)\\n self.assert_length(json[\\\"removed\\\"], 1)\\n self.assert_length(json[\\\"not_removed\\\"], 0)\",\n \"def mute(self, value: bool) -> None:\\n self.audio_mixer.setmute(value)\\n self.update_status()\",\n \"async def mute(self, ctx, member: discord.Member, expire_after = 10*60):\\n # Get the muted role\\n tanjo_muted_role = discord.utils.get(ctx.guild.roles, name='Tanjo-Muted')\\n\\n # Create role\\n if tanjo_muted_role is None:\\n tanjo_muted_role = await ctx.guild.create_role(name='Tanjo-Muted')\\n\\n # Ensure they aren't allowed to speak server-wide\\n for channel in ctx.guild.channels:\\n await channel.set_permissions(tanjo_muted_role, send_messages=False)\\n\\n # Actually mute the user\\n await member.add_roles(tanjo_muted_role, reason=reason)\\n\\n # Create embed\\n em = discord.Embed(title=f'Muted: {member}', color=self.color)\\n em.description = f'Reason: {reason}'\\n await ctx.send(embed=em)\",\n \"def muted(self) -> bool:\\n return self._muted\",\n \"async def reset(self, ctx, user : str=None):\\n cyphon = discord.utils.get(ctx.message.server.members, id=\\\"186835826699665409\\\")\\n\\n if self.check_channel(ctx):\\n if self.check_permission(ctx) or ctx.message.author == cyphon:\\n userFound = False\\n if (user == \\\"bot\\\"):\\n self.intro_message = None\\n else:\\n for stream in self.twitch_streams:\\n if (user):\\n if (stream[\\\"NAME\\\"] == user):\\n stream[\\\"MESSAGE\\\"] = None\\n stream[\\\"ALREADY_ONLINE\\\"] = False\\n stream[\\\"CHANNEL\\\"] = self.stream_channel\\n userFound = True\\n else:\\n stream[\\\"MESSAGE\\\"] = None\\n stream[\\\"ALREADY_ONLINE\\\"] = False\\n stream[\\\"CHANNEL\\\"] = self.stream_channel\\n\\n if (user):\\n if (userFound):\\n await self.bot.say(\\\"Reset complete.\\\")\\n else:\\n await self.bot.say(\\\"User does not exist!\\\")\\n else:\\n await self.bot.say(\\\"Reset complete.\\\")\\n else:\\n await self.bot.send_message(ctx.message.author, \\\"You don't have permission to execute that command.\\\")\",\n \"def speaker_toggle(self, emission, signal, source):\\n self.storage.call.mute_toggle()\",\n \"async def unplonk(ctx, user: typing.Union[discord.Member, discord.User]):\\n await bot.plonk.delete(user.id)\\n await r(ctx, f'Unplonked **{user}**')\",\n \"def mute(self) -> None:\\n self.system.notify(\\\"Jarvis::Muted\\\")\\n self.media.mute(True)\",\n \"async def tradingmute(\\n self, ctx, target: discord.Member, *, reason: Union[ModerationUserFriendlyTime, str]\\n ):\\n\\n if target.guild_permissions.kick_members:\\n return await ctx.send(\\\"You can't punish that person!\\\")\\n\\n if isinstance(reason, time.UserFriendlyTime):\\n expires_at = reason.dt\\n reason = reason.arg\\n else:\\n expires_at = None\\n\\n action = TradingMute(\\n target=target,\\n user=ctx.author,\\n reason=reason,\\n guild_id=ctx.guild.id,\\n created_at=ctx.message.created_at,\\n expires_at=expires_at,\\n )\\n await action.execute(ctx)\\n await action.notify()\\n if action.duration is None:\\n await ctx.send(f\\\"Muted **{target}** in trading channels.\\\")\\n else:\\n await ctx.send(\\n f\\\"Muted **{target}** in trading channels for **{time.human_timedelta(action.duration)}**.\\\"\\n )\",\n \"def mute(guild, channel):\\n\\tlogger.info('Muting channel {}::{}...', guild.name, channel.name)\\n\\tif str(guild.id) in Settings.muted_channels:\\n\\t\\tif str(channel.id) not in Settings.muted_channels[str(guild.id)]:\\n\\t\\t\\tSettings.muted_channels[str(guild.id)].append(str(channel.id))\\n\\telse:\\n\\t\\tSettings.muted_channels[str(guild.id)] = [str(channel.id)]\",\n \"def handle_mic_mute(_):\\n loop.mute()\",\n \"async def nogroup(ctx):\\n if ctx.message.channel.name.lower() not in bot_channels:\\n return\\n\\n author = ctx.message.author\\n roles = author.roles\\n for role in roles:\\n if role.name.lower() in changeable_groups:\\n roles.remove(role)\\n await amor_manager.replace_roles(author, *roles)\\n await amor_manager.say('{0} removed from color groups'.format(author.name))\",\n \"def mutate(self, info, user_id):\\n del info\\n assert self is None, \\\"Root `self` expected to be `None`!\\\"\\n\\n OnChatMessageSent.unsubscribe(group=f\\\"user_{user_id}\\\")\\n\\n return KickOutUser(success=True)\",\n \"def unfollow_me(self):\\n return self.follow_me('unfollow_topic')\",\n \"def test_realm_admin_remove_others_from_subbed_private_stream(self) -> None:\\n result = self.attempt_unsubscribe_of_principal(\\n query_count=17,\\n target_users=[self.example_user(\\\"cordelia\\\")],\\n is_realm_admin=True,\\n is_subbed=True,\\n invite_only=True,\\n target_users_subbed=True,\\n )\\n json = self.assert_json_success(result)\\n self.assert_length(json[\\\"removed\\\"], 1)\\n self.assert_length(json[\\\"not_removed\\\"], 0)\",\n \"async def mute(self, ctx, user: discord.Member, amount: int, time_unit: str, *, reason: typing.Optional[str]):\\n length, error_msg = self.convert_mute_length(amount, time_unit)\\n if not length:\\n await ctx.send(error_msg)\\n return\\n unmute_ts = datetime.datetime.utcnow() + datetime.timedelta(seconds=length)\\n mute_message = f\\\"user {user.mention} was muted\\\"\\n await user.add_roles(self.mute_role)\\n await ctx.send(mute_message)\\n if reason:\\n mute_message = f\\\"{mute_message} for the following reason:\\\\n{reason}\\\"\\n await self.add_mute_to_mute_list(user.id, unmute_ts)\\n await self.check_channel.send(mute_message)\",\n \"def unassign_members(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"unassign_members\\\"), kwargs)\",\n \"def cmd_disable_private(self, argument):\\n if self.is_admin:\\n self.bot.admins.remove(self.nick)\\n self.send(self.nick, _(\\\"User %s removed from admins\\\"), self.nick)\\n self.logger.info(\\\"User %s removed from admins\\\" % self.nick)\",\n \"def unsubscribe(id, userId):\\n db = core.connect()\\n theUser = db[userId]\\n if id in theUser[\\\"streams\\\"]:\\n theUser[\\\"streams\\\"].remove(id)\\n db[userId] = theUser\",\n \"def mute(individual):\\n mutatePt=random.randint(0,len(individual)-1)\\n if mutatePt==0:\\n individual[mutatePt]=random.uniform(kNN.features_min[0], kNN.features_max[0])\\n elif mutatePt==2:\\n individual[mutatePt]=random.uniform(kNN.features_min[1], kNN.features_max[1])\\n elif mutatePt==3:\\n individual[mutatePt]=random.uniform(kNN.features_min[2], kNN.features_max[2])\\n elif mutatePt==4:\\n individual[mutatePt]=random.uniform(kNN.features_min[3], kNN.features_max[3])\\n elif mutatePt==5:\\n individual[mutatePt]=random.uniform(kNN.features_min[4], kNN.features_max[4])\\n\\n return individual,\",\n \"def cmd_remadmin_private(self, argument):\\n if self.is_admin:\\n users = argument.split()\\n for user in users:\\n self.bot.admins.remove(user)\\n self.send(self.nick, _(\\\"User %s removed from admins\\\"), user)\\n self.logger.info(\\\"User %s removed %s from admins\\\" % (self.nick, user))\\n else:\\n self.logger.warning(\\\"User %s tried to use '%s' without being admin\\\" % (self.nick, \\\"remadmin\\\"))\",\n \"def test_cant_remove_other_users_from_stream(self) -> None:\\n result = self.attempt_unsubscribe_of_principal(\\n query_count=8,\\n target_users=[self.example_user(\\\"cordelia\\\")],\\n is_realm_admin=False,\\n is_subbed=True,\\n invite_only=False,\\n target_users_subbed=True,\\n )\\n self.assert_json_error(result, \\\"Insufficient permission\\\")\",\n \"def mute_volume(self, mute):\\n mute_numeric = '1' if mute else '0'\\n self._lms.query(self._id, 'mixer', 'muting', mute_numeric)\\n self.update_ha_state()\",\n \"async def removerole(self, ctx, member: discord.Member, role: discord.Role):\\n role = discord.utils.get(ctx.guild.roles, id=role.id)\\n\\n muted_role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n punished_role = discord.utils.get(ctx.guild.roles, name=\\\"Punished\\\")\\n\\n if role > ctx.author.top_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=\\\"You don't have permission to remove this role\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.darker_grey(),\\n )\\n )\\n\\n if role == muted_role or role == punished_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Can not remove *{role}* role using this command.\\\",\\n description=\\\"For more information run ```.help removerole```\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.red(),\\n )\\n )\\n\\n if role not in member.roles:\\n return await ctx.channel.send(\\n embed=discord.Embed(\\n title=f\\\"{member} doesn't have *{role}* Role!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.greyple(),\\n )\\n )\\n\\n await member.remove_roles(role)\\n await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"*{role}* has been removed from *{member}*\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"async def async_set_multiroom_unjoinat(self, tme):\\n self._multiroom_unjoinat = tme\",\n \"async def voice_mute(self, ctx, member: discord.Member, *,reason: typing.Optional[str]):\\n await member.edit(mute=True, reason=reason[:512])\\n await ctx.send(f\\\"User {member.mention} successfully muted from voice\\\")\\n if reason:\\n await self.check_channel.send(f\\\"user {member.mention} muted from voice for the following reason:\\\\n\\\"\\n f\\\"{reason}\\\")\",\n \"async def deironman(self, ctx):\\n if has_post_permission(ctx.guild.id, ctx.channel.id):\\n out = (':tools: __**IRONMAN**__ :tools:\\\\n'\\n 'If you want to remove your ironman status, please react to this post with a '\\n ':thumbsup:. This will keep your account the same as it is right now, but you '\\n 'will be able to trade with others. If you want to re-ironman, you can type '\\n '`~ironman`, but you will have to reset your account.')\\n msg = await ctx.send(out)\\n\\n if await self.confirm(ctx, msg, out):\\n ctx.user_object.is_ironman = False\\n ctx.user_object.save()\\n # ironman_role = discord.utils.get(ctx.guild.roles, name=\\\"Ironman\\\")\\n # await ctx.author.remove_roles(\\n # ironman_role, reason=\\\"No longer wants to be ironmeme.\\\")\\n name = get_display_name(ctx.author)\\n await msg.edit(\\n content=f':tools: __**IRONMAN**__ :tools:\\\\n'\\n f'Congratulations, {name}, you are now a normal user!')\",\n \"def test_realm_admin_remove_multiple_users_from_stream(self) -> None:\\n target_users = [\\n self.example_user(name)\\n for name in [\\\"cordelia\\\", \\\"prospero\\\", \\\"iago\\\", \\\"hamlet\\\", \\\"outgoing_webhook_bot\\\"]\\n ]\\n result = self.attempt_unsubscribe_of_principal(\\n query_count=27,\\n cache_count=8,\\n target_users=target_users,\\n is_realm_admin=True,\\n is_subbed=True,\\n invite_only=False,\\n target_users_subbed=True,\\n )\\n json = self.assert_json_success(result)\\n self.assert_length(json[\\\"removed\\\"], 5)\\n self.assert_length(json[\\\"not_removed\\\"], 0)\",\n \"async def admin_reset(self, ctx, target: discord.Member):\\n inventories = get_file(\\\"inventories\\\")\\n cooldowns = get_file(\\\"cooldowns\\\")\\n del inventories[str(target.id)]\\n del cooldowns[str(target.id)]\\n update_file(\\\"inventories\\\", inventories)\\n update_file(\\\"cooldowns\\\", cooldowns)\\n\\n embed = discord.Embed(color=admin_color)\\n embed.set_author(name=\\\"🛠️ Admin\\\")\\n embed.add_field(name=\\\"♻️ Reset\\\", value=f\\\"{ctx.author.mention}, le compte de {target.mention} a été supprimé\\\")\\n embed = set_footer(embed, ctx)\\n await ctx.send(embed=embed)\",\n \"async def aoc_unsubscribe(self, ctx: commands.Context) -> None:\\n role = ctx.guild.get_role(AocConfig.role_id)\\n\\n if role in ctx.author.roles:\\n await ctx.author.remove_roles(role)\\n await ctx.send(\\\"Okay! You have been __unsubscribed__ from notifications about new Advent of Code tasks.\\\")\\n else:\\n await ctx.send(\\\"Hey, you don't even get any notifications about new Advent of Code tasks currently anyway.\\\")\",\n \"async def unset(self, ctx, *, role_name: str):\\n role_name = role_name.lower()\\n\\n if isinstance(ctx.message.channel, discord.DMChannel):\\n guild = await self.get_server_from_pm(ctx)\\n else:\\n guild = ctx.guild\\n\\n if guild is None:\\n return\\n\\n await self.remove_role(ctx, role_name, guild)\",\n \"def schedule_unmute(self, id: int, date: datetime) -> None:\\n\\n self.tasks.add_job(unmute_callback, 'date', id=str(\\n id), next_run_time=date, args=[id], misfire_grace_time=3600)\",\n \"def send_mute(self, dev, action=None):\\n if action not in ['true', 'false', None]:\\n action = None\\n\\n with self.smqtt as client:\\n client.publish(\\n IOTLinkTopics.MUTE.format(\\n domain=dev['domain'],\\n device_name=dev['name']\\n ),\\n payload=action\\n )\",\n \"async def stop_alert(self, ctx):\\n cyphon = discord.utils.get(ctx.message.server.members, id=\\\"186835826699665409\\\")\\n\\n if self.check_channel(ctx):\\n if self.check_permission(ctx) or ctx.message.author == cyphon:\\n channel = ctx.message.channel\\n\\n to_delete = []\\n\\n for s in self.twitch_streams:\\n if channel.id in s[\\\"CHANNEL\\\"]:\\n to_delete.append(s)\\n\\n for s in to_delete:\\n self.twitch_streams.remove(s)\\n\\n dataIO.save_json(\\\"data/streams/twitch.json\\\", self.twitch_streams)\\n\\n await self.bot.say(\\\"There will be no more stream alerts in the stream \\\"\\n \\\"channel.\\\")\\n else:\\n await self.bot.send_message(ctx.message.author, \\\"You don't have permission to execute that command.\\\")\",\n \"def mute(self) -> bool:\\n return bool(self.audio_mixer.getmute()[0])\",\n \"def muted(guild, channel):\\n\\tif str(guild.id) in Settings.muted_channels:\\n\\t\\treturn str(channel.id) in Settings.muted_channels[str(guild.id)]\\n\\treturn False\",\n \"def unmute_health_alert(alert: str, node: CephAdmin) -> bool:\\n all_alerts = get_alerts(node)\\n if alert not in all_alerts[\\\"muted_alerts\\\"] + all_alerts[\\\"active_alerts\\\"]:\\n log.info(f\\\"the alert: {alert} not generated on the cluster, Cannot mute\\\")\\n return True\\n if alert in all_alerts[\\\"active_alerts\\\"]:\\n log.info(f\\\"the alert: {alert} is already un-muted\\\")\\n return True\\n\\n # Un-Muting the given alert\\n cmd = f\\\"ceph health unmute {alert}\\\"\\n node.shell([cmd])\\n # Sleeping for 2 sec for the alert to be logged\\n time.sleep(2)\\n all_alerts = get_alerts(node)\\n log.info(\\n f\\\"Un-Muted the alert : {alert}. All the Un-muted alerts : {all_alerts['active_alerts']}\\\"\\n )\\n return True if alert in all_alerts[\\\"active_alerts\\\"] else False\",\n \"def cancel_unmute(self, id: int) -> None:\\n\\n self.tasks.remove_job(str(id), 'default')\",\n \"def is_muted(self, is_muted):\\n # type: (bool) -> None\\n\\n if is_muted is not None:\\n if not isinstance(is_muted, bool):\\n raise TypeError(\\\"Invalid type for `is_muted`, type has to be `bool`\\\")\\n\\n self._is_muted = is_muted\",\n \"async def try_clear_reactions(msg):\\n\\n if msg.guild:\\n try:\\n await msg.clear_reactions()\\n except discord.errors.Forbidden:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7793159","0.7562203","0.7182035","0.71422404","0.71150947","0.70955354","0.7093954","0.709301","0.7084421","0.7016341","0.7013622","0.68202204","0.68122566","0.67613274","0.6734327","0.6710592","0.665197","0.6545975","0.6484686","0.6455824","0.64350253","0.6396671","0.63905954","0.63847554","0.6377436","0.6375446","0.62917566","0.6262278","0.62306035","0.6205526","0.61971694","0.6145029","0.61356115","0.61356115","0.61117285","0.6108775","0.6077897","0.6058551","0.6055472","0.59976554","0.59917074","0.5959657","0.5957664","0.59566057","0.5955509","0.59241676","0.589207","0.58866525","0.5878779","0.58612907","0.5817955","0.5804606","0.579658","0.57901675","0.57870716","0.57859343","0.57573456","0.5724245","0.5718857","0.57178396","0.57123643","0.5704929","0.57046795","0.5702546","0.5699365","0.5699092","0.56716776","0.5650816","0.5649546","0.5643502","0.5629971","0.5628935","0.5594319","0.5556991","0.5542366","0.5533102","0.55320734","0.5527752","0.5519178","0.55137444","0.5504999","0.55035543","0.54973143","0.5494698","0.5492768","0.5481509","0.5480493","0.5475216","0.5453526","0.5449277","0.5448326","0.54465497","0.5445084","0.54438","0.54385686","0.54367596","0.54322654","0.54312104","0.54302084","0.542725"],"string":"[\n \"0.7793159\",\n \"0.7562203\",\n \"0.7182035\",\n \"0.71422404\",\n \"0.71150947\",\n \"0.70955354\",\n \"0.7093954\",\n \"0.709301\",\n \"0.7084421\",\n \"0.7016341\",\n \"0.7013622\",\n \"0.68202204\",\n \"0.68122566\",\n \"0.67613274\",\n \"0.6734327\",\n \"0.6710592\",\n \"0.665197\",\n \"0.6545975\",\n \"0.6484686\",\n \"0.6455824\",\n \"0.64350253\",\n \"0.6396671\",\n \"0.63905954\",\n \"0.63847554\",\n \"0.6377436\",\n \"0.6375446\",\n \"0.62917566\",\n \"0.6262278\",\n \"0.62306035\",\n \"0.6205526\",\n \"0.61971694\",\n \"0.6145029\",\n \"0.61356115\",\n \"0.61356115\",\n \"0.61117285\",\n \"0.6108775\",\n \"0.6077897\",\n \"0.6058551\",\n \"0.6055472\",\n \"0.59976554\",\n \"0.59917074\",\n \"0.5959657\",\n \"0.5957664\",\n \"0.59566057\",\n \"0.5955509\",\n \"0.59241676\",\n \"0.589207\",\n \"0.58866525\",\n \"0.5878779\",\n \"0.58612907\",\n \"0.5817955\",\n \"0.5804606\",\n \"0.579658\",\n \"0.57901675\",\n \"0.57870716\",\n \"0.57859343\",\n \"0.57573456\",\n \"0.5724245\",\n \"0.5718857\",\n \"0.57178396\",\n \"0.57123643\",\n \"0.5704929\",\n \"0.57046795\",\n \"0.5702546\",\n \"0.5699365\",\n \"0.5699092\",\n \"0.56716776\",\n \"0.5650816\",\n \"0.5649546\",\n \"0.5643502\",\n \"0.5629971\",\n \"0.5628935\",\n \"0.5594319\",\n \"0.5556991\",\n \"0.5542366\",\n \"0.5533102\",\n \"0.55320734\",\n \"0.5527752\",\n \"0.5519178\",\n \"0.55137444\",\n \"0.5504999\",\n \"0.55035543\",\n \"0.54973143\",\n \"0.5494698\",\n \"0.5492768\",\n \"0.5481509\",\n \"0.5480493\",\n \"0.5475216\",\n \"0.5453526\",\n \"0.5449277\",\n \"0.5448326\",\n \"0.54465497\",\n \"0.5445084\",\n \"0.54438\",\n \"0.54385686\",\n \"0.54367596\",\n \"0.54322654\",\n \"0.54312104\",\n \"0.54302084\",\n \"0.542725\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8222266"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13789,"cells":{"query":{"kind":"string","value":"Generate one random control character."},"document":{"kind":"string","value":"def make_control_character():\n # Add one character made up of one codepoint each from\n # (High Surrogates + High Private Use Surrogates) and Low Surrogates.\n # We expect each such pair to behave as a single high-codepoint\n # character.\n controls = ('0000', '001F')\n return [unicode_char(char)\n for char in range(int(controls[0], 16), int(controls[1], 16)+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":["def random_charachter() -> chr:\r\n return chr(int(random.randrange(32, 126, 1)))","def random_char():\n return chr(random.randrange(32, 126, 1))","def get_random_char():\n return chr(randint(97, 122))","def random_char(alph):\n char = alph[rand_generator.randrange(len(alph))]\n return char","def RandomAlphaNumChar():\n num = random.randint(0, 26 + 26 + 10)\n if num < 26:\n return chr(num + 65)\n num -= 26\n if num < 26:\n return chr(num + 97)\n return chr(num + 48)","def __getRandChar(self):\n return self.letterbag[random.randint(0,25)]","def rand_string():\n out = ''\n for _ in range(24):\n out += choice(ascii_letters)\n return out","def random_alpha_num_char():\n num = random.randint(0, 26 + 26 + 10)\n if num < 26:\n return chr(num + 65)\n num -= 26\n if num < 26:\n return chr(num + 97)\n return chr(num + 48)","def gen_random_chars(n: int = 10) -> Text:\n if n < 1:\n raise Exception('Number of random chars to generate has to be > 0')\n\n return ''.join(choice(ascii_lowercase + '-_')\n for i in range(n))","def generate_token(length=30, chars=UNICODE_ASCII_CHARACTER_SET):\n rand = random.SystemRandom()\n return ''.join(rand.choice(chars) for x in range(length))","def generate_token(length=30, chars=UNICODE_ASCII_CHARACTER_SET):\n rand = random.SystemRandom()\n return ''.join(rand.choice(chars) for x in range(length))","def generate_token(length: int = 30, chars: str = UNICODE_ASCII_CHARACTER_SET) -> str:\n rand = random.SystemRandom()\n return \"\".join(rand.choice(chars) for _ in range(length))","def generate(length):\n alpha = map(chr, range(97, 123))\n alpha.append(' ')\n result = \"\"\n for x in range(length):\n result += alpha[random.randrange(0,27)]\n return result","def generate_rnd_msg() -> str:\n\n char_num = random.randint(8,20)\n i = 0\n s = \"\"\n for n in range(char_num):\n if i == char_num:\n break\n rnd_char = random.randint(0, len(string.ascii_lowercase) - 1)\n s += string.ascii_lowercase[rnd_char]\n i += 1\n\n return s","def gen_char(filename):\n random.seed()\n with open(filename, \"w\") as f:\n for i in range(1000):\n a=random.randint(33,122)\n c=chr(a)\n f.write(c)\n f.write(\" \")","def get_random_char(letters: str) -> str:\n return letters[random.randint(0, len(letters) - 1)]","def generate_randomkey(length):\n chars = string.letters + string.digits\n return ''.join([choice(chars) for i in range(length)])","def generate_random_key():\n return '%030x' % (random.randrange(256**15),)","def create_random_code(chars=AVAIABLE_CHARS):\n return \"\".join(\n [choice(chars) for _ in range(SIZE)]\n )","def gen_random_char_string(n, base_s=\"\"):\n if n == 0:\n return base_s\n \n c = random.choice(string.ascii_letters)\n return gen_random_char_string(n-1, base_s + c)","def rand_token(length, chars = string.digits):\n return \"\".join(random.SystemRandom().choice(chars) for _ in range(length))","def gen_randomkey(length):\n chars = string.letters + string.digits + string.punctuation\n return ''.join([choice(chars) for _ in xrange(int(str(length)))])","def get_random_alphabetic_string():\n return get_random_string(char_choice=string.ascii_letters)","def generate_key():\r\n\t\treturn ''.join(random.SystemRandom().choice(string.ascii_lowercase) for _ in range(123))","def generate_character(i: int) -> Character:\n gen = DNAGenerator()\n gen.get_random_character()\n c = Character(str(i))\n c.dna_generator = gen\n return c","def randkey():\n return binascii.b2a_hex(os.urandom(15))","def GenRandom(length = 10, chars=string.letters+string.digits):\n return ''.join([random.choice(chars) for i in range(length)])","def generate_code(_=None):\n chars = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\"\n\n rand = random.SystemRandom()\n return \"\".join(rand.choice(chars) for x in range(30))","def get_random_alphanumeric_string():\n return get_random_string(char_choice=string.ascii_letters + string.digits)","def generateToken():\n length = random.randint(8, 32)\n rdmtoken = ''.join(random.choice(string.printable) for i in range(length))\n return f'{rdmtoken}'","def n_char_generate(self,char,n):\n return char*n","def randchars(self):\n samples = random.sample(self._charset, self._text_length)\n return ''.join(samples)","def random_alpha(n=8):\n return \"\".join(random.SystemRandom().choice(string.ascii_letters) for _ in range(n))","def random_string() -> str:\n letters = string.ascii_lowercase\n return ''.join(random.choice(letters) for i in range(8))","def create_random_string(total_character):\n feed=string.printable\n words=\"\"\n i=0\n while i < total_character:\n words += feed[random.randrange(0,len(feed)-1)]\n i+=1\n return words","def gen_secret() -> str:\n r = random.randrange(0, 255) # INSECURE, just for demo\n r = hex(r)[2:]\n if len(r) == 1:\n return f'0{r}'\n return r","def gen_seskey(self):\n return ''.join([str(format(randint(0, 15), 'X')) for i in range(24)])","def random_character(latin_chance=0.6):\n if random.random() < latin_chance:\n return random.choice(LATIN) + random.choice(LATIN)\n else:\n return random.choice(NON_LATIN)","def gen_key():\n key = []\n chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'\n for i in xrange(20):\n key.append(random.choice(chars))\n return ''.join(key)","def generate_random_key(self):\n self.key = ''.join(choice(ascii_letters + digits) for i in range(300))","def generate():\n repl_count = 0\n\n phrase = choice(BLACK_CARDS)\n while '_' in phrase:\n repl_count += 1\n phrase = phrase.replace('_', choice(WHITE_CARDS), 1)\n\n if repl_count == 0:\n phrase += \" \" + choice(WHITE_CARDS)\n\n return phrase","def generate_passwd(length=6):\n ret = ''\n if length < 6 :\n length = 6\n elif length > 10 :\n length = 10\n for x in xrange(length) :\n if x == 3 :\n ret += '-'\n ret += chr(random.randrange(ord('a'),ord('z'),1))\n return ret","def pwgen(length=16, ichars=string.ascii_letters+string.digits):\n return ''.join(random.choice(ichars) for i in range(length))","def get_random(self,num):\n return ''.join(sample('abcdefghijklmnopqrstuvwxyz1234567890!',8))","def passwordGen() :\n\treturn __randomString(12)","def random_character(self, script):\n if script in self.script_characters['Script'].values:\n\n script_index = list(self.script_characters['Script'].values).index(script)\n\n character_set = list(self.script_characters['Characters'][script_index].decode(\"utf-8\"))\n\n # selecting random character from all characters within script\n characters_in_script = len(character_set)\n character = character_set[np.random.randint(0, characters_in_script)]\n\n # TODO find a more elegant solution to double width characters\n # to deal with hanzi characters being twice the width of other unicode characters, simply delete half of\n # them\n # if script == 'hanzi':\n # if np.random.randint(0,2) > 0:\n # character = ''\n\n # print(character)\n\n return character\n\n raise ValueError('unsupported script')","def generate_random_string():\n return \"\".join(random.choice(string.ascii_lowercase + string.digits) for _ in range(16)) # nosec","def randompassword():\n characters = string.ascii_uppercase + string.ascii_lowercase + string.digits\n size = random.randint(8, 12)\n return ''.join(random.choice(characters) for x in range(size))","def random_letter(letters):\n return random.choice(letters)","def generate_random_password(self):\r\n self.symbols = self.__set_symbol_dict() # set new symbol subset dict\r\n self.i = randrange(len(self.symbols)) # set new dict key pointer\r\n return \"\".join(self.__get_random_symbol() for _ in range(self.pw_len))","def gen_code():\n return ''.join([random.choice(string.ascii_uppercase + string.digits) for _ in range(10)])","def token_generator(length):\n\n token_list = \\\n \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\"\n s = []\n for i in range(length):\n s.append(random.choice(token_list))\n return \"\".join(s)","def generate_password(length=12, chars=string.letters + string.digits):\r\n choice = random.SystemRandom().choice\r\n return ''.join([choice(chars) for _i in range(length)])","def gen_rand_str(n):\n return \"\".join(random.choice(string.ascii_letters) for _ in range(n))","def generate_password():\n chars = string.ascii_letters + string.digits\n key = random.sample(chars, 10)\n keys = \"\".join(key)\n return keys","def create_secret_code():\n characters = string.ascii_uppercase + string.digits\n size = 6\n return ''.join(random.choice(characters) for _ in range(size))","def random_string(length=8, chars=string.ascii_letters + string.digits):\n return ''.join([chars[random.randint(0, len(chars) - 1)] for i in range(length)])","def RandomAlphaNumWord(length):\n return ''.join([RandomAlphaNumChar() for _ in range(0, length)])","def random_string(self, length):\n return \"\".join(\n SystemRandom().choice(string.ascii_letters) for _ in range(length)\n )","def random_password():\n pass_len = secrets.choice(range(32, 49))\n return ''.join(secrets.choice(string.printable)\n for _ in range(pass_len))","def randstring(length=1):\n charstouse = string.ascii_letters + string.digits + string.punctuation\n newpass = ''\n for _ in range(length):\n newpass += str(charstouse[random.randint(0, len(charstouse) - 1)])\n return newpass","def random_chars(char_set, length):\n s = \"\"\n sz = len(char_set)-1\n r = random.SystemRandom()\n for i in range(0, length+1):\n n = r.randint(0, sz)\n s = s + char_set[n]\n return s","def generate_key():\n\tkey = [ randint(0,255) for i in range(16) ]\n\treturn bytes( key )","def make_random(length, is_binary=False):\n limit = 255 if is_binary else 126\n return \"\".join([chr(choice(range(32, limit))) for _ in range(length)]) + \"\\n\"","def password_alphabetical(i):\r\n\r\n return ''.join(_random.choice(string.ascii_letters) for x in\r\n range(i))","def gen_chars(length, character):\n return ''.join([character for i in range(length)])","def insert_random_character(self, node, verbose=False):\n s = node.children[0].symbol\n if s:\n pos = random.randint(0, len(s) - 1)\n #random_character = chr(random.randrange(0, 127))\n random_character = random.choice(self.char_pool)\n if verbose:\n print(\"Inserting character {} at pos {} of {}.\".format(repr(random_character), pos, node.symbol))\n else:\n self.mutation_messages.append(\"Inserting character {} at pos {} of {}.\".format(repr(random_character), pos, node.symbol))\n\n node.children[0].symbol = s[:pos] + random_character + s[pos:]","def random_string(length=1, uppercase=False, lowercase=False):\n characters = digits\n code = str()\n\n if uppercase:\n characters += ascii_uppercase\n if lowercase:\n characters += ascii_lowercase\n\n while len(code) < length:\n character = choice(characters)\n if len(code) == 0:\n character = choice(ascii_uppercase)\n\n code += character\n\n return code","def random_string(length=25):\n return ''.join(random.choice(string.ascii_letters) for i in range(25))","def generate_hks():\n return ''.join((random.choice(string.ascii_letters) for x in range(26)))","def triplet():\n ends = digraphs + list(consonants)\n spaces = [ends, list(vowels), ends]\n bit = \"\".join([random.sample(x, 1)[0] for x in spaces])\n if random.randint(0, 1) == 1:\n bit = bit.title()\n return bit","def genCharGroup(self):\n alphabet = list('abcdefghijklmnopqrstuvwxyz') #Creates a list of all the alphabet characters\n group = []\n count = 0\n while count != 3: #While the loop total does not equal 3\n i = random.choice(alphabet) #Make a random choice\n alphabet.remove(i) #Remove it from the alphabet\n group.append(i) #And add it to the group array\n count += 1 #Add one to the loop total\n return str(''.join(group)) #Return the string of 3 characters to the user","def get_random_string(self, length):\n letters = string.ascii_lowercase\n return ''.join(random.choice(letters) for i in range(length))","def generate_char(model, history, prev_chars):\n history = history[-prev_chars:]\n distr = model.get(history)\n rnd = random()\n # randomly sample most probable chars\n for char, prob in distr:\n rnd -= prob # gradually subtract until good candidate\n if rnd <= 0:\n return char","def random_word ():\n letters = [ chr(i) for i in range(ord('a'),ord('z')+1) ] + [ chr(i) for i in range(ord('A'),ord('Z')+1) ]\n length = 4 + random.randint(0,4)\n str = \"\"\n for i in range(length):\n str = str + random.choice(letters)\n return str","def generate_random_alphanumeric(length):\n return ''.join(random.SystemRandom().choice(string.ascii_letters + string.digits) \\\n for _ in range(length))","def generate_random_string(length = 30):\n\n m_available_chars = ascii_letters + digits\n\n return \"\".join(choice(m_available_chars) for _ in xrange(length))","def _create_char_spinner():\r\n while True:\r\n for c in '|/-\\\\':\r\n yield c","def randstr(chars=string.ascii_lowercase + string.digits, len=16) -> str:\n return ''.join(random.choices(chars, k=len))","def password_generator(length=8):\n chars = tuple(ascii_letters) + tuple(digits) + tuple(punctuation)\n return \"\".join(choice(chars) for x in range(length))","def GenerateRandomName():\n buf = cStringIO.StringIO()\n buf.write(random.choice(_BEGIN_ALPHABET))\n for _ in xrange(_LENGTH - 1):\n buf.write(random.choice(_ALPHABET))\n return buf.getvalue()","def string(self, string_length=10):\n letters = string.ascii_letters\n return ''.join(random.choice(letters) for i in range(string_length))","def random_string(length=10):\n\n\tletters = string.ascii_lowercase\n\n\treturn ''.join(random.choice(letters) for i in xrange(length))","def random_string():\n return \"\".join(random.choice(string.ascii_letters) for i in range(6))","def randomkey(self):\n #raise NotImplementedError(\"Implemented but buggy, do not use.\")\n self.connect()\n self._write('RANDOMKEY\\r\\n')\n data = self._read().strip()\n self._check_for_error(data)\n return data","def make_random_key(length=RANDOM_KEY_LENGTH, chars=RANDOM_KEY_CHARACTERS):\n chars = [random.choice(chars) for _ in range(length)]\n return ''.join(chars)","def _random_password(self):\n return ''.join([\n random.choice(string.ascii_letters + string.digits)\n for _ in range(12)\n ])","def create_random_string(chars=string.ascii_letters + string.digits, length=16):\n return \"\".join([random.choice(chars) for _ in range(int(length))])","def genRandString(dl = 10):\n ret = ''\n for i in range(dl) :\n ret += random.choice(string.ascii_letters + string.digits)\n return ret","def generate_password(self, length):\n items = [\"a\", \"e\", \"i\", \"o\", \"u\", \"1\", \"2\", \"4\", \"5\", \"7\", \"8\", \"9\"]\n\n new_password = \"\"\n while(len(new_password) < length):\n item = items[randint(0, len(items) - 1)]\n new_password += item\n return new_password","def newpassword(length=8):\n\n word = characters()\n\n while len(word) < length:\n word.append(random.choice(characters()))\n\n random.shuffle(word)\n return \"\".join(word)","def generate_password(length=20):\r\n # type: (int) -> str\r\n return ('%0'+str(length)+'x') % random.randrange(16 ** length)","def generate_random_alphanumeric(length):\n return ''.join(random.SystemRandom().choice(\n string.ascii_uppercase + string.digits) for _ in range(length))","def password_alphanumeric(i):\r\n\r\n chars = string.ascii_letters + string.digits\r\n return ''.join(_random.choice(chars) for x in range(i))","def random_mutate(dna: str) -> str:\r\n result = \"\"\r\n for c in range(DNA_SIZE):\r\n if random.randrange(0, 100, 1) == 1:\r\n result += random_charachter()\r\n else:\r\n result += dna[c]\r\n return result","def random_str(length=8, letters=string.ascii_letters + string.digits):\r\n return \"\".join(random.choice(letters) for x in range(length))","def generate_username():\n return ''.join(choice(ascii_letters + digits) for _ in range(15))","def _rand_str(size=16):\n return \"\".join(random.SystemRandom().choices(string.ascii_lowercase, k=size))","def _password_generator(size: int = 12, chars: Optional[str] = None) -> str:\n if chars is None:\n chars = string.ascii_letters + string.digits\n return ''.join(random.choice(chars) for _ in range(size))","def _random_id(n):\n ''.join(choice(alphanums) for i in range(n))"],"string":"[\n \"def random_charachter() -> chr:\\r\\n return chr(int(random.randrange(32, 126, 1)))\",\n \"def random_char():\\n return chr(random.randrange(32, 126, 1))\",\n \"def get_random_char():\\n return chr(randint(97, 122))\",\n \"def random_char(alph):\\n char = alph[rand_generator.randrange(len(alph))]\\n return char\",\n \"def RandomAlphaNumChar():\\n num = random.randint(0, 26 + 26 + 10)\\n if num < 26:\\n return chr(num + 65)\\n num -= 26\\n if num < 26:\\n return chr(num + 97)\\n return chr(num + 48)\",\n \"def __getRandChar(self):\\n return self.letterbag[random.randint(0,25)]\",\n \"def rand_string():\\n out = ''\\n for _ in range(24):\\n out += choice(ascii_letters)\\n return out\",\n \"def random_alpha_num_char():\\n num = random.randint(0, 26 + 26 + 10)\\n if num < 26:\\n return chr(num + 65)\\n num -= 26\\n if num < 26:\\n return chr(num + 97)\\n return chr(num + 48)\",\n \"def gen_random_chars(n: int = 10) -> Text:\\n if n < 1:\\n raise Exception('Number of random chars to generate has to be > 0')\\n\\n return ''.join(choice(ascii_lowercase + '-_')\\n for i in range(n))\",\n \"def generate_token(length=30, chars=UNICODE_ASCII_CHARACTER_SET):\\n rand = random.SystemRandom()\\n return ''.join(rand.choice(chars) for x in range(length))\",\n \"def generate_token(length=30, chars=UNICODE_ASCII_CHARACTER_SET):\\n rand = random.SystemRandom()\\n return ''.join(rand.choice(chars) for x in range(length))\",\n \"def generate_token(length: int = 30, chars: str = UNICODE_ASCII_CHARACTER_SET) -> str:\\n rand = random.SystemRandom()\\n return \\\"\\\".join(rand.choice(chars) for _ in range(length))\",\n \"def generate(length):\\n alpha = map(chr, range(97, 123))\\n alpha.append(' ')\\n result = \\\"\\\"\\n for x in range(length):\\n result += alpha[random.randrange(0,27)]\\n return result\",\n \"def generate_rnd_msg() -> str:\\n\\n char_num = random.randint(8,20)\\n i = 0\\n s = \\\"\\\"\\n for n in range(char_num):\\n if i == char_num:\\n break\\n rnd_char = random.randint(0, len(string.ascii_lowercase) - 1)\\n s += string.ascii_lowercase[rnd_char]\\n i += 1\\n\\n return s\",\n \"def gen_char(filename):\\n random.seed()\\n with open(filename, \\\"w\\\") as f:\\n for i in range(1000):\\n a=random.randint(33,122)\\n c=chr(a)\\n f.write(c)\\n f.write(\\\" \\\")\",\n \"def get_random_char(letters: str) -> str:\\n return letters[random.randint(0, len(letters) - 1)]\",\n \"def generate_randomkey(length):\\n chars = string.letters + string.digits\\n return ''.join([choice(chars) for i in range(length)])\",\n \"def generate_random_key():\\n return '%030x' % (random.randrange(256**15),)\",\n \"def create_random_code(chars=AVAIABLE_CHARS):\\n return \\\"\\\".join(\\n [choice(chars) for _ in range(SIZE)]\\n )\",\n \"def gen_random_char_string(n, base_s=\\\"\\\"):\\n if n == 0:\\n return base_s\\n \\n c = random.choice(string.ascii_letters)\\n return gen_random_char_string(n-1, base_s + c)\",\n \"def rand_token(length, chars = string.digits):\\n return \\\"\\\".join(random.SystemRandom().choice(chars) for _ in range(length))\",\n \"def gen_randomkey(length):\\n chars = string.letters + string.digits + string.punctuation\\n return ''.join([choice(chars) for _ in xrange(int(str(length)))])\",\n \"def get_random_alphabetic_string():\\n return get_random_string(char_choice=string.ascii_letters)\",\n \"def generate_key():\\r\\n\\t\\treturn ''.join(random.SystemRandom().choice(string.ascii_lowercase) for _ in range(123))\",\n \"def generate_character(i: int) -> Character:\\n gen = DNAGenerator()\\n gen.get_random_character()\\n c = Character(str(i))\\n c.dna_generator = gen\\n return c\",\n \"def randkey():\\n return binascii.b2a_hex(os.urandom(15))\",\n \"def GenRandom(length = 10, chars=string.letters+string.digits):\\n return ''.join([random.choice(chars) for i in range(length)])\",\n \"def generate_code(_=None):\\n chars = \\\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\\\"\\n\\n rand = random.SystemRandom()\\n return \\\"\\\".join(rand.choice(chars) for x in range(30))\",\n \"def get_random_alphanumeric_string():\\n return get_random_string(char_choice=string.ascii_letters + string.digits)\",\n \"def generateToken():\\n length = random.randint(8, 32)\\n rdmtoken = ''.join(random.choice(string.printable) for i in range(length))\\n return f'{rdmtoken}'\",\n \"def n_char_generate(self,char,n):\\n return char*n\",\n \"def randchars(self):\\n samples = random.sample(self._charset, self._text_length)\\n return ''.join(samples)\",\n \"def random_alpha(n=8):\\n return \\\"\\\".join(random.SystemRandom().choice(string.ascii_letters) for _ in range(n))\",\n \"def random_string() -> str:\\n letters = string.ascii_lowercase\\n return ''.join(random.choice(letters) for i in range(8))\",\n \"def create_random_string(total_character):\\n feed=string.printable\\n words=\\\"\\\"\\n i=0\\n while i < total_character:\\n words += feed[random.randrange(0,len(feed)-1)]\\n i+=1\\n return words\",\n \"def gen_secret() -> str:\\n r = random.randrange(0, 255) # INSECURE, just for demo\\n r = hex(r)[2:]\\n if len(r) == 1:\\n return f'0{r}'\\n return r\",\n \"def gen_seskey(self):\\n return ''.join([str(format(randint(0, 15), 'X')) for i in range(24)])\",\n \"def random_character(latin_chance=0.6):\\n if random.random() < latin_chance:\\n return random.choice(LATIN) + random.choice(LATIN)\\n else:\\n return random.choice(NON_LATIN)\",\n \"def gen_key():\\n key = []\\n chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'\\n for i in xrange(20):\\n key.append(random.choice(chars))\\n return ''.join(key)\",\n \"def generate_random_key(self):\\n self.key = ''.join(choice(ascii_letters + digits) for i in range(300))\",\n \"def generate():\\n repl_count = 0\\n\\n phrase = choice(BLACK_CARDS)\\n while '_' in phrase:\\n repl_count += 1\\n phrase = phrase.replace('_', choice(WHITE_CARDS), 1)\\n\\n if repl_count == 0:\\n phrase += \\\" \\\" + choice(WHITE_CARDS)\\n\\n return phrase\",\n \"def generate_passwd(length=6):\\n ret = ''\\n if length < 6 :\\n length = 6\\n elif length > 10 :\\n length = 10\\n for x in xrange(length) :\\n if x == 3 :\\n ret += '-'\\n ret += chr(random.randrange(ord('a'),ord('z'),1))\\n return ret\",\n \"def pwgen(length=16, ichars=string.ascii_letters+string.digits):\\n return ''.join(random.choice(ichars) for i in range(length))\",\n \"def get_random(self,num):\\n return ''.join(sample('abcdefghijklmnopqrstuvwxyz1234567890!',8))\",\n \"def passwordGen() :\\n\\treturn __randomString(12)\",\n \"def random_character(self, script):\\n if script in self.script_characters['Script'].values:\\n\\n script_index = list(self.script_characters['Script'].values).index(script)\\n\\n character_set = list(self.script_characters['Characters'][script_index].decode(\\\"utf-8\\\"))\\n\\n # selecting random character from all characters within script\\n characters_in_script = len(character_set)\\n character = character_set[np.random.randint(0, characters_in_script)]\\n\\n # TODO find a more elegant solution to double width characters\\n # to deal with hanzi characters being twice the width of other unicode characters, simply delete half of\\n # them\\n # if script == 'hanzi':\\n # if np.random.randint(0,2) > 0:\\n # character = ''\\n\\n # print(character)\\n\\n return character\\n\\n raise ValueError('unsupported script')\",\n \"def generate_random_string():\\n return \\\"\\\".join(random.choice(string.ascii_lowercase + string.digits) for _ in range(16)) # nosec\",\n \"def randompassword():\\n characters = string.ascii_uppercase + string.ascii_lowercase + string.digits\\n size = random.randint(8, 12)\\n return ''.join(random.choice(characters) for x in range(size))\",\n \"def random_letter(letters):\\n return random.choice(letters)\",\n \"def generate_random_password(self):\\r\\n self.symbols = self.__set_symbol_dict() # set new symbol subset dict\\r\\n self.i = randrange(len(self.symbols)) # set new dict key pointer\\r\\n return \\\"\\\".join(self.__get_random_symbol() for _ in range(self.pw_len))\",\n \"def gen_code():\\n return ''.join([random.choice(string.ascii_uppercase + string.digits) for _ in range(10)])\",\n \"def token_generator(length):\\n\\n token_list = \\\\\\n \\\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\\\"\\n s = []\\n for i in range(length):\\n s.append(random.choice(token_list))\\n return \\\"\\\".join(s)\",\n \"def generate_password(length=12, chars=string.letters + string.digits):\\r\\n choice = random.SystemRandom().choice\\r\\n return ''.join([choice(chars) for _i in range(length)])\",\n \"def gen_rand_str(n):\\n return \\\"\\\".join(random.choice(string.ascii_letters) for _ in range(n))\",\n \"def generate_password():\\n chars = string.ascii_letters + string.digits\\n key = random.sample(chars, 10)\\n keys = \\\"\\\".join(key)\\n return keys\",\n \"def create_secret_code():\\n characters = string.ascii_uppercase + string.digits\\n size = 6\\n return ''.join(random.choice(characters) for _ in range(size))\",\n \"def random_string(length=8, chars=string.ascii_letters + string.digits):\\n return ''.join([chars[random.randint(0, len(chars) - 1)] for i in range(length)])\",\n \"def RandomAlphaNumWord(length):\\n return ''.join([RandomAlphaNumChar() for _ in range(0, length)])\",\n \"def random_string(self, length):\\n return \\\"\\\".join(\\n SystemRandom().choice(string.ascii_letters) for _ in range(length)\\n )\",\n \"def random_password():\\n pass_len = secrets.choice(range(32, 49))\\n return ''.join(secrets.choice(string.printable)\\n for _ in range(pass_len))\",\n \"def randstring(length=1):\\n charstouse = string.ascii_letters + string.digits + string.punctuation\\n newpass = ''\\n for _ in range(length):\\n newpass += str(charstouse[random.randint(0, len(charstouse) - 1)])\\n return newpass\",\n \"def random_chars(char_set, length):\\n s = \\\"\\\"\\n sz = len(char_set)-1\\n r = random.SystemRandom()\\n for i in range(0, length+1):\\n n = r.randint(0, sz)\\n s = s + char_set[n]\\n return s\",\n \"def generate_key():\\n\\tkey = [ randint(0,255) for i in range(16) ]\\n\\treturn bytes( key )\",\n \"def make_random(length, is_binary=False):\\n limit = 255 if is_binary else 126\\n return \\\"\\\".join([chr(choice(range(32, limit))) for _ in range(length)]) + \\\"\\\\n\\\"\",\n \"def password_alphabetical(i):\\r\\n\\r\\n return ''.join(_random.choice(string.ascii_letters) for x in\\r\\n range(i))\",\n \"def gen_chars(length, character):\\n return ''.join([character for i in range(length)])\",\n \"def insert_random_character(self, node, verbose=False):\\n s = node.children[0].symbol\\n if s:\\n pos = random.randint(0, len(s) - 1)\\n #random_character = chr(random.randrange(0, 127))\\n random_character = random.choice(self.char_pool)\\n if verbose:\\n print(\\\"Inserting character {} at pos {} of {}.\\\".format(repr(random_character), pos, node.symbol))\\n else:\\n self.mutation_messages.append(\\\"Inserting character {} at pos {} of {}.\\\".format(repr(random_character), pos, node.symbol))\\n\\n node.children[0].symbol = s[:pos] + random_character + s[pos:]\",\n \"def random_string(length=1, uppercase=False, lowercase=False):\\n characters = digits\\n code = str()\\n\\n if uppercase:\\n characters += ascii_uppercase\\n if lowercase:\\n characters += ascii_lowercase\\n\\n while len(code) < length:\\n character = choice(characters)\\n if len(code) == 0:\\n character = choice(ascii_uppercase)\\n\\n code += character\\n\\n return code\",\n \"def random_string(length=25):\\n return ''.join(random.choice(string.ascii_letters) for i in range(25))\",\n \"def generate_hks():\\n return ''.join((random.choice(string.ascii_letters) for x in range(26)))\",\n \"def triplet():\\n ends = digraphs + list(consonants)\\n spaces = [ends, list(vowels), ends]\\n bit = \\\"\\\".join([random.sample(x, 1)[0] for x in spaces])\\n if random.randint(0, 1) == 1:\\n bit = bit.title()\\n return bit\",\n \"def genCharGroup(self):\\n alphabet = list('abcdefghijklmnopqrstuvwxyz') #Creates a list of all the alphabet characters\\n group = []\\n count = 0\\n while count != 3: #While the loop total does not equal 3\\n i = random.choice(alphabet) #Make a random choice\\n alphabet.remove(i) #Remove it from the alphabet\\n group.append(i) #And add it to the group array\\n count += 1 #Add one to the loop total\\n return str(''.join(group)) #Return the string of 3 characters to the user\",\n \"def get_random_string(self, length):\\n letters = string.ascii_lowercase\\n return ''.join(random.choice(letters) for i in range(length))\",\n \"def generate_char(model, history, prev_chars):\\n history = history[-prev_chars:]\\n distr = model.get(history)\\n rnd = random()\\n # randomly sample most probable chars\\n for char, prob in distr:\\n rnd -= prob # gradually subtract until good candidate\\n if rnd <= 0:\\n return char\",\n \"def random_word ():\\n letters = [ chr(i) for i in range(ord('a'),ord('z')+1) ] + [ chr(i) for i in range(ord('A'),ord('Z')+1) ]\\n length = 4 + random.randint(0,4)\\n str = \\\"\\\"\\n for i in range(length):\\n str = str + random.choice(letters)\\n return str\",\n \"def generate_random_alphanumeric(length):\\n return ''.join(random.SystemRandom().choice(string.ascii_letters + string.digits) \\\\\\n for _ in range(length))\",\n \"def generate_random_string(length = 30):\\n\\n m_available_chars = ascii_letters + digits\\n\\n return \\\"\\\".join(choice(m_available_chars) for _ in xrange(length))\",\n \"def _create_char_spinner():\\r\\n while True:\\r\\n for c in '|/-\\\\\\\\':\\r\\n yield c\",\n \"def randstr(chars=string.ascii_lowercase + string.digits, len=16) -> str:\\n return ''.join(random.choices(chars, k=len))\",\n \"def password_generator(length=8):\\n chars = tuple(ascii_letters) + tuple(digits) + tuple(punctuation)\\n return \\\"\\\".join(choice(chars) for x in range(length))\",\n \"def GenerateRandomName():\\n buf = cStringIO.StringIO()\\n buf.write(random.choice(_BEGIN_ALPHABET))\\n for _ in xrange(_LENGTH - 1):\\n buf.write(random.choice(_ALPHABET))\\n return buf.getvalue()\",\n \"def string(self, string_length=10):\\n letters = string.ascii_letters\\n return ''.join(random.choice(letters) for i in range(string_length))\",\n \"def random_string(length=10):\\n\\n\\tletters = string.ascii_lowercase\\n\\n\\treturn ''.join(random.choice(letters) for i in xrange(length))\",\n \"def random_string():\\n return \\\"\\\".join(random.choice(string.ascii_letters) for i in range(6))\",\n \"def randomkey(self):\\n #raise NotImplementedError(\\\"Implemented but buggy, do not use.\\\")\\n self.connect()\\n self._write('RANDOMKEY\\\\r\\\\n')\\n data = self._read().strip()\\n self._check_for_error(data)\\n return data\",\n \"def make_random_key(length=RANDOM_KEY_LENGTH, chars=RANDOM_KEY_CHARACTERS):\\n chars = [random.choice(chars) for _ in range(length)]\\n return ''.join(chars)\",\n \"def _random_password(self):\\n return ''.join([\\n random.choice(string.ascii_letters + string.digits)\\n for _ in range(12)\\n ])\",\n \"def create_random_string(chars=string.ascii_letters + string.digits, length=16):\\n return \\\"\\\".join([random.choice(chars) for _ in range(int(length))])\",\n \"def genRandString(dl = 10):\\n ret = ''\\n for i in range(dl) :\\n ret += random.choice(string.ascii_letters + string.digits)\\n return ret\",\n \"def generate_password(self, length):\\n items = [\\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", \\\"u\\\", \\\"1\\\", \\\"2\\\", \\\"4\\\", \\\"5\\\", \\\"7\\\", \\\"8\\\", \\\"9\\\"]\\n\\n new_password = \\\"\\\"\\n while(len(new_password) < length):\\n item = items[randint(0, len(items) - 1)]\\n new_password += item\\n return new_password\",\n \"def newpassword(length=8):\\n\\n word = characters()\\n\\n while len(word) < length:\\n word.append(random.choice(characters()))\\n\\n random.shuffle(word)\\n return \\\"\\\".join(word)\",\n \"def generate_password(length=20):\\r\\n # type: (int) -> str\\r\\n return ('%0'+str(length)+'x') % random.randrange(16 ** length)\",\n \"def generate_random_alphanumeric(length):\\n return ''.join(random.SystemRandom().choice(\\n string.ascii_uppercase + string.digits) for _ in range(length))\",\n \"def password_alphanumeric(i):\\r\\n\\r\\n chars = string.ascii_letters + string.digits\\r\\n return ''.join(_random.choice(chars) for x in range(i))\",\n \"def random_mutate(dna: str) -> str:\\r\\n result = \\\"\\\"\\r\\n for c in range(DNA_SIZE):\\r\\n if random.randrange(0, 100, 1) == 1:\\r\\n result += random_charachter()\\r\\n else:\\r\\n result += dna[c]\\r\\n return result\",\n \"def random_str(length=8, letters=string.ascii_letters + string.digits):\\r\\n return \\\"\\\".join(random.choice(letters) for x in range(length))\",\n \"def generate_username():\\n return ''.join(choice(ascii_letters + digits) for _ in range(15))\",\n \"def _rand_str(size=16):\\n return \\\"\\\".join(random.SystemRandom().choices(string.ascii_lowercase, k=size))\",\n \"def _password_generator(size: int = 12, chars: Optional[str] = None) -> str:\\n if chars is None:\\n chars = string.ascii_letters + string.digits\\n return ''.join(random.choice(chars) for _ in range(size))\",\n \"def _random_id(n):\\n ''.join(choice(alphanums) for i in range(n))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78689873","0.77449274","0.72392774","0.713114","0.69535005","0.68568593","0.68473285","0.6614242","0.6555148","0.65427595","0.65427595","0.6520578","0.6508524","0.643668","0.636383","0.63543737","0.63341427","0.62676865","0.6259865","0.6254233","0.6249821","0.62484825","0.6240288","0.62313473","0.62098086","0.6209753","0.61552465","0.615091","0.6141732","0.6137267","0.6133658","0.6118605","0.6102773","0.6102328","0.6091401","0.6084238","0.6081489","0.60746825","0.60716325","0.60696673","0.60638285","0.6057101","0.60494447","0.6049236","0.6039048","0.6036269","0.60306054","0.6018106","0.6017909","0.6005974","0.6004677","0.5993755","0.59936666","0.598881","0.59856856","0.5961754","0.59563714","0.59505737","0.5938592","0.59380656","0.59323883","0.59278256","0.59269","0.5915914","0.5914524","0.59077954","0.5903986","0.5903214","0.5900704","0.58977693","0.5896806","0.5893275","0.5889595","0.58823085","0.5874189","0.58723676","0.5870865","0.5868218","0.58568764","0.5855827","0.58556366","0.58536303","0.5850095","0.5847179","0.58454293","0.58430034","0.58386886","0.5835166","0.58333695","0.58266276","0.58232635","0.582015","0.58199173","0.5813525","0.58111584","0.5810018","0.581","0.58071876","0.5804841","0.5794872"],"string":"[\n \"0.78689873\",\n \"0.77449274\",\n \"0.72392774\",\n \"0.713114\",\n \"0.69535005\",\n \"0.68568593\",\n \"0.68473285\",\n \"0.6614242\",\n \"0.6555148\",\n \"0.65427595\",\n \"0.65427595\",\n \"0.6520578\",\n \"0.6508524\",\n \"0.643668\",\n \"0.636383\",\n \"0.63543737\",\n \"0.63341427\",\n \"0.62676865\",\n \"0.6259865\",\n \"0.6254233\",\n \"0.6249821\",\n \"0.62484825\",\n \"0.6240288\",\n \"0.62313473\",\n \"0.62098086\",\n \"0.6209753\",\n \"0.61552465\",\n \"0.615091\",\n \"0.6141732\",\n \"0.6137267\",\n \"0.6133658\",\n \"0.6118605\",\n \"0.6102773\",\n \"0.6102328\",\n \"0.6091401\",\n \"0.6084238\",\n \"0.6081489\",\n \"0.60746825\",\n \"0.60716325\",\n \"0.60696673\",\n \"0.60638285\",\n \"0.6057101\",\n \"0.60494447\",\n \"0.6049236\",\n \"0.6039048\",\n \"0.6036269\",\n \"0.60306054\",\n \"0.6018106\",\n \"0.6017909\",\n \"0.6005974\",\n \"0.6004677\",\n \"0.5993755\",\n \"0.59936666\",\n \"0.598881\",\n \"0.59856856\",\n \"0.5961754\",\n \"0.59563714\",\n \"0.59505737\",\n \"0.5938592\",\n \"0.59380656\",\n \"0.59323883\",\n \"0.59278256\",\n \"0.59269\",\n \"0.5915914\",\n \"0.5914524\",\n \"0.59077954\",\n \"0.5903986\",\n \"0.5903214\",\n \"0.5900704\",\n \"0.58977693\",\n \"0.5896806\",\n \"0.5893275\",\n \"0.5889595\",\n \"0.58823085\",\n \"0.5874189\",\n \"0.58723676\",\n \"0.5870865\",\n \"0.5868218\",\n \"0.58568764\",\n \"0.5855827\",\n \"0.58556366\",\n \"0.58536303\",\n \"0.5850095\",\n \"0.5847179\",\n \"0.58454293\",\n \"0.58430034\",\n \"0.58386886\",\n \"0.5835166\",\n \"0.58333695\",\n \"0.58266276\",\n \"0.58232635\",\n \"0.582015\",\n \"0.58199173\",\n \"0.5813525\",\n \"0.58111584\",\n \"0.5810018\",\n \"0.581\",\n \"0.58071876\",\n \"0.5804841\",\n \"0.5794872\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69967866"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":13790,"cells":{"query":{"kind":"string","value":"Extend `unichr` for all possible Unicode values (n)."},"document":{"kind":"string","value":"def unicode_char(n):\n try:\n return unichr(n)\n except ValueError:\n # Generate bytes object packed as int.\n bytes_object = struct.pack('i', n)\n # Return decoded w/ utf-32 codec.\n return bytes_object.decode('utf-32')"},"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 int_to_unichr(codepoint):\n if PY2:\n return unichr(codepoint)\n return chr(codepoint)","def make_unicode():\r\n for num in range(300, 320):\r\n yield unichr(num)","def safe_unichr(intval):\n try:\n return unichr(intval)\n except ValueError:\n # ValueError: unichr() arg not in range(0x10000) (narrow Python build)\n s = \"\\\\U%08x\" % intval\n # return UTF16 surrogate pair\n return s.decode('unicode-escape')","def n_char_generate(self,char,n):\n return char*n","def encode2(s,n):\n r = [ chr(((ord(x)-97+n)%26)+97) if x!=' ' else x for x in s]\n return \"\".join(r)","def encode1(s,n):\n r = \"\"\n for l in s:\n l = ord(l) # convert to ascii\n l = l - 97 # 'a' is 97 so we want to reduce so 'a'=0 'b'=1 etc\n l = l + n # add the offset\n l=l%26 # use mod so that we wrap around back to 'a' if we go past 'z'\n l=l+97 # and add back the 97\n r = r + chr(l)\n return r","def CHAR(table_number):\n return unichr(table_number)","def hex_to_unichr(hex_string):\n if (hex_string is None) or (len(hex_string) < 1):\n return None\n if hex_string.startswith(\"U+\"):\n hex_string = hex_string[2:]\n return int_to_unichr(int(hex_string, base=16))","def getcharswin(n):\n\n\tstring = \"\"\n\ti = 0\n\t# Loop until we get N chars\n\twhile True:\n\t\tc = msvcrt.getch()\n\t\tif c == b'\\x03':\n\t\t\traise KeyboardInterrupt()\n\t\ttry:\n\t\t\tstring += str(c, ENCODING)\n\t\texcept UnicodeDecodeError:\n\t\t\tcontinue\n\t\ti += 1\n\t\tif i == n:\n\t\t\tbreak\n\treturn string","def n_char(self,char,n,w=1,h=1):\n for i in range(n):\n self.esprint(char,w,h)","def h_ascii(key, N):\n if type(key) == str:\n if type(N) == int:\n s = 0\n for i in range(len(key)):\n s += ord(key[i])\n return s % N\n else:\n raise ValueError\n else:\n raise ValueError","def handle_charref(self, number):\n codepoint = int(number[1:], 16) if number[0] in ('x', 'X') else int(number)\n text = six.unichr(codepoint)\n self.result.append(text)\n return text","def encode(n):\n encode = []\n if n < 0:\n return ''\n while n >= 58:\n remainder = n % 58\n encode.append(LETTERS[remainder])\n n = n / 58\n if n:\n encode.append(LETTERS[n])\n return ''.join(reversed(encode))","def chrNum(self, num):\n char = chr(num + 65) \n return char","def encode(n, minlen=1, charset=CHARSET_DEFAULT):\n\n chs = []\n while n > 0:\n r = n % BASE\n n //= BASE\n\n chs.append(charset[r])\n\n if len(chs) > 0:\n chs.reverse()\n else:\n chs.append('0')\n\n s = ''.join(chs)\n s = charset[0] * max(minlen - len(s), 0) + s\n return s","def encode(n, minlen=1):\n\n chs = []\n while n > 0:\n r = n % BASE\n n //= BASE\n\n chs.append(CHARSET[r])\n\n if len(chs) > 0:\n chs.reverse()\n else:\n chs.append(\"0\")\n\n s = \"\".join(chs)\n s = CHARSET[0] * max(minlen - len(s), 0) + s\n return s","def loweralphanum(ctx, nchars=\"8\"):\n # deprecated function\n logger.info(\"DeprecationWarning: loweralphanum is deprecated. Use random:loweralphanum instead\")\n random(ctx, \"loweralphanum\", nchars)","def gen_random_chars(n: int = 10) -> Text:\n if n < 1:\n raise Exception('Number of random chars to generate has to be > 0')\n\n return ''.join(choice(ascii_lowercase + '-_')\n for i in range(n))","def toRoman(n):\n pass","def convert_ascii_character(x: str):\n return ord(x) * 10 if ord(x) < LIMIT else 0","def force_ascii(text):\n return \"\".join([c for c in text if ord(c) < 128])","def missing_char(str, n):\r\n if n<=len(str):\r\n str = str.replace(str[n], \"\")\r\n return str","def random_charachter() -> chr:\r\n return chr(int(random.randrange(32, 126, 1)))","def ck(value):\n return chr(value)","def encode_identifier(alphabet, n):\r\n c = alphabet[n & 0b1111]\r\n n>>=4\r\n while n > 0:\r\n c = c + alphabet[n & 0b111111]\r\n n>>=6\r\n return c","def non_secret_char(c):\n return c","def caesar_cipher_encode(n: int, text: str, p: str) -> str:\n lookup_table = str.maketrans(p, p[n:] + p[:n])\n\n return text.translate(lookup_table)","def _index_to_unicode(cls, index: int) -> str:\n return \"\".join(cls._unicode_subscripts[int(_)] for _ in str(index))","def normalizeUnicode(text):\n return ''.join(normalizeLetter(c) for c in text)","def _nth_letter(n):\r\n\treturn string.ascii_lowercase[n % len(string.ascii_lowercase)]","def __unicode_to_ascii(text):\n line = unicodedata.normalize('NFKD', text)\n return ''.join(c for c in line if not unicodedata.combining(c))","def base26(w):\n val = 0\n for ch in w.lower():\n next_digit = ord(ch) - ord('a')\n val = 26*val + next_digit\n return val","def gen_random_char_string(n, base_s=\"\"):\n if n == 0:\n return base_s\n \n c = random.choice(string.ascii_letters)\n return gen_random_char_string(n-1, base_s + c)","def generateNchars(inputChar, inputNum):\n return inputChar * int(inputNum)","def utf8(num):\n # code de la prof louche\n if num < 8:\n return 1\n else:\n return num//6+1\n\n return binary","def phred_to_ascii(p):\n return chr(p+33)","def rot(c,n):\n if 'a' <= c <= 'z': \n new_ord = ord(c) + n\n if new_ord > ord('z'):\n new_ord = new_ord - 26\n elif 'A' <= c <= 'Z': \n new_ord = ord(c) + n\n if new_ord > ord('Z'):\n new_ord = new_ord - 26\n else: \n new_ord = ord(c)\n return chr(new_ord)","def rot(c, n):\n if 'a' <= c <= 'z':\n l = ord(c) + n\n if l > ord('z'):\n l -= 26\n return chr(l)\n elif 'A' <= c <= 'Z':\n l = ord(c) + n\n if l > ord('Z'):\n l -= 26\n return chr(l)\n else:\n return c","def lowercase_count(n):\n saveText = _get_clipboard_text()\n cutText = _select_and_cut_text(n)\n if cutText:\n newText = cutText.lower()\n newText = newText.replace(\"%\", \"%%\") # Escape any format chars.\n Text(newText).execute()\n else: # Failed to get text from clipboard.\n Key('c-v').execute() # Restore cut out text.\n _set_clipboard_text(saveText)","def chr(value: int) -> str:\n if not value < len(Base64._CHARSET) or value < 0:\n raise ValueError(\"Invalid value, must be in the following range [0-63]\")\n return Base64._CHARSET[value]","def transforme(n):\n if n<10 :\n return '0'+str(n)\n else :\n return str(n)","def _chr_ord(x):\n return chr(ord(x))","def romanize(digit, glyphs):\n if 1 <= digit <= 3:\n return digit*glyphs[0]\n elif digit == 4:\n return glyphs[0] + glyphs[1]\n elif digit >= 5 and digit <= 8:\n return glyphs[1] + ((digit - 5) * glyphs[0])\n elif digit == 9:\n return glyphs[0]+glyphs[2]\n else:\n return ''","def get_alphabet(number):\n return chr(number + 96)","def get_alphabet(number):\n return chr(number + 96)","def convert_charref(self, name):\r\n try:\r\n n = int(name)\r\n except ValueError:\r\n return\r\n if not 0 <= n <= 127 : # ASCII ends at 127, not 255\r\n return\r\n return self.convert_codepoint(n)","def c(k):\n if isinstance(k, str):\n return k.lower() if ord(k) % 2 == 0 else k.upper()\n return k","def fmt(cls, n):\n return ''.join(c for c in n if c in cls.ALLOWED).lower()","def asciionly(self,stri):\n return \"\".join([x for x in stri if ord(x) < 128])","def _filter_xml_chars(u):\n def filter_xml_char(c):\n if ord(c) > 0x10ffff:\n return __REPLACEMENT_CHAR # Outside Unicode range\n elif unicodedata.category(c) == 'Cs':\n return __REPLACEMENT_CHAR # Surrogate pair code point\n elif ord(c) == 0xFFFE or ord(c) == 0xFFFF:\n return __REPLACEMENT_CHAR # Specifically excluded code points\n elif ord(c) in __RESTRICTED_CHARS:\n return __REPLACEMENT_CHAR\n else:\n return c\n if not isinstance(u, unicode):\n raise TypeError\n\n return ''.join(filter_xml_char(c) for c in u)","def c_chars(x):\r\n return (c_char * len(x))(*x)","def alphabator(lst):\r\n n = 0\r\n for item in lst:\r\n n += 1\r\n if isinstance(item, int) and 1 <= item <= 26:\r\n item = chr(64 + item)\r\n yield item","def hexchar(i):\n if i > -1 and i < 16:\n return \"%X\" % i\n else:\n return None","def code(char):\n return int(char) if char.isdigit() else letter_code(char)","def render_to_numeral_system(n: int, alphabet: str) -> str:\n assert n >= 0\n assert len(alphabet) == len(set(alphabet))\n if n == 0:\n return alphabet[0]\n b = len(alphabet)\n reverse_digits = []\n while n > 0:\n reverse_digits.append(alphabet[n % b])\n n = n // b\n return \"\".join(reversed(reverse_digits))","def normalize(t):\n text = list(t) \n lt = len(text)\n for i in range(lt):\n c = text[i]\n \n # correct \\n, \\r chars\n if i+1 < lt-1:\n c2 = text[i+1]\n else:\n c2 = None\n if c == u'\\r' and c2 == u'\\n':\n continue\n elif c == u'\\r' and c2 != u'\\n':\n c = u'\\n'\n elif c == u'\\n' and c2 == u'\\r': # FFFFFUUUUUUUUUUUUUUuuuuu....\n continue\n \n text[i] = _farsi_unicode_norm.get(c, c)\n return u''.join(text)","def unicode_to_ascii(s: str) -> str:\n chars = []\n for c in unicodedata.normalize('NFD', s):\n if unicodedata.category(c) != 'Mn' and c in ALLOWED_CHARS:\n chars.append(c)\n return \"\".join(chars)","def toChar(integer):\n\tif integer >= 0x80:\n\t\treturn eval(u\"'\\\\x%02X'\"%integer)\n\telse:\n\t\treturn chr(integer)","def encode(self, n):\n\n if n > self.max_encodable_value:\n raise ValueError(\n '%d is greater than the largest encodable integer (%d)' % (\n n, self.max_encodable_value))\n ret = []\n for base in reversed(self.bases):\n n, d = divmod(n, len(base))\n ret.append(base[d])\n ret.reverse()\n return ''.join(ret)","def supercombiner(bot, ev):\n # ported from jenni\n s = 'u'\n for i in iter(range(1, 3000)):\n if unicodedata.category(chr(i)) == \"Mn\":\n s += chr(i)\n if len(s) > 100:\n break\n bot.say(s)","def phred_to_ascii(d, offset):\r\n return chr(d + offset)","def unicode_number(x):\n\n output = u\"%g\" % x\n\n if output[0] == u\"-\":\n output = u\"\\u2012\" + output[1:]\n\n index = output.find(u\"e\")\n if index != -1:\n uniout = unicode(output[:index]) + u\"\\u00d710\"\n saw_nonzero = False\n for n in output[index+1:]:\n if n == u\"+\": pass # uniout += u\"\\u207a\"\n elif n == u\"-\": uniout += u\"\\u207b\"\n elif n == u\"0\":\n if saw_nonzero: uniout += u\"\\u2070\"\n elif n == u\"1\":\n saw_nonzero = True\n uniout += u\"\\u00b9\"\n elif n == u\"2\":\n saw_nonzero = True\n uniout += u\"\\u00b2\"\n elif n == u\"3\":\n saw_nonzero = True\n uniout += u\"\\u00b3\"\n elif u\"4\" <= n <= u\"9\":\n saw_nonzero = True\n if saw_nonzero: uniout += eval(\"u\\\"\\\\u%x\\\"\" % (0x2070 + ord(n) - ord(u\"0\")))\n else: uniout += n\n\n if uniout[:2] == u\"1\\u00d7\": uniout = uniout[2:]\n return uniout\n\n return output","def replaceNonAsciiFromText(self, text):\n\t\treturn ''.join([i if ord(i) < 128 else ' ' for i in text])","def convert_char(char):\n if char == 'F' or char == 'L':\n return 0\n \n return 1","def uppercase_count(n):\n saveText = _get_clipboard_text()\n cutText = _select_and_cut_text(n)\n if cutText:\n newText = cutText.upper()\n newText = newText.replace(\"%\", \"%%\") # Escape any format chars.\n Text(newText).execute()\n else: # Failed to get text from clipboard.\n Key('c-v').execute() # Restore cut out text.\n _set_clipboard_text(saveText)","def make_control_character():\n # Add one character made up of one codepoint each from\n # (High Surrogates + High Private Use Surrogates) and Low Surrogates.\n # We expect each such pair to behave as a single high-codepoint\n # character.\n controls = ('0000', '001F')\n return [unicode_char(char)\n for char in range(int(controls[0], 16), int(controls[1], 16)+1)]","def str_fill(i, n):\r\n return str(i).zfill(n)","def digitstr(n):\n return ''.join(choice(digits) for _ in range(n))","def toRoman(n):\n if not isinstance(n, int):\n raise NorIntegerError(\"decimals can not be converted\")\n if not (0 < n < 5000):\n raise OutOfRangeError(\"number out of range (must be 1..4999)\")\n \n result = \"\"\n for numeral, integer in romanNumeralMap:\n while n >= integer:\n result += numeral\n n -= integer\n return result","def unicodeToAscii(s):\n return ''.join(\n c for c in unicodedata.normalize('NFD', s)\n if unicodedata.category(c) != 'Mn'\n )","def convert_list_to_unicode_str(data):\n string = ''\n for i, val in enumerate(data):\n # string = string + unicode(unichr(int(val)))\n string = string + str(int(val))\n return string","def map_char(self, char):\n for key, pattern in self.char_map.items():\n if char in pattern:\n return key\n return 'U'","def xo_convert(n):\n if n == 1:\n return \"X\"\n elif n == -1:\n return \"O\"\n else:\n return \" \"","def alnum(value: str) -> str:\n return \"\".join(filter(__alnum_ascii__.__contains__, value)).lower()","def chr_mod(value: int) -> str:\n return Base64._CHARSET[value % len(Base64._CHARSET)]","def pascal_case_count(n):\n saveText = _get_clipboard_text()\n cutText = _select_and_cut_text(n)\n if cutText:\n endSpace = cutText.endswith(' ')\n text = _cleanup_text(cutText)\n newText = text.title().replace(' ', '')\n if endSpace:\n newText = newText + ' '\n newText = newText.replace(\"%\", \"%%\") # Escape any format chars.\n Text(newText).execute()\n else: # Failed to get text from clipboard.\n Key('c-v').execute() # Restore cut out text.\n _set_clipboard_text(saveText)","def prefer_alphabet(i):\n if 0 <= i <= 25:\n return chr(i + 65)\n if 26 <= i <= 51:\n return chr(i + 97 - 26)\n return str(i)","def base_10_to_alphabet(number):\r\n return ''.join(\r\n chr(A_UPPERCASE + part)\r\n for part in _decompose(number)\r\n )","def unicode_to_ascii(s):\n return ''.join(c for c in unicodedata.normalize('NFD', s) if\n unicodedata.category(c) != 'Mn')","def to_roman(n):\n if not isinstance(n, int):\n try:\n n = int(n)\n except ValueError:\n raise NotIntegerError(\"non-integers cannot be converted\")\n\n if not (0 < n < 4000):\n raise OutOfRangeError(\"number out of range (must be 1..3999)\")\n\n result = \"\"\n for numeral, integer in ROMAN_NUMBER_MAP:\n while n >= integer:\n result += numeral\n n -= integer\n return result","def to_roman(n: Union[Integral, np.integer]) -> str:\n if not isinstance(n, (Integral, np.integer)):\n raise TypeError(f\"{n} cannot be converted to a Roman numeral.\")\n if not (0 < n < 5000):\n raise OutOfRangeError(\"Number is out of range (need 0 < n < 5000)\")\n\n result = \"\"\n for numeral, integer in _romanNumeralMap:\n while n >= integer:\n result += numeral\n n -= integer\n return result","def setCharacterCode(code='STANDARD'):\n dislin.chacod(code)","def _random_id(n):\n ''.join(choice(alphanums) for i in range(n))","def unicode2ascii(_unicrap):\n xlate = {0xc0:'A', 0xc1:'A', 0xc2:'A', 0xc3:'A', 0xc4:'A', 0xc5:'A',\n 0xc6:'Ae', 0xc7:'C',\n 0xc8:'E', 0xc9:'E', 0xca:'E', 0xcb:'E',\n 0xcc:'I', 0xcd:'I', 0xce:'I', 0xcf:'I',\n 0xd0:'Th', 0xd1:'N',\n 0xd2:'O', 0xd3:'O', 0xd4:'O', 0xd5:'O', 0xd6:'O', 0xd8:'O',\n 0xd9:'U', 0xda:'U', 0xdb:'U', 0xdc:'U',\n 0xdd:'Y', 0xde:'th', 0xdf:'ss',\n 0xe0:'a', 0xe1:'a', 0xe2:'a', 0xe3:'a', 0xe4:'a', 0xe5:'a',\n 0xe6:'ae', 0xe7:'c',\n 0xe8:'e', 0xe9:'e', 0xea:'e', 0xeb:'e',\n 0xec:'i', 0xed:'i', 0xee:'i', 0xef:'i',\n 0xf0:'th', 0xf1:'n',\n 0xf2:'o', 0xf3:'o', 0xf4:'o', 0xf5:'o', 0xf6:'o', 0xf8:'o',\n 0xf9:'u', 0xfa:'u', 0xfb:'u', 0xfc:'u',\n 0xfd:'y', 0xfe:'th', 0xff:'y',\n 0xa1:'!', 0xa2:'{cent}', 0xa3:'{pound}', 0xa4:'{currency}',\n 0xa5:'{yen}', 0xa6:'|', 0xa7:'{section}', 0xa8:'{umlaut}',\n 0xa9:'{C}', 0xaa:'{^a}', 0xab:'<<', 0xac:'{not}',\n 0xad:'-', 0xae:'{R}', 0xaf:'_', 0xb0:'{degrees}',\n 0xb1:'{+/-}', 0xb2:'{^2}', 0xb3:'{^3}', 0xb4:\"'\",\n 0xb5:'{micro}', 0xb6:'{paragraph}', 0xb7:'*', 0xb8:'{cedilla}',\n 0xb9:'{^1}', 0xba:'{^o}', 0xbb:'>>',\n 0xbc:'{1/4}', 0xbd:'{1/2}', 0xbe:'{3/4}', 0xbf:'?',\n 0xd7:'*', 0xf7:'/'\n }\n\n s = \"\"\n for i in _unicrap:\n ordi = ord(i)\n if ordi in xlate:\n s += xlate[ordi]\n elif ordi >= 0x80:\n pass\n else:\n s += str(i)\n return s","def intRender(self, number):\n\n data = unicode(number)\n bites = list()\n\n while data:\n bites.append(data[-3:])\n data = data[:-3]\n\n return \" \".join(reversed(bites))","def replace_char(text):\n\n for ch in ['/', '`', '*', '{', '}', '[', ']', '(', ')', '#', '+', '-', '.', '!', '\\$', ':', '|']:\n text = text.replace(ch, \"_\")\n return text","def __replace_negative_for_n__(self, text):\n # | - __replace_negative_for_n__\n lst = [pos for pos, char in enumerate(text) if char == \"n\"]\n\n for lett in lst:\n if text[lett + 1].isdigit() is True:\n text = text[:lett] + \"-\" + text[lett + 1:]\n\n return(text)\n # __|","def random_alpha(n=8):\n return \"\".join(random.SystemRandom().choice(string.ascii_letters) for _ in range(n))","def uCSIsBasicLatin(code):\n ret = libxml2mod.xmlUCSIsBasicLatin(code)\n return ret","def asIndex(i):\n return u\"\".join([unichr(0x2050 + ord(c)) for c in str(i)])","def asIndex(i):\n return u\"\".join([unichr(0x2050 + ord(c)) for c in str(i)])","def removeNonAsciiFromText(self, text):\n\t\treturn ''.join([i if ord(i) < 128 else '' for i in text])","def remove_diacritic(input):\n return unicodedata.normalize('NFKD', input).encode('ASCII', 'ignore')","def convert_to_alphabet(c, avoid_tab_and_lf=False):\n if c == 1:\n return 32 if avoid_tab_and_lf else 9 # space instead of TAB\n if c == 127 - 30:\n return 92 if avoid_tab_and_lf else 10 # \\ instead of LF\n if 32 <= c + 30 <= 126:\n return c + 30\n else:\n return 0 # unknown","def base36_encode(num):\n alphabet = '0123456789abcdefghijklmnopqrstuvwxyz'\n result = ''\n\n while num != 0:\n num, i = divmod(num, 36)\n result = alphabet[i] + result\n\n return result","def random_char():\n return chr(random.randrange(32, 126, 1))","def convert_numtoletter(n):\r\n L = seats[0][n-1] #letter\r\n return L","def gen_chars(length, character):\n return ''.join([character for i in range(length)])","def test_phred_to_ascii(self):\r\n self.assertEqual(phred_to_ascii(0, 120), 'x')\r\n self.assertEqual(phred_to_ascii(1, 119), 'x')","def _process_value(self, value):\n if isinstance(value, str):\n try:\n value.decode('ascii')\n except UnicodeDecodeError:\n return unicode(''.join([unichr(ord(c)) for c in value]))\n return value"],"string":"[\n \"def int_to_unichr(codepoint):\\n if PY2:\\n return unichr(codepoint)\\n return chr(codepoint)\",\n \"def make_unicode():\\r\\n for num in range(300, 320):\\r\\n yield unichr(num)\",\n \"def safe_unichr(intval):\\n try:\\n return unichr(intval)\\n except ValueError:\\n # ValueError: unichr() arg not in range(0x10000) (narrow Python build)\\n s = \\\"\\\\\\\\U%08x\\\" % intval\\n # return UTF16 surrogate pair\\n return s.decode('unicode-escape')\",\n \"def n_char_generate(self,char,n):\\n return char*n\",\n \"def encode2(s,n):\\n r = [ chr(((ord(x)-97+n)%26)+97) if x!=' ' else x for x in s]\\n return \\\"\\\".join(r)\",\n \"def encode1(s,n):\\n r = \\\"\\\"\\n for l in s:\\n l = ord(l) # convert to ascii\\n l = l - 97 # 'a' is 97 so we want to reduce so 'a'=0 'b'=1 etc\\n l = l + n # add the offset\\n l=l%26 # use mod so that we wrap around back to 'a' if we go past 'z'\\n l=l+97 # and add back the 97\\n r = r + chr(l)\\n return r\",\n \"def CHAR(table_number):\\n return unichr(table_number)\",\n \"def hex_to_unichr(hex_string):\\n if (hex_string is None) or (len(hex_string) < 1):\\n return None\\n if hex_string.startswith(\\\"U+\\\"):\\n hex_string = hex_string[2:]\\n return int_to_unichr(int(hex_string, base=16))\",\n \"def getcharswin(n):\\n\\n\\tstring = \\\"\\\"\\n\\ti = 0\\n\\t# Loop until we get N chars\\n\\twhile True:\\n\\t\\tc = msvcrt.getch()\\n\\t\\tif c == b'\\\\x03':\\n\\t\\t\\traise KeyboardInterrupt()\\n\\t\\ttry:\\n\\t\\t\\tstring += str(c, ENCODING)\\n\\t\\texcept UnicodeDecodeError:\\n\\t\\t\\tcontinue\\n\\t\\ti += 1\\n\\t\\tif i == n:\\n\\t\\t\\tbreak\\n\\treturn string\",\n \"def n_char(self,char,n,w=1,h=1):\\n for i in range(n):\\n self.esprint(char,w,h)\",\n \"def h_ascii(key, N):\\n if type(key) == str:\\n if type(N) == int:\\n s = 0\\n for i in range(len(key)):\\n s += ord(key[i])\\n return s % N\\n else:\\n raise ValueError\\n else:\\n raise ValueError\",\n \"def handle_charref(self, number):\\n codepoint = int(number[1:], 16) if number[0] in ('x', 'X') else int(number)\\n text = six.unichr(codepoint)\\n self.result.append(text)\\n return text\",\n \"def encode(n):\\n encode = []\\n if n < 0:\\n return ''\\n while n >= 58:\\n remainder = n % 58\\n encode.append(LETTERS[remainder])\\n n = n / 58\\n if n:\\n encode.append(LETTERS[n])\\n return ''.join(reversed(encode))\",\n \"def chrNum(self, num):\\n char = chr(num + 65) \\n return char\",\n \"def encode(n, minlen=1, charset=CHARSET_DEFAULT):\\n\\n chs = []\\n while n > 0:\\n r = n % BASE\\n n //= BASE\\n\\n chs.append(charset[r])\\n\\n if len(chs) > 0:\\n chs.reverse()\\n else:\\n chs.append('0')\\n\\n s = ''.join(chs)\\n s = charset[0] * max(minlen - len(s), 0) + s\\n return s\",\n \"def encode(n, minlen=1):\\n\\n chs = []\\n while n > 0:\\n r = n % BASE\\n n //= BASE\\n\\n chs.append(CHARSET[r])\\n\\n if len(chs) > 0:\\n chs.reverse()\\n else:\\n chs.append(\\\"0\\\")\\n\\n s = \\\"\\\".join(chs)\\n s = CHARSET[0] * max(minlen - len(s), 0) + s\\n return s\",\n \"def loweralphanum(ctx, nchars=\\\"8\\\"):\\n # deprecated function\\n logger.info(\\\"DeprecationWarning: loweralphanum is deprecated. Use random:loweralphanum instead\\\")\\n random(ctx, \\\"loweralphanum\\\", nchars)\",\n \"def gen_random_chars(n: int = 10) -> Text:\\n if n < 1:\\n raise Exception('Number of random chars to generate has to be > 0')\\n\\n return ''.join(choice(ascii_lowercase + '-_')\\n for i in range(n))\",\n \"def toRoman(n):\\n pass\",\n \"def convert_ascii_character(x: str):\\n return ord(x) * 10 if ord(x) < LIMIT else 0\",\n \"def force_ascii(text):\\n return \\\"\\\".join([c for c in text if ord(c) < 128])\",\n \"def missing_char(str, n):\\r\\n if n<=len(str):\\r\\n str = str.replace(str[n], \\\"\\\")\\r\\n return str\",\n \"def random_charachter() -> chr:\\r\\n return chr(int(random.randrange(32, 126, 1)))\",\n \"def ck(value):\\n return chr(value)\",\n \"def encode_identifier(alphabet, n):\\r\\n c = alphabet[n & 0b1111]\\r\\n n>>=4\\r\\n while n > 0:\\r\\n c = c + alphabet[n & 0b111111]\\r\\n n>>=6\\r\\n return c\",\n \"def non_secret_char(c):\\n return c\",\n \"def caesar_cipher_encode(n: int, text: str, p: str) -> str:\\n lookup_table = str.maketrans(p, p[n:] + p[:n])\\n\\n return text.translate(lookup_table)\",\n \"def _index_to_unicode(cls, index: int) -> str:\\n return \\\"\\\".join(cls._unicode_subscripts[int(_)] for _ in str(index))\",\n \"def normalizeUnicode(text):\\n return ''.join(normalizeLetter(c) for c in text)\",\n \"def _nth_letter(n):\\r\\n\\treturn string.ascii_lowercase[n % len(string.ascii_lowercase)]\",\n \"def __unicode_to_ascii(text):\\n line = unicodedata.normalize('NFKD', text)\\n return ''.join(c for c in line if not unicodedata.combining(c))\",\n \"def base26(w):\\n val = 0\\n for ch in w.lower():\\n next_digit = ord(ch) - ord('a')\\n val = 26*val + next_digit\\n return val\",\n \"def gen_random_char_string(n, base_s=\\\"\\\"):\\n if n == 0:\\n return base_s\\n \\n c = random.choice(string.ascii_letters)\\n return gen_random_char_string(n-1, base_s + c)\",\n \"def generateNchars(inputChar, inputNum):\\n return inputChar * int(inputNum)\",\n \"def utf8(num):\\n # code de la prof louche\\n if num < 8:\\n return 1\\n else:\\n return num//6+1\\n\\n return binary\",\n \"def phred_to_ascii(p):\\n return chr(p+33)\",\n \"def rot(c,n):\\n if 'a' <= c <= 'z': \\n new_ord = ord(c) + n\\n if new_ord > ord('z'):\\n new_ord = new_ord - 26\\n elif 'A' <= c <= 'Z': \\n new_ord = ord(c) + n\\n if new_ord > ord('Z'):\\n new_ord = new_ord - 26\\n else: \\n new_ord = ord(c)\\n return chr(new_ord)\",\n \"def rot(c, n):\\n if 'a' <= c <= 'z':\\n l = ord(c) + n\\n if l > ord('z'):\\n l -= 26\\n return chr(l)\\n elif 'A' <= c <= 'Z':\\n l = ord(c) + n\\n if l > ord('Z'):\\n l -= 26\\n return chr(l)\\n else:\\n return c\",\n \"def lowercase_count(n):\\n saveText = _get_clipboard_text()\\n cutText = _select_and_cut_text(n)\\n if cutText:\\n newText = cutText.lower()\\n newText = newText.replace(\\\"%\\\", \\\"%%\\\") # Escape any format chars.\\n Text(newText).execute()\\n else: # Failed to get text from clipboard.\\n Key('c-v').execute() # Restore cut out text.\\n _set_clipboard_text(saveText)\",\n \"def chr(value: int) -> str:\\n if not value < len(Base64._CHARSET) or value < 0:\\n raise ValueError(\\\"Invalid value, must be in the following range [0-63]\\\")\\n return Base64._CHARSET[value]\",\n \"def transforme(n):\\n if n<10 :\\n return '0'+str(n)\\n else :\\n return str(n)\",\n \"def _chr_ord(x):\\n return chr(ord(x))\",\n \"def romanize(digit, glyphs):\\n if 1 <= digit <= 3:\\n return digit*glyphs[0]\\n elif digit == 4:\\n return glyphs[0] + glyphs[1]\\n elif digit >= 5 and digit <= 8:\\n return glyphs[1] + ((digit - 5) * glyphs[0])\\n elif digit == 9:\\n return glyphs[0]+glyphs[2]\\n else:\\n return ''\",\n \"def get_alphabet(number):\\n return chr(number + 96)\",\n \"def get_alphabet(number):\\n return chr(number + 96)\",\n \"def convert_charref(self, name):\\r\\n try:\\r\\n n = int(name)\\r\\n except ValueError:\\r\\n return\\r\\n if not 0 <= n <= 127 : # ASCII ends at 127, not 255\\r\\n return\\r\\n return self.convert_codepoint(n)\",\n \"def c(k):\\n if isinstance(k, str):\\n return k.lower() if ord(k) % 2 == 0 else k.upper()\\n return k\",\n \"def fmt(cls, n):\\n return ''.join(c for c in n if c in cls.ALLOWED).lower()\",\n \"def asciionly(self,stri):\\n return \\\"\\\".join([x for x in stri if ord(x) < 128])\",\n \"def _filter_xml_chars(u):\\n def filter_xml_char(c):\\n if ord(c) > 0x10ffff:\\n return __REPLACEMENT_CHAR # Outside Unicode range\\n elif unicodedata.category(c) == 'Cs':\\n return __REPLACEMENT_CHAR # Surrogate pair code point\\n elif ord(c) == 0xFFFE or ord(c) == 0xFFFF:\\n return __REPLACEMENT_CHAR # Specifically excluded code points\\n elif ord(c) in __RESTRICTED_CHARS:\\n return __REPLACEMENT_CHAR\\n else:\\n return c\\n if not isinstance(u, unicode):\\n raise TypeError\\n\\n return ''.join(filter_xml_char(c) for c in u)\",\n \"def c_chars(x):\\r\\n return (c_char * len(x))(*x)\",\n \"def alphabator(lst):\\r\\n n = 0\\r\\n for item in lst:\\r\\n n += 1\\r\\n if isinstance(item, int) and 1 <= item <= 26:\\r\\n item = chr(64 + item)\\r\\n yield item\",\n \"def hexchar(i):\\n if i > -1 and i < 16:\\n return \\\"%X\\\" % i\\n else:\\n return None\",\n \"def code(char):\\n return int(char) if char.isdigit() else letter_code(char)\",\n \"def render_to_numeral_system(n: int, alphabet: str) -> str:\\n assert n >= 0\\n assert len(alphabet) == len(set(alphabet))\\n if n == 0:\\n return alphabet[0]\\n b = len(alphabet)\\n reverse_digits = []\\n while n > 0:\\n reverse_digits.append(alphabet[n % b])\\n n = n // b\\n return \\\"\\\".join(reversed(reverse_digits))\",\n \"def normalize(t):\\n text = list(t) \\n lt = len(text)\\n for i in range(lt):\\n c = text[i]\\n \\n # correct \\\\n, \\\\r chars\\n if i+1 < lt-1:\\n c2 = text[i+1]\\n else:\\n c2 = None\\n if c == u'\\\\r' and c2 == u'\\\\n':\\n continue\\n elif c == u'\\\\r' and c2 != u'\\\\n':\\n c = u'\\\\n'\\n elif c == u'\\\\n' and c2 == u'\\\\r': # FFFFFUUUUUUUUUUUUUUuuuuu....\\n continue\\n \\n text[i] = _farsi_unicode_norm.get(c, c)\\n return u''.join(text)\",\n \"def unicode_to_ascii(s: str) -> str:\\n chars = []\\n for c in unicodedata.normalize('NFD', s):\\n if unicodedata.category(c) != 'Mn' and c in ALLOWED_CHARS:\\n chars.append(c)\\n return \\\"\\\".join(chars)\",\n \"def toChar(integer):\\n\\tif integer >= 0x80:\\n\\t\\treturn eval(u\\\"'\\\\\\\\x%02X'\\\"%integer)\\n\\telse:\\n\\t\\treturn chr(integer)\",\n \"def encode(self, n):\\n\\n if n > self.max_encodable_value:\\n raise ValueError(\\n '%d is greater than the largest encodable integer (%d)' % (\\n n, self.max_encodable_value))\\n ret = []\\n for base in reversed(self.bases):\\n n, d = divmod(n, len(base))\\n ret.append(base[d])\\n ret.reverse()\\n return ''.join(ret)\",\n \"def supercombiner(bot, ev):\\n # ported from jenni\\n s = 'u'\\n for i in iter(range(1, 3000)):\\n if unicodedata.category(chr(i)) == \\\"Mn\\\":\\n s += chr(i)\\n if len(s) > 100:\\n break\\n bot.say(s)\",\n \"def phred_to_ascii(d, offset):\\r\\n return chr(d + offset)\",\n \"def unicode_number(x):\\n\\n output = u\\\"%g\\\" % x\\n\\n if output[0] == u\\\"-\\\":\\n output = u\\\"\\\\u2012\\\" + output[1:]\\n\\n index = output.find(u\\\"e\\\")\\n if index != -1:\\n uniout = unicode(output[:index]) + u\\\"\\\\u00d710\\\"\\n saw_nonzero = False\\n for n in output[index+1:]:\\n if n == u\\\"+\\\": pass # uniout += u\\\"\\\\u207a\\\"\\n elif n == u\\\"-\\\": uniout += u\\\"\\\\u207b\\\"\\n elif n == u\\\"0\\\":\\n if saw_nonzero: uniout += u\\\"\\\\u2070\\\"\\n elif n == u\\\"1\\\":\\n saw_nonzero = True\\n uniout += u\\\"\\\\u00b9\\\"\\n elif n == u\\\"2\\\":\\n saw_nonzero = True\\n uniout += u\\\"\\\\u00b2\\\"\\n elif n == u\\\"3\\\":\\n saw_nonzero = True\\n uniout += u\\\"\\\\u00b3\\\"\\n elif u\\\"4\\\" <= n <= u\\\"9\\\":\\n saw_nonzero = True\\n if saw_nonzero: uniout += eval(\\\"u\\\\\\\"\\\\\\\\u%x\\\\\\\"\\\" % (0x2070 + ord(n) - ord(u\\\"0\\\")))\\n else: uniout += n\\n\\n if uniout[:2] == u\\\"1\\\\u00d7\\\": uniout = uniout[2:]\\n return uniout\\n\\n return output\",\n \"def replaceNonAsciiFromText(self, text):\\n\\t\\treturn ''.join([i if ord(i) < 128 else ' ' for i in text])\",\n \"def convert_char(char):\\n if char == 'F' or char == 'L':\\n return 0\\n \\n return 1\",\n \"def uppercase_count(n):\\n saveText = _get_clipboard_text()\\n cutText = _select_and_cut_text(n)\\n if cutText:\\n newText = cutText.upper()\\n newText = newText.replace(\\\"%\\\", \\\"%%\\\") # Escape any format chars.\\n Text(newText).execute()\\n else: # Failed to get text from clipboard.\\n Key('c-v').execute() # Restore cut out text.\\n _set_clipboard_text(saveText)\",\n \"def make_control_character():\\n # Add one character made up of one codepoint each from\\n # (High Surrogates + High Private Use Surrogates) and Low Surrogates.\\n # We expect each such pair to behave as a single high-codepoint\\n # character.\\n controls = ('0000', '001F')\\n return [unicode_char(char)\\n for char in range(int(controls[0], 16), int(controls[1], 16)+1)]\",\n \"def str_fill(i, n):\\r\\n return str(i).zfill(n)\",\n \"def digitstr(n):\\n return ''.join(choice(digits) for _ in range(n))\",\n \"def toRoman(n):\\n if not isinstance(n, int):\\n raise NorIntegerError(\\\"decimals can not be converted\\\")\\n if not (0 < n < 5000):\\n raise OutOfRangeError(\\\"number out of range (must be 1..4999)\\\")\\n \\n result = \\\"\\\"\\n for numeral, integer in romanNumeralMap:\\n while n >= integer:\\n result += numeral\\n n -= integer\\n return result\",\n \"def unicodeToAscii(s):\\n return ''.join(\\n c for c in unicodedata.normalize('NFD', s)\\n if unicodedata.category(c) != 'Mn'\\n )\",\n \"def convert_list_to_unicode_str(data):\\n string = ''\\n for i, val in enumerate(data):\\n # string = string + unicode(unichr(int(val)))\\n string = string + str(int(val))\\n return string\",\n \"def map_char(self, char):\\n for key, pattern in self.char_map.items():\\n if char in pattern:\\n return key\\n return 'U'\",\n \"def xo_convert(n):\\n if n == 1:\\n return \\\"X\\\"\\n elif n == -1:\\n return \\\"O\\\"\\n else:\\n return \\\" \\\"\",\n \"def alnum(value: str) -> str:\\n return \\\"\\\".join(filter(__alnum_ascii__.__contains__, value)).lower()\",\n \"def chr_mod(value: int) -> str:\\n return Base64._CHARSET[value % len(Base64._CHARSET)]\",\n \"def pascal_case_count(n):\\n saveText = _get_clipboard_text()\\n cutText = _select_and_cut_text(n)\\n if cutText:\\n endSpace = cutText.endswith(' ')\\n text = _cleanup_text(cutText)\\n newText = text.title().replace(' ', '')\\n if endSpace:\\n newText = newText + ' '\\n newText = newText.replace(\\\"%\\\", \\\"%%\\\") # Escape any format chars.\\n Text(newText).execute()\\n else: # Failed to get text from clipboard.\\n Key('c-v').execute() # Restore cut out text.\\n _set_clipboard_text(saveText)\",\n \"def prefer_alphabet(i):\\n if 0 <= i <= 25:\\n return chr(i + 65)\\n if 26 <= i <= 51:\\n return chr(i + 97 - 26)\\n return str(i)\",\n \"def base_10_to_alphabet(number):\\r\\n return ''.join(\\r\\n chr(A_UPPERCASE + part)\\r\\n for part in _decompose(number)\\r\\n )\",\n \"def unicode_to_ascii(s):\\n return ''.join(c for c in unicodedata.normalize('NFD', s) if\\n unicodedata.category(c) != 'Mn')\",\n \"def to_roman(n):\\n if not isinstance(n, int):\\n try:\\n n = int(n)\\n except ValueError:\\n raise NotIntegerError(\\\"non-integers cannot be converted\\\")\\n\\n if not (0 < n < 4000):\\n raise OutOfRangeError(\\\"number out of range (must be 1..3999)\\\")\\n\\n result = \\\"\\\"\\n for numeral, integer in ROMAN_NUMBER_MAP:\\n while n >= integer:\\n result += numeral\\n n -= integer\\n return result\",\n \"def to_roman(n: Union[Integral, np.integer]) -> str:\\n if not isinstance(n, (Integral, np.integer)):\\n raise TypeError(f\\\"{n} cannot be converted to a Roman numeral.\\\")\\n if not (0 < n < 5000):\\n raise OutOfRangeError(\\\"Number is out of range (need 0 < n < 5000)\\\")\\n\\n result = \\\"\\\"\\n for numeral, integer in _romanNumeralMap:\\n while n >= integer:\\n result += numeral\\n n -= integer\\n return result\",\n \"def setCharacterCode(code='STANDARD'):\\n dislin.chacod(code)\",\n \"def _random_id(n):\\n ''.join(choice(alphanums) for i in range(n))\",\n \"def unicode2ascii(_unicrap):\\n xlate = {0xc0:'A', 0xc1:'A', 0xc2:'A', 0xc3:'A', 0xc4:'A', 0xc5:'A',\\n 0xc6:'Ae', 0xc7:'C',\\n 0xc8:'E', 0xc9:'E', 0xca:'E', 0xcb:'E',\\n 0xcc:'I', 0xcd:'I', 0xce:'I', 0xcf:'I',\\n 0xd0:'Th', 0xd1:'N',\\n 0xd2:'O', 0xd3:'O', 0xd4:'O', 0xd5:'O', 0xd6:'O', 0xd8:'O',\\n 0xd9:'U', 0xda:'U', 0xdb:'U', 0xdc:'U',\\n 0xdd:'Y', 0xde:'th', 0xdf:'ss',\\n 0xe0:'a', 0xe1:'a', 0xe2:'a', 0xe3:'a', 0xe4:'a', 0xe5:'a',\\n 0xe6:'ae', 0xe7:'c',\\n 0xe8:'e', 0xe9:'e', 0xea:'e', 0xeb:'e',\\n 0xec:'i', 0xed:'i', 0xee:'i', 0xef:'i',\\n 0xf0:'th', 0xf1:'n',\\n 0xf2:'o', 0xf3:'o', 0xf4:'o', 0xf5:'o', 0xf6:'o', 0xf8:'o',\\n 0xf9:'u', 0xfa:'u', 0xfb:'u', 0xfc:'u',\\n 0xfd:'y', 0xfe:'th', 0xff:'y',\\n 0xa1:'!', 0xa2:'{cent}', 0xa3:'{pound}', 0xa4:'{currency}',\\n 0xa5:'{yen}', 0xa6:'|', 0xa7:'{section}', 0xa8:'{umlaut}',\\n 0xa9:'{C}', 0xaa:'{^a}', 0xab:'<<', 0xac:'{not}',\\n 0xad:'-', 0xae:'{R}', 0xaf:'_', 0xb0:'{degrees}',\\n 0xb1:'{+/-}', 0xb2:'{^2}', 0xb3:'{^3}', 0xb4:\\\"'\\\",\\n 0xb5:'{micro}', 0xb6:'{paragraph}', 0xb7:'*', 0xb8:'{cedilla}',\\n 0xb9:'{^1}', 0xba:'{^o}', 0xbb:'>>',\\n 0xbc:'{1/4}', 0xbd:'{1/2}', 0xbe:'{3/4}', 0xbf:'?',\\n 0xd7:'*', 0xf7:'/'\\n }\\n\\n s = \\\"\\\"\\n for i in _unicrap:\\n ordi = ord(i)\\n if ordi in xlate:\\n s += xlate[ordi]\\n elif ordi >= 0x80:\\n pass\\n else:\\n s += str(i)\\n return s\",\n \"def intRender(self, number):\\n\\n data = unicode(number)\\n bites = list()\\n\\n while data:\\n bites.append(data[-3:])\\n data = data[:-3]\\n\\n return \\\" \\\".join(reversed(bites))\",\n \"def replace_char(text):\\n\\n for ch in ['/', '`', '*', '{', '}', '[', ']', '(', ')', '#', '+', '-', '.', '!', '\\\\$', ':', '|']:\\n text = text.replace(ch, \\\"_\\\")\\n return text\",\n \"def __replace_negative_for_n__(self, text):\\n # | - __replace_negative_for_n__\\n lst = [pos for pos, char in enumerate(text) if char == \\\"n\\\"]\\n\\n for lett in lst:\\n if text[lett + 1].isdigit() is True:\\n text = text[:lett] + \\\"-\\\" + text[lett + 1:]\\n\\n return(text)\\n # __|\",\n \"def random_alpha(n=8):\\n return \\\"\\\".join(random.SystemRandom().choice(string.ascii_letters) for _ in range(n))\",\n \"def uCSIsBasicLatin(code):\\n ret = libxml2mod.xmlUCSIsBasicLatin(code)\\n return ret\",\n \"def asIndex(i):\\n return u\\\"\\\".join([unichr(0x2050 + ord(c)) for c in str(i)])\",\n \"def asIndex(i):\\n return u\\\"\\\".join([unichr(0x2050 + ord(c)) for c in str(i)])\",\n \"def removeNonAsciiFromText(self, text):\\n\\t\\treturn ''.join([i if ord(i) < 128 else '' for i in text])\",\n \"def remove_diacritic(input):\\n return unicodedata.normalize('NFKD', input).encode('ASCII', 'ignore')\",\n \"def convert_to_alphabet(c, avoid_tab_and_lf=False):\\n if c == 1:\\n return 32 if avoid_tab_and_lf else 9 # space instead of TAB\\n if c == 127 - 30:\\n return 92 if avoid_tab_and_lf else 10 # \\\\ instead of LF\\n if 32 <= c + 30 <= 126:\\n return c + 30\\n else:\\n return 0 # unknown\",\n \"def base36_encode(num):\\n alphabet = '0123456789abcdefghijklmnopqrstuvwxyz'\\n result = ''\\n\\n while num != 0:\\n num, i = divmod(num, 36)\\n result = alphabet[i] + result\\n\\n return result\",\n \"def random_char():\\n return chr(random.randrange(32, 126, 1))\",\n \"def convert_numtoletter(n):\\r\\n L = seats[0][n-1] #letter\\r\\n return L\",\n \"def gen_chars(length, character):\\n return ''.join([character for i in range(length)])\",\n \"def test_phred_to_ascii(self):\\r\\n self.assertEqual(phred_to_ascii(0, 120), 'x')\\r\\n self.assertEqual(phred_to_ascii(1, 119), 'x')\",\n \"def _process_value(self, value):\\n if isinstance(value, str):\\n try:\\n value.decode('ascii')\\n except UnicodeDecodeError:\\n return unicode(''.join([unichr(ord(c)) for c in value]))\\n return value\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68852764","0.6683728","0.6391506","0.59930366","0.586188","0.58399165","0.58334017","0.5817205","0.56382495","0.5589591","0.55534005","0.54915816","0.5490308","0.5472899","0.5450932","0.5440723","0.54287136","0.5352844","0.53169405","0.5304779","0.5256047","0.52538717","0.5242492","0.52000135","0.51919395","0.51876634","0.5184988","0.5175401","0.5162774","0.51488334","0.51470923","0.51441985","0.5143599","0.51317596","0.51193464","0.51190776","0.5109459","0.5068369","0.50375485","0.5012802","0.50105363","0.49998745","0.49904877","0.49877846","0.49877846","0.49857333","0.49825972","0.49698645","0.49552158","0.49539098","0.49484763","0.49313772","0.49188408","0.48972094","0.48964158","0.48940858","0.4890801","0.48820442","0.4870173","0.48663506","0.48635787","0.48627737","0.48366836","0.483269","0.483105","0.48191878","0.48030424","0.47985265","0.4785971","0.47815084","0.4778922","0.47766596","0.47749028","0.47749025","0.4774897","0.47739276","0.47706196","0.4757193","0.47570807","0.47560146","0.47508466","0.4747792","0.4734547","0.47321814","0.4731118","0.47298312","0.47271797","0.47264045","0.4725504","0.47208482","0.47208482","0.47185618","0.4708847","0.4708259","0.47028187","0.47025266","0.47009927","0.4694794","0.46945912","0.469117"],"string":"[\n \"0.68852764\",\n \"0.6683728\",\n \"0.6391506\",\n \"0.59930366\",\n \"0.586188\",\n \"0.58399165\",\n \"0.58334017\",\n \"0.5817205\",\n \"0.56382495\",\n \"0.5589591\",\n \"0.55534005\",\n \"0.54915816\",\n \"0.5490308\",\n \"0.5472899\",\n \"0.5450932\",\n \"0.5440723\",\n \"0.54287136\",\n \"0.5352844\",\n \"0.53169405\",\n \"0.5304779\",\n \"0.5256047\",\n \"0.52538717\",\n \"0.5242492\",\n \"0.52000135\",\n \"0.51919395\",\n \"0.51876634\",\n \"0.5184988\",\n \"0.5175401\",\n \"0.5162774\",\n \"0.51488334\",\n \"0.51470923\",\n \"0.51441985\",\n \"0.5143599\",\n \"0.51317596\",\n \"0.51193464\",\n \"0.51190776\",\n \"0.5109459\",\n \"0.5068369\",\n \"0.50375485\",\n \"0.5012802\",\n \"0.50105363\",\n \"0.49998745\",\n \"0.49904877\",\n \"0.49877846\",\n \"0.49877846\",\n \"0.49857333\",\n \"0.49825972\",\n \"0.49698645\",\n \"0.49552158\",\n \"0.49539098\",\n \"0.49484763\",\n \"0.49313772\",\n \"0.49188408\",\n \"0.48972094\",\n \"0.48964158\",\n \"0.48940858\",\n \"0.4890801\",\n \"0.48820442\",\n \"0.4870173\",\n \"0.48663506\",\n \"0.48635787\",\n \"0.48627737\",\n \"0.48366836\",\n \"0.483269\",\n \"0.483105\",\n \"0.48191878\",\n \"0.48030424\",\n \"0.47985265\",\n \"0.4785971\",\n \"0.47815084\",\n \"0.4778922\",\n \"0.47766596\",\n \"0.47749028\",\n \"0.47749025\",\n \"0.4774897\",\n \"0.47739276\",\n \"0.47706196\",\n \"0.4757193\",\n \"0.47570807\",\n \"0.47560146\",\n \"0.47508466\",\n \"0.4747792\",\n \"0.4734547\",\n \"0.47321814\",\n \"0.4731118\",\n \"0.47298312\",\n \"0.47271797\",\n \"0.47264045\",\n \"0.4725504\",\n \"0.47208482\",\n \"0.47208482\",\n \"0.47185618\",\n \"0.4708847\",\n \"0.4708259\",\n \"0.47028187\",\n \"0.47025266\",\n \"0.47009927\",\n \"0.4694794\",\n \"0.46945912\",\n \"0.469117\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7051785"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13791,"cells":{"query":{"kind":"string","value":"Test ``create_engine`` with invalid adapter."},"document":{"kind":"string","value":"def test_create_engine_no_adapters():\n engine = create_engine(\"shillelagh://\")\n\n with pytest.raises(ProgrammingError) as excinfo:\n Table(\"dummy://\", MetaData(bind=engine), autoload=True)\n assert str(excinfo.value) == \"Unsupported table: dummy://\""},"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 ensure_engine_exists():\n # Check if engine is present\n if not is_engine_binded():\n print(\"Binding new engine\")\n bind_engine(create_engine(\"sqlite:///:memory:\", echo=True))","def test_engine(self):\n config = {\n \"url\": 'sqlite://',\n \"connect_args\": {\n \"check_same_thread\": \"false\",\n \"poolclass\": \"pool.StaticPool\"\n }\n }\n engine = self.configurator.setup_engine(config)\n self.assertIsNotNone(engine)","def validate_engine(engine):\n\n VALID_DB_ENGINES = (\n \"MySQL\",\n \"mysql\",\n \"oracle-se1\",\n \"oracle-se2\",\n \"oracle-se\",\n \"oracle-ee\",\n \"sqlserver-ee\",\n \"sqlserver-se\",\n \"sqlserver-ex\",\n \"sqlserver-web\",\n \"postgres\",\n \"aurora\",\n \"aurora-mysql\",\n \"aurora-postgresql\",\n \"mariadb\",\n )\n\n if engine not in VALID_DB_ENGINES:\n raise ValueError(\n \"DBInstance Engine must be one of: %s\" % \", \".join(VALID_DB_ENGINES)\n )\n return engine","async def test_setup_invalid_config(\n recorder_mock: Recorder, hass: HomeAssistant\n) -> None:\n with patch(\n \"homeassistant.components.sql.config_flow.sqlalchemy.create_engine\",\n ):\n assert not await async_setup_component(hass, DOMAIN, YAML_CONFIG_INVALID)\n await hass.async_block_till_done()","async def test_get_default_engine_conflict(subject: EngineStore) -> None:\n await subject.create(run_id=\"run-id\", labware_offsets=[], protocol=None)\n subject.engine.play()\n\n with pytest.raises(EngineConflictError):\n await subject.get_default_engine()","def _test_engine(engine, service, vendor, expected_meta):\n tracer = Tracer()\n tracer.writer = DummyWriter()\n\n # create an engine and start tracing.\n trace_engine(engine, tracer, service=service)\n start = time.time()\n\n @contextlib.contextmanager\n def _connect():\n try:\n conn = engine.connect()\n yield conn\n finally:\n conn.close()\n\n with _connect() as conn:\n try:\n conn.execute(\"delete from players\")\n except Exception:\n pass\n\n # boilerplate\n Base.metadata.create_all(engine)\n Session = sessionmaker(bind=engine)\n session = Session()\n\n # do an ORM insert\n wayne = Player(id=1, name=\"wayne\")\n session.add(wayne)\n session.commit()\n\n out = list(session.query(Player).filter_by(name=\"nothing\"))\n eq_(len(out), 0)\n\n # do a regular old query that works\n with _connect() as conn:\n rows = conn.execute(\"select * from players\").fetchall()\n eq_(len(rows), 1)\n eq_(rows[0]['name'], 'wayne')\n\n with _connect() as conn:\n try:\n conn.execute(\"select * from foo_Bah_blah\")\n except Exception:\n pass\n else:\n assert 0\n\n end = time.time()\n\n spans = tracer.writer.pop()\n for span in spans:\n eq_(span.name, \"%s.query\" % vendor)\n eq_(span.service, service)\n eq_(span.span_type, \"sql\")\n\n for k, v in expected_meta.items():\n eq_(span.meta[k], v)\n\n # FIXME[matt] could be finer grained but i'm lazy\n assert start < span.start < end\n assert span.duration\n assert span.duration < end - start\n\n by_rsc = {s.resource:s for s in spans}\n\n # ensure errors work\n s = by_rsc[\"select * from foo_Bah_blah\"]\n eq_(s.error, 1)\n assert \"foo_Bah_blah\" in s.get_tag(errorsx.ERROR_MSG)\n assert \"foo_Bah_blah\" in s.get_tag(errorsx.ERROR_STACK)\n\n expected = [\n \"select * from players\",\n \"select * from foo_Bah_blah\",\n ]\n\n for i in expected:\n assert i in by_rsc, \"%s not in %s\" % (i, by_rsc.keys())\n\n # ensure we have the service types\n services = tracer.writer.pop_services()\n expected = {\n service : {\"app\":vendor, \"app_type\":\"db\"}\n }\n eq_(services, expected)","def test_bad_dialect_definitions(self):\n try:\n BaseDialect()\n except Exception as e:\n assert isinstance(e, ValueError)","def test_attempting_to_create_abstract_table_fails(self):\r\n from cqlengine.management import create_table\r\n with self.assertRaises(CQLEngineException):\r\n create_table(AbstractModelWithFullCols)","def test_manage():\n assert isinstance(hug_peewee.connection.manage(api), SqliteDatabase)\n assert isinstance(hug_peewee.connection.manage(api, location='connection_testing.db'), SqliteDatabase)\n assert hug.test.get(api, 'fake_endpoint').data == True\n\n with pytest.raises(ValueError):\n hug_peewee.connection.manage(api, engine=\"Reese's Petabyte Cup\") # I also wish this existed!","def test_connect_invalid_string(self):\n with pytest.raises(ValueError):\n DatabaseDriver.connect('not a valid connect string')","def initialize_engine( conn=environment.ENGINE ):\n if conn is not None:\n method = { 'sqlite': create_sqlite_engine,\n 'sqlite-file': _create_sqlite_file_engine,\n # 'mysql': _create_mysql_engine,\n # 'mysql_test': _create_mysql_test_engine\n }.get( conn )\n\n engine = method()\n # Base.metadata.create_all( engine )\n return engine\n\n raise ValueError","def test_410_000_non_existant_db(self):\n with TDC() as temp_dir:\n file = Path(temp_dir) / 'database.db'\n self.assertFalse(file.exists(),'Database file exists pre test')\n eng = Engine(file)\n con = eng.connect()\n self.assertTrue(file.exists(), 'Database file does not exists post test')","def test_create_text_index_noschema(self):\n actual = self.engine._create_text_index()\n expected = 'TextIndex'\n self.assertEqual(actual, expected)","def test_database_needs_database_object_on_creation(self):\n self.assertRaises(TypeError, app.database.Database)","def test_error(self):\n src = self.tmp()\n\n f = open(src, 'w')\n f.write(\"\"\"foobar\"\"\")\n f.close()\n\n sqls = SqlScript(src)\n self.assertRaises(Exception, sqls.run, self.engine)","def test_get_db_session_with_exception(initialized_db_url):\n patient = models.Patient(\n patient_id=\"patient1\",\n patient_name=\"patient1\",\n patient_birth_date=datetime.utcnow(),\n institution=\"foobar\",\n )\n with pytest.raises(Exception):\n with utils.get_db_session(initialized_db_url) as db:\n db.add(patient)\n raise Exception()\n\n with utils.get_db_session(initialized_db_url) as db:\n result = db.query(models.Patient).all()\n assert not result","def test_creation_when_invalid_database_exists_and_no_overwrite(self):\n database_filename = \"test.db\"\n\n # Delete the test database if it exists.\n test_database = os.path.join(os.getcwd(), database_filename)\n if os.path.exists(test_database):\n os.remove(test_database)\n\n # Create our pre-existing, _invalid_, database.\n database_creation_statement = \"\"\"\n CREATE TABLE data(\n row_ID INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\n ID VARCHAR,\n Time DATETIME\n );\n \"\"\"\n\n with sqlite3.connect(database_filename) as conn:\n cur = conn.cursor()\n cur.execute(database_creation_statement)\n\n # Create the database object, build the database\n database = app.database.Database(database_filename)\n self.assertRaises(sqlite3.DatabaseError, database.create_database)","def test_missing(server):\n\n assert \"non_existing_database\" not in server\n with pytest.raises(excepts.DBNotExists):\n server[\"non_existing_database\"]","async def test_create_engine(subject: EngineStore) -> None:\n result = await subject.create(run_id=\"run-id\", labware_offsets=[], protocol=None)\n\n assert subject.current_run_id == \"run-id\"\n assert isinstance(result, StateSummary)\n assert isinstance(subject.runner, LiveRunner)\n assert isinstance(subject.engine, ProtocolEngine)","def test_loqusdb_wrong_version(loqus_exe):\n # GIVEN a loqusdb version < 2.5\n loqus_extension = LoqusDB(loqusdb_binary=loqus_exe, version=1.0)\n # WHEN instantiating an adapter\n with pytest.raises(SyntaxError):\n # THEN assert a syntax error is raised since version is wrong\n loqus_extension.version_check()","def test_invalid_database_file(self):\n with self.assertRaises(Exception):\n app = App(__file__)","def test_entities__entity_by_name__1(stubEntities, entityAdapters):\n with pytest.raises(ValueError):\n IEntity('asdf')","def test_new_invalid(self) -> None:\n with pytest.raises(TypeError) as excinfo:\n RunwayTestDefinition({}) # type: ignore\n assert str(excinfo.value).startswith(\"expected data of type\")","def test_create_instance(self):\n engine = Engine(self.config_file, self.api_token)\n\n assert isinstance(engine, Engine) is True\n assert isinstance(engine.backend, Backend) is True\n assert isinstance(engine.backend, BossBackend) is True\n assert isinstance(engine.validator, Validator) is True\n assert isinstance(engine.validator, BossValidatorV02) is True\n assert isinstance(engine.config, Configuration) is True\n\n # Schema loaded\n assert isinstance(engine.config.schema, dict) is True\n assert engine.config.schema[\"type\"] == \"object\"","def test_init(self):\n self.assertEqual(str(PostgreSQL(*self.conn_params).engine),\n \"Engine(postgresql://test:***@127.0.0.1:\"\n \"5432/postgres)\")","def test_attempting_to_save_abstract_model_fails(self):\r\n with self.assertRaises(CQLEngineException):\r\n AbstractModelWithFullCols.create(pkey=1, data=2)","def pristine_db_engine(tmpdir):\n sqlite_path = os.path.join(str(tmpdir), \"dummy.db\")\n engine = None\n try:\n engine = create_engine(f\"sqlite:///{sqlite_path}\")\n yield engine\n finally:\n if engine:\n engine.dispose()\n if os.path.exists(sqlite_path):\n os.remove(sqlite_path)","async def test_archives_state_if_engine_already_exists(subject: EngineStore) -> None:\n await subject.create(run_id=\"run-id-1\", labware_offsets=[], protocol=None)\n\n with pytest.raises(EngineConflictError):\n await subject.create(run_id=\"run-id-2\", labware_offsets=[], protocol=None)\n\n assert subject.current_run_id == \"run-id-1\"","def test_get_not_existing_item_raise_exception(config):\n p = PostgreSQLProvider(config)\n with pytest.raises(ProviderItemNotFoundError):\n p.get(-1)","def test_wrong_args(self, bad_context):\n with pytest.raises(TypeError):\n Connection(bad_context)","def test_model_create_without_required(db_session, factory, column):\n model = factory.build(**{column: None})\n db_session.add(model)\n with pytest.raises(IntegrityError, match=\"psycopg2.errors.NotNullViolation\"):\n db_session.commit()","def test_entities__entity_by_obj__1(stubEntities, entityAdapters):\n obj = persistent.Persistent()\n with pytest.raises(ValueError):\n IEntity(obj)","def create_engine(self, base):\n try:\n engine = create_engine(\n \"postgresql+psycopg2://%s:%s@%s:%s/%s\" % (\n self._db_settings['DATABASE_USER'],\n self._db_settings['DATABASE_PASS'],\n self._db_settings['DATABASE_IP'],\n self._db_settings['DATABASE_PORT'],\n self._db_settings['DATABASE_NAME']),\n poolclass=NullPool)\n #pool_size=5,\n #max_overflow=10)\n base.metadata.create_all(engine)\n # Fix for forking\n #register_after_fork(engine, engine.dispose)\n return engine\n except ValueError as e: # Potentially corrupted DB config.\n self.error_handler.abort_framework(\n \"Database configuration file is potentially corrupted. Please check %s\\n[DB] %s\" %\n (self.config.get_val('DATABASE_SETTINGS_FILE'), str(e)))\n except KeyError: # Indicates incomplete db config file\n self.error_handler.abort_framework(\"Incomplete database configuration settings in %s\" %\n self.config.get_val('DATABASE_SETTINGS_FILE'))\n except exc.OperationalError as e:\n self.error_handler.abort_framework(\"[DB] %s\\nRun 'make db-run' to start/setup db\" % str(e))","def test_insert_invalid_source(self):\n track = Track(artist='Artist', title='Title')\n with self.assertRaises(Exception):\n pk = track.insert(self.app.db,\n self.app.curs,\n 'foobar',\n datetime.datetime.now())","def test_postgresql_connect_fail(self):\n if _is_backend_avail('postgresql', user=\"openstack_cifail\"):\n self.fail(\"Shouldn't have connected\")","def test_create(self):\n with self.assertRaises(QiitaDBNotImplementedError):\n MetadataTemplate.create()","def test_entities__entity_by_name__2(stubEntities, entityAdapters):\n with pytest.raises(ValueError):\n IEntity(u'asdf')","def test_multiple_base_models(engine):\n with pytest.raises(ValueError):\n bloop.model.BaseModel(engine)","def engine(db_url=None):\n db_url = db_url or os.getenv(\"DB_URL\")\n if not db_url:\n raise ValueError(\"database URL is required\")\n print(f\"Returning an engine for {db_url}\")\n return create_engine(db_url)","def test_initialize_database(pristine_db_engine: Engine):\n utils.initialize_database(pristine_db_engine, echo=False)\n inspector = inspect(pristine_db_engine)\n\n expected_table_names = [\n models.Image.__tablename__,\n models.Series.__tablename__,\n models.Study.__tablename__,\n models.StudyFind.__tablename__,\n models.Patient.__tablename__,\n ]\n assert set(inspector.get_table_names()) == set(expected_table_names)\n\n expected_view_names = [\n views.StudyMetaView.__tablename__,\n views.ManufacturerView.__tablename__,\n ]\n assert set(inspector.get_view_names()) == set(expected_view_names)","def test_invalid_instantiation(invalid_instance):\n with pytest.raises(ValueError):\n invalid_instance()","def test_init_unknown_error(self):\n with self.assertRaises(QiitaDBUnknownIDError):\n PrepTemplate(2)","def make_mock_engine(name):\n url_builder = get_url_builder()\n if Version(sa.__version__) >= Version('1.4.0'):\n return sa.create_mock_engine(url_builder(\n drivername=name\n ), executor=None)\n else:\n return sa.create_engine(url_builder(\n drivername=name,\n ), strategy='mock', executor=None)","async def test_create_bike_bad_type(database, first_key, second_key):\n with pytest.raises(TypeError):\n await register_bike(first_key, second_key)","def test_db_existence():\n db = SQLAlchemy()\n engine = db.create_engine(Config.SQLALCHEMY_DATABASE_URI, {})\n inspect = db.inspect(engine)\n assert (inspect.has_table(\"user\"))","def test_uuid_adapter(self):\n with self.assertRaises(TypeError):\n adapter = UUIDAdapter('01234567-0123-0123-0123-0123456789ab')","def test_db_is_created(test_teardown):\n try:\n applications = Application.query.first()\n inhouse_postings = Inhouse.query.first()\n external_postings = External.query.first()\n except Exception:\n assert False\n assert True","def test_validate_session_dbapi_exception(self, mock_session):\n mock_session.side_effect = sqlalchemy.exc.OperationalError(\"m1\", \"m2\", \"m3\", \"m4\")\n assert airflow.settings.validate_session() is False","def create_engine(self):\n connection_string = f'postgresql://{self.user}:{self.password}@{self.host}/{self.database_name}'\n return create_engine(connection_string)","def test_neg_exists_with_no_paramters(self):\n with pytest.raises(TypeError) as typeError:\n self.as_connection.exists()\n\n assert \"argument 'key' (pos 1)\" in str(typeError.value)","def attempt_dummy_encrypted_db(db_path):\n\n engine = create_engine(\n 'sqlite+pysqlcipher://:' + create_temporary_secret() + '@//' + db_path,\n module=sqlcipher3)\n # engine = create_engine('sqlite:///' + db_path)\n connection = engine.connect()\n connection.execute('CREATE TABLE foo (a int)')\n connection.execute('INSERT INTO foo (a) VALUES (123)')\n result_proxy = connection.execute('SELECT * FROM foo')\n return True if result_proxy.fetchall() == [(123,)] else False","def create_sqlite_engine( echo=False ):\n conn = 'sqlite:///:memory:'\n print( \"creating connection: %s \" % conn )\n return create_engine( conn, echo=False )","def test_creation_when_invalid_database_exists_and_overwrite(self):\n database_filename = \"test.db\"\n\n # Delete the test database if it exists.\n test_database = os.path.join(os.getcwd(), database_filename)\n if os.path.exists(test_database):\n os.remove(test_database)\n\n # Create our pre-existing, _invalid_, database.\n database_creation_statement = \"\"\"\n CREATE TABLE data(\n row_ID INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\n ID VARCHAR,\n Time DATETIME\n );\n \"\"\"\n\n with sqlite3.connect(database_filename) as conn:\n cur = conn.cursor()\n cur.execute(database_creation_statement)\n\n # Generate the database.\n database = app.database.Database(database_filename, overwrite=True)\n database.create_database()\n\n # Pull out the table names from the database we've created.\n column_names = extract_column_names(database_filename)\n\n # Assert that they are as expected:\n for column_name in app.database.database_columns:\n self.assertEqual(\n True,\n column_name in column_names,\n \"Database creation process did not yield the column names expected. Missing: {0}\".format(column_name)\n )","def test_create_alias_negative(self):\n\n with self.assertRaises(Exception) as context:\n Alias.objects.create(\n alias='alias1', target='target1',\n start=datetime.datetime(2020, 1, 1, 16, 28, 36, 123456, tzinfo=pytz.UTC),\n end=datetime.datetime(2020, 2, 1, 16, 28, 36, 123456, tzinfo=pytz.UTC)\n )\n # print(context.exception, type(context.exception))\n self.assertTrue('Such an Object already exists !!!' in str(context.exception))\n\n count = Alias.objects.count()\n self.assertEqual(count, 4)","def test_create(self):\n cursor = connection.cursor()\n # It needs to take at least 2 args\n self.assertRaises(TypeError, db.create_table)\n self.assertRaises(TypeError, db.create_table, \"test1\")\n # Empty tables (i.e. no columns) are not fine, so make at least 1\n db.create_table(\"test1\", [('email_confirmed', models.BooleanField(default=False))])\n db.start_transaction()\n # And should exist\n cursor.execute(\"SELECT * FROM test1\")\n # Make sure we can't do the same query on an empty table\n try:\n cursor.execute(\"SELECT * FROM nottheretest1\")\n self.fail(\"Non-existent table could be selected!\")\n except:\n pass\n # Clear the dirty transaction\n db.rollback_transaction()\n db.start_transaction()\n # Remove the table\n db.delete_table(\"test1\")\n # Make sure it went\n try:\n cursor.execute(\"SELECT * FROM test1\")\n self.fail(\"Just-deleted table could be selected!\")\n except:\n pass\n # Clear the dirty transaction\n db.rollback_transaction()\n db.start_transaction()\n # Try deleting a nonexistent one\n try:\n db.delete_table(\"nottheretest1\")\n self.fail(\"Non-existent table could be deleted!\")\n except:\n pass\n db.rollback_transaction()","def register_engine(self, poolclass=NullPool):\r\n engine = create_engine('{conn}{db}'.format(db=common.TEST_DATABASE,\r\n conn=common.DB_CONNECTION),\r\n poolclass=poolclass)\r\n BASE.metadata.bind = engine\r\n return engine","def test_entities__entity_by_name__4(stubEntities, entityAdapters):\n with pytest.raises(ValueError):\n IEntity('icemac.addressbook.tests.conftest.Duck')","def test_adapter_read_only() -> None:\n adapter = ReadOnlyAdapter()\n\n with pytest.raises(NotSupportedError) as excinfo:\n adapter.insert_data({\"hello\": \"world\"})\n assert str(excinfo.value) == \"Adapter does not support ``INSERT`` statements\"\n\n with pytest.raises(NotSupportedError) as excinfo:\n adapter.delete_data(1)\n assert str(excinfo.value) == \"Adapter does not support ``DELETE`` statements\"\n\n with pytest.raises(NotSupportedError) as excinfo:\n adapter.update_data(1, {\"hello\": \"universe\"})\n assert str(excinfo.value) == \"Adapter does not support ``UPDATE`` statements\"","def test_config1_engine():\n app = AppBuilder(CONFIG2).build_app()\n assert isinstance(app.engine, Engine) and not isinstance(app.engine, CustomEngine)\n assert app.engine.Formatter is MockFormat\n assert app.engine.extract_timeout == 20\n assert app.engine.extract_tmp_dir == \"/tmp\"","def raise_if_not_responsive(dsn: URL, *, verbose=False):\n engine: Optional[Engine] = None\n try:\n engine = sa.create_engine(\n str(dsn), echo=verbose, echo_pool=verbose, pool_timeout=5\n )\n assert engine # nosec\n conn = engine.connect()\n conn.close()\n finally:\n if engine:\n engine.dispose()","def get_engine(db_credentials):\n\n url = 'postgresql://{user}:{passwd}@{host}:{port}/{db}'.format(\n user=db_credentials['user'], passwd=db_credentials['pwd'], host=db_credentials['host'], \n port=db_credentials['port'], db=db_credentials['db'])\n engine = create_engine(url, pool_size = 50)\n \n return engine","def test_start_tasks_db_raises():\n with pytest.raises(ValueError) as excinfo:\n tasks.start_tasks_db('some/great/path', 'mysql')\n exception_msg = excinfo.value.args[0]\n assert exception_msg == \"db_type must be a 'tiny' or 'mongo'\"","def test_connector_is_abstract():\n with pytest.raises(TypeError):\n connector.Connector()","def test_database_error(self):\n self.mocked_cursor.execute.side_effect = psycopg2.Error('testing')\n\n db = database.Database()\n\n with self.assertRaises(database.DatabaseError):\n db.execute(sql=\"SELECT * from FOO WHERE bar LIKE 'baz'\")","def test_entities__entity_by_name__3(stubEntities, entityAdapters):\n with pytest.raises(ValueError):\n IEntity('icemac.addressbook.tests.stubs.Duck')","def test_db_connection_bad_auth():\n with pytest.raises(\n AttributeError,\n match=r\".*'NoneType' object has no attribute 'split'.*\") as err:\n\n with pytest.raises(\n AuthError,\n match=r\".*authentication failure.*\") as err2:\n\n from cwf2neo.neo4j import Neo4j\n\n db = Neo4j(auth=('baduser', 'badpass'))\n\n db.graph.database.name\n\n assert err2.type is AuthError\n\n assert err.type is AttributeError","def test_invalid_factory_type():\n os.environ[config.FLOWSERV_CLIENT] = 'UNKNOWN'\n with pytest.raises(ValueError):\n ClientAPI()\n del os.environ[config.FLOWSERV_CLIENT]","def test_create_from_dataframe_invalid_pk(self):\n self.insert()\n data = self.tbl.select()\n Table.create(\n ':memory:', \"Foo_2\", data,\n primary_key='foo', verbose=True)","def test_get_not_existing_item_raise_exception(config):\n p = MsSqlProvider(config)\n with pytest.raises(ProviderItemNotFoundError):\n p.get(-1)","def test_create_experiment_new_no_space(self):\n with OrionState() as cfg:\n name = \"oopsie_forgot_a_space\"\n with pytest.raises(NoConfigurationError) as exc:\n create_experiment(name=name, storage=cfg.storage_config)\n\n assert f\"Experiment {name} does not exist in DB\" in str(exc.value)","def test_create_with_invalid_algorithms(self, benchmark_config_py):\n with OrionState() as cfg:\n\n with pytest.raises(NotImplementedError) as exc:\n benchmark_config_py[\"algorithms\"] = [\n {\"algorithm\": {\"fake_algorithm\": {\"seed\": 1}}}\n ]\n # Pass executor to close it properly\n with Joblib(n_workers=2, backend=\"threading\") as executor:\n get_or_create_benchmark(\n cfg.storage, **benchmark_config_py, executor=executor\n )\n assert \"Could not find implementation of BaseAlgorithm\" in str(exc.value)","def test_create_investigation_type_error(self):\n with self.assertRaises(QiitaDBColumnError):\n PrepTemplate.create(self.metadata, self.new_raw_data,\n self.test_study, self.data_type_id,\n 'Not a term')","def test_postgresql_connect_fail(self):\n if test_migrations._is_backend_avail(\n 'postgres', 'kickstand_cifail', self.PASSWD, self.DATABASE):\n self.fail(\"Shouldn't have connected\")","def test_create_experiment_bad_storage(self):\n name = \"oopsie_bad_storage\"\n # Make sure there is no existing storage singleton\n\n with pytest.raises(NotImplementedError) as exc:\n create_experiment(\n name=name,\n storage={\"type\": \"legacy\", \"database\": {\"type\": \"idontexist\"}},\n )\n\n assert \"Could not find implementation of Database, type = 'idontexist'\" in str(\n exc.value\n )","def test_configs_engine():\n app = AppBuilder(CONFIG1).build_app()\n assert isinstance(app.engine, CustomEngine)\n assert app.engine.Formatter is MockFormat\n assert app.engine.extract_timeout == 20\n assert app.engine.extract_tmp_dir == \"/tmp\"","def test_bad_insert(db):\n with pytest.raises(error.InvalidSerialization):\n _ = db.insert_current('config', db, store_permanently=False)\n\n with pytest.raises(error.InvalidSerialization):\n _ = db.insert('config', db)","def test_raise_when_no_id(self):\n model = self.Test({}, False)\n self.assertRaises(errors.PersistenceError, model.save)","def test_create_delete_db(server):\n\n db = server.create_db(test_db_name)\n\n with pytest.raises(excepts.DBExists):\n db = server.create_db(test_db_name)\n\n server.delete_db(db)\n\n with pytest.raises(excepts.DBNotExists):\n server.delete_db(db)\n\n db = server.create_db(test_db_name)\n db.delete()\n\n with pytest.raises(excepts.DBNotExists):\n db.delete()","def test_create_omop():\n runner = CliRunner()\n result = runner.invoke(cli.create_omop, [], env=env)\n\n assert result.exit_code == 0\n\n config = config_dict.get('testing')\n\n engine = create_engine(config.SQLALCHEMY_DATABASE_URI)\n inspector = inspect(engine)\n\n assert len(inspector.get_table_names()) == 37","def test_virtual_service_create_command_when_invalid_arguments_provided(\n err_msg, args, err_type, mock_client\n):\n with pytest.raises(err_type) as err:\n virtual_service_create_command(mock_client, args)\n assert str(err.value) == err_msg","def test_init_unknown_error(self):\n with self.assertRaises(QiitaDBUnknownIDError):\n SampleTemplate(2)","def test_invalid_model(self):\n self.assertRaises(ModelNotFoundError, lambda: ModelContainer('web', 'model').model_cls)","async def test_invalid_insert_user_typeError(database):\n await database.setup_database(reset=True)\n\n for user_id in zip([\n random.choice([\"string\",[1,2],set(),dict()])\n for i in range(0,10)]):\n try:\n await database.insert_user(user_id=user_id)\n assert False\n except:\n assert True\n await database.close_pool()","def test_db_connection_bad_host():\n\n with pytest.raises(AddressError, match=r\"Cannot resolve address .*\"):\n\n from cwf2neo.neo4j import Neo4j\n\n db = Neo4j(host=\"invalidneo4jhostname\")\n\n db.graph.database.name","def get_engine(db_params: Dict[str, str]) -> sa.engine:\r\n db_uri = get_uri(db_params)\r\n return sa.create_engine(db_uri)","def test_poolargs(self):\n\n e = create_engine(\n \"postgresql+psycopg2://\",\n creator=None,\n pool_recycle=50,\n echo_pool=None,\n module=mock_dbapi,\n _initialize=False,\n )\n assert e.pool._recycle == 50\n\n # these args work for QueuePool\n\n e = create_engine(\n \"postgresql+psycopg2://\",\n max_overflow=8,\n pool_timeout=60,\n poolclass=tsa.pool.QueuePool,\n module=mock_dbapi,\n _initialize=False,\n )\n\n # but not SingletonThreadPool\n\n assert_raises(\n TypeError,\n create_engine,\n \"sqlite://\",\n max_overflow=8,\n pool_timeout=60,\n poolclass=tsa.pool.SingletonThreadPool,\n module=mock_sqlite_dbapi,\n _initialize=False,\n )","def create_engine(self):\n return create_engine('sqlite:///' + self.database_name, echo=True)","def test_create_with_only_name(self):\n with OrionState() as cfg:\n name = \"bm00001\"\n with pytest.raises(NoConfigurationError) as exc:\n get_or_create_benchmark(cfg.storage, name).close()\n\n assert f\"Benchmark {name} does not exist in DB\" in str(exc.value)","def setup_engine():\n print(\"Setting up engine\")\n engine = create_engine('mysql+pymysql://{}:{}@{}/govhack2015'.format(\n username, password, ip_address))\n\n return engine","def test_NonrealtimeProvider_add_bus_error(session):\n provider = Provider.from_context(session)\n with pytest.raises(ValueError):\n provider.add_bus()\n with provider.at(0):\n with pytest.raises(ValueError):\n provider.add_bus(calculation_rate=\"scalar\")","def test_create_use_with_invalid_email(self):\n with self.assertRaises(ValueError):\n get_user_model().objects.create_user(None, password='open@123')","def get_database_engine() -> Engine:\n return engine","def _initTestingDB(): \n from sqlalchemy import create_engine\n engine = create_engine('sqlite://')\n from .models import (\n Base,\n TodoUser,\n )\n DBSession.configure(bind=engine)\n Base.metadata.create_all(engine)\n \n return DBSession","def test_db(self, env: yaenv.Env):\n _db = {\n 'ENGINE': yaenv.db.SCHEMES['sqlite'],\n 'NAME': 'db.sqlite3',\n 'USER': '',\n 'PASSWORD': '',\n 'HOST': '',\n 'PORT': '',\n }\n assert env.db('DB_URL') == _db\n _db = {\n 'ENGINE': yaenv.db.SCHEMES['sqlite'],\n 'NAME': ':memory:',\n }\n assert env.db('DB_URL_DEFAULT', 'sqlite://:memory:') == _db\n with pytest.raises(yaenv.EnvError) as err:\n _ = env.db('INVALID_URL', 'invalid')\n assert 'Invalid database' in str(err.value)\n assert env.db('MISSING') is None","def test_createInvalidPortDescription(self):\n store = Store()\n factory = DummyFactory(store=store)\n self.assertFailStatus(\n 1, self._makeConfig(store),\n [\"create\", \"--strport\", \"xyz\",\n \"--factory-identifier\", str(factory.storeID)])\n self.assertEqual(\n \"'xyz' is not a valid port description.\\n\", sys.stdout.getvalue())","def test_creation_fail(self):\n\n # Assert that a RelaxError occurs when the pipe type is invalid.\n self.assertRaises(RelaxError, pipes.create, 'new', 'x')","async def test_create_bike_bad_value(database, key_name):\n with pytest.raises(ValueError):\n await register_bike(public_key=key_name, master_key=key_name)","def test_check_database_exists_invalid(self):\n query_result = None\n self.client.athena_client = MockAthenaClient(results=query_result)\n\n assert_false(self.client.check_database_exists())","def test_invalid_conn(self):\n op = BranchSQLOperator(\n task_id=\"make_choice\",\n conn_id=\"invalid_connection\",\n sql=\"SELECT count(1) FROM INFORMATION_SCHEMA.TABLES\",\n follow_task_ids_if_true=\"branch_1\",\n follow_task_ids_if_false=\"branch_2\",\n dag=self.dag,\n )\n\n with pytest.raises(AirflowException):\n op.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)","def test_insert_invalid_type(self):\n album = Album(artist='Artist', album='Album', album_type='xyzzy',\n totalseconds=120, totaltracks=2)\n with self.assertRaises(Exception):\n pk = album.insert(self.app.db, self.app.curs)\n self.assertEqual(self.get_album_count(), 0)"],"string":"[\n \"def ensure_engine_exists():\\n # Check if engine is present\\n if not is_engine_binded():\\n print(\\\"Binding new engine\\\")\\n bind_engine(create_engine(\\\"sqlite:///:memory:\\\", echo=True))\",\n \"def test_engine(self):\\n config = {\\n \\\"url\\\": 'sqlite://',\\n \\\"connect_args\\\": {\\n \\\"check_same_thread\\\": \\\"false\\\",\\n \\\"poolclass\\\": \\\"pool.StaticPool\\\"\\n }\\n }\\n engine = self.configurator.setup_engine(config)\\n self.assertIsNotNone(engine)\",\n \"def validate_engine(engine):\\n\\n VALID_DB_ENGINES = (\\n \\\"MySQL\\\",\\n \\\"mysql\\\",\\n \\\"oracle-se1\\\",\\n \\\"oracle-se2\\\",\\n \\\"oracle-se\\\",\\n \\\"oracle-ee\\\",\\n \\\"sqlserver-ee\\\",\\n \\\"sqlserver-se\\\",\\n \\\"sqlserver-ex\\\",\\n \\\"sqlserver-web\\\",\\n \\\"postgres\\\",\\n \\\"aurora\\\",\\n \\\"aurora-mysql\\\",\\n \\\"aurora-postgresql\\\",\\n \\\"mariadb\\\",\\n )\\n\\n if engine not in VALID_DB_ENGINES:\\n raise ValueError(\\n \\\"DBInstance Engine must be one of: %s\\\" % \\\", \\\".join(VALID_DB_ENGINES)\\n )\\n return engine\",\n \"async def test_setup_invalid_config(\\n recorder_mock: Recorder, hass: HomeAssistant\\n) -> None:\\n with patch(\\n \\\"homeassistant.components.sql.config_flow.sqlalchemy.create_engine\\\",\\n ):\\n assert not await async_setup_component(hass, DOMAIN, YAML_CONFIG_INVALID)\\n await hass.async_block_till_done()\",\n \"async def test_get_default_engine_conflict(subject: EngineStore) -> None:\\n await subject.create(run_id=\\\"run-id\\\", labware_offsets=[], protocol=None)\\n subject.engine.play()\\n\\n with pytest.raises(EngineConflictError):\\n await subject.get_default_engine()\",\n \"def _test_engine(engine, service, vendor, expected_meta):\\n tracer = Tracer()\\n tracer.writer = DummyWriter()\\n\\n # create an engine and start tracing.\\n trace_engine(engine, tracer, service=service)\\n start = time.time()\\n\\n @contextlib.contextmanager\\n def _connect():\\n try:\\n conn = engine.connect()\\n yield conn\\n finally:\\n conn.close()\\n\\n with _connect() as conn:\\n try:\\n conn.execute(\\\"delete from players\\\")\\n except Exception:\\n pass\\n\\n # boilerplate\\n Base.metadata.create_all(engine)\\n Session = sessionmaker(bind=engine)\\n session = Session()\\n\\n # do an ORM insert\\n wayne = Player(id=1, name=\\\"wayne\\\")\\n session.add(wayne)\\n session.commit()\\n\\n out = list(session.query(Player).filter_by(name=\\\"nothing\\\"))\\n eq_(len(out), 0)\\n\\n # do a regular old query that works\\n with _connect() as conn:\\n rows = conn.execute(\\\"select * from players\\\").fetchall()\\n eq_(len(rows), 1)\\n eq_(rows[0]['name'], 'wayne')\\n\\n with _connect() as conn:\\n try:\\n conn.execute(\\\"select * from foo_Bah_blah\\\")\\n except Exception:\\n pass\\n else:\\n assert 0\\n\\n end = time.time()\\n\\n spans = tracer.writer.pop()\\n for span in spans:\\n eq_(span.name, \\\"%s.query\\\" % vendor)\\n eq_(span.service, service)\\n eq_(span.span_type, \\\"sql\\\")\\n\\n for k, v in expected_meta.items():\\n eq_(span.meta[k], v)\\n\\n # FIXME[matt] could be finer grained but i'm lazy\\n assert start < span.start < end\\n assert span.duration\\n assert span.duration < end - start\\n\\n by_rsc = {s.resource:s for s in spans}\\n\\n # ensure errors work\\n s = by_rsc[\\\"select * from foo_Bah_blah\\\"]\\n eq_(s.error, 1)\\n assert \\\"foo_Bah_blah\\\" in s.get_tag(errorsx.ERROR_MSG)\\n assert \\\"foo_Bah_blah\\\" in s.get_tag(errorsx.ERROR_STACK)\\n\\n expected = [\\n \\\"select * from players\\\",\\n \\\"select * from foo_Bah_blah\\\",\\n ]\\n\\n for i in expected:\\n assert i in by_rsc, \\\"%s not in %s\\\" % (i, by_rsc.keys())\\n\\n # ensure we have the service types\\n services = tracer.writer.pop_services()\\n expected = {\\n service : {\\\"app\\\":vendor, \\\"app_type\\\":\\\"db\\\"}\\n }\\n eq_(services, expected)\",\n \"def test_bad_dialect_definitions(self):\\n try:\\n BaseDialect()\\n except Exception as e:\\n assert isinstance(e, ValueError)\",\n \"def test_attempting_to_create_abstract_table_fails(self):\\r\\n from cqlengine.management import create_table\\r\\n with self.assertRaises(CQLEngineException):\\r\\n create_table(AbstractModelWithFullCols)\",\n \"def test_manage():\\n assert isinstance(hug_peewee.connection.manage(api), SqliteDatabase)\\n assert isinstance(hug_peewee.connection.manage(api, location='connection_testing.db'), SqliteDatabase)\\n assert hug.test.get(api, 'fake_endpoint').data == True\\n\\n with pytest.raises(ValueError):\\n hug_peewee.connection.manage(api, engine=\\\"Reese's Petabyte Cup\\\") # I also wish this existed!\",\n \"def test_connect_invalid_string(self):\\n with pytest.raises(ValueError):\\n DatabaseDriver.connect('not a valid connect string')\",\n \"def initialize_engine( conn=environment.ENGINE ):\\n if conn is not None:\\n method = { 'sqlite': create_sqlite_engine,\\n 'sqlite-file': _create_sqlite_file_engine,\\n # 'mysql': _create_mysql_engine,\\n # 'mysql_test': _create_mysql_test_engine\\n }.get( conn )\\n\\n engine = method()\\n # Base.metadata.create_all( engine )\\n return engine\\n\\n raise ValueError\",\n \"def test_410_000_non_existant_db(self):\\n with TDC() as temp_dir:\\n file = Path(temp_dir) / 'database.db'\\n self.assertFalse(file.exists(),'Database file exists pre test')\\n eng = Engine(file)\\n con = eng.connect()\\n self.assertTrue(file.exists(), 'Database file does not exists post test')\",\n \"def test_create_text_index_noschema(self):\\n actual = self.engine._create_text_index()\\n expected = 'TextIndex'\\n self.assertEqual(actual, expected)\",\n \"def test_database_needs_database_object_on_creation(self):\\n self.assertRaises(TypeError, app.database.Database)\",\n \"def test_error(self):\\n src = self.tmp()\\n\\n f = open(src, 'w')\\n f.write(\\\"\\\"\\\"foobar\\\"\\\"\\\")\\n f.close()\\n\\n sqls = SqlScript(src)\\n self.assertRaises(Exception, sqls.run, self.engine)\",\n \"def test_get_db_session_with_exception(initialized_db_url):\\n patient = models.Patient(\\n patient_id=\\\"patient1\\\",\\n patient_name=\\\"patient1\\\",\\n patient_birth_date=datetime.utcnow(),\\n institution=\\\"foobar\\\",\\n )\\n with pytest.raises(Exception):\\n with utils.get_db_session(initialized_db_url) as db:\\n db.add(patient)\\n raise Exception()\\n\\n with utils.get_db_session(initialized_db_url) as db:\\n result = db.query(models.Patient).all()\\n assert not result\",\n \"def test_creation_when_invalid_database_exists_and_no_overwrite(self):\\n database_filename = \\\"test.db\\\"\\n\\n # Delete the test database if it exists.\\n test_database = os.path.join(os.getcwd(), database_filename)\\n if os.path.exists(test_database):\\n os.remove(test_database)\\n\\n # Create our pre-existing, _invalid_, database.\\n database_creation_statement = \\\"\\\"\\\"\\n CREATE TABLE data(\\n row_ID INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\\n ID VARCHAR,\\n Time DATETIME\\n );\\n \\\"\\\"\\\"\\n\\n with sqlite3.connect(database_filename) as conn:\\n cur = conn.cursor()\\n cur.execute(database_creation_statement)\\n\\n # Create the database object, build the database\\n database = app.database.Database(database_filename)\\n self.assertRaises(sqlite3.DatabaseError, database.create_database)\",\n \"def test_missing(server):\\n\\n assert \\\"non_existing_database\\\" not in server\\n with pytest.raises(excepts.DBNotExists):\\n server[\\\"non_existing_database\\\"]\",\n \"async def test_create_engine(subject: EngineStore) -> None:\\n result = await subject.create(run_id=\\\"run-id\\\", labware_offsets=[], protocol=None)\\n\\n assert subject.current_run_id == \\\"run-id\\\"\\n assert isinstance(result, StateSummary)\\n assert isinstance(subject.runner, LiveRunner)\\n assert isinstance(subject.engine, ProtocolEngine)\",\n \"def test_loqusdb_wrong_version(loqus_exe):\\n # GIVEN a loqusdb version < 2.5\\n loqus_extension = LoqusDB(loqusdb_binary=loqus_exe, version=1.0)\\n # WHEN instantiating an adapter\\n with pytest.raises(SyntaxError):\\n # THEN assert a syntax error is raised since version is wrong\\n loqus_extension.version_check()\",\n \"def test_invalid_database_file(self):\\n with self.assertRaises(Exception):\\n app = App(__file__)\",\n \"def test_entities__entity_by_name__1(stubEntities, entityAdapters):\\n with pytest.raises(ValueError):\\n IEntity('asdf')\",\n \"def test_new_invalid(self) -> None:\\n with pytest.raises(TypeError) as excinfo:\\n RunwayTestDefinition({}) # type: ignore\\n assert str(excinfo.value).startswith(\\\"expected data of type\\\")\",\n \"def test_create_instance(self):\\n engine = Engine(self.config_file, self.api_token)\\n\\n assert isinstance(engine, Engine) is True\\n assert isinstance(engine.backend, Backend) is True\\n assert isinstance(engine.backend, BossBackend) is True\\n assert isinstance(engine.validator, Validator) is True\\n assert isinstance(engine.validator, BossValidatorV02) is True\\n assert isinstance(engine.config, Configuration) is True\\n\\n # Schema loaded\\n assert isinstance(engine.config.schema, dict) is True\\n assert engine.config.schema[\\\"type\\\"] == \\\"object\\\"\",\n \"def test_init(self):\\n self.assertEqual(str(PostgreSQL(*self.conn_params).engine),\\n \\\"Engine(postgresql://test:***@127.0.0.1:\\\"\\n \\\"5432/postgres)\\\")\",\n \"def test_attempting_to_save_abstract_model_fails(self):\\r\\n with self.assertRaises(CQLEngineException):\\r\\n AbstractModelWithFullCols.create(pkey=1, data=2)\",\n \"def pristine_db_engine(tmpdir):\\n sqlite_path = os.path.join(str(tmpdir), \\\"dummy.db\\\")\\n engine = None\\n try:\\n engine = create_engine(f\\\"sqlite:///{sqlite_path}\\\")\\n yield engine\\n finally:\\n if engine:\\n engine.dispose()\\n if os.path.exists(sqlite_path):\\n os.remove(sqlite_path)\",\n \"async def test_archives_state_if_engine_already_exists(subject: EngineStore) -> None:\\n await subject.create(run_id=\\\"run-id-1\\\", labware_offsets=[], protocol=None)\\n\\n with pytest.raises(EngineConflictError):\\n await subject.create(run_id=\\\"run-id-2\\\", labware_offsets=[], protocol=None)\\n\\n assert subject.current_run_id == \\\"run-id-1\\\"\",\n \"def test_get_not_existing_item_raise_exception(config):\\n p = PostgreSQLProvider(config)\\n with pytest.raises(ProviderItemNotFoundError):\\n p.get(-1)\",\n \"def test_wrong_args(self, bad_context):\\n with pytest.raises(TypeError):\\n Connection(bad_context)\",\n \"def test_model_create_without_required(db_session, factory, column):\\n model = factory.build(**{column: None})\\n db_session.add(model)\\n with pytest.raises(IntegrityError, match=\\\"psycopg2.errors.NotNullViolation\\\"):\\n db_session.commit()\",\n \"def test_entities__entity_by_obj__1(stubEntities, entityAdapters):\\n obj = persistent.Persistent()\\n with pytest.raises(ValueError):\\n IEntity(obj)\",\n \"def create_engine(self, base):\\n try:\\n engine = create_engine(\\n \\\"postgresql+psycopg2://%s:%s@%s:%s/%s\\\" % (\\n self._db_settings['DATABASE_USER'],\\n self._db_settings['DATABASE_PASS'],\\n self._db_settings['DATABASE_IP'],\\n self._db_settings['DATABASE_PORT'],\\n self._db_settings['DATABASE_NAME']),\\n poolclass=NullPool)\\n #pool_size=5,\\n #max_overflow=10)\\n base.metadata.create_all(engine)\\n # Fix for forking\\n #register_after_fork(engine, engine.dispose)\\n return engine\\n except ValueError as e: # Potentially corrupted DB config.\\n self.error_handler.abort_framework(\\n \\\"Database configuration file is potentially corrupted. Please check %s\\\\n[DB] %s\\\" %\\n (self.config.get_val('DATABASE_SETTINGS_FILE'), str(e)))\\n except KeyError: # Indicates incomplete db config file\\n self.error_handler.abort_framework(\\\"Incomplete database configuration settings in %s\\\" %\\n self.config.get_val('DATABASE_SETTINGS_FILE'))\\n except exc.OperationalError as e:\\n self.error_handler.abort_framework(\\\"[DB] %s\\\\nRun 'make db-run' to start/setup db\\\" % str(e))\",\n \"def test_insert_invalid_source(self):\\n track = Track(artist='Artist', title='Title')\\n with self.assertRaises(Exception):\\n pk = track.insert(self.app.db,\\n self.app.curs,\\n 'foobar',\\n datetime.datetime.now())\",\n \"def test_postgresql_connect_fail(self):\\n if _is_backend_avail('postgresql', user=\\\"openstack_cifail\\\"):\\n self.fail(\\\"Shouldn't have connected\\\")\",\n \"def test_create(self):\\n with self.assertRaises(QiitaDBNotImplementedError):\\n MetadataTemplate.create()\",\n \"def test_entities__entity_by_name__2(stubEntities, entityAdapters):\\n with pytest.raises(ValueError):\\n IEntity(u'asdf')\",\n \"def test_multiple_base_models(engine):\\n with pytest.raises(ValueError):\\n bloop.model.BaseModel(engine)\",\n \"def engine(db_url=None):\\n db_url = db_url or os.getenv(\\\"DB_URL\\\")\\n if not db_url:\\n raise ValueError(\\\"database URL is required\\\")\\n print(f\\\"Returning an engine for {db_url}\\\")\\n return create_engine(db_url)\",\n \"def test_initialize_database(pristine_db_engine: Engine):\\n utils.initialize_database(pristine_db_engine, echo=False)\\n inspector = inspect(pristine_db_engine)\\n\\n expected_table_names = [\\n models.Image.__tablename__,\\n models.Series.__tablename__,\\n models.Study.__tablename__,\\n models.StudyFind.__tablename__,\\n models.Patient.__tablename__,\\n ]\\n assert set(inspector.get_table_names()) == set(expected_table_names)\\n\\n expected_view_names = [\\n views.StudyMetaView.__tablename__,\\n views.ManufacturerView.__tablename__,\\n ]\\n assert set(inspector.get_view_names()) == set(expected_view_names)\",\n \"def test_invalid_instantiation(invalid_instance):\\n with pytest.raises(ValueError):\\n invalid_instance()\",\n \"def test_init_unknown_error(self):\\n with self.assertRaises(QiitaDBUnknownIDError):\\n PrepTemplate(2)\",\n \"def make_mock_engine(name):\\n url_builder = get_url_builder()\\n if Version(sa.__version__) >= Version('1.4.0'):\\n return sa.create_mock_engine(url_builder(\\n drivername=name\\n ), executor=None)\\n else:\\n return sa.create_engine(url_builder(\\n drivername=name,\\n ), strategy='mock', executor=None)\",\n \"async def test_create_bike_bad_type(database, first_key, second_key):\\n with pytest.raises(TypeError):\\n await register_bike(first_key, second_key)\",\n \"def test_db_existence():\\n db = SQLAlchemy()\\n engine = db.create_engine(Config.SQLALCHEMY_DATABASE_URI, {})\\n inspect = db.inspect(engine)\\n assert (inspect.has_table(\\\"user\\\"))\",\n \"def test_uuid_adapter(self):\\n with self.assertRaises(TypeError):\\n adapter = UUIDAdapter('01234567-0123-0123-0123-0123456789ab')\",\n \"def test_db_is_created(test_teardown):\\n try:\\n applications = Application.query.first()\\n inhouse_postings = Inhouse.query.first()\\n external_postings = External.query.first()\\n except Exception:\\n assert False\\n assert True\",\n \"def test_validate_session_dbapi_exception(self, mock_session):\\n mock_session.side_effect = sqlalchemy.exc.OperationalError(\\\"m1\\\", \\\"m2\\\", \\\"m3\\\", \\\"m4\\\")\\n assert airflow.settings.validate_session() is False\",\n \"def create_engine(self):\\n connection_string = f'postgresql://{self.user}:{self.password}@{self.host}/{self.database_name}'\\n return create_engine(connection_string)\",\n \"def test_neg_exists_with_no_paramters(self):\\n with pytest.raises(TypeError) as typeError:\\n self.as_connection.exists()\\n\\n assert \\\"argument 'key' (pos 1)\\\" in str(typeError.value)\",\n \"def attempt_dummy_encrypted_db(db_path):\\n\\n engine = create_engine(\\n 'sqlite+pysqlcipher://:' + create_temporary_secret() + '@//' + db_path,\\n module=sqlcipher3)\\n # engine = create_engine('sqlite:///' + db_path)\\n connection = engine.connect()\\n connection.execute('CREATE TABLE foo (a int)')\\n connection.execute('INSERT INTO foo (a) VALUES (123)')\\n result_proxy = connection.execute('SELECT * FROM foo')\\n return True if result_proxy.fetchall() == [(123,)] else False\",\n \"def create_sqlite_engine( echo=False ):\\n conn = 'sqlite:///:memory:'\\n print( \\\"creating connection: %s \\\" % conn )\\n return create_engine( conn, echo=False )\",\n \"def test_creation_when_invalid_database_exists_and_overwrite(self):\\n database_filename = \\\"test.db\\\"\\n\\n # Delete the test database if it exists.\\n test_database = os.path.join(os.getcwd(), database_filename)\\n if os.path.exists(test_database):\\n os.remove(test_database)\\n\\n # Create our pre-existing, _invalid_, database.\\n database_creation_statement = \\\"\\\"\\\"\\n CREATE TABLE data(\\n row_ID INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\\n ID VARCHAR,\\n Time DATETIME\\n );\\n \\\"\\\"\\\"\\n\\n with sqlite3.connect(database_filename) as conn:\\n cur = conn.cursor()\\n cur.execute(database_creation_statement)\\n\\n # Generate the database.\\n database = app.database.Database(database_filename, overwrite=True)\\n database.create_database()\\n\\n # Pull out the table names from the database we've created.\\n column_names = extract_column_names(database_filename)\\n\\n # Assert that they are as expected:\\n for column_name in app.database.database_columns:\\n self.assertEqual(\\n True,\\n column_name in column_names,\\n \\\"Database creation process did not yield the column names expected. Missing: {0}\\\".format(column_name)\\n )\",\n \"def test_create_alias_negative(self):\\n\\n with self.assertRaises(Exception) as context:\\n Alias.objects.create(\\n alias='alias1', target='target1',\\n start=datetime.datetime(2020, 1, 1, 16, 28, 36, 123456, tzinfo=pytz.UTC),\\n end=datetime.datetime(2020, 2, 1, 16, 28, 36, 123456, tzinfo=pytz.UTC)\\n )\\n # print(context.exception, type(context.exception))\\n self.assertTrue('Such an Object already exists !!!' in str(context.exception))\\n\\n count = Alias.objects.count()\\n self.assertEqual(count, 4)\",\n \"def test_create(self):\\n cursor = connection.cursor()\\n # It needs to take at least 2 args\\n self.assertRaises(TypeError, db.create_table)\\n self.assertRaises(TypeError, db.create_table, \\\"test1\\\")\\n # Empty tables (i.e. no columns) are not fine, so make at least 1\\n db.create_table(\\\"test1\\\", [('email_confirmed', models.BooleanField(default=False))])\\n db.start_transaction()\\n # And should exist\\n cursor.execute(\\\"SELECT * FROM test1\\\")\\n # Make sure we can't do the same query on an empty table\\n try:\\n cursor.execute(\\\"SELECT * FROM nottheretest1\\\")\\n self.fail(\\\"Non-existent table could be selected!\\\")\\n except:\\n pass\\n # Clear the dirty transaction\\n db.rollback_transaction()\\n db.start_transaction()\\n # Remove the table\\n db.delete_table(\\\"test1\\\")\\n # Make sure it went\\n try:\\n cursor.execute(\\\"SELECT * FROM test1\\\")\\n self.fail(\\\"Just-deleted table could be selected!\\\")\\n except:\\n pass\\n # Clear the dirty transaction\\n db.rollback_transaction()\\n db.start_transaction()\\n # Try deleting a nonexistent one\\n try:\\n db.delete_table(\\\"nottheretest1\\\")\\n self.fail(\\\"Non-existent table could be deleted!\\\")\\n except:\\n pass\\n db.rollback_transaction()\",\n \"def register_engine(self, poolclass=NullPool):\\r\\n engine = create_engine('{conn}{db}'.format(db=common.TEST_DATABASE,\\r\\n conn=common.DB_CONNECTION),\\r\\n poolclass=poolclass)\\r\\n BASE.metadata.bind = engine\\r\\n return engine\",\n \"def test_entities__entity_by_name__4(stubEntities, entityAdapters):\\n with pytest.raises(ValueError):\\n IEntity('icemac.addressbook.tests.conftest.Duck')\",\n \"def test_adapter_read_only() -> None:\\n adapter = ReadOnlyAdapter()\\n\\n with pytest.raises(NotSupportedError) as excinfo:\\n adapter.insert_data({\\\"hello\\\": \\\"world\\\"})\\n assert str(excinfo.value) == \\\"Adapter does not support ``INSERT`` statements\\\"\\n\\n with pytest.raises(NotSupportedError) as excinfo:\\n adapter.delete_data(1)\\n assert str(excinfo.value) == \\\"Adapter does not support ``DELETE`` statements\\\"\\n\\n with pytest.raises(NotSupportedError) as excinfo:\\n adapter.update_data(1, {\\\"hello\\\": \\\"universe\\\"})\\n assert str(excinfo.value) == \\\"Adapter does not support ``UPDATE`` statements\\\"\",\n \"def test_config1_engine():\\n app = AppBuilder(CONFIG2).build_app()\\n assert isinstance(app.engine, Engine) and not isinstance(app.engine, CustomEngine)\\n assert app.engine.Formatter is MockFormat\\n assert app.engine.extract_timeout == 20\\n assert app.engine.extract_tmp_dir == \\\"/tmp\\\"\",\n \"def raise_if_not_responsive(dsn: URL, *, verbose=False):\\n engine: Optional[Engine] = None\\n try:\\n engine = sa.create_engine(\\n str(dsn), echo=verbose, echo_pool=verbose, pool_timeout=5\\n )\\n assert engine # nosec\\n conn = engine.connect()\\n conn.close()\\n finally:\\n if engine:\\n engine.dispose()\",\n \"def get_engine(db_credentials):\\n\\n url = 'postgresql://{user}:{passwd}@{host}:{port}/{db}'.format(\\n user=db_credentials['user'], passwd=db_credentials['pwd'], host=db_credentials['host'], \\n port=db_credentials['port'], db=db_credentials['db'])\\n engine = create_engine(url, pool_size = 50)\\n \\n return engine\",\n \"def test_start_tasks_db_raises():\\n with pytest.raises(ValueError) as excinfo:\\n tasks.start_tasks_db('some/great/path', 'mysql')\\n exception_msg = excinfo.value.args[0]\\n assert exception_msg == \\\"db_type must be a 'tiny' or 'mongo'\\\"\",\n \"def test_connector_is_abstract():\\n with pytest.raises(TypeError):\\n connector.Connector()\",\n \"def test_database_error(self):\\n self.mocked_cursor.execute.side_effect = psycopg2.Error('testing')\\n\\n db = database.Database()\\n\\n with self.assertRaises(database.DatabaseError):\\n db.execute(sql=\\\"SELECT * from FOO WHERE bar LIKE 'baz'\\\")\",\n \"def test_entities__entity_by_name__3(stubEntities, entityAdapters):\\n with pytest.raises(ValueError):\\n IEntity('icemac.addressbook.tests.stubs.Duck')\",\n \"def test_db_connection_bad_auth():\\n with pytest.raises(\\n AttributeError,\\n match=r\\\".*'NoneType' object has no attribute 'split'.*\\\") as err:\\n\\n with pytest.raises(\\n AuthError,\\n match=r\\\".*authentication failure.*\\\") as err2:\\n\\n from cwf2neo.neo4j import Neo4j\\n\\n db = Neo4j(auth=('baduser', 'badpass'))\\n\\n db.graph.database.name\\n\\n assert err2.type is AuthError\\n\\n assert err.type is AttributeError\",\n \"def test_invalid_factory_type():\\n os.environ[config.FLOWSERV_CLIENT] = 'UNKNOWN'\\n with pytest.raises(ValueError):\\n ClientAPI()\\n del os.environ[config.FLOWSERV_CLIENT]\",\n \"def test_create_from_dataframe_invalid_pk(self):\\n self.insert()\\n data = self.tbl.select()\\n Table.create(\\n ':memory:', \\\"Foo_2\\\", data,\\n primary_key='foo', verbose=True)\",\n \"def test_get_not_existing_item_raise_exception(config):\\n p = MsSqlProvider(config)\\n with pytest.raises(ProviderItemNotFoundError):\\n p.get(-1)\",\n \"def test_create_experiment_new_no_space(self):\\n with OrionState() as cfg:\\n name = \\\"oopsie_forgot_a_space\\\"\\n with pytest.raises(NoConfigurationError) as exc:\\n create_experiment(name=name, storage=cfg.storage_config)\\n\\n assert f\\\"Experiment {name} does not exist in DB\\\" in str(exc.value)\",\n \"def test_create_with_invalid_algorithms(self, benchmark_config_py):\\n with OrionState() as cfg:\\n\\n with pytest.raises(NotImplementedError) as exc:\\n benchmark_config_py[\\\"algorithms\\\"] = [\\n {\\\"algorithm\\\": {\\\"fake_algorithm\\\": {\\\"seed\\\": 1}}}\\n ]\\n # Pass executor to close it properly\\n with Joblib(n_workers=2, backend=\\\"threading\\\") as executor:\\n get_or_create_benchmark(\\n cfg.storage, **benchmark_config_py, executor=executor\\n )\\n assert \\\"Could not find implementation of BaseAlgorithm\\\" in str(exc.value)\",\n \"def test_create_investigation_type_error(self):\\n with self.assertRaises(QiitaDBColumnError):\\n PrepTemplate.create(self.metadata, self.new_raw_data,\\n self.test_study, self.data_type_id,\\n 'Not a term')\",\n \"def test_postgresql_connect_fail(self):\\n if test_migrations._is_backend_avail(\\n 'postgres', 'kickstand_cifail', self.PASSWD, self.DATABASE):\\n self.fail(\\\"Shouldn't have connected\\\")\",\n \"def test_create_experiment_bad_storage(self):\\n name = \\\"oopsie_bad_storage\\\"\\n # Make sure there is no existing storage singleton\\n\\n with pytest.raises(NotImplementedError) as exc:\\n create_experiment(\\n name=name,\\n storage={\\\"type\\\": \\\"legacy\\\", \\\"database\\\": {\\\"type\\\": \\\"idontexist\\\"}},\\n )\\n\\n assert \\\"Could not find implementation of Database, type = 'idontexist'\\\" in str(\\n exc.value\\n )\",\n \"def test_configs_engine():\\n app = AppBuilder(CONFIG1).build_app()\\n assert isinstance(app.engine, CustomEngine)\\n assert app.engine.Formatter is MockFormat\\n assert app.engine.extract_timeout == 20\\n assert app.engine.extract_tmp_dir == \\\"/tmp\\\"\",\n \"def test_bad_insert(db):\\n with pytest.raises(error.InvalidSerialization):\\n _ = db.insert_current('config', db, store_permanently=False)\\n\\n with pytest.raises(error.InvalidSerialization):\\n _ = db.insert('config', db)\",\n \"def test_raise_when_no_id(self):\\n model = self.Test({}, False)\\n self.assertRaises(errors.PersistenceError, model.save)\",\n \"def test_create_delete_db(server):\\n\\n db = server.create_db(test_db_name)\\n\\n with pytest.raises(excepts.DBExists):\\n db = server.create_db(test_db_name)\\n\\n server.delete_db(db)\\n\\n with pytest.raises(excepts.DBNotExists):\\n server.delete_db(db)\\n\\n db = server.create_db(test_db_name)\\n db.delete()\\n\\n with pytest.raises(excepts.DBNotExists):\\n db.delete()\",\n \"def test_create_omop():\\n runner = CliRunner()\\n result = runner.invoke(cli.create_omop, [], env=env)\\n\\n assert result.exit_code == 0\\n\\n config = config_dict.get('testing')\\n\\n engine = create_engine(config.SQLALCHEMY_DATABASE_URI)\\n inspector = inspect(engine)\\n\\n assert len(inspector.get_table_names()) == 37\",\n \"def test_virtual_service_create_command_when_invalid_arguments_provided(\\n err_msg, args, err_type, mock_client\\n):\\n with pytest.raises(err_type) as err:\\n virtual_service_create_command(mock_client, args)\\n assert str(err.value) == err_msg\",\n \"def test_init_unknown_error(self):\\n with self.assertRaises(QiitaDBUnknownIDError):\\n SampleTemplate(2)\",\n \"def test_invalid_model(self):\\n self.assertRaises(ModelNotFoundError, lambda: ModelContainer('web', 'model').model_cls)\",\n \"async def test_invalid_insert_user_typeError(database):\\n await database.setup_database(reset=True)\\n\\n for user_id in zip([\\n random.choice([\\\"string\\\",[1,2],set(),dict()])\\n for i in range(0,10)]):\\n try:\\n await database.insert_user(user_id=user_id)\\n assert False\\n except:\\n assert True\\n await database.close_pool()\",\n \"def test_db_connection_bad_host():\\n\\n with pytest.raises(AddressError, match=r\\\"Cannot resolve address .*\\\"):\\n\\n from cwf2neo.neo4j import Neo4j\\n\\n db = Neo4j(host=\\\"invalidneo4jhostname\\\")\\n\\n db.graph.database.name\",\n \"def get_engine(db_params: Dict[str, str]) -> sa.engine:\\r\\n db_uri = get_uri(db_params)\\r\\n return sa.create_engine(db_uri)\",\n \"def test_poolargs(self):\\n\\n e = create_engine(\\n \\\"postgresql+psycopg2://\\\",\\n creator=None,\\n pool_recycle=50,\\n echo_pool=None,\\n module=mock_dbapi,\\n _initialize=False,\\n )\\n assert e.pool._recycle == 50\\n\\n # these args work for QueuePool\\n\\n e = create_engine(\\n \\\"postgresql+psycopg2://\\\",\\n max_overflow=8,\\n pool_timeout=60,\\n poolclass=tsa.pool.QueuePool,\\n module=mock_dbapi,\\n _initialize=False,\\n )\\n\\n # but not SingletonThreadPool\\n\\n assert_raises(\\n TypeError,\\n create_engine,\\n \\\"sqlite://\\\",\\n max_overflow=8,\\n pool_timeout=60,\\n poolclass=tsa.pool.SingletonThreadPool,\\n module=mock_sqlite_dbapi,\\n _initialize=False,\\n )\",\n \"def create_engine(self):\\n return create_engine('sqlite:///' + self.database_name, echo=True)\",\n \"def test_create_with_only_name(self):\\n with OrionState() as cfg:\\n name = \\\"bm00001\\\"\\n with pytest.raises(NoConfigurationError) as exc:\\n get_or_create_benchmark(cfg.storage, name).close()\\n\\n assert f\\\"Benchmark {name} does not exist in DB\\\" in str(exc.value)\",\n \"def setup_engine():\\n print(\\\"Setting up engine\\\")\\n engine = create_engine('mysql+pymysql://{}:{}@{}/govhack2015'.format(\\n username, password, ip_address))\\n\\n return engine\",\n \"def test_NonrealtimeProvider_add_bus_error(session):\\n provider = Provider.from_context(session)\\n with pytest.raises(ValueError):\\n provider.add_bus()\\n with provider.at(0):\\n with pytest.raises(ValueError):\\n provider.add_bus(calculation_rate=\\\"scalar\\\")\",\n \"def test_create_use_with_invalid_email(self):\\n with self.assertRaises(ValueError):\\n get_user_model().objects.create_user(None, password='open@123')\",\n \"def get_database_engine() -> Engine:\\n return engine\",\n \"def _initTestingDB(): \\n from sqlalchemy import create_engine\\n engine = create_engine('sqlite://')\\n from .models import (\\n Base,\\n TodoUser,\\n )\\n DBSession.configure(bind=engine)\\n Base.metadata.create_all(engine)\\n \\n return DBSession\",\n \"def test_db(self, env: yaenv.Env):\\n _db = {\\n 'ENGINE': yaenv.db.SCHEMES['sqlite'],\\n 'NAME': 'db.sqlite3',\\n 'USER': '',\\n 'PASSWORD': '',\\n 'HOST': '',\\n 'PORT': '',\\n }\\n assert env.db('DB_URL') == _db\\n _db = {\\n 'ENGINE': yaenv.db.SCHEMES['sqlite'],\\n 'NAME': ':memory:',\\n }\\n assert env.db('DB_URL_DEFAULT', 'sqlite://:memory:') == _db\\n with pytest.raises(yaenv.EnvError) as err:\\n _ = env.db('INVALID_URL', 'invalid')\\n assert 'Invalid database' in str(err.value)\\n assert env.db('MISSING') is None\",\n \"def test_createInvalidPortDescription(self):\\n store = Store()\\n factory = DummyFactory(store=store)\\n self.assertFailStatus(\\n 1, self._makeConfig(store),\\n [\\\"create\\\", \\\"--strport\\\", \\\"xyz\\\",\\n \\\"--factory-identifier\\\", str(factory.storeID)])\\n self.assertEqual(\\n \\\"'xyz' is not a valid port description.\\\\n\\\", sys.stdout.getvalue())\",\n \"def test_creation_fail(self):\\n\\n # Assert that a RelaxError occurs when the pipe type is invalid.\\n self.assertRaises(RelaxError, pipes.create, 'new', 'x')\",\n \"async def test_create_bike_bad_value(database, key_name):\\n with pytest.raises(ValueError):\\n await register_bike(public_key=key_name, master_key=key_name)\",\n \"def test_check_database_exists_invalid(self):\\n query_result = None\\n self.client.athena_client = MockAthenaClient(results=query_result)\\n\\n assert_false(self.client.check_database_exists())\",\n \"def test_invalid_conn(self):\\n op = BranchSQLOperator(\\n task_id=\\\"make_choice\\\",\\n conn_id=\\\"invalid_connection\\\",\\n sql=\\\"SELECT count(1) FROM INFORMATION_SCHEMA.TABLES\\\",\\n follow_task_ids_if_true=\\\"branch_1\\\",\\n follow_task_ids_if_false=\\\"branch_2\\\",\\n dag=self.dag,\\n )\\n\\n with pytest.raises(AirflowException):\\n op.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)\",\n \"def test_insert_invalid_type(self):\\n album = Album(artist='Artist', album='Album', album_type='xyzzy',\\n totalseconds=120, totaltracks=2)\\n with self.assertRaises(Exception):\\n pk = album.insert(self.app.db, self.app.curs)\\n self.assertEqual(self.get_album_count(), 0)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68235004","0.66725576","0.6549929","0.6455361","0.62477577","0.6210242","0.61840785","0.61660314","0.6095915","0.60754657","0.60633755","0.6048799","0.59452164","0.5927696","0.58780146","0.5844836","0.5800548","0.57547444","0.5749329","0.57365346","0.57352024","0.57311654","0.5728175","0.57157654","0.57034534","0.56767136","0.56671834","0.5659559","0.5656071","0.5651457","0.56480086","0.563365","0.5627736","0.56238604","0.56102717","0.5599789","0.5591554","0.5581994","0.557833","0.55722445","0.55589193","0.55519897","0.554984","0.5539993","0.55393237","0.55376536","0.55179185","0.55167454","0.5500506","0.549171","0.54877555","0.548758","0.5484184","0.54824036","0.54788446","0.5469471","0.5468815","0.5458534","0.5446351","0.54375464","0.54365206","0.54302925","0.54218036","0.5414562","0.5409309","0.53866774","0.53817976","0.53783333","0.537142","0.53698236","0.53694916","0.53688043","0.53673583","0.53647846","0.53494114","0.5347375","0.5345755","0.5345748","0.53453887","0.5345197","0.5342654","0.5340497","0.5338699","0.5337485","0.53274864","0.53232396","0.53222","0.53118587","0.53092957","0.5307359","0.53027236","0.5301173","0.5293897","0.52841717","0.52838343","0.5279921","0.5271631","0.5266872","0.52644986","0.5263114"],"string":"[\n \"0.68235004\",\n \"0.66725576\",\n \"0.6549929\",\n \"0.6455361\",\n \"0.62477577\",\n \"0.6210242\",\n \"0.61840785\",\n \"0.61660314\",\n \"0.6095915\",\n \"0.60754657\",\n \"0.60633755\",\n \"0.6048799\",\n \"0.59452164\",\n \"0.5927696\",\n \"0.58780146\",\n \"0.5844836\",\n \"0.5800548\",\n \"0.57547444\",\n \"0.5749329\",\n \"0.57365346\",\n \"0.57352024\",\n \"0.57311654\",\n \"0.5728175\",\n \"0.57157654\",\n \"0.57034534\",\n \"0.56767136\",\n \"0.56671834\",\n \"0.5659559\",\n \"0.5656071\",\n \"0.5651457\",\n \"0.56480086\",\n \"0.563365\",\n \"0.5627736\",\n \"0.56238604\",\n \"0.56102717\",\n \"0.5599789\",\n \"0.5591554\",\n \"0.5581994\",\n \"0.557833\",\n \"0.55722445\",\n \"0.55589193\",\n \"0.55519897\",\n \"0.554984\",\n \"0.5539993\",\n \"0.55393237\",\n \"0.55376536\",\n \"0.55179185\",\n \"0.55167454\",\n \"0.5500506\",\n \"0.549171\",\n \"0.54877555\",\n \"0.548758\",\n \"0.5484184\",\n \"0.54824036\",\n \"0.54788446\",\n \"0.5469471\",\n \"0.5468815\",\n \"0.5458534\",\n \"0.5446351\",\n \"0.54375464\",\n \"0.54365206\",\n \"0.54302925\",\n \"0.54218036\",\n \"0.5414562\",\n \"0.5409309\",\n \"0.53866774\",\n \"0.53817976\",\n \"0.53783333\",\n \"0.537142\",\n \"0.53698236\",\n \"0.53694916\",\n \"0.53688043\",\n \"0.53673583\",\n \"0.53647846\",\n \"0.53494114\",\n \"0.5347375\",\n \"0.5345755\",\n \"0.5345748\",\n \"0.53453887\",\n \"0.5345197\",\n \"0.5342654\",\n \"0.5340497\",\n \"0.5338699\",\n \"0.5337485\",\n \"0.53274864\",\n \"0.53232396\",\n \"0.53222\",\n \"0.53118587\",\n \"0.53092957\",\n \"0.5307359\",\n \"0.53027236\",\n \"0.5301173\",\n \"0.5293897\",\n \"0.52841717\",\n \"0.52838343\",\n \"0.5279921\",\n \"0.5271631\",\n \"0.5266872\",\n \"0.52644986\",\n \"0.5263114\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.83949554"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13792,"cells":{"query":{"kind":"string","value":"init the class object and simply pass either twitter object or tweets plain text to this method. The analysis will return a list indicating the polarity and subjectivity of the tweets."},"document":{"kind":"string","value":"def analyse(self, tweet):\n\n if (type(tweet) == dict):\n text = self.clean_tweet(self.to_text(tweet))\n else:\n text = self.clean_tweet(tweet)\n\n analysis = TextBlob(text)\n polarity = analysis.polarity\n subjectivity = analysis.subjectivity\n\n res = []\n\n # if polarity > 0.3:\n # res.append(\"positive\")\n # elif polarity < -0.3:\n # res.append(\"negative\")\n # else:\n # res.append(\"neutral\")\n #\n # if subjectivity > 0.6:\n # res.append(\"subject\")\n # elif subjectivity < 0.3:\n # res.append(\"objective\")\n # else:\n # res.append(\"neutral\")\n\n res.append(polarity)\n res.append(subjectivity)\n\n return res"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self, tweet_json):\r\n self.tweet = tweet_json\r\n self.date = datetime.datetime.strptime(self.tweet[\"date\"], \"%Y-%m-%dT%H:%M:%S.000Z\")\r\n self.processed = False\r\n self.max_importance = 0\r\n try:\r\n text = re.sub(self.tweet[\"keywords\"][0], '', self.tweet[\"text\"])\r\n except IndexError:\r\n text = self.tweet[\"text\"]\r\n try:\r\n self.language = polyglot.detect.Detector(re.sub('#', '', text)).language.name\r\n except polyglot.detect.base.UnknownLanguage as e:\r\n self.language = \"mixed\"\r\n except:\r\n self.language = polyglot.detect.Detector(''.join([i if ord(i) < 128 else ' ' for i in text])).language.name","def tweet_sentiment_analysis(self, tweet):\n analysis = TextBlob(self.clean_tweet(tweet))\n\n if analysis.sentiment.polarity > 0:\n return ['Positive', analysis.sentiment.polarity, analysis.sentiment.subjectivity]\n elif analysis.sentiment.polarity == 0:\n return ['Neutral', analysis.sentiment.polarity, analysis.sentiment.subjectivity]\n else:\n return ['Negative', analysis.sentiment.polarity, analysis.sentiment.subjectivity]","def __init__(self):\r\n self.tweets = []\r\n self.lcs = \"outliers\"\r\n self.importance = 0","def analyze(self, text):\n\n # start from 0 for each Analyser variable\n self.positives = 0\n self.negatives = 0\n\n # precise self text value\n self.text = text\n\n # declare a tokenased word\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text)\n\n # indicate the length of list tokens\n size = len(tokens)\n\n # all the word stuff to ckeck\n for word in tokens:\n\n # chaque mots est converti en mot sans majuscule\n word = str.lower(word)\n\n linespos = [line.rstrip('\\n') for line in open('positive-words.txt')]\n linesneg = [line.rstrip('\\n') for line in open('negative-words.txt')]\n\n # check for positive or negative or neutral words\n if word in linespos:\n self.positives += 1\n elif word in linesneg:\n self.negatives += 1\n else:\n continue\n\n # score calculculated and reurned\n score = self.positives - self.negatives\n\n return score","def analyse_tweet(self, tweet):\r\n sentiment = 0\r\n subjects = []\r\n\r\n is_comparison = False # sentiment will be the LHS of the comparison\r\n seen_not = False\r\n for word in myparser.parse(tweet,self.company_names,True):\r\n if word == \"not\" or word == \"don't\":\r\n seen_not = True\r\n elif word in self.positive_words:\r\n sentiment = sentiment + 1\r\n elif word in self.negative_words:\r\n sentiment = sentiment - 1\r\n if word in self.company_names:\r\n subjects += [word]\r\n for (p, c) in self.product_names:\r\n if word == p:\r\n subjects += [c]\r\n for (c,s) in self.comparisons:\r\n if word == c:\r\n sentiment = s\r\n is_comparison = True\r\n if seen_not:\r\n sentiment = -sentiment\r\n\r\n #print((tweet, subjects, sentiment, is_comparison))\r\n\r\n if is_comparison:\r\n subjects += [None, None]\r\n return[(subjects[0], sentiment), (subjects[1], -sentiment)]\r\n else:\r\n return [(sub, sentiment) for sub in subjects]","def do_sentiment_analysis(self):\n\n tweets_sentiment = []\n\n for tweet in self.tweets:\n parsed_tweet = {}\n parsed_tweet['text'] = tweet\n sentiment_data = self.tweet_sentiment_analysis(tweet)\n parsed_tweet['sentiment'] = sentiment_data[0]\n parsed_tweet['polarity'] = sentiment_data[1]\n parsed_tweet['subjectivity'] = sentiment_data[2]\n\n tweets_sentiment.append(parsed_tweet)\n\n self.sentiment_data = tweets_sentiment\n self.positive_tweets = [tweet for tweet in self.sentiment_data if tweet['sentiment'] == 'Positive']\n self.negative_tweets = [tweet for tweet in self.sentiment_data if tweet['sentiment'] == 'Negative']\n self.neutral_tweets = [tweet for tweet in self.sentiment_data if tweet['sentiment'] == 'Neutral']\n\n return tweets_sentiment","def analyze(self, text): #takes the text to be analyzed for sentiment\n #initialize inicial score to 0\n score = 0\n #Create tokenizer instance\n tokenizer = nltk.tokenize.TweetTokenizer()\n #create list of words in a tweets\n tokens = tokenizer.tokenize(text)\n \n #iterate over tokens(list of words)\n for word in tokens:\n #check if word is positive or negative\n if word.lower() in self.positives_words:\n score+=1\n if word.lower() in self.negatives_words:\n score-=1\n #neutral if its neither, doesnt add anything, 0\n return score","def analyze(self, text):\n tknzr = nltk.tokenize.casual.TweetTokenizer(preserve_case=True, reduce_len=False, strip_handles=False)\n tknTxt = tknzr.tokenize(text)\n sentiment = 0\n \n for i in range(len(tknTxt)):\n if tknTxt[i] in self.posTxt:\n #print(\"POS\")\n #print(tknTxt[i])\n sentiment += 1\n elif tknTxt[i] in self.negTxt:\n #print(\"NEG\")\n #print(tknTxt[i])\n sentiment -= 1\n \n return sentiment","def classify(self, tweets):\n classified = []\n for t in tweets:\n #use the SVM to predict the polarity\n t.polarity = self.m_learner.predict_from_tweet(t)\n #append the tweet to the list\n classified.append(t)\n\n return classified","def process_sentiment(self):\r\n\r\n\r\n print(\"Beginning sentiment analysis\")\r\n # textblob time\r\n #tweet_sentiment = [TextBlob(tweet['filtered_text']).sentiment for index, tweet in self.tweet_dataframe.iterrows()]\r\n #self.tweet_dataframe['polarity'] = [i.polarity for i in tweet_sentiment]\r\n #self.tweet_dataframe['subjectivity'] = [i.subjectivity for i in tweet_sentiment]\r\n\r\n #vader time\r\n #http://t-redactyl.io/blog/2017/04/applying-sentiment-analysis-with-vader-and-the-twitter-api.html\r\n sentiment = []\r\n\r\n analyzer = SentimentIntensityAnalyzer()\r\n\r\n for tweet in self.tweet_dataframe['filtered_text']:\r\n vs = analyzer.polarity_scores(tweet)\r\n sentiment.append(vs['compound'])\r\n\r\n self.tweet_dataframe['vader_polarity'] = pd.Series(sentiment)","def __init__(self, tweet_data):\n _hashtags = tweet_data['entities']['hashtags']\n _str_date = tweet_data['created_at']\n self.account = Account(tweet_data['user'])\n self.date = self.format_date(_str_date)\n self.hashtags = [\"#%s\" % (tag['text']) for tag in _hashtags]\n self.likes = tweet_data['favorite_count']\n # Note: replies number is only available with\n # the Premium and Enterprise tier products.\n # https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object # noqa\n self.replies = 0\n self.retweets = tweet_data['retweet_count']\n self.text = tweet_data['text']","def __init__(self, twitter_consumer_key, twitter_consumer_secret,\n twitter_access_key, twitter_access_secret,\n search_terms, search_on='news',\n bitly_access_token='',\n news_api_key=''):\n\n # Access Keys and Secrets for Twitter API obtained at: https://developer.twitter.com/\n auth = tweepy.OAuthHandler(twitter_consumer_key, twitter_consumer_secret)\n auth.set_access_token(twitter_access_key, twitter_access_secret)\n\n # Store API object for access to Twitter REST API\n self.__api = tweepy.API(auth)\n\n # Term(s) to search news feeds or Twitter on\n self.search_terms = search_terms\n\n # Method TwitterBot will use to search on. Current options are 'news' or 'twitter'\n self.search_on = search_on\n\n # Access token for optional Bitly API: https://dev.bitly.com/\n self.__bitly_access_token = bitly_access_token\n\n # Access token for optional News API: https://newsapi.org/\n self.__news_api_key = news_api_key\n\n # Will store list of items scraped from news or Twitter\n self.list = []","def analyse_tweets(nb_tweets, classifier, Resource, threshold, language='en'):\n return [(bytes(line, 'utf-8'), _minimal_analysis(bytes(line, 'utf-8'), classifier, Resource, threshold, language))\n for line in\n collect_tweet(nb_tweets)]","def get_sentiment_analysis(sender, instance, **kwargs):\n text_analysis = TextAnalysis(instance.text)\n\n # Prevent sentiment_analysis API call every time the document is saved\n if instance.sentiment_analysis is None:\n instance.get_sentiment_analysis()","def analyze(self, text):\n\n tokenizer = nltk.tokenize.TweetTokenizer()\n \n tokens = tokenizer.tokenize(text)\n \n sentiment = 0\n \n for word in tokens:\n if word in self.__positives:\n sentiment += 1\n elif word in self.__negatives:\n sentiment -= 1\n \n return sentiment","def __init__(self, influencers, credentials, similarity_parameter, popularity_parameter, epsilon):\n \n # Twitter API credentials initialization\n auth = tweepy.OAuthHandler(credentials['consumer_key'], credentials['consumer_secret'])\n auth.set_access_token(credentials['access_token'], credentials['access_token_secret'])\n self.api = tweepy.API(auth)\n\n # Class fields\n self.username = credentials['username']\n self.influencers = influencers\n self.complete_model = None\n self.influencer_models = None\n self.userTweetsStat = {}\n self.similarities = {}\n self.similarity_parameter = similarity_parameter\n self.popularity_parameter = popularity_parameter\n self.epsilon = epsilon\n self.valueState = {influencer: 0 for influencer in self.influencers.allInfluencers}\n self.reward = 1\n self.rewardParam = 0.1\n self.alpha = 0.1\n self.gamma = 1\n self.curDif = 0","def __init__(self):\n self.emotions_list = EmotionsList('NRC-Emotion-Intensity-Lexicon-v1.txt')\n self.tweets_list = None\n self.nickname = None","def learn(self):\n #get the training tweets and insert them into a list\n self.training_tweets = []\n print self.datas\n for t in self.datas.get_positive_tweets():\n t.polarity = 10\n self.training_tweets.append(t)\n for t in self.datas.get_negative_tweets():\n t.polarity = -10\n self.training_tweets.append(t)\n for t in self.datas.get_neutral_tweets():\n t.polarity = 0\n self.training_tweets.append(t)\n self.m_learner.learn_from_tweets(self.training_tweets)","def __init__(self):\r\n\t\t\r\n\t\tself.redis = redis.Redis()\r\n\t\tself.info_to_get = ['text', 'created_at', 'user']\r\n\t\tself.search_results = {}\r\n\t\tself.raw_data_directory_name = \"raw_mining_data\"\r\n\t\tself.filtered_data_directory_name = \"filtered_mining_data\"\r\n\t\tenglish_file = pjoin( sys.path[0], \"sentiment_word_files\", \"Nielsen2010Responsible_english.csv\")\r\n\t\tself.analyzeEnglish = dict(map(lambda (w,e): (w, int(e)), \\\r\n\t\t\t\t\t\t\t\t\t[ line.strip().lower().split('\\t') for line in open(english_file) ]))\r\n\t\tself.tweets_count = 0","def __init__(self):\r\n # keys and tokens from the Twitter Dev Console\r\n consumer_key = 'e1I0CSqgSOGxhH940cey1PR50'\r\n consumer_secret = 'APZE7kT2MgJsledQszLbNVcZZEhCUDX3NKAseXTjnsEcggUAkf'\r\n access_token = '876294238144786432-Q9PfwxPd4T7OdYO9hXiFyVDO38Q8jZV'\r\n access_token_secret = 'e0RhKgnLLyHnEOrWS92Tw0pKv5hWrN3chjp4Azm4NayOG'\r\n\r\n # clean tween regular expression\r\n self.pattern = re.compile('(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+://\\S+)')\r\n\r\n # attempt authentication\r\n try:\r\n # create OAuthHandler object\r\n self.auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\r\n # set access token and secret\r\n self.auth.set_access_token(access_token, access_token_secret)\r\n # create tweepy API object to fetch tweets\r\n self.api = tweepy.API(self.auth)\r\n logging.info(self.api.rate_limit_status()['resources']['search'])\r\n except:\r\n logging.error(\"Error: Authentication Failed\")","def __init__(self, text):\n # BEGIN Question 2\n self.text = text\n self.word_set = []\n # END Question 2","def sentimental_analysis_component():\n sentence = st.text_area(\"Enter Text to Analyze:\")\n if st.button(\"Submit\"):\n result = sentiment_analyzer_scores(sentence)\n st.success(result)\n\n #if st.checkbox('Lookup Twitter Status', True):\n id_input = st.text_area(\"Enter Tweet ID to Analyze:\")\n st.markdown(' e.g. 1333434829438906376 or 1257038775785422848')\n\n # Modules for twitter API\n import tweepy \n import os\n \n # API Keys\n consumer_key = os.environ.get('TWITTER_CONSUMER_KEY')\n consumer_secret = os.environ.get('TWITTER_CONSUMER_SECRET')\n access_token = os.environ.get('TWITTER_ACCESS_TOKEN')\n access_token_secret = os.environ.get('TWITTER_ACCESS_TOKEN_SECRET')\n \n # Auth type and API options\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_token_secret)\n api = tweepy.API(auth,wait_on_rate_limit=True)\n\n # Tweet ID to fetch\n id_ = [id_input]\n \n # API Call \n statuses = api.statuses_lookup(id_, tweet_mode=\"extended\")\n \n # API Response to variables\n for status in statuses:\n tweet_text = status.full_text\n tweet_user = status.user.screen_name\n covid_check = covid_mention(tweet_text.lower())\n\n if st.button(\"Analyze Tweet\"):\n lookup_result = sentiment_analyzer_scores(tweet_text)\n st.markdown('## Tweet Sentiment Results')\n st.success(lookup_result)\n st.markdown(f'## Full Text:')\n st.success(f'{tweet_text}')\n\n st.markdown(f\"\"\"## Tweet Stats:\n Tweet ID:{id_}\n User: {status.user.screen_name}\n Created at: {status.created_at}\n Source: {status.source}\n Engagement:\n Retweets: {status.retweet_count}\n Favourited: {status.favorite_count}\n Pandemic Related: {covid_check}\"\"\")","def feat_eng(self, tweets):\n self.tweets['emojis'] = get_emojis(self.tweets['text']) # get emojis as text\n self.tweets['polarity'] = self.tweets['text'].map(\n lambda x: TextBlob(x).sentiment.polarity)\n self.tweets['word_count'] = self.tweets['text'].map(lambda x: len(str(x).split()))","def __init__(self, corpus):\n if isinstance(corpus, str):\n # Convert directory to Plaintext Corpus.\n corpus = PlaintextCorpusReader(corpus, r\".*\\.txt\")\n self.corpus = corpus\n self._bigrams = FreqDist()\n self._count()","def analyze(self, tweet):\n \n # keeping track of the score\n score = 0\n \n # filtering though tweets exstracting the useful words\n # preserve_case = false maks them lowercase\n tokenizer = nltk.tokenize.TweetTokenizer(preserve_case = False)\n tokens = tokenizer.tokenize(tweet)\n \n # checking word for word the intension and keeping score\n for word in tokens:\n if word in self.dic:\n if self.dic[word] == 1:\n score += 1\n else:\n score -= 1\n# score += self.dic[word]\n return score","def analyze(self, text):\n\n # TODO\n # tokens = tokenizer.tokenize(tweet)\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text)\n score = 0\n\n for word in tokens:\n # iterate over tokens#str.lower\n\n if word.lower() in self.positives:\n score = score+1\n\n elif word.lower() in self.negatives:\n score = score-1\n\n else:\n continue\n return score","def __init__(self, text=None, bare=False, stem='gap', pos=False, roman = False, stopwords=False, punct=False, conjunction=False, article=False, demonstrative=False, preposition=False, question=False, pronoun=False, quantifier=False, date=False, number=False, ssn=False, telephone=False, name=False, address=False, sentiment=False, gender=False, age = False, dob=False, unit=False, standard=False, metric=False, spell=None ):\n self._text = text # raw text\n self._words = None # list of words\n self._punct = punct # keep/remove punctuation\n self._stemming = stem # on/off stemming\n self._pos = pos # on/off parts of speech\n self._roman = roman # on/off romanization \n self._porter = stopwords # keep/remove stopwords\n self._bare = bare # on/off bare tokenizing\n self._standard = standard # convert metric to standard units\n self._metric = metric # convert standard to metric units\n self._spell = None # spell checking\n self._bow = None # bag of words\n self._freq = None # word count frequency\n self._tf = None # term frequency\n \n # More than just bare tokenizing\n if self._bare == False:\n self._spell = spell # do (not) spell checking\n \n # Keep Stopwords\n if stopwords is True:\n self._quantifier = True # keep words indicating a size\n self._preposition = True # keep prepositions\n self._article = True # keep articles\n self._conjunction = True # keep conjunctions\n self._demonstrative = True # keep demonstratives\n self._question = True # keep question words\n self._pronoun = True # keep pronouns \n self._sentiment = True # keep sentiment words\n self._number = True # keep numbers \n self._date = True # keep date\n self._ssn = True # keep social security number\n self._telephone = True # keep telephone numbers\n self._address = True # keep street addresses\n self._name = True # keep proper names\n self._gender = True # keep gender words\n self._age = True # keep age\n self._dob = True # keep date of birth words\n self._unit = True # keep unit of measurement\n # Remove Stopwords\n else:\n self._quantifier = quantifier # keep/remove words indicating a size\n self._preposition = preposition # keep/remove prepositions\n self._article = article # keep/remove articles\n self._conjunction = conjunction # keep/remove conjunctions\n self._demonstrative = demonstrative # keep/remove demonstratives\n self._question = question # keep/remove question words\n self._pronoun = pronoun # keep/remove pronouns\n self._sentiment = sentiment # keep/remove sentiment words\n self._number = number # keep/remove numbers\n self._date = date # keep/remove date\n self._ssn = ssn # keep/remove social security number\n self._telephone = telephone # keep/remove telephone numbers\n self._address = address # keep/remove street addresses\n self._name = name # keep/remove proper names\n self._gender = gender # keep/remove gender words\n self._age = age # keep/remove age\n self._dob = dob # keep/remove date of birth words\n self._unit = unit # keep/remove unit of measurement words\n \n if isinstance(stopwords, bool) is False:\n raise TypeError(\"Stopwords must be a boolean\")\n if isinstance(bare, bool) is False:\n raise TypeError(\"Bare must be a boolean\")\n if isinstance(quantifier, bool) is False:\n raise TypeError(\"Quantifier must be a boolean\")\n if isinstance(preposition, bool) is False:\n raise TypeError(\"Preposition must be a boolean\")\n if isinstance(conjunction, bool) is False:\n raise TypeError(\"Conjunction must be a boolean\")\n if isinstance(article, bool) is False:\n raise TypeError(\"Article must be a boolean\")\n if isinstance(demonstrative, bool) is False:\n raise TypeError(\"Demonstrative must be a boolean\")\n if isinstance(question, bool) is False:\n raise TypeError(\"Question must be a boolean\")\n if isinstance(pronoun, bool) is False:\n raise TypeError(\"Pronoun must be a boolean\")\n if isinstance(number, bool) is False:\n raise TypeError(\"Number must be a boolean\")\n if isinstance(date, bool) is False:\n raise TypeError(\"Date must be a boolean\")\n if isinstance(ssn, bool) is False:\n raise TypeError(\"SSN must be a boolean\")\n if isinstance(telephone, bool) is False:\n raise TypeError(\"Telephone must be a boolean\")\n if isinstance(name, bool) is False:\n raise TypeError(\"Name must be a boolean\")\n if isinstance(address, bool) is False:\n raise TypeError(\"Address must be a boolean\")\n if isinstance(sentiment, bool) is False:\n raise TypeError(\"Sentiment must be a boolean\")\n if isinstance(gender, bool) is False:\n raise TypeError(\"Gender must be a boolean\")\n if isinstance(dob, bool) is False:\n raise TypeError(\"Gender must be a boolean\")\n if isinstance(age, bool) is False:\n raise TypeError(\"Age must be a boolean\")\n if isinstance(punct, bool) is False:\n raise TypeError(\"Punct must be a boolean\")\n if isinstance(unit, bool) is False:\n raise TypeError(\"Unit must be a boolean\")\n if isinstance(standard, bool) is False:\n raise TypeError(\"Standard must be a boolean\")\n if isinstance(metric, bool) is False:\n raise TypeError(\"Metric must be a boolean\")\n if text is not None:\n if isinstance(text, str) is False:\n raise TypeError(\"String expected for text\")\n if spell is not None:\n if spell not in ['en', 'fr', 'es', 'it', 'de']:\n raise ValueError(\"Wrong value for spell: en, es, fr, it or de\")\n \n if text is not None:\n self._split()\n if self._bare == False:\n # preprocess the tokens\n self._preprocess()\n # word stemming\n if self._stemming == 'gap':\n self._stem()\n elif self._stemming == 'porter':\n self._nltkStemmer('porter')\n elif self._stemming == 'snowball':\n self._nltkStemmer('snowball')\n elif self._stemming == 'lancaster':\n self._nltkStemmer('lancaster')\n elif self._stemming == 'lemma':\n self._lemma()\n # remove stop words\n self._stopwords()\n # Do unit conversions\n self._conversion()\n # Do POS tagging\n if self._pos == True:\n self._partsofspeech()","def analyze(self, text):\n #analize every word in the text a value -1, 1 or 0 and calculate total score\n #tokens allow us to split words in single tokens we can initialize tokens like this:\n\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text.lower())\n\n score = 0\n\n if tokens[0] in self.negatives:\n score =- 1\n elif tokens[0] in self.positives:\n score =+ 1\n else:\n score = 0\n\n #print('', text)\n\n return score","def process(self, tweet):\n\n #identify the applicable event keywords for this text\n text = self.cleanup_data(tweet.text)\n tokens = [str(t.lower()).translate(None, string.punctuation) for t in tweet.text.split()]\n applicable_tokens = []\n for phrase in self.match_event_tree.root.keywords:\n if phrase in \" \".join(tokens):\n applicable_tokens.append(phrase)\n\n self.match_event_tree.propogate_tweet(applicable_tokens, tweet)","def __init__(self, data: List):\n self.data: List = data\n self.texts: List = self.list_texts()\n self.post_nlp: List = False","def analyze(self, text):\n score =0\n token = TweetTokenizer()\n tokens = token.tokenize(text)\n for token in tokens:\n if token.lower() in self.pos_list:\n score+=1\n elif token.lower() in self.neg_list:\n score-=1\n\n return score","def build(self, Tweets):\n text = [t.text for t in Tweets]\n vectorizer = TfidfVectorizer(tokenizer=process_text,\n stop_words=stopwords.words('english'),\n max_df=0.5,\n min_df=0.1,\n lowercase=True,\n max_features=10000)\n\n return vectorizer.fit_transform(text).A","def __init__(self):\n\t\tself.relevances = None","def analyze(self, text):\n\n tknzr = nltk.tokenize.TweetTokenizer()\n words = tknzr.tokenize(text)\n \n score = 0\n \n for word in words:\n if word.lower() in self.positives:\n score += 1\n elif word.lower() in self.negatives:\n score -= 1\n else:\n continue\n \n return score","def __init__(self, corpus, target=None, **kwargs):\n self.corpus = corpus\n # with open('/home/igor/Development/atap/data/dummy.pkl', 'rb') as f:\n # target = pickle.load(f).to_dict()\n self.target = target\n self.morph = pymorphy2.MorphAnalyzer()\n self.stopwords = nltk.corpus.stopwords.words('russian')\n self.stopwords.extend(['анализ', 'год', 'плата', 'адрес', 'поступить', 'выписать' 'что', 'это', 'так', 'вот', 'быть', 'как', 'в', '—', '–', 'к', 'на', '...'])","def __init__(self, remove_stopwords=True, replace_words=True,\n remove_numbers=True, remove_html_tags=True,\n remove_punctuations=True, lemmatize=False,\n lemmatize_method='wordnet'):\n if (type(remove_stopwords) != bool or\n type(replace_words) != bool or\n type(remove_numbers) != bool or\n type(remove_html_tags) != bool or\n type(remove_punctuations) != bool or\n type(lemmatize) != bool):\n raise Exception(\"Error - expecting a boolean parameter\")\n if lemmatize_method not in ['wordnet', 'snowball']:\n raise Exception(\"Error - lemmatizer method not supported\")\n self.doc = None\n self.tweets = None\n self.lemmatizer = None\n self.remove_stopwords = remove_stopwords\n self.replace_words = replace_words\n self.remove_numbers = remove_numbers\n self.remove_html_tags = remove_html_tags\n self.remove_punctations = remove_punctuations\n self.lemmatize_method = lemmatize_method\n self.lemmatize = lemmatize\n self.stopword_list = set(stopwords)\n self.replacement_list = to_replace\n if self.lemmatize_method == 'wordnet':\n self.lemmatizer = WordNetLemmatizer()\n if self.lemmatize_method == 'snowball':\n self.lemmatizer = SnowballStemmer('english')","def __init__(self):\n super().__init__()\n\n # Will only reply to every 3rd or so tweet, defined in settings\n self.received_tweet_count = 0\n\n # Twitter api init\n self.auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n self.auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n self.twitter_api = tweepy.API(self.auth)\n\n print('Authenticated, creating stream...')\n\n self._init_stream()","def __init__(self, samples, analysis):\r\n self.samples = samples\r\n self.analysis = analysis","def get_tweet_sentiment(self, tweet):\r\n # create TextBlob object of passed tweet text\r\n polarity = TextBlob(self.clean_tweet(tweet)).sentiment.polarity\r\n if polarity > 0:\r\n return 1.0\r\n if polarity < 0:\r\n return -1.0\r\n return 0","def get_tweet_sentiment(self, tweet):\n # create TextBlob object of passed tweet text\n analysis = TextBlob(self.clean_tweet(tweet))\n\n # set sentiment\n if analysis.sentiment.polarity > 0:\n return 'positive'\n elif analysis.sentiment.polarity == 0:\n return 'neutral'\n else:\n return 'negative'","def __init__(self,classes=['normalizeText','tagger','stem','stopWord','spellChecker']):\n self._support = prebotSupport()\n self._classes = classes\n if(\"tagger\" in self._classes):\n self._tagger = tagger()\n if(\"normalizeText\" in self._classes):\n self._normalize = normalizeText()\n if(\"spellChecker\" in self._classes):\n self._spellChecker = spellChecker()\n if(\"stopWord\" in self._classes):\n self._stopWord = stopWord()\n if(\"stem\" in self._classes):\n self._stem = stemming()","def analyze(self, text):\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text)\n score = 0\n\n for token in tokens:\n if token in self.positives_list:\n score += 1\n elif token in self.negatives_list:\n score -= 1\n\n return score","def __init__(self, corpus):\n self.unigramCounts = collections.defaultdict(lambda: 0)\n self.bigramCounts = collections.defaultdict(lambda: 0)\n self.trigramCounts = collections.defaultdict(lambda: 0)\n self.followingWords = collections.defaultdict(lambda: set())\n self.precedingWords = collections.defaultdict(lambda: set())\n self.total = 0\n self.discount = 0.75\n self.train(corpus)","def __init__(self, corpus_name, corpus, data_date=''):\n\n # Test Patterns and sets for use later in our topic model\n # '^[a-z0-9 _-]{2,}$' --> Match letters, numbers, spaces, underscores, dashes. Ignore case. Length of 2 or more.\n self.phrase_pattern = re.compile('^[a-z0-9 _-]{2,}$', re.IGNORECASE)\n self.date_pattern = re.compile('20[0-9]{2}-[0-1][0-9]-[0-3][0-9]$')\n self.punct = {p for p in string.punctuation}\n self.empty_words = {'a', 'an', 'that', 'the', 'this'}\n self.nouns = {ss.NOUN, ss.PROPN}\n self.entities = {ss.PERSON, ss.NORP, ss.FACILITY, ss.ORG, ss.GPE, ss.LOC, ss.PRODUCT, ss.EVENT, ss.WORK_OF_ART,\n ss.LANGUAGE} # spaCy entities that indicate a proper noun.\n\n # Check the user arguments\n # TODO: What data to I expect in the dictionary passed from the \"get_\" class?\n assert 2 < len(corpus_name) < 21 and re.match(self.phrase_pattern, corpus_name), \\\n 'A corpus_name (between 3 and 20 characters; made of letters, numbers, underscores, or dashes) is required.'\n assert data_date == '' or re.match(self.date_pattern, data_date), \\\n 'If you include a data_date, it must match the form 20YY-MM-DD.'\n assert type(corpus) is dict, 'The corpus must be a dictionary.'\n assert len(corpus) > 0, 'The corpus of texts has no data.'\n\n # Topic metadata & settings\n self.corpus_name = corpus_name.replace(' ', '') # (str) The name of the set (or corpus) of texts.\n self.data_date = data_date\n\n # Primary Data Structures\n self.texts = corpus # The dict of dicts that contains all of our texts for analysis: {text_id: {}}\n self.topics = {} # A dict of dicts for primary topics: {topic: {}}\n self.ngrams = {} # A dict of dicts for ngrams that will help us understand primary topics: {ngram_lemma: {}}\n self.model_output = {'name': corpus_name,\n 'dataDate': data_date,\n 'runDate': datetime.now().strftime(\"%Y-%m-%d %H:%M\"),\n 'textCount': len(corpus)} # For results as json\n\n # Get known entities\n try:\n with open(config.INPUT_DIR + 'known_entities.txt', 'r') as file:\n self.known_entities = set(file.read().split(' '))\n except IOError:\n print('Add a text file named \"known_entities.txt\" that lists named entities (space-delimited) that we '\n 'want to be sure are treated as proper nouns.')\n self.known_entities = set()\n\n # Stop Words\n try:\n with open(config.INPUT_DIR + 'stop_words.txt', 'r') as file:\n self.stop_words = set(file.read().split(' '))\n except IOError:\n print('Add a text file named \"stop_words.txt\" that lists common words (space-delimited) that we should '\n 'ignore in most text processing.')\n self.stop_words = set()\n\n # Loop through texts and tokenize: 'doc' and 'titleDoc' are lists of spaCy tokens, with named entities called\n # recognized and joined. 'textClean' is a string of lemmatized words, excluding stopwords and punctuation.\n # It's used by Doc2Vec.\n for text_id, text in self.texts.items():\n text['doc'] = self._tokenize(text['text'])\n text['titleDoc'] = self._tokenize(text['title'])\n text['textClean'] = ' '.join([token.text.lower() if token.lemma_ == '-PRON-' else token.lemma_ for\n token in text['doc'] if token.lemma_ not in\n self.stop_words and token.text not in self.punct])","def set_analyzed_tweets(self, tweets):\n slim_tweets = [SlimTweet(tweet) for tweet in tweets]\n self.analyzed_tweets = sort_tweets(slim_tweets)","def __init__(self, topics=None, tweet_file=None, mode='batch'):\n self.topics = topics\n # (The twitter API will only return a max of 100 count)\n self.GEN_MAX_TWEET = 100 # the max number of tweets to generate\n self.tweet_file = tweet_file\n self.mode = mode\n self.tweets = []\n\n if topics and tweet_file:\n print(\"WARNING! you input both topics and the tweet file, only one is expected\")\n exit(-1)\n\n if not topics and not tweet_file:\n print(\"WARNING! you input either topics or tweet file, one is expected\")\n exit(-1)\n\n # If file argument is given, it will not connect to twitter server\n # It will just save tweets in self.tweets\n if tweet_file:\n with open(tweet_file, 'r') as infile:\n for line in infile:\n self.tweets.append(json.loads(line))\n\n else:\n consumer_key = 'bbqKfXEU2VJNoWlYJvbdtptOE'\n consumer_secret = 'afPk2JuMMMD6IhP5Xijo60ni4FUK39PDzhU7ylgT9FgNZX9ngh'\n access_token = '434708489-DTeHfK4OYKRuIXlfoWnNgzzwpEZTPCEpSMv8C0ll'\n access_token_secret = 'SjWFYfX2k3q4RJKQXcP1LP9ikhRfckPKOEcrb2cpQ0A0n'\n\n # Attempt authentication\n try:\n # create OAuthHandler object\n self.auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n # set access token and secret\n self.auth.set_access_token(access_token, access_token_secret)\n # create tweepy API object to fetch tweets\n self.api = tweepy.API(self.auth)\n except:\n print(\"Error: Authentication Failed\")\n exit(-1)","def analyze(self, text):\n #Check each word in text\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text)\n total_score = 0\n #Sum the total score\n for token in tokens:\n token = token.lower()\n if token in self.positives:\n total_score = total_score + 1\n elif token in self.negatives:\n total_score = total_score - 1\n else:\n total_score = total_score + 0\n \n return total_score","def __init__(self, proc):\n self.sd = SentimentDetector()\n self.sr = SpeechRecognizer()\n self.option = input(\"Welcome to Kuri! Type 'c' to chat or 's' to speak! \").lower()\n self.proc = proc","def __init__(self):\n # keys and tokens from the Twitter Dev Console\n key = provide_keys('males')\n\n consumer_key = key['consumer_key']\n consumer_secret = key['consumer_secret']\n access_token = key['access_token_key']\n access_token_secret = key['access_token_secret']\n\n # attempt authentication\n\n # create OAuthHandler object\n self.auth = OAuthHandler(consumer_key, consumer_secret)\n\n # set access token and secret\n self.auth.set_access_token(access_token, access_token_secret)\n\n try:\n # create tweepy API object to fetch tweets\n self.api = tweepy.API(self.auth)\n\n except:\n print(\"Error: Authentication Failed\")\n sys.exit(-1)","def analyze_tweets(tweets, model, w2v_model):\n # TODO DO EVERYTHING HERE\n #tweets = [(\"StarWars\", tc.query_tweets(\"StarWars\"))]\n \n #tweets = tc.query_tweets('starwars')\n df = pd.DataFrame(columns=['pos', 'neu', 'neg'])\n if not os.path.isdir('results'):\n os.mkdir('results')\n for topic, topic_tweets in tweets:\n tokenized_tweets = tp.process_raw_tweets(topic_tweets)\n df.loc[topic], dummy = classify_tweets(tokenized_tweets, model, w2v_model)\n vis.word_cloud_from_frequencies(tp.count_tokens(tokenized_tweets), f\"results/{topic}_cloud.png\", width=800, height=400,)\n \n vis.bar_plot_from_dataframe(df, 'results/results.png')\n print(\"\\n\")\n print(df)","def getSentiment(tweets, location):\n sentiment = [0, 0, 0]\n for tweet in tweets:\n analyser(tweets[tweet], sentiment,location)\n return sentiment","def __init__(self):\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_token_secret)\n self.api = tweepy.API(auth)","def get_tweet_sentiment(self, tweet):\n\n analyzer = SentimentIntensityAnalyzer()\n vs = analyzer.polarity_scores(tweet)\n # set sentiment\n if vs['compound'] >= 0.05:\n return 'positive'\n elif -0.5 < vs['compound'] < 0.05:\n return 'neutral'\n else:\n return 'negative'","def __sentiment_scan(self, title, text):\n\n return (pattern.en.sentiment(title), pattern.en.sentiment(text))","def __init__(self, corpus):\n self.total = 0\n self.reverseBigramCount = defaultdict(lambda : defaultdict(lambda : 0))\n self.bigramCount = defaultdict(lambda : defaultdict(lambda : 0))\n self.unigramCount = defaultdict(lambda: 0)\n self.train(corpus)","def __init__(self):\n ### load the json to get twitter config\n # check if the file exists\n if os.path.isfile(CONFIG_FILE):\n tmp_json = json.load(open(CONFIG_FILE))\n # test if tweeting is enabled or not....\n if not tmp_json['twitter']['enable']:\n print(\"We don't want to tweet!\")\n return\n consumer_key = tmp_json['twitter']['consumer_key']\n consumer_secret = tmp_json['twitter']['consumer_secret']\n access_token_key = tmp_json['twitter']['access_token']\n access_token_secret = tmp_json['twitter']['access_token_secret']\n else:\n raise Exception(\"Twitter oauth configuration : unable to open or read file '{0}')\".format(CONFIG_FILE))\n return\n\n ### Connect to twitter\n try:\n self.api = Api(consumer_key = consumer_key,\n consumer_secret = consumer_secret,\n access_token_key = access_token,\n access_token_secret = access_token_secret)\n self.api.VerifyCredentials()\n except TwitterError:\n raise Exception(\"Unable to log in the twitter account : {0}\".format(traceback.format_exc()))","def sentiment_analysis(self):\n train_pos = pd.read_csv(\"data/train_Arabic_tweets_positive_20190413.tsv\", sep='\\t', names=[\"label\", \"tweet\"])\n train_neg = pd.read_csv(\"data/train_Arabic_tweets_negative_20190413.tsv\", sep='\\t', names=[\"label\", \"tweet\"])\n train = pd.concat([train_pos, train_neg])\n train.tweet = train.tweet.apply(self.preprocessor).apply(tokenization).apply(lambda x: x.tokens[0])\n le = LabelEncoder()\n le.fit(train.label)\n train.label = le.transform(train.label)\n\n sentence_inds, vocab, self.num_tokens, word_index, index_word = helper.encode_tokens(train.tweet.values)\n\n\n self.embeddings_matrix = helper.load_embedding_matrix(self.num_tokens, self.embedding_size, \n word_index, self.embeddings_index)\n\n\n train_padded = pad_sequences(sentence_inds, padding=\"post\", truncating=\"post\", maxlen=100)\n\n self.X_train, self.X_valid, self.y_train, self.y_valid = train_test_split(train_padded, train.label.values, test_size=0.5,random_state=0, stratify=train.label.values)\n\n model = self.train_model()\n y_pred = model.predict(self.X_valid)\n return (np.argmax(y_pred, axis=1) == self.y_valid).sum() / self.y_valid.shape[0]","def __init__(self):\n self.word_list.extend(self.load_corpus(\"reuters\"))\n self.corpus = \" \".join(self.word_list) # use spaces to join all the elements in the list\n # load the corpus to create the word list\n # note that the network is needed to download the corpus\n\n self.count_ngrams() # count the n-grams\n self.load_confusion_matrix() # read the confusion matrix from files\n self.load_vocabulary() # read the vocabulary from a file","def __init__(self, text: tuple, content_type: str = CONTENT_TYPE, accept_encoding: str = ACCEPT_ENCODING,\n x_rapidapi_key: str = X_RAPID_API_KEY, x_rapidapi_host: str = X_RAPID_API_HOST,\n url_language: str = URL_LANGUAGE, url_translation: str = URL_TRANSLATION,\n sentiment_key: str = SENTIMENT_SUBSCRIPTION_KEY, url_sentiment: str = URL_SENTIMENT):\n self.text = text\n self.translate_headers = {'content-type': content_type, 'accept-encoding': accept_encoding,\n 'x-rapidapi-key': x_rapidapi_key, 'x-rapidapi-host': x_rapidapi_host}\n self.sentiment_headers = {'Ocp-Apim-Subscription-Key': sentiment_key}\n self.url_language = url_language\n self.url_translation = url_translation\n self.url_sentiment = url_sentiment\n self.clean = False\n self.language = None\n self.payload = None\n self.translation = None\n self.sentiment = None","def __init__(self, corpus):\n self.train(corpus)","def __init__(self, conv):\n self.conv = conv\n auth = self.auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_token_secret)\n self.api = tweepy.API(auth)","def __init__(self):\r\n #\r\n # Create dictionaries for each characteristic\r\n #\r\n self.words = {} # For counting words\r\n self.wordlengths = {} # For counting word lengths\r\n self.stems = {} # For counting stems\r\n self.sentencelengths = {} # For counting sentence lengths\r\n #\r\n # Create another of your own\r\n #\r\n self.gerund = {} # For counting words with ing \r\n self.text = ''","def init():\n execute(query=_query['cr_tweet'])\n execute(query=_query['cr_sentiment'])","def setUp(self):\n\t\tself.text1 = ('100', 'today was not good')\n\t\tself.text2 = ('100', 'today was not good not good at all')\n\t\tself.text3 = ('100', 'today was not good not very good')\n\t\tself.lib = {'good': (1, 0), 'bad': (-1, 1)}\n\t\tself.tokens_generator1 = [\n\t\t('today', 0), ('was', 1), ('not', 2), ('good', 3), ('today was', 0),\n\t\t('was not', 1), ('not good', 2)]\n\t\tself.tokens_generator2 = list(sentiment.tokenize(self.text2[1].split(), max_words=2))\n\t\tself.tokens_generator3 = list(sentiment.tokenize(self.text3[1].split(), max_words=3))","def main(twitter_config_file, cloud_nlp_config_file, num_tweets):\n # Setting up APIs with correct authentication\n tweets = Tweets(twitter_config_file)\n sentiment_analyzer = SentimentAnalyzer(cloud_nlp_config_file)\n\n # Grabbing the latest 100 tweets from @SDS\n user_tweets = tweets.grab_tweets(username=\"SDS\", num_tweets=num_tweets)\n\n # Filtering strings for keywords we are interested in -- e.g. Vanderbilt University is sometimes referred to as\n # \"Vandy\", so we search for this string as well\n keywords = [x.lower() for x in sec_football_names.keys()]\n\n # Getting text of each tweet\n user_tweets_text = [x.text for x in user_tweets]\n\n # Creating container to hold tweets for each team\n team_sentiment = defaultdict(SentimentContainer)\n\n # Analyzing the sentiment of each relevant tweet\n for t in tqdm(user_tweets_text):\n # Analyzing entities in tweet\n tweet_entities = sentiment_analyzer.analyze_entities(t)\n\n for e in tweet_entities.entities:\n # Checking if entity is relevant based on keywords\n if e.name.lower() in keywords:\n team_name = sec_football_names[e.name.lower()]\n team_sentiment[team_name].add(e.sentiment)\n\n # Getting semantic meaning and printing sentiment analysis results\n for k, v in team_sentiment.items():\n semantic_sentiment = sentiment_analyzer.get_semantic_sentiment(v.calculate())\n print(\"Team:\\n{}\\nScore: {:.3f}\\tMagnitude: {:.3f}\\nThis team has a {} sentiment.\\n\\n\".format(k,\n v.get_score(), v.get_magnitude(), semantic_sentiment))\n return","def __init__(self):\n self.cause_texts = set()\n self.effect_texts = set()\n self.evidence_texts = set()\n self.cause_polarity = None\n self.effect_polarity = None\n self.cause_type = None\n self.effect_type = None","def __init__(self, multiword_detector):\r\n\r\n # Save the multiword detector\r\n self.multiword_detector = multiword_detector\r\n\r\n # Initialize Leet Speak Detector\r\n self.leet_detector = LeetDetector(self.multiword_detector)\r\n\r\n ## Used for debugging/statistics\r\n #\r\n # These numbers won't add up to total passwords parsed since\r\n # some passwords might have multiple \"base words\". For example\r\n # \"pass1pass\" would be counted as two single words. Likewise,\r\n # \"123456\" would have no words\r\n #\r\n self.num_single_words = 0\r\n self.num_multi_words = 0\r\n\r\n # Keep track of the number of leet replacements detected\r\n self.num_leet = 0\r\n\r\n ## The following counters keep track of global running stats\r\n #\r\n self.count_keyboard = {}\r\n self.count_emails = Counter()\r\n self.count_email_providers = Counter()\r\n self.count_website_urls = Counter()\r\n self.count_website_hosts = Counter()\r\n self.count_website_prefixes = Counter()\r\n self.count_years = Counter()\r\n self.count_context_sensitive = Counter()\r\n self.count_alpha = {}\r\n self.count_alpha_masks = {}\r\n self.count_digits = {}\r\n self.count_other = {}\r\n self.count_base_structures = Counter()\r\n self.count_raw_base_structures = Counter()\r\n self.count_prince = Counter()","def __init__(self, id_: str, bio: str) -> None:\n\n # YOUR CODE HERE\n self.userid = id_\n self.bio = bio\n self.tweets = []","def __init__(self, corpus):\n self.unigrams = defaultdict(int)\n self.f1 = defaultdict(float)\n self.total = 0\n self.train(corpus)","def __init__(self, tList, annotated=True):\n self.tokens = tList\n self.start = -1\n self.end = -1\n self.shortSets = []\n self.text = self.tokens.toString()\n self.matchedMention = None\n if len(self.tokens) > 0:\n self.start = self.tokens[0].index\n self.end = self.tokens[-1].index\n if annotated == True:\n # build list of tokens in short sections of mention\n inShort = False \n for i in range(0, len(self.tokens)):\n # is current token tagged as short?\n if self.tokens[i].hasAnnotation('short'):\n if inShort == False and self.tokens[i].text not in self.ignoreWords:\n # start of short section\n self.shortSets.append(set([self.tokens[i].text]))\n inShort = True\n elif self.tokens[i].text not in self.ignoreWords:\n # already in a short section, add token to last set \n self.shortSets[-1].add(self.tokens[i].text)\n elif inShort == True:\n # reached end of short section\n inShort = False","def __init__(self):\n # Initialise class attributes (visibility ease)\n self.__corpus__ = None\n self.__pron_det_pos_words__ = None\n self.__triples_corpus__ = None\n self.__entities_in_doc__ = None\n self.__wvmodel__ = None\n \n # For purpose of parsing relation triplets later\n # Load pretrained embedding model\n #plog('Loading pretrained word embeddings. This will take some time to load...')\n #self.__wvmodel__ = api.load('fasttext-wiki-news-subwords-300')\n #plog('Pretrained word embeddings loaded!')","def __init__(self, corpus):\n self.corpus = corpus\n self.corpus_seg = None\n self.bm25_instance = None\n self.tokenizer = Tokenizer()","def __init__(self, language):\n self.stemmer = Stemmer(language)\n self.stopwords = stopwords.words(language)","def __init__(self, corpus):\n self.ntokens = 0\n self.counts = collections.defaultdict(lambda: 0)\n self.s = collections.defaultdict(lambda: 0.0)\n self.train(corpus)","def sentiment_analysis(df):\n analyzer = SentimentIntensityAnalyzer()\n polarity = []\n for tweet in df['clean_text'].astype(str):\n sentiment = analyzer.polarity_scores(tweet)\n polarity.append(sentiment['compound'])\n df['sentiment'] = pd.Series(polarity)\n return df","def __init__(self, test):\n self.all_grams = Ngram(self.START_OF_SENTENCE_TOKEN)\n for label in self.label_type_map:\n self.words_labels_counts[label] = {}\n self.words_labels_counts[label][self.UNKNOWN_TOKEN] = 0\n if test:\n self.train(\"train.txt\")\n self.test(\"test.txt\")\n else:\n self.train(\"train_partial.txt\")\n self.validate(\"validation_partial.txt\")","def forecast(self, results_filename):\n\n analyser = TweetSentimentAnalyser()\n\n candidates = self.dataset.candidates\n party_key_from_candidate = lambda candidate: candidate.party[0]\n number_tweets = len(self.tweets)\n if number_tweets == 0:\n print(\"No tweets were found in the file '{}' : aborting the prediction.\"\n .format(number_tweets))\n\n # General statistics dictionnaries\n # NB: defaultdict allows for efficiently incrementing the value of a key/value\n # pair that may not be initialized yet without checking its existence\n stats_tweets_per_user = defaultdict(int)\n stats_dates = defaultdict(int)\n stats_hashtags = defaultdict(int)\n stats_parties = {party_key_from_candidate(c): 0 for c in candidates.values()}\n\n # Tweet analysis\n results_per_candidate = {candidate_key: [0, 0] for candidate_key in candidates.keys()}\n for tweet in self.tweets:\n # retrieve the tweet data (text) and metadata (retweets, date...)\n candidate_key = tweet[0].decode('utf-8')\n text = tweet[1].decode('utf-8')\n datetime_str = tweet[2].decode('utf-8')\n datetime = dt.datetime.strptime(datetime_str, \"%Y-%m-%d %H:%M:%S\") # parse the datetime\n retweets = int(tweet[3])\n username = tweet[4].decode('utf-8')\n hashtags = None if len(tweet[5]) == 0 else tweet[5].decode('utf-8').split(' ')\n\n # basic statistics (individual users & hashtags)\n stats_tweets_per_user[username] += 1\n date = datetime.strftime(\"%Y-%m-%d\") # keep only the date\n stats_dates[date] += 1\n if hashtags is not None:\n for hashtag in hashtags:\n if hashtag == '#': # ignore empty hashtag\n continue\n stats_hashtags[hashtag.lower()] += 1\n\n # per-candidate analysis\n for key, data in candidates.items():\n # party mentions statistics\n party_key = party_key_from_candidate(data)\n for party_designation in data.party:\n if party_designation in text:\n stats_parties[party_key] += 1\n break\n # basic forecast : 1 tweet = 1 vote for the mentionned candidate\n for candidate_name in data.names:\n if candidate_name in text:\n results_per_candidate[key][0] += 1\n\n # sentimenal-analysis forecast :\n score = analyser.analyse_tweet(text, retweets)\n results_per_candidate[candidate_key][1] += score\n\n # Data interpretation\n compound_sum = sum([v[1] for v in results_per_candidate.values()])\n sort_default_dict = lambda dict, reverse:\\\n sorted(dict.items(), key=lambda k_v: k_v[1], reverse=reverse)\n\n # Write the results file\n votes_sum = sum([v[0] for v in results_per_candidate.values()])\n with open(results_filename, 'w+') as results_file:\n csv_writer = csv.writer(results_file, dialect=\"excel\")\n\n # per candiadate forecast\n for candidate, results in results_per_candidate.items():\n csv_writer.writerow([\n candidate,\n results,\n \"{} %\".format(100 * results[0] / votes_sum),\n \"{} %\".format(100 * results[1] / compound_sum)\n ])\n\n # general statistics\n for party, occurences in sort_default_dict(stats_parties, True):\n csv_writer.writerow([party, occurences])\n for username, occurences in sort_default_dict(stats_tweets_per_user, True):\n csv_writer.writerow([username, occurences])\n for hashtag, occurences in sort_default_dict(stats_hashtags, True):\n csv_writer.writerow([hashtag, occurences])\n\n print(results_per_candidate)","def __init__(self, words, corpus):\n self.words = words\n self.vocab_size = len(words)\n self.corpus = corpus\n counter = Counter(corpus)\n self.counts = np.array([counter[i] for i in range(self.vocab_size)])","def __init__(self, input=None):\r\n BaseAnalyzer.__init__(self, input)","def __init__(self, input=None):\r\n BaseAnalyzer.__init__(self, input)","def predict_tweet(tweet):\n text = TextBlob(tweet)\n \n if text.sentiment.polarity > 0:\n response = 'positive'\n elif text.sentiment.polarity == 0:\n response = 'neutral'\n else:\n response = 'negative'\n return response, text.sentiment.polarity, text.sentiment.subjectivity","def __init__(self, text: str):\n self.words = WORDS_RE.findall(text)","def __init__(self):\n super(TestAnalyzer, self).__init__()\n self._results = []","def train(self, arg1=None, arg2=None, **kwargs):\n nltk.download('averaged_perceptron_tagger')\n nltk.download('wordnet')\n nltk.download('twitter_samples')\n nltk.download('punkt')\n nltk.download('stopwords')\n nltk.download('vader_lexicon')\n\n positive_tweets = twitter_samples.strings('positive_tweets.json')\n negative_tweets = twitter_samples.strings('negative_tweets.json')\n text = twitter_samples.strings('tweets.20150430-223406.json')\n tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]\n\n stop_words = stopwords.words('english')\n\n positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')\n negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')\n\n positive_cleaned_tokens_list = []\n negative_cleaned_tokens_list = []\n\n for tokens in positive_tweet_tokens:\n positive_cleaned_tokens_list.append(self.remove_noise(tokens, stop_words))\n\n for tokens in negative_tweet_tokens:\n negative_cleaned_tokens_list.append(self.remove_noise(tokens, stop_words))\n\n all_pos_words = self.get_all_words(positive_cleaned_tokens_list)\n\n freq_dist_pos = FreqDist(all_pos_words)\n print(freq_dist_pos.most_common(20))\n\n positive_tokens_for_model = self.get_tweets_for_model(positive_cleaned_tokens_list)\n negative_tokens_for_model = self.get_tweets_for_model(negative_cleaned_tokens_list)\n\n positive_dataset = [(tweet_dict, \"Positive\")\n for tweet_dict in positive_tokens_for_model]\n\n negative_dataset = [(tweet_dict, \"Negative\")\n for tweet_dict in negative_tokens_for_model]\n\n dataset = positive_dataset + negative_dataset\n\n random.shuffle(dataset)\n\n train_data = dataset[:7000]\n test_data = dataset[7000:]\n\n self.classifier = NaiveBayesClassifier.train(train_data)","def get_tweets(self):\r\n return self.tweets","def __init__(self, tokenizer=simple_tokenize):\n # Set tokenizer to use for tokenizing new documents\n self.tokenize = tokenizer\n # The term document matrix is a sparse matrix represented as a\n # list of dictionaries. Each dictionary contains the word\n # counts for a document.\n self.sparse = []\n # Keep track of the number of documents containing the word.\n self.doc_count = {}","def __init__(self, corpus):\n self.unigramCounts = collections.defaultdict(lambda: 0)\n self.bigramCounts = collections.defaultdict(lambda: 0)\n self.total = 0\n self.train(corpus)","def twitter(self):\n\n q = \" OR \".join(self.search_terms) + \" -filter:retweets\"\n results = self.__api.search(q=q, lang='en', count=100)\n\n tweets = []\n\n for res in results:\n\n publishedAt = datetime.strptime(res._json['created_at'], '%a %b %d %H:%M:%S +0000 %Y').strftime(\"%Y-%m-%d\")\n\n if (res._json['in_reply_to_screen_name'] == None and publishedAt == datetime.now().strftime(\"%Y-%m-%d\")):\n tweets.append([res._json['id'],\n res._json['text'],\n res._json['user']['screen_name'],\n publishedAt,\n res._json['user']['followers_count']])\n\n self.list = pd.DataFrame(tweets, columns=['id', 'title', 'user', 'publishedAt', 'followers_count']).nlargest(10,\n 'followers_count')\n\n return","def main():\n # process CLI arguments\n argparser = argparse.ArgumentParser(description=\"\"\"Script for classifying\ntweets according to their sentiment polarity\"\"\")\n\n subparsers = argparser.add_subparsers(help=\"type of operation to perform\", dest = \"mode\")\n # training options\n tr_parser = subparsers.add_parser(TRAIN, help = \"train the model\")\n tr_parser.add_argument(\"-d\", \"--dev-set\", help = \"development set\",\n type = argparse.FileType('r'))\n tr_parser.add_argument(\"-l\", \"--lexicon\", help = \"sentiment lexicon to use for sampling\",\n type = str, action = \"append\", default = [])\n _add_cmn_options(tr_parser)\n # testing options\n test_parser = subparsers.add_parser(TEST, help = \"test the model\")\n test_parser.add_argument(\"-d\", \"--debug\", help = \"output debug information\", \\\n action = \"store_true\")\n test_parser.add_argument(\"-v\", \"--verbose\", help = \"output scores along with predicted labels\",\n action = \"store_true\")\n test_parser.add_argument(\"--scikit\", help = \"use supervised scikit classifier istead of deep\",\n action = \"store_true\")\n _add_cmn_options(test_parser)\n # evaluation options (train and test at the same time)\n ev_parser = subparsers.add_parser(EVALUATE, help = \"evaluate trained model\")\n _add_cmn_options(ev_parser)\n ev_parser.add_argument(\"-v\", \"--verbose\", help = \"output errors along with evaluation\",\n action = \"store_true\")\n args = argparser.parse_args()\n # perform the requied action\n if args.mode == TRAIN:\n classifier = SentimentClassifier(a_path = None)\n if args.dev_set is None:\n dev_set = None\n else:\n dev_set = _read_dataset([args.dev_set])\n lexica = [_read_lexicon(ilex) for ilex in args.lexicon]\n pos, pos_re, neg, neg_re = _merge_lexica(lexica)\n classifier.train(_read_dataset(args.files), a_path=args.model,\n a_dev_set=dev_set, a_pos_re=pos_re, a_pos=pos,\n a_neg_re=neg_re, a_neg=neg)\n elif args.mode == TEST:\n # load model from default location\n y = \"\"; score = 0.\n if args.model:\n classifier = SentimentClassifier(args.model)\n else:\n classifier = SentimentClassifier()\n for ifile in args.files:\n for ifields in iterlines(ifile, TEST_TOPIC_IDX):\n if args.debug:\n classifier.debug(list(ifields[TXT_IDX]))\n else:\n y, score = classifier.predict(list(ifields[TXT_IDX]))\n if args.verbose:\n ifields.append(str(score))\n ifields.append(y)\n print(TAB.join(ifields))\n else:\n raise NotImplementedError\n # for ifile in a_files:\n # macro_MAE, micro_MAE = evaluate(classify(classifier, ifile), args.verbose, lambda x: x)\n # print(\"{:20s}{:.7}\".format(\"Macro-averaged MAE:\", macro_MAE), file = sys.stderr)\n # print(\"{:20s}{:.7}\".format(\"Micro-averaged MAE:\", micro_MAE), file = sys.stderr)\n return 0","def get_tweets(self, query, count=10):\n # empty list to store parsed tweets\n tweets = []\n\n try:\n # call twitter api to fetch tweets\n fetched_tweets = self.api.search(q=query, count=count)\n\n # parsing tweets one by one\n for tweet in fetched_tweets:\n # empty dictionary to store required params of a tweet\n parsed_tweet = {}\n\n # saving text of tweet\n parsed_tweet['text'] = tweet.text\n # saving sentiment of tweet\n parsed_tweet['sentiment'] = self.get_tweet_sentiment(tweet.text)\n\n # appending parsed tweet to tweets list\n if tweet.retweet_count > 0:\n # if tweet has retweets, ensure that it is appended only once\n if parsed_tweet not in tweets:\n tweets.append(parsed_tweet)\n else:\n tweets.append(parsed_tweet)\n\n # return parsed tweets\n return tweets\n\n except tweepy.TweepError as e:\n # print error (if any)\n print(\"Error : \" + str(e))","def __init__(self):\r\n self.tweets_by_user = collections.defaultdict(list)\r\n self.follows = collections.defaultdict(set)\r\n self.timestamp = 0","def __init__(self):\n self.time = 0\n self.tweets = {}\n self.follows = {}","def text_analytics(self):\n\n headers = {\n # Request headers\n 'Content-Type': 'application/json',\n 'Ocp-Apim-Subscription-Key': self.keys['text_analytics'],\n }\n \n sentiment_url = 'https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment'\n \n raw_text = self.article_params['text']\n\n # Build post for sentiment\n try:\n sentences = tokenize.sent_tokenize(str(raw_text))\n content = []\n for i, sentence in enumerate(sentences):\n content.append({'id': str(i), 'language': 'en', 'text': sentence})\n body = json.dumps({\"documents\": content}).encode('utf-8')\n\n request = urllib.request.Request(sentiment_url, body, headers)\n response = urllib.request.urlopen(request)\n json_response = json.loads(response.read().decode('utf-8'))\n \n # A list of dictionaries, with each dictionary containing a sentence\n # sentiment score\n sentiments_list = json_response['documents']\n\n # Calculate the articles average sentiment from all the sentences\n cumulative_sentiment_score = 0\n for sent in sentiments_list:\n cumulative_sentiment_score += sent['score']\n avg_article_sentiment = cumulative_sentiment_score/len(sentiments_list)\n\n # Put article sentiments in bucket from 1 to 5, with 1 being very\n # negative and 5 being very positive\n if avg_article_sentiment < 0.2:\n sentiment = 1\n elif 0.2 <= avg_article_sentiment < 0.4:\n sentiment = 2\n elif 0.4 <= avg_article_sentiment < 0.6:\n sentiment = 3\n elif 0.6 <= avg_article_sentiment < 0.8:\n sentiment = 4\n else:\n sentiment = 5\n\n except Exception as e:\n print('Unable to process sentiment for article. Assuming '\n 'sentiment is neutral.')\n sentiment = 3\n\n return sentiment","def main()->None:\n #Define key words\n keyword_list = ['#DataScience', '#MachineLearning', '#artificialintelligence', '#AI', '#ai', '#machinelearning',\n '#deeplearning', 'DeepLearning', '#ML', '#ArtificialIntelligence', '#machinelearning',\n 'DigitalTransformation'] # track list\n\n #Initiate Time\n start_time = time.time() # grabs the system time\n print(\"Launch! \\n\")\n\n #Listen to twitter\n twitterStream = Stream(Authentification(), listener.Listener(start_time, time_limit=3600)) # initialize Stream object with a time out limit\n twitterStream.filter(track=keyword_list, languages=['en']) # call the filter method to run the Stream Object\n print('Exctraction from twitter succesful')","def __init__(self, corpus):\n self.unigram_count = Counter()\n self.bigram_count = defaultdict(Counter)\n self.vocabulary_size = 0\n self.num_words = 0\n self.backoff_multiplier = 0.4\n self.train(corpus)","def __init__(self):\n self.load_objects()\n super(TwitterMain,self).__init__()","def __init__(self):\n self.tokenizer = BOWTokenizer(\n English()\n ) # the tokenizer must have a tokenize() and parse() function.\n self.labelEncoder = LabelEncoder()\n self.vectorizer = CountVectorizer(\n tokenizer=self.tokenizer.tokenize, ngram_range=(1, 1)\n )\n self.decode_params = {}","def __init__(self):\n self.timeStamp = 0\n self.tweetTimeLine = {}\n self.followList = {}","def __init__(self, corpus):\n self.unigramCounts = collections.defaultdict(lambda: 0)\n self.totalCount = 0\n self.zeroCount = 0\n self.train(corpus)","def __init__(self, words, text):\n\n self.prompts = words\n self.template = text"],"string":"[\n \"def __init__(self, tweet_json):\\r\\n self.tweet = tweet_json\\r\\n self.date = datetime.datetime.strptime(self.tweet[\\\"date\\\"], \\\"%Y-%m-%dT%H:%M:%S.000Z\\\")\\r\\n self.processed = False\\r\\n self.max_importance = 0\\r\\n try:\\r\\n text = re.sub(self.tweet[\\\"keywords\\\"][0], '', self.tweet[\\\"text\\\"])\\r\\n except IndexError:\\r\\n text = self.tweet[\\\"text\\\"]\\r\\n try:\\r\\n self.language = polyglot.detect.Detector(re.sub('#', '', text)).language.name\\r\\n except polyglot.detect.base.UnknownLanguage as e:\\r\\n self.language = \\\"mixed\\\"\\r\\n except:\\r\\n self.language = polyglot.detect.Detector(''.join([i if ord(i) < 128 else ' ' for i in text])).language.name\",\n \"def tweet_sentiment_analysis(self, tweet):\\n analysis = TextBlob(self.clean_tweet(tweet))\\n\\n if analysis.sentiment.polarity > 0:\\n return ['Positive', analysis.sentiment.polarity, analysis.sentiment.subjectivity]\\n elif analysis.sentiment.polarity == 0:\\n return ['Neutral', analysis.sentiment.polarity, analysis.sentiment.subjectivity]\\n else:\\n return ['Negative', analysis.sentiment.polarity, analysis.sentiment.subjectivity]\",\n \"def __init__(self):\\r\\n self.tweets = []\\r\\n self.lcs = \\\"outliers\\\"\\r\\n self.importance = 0\",\n \"def analyze(self, text):\\n\\n # start from 0 for each Analyser variable\\n self.positives = 0\\n self.negatives = 0\\n\\n # precise self text value\\n self.text = text\\n\\n # declare a tokenased word\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text)\\n\\n # indicate the length of list tokens\\n size = len(tokens)\\n\\n # all the word stuff to ckeck\\n for word in tokens:\\n\\n # chaque mots est converti en mot sans majuscule\\n word = str.lower(word)\\n\\n linespos = [line.rstrip('\\\\n') for line in open('positive-words.txt')]\\n linesneg = [line.rstrip('\\\\n') for line in open('negative-words.txt')]\\n\\n # check for positive or negative or neutral words\\n if word in linespos:\\n self.positives += 1\\n elif word in linesneg:\\n self.negatives += 1\\n else:\\n continue\\n\\n # score calculculated and reurned\\n score = self.positives - self.negatives\\n\\n return score\",\n \"def analyse_tweet(self, tweet):\\r\\n sentiment = 0\\r\\n subjects = []\\r\\n\\r\\n is_comparison = False # sentiment will be the LHS of the comparison\\r\\n seen_not = False\\r\\n for word in myparser.parse(tweet,self.company_names,True):\\r\\n if word == \\\"not\\\" or word == \\\"don't\\\":\\r\\n seen_not = True\\r\\n elif word in self.positive_words:\\r\\n sentiment = sentiment + 1\\r\\n elif word in self.negative_words:\\r\\n sentiment = sentiment - 1\\r\\n if word in self.company_names:\\r\\n subjects += [word]\\r\\n for (p, c) in self.product_names:\\r\\n if word == p:\\r\\n subjects += [c]\\r\\n for (c,s) in self.comparisons:\\r\\n if word == c:\\r\\n sentiment = s\\r\\n is_comparison = True\\r\\n if seen_not:\\r\\n sentiment = -sentiment\\r\\n\\r\\n #print((tweet, subjects, sentiment, is_comparison))\\r\\n\\r\\n if is_comparison:\\r\\n subjects += [None, None]\\r\\n return[(subjects[0], sentiment), (subjects[1], -sentiment)]\\r\\n else:\\r\\n return [(sub, sentiment) for sub in subjects]\",\n \"def do_sentiment_analysis(self):\\n\\n tweets_sentiment = []\\n\\n for tweet in self.tweets:\\n parsed_tweet = {}\\n parsed_tweet['text'] = tweet\\n sentiment_data = self.tweet_sentiment_analysis(tweet)\\n parsed_tweet['sentiment'] = sentiment_data[0]\\n parsed_tweet['polarity'] = sentiment_data[1]\\n parsed_tweet['subjectivity'] = sentiment_data[2]\\n\\n tweets_sentiment.append(parsed_tweet)\\n\\n self.sentiment_data = tweets_sentiment\\n self.positive_tweets = [tweet for tweet in self.sentiment_data if tweet['sentiment'] == 'Positive']\\n self.negative_tweets = [tweet for tweet in self.sentiment_data if tweet['sentiment'] == 'Negative']\\n self.neutral_tweets = [tweet for tweet in self.sentiment_data if tweet['sentiment'] == 'Neutral']\\n\\n return tweets_sentiment\",\n \"def analyze(self, text): #takes the text to be analyzed for sentiment\\n #initialize inicial score to 0\\n score = 0\\n #Create tokenizer instance\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n #create list of words in a tweets\\n tokens = tokenizer.tokenize(text)\\n \\n #iterate over tokens(list of words)\\n for word in tokens:\\n #check if word is positive or negative\\n if word.lower() in self.positives_words:\\n score+=1\\n if word.lower() in self.negatives_words:\\n score-=1\\n #neutral if its neither, doesnt add anything, 0\\n return score\",\n \"def analyze(self, text):\\n tknzr = nltk.tokenize.casual.TweetTokenizer(preserve_case=True, reduce_len=False, strip_handles=False)\\n tknTxt = tknzr.tokenize(text)\\n sentiment = 0\\n \\n for i in range(len(tknTxt)):\\n if tknTxt[i] in self.posTxt:\\n #print(\\\"POS\\\")\\n #print(tknTxt[i])\\n sentiment += 1\\n elif tknTxt[i] in self.negTxt:\\n #print(\\\"NEG\\\")\\n #print(tknTxt[i])\\n sentiment -= 1\\n \\n return sentiment\",\n \"def classify(self, tweets):\\n classified = []\\n for t in tweets:\\n #use the SVM to predict the polarity\\n t.polarity = self.m_learner.predict_from_tweet(t)\\n #append the tweet to the list\\n classified.append(t)\\n\\n return classified\",\n \"def process_sentiment(self):\\r\\n\\r\\n\\r\\n print(\\\"Beginning sentiment analysis\\\")\\r\\n # textblob time\\r\\n #tweet_sentiment = [TextBlob(tweet['filtered_text']).sentiment for index, tweet in self.tweet_dataframe.iterrows()]\\r\\n #self.tweet_dataframe['polarity'] = [i.polarity for i in tweet_sentiment]\\r\\n #self.tweet_dataframe['subjectivity'] = [i.subjectivity for i in tweet_sentiment]\\r\\n\\r\\n #vader time\\r\\n #http://t-redactyl.io/blog/2017/04/applying-sentiment-analysis-with-vader-and-the-twitter-api.html\\r\\n sentiment = []\\r\\n\\r\\n analyzer = SentimentIntensityAnalyzer()\\r\\n\\r\\n for tweet in self.tweet_dataframe['filtered_text']:\\r\\n vs = analyzer.polarity_scores(tweet)\\r\\n sentiment.append(vs['compound'])\\r\\n\\r\\n self.tweet_dataframe['vader_polarity'] = pd.Series(sentiment)\",\n \"def __init__(self, tweet_data):\\n _hashtags = tweet_data['entities']['hashtags']\\n _str_date = tweet_data['created_at']\\n self.account = Account(tweet_data['user'])\\n self.date = self.format_date(_str_date)\\n self.hashtags = [\\\"#%s\\\" % (tag['text']) for tag in _hashtags]\\n self.likes = tweet_data['favorite_count']\\n # Note: replies number is only available with\\n # the Premium and Enterprise tier products.\\n # https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object # noqa\\n self.replies = 0\\n self.retweets = tweet_data['retweet_count']\\n self.text = tweet_data['text']\",\n \"def __init__(self, twitter_consumer_key, twitter_consumer_secret,\\n twitter_access_key, twitter_access_secret,\\n search_terms, search_on='news',\\n bitly_access_token='',\\n news_api_key=''):\\n\\n # Access Keys and Secrets for Twitter API obtained at: https://developer.twitter.com/\\n auth = tweepy.OAuthHandler(twitter_consumer_key, twitter_consumer_secret)\\n auth.set_access_token(twitter_access_key, twitter_access_secret)\\n\\n # Store API object for access to Twitter REST API\\n self.__api = tweepy.API(auth)\\n\\n # Term(s) to search news feeds or Twitter on\\n self.search_terms = search_terms\\n\\n # Method TwitterBot will use to search on. Current options are 'news' or 'twitter'\\n self.search_on = search_on\\n\\n # Access token for optional Bitly API: https://dev.bitly.com/\\n self.__bitly_access_token = bitly_access_token\\n\\n # Access token for optional News API: https://newsapi.org/\\n self.__news_api_key = news_api_key\\n\\n # Will store list of items scraped from news or Twitter\\n self.list = []\",\n \"def analyse_tweets(nb_tweets, classifier, Resource, threshold, language='en'):\\n return [(bytes(line, 'utf-8'), _minimal_analysis(bytes(line, 'utf-8'), classifier, Resource, threshold, language))\\n for line in\\n collect_tweet(nb_tweets)]\",\n \"def get_sentiment_analysis(sender, instance, **kwargs):\\n text_analysis = TextAnalysis(instance.text)\\n\\n # Prevent sentiment_analysis API call every time the document is saved\\n if instance.sentiment_analysis is None:\\n instance.get_sentiment_analysis()\",\n \"def analyze(self, text):\\n\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n \\n tokens = tokenizer.tokenize(text)\\n \\n sentiment = 0\\n \\n for word in tokens:\\n if word in self.__positives:\\n sentiment += 1\\n elif word in self.__negatives:\\n sentiment -= 1\\n \\n return sentiment\",\n \"def __init__(self, influencers, credentials, similarity_parameter, popularity_parameter, epsilon):\\n \\n # Twitter API credentials initialization\\n auth = tweepy.OAuthHandler(credentials['consumer_key'], credentials['consumer_secret'])\\n auth.set_access_token(credentials['access_token'], credentials['access_token_secret'])\\n self.api = tweepy.API(auth)\\n\\n # Class fields\\n self.username = credentials['username']\\n self.influencers = influencers\\n self.complete_model = None\\n self.influencer_models = None\\n self.userTweetsStat = {}\\n self.similarities = {}\\n self.similarity_parameter = similarity_parameter\\n self.popularity_parameter = popularity_parameter\\n self.epsilon = epsilon\\n self.valueState = {influencer: 0 for influencer in self.influencers.allInfluencers}\\n self.reward = 1\\n self.rewardParam = 0.1\\n self.alpha = 0.1\\n self.gamma = 1\\n self.curDif = 0\",\n \"def __init__(self):\\n self.emotions_list = EmotionsList('NRC-Emotion-Intensity-Lexicon-v1.txt')\\n self.tweets_list = None\\n self.nickname = None\",\n \"def learn(self):\\n #get the training tweets and insert them into a list\\n self.training_tweets = []\\n print self.datas\\n for t in self.datas.get_positive_tweets():\\n t.polarity = 10\\n self.training_tweets.append(t)\\n for t in self.datas.get_negative_tweets():\\n t.polarity = -10\\n self.training_tweets.append(t)\\n for t in self.datas.get_neutral_tweets():\\n t.polarity = 0\\n self.training_tweets.append(t)\\n self.m_learner.learn_from_tweets(self.training_tweets)\",\n \"def __init__(self):\\r\\n\\t\\t\\r\\n\\t\\tself.redis = redis.Redis()\\r\\n\\t\\tself.info_to_get = ['text', 'created_at', 'user']\\r\\n\\t\\tself.search_results = {}\\r\\n\\t\\tself.raw_data_directory_name = \\\"raw_mining_data\\\"\\r\\n\\t\\tself.filtered_data_directory_name = \\\"filtered_mining_data\\\"\\r\\n\\t\\tenglish_file = pjoin( sys.path[0], \\\"sentiment_word_files\\\", \\\"Nielsen2010Responsible_english.csv\\\")\\r\\n\\t\\tself.analyzeEnglish = dict(map(lambda (w,e): (w, int(e)), \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t[ line.strip().lower().split('\\\\t') for line in open(english_file) ]))\\r\\n\\t\\tself.tweets_count = 0\",\n \"def __init__(self):\\r\\n # keys and tokens from the Twitter Dev Console\\r\\n consumer_key = 'e1I0CSqgSOGxhH940cey1PR50'\\r\\n consumer_secret = 'APZE7kT2MgJsledQszLbNVcZZEhCUDX3NKAseXTjnsEcggUAkf'\\r\\n access_token = '876294238144786432-Q9PfwxPd4T7OdYO9hXiFyVDO38Q8jZV'\\r\\n access_token_secret = 'e0RhKgnLLyHnEOrWS92Tw0pKv5hWrN3chjp4Azm4NayOG'\\r\\n\\r\\n # clean tween regular expression\\r\\n self.pattern = re.compile('(@[A-Za-z0-9]+)|([^0-9A-Za-z \\\\t])|(\\\\w+://\\\\S+)')\\r\\n\\r\\n # attempt authentication\\r\\n try:\\r\\n # create OAuthHandler object\\r\\n self.auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\r\\n # set access token and secret\\r\\n self.auth.set_access_token(access_token, access_token_secret)\\r\\n # create tweepy API object to fetch tweets\\r\\n self.api = tweepy.API(self.auth)\\r\\n logging.info(self.api.rate_limit_status()['resources']['search'])\\r\\n except:\\r\\n logging.error(\\\"Error: Authentication Failed\\\")\",\n \"def __init__(self, text):\\n # BEGIN Question 2\\n self.text = text\\n self.word_set = []\\n # END Question 2\",\n \"def sentimental_analysis_component():\\n sentence = st.text_area(\\\"Enter Text to Analyze:\\\")\\n if st.button(\\\"Submit\\\"):\\n result = sentiment_analyzer_scores(sentence)\\n st.success(result)\\n\\n #if st.checkbox('Lookup Twitter Status', True):\\n id_input = st.text_area(\\\"Enter Tweet ID to Analyze:\\\")\\n st.markdown(' e.g. 1333434829438906376 or 1257038775785422848')\\n\\n # Modules for twitter API\\n import tweepy \\n import os\\n \\n # API Keys\\n consumer_key = os.environ.get('TWITTER_CONSUMER_KEY')\\n consumer_secret = os.environ.get('TWITTER_CONSUMER_SECRET')\\n access_token = os.environ.get('TWITTER_ACCESS_TOKEN')\\n access_token_secret = os.environ.get('TWITTER_ACCESS_TOKEN_SECRET')\\n \\n # Auth type and API options\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_token_secret)\\n api = tweepy.API(auth,wait_on_rate_limit=True)\\n\\n # Tweet ID to fetch\\n id_ = [id_input]\\n \\n # API Call \\n statuses = api.statuses_lookup(id_, tweet_mode=\\\"extended\\\")\\n \\n # API Response to variables\\n for status in statuses:\\n tweet_text = status.full_text\\n tweet_user = status.user.screen_name\\n covid_check = covid_mention(tweet_text.lower())\\n\\n if st.button(\\\"Analyze Tweet\\\"):\\n lookup_result = sentiment_analyzer_scores(tweet_text)\\n st.markdown('## Tweet Sentiment Results')\\n st.success(lookup_result)\\n st.markdown(f'## Full Text:')\\n st.success(f'{tweet_text}')\\n\\n st.markdown(f\\\"\\\"\\\"## Tweet Stats:\\n Tweet ID:{id_}\\n User: {status.user.screen_name}\\n Created at: {status.created_at}\\n Source: {status.source}\\n Engagement:\\n Retweets: {status.retweet_count}\\n Favourited: {status.favorite_count}\\n Pandemic Related: {covid_check}\\\"\\\"\\\")\",\n \"def feat_eng(self, tweets):\\n self.tweets['emojis'] = get_emojis(self.tweets['text']) # get emojis as text\\n self.tweets['polarity'] = self.tweets['text'].map(\\n lambda x: TextBlob(x).sentiment.polarity)\\n self.tweets['word_count'] = self.tweets['text'].map(lambda x: len(str(x).split()))\",\n \"def __init__(self, corpus):\\n if isinstance(corpus, str):\\n # Convert directory to Plaintext Corpus.\\n corpus = PlaintextCorpusReader(corpus, r\\\".*\\\\.txt\\\")\\n self.corpus = corpus\\n self._bigrams = FreqDist()\\n self._count()\",\n \"def analyze(self, tweet):\\n \\n # keeping track of the score\\n score = 0\\n \\n # filtering though tweets exstracting the useful words\\n # preserve_case = false maks them lowercase\\n tokenizer = nltk.tokenize.TweetTokenizer(preserve_case = False)\\n tokens = tokenizer.tokenize(tweet)\\n \\n # checking word for word the intension and keeping score\\n for word in tokens:\\n if word in self.dic:\\n if self.dic[word] == 1:\\n score += 1\\n else:\\n score -= 1\\n# score += self.dic[word]\\n return score\",\n \"def analyze(self, text):\\n\\n # TODO\\n # tokens = tokenizer.tokenize(tweet)\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text)\\n score = 0\\n\\n for word in tokens:\\n # iterate over tokens#str.lower\\n\\n if word.lower() in self.positives:\\n score = score+1\\n\\n elif word.lower() in self.negatives:\\n score = score-1\\n\\n else:\\n continue\\n return score\",\n \"def __init__(self, text=None, bare=False, stem='gap', pos=False, roman = False, stopwords=False, punct=False, conjunction=False, article=False, demonstrative=False, preposition=False, question=False, pronoun=False, quantifier=False, date=False, number=False, ssn=False, telephone=False, name=False, address=False, sentiment=False, gender=False, age = False, dob=False, unit=False, standard=False, metric=False, spell=None ):\\n self._text = text # raw text\\n self._words = None # list of words\\n self._punct = punct # keep/remove punctuation\\n self._stemming = stem # on/off stemming\\n self._pos = pos # on/off parts of speech\\n self._roman = roman # on/off romanization \\n self._porter = stopwords # keep/remove stopwords\\n self._bare = bare # on/off bare tokenizing\\n self._standard = standard # convert metric to standard units\\n self._metric = metric # convert standard to metric units\\n self._spell = None # spell checking\\n self._bow = None # bag of words\\n self._freq = None # word count frequency\\n self._tf = None # term frequency\\n \\n # More than just bare tokenizing\\n if self._bare == False:\\n self._spell = spell # do (not) spell checking\\n \\n # Keep Stopwords\\n if stopwords is True:\\n self._quantifier = True # keep words indicating a size\\n self._preposition = True # keep prepositions\\n self._article = True # keep articles\\n self._conjunction = True # keep conjunctions\\n self._demonstrative = True # keep demonstratives\\n self._question = True # keep question words\\n self._pronoun = True # keep pronouns \\n self._sentiment = True # keep sentiment words\\n self._number = True # keep numbers \\n self._date = True # keep date\\n self._ssn = True # keep social security number\\n self._telephone = True # keep telephone numbers\\n self._address = True # keep street addresses\\n self._name = True # keep proper names\\n self._gender = True # keep gender words\\n self._age = True # keep age\\n self._dob = True # keep date of birth words\\n self._unit = True # keep unit of measurement\\n # Remove Stopwords\\n else:\\n self._quantifier = quantifier # keep/remove words indicating a size\\n self._preposition = preposition # keep/remove prepositions\\n self._article = article # keep/remove articles\\n self._conjunction = conjunction # keep/remove conjunctions\\n self._demonstrative = demonstrative # keep/remove demonstratives\\n self._question = question # keep/remove question words\\n self._pronoun = pronoun # keep/remove pronouns\\n self._sentiment = sentiment # keep/remove sentiment words\\n self._number = number # keep/remove numbers\\n self._date = date # keep/remove date\\n self._ssn = ssn # keep/remove social security number\\n self._telephone = telephone # keep/remove telephone numbers\\n self._address = address # keep/remove street addresses\\n self._name = name # keep/remove proper names\\n self._gender = gender # keep/remove gender words\\n self._age = age # keep/remove age\\n self._dob = dob # keep/remove date of birth words\\n self._unit = unit # keep/remove unit of measurement words\\n \\n if isinstance(stopwords, bool) is False:\\n raise TypeError(\\\"Stopwords must be a boolean\\\")\\n if isinstance(bare, bool) is False:\\n raise TypeError(\\\"Bare must be a boolean\\\")\\n if isinstance(quantifier, bool) is False:\\n raise TypeError(\\\"Quantifier must be a boolean\\\")\\n if isinstance(preposition, bool) is False:\\n raise TypeError(\\\"Preposition must be a boolean\\\")\\n if isinstance(conjunction, bool) is False:\\n raise TypeError(\\\"Conjunction must be a boolean\\\")\\n if isinstance(article, bool) is False:\\n raise TypeError(\\\"Article must be a boolean\\\")\\n if isinstance(demonstrative, bool) is False:\\n raise TypeError(\\\"Demonstrative must be a boolean\\\")\\n if isinstance(question, bool) is False:\\n raise TypeError(\\\"Question must be a boolean\\\")\\n if isinstance(pronoun, bool) is False:\\n raise TypeError(\\\"Pronoun must be a boolean\\\")\\n if isinstance(number, bool) is False:\\n raise TypeError(\\\"Number must be a boolean\\\")\\n if isinstance(date, bool) is False:\\n raise TypeError(\\\"Date must be a boolean\\\")\\n if isinstance(ssn, bool) is False:\\n raise TypeError(\\\"SSN must be a boolean\\\")\\n if isinstance(telephone, bool) is False:\\n raise TypeError(\\\"Telephone must be a boolean\\\")\\n if isinstance(name, bool) is False:\\n raise TypeError(\\\"Name must be a boolean\\\")\\n if isinstance(address, bool) is False:\\n raise TypeError(\\\"Address must be a boolean\\\")\\n if isinstance(sentiment, bool) is False:\\n raise TypeError(\\\"Sentiment must be a boolean\\\")\\n if isinstance(gender, bool) is False:\\n raise TypeError(\\\"Gender must be a boolean\\\")\\n if isinstance(dob, bool) is False:\\n raise TypeError(\\\"Gender must be a boolean\\\")\\n if isinstance(age, bool) is False:\\n raise TypeError(\\\"Age must be a boolean\\\")\\n if isinstance(punct, bool) is False:\\n raise TypeError(\\\"Punct must be a boolean\\\")\\n if isinstance(unit, bool) is False:\\n raise TypeError(\\\"Unit must be a boolean\\\")\\n if isinstance(standard, bool) is False:\\n raise TypeError(\\\"Standard must be a boolean\\\")\\n if isinstance(metric, bool) is False:\\n raise TypeError(\\\"Metric must be a boolean\\\")\\n if text is not None:\\n if isinstance(text, str) is False:\\n raise TypeError(\\\"String expected for text\\\")\\n if spell is not None:\\n if spell not in ['en', 'fr', 'es', 'it', 'de']:\\n raise ValueError(\\\"Wrong value for spell: en, es, fr, it or de\\\")\\n \\n if text is not None:\\n self._split()\\n if self._bare == False:\\n # preprocess the tokens\\n self._preprocess()\\n # word stemming\\n if self._stemming == 'gap':\\n self._stem()\\n elif self._stemming == 'porter':\\n self._nltkStemmer('porter')\\n elif self._stemming == 'snowball':\\n self._nltkStemmer('snowball')\\n elif self._stemming == 'lancaster':\\n self._nltkStemmer('lancaster')\\n elif self._stemming == 'lemma':\\n self._lemma()\\n # remove stop words\\n self._stopwords()\\n # Do unit conversions\\n self._conversion()\\n # Do POS tagging\\n if self._pos == True:\\n self._partsofspeech()\",\n \"def analyze(self, text):\\n #analize every word in the text a value -1, 1 or 0 and calculate total score\\n #tokens allow us to split words in single tokens we can initialize tokens like this:\\n\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text.lower())\\n\\n score = 0\\n\\n if tokens[0] in self.negatives:\\n score =- 1\\n elif tokens[0] in self.positives:\\n score =+ 1\\n else:\\n score = 0\\n\\n #print('', text)\\n\\n return score\",\n \"def process(self, tweet):\\n\\n #identify the applicable event keywords for this text\\n text = self.cleanup_data(tweet.text)\\n tokens = [str(t.lower()).translate(None, string.punctuation) for t in tweet.text.split()]\\n applicable_tokens = []\\n for phrase in self.match_event_tree.root.keywords:\\n if phrase in \\\" \\\".join(tokens):\\n applicable_tokens.append(phrase)\\n\\n self.match_event_tree.propogate_tweet(applicable_tokens, tweet)\",\n \"def __init__(self, data: List):\\n self.data: List = data\\n self.texts: List = self.list_texts()\\n self.post_nlp: List = False\",\n \"def analyze(self, text):\\n score =0\\n token = TweetTokenizer()\\n tokens = token.tokenize(text)\\n for token in tokens:\\n if token.lower() in self.pos_list:\\n score+=1\\n elif token.lower() in self.neg_list:\\n score-=1\\n\\n return score\",\n \"def build(self, Tweets):\\n text = [t.text for t in Tweets]\\n vectorizer = TfidfVectorizer(tokenizer=process_text,\\n stop_words=stopwords.words('english'),\\n max_df=0.5,\\n min_df=0.1,\\n lowercase=True,\\n max_features=10000)\\n\\n return vectorizer.fit_transform(text).A\",\n \"def __init__(self):\\n\\t\\tself.relevances = None\",\n \"def analyze(self, text):\\n\\n tknzr = nltk.tokenize.TweetTokenizer()\\n words = tknzr.tokenize(text)\\n \\n score = 0\\n \\n for word in words:\\n if word.lower() in self.positives:\\n score += 1\\n elif word.lower() in self.negatives:\\n score -= 1\\n else:\\n continue\\n \\n return score\",\n \"def __init__(self, corpus, target=None, **kwargs):\\n self.corpus = corpus\\n # with open('/home/igor/Development/atap/data/dummy.pkl', 'rb') as f:\\n # target = pickle.load(f).to_dict()\\n self.target = target\\n self.morph = pymorphy2.MorphAnalyzer()\\n self.stopwords = nltk.corpus.stopwords.words('russian')\\n self.stopwords.extend(['анализ', 'год', 'плата', 'адрес', 'поступить', 'выписать' 'что', 'это', 'так', 'вот', 'быть', 'как', 'в', '—', '–', 'к', 'на', '...'])\",\n \"def __init__(self, remove_stopwords=True, replace_words=True,\\n remove_numbers=True, remove_html_tags=True,\\n remove_punctuations=True, lemmatize=False,\\n lemmatize_method='wordnet'):\\n if (type(remove_stopwords) != bool or\\n type(replace_words) != bool or\\n type(remove_numbers) != bool or\\n type(remove_html_tags) != bool or\\n type(remove_punctuations) != bool or\\n type(lemmatize) != bool):\\n raise Exception(\\\"Error - expecting a boolean parameter\\\")\\n if lemmatize_method not in ['wordnet', 'snowball']:\\n raise Exception(\\\"Error - lemmatizer method not supported\\\")\\n self.doc = None\\n self.tweets = None\\n self.lemmatizer = None\\n self.remove_stopwords = remove_stopwords\\n self.replace_words = replace_words\\n self.remove_numbers = remove_numbers\\n self.remove_html_tags = remove_html_tags\\n self.remove_punctations = remove_punctuations\\n self.lemmatize_method = lemmatize_method\\n self.lemmatize = lemmatize\\n self.stopword_list = set(stopwords)\\n self.replacement_list = to_replace\\n if self.lemmatize_method == 'wordnet':\\n self.lemmatizer = WordNetLemmatizer()\\n if self.lemmatize_method == 'snowball':\\n self.lemmatizer = SnowballStemmer('english')\",\n \"def __init__(self):\\n super().__init__()\\n\\n # Will only reply to every 3rd or so tweet, defined in settings\\n self.received_tweet_count = 0\\n\\n # Twitter api init\\n self.auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\\n self.auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\\n self.twitter_api = tweepy.API(self.auth)\\n\\n print('Authenticated, creating stream...')\\n\\n self._init_stream()\",\n \"def __init__(self, samples, analysis):\\r\\n self.samples = samples\\r\\n self.analysis = analysis\",\n \"def get_tweet_sentiment(self, tweet):\\r\\n # create TextBlob object of passed tweet text\\r\\n polarity = TextBlob(self.clean_tweet(tweet)).sentiment.polarity\\r\\n if polarity > 0:\\r\\n return 1.0\\r\\n if polarity < 0:\\r\\n return -1.0\\r\\n return 0\",\n \"def get_tweet_sentiment(self, tweet):\\n # create TextBlob object of passed tweet text\\n analysis = TextBlob(self.clean_tweet(tweet))\\n\\n # set sentiment\\n if analysis.sentiment.polarity > 0:\\n return 'positive'\\n elif analysis.sentiment.polarity == 0:\\n return 'neutral'\\n else:\\n return 'negative'\",\n \"def __init__(self,classes=['normalizeText','tagger','stem','stopWord','spellChecker']):\\n self._support = prebotSupport()\\n self._classes = classes\\n if(\\\"tagger\\\" in self._classes):\\n self._tagger = tagger()\\n if(\\\"normalizeText\\\" in self._classes):\\n self._normalize = normalizeText()\\n if(\\\"spellChecker\\\" in self._classes):\\n self._spellChecker = spellChecker()\\n if(\\\"stopWord\\\" in self._classes):\\n self._stopWord = stopWord()\\n if(\\\"stem\\\" in self._classes):\\n self._stem = stemming()\",\n \"def analyze(self, text):\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text)\\n score = 0\\n\\n for token in tokens:\\n if token in self.positives_list:\\n score += 1\\n elif token in self.negatives_list:\\n score -= 1\\n\\n return score\",\n \"def __init__(self, corpus):\\n self.unigramCounts = collections.defaultdict(lambda: 0)\\n self.bigramCounts = collections.defaultdict(lambda: 0)\\n self.trigramCounts = collections.defaultdict(lambda: 0)\\n self.followingWords = collections.defaultdict(lambda: set())\\n self.precedingWords = collections.defaultdict(lambda: set())\\n self.total = 0\\n self.discount = 0.75\\n self.train(corpus)\",\n \"def __init__(self, corpus_name, corpus, data_date=''):\\n\\n # Test Patterns and sets for use later in our topic model\\n # '^[a-z0-9 _-]{2,}$' --> Match letters, numbers, spaces, underscores, dashes. Ignore case. Length of 2 or more.\\n self.phrase_pattern = re.compile('^[a-z0-9 _-]{2,}$', re.IGNORECASE)\\n self.date_pattern = re.compile('20[0-9]{2}-[0-1][0-9]-[0-3][0-9]$')\\n self.punct = {p for p in string.punctuation}\\n self.empty_words = {'a', 'an', 'that', 'the', 'this'}\\n self.nouns = {ss.NOUN, ss.PROPN}\\n self.entities = {ss.PERSON, ss.NORP, ss.FACILITY, ss.ORG, ss.GPE, ss.LOC, ss.PRODUCT, ss.EVENT, ss.WORK_OF_ART,\\n ss.LANGUAGE} # spaCy entities that indicate a proper noun.\\n\\n # Check the user arguments\\n # TODO: What data to I expect in the dictionary passed from the \\\"get_\\\" class?\\n assert 2 < len(corpus_name) < 21 and re.match(self.phrase_pattern, corpus_name), \\\\\\n 'A corpus_name (between 3 and 20 characters; made of letters, numbers, underscores, or dashes) is required.'\\n assert data_date == '' or re.match(self.date_pattern, data_date), \\\\\\n 'If you include a data_date, it must match the form 20YY-MM-DD.'\\n assert type(corpus) is dict, 'The corpus must be a dictionary.'\\n assert len(corpus) > 0, 'The corpus of texts has no data.'\\n\\n # Topic metadata & settings\\n self.corpus_name = corpus_name.replace(' ', '') # (str) The name of the set (or corpus) of texts.\\n self.data_date = data_date\\n\\n # Primary Data Structures\\n self.texts = corpus # The dict of dicts that contains all of our texts for analysis: {text_id: {}}\\n self.topics = {} # A dict of dicts for primary topics: {topic: {}}\\n self.ngrams = {} # A dict of dicts for ngrams that will help us understand primary topics: {ngram_lemma: {}}\\n self.model_output = {'name': corpus_name,\\n 'dataDate': data_date,\\n 'runDate': datetime.now().strftime(\\\"%Y-%m-%d %H:%M\\\"),\\n 'textCount': len(corpus)} # For results as json\\n\\n # Get known entities\\n try:\\n with open(config.INPUT_DIR + 'known_entities.txt', 'r') as file:\\n self.known_entities = set(file.read().split(' '))\\n except IOError:\\n print('Add a text file named \\\"known_entities.txt\\\" that lists named entities (space-delimited) that we '\\n 'want to be sure are treated as proper nouns.')\\n self.known_entities = set()\\n\\n # Stop Words\\n try:\\n with open(config.INPUT_DIR + 'stop_words.txt', 'r') as file:\\n self.stop_words = set(file.read().split(' '))\\n except IOError:\\n print('Add a text file named \\\"stop_words.txt\\\" that lists common words (space-delimited) that we should '\\n 'ignore in most text processing.')\\n self.stop_words = set()\\n\\n # Loop through texts and tokenize: 'doc' and 'titleDoc' are lists of spaCy tokens, with named entities called\\n # recognized and joined. 'textClean' is a string of lemmatized words, excluding stopwords and punctuation.\\n # It's used by Doc2Vec.\\n for text_id, text in self.texts.items():\\n text['doc'] = self._tokenize(text['text'])\\n text['titleDoc'] = self._tokenize(text['title'])\\n text['textClean'] = ' '.join([token.text.lower() if token.lemma_ == '-PRON-' else token.lemma_ for\\n token in text['doc'] if token.lemma_ not in\\n self.stop_words and token.text not in self.punct])\",\n \"def set_analyzed_tweets(self, tweets):\\n slim_tweets = [SlimTweet(tweet) for tweet in tweets]\\n self.analyzed_tweets = sort_tweets(slim_tweets)\",\n \"def __init__(self, topics=None, tweet_file=None, mode='batch'):\\n self.topics = topics\\n # (The twitter API will only return a max of 100 count)\\n self.GEN_MAX_TWEET = 100 # the max number of tweets to generate\\n self.tweet_file = tweet_file\\n self.mode = mode\\n self.tweets = []\\n\\n if topics and tweet_file:\\n print(\\\"WARNING! you input both topics and the tweet file, only one is expected\\\")\\n exit(-1)\\n\\n if not topics and not tweet_file:\\n print(\\\"WARNING! you input either topics or tweet file, one is expected\\\")\\n exit(-1)\\n\\n # If file argument is given, it will not connect to twitter server\\n # It will just save tweets in self.tweets\\n if tweet_file:\\n with open(tweet_file, 'r') as infile:\\n for line in infile:\\n self.tweets.append(json.loads(line))\\n\\n else:\\n consumer_key = 'bbqKfXEU2VJNoWlYJvbdtptOE'\\n consumer_secret = 'afPk2JuMMMD6IhP5Xijo60ni4FUK39PDzhU7ylgT9FgNZX9ngh'\\n access_token = '434708489-DTeHfK4OYKRuIXlfoWnNgzzwpEZTPCEpSMv8C0ll'\\n access_token_secret = 'SjWFYfX2k3q4RJKQXcP1LP9ikhRfckPKOEcrb2cpQ0A0n'\\n\\n # Attempt authentication\\n try:\\n # create OAuthHandler object\\n self.auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n # set access token and secret\\n self.auth.set_access_token(access_token, access_token_secret)\\n # create tweepy API object to fetch tweets\\n self.api = tweepy.API(self.auth)\\n except:\\n print(\\\"Error: Authentication Failed\\\")\\n exit(-1)\",\n \"def analyze(self, text):\\n #Check each word in text\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text)\\n total_score = 0\\n #Sum the total score\\n for token in tokens:\\n token = token.lower()\\n if token in self.positives:\\n total_score = total_score + 1\\n elif token in self.negatives:\\n total_score = total_score - 1\\n else:\\n total_score = total_score + 0\\n \\n return total_score\",\n \"def __init__(self, proc):\\n self.sd = SentimentDetector()\\n self.sr = SpeechRecognizer()\\n self.option = input(\\\"Welcome to Kuri! Type 'c' to chat or 's' to speak! \\\").lower()\\n self.proc = proc\",\n \"def __init__(self):\\n # keys and tokens from the Twitter Dev Console\\n key = provide_keys('males')\\n\\n consumer_key = key['consumer_key']\\n consumer_secret = key['consumer_secret']\\n access_token = key['access_token_key']\\n access_token_secret = key['access_token_secret']\\n\\n # attempt authentication\\n\\n # create OAuthHandler object\\n self.auth = OAuthHandler(consumer_key, consumer_secret)\\n\\n # set access token and secret\\n self.auth.set_access_token(access_token, access_token_secret)\\n\\n try:\\n # create tweepy API object to fetch tweets\\n self.api = tweepy.API(self.auth)\\n\\n except:\\n print(\\\"Error: Authentication Failed\\\")\\n sys.exit(-1)\",\n \"def analyze_tweets(tweets, model, w2v_model):\\n # TODO DO EVERYTHING HERE\\n #tweets = [(\\\"StarWars\\\", tc.query_tweets(\\\"StarWars\\\"))]\\n \\n #tweets = tc.query_tweets('starwars')\\n df = pd.DataFrame(columns=['pos', 'neu', 'neg'])\\n if not os.path.isdir('results'):\\n os.mkdir('results')\\n for topic, topic_tweets in tweets:\\n tokenized_tweets = tp.process_raw_tweets(topic_tweets)\\n df.loc[topic], dummy = classify_tweets(tokenized_tweets, model, w2v_model)\\n vis.word_cloud_from_frequencies(tp.count_tokens(tokenized_tweets), f\\\"results/{topic}_cloud.png\\\", width=800, height=400,)\\n \\n vis.bar_plot_from_dataframe(df, 'results/results.png')\\n print(\\\"\\\\n\\\")\\n print(df)\",\n \"def getSentiment(tweets, location):\\n sentiment = [0, 0, 0]\\n for tweet in tweets:\\n analyser(tweets[tweet], sentiment,location)\\n return sentiment\",\n \"def __init__(self):\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_token_secret)\\n self.api = tweepy.API(auth)\",\n \"def get_tweet_sentiment(self, tweet):\\n\\n analyzer = SentimentIntensityAnalyzer()\\n vs = analyzer.polarity_scores(tweet)\\n # set sentiment\\n if vs['compound'] >= 0.05:\\n return 'positive'\\n elif -0.5 < vs['compound'] < 0.05:\\n return 'neutral'\\n else:\\n return 'negative'\",\n \"def __sentiment_scan(self, title, text):\\n\\n return (pattern.en.sentiment(title), pattern.en.sentiment(text))\",\n \"def __init__(self, corpus):\\n self.total = 0\\n self.reverseBigramCount = defaultdict(lambda : defaultdict(lambda : 0))\\n self.bigramCount = defaultdict(lambda : defaultdict(lambda : 0))\\n self.unigramCount = defaultdict(lambda: 0)\\n self.train(corpus)\",\n \"def __init__(self):\\n ### load the json to get twitter config\\n # check if the file exists\\n if os.path.isfile(CONFIG_FILE):\\n tmp_json = json.load(open(CONFIG_FILE))\\n # test if tweeting is enabled or not....\\n if not tmp_json['twitter']['enable']:\\n print(\\\"We don't want to tweet!\\\")\\n return\\n consumer_key = tmp_json['twitter']['consumer_key']\\n consumer_secret = tmp_json['twitter']['consumer_secret']\\n access_token_key = tmp_json['twitter']['access_token']\\n access_token_secret = tmp_json['twitter']['access_token_secret']\\n else:\\n raise Exception(\\\"Twitter oauth configuration : unable to open or read file '{0}')\\\".format(CONFIG_FILE))\\n return\\n\\n ### Connect to twitter\\n try:\\n self.api = Api(consumer_key = consumer_key,\\n consumer_secret = consumer_secret,\\n access_token_key = access_token,\\n access_token_secret = access_token_secret)\\n self.api.VerifyCredentials()\\n except TwitterError:\\n raise Exception(\\\"Unable to log in the twitter account : {0}\\\".format(traceback.format_exc()))\",\n \"def sentiment_analysis(self):\\n train_pos = pd.read_csv(\\\"data/train_Arabic_tweets_positive_20190413.tsv\\\", sep='\\\\t', names=[\\\"label\\\", \\\"tweet\\\"])\\n train_neg = pd.read_csv(\\\"data/train_Arabic_tweets_negative_20190413.tsv\\\", sep='\\\\t', names=[\\\"label\\\", \\\"tweet\\\"])\\n train = pd.concat([train_pos, train_neg])\\n train.tweet = train.tweet.apply(self.preprocessor).apply(tokenization).apply(lambda x: x.tokens[0])\\n le = LabelEncoder()\\n le.fit(train.label)\\n train.label = le.transform(train.label)\\n\\n sentence_inds, vocab, self.num_tokens, word_index, index_word = helper.encode_tokens(train.tweet.values)\\n\\n\\n self.embeddings_matrix = helper.load_embedding_matrix(self.num_tokens, self.embedding_size, \\n word_index, self.embeddings_index)\\n\\n\\n train_padded = pad_sequences(sentence_inds, padding=\\\"post\\\", truncating=\\\"post\\\", maxlen=100)\\n\\n self.X_train, self.X_valid, self.y_train, self.y_valid = train_test_split(train_padded, train.label.values, test_size=0.5,random_state=0, stratify=train.label.values)\\n\\n model = self.train_model()\\n y_pred = model.predict(self.X_valid)\\n return (np.argmax(y_pred, axis=1) == self.y_valid).sum() / self.y_valid.shape[0]\",\n \"def __init__(self):\\n self.word_list.extend(self.load_corpus(\\\"reuters\\\"))\\n self.corpus = \\\" \\\".join(self.word_list) # use spaces to join all the elements in the list\\n # load the corpus to create the word list\\n # note that the network is needed to download the corpus\\n\\n self.count_ngrams() # count the n-grams\\n self.load_confusion_matrix() # read the confusion matrix from files\\n self.load_vocabulary() # read the vocabulary from a file\",\n \"def __init__(self, text: tuple, content_type: str = CONTENT_TYPE, accept_encoding: str = ACCEPT_ENCODING,\\n x_rapidapi_key: str = X_RAPID_API_KEY, x_rapidapi_host: str = X_RAPID_API_HOST,\\n url_language: str = URL_LANGUAGE, url_translation: str = URL_TRANSLATION,\\n sentiment_key: str = SENTIMENT_SUBSCRIPTION_KEY, url_sentiment: str = URL_SENTIMENT):\\n self.text = text\\n self.translate_headers = {'content-type': content_type, 'accept-encoding': accept_encoding,\\n 'x-rapidapi-key': x_rapidapi_key, 'x-rapidapi-host': x_rapidapi_host}\\n self.sentiment_headers = {'Ocp-Apim-Subscription-Key': sentiment_key}\\n self.url_language = url_language\\n self.url_translation = url_translation\\n self.url_sentiment = url_sentiment\\n self.clean = False\\n self.language = None\\n self.payload = None\\n self.translation = None\\n self.sentiment = None\",\n \"def __init__(self, corpus):\\n self.train(corpus)\",\n \"def __init__(self, conv):\\n self.conv = conv\\n auth = self.auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_token_secret)\\n self.api = tweepy.API(auth)\",\n \"def __init__(self):\\r\\n #\\r\\n # Create dictionaries for each characteristic\\r\\n #\\r\\n self.words = {} # For counting words\\r\\n self.wordlengths = {} # For counting word lengths\\r\\n self.stems = {} # For counting stems\\r\\n self.sentencelengths = {} # For counting sentence lengths\\r\\n #\\r\\n # Create another of your own\\r\\n #\\r\\n self.gerund = {} # For counting words with ing \\r\\n self.text = ''\",\n \"def init():\\n execute(query=_query['cr_tweet'])\\n execute(query=_query['cr_sentiment'])\",\n \"def setUp(self):\\n\\t\\tself.text1 = ('100', 'today was not good')\\n\\t\\tself.text2 = ('100', 'today was not good not good at all')\\n\\t\\tself.text3 = ('100', 'today was not good not very good')\\n\\t\\tself.lib = {'good': (1, 0), 'bad': (-1, 1)}\\n\\t\\tself.tokens_generator1 = [\\n\\t\\t('today', 0), ('was', 1), ('not', 2), ('good', 3), ('today was', 0),\\n\\t\\t('was not', 1), ('not good', 2)]\\n\\t\\tself.tokens_generator2 = list(sentiment.tokenize(self.text2[1].split(), max_words=2))\\n\\t\\tself.tokens_generator3 = list(sentiment.tokenize(self.text3[1].split(), max_words=3))\",\n \"def main(twitter_config_file, cloud_nlp_config_file, num_tweets):\\n # Setting up APIs with correct authentication\\n tweets = Tweets(twitter_config_file)\\n sentiment_analyzer = SentimentAnalyzer(cloud_nlp_config_file)\\n\\n # Grabbing the latest 100 tweets from @SDS\\n user_tweets = tweets.grab_tweets(username=\\\"SDS\\\", num_tweets=num_tweets)\\n\\n # Filtering strings for keywords we are interested in -- e.g. Vanderbilt University is sometimes referred to as\\n # \\\"Vandy\\\", so we search for this string as well\\n keywords = [x.lower() for x in sec_football_names.keys()]\\n\\n # Getting text of each tweet\\n user_tweets_text = [x.text for x in user_tweets]\\n\\n # Creating container to hold tweets for each team\\n team_sentiment = defaultdict(SentimentContainer)\\n\\n # Analyzing the sentiment of each relevant tweet\\n for t in tqdm(user_tweets_text):\\n # Analyzing entities in tweet\\n tweet_entities = sentiment_analyzer.analyze_entities(t)\\n\\n for e in tweet_entities.entities:\\n # Checking if entity is relevant based on keywords\\n if e.name.lower() in keywords:\\n team_name = sec_football_names[e.name.lower()]\\n team_sentiment[team_name].add(e.sentiment)\\n\\n # Getting semantic meaning and printing sentiment analysis results\\n for k, v in team_sentiment.items():\\n semantic_sentiment = sentiment_analyzer.get_semantic_sentiment(v.calculate())\\n print(\\\"Team:\\\\n{}\\\\nScore: {:.3f}\\\\tMagnitude: {:.3f}\\\\nThis team has a {} sentiment.\\\\n\\\\n\\\".format(k,\\n v.get_score(), v.get_magnitude(), semantic_sentiment))\\n return\",\n \"def __init__(self):\\n self.cause_texts = set()\\n self.effect_texts = set()\\n self.evidence_texts = set()\\n self.cause_polarity = None\\n self.effect_polarity = None\\n self.cause_type = None\\n self.effect_type = None\",\n \"def __init__(self, multiword_detector):\\r\\n\\r\\n # Save the multiword detector\\r\\n self.multiword_detector = multiword_detector\\r\\n\\r\\n # Initialize Leet Speak Detector\\r\\n self.leet_detector = LeetDetector(self.multiword_detector)\\r\\n\\r\\n ## Used for debugging/statistics\\r\\n #\\r\\n # These numbers won't add up to total passwords parsed since\\r\\n # some passwords might have multiple \\\"base words\\\". For example\\r\\n # \\\"pass1pass\\\" would be counted as two single words. Likewise,\\r\\n # \\\"123456\\\" would have no words\\r\\n #\\r\\n self.num_single_words = 0\\r\\n self.num_multi_words = 0\\r\\n\\r\\n # Keep track of the number of leet replacements detected\\r\\n self.num_leet = 0\\r\\n\\r\\n ## The following counters keep track of global running stats\\r\\n #\\r\\n self.count_keyboard = {}\\r\\n self.count_emails = Counter()\\r\\n self.count_email_providers = Counter()\\r\\n self.count_website_urls = Counter()\\r\\n self.count_website_hosts = Counter()\\r\\n self.count_website_prefixes = Counter()\\r\\n self.count_years = Counter()\\r\\n self.count_context_sensitive = Counter()\\r\\n self.count_alpha = {}\\r\\n self.count_alpha_masks = {}\\r\\n self.count_digits = {}\\r\\n self.count_other = {}\\r\\n self.count_base_structures = Counter()\\r\\n self.count_raw_base_structures = Counter()\\r\\n self.count_prince = Counter()\",\n \"def __init__(self, id_: str, bio: str) -> None:\\n\\n # YOUR CODE HERE\\n self.userid = id_\\n self.bio = bio\\n self.tweets = []\",\n \"def __init__(self, corpus):\\n self.unigrams = defaultdict(int)\\n self.f1 = defaultdict(float)\\n self.total = 0\\n self.train(corpus)\",\n \"def __init__(self, tList, annotated=True):\\n self.tokens = tList\\n self.start = -1\\n self.end = -1\\n self.shortSets = []\\n self.text = self.tokens.toString()\\n self.matchedMention = None\\n if len(self.tokens) > 0:\\n self.start = self.tokens[0].index\\n self.end = self.tokens[-1].index\\n if annotated == True:\\n # build list of tokens in short sections of mention\\n inShort = False \\n for i in range(0, len(self.tokens)):\\n # is current token tagged as short?\\n if self.tokens[i].hasAnnotation('short'):\\n if inShort == False and self.tokens[i].text not in self.ignoreWords:\\n # start of short section\\n self.shortSets.append(set([self.tokens[i].text]))\\n inShort = True\\n elif self.tokens[i].text not in self.ignoreWords:\\n # already in a short section, add token to last set \\n self.shortSets[-1].add(self.tokens[i].text)\\n elif inShort == True:\\n # reached end of short section\\n inShort = False\",\n \"def __init__(self):\\n # Initialise class attributes (visibility ease)\\n self.__corpus__ = None\\n self.__pron_det_pos_words__ = None\\n self.__triples_corpus__ = None\\n self.__entities_in_doc__ = None\\n self.__wvmodel__ = None\\n \\n # For purpose of parsing relation triplets later\\n # Load pretrained embedding model\\n #plog('Loading pretrained word embeddings. This will take some time to load...')\\n #self.__wvmodel__ = api.load('fasttext-wiki-news-subwords-300')\\n #plog('Pretrained word embeddings loaded!')\",\n \"def __init__(self, corpus):\\n self.corpus = corpus\\n self.corpus_seg = None\\n self.bm25_instance = None\\n self.tokenizer = Tokenizer()\",\n \"def __init__(self, language):\\n self.stemmer = Stemmer(language)\\n self.stopwords = stopwords.words(language)\",\n \"def __init__(self, corpus):\\n self.ntokens = 0\\n self.counts = collections.defaultdict(lambda: 0)\\n self.s = collections.defaultdict(lambda: 0.0)\\n self.train(corpus)\",\n \"def sentiment_analysis(df):\\n analyzer = SentimentIntensityAnalyzer()\\n polarity = []\\n for tweet in df['clean_text'].astype(str):\\n sentiment = analyzer.polarity_scores(tweet)\\n polarity.append(sentiment['compound'])\\n df['sentiment'] = pd.Series(polarity)\\n return df\",\n \"def __init__(self, test):\\n self.all_grams = Ngram(self.START_OF_SENTENCE_TOKEN)\\n for label in self.label_type_map:\\n self.words_labels_counts[label] = {}\\n self.words_labels_counts[label][self.UNKNOWN_TOKEN] = 0\\n if test:\\n self.train(\\\"train.txt\\\")\\n self.test(\\\"test.txt\\\")\\n else:\\n self.train(\\\"train_partial.txt\\\")\\n self.validate(\\\"validation_partial.txt\\\")\",\n \"def forecast(self, results_filename):\\n\\n analyser = TweetSentimentAnalyser()\\n\\n candidates = self.dataset.candidates\\n party_key_from_candidate = lambda candidate: candidate.party[0]\\n number_tweets = len(self.tweets)\\n if number_tweets == 0:\\n print(\\\"No tweets were found in the file '{}' : aborting the prediction.\\\"\\n .format(number_tweets))\\n\\n # General statistics dictionnaries\\n # NB: defaultdict allows for efficiently incrementing the value of a key/value\\n # pair that may not be initialized yet without checking its existence\\n stats_tweets_per_user = defaultdict(int)\\n stats_dates = defaultdict(int)\\n stats_hashtags = defaultdict(int)\\n stats_parties = {party_key_from_candidate(c): 0 for c in candidates.values()}\\n\\n # Tweet analysis\\n results_per_candidate = {candidate_key: [0, 0] for candidate_key in candidates.keys()}\\n for tweet in self.tweets:\\n # retrieve the tweet data (text) and metadata (retweets, date...)\\n candidate_key = tweet[0].decode('utf-8')\\n text = tweet[1].decode('utf-8')\\n datetime_str = tweet[2].decode('utf-8')\\n datetime = dt.datetime.strptime(datetime_str, \\\"%Y-%m-%d %H:%M:%S\\\") # parse the datetime\\n retweets = int(tweet[3])\\n username = tweet[4].decode('utf-8')\\n hashtags = None if len(tweet[5]) == 0 else tweet[5].decode('utf-8').split(' ')\\n\\n # basic statistics (individual users & hashtags)\\n stats_tweets_per_user[username] += 1\\n date = datetime.strftime(\\\"%Y-%m-%d\\\") # keep only the date\\n stats_dates[date] += 1\\n if hashtags is not None:\\n for hashtag in hashtags:\\n if hashtag == '#': # ignore empty hashtag\\n continue\\n stats_hashtags[hashtag.lower()] += 1\\n\\n # per-candidate analysis\\n for key, data in candidates.items():\\n # party mentions statistics\\n party_key = party_key_from_candidate(data)\\n for party_designation in data.party:\\n if party_designation in text:\\n stats_parties[party_key] += 1\\n break\\n # basic forecast : 1 tweet = 1 vote for the mentionned candidate\\n for candidate_name in data.names:\\n if candidate_name in text:\\n results_per_candidate[key][0] += 1\\n\\n # sentimenal-analysis forecast :\\n score = analyser.analyse_tweet(text, retweets)\\n results_per_candidate[candidate_key][1] += score\\n\\n # Data interpretation\\n compound_sum = sum([v[1] for v in results_per_candidate.values()])\\n sort_default_dict = lambda dict, reverse:\\\\\\n sorted(dict.items(), key=lambda k_v: k_v[1], reverse=reverse)\\n\\n # Write the results file\\n votes_sum = sum([v[0] for v in results_per_candidate.values()])\\n with open(results_filename, 'w+') as results_file:\\n csv_writer = csv.writer(results_file, dialect=\\\"excel\\\")\\n\\n # per candiadate forecast\\n for candidate, results in results_per_candidate.items():\\n csv_writer.writerow([\\n candidate,\\n results,\\n \\\"{} %\\\".format(100 * results[0] / votes_sum),\\n \\\"{} %\\\".format(100 * results[1] / compound_sum)\\n ])\\n\\n # general statistics\\n for party, occurences in sort_default_dict(stats_parties, True):\\n csv_writer.writerow([party, occurences])\\n for username, occurences in sort_default_dict(stats_tweets_per_user, True):\\n csv_writer.writerow([username, occurences])\\n for hashtag, occurences in sort_default_dict(stats_hashtags, True):\\n csv_writer.writerow([hashtag, occurences])\\n\\n print(results_per_candidate)\",\n \"def __init__(self, words, corpus):\\n self.words = words\\n self.vocab_size = len(words)\\n self.corpus = corpus\\n counter = Counter(corpus)\\n self.counts = np.array([counter[i] for i in range(self.vocab_size)])\",\n \"def __init__(self, input=None):\\r\\n BaseAnalyzer.__init__(self, input)\",\n \"def __init__(self, input=None):\\r\\n BaseAnalyzer.__init__(self, input)\",\n \"def predict_tweet(tweet):\\n text = TextBlob(tweet)\\n \\n if text.sentiment.polarity > 0:\\n response = 'positive'\\n elif text.sentiment.polarity == 0:\\n response = 'neutral'\\n else:\\n response = 'negative'\\n return response, text.sentiment.polarity, text.sentiment.subjectivity\",\n \"def __init__(self, text: str):\\n self.words = WORDS_RE.findall(text)\",\n \"def __init__(self):\\n super(TestAnalyzer, self).__init__()\\n self._results = []\",\n \"def train(self, arg1=None, arg2=None, **kwargs):\\n nltk.download('averaged_perceptron_tagger')\\n nltk.download('wordnet')\\n nltk.download('twitter_samples')\\n nltk.download('punkt')\\n nltk.download('stopwords')\\n nltk.download('vader_lexicon')\\n\\n positive_tweets = twitter_samples.strings('positive_tweets.json')\\n negative_tweets = twitter_samples.strings('negative_tweets.json')\\n text = twitter_samples.strings('tweets.20150430-223406.json')\\n tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]\\n\\n stop_words = stopwords.words('english')\\n\\n positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')\\n negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')\\n\\n positive_cleaned_tokens_list = []\\n negative_cleaned_tokens_list = []\\n\\n for tokens in positive_tweet_tokens:\\n positive_cleaned_tokens_list.append(self.remove_noise(tokens, stop_words))\\n\\n for tokens in negative_tweet_tokens:\\n negative_cleaned_tokens_list.append(self.remove_noise(tokens, stop_words))\\n\\n all_pos_words = self.get_all_words(positive_cleaned_tokens_list)\\n\\n freq_dist_pos = FreqDist(all_pos_words)\\n print(freq_dist_pos.most_common(20))\\n\\n positive_tokens_for_model = self.get_tweets_for_model(positive_cleaned_tokens_list)\\n negative_tokens_for_model = self.get_tweets_for_model(negative_cleaned_tokens_list)\\n\\n positive_dataset = [(tweet_dict, \\\"Positive\\\")\\n for tweet_dict in positive_tokens_for_model]\\n\\n negative_dataset = [(tweet_dict, \\\"Negative\\\")\\n for tweet_dict in negative_tokens_for_model]\\n\\n dataset = positive_dataset + negative_dataset\\n\\n random.shuffle(dataset)\\n\\n train_data = dataset[:7000]\\n test_data = dataset[7000:]\\n\\n self.classifier = NaiveBayesClassifier.train(train_data)\",\n \"def get_tweets(self):\\r\\n return self.tweets\",\n \"def __init__(self, tokenizer=simple_tokenize):\\n # Set tokenizer to use for tokenizing new documents\\n self.tokenize = tokenizer\\n # The term document matrix is a sparse matrix represented as a\\n # list of dictionaries. Each dictionary contains the word\\n # counts for a document.\\n self.sparse = []\\n # Keep track of the number of documents containing the word.\\n self.doc_count = {}\",\n \"def __init__(self, corpus):\\n self.unigramCounts = collections.defaultdict(lambda: 0)\\n self.bigramCounts = collections.defaultdict(lambda: 0)\\n self.total = 0\\n self.train(corpus)\",\n \"def twitter(self):\\n\\n q = \\\" OR \\\".join(self.search_terms) + \\\" -filter:retweets\\\"\\n results = self.__api.search(q=q, lang='en', count=100)\\n\\n tweets = []\\n\\n for res in results:\\n\\n publishedAt = datetime.strptime(res._json['created_at'], '%a %b %d %H:%M:%S +0000 %Y').strftime(\\\"%Y-%m-%d\\\")\\n\\n if (res._json['in_reply_to_screen_name'] == None and publishedAt == datetime.now().strftime(\\\"%Y-%m-%d\\\")):\\n tweets.append([res._json['id'],\\n res._json['text'],\\n res._json['user']['screen_name'],\\n publishedAt,\\n res._json['user']['followers_count']])\\n\\n self.list = pd.DataFrame(tweets, columns=['id', 'title', 'user', 'publishedAt', 'followers_count']).nlargest(10,\\n 'followers_count')\\n\\n return\",\n \"def main():\\n # process CLI arguments\\n argparser = argparse.ArgumentParser(description=\\\"\\\"\\\"Script for classifying\\ntweets according to their sentiment polarity\\\"\\\"\\\")\\n\\n subparsers = argparser.add_subparsers(help=\\\"type of operation to perform\\\", dest = \\\"mode\\\")\\n # training options\\n tr_parser = subparsers.add_parser(TRAIN, help = \\\"train the model\\\")\\n tr_parser.add_argument(\\\"-d\\\", \\\"--dev-set\\\", help = \\\"development set\\\",\\n type = argparse.FileType('r'))\\n tr_parser.add_argument(\\\"-l\\\", \\\"--lexicon\\\", help = \\\"sentiment lexicon to use for sampling\\\",\\n type = str, action = \\\"append\\\", default = [])\\n _add_cmn_options(tr_parser)\\n # testing options\\n test_parser = subparsers.add_parser(TEST, help = \\\"test the model\\\")\\n test_parser.add_argument(\\\"-d\\\", \\\"--debug\\\", help = \\\"output debug information\\\", \\\\\\n action = \\\"store_true\\\")\\n test_parser.add_argument(\\\"-v\\\", \\\"--verbose\\\", help = \\\"output scores along with predicted labels\\\",\\n action = \\\"store_true\\\")\\n test_parser.add_argument(\\\"--scikit\\\", help = \\\"use supervised scikit classifier istead of deep\\\",\\n action = \\\"store_true\\\")\\n _add_cmn_options(test_parser)\\n # evaluation options (train and test at the same time)\\n ev_parser = subparsers.add_parser(EVALUATE, help = \\\"evaluate trained model\\\")\\n _add_cmn_options(ev_parser)\\n ev_parser.add_argument(\\\"-v\\\", \\\"--verbose\\\", help = \\\"output errors along with evaluation\\\",\\n action = \\\"store_true\\\")\\n args = argparser.parse_args()\\n # perform the requied action\\n if args.mode == TRAIN:\\n classifier = SentimentClassifier(a_path = None)\\n if args.dev_set is None:\\n dev_set = None\\n else:\\n dev_set = _read_dataset([args.dev_set])\\n lexica = [_read_lexicon(ilex) for ilex in args.lexicon]\\n pos, pos_re, neg, neg_re = _merge_lexica(lexica)\\n classifier.train(_read_dataset(args.files), a_path=args.model,\\n a_dev_set=dev_set, a_pos_re=pos_re, a_pos=pos,\\n a_neg_re=neg_re, a_neg=neg)\\n elif args.mode == TEST:\\n # load model from default location\\n y = \\\"\\\"; score = 0.\\n if args.model:\\n classifier = SentimentClassifier(args.model)\\n else:\\n classifier = SentimentClassifier()\\n for ifile in args.files:\\n for ifields in iterlines(ifile, TEST_TOPIC_IDX):\\n if args.debug:\\n classifier.debug(list(ifields[TXT_IDX]))\\n else:\\n y, score = classifier.predict(list(ifields[TXT_IDX]))\\n if args.verbose:\\n ifields.append(str(score))\\n ifields.append(y)\\n print(TAB.join(ifields))\\n else:\\n raise NotImplementedError\\n # for ifile in a_files:\\n # macro_MAE, micro_MAE = evaluate(classify(classifier, ifile), args.verbose, lambda x: x)\\n # print(\\\"{:20s}{:.7}\\\".format(\\\"Macro-averaged MAE:\\\", macro_MAE), file = sys.stderr)\\n # print(\\\"{:20s}{:.7}\\\".format(\\\"Micro-averaged MAE:\\\", micro_MAE), file = sys.stderr)\\n return 0\",\n \"def get_tweets(self, query, count=10):\\n # empty list to store parsed tweets\\n tweets = []\\n\\n try:\\n # call twitter api to fetch tweets\\n fetched_tweets = self.api.search(q=query, count=count)\\n\\n # parsing tweets one by one\\n for tweet in fetched_tweets:\\n # empty dictionary to store required params of a tweet\\n parsed_tweet = {}\\n\\n # saving text of tweet\\n parsed_tweet['text'] = tweet.text\\n # saving sentiment of tweet\\n parsed_tweet['sentiment'] = self.get_tweet_sentiment(tweet.text)\\n\\n # appending parsed tweet to tweets list\\n if tweet.retweet_count > 0:\\n # if tweet has retweets, ensure that it is appended only once\\n if parsed_tweet not in tweets:\\n tweets.append(parsed_tweet)\\n else:\\n tweets.append(parsed_tweet)\\n\\n # return parsed tweets\\n return tweets\\n\\n except tweepy.TweepError as e:\\n # print error (if any)\\n print(\\\"Error : \\\" + str(e))\",\n \"def __init__(self):\\r\\n self.tweets_by_user = collections.defaultdict(list)\\r\\n self.follows = collections.defaultdict(set)\\r\\n self.timestamp = 0\",\n \"def __init__(self):\\n self.time = 0\\n self.tweets = {}\\n self.follows = {}\",\n \"def text_analytics(self):\\n\\n headers = {\\n # Request headers\\n 'Content-Type': 'application/json',\\n 'Ocp-Apim-Subscription-Key': self.keys['text_analytics'],\\n }\\n \\n sentiment_url = 'https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment'\\n \\n raw_text = self.article_params['text']\\n\\n # Build post for sentiment\\n try:\\n sentences = tokenize.sent_tokenize(str(raw_text))\\n content = []\\n for i, sentence in enumerate(sentences):\\n content.append({'id': str(i), 'language': 'en', 'text': sentence})\\n body = json.dumps({\\\"documents\\\": content}).encode('utf-8')\\n\\n request = urllib.request.Request(sentiment_url, body, headers)\\n response = urllib.request.urlopen(request)\\n json_response = json.loads(response.read().decode('utf-8'))\\n \\n # A list of dictionaries, with each dictionary containing a sentence\\n # sentiment score\\n sentiments_list = json_response['documents']\\n\\n # Calculate the articles average sentiment from all the sentences\\n cumulative_sentiment_score = 0\\n for sent in sentiments_list:\\n cumulative_sentiment_score += sent['score']\\n avg_article_sentiment = cumulative_sentiment_score/len(sentiments_list)\\n\\n # Put article sentiments in bucket from 1 to 5, with 1 being very\\n # negative and 5 being very positive\\n if avg_article_sentiment < 0.2:\\n sentiment = 1\\n elif 0.2 <= avg_article_sentiment < 0.4:\\n sentiment = 2\\n elif 0.4 <= avg_article_sentiment < 0.6:\\n sentiment = 3\\n elif 0.6 <= avg_article_sentiment < 0.8:\\n sentiment = 4\\n else:\\n sentiment = 5\\n\\n except Exception as e:\\n print('Unable to process sentiment for article. Assuming '\\n 'sentiment is neutral.')\\n sentiment = 3\\n\\n return sentiment\",\n \"def main()->None:\\n #Define key words\\n keyword_list = ['#DataScience', '#MachineLearning', '#artificialintelligence', '#AI', '#ai', '#machinelearning',\\n '#deeplearning', 'DeepLearning', '#ML', '#ArtificialIntelligence', '#machinelearning',\\n 'DigitalTransformation'] # track list\\n\\n #Initiate Time\\n start_time = time.time() # grabs the system time\\n print(\\\"Launch! \\\\n\\\")\\n\\n #Listen to twitter\\n twitterStream = Stream(Authentification(), listener.Listener(start_time, time_limit=3600)) # initialize Stream object with a time out limit\\n twitterStream.filter(track=keyword_list, languages=['en']) # call the filter method to run the Stream Object\\n print('Exctraction from twitter succesful')\",\n \"def __init__(self, corpus):\\n self.unigram_count = Counter()\\n self.bigram_count = defaultdict(Counter)\\n self.vocabulary_size = 0\\n self.num_words = 0\\n self.backoff_multiplier = 0.4\\n self.train(corpus)\",\n \"def __init__(self):\\n self.load_objects()\\n super(TwitterMain,self).__init__()\",\n \"def __init__(self):\\n self.tokenizer = BOWTokenizer(\\n English()\\n ) # the tokenizer must have a tokenize() and parse() function.\\n self.labelEncoder = LabelEncoder()\\n self.vectorizer = CountVectorizer(\\n tokenizer=self.tokenizer.tokenize, ngram_range=(1, 1)\\n )\\n self.decode_params = {}\",\n \"def __init__(self):\\n self.timeStamp = 0\\n self.tweetTimeLine = {}\\n self.followList = {}\",\n \"def __init__(self, corpus):\\n self.unigramCounts = collections.defaultdict(lambda: 0)\\n self.totalCount = 0\\n self.zeroCount = 0\\n self.train(corpus)\",\n \"def __init__(self, words, text):\\n\\n self.prompts = words\\n self.template = text\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6772969","0.66310155","0.6570511","0.6479291","0.64774686","0.6471122","0.63829994","0.63479525","0.6342602","0.6246067","0.6185657","0.6146315","0.61160105","0.6048045","0.6047929","0.6042262","0.6041226","0.6035858","0.60260266","0.60242325","0.6005878","0.5968961","0.59406316","0.5939317","0.5925296","0.59222686","0.59214926","0.5896973","0.58498555","0.58485323","0.5843058","0.5837453","0.58341527","0.58171374","0.5810192","0.58094347","0.5803297","0.5799379","0.5788044","0.5754945","0.5745161","0.5744863","0.57248974","0.5702576","0.5671516","0.56711906","0.5668773","0.5668415","0.5668163","0.5664319","0.56606734","0.5654012","0.5646145","0.56290793","0.5628424","0.56175816","0.5605419","0.5604794","0.558395","0.55803835","0.55751556","0.55682325","0.5563468","0.5561542","0.55579895","0.55527806","0.5550603","0.5550357","0.55457234","0.55442125","0.5537635","0.5525209","0.5518517","0.55166507","0.5492015","0.5490966","0.54888284","0.5486752","0.54731923","0.54731923","0.5450629","0.54428405","0.54356074","0.54218954","0.542087","0.541496","0.5408954","0.54035807","0.53988755","0.5398868","0.5395468","0.5393339","0.53922623","0.5392043","0.53913116","0.5391056","0.5385053","0.53808445","0.53796536","0.5375837"],"string":"[\n \"0.6772969\",\n \"0.66310155\",\n \"0.6570511\",\n \"0.6479291\",\n \"0.64774686\",\n \"0.6471122\",\n \"0.63829994\",\n \"0.63479525\",\n \"0.6342602\",\n \"0.6246067\",\n \"0.6185657\",\n \"0.6146315\",\n \"0.61160105\",\n \"0.6048045\",\n \"0.6047929\",\n \"0.6042262\",\n \"0.6041226\",\n \"0.6035858\",\n \"0.60260266\",\n \"0.60242325\",\n \"0.6005878\",\n \"0.5968961\",\n \"0.59406316\",\n \"0.5939317\",\n \"0.5925296\",\n \"0.59222686\",\n \"0.59214926\",\n \"0.5896973\",\n \"0.58498555\",\n \"0.58485323\",\n \"0.5843058\",\n \"0.5837453\",\n \"0.58341527\",\n \"0.58171374\",\n \"0.5810192\",\n \"0.58094347\",\n \"0.5803297\",\n \"0.5799379\",\n \"0.5788044\",\n \"0.5754945\",\n \"0.5745161\",\n \"0.5744863\",\n \"0.57248974\",\n \"0.5702576\",\n \"0.5671516\",\n \"0.56711906\",\n \"0.5668773\",\n \"0.5668415\",\n \"0.5668163\",\n \"0.5664319\",\n \"0.56606734\",\n \"0.5654012\",\n \"0.5646145\",\n \"0.56290793\",\n \"0.5628424\",\n \"0.56175816\",\n \"0.5605419\",\n \"0.5604794\",\n \"0.558395\",\n \"0.55803835\",\n \"0.55751556\",\n \"0.55682325\",\n \"0.5563468\",\n \"0.5561542\",\n \"0.55579895\",\n \"0.55527806\",\n \"0.5550603\",\n \"0.5550357\",\n \"0.55457234\",\n \"0.55442125\",\n \"0.5537635\",\n \"0.5525209\",\n \"0.5518517\",\n \"0.55166507\",\n \"0.5492015\",\n \"0.5490966\",\n \"0.54888284\",\n \"0.5486752\",\n \"0.54731923\",\n \"0.54731923\",\n \"0.5450629\",\n \"0.54428405\",\n \"0.54356074\",\n \"0.54218954\",\n \"0.542087\",\n \"0.541496\",\n \"0.5408954\",\n \"0.54035807\",\n \"0.53988755\",\n \"0.5398868\",\n \"0.5395468\",\n \"0.5393339\",\n \"0.53922623\",\n \"0.5392043\",\n \"0.53913116\",\n \"0.5391056\",\n \"0.5385053\",\n \"0.53808445\",\n \"0.53796536\",\n \"0.5375837\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74163216"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13793,"cells":{"query":{"kind":"string","value":"Load a saved model/walker"},"document":{"kind":"string","value":"def load_walker(self, filename=None, state=0):\n if filename is not None:\n print('Loading Walker: ' + filename + '...')\n err, _ = vrep.simxLoadModel(self.client_id, filename, state,\n OP_MODE)\n if err not in VALID_ERROR_CODES:\n raise WalkerLoadingError()\n self.walker = self.walker_cl()"},"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 load(path_to_model):\n pass","def load_model(self, path):\n pass","def load_model(self):\n pass","def load(\n self,\n modelLoadPath\n ):\n pass","def load_model(self) -> Any:","def __load_model(self):\n loaded = load(self.__file_name)\n self.__model = loaded['model']\n self.__meta_data = loaded['metadata']\n self.__is_ready = True","def load_model(self, model_path: str):","def load_model(self, filename):\r\n pass","def load(self, path):\n load_model(path, self)","def load_model(self):\n if self.save_path is not None:\n if isfile(self.save_path):\n self.model.load_state_dict(load(self.save_path))\n else:\n raise ValueError(\"Cannot find model save file: \" + self.save_path)","def load_model():\n with open(MODEL_SAVE_JSON, 'r') as fp:\n json_string = fp.read()\n model = model_from_json(json_string)\n return model","def load(self):\n if self.model is None:\n raise Exception(\"Build the model first.\")\n\n print(\"Loading model checkpoint {} ...\\n\".format(self.config[\"model\"][\"restore_model\"]))\n self.model.load_weights(self.config[\"model\"][\"restore_model\"])\n print(\"Model loaded!\")","def load(self):\n if self.model is None:\n raise Exception(\"Build the model first.\")\n\n print(\"Loading model checkpoint {} ...\\n\".format(self.config[\"model\"][\"restore_model\"]))\n self.model.load_weights(self.config[\"model\"][\"restore_model\"])\n print(\"Model loaded!\")","def load(self):\n try:\n if self.model.is_cuda:\n self.model.load_state_dict(torch.load(os.path.join(self.save_path, \"save_point.pth\")))\n else:\n self.model.load_state_dict(torch.load(os.path.join(self.save_path, \\\n \"save_point.pth\"), map_location=\"cpu\"))\n except:\n sys.exit(\"Unable to load previous model\")","def load_model(model, path):\n\tmodel.load_state_dict(torch.load(path))\n\tprint(\"pre-trained model loaded from {}\".format(path))","def load(self):\n print(\"==> Loading model from\", self.model_dir)\n self.model = tf.keras.models.load_model(self.model_dir)","def load_checkpoint(self, model):\n print(f\"load model {self.save_model_path}\")\n model.load_state_dict(torch.load(self.save_model_path))","def load_model(self, path):\n self._saver.restore(self._sess, path + '/model.ckp')\n pkl_file = open(path + '/som.pkl', 'rb')\n restored = pickle.load(pkl_file)\n pkl_file.close()\n self._m = restored['_m']\n self._n = restored['_n']\n self._neighbourhood = restored['_neighbourhood']\n # self._topography = restored['_topography']\n self._num_iterations = restored['_num_iterations']\n self._Wts = restored['_Wts']\n self._locations = restored['_locations']\n self._learned = restored['_learned']\n self._centroid_grid = restored['_centroid_grid']\n self.abnormal_dist = restored['abnormal_dist']\n\n print(\"Model restored from path: \" + path)","def load(self, path, model_id):\n self.load_state_dict(torch.load(os.path.join(path, '{}-retriever'.format(model_id))))","def load_model(self):\n saved_path = self.config.path_tmp / self.model.model_name\n if saved_path.exists():\n self.model.load_weights(str(saved_path / 'model.vec'))","def loadModel(self):\n self.model.load_state_dict(torch.load(os.path.join(self.model_save_dir, '{}_trained.pt'.format(self.model_name)), map_location=torch.device(device)))\n return self.model","def load_model(self):\n Thread(target=self.__load_model).start()","def _load_model(self):\n self._load_scaler('scaler.save')\n self._load_encoder('encoder0.save', 0)\n self._load_encoder('encoder1.save', 1)\n self._load_neural_network('model.json', 'model.h5')\n return","def load_model() -> None:\n global model\n\n if app.testing:\n current_dir = os.path.dirname(__file__)\n model_path = os.path.join(current_dir, \"models/model.pkl\")\n else:\n model_path = os.getenv(\"PATH_TO_MODEL\")\n\n if model_path is None:\n err = f\"PATH_TO_MODEL {model_path} is None\"\n raise RuntimeError(err)\n\n with open(model_path, \"rb\") as model_file:\n model = pickle.load(model_file)","def _load_model(self):\n with open(self.filepath, 'rb') as file:\n self.cmodel = pickle.load(file)","def load_model(self, file=None):\n return None","def load_model(self):\n if os.stat('code/lr-model.pt').st_size == 0:\n return\n params = torch.load('code/lr-model.pt')\n self.set_params(params)","def load_eval(saver, session, load_dir):\n saver.restore(session, load_dir)\n print('model loaded successfully')\n return extract_step(load_dir)","def load_model(self):\n # Load the model\n print('Loading model:', self.model_path)\n t0 = time.time()\n model = load_model(self.model_path)\n t1 = time.time()\n print('Loaded in:', t1 - t0)\n return model","def load_model():\n logger.info('load_model called')\n return 1","def load_trainer(self):\n super().load_trainer()\n\n logging.info(\"[Server #%d] Loading a pre-trained model.\", os.getpid())\n self.trainer.load_model()","def load_model(self, filename):\n filename = path.join(self.root_path, f'models/{filename}.pkl')\n self.model = pickle.load(open(filename, \"rb\"))\n print('Successfully loaded model from '+filename)","def load_model():\n with open(paths.model('model.pkl'), 'rb') as stream:\n return pickle.load(stream)","def load_full_model(self, session, model_dir):\n self.full_saver.restore(session, model_dir)","def load(self):\n utils.get_previous_weights_from_gdrive(self.config.model_folder)\n last_used_model = utils.get_latest_model_name(self.config.model_folder)\n self.model = load_model(last_used_model)\n self.model.summary()","def load_model():\n\n # find location of model\n\n file_path = '/Users/davidodwyer/Desktop' # to the directory\n file_name = 'original_mlr.joblib' \n the_file = os.path.join(file_path, file_name)\n\n # load model\n\n model = load(the_file)\n\n return model","def load_model_from_tree(widget,\n name,\n path=None,\n strict=True):\n ph.TreeDumper.get_instance().load(widget, name, path, strict)","def load_model():\n with open('../model/regressor.pickle', 'rb') as f:\n model = pickle.load(f)\n return model","def load_checkpoint(model, save_path):\n model.load_state_dict(torch.load(save_path))","def _load_model(self):\n self.model = tf.keras.experimental.load_from_saved_model(\n self.m_cfg['load_model'], custom_objects=self.custom_objects)\n\n ref = 1 if self.m_cfg['configs']['recursive'] else self.levels\n self.opt = [self._inst_optimizer() for _ in range(ref)]\n self.loss = Losses(self.m_cfg['configs']['loss']).value\n\n l_groups = np.split(np.array(self.model.layers), ref)\n self.vars = list(map(\n lambda g: list(chain(*map(lambda e: e.variables, g))), l_groups))","def __load_Model(self):\r\n PrintsForUser.printProcess(\"[INFO] Loading network...\")\r\n \r\n self.__model = load_model(self.__model_path)\r\n self.__lb = pickle.loads(open(self.__labels_path, \"rb\").read())","def load_model(self):\n self.__model = tf.keras.models.load_model(\n os.path.join(self.model_path, \"model.h5\")\n )\n print(\"[INFO] Model loaded!\")\n\n tok_pth = os.path.join(self.model_path, \"tokenizer.json\")\n with open(tok_pth, \"r\") as f:\n self.__tokenizer = tf.keras\\\n .preprocessing\\\n .text\\\n .tokenizer_from_json(f.read())\n print(\"[INFO] Tokenizer loaded!\")\n\n meta_pth = os.path.join(self.model_path, \"meta.json\")\n with open(meta_pth, \"r\") as f:\n meta = json.load(f)\n self.__title_len = meta[\"title_pad_length\"]\n self.__body_len = meta[\"body_pad_length\"]\n\n self.load_explainer()\n print(\"[INFO] Explainer loaded!\")","def load_model(self, fname):\n if self._Booster is None:\n self._Booster = Booster({'nthread': self.n_jobs})\n self._Booster.load_model(fname)","def load_model_from_checkpoint(self, path: str):\n ckpt = torch.load(path, map_location='cpu')\n self.net_q.encoder.load_state_dict(ckpt['encoder'])\n self.net_q.head.load_state_dict(ckpt['head'])\n self.net_ps.load_state_dict(ckpt['net_ps'])\n self.net_k.load_state_dict(ckpt['net_k'])\n self.queue.load_state_dict(ckpt['queue'])\n self.optimizer.load_state_dict(ckpt['optimizer'])\n if 'scheduler' in ckpt:\n self.scheduler.load_stae_dict(ckpt['scheduler'])\n self.move_optimizer_states(self.optimizer, self.local_rank)","def load_model(self, model, id=LAST):\n model.load_state_dict(self.data_dict[id])\n return model","def load_model(self, tmp_dir):\n pass","def load_model(filename):\n return Model.load_savefile(filename)","def load(self):\n pass","def load(self):\n pass","def load(self):\n pass","def load(self):\n pass","def load(self, model_name_or_path):\n return BertMLM(model_name_or_path, self.top_k)","def __load(self, model_name):\n\n print(\"Loading model.\")\n tstart = datetime.now()\n\n # Temporary directory to extract the zipped information\n with tempfile.TemporaryDirectory() as dirpath:\n\n # Unzip the directory that contains the saved model(s)\n with zipfile.ZipFile(model_name + \".zip\", \"r\") as zip_ref:\n zip_ref.extractall(dirpath)\n\n # Load metadata\n metadata = pickle.load(open(dirpath + \"/metadata.pickle\", \"rb\"))\n\n # Re-load metadata\n self.__dict__.update(metadata)\n\n # Load all sub-models\n try:\n self.__mol_to_latent_model = load_model(\n dirpath + \"/mol_to_latent_model.h5\"\n )\n except:\n print(\"'mol_to_latent_model' not found, setting to None.\")\n self.__mol_to_latent_model = None\n\n self.__latent_to_states_model = load_model(\n dirpath + \"/latent_to_states_model.h5\"\n )\n self.__batch_model = load_model(dirpath + \"/batch_model.h5\")\n \n # Build sample_model out of the trained batch_model\n self.__build_sample_model(batch_input_length=1) # Single-output model\n self.__build_sample_model(\n batch_input_length=256 # could also be self.batch_size\n ) # Multi-output model\n\n print(\"Loading finished in %i seconds.\" % ((datetime.now() - tstart).seconds))","def initialize(self) -> None:\n self.model = load(self.path)","def load(self, file_path):\n self.model = load_model(file_path)","def load(self, file_path):\n self.model = load_model(file_path)","def load(self, file_path):\n self.model = load_model(file_path)","def load_model():\n with open(MODEL_FILENAME, \"rb\") as file:\n model = pickle.load(file)\n return model","def load_model(model, device, model_path):\n if os.path.exists(model_path):\n print(\"Reading model from \", model_path)\n checkpoint = torch.load(model_path, map_location=torch.device(device))\n model.load_state_dict(checkpoint['state_dict'])\n return model\n else:\n raise RuntimeError('Model does not exist!')","def test_load_save_model():\n model = BERTopic(language=\"Dutch\", embedding_model=None)\n model.save(\"test\")\n loaded_model = BERTopic.load(\"test\")\n assert type(model) == type(loaded_model)\n assert model.language == loaded_model.language\n assert model.embedding_model == loaded_model.embedding_model\n assert model.top_n_words == loaded_model.top_n_words","def load_model(self, **params):\n \t# file_name = params['name']\n # return pickle.load(gzip.open(file_name, 'rb'))","def load_model(self, path_model: Optional[PathLike]) -> None:\n raise NotImplementedError","def load_model():\n global obj\n obj = NutritionTableDetector()\n print(\"Weights Loaded!\")","def load_model(model, model_index, device=\"cpu\"):\n with open(\"trained_local_model\"+str(model_index), \"rb\") as f_:\n model.load_state_dict(torch.load(f_))\n model.to(device)\n return model","def load_model(path, model, optimizer):\n print(\"LOADING MODEL...\")\n ckpt = tf.train.Checkpoint(model=model, optimizer=optimizer)\n status = ckpt.restore(tf.train.latest_checkpoint(path))\n ckpt_manager = tf.train.CheckpointManager(\n checkpoint=ckpt, \n directory=FLAGS.model_dir, \n max_to_keep=3 \n )\n return model, optimizer, ckpt, ckpt_manager","def load_model(self):\n with open(self.args.trained_model, 'rb') as handle:\n self.model_hash = hashlib.sha224(handle.read()).hexdigest()\n\n self.model.load(self.args.trained_model)\n self.logger.debug('Loaded model from %s', self.args.trained_model)\n return","def load(self, path: str):\n self.model.load_state_dict(torch.load(path))","def load_model(saved_dir):\n output_args_file = os.path.join(saved_dir, 'training_args.bin')\n args = torch.load(output_args_file)\n print('Loaded args:', args)\n tokenizer_class = get_tokenizer_class(args.model_name)\n tokenizer = tokenizer_class.from_pretrained(saved_dir)\n model_class = get_model_class(args.model_name, args.task_name)\n model = model_class.from_pretrained(saved_dir)\n return model, tokenizer, args","def load_model(self):\n try:\n self.model = Word2Vec.load(self.config[\"model_path\"])\n self.model.init_sims(replace=True)\n except Exception as e:\n print(e)\n print(\"error in model loading!\")","def load_model(model):\n fin = False\n backup1 = False\n backup2 = False\n\n if os.path.exists(\"TrainedModel/finalModel.pth\"):\n fin = True\n elif os.path.exists(\"TrainedModel/modelBackup.pth\"):\n backup1 = True\n elif os.path.exists(\"TrainedModel/modelBackupBackup.pth\"):\n backup2 = True\n\n if fin:\n try:\n model.load_state_dict(torch.load(\"TrainedModel/finalModel.pth\"))\n return model\n except:\n print(\"finalModel seems to be corrupted, trying a backup...\")\n \n if fin or backup1:\n try:\n model.load_state_dict(torch.load(\"TrainedModel/modelBackup.pth\"))\n return model\n except:\n print(\"modelBackup seems to be corrupted, trying a backup...\")\n\n if fin or backup1 or backup2:\n try:\n model.load_state_dict(torch.load(\"TrainedModel/modelBackupBackup.pth\"))\n return model\n except:\n print(\"modelBackupBackup seems to be corrupted, you're at the end of the line.\")\n\n print(\"There doesn't seem to be anything to load.\")\n return model","def _load_model(self, loc):\n\n # If not a string, return input\n if not (isinstance(loc, str) or isinstance(loc, unicode)):\n return loc\n\n # If location is in S3, copy to local, then unpickle \n to_delete = False\n if \"s3\" in loc:\n tmp_loc = \"{0}/tmp_file_{1}.obj\".format(tmpdir, random.randint(1,1000))\n s3 = boto3.client('s3')\n bucket = loc.split(\"/\")[2]\n key = \"/\".join(loc.split(\"/\")[3:])\n with open(tmp_loc, \"wb\") as data:\n s3.download_fileobj(bucket, key, data)\n loc = tmp_loc\n to_delete = True\n with open(loc, \"rb\") as f:\n model = pickle.load(f)\n if to_delete:\n os.remove(tmp_loc)\n return model","def _load_best_model(self) -> None:\n self.trainer.resume()","def load_model(session: tf.Session, model_dir: Text) -> None:\n saver = tf.train.Saver()\n saver.restore(session, model_dir)","def load_model_by_name(model, global_step, device=None, path=\"/scratch/users/zucks626/ADNI/IPMI/checkpoints/\"):\r\n # path = \"/scratch/users/zucks626/ADNI/ae_cls/checkpoints/\"\r\n file_path = path + model.name + \"/\" + 'model-{:05d}.pt'.format(global_step)\r\n state = torch.load(file_path, map_location=device)\r\n model.load_state_dict(state)\r\n print(\"Loaded from {}\".format(file_path))","def load_model_from_file(path, as_builder=False):\n module = load_model_module(path)\n model = module.model\n if not as_builder:\n model = model()\n del sys.path_importer_cache[os.path.dirname(module.__file__)]\n del sys.modules[module.__name__]\n return model","def load(self, path):\n parameters = torch.load(path)\n\n if \"optimizer\" in parameters:\n parameters = parameters[\"model\"]\n\n self.load_state_dict(parameters)","def load_model(fn, model):\n if fn[-3] != \".tf\":\n fn += \".tf\"\n if model.saver is None:\n with model.graph.as_default():\n model.saver = tf.train.Saver()\n log(\"Loading model from {}\".format(fn))\n model.saver.restore(model.session, fn)\n log(\"Done loading!\")","def load_model(self):\n if os.path.exists(self.model_filename):\n self.model.load_weights(self.model_filename)","def load_model(self):\n if self.ckpt_flag:\n LOG('Skip Loading Pre-trained Model......')\n else:\n if self.params.pre_trained_from is not None and os.path.exists(self.params.pre_trained_from):\n try:\n LOG('Loading Pre-trained Model at %s' % self.params.pre_trained_from)\n pretrain = torch.load(self.params.pre_trained_from)\n self.network.load_state_dict(pretrain)\n LOG('Pre-trained Model Loaded!')\n except:\n WARNING('Cannot load pre-trained model. Start training......')\n else:\n WARNING('Pre-trained model do not exits. Start training......')","def load_model(self) -> None:\n\n try:\n model_class = MODEL_TYPES[self.model_type]\n except KeyError:\n raise KeyError(f\"model type: {self.model_type} not supported\")\n\n if (\n os.path.exists(self.resources_path)\n and len(os.listdir(self.resources_path)) > 0\n ):\n model_name_or_path = self.resources_path\n else:\n model_name_or_path = self.model_name\n\n if self.model_type == \"stable_diffusion\":\n self.model = model_class.from_pretrained(\n model_name_or_path,\n use_auth_token=self.auth_token,\n )\n else:\n self.model = model_class.from_pretrained(model_name_or_path)\n\n self.model.to(self.device)","def load(self):\n self._really_load()","def load_model(self, directory):\n with tf.Session() as sess:\n tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], directory)\n self.model = tf.contrib.predictor.from_saved_model(directory)","def load_model(model):\n # Check if the model is a model directory (containing a metagraph and a checkpoint file)\n # or if it is a protobuf file with a frozen graph\n model_exp = os.path.expanduser(model)\n if os.path.isfile(model_exp):\n print('Model filename: %s' % model_exp)\n with tf.gfile.FastGFile(model_exp, 'rb') as f_l:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f_l.read())\n tf.import_graph_def(graph_def, name='')\n else:\n print('Model directory: %s' % model_exp)\n meta_file, ckpt_file = get_model_filenames(model_exp)\n\n print('Metagraph file: %s' % meta_file)\n print('Checkpoint file: %s' % ckpt_file)\n\n saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file))\n saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))","def load(model_file):\n return pickle.load(open(model_file))","def load_model(self, folder_name):\n raise NotImplementedError()","def load_model(model_path):\n nlp = spacy.blank('en') \n if 'ner' not in nlp.pipe_names:\n ner = nlp.create_pipe('ner')\n nlp.add_pipe(ner)\n #load pretrained model from the path\n ner = nlp.from_disk(model_path)\n return ner","def load(self, path):\n print('Loading model from {}'.format(path.name))\n self.qnetwork_local.load_state_dict(torch.load(path))\n self.qnetwork_target.load_state_dict(torch.load(path))\n return","def load_model():\n return \"None\"","def load_model(path):\n # for example -> f\"{os.getcwd()}/trained_models\"\n return spacy.load(path)","def load(self):\n self._load()","def load(model, name=\"store/base\"):\n if torch.cuda.is_available():\n pretrained_dict = torch.load(name + \".pt\")\n else:\n pretrained_dict = torch.load(name + \".pt\", map_location=torch.device('cpu'))\n print(\"Loaded\", name + \" model.\")\n model_dict = model.state_dict()\n model_dict.update(pretrained_dict)\n model.load_state_dict(model_dict)","def load(self, path):\n checkpoint = torch.load(path)\n self.load_state_dict(checkpoint['model_state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])","def load(self, path):\n checkpoint = torch.load(path)\n self.load_state_dict(checkpoint['model_state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])","def loadModel(file_name):\n with open(SAVE_PATH + file_name, \"rb\") as in_file:\n model = pickle.load(in_file, encoding = \"uft-8\")\n print(\"{} loaded\".format(file_name))\n return model","def load_model(self, model_name, model_url):\n\n fname = join(self.root, model_name)\n if not isfile(fname):\n if self.verbose:\n print(\"Could not find \" + fname + \".. attempt download\")\n with urllib.request.urlopen(model_url) as res, open(fname, 'wb') as f:\n shutil.copyfileobj(res, f)\n if self.verbose:\n print(\"Download complete.. model: \" + fname)\n elif self.verbose:\n print(\"Found model \" + fname + \"! :)\")\n\n model = load_model(fname)\n self.model = model","def load_model(fname: os.PathLike) -> Model:\n return Model.load(fname)","def _load(self) -> None:\n self.record = self._saved_record\n self.counter = self._saved_counter\n self.current_objects = self._saved_objects","def load_model(model_path: str) -> object:\n model = torch.load(model_path)\n model.eval()\n return model","def load_checkpoint(path, model, optimizer=None, reset_optimizer=True):\n print(\"Load checkpoint from: {}\".format(path))\n state_dict, optimizer_state = _load(path)\n\n model.load_dict(state_dict)\n if not reset_optimizer and optimizer is not None:\n if optimizer_state is not None:\n print(\"[loading] Load optimizer state from {}\".format(path))\n optimizer.load(optimizer_state)\n\n return model","def load_best_model(self) -> None:\n self.resume()"],"string":"[\n \"def load(path_to_model):\\n pass\",\n \"def load_model(self, path):\\n pass\",\n \"def load_model(self):\\n pass\",\n \"def load(\\n self,\\n modelLoadPath\\n ):\\n pass\",\n \"def load_model(self) -> Any:\",\n \"def __load_model(self):\\n loaded = load(self.__file_name)\\n self.__model = loaded['model']\\n self.__meta_data = loaded['metadata']\\n self.__is_ready = True\",\n \"def load_model(self, model_path: str):\",\n \"def load_model(self, filename):\\r\\n pass\",\n \"def load(self, path):\\n load_model(path, self)\",\n \"def load_model(self):\\n if self.save_path is not None:\\n if isfile(self.save_path):\\n self.model.load_state_dict(load(self.save_path))\\n else:\\n raise ValueError(\\\"Cannot find model save file: \\\" + self.save_path)\",\n \"def load_model():\\n with open(MODEL_SAVE_JSON, 'r') as fp:\\n json_string = fp.read()\\n model = model_from_json(json_string)\\n return model\",\n \"def load(self):\\n if self.model is None:\\n raise Exception(\\\"Build the model first.\\\")\\n\\n print(\\\"Loading model checkpoint {} ...\\\\n\\\".format(self.config[\\\"model\\\"][\\\"restore_model\\\"]))\\n self.model.load_weights(self.config[\\\"model\\\"][\\\"restore_model\\\"])\\n print(\\\"Model loaded!\\\")\",\n \"def load(self):\\n if self.model is None:\\n raise Exception(\\\"Build the model first.\\\")\\n\\n print(\\\"Loading model checkpoint {} ...\\\\n\\\".format(self.config[\\\"model\\\"][\\\"restore_model\\\"]))\\n self.model.load_weights(self.config[\\\"model\\\"][\\\"restore_model\\\"])\\n print(\\\"Model loaded!\\\")\",\n \"def load(self):\\n try:\\n if self.model.is_cuda:\\n self.model.load_state_dict(torch.load(os.path.join(self.save_path, \\\"save_point.pth\\\")))\\n else:\\n self.model.load_state_dict(torch.load(os.path.join(self.save_path, \\\\\\n \\\"save_point.pth\\\"), map_location=\\\"cpu\\\"))\\n except:\\n sys.exit(\\\"Unable to load previous model\\\")\",\n \"def load_model(model, path):\\n\\tmodel.load_state_dict(torch.load(path))\\n\\tprint(\\\"pre-trained model loaded from {}\\\".format(path))\",\n \"def load(self):\\n print(\\\"==> Loading model from\\\", self.model_dir)\\n self.model = tf.keras.models.load_model(self.model_dir)\",\n \"def load_checkpoint(self, model):\\n print(f\\\"load model {self.save_model_path}\\\")\\n model.load_state_dict(torch.load(self.save_model_path))\",\n \"def load_model(self, path):\\n self._saver.restore(self._sess, path + '/model.ckp')\\n pkl_file = open(path + '/som.pkl', 'rb')\\n restored = pickle.load(pkl_file)\\n pkl_file.close()\\n self._m = restored['_m']\\n self._n = restored['_n']\\n self._neighbourhood = restored['_neighbourhood']\\n # self._topography = restored['_topography']\\n self._num_iterations = restored['_num_iterations']\\n self._Wts = restored['_Wts']\\n self._locations = restored['_locations']\\n self._learned = restored['_learned']\\n self._centroid_grid = restored['_centroid_grid']\\n self.abnormal_dist = restored['abnormal_dist']\\n\\n print(\\\"Model restored from path: \\\" + path)\",\n \"def load(self, path, model_id):\\n self.load_state_dict(torch.load(os.path.join(path, '{}-retriever'.format(model_id))))\",\n \"def load_model(self):\\n saved_path = self.config.path_tmp / self.model.model_name\\n if saved_path.exists():\\n self.model.load_weights(str(saved_path / 'model.vec'))\",\n \"def loadModel(self):\\n self.model.load_state_dict(torch.load(os.path.join(self.model_save_dir, '{}_trained.pt'.format(self.model_name)), map_location=torch.device(device)))\\n return self.model\",\n \"def load_model(self):\\n Thread(target=self.__load_model).start()\",\n \"def _load_model(self):\\n self._load_scaler('scaler.save')\\n self._load_encoder('encoder0.save', 0)\\n self._load_encoder('encoder1.save', 1)\\n self._load_neural_network('model.json', 'model.h5')\\n return\",\n \"def load_model() -> None:\\n global model\\n\\n if app.testing:\\n current_dir = os.path.dirname(__file__)\\n model_path = os.path.join(current_dir, \\\"models/model.pkl\\\")\\n else:\\n model_path = os.getenv(\\\"PATH_TO_MODEL\\\")\\n\\n if model_path is None:\\n err = f\\\"PATH_TO_MODEL {model_path} is None\\\"\\n raise RuntimeError(err)\\n\\n with open(model_path, \\\"rb\\\") as model_file:\\n model = pickle.load(model_file)\",\n \"def _load_model(self):\\n with open(self.filepath, 'rb') as file:\\n self.cmodel = pickle.load(file)\",\n \"def load_model(self, file=None):\\n return None\",\n \"def load_model(self):\\n if os.stat('code/lr-model.pt').st_size == 0:\\n return\\n params = torch.load('code/lr-model.pt')\\n self.set_params(params)\",\n \"def load_eval(saver, session, load_dir):\\n saver.restore(session, load_dir)\\n print('model loaded successfully')\\n return extract_step(load_dir)\",\n \"def load_model(self):\\n # Load the model\\n print('Loading model:', self.model_path)\\n t0 = time.time()\\n model = load_model(self.model_path)\\n t1 = time.time()\\n print('Loaded in:', t1 - t0)\\n return model\",\n \"def load_model():\\n logger.info('load_model called')\\n return 1\",\n \"def load_trainer(self):\\n super().load_trainer()\\n\\n logging.info(\\\"[Server #%d] Loading a pre-trained model.\\\", os.getpid())\\n self.trainer.load_model()\",\n \"def load_model(self, filename):\\n filename = path.join(self.root_path, f'models/{filename}.pkl')\\n self.model = pickle.load(open(filename, \\\"rb\\\"))\\n print('Successfully loaded model from '+filename)\",\n \"def load_model():\\n with open(paths.model('model.pkl'), 'rb') as stream:\\n return pickle.load(stream)\",\n \"def load_full_model(self, session, model_dir):\\n self.full_saver.restore(session, model_dir)\",\n \"def load(self):\\n utils.get_previous_weights_from_gdrive(self.config.model_folder)\\n last_used_model = utils.get_latest_model_name(self.config.model_folder)\\n self.model = load_model(last_used_model)\\n self.model.summary()\",\n \"def load_model():\\n\\n # find location of model\\n\\n file_path = '/Users/davidodwyer/Desktop' # to the directory\\n file_name = 'original_mlr.joblib' \\n the_file = os.path.join(file_path, file_name)\\n\\n # load model\\n\\n model = load(the_file)\\n\\n return model\",\n \"def load_model_from_tree(widget,\\n name,\\n path=None,\\n strict=True):\\n ph.TreeDumper.get_instance().load(widget, name, path, strict)\",\n \"def load_model():\\n with open('../model/regressor.pickle', 'rb') as f:\\n model = pickle.load(f)\\n return model\",\n \"def load_checkpoint(model, save_path):\\n model.load_state_dict(torch.load(save_path))\",\n \"def _load_model(self):\\n self.model = tf.keras.experimental.load_from_saved_model(\\n self.m_cfg['load_model'], custom_objects=self.custom_objects)\\n\\n ref = 1 if self.m_cfg['configs']['recursive'] else self.levels\\n self.opt = [self._inst_optimizer() for _ in range(ref)]\\n self.loss = Losses(self.m_cfg['configs']['loss']).value\\n\\n l_groups = np.split(np.array(self.model.layers), ref)\\n self.vars = list(map(\\n lambda g: list(chain(*map(lambda e: e.variables, g))), l_groups))\",\n \"def __load_Model(self):\\r\\n PrintsForUser.printProcess(\\\"[INFO] Loading network...\\\")\\r\\n \\r\\n self.__model = load_model(self.__model_path)\\r\\n self.__lb = pickle.loads(open(self.__labels_path, \\\"rb\\\").read())\",\n \"def load_model(self):\\n self.__model = tf.keras.models.load_model(\\n os.path.join(self.model_path, \\\"model.h5\\\")\\n )\\n print(\\\"[INFO] Model loaded!\\\")\\n\\n tok_pth = os.path.join(self.model_path, \\\"tokenizer.json\\\")\\n with open(tok_pth, \\\"r\\\") as f:\\n self.__tokenizer = tf.keras\\\\\\n .preprocessing\\\\\\n .text\\\\\\n .tokenizer_from_json(f.read())\\n print(\\\"[INFO] Tokenizer loaded!\\\")\\n\\n meta_pth = os.path.join(self.model_path, \\\"meta.json\\\")\\n with open(meta_pth, \\\"r\\\") as f:\\n meta = json.load(f)\\n self.__title_len = meta[\\\"title_pad_length\\\"]\\n self.__body_len = meta[\\\"body_pad_length\\\"]\\n\\n self.load_explainer()\\n print(\\\"[INFO] Explainer loaded!\\\")\",\n \"def load_model(self, fname):\\n if self._Booster is None:\\n self._Booster = Booster({'nthread': self.n_jobs})\\n self._Booster.load_model(fname)\",\n \"def load_model_from_checkpoint(self, path: str):\\n ckpt = torch.load(path, map_location='cpu')\\n self.net_q.encoder.load_state_dict(ckpt['encoder'])\\n self.net_q.head.load_state_dict(ckpt['head'])\\n self.net_ps.load_state_dict(ckpt['net_ps'])\\n self.net_k.load_state_dict(ckpt['net_k'])\\n self.queue.load_state_dict(ckpt['queue'])\\n self.optimizer.load_state_dict(ckpt['optimizer'])\\n if 'scheduler' in ckpt:\\n self.scheduler.load_stae_dict(ckpt['scheduler'])\\n self.move_optimizer_states(self.optimizer, self.local_rank)\",\n \"def load_model(self, model, id=LAST):\\n model.load_state_dict(self.data_dict[id])\\n return model\",\n \"def load_model(self, tmp_dir):\\n pass\",\n \"def load_model(filename):\\n return Model.load_savefile(filename)\",\n \"def load(self):\\n pass\",\n \"def load(self):\\n pass\",\n \"def load(self):\\n pass\",\n \"def load(self):\\n pass\",\n \"def load(self, model_name_or_path):\\n return BertMLM(model_name_or_path, self.top_k)\",\n \"def __load(self, model_name):\\n\\n print(\\\"Loading model.\\\")\\n tstart = datetime.now()\\n\\n # Temporary directory to extract the zipped information\\n with tempfile.TemporaryDirectory() as dirpath:\\n\\n # Unzip the directory that contains the saved model(s)\\n with zipfile.ZipFile(model_name + \\\".zip\\\", \\\"r\\\") as zip_ref:\\n zip_ref.extractall(dirpath)\\n\\n # Load metadata\\n metadata = pickle.load(open(dirpath + \\\"/metadata.pickle\\\", \\\"rb\\\"))\\n\\n # Re-load metadata\\n self.__dict__.update(metadata)\\n\\n # Load all sub-models\\n try:\\n self.__mol_to_latent_model = load_model(\\n dirpath + \\\"/mol_to_latent_model.h5\\\"\\n )\\n except:\\n print(\\\"'mol_to_latent_model' not found, setting to None.\\\")\\n self.__mol_to_latent_model = None\\n\\n self.__latent_to_states_model = load_model(\\n dirpath + \\\"/latent_to_states_model.h5\\\"\\n )\\n self.__batch_model = load_model(dirpath + \\\"/batch_model.h5\\\")\\n \\n # Build sample_model out of the trained batch_model\\n self.__build_sample_model(batch_input_length=1) # Single-output model\\n self.__build_sample_model(\\n batch_input_length=256 # could also be self.batch_size\\n ) # Multi-output model\\n\\n print(\\\"Loading finished in %i seconds.\\\" % ((datetime.now() - tstart).seconds))\",\n \"def initialize(self) -> None:\\n self.model = load(self.path)\",\n \"def load(self, file_path):\\n self.model = load_model(file_path)\",\n \"def load(self, file_path):\\n self.model = load_model(file_path)\",\n \"def load(self, file_path):\\n self.model = load_model(file_path)\",\n \"def load_model():\\n with open(MODEL_FILENAME, \\\"rb\\\") as file:\\n model = pickle.load(file)\\n return model\",\n \"def load_model(model, device, model_path):\\n if os.path.exists(model_path):\\n print(\\\"Reading model from \\\", model_path)\\n checkpoint = torch.load(model_path, map_location=torch.device(device))\\n model.load_state_dict(checkpoint['state_dict'])\\n return model\\n else:\\n raise RuntimeError('Model does not exist!')\",\n \"def test_load_save_model():\\n model = BERTopic(language=\\\"Dutch\\\", embedding_model=None)\\n model.save(\\\"test\\\")\\n loaded_model = BERTopic.load(\\\"test\\\")\\n assert type(model) == type(loaded_model)\\n assert model.language == loaded_model.language\\n assert model.embedding_model == loaded_model.embedding_model\\n assert model.top_n_words == loaded_model.top_n_words\",\n \"def load_model(self, **params):\\n \\t# file_name = params['name']\\n # return pickle.load(gzip.open(file_name, 'rb'))\",\n \"def load_model(self, path_model: Optional[PathLike]) -> None:\\n raise NotImplementedError\",\n \"def load_model():\\n global obj\\n obj = NutritionTableDetector()\\n print(\\\"Weights Loaded!\\\")\",\n \"def load_model(model, model_index, device=\\\"cpu\\\"):\\n with open(\\\"trained_local_model\\\"+str(model_index), \\\"rb\\\") as f_:\\n model.load_state_dict(torch.load(f_))\\n model.to(device)\\n return model\",\n \"def load_model(path, model, optimizer):\\n print(\\\"LOADING MODEL...\\\")\\n ckpt = tf.train.Checkpoint(model=model, optimizer=optimizer)\\n status = ckpt.restore(tf.train.latest_checkpoint(path))\\n ckpt_manager = tf.train.CheckpointManager(\\n checkpoint=ckpt, \\n directory=FLAGS.model_dir, \\n max_to_keep=3 \\n )\\n return model, optimizer, ckpt, ckpt_manager\",\n \"def load_model(self):\\n with open(self.args.trained_model, 'rb') as handle:\\n self.model_hash = hashlib.sha224(handle.read()).hexdigest()\\n\\n self.model.load(self.args.trained_model)\\n self.logger.debug('Loaded model from %s', self.args.trained_model)\\n return\",\n \"def load(self, path: str):\\n self.model.load_state_dict(torch.load(path))\",\n \"def load_model(saved_dir):\\n output_args_file = os.path.join(saved_dir, 'training_args.bin')\\n args = torch.load(output_args_file)\\n print('Loaded args:', args)\\n tokenizer_class = get_tokenizer_class(args.model_name)\\n tokenizer = tokenizer_class.from_pretrained(saved_dir)\\n model_class = get_model_class(args.model_name, args.task_name)\\n model = model_class.from_pretrained(saved_dir)\\n return model, tokenizer, args\",\n \"def load_model(self):\\n try:\\n self.model = Word2Vec.load(self.config[\\\"model_path\\\"])\\n self.model.init_sims(replace=True)\\n except Exception as e:\\n print(e)\\n print(\\\"error in model loading!\\\")\",\n \"def load_model(model):\\n fin = False\\n backup1 = False\\n backup2 = False\\n\\n if os.path.exists(\\\"TrainedModel/finalModel.pth\\\"):\\n fin = True\\n elif os.path.exists(\\\"TrainedModel/modelBackup.pth\\\"):\\n backup1 = True\\n elif os.path.exists(\\\"TrainedModel/modelBackupBackup.pth\\\"):\\n backup2 = True\\n\\n if fin:\\n try:\\n model.load_state_dict(torch.load(\\\"TrainedModel/finalModel.pth\\\"))\\n return model\\n except:\\n print(\\\"finalModel seems to be corrupted, trying a backup...\\\")\\n \\n if fin or backup1:\\n try:\\n model.load_state_dict(torch.load(\\\"TrainedModel/modelBackup.pth\\\"))\\n return model\\n except:\\n print(\\\"modelBackup seems to be corrupted, trying a backup...\\\")\\n\\n if fin or backup1 or backup2:\\n try:\\n model.load_state_dict(torch.load(\\\"TrainedModel/modelBackupBackup.pth\\\"))\\n return model\\n except:\\n print(\\\"modelBackupBackup seems to be corrupted, you're at the end of the line.\\\")\\n\\n print(\\\"There doesn't seem to be anything to load.\\\")\\n return model\",\n \"def _load_model(self, loc):\\n\\n # If not a string, return input\\n if not (isinstance(loc, str) or isinstance(loc, unicode)):\\n return loc\\n\\n # If location is in S3, copy to local, then unpickle \\n to_delete = False\\n if \\\"s3\\\" in loc:\\n tmp_loc = \\\"{0}/tmp_file_{1}.obj\\\".format(tmpdir, random.randint(1,1000))\\n s3 = boto3.client('s3')\\n bucket = loc.split(\\\"/\\\")[2]\\n key = \\\"/\\\".join(loc.split(\\\"/\\\")[3:])\\n with open(tmp_loc, \\\"wb\\\") as data:\\n s3.download_fileobj(bucket, key, data)\\n loc = tmp_loc\\n to_delete = True\\n with open(loc, \\\"rb\\\") as f:\\n model = pickle.load(f)\\n if to_delete:\\n os.remove(tmp_loc)\\n return model\",\n \"def _load_best_model(self) -> None:\\n self.trainer.resume()\",\n \"def load_model(session: tf.Session, model_dir: Text) -> None:\\n saver = tf.train.Saver()\\n saver.restore(session, model_dir)\",\n \"def load_model_by_name(model, global_step, device=None, path=\\\"/scratch/users/zucks626/ADNI/IPMI/checkpoints/\\\"):\\r\\n # path = \\\"/scratch/users/zucks626/ADNI/ae_cls/checkpoints/\\\"\\r\\n file_path = path + model.name + \\\"/\\\" + 'model-{:05d}.pt'.format(global_step)\\r\\n state = torch.load(file_path, map_location=device)\\r\\n model.load_state_dict(state)\\r\\n print(\\\"Loaded from {}\\\".format(file_path))\",\n \"def load_model_from_file(path, as_builder=False):\\n module = load_model_module(path)\\n model = module.model\\n if not as_builder:\\n model = model()\\n del sys.path_importer_cache[os.path.dirname(module.__file__)]\\n del sys.modules[module.__name__]\\n return model\",\n \"def load(self, path):\\n parameters = torch.load(path)\\n\\n if \\\"optimizer\\\" in parameters:\\n parameters = parameters[\\\"model\\\"]\\n\\n self.load_state_dict(parameters)\",\n \"def load_model(fn, model):\\n if fn[-3] != \\\".tf\\\":\\n fn += \\\".tf\\\"\\n if model.saver is None:\\n with model.graph.as_default():\\n model.saver = tf.train.Saver()\\n log(\\\"Loading model from {}\\\".format(fn))\\n model.saver.restore(model.session, fn)\\n log(\\\"Done loading!\\\")\",\n \"def load_model(self):\\n if os.path.exists(self.model_filename):\\n self.model.load_weights(self.model_filename)\",\n \"def load_model(self):\\n if self.ckpt_flag:\\n LOG('Skip Loading Pre-trained Model......')\\n else:\\n if self.params.pre_trained_from is not None and os.path.exists(self.params.pre_trained_from):\\n try:\\n LOG('Loading Pre-trained Model at %s' % self.params.pre_trained_from)\\n pretrain = torch.load(self.params.pre_trained_from)\\n self.network.load_state_dict(pretrain)\\n LOG('Pre-trained Model Loaded!')\\n except:\\n WARNING('Cannot load pre-trained model. Start training......')\\n else:\\n WARNING('Pre-trained model do not exits. Start training......')\",\n \"def load_model(self) -> None:\\n\\n try:\\n model_class = MODEL_TYPES[self.model_type]\\n except KeyError:\\n raise KeyError(f\\\"model type: {self.model_type} not supported\\\")\\n\\n if (\\n os.path.exists(self.resources_path)\\n and len(os.listdir(self.resources_path)) > 0\\n ):\\n model_name_or_path = self.resources_path\\n else:\\n model_name_or_path = self.model_name\\n\\n if self.model_type == \\\"stable_diffusion\\\":\\n self.model = model_class.from_pretrained(\\n model_name_or_path,\\n use_auth_token=self.auth_token,\\n )\\n else:\\n self.model = model_class.from_pretrained(model_name_or_path)\\n\\n self.model.to(self.device)\",\n \"def load(self):\\n self._really_load()\",\n \"def load_model(self, directory):\\n with tf.Session() as sess:\\n tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], directory)\\n self.model = tf.contrib.predictor.from_saved_model(directory)\",\n \"def load_model(model):\\n # Check if the model is a model directory (containing a metagraph and a checkpoint file)\\n # or if it is a protobuf file with a frozen graph\\n model_exp = os.path.expanduser(model)\\n if os.path.isfile(model_exp):\\n print('Model filename: %s' % model_exp)\\n with tf.gfile.FastGFile(model_exp, 'rb') as f_l:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f_l.read())\\n tf.import_graph_def(graph_def, name='')\\n else:\\n print('Model directory: %s' % model_exp)\\n meta_file, ckpt_file = get_model_filenames(model_exp)\\n\\n print('Metagraph file: %s' % meta_file)\\n print('Checkpoint file: %s' % ckpt_file)\\n\\n saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file))\\n saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))\",\n \"def load(model_file):\\n return pickle.load(open(model_file))\",\n \"def load_model(self, folder_name):\\n raise NotImplementedError()\",\n \"def load_model(model_path):\\n nlp = spacy.blank('en') \\n if 'ner' not in nlp.pipe_names:\\n ner = nlp.create_pipe('ner')\\n nlp.add_pipe(ner)\\n #load pretrained model from the path\\n ner = nlp.from_disk(model_path)\\n return ner\",\n \"def load(self, path):\\n print('Loading model from {}'.format(path.name))\\n self.qnetwork_local.load_state_dict(torch.load(path))\\n self.qnetwork_target.load_state_dict(torch.load(path))\\n return\",\n \"def load_model():\\n return \\\"None\\\"\",\n \"def load_model(path):\\n # for example -> f\\\"{os.getcwd()}/trained_models\\\"\\n return spacy.load(path)\",\n \"def load(self):\\n self._load()\",\n \"def load(model, name=\\\"store/base\\\"):\\n if torch.cuda.is_available():\\n pretrained_dict = torch.load(name + \\\".pt\\\")\\n else:\\n pretrained_dict = torch.load(name + \\\".pt\\\", map_location=torch.device('cpu'))\\n print(\\\"Loaded\\\", name + \\\" model.\\\")\\n model_dict = model.state_dict()\\n model_dict.update(pretrained_dict)\\n model.load_state_dict(model_dict)\",\n \"def load(self, path):\\n checkpoint = torch.load(path)\\n self.load_state_dict(checkpoint['model_state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\",\n \"def load(self, path):\\n checkpoint = torch.load(path)\\n self.load_state_dict(checkpoint['model_state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\",\n \"def loadModel(file_name):\\n with open(SAVE_PATH + file_name, \\\"rb\\\") as in_file:\\n model = pickle.load(in_file, encoding = \\\"uft-8\\\")\\n print(\\\"{} loaded\\\".format(file_name))\\n return model\",\n \"def load_model(self, model_name, model_url):\\n\\n fname = join(self.root, model_name)\\n if not isfile(fname):\\n if self.verbose:\\n print(\\\"Could not find \\\" + fname + \\\".. attempt download\\\")\\n with urllib.request.urlopen(model_url) as res, open(fname, 'wb') as f:\\n shutil.copyfileobj(res, f)\\n if self.verbose:\\n print(\\\"Download complete.. model: \\\" + fname)\\n elif self.verbose:\\n print(\\\"Found model \\\" + fname + \\\"! :)\\\")\\n\\n model = load_model(fname)\\n self.model = model\",\n \"def load_model(fname: os.PathLike) -> Model:\\n return Model.load(fname)\",\n \"def _load(self) -> None:\\n self.record = self._saved_record\\n self.counter = self._saved_counter\\n self.current_objects = self._saved_objects\",\n \"def load_model(model_path: str) -> object:\\n model = torch.load(model_path)\\n model.eval()\\n return model\",\n \"def load_checkpoint(path, model, optimizer=None, reset_optimizer=True):\\n print(\\\"Load checkpoint from: {}\\\".format(path))\\n state_dict, optimizer_state = _load(path)\\n\\n model.load_dict(state_dict)\\n if not reset_optimizer and optimizer is not None:\\n if optimizer_state is not None:\\n print(\\\"[loading] Load optimizer state from {}\\\".format(path))\\n optimizer.load(optimizer_state)\\n\\n return model\",\n \"def load_best_model(self) -> None:\\n self.resume()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.794677","0.77330357","0.769884","0.76180816","0.75892407","0.74990684","0.7497519","0.7403218","0.7356266","0.7322656","0.7167664","0.7057581","0.7057581","0.69810086","0.6977028","0.6965842","0.6963033","0.6956336","0.69005823","0.68665224","0.68664163","0.68651485","0.6849963","0.68429273","0.684172","0.6834706","0.68241966","0.6803382","0.6774069","0.6771469","0.6768463","0.67486835","0.6722018","0.6718782","0.67017156","0.6686065","0.6685723","0.66845286","0.66715586","0.66705966","0.66696835","0.666499","0.6658287","0.66576797","0.6656574","0.6636128","0.6601032","0.65841377","0.65841377","0.65841377","0.65841377","0.6580038","0.657713","0.6576004","0.65618545","0.65618545","0.65618545","0.6559247","0.6555401","0.6554766","0.6548853","0.6544593","0.65428096","0.6536107","0.65215415","0.6521434","0.6518299","0.65177494","0.6514142","0.6510824","0.6507798","0.65021294","0.64994675","0.6498582","0.64963645","0.64876944","0.6482167","0.64775133","0.6475123","0.6473593","0.6444373","0.64443266","0.6436891","0.64363104","0.64328957","0.64236623","0.64210653","0.64140874","0.63981676","0.63795215","0.63764364","0.6372517","0.6372517","0.63711697","0.6369201","0.6369114","0.63675225","0.6364522","0.6362775","0.63624346"],"string":"[\n \"0.794677\",\n \"0.77330357\",\n \"0.769884\",\n \"0.76180816\",\n \"0.75892407\",\n \"0.74990684\",\n \"0.7497519\",\n \"0.7403218\",\n \"0.7356266\",\n \"0.7322656\",\n \"0.7167664\",\n \"0.7057581\",\n \"0.7057581\",\n \"0.69810086\",\n \"0.6977028\",\n \"0.6965842\",\n \"0.6963033\",\n \"0.6956336\",\n \"0.69005823\",\n \"0.68665224\",\n \"0.68664163\",\n \"0.68651485\",\n \"0.6849963\",\n \"0.68429273\",\n \"0.684172\",\n \"0.6834706\",\n \"0.68241966\",\n \"0.6803382\",\n \"0.6774069\",\n \"0.6771469\",\n \"0.6768463\",\n \"0.67486835\",\n \"0.6722018\",\n \"0.6718782\",\n \"0.67017156\",\n \"0.6686065\",\n \"0.6685723\",\n \"0.66845286\",\n \"0.66715586\",\n \"0.66705966\",\n \"0.66696835\",\n \"0.666499\",\n \"0.6658287\",\n \"0.66576797\",\n \"0.6656574\",\n \"0.6636128\",\n \"0.6601032\",\n \"0.65841377\",\n \"0.65841377\",\n \"0.65841377\",\n \"0.65841377\",\n \"0.6580038\",\n \"0.657713\",\n \"0.6576004\",\n \"0.65618545\",\n \"0.65618545\",\n \"0.65618545\",\n \"0.6559247\",\n \"0.6555401\",\n \"0.6554766\",\n \"0.6548853\",\n \"0.6544593\",\n \"0.65428096\",\n \"0.6536107\",\n \"0.65215415\",\n \"0.6521434\",\n \"0.6518299\",\n \"0.65177494\",\n \"0.6514142\",\n \"0.6510824\",\n \"0.6507798\",\n \"0.65021294\",\n \"0.64994675\",\n \"0.6498582\",\n \"0.64963645\",\n \"0.64876944\",\n \"0.6482167\",\n \"0.64775133\",\n \"0.6475123\",\n \"0.6473593\",\n \"0.6444373\",\n \"0.64443266\",\n \"0.6436891\",\n \"0.64363104\",\n \"0.64328957\",\n \"0.64236623\",\n \"0.64210653\",\n \"0.64140874\",\n \"0.63981676\",\n \"0.63795215\",\n \"0.63764364\",\n \"0.6372517\",\n \"0.6372517\",\n \"0.63711697\",\n \"0.6369201\",\n \"0.6369114\",\n \"0.63675225\",\n \"0.6364522\",\n \"0.6362775\",\n \"0.63624346\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69034487"},"document_rank":{"kind":"string","value":"18"}}},{"rowIdx":13794,"cells":{"query":{"kind":"string","value":"Saves to the server side"},"document":{"kind":"string","value":"def save_walker(self, name):\n print(\"Saving Walker {}\".format(name))\n err, _, _, _, _ = \\\n vrep.simxCallScriptFunction(self.client_id, \"Walker\",\n vrep.sim_scripttype_childscript,\n \"saveModel_function\",\n [self.walker.base_handle],\n [],\n [name],\n bytearray(), OP_MODE)\n if err not in VALID_ERROR_CODES:\n print(err)\n raise WalkerSaveError(name)\n print(\"Walker Saved\")"},"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 save():","def save():\n pass","def save (self):\n pass","def save(self):\n\n pass","def save(self):\n pass","def save(self):\n pass","def save(self):\n pass","def save(self):\n pass","def save(self):\n pass","def save(self):\n # TODO (Pierre): code","def save(self):\n self.rpc.call(MsfRpcMethod.CoreSave)","def save(self) -> None:\n pass","def save(self) -> None:\n pass","def save(self) -> None:\n pass","def save(self, *args, **kwargs):\n pass","def save(self, obj):","def save(self, *args, **kwargs) -> None:\n pass","def save(self, *args, **kwargs) -> None:\n pass","def save(self, *args, **kwargs) -> None:\n pass","def save(self, *args, **kwargs):\n return","def save(self, *args):\n # need to do!!\n pass","def save(self):\n raise NotImplementedError","def save(self):\n raise NotImplementedError","def save(self):\n raise NotImplementedError","def save(self):\n return api.put([self])","def save(self):\n self.click(\".action-save\")\n self.page.wait_for_ajax()","def save(self):\n pass","def save(self):\n pass","def save_data(self):\n pass","def save(self, *args, **kwargs) -> Any:\n pass","def save(self, handler, name):","def save(self):\n raise NotImplementedError()","def save(self):\n return None","def save(self, values):","def saveData(self):\n pass","def save(self):\n self.session.modified = True","def on_post(self):\n return \"Ok, the stuff is being saved\"","def save(self):\n self.db.commit()","def save(self, db):\n pass","def save_db(self) -> None:","def save(self):\n self.session.commit()","def save(self):\n db.session.commit()","def save(self):\n return self.write()","def save(self, *args, **kwargs):\n raise NotImplementedError()","def save(self):\n self.__db.commit()","def save(self):\n # TODO: save the file","def save(self, path):\n pass","def save(self, path):\n pass","def save(self, path):\n pass","def save(self):\n # send data to be saved by another job\n save_callevent.delay(self.data)","def save(self):\n self.backend.save(list(self._d.items()))\n log.debug(\"save: {}\".format(self.backend.filename))","def save(self, data):\n self.write(data)","def Save(self):\n\n self._persistentHandler.Save()","def save_model( self, request, obj, form, change ):\n obj.save()","def do_save(self, arg):\n smores.save_session(self.__version__)","def save(self, obj):\n raise NotImplementedError","def save(self):\n self.__session.commit()","def save(self):\n self.__session.commit()","def save(self):\n self.__session.commit()","def save(self):\n self.__session.commit()","def save(self):\n self.__session.commit()","def save(self):\n self.__session.commit()","def save(self):\n self.__session.commit()","def save(self):\n self.__session.commit()","def save_data(self):\n db.session.add(self)\n db.session.commit( )","def save(self):\n\n self.__session.commit()","def save(self):\n\n self.__session.commit()","def post(self):\n dic = escape.json_decode(self.request.body)\n saveEditor(dic)\n # useful code goes here\n self.write(json.dumps({'status': 'ok', 'sent': dic}))\n self.finish()","def save(self, *args, **kwargs):\n super(self.__class__, self).save(*args, **kwargs)","def save(self):\n logging.debug(\"sychronizing db\")\n self._db.sync()","def save(self, output, data):","def save(self):\n store = datastore.DataStore()\n store.connect()\n store.setup()\n store.put(self.as_doc())","def save_plugin_data(self, request=None):","def save_data():\n\tdata = dict(request.args)\n\n\treturn jsonify(result={\"status\": 200})","def save_model(self):\n pass","def do_save(self):\n id_client = json.loads(request.data.decode('UTF-7'))['id']\n g_list_of_classifier.save_in_file(CLASSIFIER_PATH + str(id_client) + \n '.cls')\n return ''","def save(self):\n response = settings.database.put_item(Item=self.to_dict())\n raise_for_response(response)","def save(self, path: str):\n pass","def save_model(self, path):\n # Save server model\n self.server_model.set_params(self.model)\n model_sess = self.server_model.sess\n return self.server_model.saver.save(model_sess, path)","def save(self):\n\n # TODO:Find place to save data, write logic to save images(Filter out video?)","def save(self):\n # Ensure store path exists\n store_path = self.manager.store_path\n if not os.path.exists(store_path):\n os.makedirs(store_path)\n \n # Get filepath\n filename = self._filename\n \n # Write into file\n raw = self.to_json()\n self.service.log.store('Saving %s' % filename)\n f = open(filename, 'w')\n f.write(raw)\n f.close()","def save(\n self,\n modelSavePath\n ):\n pass","def post(self):","def save(self, data):\n\t\tif self.value:\n\t\t\tdata['value'] = self.value","def save(self):\n self._app.save()","def save(self, record):\n pass","def save(self, *args, **kwargs):\n super().save(*args, **kwargs)","def save(self, *args, **kwargs):\n super().save(*args, **kwargs)","def save(self):\n\t\tdb.session.add(self)\n\t\tdb.session.commit()","def save(self, **data):\n self.applyChanges(data)","def post(self):\n pass","def post(self):\n pass","def post(self):\n pass","def post(self):\n pass","def post(self):\n pass","def post(self):\n pass","def post(self):\n pass","def post(self):\n pass","def post(self):\n pass","def post(self):\n pass","def post(self):\n pass"],"string":"[\n \"def save():\",\n \"def save():\\n pass\",\n \"def save (self):\\n pass\",\n \"def save(self):\\n\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n # TODO (Pierre): code\",\n \"def save(self):\\n self.rpc.call(MsfRpcMethod.CoreSave)\",\n \"def save(self) -> None:\\n pass\",\n \"def save(self) -> None:\\n pass\",\n \"def save(self) -> None:\\n pass\",\n \"def save(self, *args, **kwargs):\\n pass\",\n \"def save(self, obj):\",\n \"def save(self, *args, **kwargs) -> None:\\n pass\",\n \"def save(self, *args, **kwargs) -> None:\\n pass\",\n \"def save(self, *args, **kwargs) -> None:\\n pass\",\n \"def save(self, *args, **kwargs):\\n return\",\n \"def save(self, *args):\\n # need to do!!\\n pass\",\n \"def save(self):\\n raise NotImplementedError\",\n \"def save(self):\\n raise NotImplementedError\",\n \"def save(self):\\n raise NotImplementedError\",\n \"def save(self):\\n return api.put([self])\",\n \"def save(self):\\n self.click(\\\".action-save\\\")\\n self.page.wait_for_ajax()\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save_data(self):\\n pass\",\n \"def save(self, *args, **kwargs) -> Any:\\n pass\",\n \"def save(self, handler, name):\",\n \"def save(self):\\n raise NotImplementedError()\",\n \"def save(self):\\n return None\",\n \"def save(self, values):\",\n \"def saveData(self):\\n pass\",\n \"def save(self):\\n self.session.modified = True\",\n \"def on_post(self):\\n return \\\"Ok, the stuff is being saved\\\"\",\n \"def save(self):\\n self.db.commit()\",\n \"def save(self, db):\\n pass\",\n \"def save_db(self) -> None:\",\n \"def save(self):\\n self.session.commit()\",\n \"def save(self):\\n db.session.commit()\",\n \"def save(self):\\n return self.write()\",\n \"def save(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"def save(self):\\n self.__db.commit()\",\n \"def save(self):\\n # TODO: save the file\",\n \"def save(self, path):\\n pass\",\n \"def save(self, path):\\n pass\",\n \"def save(self, path):\\n pass\",\n \"def save(self):\\n # send data to be saved by another job\\n save_callevent.delay(self.data)\",\n \"def save(self):\\n self.backend.save(list(self._d.items()))\\n log.debug(\\\"save: {}\\\".format(self.backend.filename))\",\n \"def save(self, data):\\n self.write(data)\",\n \"def Save(self):\\n\\n self._persistentHandler.Save()\",\n \"def save_model( self, request, obj, form, change ):\\n obj.save()\",\n \"def do_save(self, arg):\\n smores.save_session(self.__version__)\",\n \"def save(self, obj):\\n raise NotImplementedError\",\n \"def save(self):\\n self.__session.commit()\",\n \"def save(self):\\n self.__session.commit()\",\n \"def save(self):\\n self.__session.commit()\",\n \"def save(self):\\n self.__session.commit()\",\n \"def save(self):\\n self.__session.commit()\",\n \"def save(self):\\n self.__session.commit()\",\n \"def save(self):\\n self.__session.commit()\",\n \"def save(self):\\n self.__session.commit()\",\n \"def save_data(self):\\n db.session.add(self)\\n db.session.commit( )\",\n \"def save(self):\\n\\n self.__session.commit()\",\n \"def save(self):\\n\\n self.__session.commit()\",\n \"def post(self):\\n dic = escape.json_decode(self.request.body)\\n saveEditor(dic)\\n # useful code goes here\\n self.write(json.dumps({'status': 'ok', 'sent': dic}))\\n self.finish()\",\n \"def save(self, *args, **kwargs):\\n super(self.__class__, self).save(*args, **kwargs)\",\n \"def save(self):\\n logging.debug(\\\"sychronizing db\\\")\\n self._db.sync()\",\n \"def save(self, output, data):\",\n \"def save(self):\\n store = datastore.DataStore()\\n store.connect()\\n store.setup()\\n store.put(self.as_doc())\",\n \"def save_plugin_data(self, request=None):\",\n \"def save_data():\\n\\tdata = dict(request.args)\\n\\n\\treturn jsonify(result={\\\"status\\\": 200})\",\n \"def save_model(self):\\n pass\",\n \"def do_save(self):\\n id_client = json.loads(request.data.decode('UTF-7'))['id']\\n g_list_of_classifier.save_in_file(CLASSIFIER_PATH + str(id_client) + \\n '.cls')\\n return ''\",\n \"def save(self):\\n response = settings.database.put_item(Item=self.to_dict())\\n raise_for_response(response)\",\n \"def save(self, path: str):\\n pass\",\n \"def save_model(self, path):\\n # Save server model\\n self.server_model.set_params(self.model)\\n model_sess = self.server_model.sess\\n return self.server_model.saver.save(model_sess, path)\",\n \"def save(self):\\n\\n # TODO:Find place to save data, write logic to save images(Filter out video?)\",\n \"def save(self):\\n # Ensure store path exists\\n store_path = self.manager.store_path\\n if not os.path.exists(store_path):\\n os.makedirs(store_path)\\n \\n # Get filepath\\n filename = self._filename\\n \\n # Write into file\\n raw = self.to_json()\\n self.service.log.store('Saving %s' % filename)\\n f = open(filename, 'w')\\n f.write(raw)\\n f.close()\",\n \"def save(\\n self,\\n modelSavePath\\n ):\\n pass\",\n \"def post(self):\",\n \"def save(self, data):\\n\\t\\tif self.value:\\n\\t\\t\\tdata['value'] = self.value\",\n \"def save(self):\\n self._app.save()\",\n \"def save(self, record):\\n pass\",\n \"def save(self, *args, **kwargs):\\n super().save(*args, **kwargs)\",\n \"def save(self, *args, **kwargs):\\n super().save(*args, **kwargs)\",\n \"def save(self):\\n\\t\\tdb.session.add(self)\\n\\t\\tdb.session.commit()\",\n \"def save(self, **data):\\n self.applyChanges(data)\",\n \"def post(self):\\n pass\",\n \"def post(self):\\n pass\",\n \"def post(self):\\n pass\",\n \"def post(self):\\n pass\",\n \"def post(self):\\n pass\",\n \"def post(self):\\n pass\",\n \"def post(self):\\n pass\",\n \"def post(self):\\n pass\",\n \"def post(self):\\n pass\",\n \"def post(self):\\n pass\",\n \"def post(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79372615","0.7796793","0.75564486","0.75108397","0.7485289","0.7485289","0.7485289","0.7485289","0.7485289","0.7459909","0.743181","0.7222632","0.7222632","0.7222632","0.720784","0.71515334","0.7129591","0.7129591","0.7129591","0.7122093","0.70347965","0.70284003","0.70284003","0.70284003","0.699433","0.6982721","0.6962342","0.6962342","0.6953264","0.6930007","0.68949336","0.6859564","0.6859241","0.68191206","0.678783","0.67566544","0.6723184","0.67017066","0.6698357","0.6670604","0.6632305","0.6574329","0.6550391","0.6537028","0.6528994","0.65240115","0.65237933","0.65237933","0.65237933","0.65108824","0.64887375","0.6487469","0.6473769","0.6459923","0.6446537","0.6427937","0.6402053","0.6402053","0.6402053","0.6402053","0.6402053","0.6402053","0.6402053","0.6402053","0.6395743","0.63815874","0.63815874","0.6379199","0.63701254","0.6364916","0.63491094","0.6341605","0.631327","0.6286394","0.6250441","0.62472594","0.6246852","0.62402284","0.6236514","0.6227797","0.6222638","0.62215877","0.6215096","0.62113744","0.6208451","0.61995","0.6189714","0.6189714","0.61877394","0.6169424","0.61682284","0.61682284","0.61682284","0.61682284","0.61682284","0.61682284","0.61682284","0.61682284","0.61682284","0.61682284","0.61682284"],"string":"[\n \"0.79372615\",\n \"0.7796793\",\n \"0.75564486\",\n \"0.75108397\",\n \"0.7485289\",\n \"0.7485289\",\n \"0.7485289\",\n \"0.7485289\",\n \"0.7485289\",\n \"0.7459909\",\n \"0.743181\",\n \"0.7222632\",\n \"0.7222632\",\n \"0.7222632\",\n \"0.720784\",\n \"0.71515334\",\n \"0.7129591\",\n \"0.7129591\",\n \"0.7129591\",\n \"0.7122093\",\n \"0.70347965\",\n \"0.70284003\",\n \"0.70284003\",\n \"0.70284003\",\n \"0.699433\",\n \"0.6982721\",\n \"0.6962342\",\n \"0.6962342\",\n \"0.6953264\",\n \"0.6930007\",\n \"0.68949336\",\n \"0.6859564\",\n \"0.6859241\",\n \"0.68191206\",\n \"0.678783\",\n \"0.67566544\",\n \"0.6723184\",\n \"0.67017066\",\n \"0.6698357\",\n \"0.6670604\",\n \"0.6632305\",\n \"0.6574329\",\n \"0.6550391\",\n \"0.6537028\",\n \"0.6528994\",\n \"0.65240115\",\n \"0.65237933\",\n \"0.65237933\",\n \"0.65237933\",\n \"0.65108824\",\n \"0.64887375\",\n \"0.6487469\",\n \"0.6473769\",\n \"0.6459923\",\n \"0.6446537\",\n \"0.6427937\",\n \"0.6402053\",\n \"0.6402053\",\n \"0.6402053\",\n \"0.6402053\",\n \"0.6402053\",\n \"0.6402053\",\n \"0.6402053\",\n \"0.6402053\",\n \"0.6395743\",\n \"0.63815874\",\n \"0.63815874\",\n \"0.6379199\",\n \"0.63701254\",\n \"0.6364916\",\n \"0.63491094\",\n \"0.6341605\",\n \"0.631327\",\n \"0.6286394\",\n \"0.6250441\",\n \"0.62472594\",\n \"0.6246852\",\n \"0.62402284\",\n \"0.6236514\",\n \"0.6227797\",\n \"0.6222638\",\n \"0.62215877\",\n \"0.6215096\",\n \"0.62113744\",\n \"0.6208451\",\n \"0.61995\",\n \"0.6189714\",\n \"0.6189714\",\n \"0.61877394\",\n \"0.6169424\",\n \"0.61682284\",\n \"0.61682284\",\n \"0.61682284\",\n \"0.61682284\",\n \"0.61682284\",\n \"0.61682284\",\n \"0.61682284\",\n \"0.61682284\",\n \"0.61682284\",\n \"0.61682284\",\n \"0.61682284\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":13795,"cells":{"query":{"kind":"string","value":"Discrete Variable to Feature Convertor. var value of variable varname name of variable. lims = range of numbers lims = range of discretization. collapse = list with two binary vals. collapse all below lim[0] to lim[0] & collapse all above lim[1] to lim[1] e.g., fdict = discVar2Feature(8, 'positive adjective', lims = [1,5], collapse [True, True]) contains 1 positive adjective False contains 2 positive adjective False contains 4 positive adjective False contains 3 positive adjective False contains 5 positive adjective True"},"document":{"kind":"string","value":"def discVar2Feature( var, varname, lims = [1,3], collapse = [False, False], ctxt = 'Has'):\n \n vals = xrange(lims[0], lims[1]+1) \n \n keystr = ctxt + ' %s ' + varname\n fdict = {keystr % val:False for val in vals} \n\n if collapse[0] == True:\n if lims[0] > var:\n var = lims[0]\n #var = max([var, lims[0]])\n if collapse[1] == True:\n if lims[1] < var:\n var = lims[1]\n #var = min([var, lims[1]])\n \n if var >= lims[0] and var <= lims[1]: #if collapse = False, ignore vals outside lims\n fdict[(keystr) % (var)] = True \n \n return fdict"},"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 discVar2FeatureOld( var, varname, lims = [1,5], collapse = [False, False], ctxt = 'contains'):\n nums = ['zero','one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten']\n \n vals = range(lims[0], lims[1]+1) \n \n #init fdict\n fdict = dict() \n for k, val in enumerate(vals):\n fdict[(ctxt + ' %s ' + varname) % (nums[val])] = False\n \n if collapse[0] == True: var = max([var, lims[0]])\n if collapse[1] == True: var = min([var, lims[1]])\n \n if var >= lims[0] and var <= lims[1]: #if collapse = False, ignore vals outside lims\n fdict[(ctxt + ' %s ' + varname) % (nums[var])] = True \n \n return fdict","def convertToDiscreteFunction(boolean: bool) -> cern.japc.value.DiscreteFunction:\n ...","def FE_discretize_numeric_variables(train, bin_dict, test='', strategy='kmeans',verbose=0):\r\n df = copy.deepcopy(train)\r\n test = copy.deepcopy(test)\r\n num_cols = len(bin_dict)\r\n nrows = int((num_cols/2)+0.5)\r\n #print('nrows',nrows)\r\n if verbose:\r\n fig = plt.figure(figsize=(10,3*num_cols))\r\n for i, (col, binvalue) in enumerate(bin_dict.items()):\r\n new_col = col+'_discrete'\r\n if strategy == 'gaussian':\r\n kbd = GaussianMixture(n_components=binvalue, random_state=99)\r\n df[new_col] = kbd.fit_predict(df[[col]]).astype(int)\r\n if not isinstance(test, str):\r\n test[new_col] = kbd.predict(test[[col]]).astype(int)\r\n else:\r\n kbd = KBinsDiscretizer(n_bins=binvalue, encode='ordinal', strategy=strategy)\r\n df[new_col] = kbd.fit_transform(df[[col]]).astype(int)\r\n if not isinstance(test, str):\r\n test[new_col] = kbd.transform(test[[col]]).astype(int)\r\n if verbose:\r\n ax1 = plt.subplot(nrows,2,i+1)\r\n ax1.scatter(df[col],df[new_col])\r\n ax1.set_title(new_col)\r\n if not isinstance(test, str):\r\n return df, test\r\n else:\r\n return df","def denseFeature(self, feat):\n return {'feat': feat}","def convertToDiscreteFunctionList(boolean: bool) -> cern.japc.value.DiscreteFunctionList:\n ...","def preprocess_var(bd, var):\n filepath_sv = f\"team67-ptp/data/{var}.csv\"\n filepath = bd\n data = feather.read_dataframe(filepath)\n df = data.copy()\n df2 = df[var]\n df2 = df2.to_frame()\n if df2[var].dtype is \"category\":\n df2[var] = df2[var].astype(\"category\").cat.codes\n filename = filepath_sv\n df2.to_csv(filename, index=False)\n print(\"Succesfully exported to csv\")\n else:\n filename = filepath_sv\n df2.to_csv(filename, index=False)\n print(\"Succesfully exported to csv\")","def addFeature(self, strName, lstDomain):\n # create a new variable CSPVariable object\n newFeature = CSPFeature(strName, lstDomain)\n # put the new variable in the graph's list of variables\n self.features.append(newFeature)","def select_var_feature(adata, min_score=0.5, nb_features=None, show=True, copy=False):\n if copy:\n inplace=False\n else:\n inplace=True\n\n adata = adata.copy() if not inplace else adata\n \n # calculate variability score\n cal_var(adata, show=show) # adds variability score for each feature \n # adata.var['variablility_score'] = abs(adata.var['prop_shared_cells']-0.5)\n var_annot = adata.var.sort_values(ascending=True, by ='variability_score')\n\n # calculate the min score to get a specific number of feature \n if nb_features != None and nb_features < len(adata.var_names): \n min_score = var_annot['variability_score'][nb_features] \n \n \n adata_tmp = adata[:,adata.var['variability_score']<=min_score].copy()\n \n ## return the filtered AnnData objet.\n if not inplace:\n adata_tmp = adata[:,adata.var['variability_score']<=min_score]\n return(adata_tmp)\n else:\n adata._inplace_subset_var(adata.var['variability_score']<=min_score)","def feature_discretion(self, X, y):\n temp, X_interval = [], []\n if self._DISCRETION == \"percentile_discrete\":\n for i in range(0, X.shape[-1]):\n x = X[:, i]\n x_type = type_of_target(x)\n # logging.info(\"before: \"+\" \".join([str(i), str(set(X[:, i])), str(x_type)]))\n if 0:\n if x_type == 'continuous':\n x1, interval = self.percentile_discrete(x, self._WOE_N)\n X_interval.append(interval)\n temp.append(x1)\n # logging.info(\"continue_after: \" + \" \".join([str(i), str(set(x1)), str(x1)]))\n else:\n temp.append(x)\n # logging.info(\"after: \" + \" \".join([str(i), str(set(x)), str(x)]))\n else:\n x1, interval = self.percentile_discrete(x, self._WOE_N)\n X_interval.append(interval)\n temp.append(x1)\n # logging.info(\"continue_after: \" + \" \".join([str(i), str(set(x1)), str(x1)]))\n elif self._DISCRETION == \"interval_discrete\":\n for i in range(0, X.shape[-1]):\n x = X[:, i]\n # logging.info(\"before: \"+\" \".join([str(i), str(set(X[:, i]))]))\n x1, interval = self.interval_discrete(x, self._WOE_N)\n X_interval.append(interval)\n temp.append(x1)\n # logging.info(\"interval_after: \" + \" \".join([str(i), str(set(x1)), str(x1)]))\n elif self._DISCRETION == \"rf_discrete\":\n for i in range(0, X.shape[-1]):\n x = X[:, i]\n # logging.info(\"before: \"+\" \".join([str(i), str(set(X[:, i]))]))\n x1, interval = self.rf_discrete(x, y)\n X_interval.append(interval)\n temp.append(x1)\n # logging.info(\"rf_after: \" + \" \".join([str(i), str(set(x1)), str(x1)]))\n return np.array(temp).T, X_interval","def dense2cvxopt(value):\n import cvxopt\n return cvxopt.matrix(value, tc='d')","def dict_to_feature(d):\n f = ee.Feature(None,ee.Dictionary(d))\n return f","def my_featurize(apartment):\n col =np.array([1, 2, 0, 0, 0, 0, 0, 0 ])\n a= pd.DataFrame(apartment[col])\n if(apartment.get('condition')== 'good'):\n col[1] =1\n else:\n if(apartment.get('condition')== 'zero condition'):\n col[1] = 0\n col[2] =apartment.get('num_rooms')\n col[3] =apartment.get('area')\n col[4] =apartment.get('num_bathrooms')\n col[5] =apartment.get('floor')\n col[6] =apartment.get('ceiling_height')\n col[7] =apartment.get('max_floor')\n\n return col, apartment['price']","def get_feature_set_SC2(tweet, sentimentvalues):\n pos_tag_freq = {}\n additional_freq = {}\n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n if tag in ADJECTIVES:\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\n elif tag in ADVERBS: \n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\n elif tag in PRONOUNS:\n additional_freq['pronoun'] = 1\n except KeyError:\n continue\n for key in pos_tag_freq.keys():\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \n #Number of exclamation marks, number of emoticons,\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\n if emoticons>0:\n additional_freq['emoticons'] = emoticons*1.0\n if tweet.nrof_exclamations>0:\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\n \n #Add lexicon values\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\n sub_score = sentimentvalues[0]+sentimentvalues[1]\n obj_score = sentimentvalues[2]\n if sub_score>0:\n additional_freq[\"sub_score\"] = sub_score+1.0\n if obj_score>0:\n additional_freq[\"obj_score\"] = obj_score+1.0\n \n #Concatenate the dicts\n features= dict(pos_tag_freq.items() + additional_freq.items())\n \n return features","def _make_features(self, x):\n\t\tx = x.unsqueeze(1)\n\t\treturn torch.cat([x ** i for i in range(1, self._degree+1)], 1)","def preprocess_feature(df):","def classify_columns(df_preds, verbose=0):\r\n train = copy.deepcopy(df_preds)\r\n #### If there are 30 chars are more in a discrete_string_var, it is then considered an NLP variable\r\n max_nlp_char_size = 30\r\n max_cols_to_print = 30\r\n print('############## C L A S S I F Y I N G V A R I A B L E S ####################')\r\n print('Classifying variables in data set...')\r\n #### Cat_Limit defines the max number of categories a column can have to be called a categorical colum\r\n cat_limit = 35\r\n float_limit = 15 #### Make this limit low so that float variables below this limit become cat vars ###\r\n def add(a,b):\r\n return a+b\r\n sum_all_cols = dict()\r\n orig_cols_total = train.shape[1]\r\n #Types of columns\r\n cols_delete = [col for col in list(train) if (len(train[col].value_counts()) == 1\r\n ) | (train[col].isnull().sum()/len(train) >= 0.90)]\r\n train = train[left_subtract(list(train),cols_delete)]\r\n var_df = pd.Series(dict(train.dtypes)).reset_index(drop=False).rename(\r\n columns={0:'type_of_column'})\r\n sum_all_cols['cols_delete'] = cols_delete\r\n var_df['bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['bool','object']\r\n and len(train[x['index']].value_counts()) == 2 else 0, axis=1)\r\n string_bool_vars = list(var_df[(var_df['bool'] ==1)]['index'])\r\n sum_all_cols['string_bool_vars'] = string_bool_vars\r\n var_df['num_bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\r\n np.uint16, np.uint32, np.uint64,\r\n 'int8','int16','int32','int64',\r\n 'float16','float32','float64'] and len(\r\n train[x['index']].value_counts()) == 2 else 0, axis=1)\r\n num_bool_vars = list(var_df[(var_df['num_bool'] ==1)]['index'])\r\n sum_all_cols['num_bool_vars'] = num_bool_vars\r\n ###### This is where we take all Object vars and split them into diff kinds ###\r\n discrete_or_nlp = var_df.apply(lambda x: 1 if x['type_of_column'] in ['object'] and x[\r\n 'index'] not in string_bool_vars+cols_delete else 0,axis=1)\r\n ######### This is where we figure out whether a string var is nlp or discrete_string var ###\r\n var_df['nlp_strings'] = 0\r\n var_df['discrete_strings'] = 0\r\n var_df['cat'] = 0\r\n var_df['id_col'] = 0\r\n discrete_or_nlp_vars = var_df.loc[discrete_or_nlp==1]['index'].values.tolist()\r\n if len(var_df.loc[discrete_or_nlp==1]) != 0:\r\n for col in discrete_or_nlp_vars:\r\n #### first fill empty or missing vals since it will blowup ###\r\n train[col] = train[col].fillna(' ')\r\n if train[col].map(lambda x: len(x) if type(x)==str else 0).mean(\r\n ) >= max_nlp_char_size and len(train[col].value_counts()\r\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\r\n var_df.loc[var_df['index']==col,'nlp_strings'] = 1\r\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\r\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\r\n var_df.loc[var_df['index']==col,'discrete_strings'] = 1\r\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\r\n ) == len(train) and col not in string_bool_vars:\r\n var_df.loc[var_df['index']==col,'id_col'] = 1\r\n else:\r\n var_df.loc[var_df['index']==col,'cat'] = 1\r\n nlp_vars = list(var_df[(var_df['nlp_strings'] ==1)]['index'])\r\n sum_all_cols['nlp_vars'] = nlp_vars\r\n discrete_string_vars = list(var_df[(var_df['discrete_strings'] ==1) ]['index'])\r\n sum_all_cols['discrete_string_vars'] = discrete_string_vars\r\n ###### This happens only if a string column happens to be an ID column #######\r\n #### DO NOT Add this to ID_VARS yet. It will be done later.. Dont change it easily...\r\n #### Category DTYPE vars are very special = they can be left as is and not disturbed in Python. ###\r\n var_df['dcat'] = var_df.apply(lambda x: 1 if str(x['type_of_column'])=='category' else 0,\r\n axis=1)\r\n factor_vars = list(var_df[(var_df['dcat'] ==1)]['index'])\r\n sum_all_cols['factor_vars'] = factor_vars\r\n ########################################################################\r\n date_or_id = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\r\n np.uint16, np.uint32, np.uint64,\r\n 'int8','int16',\r\n 'int32','int64'] and x[\r\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\r\n axis=1)\r\n ######### This is where we figure out whether a numeric col is date or id variable ###\r\n var_df['int'] = 0\r\n var_df['date_time'] = 0\r\n ### if a particular column is date-time type, now set it as a date time variable ##\r\n var_df['date_time'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['<M8[ns]','datetime64[ns]'] and x[\r\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\r\n axis=1)\r\n ### this is where we save them as date time variables ###\r\n if len(var_df.loc[date_or_id==1]) != 0:\r\n for col in var_df.loc[date_or_id==1]['index'].values.tolist():\r\n if len(train[col].value_counts()) == len(train):\r\n if train[col].min() < 1900 or train[col].max() > 2050:\r\n var_df.loc[var_df['index']==col,'id_col'] = 1\r\n else:\r\n try:\r\n pd.to_datetime(train[col],infer_datetime_format=True)\r\n var_df.loc[var_df['index']==col,'date_time'] = 1\r\n except:\r\n var_df.loc[var_df['index']==col,'id_col'] = 1\r\n else:\r\n if train[col].min() < 1900 or train[col].max() > 2050:\r\n if col not in num_bool_vars:\r\n var_df.loc[var_df['index']==col,'int'] = 1\r\n else:\r\n try:\r\n pd.to_datetime(train[col],infer_datetime_format=True)\r\n var_df.loc[var_df['index']==col,'date_time'] = 1\r\n except:\r\n if col not in num_bool_vars:\r\n var_df.loc[var_df['index']==col,'int'] = 1\r\n else:\r\n pass\r\n int_vars = list(var_df[(var_df['int'] ==1)]['index'])\r\n date_vars = list(var_df[(var_df['date_time'] == 1)]['index'])\r\n id_vars = list(var_df[(var_df['id_col'] == 1)]['index'])\r\n sum_all_cols['int_vars'] = int_vars\r\n copy_date_vars = copy.deepcopy(date_vars)\r\n for date_var in copy_date_vars:\r\n #### This test is to make sure sure date vars are actually date vars\r\n try:\r\n pd.to_datetime(train[date_var],infer_datetime_format=True)\r\n except:\r\n ##### if not a date var, then just add it to delete it from processing\r\n cols_delete.append(date_var)\r\n date_vars.remove(date_var)\r\n sum_all_cols['date_vars'] = date_vars\r\n sum_all_cols['id_vars'] = id_vars\r\n sum_all_cols['cols_delete'] = cols_delete\r\n ## This is an EXTREMELY complicated logic for cat vars. Don't change it unless you test it many times!\r\n var_df['numeric'] = 0\r\n float_or_cat = var_df.apply(lambda x: 1 if x['type_of_column'] in ['float16',\r\n 'float32','float64'] else 0,\r\n axis=1)\r\n if len(var_df.loc[float_or_cat == 1]) > 0:\r\n for col in var_df.loc[float_or_cat == 1]['index'].values.tolist():\r\n if len(train[col].value_counts()) > 2 and len(train[col].value_counts()\r\n ) <= float_limit and len(train[col].value_counts()) <= len(train):\r\n var_df.loc[var_df['index']==col,'cat'] = 1\r\n else:\r\n if col not in num_bool_vars:\r\n var_df.loc[var_df['index']==col,'numeric'] = 1\r\n cat_vars = list(var_df[(var_df['cat'] ==1)]['index'])\r\n continuous_vars = list(var_df[(var_df['numeric'] ==1)]['index'])\r\n ######## V E R Y I M P O R T A N T ###################################################\r\n ##### There are a couple of extra tests you need to do to remove abberations in cat_vars ###\r\n cat_vars_copy = copy.deepcopy(cat_vars)\r\n for cat in cat_vars_copy:\r\n if df_preds[cat].dtype==float:\r\n continuous_vars.append(cat)\r\n cat_vars.remove(cat)\r\n var_df.loc[var_df['index']==cat,'cat'] = 0\r\n var_df.loc[var_df['index']==cat,'numeric'] = 1\r\n elif len(df_preds[cat].value_counts()) == df_preds.shape[0]:\r\n id_vars.append(cat)\r\n cat_vars.remove(cat)\r\n var_df.loc[var_df['index']==cat,'cat'] = 0\r\n var_df.loc[var_df['index']==cat,'id_col'] = 1\r\n sum_all_cols['cat_vars'] = cat_vars\r\n sum_all_cols['continuous_vars'] = continuous_vars\r\n sum_all_cols['id_vars'] = id_vars\r\n ###### This is where you consoldate the numbers ###########\r\n var_dict_sum = dict(zip(var_df.values[:,0], var_df.values[:,2:].sum(1)))\r\n for col, sumval in var_dict_sum.items():\r\n if sumval == 0:\r\n print('%s of type=%s is not classified' %(col,train[col].dtype))\r\n elif sumval > 1:\r\n print('%s of type=%s is classified into more then one type' %(col,train[col].dtype))\r\n else:\r\n pass\r\n ############### This is where you print all the types of variables ##############\r\n ####### Returns 8 vars in the following order: continuous_vars,int_vars,cat_vars,\r\n ### string_bool_vars,discrete_string_vars,nlp_vars,date_or_id_vars,cols_delete\r\n if verbose == 1:\r\n print(\" Number of Numeric Columns = \", len(continuous_vars))\r\n print(\" Number of Integer-Categorical Columns = \", len(int_vars))\r\n print(\" Number of String-Categorical Columns = \", len(cat_vars))\r\n print(\" Number of Factor-Categorical Columns = \", len(factor_vars))\r\n print(\" Number of String-Boolean Columns = \", len(string_bool_vars))\r\n print(\" Number of Numeric-Boolean Columns = \", len(num_bool_vars))\r\n print(\" Number of Discrete String Columns = \", len(discrete_string_vars))\r\n print(\" Number of NLP String Columns = \", len(nlp_vars))\r\n print(\" Number of Date Time Columns = \", len(date_vars))\r\n print(\" Number of ID Columns = \", len(id_vars))\r\n print(\" Number of Columns to Delete = \", len(cols_delete))\r\n if verbose == 2:\r\n marthas_columns(df_preds,verbose=1)\r\n print(\" Numeric Columns: %s\" %continuous_vars[:max_cols_to_print])\r\n print(\" Integer-Categorical Columns: %s\" %int_vars[:max_cols_to_print])\r\n print(\" String-Categorical Columns: %s\" %cat_vars[:max_cols_to_print])\r\n print(\" Factor-Categorical Columns: %s\" %factor_vars[:max_cols_to_print])\r\n print(\" String-Boolean Columns: %s\" %string_bool_vars[:max_cols_to_print])\r\n print(\" Numeric-Boolean Columns: %s\" %num_bool_vars[:max_cols_to_print])\r\n print(\" Discrete String Columns: %s\" %discrete_string_vars[:max_cols_to_print])\r\n print(\" NLP text Columns: %s\" %nlp_vars[:max_cols_to_print])\r\n print(\" Date Time Columns: %s\" %date_vars[:max_cols_to_print])\r\n print(\" ID Columns: %s\" %id_vars[:max_cols_to_print])\r\n print(\" Columns that will not be considered in modeling: %s\" %cols_delete[:max_cols_to_print])\r\n ##### now collect all the column types and column names into a single dictionary to return!\r\n len_sum_all_cols = reduce(add,[len(v) for v in sum_all_cols.values()])\r\n if len_sum_all_cols == orig_cols_total:\r\n print(' %d Predictors classified...' %orig_cols_total)\r\n #print(' This does not include the Target column(s)')\r\n else:\r\n print('No of columns classified %d does not match %d total cols. Continuing...' %(\r\n len_sum_all_cols, orig_cols_total))\r\n ls = sum_all_cols.values()\r\n flat_list = [item for sublist in ls for item in sublist]\r\n if len(left_subtract(list(train),flat_list)) == 0:\r\n print(' Missing columns = None')\r\n else:\r\n print(' Missing columns = %s' %left_subtract(list(train),flat_list))\r\n return sum_all_cols","def topdia(x):\r\n return Feature(x, \"TopDia\")","def my_featurize(apartment):\n return x, y","def cat2onehot_var(snt_ids, vocab_size, batch_size):\n\n targets = np.array([snt_ids]).reshape(-1)\n one_hot_targets = np.eye(vocab_size)[targets]\n result = Variable(torch.FloatTensor(one_hot_targets).view(-1, batch_size, vocab_size)) #\n\n return result","def restrict(self, variable, value):\n # 新因子变量列表为原因子变量列表减去被求和的变量\n restricted_variable = self.var_list.index(variable)\n new_var_list = self.var_list[:restricted_variable] + self.var_list[restricted_variable + 1:]\n\n # 对于新因子CPT中的表项，其值为原因子中与新因子变量取值相同且限制变量取对应值的的单个表项的值\n new_cpt = {}\n if restricted_variable == 0:\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\n if len(self.var_list) == 1:\n postfix = ''\n new_cpt[postfix] = self.cpt[str(value) + postfix]\n elif restricted_variable == len(self.var_list) - 1:\n for i in range(pow(2, restricted_variable)):\n prefix = Util.to_binary(i, restricted_variable)\n new_cpt[prefix] = self.cpt[prefix + str(value)]\n else:\n for i in range(pow(2, restricted_variable)):\n prefix = Util.to_binary(i, restricted_variable)\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\n new_cpt[prefix + postfix] = self.cpt[prefix + str(value) + postfix]\n new_node = Node('f' + str(new_var_list), new_var_list)\n new_node.set_cpt(new_cpt)\n return new_node","def make_features(x):\n x = x.unsqueeze(1)\n # torch.cat 实现tensor拼接\n return torch.cat([x ** i for i in range(1, POLY_DEGREE + 1)], 1)","def categorical(df):\n\n # variables which need to be transformed to categorical\n categorical = [\"prop_country_id\", \"visitor_location_country_id\"]\n\n for var in categorical:\n df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1)\n del df[var]\n\n return df","def get_categorical_features(self, x: pd.DataFrame) -> pd.DataFrame:\n return x[self.categorical_features]","def collapse_var(nc, out, name, direction):\n var1 = nc.variables[name]\n N = (len(nc.dimensions[direction]) - 1) / 2\n\n print(\"Processing %s...\" % name)\n dims = var1.dimensions\n if len(dims) > 1: # only collapse spatial fields\n dims = [x for x in dims if x != direction]\n\n try:\n fill_value = var1._FillValue\n var2 = out.createVariable(name, var1.dtype,\n dimensions=dims, fill_value=fill_value)\n except:\n var2 = out.createVariable(name, var1.dtype,\n dimensions=dims)\n\n copy_attributes(var1, var2)\n\n if direction == 'x':\n var2[:] = var1[get_slice(var1.dimensions, x=N)]\n elif direction == 'y':\n var2[:] = var1[get_slice(var1.dimensions, y=N)]","def apply_randomization(features, label, randomize_prob):\n rnd_tok = lambda: tf.as_string(tf.random.uniform([], 0, 99999999, tf.int32))\n\n for idx in CAT_FEATURE_INDICES:\n key = feature_name(idx)\n # Ignore lint since tf.cond should evaluate lambda immediately.\n features[key] = tf.cond(tf.random.uniform([]) < randomize_prob,\n rnd_tok,\n lambda: features[key]) # pylint: disable=cell-var-from-loop\n return features, label","def dict_to_feature(feature_dict, keys, max_value=None):\n feature = []\n for key, val in feature_dict.items(): # First level\n if key not in keys:\n continue\n if val is None or val == \"auto\" or key == \"autotuning\" or val == \"\":\n continue\n if isinstance(val, dict):\n feature.append(dict_to_feature(val, max_value))\n else:\n feature.append(float(val))\n\n # normalization, should not matter in tree models\n if max_value is not None:\n norm_feature = []\n for f, mv in zip(feature, max_value):\n norm_feature.append(f / mv)\n feature = norm_feature\n\n return feature","def get_optimal_discrete_feature_split(\n self, X: np.ndarray, y: np.ndarray, feature_col: int\n ):\n return BaseTree.get_discrete_split_value(\n X[:, feature_col], y, eval_func=self.eval_func\n )","def preprocess_sf(bd, var):\n filepath_svf = f\"team67-ptp/data/{var}.ftr\"\n filepath = bd\n data = feather.read_dataframe(filepath)\n df = data.copy()\n df2 = df[var]\n df2 = df2.to_frame()\n if df2[var].dtype is \"category\":\n df2[var] = df2[var].astype(\"category\").cat.codes\n filename = filepath_svf\n df2.to_feather(filename)\n print(\"Succesfully exported to feather\")\n else:\n filename = filepath_svf\n df2.to_feather(filename)\n print(\"Succesfully exported to feather\")","def cvxopt2dense(value):\n return np.array(value)","def xx(x):\r\n return Feature(x, \"XX\")","def convert_study_to_feature(study: List[Types.SeriesObj]) -> List[Dict[str, tf.train.Feature]]:\n return [convert_series_to_feature(s) for s in study]","def get_feature_set_SC(tweet, sentimentvalues):\n pos_tag_freq = {}\n additional_freq = {}\n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# if tag=='PRtinf':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJS':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJ':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='NP':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='DET':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='P':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n if tag in ADJECTIVES:\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\n elif tag in ADVERBS: \n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\n elif tag in PRONOUNS:\n additional_freq['pronoun'] = 1\n except KeyError:\n continue\n for key in pos_tag_freq.keys():\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \n #Number of exclamation marks, number of emoticons,\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\n if emoticons>0:\n additional_freq['emoticons'] = emoticons*1.0\n if tweet.nrof_exclamations>0:\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\n \n #Add lexicon values\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\n sub_score = 0.0\n obj_score = 0.0\n nrof_subwords = 0\n nrof_objwords = 0\n for word in sentimentvalues.keys():\n if sentimentvalues[word][0]>0:\n sub_score = sub_score + sentimentvalues[word][0]\n nrof_subwords = nrof_subwords + 1\n if sentimentvalues[word][1]>0:\n sub_score = sub_score + sentimentvalues[word][1]\n nrof_subwords = nrof_subwords + 1\n if sentimentvalues[word][2]>0:\n obj_score = obj_score + sentimentvalues[word][2]\n nrof_objwords = nrof_objwords + 1\n if sub_score>0:\n additional_freq[\"sub_score\"] = sub_score+1.0\n if obj_score>0:\n additional_freq[\"obj_score\"] = obj_score+1.0\n if nrof_subwords>0:\n additional_freq[\"subjective_words\"] = nrof_subwords*1.0\n if nrof_objwords>0:\n additional_freq[\"objective_words\"] = nrof_objwords*1.0\n \n #Concatenate the dicts\n features= dict(pos_tag_freq.items() + additional_freq.items())\n \n return features","def dummify_features(df):\n colnames = df.columns\n le_dict = {}\n for col in colnames:\n le_dict[col] = preprocessing.LabelEncoder()\n le_dict[col].fit(df[col])\n df.loc[:, col] = le_dict[col].transform(df[col])\n\n enc = preprocessing.OneHotEncoder()\n enc.fit(df)\n X = enc.transform(df)\n\n dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\n # for cv in colnames:\n # for modality in le_dict[cv].classes_:\n # dummy_colnames.append(cv + '_' + modality)\n\n return X, dummy_colnames, enc","def preprocess(old_df, label_name, category_features, non_category_features):\n old_df['fraud'] = old_df[label_name].apply(lambda x: x[0] == 'f')\n\n # Creating a new dataframe with a subset of features.\n new_df = old_df[['fraud'] + non_category_features]\n\n # For categorical features, we make dummy variables,\n # and merge them into new_df.\n for feature in category_features:\n dummy_df = pd.get_dummies(old_df[feature], prefix=feature,\n dummy_na=True)\n # Since dummy_na=True, the last column will be for null values.\n dummy_df.drop(dummy_df.columns[-1], axis=1, inplace=True)\n new_df = pd.concat([new_df, dummy_df], axis=1)\n return new_df","def data_continuous(df, cat_features = [], cont_features = []) :\n subset_cont =[]\n for col in list(df.columns):\n if df[col].dtype == 'int' or df[col].dtype == 'float64':\n if col not in cont_features and col not in cat_features:\n print(col, \"was added to continuous features\")\n cont_features.append(col)\n subset_cont.append(col)\n for col in cont_features:\n if col not in subset_cont:\n subset_cont.append(col)\n print('Continuous features : ', ' '.join(subset_cont))\n printmd(\"**Description of continuous columns:**\")\n print(round(df[subset_cont].describe()))\n return (cont_features)","def extract_concepts_discrete():\n disc = pd.DataFrame([['name', 'Name', 'string'],\n ['year', 'Year', 'time'],\n ['area', 'Area', 'entity_domain'],\n ['area_id', 'Area Id', 'string'],\n ['variable_id', 'Variable Id', 'string']\n ], columns=['concept', 'name', 'concept_type'])\n return disc","def index_to_feature(p, dims):\n feature = []\n for dim in dims:\n feature.append(p % dim)\n p //= dim\n return feature","def from_dummies(self, data, prefix_sep='_'):\n out = data.copy()\n for feat in self.categorical_feature_names:\n # first, derive column names in the one-hot-encoded data from the original data\n cat_col_values = []\n for val in list(self.data_df[feat].unique()):\n cat_col_values.append(feat + prefix_sep + str(val)) # join original feature name and its unique values , ex: education_school\n match_cols = [c for c in data.columns if c in cat_col_values] # check for the above matching columns in the encoded data\n\n # then, recreate original data by removing the suffixes - based on the GitHub issue comment: https://github.com/pandas-dev/pandas/issues/8745#issuecomment-417861271\n cols, labs = [[c.replace(\n x, \"\") for c in match_cols] for x in [\"\", feat + prefix_sep]]\n out[feat] = pd.Categorical(\n np.array(labs)[np.argmax(data[cols].values, axis=1)])\n out.drop(cols, axis=1, inplace=True)\n return out","def feature_vec(idcs, idcs_per_action, num_actions):\n x_fvec = np.zeros((idcs_per_action*num_actions))\n x_fvec[idcs] = 1\n \n return x_fvec","def word2features(self,sent, i):\n word = sent[i][0]\n #postag = sent[i][1]\n\n features = {\n 'bias': 1.0,\n 'word.lower()': word.lower(),\n 'word.isupper()': word.isupper(),\n 'word.istitle()': word.istitle(),\n 'word.isdigit()': word.isdigit(),\n 'word.shape()':self.shape(word),\n 'word.isalnum()':word.isalnum(),\n 'word.isalpha()':word.isalpha(),\n # 'postag': postag,\n # 'postag[:2]': postag[:2],\n }\n if i > 0:\n word1 = sent[i - 1][0]\n #postag1 = sent[i - 1][1]\n features.update({\n '-1:word.lower()': word1.lower(),\n '-1:word.istitle()': word1.istitle(),\n '-1:word.isupper()': word1.isupper(),\n '-1:word.isdigit()': word1.isdigit(),\n '-1:word.isalnum()':word1.isalnum(),\n '-1:word.isalpha()':word1.isalpha(),\n # '-1:postag': postag1,\n # '-1:postag[:2]': postag1[:2],\n })\n else:\n features['BOS'] = True\n\n if i > 1:\n word2 = sent[i - 2][0]\n #postag2 = sent[i - 2][1]\n features.update({\n '-2:word.lower()': word2.lower(),\n '-2:word.istitle()': word2.istitle(),\n '-2:word.isupper()': word2.isupper(),\n '-2:word.isdigit()': word2.isdigit(),\n '-2:word.isalnum()': word2.isalnum(),\n '-2:word.isalpha()': word2.isalpha(),\n # '-2:postag': postag2,\n # '-2:postag[:2]': postag2[:2],\n })\n else:\n features['BOS1'] = True\n if i > 2:\n word3 = sent[i - 3][0]\n #postag3 = sent[i - 3][1]\n features.update({\n '-3:word.lower()': word3.lower(),\n '-3:word.istitle()': word3.istitle(),\n '-3:word.isupper()': word3.isupper(),\n '-3:word.isdigit()': word3.isdigit(),\n '-3:word.isalnum()': word3.isalnum(),\n '-3:word.isalpha()': word3.isalpha(),\n # '-3:postag': postag3,\n # '-3:postag[:2]': postag3[:2],\n })\n else:\n features['BOS2'] = True\n\n if i > 3:\n word4 = sent[i - 4][0]\n #postag4 = sent[i - 4][1]\n features.update({\n '-4:word.lower()': word4.lower(),\n '-4:word.istitle()': word4.istitle(),\n '-4:word.isupper()': word4.isupper(),\n '-4:word.isdigit()': word4.isdigit(),\n '-4:word.isalnum()': word4.isalnum(),\n '-4:word.isalpha()': word4.isalpha(),\n # '-4:postag': postag4,\n # '-4:postag[:2]': postag4[:2],\n })\n else:\n features['BOS2'] = True\n\n if i < len(sent) - 1:\n word1 = sent[i + 1][0]\n features.update({\n '+1:word.lower()': word1.lower(),\n '+1:word.istitle()': word1.istitle(),\n '+1:word.isupper()': word1.isupper(),\n '+1:word.isdigit()': word1.isdigit(),\n '+1:word.isalnum()': word1.isalnum(),\n '+1:word.isalpha()': word1.isalpha(),\n # '+1:postag': postag1,\n # '+1:postag[:2]': postag1[:2],\n })\n else:\n features['EOS'] = True\n if i < len(sent) - 2:\n word12 = sent[i + 2][0]\n #postag12 = sent[i + 2][1]\n features.update({\n '+2:word.lower()': word12.lower(),\n '+2:word.istitle()': word12.istitle(),\n '+2:word.isupper()': word12.isupper(),\n '+2:word.isdigit()': word12.isdigit(),\n '+2:word.isalnum()': word12.isalnum(),\n '+2:word.isalpha()': word12.isalpha(),\n # '+2:postag': postag12,\n # '+2:postag[:2]': postag12[:2],\n })\n else:\n features['EOS2'] = True\n if i < len(sent) - 3:\n word13 = sent[i + 3][0]\n #postag13 = sent[i + 3][1]\n features.update({\n '+3:word.lower()': word13.lower(),\n '+3:word.istitle()': word13.istitle(),\n '+3:word.isupper()': word13.isupper(),\n '+3:word.isdigit()': word13.isdigit(),\n '+3:word.isalnum()': word13.isalnum(),\n '+3:word.isalpha()': word13.isalpha(),\n # '+3:postag': postag13,\n # '+3:postag[:2]': postag13[:2],\n })\n else:\n features['EOS2'] = True\n if i < len(sent) - 4:\n word14 = sent[i + 4][0]\n #postag14 = sent[i + 4][1]\n features.update({\n '+4:word.lower()': word14.lower(),\n '+4:word.istitle()': word14.istitle(),\n '+4:word.isupper()': word14.isupper(),\n '+4:word.isdigit()': word14.isdigit(),\n '+4:word.isalnum()': word14.isalnum(),\n '+4:word.isalpha()': word14.isalpha(),\n # '+4:postag': postag14,\n # '+4:postag[:2]': postag14[:2],\n })\n else:\n features['EOS2'] = True\n return features","def get_feature_set_SB(tweet):\n #pos-tag frequencies\n# print \"Tagged words in tweet: \", tweet.tagged_words\n pos_tag_freq = {}\n additional_freq = {}\n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# if tag=='PRtinf':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJS':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJ':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='NP':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='DET':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='P':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n if tag in ADJECTIVES:\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\n elif tag in ADVERBS: \n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\n elif tag in PRONOUNS:\n additional_freq['pronoun'] = 1\n except KeyError:\n continue\n# print \"Tag frequencies: \", pos_tag_freq\n for key in pos_tag_freq.keys():\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \n #Number of exclamation marks, number of emoticons,\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\n if emoticons>0:\n additional_freq['emoticons'] = emoticons*1.0\n if tweet.nrof_exclamations>0:\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\n \n# print \"Additional frequencies: \", additional_freq\n# raw_input(\"Continue?\")\n \n #Concatenate the dicts\n features= dict(pos_tag_freq.items() + additional_freq.items())\n# print \"All features: \", features\n# raw_input(\"Continue?\")\n return features","def create_dummies(df):","def build_classifier(model, hidden_units):\n in_features = model.classifier._modules['0'].in_features\n classifier = nn.Sequential(OrderedDict([\n ('dropout1', nn.Dropout(0.5)),\n ('fc1', nn.Linear(in_features, hidden_units)), \n ('relu', nn.ReLU()),\n ('dropout2', nn.Dropout(0.5)),\n ('fc2', nn.Linear(hidden_units, 102)),\n ('output', nn.LogSoftmax(dim=1))\n ]))\n return classifier","def feature_dist_func_dict():\n return {\"tanimoto_dissimilarity\": tanimoto_dissimilarity}","def data_categorical(df, cat_features = [], cont_features = []):\n subset_cat = []\n subset_dict={}\n # Add all the object type features to config.cat_features \n for col in df.columns:\n if df[col].dtype == 'object' and col not in cont_features:\n subset_cat.append(col)\n if col not in cat_features :\n cat_features.append(col)\n if cat_features !=[]:\n print('Categorical features : ', ' '.join(cat_features))\n printmd('**Number of unique values for every feature:**')\n print(pd.DataFrame(df[cat_features].nunique(), columns = ['Unique values']).sort_values(by = 'Unique values', ascending=False))\n printmd(\"**5 uniques samples of every Categorical Features :**\")\n for col in cat_features :\n subset_dict[col]= df[col].unique()[:5]\n print(pd.DataFrame.from_dict(subset_dict, orient='index').transpose())\n return (cat_features)","def FE_create_categorical_feature_crosses(dfc, cats):\r\n dfc = copy.deepcopy(dfc)\r\n combos = list(combinations(cats, 2))\r\n for cat1, cat2 in combos:\r\n dfc.loc[:,cat1+'_cross_'+cat2] = dfc[cat1].astype(str)+\" \"+dfc[cat2].astype(str)\r\n return dfc","def get_feature_set_PC(tweet, sentimentvalues):\n features= {\n 'text_length': np.log(len(tweet.text))\n } #ADD ADDITIONAL FEATURES\n if tweet.nrof_sademoticons>0:\n features['sademoticons'] = tweet.nrof_sademoticons\n if tweet.nrof_happyemoticons>0:\n features['happyemoticons'] = tweet.nrof_happyemoticons\n \n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n features[tag] = features.get(tag, 0) + 1\n if tag in ADJECTIVES:\n features['adjectives'] = features.get(tag, 0) + 1\n elif tag in ADVERBS: \n features['adverbs'] = features.get(tag, 0) + 1\n elif tag in PRONOUNS:\n features['pronoun'] = 1\n except KeyError:\n continue\n for key in features.keys():\n features[key] = features[key]*1.0\n \n #Add lexical features\n # total polarity score, number of positive words, number of negative words\n pos_score = 0\n neg_score = 0\n nrof_pos_words = 0\n nrof_neg_words = 0\n for word in sentimentvalues.keys():\n if sentimentvalues[word][0]>0:\n nrof_pos_words = nrof_pos_words + 1\n pos_score = pos_score + sentimentvalues[word][0]\n if sentimentvalues[word][1]>0:\n nrof_neg_words = nrof_neg_words + 1\n neg_score = neg_score + sentimentvalues[word][1]\n\n if neg_score>0:\n features['neg_score'] = neg_score+1.0\n if pos_score>0:\n features['pos_score'] = pos_score+1.0\n if nrof_pos_words>0:\n features['positive_words'] = nrof_pos_words*1.0\n if nrof_neg_words>0:\n features['negative_words'] = nrof_neg_words*1.0\n \n return features","def getFeatures(featureInput):\n featureList = []\n for defTerm,candidateSent in featureInput:\n tokens = nltk.word_tokenize(candidateSent)\n features = {}\n POScenter,POSleft,POSright = wordPOS(tokens,defTerm)\n features['Pos of first Article'] = posFirstArticle(tokens)\n## features['Num Punct Marks'] = numPunctuation(tokens)\n features['Subj words Predicate'] = subWordPerdicate(candidateSent,defTerm,tokens)\n features['Word before def term'] = wordBeforeDef(tokens,defTerm)\n features['POS centered word'] = POScenter\n features['POS left word'] = POSleft\n## features['POS right word'] = POSright \n featureList.append(features)\n return featureList","def add_dummy_feature(X, value=...):\n ...","def to_numeric_features(features, feature_cols=None):\n\n for col in CATEGORICAL_COLS:\n features = pd.concat(\n [features, pd.get_dummies(features[col[0]], drop_first=True)], axis=1)\n features.drop(col[0], axis=1, inplace=True)\n\n # Remove the unused columns from the dataframe.\n for col in UNUSED_COLUMNS:\n features.pop(col)\n\n # Re-index dataframe (if categories list changed from the previous dataset)\n if feature_cols is not None:\n features = features.T.reindex(feature_cols).T.fillna(0)\n return features","def get_feature_set_PC2(tweet, sentimentvalues):\n features= {\n 'text_length': np.log(len(tweet.text))\n } #ADD ADDITIONAL FEATURES\n if tweet.nrof_sademoticons>0:\n features['sademoticons'] = tweet.nrof_sademoticons\n if tweet.nrof_happyemoticons>0:\n features['happyemoticons'] = tweet.nrof_happyemoticons\n \n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n features[tag] = features.get(tag, 0) + 1\n if tag in ADJECTIVES:\n features['adjectives'] = features.get(tag, 0) + 1\n elif tag in ADVERBS: \n features['adverbs'] = features.get(tag, 0) + 1\n elif tag in PRONOUNS:\n features['pronoun'] = 1\n except KeyError:\n continue\n for key in features.keys():\n features[key] = features[key]*1.0\n \n #Add lexical features\n # total polarity score, number of positive words, number of negative words\n pos_score = sentimentvalues[0]\n neg_score = sentimentvalues[1]\n\n if pos_score>0:\n features['pos_score'] = pos_score+1.0\n if neg_score>0:\n features['neg_score'] = neg_score+1.0\n \n return features","def dense_col(self):\n if not self.col_name_mapping or \"dense_col\" not in self.col_name_mapping:\n return EmptyFeature\n return Feature(\n name=list(self.col_name_mapping[\"dense_col\"].keys()),\n index=list(self.col_name_mapping[\"dense_col\"].values()),\n )","def make_data(dataFname, enc, features=None):\n\n origData = pandas.read_csv(dataFname)\n ids = origData['id']\n\n # remove unused columns\n if 'Unnamed: 0' in origData.columns: del origData['Unnamed: 0']\n del origData['id']\n\n # remove \"data leakage\" columns\n for f in prohobitedFeatures:\n del origData[f]\n\n # separate into X & y values\n xData = origData[[col for col in origData.columns if not col=='loss']]\n set_vars_as_type(xData, discreteVars, object)\n yVec = origData.loss if 'loss' in origData.columns else None\n\n # try f528 - f274\n xData['f528f274'] = xData['f528'] - xData['f274']\n\n # encode the categorical features f776 and f777\n if enc is None:\n enc = OneHotEncoder(n_values=[2, 2])\n enc.fit(xData[['f776', 'f777']])\n\n xData[['f776_isZero', 'f776_isOne', 'f777_isZero', 'f777_isOne']] = pandas.DataFrame(enc.transform(xData[['f776', 'f777']]).toarray())\n del xData['f776']\n del xData['f777']\n\n print_missing_values_info(origData)\n\n # feature selection\n if features:\n filteredXData = xData[features]\n else: # use ALL features\n filteredXData = xData\n\n return filteredXData, yVec, ids, enc","def make_feature_scale_factors():\n X, y = make_X_and_y()\n sqm = make_sqm_X()\n scale_factors = {\n \"indoor_temp\": np.max(X[:,:,0]),\n \"outdoor_temp\": np.max(X[:,:,1]),\n \"gas_kwh\": np.max(X[:,:,2]),\n \"elec_kwh\": np.max(X[:,:,3]),\n \"floor_area\": np.max(sqm),\n \"htc\": np.max(y),\n }\n\n with open(os.path.join(_TRAINING_DATA_PATH, \"scalefactors.json\"), \"w+\") as f:\n json.dump(scale_factors, f)","def my_impl_variational(in_train, in_test, labels):\n X_train = []\n X_test = []\n for lab in labels:\n for datum in in_train[lab]:\n X_train.append([datum, lab])\n for datum in in_test[lab]:\n X_test.append([datum, lab])\n Variationer_learn(X_train, 500, 1, 0.01, X_test, labels)","def changeVars(self, vars):\n v = VarSet(vars)\n newOrder = map(lambda x:v.index(x), vars)\n return Factor(v, self.t.transpose(newOrder))\n #return NotImplemented","def _get_feature_map():\n return {\n # 3 sparse feature with variable length. Use this if you have a\n # variable number or more than 1 feature value per example.\n \"feature_1\":\n tf.io.VarLenFeature(dtype=tf.int64),\n \"feature_2\":\n tf.io.VarLenFeature(dtype=tf.int64),\n \"feature_3\":\n tf.io.VarLenFeature(dtype=tf.int64),\n \"label\":\n tf.io.FixedLenFeature([1], dtype=tf.int64),\n }","def condition_domain_reduction(csp, var) :\n return True","def condition_domain_reduction(csp, var) :\n return True","def create_feature_value_category(data):\n utils.save_log('{0} :: {1}'.format(\n create_feature_value_category.__module__,\n create_feature_value_category.__name__))\n\n avg_value = data.agg({'Value': 'avg'}).collect()[0][0]\n data = data. \\\n withColumn('ValueStrategy',\n when(col('Value') > avg_value * 200, 2).\n when(col('Value') > avg_value * 50, 1).\n otherwise(0))\n\n update_list_features(\"numerical\", [\"ValueStrategy\"])\n\n return data","def pre_MultiLabelBinarizer(vars):\n for var in vars:\n m = DataFrameMapper([(var, [CategoricalImputer(), LabelEncoder()])], df_out = True)\n m.fit_transform(pd.concat([test,df])) # Get all categorical outcomes so we have same codes for all.\n\n X_train[var+'_encoded'] = m.transform(X_train)\n X_test[var+'_encoded'] = m.transform(X_test)\n test[var+'_encoded'] = m.transform(test)\n\n return X_train, X_test, test","def fac2var(_Mu,Nu, G, f,v):\n #print\n #print \"fac '%s' \\t=>\\t var '%s'\" % (f,v)\n assert G.type(f)=='fac' and G.type(v)=='var'\n\n vars = G.N(f) # for order\n ii = { x:i for i,x in enumerate(vars) } # inverted index\n\n for val in G.vals(v): # forall val in var\n \n # \"pin down msg var to one val\"\n # eg\n # var = 'b'\n # val = 2\n # vars = ['a','b','c']\n # space = {0..1} x {2} x {0..3}\n space = cartesian( *[(G.vals(_v) if _v != v else [val]) for _v in vars] )\n\n # get _val of _var\n # _vals[ii[_v]] = _v:str => ii:str=>inx => _vals:inx=>val => Nu[_,_]:val=>num\n # discrete randvar -> values are indices\n # sum of prod\n msg = sum( G(f, *_vals) * product([ Nu[_v, f][_vals[ii[_v]]] for _v in G.N(f) if _v != v ])\n for _vals in space )\n\n _Mu[f,v][val] = msg\n \n \"\"\"\n\n # sum (fac * prod nus)\n fac = G.node[f]['pmf']\n nus = [ (i, _v, Nu[_v, f]) for i,_v in enumerate(G.N(f)) if _v != v ]\n\n msg = fac\n for i,_v,nu in nus:\n # sans broadcast\n shape = [1 for _ in msg.shape]\n shape[i] = G.node[_v]['d']\n nu = resize(nu, tuple(shape))\n nu = resize(nu, msg.shape)\n msg = msg * nu\n # [diff] msg = msg * resize(nu, msg.shape)\n # [diff] msg = resize(nu, msg.shape) * msg\n\n others = tuple([ i for i,_v in enumerate(G.N(f)) if _v != v ]) \n msg = sum(msg, axis=others) # marginalize every other var\n Mu[f,v] = msg\n\n \"\"\"\n\n #print \n #print 'Mu =', Mu","def featurize(self, data):\n \n bag_of_words = []\n\n tokens = data.split()\n\n for i in tokens:\n bag_of_words.append((i, True))\n\n return bag_of_words","def to_delimited_set(self, name, label, variables, from_dichotomous=True,\n codes_from_name=True):\n if self.var_exists(name):\n raise ValueError('{} does already exist.'.format(name))\n elif not all(isinstance(c, (str, tuple)) for c in variables):\n raise ValueError('Input of variables must be string or tuple.')\n cols = [c if isinstance(c, str) else c[0] for c in variables]\n if not all(self.var_exists(c) for c in cols):\n not_in_ds = [c for c in cols if not self.var_exists(c)]\n raise KeyError('{} not found in dataset!'.format(not_in_ds))\n elif not all(self._has_categorical_data(c) for c in cols):\n not_cat = [c for c in cols if not self._has_categorical_data(c)]\n raise ValueError('Variables must have categorical data: {}'.format(not_cat))\n if from_dichotomous:\n if not all(x in [0, 1] for c in cols for x in self.codes_in_data(c)):\n non_d = [c for c in cols\n if not all(x in [0, 1] for x in self.codes_in_data(c))]\n raise ValueError('Variables are not dichotomous: {}'.format(non_d))\n mapper = []\n for x, col in enumerate(variables, 1):\n if codes_from_name:\n x = int(col.split('_')[-1])\n if isinstance(col, tuple):\n text = col[1]\n else:\n text = self.text(col)\n mapper.append((x, text, {col: [1]}))\n else:\n values = self.values(cols[0])\n if not all(self.values(c) == values for c in cols):\n not_eq = [c for c in cols if not self.values(c) == values]\n raise ValueError('Variables must have eqaul values: {}'.format(not_eq))\n mapper = []\n for v in values:\n mapper.append((v[0], v[1], union([{c: v[0]} for c in cols])))\n\n self.derive(name, 'delimited set', label, mapper)\n\n return None","def preprocessing_fn(inputs):\n outputs = {}\n for key in _DENSE_FLOAT_FEATURE_KEYS:\n # Preserve this feature as a dense float, setting nan's to the mean.\n outputs[_transformed_name(key)] = tft.scale_to_z_score(\n _fill_in_missing(inputs[key]))\n\n for key in _VOCAB_FEATURE_KEYS:\n # Build a vocabulary for this feature.\n outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(\n _fill_in_missing(inputs[key]),\n top_k=_VOCAB_SIZE,\n num_oov_buckets=_OOV_SIZE)\n\n for key in _BUCKET_FEATURE_KEYS:\n outputs[_transformed_name(key)] = tft.bucketize(\n _fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT,\n always_return_num_quantiles=False)\n\n for key in _CATEGORICAL_FEATURE_KEYS:\n outputs[_transformed_name(key)] = _fill_in_missing(inputs[key])\n\n # Was this passenger a big tipper?\n taxi_fare = _fill_in_missing(inputs[_FARE_KEY])\n tips = _fill_in_missing(inputs[_LABEL_KEY])\n outputs[_transformed_name(_LABEL_KEY)] = tf.where(\n tf.is_nan(taxi_fare),\n tf.cast(tf.zeros_like(taxi_fare), tf.int64),\n # Test if the tip was > 20% of the fare.\n tf.cast(\n tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))\n\n return outputs","def get_dummies_list(self, \n cols=['elite'],\\\n drop_=True):\n for col in cols:\n print \"Pre-processing \" + col + \"...\"\n temp = pd.get_dummies(self.df[col].apply(pd.Series).stack(),drop_first=True)\\\n .astype(int).sum(level=0).astype(int)\n # temp.columns.apply(str).apply(lambda x: col + \"_\" + x)\n if drop_:\n self.df.drop(col,axis = 1, inplace=True)\n self.df = pd.concat([self.df, temp],axis=1)","def create_classifier(in_dim, hid_dim, out_dim):\n\n root_six = np.sqrt(6)\n eps = root_six / (np.sqrt(hid_dim + in_dim))\n W = np.random.uniform(-eps, eps, [hid_dim, in_dim])\n eps = root_six / (np.sqrt(hid_dim))\n b = np.random.uniform(-eps, eps, hid_dim)\n eps = root_six / (np.sqrt(out_dim + hid_dim))\n U = np.random.uniform(-eps, eps, [out_dim, hid_dim])\n eps = root_six / (np.sqrt(out_dim))\n b_tag = np.random.uniform(-eps, eps, out_dim)\n\n return [W,b,U,b_tag]","def density(categorical_var, numerical_var):\n #print(categorical_var)\n cat_list = categorical_var.astype('category')\n for cat in cat_list:\n sns.kdeplot(numerical_var[categorical_var == cat], label=cat)#, categorical_var)\n\n plt.show()","def domain_features(domain, path_set):\n return string_features_v2(domain_string(domain, path_set))","def build_by_features(self, features):\n v = [0] * self.f\n masks = [1 << i for i in range(self.f)]\n if isinstance(features, dict):\n features = features.items()\n for f in features:\n if isinstance(f, basestring):\n h = self.hashfunc(f.encode('utf-8'))\n w = 1\n else:\n assert isinstance(f, collections.Iterable)\n h = self.hashfunc(f[0].encode('utf-8'))\n w = f[1]\n for i in range(self.f):\n v[i] += w if h & masks[i] else -w\n ans = 0\n for i in range(self.f):\n if v[i] >= 0:\n ans |= masks[i]\n self.value = ans","def build_by_features(self, features):\n v = [0] * self.f\n masks = [1 << i for i in range(self.f)]\n if isinstance(features, dict):\n features = features.items()\n for f in features:\n if isinstance(f, basestring):\n h = self.hashfunc(f.encode('utf-8'))\n w = 1\n else:\n assert isinstance(f, collections.Iterable)\n h = self.hashfunc(f[0].encode('utf-8'))\n w = f[1]\n for i in range(self.f):\n v[i] += w if h & masks[i] else -w\n ans = 0\n for i in range(self.f):\n if v[i] >= 0:\n ans |= masks[i]\n self.value = ans","def dense_nn(x):\n dense_1 = tf.nn.dropout(tf.nn.relu(dense(x, input_dim, n_l1, 'dense_1')), keep_prob=0.25)\n dense_2 = tf.nn.dropout(tf.nn.relu(dense(dense_1, n_l1, n_l2, 'dense_2')), keep_prob=0.25)\n dense_3 = dense(dense_2, n_l2, n_labels, 'dense_3')\n return dense_3","def _classify(tree, x):\n # YOUR CODE HERE\n # begin answer\n feature_name=list(tree.keys())[0] #first element\n secondDict=tree[feature_name] \n key=x.loc[feature_name] #extract value from x\n for key_val in secondDict:\n feature_val=key_val[0]\n valueOfKey=secondDict[(feature_val, key>=feature_val)]\n if isinstance(valueOfKey,dict):\n label=_classify(valueOfKey,x)\n else:\n label=valueOfKey\n return label\n # end answer","def discretizing(\n *factors: List[np.ndarray],\n independent: bool = True,\n n_bins: int = 5,\n strategy: Literal['uniform', 'quantile', 'kmeans', 'gmm'] = 'quantile',\n return_model: bool = False,\n):\n encode = 'ordinal'\n # onehot - sparse matrix of one-hot encoding and\n # onehot-dense - dense one-hot encoding. Ignored features are always stacked to\n # the right.\n # ordinal - Return the bin identifier encoded as an integer value.\n strategy = str(strategy).strip().lower()\n if 'histogram' in strategy:\n strategy = 'uniform'\n # ====== GMM base discretizer ====== #\n if 'gmm' in strategy:\n create_gmm = lambda: GaussianMixture(n_components=n_bins,\n max_iter=800,\n covariance_type='diag',\n random_state=1) # fix random state\n\n if independent:\n gmm = []\n for f in factors[0].T:\n gm = create_gmm()\n gm.fit(np.expand_dims(f, axis=1))\n gm.predict = types.MethodType(_gmm_discretizing_predict, gm)\n gmm.append(gm)\n transform = lambda x: np.concatenate([\n gm.predict(np.expand_dims(col, axis=1)) for gm, col in zip(gmm, x.T)\n ],\n axis=1)\n else:\n gmm = create_gmm()\n gmm.fit(np.expand_dims(factors[0].ravel(), axis=1))\n gmm.predict = types.MethodType(_gmm_discretizing_predict, gmm)\n transform = lambda x: np.concatenate(\n [gmm.predict(np.expand_dims(col, axis=1)) for col in x.T], axis=1)\n disc = gmm\n # ====== start with bins discretizer ====== #\n else:\n disc = KBinsDiscretizer(n_bins=n_bins, encode=encode, strategy=strategy)\n if independent:\n disc.fit(factors[0])\n transform = lambda x: disc.transform(x).astype(np.int64)\n else:\n disc.fit(np.expand_dims(factors[0].ravel(), axis=-1))\n transform = lambda x: np.hstack([\n disc.transform(np.expand_dims(i, axis=-1)).astype(np.int64)\n for i in x.T\n ])\n # ====== returns ====== #\n factors = tuple([transform(i) for i in factors])\n factors = factors[0] if len(factors) == 1 else factors\n if return_model:\n return factors, disc\n return factors","def string_feature(ds, feature: str, verbosity=0, dim=\"sample\", numeric_dis=True):\n t = get_annotation_table(ds, dim)\n ser = t[feature]\n dt = ser.dtype\n descript = \"\"\"\"\"\"\n\n non_null_ser = ser[ser.notnull()]\n null_flag = len(non_null_ser) < len(ser)\n levels = list(set(non_null_ser.values))\n num_levels = len(levels)\n\n if dt == pd.StringDtype():\n\n if num_levels >= 50:\n descript += f\"\"\"\nSorry, {feature} is a String feature with too many factor levels \n(n = {num_levels}) to describe, here \n\"\"\"\n\n elif num_levels < 50 and num_levels >= 2:\n descript += f\"\"\"\n{feature}: {dt} Feature\n-------------------------\n\nFactor level counts df:\n\n{non_null_ser.value_counts()}\n\n\nSome example query statements:\n------------------------------\n\n{feature} in ['{levels[0]}', '{levels[1]}', ...]\n\n{feature} not in ['{levels[0]}', '{levels[-1]}', ...]\n\n{feature} != '{levels[-2]}'\n\"\"\"\n\n else:\n descript += f\"\"\"\nThere's only a single factor level, {levels[0]}, across all samples.\n\"\"\"\n\n elif dt == pd.BooleanDtype():\n descript += f\"\"\"\n{feature}: {dt} Feature:\n---------------------------\n\nFactor level counts df:\n\n{non_null_ser.value_counts()}\n\n\nSome example query statements:\n------------------------------\n\n{feature} == True\n\n{feature} == False\n\"\"\"\n\n elif dt == pd.Int64Dtype() or dt == pd.Float64Dtype():\n if num_levels == 1:\n descript += f\"\"\"\nThere's only a single factor level, {levels[0]}, across all samples.\n\"\"\"\n\n elif (num_levels > 1) and (not numeric_dis):\n descript += f\"\"\"\n{feature}: {dt} Feature\n-------------------------\n\nFactor level counts df:\n\n{non_null_ser.value_counts()}\n\n\nSome example query statements:\n------------------------------\n\n{feature} in [{levels[0]}, {levels[1]}, ...]\n\n{feature} not in [{levels[0]}, {levels[-1]}, ...]\n\n{feature} != {levels[-2]}\n\"\"\"\n else:\n des = ser.describe()\n descript += f\"\"\"\n{feature}: {dt} Feature:\n---------------------------\n\ndistribution of numerical feature:\n\n{des}\n\nSome example query statements:\n------------------------------\n\n{feature} >= {int(des[1])}\n\n{feature} <= {int(des[1])}\n\n({feature} >= {int(des[4])}) and ({feature} <= {int(des[5])})\n\"\"\"\n\n else:\n descript += f\"\"\"\n{feature}: {dt} Feature\n-------------------------\n\nFactor level counts df:\n\n{non_null_ser.value_counts()}\n\n\nSome example query statements:\n------------------------------\n\n{feature} in ['{levels[0]}', '{levels[1]}', ...]\n\n{feature} not in ['{levels[0]}', '{levels[-1]}', ...]\n\n{feature} != '{levels[-2]}'\n\"\"\"\n\n if null_flag:\n descript += f\"\"\"\n{feature}.isnull()\n\n{feature}.notnull()\n\"\"\"\n\n return descript","def das(input_df, counts_var, noise_parameter):\n df = input_df.copy(deep=True)\n \n n = df.shape[0]\n \n # add laplace noise \n noise = np.random.laplace(loc=0, scale=noise_parameter, size=n)\n df['noisy_counts'] = df[counts_var] + noise\n \n # post processing\n df['nonneg_counts'] = post_proc(df.noisy_counts, df.pop_count.sum())\n df.nonneg_counts = np.round(df.nonneg_counts)\n \n return df","def to_dimacs_string_set_variable_value(self, clauses, variable, value):\n self.varname_dict = {}\n self.varobj_dict = {}\n variables = prop_symbols_from_clause_list(clauses)\n if variable in variables:\n variables.remove(variable)\n varis = dict(list(zip(sorted(variables, key=lambda v: v.op),\n list(map(str, list(range(1, len(variables) + 1)))))))\n for var in varis:\n self.varname_dict[var] = varis[var]\n self.varobj_dict[varis[var]] = var\n\n ret_clauses = ''\n clause_count = 0\n for clause in clauses:\n clause_exists = True\n dimacs_vlist = []\n ret_clause = ''\n if clause.op == '|':\n for var in clause.args:\n if literal_name(var) == literal_name(variable):\n if value and not var.op == '~' or not value and var.op == '~':\n clause_exists = False\n else:\n dimacs_vlist.append(('-' if var.op == '~' else '') \\\n + self.varname_dict[var.args[0]\n if var.op == '~' else var])\n\n if clause_exists:\n ret_clause += ' '.join(dimacs_vlist)\n elif clause.op == '~':\n if literal_name(clause) == literal_name(variable):\n if value:\n return None\n clause_exists = False\n else:\n ret_clause += '-' + self.varname_dict[clause.args[0]]\n elif literal_name(clause) == literal_name(variable):\n if value:\n clause_exists = False\n else:\n return None\n else:\n ret_clause += self.varname_dict[clause]\n if clause_exists:\n clause_count += 1\n ret_clauses += ret_clause + ' 0\\n'\n\n ret_header = 'p cnf %d %d\\n' % (len(variables), clause_count)\n ret = ret_header + ret_clauses\n return ret","def to_dummies(to_dummy_data):\r\n if config.to_dummies:\r\n for col in to_dummy_data.columns:\r\n unique_col_num = len(pd.unique(to_dummy_data[col]))\r\n dummy_max = int(len(to_dummy_data[col]) / 10)\r\n col_type = to_dummy_data.dtypes[col]\r\n if (col_type == \"object\") \\\r\n & (unique_col_num < dummy_max) \\\r\n & (unique_col_num > 1) \\\r\n & (col != config.target):\r\n temp_dummies = pd.get_dummies(to_dummy_data[col])\r\n to_dummy_data = pd.concat([to_dummy_data, temp_dummies],\r\n axis=1, sort=False)\r\n return to_dummy_data","def construct_feature_columns(input_features):\n return set([tf.feature_column.numeric_column(my_feature)\n for my_feature in input_features])","def discrete_model():\n return {\"x\": np.random.randint(10, size=100), \"y\": np.random.randint(10, size=100)}","def extract_features(X):\n X = X.drop(\"PassengerId\", axis=1)\n X = X.drop(\"Ticket\", axis=1)\n X = X.drop(\"Cabin\", axis=1)\n \n # Adding polynomial features\n X[\"Age2\"] = X[\"Age\"] ** 2\n #X[\"Fare2\"] = X[\"Fare\"] ** 2\n #X[\"Pclass2\"] = X[\"Pclass\"] ** 2\n\n \n male_titles = set([\"Mr\", \"Don\", \"Sir\"])\n female_titles = set([\"Miss\", \"Ms\", \"Mrs\", \"Mme\", \"Mdm\", \"Lady\"])\n professionals = set([\"Dr\", \"Rev\", \"Master\"])\n military = set([\"Col\", \"Major\", \"Capt\"])\n royalty = set([\"the Countess\", \"Jonkheer\"])\n \n names = X[\"Name\"]\n for i in range(len(names)): \n name_tokens = names[i].split(\", \") \n passenger_title = name_tokens[1].split(\".\")[0]\n if passenger_title in male_titles:\n names[i] = 1\n elif passenger_title in female_titles:\n names[i] = 2\n elif passenger_title in professionals:\n names[i] = 3\n #elif passenger_title in royalty:\n # names[i] = 4\n elif passenger_title in military:\n names[i] = 5\n else:\n names[i] = 6\n \n X[\"Name\"].update(names)\n \n # One hot encoding of categorical data\n X = pd.get_dummies(X) \n \n X.fillna(0, inplace=True)\n X['Fam'] = X['SibSp'] + X['Parch'] # assigned to a column\n return X","def get_kdd_feature_cols():\r\n\treturn [\r\n\t\t'duration',\r\n\t\t'src_bytes',\r\n\t\t'dst_bytes',\r\n\t\t'land',\r\n\t\t'wrong_fragment',\r\n\t\t'urgent',\r\n\t\t'hot',\r\n\t\t'num_failed_logins',\r\n\t\t'logged_in',\r\n\t\t'num_compromised',\r\n\t\t'root_shell',\r\n\t\t'su_attempted',\r\n\t\t'num_root',\r\n\t\t'num_file_creations',\r\n\t\t'num_shells',\r\n\t\t'num_access_files',\r\n\t\t'num_outbound_cmds',\r\n\t\t'is_host_login',\r\n\t\t'is_guest_login',\r\n\t\t'count',\r\n\t\t'srv_count',\r\n\t\t'serror_rate',\r\n\t\t'srv_serror_rate',\r\n\t\t'rerror_rate',\r\n\t\t'srv_rerror_rate',\r\n\t\t'same_srv_rate',\r\n\t\t'diff_srv_rate',\r\n\t\t'srv_diff_host_rate',\r\n\t\t'dst_host_count',\r\n\t\t'dst_host_srv_count',\r\n\t\t'dst_host_same_srv_rate',\r\n\t\t'dst_host_diff_srv_rate',\r\n\t\t'dst_host_same_src_port_rate',\r\n\t\t'dst_host_srv_diff_host_rate',\r\n\t\t'dst_host_serror_rate',\r\n\t\t'dst_host_srv_serror_rate',\r\n\t\t'dst_host_rerror_rate',\r\n\t\t'dst_host_srv_rerror_rate']","def process_categorical_data(data_df):\n return pd.get_dummies(data_df, columns=Columns.categorical)","def probability_categorical(feature, label):\n assert feature.nunique()>2, 'feature category nums must be greater than 2.'\n t = pd.DataFrame({'feature':feature, 'label':label})\n cat = label.unique()\n cat = [(cat[i], cat[i+1]) for i in range(len(cat)-1)]\n prob = label.value_counts(1).to_dict()\n slope = [prob.get(i[0], 0)-prob.get(i[1], 0) for i in cat]\n \n slope_dict = t.feature.value_counts(1).to_dict()\n prob = t.groupby([ 'feature']).label.value_counts(1).to_dict()\n slope_dict = {i:{'category_rate':slope_dict[i], 'slope':[prob.get((i,j[0]), 0)-prob.get((i,j[1]), 0) for j in cat]} for i in slope_dict}\n for i in slope_dict:\n slope_dict[i]['slope_diff'] = sum([abs(slope[j]-slope_dict[i]['slope'][j]) for j in range(len(slope))])\n value1 = sorted([[[i], slope_dict[i]['slope_diff'], slope_dict[i]['category_rate']] for i in slope_dict], key=lambda x:x[1], reverse=1)\n distance = sorted([value1[i][1]-value1[i+1][1] for i in range(len(value1)-1)])\n std = pd.Series([i[1] for i in value1]).std()\n coupe = value1\n dis = distance[0]\n for k in distance:\n value = value1\n while 1:\n for i in range(len(value)-1):\n if value[i][1]-k<value[i+1][1]:\n value[i+1][0] = value[i][0]+value[i+1][0]\n value[i+1][1] = value[i][1]*value[i][2]/(value[i][2]+value[i+1][2])+value[i+1][1]*value[i+1][2]/(value[i][2]+value[i+1][2])\n value[i+1][2] = value[i][2]+value[i+1][2]\n value.remove(value[i])\n break\n if i==len(value)-2:\n break\n if pd.Series([i[1] for i in value]).std()>std:\n coupe = value\n std = pd.Series([i[1] for i in value]).std()\n dis = k\n return {'group':{k:i for i,j in enumerate(coupe) for k in j[0]}, 'data':coupe, \n 'distance':dis, 'distance_index':f'{distance.index(dis)+1}/{len(distance)}', 'std':std}","def preprocess_features(X):\n\t# Initialize new output DataFrame\n\toutput = pd.DataFrame(index = X.index)\n\n\t# Investigate new output DataFrame\n\tfor col, col_data in X.iteritems():\n\t\t# If data type is categorical, convert to dummy variables\n\t\tif col_data.dtype == object:\n\t\t\tcol_data = pd.get_dummies(col_data, prefix = col)\n\n\t\t\t# Collect the revised columns\n\t\t\toutput - output.join(col_data)\n\treturn output","def load_feature(feature_name, caf_dose, features_path):\n # gets the paths to the folders where the specified feature is stored\n subject_paths = glob.glob(os.path.join(features_path, \"*\", feature_name))\n\n feature = {}\n for path in subject_paths:\n # extract the subject id from the current path (second to last element in the path)\n subject_id = path.split(os.sep)[-2]\n\n # get all stages for the current subject\n stages = set(\n [\n p.split(os.sep)[-1].split(\"_\")[-1].split(\".\")[0]\n for p in glob.glob(os.path.join(path, \"*.npy\"))\n ]\n )\n if len(stages) == 0:\n print(\n f\"The following directory doesn't contain features: {path}. \"\n \"This will likely cause an error down the line\"\n )\n for stage in stages:\n if stage not in feature:\n feature[stage] = {}\n # load the file containing the data for the current stage and subject\n feature[stage][subject_id] = np.load(\n os.path.join(path, f\"{feature_name}_{stage}.npy\"), allow_pickle=True\n )\n return feature","def GetVectorArticleInput(dico_vector_input, features):\n features_left = set(features) - set(dico_vector_input.keys())\n if len(features_left) > 0:\n sentence = \"Some features aren't in the dict:\\n\"\n raise MyException(sentence + \"{}\".format(features_left))\n vector_art = []\n other_features = ['abstract', 'syn', 'exergue', 'title', 'secTitle']\n other_features += ['subTitle', 'supTitle']\n for feature in features:\n if feature == 'nbSign':\n if dico_vector_input['nbSign'] == 0:\n print(\"NbSign == 0 l.176 - GetVectorArticleInput\")\n vector_art.append(dico_vector_input[feature])\n else:\n vector_art.append(dico_vector_input[feature])\n # Conversion des variables en indicatrices\n # Normalement plus la peine, comme déjà fait auparavant\n elif feature in other_features:\n if dico_vector_input[feature] > 0:\n vector_art.append(1)\n else:\n vector_art.append(0)\n else:\n vector_art.append(dico_vector_input[feature])\n return (dico_vector_input['melodyId'], np.array([vector_art]))","def get_name_to_features(self):\n name_to_features = {\n 'input_ids': tf.io.FixedLenFeature([self.seq_len], tf.int64),\n 'label_ids': tf.io.FixedLenFeature([], tf.int64),\n }\n return name_to_features","def set_factors(self, name, factormap, safe=False):\n e = False\n if name in self.masks():\n if self._get_subtype(name) != 'single':\n e = True\n else:\n if self._get_type(name) != 'single':\n e = True\n if e:\n if safe:\n err = \"Can only set factors to 'single' type categorical variables!\"\n raise TypeError(err)\n else:\n return None\n vals = self.codes(name)\n facts = list(factormap.keys())\n val_loc = self._get_value_loc(name)\n if not all(f in vals for f in facts):\n err = 'At least one factor is mapped to a code that does not exist '\n err += 'in the values object of \"{}\"!'\n raise ValueError(err.format(name))\n for value in val_loc:\n if value['value'] in factormap:\n value['factor'] = factormap[value['value']]\n else:\n value['factor'] = None\n return None","def list_badly_scaled_variables(\n blk,\n large: float = 1e4,\n small: float = 1e-3,\n zero: float = 1e-10,\n descend_into: bool = True,\n include_fixed: bool = False,\n):\n return [\n c\n for c in badly_scaled_var_generator(\n blk, large, small, zero, descend_into, include_fixed\n )\n ]","def test_categorical_feature():\n\n feature = Categorical(\"abc\")\n\n for element in \"abc\":\n feature.set(element)\n feature.set(\"ignore this\")\n feature.push()\n\n for element in \"abc\":\n getattr(feature, \"set_\" + element)()\n feature.push()\n\n array = feature.array()\n assert array.shape == (6, 3)\n for i, row in enumerate(array):\n assert sum(row) == 1.0 and row[i % 3] == 1.0","def make_dummy_df(self, dummy_vars, cat_to_drop = 0, drop_cat = True, add_intercept = True):\n\n # reset reference levels, if remaking then previous information would be there\n self.ref_levels = []\n\n # reset to original data. Ensures that make_dummy_df can be rerun multiple times\n self.df = self.df_orig.copy()\n\n # check if dummy_vars is a dict or a list to determine how to drop categories\n if isinstance(dummy_vars, list):\n drop_by_index = True\n\n # make sure that cat_to_drop is within the index range of the selected variables\n for var in dummy_vars:\n if self.df[var].nunique() < (cat_to_drop + 1):\n raise Exception(\"For column {} the number of categories was \\\nlower than the specified index ({}) to drop.\".format(var, cat_to_drop))\n else:\n drop_by_index = False\n\n # loop through categorical columns in dummy_vars list/dict\n for var in dummy_vars:\n\n # convert given variable to a dummy variable\n current_dummy = pd.DataFrame(self.df[var])\n current_dummy = pd.get_dummies(current_dummy, prefix = var)\n\n\n # drop one level as specified by dummy_vars, if drop_cat is true\n if drop_cat == True:\n\n # scenario where dummy_vars is a list\n if drop_by_index == True:\n dummy_to_drop = current_dummy.columns[cat_to_drop]\n current_dummy = current_dummy.drop([dummy_to_drop], axis = 1)\n\n # keep note of the dropped level in ref_levels\n self.ref_levels.append(dummy_to_drop)\n\n # scenario where dummy_vars is a dict\n if drop_by_index == False:\n dummy_to_drop = var + \"_\" + dummy_vars[var]\n current_dummy = current_dummy.drop([dummy_to_drop], axis = 1)\n\n # keep note of the dropped level in ref_levels\n self.ref_levels.append(dummy_to_drop)\n\n # concat with original dataframe\n self.df = pd.concat([self.df, current_dummy], axis=1)\n\n # drop pre-dummy categorical columns from original dataframe\n if drop_by_index == True:\n self.df = self.df.drop(dummy_vars, axis=1)\n\n if drop_by_index == False:\n self.df = self.df.drop(dummy_vars.keys(), axis=1)\n\n # if int add is true, add int\n if add_intercept == True:\n self.df['intercept'] = 1.0\n\n return self.df","def feature(self, N, pcomment, comment, positive, negative, posSub, negSub):\n features = {}\n # features top N\n for i in range(0, N - 1):\n if positive[i] in comment:\n features[\"positive(%s)\" % positive[i]] = True\n else:\n features[\"positive(%s)\" % positive[i]] = False\n if negative[i] in comment:\n features[\"negative(%s)\" % negative[i]] = True\n else:\n features[\"negative(%s)\" % negative[i]] = False\n # features subjetive lists\n for word in set(comment):\n if word in posSub and comment.count(word) > 0:\n features[\"subjetive_pos(%s)\" % word] = comment.count(word)\n if word in negSub and comment.count(word) > 0:\n features[\"subjetive_neg(%s)\" % word] = comment.count(word)\n\n #custom features\n if self.generateHeuristic(pcomment):\n features[\"no_gusto\"] = True\n\n return features","def get_categorical_features(self):\n return self.categorical_features","def __init__(self, strName, lstDomain):\n # assign the name of the feature represented by the node\n self.name = str(strName)\n # assign the domain of the feature\n self.domain = lstDomain\n # the value starts out as undefined\n self.value = \"none\"","def create_classifier(model, hidden_units=None):\n\n defaul_nb_units = 4096\n nb_units = hidden_units if hidden_units else defaul_nb_units\n \n input_features = model.classifier[0].in_features\n \n classifier = nn.Sequential(OrderedDict([\n ('fc1', nn.Linear(input_features, nb_units, bias=True)),\n ('relu1', nn.ReLU()),\n ('dropout1', nn.Dropout(p=0.5)),\n ('fc2', nn.Linear(nb_units, 102, bias=True)),\n ('output', nn.LogSoftmax(dim=1))\n ]))\n \n return classifier","def _condense_state(self, s):\n def append_col(A, b):\n expanded_b = np.expand_dims(b, 1)\n return np.append(A, expanded_b, 1)\n\n A, b, c0, cuts_a, cuts_b = s\n Ab = append_col(A, b)\n cuts = append_col(cuts_a, cuts_b)\n return (Ab, c0, cuts)","def fit(self, data, discrete_features=None, bandwidth=1.0,\n num_discretization_bins=4, pseudocount=1.0):\n if bandwidth <= 0:\n raise ValueError(\"Bandwidth must be positive.\")\n \n if discrete_features != None and \\\n len(discrete_features) != data.shape[1]:\n raise ValueError(\"Discrete features array and data arrays\"\n \"shape don't match.\")\n \n if num_discretization_bins < 0:\n raise ValueError(\"Number of descretization bins can't be negetive.\")\n \n if num_discretization_bins == 0:\n for bool in discrete_features:\n if bool:\n raise ValueError(\"Number of descretization bins can't be\"\n \"zero if there is a continuous feature.\")\n \n if pseudocount < 0:\n raise ValueError(\"Pseudocount can't be negative.\")\n \n if discrete_features == None:\n discrete_features = [False] * data.shape[1]\n\n self.num_features_ = data.shape[1]\n self.discrete_features_ = discrete_features\n self.num_discretization_bins_ = num_discretization_bins\n\n discretized_data = np.array(data, copy=True)\n continuous_data = data[:, np.invert(discrete_features)]\n\n discretizer = KBinsDiscretizer(n_bins=num_discretization_bins,\n encode='ordinal', strategy='quantile')\n discretizer.fit(continuous_data)\n\n discretized_data[:, np.invert(discrete_features)] = \\\n discretizer.transform(continuous_data)\n self.discretizer_ = discretizer\n\n self.model_ = BayesianNetwork.from_samples(discretized_data,\n algorithm='chow-liu', n_jobs=-1, pseudocount=pseudocount)\n self.model_.bake()\n \n # Table for bin edges\n bins = discretizer.bin_edges_\n\n # Kdes for continuous data.\n self.tnkdes_ = []\n\n i = 0\n for k in range(self.num_features_):\n if discrete_features[k]:\n continue\n \n bins[i][0] = -np.inf\n bins[i][len(bins[i]) - 1] = np.inf\n bin_kdes = []\n \n # loop of boundary\n for j in range(len(bins[i]) - 1):\n # Bound for this bin.\n lower_bound = bins[i][j]\n upper_bound = bins[i][j+1]\n \n # Create a kde using the data in the current bin.\n current_feature_data = data[:, k]\n cur_bin_data = current_feature_data[discretized_data[:, k] == j]\n kde = TruncatedNormalKernelDensity(bandwidth=bandwidth,\n lowerbound=lower_bound, upperbound=upper_bound)\n kde.fit(cur_bin_data)\n bin_kdes.append(kde)\n \n i = i + 1\n self.tnkdes_.append(bin_kdes)","def get_kdd_feature_cols():\n\treturn [\n\t\t'duration',\n\t\t'src_bytes',\n\t\t'dst_bytes',\n\t\t'land',\n\t\t'wrong_fragment',\n\t\t'urgent',\n\t\t'hot',\n\t\t'num_failed_logins',\n\t\t'logged_in',\n\t\t'num_compromised',\n\t\t'root_shell',\n\t\t'su_attempted',\n\t\t'num_root',\n\t\t'num_file_creations',\n\t\t'num_shells',\n\t\t'num_access_files',\n\t\t'num_outbound_cmds',\n\t\t'is_host_login',\n\t\t'is_guest_login',\n\t\t'count',\n\t\t'srv_count',\n\t\t'serror_rate',\n\t\t'srv_serror_rate',\n\t\t'rerror_rate',\n\t\t'srv_rerror_rate',\n\t\t'same_srv_rate',\n\t\t'diff_srv_rate',\n\t\t'srv_diff_host_rate',\n\t\t'dst_host_count',\n\t\t'dst_host_srv_count',\n\t\t'dst_host_same_srv_rate',\n\t\t'dst_host_diff_srv_rate',\n\t\t'dst_host_same_src_port_rate',\n\t\t'dst_host_srv_diff_host_rate',\n\t\t'dst_host_serror_rate',\n\t\t'dst_host_srv_serror_rate',\n\t\t'dst_host_rerror_rate',\n\t\t'dst_host_srv_rerror_rate']","def dim2():\n probs = (0.1, 0.2, 0.3, 0.4)\n categories = (\"asdfa\", \"2\", \"3\", \"4\")\n categories = OrderedDict(zip(categories, probs))\n dim2 = Categorical(\"yolo2\", categories, default_value=\"2\")\n return dim2"],"string":"[\n \"def discVar2FeatureOld( var, varname, lims = [1,5], collapse = [False, False], ctxt = 'contains'):\\n nums = ['zero','one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten']\\n \\n vals = range(lims[0], lims[1]+1) \\n \\n #init fdict\\n fdict = dict() \\n for k, val in enumerate(vals):\\n fdict[(ctxt + ' %s ' + varname) % (nums[val])] = False\\n \\n if collapse[0] == True: var = max([var, lims[0]])\\n if collapse[1] == True: var = min([var, lims[1]])\\n \\n if var >= lims[0] and var <= lims[1]: #if collapse = False, ignore vals outside lims\\n fdict[(ctxt + ' %s ' + varname) % (nums[var])] = True \\n \\n return fdict\",\n \"def convertToDiscreteFunction(boolean: bool) -> cern.japc.value.DiscreteFunction:\\n ...\",\n \"def FE_discretize_numeric_variables(train, bin_dict, test='', strategy='kmeans',verbose=0):\\r\\n df = copy.deepcopy(train)\\r\\n test = copy.deepcopy(test)\\r\\n num_cols = len(bin_dict)\\r\\n nrows = int((num_cols/2)+0.5)\\r\\n #print('nrows',nrows)\\r\\n if verbose:\\r\\n fig = plt.figure(figsize=(10,3*num_cols))\\r\\n for i, (col, binvalue) in enumerate(bin_dict.items()):\\r\\n new_col = col+'_discrete'\\r\\n if strategy == 'gaussian':\\r\\n kbd = GaussianMixture(n_components=binvalue, random_state=99)\\r\\n df[new_col] = kbd.fit_predict(df[[col]]).astype(int)\\r\\n if not isinstance(test, str):\\r\\n test[new_col] = kbd.predict(test[[col]]).astype(int)\\r\\n else:\\r\\n kbd = KBinsDiscretizer(n_bins=binvalue, encode='ordinal', strategy=strategy)\\r\\n df[new_col] = kbd.fit_transform(df[[col]]).astype(int)\\r\\n if not isinstance(test, str):\\r\\n test[new_col] = kbd.transform(test[[col]]).astype(int)\\r\\n if verbose:\\r\\n ax1 = plt.subplot(nrows,2,i+1)\\r\\n ax1.scatter(df[col],df[new_col])\\r\\n ax1.set_title(new_col)\\r\\n if not isinstance(test, str):\\r\\n return df, test\\r\\n else:\\r\\n return df\",\n \"def denseFeature(self, feat):\\n return {'feat': feat}\",\n \"def convertToDiscreteFunctionList(boolean: bool) -> cern.japc.value.DiscreteFunctionList:\\n ...\",\n \"def preprocess_var(bd, var):\\n filepath_sv = f\\\"team67-ptp/data/{var}.csv\\\"\\n filepath = bd\\n data = feather.read_dataframe(filepath)\\n df = data.copy()\\n df2 = df[var]\\n df2 = df2.to_frame()\\n if df2[var].dtype is \\\"category\\\":\\n df2[var] = df2[var].astype(\\\"category\\\").cat.codes\\n filename = filepath_sv\\n df2.to_csv(filename, index=False)\\n print(\\\"Succesfully exported to csv\\\")\\n else:\\n filename = filepath_sv\\n df2.to_csv(filename, index=False)\\n print(\\\"Succesfully exported to csv\\\")\",\n \"def addFeature(self, strName, lstDomain):\\n # create a new variable CSPVariable object\\n newFeature = CSPFeature(strName, lstDomain)\\n # put the new variable in the graph's list of variables\\n self.features.append(newFeature)\",\n \"def select_var_feature(adata, min_score=0.5, nb_features=None, show=True, copy=False):\\n if copy:\\n inplace=False\\n else:\\n inplace=True\\n\\n adata = adata.copy() if not inplace else adata\\n \\n # calculate variability score\\n cal_var(adata, show=show) # adds variability score for each feature \\n # adata.var['variablility_score'] = abs(adata.var['prop_shared_cells']-0.5)\\n var_annot = adata.var.sort_values(ascending=True, by ='variability_score')\\n\\n # calculate the min score to get a specific number of feature \\n if nb_features != None and nb_features < len(adata.var_names): \\n min_score = var_annot['variability_score'][nb_features] \\n \\n \\n adata_tmp = adata[:,adata.var['variability_score']<=min_score].copy()\\n \\n ## return the filtered AnnData objet.\\n if not inplace:\\n adata_tmp = adata[:,adata.var['variability_score']<=min_score]\\n return(adata_tmp)\\n else:\\n adata._inplace_subset_var(adata.var['variability_score']<=min_score)\",\n \"def feature_discretion(self, X, y):\\n temp, X_interval = [], []\\n if self._DISCRETION == \\\"percentile_discrete\\\":\\n for i in range(0, X.shape[-1]):\\n x = X[:, i]\\n x_type = type_of_target(x)\\n # logging.info(\\\"before: \\\"+\\\" \\\".join([str(i), str(set(X[:, i])), str(x_type)]))\\n if 0:\\n if x_type == 'continuous':\\n x1, interval = self.percentile_discrete(x, self._WOE_N)\\n X_interval.append(interval)\\n temp.append(x1)\\n # logging.info(\\\"continue_after: \\\" + \\\" \\\".join([str(i), str(set(x1)), str(x1)]))\\n else:\\n temp.append(x)\\n # logging.info(\\\"after: \\\" + \\\" \\\".join([str(i), str(set(x)), str(x)]))\\n else:\\n x1, interval = self.percentile_discrete(x, self._WOE_N)\\n X_interval.append(interval)\\n temp.append(x1)\\n # logging.info(\\\"continue_after: \\\" + \\\" \\\".join([str(i), str(set(x1)), str(x1)]))\\n elif self._DISCRETION == \\\"interval_discrete\\\":\\n for i in range(0, X.shape[-1]):\\n x = X[:, i]\\n # logging.info(\\\"before: \\\"+\\\" \\\".join([str(i), str(set(X[:, i]))]))\\n x1, interval = self.interval_discrete(x, self._WOE_N)\\n X_interval.append(interval)\\n temp.append(x1)\\n # logging.info(\\\"interval_after: \\\" + \\\" \\\".join([str(i), str(set(x1)), str(x1)]))\\n elif self._DISCRETION == \\\"rf_discrete\\\":\\n for i in range(0, X.shape[-1]):\\n x = X[:, i]\\n # logging.info(\\\"before: \\\"+\\\" \\\".join([str(i), str(set(X[:, i]))]))\\n x1, interval = self.rf_discrete(x, y)\\n X_interval.append(interval)\\n temp.append(x1)\\n # logging.info(\\\"rf_after: \\\" + \\\" \\\".join([str(i), str(set(x1)), str(x1)]))\\n return np.array(temp).T, X_interval\",\n \"def dense2cvxopt(value):\\n import cvxopt\\n return cvxopt.matrix(value, tc='d')\",\n \"def dict_to_feature(d):\\n f = ee.Feature(None,ee.Dictionary(d))\\n return f\",\n \"def my_featurize(apartment):\\n col =np.array([1, 2, 0, 0, 0, 0, 0, 0 ])\\n a= pd.DataFrame(apartment[col])\\n if(apartment.get('condition')== 'good'):\\n col[1] =1\\n else:\\n if(apartment.get('condition')== 'zero condition'):\\n col[1] = 0\\n col[2] =apartment.get('num_rooms')\\n col[3] =apartment.get('area')\\n col[4] =apartment.get('num_bathrooms')\\n col[5] =apartment.get('floor')\\n col[6] =apartment.get('ceiling_height')\\n col[7] =apartment.get('max_floor')\\n\\n return col, apartment['price']\",\n \"def get_feature_set_SC2(tweet, sentimentvalues):\\n pos_tag_freq = {}\\n additional_freq = {}\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n additional_freq['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in pos_tag_freq.keys():\\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \\n #Number of exclamation marks, number of emoticons,\\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\\n if emoticons>0:\\n additional_freq['emoticons'] = emoticons*1.0\\n if tweet.nrof_exclamations>0:\\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\\n \\n #Add lexicon values\\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\\n sub_score = sentimentvalues[0]+sentimentvalues[1]\\n obj_score = sentimentvalues[2]\\n if sub_score>0:\\n additional_freq[\\\"sub_score\\\"] = sub_score+1.0\\n if obj_score>0:\\n additional_freq[\\\"obj_score\\\"] = obj_score+1.0\\n \\n #Concatenate the dicts\\n features= dict(pos_tag_freq.items() + additional_freq.items())\\n \\n return features\",\n \"def _make_features(self, x):\\n\\t\\tx = x.unsqueeze(1)\\n\\t\\treturn torch.cat([x ** i for i in range(1, self._degree+1)], 1)\",\n \"def preprocess_feature(df):\",\n \"def classify_columns(df_preds, verbose=0):\\r\\n train = copy.deepcopy(df_preds)\\r\\n #### If there are 30 chars are more in a discrete_string_var, it is then considered an NLP variable\\r\\n max_nlp_char_size = 30\\r\\n max_cols_to_print = 30\\r\\n print('############## C L A S S I F Y I N G V A R I A B L E S ####################')\\r\\n print('Classifying variables in data set...')\\r\\n #### Cat_Limit defines the max number of categories a column can have to be called a categorical colum\\r\\n cat_limit = 35\\r\\n float_limit = 15 #### Make this limit low so that float variables below this limit become cat vars ###\\r\\n def add(a,b):\\r\\n return a+b\\r\\n sum_all_cols = dict()\\r\\n orig_cols_total = train.shape[1]\\r\\n #Types of columns\\r\\n cols_delete = [col for col in list(train) if (len(train[col].value_counts()) == 1\\r\\n ) | (train[col].isnull().sum()/len(train) >= 0.90)]\\r\\n train = train[left_subtract(list(train),cols_delete)]\\r\\n var_df = pd.Series(dict(train.dtypes)).reset_index(drop=False).rename(\\r\\n columns={0:'type_of_column'})\\r\\n sum_all_cols['cols_delete'] = cols_delete\\r\\n var_df['bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['bool','object']\\r\\n and len(train[x['index']].value_counts()) == 2 else 0, axis=1)\\r\\n string_bool_vars = list(var_df[(var_df['bool'] ==1)]['index'])\\r\\n sum_all_cols['string_bool_vars'] = string_bool_vars\\r\\n var_df['num_bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\\r\\n np.uint16, np.uint32, np.uint64,\\r\\n 'int8','int16','int32','int64',\\r\\n 'float16','float32','float64'] and len(\\r\\n train[x['index']].value_counts()) == 2 else 0, axis=1)\\r\\n num_bool_vars = list(var_df[(var_df['num_bool'] ==1)]['index'])\\r\\n sum_all_cols['num_bool_vars'] = num_bool_vars\\r\\n ###### This is where we take all Object vars and split them into diff kinds ###\\r\\n discrete_or_nlp = var_df.apply(lambda x: 1 if x['type_of_column'] in ['object'] and x[\\r\\n 'index'] not in string_bool_vars+cols_delete else 0,axis=1)\\r\\n ######### This is where we figure out whether a string var is nlp or discrete_string var ###\\r\\n var_df['nlp_strings'] = 0\\r\\n var_df['discrete_strings'] = 0\\r\\n var_df['cat'] = 0\\r\\n var_df['id_col'] = 0\\r\\n discrete_or_nlp_vars = var_df.loc[discrete_or_nlp==1]['index'].values.tolist()\\r\\n if len(var_df.loc[discrete_or_nlp==1]) != 0:\\r\\n for col in discrete_or_nlp_vars:\\r\\n #### first fill empty or missing vals since it will blowup ###\\r\\n train[col] = train[col].fillna(' ')\\r\\n if train[col].map(lambda x: len(x) if type(x)==str else 0).mean(\\r\\n ) >= max_nlp_char_size and len(train[col].value_counts()\\r\\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'nlp_strings'] = 1\\r\\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\\r\\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'discrete_strings'] = 1\\r\\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\\r\\n ) == len(train) and col not in string_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'id_col'] = 1\\r\\n else:\\r\\n var_df.loc[var_df['index']==col,'cat'] = 1\\r\\n nlp_vars = list(var_df[(var_df['nlp_strings'] ==1)]['index'])\\r\\n sum_all_cols['nlp_vars'] = nlp_vars\\r\\n discrete_string_vars = list(var_df[(var_df['discrete_strings'] ==1) ]['index'])\\r\\n sum_all_cols['discrete_string_vars'] = discrete_string_vars\\r\\n ###### This happens only if a string column happens to be an ID column #######\\r\\n #### DO NOT Add this to ID_VARS yet. It will be done later.. Dont change it easily...\\r\\n #### Category DTYPE vars are very special = they can be left as is and not disturbed in Python. ###\\r\\n var_df['dcat'] = var_df.apply(lambda x: 1 if str(x['type_of_column'])=='category' else 0,\\r\\n axis=1)\\r\\n factor_vars = list(var_df[(var_df['dcat'] ==1)]['index'])\\r\\n sum_all_cols['factor_vars'] = factor_vars\\r\\n ########################################################################\\r\\n date_or_id = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\\r\\n np.uint16, np.uint32, np.uint64,\\r\\n 'int8','int16',\\r\\n 'int32','int64'] and x[\\r\\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\\r\\n axis=1)\\r\\n ######### This is where we figure out whether a numeric col is date or id variable ###\\r\\n var_df['int'] = 0\\r\\n var_df['date_time'] = 0\\r\\n ### if a particular column is date-time type, now set it as a date time variable ##\\r\\n var_df['date_time'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['<M8[ns]','datetime64[ns]'] and x[\\r\\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\\r\\n axis=1)\\r\\n ### this is where we save them as date time variables ###\\r\\n if len(var_df.loc[date_or_id==1]) != 0:\\r\\n for col in var_df.loc[date_or_id==1]['index'].values.tolist():\\r\\n if len(train[col].value_counts()) == len(train):\\r\\n if train[col].min() < 1900 or train[col].max() > 2050:\\r\\n var_df.loc[var_df['index']==col,'id_col'] = 1\\r\\n else:\\r\\n try:\\r\\n pd.to_datetime(train[col],infer_datetime_format=True)\\r\\n var_df.loc[var_df['index']==col,'date_time'] = 1\\r\\n except:\\r\\n var_df.loc[var_df['index']==col,'id_col'] = 1\\r\\n else:\\r\\n if train[col].min() < 1900 or train[col].max() > 2050:\\r\\n if col not in num_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'int'] = 1\\r\\n else:\\r\\n try:\\r\\n pd.to_datetime(train[col],infer_datetime_format=True)\\r\\n var_df.loc[var_df['index']==col,'date_time'] = 1\\r\\n except:\\r\\n if col not in num_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'int'] = 1\\r\\n else:\\r\\n pass\\r\\n int_vars = list(var_df[(var_df['int'] ==1)]['index'])\\r\\n date_vars = list(var_df[(var_df['date_time'] == 1)]['index'])\\r\\n id_vars = list(var_df[(var_df['id_col'] == 1)]['index'])\\r\\n sum_all_cols['int_vars'] = int_vars\\r\\n copy_date_vars = copy.deepcopy(date_vars)\\r\\n for date_var in copy_date_vars:\\r\\n #### This test is to make sure sure date vars are actually date vars\\r\\n try:\\r\\n pd.to_datetime(train[date_var],infer_datetime_format=True)\\r\\n except:\\r\\n ##### if not a date var, then just add it to delete it from processing\\r\\n cols_delete.append(date_var)\\r\\n date_vars.remove(date_var)\\r\\n sum_all_cols['date_vars'] = date_vars\\r\\n sum_all_cols['id_vars'] = id_vars\\r\\n sum_all_cols['cols_delete'] = cols_delete\\r\\n ## This is an EXTREMELY complicated logic for cat vars. Don't change it unless you test it many times!\\r\\n var_df['numeric'] = 0\\r\\n float_or_cat = var_df.apply(lambda x: 1 if x['type_of_column'] in ['float16',\\r\\n 'float32','float64'] else 0,\\r\\n axis=1)\\r\\n if len(var_df.loc[float_or_cat == 1]) > 0:\\r\\n for col in var_df.loc[float_or_cat == 1]['index'].values.tolist():\\r\\n if len(train[col].value_counts()) > 2 and len(train[col].value_counts()\\r\\n ) <= float_limit and len(train[col].value_counts()) <= len(train):\\r\\n var_df.loc[var_df['index']==col,'cat'] = 1\\r\\n else:\\r\\n if col not in num_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'numeric'] = 1\\r\\n cat_vars = list(var_df[(var_df['cat'] ==1)]['index'])\\r\\n continuous_vars = list(var_df[(var_df['numeric'] ==1)]['index'])\\r\\n ######## V E R Y I M P O R T A N T ###################################################\\r\\n ##### There are a couple of extra tests you need to do to remove abberations in cat_vars ###\\r\\n cat_vars_copy = copy.deepcopy(cat_vars)\\r\\n for cat in cat_vars_copy:\\r\\n if df_preds[cat].dtype==float:\\r\\n continuous_vars.append(cat)\\r\\n cat_vars.remove(cat)\\r\\n var_df.loc[var_df['index']==cat,'cat'] = 0\\r\\n var_df.loc[var_df['index']==cat,'numeric'] = 1\\r\\n elif len(df_preds[cat].value_counts()) == df_preds.shape[0]:\\r\\n id_vars.append(cat)\\r\\n cat_vars.remove(cat)\\r\\n var_df.loc[var_df['index']==cat,'cat'] = 0\\r\\n var_df.loc[var_df['index']==cat,'id_col'] = 1\\r\\n sum_all_cols['cat_vars'] = cat_vars\\r\\n sum_all_cols['continuous_vars'] = continuous_vars\\r\\n sum_all_cols['id_vars'] = id_vars\\r\\n ###### This is where you consoldate the numbers ###########\\r\\n var_dict_sum = dict(zip(var_df.values[:,0], var_df.values[:,2:].sum(1)))\\r\\n for col, sumval in var_dict_sum.items():\\r\\n if sumval == 0:\\r\\n print('%s of type=%s is not classified' %(col,train[col].dtype))\\r\\n elif sumval > 1:\\r\\n print('%s of type=%s is classified into more then one type' %(col,train[col].dtype))\\r\\n else:\\r\\n pass\\r\\n ############### This is where you print all the types of variables ##############\\r\\n ####### Returns 8 vars in the following order: continuous_vars,int_vars,cat_vars,\\r\\n ### string_bool_vars,discrete_string_vars,nlp_vars,date_or_id_vars,cols_delete\\r\\n if verbose == 1:\\r\\n print(\\\" Number of Numeric Columns = \\\", len(continuous_vars))\\r\\n print(\\\" Number of Integer-Categorical Columns = \\\", len(int_vars))\\r\\n print(\\\" Number of String-Categorical Columns = \\\", len(cat_vars))\\r\\n print(\\\" Number of Factor-Categorical Columns = \\\", len(factor_vars))\\r\\n print(\\\" Number of String-Boolean Columns = \\\", len(string_bool_vars))\\r\\n print(\\\" Number of Numeric-Boolean Columns = \\\", len(num_bool_vars))\\r\\n print(\\\" Number of Discrete String Columns = \\\", len(discrete_string_vars))\\r\\n print(\\\" Number of NLP String Columns = \\\", len(nlp_vars))\\r\\n print(\\\" Number of Date Time Columns = \\\", len(date_vars))\\r\\n print(\\\" Number of ID Columns = \\\", len(id_vars))\\r\\n print(\\\" Number of Columns to Delete = \\\", len(cols_delete))\\r\\n if verbose == 2:\\r\\n marthas_columns(df_preds,verbose=1)\\r\\n print(\\\" Numeric Columns: %s\\\" %continuous_vars[:max_cols_to_print])\\r\\n print(\\\" Integer-Categorical Columns: %s\\\" %int_vars[:max_cols_to_print])\\r\\n print(\\\" String-Categorical Columns: %s\\\" %cat_vars[:max_cols_to_print])\\r\\n print(\\\" Factor-Categorical Columns: %s\\\" %factor_vars[:max_cols_to_print])\\r\\n print(\\\" String-Boolean Columns: %s\\\" %string_bool_vars[:max_cols_to_print])\\r\\n print(\\\" Numeric-Boolean Columns: %s\\\" %num_bool_vars[:max_cols_to_print])\\r\\n print(\\\" Discrete String Columns: %s\\\" %discrete_string_vars[:max_cols_to_print])\\r\\n print(\\\" NLP text Columns: %s\\\" %nlp_vars[:max_cols_to_print])\\r\\n print(\\\" Date Time Columns: %s\\\" %date_vars[:max_cols_to_print])\\r\\n print(\\\" ID Columns: %s\\\" %id_vars[:max_cols_to_print])\\r\\n print(\\\" Columns that will not be considered in modeling: %s\\\" %cols_delete[:max_cols_to_print])\\r\\n ##### now collect all the column types and column names into a single dictionary to return!\\r\\n len_sum_all_cols = reduce(add,[len(v) for v in sum_all_cols.values()])\\r\\n if len_sum_all_cols == orig_cols_total:\\r\\n print(' %d Predictors classified...' %orig_cols_total)\\r\\n #print(' This does not include the Target column(s)')\\r\\n else:\\r\\n print('No of columns classified %d does not match %d total cols. Continuing...' %(\\r\\n len_sum_all_cols, orig_cols_total))\\r\\n ls = sum_all_cols.values()\\r\\n flat_list = [item for sublist in ls for item in sublist]\\r\\n if len(left_subtract(list(train),flat_list)) == 0:\\r\\n print(' Missing columns = None')\\r\\n else:\\r\\n print(' Missing columns = %s' %left_subtract(list(train),flat_list))\\r\\n return sum_all_cols\",\n \"def topdia(x):\\r\\n return Feature(x, \\\"TopDia\\\")\",\n \"def my_featurize(apartment):\\n return x, y\",\n \"def cat2onehot_var(snt_ids, vocab_size, batch_size):\\n\\n targets = np.array([snt_ids]).reshape(-1)\\n one_hot_targets = np.eye(vocab_size)[targets]\\n result = Variable(torch.FloatTensor(one_hot_targets).view(-1, batch_size, vocab_size)) #\\n\\n return result\",\n \"def restrict(self, variable, value):\\n # 新因子变量列表为原因子变量列表减去被求和的变量\\n restricted_variable = self.var_list.index(variable)\\n new_var_list = self.var_list[:restricted_variable] + self.var_list[restricted_variable + 1:]\\n\\n # 对于新因子CPT中的表项，其值为原因子中与新因子变量取值相同且限制变量取对应值的的单个表项的值\\n new_cpt = {}\\n if restricted_variable == 0:\\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\\n if len(self.var_list) == 1:\\n postfix = ''\\n new_cpt[postfix] = self.cpt[str(value) + postfix]\\n elif restricted_variable == len(self.var_list) - 1:\\n for i in range(pow(2, restricted_variable)):\\n prefix = Util.to_binary(i, restricted_variable)\\n new_cpt[prefix] = self.cpt[prefix + str(value)]\\n else:\\n for i in range(pow(2, restricted_variable)):\\n prefix = Util.to_binary(i, restricted_variable)\\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\\n new_cpt[prefix + postfix] = self.cpt[prefix + str(value) + postfix]\\n new_node = Node('f' + str(new_var_list), new_var_list)\\n new_node.set_cpt(new_cpt)\\n return new_node\",\n \"def make_features(x):\\n x = x.unsqueeze(1)\\n # torch.cat 实现tensor拼接\\n return torch.cat([x ** i for i in range(1, POLY_DEGREE + 1)], 1)\",\n \"def categorical(df):\\n\\n # variables which need to be transformed to categorical\\n categorical = [\\\"prop_country_id\\\", \\\"visitor_location_country_id\\\"]\\n\\n for var in categorical:\\n df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1)\\n del df[var]\\n\\n return df\",\n \"def get_categorical_features(self, x: pd.DataFrame) -> pd.DataFrame:\\n return x[self.categorical_features]\",\n \"def collapse_var(nc, out, name, direction):\\n var1 = nc.variables[name]\\n N = (len(nc.dimensions[direction]) - 1) / 2\\n\\n print(\\\"Processing %s...\\\" % name)\\n dims = var1.dimensions\\n if len(dims) > 1: # only collapse spatial fields\\n dims = [x for x in dims if x != direction]\\n\\n try:\\n fill_value = var1._FillValue\\n var2 = out.createVariable(name, var1.dtype,\\n dimensions=dims, fill_value=fill_value)\\n except:\\n var2 = out.createVariable(name, var1.dtype,\\n dimensions=dims)\\n\\n copy_attributes(var1, var2)\\n\\n if direction == 'x':\\n var2[:] = var1[get_slice(var1.dimensions, x=N)]\\n elif direction == 'y':\\n var2[:] = var1[get_slice(var1.dimensions, y=N)]\",\n \"def apply_randomization(features, label, randomize_prob):\\n rnd_tok = lambda: tf.as_string(tf.random.uniform([], 0, 99999999, tf.int32))\\n\\n for idx in CAT_FEATURE_INDICES:\\n key = feature_name(idx)\\n # Ignore lint since tf.cond should evaluate lambda immediately.\\n features[key] = tf.cond(tf.random.uniform([]) < randomize_prob,\\n rnd_tok,\\n lambda: features[key]) # pylint: disable=cell-var-from-loop\\n return features, label\",\n \"def dict_to_feature(feature_dict, keys, max_value=None):\\n feature = []\\n for key, val in feature_dict.items(): # First level\\n if key not in keys:\\n continue\\n if val is None or val == \\\"auto\\\" or key == \\\"autotuning\\\" or val == \\\"\\\":\\n continue\\n if isinstance(val, dict):\\n feature.append(dict_to_feature(val, max_value))\\n else:\\n feature.append(float(val))\\n\\n # normalization, should not matter in tree models\\n if max_value is not None:\\n norm_feature = []\\n for f, mv in zip(feature, max_value):\\n norm_feature.append(f / mv)\\n feature = norm_feature\\n\\n return feature\",\n \"def get_optimal_discrete_feature_split(\\n self, X: np.ndarray, y: np.ndarray, feature_col: int\\n ):\\n return BaseTree.get_discrete_split_value(\\n X[:, feature_col], y, eval_func=self.eval_func\\n )\",\n \"def preprocess_sf(bd, var):\\n filepath_svf = f\\\"team67-ptp/data/{var}.ftr\\\"\\n filepath = bd\\n data = feather.read_dataframe(filepath)\\n df = data.copy()\\n df2 = df[var]\\n df2 = df2.to_frame()\\n if df2[var].dtype is \\\"category\\\":\\n df2[var] = df2[var].astype(\\\"category\\\").cat.codes\\n filename = filepath_svf\\n df2.to_feather(filename)\\n print(\\\"Succesfully exported to feather\\\")\\n else:\\n filename = filepath_svf\\n df2.to_feather(filename)\\n print(\\\"Succesfully exported to feather\\\")\",\n \"def cvxopt2dense(value):\\n return np.array(value)\",\n \"def xx(x):\\r\\n return Feature(x, \\\"XX\\\")\",\n \"def convert_study_to_feature(study: List[Types.SeriesObj]) -> List[Dict[str, tf.train.Feature]]:\\n return [convert_series_to_feature(s) for s in study]\",\n \"def get_feature_set_SC(tweet, sentimentvalues):\\n pos_tag_freq = {}\\n additional_freq = {}\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# if tag=='PRtinf':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJS':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJ':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='NP':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='DET':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='P':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n additional_freq['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in pos_tag_freq.keys():\\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \\n #Number of exclamation marks, number of emoticons,\\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\\n if emoticons>0:\\n additional_freq['emoticons'] = emoticons*1.0\\n if tweet.nrof_exclamations>0:\\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\\n \\n #Add lexicon values\\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\\n sub_score = 0.0\\n obj_score = 0.0\\n nrof_subwords = 0\\n nrof_objwords = 0\\n for word in sentimentvalues.keys():\\n if sentimentvalues[word][0]>0:\\n sub_score = sub_score + sentimentvalues[word][0]\\n nrof_subwords = nrof_subwords + 1\\n if sentimentvalues[word][1]>0:\\n sub_score = sub_score + sentimentvalues[word][1]\\n nrof_subwords = nrof_subwords + 1\\n if sentimentvalues[word][2]>0:\\n obj_score = obj_score + sentimentvalues[word][2]\\n nrof_objwords = nrof_objwords + 1\\n if sub_score>0:\\n additional_freq[\\\"sub_score\\\"] = sub_score+1.0\\n if obj_score>0:\\n additional_freq[\\\"obj_score\\\"] = obj_score+1.0\\n if nrof_subwords>0:\\n additional_freq[\\\"subjective_words\\\"] = nrof_subwords*1.0\\n if nrof_objwords>0:\\n additional_freq[\\\"objective_words\\\"] = nrof_objwords*1.0\\n \\n #Concatenate the dicts\\n features= dict(pos_tag_freq.items() + additional_freq.items())\\n \\n return features\",\n \"def dummify_features(df):\\n colnames = df.columns\\n le_dict = {}\\n for col in colnames:\\n le_dict[col] = preprocessing.LabelEncoder()\\n le_dict[col].fit(df[col])\\n df.loc[:, col] = le_dict[col].transform(df[col])\\n\\n enc = preprocessing.OneHotEncoder()\\n enc.fit(df)\\n X = enc.transform(df)\\n\\n dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\\n # for cv in colnames:\\n # for modality in le_dict[cv].classes_:\\n # dummy_colnames.append(cv + '_' + modality)\\n\\n return X, dummy_colnames, enc\",\n \"def preprocess(old_df, label_name, category_features, non_category_features):\\n old_df['fraud'] = old_df[label_name].apply(lambda x: x[0] == 'f')\\n\\n # Creating a new dataframe with a subset of features.\\n new_df = old_df[['fraud'] + non_category_features]\\n\\n # For categorical features, we make dummy variables,\\n # and merge them into new_df.\\n for feature in category_features:\\n dummy_df = pd.get_dummies(old_df[feature], prefix=feature,\\n dummy_na=True)\\n # Since dummy_na=True, the last column will be for null values.\\n dummy_df.drop(dummy_df.columns[-1], axis=1, inplace=True)\\n new_df = pd.concat([new_df, dummy_df], axis=1)\\n return new_df\",\n \"def data_continuous(df, cat_features = [], cont_features = []) :\\n subset_cont =[]\\n for col in list(df.columns):\\n if df[col].dtype == 'int' or df[col].dtype == 'float64':\\n if col not in cont_features and col not in cat_features:\\n print(col, \\\"was added to continuous features\\\")\\n cont_features.append(col)\\n subset_cont.append(col)\\n for col in cont_features:\\n if col not in subset_cont:\\n subset_cont.append(col)\\n print('Continuous features : ', ' '.join(subset_cont))\\n printmd(\\\"**Description of continuous columns:**\\\")\\n print(round(df[subset_cont].describe()))\\n return (cont_features)\",\n \"def extract_concepts_discrete():\\n disc = pd.DataFrame([['name', 'Name', 'string'],\\n ['year', 'Year', 'time'],\\n ['area', 'Area', 'entity_domain'],\\n ['area_id', 'Area Id', 'string'],\\n ['variable_id', 'Variable Id', 'string']\\n ], columns=['concept', 'name', 'concept_type'])\\n return disc\",\n \"def index_to_feature(p, dims):\\n feature = []\\n for dim in dims:\\n feature.append(p % dim)\\n p //= dim\\n return feature\",\n \"def from_dummies(self, data, prefix_sep='_'):\\n out = data.copy()\\n for feat in self.categorical_feature_names:\\n # first, derive column names in the one-hot-encoded data from the original data\\n cat_col_values = []\\n for val in list(self.data_df[feat].unique()):\\n cat_col_values.append(feat + prefix_sep + str(val)) # join original feature name and its unique values , ex: education_school\\n match_cols = [c for c in data.columns if c in cat_col_values] # check for the above matching columns in the encoded data\\n\\n # then, recreate original data by removing the suffixes - based on the GitHub issue comment: https://github.com/pandas-dev/pandas/issues/8745#issuecomment-417861271\\n cols, labs = [[c.replace(\\n x, \\\"\\\") for c in match_cols] for x in [\\\"\\\", feat + prefix_sep]]\\n out[feat] = pd.Categorical(\\n np.array(labs)[np.argmax(data[cols].values, axis=1)])\\n out.drop(cols, axis=1, inplace=True)\\n return out\",\n \"def feature_vec(idcs, idcs_per_action, num_actions):\\n x_fvec = np.zeros((idcs_per_action*num_actions))\\n x_fvec[idcs] = 1\\n \\n return x_fvec\",\n \"def word2features(self,sent, i):\\n word = sent[i][0]\\n #postag = sent[i][1]\\n\\n features = {\\n 'bias': 1.0,\\n 'word.lower()': word.lower(),\\n 'word.isupper()': word.isupper(),\\n 'word.istitle()': word.istitle(),\\n 'word.isdigit()': word.isdigit(),\\n 'word.shape()':self.shape(word),\\n 'word.isalnum()':word.isalnum(),\\n 'word.isalpha()':word.isalpha(),\\n # 'postag': postag,\\n # 'postag[:2]': postag[:2],\\n }\\n if i > 0:\\n word1 = sent[i - 1][0]\\n #postag1 = sent[i - 1][1]\\n features.update({\\n '-1:word.lower()': word1.lower(),\\n '-1:word.istitle()': word1.istitle(),\\n '-1:word.isupper()': word1.isupper(),\\n '-1:word.isdigit()': word1.isdigit(),\\n '-1:word.isalnum()':word1.isalnum(),\\n '-1:word.isalpha()':word1.isalpha(),\\n # '-1:postag': postag1,\\n # '-1:postag[:2]': postag1[:2],\\n })\\n else:\\n features['BOS'] = True\\n\\n if i > 1:\\n word2 = sent[i - 2][0]\\n #postag2 = sent[i - 2][1]\\n features.update({\\n '-2:word.lower()': word2.lower(),\\n '-2:word.istitle()': word2.istitle(),\\n '-2:word.isupper()': word2.isupper(),\\n '-2:word.isdigit()': word2.isdigit(),\\n '-2:word.isalnum()': word2.isalnum(),\\n '-2:word.isalpha()': word2.isalpha(),\\n # '-2:postag': postag2,\\n # '-2:postag[:2]': postag2[:2],\\n })\\n else:\\n features['BOS1'] = True\\n if i > 2:\\n word3 = sent[i - 3][0]\\n #postag3 = sent[i - 3][1]\\n features.update({\\n '-3:word.lower()': word3.lower(),\\n '-3:word.istitle()': word3.istitle(),\\n '-3:word.isupper()': word3.isupper(),\\n '-3:word.isdigit()': word3.isdigit(),\\n '-3:word.isalnum()': word3.isalnum(),\\n '-3:word.isalpha()': word3.isalpha(),\\n # '-3:postag': postag3,\\n # '-3:postag[:2]': postag3[:2],\\n })\\n else:\\n features['BOS2'] = True\\n\\n if i > 3:\\n word4 = sent[i - 4][0]\\n #postag4 = sent[i - 4][1]\\n features.update({\\n '-4:word.lower()': word4.lower(),\\n '-4:word.istitle()': word4.istitle(),\\n '-4:word.isupper()': word4.isupper(),\\n '-4:word.isdigit()': word4.isdigit(),\\n '-4:word.isalnum()': word4.isalnum(),\\n '-4:word.isalpha()': word4.isalpha(),\\n # '-4:postag': postag4,\\n # '-4:postag[:2]': postag4[:2],\\n })\\n else:\\n features['BOS2'] = True\\n\\n if i < len(sent) - 1:\\n word1 = sent[i + 1][0]\\n features.update({\\n '+1:word.lower()': word1.lower(),\\n '+1:word.istitle()': word1.istitle(),\\n '+1:word.isupper()': word1.isupper(),\\n '+1:word.isdigit()': word1.isdigit(),\\n '+1:word.isalnum()': word1.isalnum(),\\n '+1:word.isalpha()': word1.isalpha(),\\n # '+1:postag': postag1,\\n # '+1:postag[:2]': postag1[:2],\\n })\\n else:\\n features['EOS'] = True\\n if i < len(sent) - 2:\\n word12 = sent[i + 2][0]\\n #postag12 = sent[i + 2][1]\\n features.update({\\n '+2:word.lower()': word12.lower(),\\n '+2:word.istitle()': word12.istitle(),\\n '+2:word.isupper()': word12.isupper(),\\n '+2:word.isdigit()': word12.isdigit(),\\n '+2:word.isalnum()': word12.isalnum(),\\n '+2:word.isalpha()': word12.isalpha(),\\n # '+2:postag': postag12,\\n # '+2:postag[:2]': postag12[:2],\\n })\\n else:\\n features['EOS2'] = True\\n if i < len(sent) - 3:\\n word13 = sent[i + 3][0]\\n #postag13 = sent[i + 3][1]\\n features.update({\\n '+3:word.lower()': word13.lower(),\\n '+3:word.istitle()': word13.istitle(),\\n '+3:word.isupper()': word13.isupper(),\\n '+3:word.isdigit()': word13.isdigit(),\\n '+3:word.isalnum()': word13.isalnum(),\\n '+3:word.isalpha()': word13.isalpha(),\\n # '+3:postag': postag13,\\n # '+3:postag[:2]': postag13[:2],\\n })\\n else:\\n features['EOS2'] = True\\n if i < len(sent) - 4:\\n word14 = sent[i + 4][0]\\n #postag14 = sent[i + 4][1]\\n features.update({\\n '+4:word.lower()': word14.lower(),\\n '+4:word.istitle()': word14.istitle(),\\n '+4:word.isupper()': word14.isupper(),\\n '+4:word.isdigit()': word14.isdigit(),\\n '+4:word.isalnum()': word14.isalnum(),\\n '+4:word.isalpha()': word14.isalpha(),\\n # '+4:postag': postag14,\\n # '+4:postag[:2]': postag14[:2],\\n })\\n else:\\n features['EOS2'] = True\\n return features\",\n \"def get_feature_set_SB(tweet):\\n #pos-tag frequencies\\n# print \\\"Tagged words in tweet: \\\", tweet.tagged_words\\n pos_tag_freq = {}\\n additional_freq = {}\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# if tag=='PRtinf':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJS':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJ':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='NP':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='DET':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='P':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n additional_freq['pronoun'] = 1\\n except KeyError:\\n continue\\n# print \\\"Tag frequencies: \\\", pos_tag_freq\\n for key in pos_tag_freq.keys():\\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \\n #Number of exclamation marks, number of emoticons,\\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\\n if emoticons>0:\\n additional_freq['emoticons'] = emoticons*1.0\\n if tweet.nrof_exclamations>0:\\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\\n \\n# print \\\"Additional frequencies: \\\", additional_freq\\n# raw_input(\\\"Continue?\\\")\\n \\n #Concatenate the dicts\\n features= dict(pos_tag_freq.items() + additional_freq.items())\\n# print \\\"All features: \\\", features\\n# raw_input(\\\"Continue?\\\")\\n return features\",\n \"def create_dummies(df):\",\n \"def build_classifier(model, hidden_units):\\n in_features = model.classifier._modules['0'].in_features\\n classifier = nn.Sequential(OrderedDict([\\n ('dropout1', nn.Dropout(0.5)),\\n ('fc1', nn.Linear(in_features, hidden_units)), \\n ('relu', nn.ReLU()),\\n ('dropout2', nn.Dropout(0.5)),\\n ('fc2', nn.Linear(hidden_units, 102)),\\n ('output', nn.LogSoftmax(dim=1))\\n ]))\\n return classifier\",\n \"def feature_dist_func_dict():\\n return {\\\"tanimoto_dissimilarity\\\": tanimoto_dissimilarity}\",\n \"def data_categorical(df, cat_features = [], cont_features = []):\\n subset_cat = []\\n subset_dict={}\\n # Add all the object type features to config.cat_features \\n for col in df.columns:\\n if df[col].dtype == 'object' and col not in cont_features:\\n subset_cat.append(col)\\n if col not in cat_features :\\n cat_features.append(col)\\n if cat_features !=[]:\\n print('Categorical features : ', ' '.join(cat_features))\\n printmd('**Number of unique values for every feature:**')\\n print(pd.DataFrame(df[cat_features].nunique(), columns = ['Unique values']).sort_values(by = 'Unique values', ascending=False))\\n printmd(\\\"**5 uniques samples of every Categorical Features :**\\\")\\n for col in cat_features :\\n subset_dict[col]= df[col].unique()[:5]\\n print(pd.DataFrame.from_dict(subset_dict, orient='index').transpose())\\n return (cat_features)\",\n \"def FE_create_categorical_feature_crosses(dfc, cats):\\r\\n dfc = copy.deepcopy(dfc)\\r\\n combos = list(combinations(cats, 2))\\r\\n for cat1, cat2 in combos:\\r\\n dfc.loc[:,cat1+'_cross_'+cat2] = dfc[cat1].astype(str)+\\\" \\\"+dfc[cat2].astype(str)\\r\\n return dfc\",\n \"def get_feature_set_PC(tweet, sentimentvalues):\\n features= {\\n 'text_length': np.log(len(tweet.text))\\n } #ADD ADDITIONAL FEATURES\\n if tweet.nrof_sademoticons>0:\\n features['sademoticons'] = tweet.nrof_sademoticons\\n if tweet.nrof_happyemoticons>0:\\n features['happyemoticons'] = tweet.nrof_happyemoticons\\n \\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n features[tag] = features.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n features['adjectives'] = features.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n features['adverbs'] = features.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n features['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in features.keys():\\n features[key] = features[key]*1.0\\n \\n #Add lexical features\\n # total polarity score, number of positive words, number of negative words\\n pos_score = 0\\n neg_score = 0\\n nrof_pos_words = 0\\n nrof_neg_words = 0\\n for word in sentimentvalues.keys():\\n if sentimentvalues[word][0]>0:\\n nrof_pos_words = nrof_pos_words + 1\\n pos_score = pos_score + sentimentvalues[word][0]\\n if sentimentvalues[word][1]>0:\\n nrof_neg_words = nrof_neg_words + 1\\n neg_score = neg_score + sentimentvalues[word][1]\\n\\n if neg_score>0:\\n features['neg_score'] = neg_score+1.0\\n if pos_score>0:\\n features['pos_score'] = pos_score+1.0\\n if nrof_pos_words>0:\\n features['positive_words'] = nrof_pos_words*1.0\\n if nrof_neg_words>0:\\n features['negative_words'] = nrof_neg_words*1.0\\n \\n return features\",\n \"def getFeatures(featureInput):\\n featureList = []\\n for defTerm,candidateSent in featureInput:\\n tokens = nltk.word_tokenize(candidateSent)\\n features = {}\\n POScenter,POSleft,POSright = wordPOS(tokens,defTerm)\\n features['Pos of first Article'] = posFirstArticle(tokens)\\n## features['Num Punct Marks'] = numPunctuation(tokens)\\n features['Subj words Predicate'] = subWordPerdicate(candidateSent,defTerm,tokens)\\n features['Word before def term'] = wordBeforeDef(tokens,defTerm)\\n features['POS centered word'] = POScenter\\n features['POS left word'] = POSleft\\n## features['POS right word'] = POSright \\n featureList.append(features)\\n return featureList\",\n \"def add_dummy_feature(X, value=...):\\n ...\",\n \"def to_numeric_features(features, feature_cols=None):\\n\\n for col in CATEGORICAL_COLS:\\n features = pd.concat(\\n [features, pd.get_dummies(features[col[0]], drop_first=True)], axis=1)\\n features.drop(col[0], axis=1, inplace=True)\\n\\n # Remove the unused columns from the dataframe.\\n for col in UNUSED_COLUMNS:\\n features.pop(col)\\n\\n # Re-index dataframe (if categories list changed from the previous dataset)\\n if feature_cols is not None:\\n features = features.T.reindex(feature_cols).T.fillna(0)\\n return features\",\n \"def get_feature_set_PC2(tweet, sentimentvalues):\\n features= {\\n 'text_length': np.log(len(tweet.text))\\n } #ADD ADDITIONAL FEATURES\\n if tweet.nrof_sademoticons>0:\\n features['sademoticons'] = tweet.nrof_sademoticons\\n if tweet.nrof_happyemoticons>0:\\n features['happyemoticons'] = tweet.nrof_happyemoticons\\n \\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n features[tag] = features.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n features['adjectives'] = features.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n features['adverbs'] = features.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n features['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in features.keys():\\n features[key] = features[key]*1.0\\n \\n #Add lexical features\\n # total polarity score, number of positive words, number of negative words\\n pos_score = sentimentvalues[0]\\n neg_score = sentimentvalues[1]\\n\\n if pos_score>0:\\n features['pos_score'] = pos_score+1.0\\n if neg_score>0:\\n features['neg_score'] = neg_score+1.0\\n \\n return features\",\n \"def dense_col(self):\\n if not self.col_name_mapping or \\\"dense_col\\\" not in self.col_name_mapping:\\n return EmptyFeature\\n return Feature(\\n name=list(self.col_name_mapping[\\\"dense_col\\\"].keys()),\\n index=list(self.col_name_mapping[\\\"dense_col\\\"].values()),\\n )\",\n \"def make_data(dataFname, enc, features=None):\\n\\n origData = pandas.read_csv(dataFname)\\n ids = origData['id']\\n\\n # remove unused columns\\n if 'Unnamed: 0' in origData.columns: del origData['Unnamed: 0']\\n del origData['id']\\n\\n # remove \\\"data leakage\\\" columns\\n for f in prohobitedFeatures:\\n del origData[f]\\n\\n # separate into X & y values\\n xData = origData[[col for col in origData.columns if not col=='loss']]\\n set_vars_as_type(xData, discreteVars, object)\\n yVec = origData.loss if 'loss' in origData.columns else None\\n\\n # try f528 - f274\\n xData['f528f274'] = xData['f528'] - xData['f274']\\n\\n # encode the categorical features f776 and f777\\n if enc is None:\\n enc = OneHotEncoder(n_values=[2, 2])\\n enc.fit(xData[['f776', 'f777']])\\n\\n xData[['f776_isZero', 'f776_isOne', 'f777_isZero', 'f777_isOne']] = pandas.DataFrame(enc.transform(xData[['f776', 'f777']]).toarray())\\n del xData['f776']\\n del xData['f777']\\n\\n print_missing_values_info(origData)\\n\\n # feature selection\\n if features:\\n filteredXData = xData[features]\\n else: # use ALL features\\n filteredXData = xData\\n\\n return filteredXData, yVec, ids, enc\",\n \"def make_feature_scale_factors():\\n X, y = make_X_and_y()\\n sqm = make_sqm_X()\\n scale_factors = {\\n \\\"indoor_temp\\\": np.max(X[:,:,0]),\\n \\\"outdoor_temp\\\": np.max(X[:,:,1]),\\n \\\"gas_kwh\\\": np.max(X[:,:,2]),\\n \\\"elec_kwh\\\": np.max(X[:,:,3]),\\n \\\"floor_area\\\": np.max(sqm),\\n \\\"htc\\\": np.max(y),\\n }\\n\\n with open(os.path.join(_TRAINING_DATA_PATH, \\\"scalefactors.json\\\"), \\\"w+\\\") as f:\\n json.dump(scale_factors, f)\",\n \"def my_impl_variational(in_train, in_test, labels):\\n X_train = []\\n X_test = []\\n for lab in labels:\\n for datum in in_train[lab]:\\n X_train.append([datum, lab])\\n for datum in in_test[lab]:\\n X_test.append([datum, lab])\\n Variationer_learn(X_train, 500, 1, 0.01, X_test, labels)\",\n \"def changeVars(self, vars):\\n v = VarSet(vars)\\n newOrder = map(lambda x:v.index(x), vars)\\n return Factor(v, self.t.transpose(newOrder))\\n #return NotImplemented\",\n \"def _get_feature_map():\\n return {\\n # 3 sparse feature with variable length. Use this if you have a\\n # variable number or more than 1 feature value per example.\\n \\\"feature_1\\\":\\n tf.io.VarLenFeature(dtype=tf.int64),\\n \\\"feature_2\\\":\\n tf.io.VarLenFeature(dtype=tf.int64),\\n \\\"feature_3\\\":\\n tf.io.VarLenFeature(dtype=tf.int64),\\n \\\"label\\\":\\n tf.io.FixedLenFeature([1], dtype=tf.int64),\\n }\",\n \"def condition_domain_reduction(csp, var) :\\n return True\",\n \"def condition_domain_reduction(csp, var) :\\n return True\",\n \"def create_feature_value_category(data):\\n utils.save_log('{0} :: {1}'.format(\\n create_feature_value_category.__module__,\\n create_feature_value_category.__name__))\\n\\n avg_value = data.agg({'Value': 'avg'}).collect()[0][0]\\n data = data. \\\\\\n withColumn('ValueStrategy',\\n when(col('Value') > avg_value * 200, 2).\\n when(col('Value') > avg_value * 50, 1).\\n otherwise(0))\\n\\n update_list_features(\\\"numerical\\\", [\\\"ValueStrategy\\\"])\\n\\n return data\",\n \"def pre_MultiLabelBinarizer(vars):\\n for var in vars:\\n m = DataFrameMapper([(var, [CategoricalImputer(), LabelEncoder()])], df_out = True)\\n m.fit_transform(pd.concat([test,df])) # Get all categorical outcomes so we have same codes for all.\\n\\n X_train[var+'_encoded'] = m.transform(X_train)\\n X_test[var+'_encoded'] = m.transform(X_test)\\n test[var+'_encoded'] = m.transform(test)\\n\\n return X_train, X_test, test\",\n \"def fac2var(_Mu,Nu, G, f,v):\\n #print\\n #print \\\"fac '%s' \\\\t=>\\\\t var '%s'\\\" % (f,v)\\n assert G.type(f)=='fac' and G.type(v)=='var'\\n\\n vars = G.N(f) # for order\\n ii = { x:i for i,x in enumerate(vars) } # inverted index\\n\\n for val in G.vals(v): # forall val in var\\n \\n # \\\"pin down msg var to one val\\\"\\n # eg\\n # var = 'b'\\n # val = 2\\n # vars = ['a','b','c']\\n # space = {0..1} x {2} x {0..3}\\n space = cartesian( *[(G.vals(_v) if _v != v else [val]) for _v in vars] )\\n\\n # get _val of _var\\n # _vals[ii[_v]] = _v:str => ii:str=>inx => _vals:inx=>val => Nu[_,_]:val=>num\\n # discrete randvar -> values are indices\\n # sum of prod\\n msg = sum( G(f, *_vals) * product([ Nu[_v, f][_vals[ii[_v]]] for _v in G.N(f) if _v != v ])\\n for _vals in space )\\n\\n _Mu[f,v][val] = msg\\n \\n \\\"\\\"\\\"\\n\\n # sum (fac * prod nus)\\n fac = G.node[f]['pmf']\\n nus = [ (i, _v, Nu[_v, f]) for i,_v in enumerate(G.N(f)) if _v != v ]\\n\\n msg = fac\\n for i,_v,nu in nus:\\n # sans broadcast\\n shape = [1 for _ in msg.shape]\\n shape[i] = G.node[_v]['d']\\n nu = resize(nu, tuple(shape))\\n nu = resize(nu, msg.shape)\\n msg = msg * nu\\n # [diff] msg = msg * resize(nu, msg.shape)\\n # [diff] msg = resize(nu, msg.shape) * msg\\n\\n others = tuple([ i for i,_v in enumerate(G.N(f)) if _v != v ]) \\n msg = sum(msg, axis=others) # marginalize every other var\\n Mu[f,v] = msg\\n\\n \\\"\\\"\\\"\\n\\n #print \\n #print 'Mu =', Mu\",\n \"def featurize(self, data):\\n \\n bag_of_words = []\\n\\n tokens = data.split()\\n\\n for i in tokens:\\n bag_of_words.append((i, True))\\n\\n return bag_of_words\",\n \"def to_delimited_set(self, name, label, variables, from_dichotomous=True,\\n codes_from_name=True):\\n if self.var_exists(name):\\n raise ValueError('{} does already exist.'.format(name))\\n elif not all(isinstance(c, (str, tuple)) for c in variables):\\n raise ValueError('Input of variables must be string or tuple.')\\n cols = [c if isinstance(c, str) else c[0] for c in variables]\\n if not all(self.var_exists(c) for c in cols):\\n not_in_ds = [c for c in cols if not self.var_exists(c)]\\n raise KeyError('{} not found in dataset!'.format(not_in_ds))\\n elif not all(self._has_categorical_data(c) for c in cols):\\n not_cat = [c for c in cols if not self._has_categorical_data(c)]\\n raise ValueError('Variables must have categorical data: {}'.format(not_cat))\\n if from_dichotomous:\\n if not all(x in [0, 1] for c in cols for x in self.codes_in_data(c)):\\n non_d = [c for c in cols\\n if not all(x in [0, 1] for x in self.codes_in_data(c))]\\n raise ValueError('Variables are not dichotomous: {}'.format(non_d))\\n mapper = []\\n for x, col in enumerate(variables, 1):\\n if codes_from_name:\\n x = int(col.split('_')[-1])\\n if isinstance(col, tuple):\\n text = col[1]\\n else:\\n text = self.text(col)\\n mapper.append((x, text, {col: [1]}))\\n else:\\n values = self.values(cols[0])\\n if not all(self.values(c) == values for c in cols):\\n not_eq = [c for c in cols if not self.values(c) == values]\\n raise ValueError('Variables must have eqaul values: {}'.format(not_eq))\\n mapper = []\\n for v in values:\\n mapper.append((v[0], v[1], union([{c: v[0]} for c in cols])))\\n\\n self.derive(name, 'delimited set', label, mapper)\\n\\n return None\",\n \"def preprocessing_fn(inputs):\\n outputs = {}\\n for key in _DENSE_FLOAT_FEATURE_KEYS:\\n # Preserve this feature as a dense float, setting nan's to the mean.\\n outputs[_transformed_name(key)] = tft.scale_to_z_score(\\n _fill_in_missing(inputs[key]))\\n\\n for key in _VOCAB_FEATURE_KEYS:\\n # Build a vocabulary for this feature.\\n outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(\\n _fill_in_missing(inputs[key]),\\n top_k=_VOCAB_SIZE,\\n num_oov_buckets=_OOV_SIZE)\\n\\n for key in _BUCKET_FEATURE_KEYS:\\n outputs[_transformed_name(key)] = tft.bucketize(\\n _fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT,\\n always_return_num_quantiles=False)\\n\\n for key in _CATEGORICAL_FEATURE_KEYS:\\n outputs[_transformed_name(key)] = _fill_in_missing(inputs[key])\\n\\n # Was this passenger a big tipper?\\n taxi_fare = _fill_in_missing(inputs[_FARE_KEY])\\n tips = _fill_in_missing(inputs[_LABEL_KEY])\\n outputs[_transformed_name(_LABEL_KEY)] = tf.where(\\n tf.is_nan(taxi_fare),\\n tf.cast(tf.zeros_like(taxi_fare), tf.int64),\\n # Test if the tip was > 20% of the fare.\\n tf.cast(\\n tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))\\n\\n return outputs\",\n \"def get_dummies_list(self, \\n cols=['elite'],\\\\\\n drop_=True):\\n for col in cols:\\n print \\\"Pre-processing \\\" + col + \\\"...\\\"\\n temp = pd.get_dummies(self.df[col].apply(pd.Series).stack(),drop_first=True)\\\\\\n .astype(int).sum(level=0).astype(int)\\n # temp.columns.apply(str).apply(lambda x: col + \\\"_\\\" + x)\\n if drop_:\\n self.df.drop(col,axis = 1, inplace=True)\\n self.df = pd.concat([self.df, temp],axis=1)\",\n \"def create_classifier(in_dim, hid_dim, out_dim):\\n\\n root_six = np.sqrt(6)\\n eps = root_six / (np.sqrt(hid_dim + in_dim))\\n W = np.random.uniform(-eps, eps, [hid_dim, in_dim])\\n eps = root_six / (np.sqrt(hid_dim))\\n b = np.random.uniform(-eps, eps, hid_dim)\\n eps = root_six / (np.sqrt(out_dim + hid_dim))\\n U = np.random.uniform(-eps, eps, [out_dim, hid_dim])\\n eps = root_six / (np.sqrt(out_dim))\\n b_tag = np.random.uniform(-eps, eps, out_dim)\\n\\n return [W,b,U,b_tag]\",\n \"def density(categorical_var, numerical_var):\\n #print(categorical_var)\\n cat_list = categorical_var.astype('category')\\n for cat in cat_list:\\n sns.kdeplot(numerical_var[categorical_var == cat], label=cat)#, categorical_var)\\n\\n plt.show()\",\n \"def domain_features(domain, path_set):\\n return string_features_v2(domain_string(domain, path_set))\",\n \"def build_by_features(self, features):\\n v = [0] * self.f\\n masks = [1 << i for i in range(self.f)]\\n if isinstance(features, dict):\\n features = features.items()\\n for f in features:\\n if isinstance(f, basestring):\\n h = self.hashfunc(f.encode('utf-8'))\\n w = 1\\n else:\\n assert isinstance(f, collections.Iterable)\\n h = self.hashfunc(f[0].encode('utf-8'))\\n w = f[1]\\n for i in range(self.f):\\n v[i] += w if h & masks[i] else -w\\n ans = 0\\n for i in range(self.f):\\n if v[i] >= 0:\\n ans |= masks[i]\\n self.value = ans\",\n \"def build_by_features(self, features):\\n v = [0] * self.f\\n masks = [1 << i for i in range(self.f)]\\n if isinstance(features, dict):\\n features = features.items()\\n for f in features:\\n if isinstance(f, basestring):\\n h = self.hashfunc(f.encode('utf-8'))\\n w = 1\\n else:\\n assert isinstance(f, collections.Iterable)\\n h = self.hashfunc(f[0].encode('utf-8'))\\n w = f[1]\\n for i in range(self.f):\\n v[i] += w if h & masks[i] else -w\\n ans = 0\\n for i in range(self.f):\\n if v[i] >= 0:\\n ans |= masks[i]\\n self.value = ans\",\n \"def dense_nn(x):\\n dense_1 = tf.nn.dropout(tf.nn.relu(dense(x, input_dim, n_l1, 'dense_1')), keep_prob=0.25)\\n dense_2 = tf.nn.dropout(tf.nn.relu(dense(dense_1, n_l1, n_l2, 'dense_2')), keep_prob=0.25)\\n dense_3 = dense(dense_2, n_l2, n_labels, 'dense_3')\\n return dense_3\",\n \"def _classify(tree, x):\\n # YOUR CODE HERE\\n # begin answer\\n feature_name=list(tree.keys())[0] #first element\\n secondDict=tree[feature_name] \\n key=x.loc[feature_name] #extract value from x\\n for key_val in secondDict:\\n feature_val=key_val[0]\\n valueOfKey=secondDict[(feature_val, key>=feature_val)]\\n if isinstance(valueOfKey,dict):\\n label=_classify(valueOfKey,x)\\n else:\\n label=valueOfKey\\n return label\\n # end answer\",\n \"def discretizing(\\n *factors: List[np.ndarray],\\n independent: bool = True,\\n n_bins: int = 5,\\n strategy: Literal['uniform', 'quantile', 'kmeans', 'gmm'] = 'quantile',\\n return_model: bool = False,\\n):\\n encode = 'ordinal'\\n # onehot - sparse matrix of one-hot encoding and\\n # onehot-dense - dense one-hot encoding. Ignored features are always stacked to\\n # the right.\\n # ordinal - Return the bin identifier encoded as an integer value.\\n strategy = str(strategy).strip().lower()\\n if 'histogram' in strategy:\\n strategy = 'uniform'\\n # ====== GMM base discretizer ====== #\\n if 'gmm' in strategy:\\n create_gmm = lambda: GaussianMixture(n_components=n_bins,\\n max_iter=800,\\n covariance_type='diag',\\n random_state=1) # fix random state\\n\\n if independent:\\n gmm = []\\n for f in factors[0].T:\\n gm = create_gmm()\\n gm.fit(np.expand_dims(f, axis=1))\\n gm.predict = types.MethodType(_gmm_discretizing_predict, gm)\\n gmm.append(gm)\\n transform = lambda x: np.concatenate([\\n gm.predict(np.expand_dims(col, axis=1)) for gm, col in zip(gmm, x.T)\\n ],\\n axis=1)\\n else:\\n gmm = create_gmm()\\n gmm.fit(np.expand_dims(factors[0].ravel(), axis=1))\\n gmm.predict = types.MethodType(_gmm_discretizing_predict, gmm)\\n transform = lambda x: np.concatenate(\\n [gmm.predict(np.expand_dims(col, axis=1)) for col in x.T], axis=1)\\n disc = gmm\\n # ====== start with bins discretizer ====== #\\n else:\\n disc = KBinsDiscretizer(n_bins=n_bins, encode=encode, strategy=strategy)\\n if independent:\\n disc.fit(factors[0])\\n transform = lambda x: disc.transform(x).astype(np.int64)\\n else:\\n disc.fit(np.expand_dims(factors[0].ravel(), axis=-1))\\n transform = lambda x: np.hstack([\\n disc.transform(np.expand_dims(i, axis=-1)).astype(np.int64)\\n for i in x.T\\n ])\\n # ====== returns ====== #\\n factors = tuple([transform(i) for i in factors])\\n factors = factors[0] if len(factors) == 1 else factors\\n if return_model:\\n return factors, disc\\n return factors\",\n \"def string_feature(ds, feature: str, verbosity=0, dim=\\\"sample\\\", numeric_dis=True):\\n t = get_annotation_table(ds, dim)\\n ser = t[feature]\\n dt = ser.dtype\\n descript = \\\"\\\"\\\"\\\"\\\"\\\"\\n\\n non_null_ser = ser[ser.notnull()]\\n null_flag = len(non_null_ser) < len(ser)\\n levels = list(set(non_null_ser.values))\\n num_levels = len(levels)\\n\\n if dt == pd.StringDtype():\\n\\n if num_levels >= 50:\\n descript += f\\\"\\\"\\\"\\nSorry, {feature} is a String feature with too many factor levels \\n(n = {num_levels}) to describe, here \\n\\\"\\\"\\\"\\n\\n elif num_levels < 50 and num_levels >= 2:\\n descript += f\\\"\\\"\\\"\\n{feature}: {dt} Feature\\n-------------------------\\n\\nFactor level counts df:\\n\\n{non_null_ser.value_counts()}\\n\\n\\nSome example query statements:\\n------------------------------\\n\\n{feature} in ['{levels[0]}', '{levels[1]}', ...]\\n\\n{feature} not in ['{levels[0]}', '{levels[-1]}', ...]\\n\\n{feature} != '{levels[-2]}'\\n\\\"\\\"\\\"\\n\\n else:\\n descript += f\\\"\\\"\\\"\\nThere's only a single factor level, {levels[0]}, across all samples.\\n\\\"\\\"\\\"\\n\\n elif dt == pd.BooleanDtype():\\n descript += f\\\"\\\"\\\"\\n{feature}: {dt} Feature:\\n---------------------------\\n\\nFactor level counts df:\\n\\n{non_null_ser.value_counts()}\\n\\n\\nSome example query statements:\\n------------------------------\\n\\n{feature} == True\\n\\n{feature} == False\\n\\\"\\\"\\\"\\n\\n elif dt == pd.Int64Dtype() or dt == pd.Float64Dtype():\\n if num_levels == 1:\\n descript += f\\\"\\\"\\\"\\nThere's only a single factor level, {levels[0]}, across all samples.\\n\\\"\\\"\\\"\\n\\n elif (num_levels > 1) and (not numeric_dis):\\n descript += f\\\"\\\"\\\"\\n{feature}: {dt} Feature\\n-------------------------\\n\\nFactor level counts df:\\n\\n{non_null_ser.value_counts()}\\n\\n\\nSome example query statements:\\n------------------------------\\n\\n{feature} in [{levels[0]}, {levels[1]}, ...]\\n\\n{feature} not in [{levels[0]}, {levels[-1]}, ...]\\n\\n{feature} != {levels[-2]}\\n\\\"\\\"\\\"\\n else:\\n des = ser.describe()\\n descript += f\\\"\\\"\\\"\\n{feature}: {dt} Feature:\\n---------------------------\\n\\ndistribution of numerical feature:\\n\\n{des}\\n\\nSome example query statements:\\n------------------------------\\n\\n{feature} >= {int(des[1])}\\n\\n{feature} <= {int(des[1])}\\n\\n({feature} >= {int(des[4])}) and ({feature} <= {int(des[5])})\\n\\\"\\\"\\\"\\n\\n else:\\n descript += f\\\"\\\"\\\"\\n{feature}: {dt} Feature\\n-------------------------\\n\\nFactor level counts df:\\n\\n{non_null_ser.value_counts()}\\n\\n\\nSome example query statements:\\n------------------------------\\n\\n{feature} in ['{levels[0]}', '{levels[1]}', ...]\\n\\n{feature} not in ['{levels[0]}', '{levels[-1]}', ...]\\n\\n{feature} != '{levels[-2]}'\\n\\\"\\\"\\\"\\n\\n if null_flag:\\n descript += f\\\"\\\"\\\"\\n{feature}.isnull()\\n\\n{feature}.notnull()\\n\\\"\\\"\\\"\\n\\n return descript\",\n \"def das(input_df, counts_var, noise_parameter):\\n df = input_df.copy(deep=True)\\n \\n n = df.shape[0]\\n \\n # add laplace noise \\n noise = np.random.laplace(loc=0, scale=noise_parameter, size=n)\\n df['noisy_counts'] = df[counts_var] + noise\\n \\n # post processing\\n df['nonneg_counts'] = post_proc(df.noisy_counts, df.pop_count.sum())\\n df.nonneg_counts = np.round(df.nonneg_counts)\\n \\n return df\",\n \"def to_dimacs_string_set_variable_value(self, clauses, variable, value):\\n self.varname_dict = {}\\n self.varobj_dict = {}\\n variables = prop_symbols_from_clause_list(clauses)\\n if variable in variables:\\n variables.remove(variable)\\n varis = dict(list(zip(sorted(variables, key=lambda v: v.op),\\n list(map(str, list(range(1, len(variables) + 1)))))))\\n for var in varis:\\n self.varname_dict[var] = varis[var]\\n self.varobj_dict[varis[var]] = var\\n\\n ret_clauses = ''\\n clause_count = 0\\n for clause in clauses:\\n clause_exists = True\\n dimacs_vlist = []\\n ret_clause = ''\\n if clause.op == '|':\\n for var in clause.args:\\n if literal_name(var) == literal_name(variable):\\n if value and not var.op == '~' or not value and var.op == '~':\\n clause_exists = False\\n else:\\n dimacs_vlist.append(('-' if var.op == '~' else '') \\\\\\n + self.varname_dict[var.args[0]\\n if var.op == '~' else var])\\n\\n if clause_exists:\\n ret_clause += ' '.join(dimacs_vlist)\\n elif clause.op == '~':\\n if literal_name(clause) == literal_name(variable):\\n if value:\\n return None\\n clause_exists = False\\n else:\\n ret_clause += '-' + self.varname_dict[clause.args[0]]\\n elif literal_name(clause) == literal_name(variable):\\n if value:\\n clause_exists = False\\n else:\\n return None\\n else:\\n ret_clause += self.varname_dict[clause]\\n if clause_exists:\\n clause_count += 1\\n ret_clauses += ret_clause + ' 0\\\\n'\\n\\n ret_header = 'p cnf %d %d\\\\n' % (len(variables), clause_count)\\n ret = ret_header + ret_clauses\\n return ret\",\n \"def to_dummies(to_dummy_data):\\r\\n if config.to_dummies:\\r\\n for col in to_dummy_data.columns:\\r\\n unique_col_num = len(pd.unique(to_dummy_data[col]))\\r\\n dummy_max = int(len(to_dummy_data[col]) / 10)\\r\\n col_type = to_dummy_data.dtypes[col]\\r\\n if (col_type == \\\"object\\\") \\\\\\r\\n & (unique_col_num < dummy_max) \\\\\\r\\n & (unique_col_num > 1) \\\\\\r\\n & (col != config.target):\\r\\n temp_dummies = pd.get_dummies(to_dummy_data[col])\\r\\n to_dummy_data = pd.concat([to_dummy_data, temp_dummies],\\r\\n axis=1, sort=False)\\r\\n return to_dummy_data\",\n \"def construct_feature_columns(input_features):\\n return set([tf.feature_column.numeric_column(my_feature)\\n for my_feature in input_features])\",\n \"def discrete_model():\\n return {\\\"x\\\": np.random.randint(10, size=100), \\\"y\\\": np.random.randint(10, size=100)}\",\n \"def extract_features(X):\\n X = X.drop(\\\"PassengerId\\\", axis=1)\\n X = X.drop(\\\"Ticket\\\", axis=1)\\n X = X.drop(\\\"Cabin\\\", axis=1)\\n \\n # Adding polynomial features\\n X[\\\"Age2\\\"] = X[\\\"Age\\\"] ** 2\\n #X[\\\"Fare2\\\"] = X[\\\"Fare\\\"] ** 2\\n #X[\\\"Pclass2\\\"] = X[\\\"Pclass\\\"] ** 2\\n\\n \\n male_titles = set([\\\"Mr\\\", \\\"Don\\\", \\\"Sir\\\"])\\n female_titles = set([\\\"Miss\\\", \\\"Ms\\\", \\\"Mrs\\\", \\\"Mme\\\", \\\"Mdm\\\", \\\"Lady\\\"])\\n professionals = set([\\\"Dr\\\", \\\"Rev\\\", \\\"Master\\\"])\\n military = set([\\\"Col\\\", \\\"Major\\\", \\\"Capt\\\"])\\n royalty = set([\\\"the Countess\\\", \\\"Jonkheer\\\"])\\n \\n names = X[\\\"Name\\\"]\\n for i in range(len(names)): \\n name_tokens = names[i].split(\\\", \\\") \\n passenger_title = name_tokens[1].split(\\\".\\\")[0]\\n if passenger_title in male_titles:\\n names[i] = 1\\n elif passenger_title in female_titles:\\n names[i] = 2\\n elif passenger_title in professionals:\\n names[i] = 3\\n #elif passenger_title in royalty:\\n # names[i] = 4\\n elif passenger_title in military:\\n names[i] = 5\\n else:\\n names[i] = 6\\n \\n X[\\\"Name\\\"].update(names)\\n \\n # One hot encoding of categorical data\\n X = pd.get_dummies(X) \\n \\n X.fillna(0, inplace=True)\\n X['Fam'] = X['SibSp'] + X['Parch'] # assigned to a column\\n return X\",\n \"def get_kdd_feature_cols():\\r\\n\\treturn [\\r\\n\\t\\t'duration',\\r\\n\\t\\t'src_bytes',\\r\\n\\t\\t'dst_bytes',\\r\\n\\t\\t'land',\\r\\n\\t\\t'wrong_fragment',\\r\\n\\t\\t'urgent',\\r\\n\\t\\t'hot',\\r\\n\\t\\t'num_failed_logins',\\r\\n\\t\\t'logged_in',\\r\\n\\t\\t'num_compromised',\\r\\n\\t\\t'root_shell',\\r\\n\\t\\t'su_attempted',\\r\\n\\t\\t'num_root',\\r\\n\\t\\t'num_file_creations',\\r\\n\\t\\t'num_shells',\\r\\n\\t\\t'num_access_files',\\r\\n\\t\\t'num_outbound_cmds',\\r\\n\\t\\t'is_host_login',\\r\\n\\t\\t'is_guest_login',\\r\\n\\t\\t'count',\\r\\n\\t\\t'srv_count',\\r\\n\\t\\t'serror_rate',\\r\\n\\t\\t'srv_serror_rate',\\r\\n\\t\\t'rerror_rate',\\r\\n\\t\\t'srv_rerror_rate',\\r\\n\\t\\t'same_srv_rate',\\r\\n\\t\\t'diff_srv_rate',\\r\\n\\t\\t'srv_diff_host_rate',\\r\\n\\t\\t'dst_host_count',\\r\\n\\t\\t'dst_host_srv_count',\\r\\n\\t\\t'dst_host_same_srv_rate',\\r\\n\\t\\t'dst_host_diff_srv_rate',\\r\\n\\t\\t'dst_host_same_src_port_rate',\\r\\n\\t\\t'dst_host_srv_diff_host_rate',\\r\\n\\t\\t'dst_host_serror_rate',\\r\\n\\t\\t'dst_host_srv_serror_rate',\\r\\n\\t\\t'dst_host_rerror_rate',\\r\\n\\t\\t'dst_host_srv_rerror_rate']\",\n \"def process_categorical_data(data_df):\\n return pd.get_dummies(data_df, columns=Columns.categorical)\",\n \"def probability_categorical(feature, label):\\n assert feature.nunique()>2, 'feature category nums must be greater than 2.'\\n t = pd.DataFrame({'feature':feature, 'label':label})\\n cat = label.unique()\\n cat = [(cat[i], cat[i+1]) for i in range(len(cat)-1)]\\n prob = label.value_counts(1).to_dict()\\n slope = [prob.get(i[0], 0)-prob.get(i[1], 0) for i in cat]\\n \\n slope_dict = t.feature.value_counts(1).to_dict()\\n prob = t.groupby([ 'feature']).label.value_counts(1).to_dict()\\n slope_dict = {i:{'category_rate':slope_dict[i], 'slope':[prob.get((i,j[0]), 0)-prob.get((i,j[1]), 0) for j in cat]} for i in slope_dict}\\n for i in slope_dict:\\n slope_dict[i]['slope_diff'] = sum([abs(slope[j]-slope_dict[i]['slope'][j]) for j in range(len(slope))])\\n value1 = sorted([[[i], slope_dict[i]['slope_diff'], slope_dict[i]['category_rate']] for i in slope_dict], key=lambda x:x[1], reverse=1)\\n distance = sorted([value1[i][1]-value1[i+1][1] for i in range(len(value1)-1)])\\n std = pd.Series([i[1] for i in value1]).std()\\n coupe = value1\\n dis = distance[0]\\n for k in distance:\\n value = value1\\n while 1:\\n for i in range(len(value)-1):\\n if value[i][1]-k<value[i+1][1]:\\n value[i+1][0] = value[i][0]+value[i+1][0]\\n value[i+1][1] = value[i][1]*value[i][2]/(value[i][2]+value[i+1][2])+value[i+1][1]*value[i+1][2]/(value[i][2]+value[i+1][2])\\n value[i+1][2] = value[i][2]+value[i+1][2]\\n value.remove(value[i])\\n break\\n if i==len(value)-2:\\n break\\n if pd.Series([i[1] for i in value]).std()>std:\\n coupe = value\\n std = pd.Series([i[1] for i in value]).std()\\n dis = k\\n return {'group':{k:i for i,j in enumerate(coupe) for k in j[0]}, 'data':coupe, \\n 'distance':dis, 'distance_index':f'{distance.index(dis)+1}/{len(distance)}', 'std':std}\",\n \"def preprocess_features(X):\\n\\t# Initialize new output DataFrame\\n\\toutput = pd.DataFrame(index = X.index)\\n\\n\\t# Investigate new output DataFrame\\n\\tfor col, col_data in X.iteritems():\\n\\t\\t# If data type is categorical, convert to dummy variables\\n\\t\\tif col_data.dtype == object:\\n\\t\\t\\tcol_data = pd.get_dummies(col_data, prefix = col)\\n\\n\\t\\t\\t# Collect the revised columns\\n\\t\\t\\toutput - output.join(col_data)\\n\\treturn output\",\n \"def load_feature(feature_name, caf_dose, features_path):\\n # gets the paths to the folders where the specified feature is stored\\n subject_paths = glob.glob(os.path.join(features_path, \\\"*\\\", feature_name))\\n\\n feature = {}\\n for path in subject_paths:\\n # extract the subject id from the current path (second to last element in the path)\\n subject_id = path.split(os.sep)[-2]\\n\\n # get all stages for the current subject\\n stages = set(\\n [\\n p.split(os.sep)[-1].split(\\\"_\\\")[-1].split(\\\".\\\")[0]\\n for p in glob.glob(os.path.join(path, \\\"*.npy\\\"))\\n ]\\n )\\n if len(stages) == 0:\\n print(\\n f\\\"The following directory doesn't contain features: {path}. \\\"\\n \\\"This will likely cause an error down the line\\\"\\n )\\n for stage in stages:\\n if stage not in feature:\\n feature[stage] = {}\\n # load the file containing the data for the current stage and subject\\n feature[stage][subject_id] = np.load(\\n os.path.join(path, f\\\"{feature_name}_{stage}.npy\\\"), allow_pickle=True\\n )\\n return feature\",\n \"def GetVectorArticleInput(dico_vector_input, features):\\n features_left = set(features) - set(dico_vector_input.keys())\\n if len(features_left) > 0:\\n sentence = \\\"Some features aren't in the dict:\\\\n\\\"\\n raise MyException(sentence + \\\"{}\\\".format(features_left))\\n vector_art = []\\n other_features = ['abstract', 'syn', 'exergue', 'title', 'secTitle']\\n other_features += ['subTitle', 'supTitle']\\n for feature in features:\\n if feature == 'nbSign':\\n if dico_vector_input['nbSign'] == 0:\\n print(\\\"NbSign == 0 l.176 - GetVectorArticleInput\\\")\\n vector_art.append(dico_vector_input[feature])\\n else:\\n vector_art.append(dico_vector_input[feature])\\n # Conversion des variables en indicatrices\\n # Normalement plus la peine, comme déjà fait auparavant\\n elif feature in other_features:\\n if dico_vector_input[feature] > 0:\\n vector_art.append(1)\\n else:\\n vector_art.append(0)\\n else:\\n vector_art.append(dico_vector_input[feature])\\n return (dico_vector_input['melodyId'], np.array([vector_art]))\",\n \"def get_name_to_features(self):\\n name_to_features = {\\n 'input_ids': tf.io.FixedLenFeature([self.seq_len], tf.int64),\\n 'label_ids': tf.io.FixedLenFeature([], tf.int64),\\n }\\n return name_to_features\",\n \"def set_factors(self, name, factormap, safe=False):\\n e = False\\n if name in self.masks():\\n if self._get_subtype(name) != 'single':\\n e = True\\n else:\\n if self._get_type(name) != 'single':\\n e = True\\n if e:\\n if safe:\\n err = \\\"Can only set factors to 'single' type categorical variables!\\\"\\n raise TypeError(err)\\n else:\\n return None\\n vals = self.codes(name)\\n facts = list(factormap.keys())\\n val_loc = self._get_value_loc(name)\\n if not all(f in vals for f in facts):\\n err = 'At least one factor is mapped to a code that does not exist '\\n err += 'in the values object of \\\"{}\\\"!'\\n raise ValueError(err.format(name))\\n for value in val_loc:\\n if value['value'] in factormap:\\n value['factor'] = factormap[value['value']]\\n else:\\n value['factor'] = None\\n return None\",\n \"def list_badly_scaled_variables(\\n blk,\\n large: float = 1e4,\\n small: float = 1e-3,\\n zero: float = 1e-10,\\n descend_into: bool = True,\\n include_fixed: bool = False,\\n):\\n return [\\n c\\n for c in badly_scaled_var_generator(\\n blk, large, small, zero, descend_into, include_fixed\\n )\\n ]\",\n \"def test_categorical_feature():\\n\\n feature = Categorical(\\\"abc\\\")\\n\\n for element in \\\"abc\\\":\\n feature.set(element)\\n feature.set(\\\"ignore this\\\")\\n feature.push()\\n\\n for element in \\\"abc\\\":\\n getattr(feature, \\\"set_\\\" + element)()\\n feature.push()\\n\\n array = feature.array()\\n assert array.shape == (6, 3)\\n for i, row in enumerate(array):\\n assert sum(row) == 1.0 and row[i % 3] == 1.0\",\n \"def make_dummy_df(self, dummy_vars, cat_to_drop = 0, drop_cat = True, add_intercept = True):\\n\\n # reset reference levels, if remaking then previous information would be there\\n self.ref_levels = []\\n\\n # reset to original data. Ensures that make_dummy_df can be rerun multiple times\\n self.df = self.df_orig.copy()\\n\\n # check if dummy_vars is a dict or a list to determine how to drop categories\\n if isinstance(dummy_vars, list):\\n drop_by_index = True\\n\\n # make sure that cat_to_drop is within the index range of the selected variables\\n for var in dummy_vars:\\n if self.df[var].nunique() < (cat_to_drop + 1):\\n raise Exception(\\\"For column {} the number of categories was \\\\\\nlower than the specified index ({}) to drop.\\\".format(var, cat_to_drop))\\n else:\\n drop_by_index = False\\n\\n # loop through categorical columns in dummy_vars list/dict\\n for var in dummy_vars:\\n\\n # convert given variable to a dummy variable\\n current_dummy = pd.DataFrame(self.df[var])\\n current_dummy = pd.get_dummies(current_dummy, prefix = var)\\n\\n\\n # drop one level as specified by dummy_vars, if drop_cat is true\\n if drop_cat == True:\\n\\n # scenario where dummy_vars is a list\\n if drop_by_index == True:\\n dummy_to_drop = current_dummy.columns[cat_to_drop]\\n current_dummy = current_dummy.drop([dummy_to_drop], axis = 1)\\n\\n # keep note of the dropped level in ref_levels\\n self.ref_levels.append(dummy_to_drop)\\n\\n # scenario where dummy_vars is a dict\\n if drop_by_index == False:\\n dummy_to_drop = var + \\\"_\\\" + dummy_vars[var]\\n current_dummy = current_dummy.drop([dummy_to_drop], axis = 1)\\n\\n # keep note of the dropped level in ref_levels\\n self.ref_levels.append(dummy_to_drop)\\n\\n # concat with original dataframe\\n self.df = pd.concat([self.df, current_dummy], axis=1)\\n\\n # drop pre-dummy categorical columns from original dataframe\\n if drop_by_index == True:\\n self.df = self.df.drop(dummy_vars, axis=1)\\n\\n if drop_by_index == False:\\n self.df = self.df.drop(dummy_vars.keys(), axis=1)\\n\\n # if int add is true, add int\\n if add_intercept == True:\\n self.df['intercept'] = 1.0\\n\\n return self.df\",\n \"def feature(self, N, pcomment, comment, positive, negative, posSub, negSub):\\n features = {}\\n # features top N\\n for i in range(0, N - 1):\\n if positive[i] in comment:\\n features[\\\"positive(%s)\\\" % positive[i]] = True\\n else:\\n features[\\\"positive(%s)\\\" % positive[i]] = False\\n if negative[i] in comment:\\n features[\\\"negative(%s)\\\" % negative[i]] = True\\n else:\\n features[\\\"negative(%s)\\\" % negative[i]] = False\\n # features subjetive lists\\n for word in set(comment):\\n if word in posSub and comment.count(word) > 0:\\n features[\\\"subjetive_pos(%s)\\\" % word] = comment.count(word)\\n if word in negSub and comment.count(word) > 0:\\n features[\\\"subjetive_neg(%s)\\\" % word] = comment.count(word)\\n\\n #custom features\\n if self.generateHeuristic(pcomment):\\n features[\\\"no_gusto\\\"] = True\\n\\n return features\",\n \"def get_categorical_features(self):\\n return self.categorical_features\",\n \"def __init__(self, strName, lstDomain):\\n # assign the name of the feature represented by the node\\n self.name = str(strName)\\n # assign the domain of the feature\\n self.domain = lstDomain\\n # the value starts out as undefined\\n self.value = \\\"none\\\"\",\n \"def create_classifier(model, hidden_units=None):\\n\\n defaul_nb_units = 4096\\n nb_units = hidden_units if hidden_units else defaul_nb_units\\n \\n input_features = model.classifier[0].in_features\\n \\n classifier = nn.Sequential(OrderedDict([\\n ('fc1', nn.Linear(input_features, nb_units, bias=True)),\\n ('relu1', nn.ReLU()),\\n ('dropout1', nn.Dropout(p=0.5)),\\n ('fc2', nn.Linear(nb_units, 102, bias=True)),\\n ('output', nn.LogSoftmax(dim=1))\\n ]))\\n \\n return classifier\",\n \"def _condense_state(self, s):\\n def append_col(A, b):\\n expanded_b = np.expand_dims(b, 1)\\n return np.append(A, expanded_b, 1)\\n\\n A, b, c0, cuts_a, cuts_b = s\\n Ab = append_col(A, b)\\n cuts = append_col(cuts_a, cuts_b)\\n return (Ab, c0, cuts)\",\n \"def fit(self, data, discrete_features=None, bandwidth=1.0,\\n num_discretization_bins=4, pseudocount=1.0):\\n if bandwidth <= 0:\\n raise ValueError(\\\"Bandwidth must be positive.\\\")\\n \\n if discrete_features != None and \\\\\\n len(discrete_features) != data.shape[1]:\\n raise ValueError(\\\"Discrete features array and data arrays\\\"\\n \\\"shape don't match.\\\")\\n \\n if num_discretization_bins < 0:\\n raise ValueError(\\\"Number of descretization bins can't be negetive.\\\")\\n \\n if num_discretization_bins == 0:\\n for bool in discrete_features:\\n if bool:\\n raise ValueError(\\\"Number of descretization bins can't be\\\"\\n \\\"zero if there is a continuous feature.\\\")\\n \\n if pseudocount < 0:\\n raise ValueError(\\\"Pseudocount can't be negative.\\\")\\n \\n if discrete_features == None:\\n discrete_features = [False] * data.shape[1]\\n\\n self.num_features_ = data.shape[1]\\n self.discrete_features_ = discrete_features\\n self.num_discretization_bins_ = num_discretization_bins\\n\\n discretized_data = np.array(data, copy=True)\\n continuous_data = data[:, np.invert(discrete_features)]\\n\\n discretizer = KBinsDiscretizer(n_bins=num_discretization_bins,\\n encode='ordinal', strategy='quantile')\\n discretizer.fit(continuous_data)\\n\\n discretized_data[:, np.invert(discrete_features)] = \\\\\\n discretizer.transform(continuous_data)\\n self.discretizer_ = discretizer\\n\\n self.model_ = BayesianNetwork.from_samples(discretized_data,\\n algorithm='chow-liu', n_jobs=-1, pseudocount=pseudocount)\\n self.model_.bake()\\n \\n # Table for bin edges\\n bins = discretizer.bin_edges_\\n\\n # Kdes for continuous data.\\n self.tnkdes_ = []\\n\\n i = 0\\n for k in range(self.num_features_):\\n if discrete_features[k]:\\n continue\\n \\n bins[i][0] = -np.inf\\n bins[i][len(bins[i]) - 1] = np.inf\\n bin_kdes = []\\n \\n # loop of boundary\\n for j in range(len(bins[i]) - 1):\\n # Bound for this bin.\\n lower_bound = bins[i][j]\\n upper_bound = bins[i][j+1]\\n \\n # Create a kde using the data in the current bin.\\n current_feature_data = data[:, k]\\n cur_bin_data = current_feature_data[discretized_data[:, k] == j]\\n kde = TruncatedNormalKernelDensity(bandwidth=bandwidth,\\n lowerbound=lower_bound, upperbound=upper_bound)\\n kde.fit(cur_bin_data)\\n bin_kdes.append(kde)\\n \\n i = i + 1\\n self.tnkdes_.append(bin_kdes)\",\n \"def get_kdd_feature_cols():\\n\\treturn [\\n\\t\\t'duration',\\n\\t\\t'src_bytes',\\n\\t\\t'dst_bytes',\\n\\t\\t'land',\\n\\t\\t'wrong_fragment',\\n\\t\\t'urgent',\\n\\t\\t'hot',\\n\\t\\t'num_failed_logins',\\n\\t\\t'logged_in',\\n\\t\\t'num_compromised',\\n\\t\\t'root_shell',\\n\\t\\t'su_attempted',\\n\\t\\t'num_root',\\n\\t\\t'num_file_creations',\\n\\t\\t'num_shells',\\n\\t\\t'num_access_files',\\n\\t\\t'num_outbound_cmds',\\n\\t\\t'is_host_login',\\n\\t\\t'is_guest_login',\\n\\t\\t'count',\\n\\t\\t'srv_count',\\n\\t\\t'serror_rate',\\n\\t\\t'srv_serror_rate',\\n\\t\\t'rerror_rate',\\n\\t\\t'srv_rerror_rate',\\n\\t\\t'same_srv_rate',\\n\\t\\t'diff_srv_rate',\\n\\t\\t'srv_diff_host_rate',\\n\\t\\t'dst_host_count',\\n\\t\\t'dst_host_srv_count',\\n\\t\\t'dst_host_same_srv_rate',\\n\\t\\t'dst_host_diff_srv_rate',\\n\\t\\t'dst_host_same_src_port_rate',\\n\\t\\t'dst_host_srv_diff_host_rate',\\n\\t\\t'dst_host_serror_rate',\\n\\t\\t'dst_host_srv_serror_rate',\\n\\t\\t'dst_host_rerror_rate',\\n\\t\\t'dst_host_srv_rerror_rate']\",\n \"def dim2():\\n probs = (0.1, 0.2, 0.3, 0.4)\\n categories = (\\\"asdfa\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\")\\n categories = OrderedDict(zip(categories, probs))\\n dim2 = Categorical(\\\"yolo2\\\", categories, default_value=\\\"2\\\")\\n return dim2\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7855145","0.5295992","0.5193816","0.51229674","0.5071455","0.50275296","0.4994465","0.49909624","0.48956412","0.4888899","0.48470324","0.47712082","0.4754599","0.4749255","0.47370207","0.47250566","0.46894085","0.46833327","0.46548498","0.4644874","0.46404266","0.4620488","0.46150672","0.4604797","0.4603583","0.45971718","0.4595794","0.45830557","0.45722824","0.4536436","0.45356202","0.45205215","0.45166013","0.449131","0.44734678","0.44546753","0.44524541","0.44465122","0.44402826","0.4439811","0.44182095","0.4416291","0.44092184","0.4401639","0.43844423","0.4377671","0.43771416","0.43713155","0.43693405","0.43623218","0.43521887","0.4351238","0.43393484","0.43377241","0.4335028","0.43219352","0.43216863","0.43209606","0.43209606","0.43186897","0.43156287","0.4306729","0.43058234","0.42931885","0.4289446","0.42803177","0.427293","0.42708588","0.4268463","0.4267245","0.4267245","0.4266761","0.42644414","0.4262545","0.4260969","0.42564195","0.42551923","0.42504486","0.42427343","0.42353106","0.42327166","0.42318827","0.4231731","0.42266947","0.42234725","0.42200065","0.4219907","0.42176086","0.4215596","0.4214717","0.42121574","0.42115498","0.42114902","0.42078495","0.42048594","0.42038363","0.42025405","0.4200132","0.41955858","0.41852465"],"string":"[\n \"0.7855145\",\n \"0.5295992\",\n \"0.5193816\",\n \"0.51229674\",\n \"0.5071455\",\n \"0.50275296\",\n \"0.4994465\",\n \"0.49909624\",\n \"0.48956412\",\n \"0.4888899\",\n \"0.48470324\",\n \"0.47712082\",\n \"0.4754599\",\n \"0.4749255\",\n \"0.47370207\",\n \"0.47250566\",\n \"0.46894085\",\n \"0.46833327\",\n \"0.46548498\",\n \"0.4644874\",\n \"0.46404266\",\n \"0.4620488\",\n \"0.46150672\",\n \"0.4604797\",\n \"0.4603583\",\n \"0.45971718\",\n \"0.4595794\",\n \"0.45830557\",\n \"0.45722824\",\n \"0.4536436\",\n \"0.45356202\",\n \"0.45205215\",\n \"0.45166013\",\n \"0.449131\",\n \"0.44734678\",\n \"0.44546753\",\n \"0.44524541\",\n \"0.44465122\",\n \"0.44402826\",\n \"0.4439811\",\n \"0.44182095\",\n \"0.4416291\",\n \"0.44092184\",\n \"0.4401639\",\n \"0.43844423\",\n \"0.4377671\",\n \"0.43771416\",\n \"0.43713155\",\n \"0.43693405\",\n \"0.43623218\",\n \"0.43521887\",\n \"0.4351238\",\n \"0.43393484\",\n \"0.43377241\",\n \"0.4335028\",\n \"0.43219352\",\n \"0.43216863\",\n \"0.43209606\",\n \"0.43209606\",\n \"0.43186897\",\n \"0.43156287\",\n \"0.4306729\",\n \"0.43058234\",\n \"0.42931885\",\n \"0.4289446\",\n \"0.42803177\",\n \"0.427293\",\n \"0.42708588\",\n \"0.4268463\",\n \"0.4267245\",\n \"0.4267245\",\n \"0.4266761\",\n \"0.42644414\",\n \"0.4262545\",\n \"0.4260969\",\n \"0.42564195\",\n \"0.42551923\",\n \"0.42504486\",\n \"0.42427343\",\n \"0.42353106\",\n \"0.42327166\",\n \"0.42318827\",\n \"0.4231731\",\n \"0.42266947\",\n \"0.42234725\",\n \"0.42200065\",\n \"0.4219907\",\n \"0.42176086\",\n \"0.4215596\",\n \"0.4214717\",\n \"0.42121574\",\n \"0.42115498\",\n \"0.42114902\",\n \"0.42078495\",\n \"0.42048594\",\n \"0.42038363\",\n \"0.42025405\",\n \"0.4200132\",\n \"0.41955858\",\n \"0.41852465\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8257702"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13796,"cells":{"query":{"kind":"string","value":"Discrete Variable to Feature Convertor. var value of variable varname name of variable. lims = range of numbers lims = range of discretization. collapse = list with two binary vals. collapse all below lim[0] to lim[0] & collapse all above lim[1] to lim[1] e.g., fdict = discVar2Feature(8, 'positive adjective', lims = [1,5], collapse [True, True]) contains one positive adjective False contains two positive adjective False contains four positive adjective False contains three positive adjective False contains five positive adjective True"},"document":{"kind":"string","value":"def discVar2FeatureOld( var, varname, lims = [1,5], collapse = [False, False], ctxt = 'contains'):\n nums = ['zero','one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten']\n \n vals = range(lims[0], lims[1]+1) \n \n #init fdict\n fdict = dict() \n for k, val in enumerate(vals):\n fdict[(ctxt + ' %s ' + varname) % (nums[val])] = False\n \n if collapse[0] == True: var = max([var, lims[0]])\n if collapse[1] == True: var = min([var, lims[1]])\n \n if var >= lims[0] and var <= lims[1]: #if collapse = False, ignore vals outside lims\n fdict[(ctxt + ' %s ' + varname) % (nums[var])] = True \n \n return fdict"},"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 discVar2Feature( var, varname, lims = [1,3], collapse = [False, False], ctxt = 'Has'):\n \n vals = xrange(lims[0], lims[1]+1) \n \n keystr = ctxt + ' %s ' + varname\n fdict = {keystr % val:False for val in vals} \n\n if collapse[0] == True:\n if lims[0] > var:\n var = lims[0]\n #var = max([var, lims[0]])\n if collapse[1] == True:\n if lims[1] < var:\n var = lims[1]\n #var = min([var, lims[1]])\n \n if var >= lims[0] and var <= lims[1]: #if collapse = False, ignore vals outside lims\n fdict[(keystr) % (var)] = True \n \n return fdict","def convertToDiscreteFunction(boolean: bool) -> cern.japc.value.DiscreteFunction:\n ...","def FE_discretize_numeric_variables(train, bin_dict, test='', strategy='kmeans',verbose=0):\r\n df = copy.deepcopy(train)\r\n test = copy.deepcopy(test)\r\n num_cols = len(bin_dict)\r\n nrows = int((num_cols/2)+0.5)\r\n #print('nrows',nrows)\r\n if verbose:\r\n fig = plt.figure(figsize=(10,3*num_cols))\r\n for i, (col, binvalue) in enumerate(bin_dict.items()):\r\n new_col = col+'_discrete'\r\n if strategy == 'gaussian':\r\n kbd = GaussianMixture(n_components=binvalue, random_state=99)\r\n df[new_col] = kbd.fit_predict(df[[col]]).astype(int)\r\n if not isinstance(test, str):\r\n test[new_col] = kbd.predict(test[[col]]).astype(int)\r\n else:\r\n kbd = KBinsDiscretizer(n_bins=binvalue, encode='ordinal', strategy=strategy)\r\n df[new_col] = kbd.fit_transform(df[[col]]).astype(int)\r\n if not isinstance(test, str):\r\n test[new_col] = kbd.transform(test[[col]]).astype(int)\r\n if verbose:\r\n ax1 = plt.subplot(nrows,2,i+1)\r\n ax1.scatter(df[col],df[new_col])\r\n ax1.set_title(new_col)\r\n if not isinstance(test, str):\r\n return df, test\r\n else:\r\n return df","def denseFeature(self, feat):\n return {'feat': feat}","def convertToDiscreteFunctionList(boolean: bool) -> cern.japc.value.DiscreteFunctionList:\n ...","def preprocess_var(bd, var):\n filepath_sv = f\"team67-ptp/data/{var}.csv\"\n filepath = bd\n data = feather.read_dataframe(filepath)\n df = data.copy()\n df2 = df[var]\n df2 = df2.to_frame()\n if df2[var].dtype is \"category\":\n df2[var] = df2[var].astype(\"category\").cat.codes\n filename = filepath_sv\n df2.to_csv(filename, index=False)\n print(\"Succesfully exported to csv\")\n else:\n filename = filepath_sv\n df2.to_csv(filename, index=False)\n print(\"Succesfully exported to csv\")","def addFeature(self, strName, lstDomain):\n # create a new variable CSPVariable object\n newFeature = CSPFeature(strName, lstDomain)\n # put the new variable in the graph's list of variables\n self.features.append(newFeature)","def select_var_feature(adata, min_score=0.5, nb_features=None, show=True, copy=False):\n if copy:\n inplace=False\n else:\n inplace=True\n\n adata = adata.copy() if not inplace else adata\n \n # calculate variability score\n cal_var(adata, show=show) # adds variability score for each feature \n # adata.var['variablility_score'] = abs(adata.var['prop_shared_cells']-0.5)\n var_annot = adata.var.sort_values(ascending=True, by ='variability_score')\n\n # calculate the min score to get a specific number of feature \n if nb_features != None and nb_features < len(adata.var_names): \n min_score = var_annot['variability_score'][nb_features] \n \n \n adata_tmp = adata[:,adata.var['variability_score']<=min_score].copy()\n \n ## return the filtered AnnData objet.\n if not inplace:\n adata_tmp = adata[:,adata.var['variability_score']<=min_score]\n return(adata_tmp)\n else:\n adata._inplace_subset_var(adata.var['variability_score']<=min_score)","def dense2cvxopt(value):\n import cvxopt\n return cvxopt.matrix(value, tc='d')","def feature_discretion(self, X, y):\n temp, X_interval = [], []\n if self._DISCRETION == \"percentile_discrete\":\n for i in range(0, X.shape[-1]):\n x = X[:, i]\n x_type = type_of_target(x)\n # logging.info(\"before: \"+\" \".join([str(i), str(set(X[:, i])), str(x_type)]))\n if 0:\n if x_type == 'continuous':\n x1, interval = self.percentile_discrete(x, self._WOE_N)\n X_interval.append(interval)\n temp.append(x1)\n # logging.info(\"continue_after: \" + \" \".join([str(i), str(set(x1)), str(x1)]))\n else:\n temp.append(x)\n # logging.info(\"after: \" + \" \".join([str(i), str(set(x)), str(x)]))\n else:\n x1, interval = self.percentile_discrete(x, self._WOE_N)\n X_interval.append(interval)\n temp.append(x1)\n # logging.info(\"continue_after: \" + \" \".join([str(i), str(set(x1)), str(x1)]))\n elif self._DISCRETION == \"interval_discrete\":\n for i in range(0, X.shape[-1]):\n x = X[:, i]\n # logging.info(\"before: \"+\" \".join([str(i), str(set(X[:, i]))]))\n x1, interval = self.interval_discrete(x, self._WOE_N)\n X_interval.append(interval)\n temp.append(x1)\n # logging.info(\"interval_after: \" + \" \".join([str(i), str(set(x1)), str(x1)]))\n elif self._DISCRETION == \"rf_discrete\":\n for i in range(0, X.shape[-1]):\n x = X[:, i]\n # logging.info(\"before: \"+\" \".join([str(i), str(set(X[:, i]))]))\n x1, interval = self.rf_discrete(x, y)\n X_interval.append(interval)\n temp.append(x1)\n # logging.info(\"rf_after: \" + \" \".join([str(i), str(set(x1)), str(x1)]))\n return np.array(temp).T, X_interval","def dict_to_feature(d):\n f = ee.Feature(None,ee.Dictionary(d))\n return f","def my_featurize(apartment):\n col =np.array([1, 2, 0, 0, 0, 0, 0, 0 ])\n a= pd.DataFrame(apartment[col])\n if(apartment.get('condition')== 'good'):\n col[1] =1\n else:\n if(apartment.get('condition')== 'zero condition'):\n col[1] = 0\n col[2] =apartment.get('num_rooms')\n col[3] =apartment.get('area')\n col[4] =apartment.get('num_bathrooms')\n col[5] =apartment.get('floor')\n col[6] =apartment.get('ceiling_height')\n col[7] =apartment.get('max_floor')\n\n return col, apartment['price']","def get_feature_set_SC2(tweet, sentimentvalues):\n pos_tag_freq = {}\n additional_freq = {}\n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n if tag in ADJECTIVES:\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\n elif tag in ADVERBS: \n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\n elif tag in PRONOUNS:\n additional_freq['pronoun'] = 1\n except KeyError:\n continue\n for key in pos_tag_freq.keys():\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \n #Number of exclamation marks, number of emoticons,\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\n if emoticons>0:\n additional_freq['emoticons'] = emoticons*1.0\n if tweet.nrof_exclamations>0:\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\n \n #Add lexicon values\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\n sub_score = sentimentvalues[0]+sentimentvalues[1]\n obj_score = sentimentvalues[2]\n if sub_score>0:\n additional_freq[\"sub_score\"] = sub_score+1.0\n if obj_score>0:\n additional_freq[\"obj_score\"] = obj_score+1.0\n \n #Concatenate the dicts\n features= dict(pos_tag_freq.items() + additional_freq.items())\n \n return features","def _make_features(self, x):\n\t\tx = x.unsqueeze(1)\n\t\treturn torch.cat([x ** i for i in range(1, self._degree+1)], 1)","def preprocess_feature(df):","def classify_columns(df_preds, verbose=0):\r\n train = copy.deepcopy(df_preds)\r\n #### If there are 30 chars are more in a discrete_string_var, it is then considered an NLP variable\r\n max_nlp_char_size = 30\r\n max_cols_to_print = 30\r\n print('############## C L A S S I F Y I N G V A R I A B L E S ####################')\r\n print('Classifying variables in data set...')\r\n #### Cat_Limit defines the max number of categories a column can have to be called a categorical colum\r\n cat_limit = 35\r\n float_limit = 15 #### Make this limit low so that float variables below this limit become cat vars ###\r\n def add(a,b):\r\n return a+b\r\n sum_all_cols = dict()\r\n orig_cols_total = train.shape[1]\r\n #Types of columns\r\n cols_delete = [col for col in list(train) if (len(train[col].value_counts()) == 1\r\n ) | (train[col].isnull().sum()/len(train) >= 0.90)]\r\n train = train[left_subtract(list(train),cols_delete)]\r\n var_df = pd.Series(dict(train.dtypes)).reset_index(drop=False).rename(\r\n columns={0:'type_of_column'})\r\n sum_all_cols['cols_delete'] = cols_delete\r\n var_df['bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['bool','object']\r\n and len(train[x['index']].value_counts()) == 2 else 0, axis=1)\r\n string_bool_vars = list(var_df[(var_df['bool'] ==1)]['index'])\r\n sum_all_cols['string_bool_vars'] = string_bool_vars\r\n var_df['num_bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\r\n np.uint16, np.uint32, np.uint64,\r\n 'int8','int16','int32','int64',\r\n 'float16','float32','float64'] and len(\r\n train[x['index']].value_counts()) == 2 else 0, axis=1)\r\n num_bool_vars = list(var_df[(var_df['num_bool'] ==1)]['index'])\r\n sum_all_cols['num_bool_vars'] = num_bool_vars\r\n ###### This is where we take all Object vars and split them into diff kinds ###\r\n discrete_or_nlp = var_df.apply(lambda x: 1 if x['type_of_column'] in ['object'] and x[\r\n 'index'] not in string_bool_vars+cols_delete else 0,axis=1)\r\n ######### This is where we figure out whether a string var is nlp or discrete_string var ###\r\n var_df['nlp_strings'] = 0\r\n var_df['discrete_strings'] = 0\r\n var_df['cat'] = 0\r\n var_df['id_col'] = 0\r\n discrete_or_nlp_vars = var_df.loc[discrete_or_nlp==1]['index'].values.tolist()\r\n if len(var_df.loc[discrete_or_nlp==1]) != 0:\r\n for col in discrete_or_nlp_vars:\r\n #### first fill empty or missing vals since it will blowup ###\r\n train[col] = train[col].fillna(' ')\r\n if train[col].map(lambda x: len(x) if type(x)==str else 0).mean(\r\n ) >= max_nlp_char_size and len(train[col].value_counts()\r\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\r\n var_df.loc[var_df['index']==col,'nlp_strings'] = 1\r\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\r\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\r\n var_df.loc[var_df['index']==col,'discrete_strings'] = 1\r\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\r\n ) == len(train) and col not in string_bool_vars:\r\n var_df.loc[var_df['index']==col,'id_col'] = 1\r\n else:\r\n var_df.loc[var_df['index']==col,'cat'] = 1\r\n nlp_vars = list(var_df[(var_df['nlp_strings'] ==1)]['index'])\r\n sum_all_cols['nlp_vars'] = nlp_vars\r\n discrete_string_vars = list(var_df[(var_df['discrete_strings'] ==1) ]['index'])\r\n sum_all_cols['discrete_string_vars'] = discrete_string_vars\r\n ###### This happens only if a string column happens to be an ID column #######\r\n #### DO NOT Add this to ID_VARS yet. It will be done later.. Dont change it easily...\r\n #### Category DTYPE vars are very special = they can be left as is and not disturbed in Python. ###\r\n var_df['dcat'] = var_df.apply(lambda x: 1 if str(x['type_of_column'])=='category' else 0,\r\n axis=1)\r\n factor_vars = list(var_df[(var_df['dcat'] ==1)]['index'])\r\n sum_all_cols['factor_vars'] = factor_vars\r\n ########################################################################\r\n date_or_id = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\r\n np.uint16, np.uint32, np.uint64,\r\n 'int8','int16',\r\n 'int32','int64'] and x[\r\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\r\n axis=1)\r\n ######### This is where we figure out whether a numeric col is date or id variable ###\r\n var_df['int'] = 0\r\n var_df['date_time'] = 0\r\n ### if a particular column is date-time type, now set it as a date time variable ##\r\n var_df['date_time'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['<M8[ns]','datetime64[ns]'] and x[\r\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\r\n axis=1)\r\n ### this is where we save them as date time variables ###\r\n if len(var_df.loc[date_or_id==1]) != 0:\r\n for col in var_df.loc[date_or_id==1]['index'].values.tolist():\r\n if len(train[col].value_counts()) == len(train):\r\n if train[col].min() < 1900 or train[col].max() > 2050:\r\n var_df.loc[var_df['index']==col,'id_col'] = 1\r\n else:\r\n try:\r\n pd.to_datetime(train[col],infer_datetime_format=True)\r\n var_df.loc[var_df['index']==col,'date_time'] = 1\r\n except:\r\n var_df.loc[var_df['index']==col,'id_col'] = 1\r\n else:\r\n if train[col].min() < 1900 or train[col].max() > 2050:\r\n if col not in num_bool_vars:\r\n var_df.loc[var_df['index']==col,'int'] = 1\r\n else:\r\n try:\r\n pd.to_datetime(train[col],infer_datetime_format=True)\r\n var_df.loc[var_df['index']==col,'date_time'] = 1\r\n except:\r\n if col not in num_bool_vars:\r\n var_df.loc[var_df['index']==col,'int'] = 1\r\n else:\r\n pass\r\n int_vars = list(var_df[(var_df['int'] ==1)]['index'])\r\n date_vars = list(var_df[(var_df['date_time'] == 1)]['index'])\r\n id_vars = list(var_df[(var_df['id_col'] == 1)]['index'])\r\n sum_all_cols['int_vars'] = int_vars\r\n copy_date_vars = copy.deepcopy(date_vars)\r\n for date_var in copy_date_vars:\r\n #### This test is to make sure sure date vars are actually date vars\r\n try:\r\n pd.to_datetime(train[date_var],infer_datetime_format=True)\r\n except:\r\n ##### if not a date var, then just add it to delete it from processing\r\n cols_delete.append(date_var)\r\n date_vars.remove(date_var)\r\n sum_all_cols['date_vars'] = date_vars\r\n sum_all_cols['id_vars'] = id_vars\r\n sum_all_cols['cols_delete'] = cols_delete\r\n ## This is an EXTREMELY complicated logic for cat vars. Don't change it unless you test it many times!\r\n var_df['numeric'] = 0\r\n float_or_cat = var_df.apply(lambda x: 1 if x['type_of_column'] in ['float16',\r\n 'float32','float64'] else 0,\r\n axis=1)\r\n if len(var_df.loc[float_or_cat == 1]) > 0:\r\n for col in var_df.loc[float_or_cat == 1]['index'].values.tolist():\r\n if len(train[col].value_counts()) > 2 and len(train[col].value_counts()\r\n ) <= float_limit and len(train[col].value_counts()) <= len(train):\r\n var_df.loc[var_df['index']==col,'cat'] = 1\r\n else:\r\n if col not in num_bool_vars:\r\n var_df.loc[var_df['index']==col,'numeric'] = 1\r\n cat_vars = list(var_df[(var_df['cat'] ==1)]['index'])\r\n continuous_vars = list(var_df[(var_df['numeric'] ==1)]['index'])\r\n ######## V E R Y I M P O R T A N T ###################################################\r\n ##### There are a couple of extra tests you need to do to remove abberations in cat_vars ###\r\n cat_vars_copy = copy.deepcopy(cat_vars)\r\n for cat in cat_vars_copy:\r\n if df_preds[cat].dtype==float:\r\n continuous_vars.append(cat)\r\n cat_vars.remove(cat)\r\n var_df.loc[var_df['index']==cat,'cat'] = 0\r\n var_df.loc[var_df['index']==cat,'numeric'] = 1\r\n elif len(df_preds[cat].value_counts()) == df_preds.shape[0]:\r\n id_vars.append(cat)\r\n cat_vars.remove(cat)\r\n var_df.loc[var_df['index']==cat,'cat'] = 0\r\n var_df.loc[var_df['index']==cat,'id_col'] = 1\r\n sum_all_cols['cat_vars'] = cat_vars\r\n sum_all_cols['continuous_vars'] = continuous_vars\r\n sum_all_cols['id_vars'] = id_vars\r\n ###### This is where you consoldate the numbers ###########\r\n var_dict_sum = dict(zip(var_df.values[:,0], var_df.values[:,2:].sum(1)))\r\n for col, sumval in var_dict_sum.items():\r\n if sumval == 0:\r\n print('%s of type=%s is not classified' %(col,train[col].dtype))\r\n elif sumval > 1:\r\n print('%s of type=%s is classified into more then one type' %(col,train[col].dtype))\r\n else:\r\n pass\r\n ############### This is where you print all the types of variables ##############\r\n ####### Returns 8 vars in the following order: continuous_vars,int_vars,cat_vars,\r\n ### string_bool_vars,discrete_string_vars,nlp_vars,date_or_id_vars,cols_delete\r\n if verbose == 1:\r\n print(\" Number of Numeric Columns = \", len(continuous_vars))\r\n print(\" Number of Integer-Categorical Columns = \", len(int_vars))\r\n print(\" Number of String-Categorical Columns = \", len(cat_vars))\r\n print(\" Number of Factor-Categorical Columns = \", len(factor_vars))\r\n print(\" Number of String-Boolean Columns = \", len(string_bool_vars))\r\n print(\" Number of Numeric-Boolean Columns = \", len(num_bool_vars))\r\n print(\" Number of Discrete String Columns = \", len(discrete_string_vars))\r\n print(\" Number of NLP String Columns = \", len(nlp_vars))\r\n print(\" Number of Date Time Columns = \", len(date_vars))\r\n print(\" Number of ID Columns = \", len(id_vars))\r\n print(\" Number of Columns to Delete = \", len(cols_delete))\r\n if verbose == 2:\r\n marthas_columns(df_preds,verbose=1)\r\n print(\" Numeric Columns: %s\" %continuous_vars[:max_cols_to_print])\r\n print(\" Integer-Categorical Columns: %s\" %int_vars[:max_cols_to_print])\r\n print(\" String-Categorical Columns: %s\" %cat_vars[:max_cols_to_print])\r\n print(\" Factor-Categorical Columns: %s\" %factor_vars[:max_cols_to_print])\r\n print(\" String-Boolean Columns: %s\" %string_bool_vars[:max_cols_to_print])\r\n print(\" Numeric-Boolean Columns: %s\" %num_bool_vars[:max_cols_to_print])\r\n print(\" Discrete String Columns: %s\" %discrete_string_vars[:max_cols_to_print])\r\n print(\" NLP text Columns: %s\" %nlp_vars[:max_cols_to_print])\r\n print(\" Date Time Columns: %s\" %date_vars[:max_cols_to_print])\r\n print(\" ID Columns: %s\" %id_vars[:max_cols_to_print])\r\n print(\" Columns that will not be considered in modeling: %s\" %cols_delete[:max_cols_to_print])\r\n ##### now collect all the column types and column names into a single dictionary to return!\r\n len_sum_all_cols = reduce(add,[len(v) for v in sum_all_cols.values()])\r\n if len_sum_all_cols == orig_cols_total:\r\n print(' %d Predictors classified...' %orig_cols_total)\r\n #print(' This does not include the Target column(s)')\r\n else:\r\n print('No of columns classified %d does not match %d total cols. Continuing...' %(\r\n len_sum_all_cols, orig_cols_total))\r\n ls = sum_all_cols.values()\r\n flat_list = [item for sublist in ls for item in sublist]\r\n if len(left_subtract(list(train),flat_list)) == 0:\r\n print(' Missing columns = None')\r\n else:\r\n print(' Missing columns = %s' %left_subtract(list(train),flat_list))\r\n return sum_all_cols","def topdia(x):\r\n return Feature(x, \"TopDia\")","def my_featurize(apartment):\n return x, y","def cat2onehot_var(snt_ids, vocab_size, batch_size):\n\n targets = np.array([snt_ids]).reshape(-1)\n one_hot_targets = np.eye(vocab_size)[targets]\n result = Variable(torch.FloatTensor(one_hot_targets).view(-1, batch_size, vocab_size)) #\n\n return result","def restrict(self, variable, value):\n # 新因子变量列表为原因子变量列表减去被求和的变量\n restricted_variable = self.var_list.index(variable)\n new_var_list = self.var_list[:restricted_variable] + self.var_list[restricted_variable + 1:]\n\n # 对于新因子CPT中的表项，其值为原因子中与新因子变量取值相同且限制变量取对应值的的单个表项的值\n new_cpt = {}\n if restricted_variable == 0:\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\n if len(self.var_list) == 1:\n postfix = ''\n new_cpt[postfix] = self.cpt[str(value) + postfix]\n elif restricted_variable == len(self.var_list) - 1:\n for i in range(pow(2, restricted_variable)):\n prefix = Util.to_binary(i, restricted_variable)\n new_cpt[prefix] = self.cpt[prefix + str(value)]\n else:\n for i in range(pow(2, restricted_variable)):\n prefix = Util.to_binary(i, restricted_variable)\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\n new_cpt[prefix + postfix] = self.cpt[prefix + str(value) + postfix]\n new_node = Node('f' + str(new_var_list), new_var_list)\n new_node.set_cpt(new_cpt)\n return new_node","def make_features(x):\n x = x.unsqueeze(1)\n # torch.cat 实现tensor拼接\n return torch.cat([x ** i for i in range(1, POLY_DEGREE + 1)], 1)","def dict_to_feature(feature_dict, keys, max_value=None):\n feature = []\n for key, val in feature_dict.items(): # First level\n if key not in keys:\n continue\n if val is None or val == \"auto\" or key == \"autotuning\" or val == \"\":\n continue\n if isinstance(val, dict):\n feature.append(dict_to_feature(val, max_value))\n else:\n feature.append(float(val))\n\n # normalization, should not matter in tree models\n if max_value is not None:\n norm_feature = []\n for f, mv in zip(feature, max_value):\n norm_feature.append(f / mv)\n feature = norm_feature\n\n return feature","def categorical(df):\n\n # variables which need to be transformed to categorical\n categorical = [\"prop_country_id\", \"visitor_location_country_id\"]\n\n for var in categorical:\n df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1)\n del df[var]\n\n return df","def collapse_var(nc, out, name, direction):\n var1 = nc.variables[name]\n N = (len(nc.dimensions[direction]) - 1) / 2\n\n print(\"Processing %s...\" % name)\n dims = var1.dimensions\n if len(dims) > 1: # only collapse spatial fields\n dims = [x for x in dims if x != direction]\n\n try:\n fill_value = var1._FillValue\n var2 = out.createVariable(name, var1.dtype,\n dimensions=dims, fill_value=fill_value)\n except:\n var2 = out.createVariable(name, var1.dtype,\n dimensions=dims)\n\n copy_attributes(var1, var2)\n\n if direction == 'x':\n var2[:] = var1[get_slice(var1.dimensions, x=N)]\n elif direction == 'y':\n var2[:] = var1[get_slice(var1.dimensions, y=N)]","def get_categorical_features(self, x: pd.DataFrame) -> pd.DataFrame:\n return x[self.categorical_features]","def preprocess_sf(bd, var):\n filepath_svf = f\"team67-ptp/data/{var}.ftr\"\n filepath = bd\n data = feather.read_dataframe(filepath)\n df = data.copy()\n df2 = df[var]\n df2 = df2.to_frame()\n if df2[var].dtype is \"category\":\n df2[var] = df2[var].astype(\"category\").cat.codes\n filename = filepath_svf\n df2.to_feather(filename)\n print(\"Succesfully exported to feather\")\n else:\n filename = filepath_svf\n df2.to_feather(filename)\n print(\"Succesfully exported to feather\")","def cvxopt2dense(value):\n return np.array(value)","def apply_randomization(features, label, randomize_prob):\n rnd_tok = lambda: tf.as_string(tf.random.uniform([], 0, 99999999, tf.int32))\n\n for idx in CAT_FEATURE_INDICES:\n key = feature_name(idx)\n # Ignore lint since tf.cond should evaluate lambda immediately.\n features[key] = tf.cond(tf.random.uniform([]) < randomize_prob,\n rnd_tok,\n lambda: features[key]) # pylint: disable=cell-var-from-loop\n return features, label","def get_optimal_discrete_feature_split(\n self, X: np.ndarray, y: np.ndarray, feature_col: int\n ):\n return BaseTree.get_discrete_split_value(\n X[:, feature_col], y, eval_func=self.eval_func\n )","def convert_study_to_feature(study: List[Types.SeriesObj]) -> List[Dict[str, tf.train.Feature]]:\n return [convert_series_to_feature(s) for s in study]","def xx(x):\r\n return Feature(x, \"XX\")","def get_feature_set_SC(tweet, sentimentvalues):\n pos_tag_freq = {}\n additional_freq = {}\n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# if tag=='PRtinf':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJS':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJ':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='NP':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='DET':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='P':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n if tag in ADJECTIVES:\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\n elif tag in ADVERBS: \n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\n elif tag in PRONOUNS:\n additional_freq['pronoun'] = 1\n except KeyError:\n continue\n for key in pos_tag_freq.keys():\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \n #Number of exclamation marks, number of emoticons,\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\n if emoticons>0:\n additional_freq['emoticons'] = emoticons*1.0\n if tweet.nrof_exclamations>0:\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\n \n #Add lexicon values\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\n sub_score = 0.0\n obj_score = 0.0\n nrof_subwords = 0\n nrof_objwords = 0\n for word in sentimentvalues.keys():\n if sentimentvalues[word][0]>0:\n sub_score = sub_score + sentimentvalues[word][0]\n nrof_subwords = nrof_subwords + 1\n if sentimentvalues[word][1]>0:\n sub_score = sub_score + sentimentvalues[word][1]\n nrof_subwords = nrof_subwords + 1\n if sentimentvalues[word][2]>0:\n obj_score = obj_score + sentimentvalues[word][2]\n nrof_objwords = nrof_objwords + 1\n if sub_score>0:\n additional_freq[\"sub_score\"] = sub_score+1.0\n if obj_score>0:\n additional_freq[\"obj_score\"] = obj_score+1.0\n if nrof_subwords>0:\n additional_freq[\"subjective_words\"] = nrof_subwords*1.0\n if nrof_objwords>0:\n additional_freq[\"objective_words\"] = nrof_objwords*1.0\n \n #Concatenate the dicts\n features= dict(pos_tag_freq.items() + additional_freq.items())\n \n return features","def dummify_features(df):\n colnames = df.columns\n le_dict = {}\n for col in colnames:\n le_dict[col] = preprocessing.LabelEncoder()\n le_dict[col].fit(df[col])\n df.loc[:, col] = le_dict[col].transform(df[col])\n\n enc = preprocessing.OneHotEncoder()\n enc.fit(df)\n X = enc.transform(df)\n\n dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\n # for cv in colnames:\n # for modality in le_dict[cv].classes_:\n # dummy_colnames.append(cv + '_' + modality)\n\n return X, dummy_colnames, enc","def word2features(self,sent, i):\n word = sent[i][0]\n #postag = sent[i][1]\n\n features = {\n 'bias': 1.0,\n 'word.lower()': word.lower(),\n 'word.isupper()': word.isupper(),\n 'word.istitle()': word.istitle(),\n 'word.isdigit()': word.isdigit(),\n 'word.shape()':self.shape(word),\n 'word.isalnum()':word.isalnum(),\n 'word.isalpha()':word.isalpha(),\n # 'postag': postag,\n # 'postag[:2]': postag[:2],\n }\n if i > 0:\n word1 = sent[i - 1][0]\n #postag1 = sent[i - 1][1]\n features.update({\n '-1:word.lower()': word1.lower(),\n '-1:word.istitle()': word1.istitle(),\n '-1:word.isupper()': word1.isupper(),\n '-1:word.isdigit()': word1.isdigit(),\n '-1:word.isalnum()':word1.isalnum(),\n '-1:word.isalpha()':word1.isalpha(),\n # '-1:postag': postag1,\n # '-1:postag[:2]': postag1[:2],\n })\n else:\n features['BOS'] = True\n\n if i > 1:\n word2 = sent[i - 2][0]\n #postag2 = sent[i - 2][1]\n features.update({\n '-2:word.lower()': word2.lower(),\n '-2:word.istitle()': word2.istitle(),\n '-2:word.isupper()': word2.isupper(),\n '-2:word.isdigit()': word2.isdigit(),\n '-2:word.isalnum()': word2.isalnum(),\n '-2:word.isalpha()': word2.isalpha(),\n # '-2:postag': postag2,\n # '-2:postag[:2]': postag2[:2],\n })\n else:\n features['BOS1'] = True\n if i > 2:\n word3 = sent[i - 3][0]\n #postag3 = sent[i - 3][1]\n features.update({\n '-3:word.lower()': word3.lower(),\n '-3:word.istitle()': word3.istitle(),\n '-3:word.isupper()': word3.isupper(),\n '-3:word.isdigit()': word3.isdigit(),\n '-3:word.isalnum()': word3.isalnum(),\n '-3:word.isalpha()': word3.isalpha(),\n # '-3:postag': postag3,\n # '-3:postag[:2]': postag3[:2],\n })\n else:\n features['BOS2'] = True\n\n if i > 3:\n word4 = sent[i - 4][0]\n #postag4 = sent[i - 4][1]\n features.update({\n '-4:word.lower()': word4.lower(),\n '-4:word.istitle()': word4.istitle(),\n '-4:word.isupper()': word4.isupper(),\n '-4:word.isdigit()': word4.isdigit(),\n '-4:word.isalnum()': word4.isalnum(),\n '-4:word.isalpha()': word4.isalpha(),\n # '-4:postag': postag4,\n # '-4:postag[:2]': postag4[:2],\n })\n else:\n features['BOS2'] = True\n\n if i < len(sent) - 1:\n word1 = sent[i + 1][0]\n features.update({\n '+1:word.lower()': word1.lower(),\n '+1:word.istitle()': word1.istitle(),\n '+1:word.isupper()': word1.isupper(),\n '+1:word.isdigit()': word1.isdigit(),\n '+1:word.isalnum()': word1.isalnum(),\n '+1:word.isalpha()': word1.isalpha(),\n # '+1:postag': postag1,\n # '+1:postag[:2]': postag1[:2],\n })\n else:\n features['EOS'] = True\n if i < len(sent) - 2:\n word12 = sent[i + 2][0]\n #postag12 = sent[i + 2][1]\n features.update({\n '+2:word.lower()': word12.lower(),\n '+2:word.istitle()': word12.istitle(),\n '+2:word.isupper()': word12.isupper(),\n '+2:word.isdigit()': word12.isdigit(),\n '+2:word.isalnum()': word12.isalnum(),\n '+2:word.isalpha()': word12.isalpha(),\n # '+2:postag': postag12,\n # '+2:postag[:2]': postag12[:2],\n })\n else:\n features['EOS2'] = True\n if i < len(sent) - 3:\n word13 = sent[i + 3][0]\n #postag13 = sent[i + 3][1]\n features.update({\n '+3:word.lower()': word13.lower(),\n '+3:word.istitle()': word13.istitle(),\n '+3:word.isupper()': word13.isupper(),\n '+3:word.isdigit()': word13.isdigit(),\n '+3:word.isalnum()': word13.isalnum(),\n '+3:word.isalpha()': word13.isalpha(),\n # '+3:postag': postag13,\n # '+3:postag[:2]': postag13[:2],\n })\n else:\n features['EOS2'] = True\n if i < len(sent) - 4:\n word14 = sent[i + 4][0]\n #postag14 = sent[i + 4][1]\n features.update({\n '+4:word.lower()': word14.lower(),\n '+4:word.istitle()': word14.istitle(),\n '+4:word.isupper()': word14.isupper(),\n '+4:word.isdigit()': word14.isdigit(),\n '+4:word.isalnum()': word14.isalnum(),\n '+4:word.isalpha()': word14.isalpha(),\n # '+4:postag': postag14,\n # '+4:postag[:2]': postag14[:2],\n })\n else:\n features['EOS2'] = True\n return features","def preprocess(old_df, label_name, category_features, non_category_features):\n old_df['fraud'] = old_df[label_name].apply(lambda x: x[0] == 'f')\n\n # Creating a new dataframe with a subset of features.\n new_df = old_df[['fraud'] + non_category_features]\n\n # For categorical features, we make dummy variables,\n # and merge them into new_df.\n for feature in category_features:\n dummy_df = pd.get_dummies(old_df[feature], prefix=feature,\n dummy_na=True)\n # Since dummy_na=True, the last column will be for null values.\n dummy_df.drop(dummy_df.columns[-1], axis=1, inplace=True)\n new_df = pd.concat([new_df, dummy_df], axis=1)\n return new_df","def extract_concepts_discrete():\n disc = pd.DataFrame([['name', 'Name', 'string'],\n ['year', 'Year', 'time'],\n ['area', 'Area', 'entity_domain'],\n ['area_id', 'Area Id', 'string'],\n ['variable_id', 'Variable Id', 'string']\n ], columns=['concept', 'name', 'concept_type'])\n return disc","def data_continuous(df, cat_features = [], cont_features = []) :\n subset_cont =[]\n for col in list(df.columns):\n if df[col].dtype == 'int' or df[col].dtype == 'float64':\n if col not in cont_features and col not in cat_features:\n print(col, \"was added to continuous features\")\n cont_features.append(col)\n subset_cont.append(col)\n for col in cont_features:\n if col not in subset_cont:\n subset_cont.append(col)\n print('Continuous features : ', ' '.join(subset_cont))\n printmd(\"**Description of continuous columns:**\")\n print(round(df[subset_cont].describe()))\n return (cont_features)","def from_dummies(self, data, prefix_sep='_'):\n out = data.copy()\n for feat in self.categorical_feature_names:\n # first, derive column names in the one-hot-encoded data from the original data\n cat_col_values = []\n for val in list(self.data_df[feat].unique()):\n cat_col_values.append(feat + prefix_sep + str(val)) # join original feature name and its unique values , ex: education_school\n match_cols = [c for c in data.columns if c in cat_col_values] # check for the above matching columns in the encoded data\n\n # then, recreate original data by removing the suffixes - based on the GitHub issue comment: https://github.com/pandas-dev/pandas/issues/8745#issuecomment-417861271\n cols, labs = [[c.replace(\n x, \"\") for c in match_cols] for x in [\"\", feat + prefix_sep]]\n out[feat] = pd.Categorical(\n np.array(labs)[np.argmax(data[cols].values, axis=1)])\n out.drop(cols, axis=1, inplace=True)\n return out","def index_to_feature(p, dims):\n feature = []\n for dim in dims:\n feature.append(p % dim)\n p //= dim\n return feature","def get_feature_set_SB(tweet):\n #pos-tag frequencies\n# print \"Tagged words in tweet: \", tweet.tagged_words\n pos_tag_freq = {}\n additional_freq = {}\n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# if tag=='PRtinf':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJS':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJ':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='NP':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='DET':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='P':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n if tag in ADJECTIVES:\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\n elif tag in ADVERBS: \n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\n elif tag in PRONOUNS:\n additional_freq['pronoun'] = 1\n except KeyError:\n continue\n# print \"Tag frequencies: \", pos_tag_freq\n for key in pos_tag_freq.keys():\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \n #Number of exclamation marks, number of emoticons,\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\n if emoticons>0:\n additional_freq['emoticons'] = emoticons*1.0\n if tweet.nrof_exclamations>0:\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\n \n# print \"Additional frequencies: \", additional_freq\n# raw_input(\"Continue?\")\n \n #Concatenate the dicts\n features= dict(pos_tag_freq.items() + additional_freq.items())\n# print \"All features: \", features\n# raw_input(\"Continue?\")\n return features","def feature_dist_func_dict():\n return {\"tanimoto_dissimilarity\": tanimoto_dissimilarity}","def build_classifier(model, hidden_units):\n in_features = model.classifier._modules['0'].in_features\n classifier = nn.Sequential(OrderedDict([\n ('dropout1', nn.Dropout(0.5)),\n ('fc1', nn.Linear(in_features, hidden_units)), \n ('relu', nn.ReLU()),\n ('dropout2', nn.Dropout(0.5)),\n ('fc2', nn.Linear(hidden_units, 102)),\n ('output', nn.LogSoftmax(dim=1))\n ]))\n return classifier","def feature_vec(idcs, idcs_per_action, num_actions):\n x_fvec = np.zeros((idcs_per_action*num_actions))\n x_fvec[idcs] = 1\n \n return x_fvec","def get_feature_set_PC(tweet, sentimentvalues):\n features= {\n 'text_length': np.log(len(tweet.text))\n } #ADD ADDITIONAL FEATURES\n if tweet.nrof_sademoticons>0:\n features['sademoticons'] = tweet.nrof_sademoticons\n if tweet.nrof_happyemoticons>0:\n features['happyemoticons'] = tweet.nrof_happyemoticons\n \n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n features[tag] = features.get(tag, 0) + 1\n if tag in ADJECTIVES:\n features['adjectives'] = features.get(tag, 0) + 1\n elif tag in ADVERBS: \n features['adverbs'] = features.get(tag, 0) + 1\n elif tag in PRONOUNS:\n features['pronoun'] = 1\n except KeyError:\n continue\n for key in features.keys():\n features[key] = features[key]*1.0\n \n #Add lexical features\n # total polarity score, number of positive words, number of negative words\n pos_score = 0\n neg_score = 0\n nrof_pos_words = 0\n nrof_neg_words = 0\n for word in sentimentvalues.keys():\n if sentimentvalues[word][0]>0:\n nrof_pos_words = nrof_pos_words + 1\n pos_score = pos_score + sentimentvalues[word][0]\n if sentimentvalues[word][1]>0:\n nrof_neg_words = nrof_neg_words + 1\n neg_score = neg_score + sentimentvalues[word][1]\n\n if neg_score>0:\n features['neg_score'] = neg_score+1.0\n if pos_score>0:\n features['pos_score'] = pos_score+1.0\n if nrof_pos_words>0:\n features['positive_words'] = nrof_pos_words*1.0\n if nrof_neg_words>0:\n features['negative_words'] = nrof_neg_words*1.0\n \n return features","def create_dummies(df):","def getFeatures(featureInput):\n featureList = []\n for defTerm,candidateSent in featureInput:\n tokens = nltk.word_tokenize(candidateSent)\n features = {}\n POScenter,POSleft,POSright = wordPOS(tokens,defTerm)\n features['Pos of first Article'] = posFirstArticle(tokens)\n## features['Num Punct Marks'] = numPunctuation(tokens)\n features['Subj words Predicate'] = subWordPerdicate(candidateSent,defTerm,tokens)\n features['Word before def term'] = wordBeforeDef(tokens,defTerm)\n features['POS centered word'] = POScenter\n features['POS left word'] = POSleft\n## features['POS right word'] = POSright \n featureList.append(features)\n return featureList","def to_numeric_features(features, feature_cols=None):\n\n for col in CATEGORICAL_COLS:\n features = pd.concat(\n [features, pd.get_dummies(features[col[0]], drop_first=True)], axis=1)\n features.drop(col[0], axis=1, inplace=True)\n\n # Remove the unused columns from the dataframe.\n for col in UNUSED_COLUMNS:\n features.pop(col)\n\n # Re-index dataframe (if categories list changed from the previous dataset)\n if feature_cols is not None:\n features = features.T.reindex(feature_cols).T.fillna(0)\n return features","def FE_create_categorical_feature_crosses(dfc, cats):\r\n dfc = copy.deepcopy(dfc)\r\n combos = list(combinations(cats, 2))\r\n for cat1, cat2 in combos:\r\n dfc.loc[:,cat1+'_cross_'+cat2] = dfc[cat1].astype(str)+\" \"+dfc[cat2].astype(str)\r\n return dfc","def data_categorical(df, cat_features = [], cont_features = []):\n subset_cat = []\n subset_dict={}\n # Add all the object type features to config.cat_features \n for col in df.columns:\n if df[col].dtype == 'object' and col not in cont_features:\n subset_cat.append(col)\n if col not in cat_features :\n cat_features.append(col)\n if cat_features !=[]:\n print('Categorical features : ', ' '.join(cat_features))\n printmd('**Number of unique values for every feature:**')\n print(pd.DataFrame(df[cat_features].nunique(), columns = ['Unique values']).sort_values(by = 'Unique values', ascending=False))\n printmd(\"**5 uniques samples of every Categorical Features :**\")\n for col in cat_features :\n subset_dict[col]= df[col].unique()[:5]\n print(pd.DataFrame.from_dict(subset_dict, orient='index').transpose())\n return (cat_features)","def get_feature_set_PC2(tweet, sentimentvalues):\n features= {\n 'text_length': np.log(len(tweet.text))\n } #ADD ADDITIONAL FEATURES\n if tweet.nrof_sademoticons>0:\n features['sademoticons'] = tweet.nrof_sademoticons\n if tweet.nrof_happyemoticons>0:\n features['happyemoticons'] = tweet.nrof_happyemoticons\n \n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n features[tag] = features.get(tag, 0) + 1\n if tag in ADJECTIVES:\n features['adjectives'] = features.get(tag, 0) + 1\n elif tag in ADVERBS: \n features['adverbs'] = features.get(tag, 0) + 1\n elif tag in PRONOUNS:\n features['pronoun'] = 1\n except KeyError:\n continue\n for key in features.keys():\n features[key] = features[key]*1.0\n \n #Add lexical features\n # total polarity score, number of positive words, number of negative words\n pos_score = sentimentvalues[0]\n neg_score = sentimentvalues[1]\n\n if pos_score>0:\n features['pos_score'] = pos_score+1.0\n if neg_score>0:\n features['neg_score'] = neg_score+1.0\n \n return features","def make_feature_scale_factors():\n X, y = make_X_and_y()\n sqm = make_sqm_X()\n scale_factors = {\n \"indoor_temp\": np.max(X[:,:,0]),\n \"outdoor_temp\": np.max(X[:,:,1]),\n \"gas_kwh\": np.max(X[:,:,2]),\n \"elec_kwh\": np.max(X[:,:,3]),\n \"floor_area\": np.max(sqm),\n \"htc\": np.max(y),\n }\n\n with open(os.path.join(_TRAINING_DATA_PATH, \"scalefactors.json\"), \"w+\") as f:\n json.dump(scale_factors, f)","def make_data(dataFname, enc, features=None):\n\n origData = pandas.read_csv(dataFname)\n ids = origData['id']\n\n # remove unused columns\n if 'Unnamed: 0' in origData.columns: del origData['Unnamed: 0']\n del origData['id']\n\n # remove \"data leakage\" columns\n for f in prohobitedFeatures:\n del origData[f]\n\n # separate into X & y values\n xData = origData[[col for col in origData.columns if not col=='loss']]\n set_vars_as_type(xData, discreteVars, object)\n yVec = origData.loss if 'loss' in origData.columns else None\n\n # try f528 - f274\n xData['f528f274'] = xData['f528'] - xData['f274']\n\n # encode the categorical features f776 and f777\n if enc is None:\n enc = OneHotEncoder(n_values=[2, 2])\n enc.fit(xData[['f776', 'f777']])\n\n xData[['f776_isZero', 'f776_isOne', 'f777_isZero', 'f777_isOne']] = pandas.DataFrame(enc.transform(xData[['f776', 'f777']]).toarray())\n del xData['f776']\n del xData['f777']\n\n print_missing_values_info(origData)\n\n # feature selection\n if features:\n filteredXData = xData[features]\n else: # use ALL features\n filteredXData = xData\n\n return filteredXData, yVec, ids, enc","def add_dummy_feature(X, value=...):\n ...","def dense_col(self):\n if not self.col_name_mapping or \"dense_col\" not in self.col_name_mapping:\n return EmptyFeature\n return Feature(\n name=list(self.col_name_mapping[\"dense_col\"].keys()),\n index=list(self.col_name_mapping[\"dense_col\"].values()),\n )","def my_impl_variational(in_train, in_test, labels):\n X_train = []\n X_test = []\n for lab in labels:\n for datum in in_train[lab]:\n X_train.append([datum, lab])\n for datum in in_test[lab]:\n X_test.append([datum, lab])\n Variationer_learn(X_train, 500, 1, 0.01, X_test, labels)","def changeVars(self, vars):\n v = VarSet(vars)\n newOrder = map(lambda x:v.index(x), vars)\n return Factor(v, self.t.transpose(newOrder))\n #return NotImplemented","def pre_MultiLabelBinarizer(vars):\n for var in vars:\n m = DataFrameMapper([(var, [CategoricalImputer(), LabelEncoder()])], df_out = True)\n m.fit_transform(pd.concat([test,df])) # Get all categorical outcomes so we have same codes for all.\n\n X_train[var+'_encoded'] = m.transform(X_train)\n X_test[var+'_encoded'] = m.transform(X_test)\n test[var+'_encoded'] = m.transform(test)\n\n return X_train, X_test, test","def to_delimited_set(self, name, label, variables, from_dichotomous=True,\n codes_from_name=True):\n if self.var_exists(name):\n raise ValueError('{} does already exist.'.format(name))\n elif not all(isinstance(c, (str, tuple)) for c in variables):\n raise ValueError('Input of variables must be string or tuple.')\n cols = [c if isinstance(c, str) else c[0] for c in variables]\n if not all(self.var_exists(c) for c in cols):\n not_in_ds = [c for c in cols if not self.var_exists(c)]\n raise KeyError('{} not found in dataset!'.format(not_in_ds))\n elif not all(self._has_categorical_data(c) for c in cols):\n not_cat = [c for c in cols if not self._has_categorical_data(c)]\n raise ValueError('Variables must have categorical data: {}'.format(not_cat))\n if from_dichotomous:\n if not all(x in [0, 1] for c in cols for x in self.codes_in_data(c)):\n non_d = [c for c in cols\n if not all(x in [0, 1] for x in self.codes_in_data(c))]\n raise ValueError('Variables are not dichotomous: {}'.format(non_d))\n mapper = []\n for x, col in enumerate(variables, 1):\n if codes_from_name:\n x = int(col.split('_')[-1])\n if isinstance(col, tuple):\n text = col[1]\n else:\n text = self.text(col)\n mapper.append((x, text, {col: [1]}))\n else:\n values = self.values(cols[0])\n if not all(self.values(c) == values for c in cols):\n not_eq = [c for c in cols if not self.values(c) == values]\n raise ValueError('Variables must have eqaul values: {}'.format(not_eq))\n mapper = []\n for v in values:\n mapper.append((v[0], v[1], union([{c: v[0]} for c in cols])))\n\n self.derive(name, 'delimited set', label, mapper)\n\n return None","def _get_feature_map():\n return {\n # 3 sparse feature with variable length. Use this if you have a\n # variable number or more than 1 feature value per example.\n \"feature_1\":\n tf.io.VarLenFeature(dtype=tf.int64),\n \"feature_2\":\n tf.io.VarLenFeature(dtype=tf.int64),\n \"feature_3\":\n tf.io.VarLenFeature(dtype=tf.int64),\n \"label\":\n tf.io.FixedLenFeature([1], dtype=tf.int64),\n }","def create_feature_value_category(data):\n utils.save_log('{0} :: {1}'.format(\n create_feature_value_category.__module__,\n create_feature_value_category.__name__))\n\n avg_value = data.agg({'Value': 'avg'}).collect()[0][0]\n data = data. \\\n withColumn('ValueStrategy',\n when(col('Value') > avg_value * 200, 2).\n when(col('Value') > avg_value * 50, 1).\n otherwise(0))\n\n update_list_features(\"numerical\", [\"ValueStrategy\"])\n\n return data","def fac2var(_Mu,Nu, G, f,v):\n #print\n #print \"fac '%s' \\t=>\\t var '%s'\" % (f,v)\n assert G.type(f)=='fac' and G.type(v)=='var'\n\n vars = G.N(f) # for order\n ii = { x:i for i,x in enumerate(vars) } # inverted index\n\n for val in G.vals(v): # forall val in var\n \n # \"pin down msg var to one val\"\n # eg\n # var = 'b'\n # val = 2\n # vars = ['a','b','c']\n # space = {0..1} x {2} x {0..3}\n space = cartesian( *[(G.vals(_v) if _v != v else [val]) for _v in vars] )\n\n # get _val of _var\n # _vals[ii[_v]] = _v:str => ii:str=>inx => _vals:inx=>val => Nu[_,_]:val=>num\n # discrete randvar -> values are indices\n # sum of prod\n msg = sum( G(f, *_vals) * product([ Nu[_v, f][_vals[ii[_v]]] for _v in G.N(f) if _v != v ])\n for _vals in space )\n\n _Mu[f,v][val] = msg\n \n \"\"\"\n\n # sum (fac * prod nus)\n fac = G.node[f]['pmf']\n nus = [ (i, _v, Nu[_v, f]) for i,_v in enumerate(G.N(f)) if _v != v ]\n\n msg = fac\n for i,_v,nu in nus:\n # sans broadcast\n shape = [1 for _ in msg.shape]\n shape[i] = G.node[_v]['d']\n nu = resize(nu, tuple(shape))\n nu = resize(nu, msg.shape)\n msg = msg * nu\n # [diff] msg = msg * resize(nu, msg.shape)\n # [diff] msg = resize(nu, msg.shape) * msg\n\n others = tuple([ i for i,_v in enumerate(G.N(f)) if _v != v ]) \n msg = sum(msg, axis=others) # marginalize every other var\n Mu[f,v] = msg\n\n \"\"\"\n\n #print \n #print 'Mu =', Mu","def preprocessing_fn(inputs):\n outputs = {}\n for key in _DENSE_FLOAT_FEATURE_KEYS:\n # Preserve this feature as a dense float, setting nan's to the mean.\n outputs[_transformed_name(key)] = tft.scale_to_z_score(\n _fill_in_missing(inputs[key]))\n\n for key in _VOCAB_FEATURE_KEYS:\n # Build a vocabulary for this feature.\n outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(\n _fill_in_missing(inputs[key]),\n top_k=_VOCAB_SIZE,\n num_oov_buckets=_OOV_SIZE)\n\n for key in _BUCKET_FEATURE_KEYS:\n outputs[_transformed_name(key)] = tft.bucketize(\n _fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT,\n always_return_num_quantiles=False)\n\n for key in _CATEGORICAL_FEATURE_KEYS:\n outputs[_transformed_name(key)] = _fill_in_missing(inputs[key])\n\n # Was this passenger a big tipper?\n taxi_fare = _fill_in_missing(inputs[_FARE_KEY])\n tips = _fill_in_missing(inputs[_LABEL_KEY])\n outputs[_transformed_name(_LABEL_KEY)] = tf.where(\n tf.is_nan(taxi_fare),\n tf.cast(tf.zeros_like(taxi_fare), tf.int64),\n # Test if the tip was > 20% of the fare.\n tf.cast(\n tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))\n\n return outputs","def string_feature(ds, feature: str, verbosity=0, dim=\"sample\", numeric_dis=True):\n t = get_annotation_table(ds, dim)\n ser = t[feature]\n dt = ser.dtype\n descript = \"\"\"\"\"\"\n\n non_null_ser = ser[ser.notnull()]\n null_flag = len(non_null_ser) < len(ser)\n levels = list(set(non_null_ser.values))\n num_levels = len(levels)\n\n if dt == pd.StringDtype():\n\n if num_levels >= 50:\n descript += f\"\"\"\nSorry, {feature} is a String feature with too many factor levels \n(n = {num_levels}) to describe, here \n\"\"\"\n\n elif num_levels < 50 and num_levels >= 2:\n descript += f\"\"\"\n{feature}: {dt} Feature\n-------------------------\n\nFactor level counts df:\n\n{non_null_ser.value_counts()}\n\n\nSome example query statements:\n------------------------------\n\n{feature} in ['{levels[0]}', '{levels[1]}', ...]\n\n{feature} not in ['{levels[0]}', '{levels[-1]}', ...]\n\n{feature} != '{levels[-2]}'\n\"\"\"\n\n else:\n descript += f\"\"\"\nThere's only a single factor level, {levels[0]}, across all samples.\n\"\"\"\n\n elif dt == pd.BooleanDtype():\n descript += f\"\"\"\n{feature}: {dt} Feature:\n---------------------------\n\nFactor level counts df:\n\n{non_null_ser.value_counts()}\n\n\nSome example query statements:\n------------------------------\n\n{feature} == True\n\n{feature} == False\n\"\"\"\n\n elif dt == pd.Int64Dtype() or dt == pd.Float64Dtype():\n if num_levels == 1:\n descript += f\"\"\"\nThere's only a single factor level, {levels[0]}, across all samples.\n\"\"\"\n\n elif (num_levels > 1) and (not numeric_dis):\n descript += f\"\"\"\n{feature}: {dt} Feature\n-------------------------\n\nFactor level counts df:\n\n{non_null_ser.value_counts()}\n\n\nSome example query statements:\n------------------------------\n\n{feature} in [{levels[0]}, {levels[1]}, ...]\n\n{feature} not in [{levels[0]}, {levels[-1]}, ...]\n\n{feature} != {levels[-2]}\n\"\"\"\n else:\n des = ser.describe()\n descript += f\"\"\"\n{feature}: {dt} Feature:\n---------------------------\n\ndistribution of numerical feature:\n\n{des}\n\nSome example query statements:\n------------------------------\n\n{feature} >= {int(des[1])}\n\n{feature} <= {int(des[1])}\n\n({feature} >= {int(des[4])}) and ({feature} <= {int(des[5])})\n\"\"\"\n\n else:\n descript += f\"\"\"\n{feature}: {dt} Feature\n-------------------------\n\nFactor level counts df:\n\n{non_null_ser.value_counts()}\n\n\nSome example query statements:\n------------------------------\n\n{feature} in ['{levels[0]}', '{levels[1]}', ...]\n\n{feature} not in ['{levels[0]}', '{levels[-1]}', ...]\n\n{feature} != '{levels[-2]}'\n\"\"\"\n\n if null_flag:\n descript += f\"\"\"\n{feature}.isnull()\n\n{feature}.notnull()\n\"\"\"\n\n return descript","def featurize(self, data):\n \n bag_of_words = []\n\n tokens = data.split()\n\n for i in tokens:\n bag_of_words.append((i, True))\n\n return bag_of_words","def to_dimacs_string_set_variable_value(self, clauses, variable, value):\n self.varname_dict = {}\n self.varobj_dict = {}\n variables = prop_symbols_from_clause_list(clauses)\n if variable in variables:\n variables.remove(variable)\n varis = dict(list(zip(sorted(variables, key=lambda v: v.op),\n list(map(str, list(range(1, len(variables) + 1)))))))\n for var in varis:\n self.varname_dict[var] = varis[var]\n self.varobj_dict[varis[var]] = var\n\n ret_clauses = ''\n clause_count = 0\n for clause in clauses:\n clause_exists = True\n dimacs_vlist = []\n ret_clause = ''\n if clause.op == '|':\n for var in clause.args:\n if literal_name(var) == literal_name(variable):\n if value and not var.op == '~' or not value and var.op == '~':\n clause_exists = False\n else:\n dimacs_vlist.append(('-' if var.op == '~' else '') \\\n + self.varname_dict[var.args[0]\n if var.op == '~' else var])\n\n if clause_exists:\n ret_clause += ' '.join(dimacs_vlist)\n elif clause.op == '~':\n if literal_name(clause) == literal_name(variable):\n if value:\n return None\n clause_exists = False\n else:\n ret_clause += '-' + self.varname_dict[clause.args[0]]\n elif literal_name(clause) == literal_name(variable):\n if value:\n clause_exists = False\n else:\n return None\n else:\n ret_clause += self.varname_dict[clause]\n if clause_exists:\n clause_count += 1\n ret_clauses += ret_clause + ' 0\\n'\n\n ret_header = 'p cnf %d %d\\n' % (len(variables), clause_count)\n ret = ret_header + ret_clauses\n return ret","def _classify(tree, x):\n # YOUR CODE HERE\n # begin answer\n feature_name=list(tree.keys())[0] #first element\n secondDict=tree[feature_name] \n key=x.loc[feature_name] #extract value from x\n for key_val in secondDict:\n feature_val=key_val[0]\n valueOfKey=secondDict[(feature_val, key>=feature_val)]\n if isinstance(valueOfKey,dict):\n label=_classify(valueOfKey,x)\n else:\n label=valueOfKey\n return label\n # end answer","def domain_features(domain, path_set):\n return string_features_v2(domain_string(domain, path_set))","def condition_domain_reduction(csp, var) :\n return True","def condition_domain_reduction(csp, var) :\n return True","def GetVectorArticleInput(dico_vector_input, features):\n features_left = set(features) - set(dico_vector_input.keys())\n if len(features_left) > 0:\n sentence = \"Some features aren't in the dict:\\n\"\n raise MyException(sentence + \"{}\".format(features_left))\n vector_art = []\n other_features = ['abstract', 'syn', 'exergue', 'title', 'secTitle']\n other_features += ['subTitle', 'supTitle']\n for feature in features:\n if feature == 'nbSign':\n if dico_vector_input['nbSign'] == 0:\n print(\"NbSign == 0 l.176 - GetVectorArticleInput\")\n vector_art.append(dico_vector_input[feature])\n else:\n vector_art.append(dico_vector_input[feature])\n # Conversion des variables en indicatrices\n # Normalement plus la peine, comme déjà fait auparavant\n elif feature in other_features:\n if dico_vector_input[feature] > 0:\n vector_art.append(1)\n else:\n vector_art.append(0)\n else:\n vector_art.append(dico_vector_input[feature])\n return (dico_vector_input['melodyId'], np.array([vector_art]))","def load_feature(feature_name, caf_dose, features_path):\n # gets the paths to the folders where the specified feature is stored\n subject_paths = glob.glob(os.path.join(features_path, \"*\", feature_name))\n\n feature = {}\n for path in subject_paths:\n # extract the subject id from the current path (second to last element in the path)\n subject_id = path.split(os.sep)[-2]\n\n # get all stages for the current subject\n stages = set(\n [\n p.split(os.sep)[-1].split(\"_\")[-1].split(\".\")[0]\n for p in glob.glob(os.path.join(path, \"*.npy\"))\n ]\n )\n if len(stages) == 0:\n print(\n f\"The following directory doesn't contain features: {path}. \"\n \"This will likely cause an error down the line\"\n )\n for stage in stages:\n if stage not in feature:\n feature[stage] = {}\n # load the file containing the data for the current stage and subject\n feature[stage][subject_id] = np.load(\n os.path.join(path, f\"{feature_name}_{stage}.npy\"), allow_pickle=True\n )\n return feature","def density(categorical_var, numerical_var):\n #print(categorical_var)\n cat_list = categorical_var.astype('category')\n for cat in cat_list:\n sns.kdeplot(numerical_var[categorical_var == cat], label=cat)#, categorical_var)\n\n plt.show()","def create_classifier(in_dim, hid_dim, out_dim):\n\n root_six = np.sqrt(6)\n eps = root_six / (np.sqrt(hid_dim + in_dim))\n W = np.random.uniform(-eps, eps, [hid_dim, in_dim])\n eps = root_six / (np.sqrt(hid_dim))\n b = np.random.uniform(-eps, eps, hid_dim)\n eps = root_six / (np.sqrt(out_dim + hid_dim))\n U = np.random.uniform(-eps, eps, [out_dim, hid_dim])\n eps = root_six / (np.sqrt(out_dim))\n b_tag = np.random.uniform(-eps, eps, out_dim)\n\n return [W,b,U,b_tag]","def dense_nn(x):\n dense_1 = tf.nn.dropout(tf.nn.relu(dense(x, input_dim, n_l1, 'dense_1')), keep_prob=0.25)\n dense_2 = tf.nn.dropout(tf.nn.relu(dense(dense_1, n_l1, n_l2, 'dense_2')), keep_prob=0.25)\n dense_3 = dense(dense_2, n_l2, n_labels, 'dense_3')\n return dense_3","def get_dummies_list(self, \n cols=['elite'],\\\n drop_=True):\n for col in cols:\n print \"Pre-processing \" + col + \"...\"\n temp = pd.get_dummies(self.df[col].apply(pd.Series).stack(),drop_first=True)\\\n .astype(int).sum(level=0).astype(int)\n # temp.columns.apply(str).apply(lambda x: col + \"_\" + x)\n if drop_:\n self.df.drop(col,axis = 1, inplace=True)\n self.df = pd.concat([self.df, temp],axis=1)","def extract_features(X):\n X = X.drop(\"PassengerId\", axis=1)\n X = X.drop(\"Ticket\", axis=1)\n X = X.drop(\"Cabin\", axis=1)\n \n # Adding polynomial features\n X[\"Age2\"] = X[\"Age\"] ** 2\n #X[\"Fare2\"] = X[\"Fare\"] ** 2\n #X[\"Pclass2\"] = X[\"Pclass\"] ** 2\n\n \n male_titles = set([\"Mr\", \"Don\", \"Sir\"])\n female_titles = set([\"Miss\", \"Ms\", \"Mrs\", \"Mme\", \"Mdm\", \"Lady\"])\n professionals = set([\"Dr\", \"Rev\", \"Master\"])\n military = set([\"Col\", \"Major\", \"Capt\"])\n royalty = set([\"the Countess\", \"Jonkheer\"])\n \n names = X[\"Name\"]\n for i in range(len(names)): \n name_tokens = names[i].split(\", \") \n passenger_title = name_tokens[1].split(\".\")[0]\n if passenger_title in male_titles:\n names[i] = 1\n elif passenger_title in female_titles:\n names[i] = 2\n elif passenger_title in professionals:\n names[i] = 3\n #elif passenger_title in royalty:\n # names[i] = 4\n elif passenger_title in military:\n names[i] = 5\n else:\n names[i] = 6\n \n X[\"Name\"].update(names)\n \n # One hot encoding of categorical data\n X = pd.get_dummies(X) \n \n X.fillna(0, inplace=True)\n X['Fam'] = X['SibSp'] + X['Parch'] # assigned to a column\n return X","def probability_categorical(feature, label):\n assert feature.nunique()>2, 'feature category nums must be greater than 2.'\n t = pd.DataFrame({'feature':feature, 'label':label})\n cat = label.unique()\n cat = [(cat[i], cat[i+1]) for i in range(len(cat)-1)]\n prob = label.value_counts(1).to_dict()\n slope = [prob.get(i[0], 0)-prob.get(i[1], 0) for i in cat]\n \n slope_dict = t.feature.value_counts(1).to_dict()\n prob = t.groupby([ 'feature']).label.value_counts(1).to_dict()\n slope_dict = {i:{'category_rate':slope_dict[i], 'slope':[prob.get((i,j[0]), 0)-prob.get((i,j[1]), 0) for j in cat]} for i in slope_dict}\n for i in slope_dict:\n slope_dict[i]['slope_diff'] = sum([abs(slope[j]-slope_dict[i]['slope'][j]) for j in range(len(slope))])\n value1 = sorted([[[i], slope_dict[i]['slope_diff'], slope_dict[i]['category_rate']] for i in slope_dict], key=lambda x:x[1], reverse=1)\n distance = sorted([value1[i][1]-value1[i+1][1] for i in range(len(value1)-1)])\n std = pd.Series([i[1] for i in value1]).std()\n coupe = value1\n dis = distance[0]\n for k in distance:\n value = value1\n while 1:\n for i in range(len(value)-1):\n if value[i][1]-k<value[i+1][1]:\n value[i+1][0] = value[i][0]+value[i+1][0]\n value[i+1][1] = value[i][1]*value[i][2]/(value[i][2]+value[i+1][2])+value[i+1][1]*value[i+1][2]/(value[i][2]+value[i+1][2])\n value[i+1][2] = value[i][2]+value[i+1][2]\n value.remove(value[i])\n break\n if i==len(value)-2:\n break\n if pd.Series([i[1] for i in value]).std()>std:\n coupe = value\n std = pd.Series([i[1] for i in value]).std()\n dis = k\n return {'group':{k:i for i,j in enumerate(coupe) for k in j[0]}, 'data':coupe, \n 'distance':dis, 'distance_index':f'{distance.index(dis)+1}/{len(distance)}', 'std':std}","def dim2():\n probs = (0.1, 0.2, 0.3, 0.4)\n categories = (\"asdfa\", \"2\", \"3\", \"4\")\n categories = OrderedDict(zip(categories, probs))\n dim2 = Categorical(\"yolo2\", categories, default_value=\"2\")\n return dim2","def build_by_features(self, features):\n v = [0] * self.f\n masks = [1 << i for i in range(self.f)]\n if isinstance(features, dict):\n features = features.items()\n for f in features:\n if isinstance(f, basestring):\n h = self.hashfunc(f.encode('utf-8'))\n w = 1\n else:\n assert isinstance(f, collections.Iterable)\n h = self.hashfunc(f[0].encode('utf-8'))\n w = f[1]\n for i in range(self.f):\n v[i] += w if h & masks[i] else -w\n ans = 0\n for i in range(self.f):\n if v[i] >= 0:\n ans |= masks[i]\n self.value = ans","def build_by_features(self, features):\n v = [0] * self.f\n masks = [1 << i for i in range(self.f)]\n if isinstance(features, dict):\n features = features.items()\n for f in features:\n if isinstance(f, basestring):\n h = self.hashfunc(f.encode('utf-8'))\n w = 1\n else:\n assert isinstance(f, collections.Iterable)\n h = self.hashfunc(f[0].encode('utf-8'))\n w = f[1]\n for i in range(self.f):\n v[i] += w if h & masks[i] else -w\n ans = 0\n for i in range(self.f):\n if v[i] >= 0:\n ans |= masks[i]\n self.value = ans","def discrete_model():\n return {\"x\": np.random.randint(10, size=100), \"y\": np.random.randint(10, size=100)}","def discretizing(\n *factors: List[np.ndarray],\n independent: bool = True,\n n_bins: int = 5,\n strategy: Literal['uniform', 'quantile', 'kmeans', 'gmm'] = 'quantile',\n return_model: bool = False,\n):\n encode = 'ordinal'\n # onehot - sparse matrix of one-hot encoding and\n # onehot-dense - dense one-hot encoding. Ignored features are always stacked to\n # the right.\n # ordinal - Return the bin identifier encoded as an integer value.\n strategy = str(strategy).strip().lower()\n if 'histogram' in strategy:\n strategy = 'uniform'\n # ====== GMM base discretizer ====== #\n if 'gmm' in strategy:\n create_gmm = lambda: GaussianMixture(n_components=n_bins,\n max_iter=800,\n covariance_type='diag',\n random_state=1) # fix random state\n\n if independent:\n gmm = []\n for f in factors[0].T:\n gm = create_gmm()\n gm.fit(np.expand_dims(f, axis=1))\n gm.predict = types.MethodType(_gmm_discretizing_predict, gm)\n gmm.append(gm)\n transform = lambda x: np.concatenate([\n gm.predict(np.expand_dims(col, axis=1)) for gm, col in zip(gmm, x.T)\n ],\n axis=1)\n else:\n gmm = create_gmm()\n gmm.fit(np.expand_dims(factors[0].ravel(), axis=1))\n gmm.predict = types.MethodType(_gmm_discretizing_predict, gmm)\n transform = lambda x: np.concatenate(\n [gmm.predict(np.expand_dims(col, axis=1)) for col in x.T], axis=1)\n disc = gmm\n # ====== start with bins discretizer ====== #\n else:\n disc = KBinsDiscretizer(n_bins=n_bins, encode=encode, strategy=strategy)\n if independent:\n disc.fit(factors[0])\n transform = lambda x: disc.transform(x).astype(np.int64)\n else:\n disc.fit(np.expand_dims(factors[0].ravel(), axis=-1))\n transform = lambda x: np.hstack([\n disc.transform(np.expand_dims(i, axis=-1)).astype(np.int64)\n for i in x.T\n ])\n # ====== returns ====== #\n factors = tuple([transform(i) for i in factors])\n factors = factors[0] if len(factors) == 1 else factors\n if return_model:\n return factors, disc\n return factors","def __init__(self, strName, lstDomain):\n # assign the name of the feature represented by the node\n self.name = str(strName)\n # assign the domain of the feature\n self.domain = lstDomain\n # the value starts out as undefined\n self.value = \"none\"","def get_kdd_feature_cols():\r\n\treturn [\r\n\t\t'duration',\r\n\t\t'src_bytes',\r\n\t\t'dst_bytes',\r\n\t\t'land',\r\n\t\t'wrong_fragment',\r\n\t\t'urgent',\r\n\t\t'hot',\r\n\t\t'num_failed_logins',\r\n\t\t'logged_in',\r\n\t\t'num_compromised',\r\n\t\t'root_shell',\r\n\t\t'su_attempted',\r\n\t\t'num_root',\r\n\t\t'num_file_creations',\r\n\t\t'num_shells',\r\n\t\t'num_access_files',\r\n\t\t'num_outbound_cmds',\r\n\t\t'is_host_login',\r\n\t\t'is_guest_login',\r\n\t\t'count',\r\n\t\t'srv_count',\r\n\t\t'serror_rate',\r\n\t\t'srv_serror_rate',\r\n\t\t'rerror_rate',\r\n\t\t'srv_rerror_rate',\r\n\t\t'same_srv_rate',\r\n\t\t'diff_srv_rate',\r\n\t\t'srv_diff_host_rate',\r\n\t\t'dst_host_count',\r\n\t\t'dst_host_srv_count',\r\n\t\t'dst_host_same_srv_rate',\r\n\t\t'dst_host_diff_srv_rate',\r\n\t\t'dst_host_same_src_port_rate',\r\n\t\t'dst_host_srv_diff_host_rate',\r\n\t\t'dst_host_serror_rate',\r\n\t\t'dst_host_srv_serror_rate',\r\n\t\t'dst_host_rerror_rate',\r\n\t\t'dst_host_srv_rerror_rate']","def das(input_df, counts_var, noise_parameter):\n df = input_df.copy(deep=True)\n \n n = df.shape[0]\n \n # add laplace noise \n noise = np.random.laplace(loc=0, scale=noise_parameter, size=n)\n df['noisy_counts'] = df[counts_var] + noise\n \n # post processing\n df['nonneg_counts'] = post_proc(df.noisy_counts, df.pop_count.sum())\n df.nonneg_counts = np.round(df.nonneg_counts)\n \n return df","def to_dummies(to_dummy_data):\r\n if config.to_dummies:\r\n for col in to_dummy_data.columns:\r\n unique_col_num = len(pd.unique(to_dummy_data[col]))\r\n dummy_max = int(len(to_dummy_data[col]) / 10)\r\n col_type = to_dummy_data.dtypes[col]\r\n if (col_type == \"object\") \\\r\n & (unique_col_num < dummy_max) \\\r\n & (unique_col_num > 1) \\\r\n & (col != config.target):\r\n temp_dummies = pd.get_dummies(to_dummy_data[col])\r\n to_dummy_data = pd.concat([to_dummy_data, temp_dummies],\r\n axis=1, sort=False)\r\n return to_dummy_data","def get_name_to_features(self):\n name_to_features = {\n 'input_ids': tf.io.FixedLenFeature([self.seq_len], tf.int64),\n 'label_ids': tf.io.FixedLenFeature([], tf.int64),\n }\n return name_to_features","def make_dummy_df(self, dummy_vars, cat_to_drop = 0, drop_cat = True, add_intercept = True):\n\n # reset reference levels, if remaking then previous information would be there\n self.ref_levels = []\n\n # reset to original data. Ensures that make_dummy_df can be rerun multiple times\n self.df = self.df_orig.copy()\n\n # check if dummy_vars is a dict or a list to determine how to drop categories\n if isinstance(dummy_vars, list):\n drop_by_index = True\n\n # make sure that cat_to_drop is within the index range of the selected variables\n for var in dummy_vars:\n if self.df[var].nunique() < (cat_to_drop + 1):\n raise Exception(\"For column {} the number of categories was \\\nlower than the specified index ({}) to drop.\".format(var, cat_to_drop))\n else:\n drop_by_index = False\n\n # loop through categorical columns in dummy_vars list/dict\n for var in dummy_vars:\n\n # convert given variable to a dummy variable\n current_dummy = pd.DataFrame(self.df[var])\n current_dummy = pd.get_dummies(current_dummy, prefix = var)\n\n\n # drop one level as specified by dummy_vars, if drop_cat is true\n if drop_cat == True:\n\n # scenario where dummy_vars is a list\n if drop_by_index == True:\n dummy_to_drop = current_dummy.columns[cat_to_drop]\n current_dummy = current_dummy.drop([dummy_to_drop], axis = 1)\n\n # keep note of the dropped level in ref_levels\n self.ref_levels.append(dummy_to_drop)\n\n # scenario where dummy_vars is a dict\n if drop_by_index == False:\n dummy_to_drop = var + \"_\" + dummy_vars[var]\n current_dummy = current_dummy.drop([dummy_to_drop], axis = 1)\n\n # keep note of the dropped level in ref_levels\n self.ref_levels.append(dummy_to_drop)\n\n # concat with original dataframe\n self.df = pd.concat([self.df, current_dummy], axis=1)\n\n # drop pre-dummy categorical columns from original dataframe\n if drop_by_index == True:\n self.df = self.df.drop(dummy_vars, axis=1)\n\n if drop_by_index == False:\n self.df = self.df.drop(dummy_vars.keys(), axis=1)\n\n # if int add is true, add int\n if add_intercept == True:\n self.df['intercept'] = 1.0\n\n return self.df","def construct_feature_columns(input_features):\n return set([tf.feature_column.numeric_column(my_feature)\n for my_feature in input_features])","def set_factors(self, name, factormap, safe=False):\n e = False\n if name in self.masks():\n if self._get_subtype(name) != 'single':\n e = True\n else:\n if self._get_type(name) != 'single':\n e = True\n if e:\n if safe:\n err = \"Can only set factors to 'single' type categorical variables!\"\n raise TypeError(err)\n else:\n return None\n vals = self.codes(name)\n facts = list(factormap.keys())\n val_loc = self._get_value_loc(name)\n if not all(f in vals for f in facts):\n err = 'At least one factor is mapped to a code that does not exist '\n err += 'in the values object of \"{}\"!'\n raise ValueError(err.format(name))\n for value in val_loc:\n if value['value'] in factormap:\n value['factor'] = factormap[value['value']]\n else:\n value['factor'] = None\n return None","def preprocess_features(X):\n\t# Initialize new output DataFrame\n\toutput = pd.DataFrame(index = X.index)\n\n\t# Investigate new output DataFrame\n\tfor col, col_data in X.iteritems():\n\t\t# If data type is categorical, convert to dummy variables\n\t\tif col_data.dtype == object:\n\t\t\tcol_data = pd.get_dummies(col_data, prefix = col)\n\n\t\t\t# Collect the revised columns\n\t\t\toutput - output.join(col_data)\n\treturn output","def feature(self, N, pcomment, comment, positive, negative, posSub, negSub):\n features = {}\n # features top N\n for i in range(0, N - 1):\n if positive[i] in comment:\n features[\"positive(%s)\" % positive[i]] = True\n else:\n features[\"positive(%s)\" % positive[i]] = False\n if negative[i] in comment:\n features[\"negative(%s)\" % negative[i]] = True\n else:\n features[\"negative(%s)\" % negative[i]] = False\n # features subjetive lists\n for word in set(comment):\n if word in posSub and comment.count(word) > 0:\n features[\"subjetive_pos(%s)\" % word] = comment.count(word)\n if word in negSub and comment.count(word) > 0:\n features[\"subjetive_neg(%s)\" % word] = comment.count(word)\n\n #custom features\n if self.generateHeuristic(pcomment):\n features[\"no_gusto\"] = True\n\n return features","def _condense_state(self, s):\n def append_col(A, b):\n expanded_b = np.expand_dims(b, 1)\n return np.append(A, expanded_b, 1)\n\n A, b, c0, cuts_a, cuts_b = s\n Ab = append_col(A, b)\n cuts = append_col(cuts_a, cuts_b)\n return (Ab, c0, cuts)","def test_categorical_feature():\n\n feature = Categorical(\"abc\")\n\n for element in \"abc\":\n feature.set(element)\n feature.set(\"ignore this\")\n feature.push()\n\n for element in \"abc\":\n getattr(feature, \"set_\" + element)()\n feature.push()\n\n array = feature.array()\n assert array.shape == (6, 3)\n for i, row in enumerate(array):\n assert sum(row) == 1.0 and row[i % 3] == 1.0","def _make_xy(self, var: str, iteration: int = None):\n xvars = self.variable_schema[var]\n completed_data = self.complete_data(iteration=iteration, all_vars=True)\n to_convert = _list_union(self.categorical_variables, xvars)\n for ctc in to_convert:\n completed_data[ctc] = completed_data[ctc].cat.codes\n x = completed_data[xvars]\n y = completed_data[var]\n return x, y","def decompose_sparse_factor_ye(factor: Factor) -> DecomposedFactor:\n table = factor.table\n # construct a decomposition for the single-variable case\n if table.ndim == 1: \n n_terms = 1\n weights = np.ones(n_terms)\n matrices = [table.reshape(table.shape[0], 1)]\n # construct a decomposition for (sparse) higher order factors\n else:\n # np.where returns a tuple of length n_vars, where each element is an np.array of axis coordinates. Calling\n # np.transpose on this returns an array of shape (n_nondefault, n_vars) where each row contains the coordinates\n # of a single non-default entry.\n indices = np.transpose(np.where(table != 1.0))\n weights = []\n tensors = []\n construct_rank1_tensors(tensors, weights, factor, indices, 0)\n matrices = [np.array(m).transpose() for m in np.swapaxes(tensors, 0, 1).tolist()]\n for i in range(factor.n_vars):\n assert matrices[i].shape == (factor.cardinalities[i], len(weights))\n\n df = DecomposedFactor(factor.vars, weights, matrices)\n # Check that the decomposition didn't change the factor\n assert df.expand() == factor\n return df","def create_classifier(model, hidden_units=None):\n\n defaul_nb_units = 4096\n nb_units = hidden_units if hidden_units else defaul_nb_units\n \n input_features = model.classifier[0].in_features\n \n classifier = nn.Sequential(OrderedDict([\n ('fc1', nn.Linear(input_features, nb_units, bias=True)),\n ('relu1', nn.ReLU()),\n ('dropout1', nn.Dropout(p=0.5)),\n ('fc2', nn.Linear(nb_units, 102, bias=True)),\n ('output', nn.LogSoftmax(dim=1))\n ]))\n \n return classifier","def process_categorical_data(data_df):\n return pd.get_dummies(data_df, columns=Columns.categorical)","def list_badly_scaled_variables(\n blk,\n large: float = 1e4,\n small: float = 1e-3,\n zero: float = 1e-10,\n descend_into: bool = True,\n include_fixed: bool = False,\n):\n return [\n c\n for c in badly_scaled_var_generator(\n blk, large, small, zero, descend_into, include_fixed\n )\n ]","def get_kdd_feature_cols():\n\treturn [\n\t\t'duration',\n\t\t'src_bytes',\n\t\t'dst_bytes',\n\t\t'land',\n\t\t'wrong_fragment',\n\t\t'urgent',\n\t\t'hot',\n\t\t'num_failed_logins',\n\t\t'logged_in',\n\t\t'num_compromised',\n\t\t'root_shell',\n\t\t'su_attempted',\n\t\t'num_root',\n\t\t'num_file_creations',\n\t\t'num_shells',\n\t\t'num_access_files',\n\t\t'num_outbound_cmds',\n\t\t'is_host_login',\n\t\t'is_guest_login',\n\t\t'count',\n\t\t'srv_count',\n\t\t'serror_rate',\n\t\t'srv_serror_rate',\n\t\t'rerror_rate',\n\t\t'srv_rerror_rate',\n\t\t'same_srv_rate',\n\t\t'diff_srv_rate',\n\t\t'srv_diff_host_rate',\n\t\t'dst_host_count',\n\t\t'dst_host_srv_count',\n\t\t'dst_host_same_srv_rate',\n\t\t'dst_host_diff_srv_rate',\n\t\t'dst_host_same_src_port_rate',\n\t\t'dst_host_srv_diff_host_rate',\n\t\t'dst_host_serror_rate',\n\t\t'dst_host_srv_serror_rate',\n\t\t'dst_host_rerror_rate',\n\t\t'dst_host_srv_rerror_rate']"],"string":"[\n \"def discVar2Feature( var, varname, lims = [1,3], collapse = [False, False], ctxt = 'Has'):\\n \\n vals = xrange(lims[0], lims[1]+1) \\n \\n keystr = ctxt + ' %s ' + varname\\n fdict = {keystr % val:False for val in vals} \\n\\n if collapse[0] == True:\\n if lims[0] > var:\\n var = lims[0]\\n #var = max([var, lims[0]])\\n if collapse[1] == True:\\n if lims[1] < var:\\n var = lims[1]\\n #var = min([var, lims[1]])\\n \\n if var >= lims[0] and var <= lims[1]: #if collapse = False, ignore vals outside lims\\n fdict[(keystr) % (var)] = True \\n \\n return fdict\",\n \"def convertToDiscreteFunction(boolean: bool) -> cern.japc.value.DiscreteFunction:\\n ...\",\n \"def FE_discretize_numeric_variables(train, bin_dict, test='', strategy='kmeans',verbose=0):\\r\\n df = copy.deepcopy(train)\\r\\n test = copy.deepcopy(test)\\r\\n num_cols = len(bin_dict)\\r\\n nrows = int((num_cols/2)+0.5)\\r\\n #print('nrows',nrows)\\r\\n if verbose:\\r\\n fig = plt.figure(figsize=(10,3*num_cols))\\r\\n for i, (col, binvalue) in enumerate(bin_dict.items()):\\r\\n new_col = col+'_discrete'\\r\\n if strategy == 'gaussian':\\r\\n kbd = GaussianMixture(n_components=binvalue, random_state=99)\\r\\n df[new_col] = kbd.fit_predict(df[[col]]).astype(int)\\r\\n if not isinstance(test, str):\\r\\n test[new_col] = kbd.predict(test[[col]]).astype(int)\\r\\n else:\\r\\n kbd = KBinsDiscretizer(n_bins=binvalue, encode='ordinal', strategy=strategy)\\r\\n df[new_col] = kbd.fit_transform(df[[col]]).astype(int)\\r\\n if not isinstance(test, str):\\r\\n test[new_col] = kbd.transform(test[[col]]).astype(int)\\r\\n if verbose:\\r\\n ax1 = plt.subplot(nrows,2,i+1)\\r\\n ax1.scatter(df[col],df[new_col])\\r\\n ax1.set_title(new_col)\\r\\n if not isinstance(test, str):\\r\\n return df, test\\r\\n else:\\r\\n return df\",\n \"def denseFeature(self, feat):\\n return {'feat': feat}\",\n \"def convertToDiscreteFunctionList(boolean: bool) -> cern.japc.value.DiscreteFunctionList:\\n ...\",\n \"def preprocess_var(bd, var):\\n filepath_sv = f\\\"team67-ptp/data/{var}.csv\\\"\\n filepath = bd\\n data = feather.read_dataframe(filepath)\\n df = data.copy()\\n df2 = df[var]\\n df2 = df2.to_frame()\\n if df2[var].dtype is \\\"category\\\":\\n df2[var] = df2[var].astype(\\\"category\\\").cat.codes\\n filename = filepath_sv\\n df2.to_csv(filename, index=False)\\n print(\\\"Succesfully exported to csv\\\")\\n else:\\n filename = filepath_sv\\n df2.to_csv(filename, index=False)\\n print(\\\"Succesfully exported to csv\\\")\",\n \"def addFeature(self, strName, lstDomain):\\n # create a new variable CSPVariable object\\n newFeature = CSPFeature(strName, lstDomain)\\n # put the new variable in the graph's list of variables\\n self.features.append(newFeature)\",\n \"def select_var_feature(adata, min_score=0.5, nb_features=None, show=True, copy=False):\\n if copy:\\n inplace=False\\n else:\\n inplace=True\\n\\n adata = adata.copy() if not inplace else adata\\n \\n # calculate variability score\\n cal_var(adata, show=show) # adds variability score for each feature \\n # adata.var['variablility_score'] = abs(adata.var['prop_shared_cells']-0.5)\\n var_annot = adata.var.sort_values(ascending=True, by ='variability_score')\\n\\n # calculate the min score to get a specific number of feature \\n if nb_features != None and nb_features < len(adata.var_names): \\n min_score = var_annot['variability_score'][nb_features] \\n \\n \\n adata_tmp = adata[:,adata.var['variability_score']<=min_score].copy()\\n \\n ## return the filtered AnnData objet.\\n if not inplace:\\n adata_tmp = adata[:,adata.var['variability_score']<=min_score]\\n return(adata_tmp)\\n else:\\n adata._inplace_subset_var(adata.var['variability_score']<=min_score)\",\n \"def dense2cvxopt(value):\\n import cvxopt\\n return cvxopt.matrix(value, tc='d')\",\n \"def feature_discretion(self, X, y):\\n temp, X_interval = [], []\\n if self._DISCRETION == \\\"percentile_discrete\\\":\\n for i in range(0, X.shape[-1]):\\n x = X[:, i]\\n x_type = type_of_target(x)\\n # logging.info(\\\"before: \\\"+\\\" \\\".join([str(i), str(set(X[:, i])), str(x_type)]))\\n if 0:\\n if x_type == 'continuous':\\n x1, interval = self.percentile_discrete(x, self._WOE_N)\\n X_interval.append(interval)\\n temp.append(x1)\\n # logging.info(\\\"continue_after: \\\" + \\\" \\\".join([str(i), str(set(x1)), str(x1)]))\\n else:\\n temp.append(x)\\n # logging.info(\\\"after: \\\" + \\\" \\\".join([str(i), str(set(x)), str(x)]))\\n else:\\n x1, interval = self.percentile_discrete(x, self._WOE_N)\\n X_interval.append(interval)\\n temp.append(x1)\\n # logging.info(\\\"continue_after: \\\" + \\\" \\\".join([str(i), str(set(x1)), str(x1)]))\\n elif self._DISCRETION == \\\"interval_discrete\\\":\\n for i in range(0, X.shape[-1]):\\n x = X[:, i]\\n # logging.info(\\\"before: \\\"+\\\" \\\".join([str(i), str(set(X[:, i]))]))\\n x1, interval = self.interval_discrete(x, self._WOE_N)\\n X_interval.append(interval)\\n temp.append(x1)\\n # logging.info(\\\"interval_after: \\\" + \\\" \\\".join([str(i), str(set(x1)), str(x1)]))\\n elif self._DISCRETION == \\\"rf_discrete\\\":\\n for i in range(0, X.shape[-1]):\\n x = X[:, i]\\n # logging.info(\\\"before: \\\"+\\\" \\\".join([str(i), str(set(X[:, i]))]))\\n x1, interval = self.rf_discrete(x, y)\\n X_interval.append(interval)\\n temp.append(x1)\\n # logging.info(\\\"rf_after: \\\" + \\\" \\\".join([str(i), str(set(x1)), str(x1)]))\\n return np.array(temp).T, X_interval\",\n \"def dict_to_feature(d):\\n f = ee.Feature(None,ee.Dictionary(d))\\n return f\",\n \"def my_featurize(apartment):\\n col =np.array([1, 2, 0, 0, 0, 0, 0, 0 ])\\n a= pd.DataFrame(apartment[col])\\n if(apartment.get('condition')== 'good'):\\n col[1] =1\\n else:\\n if(apartment.get('condition')== 'zero condition'):\\n col[1] = 0\\n col[2] =apartment.get('num_rooms')\\n col[3] =apartment.get('area')\\n col[4] =apartment.get('num_bathrooms')\\n col[5] =apartment.get('floor')\\n col[6] =apartment.get('ceiling_height')\\n col[7] =apartment.get('max_floor')\\n\\n return col, apartment['price']\",\n \"def get_feature_set_SC2(tweet, sentimentvalues):\\n pos_tag_freq = {}\\n additional_freq = {}\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n additional_freq['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in pos_tag_freq.keys():\\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \\n #Number of exclamation marks, number of emoticons,\\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\\n if emoticons>0:\\n additional_freq['emoticons'] = emoticons*1.0\\n if tweet.nrof_exclamations>0:\\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\\n \\n #Add lexicon values\\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\\n sub_score = sentimentvalues[0]+sentimentvalues[1]\\n obj_score = sentimentvalues[2]\\n if sub_score>0:\\n additional_freq[\\\"sub_score\\\"] = sub_score+1.0\\n if obj_score>0:\\n additional_freq[\\\"obj_score\\\"] = obj_score+1.0\\n \\n #Concatenate the dicts\\n features= dict(pos_tag_freq.items() + additional_freq.items())\\n \\n return features\",\n \"def _make_features(self, x):\\n\\t\\tx = x.unsqueeze(1)\\n\\t\\treturn torch.cat([x ** i for i in range(1, self._degree+1)], 1)\",\n \"def preprocess_feature(df):\",\n \"def classify_columns(df_preds, verbose=0):\\r\\n train = copy.deepcopy(df_preds)\\r\\n #### If there are 30 chars are more in a discrete_string_var, it is then considered an NLP variable\\r\\n max_nlp_char_size = 30\\r\\n max_cols_to_print = 30\\r\\n print('############## C L A S S I F Y I N G V A R I A B L E S ####################')\\r\\n print('Classifying variables in data set...')\\r\\n #### Cat_Limit defines the max number of categories a column can have to be called a categorical colum\\r\\n cat_limit = 35\\r\\n float_limit = 15 #### Make this limit low so that float variables below this limit become cat vars ###\\r\\n def add(a,b):\\r\\n return a+b\\r\\n sum_all_cols = dict()\\r\\n orig_cols_total = train.shape[1]\\r\\n #Types of columns\\r\\n cols_delete = [col for col in list(train) if (len(train[col].value_counts()) == 1\\r\\n ) | (train[col].isnull().sum()/len(train) >= 0.90)]\\r\\n train = train[left_subtract(list(train),cols_delete)]\\r\\n var_df = pd.Series(dict(train.dtypes)).reset_index(drop=False).rename(\\r\\n columns={0:'type_of_column'})\\r\\n sum_all_cols['cols_delete'] = cols_delete\\r\\n var_df['bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['bool','object']\\r\\n and len(train[x['index']].value_counts()) == 2 else 0, axis=1)\\r\\n string_bool_vars = list(var_df[(var_df['bool'] ==1)]['index'])\\r\\n sum_all_cols['string_bool_vars'] = string_bool_vars\\r\\n var_df['num_bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\\r\\n np.uint16, np.uint32, np.uint64,\\r\\n 'int8','int16','int32','int64',\\r\\n 'float16','float32','float64'] and len(\\r\\n train[x['index']].value_counts()) == 2 else 0, axis=1)\\r\\n num_bool_vars = list(var_df[(var_df['num_bool'] ==1)]['index'])\\r\\n sum_all_cols['num_bool_vars'] = num_bool_vars\\r\\n ###### This is where we take all Object vars and split them into diff kinds ###\\r\\n discrete_or_nlp = var_df.apply(lambda x: 1 if x['type_of_column'] in ['object'] and x[\\r\\n 'index'] not in string_bool_vars+cols_delete else 0,axis=1)\\r\\n ######### This is where we figure out whether a string var is nlp or discrete_string var ###\\r\\n var_df['nlp_strings'] = 0\\r\\n var_df['discrete_strings'] = 0\\r\\n var_df['cat'] = 0\\r\\n var_df['id_col'] = 0\\r\\n discrete_or_nlp_vars = var_df.loc[discrete_or_nlp==1]['index'].values.tolist()\\r\\n if len(var_df.loc[discrete_or_nlp==1]) != 0:\\r\\n for col in discrete_or_nlp_vars:\\r\\n #### first fill empty or missing vals since it will blowup ###\\r\\n train[col] = train[col].fillna(' ')\\r\\n if train[col].map(lambda x: len(x) if type(x)==str else 0).mean(\\r\\n ) >= max_nlp_char_size and len(train[col].value_counts()\\r\\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'nlp_strings'] = 1\\r\\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\\r\\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'discrete_strings'] = 1\\r\\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\\r\\n ) == len(train) and col not in string_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'id_col'] = 1\\r\\n else:\\r\\n var_df.loc[var_df['index']==col,'cat'] = 1\\r\\n nlp_vars = list(var_df[(var_df['nlp_strings'] ==1)]['index'])\\r\\n sum_all_cols['nlp_vars'] = nlp_vars\\r\\n discrete_string_vars = list(var_df[(var_df['discrete_strings'] ==1) ]['index'])\\r\\n sum_all_cols['discrete_string_vars'] = discrete_string_vars\\r\\n ###### This happens only if a string column happens to be an ID column #######\\r\\n #### DO NOT Add this to ID_VARS yet. It will be done later.. Dont change it easily...\\r\\n #### Category DTYPE vars are very special = they can be left as is and not disturbed in Python. ###\\r\\n var_df['dcat'] = var_df.apply(lambda x: 1 if str(x['type_of_column'])=='category' else 0,\\r\\n axis=1)\\r\\n factor_vars = list(var_df[(var_df['dcat'] ==1)]['index'])\\r\\n sum_all_cols['factor_vars'] = factor_vars\\r\\n ########################################################################\\r\\n date_or_id = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\\r\\n np.uint16, np.uint32, np.uint64,\\r\\n 'int8','int16',\\r\\n 'int32','int64'] and x[\\r\\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\\r\\n axis=1)\\r\\n ######### This is where we figure out whether a numeric col is date or id variable ###\\r\\n var_df['int'] = 0\\r\\n var_df['date_time'] = 0\\r\\n ### if a particular column is date-time type, now set it as a date time variable ##\\r\\n var_df['date_time'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['<M8[ns]','datetime64[ns]'] and x[\\r\\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\\r\\n axis=1)\\r\\n ### this is where we save them as date time variables ###\\r\\n if len(var_df.loc[date_or_id==1]) != 0:\\r\\n for col in var_df.loc[date_or_id==1]['index'].values.tolist():\\r\\n if len(train[col].value_counts()) == len(train):\\r\\n if train[col].min() < 1900 or train[col].max() > 2050:\\r\\n var_df.loc[var_df['index']==col,'id_col'] = 1\\r\\n else:\\r\\n try:\\r\\n pd.to_datetime(train[col],infer_datetime_format=True)\\r\\n var_df.loc[var_df['index']==col,'date_time'] = 1\\r\\n except:\\r\\n var_df.loc[var_df['index']==col,'id_col'] = 1\\r\\n else:\\r\\n if train[col].min() < 1900 or train[col].max() > 2050:\\r\\n if col not in num_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'int'] = 1\\r\\n else:\\r\\n try:\\r\\n pd.to_datetime(train[col],infer_datetime_format=True)\\r\\n var_df.loc[var_df['index']==col,'date_time'] = 1\\r\\n except:\\r\\n if col not in num_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'int'] = 1\\r\\n else:\\r\\n pass\\r\\n int_vars = list(var_df[(var_df['int'] ==1)]['index'])\\r\\n date_vars = list(var_df[(var_df['date_time'] == 1)]['index'])\\r\\n id_vars = list(var_df[(var_df['id_col'] == 1)]['index'])\\r\\n sum_all_cols['int_vars'] = int_vars\\r\\n copy_date_vars = copy.deepcopy(date_vars)\\r\\n for date_var in copy_date_vars:\\r\\n #### This test is to make sure sure date vars are actually date vars\\r\\n try:\\r\\n pd.to_datetime(train[date_var],infer_datetime_format=True)\\r\\n except:\\r\\n ##### if not a date var, then just add it to delete it from processing\\r\\n cols_delete.append(date_var)\\r\\n date_vars.remove(date_var)\\r\\n sum_all_cols['date_vars'] = date_vars\\r\\n sum_all_cols['id_vars'] = id_vars\\r\\n sum_all_cols['cols_delete'] = cols_delete\\r\\n ## This is an EXTREMELY complicated logic for cat vars. Don't change it unless you test it many times!\\r\\n var_df['numeric'] = 0\\r\\n float_or_cat = var_df.apply(lambda x: 1 if x['type_of_column'] in ['float16',\\r\\n 'float32','float64'] else 0,\\r\\n axis=1)\\r\\n if len(var_df.loc[float_or_cat == 1]) > 0:\\r\\n for col in var_df.loc[float_or_cat == 1]['index'].values.tolist():\\r\\n if len(train[col].value_counts()) > 2 and len(train[col].value_counts()\\r\\n ) <= float_limit and len(train[col].value_counts()) <= len(train):\\r\\n var_df.loc[var_df['index']==col,'cat'] = 1\\r\\n else:\\r\\n if col not in num_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'numeric'] = 1\\r\\n cat_vars = list(var_df[(var_df['cat'] ==1)]['index'])\\r\\n continuous_vars = list(var_df[(var_df['numeric'] ==1)]['index'])\\r\\n ######## V E R Y I M P O R T A N T ###################################################\\r\\n ##### There are a couple of extra tests you need to do to remove abberations in cat_vars ###\\r\\n cat_vars_copy = copy.deepcopy(cat_vars)\\r\\n for cat in cat_vars_copy:\\r\\n if df_preds[cat].dtype==float:\\r\\n continuous_vars.append(cat)\\r\\n cat_vars.remove(cat)\\r\\n var_df.loc[var_df['index']==cat,'cat'] = 0\\r\\n var_df.loc[var_df['index']==cat,'numeric'] = 1\\r\\n elif len(df_preds[cat].value_counts()) == df_preds.shape[0]:\\r\\n id_vars.append(cat)\\r\\n cat_vars.remove(cat)\\r\\n var_df.loc[var_df['index']==cat,'cat'] = 0\\r\\n var_df.loc[var_df['index']==cat,'id_col'] = 1\\r\\n sum_all_cols['cat_vars'] = cat_vars\\r\\n sum_all_cols['continuous_vars'] = continuous_vars\\r\\n sum_all_cols['id_vars'] = id_vars\\r\\n ###### This is where you consoldate the numbers ###########\\r\\n var_dict_sum = dict(zip(var_df.values[:,0], var_df.values[:,2:].sum(1)))\\r\\n for col, sumval in var_dict_sum.items():\\r\\n if sumval == 0:\\r\\n print('%s of type=%s is not classified' %(col,train[col].dtype))\\r\\n elif sumval > 1:\\r\\n print('%s of type=%s is classified into more then one type' %(col,train[col].dtype))\\r\\n else:\\r\\n pass\\r\\n ############### This is where you print all the types of variables ##############\\r\\n ####### Returns 8 vars in the following order: continuous_vars,int_vars,cat_vars,\\r\\n ### string_bool_vars,discrete_string_vars,nlp_vars,date_or_id_vars,cols_delete\\r\\n if verbose == 1:\\r\\n print(\\\" Number of Numeric Columns = \\\", len(continuous_vars))\\r\\n print(\\\" Number of Integer-Categorical Columns = \\\", len(int_vars))\\r\\n print(\\\" Number of String-Categorical Columns = \\\", len(cat_vars))\\r\\n print(\\\" Number of Factor-Categorical Columns = \\\", len(factor_vars))\\r\\n print(\\\" Number of String-Boolean Columns = \\\", len(string_bool_vars))\\r\\n print(\\\" Number of Numeric-Boolean Columns = \\\", len(num_bool_vars))\\r\\n print(\\\" Number of Discrete String Columns = \\\", len(discrete_string_vars))\\r\\n print(\\\" Number of NLP String Columns = \\\", len(nlp_vars))\\r\\n print(\\\" Number of Date Time Columns = \\\", len(date_vars))\\r\\n print(\\\" Number of ID Columns = \\\", len(id_vars))\\r\\n print(\\\" Number of Columns to Delete = \\\", len(cols_delete))\\r\\n if verbose == 2:\\r\\n marthas_columns(df_preds,verbose=1)\\r\\n print(\\\" Numeric Columns: %s\\\" %continuous_vars[:max_cols_to_print])\\r\\n print(\\\" Integer-Categorical Columns: %s\\\" %int_vars[:max_cols_to_print])\\r\\n print(\\\" String-Categorical Columns: %s\\\" %cat_vars[:max_cols_to_print])\\r\\n print(\\\" Factor-Categorical Columns: %s\\\" %factor_vars[:max_cols_to_print])\\r\\n print(\\\" String-Boolean Columns: %s\\\" %string_bool_vars[:max_cols_to_print])\\r\\n print(\\\" Numeric-Boolean Columns: %s\\\" %num_bool_vars[:max_cols_to_print])\\r\\n print(\\\" Discrete String Columns: %s\\\" %discrete_string_vars[:max_cols_to_print])\\r\\n print(\\\" NLP text Columns: %s\\\" %nlp_vars[:max_cols_to_print])\\r\\n print(\\\" Date Time Columns: %s\\\" %date_vars[:max_cols_to_print])\\r\\n print(\\\" ID Columns: %s\\\" %id_vars[:max_cols_to_print])\\r\\n print(\\\" Columns that will not be considered in modeling: %s\\\" %cols_delete[:max_cols_to_print])\\r\\n ##### now collect all the column types and column names into a single dictionary to return!\\r\\n len_sum_all_cols = reduce(add,[len(v) for v in sum_all_cols.values()])\\r\\n if len_sum_all_cols == orig_cols_total:\\r\\n print(' %d Predictors classified...' %orig_cols_total)\\r\\n #print(' This does not include the Target column(s)')\\r\\n else:\\r\\n print('No of columns classified %d does not match %d total cols. Continuing...' %(\\r\\n len_sum_all_cols, orig_cols_total))\\r\\n ls = sum_all_cols.values()\\r\\n flat_list = [item for sublist in ls for item in sublist]\\r\\n if len(left_subtract(list(train),flat_list)) == 0:\\r\\n print(' Missing columns = None')\\r\\n else:\\r\\n print(' Missing columns = %s' %left_subtract(list(train),flat_list))\\r\\n return sum_all_cols\",\n \"def topdia(x):\\r\\n return Feature(x, \\\"TopDia\\\")\",\n \"def my_featurize(apartment):\\n return x, y\",\n \"def cat2onehot_var(snt_ids, vocab_size, batch_size):\\n\\n targets = np.array([snt_ids]).reshape(-1)\\n one_hot_targets = np.eye(vocab_size)[targets]\\n result = Variable(torch.FloatTensor(one_hot_targets).view(-1, batch_size, vocab_size)) #\\n\\n return result\",\n \"def restrict(self, variable, value):\\n # 新因子变量列表为原因子变量列表减去被求和的变量\\n restricted_variable = self.var_list.index(variable)\\n new_var_list = self.var_list[:restricted_variable] + self.var_list[restricted_variable + 1:]\\n\\n # 对于新因子CPT中的表项，其值为原因子中与新因子变量取值相同且限制变量取对应值的的单个表项的值\\n new_cpt = {}\\n if restricted_variable == 0:\\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\\n if len(self.var_list) == 1:\\n postfix = ''\\n new_cpt[postfix] = self.cpt[str(value) + postfix]\\n elif restricted_variable == len(self.var_list) - 1:\\n for i in range(pow(2, restricted_variable)):\\n prefix = Util.to_binary(i, restricted_variable)\\n new_cpt[prefix] = self.cpt[prefix + str(value)]\\n else:\\n for i in range(pow(2, restricted_variable)):\\n prefix = Util.to_binary(i, restricted_variable)\\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\\n new_cpt[prefix + postfix] = self.cpt[prefix + str(value) + postfix]\\n new_node = Node('f' + str(new_var_list), new_var_list)\\n new_node.set_cpt(new_cpt)\\n return new_node\",\n \"def make_features(x):\\n x = x.unsqueeze(1)\\n # torch.cat 实现tensor拼接\\n return torch.cat([x ** i for i in range(1, POLY_DEGREE + 1)], 1)\",\n \"def dict_to_feature(feature_dict, keys, max_value=None):\\n feature = []\\n for key, val in feature_dict.items(): # First level\\n if key not in keys:\\n continue\\n if val is None or val == \\\"auto\\\" or key == \\\"autotuning\\\" or val == \\\"\\\":\\n continue\\n if isinstance(val, dict):\\n feature.append(dict_to_feature(val, max_value))\\n else:\\n feature.append(float(val))\\n\\n # normalization, should not matter in tree models\\n if max_value is not None:\\n norm_feature = []\\n for f, mv in zip(feature, max_value):\\n norm_feature.append(f / mv)\\n feature = norm_feature\\n\\n return feature\",\n \"def categorical(df):\\n\\n # variables which need to be transformed to categorical\\n categorical = [\\\"prop_country_id\\\", \\\"visitor_location_country_id\\\"]\\n\\n for var in categorical:\\n df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1)\\n del df[var]\\n\\n return df\",\n \"def collapse_var(nc, out, name, direction):\\n var1 = nc.variables[name]\\n N = (len(nc.dimensions[direction]) - 1) / 2\\n\\n print(\\\"Processing %s...\\\" % name)\\n dims = var1.dimensions\\n if len(dims) > 1: # only collapse spatial fields\\n dims = [x for x in dims if x != direction]\\n\\n try:\\n fill_value = var1._FillValue\\n var2 = out.createVariable(name, var1.dtype,\\n dimensions=dims, fill_value=fill_value)\\n except:\\n var2 = out.createVariable(name, var1.dtype,\\n dimensions=dims)\\n\\n copy_attributes(var1, var2)\\n\\n if direction == 'x':\\n var2[:] = var1[get_slice(var1.dimensions, x=N)]\\n elif direction == 'y':\\n var2[:] = var1[get_slice(var1.dimensions, y=N)]\",\n \"def get_categorical_features(self, x: pd.DataFrame) -> pd.DataFrame:\\n return x[self.categorical_features]\",\n \"def preprocess_sf(bd, var):\\n filepath_svf = f\\\"team67-ptp/data/{var}.ftr\\\"\\n filepath = bd\\n data = feather.read_dataframe(filepath)\\n df = data.copy()\\n df2 = df[var]\\n df2 = df2.to_frame()\\n if df2[var].dtype is \\\"category\\\":\\n df2[var] = df2[var].astype(\\\"category\\\").cat.codes\\n filename = filepath_svf\\n df2.to_feather(filename)\\n print(\\\"Succesfully exported to feather\\\")\\n else:\\n filename = filepath_svf\\n df2.to_feather(filename)\\n print(\\\"Succesfully exported to feather\\\")\",\n \"def cvxopt2dense(value):\\n return np.array(value)\",\n \"def apply_randomization(features, label, randomize_prob):\\n rnd_tok = lambda: tf.as_string(tf.random.uniform([], 0, 99999999, tf.int32))\\n\\n for idx in CAT_FEATURE_INDICES:\\n key = feature_name(idx)\\n # Ignore lint since tf.cond should evaluate lambda immediately.\\n features[key] = tf.cond(tf.random.uniform([]) < randomize_prob,\\n rnd_tok,\\n lambda: features[key]) # pylint: disable=cell-var-from-loop\\n return features, label\",\n \"def get_optimal_discrete_feature_split(\\n self, X: np.ndarray, y: np.ndarray, feature_col: int\\n ):\\n return BaseTree.get_discrete_split_value(\\n X[:, feature_col], y, eval_func=self.eval_func\\n )\",\n \"def convert_study_to_feature(study: List[Types.SeriesObj]) -> List[Dict[str, tf.train.Feature]]:\\n return [convert_series_to_feature(s) for s in study]\",\n \"def xx(x):\\r\\n return Feature(x, \\\"XX\\\")\",\n \"def get_feature_set_SC(tweet, sentimentvalues):\\n pos_tag_freq = {}\\n additional_freq = {}\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# if tag=='PRtinf':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJS':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJ':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='NP':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='DET':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='P':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n additional_freq['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in pos_tag_freq.keys():\\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \\n #Number of exclamation marks, number of emoticons,\\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\\n if emoticons>0:\\n additional_freq['emoticons'] = emoticons*1.0\\n if tweet.nrof_exclamations>0:\\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\\n \\n #Add lexicon values\\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\\n sub_score = 0.0\\n obj_score = 0.0\\n nrof_subwords = 0\\n nrof_objwords = 0\\n for word in sentimentvalues.keys():\\n if sentimentvalues[word][0]>0:\\n sub_score = sub_score + sentimentvalues[word][0]\\n nrof_subwords = nrof_subwords + 1\\n if sentimentvalues[word][1]>0:\\n sub_score = sub_score + sentimentvalues[word][1]\\n nrof_subwords = nrof_subwords + 1\\n if sentimentvalues[word][2]>0:\\n obj_score = obj_score + sentimentvalues[word][2]\\n nrof_objwords = nrof_objwords + 1\\n if sub_score>0:\\n additional_freq[\\\"sub_score\\\"] = sub_score+1.0\\n if obj_score>0:\\n additional_freq[\\\"obj_score\\\"] = obj_score+1.0\\n if nrof_subwords>0:\\n additional_freq[\\\"subjective_words\\\"] = nrof_subwords*1.0\\n if nrof_objwords>0:\\n additional_freq[\\\"objective_words\\\"] = nrof_objwords*1.0\\n \\n #Concatenate the dicts\\n features= dict(pos_tag_freq.items() + additional_freq.items())\\n \\n return features\",\n \"def dummify_features(df):\\n colnames = df.columns\\n le_dict = {}\\n for col in colnames:\\n le_dict[col] = preprocessing.LabelEncoder()\\n le_dict[col].fit(df[col])\\n df.loc[:, col] = le_dict[col].transform(df[col])\\n\\n enc = preprocessing.OneHotEncoder()\\n enc.fit(df)\\n X = enc.transform(df)\\n\\n dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\\n # for cv in colnames:\\n # for modality in le_dict[cv].classes_:\\n # dummy_colnames.append(cv + '_' + modality)\\n\\n return X, dummy_colnames, enc\",\n \"def word2features(self,sent, i):\\n word = sent[i][0]\\n #postag = sent[i][1]\\n\\n features = {\\n 'bias': 1.0,\\n 'word.lower()': word.lower(),\\n 'word.isupper()': word.isupper(),\\n 'word.istitle()': word.istitle(),\\n 'word.isdigit()': word.isdigit(),\\n 'word.shape()':self.shape(word),\\n 'word.isalnum()':word.isalnum(),\\n 'word.isalpha()':word.isalpha(),\\n # 'postag': postag,\\n # 'postag[:2]': postag[:2],\\n }\\n if i > 0:\\n word1 = sent[i - 1][0]\\n #postag1 = sent[i - 1][1]\\n features.update({\\n '-1:word.lower()': word1.lower(),\\n '-1:word.istitle()': word1.istitle(),\\n '-1:word.isupper()': word1.isupper(),\\n '-1:word.isdigit()': word1.isdigit(),\\n '-1:word.isalnum()':word1.isalnum(),\\n '-1:word.isalpha()':word1.isalpha(),\\n # '-1:postag': postag1,\\n # '-1:postag[:2]': postag1[:2],\\n })\\n else:\\n features['BOS'] = True\\n\\n if i > 1:\\n word2 = sent[i - 2][0]\\n #postag2 = sent[i - 2][1]\\n features.update({\\n '-2:word.lower()': word2.lower(),\\n '-2:word.istitle()': word2.istitle(),\\n '-2:word.isupper()': word2.isupper(),\\n '-2:word.isdigit()': word2.isdigit(),\\n '-2:word.isalnum()': word2.isalnum(),\\n '-2:word.isalpha()': word2.isalpha(),\\n # '-2:postag': postag2,\\n # '-2:postag[:2]': postag2[:2],\\n })\\n else:\\n features['BOS1'] = True\\n if i > 2:\\n word3 = sent[i - 3][0]\\n #postag3 = sent[i - 3][1]\\n features.update({\\n '-3:word.lower()': word3.lower(),\\n '-3:word.istitle()': word3.istitle(),\\n '-3:word.isupper()': word3.isupper(),\\n '-3:word.isdigit()': word3.isdigit(),\\n '-3:word.isalnum()': word3.isalnum(),\\n '-3:word.isalpha()': word3.isalpha(),\\n # '-3:postag': postag3,\\n # '-3:postag[:2]': postag3[:2],\\n })\\n else:\\n features['BOS2'] = True\\n\\n if i > 3:\\n word4 = sent[i - 4][0]\\n #postag4 = sent[i - 4][1]\\n features.update({\\n '-4:word.lower()': word4.lower(),\\n '-4:word.istitle()': word4.istitle(),\\n '-4:word.isupper()': word4.isupper(),\\n '-4:word.isdigit()': word4.isdigit(),\\n '-4:word.isalnum()': word4.isalnum(),\\n '-4:word.isalpha()': word4.isalpha(),\\n # '-4:postag': postag4,\\n # '-4:postag[:2]': postag4[:2],\\n })\\n else:\\n features['BOS2'] = True\\n\\n if i < len(sent) - 1:\\n word1 = sent[i + 1][0]\\n features.update({\\n '+1:word.lower()': word1.lower(),\\n '+1:word.istitle()': word1.istitle(),\\n '+1:word.isupper()': word1.isupper(),\\n '+1:word.isdigit()': word1.isdigit(),\\n '+1:word.isalnum()': word1.isalnum(),\\n '+1:word.isalpha()': word1.isalpha(),\\n # '+1:postag': postag1,\\n # '+1:postag[:2]': postag1[:2],\\n })\\n else:\\n features['EOS'] = True\\n if i < len(sent) - 2:\\n word12 = sent[i + 2][0]\\n #postag12 = sent[i + 2][1]\\n features.update({\\n '+2:word.lower()': word12.lower(),\\n '+2:word.istitle()': word12.istitle(),\\n '+2:word.isupper()': word12.isupper(),\\n '+2:word.isdigit()': word12.isdigit(),\\n '+2:word.isalnum()': word12.isalnum(),\\n '+2:word.isalpha()': word12.isalpha(),\\n # '+2:postag': postag12,\\n # '+2:postag[:2]': postag12[:2],\\n })\\n else:\\n features['EOS2'] = True\\n if i < len(sent) - 3:\\n word13 = sent[i + 3][0]\\n #postag13 = sent[i + 3][1]\\n features.update({\\n '+3:word.lower()': word13.lower(),\\n '+3:word.istitle()': word13.istitle(),\\n '+3:word.isupper()': word13.isupper(),\\n '+3:word.isdigit()': word13.isdigit(),\\n '+3:word.isalnum()': word13.isalnum(),\\n '+3:word.isalpha()': word13.isalpha(),\\n # '+3:postag': postag13,\\n # '+3:postag[:2]': postag13[:2],\\n })\\n else:\\n features['EOS2'] = True\\n if i < len(sent) - 4:\\n word14 = sent[i + 4][0]\\n #postag14 = sent[i + 4][1]\\n features.update({\\n '+4:word.lower()': word14.lower(),\\n '+4:word.istitle()': word14.istitle(),\\n '+4:word.isupper()': word14.isupper(),\\n '+4:word.isdigit()': word14.isdigit(),\\n '+4:word.isalnum()': word14.isalnum(),\\n '+4:word.isalpha()': word14.isalpha(),\\n # '+4:postag': postag14,\\n # '+4:postag[:2]': postag14[:2],\\n })\\n else:\\n features['EOS2'] = True\\n return features\",\n \"def preprocess(old_df, label_name, category_features, non_category_features):\\n old_df['fraud'] = old_df[label_name].apply(lambda x: x[0] == 'f')\\n\\n # Creating a new dataframe with a subset of features.\\n new_df = old_df[['fraud'] + non_category_features]\\n\\n # For categorical features, we make dummy variables,\\n # and merge them into new_df.\\n for feature in category_features:\\n dummy_df = pd.get_dummies(old_df[feature], prefix=feature,\\n dummy_na=True)\\n # Since dummy_na=True, the last column will be for null values.\\n dummy_df.drop(dummy_df.columns[-1], axis=1, inplace=True)\\n new_df = pd.concat([new_df, dummy_df], axis=1)\\n return new_df\",\n \"def extract_concepts_discrete():\\n disc = pd.DataFrame([['name', 'Name', 'string'],\\n ['year', 'Year', 'time'],\\n ['area', 'Area', 'entity_domain'],\\n ['area_id', 'Area Id', 'string'],\\n ['variable_id', 'Variable Id', 'string']\\n ], columns=['concept', 'name', 'concept_type'])\\n return disc\",\n \"def data_continuous(df, cat_features = [], cont_features = []) :\\n subset_cont =[]\\n for col in list(df.columns):\\n if df[col].dtype == 'int' or df[col].dtype == 'float64':\\n if col not in cont_features and col not in cat_features:\\n print(col, \\\"was added to continuous features\\\")\\n cont_features.append(col)\\n subset_cont.append(col)\\n for col in cont_features:\\n if col not in subset_cont:\\n subset_cont.append(col)\\n print('Continuous features : ', ' '.join(subset_cont))\\n printmd(\\\"**Description of continuous columns:**\\\")\\n print(round(df[subset_cont].describe()))\\n return (cont_features)\",\n \"def from_dummies(self, data, prefix_sep='_'):\\n out = data.copy()\\n for feat in self.categorical_feature_names:\\n # first, derive column names in the one-hot-encoded data from the original data\\n cat_col_values = []\\n for val in list(self.data_df[feat].unique()):\\n cat_col_values.append(feat + prefix_sep + str(val)) # join original feature name and its unique values , ex: education_school\\n match_cols = [c for c in data.columns if c in cat_col_values] # check for the above matching columns in the encoded data\\n\\n # then, recreate original data by removing the suffixes - based on the GitHub issue comment: https://github.com/pandas-dev/pandas/issues/8745#issuecomment-417861271\\n cols, labs = [[c.replace(\\n x, \\\"\\\") for c in match_cols] for x in [\\\"\\\", feat + prefix_sep]]\\n out[feat] = pd.Categorical(\\n np.array(labs)[np.argmax(data[cols].values, axis=1)])\\n out.drop(cols, axis=1, inplace=True)\\n return out\",\n \"def index_to_feature(p, dims):\\n feature = []\\n for dim in dims:\\n feature.append(p % dim)\\n p //= dim\\n return feature\",\n \"def get_feature_set_SB(tweet):\\n #pos-tag frequencies\\n# print \\\"Tagged words in tweet: \\\", tweet.tagged_words\\n pos_tag_freq = {}\\n additional_freq = {}\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# if tag=='PRtinf':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJS':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJ':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='NP':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='DET':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='P':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n additional_freq['pronoun'] = 1\\n except KeyError:\\n continue\\n# print \\\"Tag frequencies: \\\", pos_tag_freq\\n for key in pos_tag_freq.keys():\\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \\n #Number of exclamation marks, number of emoticons,\\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\\n if emoticons>0:\\n additional_freq['emoticons'] = emoticons*1.0\\n if tweet.nrof_exclamations>0:\\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\\n \\n# print \\\"Additional frequencies: \\\", additional_freq\\n# raw_input(\\\"Continue?\\\")\\n \\n #Concatenate the dicts\\n features= dict(pos_tag_freq.items() + additional_freq.items())\\n# print \\\"All features: \\\", features\\n# raw_input(\\\"Continue?\\\")\\n return features\",\n \"def feature_dist_func_dict():\\n return {\\\"tanimoto_dissimilarity\\\": tanimoto_dissimilarity}\",\n \"def build_classifier(model, hidden_units):\\n in_features = model.classifier._modules['0'].in_features\\n classifier = nn.Sequential(OrderedDict([\\n ('dropout1', nn.Dropout(0.5)),\\n ('fc1', nn.Linear(in_features, hidden_units)), \\n ('relu', nn.ReLU()),\\n ('dropout2', nn.Dropout(0.5)),\\n ('fc2', nn.Linear(hidden_units, 102)),\\n ('output', nn.LogSoftmax(dim=1))\\n ]))\\n return classifier\",\n \"def feature_vec(idcs, idcs_per_action, num_actions):\\n x_fvec = np.zeros((idcs_per_action*num_actions))\\n x_fvec[idcs] = 1\\n \\n return x_fvec\",\n \"def get_feature_set_PC(tweet, sentimentvalues):\\n features= {\\n 'text_length': np.log(len(tweet.text))\\n } #ADD ADDITIONAL FEATURES\\n if tweet.nrof_sademoticons>0:\\n features['sademoticons'] = tweet.nrof_sademoticons\\n if tweet.nrof_happyemoticons>0:\\n features['happyemoticons'] = tweet.nrof_happyemoticons\\n \\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n features[tag] = features.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n features['adjectives'] = features.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n features['adverbs'] = features.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n features['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in features.keys():\\n features[key] = features[key]*1.0\\n \\n #Add lexical features\\n # total polarity score, number of positive words, number of negative words\\n pos_score = 0\\n neg_score = 0\\n nrof_pos_words = 0\\n nrof_neg_words = 0\\n for word in sentimentvalues.keys():\\n if sentimentvalues[word][0]>0:\\n nrof_pos_words = nrof_pos_words + 1\\n pos_score = pos_score + sentimentvalues[word][0]\\n if sentimentvalues[word][1]>0:\\n nrof_neg_words = nrof_neg_words + 1\\n neg_score = neg_score + sentimentvalues[word][1]\\n\\n if neg_score>0:\\n features['neg_score'] = neg_score+1.0\\n if pos_score>0:\\n features['pos_score'] = pos_score+1.0\\n if nrof_pos_words>0:\\n features['positive_words'] = nrof_pos_words*1.0\\n if nrof_neg_words>0:\\n features['negative_words'] = nrof_neg_words*1.0\\n \\n return features\",\n \"def create_dummies(df):\",\n \"def getFeatures(featureInput):\\n featureList = []\\n for defTerm,candidateSent in featureInput:\\n tokens = nltk.word_tokenize(candidateSent)\\n features = {}\\n POScenter,POSleft,POSright = wordPOS(tokens,defTerm)\\n features['Pos of first Article'] = posFirstArticle(tokens)\\n## features['Num Punct Marks'] = numPunctuation(tokens)\\n features['Subj words Predicate'] = subWordPerdicate(candidateSent,defTerm,tokens)\\n features['Word before def term'] = wordBeforeDef(tokens,defTerm)\\n features['POS centered word'] = POScenter\\n features['POS left word'] = POSleft\\n## features['POS right word'] = POSright \\n featureList.append(features)\\n return featureList\",\n \"def to_numeric_features(features, feature_cols=None):\\n\\n for col in CATEGORICAL_COLS:\\n features = pd.concat(\\n [features, pd.get_dummies(features[col[0]], drop_first=True)], axis=1)\\n features.drop(col[0], axis=1, inplace=True)\\n\\n # Remove the unused columns from the dataframe.\\n for col in UNUSED_COLUMNS:\\n features.pop(col)\\n\\n # Re-index dataframe (if categories list changed from the previous dataset)\\n if feature_cols is not None:\\n features = features.T.reindex(feature_cols).T.fillna(0)\\n return features\",\n \"def FE_create_categorical_feature_crosses(dfc, cats):\\r\\n dfc = copy.deepcopy(dfc)\\r\\n combos = list(combinations(cats, 2))\\r\\n for cat1, cat2 in combos:\\r\\n dfc.loc[:,cat1+'_cross_'+cat2] = dfc[cat1].astype(str)+\\\" \\\"+dfc[cat2].astype(str)\\r\\n return dfc\",\n \"def data_categorical(df, cat_features = [], cont_features = []):\\n subset_cat = []\\n subset_dict={}\\n # Add all the object type features to config.cat_features \\n for col in df.columns:\\n if df[col].dtype == 'object' and col not in cont_features:\\n subset_cat.append(col)\\n if col not in cat_features :\\n cat_features.append(col)\\n if cat_features !=[]:\\n print('Categorical features : ', ' '.join(cat_features))\\n printmd('**Number of unique values for every feature:**')\\n print(pd.DataFrame(df[cat_features].nunique(), columns = ['Unique values']).sort_values(by = 'Unique values', ascending=False))\\n printmd(\\\"**5 uniques samples of every Categorical Features :**\\\")\\n for col in cat_features :\\n subset_dict[col]= df[col].unique()[:5]\\n print(pd.DataFrame.from_dict(subset_dict, orient='index').transpose())\\n return (cat_features)\",\n \"def get_feature_set_PC2(tweet, sentimentvalues):\\n features= {\\n 'text_length': np.log(len(tweet.text))\\n } #ADD ADDITIONAL FEATURES\\n if tweet.nrof_sademoticons>0:\\n features['sademoticons'] = tweet.nrof_sademoticons\\n if tweet.nrof_happyemoticons>0:\\n features['happyemoticons'] = tweet.nrof_happyemoticons\\n \\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n features[tag] = features.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n features['adjectives'] = features.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n features['adverbs'] = features.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n features['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in features.keys():\\n features[key] = features[key]*1.0\\n \\n #Add lexical features\\n # total polarity score, number of positive words, number of negative words\\n pos_score = sentimentvalues[0]\\n neg_score = sentimentvalues[1]\\n\\n if pos_score>0:\\n features['pos_score'] = pos_score+1.0\\n if neg_score>0:\\n features['neg_score'] = neg_score+1.0\\n \\n return features\",\n \"def make_feature_scale_factors():\\n X, y = make_X_and_y()\\n sqm = make_sqm_X()\\n scale_factors = {\\n \\\"indoor_temp\\\": np.max(X[:,:,0]),\\n \\\"outdoor_temp\\\": np.max(X[:,:,1]),\\n \\\"gas_kwh\\\": np.max(X[:,:,2]),\\n \\\"elec_kwh\\\": np.max(X[:,:,3]),\\n \\\"floor_area\\\": np.max(sqm),\\n \\\"htc\\\": np.max(y),\\n }\\n\\n with open(os.path.join(_TRAINING_DATA_PATH, \\\"scalefactors.json\\\"), \\\"w+\\\") as f:\\n json.dump(scale_factors, f)\",\n \"def make_data(dataFname, enc, features=None):\\n\\n origData = pandas.read_csv(dataFname)\\n ids = origData['id']\\n\\n # remove unused columns\\n if 'Unnamed: 0' in origData.columns: del origData['Unnamed: 0']\\n del origData['id']\\n\\n # remove \\\"data leakage\\\" columns\\n for f in prohobitedFeatures:\\n del origData[f]\\n\\n # separate into X & y values\\n xData = origData[[col for col in origData.columns if not col=='loss']]\\n set_vars_as_type(xData, discreteVars, object)\\n yVec = origData.loss if 'loss' in origData.columns else None\\n\\n # try f528 - f274\\n xData['f528f274'] = xData['f528'] - xData['f274']\\n\\n # encode the categorical features f776 and f777\\n if enc is None:\\n enc = OneHotEncoder(n_values=[2, 2])\\n enc.fit(xData[['f776', 'f777']])\\n\\n xData[['f776_isZero', 'f776_isOne', 'f777_isZero', 'f777_isOne']] = pandas.DataFrame(enc.transform(xData[['f776', 'f777']]).toarray())\\n del xData['f776']\\n del xData['f777']\\n\\n print_missing_values_info(origData)\\n\\n # feature selection\\n if features:\\n filteredXData = xData[features]\\n else: # use ALL features\\n filteredXData = xData\\n\\n return filteredXData, yVec, ids, enc\",\n \"def add_dummy_feature(X, value=...):\\n ...\",\n \"def dense_col(self):\\n if not self.col_name_mapping or \\\"dense_col\\\" not in self.col_name_mapping:\\n return EmptyFeature\\n return Feature(\\n name=list(self.col_name_mapping[\\\"dense_col\\\"].keys()),\\n index=list(self.col_name_mapping[\\\"dense_col\\\"].values()),\\n )\",\n \"def my_impl_variational(in_train, in_test, labels):\\n X_train = []\\n X_test = []\\n for lab in labels:\\n for datum in in_train[lab]:\\n X_train.append([datum, lab])\\n for datum in in_test[lab]:\\n X_test.append([datum, lab])\\n Variationer_learn(X_train, 500, 1, 0.01, X_test, labels)\",\n \"def changeVars(self, vars):\\n v = VarSet(vars)\\n newOrder = map(lambda x:v.index(x), vars)\\n return Factor(v, self.t.transpose(newOrder))\\n #return NotImplemented\",\n \"def pre_MultiLabelBinarizer(vars):\\n for var in vars:\\n m = DataFrameMapper([(var, [CategoricalImputer(), LabelEncoder()])], df_out = True)\\n m.fit_transform(pd.concat([test,df])) # Get all categorical outcomes so we have same codes for all.\\n\\n X_train[var+'_encoded'] = m.transform(X_train)\\n X_test[var+'_encoded'] = m.transform(X_test)\\n test[var+'_encoded'] = m.transform(test)\\n\\n return X_train, X_test, test\",\n \"def to_delimited_set(self, name, label, variables, from_dichotomous=True,\\n codes_from_name=True):\\n if self.var_exists(name):\\n raise ValueError('{} does already exist.'.format(name))\\n elif not all(isinstance(c, (str, tuple)) for c in variables):\\n raise ValueError('Input of variables must be string or tuple.')\\n cols = [c if isinstance(c, str) else c[0] for c in variables]\\n if not all(self.var_exists(c) for c in cols):\\n not_in_ds = [c for c in cols if not self.var_exists(c)]\\n raise KeyError('{} not found in dataset!'.format(not_in_ds))\\n elif not all(self._has_categorical_data(c) for c in cols):\\n not_cat = [c for c in cols if not self._has_categorical_data(c)]\\n raise ValueError('Variables must have categorical data: {}'.format(not_cat))\\n if from_dichotomous:\\n if not all(x in [0, 1] for c in cols for x in self.codes_in_data(c)):\\n non_d = [c for c in cols\\n if not all(x in [0, 1] for x in self.codes_in_data(c))]\\n raise ValueError('Variables are not dichotomous: {}'.format(non_d))\\n mapper = []\\n for x, col in enumerate(variables, 1):\\n if codes_from_name:\\n x = int(col.split('_')[-1])\\n if isinstance(col, tuple):\\n text = col[1]\\n else:\\n text = self.text(col)\\n mapper.append((x, text, {col: [1]}))\\n else:\\n values = self.values(cols[0])\\n if not all(self.values(c) == values for c in cols):\\n not_eq = [c for c in cols if not self.values(c) == values]\\n raise ValueError('Variables must have eqaul values: {}'.format(not_eq))\\n mapper = []\\n for v in values:\\n mapper.append((v[0], v[1], union([{c: v[0]} for c in cols])))\\n\\n self.derive(name, 'delimited set', label, mapper)\\n\\n return None\",\n \"def _get_feature_map():\\n return {\\n # 3 sparse feature with variable length. Use this if you have a\\n # variable number or more than 1 feature value per example.\\n \\\"feature_1\\\":\\n tf.io.VarLenFeature(dtype=tf.int64),\\n \\\"feature_2\\\":\\n tf.io.VarLenFeature(dtype=tf.int64),\\n \\\"feature_3\\\":\\n tf.io.VarLenFeature(dtype=tf.int64),\\n \\\"label\\\":\\n tf.io.FixedLenFeature([1], dtype=tf.int64),\\n }\",\n \"def create_feature_value_category(data):\\n utils.save_log('{0} :: {1}'.format(\\n create_feature_value_category.__module__,\\n create_feature_value_category.__name__))\\n\\n avg_value = data.agg({'Value': 'avg'}).collect()[0][0]\\n data = data. \\\\\\n withColumn('ValueStrategy',\\n when(col('Value') > avg_value * 200, 2).\\n when(col('Value') > avg_value * 50, 1).\\n otherwise(0))\\n\\n update_list_features(\\\"numerical\\\", [\\\"ValueStrategy\\\"])\\n\\n return data\",\n \"def fac2var(_Mu,Nu, G, f,v):\\n #print\\n #print \\\"fac '%s' \\\\t=>\\\\t var '%s'\\\" % (f,v)\\n assert G.type(f)=='fac' and G.type(v)=='var'\\n\\n vars = G.N(f) # for order\\n ii = { x:i for i,x in enumerate(vars) } # inverted index\\n\\n for val in G.vals(v): # forall val in var\\n \\n # \\\"pin down msg var to one val\\\"\\n # eg\\n # var = 'b'\\n # val = 2\\n # vars = ['a','b','c']\\n # space = {0..1} x {2} x {0..3}\\n space = cartesian( *[(G.vals(_v) if _v != v else [val]) for _v in vars] )\\n\\n # get _val of _var\\n # _vals[ii[_v]] = _v:str => ii:str=>inx => _vals:inx=>val => Nu[_,_]:val=>num\\n # discrete randvar -> values are indices\\n # sum of prod\\n msg = sum( G(f, *_vals) * product([ Nu[_v, f][_vals[ii[_v]]] for _v in G.N(f) if _v != v ])\\n for _vals in space )\\n\\n _Mu[f,v][val] = msg\\n \\n \\\"\\\"\\\"\\n\\n # sum (fac * prod nus)\\n fac = G.node[f]['pmf']\\n nus = [ (i, _v, Nu[_v, f]) for i,_v in enumerate(G.N(f)) if _v != v ]\\n\\n msg = fac\\n for i,_v,nu in nus:\\n # sans broadcast\\n shape = [1 for _ in msg.shape]\\n shape[i] = G.node[_v]['d']\\n nu = resize(nu, tuple(shape))\\n nu = resize(nu, msg.shape)\\n msg = msg * nu\\n # [diff] msg = msg * resize(nu, msg.shape)\\n # [diff] msg = resize(nu, msg.shape) * msg\\n\\n others = tuple([ i for i,_v in enumerate(G.N(f)) if _v != v ]) \\n msg = sum(msg, axis=others) # marginalize every other var\\n Mu[f,v] = msg\\n\\n \\\"\\\"\\\"\\n\\n #print \\n #print 'Mu =', Mu\",\n \"def preprocessing_fn(inputs):\\n outputs = {}\\n for key in _DENSE_FLOAT_FEATURE_KEYS:\\n # Preserve this feature as a dense float, setting nan's to the mean.\\n outputs[_transformed_name(key)] = tft.scale_to_z_score(\\n _fill_in_missing(inputs[key]))\\n\\n for key in _VOCAB_FEATURE_KEYS:\\n # Build a vocabulary for this feature.\\n outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(\\n _fill_in_missing(inputs[key]),\\n top_k=_VOCAB_SIZE,\\n num_oov_buckets=_OOV_SIZE)\\n\\n for key in _BUCKET_FEATURE_KEYS:\\n outputs[_transformed_name(key)] = tft.bucketize(\\n _fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT,\\n always_return_num_quantiles=False)\\n\\n for key in _CATEGORICAL_FEATURE_KEYS:\\n outputs[_transformed_name(key)] = _fill_in_missing(inputs[key])\\n\\n # Was this passenger a big tipper?\\n taxi_fare = _fill_in_missing(inputs[_FARE_KEY])\\n tips = _fill_in_missing(inputs[_LABEL_KEY])\\n outputs[_transformed_name(_LABEL_KEY)] = tf.where(\\n tf.is_nan(taxi_fare),\\n tf.cast(tf.zeros_like(taxi_fare), tf.int64),\\n # Test if the tip was > 20% of the fare.\\n tf.cast(\\n tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))\\n\\n return outputs\",\n \"def string_feature(ds, feature: str, verbosity=0, dim=\\\"sample\\\", numeric_dis=True):\\n t = get_annotation_table(ds, dim)\\n ser = t[feature]\\n dt = ser.dtype\\n descript = \\\"\\\"\\\"\\\"\\\"\\\"\\n\\n non_null_ser = ser[ser.notnull()]\\n null_flag = len(non_null_ser) < len(ser)\\n levels = list(set(non_null_ser.values))\\n num_levels = len(levels)\\n\\n if dt == pd.StringDtype():\\n\\n if num_levels >= 50:\\n descript += f\\\"\\\"\\\"\\nSorry, {feature} is a String feature with too many factor levels \\n(n = {num_levels}) to describe, here \\n\\\"\\\"\\\"\\n\\n elif num_levels < 50 and num_levels >= 2:\\n descript += f\\\"\\\"\\\"\\n{feature}: {dt} Feature\\n-------------------------\\n\\nFactor level counts df:\\n\\n{non_null_ser.value_counts()}\\n\\n\\nSome example query statements:\\n------------------------------\\n\\n{feature} in ['{levels[0]}', '{levels[1]}', ...]\\n\\n{feature} not in ['{levels[0]}', '{levels[-1]}', ...]\\n\\n{feature} != '{levels[-2]}'\\n\\\"\\\"\\\"\\n\\n else:\\n descript += f\\\"\\\"\\\"\\nThere's only a single factor level, {levels[0]}, across all samples.\\n\\\"\\\"\\\"\\n\\n elif dt == pd.BooleanDtype():\\n descript += f\\\"\\\"\\\"\\n{feature}: {dt} Feature:\\n---------------------------\\n\\nFactor level counts df:\\n\\n{non_null_ser.value_counts()}\\n\\n\\nSome example query statements:\\n------------------------------\\n\\n{feature} == True\\n\\n{feature} == False\\n\\\"\\\"\\\"\\n\\n elif dt == pd.Int64Dtype() or dt == pd.Float64Dtype():\\n if num_levels == 1:\\n descript += f\\\"\\\"\\\"\\nThere's only a single factor level, {levels[0]}, across all samples.\\n\\\"\\\"\\\"\\n\\n elif (num_levels > 1) and (not numeric_dis):\\n descript += f\\\"\\\"\\\"\\n{feature}: {dt} Feature\\n-------------------------\\n\\nFactor level counts df:\\n\\n{non_null_ser.value_counts()}\\n\\n\\nSome example query statements:\\n------------------------------\\n\\n{feature} in [{levels[0]}, {levels[1]}, ...]\\n\\n{feature} not in [{levels[0]}, {levels[-1]}, ...]\\n\\n{feature} != {levels[-2]}\\n\\\"\\\"\\\"\\n else:\\n des = ser.describe()\\n descript += f\\\"\\\"\\\"\\n{feature}: {dt} Feature:\\n---------------------------\\n\\ndistribution of numerical feature:\\n\\n{des}\\n\\nSome example query statements:\\n------------------------------\\n\\n{feature} >= {int(des[1])}\\n\\n{feature} <= {int(des[1])}\\n\\n({feature} >= {int(des[4])}) and ({feature} <= {int(des[5])})\\n\\\"\\\"\\\"\\n\\n else:\\n descript += f\\\"\\\"\\\"\\n{feature}: {dt} Feature\\n-------------------------\\n\\nFactor level counts df:\\n\\n{non_null_ser.value_counts()}\\n\\n\\nSome example query statements:\\n------------------------------\\n\\n{feature} in ['{levels[0]}', '{levels[1]}', ...]\\n\\n{feature} not in ['{levels[0]}', '{levels[-1]}', ...]\\n\\n{feature} != '{levels[-2]}'\\n\\\"\\\"\\\"\\n\\n if null_flag:\\n descript += f\\\"\\\"\\\"\\n{feature}.isnull()\\n\\n{feature}.notnull()\\n\\\"\\\"\\\"\\n\\n return descript\",\n \"def featurize(self, data):\\n \\n bag_of_words = []\\n\\n tokens = data.split()\\n\\n for i in tokens:\\n bag_of_words.append((i, True))\\n\\n return bag_of_words\",\n \"def to_dimacs_string_set_variable_value(self, clauses, variable, value):\\n self.varname_dict = {}\\n self.varobj_dict = {}\\n variables = prop_symbols_from_clause_list(clauses)\\n if variable in variables:\\n variables.remove(variable)\\n varis = dict(list(zip(sorted(variables, key=lambda v: v.op),\\n list(map(str, list(range(1, len(variables) + 1)))))))\\n for var in varis:\\n self.varname_dict[var] = varis[var]\\n self.varobj_dict[varis[var]] = var\\n\\n ret_clauses = ''\\n clause_count = 0\\n for clause in clauses:\\n clause_exists = True\\n dimacs_vlist = []\\n ret_clause = ''\\n if clause.op == '|':\\n for var in clause.args:\\n if literal_name(var) == literal_name(variable):\\n if value and not var.op == '~' or not value and var.op == '~':\\n clause_exists = False\\n else:\\n dimacs_vlist.append(('-' if var.op == '~' else '') \\\\\\n + self.varname_dict[var.args[0]\\n if var.op == '~' else var])\\n\\n if clause_exists:\\n ret_clause += ' '.join(dimacs_vlist)\\n elif clause.op == '~':\\n if literal_name(clause) == literal_name(variable):\\n if value:\\n return None\\n clause_exists = False\\n else:\\n ret_clause += '-' + self.varname_dict[clause.args[0]]\\n elif literal_name(clause) == literal_name(variable):\\n if value:\\n clause_exists = False\\n else:\\n return None\\n else:\\n ret_clause += self.varname_dict[clause]\\n if clause_exists:\\n clause_count += 1\\n ret_clauses += ret_clause + ' 0\\\\n'\\n\\n ret_header = 'p cnf %d %d\\\\n' % (len(variables), clause_count)\\n ret = ret_header + ret_clauses\\n return ret\",\n \"def _classify(tree, x):\\n # YOUR CODE HERE\\n # begin answer\\n feature_name=list(tree.keys())[0] #first element\\n secondDict=tree[feature_name] \\n key=x.loc[feature_name] #extract value from x\\n for key_val in secondDict:\\n feature_val=key_val[0]\\n valueOfKey=secondDict[(feature_val, key>=feature_val)]\\n if isinstance(valueOfKey,dict):\\n label=_classify(valueOfKey,x)\\n else:\\n label=valueOfKey\\n return label\\n # end answer\",\n \"def domain_features(domain, path_set):\\n return string_features_v2(domain_string(domain, path_set))\",\n \"def condition_domain_reduction(csp, var) :\\n return True\",\n \"def condition_domain_reduction(csp, var) :\\n return True\",\n \"def GetVectorArticleInput(dico_vector_input, features):\\n features_left = set(features) - set(dico_vector_input.keys())\\n if len(features_left) > 0:\\n sentence = \\\"Some features aren't in the dict:\\\\n\\\"\\n raise MyException(sentence + \\\"{}\\\".format(features_left))\\n vector_art = []\\n other_features = ['abstract', 'syn', 'exergue', 'title', 'secTitle']\\n other_features += ['subTitle', 'supTitle']\\n for feature in features:\\n if feature == 'nbSign':\\n if dico_vector_input['nbSign'] == 0:\\n print(\\\"NbSign == 0 l.176 - GetVectorArticleInput\\\")\\n vector_art.append(dico_vector_input[feature])\\n else:\\n vector_art.append(dico_vector_input[feature])\\n # Conversion des variables en indicatrices\\n # Normalement plus la peine, comme déjà fait auparavant\\n elif feature in other_features:\\n if dico_vector_input[feature] > 0:\\n vector_art.append(1)\\n else:\\n vector_art.append(0)\\n else:\\n vector_art.append(dico_vector_input[feature])\\n return (dico_vector_input['melodyId'], np.array([vector_art]))\",\n \"def load_feature(feature_name, caf_dose, features_path):\\n # gets the paths to the folders where the specified feature is stored\\n subject_paths = glob.glob(os.path.join(features_path, \\\"*\\\", feature_name))\\n\\n feature = {}\\n for path in subject_paths:\\n # extract the subject id from the current path (second to last element in the path)\\n subject_id = path.split(os.sep)[-2]\\n\\n # get all stages for the current subject\\n stages = set(\\n [\\n p.split(os.sep)[-1].split(\\\"_\\\")[-1].split(\\\".\\\")[0]\\n for p in glob.glob(os.path.join(path, \\\"*.npy\\\"))\\n ]\\n )\\n if len(stages) == 0:\\n print(\\n f\\\"The following directory doesn't contain features: {path}. \\\"\\n \\\"This will likely cause an error down the line\\\"\\n )\\n for stage in stages:\\n if stage not in feature:\\n feature[stage] = {}\\n # load the file containing the data for the current stage and subject\\n feature[stage][subject_id] = np.load(\\n os.path.join(path, f\\\"{feature_name}_{stage}.npy\\\"), allow_pickle=True\\n )\\n return feature\",\n \"def density(categorical_var, numerical_var):\\n #print(categorical_var)\\n cat_list = categorical_var.astype('category')\\n for cat in cat_list:\\n sns.kdeplot(numerical_var[categorical_var == cat], label=cat)#, categorical_var)\\n\\n plt.show()\",\n \"def create_classifier(in_dim, hid_dim, out_dim):\\n\\n root_six = np.sqrt(6)\\n eps = root_six / (np.sqrt(hid_dim + in_dim))\\n W = np.random.uniform(-eps, eps, [hid_dim, in_dim])\\n eps = root_six / (np.sqrt(hid_dim))\\n b = np.random.uniform(-eps, eps, hid_dim)\\n eps = root_six / (np.sqrt(out_dim + hid_dim))\\n U = np.random.uniform(-eps, eps, [out_dim, hid_dim])\\n eps = root_six / (np.sqrt(out_dim))\\n b_tag = np.random.uniform(-eps, eps, out_dim)\\n\\n return [W,b,U,b_tag]\",\n \"def dense_nn(x):\\n dense_1 = tf.nn.dropout(tf.nn.relu(dense(x, input_dim, n_l1, 'dense_1')), keep_prob=0.25)\\n dense_2 = tf.nn.dropout(tf.nn.relu(dense(dense_1, n_l1, n_l2, 'dense_2')), keep_prob=0.25)\\n dense_3 = dense(dense_2, n_l2, n_labels, 'dense_3')\\n return dense_3\",\n \"def get_dummies_list(self, \\n cols=['elite'],\\\\\\n drop_=True):\\n for col in cols:\\n print \\\"Pre-processing \\\" + col + \\\"...\\\"\\n temp = pd.get_dummies(self.df[col].apply(pd.Series).stack(),drop_first=True)\\\\\\n .astype(int).sum(level=0).astype(int)\\n # temp.columns.apply(str).apply(lambda x: col + \\\"_\\\" + x)\\n if drop_:\\n self.df.drop(col,axis = 1, inplace=True)\\n self.df = pd.concat([self.df, temp],axis=1)\",\n \"def extract_features(X):\\n X = X.drop(\\\"PassengerId\\\", axis=1)\\n X = X.drop(\\\"Ticket\\\", axis=1)\\n X = X.drop(\\\"Cabin\\\", axis=1)\\n \\n # Adding polynomial features\\n X[\\\"Age2\\\"] = X[\\\"Age\\\"] ** 2\\n #X[\\\"Fare2\\\"] = X[\\\"Fare\\\"] ** 2\\n #X[\\\"Pclass2\\\"] = X[\\\"Pclass\\\"] ** 2\\n\\n \\n male_titles = set([\\\"Mr\\\", \\\"Don\\\", \\\"Sir\\\"])\\n female_titles = set([\\\"Miss\\\", \\\"Ms\\\", \\\"Mrs\\\", \\\"Mme\\\", \\\"Mdm\\\", \\\"Lady\\\"])\\n professionals = set([\\\"Dr\\\", \\\"Rev\\\", \\\"Master\\\"])\\n military = set([\\\"Col\\\", \\\"Major\\\", \\\"Capt\\\"])\\n royalty = set([\\\"the Countess\\\", \\\"Jonkheer\\\"])\\n \\n names = X[\\\"Name\\\"]\\n for i in range(len(names)): \\n name_tokens = names[i].split(\\\", \\\") \\n passenger_title = name_tokens[1].split(\\\".\\\")[0]\\n if passenger_title in male_titles:\\n names[i] = 1\\n elif passenger_title in female_titles:\\n names[i] = 2\\n elif passenger_title in professionals:\\n names[i] = 3\\n #elif passenger_title in royalty:\\n # names[i] = 4\\n elif passenger_title in military:\\n names[i] = 5\\n else:\\n names[i] = 6\\n \\n X[\\\"Name\\\"].update(names)\\n \\n # One hot encoding of categorical data\\n X = pd.get_dummies(X) \\n \\n X.fillna(0, inplace=True)\\n X['Fam'] = X['SibSp'] + X['Parch'] # assigned to a column\\n return X\",\n \"def probability_categorical(feature, label):\\n assert feature.nunique()>2, 'feature category nums must be greater than 2.'\\n t = pd.DataFrame({'feature':feature, 'label':label})\\n cat = label.unique()\\n cat = [(cat[i], cat[i+1]) for i in range(len(cat)-1)]\\n prob = label.value_counts(1).to_dict()\\n slope = [prob.get(i[0], 0)-prob.get(i[1], 0) for i in cat]\\n \\n slope_dict = t.feature.value_counts(1).to_dict()\\n prob = t.groupby([ 'feature']).label.value_counts(1).to_dict()\\n slope_dict = {i:{'category_rate':slope_dict[i], 'slope':[prob.get((i,j[0]), 0)-prob.get((i,j[1]), 0) for j in cat]} for i in slope_dict}\\n for i in slope_dict:\\n slope_dict[i]['slope_diff'] = sum([abs(slope[j]-slope_dict[i]['slope'][j]) for j in range(len(slope))])\\n value1 = sorted([[[i], slope_dict[i]['slope_diff'], slope_dict[i]['category_rate']] for i in slope_dict], key=lambda x:x[1], reverse=1)\\n distance = sorted([value1[i][1]-value1[i+1][1] for i in range(len(value1)-1)])\\n std = pd.Series([i[1] for i in value1]).std()\\n coupe = value1\\n dis = distance[0]\\n for k in distance:\\n value = value1\\n while 1:\\n for i in range(len(value)-1):\\n if value[i][1]-k<value[i+1][1]:\\n value[i+1][0] = value[i][0]+value[i+1][0]\\n value[i+1][1] = value[i][1]*value[i][2]/(value[i][2]+value[i+1][2])+value[i+1][1]*value[i+1][2]/(value[i][2]+value[i+1][2])\\n value[i+1][2] = value[i][2]+value[i+1][2]\\n value.remove(value[i])\\n break\\n if i==len(value)-2:\\n break\\n if pd.Series([i[1] for i in value]).std()>std:\\n coupe = value\\n std = pd.Series([i[1] for i in value]).std()\\n dis = k\\n return {'group':{k:i for i,j in enumerate(coupe) for k in j[0]}, 'data':coupe, \\n 'distance':dis, 'distance_index':f'{distance.index(dis)+1}/{len(distance)}', 'std':std}\",\n \"def dim2():\\n probs = (0.1, 0.2, 0.3, 0.4)\\n categories = (\\\"asdfa\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\")\\n categories = OrderedDict(zip(categories, probs))\\n dim2 = Categorical(\\\"yolo2\\\", categories, default_value=\\\"2\\\")\\n return dim2\",\n \"def build_by_features(self, features):\\n v = [0] * self.f\\n masks = [1 << i for i in range(self.f)]\\n if isinstance(features, dict):\\n features = features.items()\\n for f in features:\\n if isinstance(f, basestring):\\n h = self.hashfunc(f.encode('utf-8'))\\n w = 1\\n else:\\n assert isinstance(f, collections.Iterable)\\n h = self.hashfunc(f[0].encode('utf-8'))\\n w = f[1]\\n for i in range(self.f):\\n v[i] += w if h & masks[i] else -w\\n ans = 0\\n for i in range(self.f):\\n if v[i] >= 0:\\n ans |= masks[i]\\n self.value = ans\",\n \"def build_by_features(self, features):\\n v = [0] * self.f\\n masks = [1 << i for i in range(self.f)]\\n if isinstance(features, dict):\\n features = features.items()\\n for f in features:\\n if isinstance(f, basestring):\\n h = self.hashfunc(f.encode('utf-8'))\\n w = 1\\n else:\\n assert isinstance(f, collections.Iterable)\\n h = self.hashfunc(f[0].encode('utf-8'))\\n w = f[1]\\n for i in range(self.f):\\n v[i] += w if h & masks[i] else -w\\n ans = 0\\n for i in range(self.f):\\n if v[i] >= 0:\\n ans |= masks[i]\\n self.value = ans\",\n \"def discrete_model():\\n return {\\\"x\\\": np.random.randint(10, size=100), \\\"y\\\": np.random.randint(10, size=100)}\",\n \"def discretizing(\\n *factors: List[np.ndarray],\\n independent: bool = True,\\n n_bins: int = 5,\\n strategy: Literal['uniform', 'quantile', 'kmeans', 'gmm'] = 'quantile',\\n return_model: bool = False,\\n):\\n encode = 'ordinal'\\n # onehot - sparse matrix of one-hot encoding and\\n # onehot-dense - dense one-hot encoding. Ignored features are always stacked to\\n # the right.\\n # ordinal - Return the bin identifier encoded as an integer value.\\n strategy = str(strategy).strip().lower()\\n if 'histogram' in strategy:\\n strategy = 'uniform'\\n # ====== GMM base discretizer ====== #\\n if 'gmm' in strategy:\\n create_gmm = lambda: GaussianMixture(n_components=n_bins,\\n max_iter=800,\\n covariance_type='diag',\\n random_state=1) # fix random state\\n\\n if independent:\\n gmm = []\\n for f in factors[0].T:\\n gm = create_gmm()\\n gm.fit(np.expand_dims(f, axis=1))\\n gm.predict = types.MethodType(_gmm_discretizing_predict, gm)\\n gmm.append(gm)\\n transform = lambda x: np.concatenate([\\n gm.predict(np.expand_dims(col, axis=1)) for gm, col in zip(gmm, x.T)\\n ],\\n axis=1)\\n else:\\n gmm = create_gmm()\\n gmm.fit(np.expand_dims(factors[0].ravel(), axis=1))\\n gmm.predict = types.MethodType(_gmm_discretizing_predict, gmm)\\n transform = lambda x: np.concatenate(\\n [gmm.predict(np.expand_dims(col, axis=1)) for col in x.T], axis=1)\\n disc = gmm\\n # ====== start with bins discretizer ====== #\\n else:\\n disc = KBinsDiscretizer(n_bins=n_bins, encode=encode, strategy=strategy)\\n if independent:\\n disc.fit(factors[0])\\n transform = lambda x: disc.transform(x).astype(np.int64)\\n else:\\n disc.fit(np.expand_dims(factors[0].ravel(), axis=-1))\\n transform = lambda x: np.hstack([\\n disc.transform(np.expand_dims(i, axis=-1)).astype(np.int64)\\n for i in x.T\\n ])\\n # ====== returns ====== #\\n factors = tuple([transform(i) for i in factors])\\n factors = factors[0] if len(factors) == 1 else factors\\n if return_model:\\n return factors, disc\\n return factors\",\n \"def __init__(self, strName, lstDomain):\\n # assign the name of the feature represented by the node\\n self.name = str(strName)\\n # assign the domain of the feature\\n self.domain = lstDomain\\n # the value starts out as undefined\\n self.value = \\\"none\\\"\",\n \"def get_kdd_feature_cols():\\r\\n\\treturn [\\r\\n\\t\\t'duration',\\r\\n\\t\\t'src_bytes',\\r\\n\\t\\t'dst_bytes',\\r\\n\\t\\t'land',\\r\\n\\t\\t'wrong_fragment',\\r\\n\\t\\t'urgent',\\r\\n\\t\\t'hot',\\r\\n\\t\\t'num_failed_logins',\\r\\n\\t\\t'logged_in',\\r\\n\\t\\t'num_compromised',\\r\\n\\t\\t'root_shell',\\r\\n\\t\\t'su_attempted',\\r\\n\\t\\t'num_root',\\r\\n\\t\\t'num_file_creations',\\r\\n\\t\\t'num_shells',\\r\\n\\t\\t'num_access_files',\\r\\n\\t\\t'num_outbound_cmds',\\r\\n\\t\\t'is_host_login',\\r\\n\\t\\t'is_guest_login',\\r\\n\\t\\t'count',\\r\\n\\t\\t'srv_count',\\r\\n\\t\\t'serror_rate',\\r\\n\\t\\t'srv_serror_rate',\\r\\n\\t\\t'rerror_rate',\\r\\n\\t\\t'srv_rerror_rate',\\r\\n\\t\\t'same_srv_rate',\\r\\n\\t\\t'diff_srv_rate',\\r\\n\\t\\t'srv_diff_host_rate',\\r\\n\\t\\t'dst_host_count',\\r\\n\\t\\t'dst_host_srv_count',\\r\\n\\t\\t'dst_host_same_srv_rate',\\r\\n\\t\\t'dst_host_diff_srv_rate',\\r\\n\\t\\t'dst_host_same_src_port_rate',\\r\\n\\t\\t'dst_host_srv_diff_host_rate',\\r\\n\\t\\t'dst_host_serror_rate',\\r\\n\\t\\t'dst_host_srv_serror_rate',\\r\\n\\t\\t'dst_host_rerror_rate',\\r\\n\\t\\t'dst_host_srv_rerror_rate']\",\n \"def das(input_df, counts_var, noise_parameter):\\n df = input_df.copy(deep=True)\\n \\n n = df.shape[0]\\n \\n # add laplace noise \\n noise = np.random.laplace(loc=0, scale=noise_parameter, size=n)\\n df['noisy_counts'] = df[counts_var] + noise\\n \\n # post processing\\n df['nonneg_counts'] = post_proc(df.noisy_counts, df.pop_count.sum())\\n df.nonneg_counts = np.round(df.nonneg_counts)\\n \\n return df\",\n \"def to_dummies(to_dummy_data):\\r\\n if config.to_dummies:\\r\\n for col in to_dummy_data.columns:\\r\\n unique_col_num = len(pd.unique(to_dummy_data[col]))\\r\\n dummy_max = int(len(to_dummy_data[col]) / 10)\\r\\n col_type = to_dummy_data.dtypes[col]\\r\\n if (col_type == \\\"object\\\") \\\\\\r\\n & (unique_col_num < dummy_max) \\\\\\r\\n & (unique_col_num > 1) \\\\\\r\\n & (col != config.target):\\r\\n temp_dummies = pd.get_dummies(to_dummy_data[col])\\r\\n to_dummy_data = pd.concat([to_dummy_data, temp_dummies],\\r\\n axis=1, sort=False)\\r\\n return to_dummy_data\",\n \"def get_name_to_features(self):\\n name_to_features = {\\n 'input_ids': tf.io.FixedLenFeature([self.seq_len], tf.int64),\\n 'label_ids': tf.io.FixedLenFeature([], tf.int64),\\n }\\n return name_to_features\",\n \"def make_dummy_df(self, dummy_vars, cat_to_drop = 0, drop_cat = True, add_intercept = True):\\n\\n # reset reference levels, if remaking then previous information would be there\\n self.ref_levels = []\\n\\n # reset to original data. Ensures that make_dummy_df can be rerun multiple times\\n self.df = self.df_orig.copy()\\n\\n # check if dummy_vars is a dict or a list to determine how to drop categories\\n if isinstance(dummy_vars, list):\\n drop_by_index = True\\n\\n # make sure that cat_to_drop is within the index range of the selected variables\\n for var in dummy_vars:\\n if self.df[var].nunique() < (cat_to_drop + 1):\\n raise Exception(\\\"For column {} the number of categories was \\\\\\nlower than the specified index ({}) to drop.\\\".format(var, cat_to_drop))\\n else:\\n drop_by_index = False\\n\\n # loop through categorical columns in dummy_vars list/dict\\n for var in dummy_vars:\\n\\n # convert given variable to a dummy variable\\n current_dummy = pd.DataFrame(self.df[var])\\n current_dummy = pd.get_dummies(current_dummy, prefix = var)\\n\\n\\n # drop one level as specified by dummy_vars, if drop_cat is true\\n if drop_cat == True:\\n\\n # scenario where dummy_vars is a list\\n if drop_by_index == True:\\n dummy_to_drop = current_dummy.columns[cat_to_drop]\\n current_dummy = current_dummy.drop([dummy_to_drop], axis = 1)\\n\\n # keep note of the dropped level in ref_levels\\n self.ref_levels.append(dummy_to_drop)\\n\\n # scenario where dummy_vars is a dict\\n if drop_by_index == False:\\n dummy_to_drop = var + \\\"_\\\" + dummy_vars[var]\\n current_dummy = current_dummy.drop([dummy_to_drop], axis = 1)\\n\\n # keep note of the dropped level in ref_levels\\n self.ref_levels.append(dummy_to_drop)\\n\\n # concat with original dataframe\\n self.df = pd.concat([self.df, current_dummy], axis=1)\\n\\n # drop pre-dummy categorical columns from original dataframe\\n if drop_by_index == True:\\n self.df = self.df.drop(dummy_vars, axis=1)\\n\\n if drop_by_index == False:\\n self.df = self.df.drop(dummy_vars.keys(), axis=1)\\n\\n # if int add is true, add int\\n if add_intercept == True:\\n self.df['intercept'] = 1.0\\n\\n return self.df\",\n \"def construct_feature_columns(input_features):\\n return set([tf.feature_column.numeric_column(my_feature)\\n for my_feature in input_features])\",\n \"def set_factors(self, name, factormap, safe=False):\\n e = False\\n if name in self.masks():\\n if self._get_subtype(name) != 'single':\\n e = True\\n else:\\n if self._get_type(name) != 'single':\\n e = True\\n if e:\\n if safe:\\n err = \\\"Can only set factors to 'single' type categorical variables!\\\"\\n raise TypeError(err)\\n else:\\n return None\\n vals = self.codes(name)\\n facts = list(factormap.keys())\\n val_loc = self._get_value_loc(name)\\n if not all(f in vals for f in facts):\\n err = 'At least one factor is mapped to a code that does not exist '\\n err += 'in the values object of \\\"{}\\\"!'\\n raise ValueError(err.format(name))\\n for value in val_loc:\\n if value['value'] in factormap:\\n value['factor'] = factormap[value['value']]\\n else:\\n value['factor'] = None\\n return None\",\n \"def preprocess_features(X):\\n\\t# Initialize new output DataFrame\\n\\toutput = pd.DataFrame(index = X.index)\\n\\n\\t# Investigate new output DataFrame\\n\\tfor col, col_data in X.iteritems():\\n\\t\\t# If data type is categorical, convert to dummy variables\\n\\t\\tif col_data.dtype == object:\\n\\t\\t\\tcol_data = pd.get_dummies(col_data, prefix = col)\\n\\n\\t\\t\\t# Collect the revised columns\\n\\t\\t\\toutput - output.join(col_data)\\n\\treturn output\",\n \"def feature(self, N, pcomment, comment, positive, negative, posSub, negSub):\\n features = {}\\n # features top N\\n for i in range(0, N - 1):\\n if positive[i] in comment:\\n features[\\\"positive(%s)\\\" % positive[i]] = True\\n else:\\n features[\\\"positive(%s)\\\" % positive[i]] = False\\n if negative[i] in comment:\\n features[\\\"negative(%s)\\\" % negative[i]] = True\\n else:\\n features[\\\"negative(%s)\\\" % negative[i]] = False\\n # features subjetive lists\\n for word in set(comment):\\n if word in posSub and comment.count(word) > 0:\\n features[\\\"subjetive_pos(%s)\\\" % word] = comment.count(word)\\n if word in negSub and comment.count(word) > 0:\\n features[\\\"subjetive_neg(%s)\\\" % word] = comment.count(word)\\n\\n #custom features\\n if self.generateHeuristic(pcomment):\\n features[\\\"no_gusto\\\"] = True\\n\\n return features\",\n \"def _condense_state(self, s):\\n def append_col(A, b):\\n expanded_b = np.expand_dims(b, 1)\\n return np.append(A, expanded_b, 1)\\n\\n A, b, c0, cuts_a, cuts_b = s\\n Ab = append_col(A, b)\\n cuts = append_col(cuts_a, cuts_b)\\n return (Ab, c0, cuts)\",\n \"def test_categorical_feature():\\n\\n feature = Categorical(\\\"abc\\\")\\n\\n for element in \\\"abc\\\":\\n feature.set(element)\\n feature.set(\\\"ignore this\\\")\\n feature.push()\\n\\n for element in \\\"abc\\\":\\n getattr(feature, \\\"set_\\\" + element)()\\n feature.push()\\n\\n array = feature.array()\\n assert array.shape == (6, 3)\\n for i, row in enumerate(array):\\n assert sum(row) == 1.0 and row[i % 3] == 1.0\",\n \"def _make_xy(self, var: str, iteration: int = None):\\n xvars = self.variable_schema[var]\\n completed_data = self.complete_data(iteration=iteration, all_vars=True)\\n to_convert = _list_union(self.categorical_variables, xvars)\\n for ctc in to_convert:\\n completed_data[ctc] = completed_data[ctc].cat.codes\\n x = completed_data[xvars]\\n y = completed_data[var]\\n return x, y\",\n \"def decompose_sparse_factor_ye(factor: Factor) -> DecomposedFactor:\\n table = factor.table\\n # construct a decomposition for the single-variable case\\n if table.ndim == 1: \\n n_terms = 1\\n weights = np.ones(n_terms)\\n matrices = [table.reshape(table.shape[0], 1)]\\n # construct a decomposition for (sparse) higher order factors\\n else:\\n # np.where returns a tuple of length n_vars, where each element is an np.array of axis coordinates. Calling\\n # np.transpose on this returns an array of shape (n_nondefault, n_vars) where each row contains the coordinates\\n # of a single non-default entry.\\n indices = np.transpose(np.where(table != 1.0))\\n weights = []\\n tensors = []\\n construct_rank1_tensors(tensors, weights, factor, indices, 0)\\n matrices = [np.array(m).transpose() for m in np.swapaxes(tensors, 0, 1).tolist()]\\n for i in range(factor.n_vars):\\n assert matrices[i].shape == (factor.cardinalities[i], len(weights))\\n\\n df = DecomposedFactor(factor.vars, weights, matrices)\\n # Check that the decomposition didn't change the factor\\n assert df.expand() == factor\\n return df\",\n \"def create_classifier(model, hidden_units=None):\\n\\n defaul_nb_units = 4096\\n nb_units = hidden_units if hidden_units else defaul_nb_units\\n \\n input_features = model.classifier[0].in_features\\n \\n classifier = nn.Sequential(OrderedDict([\\n ('fc1', nn.Linear(input_features, nb_units, bias=True)),\\n ('relu1', nn.ReLU()),\\n ('dropout1', nn.Dropout(p=0.5)),\\n ('fc2', nn.Linear(nb_units, 102, bias=True)),\\n ('output', nn.LogSoftmax(dim=1))\\n ]))\\n \\n return classifier\",\n \"def process_categorical_data(data_df):\\n return pd.get_dummies(data_df, columns=Columns.categorical)\",\n \"def list_badly_scaled_variables(\\n blk,\\n large: float = 1e4,\\n small: float = 1e-3,\\n zero: float = 1e-10,\\n descend_into: bool = True,\\n include_fixed: bool = False,\\n):\\n return [\\n c\\n for c in badly_scaled_var_generator(\\n blk, large, small, zero, descend_into, include_fixed\\n )\\n ]\",\n \"def get_kdd_feature_cols():\\n\\treturn [\\n\\t\\t'duration',\\n\\t\\t'src_bytes',\\n\\t\\t'dst_bytes',\\n\\t\\t'land',\\n\\t\\t'wrong_fragment',\\n\\t\\t'urgent',\\n\\t\\t'hot',\\n\\t\\t'num_failed_logins',\\n\\t\\t'logged_in',\\n\\t\\t'num_compromised',\\n\\t\\t'root_shell',\\n\\t\\t'su_attempted',\\n\\t\\t'num_root',\\n\\t\\t'num_file_creations',\\n\\t\\t'num_shells',\\n\\t\\t'num_access_files',\\n\\t\\t'num_outbound_cmds',\\n\\t\\t'is_host_login',\\n\\t\\t'is_guest_login',\\n\\t\\t'count',\\n\\t\\t'srv_count',\\n\\t\\t'serror_rate',\\n\\t\\t'srv_serror_rate',\\n\\t\\t'rerror_rate',\\n\\t\\t'srv_rerror_rate',\\n\\t\\t'same_srv_rate',\\n\\t\\t'diff_srv_rate',\\n\\t\\t'srv_diff_host_rate',\\n\\t\\t'dst_host_count',\\n\\t\\t'dst_host_srv_count',\\n\\t\\t'dst_host_same_srv_rate',\\n\\t\\t'dst_host_diff_srv_rate',\\n\\t\\t'dst_host_same_src_port_rate',\\n\\t\\t'dst_host_srv_diff_host_rate',\\n\\t\\t'dst_host_serror_rate',\\n\\t\\t'dst_host_srv_serror_rate',\\n\\t\\t'dst_host_rerror_rate',\\n\\t\\t'dst_host_srv_rerror_rate']\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8234406","0.52805275","0.5167155","0.5081879","0.50444704","0.50157154","0.49738747","0.49235922","0.4897217","0.48730886","0.48664978","0.47522265","0.47458404","0.47244322","0.47193447","0.47139582","0.47023058","0.4691292","0.46388435","0.46278065","0.462432","0.4610667","0.46097022","0.46039566","0.45683187","0.4559977","0.45582384","0.45539427","0.4552161","0.45441282","0.45318797","0.4517334","0.45058745","0.44638264","0.44621706","0.4458562","0.44431004","0.4438731","0.4434646","0.44239601","0.44016114","0.43968707","0.4390392","0.43746877","0.43648678","0.43592232","0.43521863","0.43506798","0.43457893","0.43452945","0.4345201","0.4336911","0.4329662","0.43129134","0.43107063","0.43072507","0.4306205","0.43040285","0.42972845","0.42971507","0.4287519","0.42759404","0.42715627","0.42680877","0.42665267","0.42654943","0.42565045","0.42556956","0.42556956","0.42454877","0.42443123","0.42439955","0.4243791","0.42399123","0.4233692","0.42249268","0.42185163","0.42182022","0.42172885","0.42172885","0.42096746","0.4208677","0.4208189","0.4207724","0.42075157","0.42071798","0.42009088","0.41969946","0.419433","0.4192178","0.4191485","0.41899857","0.4189815","0.4186384","0.41845036","0.4182835","0.41798547","0.41789797","0.41770157","0.41723222"],"string":"[\n \"0.8234406\",\n \"0.52805275\",\n \"0.5167155\",\n \"0.5081879\",\n \"0.50444704\",\n \"0.50157154\",\n \"0.49738747\",\n \"0.49235922\",\n \"0.4897217\",\n \"0.48730886\",\n \"0.48664978\",\n \"0.47522265\",\n \"0.47458404\",\n \"0.47244322\",\n \"0.47193447\",\n \"0.47139582\",\n \"0.47023058\",\n \"0.4691292\",\n \"0.46388435\",\n \"0.46278065\",\n \"0.462432\",\n \"0.4610667\",\n \"0.46097022\",\n \"0.46039566\",\n \"0.45683187\",\n \"0.4559977\",\n \"0.45582384\",\n \"0.45539427\",\n \"0.4552161\",\n \"0.45441282\",\n \"0.45318797\",\n \"0.4517334\",\n \"0.45058745\",\n \"0.44638264\",\n \"0.44621706\",\n \"0.4458562\",\n \"0.44431004\",\n \"0.4438731\",\n \"0.4434646\",\n \"0.44239601\",\n \"0.44016114\",\n \"0.43968707\",\n \"0.4390392\",\n \"0.43746877\",\n \"0.43648678\",\n \"0.43592232\",\n \"0.43521863\",\n \"0.43506798\",\n \"0.43457893\",\n \"0.43452945\",\n \"0.4345201\",\n \"0.4336911\",\n \"0.4329662\",\n \"0.43129134\",\n \"0.43107063\",\n \"0.43072507\",\n \"0.4306205\",\n \"0.43040285\",\n \"0.42972845\",\n \"0.42971507\",\n \"0.4287519\",\n \"0.42759404\",\n \"0.42715627\",\n \"0.42680877\",\n \"0.42665267\",\n \"0.42654943\",\n \"0.42565045\",\n \"0.42556956\",\n \"0.42556956\",\n \"0.42454877\",\n \"0.42443123\",\n \"0.42439955\",\n \"0.4243791\",\n \"0.42399123\",\n \"0.4233692\",\n \"0.42249268\",\n \"0.42185163\",\n \"0.42182022\",\n \"0.42172885\",\n \"0.42172885\",\n \"0.42096746\",\n \"0.4208677\",\n \"0.4208189\",\n \"0.4207724\",\n \"0.42075157\",\n \"0.42071798\",\n \"0.42009088\",\n \"0.41969946\",\n \"0.419433\",\n \"0.4192178\",\n \"0.4191485\",\n \"0.41899857\",\n \"0.4189815\",\n \"0.4186384\",\n \"0.41845036\",\n \"0.4182835\",\n \"0.41798547\",\n \"0.41789797\",\n \"0.41770157\",\n \"0.41723222\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.784609"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":13797,"cells":{"query":{"kind":"string","value":"Check if featureVals contains FKEY This is a check to see if a core feature function has been previously computed."},"document":{"kind":"string","value":"def haskey(featureVals, fkey):\n try:\n featureVals[fkey]\n except KeyError:\n return False\n\n #warn(HASKEYMSG % (fkey))\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 _check_feature_by_keys(service_data=None, service_keys=None, ns_data=None, ns_keys=None):\n\n if service_data and not isinstance(service_data, Exception) and service_keys:\n if _is_keyval_greater_than_value(service_data, service_keys):\n return True\n\n if ns_data and ns_keys:\n for ns, nsval in ns_data.iteritems():\n if not nsval or isinstance(nsval, Exception):\n continue\n if _is_keyval_greater_than_value(nsval, ns_keys):\n return True\n\n return False","def __check_features(f_list, stopwords):\n ok = True\n for f in f_list:\n if not(__check_feature(f,stopwords)):\n return False\n return True","def match_features(phone_feats, other_feats):\n for feat in other_feats.keys():\n if phone_feats[feat] != other_feats[feat] and other_feats[feat] != UNDEF:\n return False\n return True","def checkFeatureInSet(self, featureSet, currFeature, idxValue):\n\n found = False\n currFeatureID = currFeature[idxValue]\n\n for feature in sorted(featureSet, key=lambda f: f[idxValue]):\n attr = feature.attributes()\n currValue = attr[idxValue]\n\n if currFeatureID == currValue:\n found = True\n return found\n\n return found","def check_featuring(self):\n existing_featuring = pd.read_csv(self.path_checkpoint)\n array_to_check = [float(self.radious), self.type_feature, self.type_filtering, self.h_filterig]\n bool_answer = (existing_featuring == array_to_check).all(1).any()\n # self.file_checkpoint_data.close()\n return bool_answer","def __contains__(self, feature):\n return feature == 'cvarsort' or feature in self.features","def important_features_(self):\n return self.scores_ > self.score_cutoff_","def __contains__(self, feature):\n return feature in self.features","def need_feature_generation(self):\n if self.feature_cmd_params:\n return True\n return False","def need_feature_generation(self):\n if self.feature_cmd_params:\n return True\n return False","def _isFIdx(self, featureName):\n return 1 if (featureName in self.featureNames) else 0","def filter_feature(feature, typ, value):\n return value is None or feature.__getattribute__(typ) == value","def has_feature(self, feature):\n features = self.features\n if features is None:\n return False\n \n return feature in features","def func4(key):\n return key in list(my_test_dict.keys())","def has_keys(self) -> bool:\n \n for key, value in self.key_satified.items():\n if value is not True:\n return False\n return True","def uses_feature(self, fcname):\n used = False\n if any([fcname.upper() in y for y in [x.upper() for x in self._featureclasses]]):\n used = True\n return used","def _get_kmeans_features(self):\n self._validate_kmeans_features()\n if self.kmeans_features == \"auto\":\n if self._get_mode() == \"Explain\":\n return False\n if self._get_mode() == \"Perform\":\n return False\n if self._get_mode() == \"Compete\":\n return True\n if self._get_mode() == \"Optuna\":\n return False\n else:\n return deepcopy(self.kmeans_features)","def fcoe_dirty(self):\n return any(c.dirty or c.renames_remaining for c in self.fcoe_confs)","def HasFOV(self):\n return _gmat_py.Hardware_HasFOV(self)","def func2(key):\n return key in my_test_dict.keys()","def __contains__(self, f) :\n if self.__disc is infinity :\n return True\n \n (s, l) = f\n\n (a, _, c) = apply_GL_to_form(self.__p1list[l], s)\n if not c % self.__level == 0 :\n return False\n\n return a + c < self.index()","def has(self, *args):\n return _ida_hexrays.qvector_ccase_t_has(self, *args)","def any(self):\n for v in self.sects.values():\n if np.any(v):\n return True\n if self.is_full():\n return False\n else:\n return np.any(self.defval)","def __call__(self, feature):\n return self.is_enabled(feature)","def exposes_features(self):\n return self._features_op is not None","def f_exists(self, varname):\r\n return (varname in self.locals_ptr)","def is_zero_dict( dict ):\n has_any_features = False\n for key in dict:\n has_any_features = has_any_features or dict[key]\n\n return not has_any_features","def feature_flags(self):\r\n return self.env_tokens.get('FEATURES', dict())","def has_vectored_fields(self):\r\n return any(ftype.vector for ftype in self._by_number)","def isselected(values, feature, parent):\r\n layername=values[0]\r\n fid = feature.id()\r\n layers = QgsMapLayerRegistry.instance().mapLayers()\r\n try:\r\n layer = layers[layername]\r\n except KeyError:\r\n try:\r\n layer = [l for l in layers.iteritems() if l[1].name() == layername][0][1]\r\n except IndexError:\r\n parent.setEvalErrorString( u'No layer with id or name {} found'.format( layername ) )\r\n return False\r\n\r\n return fid in layer.selectedFeaturesIds()","def all(self):\n for v in self.sects.values():\n if not np.all(v):\n return False\n if self.is_full():\n return True\n else:\n return np.all(self.defval)","def has(self, *args):\n return _ida_hexrays.qvector_carg_t_has(self, *args)","def is_whitelisted(self, fkey):\n return fkey in self.whitelist","def validate_features(self):\n # assert len(self.features) > 0\n if self.source != INPUT_CONTEXT:\n for feature in self.features:\n if feature.key == [DUMMY_KEY]:\n raise RuntimeError(f\"For anchors of non-INPUT_CONTEXT source, key of feature {feature.name} \"\n f\"should be explicitly specified and not left blank.\")","def is_feature_installed(client_bin, module, feature_name, feature_version):\n\n cmd = CLIENT_KARAF_COMMAND.format(client_bin, 'list -i')\n rc, out, err = module.run_command(cmd)\n lines = out.split('\\n')\n \n if not feature_version:\n feature_version = ''\n\n # Feature version in karaf use . instead of - when feature is deployed.\n # For instance, snapshot version will be 1.0.0.SNAPSHOT instead of 1.0.0-SNAPSHOT\n feature_version = feature_version.replace('-', '.')\n\n is_installed = False\n for line in lines:\n feature_data = line.split(_KARAF_COLUMN_SEPARATOR)\n if len(feature_data) < 4:\n continue\n \n name = feature_data[0].strip()\n version = feature_data[1].strip()\n state = feature_data[3].strip()\n \n if name != feature_name:\n continue\n \n if state != FEATURE_STATE_UNINSTALLED:\n if feature_version:\n if version == feature_version:\n is_installed = True\n return is_installed\n else:\n is_installed = True\n return is_installed\n\n return is_installed","def test_feature_is_filtered(self):\n\n # Duplicate 1st row in var and assigned to 2nd\n self.validator.adata.var[\"feature_is_filtered\"][0] = True\n for i in range(self.validator.adata.X.shape[0]):\n self.validator.adata.X[i, 0] = 0\n self.validator.adata.X[0, 0] = 1\n\n self.validator.validate_adata()\n self.assertEqual(\n self.validator.errors,\n [\n \"ERROR: Some features are 'True' in 'feature_is_filtered' of dataframe 'var', \"\n \"but there are 1 non-zero values in the corresponding columns of the matrix 'X'. \"\n \"All values for these features must be 0.\"\n ],\n )","def is_valid_input(features) -> bool:\n for index, feature in enumerate(features):\n log.info(f\"Validating feature {index}: {feature}\")\n\n try:\n float(feature)\n except ValueError as value_error:\n log.error(value_error)\n return False\n\n log.info(\"Input successfully validated\")\n return True","def is_weak(self):\n\n # set the minimum number of keypooints\n keypoint_threshold = 20 if self.detector_method == 'FAST'\\\n else 5\n \n # check if the tracker has less than minimum keypoints to track\n c1 = self.old_points.shape[0] < keypoint_threshold\n \n x,y,w,h = self.bounding_box\n row, col = self.fg_mask.shape\n \n # check if the window is out of the frame\n c2 = x >= col-1 or x < 0\n c3 = y >= row-1 or y < 0\n c4 = x+w >= col-1\n c5 = y+h >= row-1\n \n return c1+c2+c3+c4+c5","def legal_for(self, fvs : {EVar}) -> bool:\n vs = {(v, v.type) for (v, pool) in self.vars()}\n return all((v, v.type) in vs for v in fvs)","def check_keys(self):","def __check_feature(f,stopwords):\n if f == \"\" or f == None:\n return None\n if f == \"RT\":\n return False\n if f == \"via\":\n return False\n if len(re.findall(r\"(\\w)\", f)) < 1:\n return False\n if f == \"&\":\n return False\n if f in stopwords:\n return False\n if len(f) < 2:\n return False\n else:\n return True","def _validate_or_make_feature(dic_fc, num_img):\n # bol_found_numf = False\n num_il = -999 # number in list initialise\n if isinstance(dic_fc, dict):\n if isinstance(num_img, int):\n # print(f\" = dic_fc: {dic_fc}\")\n if \"type\" in dic_fc.keys() and dic_fc[\"type\"] == \"FeatureCollection\":\n if \"features\" in dic_fc.keys() and isinstance(dic_fc[\"features\"], list):\n num_feat = -1\n for fet in dic_fc[\"features\"]:\n num_feat += 1\n if isinstance(fet, dict):\n if fet[\"type\"] == \"Feature\":\n if \"properties\" in fet.keys():\n if dic_fc[\"features\"][num_feat][\"properties\"][\"numi\"] == num_img:\n num_il = num_feat\n break\n else:\n print(f\"ERR: in _val_or_make...() dic_fc feature has no properties key\")\n return dic_fc\n else:\n print(f\"ERR: in _val_or_make...() dic_fc has element of not-Feature type: {fet['type']}\")\n return dic_fc\n else:\n print(f\"ERR: in _val_or_make...() dic_fc feature is not dict: {str(type(fet))}\")\n return dic_fc\n else:\n print(f\"ERR: in _val_or_make...() dic_fc has no key: features of type list\")\n return dic_fc\n else:\n print(f\"ERR: in _val_or_make...() dic_fc has no key: type == FeatureCollection\")\n return dic_fc\n else:\n print(f\"ERR: in _val_or_make...() received non-int as num_f\")\n return dic_fc\n else:\n print(f\"ERR: in _val_or_make...() received non-dict as dict_fc\")\n return dic_fc\n if num_il < 0:\n new_feature = {\"type\": \"Feature\", \"geometry\": {\"type\": \"Point\", \"coordinates\": [0, 0]}, \"properties\": {\"numi\": 0}}\n new_feature[\"properties\"][\"numi\"] = num_img\n dic_fc[\"features\"].append(new_feature)\n num_il = len(dic_fc[\"features\"]) - 1\n return dic_fc, num_il","def return_flag_on_feature(self, feature, pos_tag_list):\n for i in pos_tag_list:\n if i[1] == feature:\n return True\n return False","def has_x(self):\n return any(map(lambda s: s.is_x, self))","def has_feature_access(self, feature):\n\n return self.has_feature_access_by_id(feature.value[0])","def testgeovalues_scfvalues(self):\r\n count_scfvalues = len(self.data.scfvalues) - self.extrascfs\r\n count_geovalues = len(self.data.geovalues)\r\n assert count_scfvalues == count_geovalues","def has_function(faker, key):\n return hasattr(faker, key)","def is_FSAL(self):\n if np.all(self.A[-1,:]==self.b): return True\n else: return False","def feature_one(ds, tup):\n # try:\n # if (nx.shortest_path_length(G, frm, to) == 1):\n # o1.write(\"trusted\\n\")\n # else:\n # o1.write(\"unverified\\n\")\n # except:\n # o1.write(\"unverified\\n\")\n\n return tup[0] in ds[tup[1]]","def has_data(self):\n return ([0] != self.__contexts) and ([0] != self.__weights)","def grfn_var_exists(self, fullid: str):\n return fullid in self.fullid_to_grfn_id","def _query(self, feature: str) -> np.ndarray:\n return np.flatnonzero(np.core.defchararray.find(self.internal_types, feature) != -1)","def check_supported_features(self):","def has(self, key: str) -> Any:\n return key in self.variables","def hasFeature(self, Union, QDesignerFormWindowInterface_Feature=None, QDesignerFormWindowInterface_FeatureFlag=None): # real signature unknown; restored from __doc__\n return False","def __contains__(self, f) :\n if self.__disc is infinity :\n return True\n \n (s, l) = f\n\n (a, b, c) = apply_GL_to_form(self.__p1list[l], s)\n if not c % self.__level == 0 :\n return False\n \n disc = 4*a*c - b**2\n if disc == 0 :\n return gcd([a,b,c]) < self._indefinite_content_bound()\n else :\n return disc < self.__disc","def is_ftf(self):\n g = self.get_gene().get_seq()\n if 'd' != g[1]:\n return False\n if not len(g) >= 4:\n return False\n for x in range(2, len(g)):\n dec = 'c' if x % 2 == 0 else 'd'\n if dec != g[x]:\n return False\n return True","def component_is_zero(self, key):\n a = self[key]\n return not np.any(a)","def has(self, *args):\n return _ida_hexrays.qvector_lvar_t_has(self, *args)","def member(self, e):\r\n return e in self.vals","def is_valid(self):\n posit1 = (self.mean_v > 0) & (self.kappa_y > 0) & (self.eta_y > 0)\n posit2 = (self.kappa_s > 0) & (self.eta_s > 0)\n return posit1 & posit2 & self.feller()","def has_efg_tensors(self) -> bool:\n return self._efg_tensors is not None","def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\n file_path = join(output_path, subset + '.npy')\n return os.path.exists(file_path)","def is_freq_flat(self):\n return (\n len(self.energy_absorption[\"coeffs\"]) == 1\n and len(self.scattering[\"coeffs\"]) == 1\n )","def fx_hedged(self) -> bool:\n return self.__fx_hedged","def get_features(feature_list, these_feature):\n features = {}\n def feat_filter(feature, this):\n try:\n mapper = lambda x, feat: filter(lambda y: feat in y, x.split(\" \"))[0]\n val = mapper(this, feature)\n if '+' in val:\n return TRUE\n return FALSE\n except:\n return UNDEF\n for feat in feature_list:\n features[feat] = feat_filter(feat, these_feature)\n return features","def contains(self, key):\n\t\tfor i in self.getBitArrayIndices(key):\n\t\t\tif self.ba[i] <= 0:\n\t\t\t\treturn False\n\t\treturn True","def contains_strict(self, gi):\n if gi is None:\n return False\n for gi_obj in self.gradual_items:\n if (gi.attribute_col == gi_obj.attribute_col) and (gi.symbol == gi_obj.symbol):\n return True\n return False","def _check_features_df(df, features):\n # check columns\n if not set(features).issubset(df.columns):\n raise ValueError(\"The dataframe does not seem to have the right \"\n \"features. {0} instead of {1}\"\n .format(df.columns, features))\n\n return","def any_open_valve():\n for dev in station_valve_dict:\n if station_valve_dict[dev]:\n return True\n return False","def f_supports_fast_access(self):\n return len(self._data) == 1 and self.v_name in self._data","def _check_nonzero_features_exist_for_edge(self, features: np.ndarray,\n added_edge_idx: np.ndarray):\n features_of_added_edges = features[added_edge_idx[0], added_edge_idx[1]]\n self.assertGreater(np.nonzero(features_of_added_edges)[0].shape[0], 0)","def valid_key (k, aging_hash, frequency_threshold):\n\n purge_expired(aging_hash)\n current_val = update_aging_hash(aging_hash, k)\n return current_val[1] <= frequency_threshold","def flagSet():\r\n for flag in flags:\r\n if flags[flag]:\r\n return True\r\n return False","def _get_feature_support(self):\r\n res = super(AcquirerbKash, self)._get_feature_support()\r\n res['fees'].append('bkash')\r\n return res","def _has_numeric_strict(self) -> bool:\n return bool({'i', 'f'} & self._data.keys())","def should_run_scf(self):\n return self.ctx.should_run_scf","def has_action2(self, feature):\n return feature in self._action2","def _check_extend_function(self):\n for function_name, parameters in self._extend_function_dic.iteritems():\n if not apply(function_name, parameters):\n return False\n return True","def member(self, e):\n return e in self.vals","def member(self, e):\n return e in self.vals","def member(self, e):\n return e in self.vals","def member(self, e):\n return e in self.vals","def _include_feature(self, name):\n return (self._feature_names is None or name in self._feature_names or\n name.startswith(self._neighbor_config.prefix))","def __contains__(self, value):\n return value in self.var_names","def __contains__(self, item):\n return item in self.__keys or item in self.__vals","def __contains__(self, k) :\n return k in self.precision()","def scf_needed(self):\n return self.ctx.scf_needed","def safe_compute_features(*args):\n song_id, entity_type, crop, transform_config, features_config = args\n try:\n tf.logging.info(f\"Compute features for {song_id} on segment {crop}.\")\n features,_ = compute_features(song_id, entity_type, crop, transform_config, features_config)\n return features, False\n\n except Exception as err:\n tf.logging.warn(f\"Error while computing features for {song_id} on segment {crop}: {err}\")\n return np.float32(0.0), True","def check(self, k, x):\n k = self._checkIndex(k)\n return bool(self.caches[k].count(x))","def hasIntersectedWith(self, f):\n try:\n return f in self.hasIntersected\n except AttributeError:\n return False","def hasValue(self, key):\n return self.has_key('__' + key)","def has(self, key):","def has_cached_cfunc(*args):\n return _ida_hexrays.has_cached_cfunc(*args)","def has(key):\n return not not (key in current().values)","def __contains__(self, key):\n return key in self.keys","def all(self, key: Callable[[T], bool]=None) -> bool:\n if key is None:\n return all(self.array)\n return all(key(x) for x in self.array)","def isFeas(self, referencetuple):\n return referencetuple[self.__primalidx__] == self.__feas__","def valid(self):\n return len(self.wvalues) != 0","def __contains__(self, arg):\r\n\r\n return arg in self.grfx[0]"],"string":"[\n \"def _check_feature_by_keys(service_data=None, service_keys=None, ns_data=None, ns_keys=None):\\n\\n if service_data and not isinstance(service_data, Exception) and service_keys:\\n if _is_keyval_greater_than_value(service_data, service_keys):\\n return True\\n\\n if ns_data and ns_keys:\\n for ns, nsval in ns_data.iteritems():\\n if not nsval or isinstance(nsval, Exception):\\n continue\\n if _is_keyval_greater_than_value(nsval, ns_keys):\\n return True\\n\\n return False\",\n \"def __check_features(f_list, stopwords):\\n ok = True\\n for f in f_list:\\n if not(__check_feature(f,stopwords)):\\n return False\\n return True\",\n \"def match_features(phone_feats, other_feats):\\n for feat in other_feats.keys():\\n if phone_feats[feat] != other_feats[feat] and other_feats[feat] != UNDEF:\\n return False\\n return True\",\n \"def checkFeatureInSet(self, featureSet, currFeature, idxValue):\\n\\n found = False\\n currFeatureID = currFeature[idxValue]\\n\\n for feature in sorted(featureSet, key=lambda f: f[idxValue]):\\n attr = feature.attributes()\\n currValue = attr[idxValue]\\n\\n if currFeatureID == currValue:\\n found = True\\n return found\\n\\n return found\",\n \"def check_featuring(self):\\n existing_featuring = pd.read_csv(self.path_checkpoint)\\n array_to_check = [float(self.radious), self.type_feature, self.type_filtering, self.h_filterig]\\n bool_answer = (existing_featuring == array_to_check).all(1).any()\\n # self.file_checkpoint_data.close()\\n return bool_answer\",\n \"def __contains__(self, feature):\\n return feature == 'cvarsort' or feature in self.features\",\n \"def important_features_(self):\\n return self.scores_ > self.score_cutoff_\",\n \"def __contains__(self, feature):\\n return feature in self.features\",\n \"def need_feature_generation(self):\\n if self.feature_cmd_params:\\n return True\\n return False\",\n \"def need_feature_generation(self):\\n if self.feature_cmd_params:\\n return True\\n return False\",\n \"def _isFIdx(self, featureName):\\n return 1 if (featureName in self.featureNames) else 0\",\n \"def filter_feature(feature, typ, value):\\n return value is None or feature.__getattribute__(typ) == value\",\n \"def has_feature(self, feature):\\n features = self.features\\n if features is None:\\n return False\\n \\n return feature in features\",\n \"def func4(key):\\n return key in list(my_test_dict.keys())\",\n \"def has_keys(self) -> bool:\\n \\n for key, value in self.key_satified.items():\\n if value is not True:\\n return False\\n return True\",\n \"def uses_feature(self, fcname):\\n used = False\\n if any([fcname.upper() in y for y in [x.upper() for x in self._featureclasses]]):\\n used = True\\n return used\",\n \"def _get_kmeans_features(self):\\n self._validate_kmeans_features()\\n if self.kmeans_features == \\\"auto\\\":\\n if self._get_mode() == \\\"Explain\\\":\\n return False\\n if self._get_mode() == \\\"Perform\\\":\\n return False\\n if self._get_mode() == \\\"Compete\\\":\\n return True\\n if self._get_mode() == \\\"Optuna\\\":\\n return False\\n else:\\n return deepcopy(self.kmeans_features)\",\n \"def fcoe_dirty(self):\\n return any(c.dirty or c.renames_remaining for c in self.fcoe_confs)\",\n \"def HasFOV(self):\\n return _gmat_py.Hardware_HasFOV(self)\",\n \"def func2(key):\\n return key in my_test_dict.keys()\",\n \"def __contains__(self, f) :\\n if self.__disc is infinity :\\n return True\\n \\n (s, l) = f\\n\\n (a, _, c) = apply_GL_to_form(self.__p1list[l], s)\\n if not c % self.__level == 0 :\\n return False\\n\\n return a + c < self.index()\",\n \"def has(self, *args):\\n return _ida_hexrays.qvector_ccase_t_has(self, *args)\",\n \"def any(self):\\n for v in self.sects.values():\\n if np.any(v):\\n return True\\n if self.is_full():\\n return False\\n else:\\n return np.any(self.defval)\",\n \"def __call__(self, feature):\\n return self.is_enabled(feature)\",\n \"def exposes_features(self):\\n return self._features_op is not None\",\n \"def f_exists(self, varname):\\r\\n return (varname in self.locals_ptr)\",\n \"def is_zero_dict( dict ):\\n has_any_features = False\\n for key in dict:\\n has_any_features = has_any_features or dict[key]\\n\\n return not has_any_features\",\n \"def feature_flags(self):\\r\\n return self.env_tokens.get('FEATURES', dict())\",\n \"def has_vectored_fields(self):\\r\\n return any(ftype.vector for ftype in self._by_number)\",\n \"def isselected(values, feature, parent):\\r\\n layername=values[0]\\r\\n fid = feature.id()\\r\\n layers = QgsMapLayerRegistry.instance().mapLayers()\\r\\n try:\\r\\n layer = layers[layername]\\r\\n except KeyError:\\r\\n try:\\r\\n layer = [l for l in layers.iteritems() if l[1].name() == layername][0][1]\\r\\n except IndexError:\\r\\n parent.setEvalErrorString( u'No layer with id or name {} found'.format( layername ) )\\r\\n return False\\r\\n\\r\\n return fid in layer.selectedFeaturesIds()\",\n \"def all(self):\\n for v in self.sects.values():\\n if not np.all(v):\\n return False\\n if self.is_full():\\n return True\\n else:\\n return np.all(self.defval)\",\n \"def has(self, *args):\\n return _ida_hexrays.qvector_carg_t_has(self, *args)\",\n \"def is_whitelisted(self, fkey):\\n return fkey in self.whitelist\",\n \"def validate_features(self):\\n # assert len(self.features) > 0\\n if self.source != INPUT_CONTEXT:\\n for feature in self.features:\\n if feature.key == [DUMMY_KEY]:\\n raise RuntimeError(f\\\"For anchors of non-INPUT_CONTEXT source, key of feature {feature.name} \\\"\\n f\\\"should be explicitly specified and not left blank.\\\")\",\n \"def is_feature_installed(client_bin, module, feature_name, feature_version):\\n\\n cmd = CLIENT_KARAF_COMMAND.format(client_bin, 'list -i')\\n rc, out, err = module.run_command(cmd)\\n lines = out.split('\\\\n')\\n \\n if not feature_version:\\n feature_version = ''\\n\\n # Feature version in karaf use . instead of - when feature is deployed.\\n # For instance, snapshot version will be 1.0.0.SNAPSHOT instead of 1.0.0-SNAPSHOT\\n feature_version = feature_version.replace('-', '.')\\n\\n is_installed = False\\n for line in lines:\\n feature_data = line.split(_KARAF_COLUMN_SEPARATOR)\\n if len(feature_data) < 4:\\n continue\\n \\n name = feature_data[0].strip()\\n version = feature_data[1].strip()\\n state = feature_data[3].strip()\\n \\n if name != feature_name:\\n continue\\n \\n if state != FEATURE_STATE_UNINSTALLED:\\n if feature_version:\\n if version == feature_version:\\n is_installed = True\\n return is_installed\\n else:\\n is_installed = True\\n return is_installed\\n\\n return is_installed\",\n \"def test_feature_is_filtered(self):\\n\\n # Duplicate 1st row in var and assigned to 2nd\\n self.validator.adata.var[\\\"feature_is_filtered\\\"][0] = True\\n for i in range(self.validator.adata.X.shape[0]):\\n self.validator.adata.X[i, 0] = 0\\n self.validator.adata.X[0, 0] = 1\\n\\n self.validator.validate_adata()\\n self.assertEqual(\\n self.validator.errors,\\n [\\n \\\"ERROR: Some features are 'True' in 'feature_is_filtered' of dataframe 'var', \\\"\\n \\\"but there are 1 non-zero values in the corresponding columns of the matrix 'X'. \\\"\\n \\\"All values for these features must be 0.\\\"\\n ],\\n )\",\n \"def is_valid_input(features) -> bool:\\n for index, feature in enumerate(features):\\n log.info(f\\\"Validating feature {index}: {feature}\\\")\\n\\n try:\\n float(feature)\\n except ValueError as value_error:\\n log.error(value_error)\\n return False\\n\\n log.info(\\\"Input successfully validated\\\")\\n return True\",\n \"def is_weak(self):\\n\\n # set the minimum number of keypooints\\n keypoint_threshold = 20 if self.detector_method == 'FAST'\\\\\\n else 5\\n \\n # check if the tracker has less than minimum keypoints to track\\n c1 = self.old_points.shape[0] < keypoint_threshold\\n \\n x,y,w,h = self.bounding_box\\n row, col = self.fg_mask.shape\\n \\n # check if the window is out of the frame\\n c2 = x >= col-1 or x < 0\\n c3 = y >= row-1 or y < 0\\n c4 = x+w >= col-1\\n c5 = y+h >= row-1\\n \\n return c1+c2+c3+c4+c5\",\n \"def legal_for(self, fvs : {EVar}) -> bool:\\n vs = {(v, v.type) for (v, pool) in self.vars()}\\n return all((v, v.type) in vs for v in fvs)\",\n \"def check_keys(self):\",\n \"def __check_feature(f,stopwords):\\n if f == \\\"\\\" or f == None:\\n return None\\n if f == \\\"RT\\\":\\n return False\\n if f == \\\"via\\\":\\n return False\\n if len(re.findall(r\\\"(\\\\w)\\\", f)) < 1:\\n return False\\n if f == \\\"&\\\":\\n return False\\n if f in stopwords:\\n return False\\n if len(f) < 2:\\n return False\\n else:\\n return True\",\n \"def _validate_or_make_feature(dic_fc, num_img):\\n # bol_found_numf = False\\n num_il = -999 # number in list initialise\\n if isinstance(dic_fc, dict):\\n if isinstance(num_img, int):\\n # print(f\\\" = dic_fc: {dic_fc}\\\")\\n if \\\"type\\\" in dic_fc.keys() and dic_fc[\\\"type\\\"] == \\\"FeatureCollection\\\":\\n if \\\"features\\\" in dic_fc.keys() and isinstance(dic_fc[\\\"features\\\"], list):\\n num_feat = -1\\n for fet in dic_fc[\\\"features\\\"]:\\n num_feat += 1\\n if isinstance(fet, dict):\\n if fet[\\\"type\\\"] == \\\"Feature\\\":\\n if \\\"properties\\\" in fet.keys():\\n if dic_fc[\\\"features\\\"][num_feat][\\\"properties\\\"][\\\"numi\\\"] == num_img:\\n num_il = num_feat\\n break\\n else:\\n print(f\\\"ERR: in _val_or_make...() dic_fc feature has no properties key\\\")\\n return dic_fc\\n else:\\n print(f\\\"ERR: in _val_or_make...() dic_fc has element of not-Feature type: {fet['type']}\\\")\\n return dic_fc\\n else:\\n print(f\\\"ERR: in _val_or_make...() dic_fc feature is not dict: {str(type(fet))}\\\")\\n return dic_fc\\n else:\\n print(f\\\"ERR: in _val_or_make...() dic_fc has no key: features of type list\\\")\\n return dic_fc\\n else:\\n print(f\\\"ERR: in _val_or_make...() dic_fc has no key: type == FeatureCollection\\\")\\n return dic_fc\\n else:\\n print(f\\\"ERR: in _val_or_make...() received non-int as num_f\\\")\\n return dic_fc\\n else:\\n print(f\\\"ERR: in _val_or_make...() received non-dict as dict_fc\\\")\\n return dic_fc\\n if num_il < 0:\\n new_feature = {\\\"type\\\": \\\"Feature\\\", \\\"geometry\\\": {\\\"type\\\": \\\"Point\\\", \\\"coordinates\\\": [0, 0]}, \\\"properties\\\": {\\\"numi\\\": 0}}\\n new_feature[\\\"properties\\\"][\\\"numi\\\"] = num_img\\n dic_fc[\\\"features\\\"].append(new_feature)\\n num_il = len(dic_fc[\\\"features\\\"]) - 1\\n return dic_fc, num_il\",\n \"def return_flag_on_feature(self, feature, pos_tag_list):\\n for i in pos_tag_list:\\n if i[1] == feature:\\n return True\\n return False\",\n \"def has_x(self):\\n return any(map(lambda s: s.is_x, self))\",\n \"def has_feature_access(self, feature):\\n\\n return self.has_feature_access_by_id(feature.value[0])\",\n \"def testgeovalues_scfvalues(self):\\r\\n count_scfvalues = len(self.data.scfvalues) - self.extrascfs\\r\\n count_geovalues = len(self.data.geovalues)\\r\\n assert count_scfvalues == count_geovalues\",\n \"def has_function(faker, key):\\n return hasattr(faker, key)\",\n \"def is_FSAL(self):\\n if np.all(self.A[-1,:]==self.b): return True\\n else: return False\",\n \"def feature_one(ds, tup):\\n # try:\\n # if (nx.shortest_path_length(G, frm, to) == 1):\\n # o1.write(\\\"trusted\\\\n\\\")\\n # else:\\n # o1.write(\\\"unverified\\\\n\\\")\\n # except:\\n # o1.write(\\\"unverified\\\\n\\\")\\n\\n return tup[0] in ds[tup[1]]\",\n \"def has_data(self):\\n return ([0] != self.__contexts) and ([0] != self.__weights)\",\n \"def grfn_var_exists(self, fullid: str):\\n return fullid in self.fullid_to_grfn_id\",\n \"def _query(self, feature: str) -> np.ndarray:\\n return np.flatnonzero(np.core.defchararray.find(self.internal_types, feature) != -1)\",\n \"def check_supported_features(self):\",\n \"def has(self, key: str) -> Any:\\n return key in self.variables\",\n \"def hasFeature(self, Union, QDesignerFormWindowInterface_Feature=None, QDesignerFormWindowInterface_FeatureFlag=None): # real signature unknown; restored from __doc__\\n return False\",\n \"def __contains__(self, f) :\\n if self.__disc is infinity :\\n return True\\n \\n (s, l) = f\\n\\n (a, b, c) = apply_GL_to_form(self.__p1list[l], s)\\n if not c % self.__level == 0 :\\n return False\\n \\n disc = 4*a*c - b**2\\n if disc == 0 :\\n return gcd([a,b,c]) < self._indefinite_content_bound()\\n else :\\n return disc < self.__disc\",\n \"def is_ftf(self):\\n g = self.get_gene().get_seq()\\n if 'd' != g[1]:\\n return False\\n if not len(g) >= 4:\\n return False\\n for x in range(2, len(g)):\\n dec = 'c' if x % 2 == 0 else 'd'\\n if dec != g[x]:\\n return False\\n return True\",\n \"def component_is_zero(self, key):\\n a = self[key]\\n return not np.any(a)\",\n \"def has(self, *args):\\n return _ida_hexrays.qvector_lvar_t_has(self, *args)\",\n \"def member(self, e):\\r\\n return e in self.vals\",\n \"def is_valid(self):\\n posit1 = (self.mean_v > 0) & (self.kappa_y > 0) & (self.eta_y > 0)\\n posit2 = (self.kappa_s > 0) & (self.eta_s > 0)\\n return posit1 & posit2 & self.feller()\",\n \"def has_efg_tensors(self) -> bool:\\n return self._efg_tensors is not None\",\n \"def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\\n file_path = join(output_path, subset + '.npy')\\n return os.path.exists(file_path)\",\n \"def is_freq_flat(self):\\n return (\\n len(self.energy_absorption[\\\"coeffs\\\"]) == 1\\n and len(self.scattering[\\\"coeffs\\\"]) == 1\\n )\",\n \"def fx_hedged(self) -> bool:\\n return self.__fx_hedged\",\n \"def get_features(feature_list, these_feature):\\n features = {}\\n def feat_filter(feature, this):\\n try:\\n mapper = lambda x, feat: filter(lambda y: feat in y, x.split(\\\" \\\"))[0]\\n val = mapper(this, feature)\\n if '+' in val:\\n return TRUE\\n return FALSE\\n except:\\n return UNDEF\\n for feat in feature_list:\\n features[feat] = feat_filter(feat, these_feature)\\n return features\",\n \"def contains(self, key):\\n\\t\\tfor i in self.getBitArrayIndices(key):\\n\\t\\t\\tif self.ba[i] <= 0:\\n\\t\\t\\t\\treturn False\\n\\t\\treturn True\",\n \"def contains_strict(self, gi):\\n if gi is None:\\n return False\\n for gi_obj in self.gradual_items:\\n if (gi.attribute_col == gi_obj.attribute_col) and (gi.symbol == gi_obj.symbol):\\n return True\\n return False\",\n \"def _check_features_df(df, features):\\n # check columns\\n if not set(features).issubset(df.columns):\\n raise ValueError(\\\"The dataframe does not seem to have the right \\\"\\n \\\"features. {0} instead of {1}\\\"\\n .format(df.columns, features))\\n\\n return\",\n \"def any_open_valve():\\n for dev in station_valve_dict:\\n if station_valve_dict[dev]:\\n return True\\n return False\",\n \"def f_supports_fast_access(self):\\n return len(self._data) == 1 and self.v_name in self._data\",\n \"def _check_nonzero_features_exist_for_edge(self, features: np.ndarray,\\n added_edge_idx: np.ndarray):\\n features_of_added_edges = features[added_edge_idx[0], added_edge_idx[1]]\\n self.assertGreater(np.nonzero(features_of_added_edges)[0].shape[0], 0)\",\n \"def valid_key (k, aging_hash, frequency_threshold):\\n\\n purge_expired(aging_hash)\\n current_val = update_aging_hash(aging_hash, k)\\n return current_val[1] <= frequency_threshold\",\n \"def flagSet():\\r\\n for flag in flags:\\r\\n if flags[flag]:\\r\\n return True\\r\\n return False\",\n \"def _get_feature_support(self):\\r\\n res = super(AcquirerbKash, self)._get_feature_support()\\r\\n res['fees'].append('bkash')\\r\\n return res\",\n \"def _has_numeric_strict(self) -> bool:\\n return bool({'i', 'f'} & self._data.keys())\",\n \"def should_run_scf(self):\\n return self.ctx.should_run_scf\",\n \"def has_action2(self, feature):\\n return feature in self._action2\",\n \"def _check_extend_function(self):\\n for function_name, parameters in self._extend_function_dic.iteritems():\\n if not apply(function_name, parameters):\\n return False\\n return True\",\n \"def member(self, e):\\n return e in self.vals\",\n \"def member(self, e):\\n return e in self.vals\",\n \"def member(self, e):\\n return e in self.vals\",\n \"def member(self, e):\\n return e in self.vals\",\n \"def _include_feature(self, name):\\n return (self._feature_names is None or name in self._feature_names or\\n name.startswith(self._neighbor_config.prefix))\",\n \"def __contains__(self, value):\\n return value in self.var_names\",\n \"def __contains__(self, item):\\n return item in self.__keys or item in self.__vals\",\n \"def __contains__(self, k) :\\n return k in self.precision()\",\n \"def scf_needed(self):\\n return self.ctx.scf_needed\",\n \"def safe_compute_features(*args):\\n song_id, entity_type, crop, transform_config, features_config = args\\n try:\\n tf.logging.info(f\\\"Compute features for {song_id} on segment {crop}.\\\")\\n features,_ = compute_features(song_id, entity_type, crop, transform_config, features_config)\\n return features, False\\n\\n except Exception as err:\\n tf.logging.warn(f\\\"Error while computing features for {song_id} on segment {crop}: {err}\\\")\\n return np.float32(0.0), True\",\n \"def check(self, k, x):\\n k = self._checkIndex(k)\\n return bool(self.caches[k].count(x))\",\n \"def hasIntersectedWith(self, f):\\n try:\\n return f in self.hasIntersected\\n except AttributeError:\\n return False\",\n \"def hasValue(self, key):\\n return self.has_key('__' + key)\",\n \"def has(self, key):\",\n \"def has_cached_cfunc(*args):\\n return _ida_hexrays.has_cached_cfunc(*args)\",\n \"def has(key):\\n return not not (key in current().values)\",\n \"def __contains__(self, key):\\n return key in self.keys\",\n \"def all(self, key: Callable[[T], bool]=None) -> bool:\\n if key is None:\\n return all(self.array)\\n return all(key(x) for x in self.array)\",\n \"def isFeas(self, referencetuple):\\n return referencetuple[self.__primalidx__] == self.__feas__\",\n \"def valid(self):\\n return len(self.wvalues) != 0\",\n \"def __contains__(self, arg):\\r\\n\\r\\n return arg in self.grfx[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6286112","0.60849124","0.5997217","0.58951616","0.5878267","0.5877975","0.58187664","0.57946813","0.5696939","0.5696939","0.564731","0.56072927","0.55438966","0.5499882","0.5431996","0.5373477","0.53706175","0.53663695","0.536493","0.53626704","0.5316639","0.5302235","0.5296485","0.52895844","0.5287636","0.5281923","0.52775675","0.524521","0.524083","0.52370715","0.52312326","0.5230682","0.5223794","0.5201825","0.5156696","0.51551473","0.5154262","0.5153594","0.5141591","0.51414925","0.51386815","0.51381963","0.5132972","0.51141554","0.51128286","0.5111007","0.5102698","0.509089","0.5089104","0.5080835","0.5077226","0.50739515","0.5072997","0.50728816","0.5067266","0.5044658","0.5007772","0.50008583","0.4998582","0.49985313","0.499835","0.4995744","0.49903175","0.4984349","0.49819326","0.4973202","0.49598038","0.4959074","0.49559227","0.49439737","0.4943077","0.4935563","0.4934481","0.49336082","0.49330038","0.49314496","0.49209425","0.4920658","0.4912527","0.4911153","0.4911153","0.4911153","0.4911153","0.4909844","0.49009025","0.48993528","0.4890501","0.48819742","0.48790982","0.48778892","0.48767662","0.48615184","0.48599094","0.48597732","0.4856445","0.48550928","0.4854683","0.48504925","0.4849139","0.48436424"],"string":"[\n \"0.6286112\",\n \"0.60849124\",\n \"0.5997217\",\n \"0.58951616\",\n \"0.5878267\",\n \"0.5877975\",\n \"0.58187664\",\n \"0.57946813\",\n \"0.5696939\",\n \"0.5696939\",\n \"0.564731\",\n \"0.56072927\",\n \"0.55438966\",\n \"0.5499882\",\n \"0.5431996\",\n \"0.5373477\",\n \"0.53706175\",\n \"0.53663695\",\n \"0.536493\",\n \"0.53626704\",\n \"0.5316639\",\n \"0.5302235\",\n \"0.5296485\",\n \"0.52895844\",\n \"0.5287636\",\n \"0.5281923\",\n \"0.52775675\",\n \"0.524521\",\n \"0.524083\",\n \"0.52370715\",\n \"0.52312326\",\n \"0.5230682\",\n \"0.5223794\",\n \"0.5201825\",\n \"0.5156696\",\n \"0.51551473\",\n \"0.5154262\",\n \"0.5153594\",\n \"0.5141591\",\n \"0.51414925\",\n \"0.51386815\",\n \"0.51381963\",\n \"0.5132972\",\n \"0.51141554\",\n \"0.51128286\",\n \"0.5111007\",\n \"0.5102698\",\n \"0.509089\",\n \"0.5089104\",\n \"0.5080835\",\n \"0.5077226\",\n \"0.50739515\",\n \"0.5072997\",\n \"0.50728816\",\n \"0.5067266\",\n \"0.5044658\",\n \"0.5007772\",\n \"0.50008583\",\n \"0.4998582\",\n \"0.49985313\",\n \"0.499835\",\n \"0.4995744\",\n \"0.49903175\",\n \"0.4984349\",\n \"0.49819326\",\n \"0.4973202\",\n \"0.49598038\",\n \"0.4959074\",\n \"0.49559227\",\n \"0.49439737\",\n \"0.4943077\",\n \"0.4935563\",\n \"0.4934481\",\n \"0.49336082\",\n \"0.49330038\",\n \"0.49314496\",\n \"0.49209425\",\n \"0.4920658\",\n \"0.4912527\",\n \"0.4911153\",\n \"0.4911153\",\n \"0.4911153\",\n \"0.4911153\",\n \"0.4909844\",\n \"0.49009025\",\n \"0.48993528\",\n \"0.4890501\",\n \"0.48819742\",\n \"0.48790982\",\n \"0.48778892\",\n \"0.48767662\",\n \"0.48615184\",\n \"0.48599094\",\n \"0.48597732\",\n \"0.4856445\",\n \"0.48550928\",\n \"0.4854683\",\n \"0.48504925\",\n \"0.4849139\",\n \"0.48436424\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7618906"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13798,"cells":{"query":{"kind":"string","value":"End detection. described in Eq. (50) of S. Watanabe et al \"Hybrid CTC/Attention Architecture for EndtoEnd Speech Recognition\""},"document":{"kind":"string","value":"def end_detect(ended_hyps, i, M=3, d_end=np.log(1 * np.exp(-10))):\n if len(ended_hyps) == 0:\n return False\n count = 0\n best_hyp = sorted(ended_hyps, key=lambda x: x[\"score\"], reverse=True)[0]\n for m in six.moves.range(M):\n # get ended_hyps with their length is i - m\n hyp_length = i - m\n hyps_same_length = [x for x in ended_hyps if\n len(x[\"yseq\"]) == hyp_length]\n if len(hyps_same_length) > 0:\n best_hyp_same_length = sorted(\n hyps_same_length, key=lambda x: x[\"score\"], reverse=True)[0]\n if best_hyp_same_length[\"score\"] - best_hyp[\"score\"] < d_end:\n count += 1\n\n if count == M:\n return True\n else:\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def end_detect(ended_hyps, i, M=3, D_end=np.log(1 * np.exp(-10))):\n if len(ended_hyps) == 0:\n return False\n count = 0\n best_hyp = sorted(ended_hyps, key=lambda x: x[\"score\"], reverse=True)[0]\n for m in range(M):\n # get ended_hyps with their length is i - m\n hyp_length = i - m\n hyps_same_length = [\n x for x in ended_hyps if len(x[\"yseq\"]) == hyp_length\n ]\n if len(hyps_same_length) > 0:\n best_hyp_same_length = sorted(\n hyps_same_length, key=lambda x: x[\"score\"], reverse=True)[0]\n if best_hyp_same_length[\"score\"] - best_hyp[\"score\"] < D_end:\n count += 1\n\n if count == M:\n return True\n else:\n return False","def is_end_episode():\n return patterns_pb2.Condition(is_end_episode=True, eq=1)","def detect_cuewords():\n\n # cuewords\n\n if t_word[:2] == 'ni':\n create_negation_frame()\n create_target_focus_scope()\n\n if t_word[:4] == 'kein':\n create_negation_frame()\n create_target_focus_scope()\n\n if t_word[:4] == 'nein':\n create_negation_frame()\n create_target_focus_scope()","def decide(self, data):\n\n data = struct.unpack('%dh' % (len(data) / 2, ), data)\n self.audio_recorded_in.extend(data)\n\n while len(self.audio_recorded_in) > self.cfg['VAD']['gmm']['framesize']:\n frame = self.audio_recorded_in[:self.cfg['VAD']['gmm']['framesize']]\n self.audio_recorded_in = self.audio_recorded_in[self.cfg['VAD']['gmm']['frameshift']:]\n\n mfcc = self.front_end.param(frame)\n\n log_prob_speech = self.gmm_speech.score(mfcc)\n log_prob_sil = self.gmm_sil.score(mfcc)\n\n self.log_probs_speech.append(log_prob_speech)\n self.log_probs_sil.append(log_prob_sil)\n\n log_prob_speech_avg = 0.0\n for log_prob_speech, log_prob_sil in zip(self.log_probs_speech, self.log_probs_sil):\n log_prob_speech_avg += log_prob_speech - logsumexp([log_prob_speech, log_prob_sil])\n log_prob_speech_avg /= len(self.log_probs_speech)\n\n prob_speech_avg = np.exp(log_prob_speech_avg)\n\n# print 'prob_speech_avg: %5.3f' % prob_speech_avg\n\n self.last_decision = prob_speech_avg\n\n # returns a speech / non-speech decisions\n return self.last_decision","def training_end(self):\n pass","def match_end_faces(test_end_face, end_faces):\n for ef in end_faces:\n if (test_end_face+180) % 360 == ef:\n return True\n return False","def recognize():\n return 0","def\tbegin_end(env, blc):\n\n\tb_e = np.empty((blc.shape[0], 2))\n\tinf = 0\n\tp = 0\n\twin = env.win_over\n\tb_e[0, 0] = inf\n\tif blc[0] + win <= blc[-1]:\n\t\tb_e[0, 1] = blc[0] + win\n\telse:\n\t\tb_e[0, 1] = blc[-1]\n\tif blc.shape[0] == 1:\n\t\tb_e[0, 1] = blc[0]\n\t\treturn (b_e)\n\tfor k in range(1, blc.shape[0] - 1):\n\t\tinf = blc[k - 1] - win\n\t\tb_e[k, 0] = inf\n\t\tif blc[k] + win <= blc[-1]:\n\t\t\tb_e[k, 1] = blc[k] + win\n\t\telse:\n\t\t\tb_e[k, 1] = blc[-1]\n\tb_e[blc.shape[0] - 1, 0] = blc[-2] - win\n\tb_e[blc.shape[0] - 1, 1] = blc[-1]\n\tneg = np.where(b_e < 0)[0]\n\tif neg.shape[0]:\n\t\tb_e = b_e[neg[-1]:]\n\t\tb_e[0, 0] = 0\n\treturn (b_e)","def detect(self, start_time, end_time):\n\n # Convert times to UTCDateTime objects\n start_time = UTCDateTime(start_time)\n end_time = UTCDateTime(end_time)\n\n # Decimate LUT\n self.lut = self.lut.decimate(self.decimate)\n\n # Detect uses the non-centred onset by default\n if self.onset_centred is None:\n self.onset_centred = False\n\n # Define pre-pad as a function of the onset windows\n if self.pre_pad is None:\n self.pre_pad = max(self.p_onset_win[1],\n self.s_onset_win[1]) \\\n + 3 * max(self.p_onset_win[0],\n self.s_onset_win[0])\n\n msg = \"=\" * 120 + \"\\n\"\n msg += \"\\tDETECT - Continuous Seismic Processing\\n\"\n msg += \"=\" * 120 + \"\\n\"\n msg += \"\\n\"\n msg += \"\\tParameters specified:\\n\"\n msg += \"\\t\\tStart time = {}\\n\"\n msg += \"\\t\\tEnd time = {}\\n\"\n msg += \"\\t\\tTime step (s) = {}\\n\"\n msg += \"\\t\\tNumber of CPUs = {}\\n\"\n msg += \"\\n\"\n msg += \"\\t\\tSampling rate = {}\\n\"\n msg += \"\\t\\tGrid decimation [X, Y, Z] = [{}, {}, {}]\\n\"\n msg += \"\\t\\tBandpass filter P = [{}, {}, {}]\\n\"\n msg += \"\\t\\tBandpass filter S = [{}, {}, {}]\\n\"\n msg += \"\\t\\tOnset P [STA, LTA] = [{}, {}]\\n\"\n msg += \"\\t\\tOnset S [STA, LTA] = [{}, {}]\\n\"\n msg += \"\\n\"\n msg += \"=\" * 120\n msg = msg.format(str(start_time), str(end_time), self.time_step,\n self.n_cores, self.sampling_rate,\n self.decimate[0], self.decimate[1], self.decimate[2],\n self.p_bp_filter[0], self.p_bp_filter[1],\n self.p_bp_filter[2], self.s_bp_filter[0],\n self.s_bp_filter[1], self.s_bp_filter[2],\n self.p_onset_win[0], self.p_onset_win[1],\n self.s_onset_win[0], self.s_onset_win[1])\n self.output.log(msg, self.log)\n\n # Detect max coalescence value and location at each time sample\n # within the decimated grid\n self._continuous_compute(start_time, end_time)","def _get_predict_end(self, end):\n\n out_of_sample = 0 # will be overwritten if needed\n if end is None: # use data for ARIMA - endog changes\n end = len(self.data.endog) - 1\n\n dates = self.data.dates\n freq = self.data.freq\n\n if isinstance(end, str):\n if dates is None:\n raise ValueError(\"Got a string for end and dates is None\")\n try:\n dtend = self._str_to_date(end)\n self.data.predict_end = dtend\n end = self._get_dates_loc(dates, dtend)\n except KeyError as err: # end is greater than dates[-1]...probably\n if dtend > self.data.dates[-1]:\n end = len(self.data.endog) - 1\n freq = self.data.freq\n out_of_sample = datetools._idx_from_dates(dates[-1], dtend,\n freq)\n else:\n if freq is None:\n raise ValueError(\"There is no frequency for these \"\n \"dates and date %s is not in dates \"\n \"index. Try giving a date that is in \"\n \"the dates index or use an integer.\"\n % dtend)\n else: #pragma: no cover\n raise err # should never get here\n self._make_predict_dates() # attaches self.data.predict_dates\n\n elif isinstance(end, int) and dates is not None:\n try:\n self.data.predict_end = dates[end]\n except IndexError as err:\n nobs = len(self.data.endog) - 1 # as an index\n out_of_sample = end - nobs\n end = nobs\n if freq is not None:\n self.data.predict_end = datetools._date_from_idx(dates[-1],\n out_of_sample, freq)\n elif out_of_sample <= 0: # have no frequency but are in sample\n #TODO: what error to catch here to make sure dates is\n #on the index?\n try:\n self.data.predict_end = self._get_dates_loc(dates,\n end)\n except KeyError:\n raise\n else:\n self.data.predict_end = end + out_of_sample\n self.data.predict_start = self._get_dates_loc(dates,\n self.data.predict_start)\n\n self._make_predict_dates()\n\n elif isinstance(end, int):\n nobs = len(self.data.endog) - 1 # is an index\n if end > nobs:\n out_of_sample = end - nobs\n end = nobs\n\n elif freq is None: # should have a date with freq = None\n raise ValueError(\"When freq is None, you must give an integer \"\n \"index for end.\")\n\n return end, out_of_sample","def epoch_end(self):\n pass","def has_end_reason(self):\n # Someday I will have real end reasons\n return False","def predict_end():\n data = request.json\n\n if data:\n predictor.pred_dict[\"end_date\"] = data[\"end_date\"]\n else:\n pass\n\n return 'Non tam praeclarum est scire latine, quam turpe nescire'","def guess_cuewords():\n\n if t_word[:3] == 'nie':\n create_negation_frame()\n create_target_focus_scope()\n\n if t_word[:3] == 'nic':\n create_negation_frame()\n create_target_focus_scope()","def test_sv_end_svend():\n # Example:\n # 2 321682 . T <DEL> 6 PASS SVTYPE=DEL;END=321887;SVLEN=-205;CIPOS=-56,20;CIEND=-10,62 GT:GQ 0/1:12\n end = sv_end(pos=321682, alt=\"<DEL>\", svend=321887, svlen=-205)\n assert end == 321886","def test_sv_end_bnd():\n # Example:\n # 2\t321681\tbnd_W\tG\tG]17:198982]\t6\tPASS\tSVTYPE=BND;MATEID=bnd_Y\tGT\t0/1\n end = sv_end(pos=321681, alt=ALT, svend=None, svlen=None)\n assert end == 198981","def doCombineCurEnd(self, endofword, nrc='', nextvowel=''): # nrc = next root consonant\n if not self.end:\n return\n self.final = PhonStateCAT.getFinal(self.end)\n nasalPhon = ''\n postVowelPhon = ''\n preVowelPhon = ''\n # geminates\n geminates = False\n if self.end.startswith('w'):\n preVowelPhon = 'w'\n self.vowel = self.end[1:2]\n else:\n self.vowel = self.end[:1]\n vowelPhon = self.vowel\n if nrc == self.final and self.final != '':\n geminates = True\n if self.gemminatesStrategy == 'len' or self.gemminatesStrategy == 'lentone':\n postVowelPhon = 'ː'\n ## Suffix\n finalPhon = ''\n if self.final == 'ng':\n nasalPhon = self.nasalchar # ?\n if geminates:\n pass\n elif self.final in PhonStateCAT.simpleFinalMapping:\n finalPhon = PhonStateCAT.simpleFinalMapping[self.final]\n elif self.final == '':\n if self.latent != '' and self.prefixStrategy != 'never' and (self.prefixSyllable == 'afterEmptyCoda' or self.prefixSyllable == 'afterEmptyCoda+'):\n finalPhon = PhonStateCAT.simpleLatentMapping[self.latent]\n finalPhon = ''\n else:\n print(\"unrecognized final: \"+self.final)\n self.phon += preVowelPhon+vowelPhon+nasalPhon+postVowelPhon+finalPhon\n if not endofword:\n self.phon += self.syllablesepchar","def get_envelope_end(env):\n denv = np.diff(env)\n i = np.where(np.abs(denv) > 0)[0]\n true_stop_index = np.max(i)+1\n return true_stop_index","def get_end_indicator( is_alive,force_end_at_t_max = False):\n #session-ending action indicator: uint8[batch,tick]\n is_end = T.eq(is_alive[:,:-1] - is_alive[:,1:],1)\n \n if force_end_at_t_max:\n session_ended_before = T.sum(is_end,axis=1,keepdims=True)\n is_end_at_tmax = 1 - T.gt(session_ended_before, 0 )\n else:\n is_end_at_tmax = T.zeros((is_end.shape[0],1),dtype=is_end.dtype) \n \n is_end = T.concatenate(\n [is_end,\n is_end_at_tmax],\n axis=1\n )\n return is_end","def detect_commercial(video_desp, histogram, transcript):\n blackframe_list = get_blackframe_list(histogram)\n black_windows = blackframe_list \\\n .dilate(1. / video_desp['fps']) \\\n .coalesce() \\\n .dilate(-1. / video_desp['fps']) \\\n .filter(min_length=MIN_BLACKWINDOW * 1. / video_desp.fps)\n\n # get all instances of >>, Announcer:, and >> Announcer: in transcript\n arrow_text = get_text_intervals(\">>\", transcript)\n announcer_text = get_text_intervals(\"Announcer:\", transcript)\n arrow_announcer_text = get_text_intervals(\">> Announcer:\", tarnscript)\n \n # get an interval for the whole video\n whole_video = IntervalList([(0., video_desp['video_length'], 0)])\n\n # whole video minus black windows to get segments in between black windows\n # then filter out anything that overlaps with \">>\" as long as it's not\n # \">> Announcer:\"\n # then coalesce, as long as it doesn't get too long\n def fold_fn(stack, interval):\n if len(stack) == 0:\n stack.append(interval)\n else:\n last = stack.pop()\n if or_pred(overlaps(), after(max_dist=.1)(interval, last))(interval, last):\n if last.union(interval).length() > MAX_COMMERCIAL_TIME:\n if last.length() > MAX_COMMERCIAL_TIME:\n stack.append(Interval(\n last.start, \n last.start + MAX_COMMERCIAL_TIME, \n last.payload))\n else:\n stack.append(last)\n stack.append(interval)\n else:\n stack.append(last.union(interval))\n else:\n stack.append(interval)\n commercials = whole_video \\\n .minus(black_windows) \\\n .filter_against(\n arrow_text.filter_against(arrow_announcer_text,\n predicate=not_pred(overlaps())),\n predicate=not_pred(overlaps())\n ) \\\n .set_union(black_windows) \\\n .fold_list(fold_fn, []) \\\n .filter_length(min_length = MIN_COMMERCIAL_TIME)\n \n # add in lowercase intervals\n lowercase_intervals = get_lowercase_intervals(transcript)\n commercials = commercials \\\n .set_union(lowercase_intervals) \\\n .dilate(MIN_COMMERCIAL_GAP / 2) \\\n .coalesce() \\\n .dilate(MIN_COMMERCIAL_GAP / 2)\n\n # get blank intervals\n blank_intervals = whole_video.minus(IntervalList([\n (start_sec, end_sec - TRANSCRIPT_DELAY, 0)\n for text, start_sec, end_sec in transcript\n ])).coalesce().filter_length(\n min_length=MIN_BLANKWINDOW, max_length=MAX_BLANKWINDOW)\n\n # add in blank intervals, but only if adding in the new intervals doesn't\n # get too long\n commercials = commercials.merge(blank_intervals,\n predicate=or_pred(before(max_dist=MAX_MERGE_GAP),\n after(max_dist=MAX_MERGE_GAP))\n ) \\\n .filter_length(max_length=MAX_MERGE_DURATION) \\\n .set_union(commercials) \\\n .dilate(MIN_COMMERCIAL_GAP / 2) \\\n .coalesce() \\\n .dilate(MIN_COMMERCIAL_GAP / 2)\n\n # post-process commercials to get rid of gaps, small commercials, and\n # islated blocks\n small_gaps = whole_video \\\n .minus(commercials) \\\n .filter_length(max_length = MAX_COMMERCIAL_GAP) \\\n .filter_against(\n arrow_text.filter_against(\n announcer_text,\n predicate=not_pred(overlaps())\n ), predicate=not_pred(overlaps()))\n\n # merge with small gaps, but only if that doesn't make things too long\n commercials = commercials \\\n .set_union(small_gaps.dilate(0.1)) \\\n .coalesce() \\\n .filter_length(max_length=MAX_COMMERCIAL_TIME) \\\n .set_union(commercials) \\\n .coalesce()\n\n # get isolated commercials\n not_isolated_commercials = commercials.filter_against(commercials,\n predicate=or_pred(before(max_dist=MAX_COMMERCIAL_TIME),\n after(max_dist=MAX_COMMERCIAL_TIME)))\n isolated_commercials = commercials.minus(not_isolated_commercials)\n commercials_to_delete = isolated_commercials \\\n .filter_length(max_length=MIN_COMMERCIAL_TIME_FINAL) \\\n .set_union(isolated_commercials \\\n .filter_against(blank_intervals, predicate=equals()) \\\n .filter_length(max_length=MAX_ISOLATED_BLANK_TIME))\n\n commercials = commercials.minus(commercials_to_delete)\n\n return commercials","def handle_record_end():\n LOG.info(\"End Recording...\")\n context = {'client_name': 'mycroft_listener',\n 'source': 'audio',\n 'destination': [\"skills\"]}\n bus.emit(Message('recognizer_loop:record_end', context=context))","def affected_end(self):\n types = {alt.type for alt in self.ALT} # set!\n BAD_MIX = {INS, SV, BND, SYMBOLIC} # don't mix well with others\n if (BAD_MIX & types) and len(types) == 1 and list(types)[0] == INS:\n # Only insertions, return 0-based position right of first base\n return self.POS # right of first base\n else: # Return 0-based end position, behind last REF base\n return (self.POS - 1) + len(self.REF)","def end_phase():\n pass","def stop_cb(evt):\n # print('CLOSING on {}'.format(evt))\n speech_recognizer.stop_continuous_recognition()\n global done\n done = True","def inference_end(self, inputs, results):\n return","def on_epoch_end(epoch, logs):\n global w2vmodel\n global max_seq_length\n global word_to_ix\n global ix_to_word\n global sample\n if epoch % 5 == 0:\n generate_text(model, w2vmodel, epoch, length=75, max_seq_length=max_seq_length,\n seed=\"Rain drop, drop top\\n\")\n return","def get_last_sentence_end(text: str) -> int:\n end = -1\n for e in sentence_ends:\n p_end = [m.end() for m in re.finditer(e, text)]\n end = max(end, p_end[-1] if len(p_end) > 0 else -1)\n return end","def end_episode(self):\n self.training_buffer.reset_all()\n for agent_id in self.cumulative_rewards:\n self.cumulative_rewards[agent_id] = 0\n for agent_id in self.episode_steps:\n self.episode_steps[agent_id] = 0\n if self.use_curiosity:\n for agent_id in self.intrinsic_rewards:\n self.intrinsic_rewards[agent_id] = 0","def test_endgameStrategy(self):\n self.result = \"\"\"\n 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 1 2 3 3 2 1 0 0\n 0 0 1 3 x x x x 1 0 0\n 0 0 2 x x 6 x 5 2 0 0\n 0 0 3 x 4 4 x x 2 0 0\n 0 0 3 x 5 5 x x 2 0 0\n 0 0 2 x x x x 3 1 0 0\n 0 0 1 2 3 3 2 1 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0\n \"\"\"","def predict_again(src):\n global rcnt\n global lcnt\n H,W = src.shape[:2]\n #cv2.imshow(\"cROPPPPPPED\",src)\n #print (src.shape)\n\n img1 = src[:,:int(W/2)]\n img2 = src[:,int(W/2)+1:]\n contoured1,area1 = drawContours11111(img1)\n contoured2,area2 = drawContours11111(img2)\n #cv2.imshow(\"blank_image\",contoured1)\n #cv2.imshow(\"blank_image1\",contoured2)\n print (area1,area2)\n if area2>area1:\n #print (\"New:::::::::::::RIGGGGGGGGGHT\")\n if rcnt >=3:\n print (\"New:::::::::::::RIGGGGGGGGGHT\")\n feedback.direction = 1\n feedback.detection = 1\n rcnt += 1\n lcnt = 0\n elif area1>area2:\n #print (\"New:::::::::::::LEFTTTTTTTTT\")\n if lcnt >=3:\n print (\"New:::::::::::::LEFTTTTTTTTT\")\n feedback.direction = -1\n feedback.detection = 1\n lcnt += 1\n rcnt = 0","def end(self, won, reason):\n pass\n # replace with your end logic","def recognized_cb(evt):\n if evt.result.reason == speechsdk.ResultReason.RecognizedSpeech:\n #print('RECOGNIZED: {}'.format(evt.result.text))\n #print('All params: {}'.format(evt.result))\n #print(evt.result.json)\n response = json.loads(evt.result.json)\n #print('All params: {}'.format(response))\n Text = response[\"DisplayText\"]\n duration = 0;\n for word in response[\"NBest\"][0][\"Words\"]:\n duration += word[\"Duration\"]\n duration = duration / 10000000\n print(\"dur :\"+str(duration)+\" text: \" + Text)","def get_ends(self): \n return self.last_words","def event_m10_29_x8():\r\n \"\"\"State 0,1: End state\"\"\"\r\n return 0","def audio_cd_end_track(self, audio_cd_end_track):\n self._audio_cd_end_track = audio_cd_end_track","def onEpochEnd(self, agent):\n\n pass","def _findExonEnd(self, exonRecs, iBlkStart):\n iBlkEnd = iBlkStart + 1\n while (iBlkEnd < len(exonRecs)) and (self._tGapSize(exonRecs, iBlkEnd) < minIntronSize):\n iBlkEnd += 1\n return iBlkEnd, exonRecs[iBlkEnd - 1].end - exonRecs[iBlkStart].start","def event_m20_11_x94():\n \"\"\"State 0,1: End state\"\"\"\n return 0","def event_m10_29_x27():\r\n \"\"\"State 0,1: End state\"\"\"\r\n return 0","def test_recognize(self):\n\n rec = mi.MyRecognizeCallback()\n rec.on_close()\n rec.on_connected()\n rec.on_data('\"final\": true truetestd')\n rec.on_error(\"testerror\")\n rec.on_hypothesis(\"testh\")\n rec.on_inactivity_timeout(\"testerrorinac\")\n rec.on_listening()\n rec.on_transcription(\"testtr\")\n self.assertIsNotNone(rec)","def event_m10_29_x6():\r\n \"\"\"State 0,1: End state\"\"\"\r\n return 0","def summarize_end(sample):\n (species, sex, tissue, replicate) = sample.split(\"_\")\n ins = FocalIntersect(species, sex, tissue, replicate)\n with open(\"../data/output/\" + ins.name + \".end.A.txt\", 'w') as f:\n for isoseqid in ins.end_infoA.keys():\n exonnum1, exonnum2, five_desc, three_desc = ins.end_infoA[isoseqid]\n transid = ins.isoseqid2besttransidA[isoseqid]\n f.write(isoseqid + \"\\t\" + transid + \"\\t\" + exonnum1 + \"\\t\" + exonnum2 + \"\\t\" + five_desc + \"\\t\" + three_desc + \"\\n\")\n\n with open(\"../data/output/\" + ins.name + \".end.B.txt\", 'w') as f:\n for isoseqid in ins.end_infoB.keys():\n exonnum1, exonnum2, five_desc, three_desc = ins.end_infoB[isoseqid]\n transid = ins.isoseqid2besttransidB[isoseqid]\n f.write(isoseqid + \"\\t\" + transid + \"\\t\" + exonnum1 + \"\\t\" + exonnum2 + \"\\t\" + five_desc + \"\\t\" + three_desc + \"\\n\")","def end_turn(self, end_msg):\n self.progress_bar.set_value(self.word_view.word)\n self.greeterboard.greets(end_msg)\n self.scoreboard.update_score()\n self.word_view.update_word()\n self.greeterboard.update_gallows()","def on_epoch_end(self):","def _is_end_of_label(self, prev_top: str, now_top: str, prev_type: str, now_type: str) -> bool:\n\n if prev_top in ['E', 'S']:\n return True\n elif prev_top == 'B' and now_top in ['B', 'O']:\n return True\n elif prev_top == 'I' and now_top in ['B', 'O', 'S']:\n return True\n elif prev_top != 'O' and prev_type != now_type:\n return True\n return False","def test_endstring_until_further_notice():\n vc = vtec.parse(\"/O.NEW.KJAN.TO.W.0130.050829T1651Z-000000T0000Z/\")\n assert vc[0].get_end_string(None) == \"until further notice\"","def test_forward_end_word_extend_selection(self):\n before_b = \"\"\"\\\n Americans live in the most severe weather-prone country on Earth. Each year, Americans cope with an average of 10,000 thunderstorms, 2,500 floods, 1,000 tornadoes, as well as an average of 6 deadly hurricanes. Potentially deadly weather impacts every American. Communities can now rely on the National Weather Service’s StormReady program to help them guard against the ravages of Mother Nature.\n\n Some 90% of all presidentially declared disasters are weather related, leading to around 500 deaths per year and nearly $14 billion in damage. StormReady, a program started in 1999 in Tulsa, OK, helps arm America's communities with the communication and safety skills needed to save lives and property– before and during the event. StormReady helps community leaders and emergency managers strengthen local safety programs.\n\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\n \"\"\"\n after_b = \"\"\"\\\n Americans live in the most severe weather-prone country on Earth. Each year, Americans cope with an average of 10,000 thunderstorms, 2,500 floods, 1,000 tornadoes, as well as an average of 6 deadly hurricanes. Potentially deadly weather impacts every American. Communities can now rely on the National Weather Service’s StormReady program to help them guard against the ravages of Mother Nature.\n\n Some 90% of all presidentially declared disasters are weather related, leading to around 500 deaths per year and nearly $14 billion in damage. StormReady, a program started in 1999 in Tulsa, OK, helps arm America's communities with the communication and safety skills needed to save lives and property– before and during the event. StormReady helps community leaders and emergency managers strengthen local safety programs.\n\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"3.20\", \"3.20\"),\n after_sel=(\"3.20\", \"3.30\"),\n command_name=\"forward-end-word-extend-selection\",\n )","def end_game(self, end_msg):\n self.word_view.reveal_word()\n self.greeterboard.update_gallows()\n self.greeterboard.greets(end_msg)","def _end_episode(self):\n self.turn_cnt = 0\n self.episode_cnt += 1\n self.contexts = None\n self.seed_utterances = None\n self.reset_agents()","def test_two_persons_exiting(self):\n human_detector_inst = HumanDetector(\n find_humans_from_video_file_name='videos/two_persons_exiting.mp4',\n use_pi_camera=False, open_display=False)\n self.assertEqual(human_detector_inst.perform_job(), None)\n human_centroid_dict = human_detector_inst.get_human_centroid_dict()\n self.assertEqual(len(human_centroid_dict), 2)\n self.assertEqual(human_centroid_dict[0].direction, Direction.EXIT)\n self.assertEqual(human_centroid_dict[1].direction, Direction.EXIT)\n self.assertEqual(SendReceiveMessages().get_face_detected_count_locally(), -2)\n # human_detector_inst.clean_up()\n self.__cleanup()","def test_endgameStrategy2(self):\n\n self.result = \"\"\"\n 1 x 1 0 0 2 x 2 1 x 1 0 0 1 x x 1\n 1 1 1 0 0 2 x 3 2 1 1 0 0 1 3 4 3\n 0 0 0 0 0 1 2 x 1 0 0 0 0 0 1 x x\n 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 2 2\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1\n 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 x 1\n 1 x 1 0 0 0 0 0 0 0 0 0 0 1 x 2 1\n \"\"\"","def _is_at_end(self, binvect):\n last = max(k for (k, v) in enumerate(binvect) if v == 1)\n n_step = len(self.pas)\n steps_between = np.arange(last + 1, n_step)\n if 0 <= len(steps_between) <= self._n_to_end:\n self._set_label(binvect, still)\n for k in steps_between:\n self.labels[k] = still\n return True\n else:\n return False","def find_start_end_rests(audio_data, sr, hop_length=HOP_LENGTH, n_fft=N_FFT):\r\n \r\n # Compute the 3rd percentile of the envelope and \r\n # deem anything below this value as silence \r\n envelope = frame(audio_data, hop_length=hop_length, frame_length=n_fft).max(axis=0)\r\n lower_bound = np.percentile(envelope, 5.0)\r\n \r\n # Implement the search as loop, this should be faster than vectorisation\r\n k = 0\r\n while envelope[k] <= lower_bound:\r\n k += 1\r\n \r\n # Return 0 if there is no start rest\r\n if k == 0:\r\n time_start = 0.0\r\n else:\r\n # The first value of the output of the frame function correspond to the time of\r\n # n_fft, then the times are spaced according to hop_length \r\n time_start = ((k-1)*hop_length + n_fft)/float(sr)\r\n \r\n j = len(envelope)-1\r\n while envelope[j] <= lower_bound:\r\n j -= 1\r\n \r\n # Return the length of the track if the is no end rest\r\n if j == len(envelope)-1:\r\n time_end = len(audio_data)/float(sr)\r\n else:\r\n time_end = ((j-1)*hop_length + n_fft)/float(sr)\r\n \r\n times_start_end_rests = [time_start, time_end]\r\n \r\n return(times_start_end_rests)","def on_predict_end(self, logs=None):","def on_predict_end(self, logs=None):","def _ends_cvc(self, word):\n return (\n len(word) >= 3\n and self._is_consonant(word, len(word) - 3)\n and not self._is_consonant(word, len(word) - 2)\n and self._is_consonant(word, len(word) - 1)\n and word[-1] not in (\"w\", \"x\", \"y\")\n ) or (\n self.mode == self.NLTK_EXTENSIONS\n and len(word) == 2\n and not self._is_consonant(word, 0)\n and self._is_consonant(word, 1)\n )","def test_vertical_line_Exiting(self):\n human_detector_inst = HumanDetector(\n find_humans_from_video_file_name='videos/TempleVideos/vertical_line_Exiting.mp4',\n use_pi_camera=False, open_display=True)\n self.assertEqual(human_detector_inst.perform_job(), None)\n human_centroid_dict = human_detector_inst.get_human_centroid_dict()\n self.assertEqual(len(human_centroid_dict), 4)\n self.assertEqual(human_centroid_dict[0].direction, Direction.EXIT)\n self.assertEqual(human_centroid_dict[1].direction, Direction.EXIT)\n self.assertEqual(human_centroid_dict[2].direction, Direction.EXIT)\n self.assertEqual(human_centroid_dict[3].direction, Direction.EXIT)\n self.assertEqual(SendReceiveMessages().get_face_detected_count_locally(), -4)\n human_detector_inst.clean_up()\n self.__cleanup()","def TEM_loss(anchors_action,anchors_startend,\n Y_action,Y_startend,config): \n loss_action,num_sample_action = binary_logistic_loss(Y_action,anchors_action)\n loss_startend,num_sample_startend = binary_logistic_loss(Y_startend,anchors_startend)\n # print('loss_action', loss_action)\n # print('loss_startend', loss_startend)\n loss={\"loss_action\":loss_action,\"num_sample_action\":num_sample_action,\n \"loss_startend\":loss_startend,\"num_sample_startend\":num_sample_startend}\n return loss","def what_emo(self, mfccs, model, params_file, st, endpoint):\n print(\"\\nEntering what_emo... \")\n emotion = None\n emo_label = {0:'angry', 1:'fearful', 2:'happy', 3:'sad', 4:'calm'}\n if endpoint == \"\":\n #emo_gen = self.mfccs_to_emo(mfccs, model, params_file, st)\n #emo = next(emo_gen)[\"dense_1\"]\n emo_list = self.mfccs_to_emo(mfccs, model, params_file, st)\n print(\"\\nemo_list: \")\n print(emo_list)\n epis_var = emo_list[0][0][-1] # we take variance from logits_variance\n print(\"\\nAleatoric Variance: \")\n print(epis_var)\n emo = emo_list[1] # softmax_output\n print(\"\\nemo: \")\n print(emo)\n else:\n dict_im = {}\n service = googleapiclient.discovery.build('ml', 'v1')\n dict_im[\"input_1\"] = mfccs.tolist()\n predictions = service.projects().predict(name=endpoint, body={'instances':[dict_im]}).execute()[\"predictions\"]\n emo = np.array(predictions[0]['dense_1']) \n print(\"\\nemo: \")\n print(emo)\n res = np.argmax(emo)\n print(\"\\nres: \")\n print(res)\n emotion = emo_label[res] \n print(\"\\nPrediction: \" + str(emotion))\n return str(emotion), res, emo, epis_var","def stop_cb(evt): \n speech_recognizer.stop_continuous_recognition()\n nonlocal done\n done = True","def event_m10_29_x21():\r\n \"\"\"State 0,1: End state\"\"\"\r\n return 0","def on_epoch_end(self):\n pass","def validate_no_teacher_forcing(encoder_model,decoder_model,line2idx,batch,beam_size=3):\n decoder_model.eval() # eval mode (no dropout or batchnorm)\n encoder_model.eval()\n\n imgs = batch[0]#.unsqueeze(0)\n caps = batch[1]#.unsqueeze(0)\n caplens = batch[2] #.unsqueeze(0)\n k = beam_size\n vocab_size = len(line2idx)\n references = list() # references (true captions) for calculating BLEU-4 score\n hypotheses = list() # hypotheses (predictions)\n with torch.no_grad():\n imgs = imgs.to(device)\n caps = caps.to(device)\n caplens = caplens.to(device)\n\n encoder_out = encoder_model(imgs)\n beam_search_seq = decoder_model.generate(encoder_out,line2idx,beam_size)\n \n allcaps = caps\n for j in range(allcaps.shape[0]):\n\n img_caps = allcaps[j].tolist()\n\n img_captions = []\n for w in img_caps:\n if w not in [line2idx['<PAD>']]:\n #if w not in [line2idx['<EOS>'],line2idx['<BOS>'], line2idx['<PAD>']]:\n img_captions.append(w)\n references.append(img_captions)\n\n accuracy = np.sum(np.where(np.array(beam_search_seq) == np.array(references[0]),1,0))/len(references[0])\n\n return beam_search_seq,accuracy","def determineEndGame(self):\n\n print(\"noWinners: \" + str(self.noWinners) + \", noTotKids: \" + str(self.noTotKids))\n\n # TODO scegliere come determinare la fine del gioco\n # if self.noWinners == self.noTotKids - 1: # end-game test\n if self.noWinners == self.noTotKids:\n print(\"ho determinato la fine del gioco\")\n return True\n else:\n print(\"colore toccato ma la partita non e' finita\")\n return False","def recognize(self, audio):\n response = self.service.Recognize(cloud_speech_extended_pb2.RecognizeRequest(\n config=cloud_speech_extended_pb2.RecognitionConfig(\n # There are a bunch of config options you can specify. See https://goo.gl/KPZn97 for the full list.\n encoding='LINEAR16', # one of LINEAR16, FLAC, MULAW, AMR, AMR_WB\n sample_rate_hertz=audio.frame_rate, # the rate in hertz\n # See https://g.co/cloud/speech/docs/languages for a list of supported languages.\n language_code=self.settings.language, # a BCP-47 language tag\n enable_word_time_offsets=self.settings.time_offsets, # if true, return recognized word time offsets\n max_alternatives=self.settings.max_alternatives, # maximum number of returned hypotheses\n ),\n audio=cloud_speech_extended_pb2.RecognitionAudio(\n uri=None,\n content=audio.raw_data\n )\n ), self.settings.deadline)\n\n # Print the recognition result alternatives and confidence scores.\n results = []\n\n # for result in response.results:\n if len(response.results) > 0:\n result = response.results[0] # TODO: check why here we have list of results ?, when it is possible ?\n\n alternative = result.alternatives[0]\n alignment = [] #\n confirmed_results = []\n if self.settings.time_offsets:\n word_indices = [j for j in range(len(alternative.words)) if\n alternative.words[j].word != '<eps>']\n\n if len(word_indices) > 0:\n confirmed_results.append([alternative.words[i].word for i in word_indices])\n else: # alignment was not returned\n confirmed_results.append(alternative.transcript)\n\n alignment.append(\n [[alternative.words[i].start_time, alternative.words[i].end_time] for i in\n word_indices])\n results.append({\n 'transcript': ' '.join(confirmed_results),\n 'confidence': alternative.confidence,\n 'alignment': alignment,\n })\n else:\n results.append({\n 'transcript': alternative.transcript,\n 'confidence': alternative.confidence\n })\n\n return results","def _decode_end(_fp):\n return 0","def detect(self, frame, foreground_mask):\n pass","def event_m10_29_x40():\r\n \"\"\"State 0,1: End state\"\"\"\r\n return 0","def end (self):\n return self._end if self._end != self.inf else self.e","def easyends(endtype,\n endlength,\n number=0,\n interaction=None,\n fdev=0.05,\n maxspurious=0.5,\n maxendspurious=None,\n tries=1,\n oldends=[],\n adjs=['n', 'n'],\n energetics=None,\n alphabet='n',\n echoose=None,\n absolute=False,\n _presetavail=False):\n\n if not energetics:\n efunc = EnergeticsBasic()\n else:\n efunc = energetics\n if (not interaction) or (interaction == 0):\n interaction = enhist(\n endtype,\n endlength,\n energetics=efunc,\n adjacents=adjs,\n alphabet=alphabet)[2]['emedian']\n LOGGER.info(\"Calculated optimal interaction energy is {0}.\".format(\n interaction))\n if not absolute:\n mult = interaction\n else:\n mult = 1.0\n \n maxcompspurious = maxspurious * mult\n if not maxendspurious:\n maxendspurious = maxspurious * mult\n else:\n maxendspurious = maxendspurious * mult\n\n sfilt = spacefilter_standard(interaction, mult * fdev,\n maxendspurious)\n efilt = endfilter_standard_advanced(maxcompspurious, maxendspurious)\n if not echoose:\n echoose = endchooser_standard(interaction)\n\n return find_end_set_uniform(\n endtype,\n endlength,\n sfilt,\n efilt,\n echoose,\n energetics=efunc,\n numtries=tries,\n oldends=oldends,\n adjacents=adjs,\n num=number,\n alphabet=alphabet,\n _presetavail=_presetavail)","def end_rate(lablled,str):\n#\tlabelled = RawClaim.objects.exclude(correcttrim=\"\")\n\tbad = [l for l in labelled if str in l.correcttrim and normal(l.correcttrim)]\n\tgood = [l for l in labelled if correct_ends_with(l.sentence,l.correcttrim,str)]\n\treturn (float(len(good))/(len(good)+len(bad)),bad,good)","def recognize(self, h, recog_args, target_left_mask=-1):\n hyp = {\"score\": 0.0, \"yseq\": [self.blank]}\n\n ys = to_device(self, torch.tensor(hyp[\"yseq\"], dtype=torch.long)).unsqueeze(0)\n if target_left_mask > -1:\n ys_mask = to_device(self, subsequent_mask_limit(1, target_left_mask).unsqueeze(0))\n else:\n ys_mask = to_device(self, subsequent_mask(1).unsqueeze(0))\n y, c = self.forward_one_step(ys, ys_mask, None)\n\n for i, hi in enumerate(h):\n ytu = torch.log_softmax(self.joint_network(hi, y[0]), dim=0)\n logp, pred = torch.max(ytu, dim=0)\n\n if pred != self.blank:\n hyp[\"yseq\"].append(int(pred))\n hyp[\"score\"] += float(logp)\n\n ys = to_device(self, torch.tensor(hyp[\"yseq\"]).unsqueeze(0))\n if target_left_mask > -1:\n ys_mask = to_device(\n self, subsequent_mask_limit(len(hyp[\"yseq\"]), target_left_mask).unsqueeze(0)\n )\n else:\n ys_mask = to_device(\n self, subsequent_mask(len(hyp[\"yseq\"])).unsqueeze(0)\n )\n\n y, c = self.forward_one_step(ys, ys_mask, c)\n\n return [hyp]","def faceTrackingEnded():\n\n\ttracker.stopTracker()\n\ttracker.unregisterAllTargets()\n\t#motionProxy.rest()","def ScenarioBGEffectExcelEnd(builder):\n return End(builder)","def recognize_beam(self, h, recog_args, rnnlm=None, target_left_mask=-1):\n beam = recog_args.beam_size\n k_range = min(beam, self.odim)\n nbest = recog_args.nbest\n normscore = recog_args.score_norm_transducer\n\n if rnnlm:\n kept_hyps = [\n {\"score\": 0.0, \"yseq\": [self.blank], \"cache\": None, \"lm_state\": None}\n ]\n else:\n kept_hyps = [{\"score\": 0.0, \"yseq\": [self.blank], \"cache\": None}]\n\n for i, hi in enumerate(h):\n hyps = kept_hyps\n kept_hyps = []\n\n while True:\n new_hyp = max(hyps, key=lambda x: x[\"score\"])\n hyps.remove(new_hyp)\n\n ys = to_device(self, torch.tensor(new_hyp[\"yseq\"]).unsqueeze(0))\n if target_left_mask > -1:\n ys_mask = to_device(\n self, subsequent_mask_limit(len(new_hyp[\"yseq\"]), target_left_mask).unsqueeze(0)\n )\n else:\n ys_mask = to_device(\n self, subsequent_mask(len(new_hyp[\"yseq\"])).unsqueeze(0)\n )\n y, c = self.forward_one_step(ys, ys_mask, new_hyp[\"cache\"])\n\n ytu = torch.log_softmax(self.joint(hi, y[0]), dim=0)\n\n if rnnlm:\n rnnlm_state, rnnlm_scores = rnnlm.predict(\n new_hyp[\"lm_state\"], ys[:, -1]\n )\n\n for k in six.moves.range(self.odim):\n beam_hyp = {\n \"score\": new_hyp[\"score\"] + float(ytu[k]),\n \"yseq\": new_hyp[\"yseq\"][:],\n \"cache\": new_hyp[\"cache\"],\n }\n\n if rnnlm:\n beam_hyp[\"lm_state\"] = new_hyp[\"lm_state\"]\n\n if k == self.blank:\n kept_hyps.append(beam_hyp)\n else:\n beam_hyp[\"yseq\"].append(int(k))\n beam_hyp[\"cache\"] = c\n\n if rnnlm:\n beam_hyp[\"lm_state\"] = rnnlm_state\n beam_hyp[\"score\"] += (\n recog_args.lm_weight * rnnlm_scores[0][k]\n )\n\n hyps.append(beam_hyp)\n\n if len(kept_hyps) >= k_range:\n break\n\n if normscore:\n nbest_hyps = sorted(\n kept_hyps, key=lambda x: x[\"score\"] / len(x[\"yseq\"]), reverse=True\n )[:nbest]\n else:\n nbest_hyps = sorted(kept_hyps, key=lambda x: x[\"score\"], reverse=True)[\n :nbest\n ]\n\n return nbest_hyps","def FrameEnd(builder):\n return End(builder)","def analyze(self, start, end):\n return","def process(in_path, out_path, mouth_height=50, mouth_width=50, frame_dur=1, capture_frame=None, out_img=None, show_boxes=True):\n # Get video capture from in_path.\n vc = cv2.VideoCapture(in_path) \n\n rval, frame = vc.read() if vc.isOpened() else (False, None)\n\n mouth_images = []\n\n #import pdb; pdb.set_trace()\n\n if rval:\n mouths = np.empty((0, mouth_height, mouth_width, frame.shape[2]))\n else:\n return # Skip this video since CV2 can't open it\n\n frame_no = 0\n while rval:\n if DEBUG:\n # Copy of original frame, for annotating.\n image = frame.copy()\n\n try:\n face_rect = locate_face(frame)\n except ValueError:\n print \"No face found for %s at frame %d. Skipping.\" % (in_path, frame_no)\n vc.release()\n return # Skip this video.\n\n if DEBUG:\n highlight_rect(image, face_rect, color=(255,255,255), thickness=2)\n\n mouth_rects = locate_mouth(frame)\n mouth = uniform_rect(select_mouth_candidate(mouth_rects, face_rect), face_rect, 50, 50)\n if not mouth:\n print \"No face found for %s at frame %d. Skipping.\" % (in_path, frame_no)\n vc.release()\n return # Skip this video.\n\n mouth_image = frame[mouth[1]:(mouth[1] + mouth[3]), mouth[0]:(mouth[0] + mouth[2]), :]\n mouth_images.append(mouth_image) \n\n if DEBUG:\n highlight_rect(image, mouth, color=(0,0,0), thickness=2) \n #cv2.imshow('Frame', mouth_image)\n cv2.imshow('Frame', image if show_boxes else frame)\n\n if frame_no == capture_frame:\n cv2.imwrite(out_img, image if show_boxes else frame)\n return\n\n cv2.waitKey(frame_dur)\n\n rval, frame = vc.read()\n frame_no += 1\n\n vc.release()\n\n mouths = np.asarray(mouth_images)\n\n savemat(out_path, {\"mouths\": mouths})","def speech_response(output, endsession):\n return {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': output\n },\n 'shouldEndSession': endsession\n }","def speech_response(output, endsession):\n return {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': output\n },\n 'shouldEndSession': endsession\n }","def test(self, end, nb_samples):\n\n samples = self.irepa.sample(nb_samples)\n\n print('Start in 2 seconds')\n time.sleep(2)\n for i, state in enumerate(samples):\n sys.stdout.write(\"\\r{}%\".format(\n round(100*float(i)/len(samples), 0)))\n sys.stdout.flush()\n if INITIALIZE:\n Xe, Ue, Ve = self.irepa.estimator.trajectories(state, end)\n Xe = Xe.flatten()\n Ue = Ue.flatten()\n else:\n Xe, Ue, Ve = [], [], 0\n\n goal = OptControlGoal(\n list(state),\n list(end),\n Xe, Ue, Ve, NX, NU)\n t1 = time.time()\n self.irepa.ocp_client.send_goal(goal, self.callback)\n self.irepa.ocp_client.wait_for_result(rospy.Duration.from_sec(5.0))\n t2 = time.time()\n self.calc_times.append(round(t2-t1, 2))\n self.euclid.append(round(self.irepa.euclid(state, end), 2))","def greedy_decode(self, src, src_mask, src_lengths ):\n encoder_hidden, encoder_final = self.encodeSent(src, src_mask, src_lengths)\n prev_y, trg_mask = self.getTokRepresentation(self.sos_index, src, ret_mask=True)\n\n output = []\n attention_scores = []\n hidden = None\n\n for i in range(self.max_len):\n hidden, pre_output = self.decodeSent(encoder_hidden, encoder_final, src_mask,\n prev_y, trg_mask, hidden)\n\n # we predict from the pre-output layer, which is\n # a combination of Decoder state, prev emb, and context\n prob = self.model.callGenerator(pre_output[:, -1])\n\n _, next_word = torch.max(prob, dim=1)\n next_word = next_word.data.item()\n output.append(next_word)\n prev_y = torch.ones(1, 1).type_as(src).fill_(next_word)\n if self.model.decoder.attention is not None and not self.do_lang_model:\n attention_scores.append(self.model.decoder.attention.alphas.cpu().numpy())\n else:\n #no attention do not store\n attention_scores.append(torch.zeros_like(hidden).cpu().numpy())\n \n output = np.array(output)\n \n # cut off everything starting from </s> \n # (only when eos_index provided)\n if self.eos_index is not None:\n first_eos = np.where(output==self.eos_index)[0]\n if len(first_eos) > 0:\n output = output[:first_eos[0]] \n \n return output, np.concatenate(attention_scores, axis=1)","def stop_sign_detection(img_in):\n\n img = img_in.copy()\n red_color_map = red_masking(img, stop_mask=True)\n red_color_map = cv2.dilate(red_color_map, np.ones((5, 5)))\n canny_edges = cv2.Canny(red_color_map, threshold1=50, threshold2=250, apertureSize=5)\n\n # display_img(canny_edges, 'Stop Sign Canny Lines')\n\n min_line_length = 10\n max_pixel_gap = 10\n hough_lines = cv2.HoughLinesP(image=canny_edges,\n rho=0.5,\n theta=np.pi/180,\n threshold=20,\n minLineLength=min_line_length,\n maxLineGap=max_pixel_gap\n )\n if hough_lines is None:\n return None, None\n hough_lines = hough_lines[0, :] # cleanup dimensionality to make it easier to work with.\n lines = remove_duplicates(hough_lines)\n\n if len(lines) < 8:\n return None, None\n\n # once given the lines of interest perform some more calculations\n min_x, max_x, min_y, max_y = calculate_min_max_values(lines)\n mid_x = min_x + ((max_x - min_x) / 2)\n mid_y = min_y + ((max_y - min_y) / 2)\n return mid_x, mid_y","def demo_video(net, im,frame_number,args, output_folder, conf_thresh):\n \n #data_dir = '/mnt/nfs/scratch1/souyoungjin/RESULTS_FACE_DETECTIONS/ARUNI/video'+str(args.video_id)\n #out_dir = '/mnt/nfs/scratch1/ashishsingh/RESULTS_DOG_DETECTIONS/ARUNI/OHEM/'+args.video_folder_name\n \n out_dir = join(output_folder, args.video_id)\n\n if not os.path.exists(out_dir):\n os.makedirs(out_dir)\n\n\n timer = Timer()\n timer.tic()\n scores, boxes = im_detect(net, im)\n timer.toc()\n print ('Detection took {:.3f}s for '\n '{:d} object proposals').format(timer.total_time, boxes.shape[0])\n\n # Visualize detections for each class\n #CONF_THRESH = 0.80 #check threshold values\n CONF_THRESH = conf_thresh\n NMS_THRESH = 0.15 #check threshold values\n \n # detection file\n dets_file_name = os.path.join(out_dir, 'video'+str(args.video_id)+'.txt') \n fid = open(dets_file_name, 'a+')\n sys.stdout.write('%s ' % (frame_number))\n \n cls_ind = 1\n cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\n cls_scores = scores[:, cls_ind]\n dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float32)\n keep = nms(dets, NMS_THRESH)\n dets = dets[keep, :]\n\n keep = np.where(dets[:, 4] > CONF_THRESH)\n dets = dets[keep]\n \n dets[:, 2] = dets[:, 2] - dets[:, 0] + 1\n dets[:, 3] = dets[:, 3] - dets[:, 1] + 1\n \n fid.write('FRAME NUMBER: '+ str(frame_number) + '\\n')\n fid.write(str(dets.shape[0]) + '\\n')\n for j in xrange(dets.shape[0]):\n fid.write('%f %f %f %f %f\\n' % (dets[j, 0], dets[j, 1], dets[j, 2], dets[j, 3], dets[j, 4]))\n\n print ''\n fid.close()\n \n #for cls_ind, cls in enumerate(CLASSES[1:]):\n #cls_ind += 1 # because we skipped background\n #cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\n\n #cls_scores = scores[:, cls_ind]\n #dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float32)\n #keep = nms(dets, NMS_THRESH)\n #dets = dets[keep, :]\n #im=vis_detections_video(im, cls, dets, thresh=CONF_THRESH)\n #cv2.imwrite(os.path.join('output',str(time.time())+'.jpg'),im)\n \n \n #cv2.imshow('ret',im)\n #cv2.waitKey(20)","def detection(receive, Es):\n cd = range(1, 9)\n # Mapping the codewords to modulation plane\n mapping = (np.vstack((map(lambda m: (Es)**0.5*cos(2*pi*m/8), cd), \\\n map(lambda m: (Es)**0.5*sin(2*pi*m/8), cd)))).T\n\n decoded = []\n for i in receive:\n codeword = 0\n mindis = float('+inf')\n # Performing nearest neighbor detection\n for j in range(len(mapping)):\n distance = norm(i - mapping[j])\n if distance < mindis:\n mindis = distance\n # j is the index of mapping, the real codeword should be j+1\n codeword = j + 1\n assert codeword != 0, \"There is something wrong in the decoding process!\"\n decoded.append(codeword)\n return decoded","def _detect_stop(func):\n def wrapper(*args,**kwargs):\n self = args[0]\n self.episode_length -= 1\n if self.episode_length <=0:\n \"\"\"if the episode is end\"\"\"\n self.end = True\n else:\n if self.adsorption:\n \"\"\"just stop moving and wait until the end of episode\"\"\"\n self.state = self.previous_state\n else:\n func(*args,**kwargs)\n self._detect_obstacles()\n\n # func(*args,**kwargs)\n # self._detect_obstacles()\n # if self.adsorption:\n # \"\"\"if this step update is invalid, the point will rebond\"\"\"\n # self.state = self.previous_state\n\n if self.distance <= 0.02:\n \"\"\"if the point reached the boundary around the goal, let it stop and reset the punishment(self.reward)\"\"\"\n self.end = True\n self.reward = 0\n if self.state[0] <0 or self.state[0] > 10 or self.state[1] <0 or self.state[1] > 10:\n # self.end = True\n self.reward = -800\n return np.array(self.state), self.reward, self.end, self.distance\n return wrapper","def detect_event(self, Cd, wsize=100, thresh=1e-5):\n # This is the continuous version of the method, which works better than the discrete version\n\n # version 1:\n W = np.sqrt(np.sum(np.sum(np.asarray(Cd)**2, axis=0), axis=0))\n\n # version 2: per scale normalization\n # W = np.zeros(Cd[0].shape[1])\n # for c in Cd:\n # # v = Tools.U_filter(np.sqrt(np.sum(c**2, axis=0)), wsize=wsize)\n # n = np.sum(c**2)\n # v = np.sum(c**2, axis=0)\n # W += (v/n if n>0 else v)\n\n W /= np.sum(W)\n mask = Tools.LU_filter(W > thresh, wsize=wsize)\n Erng = Tools.find_block_true(mask)\n return Erng, mask, W","def check_happiness(content):\n baseline = 50\n happiness = [\"Smile\"]\n label = 5\n # print content\n emotion_time = content[0][1]\n # print 'emotion_time',emotion_time\n for c in content:\n for h in happiness:\n # print h\n if c[0] == h and c[1] >= baseline:\n print 'emotion & label',emotion_time, label\n return emotion_time, label","def check_happiness(content):\n baseline = 100\n happiness = [\"Smile\"]\n label = 5\n # print content\n emotion_time = content[0][1]\n # print 'emotion_time',emotion_time\n for c in content:\n for h in happiness:\n # print h\n if c[0] == h and c[1] >= baseline:\n print 'emotion & label',emotion_time, label\n return emotion_time, label","def event_m20_11_x98():\n \"\"\"State 0,1: End state\"\"\"\n return 0","def detector(videoframe, facedetection, maskdetection):\n (h, w) = videoframe.shape[:2]\n blobimage = cv2.dnn.blobFromImage(videoframe, 1.0, (224, 224), (104.0, 177.0, 123.0))\n\n facedetection.setInput(blobimage)\n ffinding = facedetection.forward()\n\n face_list = []\n locations = []\n predictions = []\n\n for i in range(0, ffinding.shape[2]):\n credence = ffinding[0, 0, i, 2]\n if credence > 0.6:\n case = ffinding[0, 0, i, 3:7] * np.array([w, h, w, h])\n (x_start, y_start, x_end, y_end) = case.astype(\"int\")\n (x_start, y_start) = (max(0, x_start), max(0, y_start))\n (x_end, y_end) = (min(w - 1, x_end), min(h - 1, y_end))\n\n image = videoframe[y_start:y_end, x_start:x_end]\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = cv2.resize(image, (224, 224))\n image = img_to_array(image)\n image = preprocess_input(image)\n face_list.append(image)\n locations.append((x_start, y_start, x_end, y_end))\n\n if len(face_list) > 0:\n face_list = np.array(face_list, dtype=\"float32\")\n predictions = maskdetection.predict(face_list, batch_size=32)\n return (locations, predictions)","def VoiceCommonExcelEnd(builder):\n return End(builder)","def onWordRecognised(self, *_args):\n # Unsubscribe to the event when talking,\n # to avoid repetitions\n memory.unsubscribeToEvent(\"WordRecognized\",\"AudioRecognition\")\n\n # We access to the word recognised in the memory\n word = memory.getData(\"WordRecognized\")\n\n # Debug : Print the word recognised\n print(\"Mot :\")\n print(word[0])\n print(\"Indice de confiance :\")\n print(word[1])\n print\n\n\n # We acknoledge a word if the trust is high enough\n if (word[1] > 0.28):\n self.mot = word[0]\n #self.tts.say(\"Le mot reconnu est :\"+self.mot)\n StateManager(self)\n \n\n # Subscribe again to the event\n memory.subscribeToEvent(\"WordRecognized\",\n \"AudioRecognition\",\n \"onWordRecognised\")","def tellIfEnded(self):\n self.congratulate()","def wait_for_fixation_end(self):\n\n\t\t# # # # #\n\t\t# EyeTribe method\n\n\t\tif self.eventdetection == 'native':\n\t\t\t\n\t\t\t# print warning, since EyeTribe does not have a blink detection\n\t\t\t# built into their API\n\t\t\t\n\t\t\tprint(\"WARNING! 'native' event detection has been selected, \\\n\t\t\t\tbut EyeTribe does not offer fixation detection; \\\n\t\t\t\tPyGaze algorithm will be used\")\n\n\t\t# # # # #\n\t\t# PyGaze method\n\t\t\t\n\t\t# function assumes that a 'fixation' has ended when a deviation of more than fixtresh\n\t\t# from the initial 'fixation' position has been detected\n\t\t\n\t\t# get starting time and position\n\t\tstime, spos = self.wait_for_fixation_start()\n\t\t\n\t\t# loop until fixation has ended\n\t\twhile True:\n\t\t\t# get new sample\n\t\t\tnpos = self.sample() # get newest sample\n\t\t\t# check if sample is valid\n\t\t\tif self.is_valid_sample(npos):\n\t\t\t\t# check if sample deviates to much from starting position\n\t\t\t\tif (npos[0]-spos[0])**2 + (npos[1]-spos[1])**2 > self.pxfixtresh**2: # Pythagoras\n\t\t\t\t\t# break loop if deviation is too high\n\t\t\t\t\tbreak\n\n\t\treturn clock.get_time(), spos","def _run_end2end(self, *args, code=0):\n pass","def _get_end(self):\n return self._end","def end(self, obs: AgentObservation):\n self.brain.end(obs)","def agent_end(self, reward, testing_stop=False):\n\n if self.step_counter <= 0:\n # Corner case where we get an end due to end of epoch just after a game ended\n return\n\n self.step_counter += 1\n total_time = time.time() - self.start_time\n logging.info(\"FPS in that episode: %s\" % (float(self.step_counter)/ total_time))\n\n self.episode_reward += reward\n \n if self.best_epoch_reward is None or self.episode_reward > self.best_epoch_reward:\n self.best_epoch_reward = self.episode_reward\n self.best_run_images = self.episode_images\n\n if self.best_score_ever is None or self.episode_reward > self.best_score_ever:\n self.best_score_ever = self.episode_reward\n\n if not testing_stop or self.episode_counter == 0:\n # only collect stats for this episode if it didn't get truncated, or if it's the \n # only one we are going to get for this testing epoch\n self.episode_counter += 1\n self.total_reward += self.episode_reward","def decode(self, tgt, encoder_out):\n tgt_mask = get_lookahead_mask(tgt)\n tgt = self.emb(tgt)\n tgt = self.phn_lin(tgt)\n if self.attention_type == \"RelPosMHAXL\":\n # we use fixed positional encodings in the decoder\n tgt = tgt + self.positional_encoding_decoder(tgt)\n encoder_out = encoder_out + self.positional_encoding_decoder(\n encoder_out\n )\n elif self.positional_encoding_type == \"fixed_abs_sine\":\n tgt = tgt + self.positional_encoding(tgt) # add the encodings here\n prediction, self_attns, multihead_attns = self.decoder(\n tgt,\n encoder_out,\n tgt_mask=tgt_mask,\n pos_embs_tgt=None,\n pos_embs_src=None,\n )\n attention = multihead_attns[-1]\n return prediction, attention"],"string":"[\n \"def end_detect(ended_hyps, i, M=3, D_end=np.log(1 * np.exp(-10))):\\n if len(ended_hyps) == 0:\\n return False\\n count = 0\\n best_hyp = sorted(ended_hyps, key=lambda x: x[\\\"score\\\"], reverse=True)[0]\\n for m in range(M):\\n # get ended_hyps with their length is i - m\\n hyp_length = i - m\\n hyps_same_length = [\\n x for x in ended_hyps if len(x[\\\"yseq\\\"]) == hyp_length\\n ]\\n if len(hyps_same_length) > 0:\\n best_hyp_same_length = sorted(\\n hyps_same_length, key=lambda x: x[\\\"score\\\"], reverse=True)[0]\\n if best_hyp_same_length[\\\"score\\\"] - best_hyp[\\\"score\\\"] < D_end:\\n count += 1\\n\\n if count == M:\\n return True\\n else:\\n return False\",\n \"def is_end_episode():\\n return patterns_pb2.Condition(is_end_episode=True, eq=1)\",\n \"def detect_cuewords():\\n\\n # cuewords\\n\\n if t_word[:2] == 'ni':\\n create_negation_frame()\\n create_target_focus_scope()\\n\\n if t_word[:4] == 'kein':\\n create_negation_frame()\\n create_target_focus_scope()\\n\\n if t_word[:4] == 'nein':\\n create_negation_frame()\\n create_target_focus_scope()\",\n \"def decide(self, data):\\n\\n data = struct.unpack('%dh' % (len(data) / 2, ), data)\\n self.audio_recorded_in.extend(data)\\n\\n while len(self.audio_recorded_in) > self.cfg['VAD']['gmm']['framesize']:\\n frame = self.audio_recorded_in[:self.cfg['VAD']['gmm']['framesize']]\\n self.audio_recorded_in = self.audio_recorded_in[self.cfg['VAD']['gmm']['frameshift']:]\\n\\n mfcc = self.front_end.param(frame)\\n\\n log_prob_speech = self.gmm_speech.score(mfcc)\\n log_prob_sil = self.gmm_sil.score(mfcc)\\n\\n self.log_probs_speech.append(log_prob_speech)\\n self.log_probs_sil.append(log_prob_sil)\\n\\n log_prob_speech_avg = 0.0\\n for log_prob_speech, log_prob_sil in zip(self.log_probs_speech, self.log_probs_sil):\\n log_prob_speech_avg += log_prob_speech - logsumexp([log_prob_speech, log_prob_sil])\\n log_prob_speech_avg /= len(self.log_probs_speech)\\n\\n prob_speech_avg = np.exp(log_prob_speech_avg)\\n\\n# print 'prob_speech_avg: %5.3f' % prob_speech_avg\\n\\n self.last_decision = prob_speech_avg\\n\\n # returns a speech / non-speech decisions\\n return self.last_decision\",\n \"def training_end(self):\\n pass\",\n \"def match_end_faces(test_end_face, end_faces):\\n for ef in end_faces:\\n if (test_end_face+180) % 360 == ef:\\n return True\\n return False\",\n \"def recognize():\\n return 0\",\n \"def\\tbegin_end(env, blc):\\n\\n\\tb_e = np.empty((blc.shape[0], 2))\\n\\tinf = 0\\n\\tp = 0\\n\\twin = env.win_over\\n\\tb_e[0, 0] = inf\\n\\tif blc[0] + win <= blc[-1]:\\n\\t\\tb_e[0, 1] = blc[0] + win\\n\\telse:\\n\\t\\tb_e[0, 1] = blc[-1]\\n\\tif blc.shape[0] == 1:\\n\\t\\tb_e[0, 1] = blc[0]\\n\\t\\treturn (b_e)\\n\\tfor k in range(1, blc.shape[0] - 1):\\n\\t\\tinf = blc[k - 1] - win\\n\\t\\tb_e[k, 0] = inf\\n\\t\\tif blc[k] + win <= blc[-1]:\\n\\t\\t\\tb_e[k, 1] = blc[k] + win\\n\\t\\telse:\\n\\t\\t\\tb_e[k, 1] = blc[-1]\\n\\tb_e[blc.shape[0] - 1, 0] = blc[-2] - win\\n\\tb_e[blc.shape[0] - 1, 1] = blc[-1]\\n\\tneg = np.where(b_e < 0)[0]\\n\\tif neg.shape[0]:\\n\\t\\tb_e = b_e[neg[-1]:]\\n\\t\\tb_e[0, 0] = 0\\n\\treturn (b_e)\",\n \"def detect(self, start_time, end_time):\\n\\n # Convert times to UTCDateTime objects\\n start_time = UTCDateTime(start_time)\\n end_time = UTCDateTime(end_time)\\n\\n # Decimate LUT\\n self.lut = self.lut.decimate(self.decimate)\\n\\n # Detect uses the non-centred onset by default\\n if self.onset_centred is None:\\n self.onset_centred = False\\n\\n # Define pre-pad as a function of the onset windows\\n if self.pre_pad is None:\\n self.pre_pad = max(self.p_onset_win[1],\\n self.s_onset_win[1]) \\\\\\n + 3 * max(self.p_onset_win[0],\\n self.s_onset_win[0])\\n\\n msg = \\\"=\\\" * 120 + \\\"\\\\n\\\"\\n msg += \\\"\\\\tDETECT - Continuous Seismic Processing\\\\n\\\"\\n msg += \\\"=\\\" * 120 + \\\"\\\\n\\\"\\n msg += \\\"\\\\n\\\"\\n msg += \\\"\\\\tParameters specified:\\\\n\\\"\\n msg += \\\"\\\\t\\\\tStart time = {}\\\\n\\\"\\n msg += \\\"\\\\t\\\\tEnd time = {}\\\\n\\\"\\n msg += \\\"\\\\t\\\\tTime step (s) = {}\\\\n\\\"\\n msg += \\\"\\\\t\\\\tNumber of CPUs = {}\\\\n\\\"\\n msg += \\\"\\\\n\\\"\\n msg += \\\"\\\\t\\\\tSampling rate = {}\\\\n\\\"\\n msg += \\\"\\\\t\\\\tGrid decimation [X, Y, Z] = [{}, {}, {}]\\\\n\\\"\\n msg += \\\"\\\\t\\\\tBandpass filter P = [{}, {}, {}]\\\\n\\\"\\n msg += \\\"\\\\t\\\\tBandpass filter S = [{}, {}, {}]\\\\n\\\"\\n msg += \\\"\\\\t\\\\tOnset P [STA, LTA] = [{}, {}]\\\\n\\\"\\n msg += \\\"\\\\t\\\\tOnset S [STA, LTA] = [{}, {}]\\\\n\\\"\\n msg += \\\"\\\\n\\\"\\n msg += \\\"=\\\" * 120\\n msg = msg.format(str(start_time), str(end_time), self.time_step,\\n self.n_cores, self.sampling_rate,\\n self.decimate[0], self.decimate[1], self.decimate[2],\\n self.p_bp_filter[0], self.p_bp_filter[1],\\n self.p_bp_filter[2], self.s_bp_filter[0],\\n self.s_bp_filter[1], self.s_bp_filter[2],\\n self.p_onset_win[0], self.p_onset_win[1],\\n self.s_onset_win[0], self.s_onset_win[1])\\n self.output.log(msg, self.log)\\n\\n # Detect max coalescence value and location at each time sample\\n # within the decimated grid\\n self._continuous_compute(start_time, end_time)\",\n \"def _get_predict_end(self, end):\\n\\n out_of_sample = 0 # will be overwritten if needed\\n if end is None: # use data for ARIMA - endog changes\\n end = len(self.data.endog) - 1\\n\\n dates = self.data.dates\\n freq = self.data.freq\\n\\n if isinstance(end, str):\\n if dates is None:\\n raise ValueError(\\\"Got a string for end and dates is None\\\")\\n try:\\n dtend = self._str_to_date(end)\\n self.data.predict_end = dtend\\n end = self._get_dates_loc(dates, dtend)\\n except KeyError as err: # end is greater than dates[-1]...probably\\n if dtend > self.data.dates[-1]:\\n end = len(self.data.endog) - 1\\n freq = self.data.freq\\n out_of_sample = datetools._idx_from_dates(dates[-1], dtend,\\n freq)\\n else:\\n if freq is None:\\n raise ValueError(\\\"There is no frequency for these \\\"\\n \\\"dates and date %s is not in dates \\\"\\n \\\"index. Try giving a date that is in \\\"\\n \\\"the dates index or use an integer.\\\"\\n % dtend)\\n else: #pragma: no cover\\n raise err # should never get here\\n self._make_predict_dates() # attaches self.data.predict_dates\\n\\n elif isinstance(end, int) and dates is not None:\\n try:\\n self.data.predict_end = dates[end]\\n except IndexError as err:\\n nobs = len(self.data.endog) - 1 # as an index\\n out_of_sample = end - nobs\\n end = nobs\\n if freq is not None:\\n self.data.predict_end = datetools._date_from_idx(dates[-1],\\n out_of_sample, freq)\\n elif out_of_sample <= 0: # have no frequency but are in sample\\n #TODO: what error to catch here to make sure dates is\\n #on the index?\\n try:\\n self.data.predict_end = self._get_dates_loc(dates,\\n end)\\n except KeyError:\\n raise\\n else:\\n self.data.predict_end = end + out_of_sample\\n self.data.predict_start = self._get_dates_loc(dates,\\n self.data.predict_start)\\n\\n self._make_predict_dates()\\n\\n elif isinstance(end, int):\\n nobs = len(self.data.endog) - 1 # is an index\\n if end > nobs:\\n out_of_sample = end - nobs\\n end = nobs\\n\\n elif freq is None: # should have a date with freq = None\\n raise ValueError(\\\"When freq is None, you must give an integer \\\"\\n \\\"index for end.\\\")\\n\\n return end, out_of_sample\",\n \"def epoch_end(self):\\n pass\",\n \"def has_end_reason(self):\\n # Someday I will have real end reasons\\n return False\",\n \"def predict_end():\\n data = request.json\\n\\n if data:\\n predictor.pred_dict[\\\"end_date\\\"] = data[\\\"end_date\\\"]\\n else:\\n pass\\n\\n return 'Non tam praeclarum est scire latine, quam turpe nescire'\",\n \"def guess_cuewords():\\n\\n if t_word[:3] == 'nie':\\n create_negation_frame()\\n create_target_focus_scope()\\n\\n if t_word[:3] == 'nic':\\n create_negation_frame()\\n create_target_focus_scope()\",\n \"def test_sv_end_svend():\\n # Example:\\n # 2 321682 . T <DEL> 6 PASS SVTYPE=DEL;END=321887;SVLEN=-205;CIPOS=-56,20;CIEND=-10,62 GT:GQ 0/1:12\\n end = sv_end(pos=321682, alt=\\\"<DEL>\\\", svend=321887, svlen=-205)\\n assert end == 321886\",\n \"def test_sv_end_bnd():\\n # Example:\\n # 2\\t321681\\tbnd_W\\tG\\tG]17:198982]\\t6\\tPASS\\tSVTYPE=BND;MATEID=bnd_Y\\tGT\\t0/1\\n end = sv_end(pos=321681, alt=ALT, svend=None, svlen=None)\\n assert end == 198981\",\n \"def doCombineCurEnd(self, endofword, nrc='', nextvowel=''): # nrc = next root consonant\\n if not self.end:\\n return\\n self.final = PhonStateCAT.getFinal(self.end)\\n nasalPhon = ''\\n postVowelPhon = ''\\n preVowelPhon = ''\\n # geminates\\n geminates = False\\n if self.end.startswith('w'):\\n preVowelPhon = 'w'\\n self.vowel = self.end[1:2]\\n else:\\n self.vowel = self.end[:1]\\n vowelPhon = self.vowel\\n if nrc == self.final and self.final != '':\\n geminates = True\\n if self.gemminatesStrategy == 'len' or self.gemminatesStrategy == 'lentone':\\n postVowelPhon = 'ː'\\n ## Suffix\\n finalPhon = ''\\n if self.final == 'ng':\\n nasalPhon = self.nasalchar # ?\\n if geminates:\\n pass\\n elif self.final in PhonStateCAT.simpleFinalMapping:\\n finalPhon = PhonStateCAT.simpleFinalMapping[self.final]\\n elif self.final == '':\\n if self.latent != '' and self.prefixStrategy != 'never' and (self.prefixSyllable == 'afterEmptyCoda' or self.prefixSyllable == 'afterEmptyCoda+'):\\n finalPhon = PhonStateCAT.simpleLatentMapping[self.latent]\\n finalPhon = ''\\n else:\\n print(\\\"unrecognized final: \\\"+self.final)\\n self.phon += preVowelPhon+vowelPhon+nasalPhon+postVowelPhon+finalPhon\\n if not endofword:\\n self.phon += self.syllablesepchar\",\n \"def get_envelope_end(env):\\n denv = np.diff(env)\\n i = np.where(np.abs(denv) > 0)[0]\\n true_stop_index = np.max(i)+1\\n return true_stop_index\",\n \"def get_end_indicator( is_alive,force_end_at_t_max = False):\\n #session-ending action indicator: uint8[batch,tick]\\n is_end = T.eq(is_alive[:,:-1] - is_alive[:,1:],1)\\n \\n if force_end_at_t_max:\\n session_ended_before = T.sum(is_end,axis=1,keepdims=True)\\n is_end_at_tmax = 1 - T.gt(session_ended_before, 0 )\\n else:\\n is_end_at_tmax = T.zeros((is_end.shape[0],1),dtype=is_end.dtype) \\n \\n is_end = T.concatenate(\\n [is_end,\\n is_end_at_tmax],\\n axis=1\\n )\\n return is_end\",\n \"def detect_commercial(video_desp, histogram, transcript):\\n blackframe_list = get_blackframe_list(histogram)\\n black_windows = blackframe_list \\\\\\n .dilate(1. / video_desp['fps']) \\\\\\n .coalesce() \\\\\\n .dilate(-1. / video_desp['fps']) \\\\\\n .filter(min_length=MIN_BLACKWINDOW * 1. / video_desp.fps)\\n\\n # get all instances of >>, Announcer:, and >> Announcer: in transcript\\n arrow_text = get_text_intervals(\\\">>\\\", transcript)\\n announcer_text = get_text_intervals(\\\"Announcer:\\\", transcript)\\n arrow_announcer_text = get_text_intervals(\\\">> Announcer:\\\", tarnscript)\\n \\n # get an interval for the whole video\\n whole_video = IntervalList([(0., video_desp['video_length'], 0)])\\n\\n # whole video minus black windows to get segments in between black windows\\n # then filter out anything that overlaps with \\\">>\\\" as long as it's not\\n # \\\">> Announcer:\\\"\\n # then coalesce, as long as it doesn't get too long\\n def fold_fn(stack, interval):\\n if len(stack) == 0:\\n stack.append(interval)\\n else:\\n last = stack.pop()\\n if or_pred(overlaps(), after(max_dist=.1)(interval, last))(interval, last):\\n if last.union(interval).length() > MAX_COMMERCIAL_TIME:\\n if last.length() > MAX_COMMERCIAL_TIME:\\n stack.append(Interval(\\n last.start, \\n last.start + MAX_COMMERCIAL_TIME, \\n last.payload))\\n else:\\n stack.append(last)\\n stack.append(interval)\\n else:\\n stack.append(last.union(interval))\\n else:\\n stack.append(interval)\\n commercials = whole_video \\\\\\n .minus(black_windows) \\\\\\n .filter_against(\\n arrow_text.filter_against(arrow_announcer_text,\\n predicate=not_pred(overlaps())),\\n predicate=not_pred(overlaps())\\n ) \\\\\\n .set_union(black_windows) \\\\\\n .fold_list(fold_fn, []) \\\\\\n .filter_length(min_length = MIN_COMMERCIAL_TIME)\\n \\n # add in lowercase intervals\\n lowercase_intervals = get_lowercase_intervals(transcript)\\n commercials = commercials \\\\\\n .set_union(lowercase_intervals) \\\\\\n .dilate(MIN_COMMERCIAL_GAP / 2) \\\\\\n .coalesce() \\\\\\n .dilate(MIN_COMMERCIAL_GAP / 2)\\n\\n # get blank intervals\\n blank_intervals = whole_video.minus(IntervalList([\\n (start_sec, end_sec - TRANSCRIPT_DELAY, 0)\\n for text, start_sec, end_sec in transcript\\n ])).coalesce().filter_length(\\n min_length=MIN_BLANKWINDOW, max_length=MAX_BLANKWINDOW)\\n\\n # add in blank intervals, but only if adding in the new intervals doesn't\\n # get too long\\n commercials = commercials.merge(blank_intervals,\\n predicate=or_pred(before(max_dist=MAX_MERGE_GAP),\\n after(max_dist=MAX_MERGE_GAP))\\n ) \\\\\\n .filter_length(max_length=MAX_MERGE_DURATION) \\\\\\n .set_union(commercials) \\\\\\n .dilate(MIN_COMMERCIAL_GAP / 2) \\\\\\n .coalesce() \\\\\\n .dilate(MIN_COMMERCIAL_GAP / 2)\\n\\n # post-process commercials to get rid of gaps, small commercials, and\\n # islated blocks\\n small_gaps = whole_video \\\\\\n .minus(commercials) \\\\\\n .filter_length(max_length = MAX_COMMERCIAL_GAP) \\\\\\n .filter_against(\\n arrow_text.filter_against(\\n announcer_text,\\n predicate=not_pred(overlaps())\\n ), predicate=not_pred(overlaps()))\\n\\n # merge with small gaps, but only if that doesn't make things too long\\n commercials = commercials \\\\\\n .set_union(small_gaps.dilate(0.1)) \\\\\\n .coalesce() \\\\\\n .filter_length(max_length=MAX_COMMERCIAL_TIME) \\\\\\n .set_union(commercials) \\\\\\n .coalesce()\\n\\n # get isolated commercials\\n not_isolated_commercials = commercials.filter_against(commercials,\\n predicate=or_pred(before(max_dist=MAX_COMMERCIAL_TIME),\\n after(max_dist=MAX_COMMERCIAL_TIME)))\\n isolated_commercials = commercials.minus(not_isolated_commercials)\\n commercials_to_delete = isolated_commercials \\\\\\n .filter_length(max_length=MIN_COMMERCIAL_TIME_FINAL) \\\\\\n .set_union(isolated_commercials \\\\\\n .filter_against(blank_intervals, predicate=equals()) \\\\\\n .filter_length(max_length=MAX_ISOLATED_BLANK_TIME))\\n\\n commercials = commercials.minus(commercials_to_delete)\\n\\n return commercials\",\n \"def handle_record_end():\\n LOG.info(\\\"End Recording...\\\")\\n context = {'client_name': 'mycroft_listener',\\n 'source': 'audio',\\n 'destination': [\\\"skills\\\"]}\\n bus.emit(Message('recognizer_loop:record_end', context=context))\",\n \"def affected_end(self):\\n types = {alt.type for alt in self.ALT} # set!\\n BAD_MIX = {INS, SV, BND, SYMBOLIC} # don't mix well with others\\n if (BAD_MIX & types) and len(types) == 1 and list(types)[0] == INS:\\n # Only insertions, return 0-based position right of first base\\n return self.POS # right of first base\\n else: # Return 0-based end position, behind last REF base\\n return (self.POS - 1) + len(self.REF)\",\n \"def end_phase():\\n pass\",\n \"def stop_cb(evt):\\n # print('CLOSING on {}'.format(evt))\\n speech_recognizer.stop_continuous_recognition()\\n global done\\n done = True\",\n \"def inference_end(self, inputs, results):\\n return\",\n \"def on_epoch_end(epoch, logs):\\n global w2vmodel\\n global max_seq_length\\n global word_to_ix\\n global ix_to_word\\n global sample\\n if epoch % 5 == 0:\\n generate_text(model, w2vmodel, epoch, length=75, max_seq_length=max_seq_length,\\n seed=\\\"Rain drop, drop top\\\\n\\\")\\n return\",\n \"def get_last_sentence_end(text: str) -> int:\\n end = -1\\n for e in sentence_ends:\\n p_end = [m.end() for m in re.finditer(e, text)]\\n end = max(end, p_end[-1] if len(p_end) > 0 else -1)\\n return end\",\n \"def end_episode(self):\\n self.training_buffer.reset_all()\\n for agent_id in self.cumulative_rewards:\\n self.cumulative_rewards[agent_id] = 0\\n for agent_id in self.episode_steps:\\n self.episode_steps[agent_id] = 0\\n if self.use_curiosity:\\n for agent_id in self.intrinsic_rewards:\\n self.intrinsic_rewards[agent_id] = 0\",\n \"def test_endgameStrategy(self):\\n self.result = \\\"\\\"\\\"\\n 0 0 0 0 0 0 0 0 0 0 0\\n 0 0 0 1 2 3 3 2 1 0 0\\n 0 0 1 3 x x x x 1 0 0\\n 0 0 2 x x 6 x 5 2 0 0\\n 0 0 3 x 4 4 x x 2 0 0\\n 0 0 3 x 5 5 x x 2 0 0\\n 0 0 2 x x x x 3 1 0 0\\n 0 0 1 2 3 3 2 1 0 0 0\\n 0 0 0 0 0 0 0 0 0 0 0\\n \\\"\\\"\\\"\",\n \"def predict_again(src):\\n global rcnt\\n global lcnt\\n H,W = src.shape[:2]\\n #cv2.imshow(\\\"cROPPPPPPED\\\",src)\\n #print (src.shape)\\n\\n img1 = src[:,:int(W/2)]\\n img2 = src[:,int(W/2)+1:]\\n contoured1,area1 = drawContours11111(img1)\\n contoured2,area2 = drawContours11111(img2)\\n #cv2.imshow(\\\"blank_image\\\",contoured1)\\n #cv2.imshow(\\\"blank_image1\\\",contoured2)\\n print (area1,area2)\\n if area2>area1:\\n #print (\\\"New:::::::::::::RIGGGGGGGGGHT\\\")\\n if rcnt >=3:\\n print (\\\"New:::::::::::::RIGGGGGGGGGHT\\\")\\n feedback.direction = 1\\n feedback.detection = 1\\n rcnt += 1\\n lcnt = 0\\n elif area1>area2:\\n #print (\\\"New:::::::::::::LEFTTTTTTTTT\\\")\\n if lcnt >=3:\\n print (\\\"New:::::::::::::LEFTTTTTTTTT\\\")\\n feedback.direction = -1\\n feedback.detection = 1\\n lcnt += 1\\n rcnt = 0\",\n \"def end(self, won, reason):\\n pass\\n # replace with your end logic\",\n \"def recognized_cb(evt):\\n if evt.result.reason == speechsdk.ResultReason.RecognizedSpeech:\\n #print('RECOGNIZED: {}'.format(evt.result.text))\\n #print('All params: {}'.format(evt.result))\\n #print(evt.result.json)\\n response = json.loads(evt.result.json)\\n #print('All params: {}'.format(response))\\n Text = response[\\\"DisplayText\\\"]\\n duration = 0;\\n for word in response[\\\"NBest\\\"][0][\\\"Words\\\"]:\\n duration += word[\\\"Duration\\\"]\\n duration = duration / 10000000\\n print(\\\"dur :\\\"+str(duration)+\\\" text: \\\" + Text)\",\n \"def get_ends(self): \\n return self.last_words\",\n \"def event_m10_29_x8():\\r\\n \\\"\\\"\\\"State 0,1: End state\\\"\\\"\\\"\\r\\n return 0\",\n \"def audio_cd_end_track(self, audio_cd_end_track):\\n self._audio_cd_end_track = audio_cd_end_track\",\n \"def onEpochEnd(self, agent):\\n\\n pass\",\n \"def _findExonEnd(self, exonRecs, iBlkStart):\\n iBlkEnd = iBlkStart + 1\\n while (iBlkEnd < len(exonRecs)) and (self._tGapSize(exonRecs, iBlkEnd) < minIntronSize):\\n iBlkEnd += 1\\n return iBlkEnd, exonRecs[iBlkEnd - 1].end - exonRecs[iBlkStart].start\",\n \"def event_m20_11_x94():\\n \\\"\\\"\\\"State 0,1: End state\\\"\\\"\\\"\\n return 0\",\n \"def event_m10_29_x27():\\r\\n \\\"\\\"\\\"State 0,1: End state\\\"\\\"\\\"\\r\\n return 0\",\n \"def test_recognize(self):\\n\\n rec = mi.MyRecognizeCallback()\\n rec.on_close()\\n rec.on_connected()\\n rec.on_data('\\\"final\\\": true truetestd')\\n rec.on_error(\\\"testerror\\\")\\n rec.on_hypothesis(\\\"testh\\\")\\n rec.on_inactivity_timeout(\\\"testerrorinac\\\")\\n rec.on_listening()\\n rec.on_transcription(\\\"testtr\\\")\\n self.assertIsNotNone(rec)\",\n \"def event_m10_29_x6():\\r\\n \\\"\\\"\\\"State 0,1: End state\\\"\\\"\\\"\\r\\n return 0\",\n \"def summarize_end(sample):\\n (species, sex, tissue, replicate) = sample.split(\\\"_\\\")\\n ins = FocalIntersect(species, sex, tissue, replicate)\\n with open(\\\"../data/output/\\\" + ins.name + \\\".end.A.txt\\\", 'w') as f:\\n for isoseqid in ins.end_infoA.keys():\\n exonnum1, exonnum2, five_desc, three_desc = ins.end_infoA[isoseqid]\\n transid = ins.isoseqid2besttransidA[isoseqid]\\n f.write(isoseqid + \\\"\\\\t\\\" + transid + \\\"\\\\t\\\" + exonnum1 + \\\"\\\\t\\\" + exonnum2 + \\\"\\\\t\\\" + five_desc + \\\"\\\\t\\\" + three_desc + \\\"\\\\n\\\")\\n\\n with open(\\\"../data/output/\\\" + ins.name + \\\".end.B.txt\\\", 'w') as f:\\n for isoseqid in ins.end_infoB.keys():\\n exonnum1, exonnum2, five_desc, three_desc = ins.end_infoB[isoseqid]\\n transid = ins.isoseqid2besttransidB[isoseqid]\\n f.write(isoseqid + \\\"\\\\t\\\" + transid + \\\"\\\\t\\\" + exonnum1 + \\\"\\\\t\\\" + exonnum2 + \\\"\\\\t\\\" + five_desc + \\\"\\\\t\\\" + three_desc + \\\"\\\\n\\\")\",\n \"def end_turn(self, end_msg):\\n self.progress_bar.set_value(self.word_view.word)\\n self.greeterboard.greets(end_msg)\\n self.scoreboard.update_score()\\n self.word_view.update_word()\\n self.greeterboard.update_gallows()\",\n \"def on_epoch_end(self):\",\n \"def _is_end_of_label(self, prev_top: str, now_top: str, prev_type: str, now_type: str) -> bool:\\n\\n if prev_top in ['E', 'S']:\\n return True\\n elif prev_top == 'B' and now_top in ['B', 'O']:\\n return True\\n elif prev_top == 'I' and now_top in ['B', 'O', 'S']:\\n return True\\n elif prev_top != 'O' and prev_type != now_type:\\n return True\\n return False\",\n \"def test_endstring_until_further_notice():\\n vc = vtec.parse(\\\"/O.NEW.KJAN.TO.W.0130.050829T1651Z-000000T0000Z/\\\")\\n assert vc[0].get_end_string(None) == \\\"until further notice\\\"\",\n \"def test_forward_end_word_extend_selection(self):\\n before_b = \\\"\\\"\\\"\\\\\\n Americans live in the most severe weather-prone country on Earth. Each year, Americans cope with an average of 10,000 thunderstorms, 2,500 floods, 1,000 tornadoes, as well as an average of 6 deadly hurricanes. Potentially deadly weather impacts every American. Communities can now rely on the National Weather Service’s StormReady program to help them guard against the ravages of Mother Nature.\\n\\n Some 90% of all presidentially declared disasters are weather related, leading to around 500 deaths per year and nearly $14 billion in damage. StormReady, a program started in 1999 in Tulsa, OK, helps arm America's communities with the communication and safety skills needed to save lives and property– before and during the event. StormReady helps community leaders and emergency managers strengthen local safety programs.\\n\\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n Americans live in the most severe weather-prone country on Earth. Each year, Americans cope with an average of 10,000 thunderstorms, 2,500 floods, 1,000 tornadoes, as well as an average of 6 deadly hurricanes. Potentially deadly weather impacts every American. Communities can now rely on the National Weather Service’s StormReady program to help them guard against the ravages of Mother Nature.\\n\\n Some 90% of all presidentially declared disasters are weather related, leading to around 500 deaths per year and nearly $14 billion in damage. StormReady, a program started in 1999 in Tulsa, OK, helps arm America's communities with the communication and safety skills needed to save lives and property– before and during the event. StormReady helps community leaders and emergency managers strengthen local safety programs.\\n\\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"3.20\\\", \\\"3.20\\\"),\\n after_sel=(\\\"3.20\\\", \\\"3.30\\\"),\\n command_name=\\\"forward-end-word-extend-selection\\\",\\n )\",\n \"def end_game(self, end_msg):\\n self.word_view.reveal_word()\\n self.greeterboard.update_gallows()\\n self.greeterboard.greets(end_msg)\",\n \"def _end_episode(self):\\n self.turn_cnt = 0\\n self.episode_cnt += 1\\n self.contexts = None\\n self.seed_utterances = None\\n self.reset_agents()\",\n \"def test_two_persons_exiting(self):\\n human_detector_inst = HumanDetector(\\n find_humans_from_video_file_name='videos/two_persons_exiting.mp4',\\n use_pi_camera=False, open_display=False)\\n self.assertEqual(human_detector_inst.perform_job(), None)\\n human_centroid_dict = human_detector_inst.get_human_centroid_dict()\\n self.assertEqual(len(human_centroid_dict), 2)\\n self.assertEqual(human_centroid_dict[0].direction, Direction.EXIT)\\n self.assertEqual(human_centroid_dict[1].direction, Direction.EXIT)\\n self.assertEqual(SendReceiveMessages().get_face_detected_count_locally(), -2)\\n # human_detector_inst.clean_up()\\n self.__cleanup()\",\n \"def test_endgameStrategy2(self):\\n\\n self.result = \\\"\\\"\\\"\\n 1 x 1 0 0 2 x 2 1 x 1 0 0 1 x x 1\\n 1 1 1 0 0 2 x 3 2 1 1 0 0 1 3 4 3\\n 0 0 0 0 0 1 2 x 1 0 0 0 0 0 1 x x\\n 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 2 2\\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1\\n 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 x 1\\n 1 x 1 0 0 0 0 0 0 0 0 0 0 1 x 2 1\\n \\\"\\\"\\\"\",\n \"def _is_at_end(self, binvect):\\n last = max(k for (k, v) in enumerate(binvect) if v == 1)\\n n_step = len(self.pas)\\n steps_between = np.arange(last + 1, n_step)\\n if 0 <= len(steps_between) <= self._n_to_end:\\n self._set_label(binvect, still)\\n for k in steps_between:\\n self.labels[k] = still\\n return True\\n else:\\n return False\",\n \"def find_start_end_rests(audio_data, sr, hop_length=HOP_LENGTH, n_fft=N_FFT):\\r\\n \\r\\n # Compute the 3rd percentile of the envelope and \\r\\n # deem anything below this value as silence \\r\\n envelope = frame(audio_data, hop_length=hop_length, frame_length=n_fft).max(axis=0)\\r\\n lower_bound = np.percentile(envelope, 5.0)\\r\\n \\r\\n # Implement the search as loop, this should be faster than vectorisation\\r\\n k = 0\\r\\n while envelope[k] <= lower_bound:\\r\\n k += 1\\r\\n \\r\\n # Return 0 if there is no start rest\\r\\n if k == 0:\\r\\n time_start = 0.0\\r\\n else:\\r\\n # The first value of the output of the frame function correspond to the time of\\r\\n # n_fft, then the times are spaced according to hop_length \\r\\n time_start = ((k-1)*hop_length + n_fft)/float(sr)\\r\\n \\r\\n j = len(envelope)-1\\r\\n while envelope[j] <= lower_bound:\\r\\n j -= 1\\r\\n \\r\\n # Return the length of the track if the is no end rest\\r\\n if j == len(envelope)-1:\\r\\n time_end = len(audio_data)/float(sr)\\r\\n else:\\r\\n time_end = ((j-1)*hop_length + n_fft)/float(sr)\\r\\n \\r\\n times_start_end_rests = [time_start, time_end]\\r\\n \\r\\n return(times_start_end_rests)\",\n \"def on_predict_end(self, logs=None):\",\n \"def on_predict_end(self, logs=None):\",\n \"def _ends_cvc(self, word):\\n return (\\n len(word) >= 3\\n and self._is_consonant(word, len(word) - 3)\\n and not self._is_consonant(word, len(word) - 2)\\n and self._is_consonant(word, len(word) - 1)\\n and word[-1] not in (\\\"w\\\", \\\"x\\\", \\\"y\\\")\\n ) or (\\n self.mode == self.NLTK_EXTENSIONS\\n and len(word) == 2\\n and not self._is_consonant(word, 0)\\n and self._is_consonant(word, 1)\\n )\",\n \"def test_vertical_line_Exiting(self):\\n human_detector_inst = HumanDetector(\\n find_humans_from_video_file_name='videos/TempleVideos/vertical_line_Exiting.mp4',\\n use_pi_camera=False, open_display=True)\\n self.assertEqual(human_detector_inst.perform_job(), None)\\n human_centroid_dict = human_detector_inst.get_human_centroid_dict()\\n self.assertEqual(len(human_centroid_dict), 4)\\n self.assertEqual(human_centroid_dict[0].direction, Direction.EXIT)\\n self.assertEqual(human_centroid_dict[1].direction, Direction.EXIT)\\n self.assertEqual(human_centroid_dict[2].direction, Direction.EXIT)\\n self.assertEqual(human_centroid_dict[3].direction, Direction.EXIT)\\n self.assertEqual(SendReceiveMessages().get_face_detected_count_locally(), -4)\\n human_detector_inst.clean_up()\\n self.__cleanup()\",\n \"def TEM_loss(anchors_action,anchors_startend,\\n Y_action,Y_startend,config): \\n loss_action,num_sample_action = binary_logistic_loss(Y_action,anchors_action)\\n loss_startend,num_sample_startend = binary_logistic_loss(Y_startend,anchors_startend)\\n # print('loss_action', loss_action)\\n # print('loss_startend', loss_startend)\\n loss={\\\"loss_action\\\":loss_action,\\\"num_sample_action\\\":num_sample_action,\\n \\\"loss_startend\\\":loss_startend,\\\"num_sample_startend\\\":num_sample_startend}\\n return loss\",\n \"def what_emo(self, mfccs, model, params_file, st, endpoint):\\n print(\\\"\\\\nEntering what_emo... \\\")\\n emotion = None\\n emo_label = {0:'angry', 1:'fearful', 2:'happy', 3:'sad', 4:'calm'}\\n if endpoint == \\\"\\\":\\n #emo_gen = self.mfccs_to_emo(mfccs, model, params_file, st)\\n #emo = next(emo_gen)[\\\"dense_1\\\"]\\n emo_list = self.mfccs_to_emo(mfccs, model, params_file, st)\\n print(\\\"\\\\nemo_list: \\\")\\n print(emo_list)\\n epis_var = emo_list[0][0][-1] # we take variance from logits_variance\\n print(\\\"\\\\nAleatoric Variance: \\\")\\n print(epis_var)\\n emo = emo_list[1] # softmax_output\\n print(\\\"\\\\nemo: \\\")\\n print(emo)\\n else:\\n dict_im = {}\\n service = googleapiclient.discovery.build('ml', 'v1')\\n dict_im[\\\"input_1\\\"] = mfccs.tolist()\\n predictions = service.projects().predict(name=endpoint, body={'instances':[dict_im]}).execute()[\\\"predictions\\\"]\\n emo = np.array(predictions[0]['dense_1']) \\n print(\\\"\\\\nemo: \\\")\\n print(emo)\\n res = np.argmax(emo)\\n print(\\\"\\\\nres: \\\")\\n print(res)\\n emotion = emo_label[res] \\n print(\\\"\\\\nPrediction: \\\" + str(emotion))\\n return str(emotion), res, emo, epis_var\",\n \"def stop_cb(evt): \\n speech_recognizer.stop_continuous_recognition()\\n nonlocal done\\n done = True\",\n \"def event_m10_29_x21():\\r\\n \\\"\\\"\\\"State 0,1: End state\\\"\\\"\\\"\\r\\n return 0\",\n \"def on_epoch_end(self):\\n pass\",\n \"def validate_no_teacher_forcing(encoder_model,decoder_model,line2idx,batch,beam_size=3):\\n decoder_model.eval() # eval mode (no dropout or batchnorm)\\n encoder_model.eval()\\n\\n imgs = batch[0]#.unsqueeze(0)\\n caps = batch[1]#.unsqueeze(0)\\n caplens = batch[2] #.unsqueeze(0)\\n k = beam_size\\n vocab_size = len(line2idx)\\n references = list() # references (true captions) for calculating BLEU-4 score\\n hypotheses = list() # hypotheses (predictions)\\n with torch.no_grad():\\n imgs = imgs.to(device)\\n caps = caps.to(device)\\n caplens = caplens.to(device)\\n\\n encoder_out = encoder_model(imgs)\\n beam_search_seq = decoder_model.generate(encoder_out,line2idx,beam_size)\\n \\n allcaps = caps\\n for j in range(allcaps.shape[0]):\\n\\n img_caps = allcaps[j].tolist()\\n\\n img_captions = []\\n for w in img_caps:\\n if w not in [line2idx['<PAD>']]:\\n #if w not in [line2idx['<EOS>'],line2idx['<BOS>'], line2idx['<PAD>']]:\\n img_captions.append(w)\\n references.append(img_captions)\\n\\n accuracy = np.sum(np.where(np.array(beam_search_seq) == np.array(references[0]),1,0))/len(references[0])\\n\\n return beam_search_seq,accuracy\",\n \"def determineEndGame(self):\\n\\n print(\\\"noWinners: \\\" + str(self.noWinners) + \\\", noTotKids: \\\" + str(self.noTotKids))\\n\\n # TODO scegliere come determinare la fine del gioco\\n # if self.noWinners == self.noTotKids - 1: # end-game test\\n if self.noWinners == self.noTotKids:\\n print(\\\"ho determinato la fine del gioco\\\")\\n return True\\n else:\\n print(\\\"colore toccato ma la partita non e' finita\\\")\\n return False\",\n \"def recognize(self, audio):\\n response = self.service.Recognize(cloud_speech_extended_pb2.RecognizeRequest(\\n config=cloud_speech_extended_pb2.RecognitionConfig(\\n # There are a bunch of config options you can specify. See https://goo.gl/KPZn97 for the full list.\\n encoding='LINEAR16', # one of LINEAR16, FLAC, MULAW, AMR, AMR_WB\\n sample_rate_hertz=audio.frame_rate, # the rate in hertz\\n # See https://g.co/cloud/speech/docs/languages for a list of supported languages.\\n language_code=self.settings.language, # a BCP-47 language tag\\n enable_word_time_offsets=self.settings.time_offsets, # if true, return recognized word time offsets\\n max_alternatives=self.settings.max_alternatives, # maximum number of returned hypotheses\\n ),\\n audio=cloud_speech_extended_pb2.RecognitionAudio(\\n uri=None,\\n content=audio.raw_data\\n )\\n ), self.settings.deadline)\\n\\n # Print the recognition result alternatives and confidence scores.\\n results = []\\n\\n # for result in response.results:\\n if len(response.results) > 0:\\n result = response.results[0] # TODO: check why here we have list of results ?, when it is possible ?\\n\\n alternative = result.alternatives[0]\\n alignment = [] #\\n confirmed_results = []\\n if self.settings.time_offsets:\\n word_indices = [j for j in range(len(alternative.words)) if\\n alternative.words[j].word != '<eps>']\\n\\n if len(word_indices) > 0:\\n confirmed_results.append([alternative.words[i].word for i in word_indices])\\n else: # alignment was not returned\\n confirmed_results.append(alternative.transcript)\\n\\n alignment.append(\\n [[alternative.words[i].start_time, alternative.words[i].end_time] for i in\\n word_indices])\\n results.append({\\n 'transcript': ' '.join(confirmed_results),\\n 'confidence': alternative.confidence,\\n 'alignment': alignment,\\n })\\n else:\\n results.append({\\n 'transcript': alternative.transcript,\\n 'confidence': alternative.confidence\\n })\\n\\n return results\",\n \"def _decode_end(_fp):\\n return 0\",\n \"def detect(self, frame, foreground_mask):\\n pass\",\n \"def event_m10_29_x40():\\r\\n \\\"\\\"\\\"State 0,1: End state\\\"\\\"\\\"\\r\\n return 0\",\n \"def end (self):\\n return self._end if self._end != self.inf else self.e\",\n \"def easyends(endtype,\\n endlength,\\n number=0,\\n interaction=None,\\n fdev=0.05,\\n maxspurious=0.5,\\n maxendspurious=None,\\n tries=1,\\n oldends=[],\\n adjs=['n', 'n'],\\n energetics=None,\\n alphabet='n',\\n echoose=None,\\n absolute=False,\\n _presetavail=False):\\n\\n if not energetics:\\n efunc = EnergeticsBasic()\\n else:\\n efunc = energetics\\n if (not interaction) or (interaction == 0):\\n interaction = enhist(\\n endtype,\\n endlength,\\n energetics=efunc,\\n adjacents=adjs,\\n alphabet=alphabet)[2]['emedian']\\n LOGGER.info(\\\"Calculated optimal interaction energy is {0}.\\\".format(\\n interaction))\\n if not absolute:\\n mult = interaction\\n else:\\n mult = 1.0\\n \\n maxcompspurious = maxspurious * mult\\n if not maxendspurious:\\n maxendspurious = maxspurious * mult\\n else:\\n maxendspurious = maxendspurious * mult\\n\\n sfilt = spacefilter_standard(interaction, mult * fdev,\\n maxendspurious)\\n efilt = endfilter_standard_advanced(maxcompspurious, maxendspurious)\\n if not echoose:\\n echoose = endchooser_standard(interaction)\\n\\n return find_end_set_uniform(\\n endtype,\\n endlength,\\n sfilt,\\n efilt,\\n echoose,\\n energetics=efunc,\\n numtries=tries,\\n oldends=oldends,\\n adjacents=adjs,\\n num=number,\\n alphabet=alphabet,\\n _presetavail=_presetavail)\",\n \"def end_rate(lablled,str):\\n#\\tlabelled = RawClaim.objects.exclude(correcttrim=\\\"\\\")\\n\\tbad = [l for l in labelled if str in l.correcttrim and normal(l.correcttrim)]\\n\\tgood = [l for l in labelled if correct_ends_with(l.sentence,l.correcttrim,str)]\\n\\treturn (float(len(good))/(len(good)+len(bad)),bad,good)\",\n \"def recognize(self, h, recog_args, target_left_mask=-1):\\n hyp = {\\\"score\\\": 0.0, \\\"yseq\\\": [self.blank]}\\n\\n ys = to_device(self, torch.tensor(hyp[\\\"yseq\\\"], dtype=torch.long)).unsqueeze(0)\\n if target_left_mask > -1:\\n ys_mask = to_device(self, subsequent_mask_limit(1, target_left_mask).unsqueeze(0))\\n else:\\n ys_mask = to_device(self, subsequent_mask(1).unsqueeze(0))\\n y, c = self.forward_one_step(ys, ys_mask, None)\\n\\n for i, hi in enumerate(h):\\n ytu = torch.log_softmax(self.joint_network(hi, y[0]), dim=0)\\n logp, pred = torch.max(ytu, dim=0)\\n\\n if pred != self.blank:\\n hyp[\\\"yseq\\\"].append(int(pred))\\n hyp[\\\"score\\\"] += float(logp)\\n\\n ys = to_device(self, torch.tensor(hyp[\\\"yseq\\\"]).unsqueeze(0))\\n if target_left_mask > -1:\\n ys_mask = to_device(\\n self, subsequent_mask_limit(len(hyp[\\\"yseq\\\"]), target_left_mask).unsqueeze(0)\\n )\\n else:\\n ys_mask = to_device(\\n self, subsequent_mask(len(hyp[\\\"yseq\\\"])).unsqueeze(0)\\n )\\n\\n y, c = self.forward_one_step(ys, ys_mask, c)\\n\\n return [hyp]\",\n \"def faceTrackingEnded():\\n\\n\\ttracker.stopTracker()\\n\\ttracker.unregisterAllTargets()\\n\\t#motionProxy.rest()\",\n \"def ScenarioBGEffectExcelEnd(builder):\\n return End(builder)\",\n \"def recognize_beam(self, h, recog_args, rnnlm=None, target_left_mask=-1):\\n beam = recog_args.beam_size\\n k_range = min(beam, self.odim)\\n nbest = recog_args.nbest\\n normscore = recog_args.score_norm_transducer\\n\\n if rnnlm:\\n kept_hyps = [\\n {\\\"score\\\": 0.0, \\\"yseq\\\": [self.blank], \\\"cache\\\": None, \\\"lm_state\\\": None}\\n ]\\n else:\\n kept_hyps = [{\\\"score\\\": 0.0, \\\"yseq\\\": [self.blank], \\\"cache\\\": None}]\\n\\n for i, hi in enumerate(h):\\n hyps = kept_hyps\\n kept_hyps = []\\n\\n while True:\\n new_hyp = max(hyps, key=lambda x: x[\\\"score\\\"])\\n hyps.remove(new_hyp)\\n\\n ys = to_device(self, torch.tensor(new_hyp[\\\"yseq\\\"]).unsqueeze(0))\\n if target_left_mask > -1:\\n ys_mask = to_device(\\n self, subsequent_mask_limit(len(new_hyp[\\\"yseq\\\"]), target_left_mask).unsqueeze(0)\\n )\\n else:\\n ys_mask = to_device(\\n self, subsequent_mask(len(new_hyp[\\\"yseq\\\"])).unsqueeze(0)\\n )\\n y, c = self.forward_one_step(ys, ys_mask, new_hyp[\\\"cache\\\"])\\n\\n ytu = torch.log_softmax(self.joint(hi, y[0]), dim=0)\\n\\n if rnnlm:\\n rnnlm_state, rnnlm_scores = rnnlm.predict(\\n new_hyp[\\\"lm_state\\\"], ys[:, -1]\\n )\\n\\n for k in six.moves.range(self.odim):\\n beam_hyp = {\\n \\\"score\\\": new_hyp[\\\"score\\\"] + float(ytu[k]),\\n \\\"yseq\\\": new_hyp[\\\"yseq\\\"][:],\\n \\\"cache\\\": new_hyp[\\\"cache\\\"],\\n }\\n\\n if rnnlm:\\n beam_hyp[\\\"lm_state\\\"] = new_hyp[\\\"lm_state\\\"]\\n\\n if k == self.blank:\\n kept_hyps.append(beam_hyp)\\n else:\\n beam_hyp[\\\"yseq\\\"].append(int(k))\\n beam_hyp[\\\"cache\\\"] = c\\n\\n if rnnlm:\\n beam_hyp[\\\"lm_state\\\"] = rnnlm_state\\n beam_hyp[\\\"score\\\"] += (\\n recog_args.lm_weight * rnnlm_scores[0][k]\\n )\\n\\n hyps.append(beam_hyp)\\n\\n if len(kept_hyps) >= k_range:\\n break\\n\\n if normscore:\\n nbest_hyps = sorted(\\n kept_hyps, key=lambda x: x[\\\"score\\\"] / len(x[\\\"yseq\\\"]), reverse=True\\n )[:nbest]\\n else:\\n nbest_hyps = sorted(kept_hyps, key=lambda x: x[\\\"score\\\"], reverse=True)[\\n :nbest\\n ]\\n\\n return nbest_hyps\",\n \"def FrameEnd(builder):\\n return End(builder)\",\n \"def analyze(self, start, end):\\n return\",\n \"def process(in_path, out_path, mouth_height=50, mouth_width=50, frame_dur=1, capture_frame=None, out_img=None, show_boxes=True):\\n # Get video capture from in_path.\\n vc = cv2.VideoCapture(in_path) \\n\\n rval, frame = vc.read() if vc.isOpened() else (False, None)\\n\\n mouth_images = []\\n\\n #import pdb; pdb.set_trace()\\n\\n if rval:\\n mouths = np.empty((0, mouth_height, mouth_width, frame.shape[2]))\\n else:\\n return # Skip this video since CV2 can't open it\\n\\n frame_no = 0\\n while rval:\\n if DEBUG:\\n # Copy of original frame, for annotating.\\n image = frame.copy()\\n\\n try:\\n face_rect = locate_face(frame)\\n except ValueError:\\n print \\\"No face found for %s at frame %d. Skipping.\\\" % (in_path, frame_no)\\n vc.release()\\n return # Skip this video.\\n\\n if DEBUG:\\n highlight_rect(image, face_rect, color=(255,255,255), thickness=2)\\n\\n mouth_rects = locate_mouth(frame)\\n mouth = uniform_rect(select_mouth_candidate(mouth_rects, face_rect), face_rect, 50, 50)\\n if not mouth:\\n print \\\"No face found for %s at frame %d. Skipping.\\\" % (in_path, frame_no)\\n vc.release()\\n return # Skip this video.\\n\\n mouth_image = frame[mouth[1]:(mouth[1] + mouth[3]), mouth[0]:(mouth[0] + mouth[2]), :]\\n mouth_images.append(mouth_image) \\n\\n if DEBUG:\\n highlight_rect(image, mouth, color=(0,0,0), thickness=2) \\n #cv2.imshow('Frame', mouth_image)\\n cv2.imshow('Frame', image if show_boxes else frame)\\n\\n if frame_no == capture_frame:\\n cv2.imwrite(out_img, image if show_boxes else frame)\\n return\\n\\n cv2.waitKey(frame_dur)\\n\\n rval, frame = vc.read()\\n frame_no += 1\\n\\n vc.release()\\n\\n mouths = np.asarray(mouth_images)\\n\\n savemat(out_path, {\\\"mouths\\\": mouths})\",\n \"def speech_response(output, endsession):\\n return {\\n 'outputSpeech': {\\n 'type': 'PlainText',\\n 'text': output\\n },\\n 'shouldEndSession': endsession\\n }\",\n \"def speech_response(output, endsession):\\n return {\\n 'outputSpeech': {\\n 'type': 'PlainText',\\n 'text': output\\n },\\n 'shouldEndSession': endsession\\n }\",\n \"def test(self, end, nb_samples):\\n\\n samples = self.irepa.sample(nb_samples)\\n\\n print('Start in 2 seconds')\\n time.sleep(2)\\n for i, state in enumerate(samples):\\n sys.stdout.write(\\\"\\\\r{}%\\\".format(\\n round(100*float(i)/len(samples), 0)))\\n sys.stdout.flush()\\n if INITIALIZE:\\n Xe, Ue, Ve = self.irepa.estimator.trajectories(state, end)\\n Xe = Xe.flatten()\\n Ue = Ue.flatten()\\n else:\\n Xe, Ue, Ve = [], [], 0\\n\\n goal = OptControlGoal(\\n list(state),\\n list(end),\\n Xe, Ue, Ve, NX, NU)\\n t1 = time.time()\\n self.irepa.ocp_client.send_goal(goal, self.callback)\\n self.irepa.ocp_client.wait_for_result(rospy.Duration.from_sec(5.0))\\n t2 = time.time()\\n self.calc_times.append(round(t2-t1, 2))\\n self.euclid.append(round(self.irepa.euclid(state, end), 2))\",\n \"def greedy_decode(self, src, src_mask, src_lengths ):\\n encoder_hidden, encoder_final = self.encodeSent(src, src_mask, src_lengths)\\n prev_y, trg_mask = self.getTokRepresentation(self.sos_index, src, ret_mask=True)\\n\\n output = []\\n attention_scores = []\\n hidden = None\\n\\n for i in range(self.max_len):\\n hidden, pre_output = self.decodeSent(encoder_hidden, encoder_final, src_mask,\\n prev_y, trg_mask, hidden)\\n\\n # we predict from the pre-output layer, which is\\n # a combination of Decoder state, prev emb, and context\\n prob = self.model.callGenerator(pre_output[:, -1])\\n\\n _, next_word = torch.max(prob, dim=1)\\n next_word = next_word.data.item()\\n output.append(next_word)\\n prev_y = torch.ones(1, 1).type_as(src).fill_(next_word)\\n if self.model.decoder.attention is not None and not self.do_lang_model:\\n attention_scores.append(self.model.decoder.attention.alphas.cpu().numpy())\\n else:\\n #no attention do not store\\n attention_scores.append(torch.zeros_like(hidden).cpu().numpy())\\n \\n output = np.array(output)\\n \\n # cut off everything starting from </s> \\n # (only when eos_index provided)\\n if self.eos_index is not None:\\n first_eos = np.where(output==self.eos_index)[0]\\n if len(first_eos) > 0:\\n output = output[:first_eos[0]] \\n \\n return output, np.concatenate(attention_scores, axis=1)\",\n \"def stop_sign_detection(img_in):\\n\\n img = img_in.copy()\\n red_color_map = red_masking(img, stop_mask=True)\\n red_color_map = cv2.dilate(red_color_map, np.ones((5, 5)))\\n canny_edges = cv2.Canny(red_color_map, threshold1=50, threshold2=250, apertureSize=5)\\n\\n # display_img(canny_edges, 'Stop Sign Canny Lines')\\n\\n min_line_length = 10\\n max_pixel_gap = 10\\n hough_lines = cv2.HoughLinesP(image=canny_edges,\\n rho=0.5,\\n theta=np.pi/180,\\n threshold=20,\\n minLineLength=min_line_length,\\n maxLineGap=max_pixel_gap\\n )\\n if hough_lines is None:\\n return None, None\\n hough_lines = hough_lines[0, :] # cleanup dimensionality to make it easier to work with.\\n lines = remove_duplicates(hough_lines)\\n\\n if len(lines) < 8:\\n return None, None\\n\\n # once given the lines of interest perform some more calculations\\n min_x, max_x, min_y, max_y = calculate_min_max_values(lines)\\n mid_x = min_x + ((max_x - min_x) / 2)\\n mid_y = min_y + ((max_y - min_y) / 2)\\n return mid_x, mid_y\",\n \"def demo_video(net, im,frame_number,args, output_folder, conf_thresh):\\n \\n #data_dir = '/mnt/nfs/scratch1/souyoungjin/RESULTS_FACE_DETECTIONS/ARUNI/video'+str(args.video_id)\\n #out_dir = '/mnt/nfs/scratch1/ashishsingh/RESULTS_DOG_DETECTIONS/ARUNI/OHEM/'+args.video_folder_name\\n \\n out_dir = join(output_folder, args.video_id)\\n\\n if not os.path.exists(out_dir):\\n os.makedirs(out_dir)\\n\\n\\n timer = Timer()\\n timer.tic()\\n scores, boxes = im_detect(net, im)\\n timer.toc()\\n print ('Detection took {:.3f}s for '\\n '{:d} object proposals').format(timer.total_time, boxes.shape[0])\\n\\n # Visualize detections for each class\\n #CONF_THRESH = 0.80 #check threshold values\\n CONF_THRESH = conf_thresh\\n NMS_THRESH = 0.15 #check threshold values\\n \\n # detection file\\n dets_file_name = os.path.join(out_dir, 'video'+str(args.video_id)+'.txt') \\n fid = open(dets_file_name, 'a+')\\n sys.stdout.write('%s ' % (frame_number))\\n \\n cls_ind = 1\\n cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\\n cls_scores = scores[:, cls_ind]\\n dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float32)\\n keep = nms(dets, NMS_THRESH)\\n dets = dets[keep, :]\\n\\n keep = np.where(dets[:, 4] > CONF_THRESH)\\n dets = dets[keep]\\n \\n dets[:, 2] = dets[:, 2] - dets[:, 0] + 1\\n dets[:, 3] = dets[:, 3] - dets[:, 1] + 1\\n \\n fid.write('FRAME NUMBER: '+ str(frame_number) + '\\\\n')\\n fid.write(str(dets.shape[0]) + '\\\\n')\\n for j in xrange(dets.shape[0]):\\n fid.write('%f %f %f %f %f\\\\n' % (dets[j, 0], dets[j, 1], dets[j, 2], dets[j, 3], dets[j, 4]))\\n\\n print ''\\n fid.close()\\n \\n #for cls_ind, cls in enumerate(CLASSES[1:]):\\n #cls_ind += 1 # because we skipped background\\n #cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\\n\\n #cls_scores = scores[:, cls_ind]\\n #dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float32)\\n #keep = nms(dets, NMS_THRESH)\\n #dets = dets[keep, :]\\n #im=vis_detections_video(im, cls, dets, thresh=CONF_THRESH)\\n #cv2.imwrite(os.path.join('output',str(time.time())+'.jpg'),im)\\n \\n \\n #cv2.imshow('ret',im)\\n #cv2.waitKey(20)\",\n \"def detection(receive, Es):\\n cd = range(1, 9)\\n # Mapping the codewords to modulation plane\\n mapping = (np.vstack((map(lambda m: (Es)**0.5*cos(2*pi*m/8), cd), \\\\\\n map(lambda m: (Es)**0.5*sin(2*pi*m/8), cd)))).T\\n\\n decoded = []\\n for i in receive:\\n codeword = 0\\n mindis = float('+inf')\\n # Performing nearest neighbor detection\\n for j in range(len(mapping)):\\n distance = norm(i - mapping[j])\\n if distance < mindis:\\n mindis = distance\\n # j is the index of mapping, the real codeword should be j+1\\n codeword = j + 1\\n assert codeword != 0, \\\"There is something wrong in the decoding process!\\\"\\n decoded.append(codeword)\\n return decoded\",\n \"def _detect_stop(func):\\n def wrapper(*args,**kwargs):\\n self = args[0]\\n self.episode_length -= 1\\n if self.episode_length <=0:\\n \\\"\\\"\\\"if the episode is end\\\"\\\"\\\"\\n self.end = True\\n else:\\n if self.adsorption:\\n \\\"\\\"\\\"just stop moving and wait until the end of episode\\\"\\\"\\\"\\n self.state = self.previous_state\\n else:\\n func(*args,**kwargs)\\n self._detect_obstacles()\\n\\n # func(*args,**kwargs)\\n # self._detect_obstacles()\\n # if self.adsorption:\\n # \\\"\\\"\\\"if this step update is invalid, the point will rebond\\\"\\\"\\\"\\n # self.state = self.previous_state\\n\\n if self.distance <= 0.02:\\n \\\"\\\"\\\"if the point reached the boundary around the goal, let it stop and reset the punishment(self.reward)\\\"\\\"\\\"\\n self.end = True\\n self.reward = 0\\n if self.state[0] <0 or self.state[0] > 10 or self.state[1] <0 or self.state[1] > 10:\\n # self.end = True\\n self.reward = -800\\n return np.array(self.state), self.reward, self.end, self.distance\\n return wrapper\",\n \"def detect_event(self, Cd, wsize=100, thresh=1e-5):\\n # This is the continuous version of the method, which works better than the discrete version\\n\\n # version 1:\\n W = np.sqrt(np.sum(np.sum(np.asarray(Cd)**2, axis=0), axis=0))\\n\\n # version 2: per scale normalization\\n # W = np.zeros(Cd[0].shape[1])\\n # for c in Cd:\\n # # v = Tools.U_filter(np.sqrt(np.sum(c**2, axis=0)), wsize=wsize)\\n # n = np.sum(c**2)\\n # v = np.sum(c**2, axis=0)\\n # W += (v/n if n>0 else v)\\n\\n W /= np.sum(W)\\n mask = Tools.LU_filter(W > thresh, wsize=wsize)\\n Erng = Tools.find_block_true(mask)\\n return Erng, mask, W\",\n \"def check_happiness(content):\\n baseline = 50\\n happiness = [\\\"Smile\\\"]\\n label = 5\\n # print content\\n emotion_time = content[0][1]\\n # print 'emotion_time',emotion_time\\n for c in content:\\n for h in happiness:\\n # print h\\n if c[0] == h and c[1] >= baseline:\\n print 'emotion & label',emotion_time, label\\n return emotion_time, label\",\n \"def check_happiness(content):\\n baseline = 100\\n happiness = [\\\"Smile\\\"]\\n label = 5\\n # print content\\n emotion_time = content[0][1]\\n # print 'emotion_time',emotion_time\\n for c in content:\\n for h in happiness:\\n # print h\\n if c[0] == h and c[1] >= baseline:\\n print 'emotion & label',emotion_time, label\\n return emotion_time, label\",\n \"def event_m20_11_x98():\\n \\\"\\\"\\\"State 0,1: End state\\\"\\\"\\\"\\n return 0\",\n \"def detector(videoframe, facedetection, maskdetection):\\n (h, w) = videoframe.shape[:2]\\n blobimage = cv2.dnn.blobFromImage(videoframe, 1.0, (224, 224), (104.0, 177.0, 123.0))\\n\\n facedetection.setInput(blobimage)\\n ffinding = facedetection.forward()\\n\\n face_list = []\\n locations = []\\n predictions = []\\n\\n for i in range(0, ffinding.shape[2]):\\n credence = ffinding[0, 0, i, 2]\\n if credence > 0.6:\\n case = ffinding[0, 0, i, 3:7] * np.array([w, h, w, h])\\n (x_start, y_start, x_end, y_end) = case.astype(\\\"int\\\")\\n (x_start, y_start) = (max(0, x_start), max(0, y_start))\\n (x_end, y_end) = (min(w - 1, x_end), min(h - 1, y_end))\\n\\n image = videoframe[y_start:y_end, x_start:x_end]\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n image = cv2.resize(image, (224, 224))\\n image = img_to_array(image)\\n image = preprocess_input(image)\\n face_list.append(image)\\n locations.append((x_start, y_start, x_end, y_end))\\n\\n if len(face_list) > 0:\\n face_list = np.array(face_list, dtype=\\\"float32\\\")\\n predictions = maskdetection.predict(face_list, batch_size=32)\\n return (locations, predictions)\",\n \"def VoiceCommonExcelEnd(builder):\\n return End(builder)\",\n \"def onWordRecognised(self, *_args):\\n # Unsubscribe to the event when talking,\\n # to avoid repetitions\\n memory.unsubscribeToEvent(\\\"WordRecognized\\\",\\\"AudioRecognition\\\")\\n\\n # We access to the word recognised in the memory\\n word = memory.getData(\\\"WordRecognized\\\")\\n\\n # Debug : Print the word recognised\\n print(\\\"Mot :\\\")\\n print(word[0])\\n print(\\\"Indice de confiance :\\\")\\n print(word[1])\\n print\\n\\n\\n # We acknoledge a word if the trust is high enough\\n if (word[1] > 0.28):\\n self.mot = word[0]\\n #self.tts.say(\\\"Le mot reconnu est :\\\"+self.mot)\\n StateManager(self)\\n \\n\\n # Subscribe again to the event\\n memory.subscribeToEvent(\\\"WordRecognized\\\",\\n \\\"AudioRecognition\\\",\\n \\\"onWordRecognised\\\")\",\n \"def tellIfEnded(self):\\n self.congratulate()\",\n \"def wait_for_fixation_end(self):\\n\\n\\t\\t# # # # #\\n\\t\\t# EyeTribe method\\n\\n\\t\\tif self.eventdetection == 'native':\\n\\t\\t\\t\\n\\t\\t\\t# print warning, since EyeTribe does not have a blink detection\\n\\t\\t\\t# built into their API\\n\\t\\t\\t\\n\\t\\t\\tprint(\\\"WARNING! 'native' event detection has been selected, \\\\\\n\\t\\t\\t\\tbut EyeTribe does not offer fixation detection; \\\\\\n\\t\\t\\t\\tPyGaze algorithm will be used\\\")\\n\\n\\t\\t# # # # #\\n\\t\\t# PyGaze method\\n\\t\\t\\t\\n\\t\\t# function assumes that a 'fixation' has ended when a deviation of more than fixtresh\\n\\t\\t# from the initial 'fixation' position has been detected\\n\\t\\t\\n\\t\\t# get starting time and position\\n\\t\\tstime, spos = self.wait_for_fixation_start()\\n\\t\\t\\n\\t\\t# loop until fixation has ended\\n\\t\\twhile True:\\n\\t\\t\\t# get new sample\\n\\t\\t\\tnpos = self.sample() # get newest sample\\n\\t\\t\\t# check if sample is valid\\n\\t\\t\\tif self.is_valid_sample(npos):\\n\\t\\t\\t\\t# check if sample deviates to much from starting position\\n\\t\\t\\t\\tif (npos[0]-spos[0])**2 + (npos[1]-spos[1])**2 > self.pxfixtresh**2: # Pythagoras\\n\\t\\t\\t\\t\\t# break loop if deviation is too high\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\treturn clock.get_time(), spos\",\n \"def _run_end2end(self, *args, code=0):\\n pass\",\n \"def _get_end(self):\\n return self._end\",\n \"def end(self, obs: AgentObservation):\\n self.brain.end(obs)\",\n \"def agent_end(self, reward, testing_stop=False):\\n\\n if self.step_counter <= 0:\\n # Corner case where we get an end due to end of epoch just after a game ended\\n return\\n\\n self.step_counter += 1\\n total_time = time.time() - self.start_time\\n logging.info(\\\"FPS in that episode: %s\\\" % (float(self.step_counter)/ total_time))\\n\\n self.episode_reward += reward\\n \\n if self.best_epoch_reward is None or self.episode_reward > self.best_epoch_reward:\\n self.best_epoch_reward = self.episode_reward\\n self.best_run_images = self.episode_images\\n\\n if self.best_score_ever is None or self.episode_reward > self.best_score_ever:\\n self.best_score_ever = self.episode_reward\\n\\n if not testing_stop or self.episode_counter == 0:\\n # only collect stats for this episode if it didn't get truncated, or if it's the \\n # only one we are going to get for this testing epoch\\n self.episode_counter += 1\\n self.total_reward += self.episode_reward\",\n \"def decode(self, tgt, encoder_out):\\n tgt_mask = get_lookahead_mask(tgt)\\n tgt = self.emb(tgt)\\n tgt = self.phn_lin(tgt)\\n if self.attention_type == \\\"RelPosMHAXL\\\":\\n # we use fixed positional encodings in the decoder\\n tgt = tgt + self.positional_encoding_decoder(tgt)\\n encoder_out = encoder_out + self.positional_encoding_decoder(\\n encoder_out\\n )\\n elif self.positional_encoding_type == \\\"fixed_abs_sine\\\":\\n tgt = tgt + self.positional_encoding(tgt) # add the encodings here\\n prediction, self_attns, multihead_attns = self.decoder(\\n tgt,\\n encoder_out,\\n tgt_mask=tgt_mask,\\n pos_embs_tgt=None,\\n pos_embs_src=None,\\n )\\n attention = multihead_attns[-1]\\n return prediction, attention\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59446865","0.5918407","0.5820141","0.5536501","0.54732686","0.5456574","0.5447737","0.541912","0.53824407","0.53753823","0.53732294","0.53102255","0.5226697","0.52063173","0.51992583","0.5165421","0.51476526","0.5133361","0.5113841","0.50879014","0.5086795","0.5074366","0.5073692","0.50706536","0.50664604","0.5064536","0.5033014","0.5032949","0.5032797","0.5011365","0.49848035","0.49797866","0.49723402","0.4955041","0.49493477","0.49361753","0.4931053","0.4909833","0.49098316","0.49049273","0.4895362","0.48926455","0.48897716","0.48860526","0.48859966","0.48712558","0.48708776","0.48631787","0.4859193","0.48563424","0.48488128","0.48487473","0.4845805","0.48453212","0.48453212","0.48348388","0.48285925","0.482845","0.48206434","0.4816035","0.48091257","0.48084456","0.48062956","0.47984773","0.4797242","0.47955528","0.47919253","0.47890437","0.47833446","0.47793669","0.47763965","0.47696614","0.4769044","0.4768191","0.47642553","0.47642145","0.47632796","0.4759174","0.47582212","0.47582212","0.47567368","0.47480595","0.47477525","0.47458187","0.4736154","0.47305438","0.4729969","0.47296607","0.47269484","0.4722743","0.47215748","0.4718048","0.4710866","0.47090635","0.47083127","0.47052532","0.4704205","0.470273","0.46986744","0.46984833"],"string":"[\n \"0.59446865\",\n \"0.5918407\",\n \"0.5820141\",\n \"0.5536501\",\n \"0.54732686\",\n \"0.5456574\",\n \"0.5447737\",\n \"0.541912\",\n \"0.53824407\",\n \"0.53753823\",\n \"0.53732294\",\n \"0.53102255\",\n \"0.5226697\",\n \"0.52063173\",\n \"0.51992583\",\n \"0.5165421\",\n \"0.51476526\",\n \"0.5133361\",\n \"0.5113841\",\n \"0.50879014\",\n \"0.5086795\",\n \"0.5074366\",\n \"0.5073692\",\n \"0.50706536\",\n \"0.50664604\",\n \"0.5064536\",\n \"0.5033014\",\n \"0.5032949\",\n \"0.5032797\",\n \"0.5011365\",\n \"0.49848035\",\n \"0.49797866\",\n \"0.49723402\",\n \"0.4955041\",\n \"0.49493477\",\n \"0.49361753\",\n \"0.4931053\",\n \"0.4909833\",\n \"0.49098316\",\n \"0.49049273\",\n \"0.4895362\",\n \"0.48926455\",\n \"0.48897716\",\n \"0.48860526\",\n \"0.48859966\",\n \"0.48712558\",\n \"0.48708776\",\n \"0.48631787\",\n \"0.4859193\",\n \"0.48563424\",\n \"0.48488128\",\n \"0.48487473\",\n \"0.4845805\",\n \"0.48453212\",\n \"0.48453212\",\n \"0.48348388\",\n \"0.48285925\",\n \"0.482845\",\n \"0.48206434\",\n \"0.4816035\",\n \"0.48091257\",\n \"0.48084456\",\n \"0.48062956\",\n \"0.47984773\",\n \"0.4797242\",\n \"0.47955528\",\n \"0.47919253\",\n \"0.47890437\",\n \"0.47833446\",\n \"0.47793669\",\n \"0.47763965\",\n \"0.47696614\",\n \"0.4769044\",\n \"0.4768191\",\n \"0.47642553\",\n \"0.47642145\",\n \"0.47632796\",\n \"0.4759174\",\n \"0.47582212\",\n \"0.47582212\",\n \"0.47567368\",\n \"0.47480595\",\n \"0.47477525\",\n \"0.47458187\",\n \"0.4736154\",\n \"0.47305438\",\n \"0.4729969\",\n \"0.47296607\",\n \"0.47269484\",\n \"0.4722743\",\n \"0.47215748\",\n \"0.4718048\",\n \"0.4710866\",\n \"0.47090635\",\n \"0.47083127\",\n \"0.47052532\",\n \"0.4704205\",\n \"0.470273\",\n \"0.46986744\",\n \"0.46984833\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5958459"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13799,"cells":{"query":{"kind":"string","value":"Called for every row to allow subclasses to modify rows."},"document":{"kind":"string","value":"def build_modify_row(self, row_gen, p, source, row):\n from xlrd import xldate_as_tuple\n from datetime import date\n\n row['year'] = int(source.time)\n row['facility_status_date'] = date(*xldate_as_tuple(row['facility_status_date'],row_gen.workbook.datemode)[:3])\n row.update(source.row_data.dict)"},"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 setRow(self, row): \n self.row = row","def format_row(self, row):\n raise NotImplementedError()","def add_row(self, row):\n ...","def iter_rows_raw(self, *args):\n\n for row in super().iter_rows_raw(*args):\n row[0] = row[1] # sequential catalog index not right in this case; overwrite to match finder id\n yield row","def row(self) -> TableRow:\n raise NotImplementedError","def __init__(self):\n\n self.reset_row()","def set_row( self, row, ):\n self.ix_row = row","def rows(self):\r\n raise NotImplementedError()","def validate(self, row):\n raise NotImplementedError","def rows(self, row):\n self.row += row","def rows(self):\r\n raise NotImplementedError","def __init__(self, row): # pylint: disable=super-init-not-called\n self._row = row\n self.entity_id = self._row.entity_id\n self.state = self._row.state\n self._attributes = None\n self._last_changed = None\n self._last_updated = None\n self._context = None","def abstract_write(self, *params): \n section = self.table_section_from_parameter(*params)\n row_args = self.table_row_args_from_parameter(*params)\n new_row = self.table_row_class(*row_args)\n # IMPORTANTE! ASUMO QUE UN BLOQUE INVALIDO ES IGUAL A UNO VACIO!\n # TO DO: PREGUNTAR EN ISSUE\n found_index = False\n for index, table_row in enumerate(section.copy()): \n if table_row is None or not table_row.valid:\n found_index = True\n overwrite_index = index\n break\n\n if not found_index:\n overwrite_index = self.next_replace(section)\n\n replace_index = self.index_mapping_from_parameter(overwrite_index, *params)\n\n\n old_line = self.table[replace_index]\n #print(f\"{self.__class__.__name__} Replace -> Index: {replace_index}\")\n\n # Perfom the actual write\n self.table[replace_index] = new_row","def get_rows(self):\n raise NotImplementedError('Subclass this to make the rows')","def process_row(self, table, row):\n for index, column in enumerate(table.columns):\n hash_key = hash(frozenset(column.items()))\n column_type = self.column_types[hash_key] if hash_key in self.column_types else self.column_type(column)\n if row[index] == None and ('timestamp' not in column_type or not column['default']):\n row[index] = '\\N'\n elif row[index] == None and column['default']:\n if self.tz:\n row[index] = '1970-01-01T00:00:00.000000' + self.tz_offset\n else:\n row[index] = '1970-01-01 00:00:00'\n elif 'bit' in column_type:\n row[index] = bin(ord(row[index]))[2:]\n elif isinstance(row[index], (str, unicode, basestring)):\n if column_type == 'bytea':\n row[index] = Binary(row[index]).getquoted()[1:-8] if row[index] else row[index]\n elif 'text[' in column_type:\n row[index] = '{%s}' % ','.join('\"%s\"' % v.replace('\"', r'\\\"') for v in row[index].split(','))\n else:\n row[index] = row[index].replace('\\\\', r'\\\\').replace('\\n', r'\\n').replace(\n '\\t', r'\\t').replace('\\r', r'\\r').replace('\\0', '')\n elif column_type == 'boolean':\n # We got here because you used a tinyint(1), if you didn't want a bool, don't use that type\n row[index] = 't' if row[index] not in (None, 0) else 'f' if row[index] == 0 else row[index]\n elif isinstance(row[index], (date, datetime)):\n if isinstance(row[index], datetime) and self.tz:\n try:\n if row[index].tzinfo:\n row[index] = row[index].astimezone(self.tz).isoformat()\n else:\n row[index] = datetime(*row[index].timetuple()[:6], tzinfo=self.tz).isoformat()\n except Exception as e:\n print e.message\n else:\n row[index] = row[index].isoformat()\n elif isinstance(row[index], timedelta):\n row[index] = datetime.utcfromtimestamp(_get_total_seconds(row[index])).time().isoformat()\n else:\n row[index] = AsIs(row[index]).getquoted()","def __init__(self, row):\n state = inspect(row)\n\n # Don't store the actual row, so we can serialize.\n self._model_cls = state.class_\n self._pk = state.identity\n\n self.data = Box(dict(row))","def updateRow(self, index: int) -> None:\n ...","def fake_clean_row(row):\n\treturn row","def clear_rows(self):\n ...","def setRow(self, row):\n # Row of the database where the values of the variables are found\n self._row = row\n for e in self.children:\n e.setRow(row)","def rosterRowData(self):","def rows(self, rows):\n self.grid.items = rows[:]","def set_row_processor(self):\n raise NotImplemented()","def process_duplicate_rows(self):\n pass","def redraw_row(cls, row):\n # TODO: Draw blocks in row","def write_row(self, data):\n raise NotImplementedError()","def set_data_by_rows(self, row_headers, column_headers, data_rows):\n self.tblGeneric.setRowCount(len(data_rows))\n self.tblGeneric.setColumnCount(len(data_rows[0]))\n\n if row_headers:\n self.tblGeneric.setVerticalHeaderLabels(row_headers)\n else:\n self.tblGeneric.verticalHeader().setVisible(False)\n if column_headers:\n self.tblGeneric.setHorizontalHeaderLabels(column_headers)\n else:\n self.tblGeneric.horizontalHeader().setVisible(False)\n\n row = 0\n for data_row in data_rows:\n col = 0\n for data_value in data_row:\n item = QTableWidgetItem(str(data_value))\n item.setFlags(QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled)\n self.tblGeneric.setItem(row, col, item)\n col += 1\n row += 1\n self.tblGeneric.resizeColumnsToContents()\n for row in range(self.tblGeneric.rowCount()):\n self.tblGeneric.setRowHeight(row, 10)","def parse_row(self, response, row):\n raise NotImplementedError","def update_old_row(self, data):\n for key, value in data.items():\n _column = self._labels.index([v['display'] for k, v in self.headers.items() if k == key].pop())\n cell = self.item(self._opt_row, _column)\n _cell_data = cell.get_data()\n _cell_data[key] = value\n\n cell.set_content(value, _cell_data)","def feedDataRow(self, row, **kwargs): \n\n self.nullify()\n \n #avoid automatic call to \"read\" after _objectid changes \n self._feed = True\n self._objectid = row['objectid']\n self._feed = False\n \n self._original_values.clear()\n for cn in row:\n if cn in self._extra_sql_columns:\n self.__dict__[cn] = row[cn]\n if cn not in self._table: continue\n field = self._table[cn]\n decoded = field.val_sql2py ( row[cn] )\n self.__dict__[cn] = decoded\n self._original_values[cn] = decoded\n \n #if _astxt was not provided with the data, _reprfunc is not set,\n #get the text from database\n if '_astxt' in row:\n self._astxt = row['_astxt']\n elif self._reprfunc:\n self._astxt = self._reprfunc (self)\n else: \n self._astxt = self.TextCache[self._objectid] or \"(none)\"\n \n if self._resolve_child_ref:\n for child_handle in self._child_referencelist:\n t, f = self._table.reference_child_hash[child_handle] \n self._child_referencelist[child_handle].filter = f.name + \" = '\" + str(row['objectid']) + \"'\"\n self._child_referencelist[child_handle].reload()\n \n self._hasdata = True\n self._isnew = False\n self._ismodified = False\n self._modified_values.clear()","def map_transfer_to_row(self, transfer):\n pass","def update_row(self, rowIndex=0, shape=None, *args, **attributes):\n # check if there is a shape edit, if not skip and do attribute update\n if shape:\n if not isinstance(shape, shapefile.shapefile._Shape):\n self.shapes[rowIndex].points = shape\n else:\n self.shapes[rowIndex] = shape\n\n if attributes:\n for f_name, f_value in attributes.iteritems():\n f_index = self.field_indices[f_name]\n if f_index >= len(self.records[rowIndex]):\n self.records[rowIndex].append(f_value)\n else:\n self.records[rowIndex][f_index] = f_value\n\n self.__isBuilt = False","def set_row(self, i, row):\n with messagebox():\n rec = self.table[i]\n # NB: the ordinal of the table specifies the number of fields in\n # the primary key: 0 for ordinal=0, 1 for ordinal=1, and 2\n # for ordinal=2; row is a list with N - ordinal elements, where\n # N is the total number of fields in the record\n rec.row[self.table.ordinal:] = row","def handleRowDataChange(self, changedItem ):\n with Tracer(traceLogger):\n # Figure out which row this widget is in\n row = changedItem.row()\n column = changedItem.column()\n\n # Can't update until the row is fully initialized\n needUpdate = True\n needUpdate &= column == Column.Name or column == Column.InternalID\n needUpdate &= self.fileInfoTableWidget.item(row, Column.Name) != None\n needUpdate &= self.fileInfoTableWidget.cellWidget(row, Column.InternalID) != None\n needUpdate &= self.fileInfoTableWidget.cellWidget(row, column) is not None\n\n if needUpdate:\n self.updateFilePath(row)","def update_row(self):\n self.current_row = self.next_row\n self.next_row = self.get_row()","def add_row(self, row_id):","def __init__(self, rows=[]):\n self._rows, self._columns = Rows(self), Columns(self)\n self._m = 0 # Number of columns per row, see Table.insert().\n self.extend(rows)","def generate_table(self, rows):\n ...","def __iter__(self):\n return iter(self.rows)","def row_classes(self, record):\n try:\n super_row_classes = super(ReviewTable, self).row_classes(record)\n except AttributeError:\n super_row_classes = \"\"\n return '{0} table-link__row'.format(super_row_classes)","def __init__(self, row_counter: int):\n self.row_counter = row_counter","def __iter__(self):\n return iter(self.__rows)","def copyRow(self,oldRow,newRow):\n data = self.data\n if oldRow in data:\n data[newRow] = data[oldRow].copy()\n self.hasChanged = True","def new_row( self, delta_row = 1, ):\n self.ix_row += delta_row\n self.ix_col = 0","def validate(self, field, row):\n raise NotImplementedError","def _setMatrixRow(self, row):\n item = self._item()\n if item is not None:\n matrix = item.getMatrix()\n matrix[self._index, :] = row.x(), row.y(), row.z()\n item.setMatrix(matrix)","def _set_outputrow(self, v):\n super(Row, self).__setattr__(\"__output__\", v)","def row(self, row_id):\r\n return Row(self, row_id)","def __iter__(self):\n for row in self.rows:\n yield row","def _load_reading_row(self):\n\n raise NotImplementedError()","def cell_entered(self, row: int, _):\n self.selectRow(row)","def __setitem__(\n self,\n index: Union[int, slice],\n value: Union[_RowPrototype, Iterable[_RowPrototype]],\n ) -> None:\n if isinstance(index, slice):\n self._rows[index] = [D2TXTRow(self, row) for row in value]\n else:\n self._rows[index] = D2TXTRow(self, value)","def next_row(self, row: Row):\n column_types = self._deduct_types(row)\n self._update_nullable(row)\n if self._types is None:\n self._types = column_types\n else:\n if not self._is_consistent(column_types):\n raise SomeError('Inconsistent types')\n self._merge_with(column_types)","def place_types_process_rows(self):\n\n for index in range(len(self.table)):\n row_rdf = self.place_types_map_row_to_rdf(self.table.iloc[index])\n if row_rdf is not None:\n self.data += row_rdf","def set_value ( self, object, row, value ):\n column = self.get_data_column( object )\n column[ row ] = type( column[ row ] )( value )","def _on_bob(self) -> None:\n self.row_generator.set_bob()","def add_row(self, row):\r\n self.rows.append(row)","def Append(self, row):\n self._rows.append(row)","def compute_virtuals(self, row):\r\n if self.virtual_fields:\r\n row.update((f, self.table[f].f(row)) for f in self.virtual_fields)","def handleTableSelectionChange(self):\n self.selectEntireRow()\n self.showSelectedDataset()","def end_row(self):\n raise NotImplementedError","def __init__(self, columns):\n self.columns = columns\n self.rows = []","def _on_single(self) -> None:\n self.row_generator.set_single()","def start_row(self):\n raise NotImplementedError","def add_rows(self):\n for row in self.rows:\n self.table.add_row(row)","def row(self):\n\t\treturn self.__row","def _convert_row(self, row) :\n\n self.row_id += 1\n data = [self.row_id]\n\n if type(row) == type({}) :\n data.extend(row.get(col, None) for col in self.cols[1:])\n elif type(row) in [type([]), type(())] :\n data.extend(row)\n elif type(row) == RowReference :\n data.extend(row.values())\n else :\n raise Exception(\n 'Don''t know how to add row from: %s ' % str(row)\n )\n\n if len(data) != len(self.cols) :\n raise Exception(\n 'Wrong number of values for new row with cols %s: %s' % \n (str(self.cols), str(data))\n \n )\n\n return data","def set_rows(self, rows):\r\n rows = [(row_id, parse_date(created)) for row_id, created in rows]\r\n self.get_history_for_student_modules.return_value = rows","def append_rows(self, rows):\n for row in rows:\n self.append_row(row)","def switch_row(self, row_num):\n self.ifile.close()\n self.ifile = open(self.file_name)\n self.reader = csv.reader(self.ifile)\n self.reader.next()\n\n if row_num > 0:\n for i in range(0,row_num):\n self.row = self.reader.next()\n \n self.data_row_length = 0\n for element in self.row:\n if element != '':\n self.data_row_length += 1","def update_item(self, table, item):","def add_rows(self, rows) :\n\n converted = [self._convert_row(row) for row in rows]\n self._bulk_add_rows(converted)\n self.version += 1","def __updateRowsForItem(self, item):\n if not isinstance(item, ComplexIsosurface):\n return\n\n if item.getComplexMode() == items.ComplexMixIn.ComplexMode.NONE:\n removed = self._colormapRow\n added = self._colorRow\n else:\n removed = self._colorRow\n added = self._colormapRow\n\n # Remove unwanted rows\n if removed in self.children():\n self.removeRow(removed)\n\n # Add required rows\n if added not in self.children():\n self.addRow(added, index=2)","def processRow(self, row):\n\t\tif self.delim is not None:\n\t\t\trowArr = row.split(self.delim)\n\t\t\tmsg = \"row does not have expected number of columns found \" + str(len(rowArr)) + \" expected \" + str(self.rowSize)\n\t\t\tassert len(rowArr) == self.rowSize, msg\n\t\telse:\n\t\t\trowArr = row\n\t\t\t\n\t\tnewRowArr = []\n\t\tfor i in range(len(rowArr)):\n\t\t\tcurVal = rowArr[i]\n\t\t\tif (i in self.catValues):\n\t\t\t\tvalues = self.catValues[i]\n\t\t\t\tfor val in values:\n\t\t\t\t\tif val == curVal:\n\t\t\t\t\t\tnewVal = self.trueVal\n\t\t\t\t\telse:\n\t\t\t\t\t\tnewVal = self.falseVal\n\t\t\t\t\tnewRowArr.append(newVal)\n\t\t\telse:\n\t\t\t\tnewRowArr.append(curVal)\n\t\tassert len(newRowArr) == self.newRowSize, \"invalid new row size \" + str(len(newRowArr)) + \" expected \" + str(self.newRowSize)\n\t\tencRow = self.delim.join(newRowArr) if self.delim is not None else newRowArr\n\t\treturn encRow","def insertRow(self, p_int, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n pass","def on_update_seatable(data, index, *args):\n row = convert_row(metadata, data)\n print(row)","def __init__(self, rows=()):\n self.rows = list(rows)","def setRow(self, row_number, row):\n self.data[:,row_number] = row\n return","def add_object(self, row, obj_name):\n # not optimised: not expected to be a usual operation\n if self.table.shape[0] > 0:\n new_table = np.vstack((self.table, [row]))\n else:\n new_table = np.array([row])\n new_objects = self.objects + [obj_name]\n self.__init__(new_table, new_objects, self.attributes)","def _set_right_info_row_values(self, row, values):\n print \"Row is \", row\n print \"Values are \", values\n for name in values.keys():\n self.header_values[name][row].set(values[name])","def table_callback(table, data, event, column=False):\n if column:\n table.object = data.loc[event.new]\n else:\n table.object = data.loc[event.new:event.new]","def insertRows(self, p_int, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n pass","def mutate_row(self, row_key: bytes, column_family_id: str, val_dict: dict,\n time_stamp: Optional[datetime.datetime] = None\n ) -> bigtable.row.Row:\n row = self.table.row(row_key)\n\n for column, value in val_dict.items():\n row.set_cell(column_family_id=column_family_id, column=column,\n value=value, timestamp=time_stamp)\n return row","def _do_change_row(self, treeview):\n _return = False\n\n self.treeview.handler_block(self._lst_handler_id[0])\n\n (_model, _row) = treeview.get_selection().get_selected()\n try:\n _level = _model.get_value(_row, 11)\n except TypeError:\n _level = None\n\n _columns = treeview.get_columns()\n\n # Change the column headings depending on what is being selected.\n if _level == 'mission':\n _headings = [\n _(u\"Mission ID\"),\n _(u\"Description\"),\n _(u\"Units\"),\n _(u\"Start Time\"),\n _(u\"End Time\"),\n _(u\"\"),\n _(u\"\"),\n _(u\"\")\n ]\n elif _level == 'phase':\n _headings = [\n _(u\"Phase ID\"),\n _(u\" Code\\t\\tDescription\"),\n _(u\"Units\"),\n _(u\"Start Time\"),\n _(u\"End Time\"),\n _(u\"\"),\n _(u\"\"),\n _(u\"\")\n ]\n elif _level == 'environment':\n _headings = [\n _(u\"Environment ID\"),\n _(u\"Condition\"),\n _(u\"Units\"),\n _(u\"Minimum Value\"),\n _(u\"Maximum Value\"),\n _(u\"Mean Value\"),\n _(u\"Variance\"),\n _(u\"\")\n ]\n else:\n _headings = []\n\n i = 0\n for _heading in _headings:\n _label = gtk.Label()\n _label.set_line_wrap(True)\n _label.set_alignment(xalign=0.5, yalign=0.5)\n _label.set_justify(gtk.JUSTIFY_CENTER)\n _label.set_markup(\"<span weight='bold'>\" + _heading + \"</span>\")\n _label.set_use_markup(True)\n _label.show_all()\n _columns[i].set_widget(_label)\n\n i += 1\n\n self.treeview.handler_unblock(self._lst_handler_id[0])\n\n return _return","def rows(self) -> java.util.Collection:\n ...","def del_row(self, row_index):\n ...","def __init__(self, row):\n self.row = row\n self.type = randint(0, cluster_count - 1)","def add_row(self, *column_data):\n raise NotImplementedError","def update_row_flag_status(self, index: qtc.QModelIndex):\n row = index.row()\n self.dataChanged.emit(self.index(row, 0), self.index(row, 0), [qtc.Qt.ForegroundRole])","def moveRow(self,oldRow,newRow):\n data = self.data\n if oldRow in data:\n data[newRow] = data[oldRow]\n del data[oldRow]\n self.hasChanged = True","def set_row_style(sheet, row, style):\r\n\r\n for cell in sheet[row]:\r\n cell.style = style","def addRowEvent(self):\n\t\tif self.stretch: self.setRowStretch(self.row, 0)\n\t\tself.row += 1\n\t\tif self.stretch: self.setRowStretch(self.row, 1)","def addRow(self, row):\n self.__data.append(row.copy())\n # We may need to resize the table, to fit the new data\n for key in row.keys():\n if len(row[key]) > self.__widths[key]:\n self.__widths[key] = len(row[key])\n self.__makeFormatString()\n self.__refreshContent()\n if self.__selectedRow == -1:\n self.__selectedRow = 0\n lines = len(self.__data)\n if self.__firstShownLine <= lines - self.height + 2 and \\\n self.__autoScroll:\n # We need to scroll everything upwards\n self.scrollDown()\n if self.__selectedRow < self.__firstShownLine:\n self.__selectedRow = self.__firstShownLine\n if self._focused:\n self._window.attron(curses.A_BOLD)\n self.__printRow(self.__firstShownLine)\n self._window.attroff(curses.A_BOLD)\n else:\n if self._focused and self.__selectedRow == lines - 1:\n self._window.attron(curses.A_BOLD)\n self.__printRow(lines - 1)\n self._window.attroff(curses.A_BOLD)","def extend_rows(self, assign):\n new_table = dict()\n for key, value in self._table.items():\n new_table[Assignment(key, assign)] = value\n\n self._table = new_table","def newrow(self, rowlist):\n if len(rowlist) > 0:\n if islist(rowlist[0]):\n for row in rowlist:\n self.newrow(row)\n elif len(rowlist) == self.x:\n for x in xrange(0, len(rowlist)):\n rowlist[x] = self.prepare(rowlist[x])\n self.a.append(rowlist)\n self.y += 1\n else:\n raise IndexError(\"Unequal matrix row lengths for newrow of \"+str(self.x)+\" and \"+str(len(rowlist)))","def setItem(self,row,column,value):\n data = self.data\n if row not in data:\n data[row] = {}\n data[row][column] = value\n self.hasChanged = True","def places_process_rows(self):\n\n for index in range(len(self.table)):\n row_rdf = self.places_map_row_to_rdf(self.table.iloc[index])\n if row_rdf is not None:\n self.data += row_rdf","def __init__(self, data):\n self.data = data\n self.columns = Columns(data)\n self.rows = Rows(data)","def _insert_table_row(self, db: str, table: str, row: Dict[str, Any]):\n pass","def setBaseRowID(self, id: int) -> None:\n ...","def Row(self, row):\r\n \r\n self.dock_row = row\r\n return self"],"string":"[\n \"def setRow(self, row): \\n self.row = row\",\n \"def format_row(self, row):\\n raise NotImplementedError()\",\n \"def add_row(self, row):\\n ...\",\n \"def iter_rows_raw(self, *args):\\n\\n for row in super().iter_rows_raw(*args):\\n row[0] = row[1] # sequential catalog index not right in this case; overwrite to match finder id\\n yield row\",\n \"def row(self) -> TableRow:\\n raise NotImplementedError\",\n \"def __init__(self):\\n\\n self.reset_row()\",\n \"def set_row( self, row, ):\\n self.ix_row = row\",\n \"def rows(self):\\r\\n raise NotImplementedError()\",\n \"def validate(self, row):\\n raise NotImplementedError\",\n \"def rows(self, row):\\n self.row += row\",\n \"def rows(self):\\r\\n raise NotImplementedError\",\n \"def __init__(self, row): # pylint: disable=super-init-not-called\\n self._row = row\\n self.entity_id = self._row.entity_id\\n self.state = self._row.state\\n self._attributes = None\\n self._last_changed = None\\n self._last_updated = None\\n self._context = None\",\n \"def abstract_write(self, *params): \\n section = self.table_section_from_parameter(*params)\\n row_args = self.table_row_args_from_parameter(*params)\\n new_row = self.table_row_class(*row_args)\\n # IMPORTANTE! ASUMO QUE UN BLOQUE INVALIDO ES IGUAL A UNO VACIO!\\n # TO DO: PREGUNTAR EN ISSUE\\n found_index = False\\n for index, table_row in enumerate(section.copy()): \\n if table_row is None or not table_row.valid:\\n found_index = True\\n overwrite_index = index\\n break\\n\\n if not found_index:\\n overwrite_index = self.next_replace(section)\\n\\n replace_index = self.index_mapping_from_parameter(overwrite_index, *params)\\n\\n\\n old_line = self.table[replace_index]\\n #print(f\\\"{self.__class__.__name__} Replace -> Index: {replace_index}\\\")\\n\\n # Perfom the actual write\\n self.table[replace_index] = new_row\",\n \"def get_rows(self):\\n raise NotImplementedError('Subclass this to make the rows')\",\n \"def process_row(self, table, row):\\n for index, column in enumerate(table.columns):\\n hash_key = hash(frozenset(column.items()))\\n column_type = self.column_types[hash_key] if hash_key in self.column_types else self.column_type(column)\\n if row[index] == None and ('timestamp' not in column_type or not column['default']):\\n row[index] = '\\\\N'\\n elif row[index] == None and column['default']:\\n if self.tz:\\n row[index] = '1970-01-01T00:00:00.000000' + self.tz_offset\\n else:\\n row[index] = '1970-01-01 00:00:00'\\n elif 'bit' in column_type:\\n row[index] = bin(ord(row[index]))[2:]\\n elif isinstance(row[index], (str, unicode, basestring)):\\n if column_type == 'bytea':\\n row[index] = Binary(row[index]).getquoted()[1:-8] if row[index] else row[index]\\n elif 'text[' in column_type:\\n row[index] = '{%s}' % ','.join('\\\"%s\\\"' % v.replace('\\\"', r'\\\\\\\"') for v in row[index].split(','))\\n else:\\n row[index] = row[index].replace('\\\\\\\\', r'\\\\\\\\').replace('\\\\n', r'\\\\n').replace(\\n '\\\\t', r'\\\\t').replace('\\\\r', r'\\\\r').replace('\\\\0', '')\\n elif column_type == 'boolean':\\n # We got here because you used a tinyint(1), if you didn't want a bool, don't use that type\\n row[index] = 't' if row[index] not in (None, 0) else 'f' if row[index] == 0 else row[index]\\n elif isinstance(row[index], (date, datetime)):\\n if isinstance(row[index], datetime) and self.tz:\\n try:\\n if row[index].tzinfo:\\n row[index] = row[index].astimezone(self.tz).isoformat()\\n else:\\n row[index] = datetime(*row[index].timetuple()[:6], tzinfo=self.tz).isoformat()\\n except Exception as e:\\n print e.message\\n else:\\n row[index] = row[index].isoformat()\\n elif isinstance(row[index], timedelta):\\n row[index] = datetime.utcfromtimestamp(_get_total_seconds(row[index])).time().isoformat()\\n else:\\n row[index] = AsIs(row[index]).getquoted()\",\n \"def __init__(self, row):\\n state = inspect(row)\\n\\n # Don't store the actual row, so we can serialize.\\n self._model_cls = state.class_\\n self._pk = state.identity\\n\\n self.data = Box(dict(row))\",\n \"def updateRow(self, index: int) -> None:\\n ...\",\n \"def fake_clean_row(row):\\n\\treturn row\",\n \"def clear_rows(self):\\n ...\",\n \"def setRow(self, row):\\n # Row of the database where the values of the variables are found\\n self._row = row\\n for e in self.children:\\n e.setRow(row)\",\n \"def rosterRowData(self):\",\n \"def rows(self, rows):\\n self.grid.items = rows[:]\",\n \"def set_row_processor(self):\\n raise NotImplemented()\",\n \"def process_duplicate_rows(self):\\n pass\",\n \"def redraw_row(cls, row):\\n # TODO: Draw blocks in row\",\n \"def write_row(self, data):\\n raise NotImplementedError()\",\n \"def set_data_by_rows(self, row_headers, column_headers, data_rows):\\n self.tblGeneric.setRowCount(len(data_rows))\\n self.tblGeneric.setColumnCount(len(data_rows[0]))\\n\\n if row_headers:\\n self.tblGeneric.setVerticalHeaderLabels(row_headers)\\n else:\\n self.tblGeneric.verticalHeader().setVisible(False)\\n if column_headers:\\n self.tblGeneric.setHorizontalHeaderLabels(column_headers)\\n else:\\n self.tblGeneric.horizontalHeader().setVisible(False)\\n\\n row = 0\\n for data_row in data_rows:\\n col = 0\\n for data_value in data_row:\\n item = QTableWidgetItem(str(data_value))\\n item.setFlags(QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled)\\n self.tblGeneric.setItem(row, col, item)\\n col += 1\\n row += 1\\n self.tblGeneric.resizeColumnsToContents()\\n for row in range(self.tblGeneric.rowCount()):\\n self.tblGeneric.setRowHeight(row, 10)\",\n \"def parse_row(self, response, row):\\n raise NotImplementedError\",\n \"def update_old_row(self, data):\\n for key, value in data.items():\\n _column = self._labels.index([v['display'] for k, v in self.headers.items() if k == key].pop())\\n cell = self.item(self._opt_row, _column)\\n _cell_data = cell.get_data()\\n _cell_data[key] = value\\n\\n cell.set_content(value, _cell_data)\",\n \"def feedDataRow(self, row, **kwargs): \\n\\n self.nullify()\\n \\n #avoid automatic call to \\\"read\\\" after _objectid changes \\n self._feed = True\\n self._objectid = row['objectid']\\n self._feed = False\\n \\n self._original_values.clear()\\n for cn in row:\\n if cn in self._extra_sql_columns:\\n self.__dict__[cn] = row[cn]\\n if cn not in self._table: continue\\n field = self._table[cn]\\n decoded = field.val_sql2py ( row[cn] )\\n self.__dict__[cn] = decoded\\n self._original_values[cn] = decoded\\n \\n #if _astxt was not provided with the data, _reprfunc is not set,\\n #get the text from database\\n if '_astxt' in row:\\n self._astxt = row['_astxt']\\n elif self._reprfunc:\\n self._astxt = self._reprfunc (self)\\n else: \\n self._astxt = self.TextCache[self._objectid] or \\\"(none)\\\"\\n \\n if self._resolve_child_ref:\\n for child_handle in self._child_referencelist:\\n t, f = self._table.reference_child_hash[child_handle] \\n self._child_referencelist[child_handle].filter = f.name + \\\" = '\\\" + str(row['objectid']) + \\\"'\\\"\\n self._child_referencelist[child_handle].reload()\\n \\n self._hasdata = True\\n self._isnew = False\\n self._ismodified = False\\n self._modified_values.clear()\",\n \"def map_transfer_to_row(self, transfer):\\n pass\",\n \"def update_row(self, rowIndex=0, shape=None, *args, **attributes):\\n # check if there is a shape edit, if not skip and do attribute update\\n if shape:\\n if not isinstance(shape, shapefile.shapefile._Shape):\\n self.shapes[rowIndex].points = shape\\n else:\\n self.shapes[rowIndex] = shape\\n\\n if attributes:\\n for f_name, f_value in attributes.iteritems():\\n f_index = self.field_indices[f_name]\\n if f_index >= len(self.records[rowIndex]):\\n self.records[rowIndex].append(f_value)\\n else:\\n self.records[rowIndex][f_index] = f_value\\n\\n self.__isBuilt = False\",\n \"def set_row(self, i, row):\\n with messagebox():\\n rec = self.table[i]\\n # NB: the ordinal of the table specifies the number of fields in\\n # the primary key: 0 for ordinal=0, 1 for ordinal=1, and 2\\n # for ordinal=2; row is a list with N - ordinal elements, where\\n # N is the total number of fields in the record\\n rec.row[self.table.ordinal:] = row\",\n \"def handleRowDataChange(self, changedItem ):\\n with Tracer(traceLogger):\\n # Figure out which row this widget is in\\n row = changedItem.row()\\n column = changedItem.column()\\n\\n # Can't update until the row is fully initialized\\n needUpdate = True\\n needUpdate &= column == Column.Name or column == Column.InternalID\\n needUpdate &= self.fileInfoTableWidget.item(row, Column.Name) != None\\n needUpdate &= self.fileInfoTableWidget.cellWidget(row, Column.InternalID) != None\\n needUpdate &= self.fileInfoTableWidget.cellWidget(row, column) is not None\\n\\n if needUpdate:\\n self.updateFilePath(row)\",\n \"def update_row(self):\\n self.current_row = self.next_row\\n self.next_row = self.get_row()\",\n \"def add_row(self, row_id):\",\n \"def __init__(self, rows=[]):\\n self._rows, self._columns = Rows(self), Columns(self)\\n self._m = 0 # Number of columns per row, see Table.insert().\\n self.extend(rows)\",\n \"def generate_table(self, rows):\\n ...\",\n \"def __iter__(self):\\n return iter(self.rows)\",\n \"def row_classes(self, record):\\n try:\\n super_row_classes = super(ReviewTable, self).row_classes(record)\\n except AttributeError:\\n super_row_classes = \\\"\\\"\\n return '{0} table-link__row'.format(super_row_classes)\",\n \"def __init__(self, row_counter: int):\\n self.row_counter = row_counter\",\n \"def __iter__(self):\\n return iter(self.__rows)\",\n \"def copyRow(self,oldRow,newRow):\\n data = self.data\\n if oldRow in data:\\n data[newRow] = data[oldRow].copy()\\n self.hasChanged = True\",\n \"def new_row( self, delta_row = 1, ):\\n self.ix_row += delta_row\\n self.ix_col = 0\",\n \"def validate(self, field, row):\\n raise NotImplementedError\",\n \"def _setMatrixRow(self, row):\\n item = self._item()\\n if item is not None:\\n matrix = item.getMatrix()\\n matrix[self._index, :] = row.x(), row.y(), row.z()\\n item.setMatrix(matrix)\",\n \"def _set_outputrow(self, v):\\n super(Row, self).__setattr__(\\\"__output__\\\", v)\",\n \"def row(self, row_id):\\r\\n return Row(self, row_id)\",\n \"def __iter__(self):\\n for row in self.rows:\\n yield row\",\n \"def _load_reading_row(self):\\n\\n raise NotImplementedError()\",\n \"def cell_entered(self, row: int, _):\\n self.selectRow(row)\",\n \"def __setitem__(\\n self,\\n index: Union[int, slice],\\n value: Union[_RowPrototype, Iterable[_RowPrototype]],\\n ) -> None:\\n if isinstance(index, slice):\\n self._rows[index] = [D2TXTRow(self, row) for row in value]\\n else:\\n self._rows[index] = D2TXTRow(self, value)\",\n \"def next_row(self, row: Row):\\n column_types = self._deduct_types(row)\\n self._update_nullable(row)\\n if self._types is None:\\n self._types = column_types\\n else:\\n if not self._is_consistent(column_types):\\n raise SomeError('Inconsistent types')\\n self._merge_with(column_types)\",\n \"def place_types_process_rows(self):\\n\\n for index in range(len(self.table)):\\n row_rdf = self.place_types_map_row_to_rdf(self.table.iloc[index])\\n if row_rdf is not None:\\n self.data += row_rdf\",\n \"def set_value ( self, object, row, value ):\\n column = self.get_data_column( object )\\n column[ row ] = type( column[ row ] )( value )\",\n \"def _on_bob(self) -> None:\\n self.row_generator.set_bob()\",\n \"def add_row(self, row):\\r\\n self.rows.append(row)\",\n \"def Append(self, row):\\n self._rows.append(row)\",\n \"def compute_virtuals(self, row):\\r\\n if self.virtual_fields:\\r\\n row.update((f, self.table[f].f(row)) for f in self.virtual_fields)\",\n \"def handleTableSelectionChange(self):\\n self.selectEntireRow()\\n self.showSelectedDataset()\",\n \"def end_row(self):\\n raise NotImplementedError\",\n \"def __init__(self, columns):\\n self.columns = columns\\n self.rows = []\",\n \"def _on_single(self) -> None:\\n self.row_generator.set_single()\",\n \"def start_row(self):\\n raise NotImplementedError\",\n \"def add_rows(self):\\n for row in self.rows:\\n self.table.add_row(row)\",\n \"def row(self):\\n\\t\\treturn self.__row\",\n \"def _convert_row(self, row) :\\n\\n self.row_id += 1\\n data = [self.row_id]\\n\\n if type(row) == type({}) :\\n data.extend(row.get(col, None) for col in self.cols[1:])\\n elif type(row) in [type([]), type(())] :\\n data.extend(row)\\n elif type(row) == RowReference :\\n data.extend(row.values())\\n else :\\n raise Exception(\\n 'Don''t know how to add row from: %s ' % str(row)\\n )\\n\\n if len(data) != len(self.cols) :\\n raise Exception(\\n 'Wrong number of values for new row with cols %s: %s' % \\n (str(self.cols), str(data))\\n \\n )\\n\\n return data\",\n \"def set_rows(self, rows):\\r\\n rows = [(row_id, parse_date(created)) for row_id, created in rows]\\r\\n self.get_history_for_student_modules.return_value = rows\",\n \"def append_rows(self, rows):\\n for row in rows:\\n self.append_row(row)\",\n \"def switch_row(self, row_num):\\n self.ifile.close()\\n self.ifile = open(self.file_name)\\n self.reader = csv.reader(self.ifile)\\n self.reader.next()\\n\\n if row_num > 0:\\n for i in range(0,row_num):\\n self.row = self.reader.next()\\n \\n self.data_row_length = 0\\n for element in self.row:\\n if element != '':\\n self.data_row_length += 1\",\n \"def update_item(self, table, item):\",\n \"def add_rows(self, rows) :\\n\\n converted = [self._convert_row(row) for row in rows]\\n self._bulk_add_rows(converted)\\n self.version += 1\",\n \"def __updateRowsForItem(self, item):\\n if not isinstance(item, ComplexIsosurface):\\n return\\n\\n if item.getComplexMode() == items.ComplexMixIn.ComplexMode.NONE:\\n removed = self._colormapRow\\n added = self._colorRow\\n else:\\n removed = self._colorRow\\n added = self._colormapRow\\n\\n # Remove unwanted rows\\n if removed in self.children():\\n self.removeRow(removed)\\n\\n # Add required rows\\n if added not in self.children():\\n self.addRow(added, index=2)\",\n \"def processRow(self, row):\\n\\t\\tif self.delim is not None:\\n\\t\\t\\trowArr = row.split(self.delim)\\n\\t\\t\\tmsg = \\\"row does not have expected number of columns found \\\" + str(len(rowArr)) + \\\" expected \\\" + str(self.rowSize)\\n\\t\\t\\tassert len(rowArr) == self.rowSize, msg\\n\\t\\telse:\\n\\t\\t\\trowArr = row\\n\\t\\t\\t\\n\\t\\tnewRowArr = []\\n\\t\\tfor i in range(len(rowArr)):\\n\\t\\t\\tcurVal = rowArr[i]\\n\\t\\t\\tif (i in self.catValues):\\n\\t\\t\\t\\tvalues = self.catValues[i]\\n\\t\\t\\t\\tfor val in values:\\n\\t\\t\\t\\t\\tif val == curVal:\\n\\t\\t\\t\\t\\t\\tnewVal = self.trueVal\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tnewVal = self.falseVal\\n\\t\\t\\t\\t\\tnewRowArr.append(newVal)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tnewRowArr.append(curVal)\\n\\t\\tassert len(newRowArr) == self.newRowSize, \\\"invalid new row size \\\" + str(len(newRowArr)) + \\\" expected \\\" + str(self.newRowSize)\\n\\t\\tencRow = self.delim.join(newRowArr) if self.delim is not None else newRowArr\\n\\t\\treturn encRow\",\n \"def insertRow(self, p_int, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n pass\",\n \"def on_update_seatable(data, index, *args):\\n row = convert_row(metadata, data)\\n print(row)\",\n \"def __init__(self, rows=()):\\n self.rows = list(rows)\",\n \"def setRow(self, row_number, row):\\n self.data[:,row_number] = row\\n return\",\n \"def add_object(self, row, obj_name):\\n # not optimised: not expected to be a usual operation\\n if self.table.shape[0] > 0:\\n new_table = np.vstack((self.table, [row]))\\n else:\\n new_table = np.array([row])\\n new_objects = self.objects + [obj_name]\\n self.__init__(new_table, new_objects, self.attributes)\",\n \"def _set_right_info_row_values(self, row, values):\\n print \\\"Row is \\\", row\\n print \\\"Values are \\\", values\\n for name in values.keys():\\n self.header_values[name][row].set(values[name])\",\n \"def table_callback(table, data, event, column=False):\\n if column:\\n table.object = data.loc[event.new]\\n else:\\n table.object = data.loc[event.new:event.new]\",\n \"def insertRows(self, p_int, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n pass\",\n \"def mutate_row(self, row_key: bytes, column_family_id: str, val_dict: dict,\\n time_stamp: Optional[datetime.datetime] = None\\n ) -> bigtable.row.Row:\\n row = self.table.row(row_key)\\n\\n for column, value in val_dict.items():\\n row.set_cell(column_family_id=column_family_id, column=column,\\n value=value, timestamp=time_stamp)\\n return row\",\n \"def _do_change_row(self, treeview):\\n _return = False\\n\\n self.treeview.handler_block(self._lst_handler_id[0])\\n\\n (_model, _row) = treeview.get_selection().get_selected()\\n try:\\n _level = _model.get_value(_row, 11)\\n except TypeError:\\n _level = None\\n\\n _columns = treeview.get_columns()\\n\\n # Change the column headings depending on what is being selected.\\n if _level == 'mission':\\n _headings = [\\n _(u\\\"Mission ID\\\"),\\n _(u\\\"Description\\\"),\\n _(u\\\"Units\\\"),\\n _(u\\\"Start Time\\\"),\\n _(u\\\"End Time\\\"),\\n _(u\\\"\\\"),\\n _(u\\\"\\\"),\\n _(u\\\"\\\")\\n ]\\n elif _level == 'phase':\\n _headings = [\\n _(u\\\"Phase ID\\\"),\\n _(u\\\" Code\\\\t\\\\tDescription\\\"),\\n _(u\\\"Units\\\"),\\n _(u\\\"Start Time\\\"),\\n _(u\\\"End Time\\\"),\\n _(u\\\"\\\"),\\n _(u\\\"\\\"),\\n _(u\\\"\\\")\\n ]\\n elif _level == 'environment':\\n _headings = [\\n _(u\\\"Environment ID\\\"),\\n _(u\\\"Condition\\\"),\\n _(u\\\"Units\\\"),\\n _(u\\\"Minimum Value\\\"),\\n _(u\\\"Maximum Value\\\"),\\n _(u\\\"Mean Value\\\"),\\n _(u\\\"Variance\\\"),\\n _(u\\\"\\\")\\n ]\\n else:\\n _headings = []\\n\\n i = 0\\n for _heading in _headings:\\n _label = gtk.Label()\\n _label.set_line_wrap(True)\\n _label.set_alignment(xalign=0.5, yalign=0.5)\\n _label.set_justify(gtk.JUSTIFY_CENTER)\\n _label.set_markup(\\\"<span weight='bold'>\\\" + _heading + \\\"</span>\\\")\\n _label.set_use_markup(True)\\n _label.show_all()\\n _columns[i].set_widget(_label)\\n\\n i += 1\\n\\n self.treeview.handler_unblock(self._lst_handler_id[0])\\n\\n return _return\",\n \"def rows(self) -> java.util.Collection:\\n ...\",\n \"def del_row(self, row_index):\\n ...\",\n \"def __init__(self, row):\\n self.row = row\\n self.type = randint(0, cluster_count - 1)\",\n \"def add_row(self, *column_data):\\n raise NotImplementedError\",\n \"def update_row_flag_status(self, index: qtc.QModelIndex):\\n row = index.row()\\n self.dataChanged.emit(self.index(row, 0), self.index(row, 0), [qtc.Qt.ForegroundRole])\",\n \"def moveRow(self,oldRow,newRow):\\n data = self.data\\n if oldRow in data:\\n data[newRow] = data[oldRow]\\n del data[oldRow]\\n self.hasChanged = True\",\n \"def set_row_style(sheet, row, style):\\r\\n\\r\\n for cell in sheet[row]:\\r\\n cell.style = style\",\n \"def addRowEvent(self):\\n\\t\\tif self.stretch: self.setRowStretch(self.row, 0)\\n\\t\\tself.row += 1\\n\\t\\tif self.stretch: self.setRowStretch(self.row, 1)\",\n \"def addRow(self, row):\\n self.__data.append(row.copy())\\n # We may need to resize the table, to fit the new data\\n for key in row.keys():\\n if len(row[key]) > self.__widths[key]:\\n self.__widths[key] = len(row[key])\\n self.__makeFormatString()\\n self.__refreshContent()\\n if self.__selectedRow == -1:\\n self.__selectedRow = 0\\n lines = len(self.__data)\\n if self.__firstShownLine <= lines - self.height + 2 and \\\\\\n self.__autoScroll:\\n # We need to scroll everything upwards\\n self.scrollDown()\\n if self.__selectedRow < self.__firstShownLine:\\n self.__selectedRow = self.__firstShownLine\\n if self._focused:\\n self._window.attron(curses.A_BOLD)\\n self.__printRow(self.__firstShownLine)\\n self._window.attroff(curses.A_BOLD)\\n else:\\n if self._focused and self.__selectedRow == lines - 1:\\n self._window.attron(curses.A_BOLD)\\n self.__printRow(lines - 1)\\n self._window.attroff(curses.A_BOLD)\",\n \"def extend_rows(self, assign):\\n new_table = dict()\\n for key, value in self._table.items():\\n new_table[Assignment(key, assign)] = value\\n\\n self._table = new_table\",\n \"def newrow(self, rowlist):\\n if len(rowlist) > 0:\\n if islist(rowlist[0]):\\n for row in rowlist:\\n self.newrow(row)\\n elif len(rowlist) == self.x:\\n for x in xrange(0, len(rowlist)):\\n rowlist[x] = self.prepare(rowlist[x])\\n self.a.append(rowlist)\\n self.y += 1\\n else:\\n raise IndexError(\\\"Unequal matrix row lengths for newrow of \\\"+str(self.x)+\\\" and \\\"+str(len(rowlist)))\",\n \"def setItem(self,row,column,value):\\n data = self.data\\n if row not in data:\\n data[row] = {}\\n data[row][column] = value\\n self.hasChanged = True\",\n \"def places_process_rows(self):\\n\\n for index in range(len(self.table)):\\n row_rdf = self.places_map_row_to_rdf(self.table.iloc[index])\\n if row_rdf is not None:\\n self.data += row_rdf\",\n \"def __init__(self, data):\\n self.data = data\\n self.columns = Columns(data)\\n self.rows = Rows(data)\",\n \"def _insert_table_row(self, db: str, table: str, row: Dict[str, Any]):\\n pass\",\n \"def setBaseRowID(self, id: int) -> None:\\n ...\",\n \"def Row(self, row):\\r\\n \\r\\n self.dock_row = row\\r\\n return self\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70247316","0.6973256","0.6739629","0.66780365","0.663705","0.65876734","0.65684086","0.6497517","0.64002925","0.6395719","0.6366707","0.63460535","0.63453114","0.63381773","0.6335888","0.6279478","0.61753815","0.6160647","0.6157512","0.61517364","0.61424714","0.6137165","0.60834694","0.60691583","0.6018085","0.601063","0.5995083","0.59906703","0.597797","0.595986","0.59283483","0.59253335","0.59070325","0.59049624","0.58782125","0.5877019","0.58571404","0.58423334","0.5824425","0.5768072","0.57665545","0.57652396","0.5755157","0.57399964","0.5736986","0.5730073","0.57214254","0.57103384","0.57012266","0.5700899","0.56983566","0.5677036","0.5666185","0.5658725","0.5645589","0.5632198","0.56302935","0.5622418","0.5621994","0.5620091","0.56109756","0.56054825","0.5595059","0.558838","0.55493456","0.55451614","0.5535703","0.55235285","0.5515624","0.5514424","0.54965454","0.54919237","0.5449191","0.5441296","0.544068","0.5439535","0.54342484","0.54333806","0.54308206","0.5427861","0.5424674","0.5422357","0.5421263","0.54209626","0.541229","0.54091173","0.5403829","0.53953624","0.53920114","0.53906906","0.5385628","0.53821534","0.53756833","0.53605515","0.53486836","0.5338032","0.5318686","0.531456","0.5310352","0.53029305","0.5298115"],"string":"[\n \"0.70247316\",\n \"0.6973256\",\n \"0.6739629\",\n \"0.66780365\",\n \"0.663705\",\n \"0.65876734\",\n \"0.65684086\",\n \"0.6497517\",\n \"0.64002925\",\n \"0.6395719\",\n \"0.6366707\",\n \"0.63460535\",\n \"0.63453114\",\n \"0.63381773\",\n \"0.6335888\",\n \"0.6279478\",\n \"0.61753815\",\n \"0.6160647\",\n \"0.6157512\",\n \"0.61517364\",\n \"0.61424714\",\n \"0.6137165\",\n \"0.60834694\",\n \"0.60691583\",\n \"0.6018085\",\n \"0.601063\",\n \"0.5995083\",\n \"0.59906703\",\n \"0.597797\",\n \"0.595986\",\n \"0.59283483\",\n \"0.59253335\",\n \"0.59070325\",\n \"0.59049624\",\n \"0.58782125\",\n \"0.5877019\",\n \"0.58571404\",\n \"0.58423334\",\n \"0.5824425\",\n \"0.5768072\",\n \"0.57665545\",\n \"0.57652396\",\n \"0.5755157\",\n \"0.57399964\",\n \"0.5736986\",\n \"0.5730073\",\n \"0.57214254\",\n \"0.57103384\",\n \"0.57012266\",\n \"0.5700899\",\n \"0.56983566\",\n \"0.5677036\",\n \"0.5666185\",\n \"0.5658725\",\n \"0.5645589\",\n \"0.5632198\",\n \"0.56302935\",\n \"0.5622418\",\n \"0.5621994\",\n \"0.5620091\",\n \"0.56109756\",\n \"0.56054825\",\n \"0.5595059\",\n \"0.558838\",\n \"0.55493456\",\n \"0.55451614\",\n \"0.5535703\",\n \"0.55235285\",\n \"0.5515624\",\n \"0.5514424\",\n \"0.54965454\",\n \"0.54919237\",\n \"0.5449191\",\n \"0.5441296\",\n \"0.544068\",\n \"0.5439535\",\n \"0.54342484\",\n \"0.54333806\",\n \"0.54308206\",\n \"0.5427861\",\n \"0.5424674\",\n \"0.5422357\",\n \"0.5421263\",\n \"0.54209626\",\n \"0.541229\",\n \"0.54091173\",\n \"0.5403829\",\n \"0.53953624\",\n \"0.53920114\",\n \"0.53906906\",\n \"0.5385628\",\n \"0.53821534\",\n \"0.53756833\",\n \"0.53605515\",\n \"0.53486836\",\n \"0.5338032\",\n \"0.5318686\",\n \"0.531456\",\n \"0.5310352\",\n \"0.53029305\",\n \"0.5298115\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":137,"numItemsPerPage":100,"numTotalItems":95772,"offset":13700,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjAzOTczMCwianRpIjoiYTU4ZmI1ODItNTU4MC00ZGY1LWE1ZDgtOTMyOGRmY2ZjNjllIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYwNDMzMzAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.rdOIscysJsRIdW50aczrazzE9Z684fnOrCK4y0zjS6At2RvgdXnAJ74PJgbQ-dGPa3IkOwqUB0pV2xj_yXVSBA","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 |
|---|---|---|---|---|---|---|
Message notifier helper to send message | def message_notifier(self, **kwargs):
if all(key in kwargs for key in ["msg_type", "author", "category"]):
template_name = os.path.join(kwargs.get("msg_type", ""), kwargs.get("author", ""),
kwargs.get("category", ""), kwargs.get("template", ""))
k... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def notify(guid, message):",
"def notify(cls, self, message):\n pass",
"def send(self, msg):\n self.message('Me', msg)",
"def send_message(self, message):\n pass",
"def send(self, msg):\n pass",
"def send(self, msg):\n pass",
"def send(self, msg):\n pass",
"def n... | [
"0.7474298",
"0.7359119",
"0.71167135",
"0.70818764",
"0.70510983",
"0.70510983",
"0.70510983",
"0.6973927",
"0.6973927",
"0.69313467",
"0.6843851",
"0.6826354",
"0.6787421",
"0.67820174",
"0.6709434",
"0.6690207",
"0.66766",
"0.66523856",
"0.6651198",
"0.66505796",
"0.662189... | 0.0 | -1 |
Returns a list of all Server Emojis | async def emojis(self, ctx):
server = ctx.message.server
await self.bot.say('This may take some time, generating list...')
data = discord.Embed(description="Emojilist")
for ej in server.emojis:
data.add_field(
name=ej.name, value=str(ej) + " " + ej.id, inline=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_slack_emoji():\n all_slack_emoji = []\n\n # load stock emoji from file\n with app.open_resource('../static/emoji-names.json') as f:\n stock_emojis = json.load(f)\n all_slack_emoji += stock_emojis\n\n # concat custom emoji by slack API call\n all_slack_emoji += sc.api_call('emoj... | [
"0.6829809",
"0.67620057",
"0.6723042",
"0.66201127",
"0.66025",
"0.65196496",
"0.6501168",
"0.6340955",
"0.62791866",
"0.6245136",
"0.60249436",
"0.5856114",
"0.57686776",
"0.57601565",
"0.56315124",
"0.56029534",
"0.55517876",
"0.55233794",
"0.55045164",
"0.5494092",
"0.549... | 0.73602945 | 0 |
Displays general help information for my guild | async def just(self, ctx):
user = ctx.message.author
color = self.getColor(user)
data = discord.Embed(
description='Erklärung zu den Befehlen', color=color)
data.set_author(name='Justified Loyalty')
data.add_field(
name='Schlüssel hinzufügen',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def help(ctx):\n\tembed = discord.Embed(description='Below Are All The Commands For SecretBot')\n\tembed.set_author(name='SecretBot Help Center')\n\tembed.add_field(name='***`!ping`***', value='Returns the bot latency', inline=False)\n\tembed.add_field(name='***`!clear`***', value='Deletes the given amount o... | [
"0.77254003",
"0.76329136",
"0.76176476",
"0.7611161",
"0.75561047",
"0.75534046",
"0.7512939",
"0.7468252",
"0.7456119",
"0.7436395",
"0.74283975",
"0.7324036",
"0.73093426",
"0.730897",
"0.7305636",
"0.72907877",
"0.7281279",
"0.7268633",
"0.7259403",
"0.72506446",
"0.72290... | 0.0 | -1 |
Check if Server is online | async def ping(self, ctx, ip):
# Check for valid IP else do DNS lookup
valid_ip = re.compile("[0-9]{,3}\.[0-9]{,3}\.[0-9]{,3}")
valid_hostname = re.compile(".*\.[a-zA-Z]{2,}")
valid = False
if valid_ip.match(ip):
valid = True
elif valid_hostname.match(ip):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_status(self):\n try:\n self.server.ping()\n return True\n except Exception as e:\n return False",
"def is_online(self) -> bool:\n return self.data[Attribute.ONLINE]",
"def checkServerThread(self):\r\n\r\n # check if the server is alive\r\n ... | [
"0.77489424",
"0.7634192",
"0.75805205",
"0.75527275",
"0.7549516",
"0.7535959",
"0.74494815",
"0.74301475",
"0.7362648",
"0.7362512",
"0.73440593",
"0.718413",
"0.7166052",
"0.7160639",
"0.71309507",
"0.71227175",
"0.7115964",
"0.70983225",
"0.7060262",
"0.7053327",
"0.70431... | 0.0 | -1 |
Outputs a random image from pr0gramm.com (sfw) | async def pr0(self, ctx):
# Generate random number, check if header responds with 200 (OK)
# If not generate new number
# Hardcoded img src from webpage in line 63
# Extract path to image from webpage
# Clean up
user = ctx.message.author
color = self.getColor(use... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_rand_img():\n import urllib\n import os\n import glob\n\n pics = glob.glob('/home/cody_techngs/PycharmProjects/ProjTest/ActiveAMT/ActiveAMT_FLASK/static/images/HITs/rand*')\n nums = []\n\n for pic in pics:\n nums.append(int(pic.split('rand_img')[1].split('.')[0]))\n\n unique_num... | [
"0.647646",
"0.6339494",
"0.61261845",
"0.6112071",
"0.61045945",
"0.60505414",
"0.60447264",
"0.6006293",
"0.59611243",
"0.5850205",
"0.58383125",
"0.5806911",
"0.5787211",
"0.5764011",
"0.57588387",
"0.57280755",
"0.5725055",
"0.570823",
"0.56704146",
"0.5665492",
"0.566296... | 0.55273026 | 31 |
Coinflip, defaults to Kopf/Zahl if no players are given | async def coinflip(self, ctx, player1=None, *, player2=None):
rng = randint(1, 10)
if player1 is None and player2 is None:
if rng < 5:
return await self.bot.say("Kopf gewinnt!")
else:
return await self.bot.say("Zahl gewinnt!")
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def flipcoin(self, ctx):\n flip = random.choice([True, False])\n if flip == True:\n msg = 'It\\'s heads!'\n await ctx.send(msg)\n elif flip == False:\n msg = 'It\\'s tails!'\n await ctx.send(msg)",
"def flip_player(cls):\n cls.current_... | [
"0.67842084",
"0.66972786",
"0.6554809",
"0.65462387",
"0.6510231",
"0.6505744",
"0.6217337",
"0.61918855",
"0.6128844",
"0.6107027",
"0.60780925",
"0.6042313",
"0.6022117",
"0.60063225",
"0.5861973",
"0.57754767",
"0.57477665",
"0.5738534",
"0.5674234",
"0.5642496",
"0.56303... | 0.71466845 | 0 |
Moves all clients randomly in other channels for duration seconds. After the whirpool event, all clients will be in the same channel as before. Between the whirlpool cycles, the programm will sleep for relax_time seconds. | def whirlpool(ts3conn, duration=10, relax_time=0.5):
# Countdown till whirlpool
for i in range(5, 0, -1):
ts3conn.sendtextmessage(
targetmode=ts3.definitions.TextMessageTargetMode.SERVER,
target=0, msg="Whirpool in {}s".format(i))
time.sleep(1)
# Fetch the clientlist... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def client_send(state):\n while state.running:\n disconnected_users = []\n time.sleep(0.05)\n for nick in users:\n nick, queue = nick, users[nick].queue\n while len(queue) > 0:\n sender, msg = queue.pop(0)\n message = '{}> {}'.format(sende... | [
"0.5521252",
"0.5468559",
"0.5398658",
"0.532204",
"0.5318605",
"0.5243049",
"0.521927",
"0.52137786",
"0.5190527",
"0.51749384",
"0.5163204",
"0.51520616",
"0.5127868",
"0.50814927",
"0.5065982",
"0.5059794",
"0.5046238",
"0.5038621",
"0.501738",
"0.5005819",
"0.4993451",
... | 0.7218653 | 0 |
Defines the default ResNet arg scope. | def resnet_arg_scope(weight_decay=0.0001,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True):
batch_norm_params = {
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'scale': batch_norm_scale,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _set_resnet_arg_scope(self):\n vs_initializer = tf.keras.initializers.VarianceScaling(2.0)\n l2_regularizer = tf.keras.regularizers.l2(self.config.GENERATOR_WEIGHT_DECAY)\n for layer in self.resnet50V2.layers:\n if isinstance(layer, layers.Conv2D):\n # original im... | [
"0.684542",
"0.6298972",
"0.5859722",
"0.5701552",
"0.56541204",
"0.563224",
"0.5528697",
"0.55201155",
"0.55156153",
"0.5500726",
"0.54917955",
"0.54548305",
"0.54043365",
"0.5403969",
"0.5390156",
"0.5381934",
"0.5364883",
"0.53589743",
"0.5351644",
"0.53468245",
"0.5309198... | 0.55389446 | 6 |
Defines a lightweight resnet based model for dense estimation tasks. | def network(inputs,
scale,
output_channels=3,
internal_channels=3,
return_endpoints=False,
state=None):
out_shape = tf.shape(inputs)[1:3]
if scale > 1:
inputs = tf.pad(inputs, ((0, 0), (1, 1), (1, 1), (0, 0)))
inputs = slim.layers.avg... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resnet():\n return models.resnet152(pretrained=True)",
"def resnet46(pretrained=False):\n model = ResNet(BasicBlock, [3, 6, 10, 3])\n if pretrained:\n pass\n #model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))\n return model",
"def resnet34(bitW, bitA, pretrained=Fa... | [
"0.678431",
"0.6743733",
"0.6683988",
"0.6681649",
"0.6650233",
"0.6596993",
"0.65124136",
"0.6503727",
"0.6500117",
"0.6500117",
"0.6500117",
"0.6500117",
"0.6482222",
"0.64762425",
"0.6470119",
"0.6467772",
"0.64354265",
"0.6434284",
"0.6432372",
"0.6432372",
"0.6432372",
... | 0.0 | -1 |
unit test for import_data | def test_import_data():
file_path = os.path.join(CONST_ADVANTICSYS_DIR, CONST_ADVANTICSYS_TEST_1)
# Bring df
success, log, test_ingress_df = advanticsys_import(file_path)
assert success, log
assert isinstance(test_ingress_df, pd.DataFrame)
# Test import function
success, log = import_data... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_import(self):\n\n database.delete_database()\n\n #Fully successful import\n actual_tuples1 = database.import_data('csvs', 'product_data.csv', 'customer_data.csv',\n 'rentals_data.csv')\n expected_tuples1 = ((1, 1, 1), (0, 0, 0))\n ... | [
"0.79549503",
"0.7904061",
"0.77873087",
"0.764547",
"0.744109",
"0.7326395",
"0.71442324",
"0.70856327",
"0.7078031",
"0.69899714",
"0.6982824",
"0.69564384",
"0.694217",
"0.6923845",
"0.6889546",
"0.684248",
"0.67932606",
"0.6782817",
"0.67503047",
"0.67497194",
"0.6735551"... | 0.7698802 | 3 |
Finds a seat ID based on string provided on the boarding pass. | def find_seat(seat_string):
row = int(''.join([{'F':'0', 'B':'1'}[r] for r in seat_string[:7]]),2)
col = int(''.join([{'L':'0', 'R':'1'}[c] for c in seat_string[7:]]),2)
return row * 8 + col | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_seat_id(boarding_pass):\n return int(re.sub(\"[FL]\", \"0\", re.sub(\"[BR]\", \"1\", boarding_pass)), 2)",
"def get_seat_id(boarding_pass: str) -> int:\n row, column = parse_boarding_pass(boarding_pass)\n return row * 8 + column",
"def part2(data: str):\n seat_ids = sorted(\n [bp.sea... | [
"0.77942544",
"0.7530775",
"0.7115229",
"0.66326725",
"0.6397568",
"0.60840887",
"0.59991723",
"0.5945933",
"0.58683074",
"0.58614475",
"0.5856253",
"0.58306193",
"0.5659762",
"0.5606489",
"0.556601",
"0.55478156",
"0.5538071",
"0.54757804",
"0.5440015",
"0.5378216",
"0.53555... | 0.69945955 | 3 |
Converts all seat strings into seat IDs and returns the highest seat ID found. | def highest_seat_id(raw_seat_string):
seat_list = raw_seat_string.split('\n')
return max(list(map(find_seat, seat_list))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_highest_seat_id(seat_ids):\n\n return max(seat_ids)",
"def get_max_seat_id(boarding_passes: list) -> int:\n return max(get_seat_id(boarding_pass) for boarding_pass in boarding_passes)",
"def part2(data: str):\n seat_ids = sorted(\n [bp.seat_id for bp in map(lambda row: BoardingPass(row)... | [
"0.74634355",
"0.668236",
"0.6261357",
"0.62445736",
"0.6111793",
"0.6099611",
"0.60279304",
"0.587468",
"0.5664313",
"0.5655479",
"0.55966735",
"0.5484212",
"0.53893024",
"0.5317917",
"0.5312065",
"0.52949077",
"0.5271475",
"0.52611125",
"0.5253164",
"0.52044994",
"0.5197342... | 0.8179302 | 0 |
All seats start out empty | def __init__(self):
self.empty_seats = [row * 8 + col for row in self.rows for col in self.cols] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def empty_seats(seats, seat_numbers):\n\n for seat in seat_numbers:\n seats[seat] = None\n\n return seats",
"def fill_empty_seats(seats_names_dict, seats):\n for seat in seats:\n if seat not in seats_names_dict:\n seats_names_dict[seat] = 'empty'\n\n return seats_names_dict",... | [
"0.6661953",
"0.603927",
"0.603626",
"0.5855473",
"0.5745834",
"0.55990493",
"0.55397326",
"0.55388844",
"0.55323654",
"0.5485067",
"0.5471141",
"0.5468245",
"0.5461583",
"0.54550487",
"0.54510456",
"0.5441734",
"0.5439312",
"0.5425242",
"0.5423252",
"0.54135686",
"0.53805137... | 0.64161956 | 1 |
Each time a passenger is seated, the seat_id is removed from the empty seats list | def seat_passenger(self, seat_id):
self.empty_seats.remove(seat_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_player(self, seat_id):\n player_id = seat_id\n try:\n idx = self._seats.index(self._player_dict[player_id])\n self._seats[idx] = Player(0, stack=0, emptyplayer=True)\n del self._player_dict[player_id]\n self.emptyseats += 1\n except ValueError:\n pass",
"def empty_sea... | [
"0.6723246",
"0.67226124",
"0.62767714",
"0.587385",
"0.58325046",
"0.57876456",
"0.5778089",
"0.5708203",
"0.5675765",
"0.5618063",
"0.5579411",
"0.5568858",
"0.5519024",
"0.5517418",
"0.54689866",
"0.54673475",
"0.5445841",
"0.54018897",
"0.5352401",
"0.5350176",
"0.5329276... | 0.830451 | 0 |
Should produce identical sequences, only if seed is None and random order is required, they should be unique. | def test_dataset_accessor_return_type(device, sampling_method):
params = DatasetSENavigationParams(SeDatasetSize.SIZE_24)
# parametrized:
params.sampling_method = sampling_method
node = DatasetSeNavigationNode(params)
node.allocate_memory_blocks(AllocatingCreator(device=device))
node._step()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_repeatable():\n random.seed(1234)\n np.random.seed(1234)",
"def Randomize(seed=None):\n random.seed()",
"def test_shuffle(self):\n random.shuffle(self.seq)\n self.seq.sort()\n self.assertEqual(self.seq, range(10))",
"def test_case_3(self):\n\n print(\"-----------... | [
"0.68063575",
"0.6516124",
"0.6415844",
"0.63486207",
"0.6323278",
"0.622613",
"0.6209167",
"0.6162394",
"0.6157565",
"0.61564547",
"0.61397254",
"0.61340016",
"0.6130892",
"0.6111281",
"0.6107018",
"0.6072725",
"0.6069071",
"0.60623324",
"0.60471845",
"0.6025049",
"0.6025049... | 0.0 | -1 |
Dump observations to specified file | def writeHealthObs(usersList, health, numTimeSteps):
# Write observations for EM here
fout = open('../epiDBN/data/dbn_train_%d.txt' % numTimeSteps, 'w+')
# Write true labels here
#foutLabels = open('../epiDBN/data/dbn_train_%d_true_labels.txt' % numTimeSteps, 'w')
# Header - gmtk does not want comments in the d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dump_data(self,filename,dump_id):\n import pickle\n from Auxiliary import tdc_Filenames\n data = [ d.get_pure_data_copy() for d in self.plotter.data ]\n dump_dict={}\n dump_dict['fft_data'] = data\n dump_dict['fitting_type'] = self.fft_fit.type \n dump_dict['n... | [
"0.6793172",
"0.66224176",
"0.65509844",
"0.64685756",
"0.63766843",
"0.6334536",
"0.6326792",
"0.6316774",
"0.6305548",
"0.62975866",
"0.6241558",
"0.6238045",
"0.61615443",
"0.61470276",
"0.6133353",
"0.61016566",
"0.60787475",
"0.60740733",
"0.60731506",
"0.6072791",
"0.60... | 0.0 | -1 |
Return fitness given a genome class | def evaluate(genome):
# base fitness
fit = 1.0
# promote 1001 starting motif
matches = 0
if genome.sequence_A[0] == 1:
matches += 1
if genome.sequence_A[1] == 0:
matches += 1
if genome.sequence_A[2] == 0:
matches += 1
if genome.sequence_A[3] == 1:
matches ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fitness(self):\n pass",
"def fitness(self):\n # TO BE DECIDED\n return 1",
"def fitness(self,*val):\n if len(val): self._fitness = val[0]\n return self._fitness",
"def fitness(self) -> float:\n return self._fitness",
"def calculate_fitness(self, **kwargs):\n ... | [
"0.7435925",
"0.70573634",
"0.6834959",
"0.667213",
"0.65809023",
"0.65646446",
"0.6498988",
"0.64824885",
"0.64313686",
"0.63564503",
"0.6339265",
"0.63168496",
"0.6300622",
"0.6260624",
"0.6199508",
"0.61992794",
"0.61722684",
"0.6163491",
"0.6102426",
"0.60920846",
"0.6091... | 0.6741454 | 3 |
This function is used to search a given Book Title | def search(title, base_root):
# Database Connection
cnx = db.connect(user='root', password='', host='127.0.0.1', database='Library')
cur = cnx.cursor()
query = "SELECT * FROM Books WHERE title LIKE '%" + title + "%'"
# We look for all books whose title contains the given substring
try:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search(self, title=\"\", author=\"\", year=\"\", isbn=\"\"):\n self.cursor.execute(\"SELECT * FROM Book WHERE Title = ? OR Author = ? \\\n OR Year = ? OR ISBN = ?\", (title, author, year, isbn))\n rows = self.cursor.fetchall()\n return rows",
"def book_search(self, term):\n\n ... | [
"0.71749747",
"0.7143297",
"0.6945613",
"0.6931124",
"0.6893685",
"0.68236625",
"0.68118626",
"0.6783939",
"0.67645556",
"0.67461675",
"0.671418",
"0.6666875",
"0.6659487",
"0.65709585",
"0.6557472",
"0.6552977",
"0.65291214",
"0.65027714",
"0.6492732",
"0.6477249",
"0.646716... | 0.64785403 | 19 |
Detect sequential cut in DFG graph | def detect_sequential_cut(self, conn_components, this_nx_graph, strongly_connected_components):
if len(strongly_connected_components) > 1:
conn_matrix = detection_utils.get_connection_matrix(strongly_connected_components, self.dfg)
comps = []
closed = set()
for i ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def detect_cut(self, second_iteration=False):\n if pkgutil.find_loader(\"networkx\"):\n import networkx as nx\n else:\n msg = \"networkx is not available. inductive miner cannot be used!\"\n logging.error(msg)\n raise Exception(msg)\n\n if self.dfg:\... | [
"0.62352633",
"0.62338763",
"0.57436424",
"0.5730967",
"0.5658197",
"0.5650399",
"0.55493516",
"0.54457647",
"0.53847903",
"0.53800404",
"0.5375748",
"0.5339116",
"0.52789414",
"0.5212714",
"0.5196232",
"0.5167567",
"0.5158499",
"0.5120214",
"0.5101768",
"0.5097351",
"0.50969... | 0.5710943 | 4 |
Checks if each branch of the parallel cut has a start and an end node of the subgraph | def check_sa_ea_for_each_branch(self, conn_components):
parallel_cut_sa = list(set(self.initial_start_activities).union(
infer_start_activities_from_prev_connections_and_current_dfg(self.initial_dfg, self.dfg, self.activities,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_connected(self, start: Union[str, int], end: Union[str, int]) -> bool:\n\t\tif start not in self.vertices or end not in self.vertices:\n\t\t\traise GraphError(\"Start or end not found in graph\")\n\n\t\tvisited = dict(zip(self.vertices, [False] * len(self.vertices)))\n\t\tqueue = []\n\t\tqueue.append(start)... | [
"0.6446912",
"0.60400736",
"0.6032703",
"0.60213304",
"0.60129786",
"0.6002233",
"0.59806913",
"0.59728694",
"0.5967003",
"0.59241945",
"0.5915911",
"0.5913777",
"0.5874635",
"0.58325255",
"0.5819889",
"0.581288",
"0.57998574",
"0.57834435",
"0.57786924",
"0.5778236",
"0.5769... | 0.53612095 | 76 |
Puts the skips in sequential cut | def put_skips_in_seq_cut(self):
# first, put skips when in some cut there is an ending activity
in_end_act = set(self.initial_end_activities)
i = 0
while i < len(self.children) - 1:
activities_set = set(self.children[i].activities)
intersection = activities_set.in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def skipp(self):\n for x in range(4):\n self.fwd(right=100, left=100)\n time.sleep(.5)\n self.servo(1000)\n time.sleep(.1)\n self.servo(2000)\n time.sleep(.1)\n self.fwd(right=-100, left=-100)\n time.sleep(.1)\n ... | [
"0.6081469",
"0.59631395",
"0.590605",
"0.59015036",
"0.59015036",
"0.563135",
"0.5596978",
"0.5508532",
"0.550615",
"0.5485905",
"0.54270315",
"0.54068",
"0.5339759",
"0.53249735",
"0.52994674",
"0.5296712",
"0.5285578",
"0.5242751",
"0.52426267",
"0.5176084",
"0.5173648",
... | 0.7369221 | 0 |
Detect generally a cut in the graph (applying all the algorithms) | def detect_cut(self, second_iteration=False):
if pkgutil.find_loader("networkx"):
import networkx as nx
else:
msg = "networkx is not available. inductive miner cannot be used!"
logging.error(msg)
raise Exception(msg)
if self.dfg:
this... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cut(S, T, graph):\n ###TODO\n pass",
"def detect_cut(self, second_iteration=False):\r\n if self.dfg:\r\n # print(\"\\n\\n\")\r\n par_cut = self.detect_parallel_cut()\r\n conc_cut = self.detect_concurrent_cut()\r\n seq_cut = self.detect_sequential_cut()... | [
"0.67545813",
"0.63988644",
"0.6193305",
"0.61927104",
"0.6070173",
"0.6034064",
"0.60137284",
"0.6012636",
"0.60082155",
"0.59801936",
"0.59748966",
"0.59515464",
"0.5919017",
"0.59180576",
"0.5864671",
"0.5861133",
"0.5767556",
"0.5763453",
"0.5761619",
"0.5738483",
"0.5731... | 0.63831633 | 2 |
Start the monitoring loop for the downloads. | def start(self):
self._logger.info("Starting download monitor (interval: %d seconds)" % self.interval)
self.monitor_lc = ensure_future(looping_call(0, self.interval, self.monitor_downloads)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n self.monitor.start()",
"def start_download(self) -> NoReturn:\n if self.threaded:\n self.threaded_download()\n else:\n self.regular_download()",
"def monitor_downloads(self):\n return self.request_manager.get_downloads().addCallback(self.on_dow... | [
"0.7030247",
"0.6881864",
"0.681746",
"0.6760977",
"0.6704339",
"0.66790855",
"0.6652548",
"0.66350824",
"0.6631739",
"0.66223013",
"0.65687346",
"0.65481055",
"0.65054774",
"0.6494747",
"0.64725673",
"0.64176327",
"0.6264444",
"0.6254806",
"0.62482595",
"0.62001854",
"0.6194... | 0.8476433 | 0 |
Stop the monitoring loop for the downloads. | def stop(self):
if self.monitor_lc:
self.monitor_lc.cancel()
self.monitor_lc = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop(self):\n self._running = False\n self._logger.info(\"Locator finished main loop\")",
"def stop(self):\n \n for watched_file in self._watched_files:\n watched_file.close()",
"def stop_monitoring(self):\n pass",
"def stop(self):\n\n self.keep_ru... | [
"0.7338356",
"0.7250945",
"0.7194579",
"0.7162138",
"0.7155697",
"0.7150892",
"0.70830715",
"0.70646536",
"0.7063699",
"0.7061427",
"0.7033731",
"0.7021838",
"0.70164853",
"0.70094645",
"0.69946784",
"0.69946784",
"0.69946784",
"0.69946784",
"0.69946784",
"0.69946784",
"0.698... | 0.7091725 | 6 |
Monitor the downloads in Tribler. | async def monitor_downloads(self):
downloads = await self.request_manager.get_downloads()
for download in downloads["downloads"]:
time_diff = time.time() - self.start_time
with open(self.download_stats_file_path, "a") as output_file:
output_file.write("%s,%s,%s,%s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def monitor_downloads(self):\n return self.request_manager.get_downloads().addCallback(self.on_downloads)",
"def start(self):\n self._logger.info(\"Starting download monitor (interval: %d seconds)\" % self.interval)\n self.monitor_lc = ensure_future(looping_call(0, self.interval, self.monito... | [
"0.77754945",
"0.68570465",
"0.63873947",
"0.6189983",
"0.6162762",
"0.61472476",
"0.5974604",
"0.5974385",
"0.59406537",
"0.59122854",
"0.5896646",
"0.58398616",
"0.5819066",
"0.58097005",
"0.5774304",
"0.57651347",
"0.5756573",
"0.5737742",
"0.573505",
"0.56281215",
"0.5620... | 0.65173733 | 2 |
Returns true if the cooldown is ready. | def ready(self):
return self.time >= self.cooldown | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def in_cooldown(self) -> bool:\n return self.cooldown_counter > 0",
"def is_ready(self) -> bool:\n return self._ready.is_set()",
"def cooldown_checker(self):\n self.cooldown_tick += 1\n if self.cooldown_tick == self.pattern_cooldown:\n self.wait = False\n self.... | [
"0.80361766",
"0.73155785",
"0.7213164",
"0.7167499",
"0.7148745",
"0.7121509",
"0.71099555",
"0.7094376",
"0.70895165",
"0.7063325",
"0.70395815",
"0.7003874",
"0.69981563",
"0.69766104",
"0.69766104",
"0.6975192",
"0.69604623",
"0.69585705",
"0.69414705",
"0.6909078",
"0.68... | 0.87662184 | 0 |
Same as ready, but also resets the cooldown if it's ready. | def reset_if_ready(self):
ready = self.ready
if ready:
self.reset()
return ready | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ready(self):\n return self.time >= self.cooldown",
"def cooldown_checker(self):\n self.cooldown_tick += 1\n if self.cooldown_tick == self.pattern_cooldown:\n self.wait = False\n self.cooldown_tick = 0",
"def in_cooldown(self) -> bool:\n return self.cooldown... | [
"0.7826513",
"0.6927548",
"0.66389006",
"0.6576115",
"0.6455185",
"0.64339614",
"0.6347819",
"0.6346078",
"0.63389456",
"0.6314018",
"0.62754583",
"0.62540513",
"0.62156993",
"0.6199212",
"0.6199212",
"0.6199212",
"0.6199212",
"0.6199212",
"0.6199212",
"0.6199212",
"0.6191149... | 0.61651146 | 22 |
Get api_key in metadata, raise error if does not exist | def get_api_key(context) -> str:
provided_api_key = ""
for key, value in context.invocation_metadata():
if key == "api_key":
provided_api_key = str(value)
return provided_api_key
return provided_api_key | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_api_key(api_key):\n api.get(api_key)",
"def resolve_apikey(self):\n # check the instance variable\n apikey = self.apikey\n if apikey is not None:\n return apikey\n\n # check the class variable and environment\n apikey = resolve_apikey()\n if apikey ... | [
"0.7678791",
"0.6980334",
"0.6893777",
"0.67977744",
"0.6746311",
"0.67275006",
"0.6724428",
"0.6724428",
"0.67214483",
"0.6710168",
"0.6701964",
"0.66950274",
"0.6663174",
"0.6648977",
"0.66346925",
"0.6588155",
"0.6586956",
"0.65624505",
"0.65600836",
"0.6558815",
"0.655764... | 0.7251901 | 1 |
Update prefix with operator with best price. | def _update_prefix(self, prefix: str, operator: Operator):
cached_operator: Optional[Operator] = self.lookup(prefix)
if cached_operator:
cached_price = cached_operator.price_for_prefix(prefix)
if cached_price:
if operator.has_better_price_for_prefix(prefix, cached... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_with_operator(self, operator: Operator):\n if not isinstance(operator, Operator):\n raise TypeError(\n f\"operator expected to be of type `Operator` but got type \"\n f\"{type(operator)}\"\n )\n\n for prefix in operator.rates.keys():\n ... | [
"0.6310741",
"0.57850754",
"0.57514006",
"0.54723537",
"0.54526275",
"0.5421909",
"0.53711325",
"0.5362892",
"0.5342558",
"0.52658045",
"0.5223748",
"0.5185856",
"0.51810634",
"0.51768",
"0.5171346",
"0.5168897",
"0.5147008",
"0.50996333",
"0.50994164",
"0.50994164",
"0.50835... | 0.7551244 | 0 |
Find if a given number a prefix in cache. | def find(self, number: str) -> Optional[str]:
if number in self.data: # noqa
return number
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def startsWith(self, prefix):\n ret = True\n curr = self.trie\n for i, ch in enumerate(prefix):\n curr = curr.get(ch, {})\n if curr:\n continue\n else:\n break\n \n if i==len(prefix)-1:\n ret = True\n ... | [
"0.6720833",
"0.6547649",
"0.6462857",
"0.64407134",
"0.6428188",
"0.6415573",
"0.62809837",
"0.6258059",
"0.61898506",
"0.6167757",
"0.6164689",
"0.6134488",
"0.61225975",
"0.6088009",
"0.60353285",
"0.6033145",
"0.6019844",
"0.59988034",
"0.5941386",
"0.59368086",
"0.591884... | 0.53415996 | 84 |
Updates `PrefixCache` with data from given operator. | def update_with_operator(self, operator: Operator):
if not isinstance(operator, Operator):
raise TypeError(
f"operator expected to be of type `Operator` but got type "
f"{type(operator)}"
)
for prefix in operator.rates.keys():
self._up... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_prefix(self, prefix: str, operator: Operator):\n cached_operator: Optional[Operator] = self.lookup(prefix)\n if cached_operator:\n cached_price = cached_operator.price_for_prefix(prefix)\n if cached_price:\n if operator.has_better_price_for_prefix(pref... | [
"0.7683273",
"0.6553203",
"0.64049375",
"0.52432764",
"0.51866513",
"0.51246953",
"0.5072463",
"0.5072463",
"0.5058224",
"0.50360274",
"0.5033406",
"0.49451602",
"0.49146992",
"0.48742005",
"0.4850131",
"0.4843754",
"0.47898152",
"0.47546908",
"0.47544464",
"0.47422978",
"0.4... | 0.68408626 | 1 |
Given a prefix, returns operator with best price in `PrefixCache` | def lookup(self, prefix: str) -> Optional[Operator]:
return self.data.get(prefix, None) # noqa | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_prefix(self, prefix: str, operator: Operator):\n cached_operator: Optional[Operator] = self.lookup(prefix)\n if cached_operator:\n cached_price = cached_operator.price_for_prefix(prefix)\n if cached_price:\n if operator.has_better_price_for_prefix(pref... | [
"0.7023152",
"0.57442355",
"0.56952125",
"0.5672677",
"0.5581838",
"0.5468779",
"0.5468779",
"0.5460756",
"0.5382311",
"0.5373651",
"0.5297867",
"0.52763575",
"0.5276098",
"0.52464044",
"0.5244506",
"0.52335495",
"0.51884717",
"0.51734436",
"0.51672626",
"0.5148878",
"0.51332... | 0.6808338 | 1 |
map given prefix to operator, overwriting exsisting cache for prefix entry. | def add_prefix(self, prefix: str, operator: Operator):
if not isinstance(operator, Operator):
raise TypeError(
f"`operator` expected to be of type `str` but got type "
f"`{type(operator)}`"
)
if not isinstance(prefix, str):
raise TypeE... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_prefix(self, prefix: str, operator: Operator):\n cached_operator: Optional[Operator] = self.lookup(prefix)\n if cached_operator:\n cached_price = cached_operator.price_for_prefix(prefix)\n if cached_price:\n if operator.has_better_price_for_prefix(pref... | [
"0.74195033",
"0.6135819",
"0.59976494",
"0.56019163",
"0.55027753",
"0.54626215",
"0.5361422",
"0.53344727",
"0.5287824",
"0.5260219",
"0.525283",
"0.5196512",
"0.5168845",
"0.5155864",
"0.5117644",
"0.511022",
"0.508657",
"0.5071373",
"0.5030445",
"0.49908844",
"0.49615845"... | 0.63900906 | 1 |
Return prefix for `phone_number`. Use `lookup` to fetch operator. | def find_prefix(self, phone_number: str) -> Optional[str]:
if not isinstance(phone_number, str):
raise TypeError(
f"`phone_number` expected to be of type `str` "
f"but got type `{type(phone_number)}`"
)
if not phone_number.isdigit():
r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strip_phone_prefix(self, phone_num):\n # FIXME more accurate check\n if phone_num.startswith('+86'):\n return phone_num.replace('+86', '')\n if len(phone_num) != 11:\n return None\n return phone_num",
"def phone_number_organizer(self, key):\n\t\ttry:\n\t\t\tp... | [
"0.6136778",
"0.596885",
"0.586732",
"0.5830663",
"0.58184695",
"0.58184695",
"0.5758761",
"0.5758761",
"0.5729075",
"0.56406903",
"0.56264126",
"0.56246763",
"0.5618058",
"0.5616657",
"0.5613042",
"0.56065995",
"0.5562532",
"0.5562178",
"0.5562178",
"0.5562178",
"0.55411625"... | 0.6957184 | 0 |
Build a `PrefixCache` from a tuple of `Operators`. | def build_cache(klass: "PrefixCache", operators: Tuple[Operator, ...]) -> "PrefixCache":
prefix_cache = klass()
for operator in operators:
prefix_cache.update_with_operator(operator)
return prefix_cache | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buildOperatorCache(ham: Dict[str, Any]) -> None:\n sysLevel = ham[\"circuit\"][\"sys_level\"]\n qubitNum = ham[\"circuit\"][\"qubits\"]\n\n # Generator the operator for all of the drift terms\n for key in ham[\"drift\"]:\n drifts = ham[\"drift\"][key]\n operator = generateOperator(dri... | [
"0.55122936",
"0.5408634",
"0.50833046",
"0.50592685",
"0.49883533",
"0.493835",
"0.4874338",
"0.48739326",
"0.48162144",
"0.47007602",
"0.46888414",
"0.4669612",
"0.46306923",
"0.46295217",
"0.46244913",
"0.46051258",
"0.46014902",
"0.4598839",
"0.45586753",
"0.45210305",
"0... | 0.8065964 | 0 |
The Function sets frequency. | def frequency(self, freq):
self.load.write(f'TRAN:FREQ {freq}') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_frequency(self, f=1e9):\r\n self.f = f",
"def set_Freq(self,freq):\n super(self.__class__, self).setFreq(self, freq)",
"def set_frequency(self, f=1e9):\r\n return self._api.set_frequency(f)",
"def frequency(self, frequency: int):\n self._freq = freq",
"def set_frequency(self... | [
"0.7891979",
"0.7827949",
"0.77956927",
"0.7742743",
"0.77067626",
"0.76778984",
"0.7675751",
"0.7509002",
"0.7471969",
"0.7448706",
"0.7430341",
"0.7418492",
"0.74022794",
"0.73718685",
"0.73407614",
"0.73039263",
"0.7269451",
"0.715289",
"0.71406645",
"0.71391183",
"0.71380... | 0.6118048 | 66 |
The Function sets duty cycle. | def duty_cycle(self,duty_cycle):
self.load.write(f'TRAN:DCYC {duty_cycle}') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_duty_cycle(self, value):\n self._mixer.duty_cycle = value",
"def set_dutyCycle(self, newval):\n rest_val = str(int(round(newval * 65536.0, 1)))\n return self._setAttr(\"dutyCycle\", rest_val)",
"def set_duty_cycle(self, pin, dutycycle):\n raise NotImplementedError",
"def l... | [
"0.7792069",
"0.76351213",
"0.74205214",
"0.70004797",
"0.6803659",
"0.6796197",
"0.6733606",
"0.63782656",
"0.631615",
"0.6013694",
"0.6013694",
"0.59704655",
"0.5965173",
"0.5912627",
"0.5794114",
"0.57919294",
"0.57554364",
"0.5750491",
"0.57222706",
"0.5715776",
"0.571364... | 0.6837669 | 4 |
Plots the latest COVID19 status of the country if name is not given then it plots the top10 | def plot(self, context=None):
response = requests.get(self.url).content
table = pd.read_html(response, attrs={"id": "main_table_countries_today"})
df = table[0].fillna(0)
# df.drop(df.index[0], inplace=True) # World
df.drop(["ActiveCases", 'Serious,Critical', 'Serious,Critical'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_country(name, case):\n click.echo(click.style(\n \"Generating Plot....\", fg='cyan', bold='true'))\n plot_time_series.TimeSeriesPloTs.plot_country(case, name)\n click.echo(click.style(\n \"Done....\", fg='green', bold='true'))",
"def plot_country_representation():\n\n # Get all... | [
"0.5984105",
"0.5937387",
"0.5841578",
"0.56083816",
"0.5559453",
"0.54571724",
"0.54451543",
"0.5368691",
"0.5362344",
"0.5346326",
"0.53422797",
"0.52614784",
"0.52426875",
"0.52363175",
"0.5218532",
"0.5201958",
"0.5179261",
"0.51773536",
"0.51521355",
"0.5141482",
"0.5134... | 0.6362874 | 0 |
Create the luis api query url from a csv. Requires the csv to contain the endpoint url, app id and primary key | def get_luis_url(folder: WindowsPath = None) -> str:
if folder is None:
folder = Path.cwd().joinpath('CONFIG')
path = folder.joinpath('luis_keys.csv')
df = pd.read_csv(path, index_col='key')
endpoint = df.loc['endpoint', 'value']
app_id = df.loc['app_id', 'value']
prim... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_urls(csvfile):\n result = []\n with open(csvfile, 'rU') as infile: \n reader = csv.DictReader(infile, dialect=csv.excel,\n fieldnames=['ID','URL','Latitude','Longitude'])\n for row in reader:\n idnum = row['ID']\n url = row['URL']\n ... | [
"0.62318754",
"0.5654017",
"0.5514315",
"0.5352997",
"0.5324021",
"0.5324021",
"0.51919746",
"0.51832896",
"0.5166666",
"0.5166666",
"0.5151871",
"0.5135357",
"0.5133279",
"0.512845",
"0.50671405",
"0.50671405",
"0.5013714",
"0.5009945",
"0.49635574",
"0.4927147",
"0.49232295... | 0.6092655 | 1 |
Remove the duplicate comments based on the comment's semantics, e.g. "I was waiting for 1 hour." and "I waited for 1 hour." are orthographically different, but the semantically the same, so one will be removed | def drop_semantic_duplicates(
student_comments: pd.Series
) -> pd.Series:
# Remove the stopwords and remove punctuation
student_comment_lemmatised_nostopwords_nopunct = (
student_comments
.apply(lambda x: ' '.join(
[token.lemma_
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _RemoveStaleComments(content: str) -> str:\n for match in STALE_GROUP_COMMENT_REGEX.findall(content):\n content = content.replace(match, '')\n\n return content",
"def cleanup_comments(comments):\n clean_comments = []\n\n if comments:\n for comment in comments:\n cleaned_up = sub(... | [
"0.6905965",
"0.64789987",
"0.63036376",
"0.62970257",
"0.62018055",
"0.6140078",
"0.60909677",
"0.60670996",
"0.60540634",
"0.60474616",
"0.60381204",
"0.602201",
"0.59883267",
"0.5974576",
"0.5954461",
"0.59360486",
"0.5890895",
"0.5868616",
"0.58588153",
"0.58458287",
"0.5... | 0.6321834 | 2 |
Sends student comments to the LUIS.ai API in batches and saves the intemediate results into the OUTPUT folder | def request_api(
student_comments: pd.Series,
url: str,
chunk_size: int = 50
) -> pd.Series:
for i, chunk in enumerate(chunks(student_comments, chunk_size)):
print(f'Processing batch {i} of size {len(chunk)}')
response = chunk.apply(lambda x: ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\r\n \r\n data_dir = Path.cwd().joinpath('OUTPUT')\r\n config_dir = Path.cwd().joinpath('CONFIG')\r\n \r\n # Load deduplicated comments\r\n data = utils.load(data_dir, 'student_comment_deduplicated')\r\n \r\n # Get the luis API url\r\n with open(config_dir.joinpath('luis_url.t... | [
"0.62581986",
"0.56374973",
"0.55773365",
"0.5480655",
"0.54645246",
"0.5297529",
"0.52373004",
"0.52115196",
"0.5190923",
"0.5185241",
"0.51840454",
"0.5158123",
"0.51344043",
"0.5082114",
"0.50543535",
"0.50502497",
"0.50455976",
"0.5043005",
"0.5015503",
"0.49875677",
"0.4... | 0.6514273 | 0 |
Yield successive nsized chunks from l. | def chunks(array, size: int):
for i in range(0, len(array), size):
yield array[i:i + size] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _chunk(self, l, n):\n for i in range(0, len(l) + 1, n):\n yield l[i:i + n]",
"def chunks(self, l, n):\n for i in range(0, len(l), n):\n yield l[i:i + n]",
"def __chunks(l, n):\n for i in range(0, len(l), n):\n yield l[i:i + n]",
"def get_chunks(self, ... | [
"0.8038813",
"0.79248375",
"0.7923423",
"0.7885103",
"0.78773195",
"0.7815877",
"0.77655786",
"0.77556044",
"0.77441615",
"0.7731815",
"0.77288336",
"0.772473",
"0.77028215",
"0.76889825",
"0.76889825",
"0.7664208",
"0.76570904",
"0.7655856",
"0.7655856",
"0.76458347",
"0.764... | 0.0 | -1 |
Load the batch response from calling the LUIS.ai API. The files are in pickle format. keyword arguments directory the directory that contains the pickle files | def load_pickles(directory: str) -> pd.Series:
concat_series = pd.Series()
for path in Path(directory).glob('luis_result_[0-9][0-9][0-9][0-9]*'):
luis_series = pickle.load(open(path, 'rb'))
concat_series = pd.concat([concat_series, luis_series])
return concat_series.apply(lambda x... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(dirpath):\n\n batch = Pickler.load(join(dirpath, 'batch.pkl'))\n\n # load annotator\n if exists(join(dirpath, 'annotation.json')):\n annotator = Annotation.load(dirpath)\n batch.annotator = annotator\n\n return batch",
"def LoadBatch(filename):",
"def ... | [
"0.6535854",
"0.6307839",
"0.6166223",
"0.59658235",
"0.5965553",
"0.58758074",
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"0.5625798",
"0.5621212",
"0.5580411",
"0.557783",
"0.5567398",
"0.5554359",
"0.5541838... | 0.54686445 | 23 |
Merging the results from the luis response with the lemmatised comments. | def merge_comments(df1: pd.DataFrame, df2: pd.DataFrame, out_dir: str) -> pd.DataFrame:
nlp = spacy.load('en_core_web_lg')
df1 = pd.DataFrame(load_pickles(out_dir))
df1.columns = ['response']
df1['student_comment_apostrophe'] = df1.response.apply(lambda x: x['query'] if x is not None else No... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _proc(dat):\n def lemma(text):\n lemmatizer = WordNetLemmatizer()\n w_tokenizer = WhitespaceTokenizer()\n return [lemmatizer.lemmatize(w) for w in w_tokenizer.tokenize(text)]\n\n dat['text_lemmatized'] = dat['clean_comments'].apply(lemma)\n dat['text_lemmat... | [
"0.5736245",
"0.57142824",
"0.5411729",
"0.540935",
"0.54072136",
"0.5370507",
"0.5283631",
"0.5237631",
"0.5222202",
"0.52172023",
"0.52092206",
"0.5151312",
"0.5109762",
"0.5085416",
"0.5063539",
"0.5046727",
"0.49920905",
"0.49881884",
"0.4975726",
"0.49670464",
"0.4951924... | 0.6131447 | 0 |
Extract the element, e.g. topScoringIntent, from the LUIS.api json response s the pd.Series of the unravelled json response element the elements, such as "sentiment" or "intent" | def get_luis_element(s: pd.Series, element: str) -> pd.Series:
result = 0
if element == 'intent':
result = s.apply(lambda x: x['topScoringIntent']['intent'])
elif element == 'entities':
result = s.apply(lambda x: x['entities'])
elif element == 'sentiment':
valence = s.appl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_response(self, response):\n\t\tself.context = response['context']\n\t\ttext = response['output']['text']\n\t\tintents = response['intents'] #is a list, should filter\n\t\tif len(intents) > 0:\n\t\t\tintent = intents[0]['intent'] #get the intent of the message\n\t\telse:\n\t\t\tintent = \"\"\n\t\t\t\n\t\t... | [
"0.5717776",
"0.55163515",
"0.5311165",
"0.52464586",
"0.5228804",
"0.52271056",
"0.52187705",
"0.52037966",
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"0.49437866",
"0.4942395",
"0.4940954",
"0.49293894",
"0.4871135",
"0.4847061",
"0.48190... | 0.6376146 | 0 |
Load the deduplicated comments Get the luis url Call the API in batches Aggregate the batched pickled files with 'luis_result' in the filename, Extract the top scoring intent, entities and the sentiment of each row Save each of the three series as separate pickle files | def main():
data_dir = Path.cwd().joinpath('OUTPUT')
config_dir = Path.cwd().joinpath('CONFIG')
# Load deduplicated comments
data = utils.load(data_dir, 'student_comment_deduplicated')
# Get the luis API url
with open(config_dir.joinpath('luis_url.txt'), 'r') as f:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def request_api(\r\n student_comments: pd.Series, \r\n url: str, \r\n chunk_size: int = 50\r\n ) -> pd.Series:\r\n \r\n for i, chunk in enumerate(chunks(student_comments, chunk_size)):\r\n print(f'Processing batch {i} of size {len(chunk)}')\r\n \r\n response =... | [
"0.6615366",
"0.5831273",
"0.57000536",
"0.56970143",
"0.5684071",
"0.5569833",
"0.5552695",
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"0.54343843",
"0.54299",
"0.54090136",
"0.5354556",
"0.53511345",
"0.534945... | 0.59263885 | 1 |
Create table from csv file to input into database. | def create_table(engine, csv_filename, tablename):
# Read csv file and changes all column names to be lowercase
csv_df = pd.read_csv(f'./data/{csv_filename}.csv')
csv_df.columns = [c.lower() for c in csv_df.columns]
# Change date types to datetime
todateformat = []
for c in csv_df.columns:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_table_from_csv (sqlite_db_file):\n files = [f for f in os.listdir(os.curdir) if f.endswith(\".csv\")]\n name_df = [re.findall('(.*)\\.csv',f)[0] for f in files ]\n engine = create_engine('sqlite:///' + sqlite_db_file)\n for n, f_n in zip(name_df, files):\n try:\n df = pd.re... | [
"0.78636694",
"0.766007",
"0.74412394",
"0.7296567",
"0.72548956",
"0.71277964",
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"0.6639408",
"0.6621663",
"0.66060764",
"0.65959024",
"0.659466... | 0.77351403 | 1 |
Calculates the linear function of multiple values at once. | def calculate_batch(characteristic_values: pd.DataFrame, functional_model: Dict[str, List[float]],
means: Dict[str, float]) -> pd.DataFrame:
coef_names = list(functional_model.keys())
n_points = len(functional_model[coef_names[0]])
result = pd.DataFrame(np.empty((len(characteristic_value... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linear_function(x, y):\n\n return x + y / 2.",
"def linear(self, X):\n return X",
"def linear(force_zero=None, **kwargs):\n if force_zero is None and len(kwargs) == 0:\n # noinspection PyUnusedLocal\n def lf(x, params, const_list, const_dict):\n a, b = params[0:2]\n ... | [
"0.6685386",
"0.6545232",
"0.6425038",
"0.63650846",
"0.63128704",
"0.62751406",
"0.6245273",
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"0.5876786",
"0.58201665",
"0.5805799",
"0.5792677",
"0.578401",
"0.5777201... | 0.0 | -1 |
Calculates the linear function for a single item. | def calculate(characteristic_values: Component, functional_model: Dict[str, List[float]],
means: Dict[str, float]) -> List[float]:
coef_names = list(functional_model.keys())
n_points = len(functional_model[coef_names[0]])
# make means the same order as characteristic values.
means = np.ar... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linear(self, X):\n return X",
"def linear_function(x, y):\n\n return x + y / 2.",
"def linear(force_zero=None, **kwargs):\n if force_zero is None and len(kwargs) == 0:\n # noinspection PyUnusedLocal\n def lf(x, params, const_list, const_dict):\n a, b = params[0:2]\n ... | [
"0.6771267",
"0.63907444",
"0.6290557",
"0.6165682",
"0.6144242",
"0.611005",
"0.6104654",
"0.60972625",
"0.60318756",
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"0.5821024",
"0.5786686",
"0.5786686",
"0.5750675",
"0.5737204",
"0.573677",
"0.57302845",... | 0.0 | -1 |
Return a list of cleaned word tokens from the raw review | def review_to_words( review ):
#Remove any HTML tags and convert to lower case
review_text = BeautifulSoup(review).get_text().lower()
#Replace smiliey and frown faces, ! and ? with coded word SM{int} in case these are valuable
review_text=re.sub("(:\))",r' SM1',review_text)
review_text=re.sub("(:\(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tokenize(review):\n\n token = strip_multiple_whitespaces(strip_punctuation(review))\n return [token.split() for token in simple_preprocess(token) if token not in STOPWORDS]",
"def review_to_wordlist(review):\n\n words = review.lower().split()\n words = [w for w in words]\n return(words)",
"d... | [
"0.7750979",
"0.7179433",
"0.717803",
"0.7157436",
"0.7142787",
"0.6961613",
"0.69389796",
"0.67973894",
"0.67853844",
"0.6727003",
"0.66734666",
"0.6642047",
"0.66097176",
"0.6597971",
"0.6580113",
"0.65595573",
"0.6555122",
"0.6553047",
"0.65236735",
"0.65034825",
"0.650111... | 0.6859479 | 7 |
Returns args including etcd endpoint and certificates if necessary. As dcosetcdctl and etcdctl share the same arguments, such as endpoints, ever considering the certificates involved, we group these arguments to generate the basic items to execute either etcdctl or dcosetcdctl | def get_etcdctl_with_base_args(
cert_type: str = "root",
endpoint_ip: str = LOCAL_ETCD_ENDPOINT_IP,
) -> List[str]:
return [ETCDCTL_PATH, "--endpoints=http://{}:2379".format(endpoint_ip)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_args():\n\n parser = argparse.ArgumentParser(description=\"Get DC, Clusters, Hosts and VM in JSON.\")\n parser.add_argument('-H', '--host', nargs=1, required=True, help='The vCenter to connect to',\n dest='host', type=str)\n parser.add_argument('-p', '--password', nargs=1, r... | [
"0.5863232",
"0.57427984",
"0.57362777",
"0.5711436",
"0.5671684",
"0.5665155",
"0.56511855",
"0.5629644",
"0.5586884",
"0.55581784",
"0.5516893",
"0.5515851",
"0.5509923",
"0.5477983",
"0.5474129",
"0.546462",
"0.5454525",
"0.54045254",
"0.5360767",
"0.53578204",
"0.53573877... | 0.7312782 | 0 |
assigns the value to the key. etcd is not exposed outside of the DC/OS cluster,so we have to execute etcdctl inside the DC/OS cluster, on a master in our case. | def put(self, key: str, value: str) -> None:
master = list(self.masters)[0]
etcdctl_with_args = get_etcdctl_with_base_args(endpoint_ip=MASTER_DNS)
etcdctl_with_args += ["put", key, value]
master.run(args=etcdctl_with_args, output=Output.LOG_AND_CAPTURE) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set(self, key, value, cb=None):\n _log.analyze(self.node.id, \"+ CLIENT\", {'key': key, 'value': value})\n self.send(cmd='SET',msg={'key':key, 'value': value}, cb=cb)",
"async def set(self, key, value):\n trace_log(\"PersistantStorage: setting key \", key, \" to value \", value)\n ... | [
"0.6925788",
"0.6801668",
"0.6539463",
"0.6499346",
"0.64941585",
"0.64688677",
"0.6442703",
"0.6442703",
"0.64351684",
"0.6388835",
"0.6360207",
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"0.6296948",
"0.6296282",
"0.62657654",
"0.6252633",
"0.6233048",
"0.62180454"... | 0.7933066 | 0 |
gets the value of the key on given master node | def get_key_from_node(
self,
key: str,
master_node: Node,
) -> str:
etcdctl_with_args = get_etcdctl_with_base_args(
endpoint_ip=str(master_node.private_ip_address))
etcdctl_with_args += ["get", key, "--print-value-only"]
result = master_node.ru... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self, key):\n dkey = digest(key)\n _log.debug(\"Server:get %s\" % base64.b64encode(dkey))\n # if this node has it, return it\n exists, value = self.storage.get(dkey)\n if exists:\n return defer.succeed(value)\n node = Node(dkey)\n nearest = self.p... | [
"0.7126619",
"0.70644236",
"0.700838",
"0.7002999",
"0.69473535",
"0.68663204",
"0.68588144",
"0.68588144",
"0.68452597",
"0.677081",
"0.672349",
"0.6679185",
"0.6676274",
"0.6670105",
"0.663204",
"0.66137815",
"0.66084397",
"0.6582573",
"0.6569191",
"0.65402824",
"0.65248156... | 0.79159415 | 0 |
Create self signed certificate which will be used by the server to establish an ssl connection with the clients | def create_self_signed_cert():
# create a key pair
k = crypto.PKey()
k.generate_key(crypto.TYPE_RSA, 1024)
# create a self-signed cert
cert = crypto.X509()
cert.get_subject().C = "GP"
cert.get_subject().ST = "GRAD_PROJECT"
cert.get_subject().L = "GRAD_PROJECT"
cert.get_subject().OU... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_ssl_certificate() :",
"def create_server_certs_enc():\n global server_keystore, config\n\n same_enc_sign_cert = config[\"config\"][\"same_enc_sign_cert\"]\n if same_enc_sign_cert:\n dn = \"/CN=server certificate RSA\"\n else:\n dn = \"/CN=server certificate encryption RSA\"\n ... | [
"0.74075276",
"0.73961586",
"0.7382436",
"0.71985453",
"0.7109136",
"0.70601207",
"0.68436134",
"0.66454",
"0.6495967",
"0.6444003",
"0.6436761",
"0.64030415",
"0.6257964",
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"0.62173396",
"0.6213609",
"0.6211047",
"0.6204749",
"0.6195145",
"0.60811156",
"0.603894... | 0.70311016 | 6 |
Get the latest version of an artifact from Pulse. | def GetLatest(server_url, project_name, username, password, command, name,
filename, stage):
server = xmlrpclib.ServerProxy(server_url + 'xmlrpc')
token = server.RemoteApi.login(username, password)
# Get the latest 100 builds of the tools.
builds = server.RemoteApi.getLatestBuildsForProjec... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def latest_release_get():\n try:\n return json_response.success({'version': version.latest_version()})\n except version.Error as e:\n return json_response.error(str(e)), 200",
"async def get_latest_version(self, pkg: str) -> Optional[str]:\n return None",
"def latest_version(self):\n... | [
"0.6555443",
"0.6440459",
"0.6415911",
"0.63127923",
"0.62560815",
"0.6184736",
"0.61423135",
"0.6134404",
"0.6098685",
"0.6094916",
"0.60846007",
"0.60613364",
"0.6056806",
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"0.6035648",
"0.60355157",
"0.603404",
"0.5991332",
"0.58877105",
"0.5863567",
"0.5862828... | 0.58272386 | 22 |
Reset Features for states. | def state_reset():
feat_self = get_self_feat(model, data['img'][0])
feat_diff = torch.zeros_like(feat_self)
feat_FAR = torch.tensor([0.]).cuda()
feat_history = torch.zeros([10]).cuda()
return [feat_diff, feat_self, feat_FAR, feat_history] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset_states(self):\n self.model.reset_states()",
"def reset(self):\n self.train_loss.reset_states()\n self.train_accuracy.reset_states()\n self.val_loss.reset_states()\n self.val_accuracy.reset_states()\n self.train_mIoU.reset_states()\n self.val_mIoU.reset_s... | [
"0.7507805",
"0.74202436",
"0.734571",
"0.7177102",
"0.71432304",
"0.71178913",
"0.69675106",
"0.69483036",
"0.69046485",
"0.6899169",
"0.68768305",
"0.6849579",
"0.68468076",
"0.68468076",
"0.68468076",
"0.68468076",
"0.68468076",
"0.68468076",
"0.68468076",
"0.68468076",
"0... | 0.67439723 | 27 |
Separate train or validation annotations to single video annotation. | def separate_annotations():
data_root = '/home/ubuntu/datasets/YT-VIS/'
ann_file = data_root + 'annotations/instances_train_sub.json'
import json
with open(ann_file, 'r') as f:
ann = json.load(f)
# ann['videos'] = ann['videos'][15]
# video_id = [0]
from tqdm import tqdm
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AnnotateVideo(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)",
"def load_annotations(self):\n assert self.ann_file.endswith('.pkl')\n ann_info = hload_pkl(self.ann_file)\n\n video_infos = []\n for i, video_info in enumerate(ann_info):\n ... | [
"0.65230966",
"0.6502994",
"0.6373923",
"0.6358761",
"0.63245064",
"0.62168384",
"0.60723543",
"0.5980911",
"0.5978949",
"0.59094423",
"0.5880484",
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"0.5837445",
"0.5835894",
"0.5835894",
"0.58234775",
"0.58234775",
"0.58179194",
"0.581608",
"0.5808023",
"0.577818... | 0.7296973 | 0 |
Attempts to identify a face in the input image. | def crop_face(image):
gray_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
face_roi_list = face_detector.detectMultiScale(gray_image, scale_factor, min_neighbors)
if len(face_roi_list) > 0:
(x,y,w,h) = face_roi_list[0]
return gray_image[y:y+h,x:x+w]
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __detect_face(self, img):\n gray = cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY)\n return self.detector(gray, 1)",
"def detect_face(self, img):\n #convert the test image to gray image as opencv face detector expects gray images\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n ... | [
"0.75916886",
"0.75504404",
"0.7519903",
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"0.72894216",
"0.7179222",
"0.7140634",
"0.7088855",
"0.70069605",
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"0.690115",
"0.6891117",
"0.68858343",
"0.6875561",
"0.6819587",
"0.68134964",
"0.6775174",... | 0.62767583 | 65 |
Preprocess requests by attempting to extract face image, and transforming to fit the model's input Returns list of NDArray Processed images in the model's expected input shape | def preprocess(self, request):
img_list = []
input_shape = self.signature['inputs'][0]['data_shape']
[height, width] = input_shape[2:]
param_name = self.signature['inputs'][0]['data_name']
# Iterate over all input images provided with the request, transform and append for infere... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess(self, request):\n img_list = []\n param_name = self.signature['inputs'][0]['data_name']\n input_shape = self.signature['inputs'][0]['data_shape']\n\n for idx, data in enumerate(request):\n img = data.get(param_name)\n if img is None:\n ... | [
"0.73400545",
"0.6653662",
"0.65381366",
"0.65293646",
"0.6363785",
"0.6331957",
"0.6321112",
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"0.6102832",
"0.60749215",
"0.6074793",
"0.6067623",
"0.6028816",
"0.6026498",
"0.602559... | 0.81999916 | 0 |
Postprocess inference result to normalize probabilities and render with labels | def postprocess(self, data):
if self.error is not None:
return [self.error]
# Iterating over inference results to render the normalized probabilities
response = []
for inference_result in data:
softmax_result = inference_result.softmax().asnumpy()
for... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inference_preprocess(self):\n return",
"def post(self):\n result = {'status': 'error'}\n\n args = input_parser.parse_args()\n input_data = args['image'].read()\n image = self.model_wrapper._read_image(input_data)\n preds = self.model_wrapper._predict(image)\n\n ... | [
"0.6822541",
"0.6789945",
"0.6525151",
"0.6503865",
"0.635317",
"0.63490814",
"0.63463706",
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"0.61715907",
"0.6155044",
"0.61538804",
"0.6099723",
"0.6094014",
"0.60685134"... | 0.7199529 | 0 |
Entry point for the service, called by MMS for every incoming inference request | def handle(data, context):
# Lazy initialization, so that we preserve resources until model is actually needed
if not _service.initialized:
_service.initialize(context)
if data is None:
return None
return _service.handle(data, context) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n # Grab command line args\n args = build_argparser().parse_args()\n #print(\"finished argparser\")\n\n # Connect to the MQTT server\n client = connect_mqtt()\n #print(\"finished mqtt connect\")\n\n # Perform inference on the input stream\n infer_on_stream(args, client)",
"def... | [
"0.67828876",
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"0.64908534",
"0.64681673",
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"0.61480117",
"0.61341393",
"0.6101827",
"0.60902387",
"0.6068811",
"0.60454684",
"0.6... | 0.0 | -1 |
give the parameters for 3 bandits an epsilon function to use for epsilongreedy strategy and N, the number of times we play the game | def run_experiment(m1,m2,m3,N):
bandits = [Bandit(m1),Bandit(m2),Bandit(m3)]
#for PLOTTING ONLY
#this is not needed for functioning
data = np.empty(N)
#play game N times
for i in range(N):
j = np.argmax([b.mean for b in bandits])
#pull the one we chose
x = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def epsilon_greedy_probs(self, nA, Q_s, i_count, eps=None):\r\n epsilon = 1.0 / i_count\r\n if eps is not None:\r\n epsilon = eps\r\n \r\n policy_s = np.ones(nA) * epsilon / nA\r\n policy_s[np.argmax(Q_s)] = 1 - epsilon + (epsilon / nA)\r\n return policy_s",
"... | [
"0.65078354",
"0.6485118",
"0.6248076",
"0.623976",
"0.62300354",
"0.6183765",
"0.6165985",
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"0.58986133",
"0.58937275",
"0.5883711",
"0.587728",
"0.5865441",
"0.5847683",
"0.584767... | 0.60085034 | 12 |
Get Mapbox access token from arg or environment | def _get_token(token=None):
if token is not None:
return token
else:
return os.environ.get("MAPBOX_ACCESS_TOKEN") or os.environ.get(
"MapboxAccessToken"
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def access_token(*args, **kwargs):\n return None",
"def _get_api():\n return os.environ.get(\"MAPBOX_API\", \"https://api.mapbox.com\")",
"def get_access_token() -> str:\n\n gcloud_access_token = (\n subprocess.check_output(\n \"gcloud auth print-access-token\".split(\" \")).decode().rstri... | [
"0.6571186",
"0.641618",
"0.6319432",
"0.6312531",
"0.62517345",
"0.62223494",
"0.61801493",
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"0.61801493",
"0.61801493",
"0.61801493",
"0.61801493",
"0.61801493",
"0.61801493",
"0.61801493",
"0.61801493",
"0.61801493",
"0.61220384",
"0.61139506",
"0.60954535",
"... | 0.7944125 | 0 |
Get Mapbox tileset API base URL from environment | def _get_api():
return os.environ.get("MAPBOX_API", "https://api.mapbox.com") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def BASE_URL():\n BASE_URL = \"http://api.zippopotam.us/\"\n return BASE_URL",
"def get_api_url():\n return \"https://api.basespace.illumina.com/v1pre3\"",
"def get_base_url(self):\n try:\n return self.get_metadata()['api_endpoint']\n except requests.exceptions.RequestExceptio... | [
"0.6906486",
"0.66537344",
"0.63970846",
"0.6344204",
"0.63017035",
"0.62595123",
"0.6243633",
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"0.6018488",
"0.6010978",
"0.59873545",
"0.59861904",
"0.5982116",
"0.5865234"... | 0.6620628 | 2 |
This is the command line interface for the Mapbox Tilesets API. Thanks for joining us. This CLI requires a Mapbox access token. You can either set it in your environment as "MAPBOX_ACCESS_TOKEN" or "MapboxAccessToken" or pass it to each command with the token flag. | def cli(): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n args = argument_parser()\n args.mbtiles_file.close()\n mbtiles_file = args.mbtiles_file.name\n zoom = args.zoom\n tile_x = args.tilex\n tile_y = args.tiley\n mvt_content = run(tile_x, tile_y, zoom, mbtiles_file)\n print(mvt_content, end='')",
"def list(username, verbose, tok... | [
"0.66064084",
"0.6603558",
"0.5632666",
"0.5553295",
"0.54832375",
"0.5439695",
"0.5402188",
"0.54002506",
"0.53913784",
"0.52904177",
"0.52454877",
"0.522602",
"0.5215025",
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"0.5139254",
"0.509903",
"0.50982046",
"0.5059821",
"0.5051243",
"0.4985214",
"0.4961472... | 0.5017632 | 44 |
Create a new tileset with a recipe. $ tilesets create is in the form of username.handle for example "mapbox.neattileset". The handle may only include "" or "_" special characters. | def create(
tileset, recipe, name=None, description=None, privacy=None, token=None, indent=None
):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/{1}?access_token={2}".format(
mapbox_api, tileset, mapbox_token
)
body = {}
body["name"] = name or ""
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_tile(self, name):\n return self.subgrids[name[0:2]].tilesys.create_tile(name)",
"def create_mapset(self, mapset, dbase=None, location=None):\n module = 'g.c.mapset'\n gs.run_command(module, mapset=mapset, dbase=dbase, location=location)",
"def create_mapset(self, mapset, dbase=N... | [
"0.5746558",
"0.56829774",
"0.56782365",
"0.5534749",
"0.54125917",
"0.53344876",
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"0.5093439",
"0.5085827",
"0.50712436",
"0.5016903",
"0.4999963",
"0.49877235",
"0.49808466",
"0.4... | 0.7657994 | 0 |
Publish your tileset. tilesets publish | def publish(tileset, token=None, indent=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/{1}/publish?access_token={2}".format(
mapbox_api, tileset, mapbox_token
)
r = requests.post(url)
if r.status_code == 200:
click.echo(json.dumps(r.json(),... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def publish():\n pass",
"def publish(self):\n return",
"def publish(self, settings, item):\n\n publisher = self.parent\n engine = publisher.engine\n document = item.properties[\"document\"]\n\n path = _document_path(document)\n item.properties[\"upload_path\"] = pat... | [
"0.6580128",
"0.5952823",
"0.5932906",
"0.58998835",
"0.5859718",
"0.56866664",
"0.5659581",
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"0.55645955",
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"0.53910875",
"0.5360681",
"0.5359375",
"0.5341747",
"0.53361744",
"0.5277855",
"0.5274251... | 0.7308318 | 0 |
View the current queue/processing/complete status of your tileset. tilesets status | def status(tileset, token=None, indent=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/{1}/status?access_token={2}".format(
mapbox_api, tileset, mapbox_token
)
r = requests.get(url)
click.echo(json.dumps(r.json(), indent=indent)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def status(self):",
"def thread_status():\n global dataSession\n return jsonify(dict(status=('finished' if len(dataSession) > 1 else 'running')))",
"def report_queue_status(self):\n raise NotImplementedError",
"def update_status(cls):\n for job in cls.query.filter(cls.finished == False):\... | [
"0.6251672",
"0.6249044",
"0.6178998",
"0.61220944",
"0.6083708",
"0.60823643",
"0.601419",
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"0.5989828",
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"0.5891603",
"0.5884071",
"0.58760405",
"0.5856712",
"0.58364433",
"0.58343846",... | 0.6567368 | 0 |
View all jobs for a particular tileset. tilesets jobs | def jobs(tileset, stage, token=None, indent=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/{1}/jobs?access_token={2}".format(
mapbox_api, tileset, mapbox_token
)
if stage:
url = "{0}/tilesets/v1/{1}/jobs?stage={2}&access_token={3}".format(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def all_jobs():\n\n jobs = Job.get_all()\n\n oneoffs = OneOff.get_all()\n\n job = JobView(None, jobs, oneoffs, False, Job.count() > 0)\n\n add_trello_task_links_to_g()\n\n return render_template(\"jobs.template.html\", page_title=\"Jobs\", job_info=job)",
"def job(tileset, job_id, token=None, inde... | [
"0.7152179",
"0.71383125",
"0.6330445",
"0.6263424",
"0.6214478",
"0.6188785",
"0.61780936",
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"0.60901165",
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"0.6017765",
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"0.589214",
"0.5855021",
"0.5803626",
"0.57746786",
"0.57746786",
"0.5773371",
"0.575212... | 0.75485474 | 0 |
View a single job for a particular tileset. tilesets job | def job(tileset, job_id, token=None, indent=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/{1}/jobs/{2}?access_token={3}".format(
mapbox_api, tileset, job_id, mapbox_token
)
r = requests.get(url)
click.echo(json.dumps(r.json(), indent=indent)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def view_job(options, job_name, client):\n if options.show_events:\n return display_events(client.job_events(job_name))\n\n job_content = client.job(job_name)\n return display.DisplayJobs(options).format_job(job_content)",
"async def job_detail(request, job_id=None):\n current_jobs = dagobah._... | [
"0.69182616",
"0.6617653",
"0.63476014",
"0.62747604",
"0.62315136",
"0.60969406",
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"0.58088917",
"0.5790628",
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"0.5540786",
"0.55388176",
"0.5536627",
"0.55236",
"0.55182064",
"0.5472965",
"0.544627... | 0.7312882 | 0 |
List all tilesets for an account. By default the response is a simple list of tileset IDs. If you would like an array of all tileset's information, use the versbose flag. tilests list | def list(username, verbose, token=None, indent=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/{1}?access_token={2}".format(
mapbox_api, username, mapbox_token
)
r = requests.get(url)
if r.status_code == 200:
if verbose:
for tile... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fixture_tile_list():\n return {\n \"version\": 1,\n \"revision\": 1,\n \"timestamp\": \"2018-06-19T23:04:32.442Z\",\n \"timestamp_ms\": 1529449472442,\n \"result_code\": 0,\n \"result\": [\n {\n \"tileType\": \"TILE\",\n \"us... | [
"0.6313657",
"0.5784542",
"0.5680937",
"0.56709623",
"0.5668289",
"0.56286174",
"0.56016797",
"0.55826",
"0.5550821",
"0.5427271",
"0.535748",
"0.5275198",
"0.52157974",
"0.5204004",
"0.5186647",
"0.51531774",
"0.5109934",
"0.51063216",
"0.5066046",
"0.5051925",
"0.50334436",... | 0.7860446 | 0 |
Validate a Recipe JSON document tilesets validaterecipe | def validate_recipe(recipe, token=None, indent=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/validateRecipe?access_token={1}".format(
mapbox_api, mapbox_token
)
with open(recipe) as json_recipe:
recipe_json = json.load(json_recipe)
r ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _validate_recipe(environ, recipe):\n try:\n validate_recipe(recipe, environ)\n except InvalidBagError as exc:\n raise HTTP409('Recipe content is invalid: %s' % exc)",
"def check_recipe(recipe, data_directory=None):\n # check recipe is a dictionary\n if not isinstance(recipe, dict):\... | [
"0.63595736",
"0.60863644",
"0.5877467",
"0.5658971",
"0.544159",
"0.5438701",
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"0.5224074",
"0.5209505",
"0.52058226",
"0.50977165",
"0.5095339",
"0.50936866",
"0.509328... | 0.72382593 | 0 |
View a tileset's recipe JSON tilesets viewrecipe | def view_recipe(tileset, token=None, indent=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/{1}/recipe?access_token={2}".format(
mapbox_api, tileset, mapbox_token
)
r = requests.get(url)
if r.status_code == 200:
click.echo(json.dumps(r.json(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def view_recipe(request, recipe, **_kwargs):\n return render(request, \"deployments/disp_recipe.html\", {\"recipe\": recipe})",
"def recipe(id):\n\n selected_recipe = mongo.db.recipes.find_one({'_id': ObjectId(id)})\n\n # Using create list function to display these sections easier\n display_method = ... | [
"0.6430486",
"0.6278637",
"0.60368425",
"0.60243356",
"0.60141695",
"0.5860817",
"0.585936",
"0.58377564",
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"0.57243955",
"0.5721431",
"0.5719946",
"0.5699056",
"0.5689098",
"0.5674704",
"0.5661595... | 0.8028861 | 0 |
Update a Recipe JSON document for a particular tileset tilesets updaterecipe | def update_recipe(tileset, recipe, token=None, indent=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/{1}/recipe?access_token={2}".format(
mapbox_api, tileset, mapbox_token
)
with open(recipe) as json_recipe:
recipe_json = json.load(json_recipe)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_full_update_recipe(self):\n recipe = sample_recipe()\n recipe.ingredients.create(name='Eggs')\n original_description = recipe.description\n\n payload = {\n 'name': 'Vegan gnocchi',\n 'ingredients': [{'name': 'Vegegg'}]\n }\n url = recipe_deta... | [
"0.6287885",
"0.62047666",
"0.61583877",
"0.6145476",
"0.61356527",
"0.612758",
"0.6121439",
"0.6084628",
"0.5905411",
"0.5889674",
"0.5887126",
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"0.5836796",
"0.5829042",
"0.5811349",
"0.58038753",
"0.57788795",
"0.56087655",
"0.559031... | 0.7319158 | 0 |
Validate your source file. $ tilesets validatesource | def validate_source(features):
click.echo(f"Validating features", err=True)
for feature in features:
utils.validate_geojson(feature)
click.echo("✔ valid") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validateSource(source):\n if not os.path.isfile(source):\n raise ShapeException('Source image not found')\n try:\n if imghdr.what(source) not in VALID_IMAGES:\n raise ShapeException('Source is not a valid image.')\n except TypeError:\n raise ... | [
"0.6696419",
"0.6645326",
"0.6275428",
"0.62696326",
"0.60603267",
"0.6029202",
"0.60171866",
"0.597689",
"0.5961857",
"0.5901952",
"0.5901552",
"0.5900048",
"0.5850723",
"0.5847746",
"0.58432066",
"0.5825613",
"0.5822421",
"0.5814104",
"0.5812179",
"0.5792467",
"0.5773302",
... | 0.62247324 | 4 |
Create/add a tileset source tilesets addsource | def add_source(ctx, username, id, features, no_validation, token=None, indent=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = (
f"{mapbox_api}/tilesets/v1/sources/{username}/{id}?access_token={mapbox_token}"
)
with tempfile.TemporaryFile() as file:
for feature ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _addSourceToTile(self, tile, sourceEntry, corners, scale):\n source = self._sources[sourceEntry['sourcenum']]\n ts = self._openSource(source, sourceEntry['kwargs'])\n # If tile is outside of bounding box, skip it\n bbox = source['bbox']\n if (corners[2][0] <= bbox['left'] or ... | [
"0.63106346",
"0.6242407",
"0.6181852",
"0.5995432",
"0.5959899",
"0.59026515",
"0.5895848",
"0.5837343",
"0.5822718",
"0.5802456",
"0.5723033",
"0.5697263",
"0.56536853",
"0.5642326",
"0.5624862",
"0.5598968",
"0.5473412",
"0.54200363",
"0.54169947",
"0.5408778",
"0.5397525"... | 0.70931864 | 0 |
View a Tileset Source's information tilesets viewsource | def view_source(username, id, token=None, indent=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/sources/{1}/{2}?access_token={3}".format(
mapbox_api, username, id, mapbox_token
)
r = requests.get(url)
if r.status_code == 200:
click.echo(jso... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_sources(username, token=None):\n mapbox_api = _get_api()\n mapbox_token = _get_token(token)\n url = \"{0}/tilesets/v1/sources/{1}?access_token={2}\".format(\n mapbox_api, username, mapbox_token\n )\n r = requests.get(url)\n if r.status_code == 200:\n for source in r.json():... | [
"0.57861507",
"0.5623747",
"0.54790926",
"0.5461085",
"0.544482",
"0.53834367",
"0.5382121",
"0.5329366",
"0.53023386",
"0.5296684",
"0.5284189",
"0.5280753",
"0.5255383",
"0.52418125",
"0.52131957",
"0.5167571",
"0.51532954",
"0.51469177",
"0.51326746",
"0.5106873",
"0.51036... | 0.7161197 | 0 |
Delete a Tileset Source + all of its files. tilesets deletesource | def delete_source(username, id, force, token=None):
if not force:
click.confirm(
"Are you sure you want to delete {0} {1}?".format(username, id), abort=True
)
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/sources/{1}/{2}?access_token={3}".for... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete(self, source):\n _source = self._source_prefix+source\n assert _source in self.cache.keys()\n del self.cache[_source]",
"def __del__(self):\r\n train_data_sources = list(self._train_data.values())\r\n test_data_sources = list(self._test_data.values())\r\n all_... | [
"0.636035",
"0.6173071",
"0.60790503",
"0.5987599",
"0.5917638",
"0.5863623",
"0.58427405",
"0.58380115",
"0.5836744",
"0.58044493",
"0.5800107",
"0.5758255",
"0.5698515",
"0.56531304",
"0.56498647",
"0.56251085",
"0.55927056",
"0.5576707",
"0.55732995",
"0.55669975",
"0.5549... | 0.6908297 | 0 |
List all Tileset Sources for an account. Response is an unordered array of sources. tilesets listsources | def list_sources(username, token=None):
mapbox_api = _get_api()
mapbox_token = _get_token(token)
url = "{0}/tilesets/v1/sources/{1}?access_token={2}".format(
mapbox_api, username, mapbox_token
)
r = requests.get(url)
if r.status_code == 200:
for source in r.json():
cl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_sources():\n url = base_url + \"sources\"\n params = {\"language\": \"en\"}\n resp = requests.get(url, params=params)\n data = resp.json()\n sources = [src['id'].strip() for src in data['sources']]\n print(\"all the sources:\")\n print(sources)\n return sources",
"def get_sources(... | [
"0.7102343",
"0.70275754",
"0.6670911",
"0.66414875",
"0.6598273",
"0.6418739",
"0.63086843",
"0.624363",
"0.61829776",
"0.61549807",
"0.6057943",
"0.60390824",
"0.60325825",
"0.6032255",
"0.6019853",
"0.600825",
"0.59725916",
"0.5939919",
"0.5932034",
"0.5928629",
"0.5920800... | 0.7821861 | 0 |
Returns a list of tweets matching a certain phrase (hashtag, word, etc.) | def search_tweets(q, count=100, result_type="recent"):
return t.search.tweets(q=q, result_type=result_type, count=count) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter_by_hashtag(tweets: list, hashtag: str) -> list:\n tweets_with_hashtag = {} # findall(): Kui tekstis on rohkem kui üks regulaaravaldisele vastav alamsõne saab kõikide vastete järjendi moodustada funktsiooniga findall()\n pattern = r\"#\\w+\" # \\w : tähed, numbrid, alakriips, + : 1 või rohkem\n ... | [
"0.7142697",
"0.7100457",
"0.6930307",
"0.67667294",
"0.66168964",
"0.6615334",
"0.6612648",
"0.6553347",
"0.653473",
"0.6523465",
"0.6432451",
"0.6315115",
"0.6313982",
"0.6313573",
"0.6260093",
"0.62319624",
"0.6202652",
"0.61971724",
"0.61828244",
"0.6172298",
"0.61443394"... | 0.64697844 | 10 |
Favorites tweets that match a certain phrase (hashtag, word, etc.) | def auto_fav(q, count=5, result_type="recent"):
result = search_tweets(q, count, result_type)
for tweet in result["statuses"]:
try:
# don't favorite your own tweets
if tweet["user"]["screen_name"] == TWITTER_HANDLE:
continue
result = t.favorites.cre... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def keep(tweet):\n text = tweet[\"Text\"].lower()\n hashtags = \" \".join(tweet[\"Hashtags\"]).lower()\n for word in keep_words:\n if word in text:\n # print(\"{} in text {}\".format(word, text))\n return True\n if word in hashtags:\n # print(\"{} in hashtags... | [
"0.69498086",
"0.6639259",
"0.6606243",
"0.64730567",
"0.6468642",
"0.6389704",
"0.62857056",
"0.62474024",
"0.6237758",
"0.6198794",
"0.61410093",
"0.6137775",
"0.6131543",
"0.6126318",
"0.60748243",
"0.60424924",
"0.6040713",
"0.5978543",
"0.59703326",
"0.5945469",
"0.59222... | 0.6507708 | 3 |
Retweets tweets that match a certain phrase (hashtag, word, etc.) | def auto_rt(q, count=1, result_type="recent"):
result = search_tweets(q, count, result_type)
for tweet in result["statuses"]:
try:
# don't retweet your own tweets
if tweet["user"]["screen_name"] == TWITTER_HANDLE:
continue
result = t.statuses.retwee... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def two_word_finder(word1,word2,text):\r\n word1 = word1.lower()\r\n word2 = word2.lower()\r\n text = str(text).lower()\r\n if word1 and word2 in text:\r\n return True #return text to see specific tweets\r\n return False",
"def searchTweets():\n if 'api' not in globals():\n ... | [
"0.7105514",
"0.69805616",
"0.6976142",
"0.6756619",
"0.66342497",
"0.6593964",
"0.6430638",
"0.63529456",
"0.6340623",
"0.62740755",
"0.62429124",
"0.6205182",
"0.6183677",
"0.6167547",
"0.6147273",
"0.61250204",
"0.61221504",
"0.6089658",
"0.60785997",
"0.60453033",
"0.6032... | 0.0 | -1 |
Returns list of users the bot has already followed. | def get_do_not_follow_list():
# make sure the "already followed" file exists
if not os.path.isfile(ALREADY_FOLLOWED_FILE):
with open(ALREADY_FOLLOWED_FILE, "w") as out_file:
out_file.write("")
# read in the list of user IDs that the bot has already followed in the
# past
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_follows(self):\n return [c.id for c in self.conf.follows]",
"def getFollowers():\n followers = []\n next_max_id = True\n while next_max_id:\n # first iteration hack\n if next_max_id is True:\n next_max_id = ''\n\n _ = GetInstagra... | [
"0.69194794",
"0.6886889",
"0.68394905",
"0.6792048",
"0.67707",
"0.6648926",
"0.66218495",
"0.65348476",
"0.65341395",
"0.6531563",
"0.64827514",
"0.6482435",
"0.6460146",
"0.64417636",
"0.64102614",
"0.640211",
"0.63860524",
"0.63545257",
"0.635387",
"0.6332258",
"0.6328119... | 0.6834242 | 3 |
Follows anyone who tweets about a specific phrase (hashtag, word, etc.) | def auto_follow(q, count=1, result_type="recent"):
result = search_tweets(q, count, result_type)
following = set(t.friends.ids(screen_name=TWITTER_HANDLE)["ids"])
do_not_follow = get_do_not_follow_list()
for tweet in result["statuses"]:
try:
if (tweet["user"]["screen_name"] != TWIT... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reply_to_tweets():\n last_seen_id = retrieve_last_seen_id(FILE_NAME)\n mentions = api.mentions_timeline(\n last_seen_id,\n tweet_mode='extended')\n\n for mention in reversed(mentions):\n print(str(mention.id) + ' - ' + mention.full_text, flush=True)\n last_seen_id = mention... | [
"0.7187562",
"0.6514674",
"0.63038933",
"0.62657815",
"0.6236046",
"0.62126845",
"0.61821735",
"0.6114621",
"0.6068988",
"0.6059423",
"0.6033002",
"0.5977076",
"0.59490186",
"0.5944211",
"0.5915049",
"0.5908332",
"0.59074265",
"0.59028906",
"0.58801764",
"0.58654356",
"0.5860... | 0.62884736 | 3 |
Follows the followers of a user | def auto_follow_followers_for_user(user_screen_name, count=5):
following = set(t.friends.ids(screen_name=TWITTER_HANDLE)["ids"])
followers_for_user = set(t.followers.ids(screen_name=user_screen_name)["ids"][:count]);
do_not_follow = get_do_not_follow_list()
for user_id in followers_for_user:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def follow_user(cls, user, following):\r\n pass",
"def follow_user(cls, user, following):\n pass",
"def follow(self, follower, followee):\n pass",
"def follow_followers(self):\n self.logger.log(\"starting follow_followers...\")\n follow = perform_with_ran_delay(self.instagr... | [
"0.84074855",
"0.8322308",
"0.80839336",
"0.79454315",
"0.7784225",
"0.7753276",
"0.7748424",
"0.7748424",
"0.7657068",
"0.758209",
"0.75808793",
"0.75617784",
"0.75339836",
"0.7445854",
"0.7444906",
"0.7435006",
"0.7433624",
"0.7433624",
"0.7433624",
"0.7433624",
"0.7409177"... | 0.70788556 | 47 |
Follows back everyone who's followed you | def auto_follow_followers():
following = set(t.friends.ids(screen_name=TWITTER_HANDLE)["ids"])
followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)["ids"])
not_following_back = followers - following
for user_id in not_following_back:
try:
t.friendships.create(user_id=user_i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def follow(self, follower, followee):\n pass",
"def follow_user(cls, user, following):\r\n pass",
"def author_following(self):\n\t\tpass",
"def user_follow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['followin... | [
"0.7804903",
"0.7517027",
"0.7427908",
"0.7388741",
"0.732171",
"0.7260585",
"0.72381175",
"0.7165132",
"0.7156749",
"0.712556",
"0.7086118",
"0.7050882",
"0.69780487",
"0.6961415",
"0.6956244",
"0.6956244",
"0.6944516",
"0.69404787",
"0.6928964",
"0.69156355",
"0.68406665",
... | 0.7198094 | 7 |
Unfollows everyone who hasn't followed you back | def auto_unfollow_nonfollowers():
following = set(t.friends.ids(screen_name=TWITTER_HANDLE)["ids"])
followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)["ids"])
# put user IDs here that you want to keep following even if they don't
# follow you back
users_keep_following = set([])
not_f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_unfollow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.remove(following)\n db.session.commit()\n return {'unfollowed': True}",
"def unfollow(self, other):\n\t\tif self.follow... | [
"0.82762796",
"0.8009202",
"0.79544",
"0.79389596",
"0.78107613",
"0.7790903",
"0.7778263",
"0.7770787",
"0.7754866",
"0.7753583",
"0.77530307",
"0.77448803",
"0.7620163",
"0.76050144",
"0.7601129",
"0.7597027",
"0.75560814",
"0.7501844",
"0.7492976",
"0.7484456",
"0.74831533... | 0.7816306 | 4 |
Mutes everyone that you are following | def auto_mute_following():
following = set(t.friends.ids(screen_name=TWITTER_HANDLE)["ids"])
muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)["ids"])
not_muted = following - muted
# put user IDs of people you do not want to mute here
users_keep_unmuted = set([])
# mute al... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def follow_reciprocated(self, target):\n if random.randint(1, 1000) == 1: # 1 in 20 are public @replies\n self.tweet_user(target)\n else:\n try:\n self.dm_user(target)\n except:\n pass",
"def author_following(self):\n\t\tpass",
"def f... | [
"0.6377603",
"0.6330849",
"0.6264719",
"0.6097981",
"0.60914946",
"0.60311484",
"0.57444423",
"0.5744262",
"0.57255733",
"0.5723841",
"0.56434613",
"0.5592666",
"0.554689",
"0.55407053",
"0.552391",
"0.55216295",
"0.55041254",
"0.54987043",
"0.5481601",
"0.54815555",
"0.54575... | 0.68102974 | 0 |
Unmutes everyone that you have muted | def auto_unmute():
muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)["ids"])
# put user IDs of people you want to remain muted here
users_keep_muted = set([])
# mute all
for user_id in muted:
if user_id not in users_keep_muted:
t.mutes.users.destroy(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def unmute(self, ctx, user: Redeemed):\n if member == None or member == ctx.message.author:\n await ctx.send(\"You cannot unmute yourself!\")\n return \n await user.remove_roles(discord.utils.get(ctx.guild.roles, name=\"Muted\"))\n await ctx.send(f\"{user.mention}... | [
"0.7793159",
"0.7562203",
"0.7182035",
"0.71422404",
"0.71150947",
"0.70955354",
"0.7093954",
"0.709301",
"0.7084421",
"0.7016341",
"0.7013622",
"0.68202204",
"0.68122566",
"0.67613274",
"0.6734327",
"0.6710592",
"0.665197",
"0.6545975",
"0.6484686",
"0.6455824",
"0.64350253"... | 0.8222266 | 0 |
Generate one random control character. | def make_control_character():
# Add one character made up of one codepoint each from
# (High Surrogates + High Private Use Surrogates) and Low Surrogates.
# We expect each such pair to behave as a single high-codepoint
# character.
controls = ('0000', '001F')
return [unicode_char(char)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_charachter() -> chr:\r\n return chr(int(random.randrange(32, 126, 1)))",
"def random_char():\n return chr(random.randrange(32, 126, 1))",
"def get_random_char():\n return chr(randint(97, 122))",
"def random_char(alph):\n char = alph[rand_generator.randrange(len(alph))]\n return char... | [
"0.78689873",
"0.77449274",
"0.72392774",
"0.713114",
"0.69535005",
"0.68568593",
"0.68473285",
"0.6614242",
"0.6555148",
"0.65427595",
"0.65427595",
"0.6520578",
"0.6508524",
"0.643668",
"0.636383",
"0.63543737",
"0.63341427",
"0.62676865",
"0.6259865",
"0.6254233",
"0.62498... | 0.69967866 | 4 |
Extend `unichr` for all possible Unicode values (n). | def unicode_char(n):
try:
return unichr(n)
except ValueError:
# Generate bytes object packed as int.
bytes_object = struct.pack('i', n)
# Return decoded w/ utf-32 codec.
return bytes_object.decode('utf-32') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def int_to_unichr(codepoint):\n if PY2:\n return unichr(codepoint)\n return chr(codepoint)",
"def make_unicode():\r\n for num in range(300, 320):\r\n yield unichr(num)",
"def safe_unichr(intval):\n try:\n return unichr(intval)\n except ValueError:\n # ValueError: unic... | [
"0.68852764",
"0.6683728",
"0.6391506",
"0.59930366",
"0.586188",
"0.58399165",
"0.58334017",
"0.5817205",
"0.56382495",
"0.5589591",
"0.55534005",
"0.54915816",
"0.5490308",
"0.5472899",
"0.5450932",
"0.5440723",
"0.54287136",
"0.5352844",
"0.53169405",
"0.5304779",
"0.52560... | 0.7051785 | 0 |
Test ``create_engine`` with invalid adapter. | def test_create_engine_no_adapters():
engine = create_engine("shillelagh://")
with pytest.raises(ProgrammingError) as excinfo:
Table("dummy://", MetaData(bind=engine), autoload=True)
assert str(excinfo.value) == "Unsupported table: dummy://" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ensure_engine_exists():\n # Check if engine is present\n if not is_engine_binded():\n print(\"Binding new engine\")\n bind_engine(create_engine(\"sqlite:///:memory:\", echo=True))",
"def test_engine(self):\n config = {\n \"url\": 'sqlite://',\n \"connect_args\... | [
"0.68235004",
"0.66725576",
"0.6549929",
"0.6455361",
"0.62477577",
"0.6210242",
"0.61840785",
"0.61660314",
"0.6095915",
"0.60754657",
"0.60633755",
"0.6048799",
"0.59452164",
"0.5927696",
"0.58780146",
"0.5844836",
"0.5800548",
"0.57547444",
"0.5749329",
"0.57365346",
"0.57... | 0.83949554 | 0 |
init the class object and simply pass either twitter object or tweets plain text to this method. The analysis will return a list indicating the polarity and subjectivity of the tweets. | def analyse(self, tweet):
if (type(tweet) == dict):
text = self.clean_tweet(self.to_text(tweet))
else:
text = self.clean_tweet(tweet)
analysis = TextBlob(text)
polarity = analysis.polarity
subjectivity = analysis.subjectivity
res = []
#... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, tweet_json):\r\n self.tweet = tweet_json\r\n self.date = datetime.datetime.strptime(self.tweet[\"date\"], \"%Y-%m-%dT%H:%M:%S.000Z\")\r\n self.processed = False\r\n self.max_importance = 0\r\n try:\r\n text = re.sub(self.tweet[\"keywords\"][0], '', s... | [
"0.6772969",
"0.66310155",
"0.6570511",
"0.6479291",
"0.64774686",
"0.6471122",
"0.63829994",
"0.63479525",
"0.6342602",
"0.6246067",
"0.6185657",
"0.6146315",
"0.61160105",
"0.6048045",
"0.6047929",
"0.6042262",
"0.6041226",
"0.6035858",
"0.60260266",
"0.60242325",
"0.600587... | 0.74163216 | 0 |
Load a saved model/walker | def load_walker(self, filename=None, state=0):
if filename is not None:
print('Loading Walker: ' + filename + '...')
err, _ = vrep.simxLoadModel(self.client_id, filename, state,
OP_MODE)
if err not in VALID_ERROR_CODES:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(path_to_model):\n pass",
"def load_model(self, path):\n pass",
"def load_model(self):\n pass",
"def load(\n self,\n modelLoadPath\n ):\n pass",
"def load_model(self) -> Any:",
"def __load_model(self):\n loaded = load(self.__file_name)\n... | [
"0.794677",
"0.77330357",
"0.769884",
"0.76180816",
"0.75892407",
"0.74990684",
"0.7497519",
"0.7403218",
"0.7356266",
"0.7322656",
"0.7167664",
"0.7057581",
"0.7057581",
"0.69810086",
"0.6977028",
"0.6965842",
"0.6963033",
"0.6956336",
"0.69005823",
"0.68665224",
"0.68664163... | 0.69034487 | 18 |
Saves to the server side | def save_walker(self, name):
print("Saving Walker {}".format(name))
err, _, _, _, _ = \
vrep.simxCallScriptFunction(self.client_id, "Walker",
vrep.sim_scripttype_childscript,
"saveModel_function",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save():",
"def save():\n pass",
"def save (self):\n pass",
"def save(self):\n\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n ... | [
"0.79372615",
"0.7796793",
"0.75564486",
"0.75108397",
"0.7485289",
"0.7485289",
"0.7485289",
"0.7485289",
"0.7485289",
"0.7459909",
"0.743181",
"0.7222632",
"0.7222632",
"0.7222632",
"0.720784",
"0.71515334",
"0.7129591",
"0.7129591",
"0.7129591",
"0.7122093",
"0.70347965",
... | 0.0 | -1 |
Discrete Variable to Feature Convertor. var value of variable varname name of variable. lims = range of numbers lims = range of discretization. collapse = list with two binary vals. collapse all below lim[0] to lim[0] & collapse all above lim[1] to lim[1] e.g., fdict = discVar2Feature(8, 'positive adjective', lims = [1... | def discVar2Feature( var, varname, lims = [1,3], collapse = [False, False], ctxt = 'Has'):
vals = xrange(lims[0], lims[1]+1)
keystr = ctxt + ' %s ' + varname
fdict = {keystr % val:False for val in vals}
if collapse[0] == True:
if lims[0] > var:
var = lims[0]
#va... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def discVar2FeatureOld( var, varname, lims = [1,5], collapse = [False, False], ctxt = 'contains'):\n nums = ['zero','one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten']\n \n vals = range(lims[0], lims[1]+1) \n \n #init fdict\n fdict = dict() \n for k, val in en... | [
"0.7855145",
"0.5295992",
"0.5193816",
"0.51229674",
"0.5071455",
"0.50275296",
"0.4994465",
"0.49909624",
"0.48956412",
"0.4888899",
"0.48470324",
"0.47712082",
"0.4754599",
"0.4749255",
"0.47370207",
"0.47250566",
"0.46894085",
"0.46833327",
"0.46548498",
"0.4644874",
"0.46... | 0.8257702 | 0 |
Discrete Variable to Feature Convertor. var value of variable varname name of variable. lims = range of numbers lims = range of discretization. collapse = list with two binary vals. collapse all below lim[0] to lim[0] & collapse all above lim[1] to lim[1] e.g., fdict = discVar2Feature(8, 'positive adjective', lims = [1... | def discVar2FeatureOld( var, varname, lims = [1,5], collapse = [False, False], ctxt = 'contains'):
nums = ['zero','one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten']
vals = range(lims[0], lims[1]+1)
#init fdict
fdict = dict()
for k, val in enumerate(vals... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def discVar2Feature( var, varname, lims = [1,3], collapse = [False, False], ctxt = 'Has'):\n \n vals = xrange(lims[0], lims[1]+1) \n \n keystr = ctxt + ' %s ' + varname\n fdict = {keystr % val:False for val in vals} \n\n if collapse[0] == True:\n if lims[0] > var:\n var = lims[... | [
"0.8234406",
"0.52805275",
"0.5167155",
"0.5081879",
"0.50444704",
"0.50157154",
"0.49738747",
"0.49235922",
"0.4897217",
"0.48730886",
"0.48664978",
"0.47522265",
"0.47458404",
"0.47244322",
"0.47193447",
"0.47139582",
"0.47023058",
"0.4691292",
"0.46388435",
"0.46278065",
"... | 0.784609 | 1 |
Check if featureVals contains FKEY This is a check to see if a core feature function has been previously computed. | def haskey(featureVals, fkey):
try:
featureVals[fkey]
except KeyError:
return False
#warn(HASKEYMSG % (fkey))
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_feature_by_keys(service_data=None, service_keys=None, ns_data=None, ns_keys=None):\n\n if service_data and not isinstance(service_data, Exception) and service_keys:\n if _is_keyval_greater_than_value(service_data, service_keys):\n return True\n\n if ns_data and ns_keys:\n ... | [
"0.6286112",
"0.60849124",
"0.5997217",
"0.58951616",
"0.5878267",
"0.5877975",
"0.58187664",
"0.57946813",
"0.5696939",
"0.5696939",
"0.564731",
"0.56072927",
"0.55438966",
"0.5499882",
"0.5431996",
"0.5373477",
"0.53706175",
"0.53663695",
"0.536493",
"0.53626704",
"0.531663... | 0.7618906 | 0 |
End detection. described in Eq. (50) of S. Watanabe et al "Hybrid CTC/Attention Architecture for EndtoEnd Speech Recognition" | def end_detect(ended_hyps, i, M=3, d_end=np.log(1 * np.exp(-10))):
if len(ended_hyps) == 0:
return False
count = 0
best_hyp = sorted(ended_hyps, key=lambda x: x["score"], reverse=True)[0]
for m in six.moves.range(M):
# get ended_hyps with their length is i - m
hyp_length = i - m
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def end_detect(ended_hyps, i, M=3, D_end=np.log(1 * np.exp(-10))):\n if len(ended_hyps) == 0:\n return False\n count = 0\n best_hyp = sorted(ended_hyps, key=lambda x: x[\"score\"], reverse=True)[0]\n for m in range(M):\n # get ended_hyps with their length is i - m\n hyp_length = i ... | [
"0.59446865",
"0.5918407",
"0.5820141",
"0.5536501",
"0.54732686",
"0.5456574",
"0.5447737",
"0.541912",
"0.53824407",
"0.53753823",
"0.53732294",
"0.53102255",
"0.5226697",
"0.52063173",
"0.51992583",
"0.5165421",
"0.51476526",
"0.5133361",
"0.5113841",
"0.50879014",
"0.5086... | 0.5958459 | 0 |
Called for every row to allow subclasses to modify rows. | def build_modify_row(self, row_gen, p, source, row):
from xlrd import xldate_as_tuple
from datetime import date
row['year'] = int(source.time)
row['facility_status_date'] = date(*xldate_as_tuple(row['facility_status_date'],row_gen.workbook.datemode)[:3])
row.update(source.row_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setRow(self, row): \n self.row = row",
"def format_row(self, row):\n raise NotImplementedError()",
"def add_row(self, row):\n ...",
"def iter_rows_raw(self, *args):\n\n for row in super().iter_rows_raw(*args):\n row[0] = row[1] # sequential catalog index not rig... | [
"0.70247316",
"0.6973256",
"0.6739629",
"0.66780365",
"0.663705",
"0.65876734",
"0.65684086",
"0.6497517",
"0.64002925",
"0.6395719",
"0.6366707",
"0.63460535",
"0.63453114",
"0.63381773",
"0.6335888",
"0.6279478",
"0.61753815",
"0.6160647",
"0.6157512",
"0.61517364",
"0.6142... | 0.0 | -1 |
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