created_after and tweet.from_user != user:\\n ids.append(str(tweet.id))\\n if ids:\\n print \\\"\\\\n\\\".join(ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad3a8ed8a9cc860672b04d0b56d5889\",\n \"score\": \"0.5839854\",\n \"text\": \"def on_status(self, status):\\n try:\\n if not status.retweeted and status.user.id_str in self.ids:\\n self.tweet_counter += 1\\n self.logger.info(\\\"Tweet counter: %s\\\" % self.tweet_counter)\\n self.logger.info('%s %s: %s' % (status.id, status.user.screen_name, status.text))\\n\\n orig_tweet = status._json\\n # url_struct = status.entities['urls'][0]\\n\\n if 'retweeted_status' in orig_tweet:\\n self.logger.info(\\\"retweeted_status......................\\\")\\n tweet_fnl = orig_tweet['retweeted_status']\\n else:\\n tweet_fnl = orig_tweet\\n if 'extended_tweet' in tweet_fnl:\\n self.logger.info(\\\"extended_tweet......................\\\")\\n urls = tweet_fnl['extended_tweet']['entities']['urls']\\n else:\\n urls = tweet_fnl['entities']['urls']\\n tweet_id = tweet_fnl['id']\\n tweet_screen_name = tweet_fnl['user']['screen_name']\\n if len(urls) == 0:\\n self.logger.info(\\\"Empty url_struct for id %s and user %s.\\\\n\\\" % (tweet_id, tweet_screen_name))\\n return True\\n\\n url_struct = urls[0]\\n url = url_struct['url']\\n article_content, html_b64 = self.parse_article_from_url(url, tweet_id)\\n\\n output = {\\n 'tweet': tweet_fnl,\\n 'text': article_content\\n }\\n html_out = {\\n 'tweet_id': tweet_id,\\n 'tweet_screen_name': tweet_screen_name,\\n 'url': url,\\n 'html_article': html_b64\\n }\\n try:\\n self.output_file_handle.write(json.dumps(output))\\n self.html_file_handle.write(json.dumps(html_out))\\n except Exception as inst:\\n self.logger.info(\\\"Error %s while dumping json.\\\\n\\\" % inst)\\n return True\\n self.output_file_handle.write('\\\\n')\\n self.html_file_handle.write('\\\\n')\\n self.output_file_handle.flush()\\n self.html_file_handle.flush()\\n\\n self.logger.info(\\\"Finished retrieval process for url: %s\\\\n\\\" % url)\\n return True\\n except Exception as inst:\\n self.logger.info(\\\"Error %s while processing the tweet. Skipping.\\\\n\\\" % inst)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2f1eb66420fba7559d88ade5de9a093\",\n \"score\": \"0.5833325\",\n \"text\": \"def index(self):\\n token = self.params.get('oauth_token')\\n self.auth_obj = TwitterOAuth()\\n self.auth_obj.request = self.request\\n self.auth_obj.request.args = self.request.params\\n self.auth_obj.get_authenticated_user(self._post_action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c9330115951b629a494734275972c2\",\n \"score\": \"0.5829845\",\n \"text\": \"def tweet(filenames):\\n\\n while True:\\n chains = make_text(make_chains(open_and_read_file(filenames)))\\n print(chains)\\n print()\\n print(\\\"Would you like to print this tweet? [y/n/q]\\\")\\n user_input = input(\\\"> \\\").lower()\\n print()\\n if user_input == 'q':\\n break\\n elif user_input == 'y':\\n api = twitter.Api(\\n consumer_key=os.environ[\\\"TWITTER_CONSUMER_KEY\\\"],\\n consumer_secret=os.environ[\\\"TWITTER_CONSUMER_SECRET\\\"],\\n access_token_key=os.environ[\\\"TWITTER_ACCESS_TOKEN_KEY\\\"],\\n access_token_secret=os.environ[\\\"TWITTER_ACCESS_TOKEN_SECRET\\\"])\\n\\n print(api.VerifyCredentials())\\n\\n new_tweet = api.PostUpdate(chains)\\n else:\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fccc8798958ba7789177a5c2a4784250\",\n \"score\": \"0.5815736\",\n \"text\": \"def make_tweet(self, request, queryset):\\n auth = tweepy.OAuthHandler(settings.TWITTER_CONSUMER_KEY,\\n settings.TWITTER_CONSUMER_SECRET)\\n auth.set_access_token(settings.TWITTER_ACCESS_KEY,\\n settings.TWITTER_ACCESS_SECRET)\\n api = tweepy.API(auth)\\n for entry in queryset:\\n short_url = entry.short_url\\n message = '%s %s' % (entry.title[:139 - len(short_url)], short_url)\\n api.update_status(status=message)\\n self.message_user(\\n request, _('The selected entries have been tweeted.'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ef1e191c6bef94e504e97536fceb88d\",\n \"score\": \"0.581363\",\n \"text\": \"def twitter_login():\\n api = twitter.Api(consumer_key=CONSUMER_KEY,\\n consumer_secret=CONSUMER_SECRET,\\n access_token_key=ACCESS_TOKEN_KEY,\\n access_token_secret=ACCESS_TOKEN_SECRET)\\n return api\",\n \"title\": \"\"\n },\n {\n \"docid\": \"705787fdb712f4b69942b7076dd5071d\",\n \"score\": \"0.580854\",\n \"text\": \"def tweet(client, target, origin, message):\\n service.storage_for(client.bot).api.statuses.update(status=message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a3472b64c707cf9c5a3e58261813ab\",\n \"score\": \"0.58006686\",\n \"text\": \"def signIntoTwitter():\\n print \\\"Signing you in...\\\"\\n #authorize ourselves using OAuth by just passing the ckey csecret\\n auth = OAuthHandler(ckey, csecret)\\n auth.set_access_token(atoken, asecret)\\n api = API(auth)\\n my = api.me()\\n\\n print 'Hello ',my.name\\n print 'Twitter Friends: ',my.friends_count\\n\\n return api\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11d3439202150094c7fa62b6559dfc6b\",\n \"score\": \"0.5795763\",\n \"text\": \"def call_twitter_api():\\n if consumerKey and consumerSecret and accessToken and accessTokenSecret:\\n auth = tweepy.OAuthHandler(consumerKey, consumerSecret)\\n auth.set_access_token(accessToken, accessTokenSecret)\\n api = tweepy.API(auth)\\n\\n resp = get_twitter_response(api)\\n trend_list = extract_twitter_trends(resp)\\n\\n return trend_list[:10]\\n else:\\n print('Missing OAuth key or token')\\n raise ValueError('Missing OAuth key or token.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c3b72fff8bb248e941f4041f174b5f0\",\n \"score\": \"0.5789365\",\n \"text\": \"def postTweet(city):\\n try:\\n status = twitterApi.PostUpdate(city + \\\" niggas? They trained to go.\\\")\\n\\n except twitter.error.TwitterError as e:\\n print('There was an error: ' + e.message[0]['message'])\\n\\n else:\\n print(\\\"%s just posted: %s\\\" % (status.user.name, status.text))\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aae4e64bfb478b056b8d549d34343240\",\n \"score\": \"0.577743\",\n \"text\": \"async def retweet(self, tweet: str = \\\"\\\"):\\n if tweet == \\\"\\\":\\n return\\n elif not tweet.isnumeric():\\n split = tweet.split('/')\\n rt = split[-1]\\n if not rt.isnumeric():\\n rt = split[-2]\\n else:\\n rt = tweet\\n url = 'https://api.twitter.com/1.1/statuses/retweet/' + rt + '.json'\\n t_params = self._generate_parameters()\\n t_header = self._generate_header('post', url, t_params)\\n # enc_msg = self._percent_enc(message)\\n async with session.post(\\n url, data={}, headers=t_header) as r:\\n json = await r.json()\\n if r.status != 200:\\n await self.bot.say(json['errors'][0]['message'])\\n return\\n await self.bot.say('retweeted')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6053569904aa9d1692ad166dcdd2998c\",\n \"score\": \"0.57762825\",\n \"text\": \"def setup_twitter():\\n if isinstance(env.twitter_period, int):\\n srv, ssn, acn = get_webf_session()\\n srv.create_cronjob(ssn, \\\"*/%s * * * * %s poll_twitter\\\" % (\\n env.twitter_period, env.manage))\\n manage(\\\"poll_twitter\\\")\\n print(\\\"New cronjob. Twitter will be polled every %s minutes. \\\"\\n \\\"Please make sure you have configured your Twitter credentials \\\"\\n \\\"in your site settings.\\\" % env.twitter_period)\\n else:\\n abort(\\\"TWITTER_PERIOD not set correctly in deployment settings.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ffd2a42fd8f25b239e606a072d4a2a6\",\n \"score\": \"0.57716495\",\n \"text\": \"def test_get_twitter_link(self):\\n self.get_using_valid_provider(\\\"twitter\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f32351e4331d0a8a4d3aa20dd96e49a\",\n \"score\": \"0.5765948\",\n \"text\": \"def fetch_tweets(self):\\n\\n if not self._active:\\n self._active = True\\n\\n QtCore.QTimer.singleShot(0, self.ask_twitter)\\n else:\\n self._active = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09034cb6dbe9979d5f34e0819c5ab338\",\n \"score\": \"0.5765622\",\n \"text\": \"def init_api():\\n global twitter\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_token_secret)\\n twitter = tweepy.API(auth)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"870d5f85402055bcb7a991c2f6b343b5\",\n \"score\": \"0.5763559\",\n \"text\": \"def send_my_tweet(my_tweet_text, local_screenshot):\\n TWITTER.update_with_media(local_screenshot, status=my_tweet_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2eb64fa2fd2b873c549827908dfa6bd\",\n \"score\": \"0.5761442\",\n \"text\": \"def on_status(self, status):\\n # get each tweet's screen_name, text and location\\n tweet_data = get_tweet_content(status, location=True) \\n\\n # ignore retweets and tweets that do not contain the topic\\n if (tweet_data['text'].startswith('RT') or\\n self.topic.lower() not in tweet_data['text'].lower()):\\n return\\n\\n self.counts_dict['total_tweets'] += 1 # original tweet\\n\\n # ignore tweets with no location \\n if not status.user.location: \\n return\\n\\n self.counts_dict['locations'] += 1 # tweet with location\\n self.tweets_list.append(tweet_data) # store the tweet\\n print(f'{status.user.screen_name}: {tweet_data[\\\"text\\\"]}\\\\n')\\n\\n # if TWEET_LIMIT is reached, return False to terminate streaming\\n return self.counts_dict['locations'] < self.TWEET_LIMIT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e033c0d4897b500a7feec7a6de4d7d0\",\n \"score\": \"0.57400143\",\n \"text\": \"def handle_tweet(self, data):\\n # self.nb_tweet += 1\\n data = data.decode('utf-8')\\n self.buffer += data\\n if data.endswith('\\\\r\\\\n') and self.buffer.strip():\\n try:\\n message = json.loads(self.buffer)\\n self.buffer = ''\\n\\n if message.get('limit'):\\n pass\\n # logger.warning('Rate limiting caused us to miss %s tweets' % (message['limit'].get('track')))\\n elif message.get('disconnect'):\\n pass\\n logger.warning('Got disconnected'.format(message['disconnected'].get('reason')))\\n raise Exception('Got disconnect: %s' % message['disconnect'].get('reason'))\\n elif message.get('warning'):\\n logger.warning('Got warning: {}'.format(message['warning'].get('message')))\\n pass\\n else:\\n print(message)\\n # self.process_tweet(message)\\n except ValueError:\\n pass\\n\\n if datetime.datetime.now() - self.last_run > datetime.timedelta(seconds=120):\\n raise TimeOutTweet()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8466e42c4def14db404facfbaf5821b6\",\n \"score\": \"0.573342\",\n \"text\": \"def handle_webhook():\\n webhook_json = request.get_json()\\n yext_account_id = webhook_json['meta']['appSpecificAccountId']\\n name = webhook_json['location']['locationName']\\n twitter_access_token = datastore.get_twitter_oauth_token(yext_account_id)\\n r = twitter.post('statuses/update.json',\\n data={\\n 'status': '%s has changed their location data on Yext!' % (name)\\n },\\n token=twitter_access_token)\\n return 'Twitter Response: %s' % (r.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae5a9df16d5ed6feed9a90d5530b2b1\",\n \"score\": \"0.5727241\",\n \"text\": \"def handler(event, context):\\n send_tweet(random.choice(potential_tweets))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b5b344c9795b66c03449ccf7fb79cfb\",\n \"score\": \"0.5724524\",\n \"text\": \"def search(self):\\n consumer_key, consumer_secret, oauth_secret, oauth_token_secret = tweet.get_config_parameters()\\n\\n oauthc = OAuthClient(consumer_key, consumer_secret)\\n new_token = oauth.Token(oauth_secret, oauth_token_secret)\\n client = oauth.Client(oauthc.consumer, new_token)\\n response = client.request(self.__query, method='GET')\\n\\n json_dict = json.loads(response[1].decode())\\n statuses = json_dict['statuses']\\n for status in statuses:\\n print(\\\"User: {} said: {} at: {}\\\".format(status['user']['screen_name'],\\n status['text'],\\n status['created_at']))\\n print('=' * 80)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e32ab49b3c5e1f00e5e82e60080d8d10\",\n \"score\": \"0.5713354\",\n \"text\": \"def _post_action(self, user):\\n if user:\\n d = {'type':TwitterOAuth.TYPE,\\n 'nickname':user.get('username', ''),\\n 'email':'',\\n 'userid':user.get('id', ''),\\n 'realname':user.get('name', ''),\\n 'icon_url':user.get('profile_image_url', ''),\\n }\\n token = user.get('access_token', '')\\n if token:\\n if token.get('secret', '') and token.get('key', ''):\\n d['access_secret'] = token.get('secret', '')\\n d['access_key'] = token.get('key', '')\\n memcache.set(self.cookies.get(OAUTH_ACCESS_TOKEN_COOKIE),\\n d, namespace = TWITTER_NAMESPACE, time = EXPIRE)\\n rurl = self.session.get('referer', '')\\n if rurl:\\n # clear 'referer' key in session object.\\n del self.session['referer']\\n self.session.put()\\n self.redirect(rurl)\\n else:\\n self.redirect('/')\\n\\n self.render('blank')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8796c5371cd1a71eea3c44a48556e713\",\n \"score\": \"0.5708267\",\n \"text\": \"def run(self):\\n twitter_userstream = twitter.TwitterStream(\\n auth=self.oauth,\\n domain='userstream.twitter.com')\\n for msg in twitter_userstream.user():\\n if not self.run:\\n break\\n print ('Incoming Twitter stream message:')\\n print ('-' * 72)\\n pprint.pprint(msg)\\n print ('-' * 72)\\n if 'text' not in msg:\\n # Not a status update, so skip this...\\n continue\\n self.send_message(u'_Received tweet from @%s:_\\\\n%s' % (\\n msg['user']['screen_name'],\\n msg['text']),\\n parse_mode='Markdown')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d6eebf51a992868e1d11cd69bdd4c7c\",\n \"score\": \"0.5707768\",\n \"text\": \"def get_twitter_oauth_token():\\n return keys['oauth_token']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3028d0e41b7665ff91384bbe021bd1cd\",\n \"score\": \"0.57071304\",\n \"text\": \"def twitter_post(username,password,message):\\n import urllib, urllib2, base64, gluon.contrib.simplejson\\n args = urllib.urlencode([('status',message)])\\n headers={}\\n headers['Authorization'] = 'Basic '+ base64.b64encode(username + ':' + password)\\n req = urllib2.Request('http://twitter.com/statuses/update.json', args, headers)\\n try:\\n return gluon.contrib.simplejson.loads(urllib2.urlopen(req).read())\\n except urllib2.HTTPError, e:\\n return e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6828a2b035992e86066a077ad9bb6ca\",\n \"score\": \"0.5695469\",\n \"text\": \"def go(self, settings, command):\\n bot = TwitterBot(settings)\\n\\n result = 1\\n if command == 'post_message':\\n result = bot.post_message()\\n elif command == 'reply_to_mentions':\\n result = bot.reply_to_mentions()\\n else:\\n print(\\\"Command must be either 'post_message' or 'reply_to_mentions'\\\")\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cf7a2491b856ffaa854929fd97d798d\",\n \"score\": \"0.5693171\",\n \"text\": \"def get_twitter():\\n #twitter authorization\\n consumer_key = 'rUloSGBGvaS4FGKqVWMRZOfly'\\n consumer_secret = 'gshJohkZqPnMk2StyBPEnU03ZzyYuhaTwReGp2YAkoqKUH9JfK'\\n access_token = '428180671-oyVOMEOw77UYpTRCaHs4LZxd8o3PlAiYe0OPNaU5'\\n access_token_secret = 'UNNQWBVFPFw5OEm2ChKbMkBqYXwWOx3yxWuFrLsJKIBhn'\\n return TwitterAPI(consumer_key, consumer_secret, access_token, access_token_secret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f57c58f1f488b8e097dabdf7d7a5f838\",\n \"score\": \"0.56830376\",\n \"text\": \"def twitter_request(self, path, access_token, callback = None,\\n post_args = None, **args):\\n api = self.auth_obj\\n if not api:\\n raise ValueError((\\\"auth_obj is None. You must do \\\"\\n \\\"oauth authentication before sending request.\\\"))\\n if not callback:\\n callback = self._dummy_callback\\n api.twitter_request(\\n path,\\n post_args = post_args,\\n access_token = access_token,\\n callback = callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e3e393cbab8deddef2b1d047f645908\",\n \"score\": \"0.5679568\",\n \"text\": \"def get_timeline(self, timeline):\\n\\t\\tif len(sys.argv) == 3:\\n\\t\\t\\tnumber_of_tweets = str(sys.argv[2])\\n\\t\\telse:\\n\\t\\t\\tnumber_of_tweets = str(10)\\n\\n\\t\\ttry:\\n\\t\\t\\tdata = urllib2.urlopen('http://twitter.com/statuses/' \\\\\\n\\t\\t\\t+ timeline + '.json?count=' + number_of_tweets).read()\\n\\n\\t\\texcept urllib2.HTTPError, e:\\n\\t\\t\\tprint e.code\\n\\t\\t\\tprint e.read()\\n\\t\\t\\tsys.exit(1)\\n\\n\\t\\tself.print_tweets(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a2f2c786bc60ac34b588d0a55917566\",\n \"score\": \"0.5678457\",\n \"text\": \"def fetchTweets(queryTopic,twitter):\\r\\n \\r\\n raw_data = twitter.search(q=str(queryTopic), count= 10, lang='en')\\r\\n\\r\\n tweets = []\\r\\n\\r\\n #search through JSON data and extract the tweets only.\\r\\n for tweet in raw_data['statuses']:\\r\\n tweets.append((tweet['text']).encode('ascii', 'ignore'))\\r\\n \\r\\n \\r\\n for i in range(0,len(tweets)):\\r\\n #removing all links, because really its just gonna mess up topic modeling\\r\\n tweets[i] =re.sub(r'\\\\w+:\\\\/{2}[\\\\d\\\\w-]+(\\\\.[\\\\d\\\\w-]+)*(?:(?:\\\\/[^\\\\s/]*))*', '', tweets[i])\\r\\n #removing #'s, '\\\\n''s, and 'RT'\\r\\n tweets[i] = tweets[i].replace(\\\"#\\\",\\\"\\\")\\r\\n tweets[i] = tweets[i].replace(\\\"\\\\n\\\",\\\"\\\")\\r\\n if tweets[i][:2] == \\\"RT\\\":\\r\\n while(tweets[i][:2] != ': '):\\r\\n tweets[i] = tweets[i][1:]\\r\\n tweets[i] = tweets[i][2:]\\r\\n \\r\\n \\r\\n tweets = filter(lambda x: len(x) > 3, tweets)\\r\\n \\r\\n return tweets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67bdbf5191b5070b5014a213c09932ee\",\n \"score\": \"0.56722087\",\n \"text\": \"async def twintail(self):\\n res = tools.fetch('twintails')\\n await self.client.say(res.url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4440c9c1635ff84b5ef0fabd25fd76f3\",\n \"score\": \"0.56706536\",\n \"text\": \"def __init__(self):\\n # parse twitter keys\\n settings = self.getSettings(sys.argv)\\n self.consumer_key = settings[0]\\n self.consumer_secret = settings[1]\\n self.access_token = settings[2]\\n self.access_token_secret = settings[3]\\n self.twitterConnect() #after getting keys, connect to twitter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ac02d8c18bdd30f5e4030a917bac32\",\n \"score\": \"0.5658061\",\n \"text\": \"def isTwitter(cls, s):\\n return s and s.startswith('@')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cbee872e885c6ddab5ca2b9249512fd\",\n \"score\": \"0.5648784\",\n \"text\": \"def retweet():\\n tw_id = request.args.get(\\\"tweet\\\")\\n\\n tws = session[\\\"tweets\\\"]\\n tws[tw_id][\\\"retweet_time\\\"] = datetime.now().strftime(\\\"%m/%d/%Y %H:%M:%S\\\")\\n tws[tw_id][\\\"retweeter\\\"] = session[\\\"user\\\"]\\n\\n session[\\\"tweets\\\"] = tws\\n save_tweets()\\n\\n return redirect(\\\"/personal_feed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee808d7ffdf7b6b9a267d9f362d7c36\",\n \"score\": \"0.5641918\",\n \"text\": \"def twitter_setup():\\n # Authentication and access using keys:\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_token_secret)\\n\\n # Return API with authentication:\\n api = tweepy.API(auth)\\n return api\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23e0ef0570ea5c16ec7e934374bd9db5\",\n \"score\": \"0.56391245\",\n \"text\": \"def __init__(self,\\n api=None,\\n search=['None'],\\n time_limit=0,\\n tweets_limit=0,\\n start_time=datetime.datetime.now(),\\n start_counter=0\\n ):\\n TweetsListener.__init__(self,\\n api,\\n search,\\n time_limit,\\n tweets_limit,\\n start_time,\\n start_counter)\\n self.POSITIVE = 'Positive'\\n self.NEGATIVE = 'Negative'\\n self.NEUTRAL = 'Neutral'\\n self.CONFUSED = 'Confused'\\n self.emoticons = {self.POSITIVE:'😀|😁|😂|😃|😄|😅|😆|😇|😈|😉|😊|😋|😌|😍|😎|😏|😗|😘|😙|😚|😛|😜|😝|😸|😹|😺|😻|😼|😽',\\n self.NEGATIVE : '😒|😓|😔|😖|😞|😟|😠|😡|😢|😣|😤|😥|😦|😧|😨|😩|😪|😫|😬|😭|😾|😿|😰|😱|🙀',\\n self.NEUTRAL : '😐|😑|😳|😮|😯|😶|😴|😵|😲',\\n self.CONFUSED: '😕'\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddbb4e1e7d7df342ee880c47dbacadf6\",\n \"score\": \"0.5627791\",\n \"text\": \"def post(self):\\n tweet = self.get_random_tweet()\\n\\n # wait before posting a tweet\\n sleep(self.get_wait_time(tweet))\\n\\n tweet_text = '{}{}'.format(\\n tweet['text'],\\n self.get_emoji_of_the_day_text()\\n )\\n\\n self.api.update_status(\\n status=tweet_text,\\n lat='45.814632', # this is fixed, duh\\n long='15.973277'\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993475,"cells":{"query_id":{"kind":"string","value":"b840234fa9d8f1681256cc2189f82323"},"query":{"kind":"string","value":"r\"\"\"Call the update phrase matcher method over HTTP."},"positive_passages":{"kind":"list like","value":[{"docid":"fcdc30237ae20d3245c69c5b7c8cd8ff","score":"0.62303835","text":"def __call__(\n self,\n request: contact_center_insights.UpdatePhraseMatcherRequest,\n *,\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\n timeout: Optional[float] = None,\n metadata: Sequence[Tuple[str, str]] = (),\n ) -> resources.PhraseMatcher:\n\n http_options: List[Dict[str, str]] = [\n {\n \"method\": \"patch\",\n \"uri\": \"/v1/{phrase_matcher.name=projects/*/locations/*/phraseMatchers/*}\",\n \"body\": \"phrase_matcher\",\n },\n ]\n request, metadata = self._interceptor.pre_update_phrase_matcher(\n request, metadata\n )\n pb_request = contact_center_insights.UpdatePhraseMatcherRequest.pb(request)\n transcoded_request = path_template.transcode(http_options, pb_request)\n\n # Jsonify the request body\n\n body = json_format.MessageToJson(\n transcoded_request[\"body\"],\n including_default_value_fields=False,\n use_integers_for_enums=True,\n )\n uri = transcoded_request[\"uri\"]\n method = transcoded_request[\"method\"]\n\n # Jsonify the query params\n query_params = json.loads(\n json_format.MessageToJson(\n transcoded_request[\"query_params\"],\n including_default_value_fields=False,\n use_integers_for_enums=True,\n )\n )\n query_params.update(self._get_unset_required_fields(query_params))\n\n query_params[\"$alt\"] = \"json;enum-encoding=int\"\n\n # Send the request\n headers = dict(metadata)\n headers[\"Content-Type\"] = \"application/json\"\n response = getattr(self._session, method)(\n \"{host}{uri}\".format(host=self._host, uri=uri),\n timeout=timeout,\n headers=headers,\n params=rest_helpers.flatten_query_params(query_params, strict=True),\n data=body,\n )\n\n # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception\n # subclass.\n if response.status_code >= 400:\n raise core_exceptions.from_http_response(response)\n\n # Return the response\n resp = resources.PhraseMatcher()\n pb_resp = resources.PhraseMatcher.pb(resp)\n\n json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True)\n resp = self._interceptor.post_update_phrase_matcher(resp)\n return resp","title":""}],"string":"[\n {\n \"docid\": \"fcdc30237ae20d3245c69c5b7c8cd8ff\",\n \"score\": \"0.62303835\",\n \"text\": \"def __call__(\\n self,\\n request: contact_center_insights.UpdatePhraseMatcherRequest,\\n *,\\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\\n timeout: Optional[float] = None,\\n metadata: Sequence[Tuple[str, str]] = (),\\n ) -> resources.PhraseMatcher:\\n\\n http_options: List[Dict[str, str]] = [\\n {\\n \\\"method\\\": \\\"patch\\\",\\n \\\"uri\\\": \\\"/v1/{phrase_matcher.name=projects/*/locations/*/phraseMatchers/*}\\\",\\n \\\"body\\\": \\\"phrase_matcher\\\",\\n },\\n ]\\n request, metadata = self._interceptor.pre_update_phrase_matcher(\\n request, metadata\\n )\\n pb_request = contact_center_insights.UpdatePhraseMatcherRequest.pb(request)\\n transcoded_request = path_template.transcode(http_options, pb_request)\\n\\n # Jsonify the request body\\n\\n body = json_format.MessageToJson(\\n transcoded_request[\\\"body\\\"],\\n including_default_value_fields=False,\\n use_integers_for_enums=True,\\n )\\n uri = transcoded_request[\\\"uri\\\"]\\n method = transcoded_request[\\\"method\\\"]\\n\\n # Jsonify the query params\\n query_params = json.loads(\\n json_format.MessageToJson(\\n transcoded_request[\\\"query_params\\\"],\\n including_default_value_fields=False,\\n use_integers_for_enums=True,\\n )\\n )\\n query_params.update(self._get_unset_required_fields(query_params))\\n\\n query_params[\\\"$alt\\\"] = \\\"json;enum-encoding=int\\\"\\n\\n # Send the request\\n headers = dict(metadata)\\n headers[\\\"Content-Type\\\"] = \\\"application/json\\\"\\n response = getattr(self._session, method)(\\n \\\"{host}{uri}\\\".format(host=self._host, uri=uri),\\n timeout=timeout,\\n headers=headers,\\n params=rest_helpers.flatten_query_params(query_params, strict=True),\\n data=body,\\n )\\n\\n # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception\\n # subclass.\\n if response.status_code >= 400:\\n raise core_exceptions.from_http_response(response)\\n\\n # Return the response\\n resp = resources.PhraseMatcher()\\n pb_resp = resources.PhraseMatcher.pb(resp)\\n\\n json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True)\\n resp = self._interceptor.post_update_phrase_matcher(resp)\\n return resp\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2e2b4ff5679148e0a86d475b255aa123","score":"0.70168084","text":"def post_update_phrase_matcher(\n self, response: resources.PhraseMatcher\n ) -> resources.PhraseMatcher:\n return response","title":""},{"docid":"c4882d357b086afe3205dc8133d5987a","score":"0.5941446","text":"def test_update(self):\n SCRIPT = 'ctx._source.thing += count'\n with patch.object(self.conn, 'send_request') as send_request:\n self.conn.update('some_index',\n 'some_type',\n 3,\n SCRIPT,\n params={'count': 5},\n lang='python')\n send_request.assert_called_once_with(\n 'POST', ['some_index', 'some_type', 3, '_update'],\n body={'script': SCRIPT,\n 'params': {'count': 5},\n 'lang': 'python'},\n query_params={})","title":""},{"docid":"c4882d357b086afe3205dc8133d5987a","score":"0.5941446","text":"def test_update(self):\n SCRIPT = 'ctx._source.thing += count'\n with patch.object(self.conn, 'send_request') as send_request:\n self.conn.update('some_index',\n 'some_type',\n 3,\n SCRIPT,\n params={'count': 5},\n lang='python')\n send_request.assert_called_once_with(\n 'POST', ['some_index', 'some_type', 3, '_update'],\n body={'script': SCRIPT,\n 'params': {'count': 5},\n 'lang': 'python'},\n query_params={})","title":""},{"docid":"cfbeee3e444a5a3e78ab5669cdf969a2","score":"0.59103394","text":"def update(self, *args):\n label = '/'.join(args)\n body_length = int(cherrypy.request.headers.get('Content-Length', 0))\n data = cherrypy.request.rfile.read(body_length)\n return updater.HandleUpdatePing(data, label)","title":""},{"docid":"69e3d5aaec3e9ce58ccca80a329887d4","score":"0.5771502","text":"def pat(update, context):\n response(update.message, 'pat')","title":""},{"docid":"8da798fc8f57b7f4a735bd050067b7e5","score":"0.5631919","text":"def put(self, request, cbid):\n try:\n self.init_time = datetime.datetime.now()\n print(\"Start:\" + str((datetime.datetime.now() - self.init_time).total_seconds() * 1000))\n result = ServiceManager(cbid).run_chatbot(request.data)\n print(\"Done:\" + str((datetime.datetime.now() - self.init_time).total_seconds() * 1000))\n return Response(json.loads(result))\n except Exception as e:\n return_data = {\"status\": \"404\", \"result\": str(e)}\n return Response(json.dumps(return_data))","title":""},{"docid":"c44b0bd6769964271782d54949189e84","score":"0.55970174","text":"def update_text(self, text):","title":""},{"docid":"7e89519f98575ad6f989136dfaf25acd","score":"0.5555657","text":"def post_get_phrase_matcher(\n self, response: resources.PhraseMatcher\n ) -> resources.PhraseMatcher:\n return response","title":""},{"docid":"d65b090eb183e93c7f4bf1dfba423e3e","score":"0.5513382","text":"def respond():\n update = telegram.Update.de_json(request.get_json(force=True), bot)\n text = update.message.text.encode('utf-8').decode()\n\n if text == \"/start\":\n controller_telegram.start(update, True)\n elif text == \"/restart\":\n controller_telegram.start(update, True)\n elif text == \"/help\":\n controller_telegram.help(update, True)\n elif text == \"/exit\":\n controller_telegram.exit(update, True)\n else:\n controller_telegram.continue_conv(update, True)\n return 'ok'","title":""},{"docid":"7534610c3485f286e5bfed617728ba31","score":"0.5426643","text":"def update(request):","title":""},{"docid":"e7d211441fc6b90896898db139e970d1","score":"0.5377268","text":"def edit_update(request, petition, update_position, description):\n\n updates = petition.updates.all()\n\n updated = False\n indexes = \"\"\n for index, update in enumerate(updates):\n indexes += str(index)+\", \"\n if index == update_position - 1:\n update.description = description\n update.save()\n updated = True\n\n if updated:\n data = {\n \"command\": \"refresh-petition\",\n \"petition_id\": petition.id,\n }\n send_update(data)\n\n # Send email regarding updates.\n petition_update(petition.id, request.META['HTTP_HOST'])\n\n petition.save()\n\n return JsonResponse({\"editUpdate\": \"Done.\"})\n\n return JsonResponse({\"Error\": \"Did not find update: \"+str(update_position)+\", searched: \"+indexes})","title":""},{"docid":"0e5e62482e87af79d81aff78280c9577","score":"0.5335206","text":"def do_UPDATE(self):\n self.send_error(501)\n return","title":""},{"docid":"f3eacc3ca34f14a028628fe28ac3e4b4","score":"0.53280634","text":"def update(self, **kwargs):\n ...","title":""},{"docid":"f3eacc3ca34f14a028628fe28ac3e4b4","score":"0.53280634","text":"def update(self, **kwargs):\n ...","title":""},{"docid":"607e4d779d1d6fc9118403629b7ea639","score":"0.5327661","text":"def test_update_song(self):\n song_post_payload = {\n \"title\": \"Hold on\",\n \"duration\": 240\n }\n \n song__update_payload = {\n \"title\": \"God's plan\",\n \"duration\": 344\n }\n \n rev = self.app.post('/song', headers={\"Content-Type\": \"application/json\"}, data=json.dumps(song_post_payload))\n self.assertEqual(rev.status_code, 200)\n\n response= self.app.put('/song/1', headers={\"Content-Type\": \"application/json\"}, data=json.dumps(song__update_payload))\n \n self.assertEqual(response.status_code, 200)\n self.assertIn(\"God\", str(response.data))","title":""},{"docid":"30efbf8736ab9cfc0bfd80491da87e81","score":"0.53041345","text":"def test_update_document(self):\n body = Entry()\n response = self.client.open(\n '/v2/document',\n method='PUT',\n data=json.dumps(body),\n content_type='application/json')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","title":""},{"docid":"05a9ff3aa0fa6fcad1626a2db2af4e6b","score":"0.52589613","text":"def update_command():\n db.update(bid, title_text.get(), author_text.get(), year_text.get(), isbn_text.get())\n view_command()","title":""},{"docid":"1424b3f12a5474e1987b87752c95727b","score":"0.5253685","text":"def put(self, request: Request) -> Response:\n pk = request.query_params.get('topic')\n data = change_answer_status(request, pk)\n if data.get('success'):\n return Response(data, status=200)\n else:\n return Response(data, status=422)","title":""},{"docid":"3ee9fb94688ec6c35496ff7d4e017acc","score":"0.52454907","text":"def tweet(bot, update):\n pass","title":""},{"docid":"9129217867f31d36f2d8e4b5a5ba48bb","score":"0.52191246","text":"def put(self):\n self.handler(self.request.method)","title":""},{"docid":"557c8f4c84f2164f4bfabd4a0c3df799","score":"0.52188444","text":"def webhook():\n # Create update object from json-format request data\n update = Update.de_json(request.get_json(), bot)\n # process update\n dp.process_update(update)\n return \"ok\"","title":""},{"docid":"b0e58137cee53c5850b14e4ed12187ea","score":"0.52107716","text":"def update_record(self):\n\n package = \"web/run\"\n json_payload = self.record\n method = f\"{json_payload['maryID']}/update\"\n\n print(\"Sending update request\")\n try:\n r = requests.put(f\"{self.api_url}/{package}/{method}\", json=json_payload)\n if r.status_code == requests.codes.OK:\n return r.json()\n else:\n print(\"failed request status code \", r.status_code)\n except Exception as e:\n # logging.error(\"failed\")\n print(\"failed request, check response \", e)\n # return(r)","title":""},{"docid":"77c1f577a4566a7d09c73a2328360110","score":"0.5206073","text":"def main(**kwargs):\n print(f'Received: \"{kwargs}\"')\n message = telebot.types.Update.de_json(kwargs)\n message = message.message or message.edited_message\n if message and message.text and message.text[0] == '/':\n print(f'Echo on \"{message.text}\"')\n route_command(message.text.lower(), message)\n else:\n echo(message, message.chat.username)","title":""},{"docid":"5d13172ad2ec7f9358bde5d789eff2b0","score":"0.51972747","text":"def test_update_endpoint(self):\n pass","title":""},{"docid":"b224e3be3fbac85ac3430249d7944252","score":"0.5195553","text":"async def update(self, access_token: AP, update_dict: Dict[str, Any]) -> AP:\n ... # pragma: no cover","title":""},{"docid":"2457c88bcc5d52bec4848a39f4115dcf","score":"0.51883966","text":"def update(self, data):","title":""},{"docid":"2457c88bcc5d52bec4848a39f4115dcf","score":"0.51883966","text":"def update(self, data):","title":""},{"docid":"2457c88bcc5d52bec4848a39f4115dcf","score":"0.51883966","text":"def update(self, data):","title":""},{"docid":"2457c88bcc5d52bec4848a39f4115dcf","score":"0.51883966","text":"def update(self, data):","title":""},{"docid":"2457c88bcc5d52bec4848a39f4115dcf","score":"0.51883966","text":"def update(self, data):","title":""},{"docid":"2457c88bcc5d52bec4848a39f4115dcf","score":"0.51883966","text":"def update(self, data):","title":""},{"docid":"f7704debd7e80fb2054f261c74392d9d","score":"0.51827466","text":"def test_patch(self, view, mrequest, mget_contest, mcommand):\n id = uuid4().hex\n mget_contest.return_value.id = id\n mrequest.json_body = {\n 'name': 'new name',\n }\n\n view.patch()\n\n mcommand.update_by_id.assert_called_once_with(\n id,\n {\n 'name': 'new name'\n })","title":""},{"docid":"a6ac3dd31ef48e5c138538910b70cbff","score":"0.5169902","text":"def update(self, messages):","title":""},{"docid":"a9289b0e23a20d990afd19c8dba143b0","score":"0.5159581","text":"def test_update_document_with_form(self):\n data = dict(name='name_example',\n status='status_example')\n response = self.client.open(\n '/v2/document/{documentId}'.format(documentId=789),\n method='POST',\n data=data,\n content_type='application/x-www-form-urlencoded')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","title":""},{"docid":"52e601a00bd1bf5fee2ad54477cc5cd6","score":"0.5153948","text":"def webhook_handler():\n if request.method == \"POST\":\n update = telegram.Update.de_json(request.get_json(force=True), bot)\n dispatcher.process_update(update)\n return 'ok'","title":""},{"docid":"1faaf73b697b3a85f799212a0acb1a16","score":"0.5147767","text":"def _send_solr_command(self, core_url, json_command):\n\n # Check document language and dispatch to correct core\n # url = urlparse.urljoin(core_url, \"update/json/\")]\n update_url = 'update/json'\n url = '/'.join([core_url.rstrip('/'), update_url.lstrip('/')])\n try:\n request = urllib2.Request(url, json_command, {'Content-Type':'application/json'})\n response = urllib2.urlopen(request).read()\n except urllib2.HTTPError as e:\n logger.error(\"Failed to send update to Solr endpoint [%s]: %s\", core_url, e, exc_info=True)\n # return False\n print json_command\n return True","title":""},{"docid":"c4116f3b56ee883cc031a8e3b4d5885c","score":"0.5144614","text":"def update(self, data) -> None:\n ...","title":""},{"docid":"c4116f3b56ee883cc031a8e3b4d5885c","score":"0.5144614","text":"def update(self, data) -> None:\n ...","title":""},{"docid":"900185a17c3e6f000c5c0a6f8ae22308","score":"0.51424974","text":"def update():\n\n # call function\n ThreadedUpdate()\n print(f'process_update_call manually ran by user \"/update\"', file=sys.stderr)\n return redirect(\"/\")","title":""},{"docid":"abe8b6faf123ea731706f97c226816c3","score":"0.5138806","text":"def put_sentence():\n sentence = request.form['sentence']\n was_funny = request.form['wasFunny']\n app.logger.info(\"Received sentence %s with funny = %s\" % (sentence, was_funny))\n response = insert_sentence_into_db(sentence, was_funny)\n return jsonify(response=response)","title":""},{"docid":"e833f20c9fe3ac2694928504db968a74","score":"0.5131859","text":"def update(self, _id, data):","title":""},{"docid":"0d63b9117d42442ed242cea9fe08a98e","score":"0.5128198","text":"def update(self, _):\n pass","title":""},{"docid":"22a3b8b1fa4b2861bc03d8f848d99815","score":"0.5117782","text":"def echo_text(bot, update):\n reply = update.message.text\n bot.send_message(chat_id=update.message.chat_id, text=reply)","title":""},{"docid":"741577cec18c9018966981df5a277e0c","score":"0.5115981","text":"def applyUpdate(self, data):\n return self.session.request('update/', 'POST',\n self.getXML(data, 'applyUpdateRequest'))","title":""},{"docid":"93609bc2b96988729d326de624e3a2d1","score":"0.5113203","text":"def update(self,s):\r\n return","title":""},{"docid":"089b6c7fe4994423787cbe5bbe98a7fd","score":"0.5111034","text":"def echo(bot, update):\r\n update.message.reply_text(update.message.text)","title":""},{"docid":"8f26d3cf25b4f28e63129930c2da3b47","score":"0.5108115","text":"def updateContent(content, environ):","title":""},{"docid":"e41f318faddcbae5a3617db9dbd43acf","score":"0.51063824","text":"def do_updates(self):\n self.word.verify_guess(self.userinput.guess)\n self.userinput.check_mistakes()","title":""},{"docid":"79393cc996623d6b508d1ec53c3c85fe","score":"0.5098644","text":"def test_update_amendment(self):\n pass","title":""},{"docid":"8a89df86e1ee3531aa706c2b742f8453","score":"0.5097057","text":"def echo(update: Update, _: CallbackContext) -> None:\n\n update.message.reply_text(bot(update.message.text))","title":""},{"docid":"d678fed542a5df09432e032c6c9fb72a","score":"0.50965756","text":"def update(self, data: Any) -> None:\n ...","title":""},{"docid":"67fe04d2d07232089e88941601324f49","score":"0.508772","text":"def echo(update: Update, _: CallbackContext) -> None:\n update.message.reply_text(update.message.text)","title":""},{"docid":"db6df0c9e8c47ec97309dfdeae47935a","score":"0.5078755","text":"def post_create_phrase_matcher(\n self, response: resources.PhraseMatcher\n ) -> resources.PhraseMatcher:\n return response","title":""},{"docid":"2f9b6aeb519b66911d0f644255ec145e","score":"0.50774574","text":"def test_update():","title":""},{"docid":"bd0e4b272c49cb1ad4a40063021a7704","score":"0.50731313","text":"def button_handler(update: Update, _: CallbackContext) -> None:\n query = update.callback_query\n query.answer()\n\n query.edit_message_text(query.data)","title":""},{"docid":"7bf9ccf182dc85597699563707c3799d","score":"0.5071008","text":"def update(self, request, pk=None):\n\t\treturn Response({'http_method': 'POST'})","title":""},{"docid":"c19e34555982832293e6a3abd8ef34ff","score":"0.5064803","text":"def pre_update_phrase_matcher(\n self,\n request: contact_center_insights.UpdatePhraseMatcherRequest,\n metadata: Sequence[Tuple[str, str]],\n ) -> Tuple[\n contact_center_insights.UpdatePhraseMatcherRequest, Sequence[Tuple[str, str]]\n ]:\n return request, metadata","title":""},{"docid":"c1431a29313626f2c4b3aa54ba2253de","score":"0.5062203","text":"async def update(request: SlackApiRequest, app: SirBot) -> dict:\n slack = app.plugins[\"slack\"].api\n slack_id = request.query[\"slack_id\"]\n military_status = request.query[\"military_status\"]\n if military_status not in ('current', 'spouse'):\n return\n user_message = base_user_message(slack_id)\n\n if military_status == 'current':\n user_message['text'] = identify_military_ad(slack_id)\n elif military_status == 'spouse':\n user_message['text'] = identify_military_spouse(slack_id)\n\n send_user_greetings([user_message], slack)\n\n return {\"success\": \"true\"}","title":""},{"docid":"6a7574020e65d1ccb0cdc5c971d96697","score":"0.50616103","text":"def webhook_handler():\n if request.method == \"POST\":\n update = telegram.Update.de_json(request.get_json(force=True), bot)\n\n # Update dispatcher process that handler to process this message\n dispatcher.process_update(update)\n return 'ok'","title":""},{"docid":"4a1dca1ebd7aaffe64f4713e4e7d8428","score":"0.5050154","text":"def updatePostRequests(self, request):\n update(request)\n return message_types.VoidMessage()","title":""},{"docid":"3ced5eca035c328938893bf30aa35843","score":"0.50450885","text":"def update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"3ced5eca035c328938893bf30aa35843","score":"0.50450885","text":"def update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"3ced5eca035c328938893bf30aa35843","score":"0.50450885","text":"def update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"3ced5eca035c328938893bf30aa35843","score":"0.50450885","text":"def update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"3ced5eca035c328938893bf30aa35843","score":"0.50450885","text":"def update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"3ced5eca035c328938893bf30aa35843","score":"0.50450885","text":"def update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"3ced5eca035c328938893bf30aa35843","score":"0.50450885","text":"def update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"3ced5eca035c328938893bf30aa35843","score":"0.50450885","text":"def update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"3ced5eca035c328938893bf30aa35843","score":"0.50450885","text":"def update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"3ced5eca035c328938893bf30aa35843","score":"0.50450885","text":"def update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"183fa1d3ed7f111d9b8655e8436c3706","score":"0.50445575","text":"def patch_method(self, url, data):\n resp = self.response.patch(url, data)\n print(resp.status_code)\n print(resp.content)","title":""},{"docid":"c6ab4118157e051488ce23ea36d97272","score":"0.50413513","text":"def do_send_update(words):\n if test_locked():\n # good; there's something there to listen.\n with open(COMMUNICATION_FILE, 'a') as fout:\n fout.write('\\n'.join(words)+'\\n')\n return 0\n sys.stderr.write('The cyanbar was not running.\\n')\n return 1","title":""},{"docid":"ea77e03395a5228527a575bbadf1dc50","score":"0.50294477","text":"def put(self, request, *args, **kwrags):\n code = self.request.PUT.get('code') \n name = self.request.PUT.get('name') \n save = self.request.GET.get('save') == 'true' \n output = self.run_code(code) \n if save: \n instance = self.get_object() \n setattr(instance, 'name', name) # change name\n setattr(instance, 'code', code) # change code\n instance.save()\n return self.response(status='Successfully Run and Change',\n output=output) # return response ","title":""},{"docid":"c6bd79f097c3bd8787bf4be37f96ff9d","score":"0.50271964","text":"def upd(self, instance, text):\n self.manager.dicts = text","title":""},{"docid":"8fd71d7a043f9cb18fa37631d695a56e","score":"0.50180864","text":"def main():\r\n # api = get_twitter_api()\r\n # api.PostUpdate()\r","title":""},{"docid":"53a6e48d74fc071ae16756b70be054f2","score":"0.5017476","text":"def update(self, *args, **kwargs) -> Any:\n pass","title":""},{"docid":"b708b8a2941a794b9d3cff6beaeb199b","score":"0.5015711","text":"def do_POST(self):\n if self.path.startswith(\"/service\"):\n length = int(self.headers.getheader('content-length'))\n qs = self.rfile.read(length)\n para = cgi.parse_qs(qs, keep_blank_values=1)\n\n input_text, options = self.qs2options(para)\n \n self.response(input_text, options)\n else:\n self.send_error(404, \"File not found\")","title":""},{"docid":"73df1677a03491c1ba247f53f8018964","score":"0.5009754","text":"def echo(bot, update):\n update.message.reply_text(update.message.text)","title":""},{"docid":"f8689ad05bbfb3d3df73be3f52a6e68b","score":"0.50068843","text":"def update(self, *args, **kwargs) -> None:\n pass","title":""},{"docid":"0e71f155c30e5fa9e8b3e02c10ec9fcc","score":"0.50059116","text":"def post_sparql(json_data):\n stm = SPARQL_UPDATE_TEMPLATE % json_data\n response_sparql = requests.post(SPARQL_ENDPOINT, data={\"update\": stm})\n\n try:\n response_sparql.raise_for_status()\n except:\n print(stm)\n print(response_sparql.text)\n raise","title":""},{"docid":"eea24544444afb9bf738852d0f53a56a","score":"0.49978942","text":"def do_update(self, args):\n self.__handle_update('update', args)","title":""},{"docid":"ac1efe53fcb9a380bd1d2160f6594233","score":"0.49949604","text":"def get(self):\n self.write(\"Send me some POST requests!\")","title":""},{"docid":"8384b23296e9c49e8e83664b2e0dec31","score":"0.4984063","text":"def test_updatePetWithForm(self):\n body = {\"name\":\"string\",\"status\":\"string\"}\n response = self.client.open(\n '/pet/{petId}'.format(petId=0),\n method='POST',\n data=json.dumps(body),\n content_type='application/x-www-form-urlencoded')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","title":""},{"docid":"09e8fe4c753cf9d685d5cffbfcaa34ca","score":"0.49806017","text":"def update(self):\n self.ser.handle_request()","title":""},{"docid":"5fd6d114d1003d6a69144b3474573144","score":"0.49790567","text":"def parseAsPythonRequests(self):\n request = HTTPRequest(self.textArea.text)\n url = self.httpService.toString()\n url += request.path\n headerValuesSplit = str(request.headers.values).split('\\n')[1:]\n headerNames = [header.split(':')[0] for header in headerValuesSplit]\n headerValues = []\n for value in headerValuesSplit:\n if value.count(':') > 1:\n headerValues.append(':'.join(value.split(':')[1:]).strip())\n else:\n headerValues.append(''.join(value.split(':')[1:]).strip())\n \n url = ''.join(url)\n header_dict = dict(zip(headerNames, headerValues))\n headers = {k: v for k, v in header_dict.items() if v}\n verb = request.command.lower()\n data = ''\n\n dataVerbs = [\"PUT, POST, DELETE\"]\n if request.command in dataVerbs or int(request.headers.getheader('content-length')):\n length = int(request.headers.getheader('content-length'))\n body = request.rfile.read(length)\n data = urlparse.parse_qsl(body)\n\n if data:\n command = \\\n\"\"\"import requests\n\ns=requests.Session()\n\ns.headers = {}\n\ndata = {}\n\nresp = s.{}('{}', data)\"\"\".format(headers, data, verb, url)\n else:\n command =\\\n\"\"\"import requests\n\ns = requests.Session()\n\ns.headers = {}\n\nresp = s.{}('{}'')\"\"\".format(headers, verb, url)\n\n return command","title":""},{"docid":"5a535be76d51a3e1b4b025008a94b7b2","score":"0.49732968","text":"def _patch_request(self,path,body=\"\"):\n r = self._session.patch(self._base_url+path,body=body)\n return r","title":""},{"docid":"e6bc3ac2b17812fd05476475d50b4405","score":"0.49650228","text":"def change():\r\n\tfile = request.form['file']\r\n\ttext = request.form['text']\r\n\t\r\n\twith open(file, 'w') as fout:\r\n\t\tfout.write(text)\r\n\tprint(f'updated: {file}')\r\n\treturn \"file updated\"","title":""},{"docid":"c7e8ce847e71e9e1b63143ad70343102","score":"0.49645957","text":"def test_update_plain_conversation(self):\n pass","title":""},{"docid":"7ef4518e0e0aaa8bda622da2fada2115","score":"0.49568436","text":"def handle(self, *args, **options):\n services = RestService.objects.filter(name=TEXTIT)\n force = options.get(\"apply\")\n if force and force.lower() != \"true\":\n self.stderr.write(\"--apply expects 'true' as a parameter value.\")\n else:\n version_1 = re.compile(r\"\\/v1/runs\")\n version_2 = \"/v2/flow_starts\"\n\n for service in services:\n if version_1.findall(service.service_url):\n original = service.service_url\n new_uri = re.sub(version_1, version_2, service.service_url)\n params = {\"v1_url\": original, \"v2_url\": new_uri}\n if force.lower() == \"true\":\n service.service_url = new_uri\n service.save()\n self.stdout.write(\n _(\"Changed %(v1_url)s to %(v2_url)s\" % params)\n )\n else:\n self.stdout.write(\n _(\"Will change %(v1_url)s to %(v2_url)s\" % params)\n )","title":""},{"docid":"66cbf55b1536fc93a5239ff9b142f035","score":"0.4954937","text":"def testUpdateMethodDictionary(self):\n b1 = BaseModel()\n\n with unittest.mock.patch(\"sys.stdout\", new=StringIO()) as f:\n HBNBCommand().onecmd(\"BaseModel.update(\\\"{}\\\", \".format(b1.id) +\n \"{\\\"name\\\": \\\"Ben Keener\\\", \\\"foo\\\": \" +\n \"\\\"bar\\\"})\")\n self.assertEqual(\"\", f.getvalue().rstrip())\n\n self.assertEqual(b1.foo, \"bar\")\n self.assertEqual(b1.name, \"Ben Keener\")","title":""},{"docid":"8bbfb5c5cf7d726bab33d0533f3dbd67","score":"0.49535218","text":"async def reply_text(message, update):\n\n try:\n raw_message = html.unescape(message)\n if len(raw_message) > 4096:\n all_messages = textwrap.wrap(raw_message, 4096)\n for msg in all_messages:\n await update.message.reply_text(\n text=msg,\n parse_mode=ParseMode.MARKDOWN,\n reply_to_message_id=None,\n )\n else:\n await update.message.reply_text(\n text=html.unescape(message),\n parse_mode=ParseMode.MARKDOWN,\n reply_to_message_id=None,\n )\n except BadRequest:\n await update.message.reply_text(\n text=html.unescape(\n \"Sucedió un error, no podemos mostrarte lo que solicitaste :(\"\n ),\n parse_mode=ParseMode.MARKDOWN,\n reply_to_message_id=None,\n )","title":""},{"docid":"560a379bf6d79666f278eaa758c71783","score":"0.49523938","text":"def update_interpretation():\n try:\n current_app.logger.info(\"Received update interpretation request.\")\n if request.headers['Content-Type'] == 'application/json':\n current_app.logger.debug(\"Content type is application/json\")\n interpretation_object = request.json\n current_app.logger.debug(\"interpretation_object: \" , interpretation_object)\n output = bpc.update_interpretation(_get_interpretation_collection(), interpretation_object)\n current_app.logger.debug(output)\n \n if output == 1:\n return str(output), status.HTTP_202_ACCEPTED\n elif output > 1:\n return str(output), status.HTTP_409_CONFLICT\n else:\n return str(output), status.HTTP_404_NOT_FOUND\n except Exception as exc:\n current_app.logger.error(\"Unable to reach MongoDB.\")\n current_app.logger.error(exc)\n return (\"Unable to reach MongoDB\"), status.HTTP_503_SERVICE_UNAVAILABLE","title":""},{"docid":"56ad210ef13e5259cb53241874d57bb3","score":"0.49471852","text":"def test_edit_party(self):\n response1 = self.Client.post('/api/v1/parties', data=json.dumps(self.new_party), content_type='application/json')\n response = self.Client.put('/api/v1/parties/1', data=json.dumps(self.edit_party), content_type='application/json')\n result = json.loads(response.data.decode())\n self.assertEqual(result['Message'], \"Your party update is successful\")\n self.assertEqual(200, response.status_code)","title":""},{"docid":"d850d8f04c6185ed0ba8ae849fc4dd13","score":"0.4939434","text":"def echo(update, context):\r\n update.message.reply_text(update.message.text)","title":""},{"docid":"8f3d7bb954a1b2acc0a2cd57d0b62534","score":"0.49321786","text":"def updateCommand():\r\n print(\"Update!\")","title":""},{"docid":"c6201ea341a4654efaaacc1508fa3bad","score":"0.49319753","text":"def test_any_word(self):\n handler = SearchTermHandler(search_terms='alice bob carol')\n handler.prepare()\n \n req = HttpRequest()\n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice+bob+carol')}\n self.assertTrue(handler.check(req))\n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+alice')}\n self.assertTrue(handler.check(req))\n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+alice+')}\n self.assertTrue(handler.check(req))\n\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice+')}\n self.assertTrue(handler.check(req))\n\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice')}\n self.assertTrue(handler.check(req)) \n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice+++')}\n self.assertTrue(handler.check(req))\n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice+carol')}\n self.assertTrue(handler.check(req))\n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice++carol')}\n self.assertTrue(handler.check(req))\n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice+bob')}\n self.assertTrue(handler.check(req))\n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+carol')}\n self.assertTrue(handler.check(req))\n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+carol+')}\n self.assertTrue(handler.check(req))\n\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=carol+')}\n self.assertTrue(handler.check(req))\n\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+bob')}\n self.assertTrue(handler.check(req))\n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+bob+')}\n self.assertTrue(handler.check(req))\n\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=bob+')}\n self.assertTrue(handler.check(req))\n\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=bob')}\n self.assertTrue(handler.check(req))\n\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alicebob')}\n self.assertFalse(handler.check(req))\n \n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=')}\n self.assertFalse(handler.check(req))","title":""},{"docid":"bf6a073365d36f6e49f96dafae2159da","score":"0.492688","text":"def update(self, request, *args, **kwargs):\n return Response(status=status.HTTP_405_METHOD_NOT_ALLOWED)","title":""},{"docid":"c3661439b26dadb4d230d52d7b1bb533","score":"0.4926483","text":"def update(context):","title":""},{"docid":"c56695614a7918c396221c17e213ccc4","score":"0.49256507","text":"def do_PUT(self):\n self._route('PUT', self.path)","title":""},{"docid":"9e3962703eb9712e7e9888196e56d53d","score":"0.49252298","text":"def module_update():\n mid = request.form['mid']\n name = request.form['name']\n namespace = request.form['namespace']\n version = request.form['version']\n permission = request.form['permission']\n\n moduleservice = Module_Service(MONGO)\n data, code, message = moduleservice.update(mid, name, namespace, version, permission)\n return __result(data, code, message)","title":""}],"string":"[\n {\n \"docid\": \"2e2b4ff5679148e0a86d475b255aa123\",\n \"score\": \"0.70168084\",\n \"text\": \"def post_update_phrase_matcher(\\n self, response: resources.PhraseMatcher\\n ) -> resources.PhraseMatcher:\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4882d357b086afe3205dc8133d5987a\",\n \"score\": \"0.5941446\",\n \"text\": \"def test_update(self):\\n SCRIPT = 'ctx._source.thing += count'\\n with patch.object(self.conn, 'send_request') as send_request:\\n self.conn.update('some_index',\\n 'some_type',\\n 3,\\n SCRIPT,\\n params={'count': 5},\\n lang='python')\\n send_request.assert_called_once_with(\\n 'POST', ['some_index', 'some_type', 3, '_update'],\\n body={'script': SCRIPT,\\n 'params': {'count': 5},\\n 'lang': 'python'},\\n query_params={})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4882d357b086afe3205dc8133d5987a\",\n \"score\": \"0.5941446\",\n \"text\": \"def test_update(self):\\n SCRIPT = 'ctx._source.thing += count'\\n with patch.object(self.conn, 'send_request') as send_request:\\n self.conn.update('some_index',\\n 'some_type',\\n 3,\\n SCRIPT,\\n params={'count': 5},\\n lang='python')\\n send_request.assert_called_once_with(\\n 'POST', ['some_index', 'some_type', 3, '_update'],\\n body={'script': SCRIPT,\\n 'params': {'count': 5},\\n 'lang': 'python'},\\n query_params={})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfbeee3e444a5a3e78ab5669cdf969a2\",\n \"score\": \"0.59103394\",\n \"text\": \"def update(self, *args):\\n label = '/'.join(args)\\n body_length = int(cherrypy.request.headers.get('Content-Length', 0))\\n data = cherrypy.request.rfile.read(body_length)\\n return updater.HandleUpdatePing(data, label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e3d5aaec3e9ce58ccca80a329887d4\",\n \"score\": \"0.5771502\",\n \"text\": \"def pat(update, context):\\n response(update.message, 'pat')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8da798fc8f57b7f4a735bd050067b7e5\",\n \"score\": \"0.5631919\",\n \"text\": \"def put(self, request, cbid):\\n try:\\n self.init_time = datetime.datetime.now()\\n print(\\\"Start:\\\" + str((datetime.datetime.now() - self.init_time).total_seconds() * 1000))\\n result = ServiceManager(cbid).run_chatbot(request.data)\\n print(\\\"Done:\\\" + str((datetime.datetime.now() - self.init_time).total_seconds() * 1000))\\n return Response(json.loads(result))\\n except Exception as e:\\n return_data = {\\\"status\\\": \\\"404\\\", \\\"result\\\": str(e)}\\n return Response(json.dumps(return_data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c44b0bd6769964271782d54949189e84\",\n \"score\": \"0.55970174\",\n \"text\": \"def update_text(self, text):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e89519f98575ad6f989136dfaf25acd\",\n \"score\": \"0.5555657\",\n \"text\": \"def post_get_phrase_matcher(\\n self, response: resources.PhraseMatcher\\n ) -> resources.PhraseMatcher:\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d65b090eb183e93c7f4bf1dfba423e3e\",\n \"score\": \"0.5513382\",\n \"text\": \"def respond():\\n update = telegram.Update.de_json(request.get_json(force=True), bot)\\n text = update.message.text.encode('utf-8').decode()\\n\\n if text == \\\"/start\\\":\\n controller_telegram.start(update, True)\\n elif text == \\\"/restart\\\":\\n controller_telegram.start(update, True)\\n elif text == \\\"/help\\\":\\n controller_telegram.help(update, True)\\n elif text == \\\"/exit\\\":\\n controller_telegram.exit(update, True)\\n else:\\n controller_telegram.continue_conv(update, True)\\n return 'ok'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7534610c3485f286e5bfed617728ba31\",\n \"score\": \"0.5426643\",\n \"text\": \"def update(request):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7d211441fc6b90896898db139e970d1\",\n \"score\": \"0.5377268\",\n \"text\": \"def edit_update(request, petition, update_position, description):\\n\\n updates = petition.updates.all()\\n\\n updated = False\\n indexes = \\\"\\\"\\n for index, update in enumerate(updates):\\n indexes += str(index)+\\\", \\\"\\n if index == update_position - 1:\\n update.description = description\\n update.save()\\n updated = True\\n\\n if updated:\\n data = {\\n \\\"command\\\": \\\"refresh-petition\\\",\\n \\\"petition_id\\\": petition.id,\\n }\\n send_update(data)\\n\\n # Send email regarding updates.\\n petition_update(petition.id, request.META['HTTP_HOST'])\\n\\n petition.save()\\n\\n return JsonResponse({\\\"editUpdate\\\": \\\"Done.\\\"})\\n\\n return JsonResponse({\\\"Error\\\": \\\"Did not find update: \\\"+str(update_position)+\\\", searched: \\\"+indexes})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5e62482e87af79d81aff78280c9577\",\n \"score\": \"0.5335206\",\n \"text\": \"def do_UPDATE(self):\\n self.send_error(501)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3eacc3ca34f14a028628fe28ac3e4b4\",\n \"score\": \"0.53280634\",\n \"text\": \"def update(self, **kwargs):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3eacc3ca34f14a028628fe28ac3e4b4\",\n \"score\": \"0.53280634\",\n \"text\": \"def update(self, **kwargs):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607e4d779d1d6fc9118403629b7ea639\",\n \"score\": \"0.5327661\",\n \"text\": \"def test_update_song(self):\\n song_post_payload = {\\n \\\"title\\\": \\\"Hold on\\\",\\n \\\"duration\\\": 240\\n }\\n \\n song__update_payload = {\\n \\\"title\\\": \\\"God's plan\\\",\\n \\\"duration\\\": 344\\n }\\n \\n rev = self.app.post('/song', headers={\\\"Content-Type\\\": \\\"application/json\\\"}, data=json.dumps(song_post_payload))\\n self.assertEqual(rev.status_code, 200)\\n\\n response= self.app.put('/song/1', headers={\\\"Content-Type\\\": \\\"application/json\\\"}, data=json.dumps(song__update_payload))\\n \\n self.assertEqual(response.status_code, 200)\\n self.assertIn(\\\"God\\\", str(response.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30efbf8736ab9cfc0bfd80491da87e81\",\n \"score\": \"0.53041345\",\n \"text\": \"def test_update_document(self):\\n body = Entry()\\n response = self.client.open(\\n '/v2/document',\\n method='PUT',\\n data=json.dumps(body),\\n content_type='application/json')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05a9ff3aa0fa6fcad1626a2db2af4e6b\",\n \"score\": \"0.52589613\",\n \"text\": \"def update_command():\\n db.update(bid, title_text.get(), author_text.get(), year_text.get(), isbn_text.get())\\n view_command()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1424b3f12a5474e1987b87752c95727b\",\n \"score\": \"0.5253685\",\n \"text\": \"def put(self, request: Request) -> Response:\\n pk = request.query_params.get('topic')\\n data = change_answer_status(request, pk)\\n if data.get('success'):\\n return Response(data, status=200)\\n else:\\n return Response(data, status=422)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee9fb94688ec6c35496ff7d4e017acc\",\n \"score\": \"0.52454907\",\n \"text\": \"def tweet(bot, update):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9129217867f31d36f2d8e4b5a5ba48bb\",\n \"score\": \"0.52191246\",\n \"text\": \"def put(self):\\n self.handler(self.request.method)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"557c8f4c84f2164f4bfabd4a0c3df799\",\n \"score\": \"0.52188444\",\n \"text\": \"def webhook():\\n # Create update object from json-format request data\\n update = Update.de_json(request.get_json(), bot)\\n # process update\\n dp.process_update(update)\\n return \\\"ok\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0e58137cee53c5850b14e4ed12187ea\",\n \"score\": \"0.52107716\",\n \"text\": \"def update_record(self):\\n\\n package = \\\"web/run\\\"\\n json_payload = self.record\\n method = f\\\"{json_payload['maryID']}/update\\\"\\n\\n print(\\\"Sending update request\\\")\\n try:\\n r = requests.put(f\\\"{self.api_url}/{package}/{method}\\\", json=json_payload)\\n if r.status_code == requests.codes.OK:\\n return r.json()\\n else:\\n print(\\\"failed request status code \\\", r.status_code)\\n except Exception as e:\\n # logging.error(\\\"failed\\\")\\n print(\\\"failed request, check response \\\", e)\\n # return(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c1f577a4566a7d09c73a2328360110\",\n \"score\": \"0.5206073\",\n \"text\": \"def main(**kwargs):\\n print(f'Received: \\\"{kwargs}\\\"')\\n message = telebot.types.Update.de_json(kwargs)\\n message = message.message or message.edited_message\\n if message and message.text and message.text[0] == '/':\\n print(f'Echo on \\\"{message.text}\\\"')\\n route_command(message.text.lower(), message)\\n else:\\n echo(message, message.chat.username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d13172ad2ec7f9358bde5d789eff2b0\",\n \"score\": \"0.51972747\",\n \"text\": \"def test_update_endpoint(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b224e3be3fbac85ac3430249d7944252\",\n \"score\": \"0.5195553\",\n \"text\": \"async def update(self, access_token: AP, update_dict: Dict[str, Any]) -> AP:\\n ... # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2457c88bcc5d52bec4848a39f4115dcf\",\n \"score\": \"0.51883966\",\n \"text\": \"def update(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2457c88bcc5d52bec4848a39f4115dcf\",\n \"score\": \"0.51883966\",\n \"text\": \"def update(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2457c88bcc5d52bec4848a39f4115dcf\",\n \"score\": \"0.51883966\",\n \"text\": \"def update(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2457c88bcc5d52bec4848a39f4115dcf\",\n \"score\": \"0.51883966\",\n \"text\": \"def update(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2457c88bcc5d52bec4848a39f4115dcf\",\n \"score\": \"0.51883966\",\n \"text\": \"def update(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2457c88bcc5d52bec4848a39f4115dcf\",\n \"score\": \"0.51883966\",\n \"text\": \"def update(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7704debd7e80fb2054f261c74392d9d\",\n \"score\": \"0.51827466\",\n \"text\": \"def test_patch(self, view, mrequest, mget_contest, mcommand):\\n id = uuid4().hex\\n mget_contest.return_value.id = id\\n mrequest.json_body = {\\n 'name': 'new name',\\n }\\n\\n view.patch()\\n\\n mcommand.update_by_id.assert_called_once_with(\\n id,\\n {\\n 'name': 'new name'\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6ac3dd31ef48e5c138538910b70cbff\",\n \"score\": \"0.5169902\",\n \"text\": \"def update(self, messages):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9289b0e23a20d990afd19c8dba143b0\",\n \"score\": \"0.5159581\",\n \"text\": \"def test_update_document_with_form(self):\\n data = dict(name='name_example',\\n status='status_example')\\n response = self.client.open(\\n '/v2/document/{documentId}'.format(documentId=789),\\n method='POST',\\n data=data,\\n content_type='application/x-www-form-urlencoded')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52e601a00bd1bf5fee2ad54477cc5cd6\",\n \"score\": \"0.5153948\",\n \"text\": \"def webhook_handler():\\n if request.method == \\\"POST\\\":\\n update = telegram.Update.de_json(request.get_json(force=True), bot)\\n dispatcher.process_update(update)\\n return 'ok'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1faaf73b697b3a85f799212a0acb1a16\",\n \"score\": \"0.5147767\",\n \"text\": \"def _send_solr_command(self, core_url, json_command):\\n\\n # Check document language and dispatch to correct core\\n # url = urlparse.urljoin(core_url, \\\"update/json/\\\")]\\n update_url = 'update/json'\\n url = '/'.join([core_url.rstrip('/'), update_url.lstrip('/')])\\n try:\\n request = urllib2.Request(url, json_command, {'Content-Type':'application/json'})\\n response = urllib2.urlopen(request).read()\\n except urllib2.HTTPError as e:\\n logger.error(\\\"Failed to send update to Solr endpoint [%s]: %s\\\", core_url, e, exc_info=True)\\n # return False\\n print json_command\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4116f3b56ee883cc031a8e3b4d5885c\",\n \"score\": \"0.5144614\",\n \"text\": \"def update(self, data) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4116f3b56ee883cc031a8e3b4d5885c\",\n \"score\": \"0.5144614\",\n \"text\": \"def update(self, data) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900185a17c3e6f000c5c0a6f8ae22308\",\n \"score\": \"0.51424974\",\n \"text\": \"def update():\\n\\n # call function\\n ThreadedUpdate()\\n print(f'process_update_call manually ran by user \\\"/update\\\"', file=sys.stderr)\\n return redirect(\\\"/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe8b6faf123ea731706f97c226816c3\",\n \"score\": \"0.5138806\",\n \"text\": \"def put_sentence():\\n sentence = request.form['sentence']\\n was_funny = request.form['wasFunny']\\n app.logger.info(\\\"Received sentence %s with funny = %s\\\" % (sentence, was_funny))\\n response = insert_sentence_into_db(sentence, was_funny)\\n return jsonify(response=response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e833f20c9fe3ac2694928504db968a74\",\n \"score\": \"0.5131859\",\n \"text\": \"def update(self, _id, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d63b9117d42442ed242cea9fe08a98e\",\n \"score\": \"0.5128198\",\n \"text\": \"def update(self, _):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a3b8b1fa4b2861bc03d8f848d99815\",\n \"score\": \"0.5117782\",\n \"text\": \"def echo_text(bot, update):\\n reply = update.message.text\\n bot.send_message(chat_id=update.message.chat_id, text=reply)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"741577cec18c9018966981df5a277e0c\",\n \"score\": \"0.5115981\",\n \"text\": \"def applyUpdate(self, data):\\n return self.session.request('update/', 'POST',\\n self.getXML(data, 'applyUpdateRequest'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93609bc2b96988729d326de624e3a2d1\",\n \"score\": \"0.5113203\",\n \"text\": \"def update(self,s):\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"089b6c7fe4994423787cbe5bbe98a7fd\",\n \"score\": \"0.5111034\",\n \"text\": \"def echo(bot, update):\\r\\n update.message.reply_text(update.message.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f26d3cf25b4f28e63129930c2da3b47\",\n \"score\": \"0.5108115\",\n \"text\": \"def updateContent(content, environ):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e41f318faddcbae5a3617db9dbd43acf\",\n \"score\": \"0.51063824\",\n \"text\": \"def do_updates(self):\\n self.word.verify_guess(self.userinput.guess)\\n self.userinput.check_mistakes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79393cc996623d6b508d1ec53c3c85fe\",\n \"score\": \"0.5098644\",\n \"text\": \"def test_update_amendment(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a89df86e1ee3531aa706c2b742f8453\",\n \"score\": \"0.5097057\",\n \"text\": \"def echo(update: Update, _: CallbackContext) -> None:\\n\\n update.message.reply_text(bot(update.message.text))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d678fed542a5df09432e032c6c9fb72a\",\n \"score\": \"0.50965756\",\n \"text\": \"def update(self, data: Any) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67fe04d2d07232089e88941601324f49\",\n \"score\": \"0.508772\",\n \"text\": \"def echo(update: Update, _: CallbackContext) -> None:\\n update.message.reply_text(update.message.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db6df0c9e8c47ec97309dfdeae47935a\",\n \"score\": \"0.5078755\",\n \"text\": \"def post_create_phrase_matcher(\\n self, response: resources.PhraseMatcher\\n ) -> resources.PhraseMatcher:\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9b6aeb519b66911d0f644255ec145e\",\n \"score\": \"0.50774574\",\n \"text\": \"def test_update():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd0e4b272c49cb1ad4a40063021a7704\",\n \"score\": \"0.50731313\",\n \"text\": \"def button_handler(update: Update, _: CallbackContext) -> None:\\n query = update.callback_query\\n query.answer()\\n\\n query.edit_message_text(query.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf9ccf182dc85597699563707c3799d\",\n \"score\": \"0.5071008\",\n \"text\": \"def update(self, request, pk=None):\\n\\t\\treturn Response({'http_method': 'POST'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c19e34555982832293e6a3abd8ef34ff\",\n \"score\": \"0.5064803\",\n \"text\": \"def pre_update_phrase_matcher(\\n self,\\n request: contact_center_insights.UpdatePhraseMatcherRequest,\\n metadata: Sequence[Tuple[str, str]],\\n ) -> Tuple[\\n contact_center_insights.UpdatePhraseMatcherRequest, Sequence[Tuple[str, str]]\\n ]:\\n return request, metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1431a29313626f2c4b3aa54ba2253de\",\n \"score\": \"0.5062203\",\n \"text\": \"async def update(request: SlackApiRequest, app: SirBot) -> dict:\\n slack = app.plugins[\\\"slack\\\"].api\\n slack_id = request.query[\\\"slack_id\\\"]\\n military_status = request.query[\\\"military_status\\\"]\\n if military_status not in ('current', 'spouse'):\\n return\\n user_message = base_user_message(slack_id)\\n\\n if military_status == 'current':\\n user_message['text'] = identify_military_ad(slack_id)\\n elif military_status == 'spouse':\\n user_message['text'] = identify_military_spouse(slack_id)\\n\\n send_user_greetings([user_message], slack)\\n\\n return {\\\"success\\\": \\\"true\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a7574020e65d1ccb0cdc5c971d96697\",\n \"score\": \"0.50616103\",\n \"text\": \"def webhook_handler():\\n if request.method == \\\"POST\\\":\\n update = telegram.Update.de_json(request.get_json(force=True), bot)\\n\\n # Update dispatcher process that handler to process this message\\n dispatcher.process_update(update)\\n return 'ok'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a1dca1ebd7aaffe64f4713e4e7d8428\",\n \"score\": \"0.5050154\",\n \"text\": \"def updatePostRequests(self, request):\\n update(request)\\n return message_types.VoidMessage()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ced5eca035c328938893bf30aa35843\",\n \"score\": \"0.50450885\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ced5eca035c328938893bf30aa35843\",\n \"score\": \"0.50450885\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ced5eca035c328938893bf30aa35843\",\n \"score\": \"0.50450885\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ced5eca035c328938893bf30aa35843\",\n \"score\": \"0.50450885\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ced5eca035c328938893bf30aa35843\",\n \"score\": \"0.50450885\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ced5eca035c328938893bf30aa35843\",\n \"score\": \"0.50450885\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ced5eca035c328938893bf30aa35843\",\n \"score\": \"0.50450885\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ced5eca035c328938893bf30aa35843\",\n \"score\": \"0.50450885\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ced5eca035c328938893bf30aa35843\",\n \"score\": \"0.50450885\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ced5eca035c328938893bf30aa35843\",\n \"score\": \"0.50450885\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"183fa1d3ed7f111d9b8655e8436c3706\",\n \"score\": \"0.50445575\",\n \"text\": \"def patch_method(self, url, data):\\n resp = self.response.patch(url, data)\\n print(resp.status_code)\\n print(resp.content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6ab4118157e051488ce23ea36d97272\",\n \"score\": \"0.50413513\",\n \"text\": \"def do_send_update(words):\\n if test_locked():\\n # good; there's something there to listen.\\n with open(COMMUNICATION_FILE, 'a') as fout:\\n fout.write('\\\\n'.join(words)+'\\\\n')\\n return 0\\n sys.stderr.write('The cyanbar was not running.\\\\n')\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea77e03395a5228527a575bbadf1dc50\",\n \"score\": \"0.50294477\",\n \"text\": \"def put(self, request, *args, **kwrags):\\n code = self.request.PUT.get('code') \\n name = self.request.PUT.get('name') \\n save = self.request.GET.get('save') == 'true' \\n output = self.run_code(code) \\n if save: \\n instance = self.get_object() \\n setattr(instance, 'name', name) # change name\\n setattr(instance, 'code', code) # change code\\n instance.save()\\n return self.response(status='Successfully Run and Change',\\n output=output) # return response \",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6bd79f097c3bd8787bf4be37f96ff9d\",\n \"score\": \"0.50271964\",\n \"text\": \"def upd(self, instance, text):\\n self.manager.dicts = text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd71d7a043f9cb18fa37631d695a56e\",\n \"score\": \"0.50180864\",\n \"text\": \"def main():\\r\\n # api = get_twitter_api()\\r\\n # api.PostUpdate()\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a6e48d74fc071ae16756b70be054f2\",\n \"score\": \"0.5017476\",\n \"text\": \"def update(self, *args, **kwargs) -> Any:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b708b8a2941a794b9d3cff6beaeb199b\",\n \"score\": \"0.5015711\",\n \"text\": \"def do_POST(self):\\n if self.path.startswith(\\\"/service\\\"):\\n length = int(self.headers.getheader('content-length'))\\n qs = self.rfile.read(length)\\n para = cgi.parse_qs(qs, keep_blank_values=1)\\n\\n input_text, options = self.qs2options(para)\\n \\n self.response(input_text, options)\\n else:\\n self.send_error(404, \\\"File not found\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73df1677a03491c1ba247f53f8018964\",\n \"score\": \"0.5009754\",\n \"text\": \"def echo(bot, update):\\n update.message.reply_text(update.message.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8689ad05bbfb3d3df73be3f52a6e68b\",\n \"score\": \"0.50068843\",\n \"text\": \"def update(self, *args, **kwargs) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e71f155c30e5fa9e8b3e02c10ec9fcc\",\n \"score\": \"0.50059116\",\n \"text\": \"def post_sparql(json_data):\\n stm = SPARQL_UPDATE_TEMPLATE % json_data\\n response_sparql = requests.post(SPARQL_ENDPOINT, data={\\\"update\\\": stm})\\n\\n try:\\n response_sparql.raise_for_status()\\n except:\\n print(stm)\\n print(response_sparql.text)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eea24544444afb9bf738852d0f53a56a\",\n \"score\": \"0.49978942\",\n \"text\": \"def do_update(self, args):\\n self.__handle_update('update', args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac1efe53fcb9a380bd1d2160f6594233\",\n \"score\": \"0.49949604\",\n \"text\": \"def get(self):\\n self.write(\\\"Send me some POST requests!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8384b23296e9c49e8e83664b2e0dec31\",\n \"score\": \"0.4984063\",\n \"text\": \"def test_updatePetWithForm(self):\\n body = {\\\"name\\\":\\\"string\\\",\\\"status\\\":\\\"string\\\"}\\n response = self.client.open(\\n '/pet/{petId}'.format(petId=0),\\n method='POST',\\n data=json.dumps(body),\\n content_type='application/x-www-form-urlencoded')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e8fe4c753cf9d685d5cffbfcaa34ca\",\n \"score\": \"0.49806017\",\n \"text\": \"def update(self):\\n self.ser.handle_request()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fd6d114d1003d6a69144b3474573144\",\n \"score\": \"0.49790567\",\n \"text\": \"def parseAsPythonRequests(self):\\n request = HTTPRequest(self.textArea.text)\\n url = self.httpService.toString()\\n url += request.path\\n headerValuesSplit = str(request.headers.values).split('\\\\n')[1:]\\n headerNames = [header.split(':')[0] for header in headerValuesSplit]\\n headerValues = []\\n for value in headerValuesSplit:\\n if value.count(':') > 1:\\n headerValues.append(':'.join(value.split(':')[1:]).strip())\\n else:\\n headerValues.append(''.join(value.split(':')[1:]).strip())\\n \\n url = ''.join(url)\\n header_dict = dict(zip(headerNames, headerValues))\\n headers = {k: v for k, v in header_dict.items() if v}\\n verb = request.command.lower()\\n data = ''\\n\\n dataVerbs = [\\\"PUT, POST, DELETE\\\"]\\n if request.command in dataVerbs or int(request.headers.getheader('content-length')):\\n length = int(request.headers.getheader('content-length'))\\n body = request.rfile.read(length)\\n data = urlparse.parse_qsl(body)\\n\\n if data:\\n command = \\\\\\n\\\"\\\"\\\"import requests\\n\\ns=requests.Session()\\n\\ns.headers = {}\\n\\ndata = {}\\n\\nresp = s.{}('{}', data)\\\"\\\"\\\".format(headers, data, verb, url)\\n else:\\n command =\\\\\\n\\\"\\\"\\\"import requests\\n\\ns = requests.Session()\\n\\ns.headers = {}\\n\\nresp = s.{}('{}'')\\\"\\\"\\\".format(headers, verb, url)\\n\\n return command\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a535be76d51a3e1b4b025008a94b7b2\",\n \"score\": \"0.49732968\",\n \"text\": \"def _patch_request(self,path,body=\\\"\\\"):\\n r = self._session.patch(self._base_url+path,body=body)\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6bc3ac2b17812fd05476475d50b4405\",\n \"score\": \"0.49650228\",\n \"text\": \"def change():\\r\\n\\tfile = request.form['file']\\r\\n\\ttext = request.form['text']\\r\\n\\t\\r\\n\\twith open(file, 'w') as fout:\\r\\n\\t\\tfout.write(text)\\r\\n\\tprint(f'updated: {file}')\\r\\n\\treturn \\\"file updated\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7e8ce847e71e9e1b63143ad70343102\",\n \"score\": \"0.49645957\",\n \"text\": \"def test_update_plain_conversation(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef4518e0e0aaa8bda622da2fada2115\",\n \"score\": \"0.49568436\",\n \"text\": \"def handle(self, *args, **options):\\n services = RestService.objects.filter(name=TEXTIT)\\n force = options.get(\\\"apply\\\")\\n if force and force.lower() != \\\"true\\\":\\n self.stderr.write(\\\"--apply expects 'true' as a parameter value.\\\")\\n else:\\n version_1 = re.compile(r\\\"\\\\/v1/runs\\\")\\n version_2 = \\\"/v2/flow_starts\\\"\\n\\n for service in services:\\n if version_1.findall(service.service_url):\\n original = service.service_url\\n new_uri = re.sub(version_1, version_2, service.service_url)\\n params = {\\\"v1_url\\\": original, \\\"v2_url\\\": new_uri}\\n if force.lower() == \\\"true\\\":\\n service.service_url = new_uri\\n service.save()\\n self.stdout.write(\\n _(\\\"Changed %(v1_url)s to %(v2_url)s\\\" % params)\\n )\\n else:\\n self.stdout.write(\\n _(\\\"Will change %(v1_url)s to %(v2_url)s\\\" % params)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66cbf55b1536fc93a5239ff9b142f035\",\n \"score\": \"0.4954937\",\n \"text\": \"def testUpdateMethodDictionary(self):\\n b1 = BaseModel()\\n\\n with unittest.mock.patch(\\\"sys.stdout\\\", new=StringIO()) as f:\\n HBNBCommand().onecmd(\\\"BaseModel.update(\\\\\\\"{}\\\\\\\", \\\".format(b1.id) +\\n \\\"{\\\\\\\"name\\\\\\\": \\\\\\\"Ben Keener\\\\\\\", \\\\\\\"foo\\\\\\\": \\\" +\\n \\\"\\\\\\\"bar\\\\\\\"})\\\")\\n self.assertEqual(\\\"\\\", f.getvalue().rstrip())\\n\\n self.assertEqual(b1.foo, \\\"bar\\\")\\n self.assertEqual(b1.name, \\\"Ben Keener\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bbfb5c5cf7d726bab33d0533f3dbd67\",\n \"score\": \"0.49535218\",\n \"text\": \"async def reply_text(message, update):\\n\\n try:\\n raw_message = html.unescape(message)\\n if len(raw_message) > 4096:\\n all_messages = textwrap.wrap(raw_message, 4096)\\n for msg in all_messages:\\n await update.message.reply_text(\\n text=msg,\\n parse_mode=ParseMode.MARKDOWN,\\n reply_to_message_id=None,\\n )\\n else:\\n await update.message.reply_text(\\n text=html.unescape(message),\\n parse_mode=ParseMode.MARKDOWN,\\n reply_to_message_id=None,\\n )\\n except BadRequest:\\n await update.message.reply_text(\\n text=html.unescape(\\n \\\"Sucedió un error, no podemos mostrarte lo que solicitaste :(\\\"\\n ),\\n parse_mode=ParseMode.MARKDOWN,\\n reply_to_message_id=None,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"560a379bf6d79666f278eaa758c71783\",\n \"score\": \"0.49523938\",\n \"text\": \"def update_interpretation():\\n try:\\n current_app.logger.info(\\\"Received update interpretation request.\\\")\\n if request.headers['Content-Type'] == 'application/json':\\n current_app.logger.debug(\\\"Content type is application/json\\\")\\n interpretation_object = request.json\\n current_app.logger.debug(\\\"interpretation_object: \\\" , interpretation_object)\\n output = bpc.update_interpretation(_get_interpretation_collection(), interpretation_object)\\n current_app.logger.debug(output)\\n \\n if output == 1:\\n return str(output), status.HTTP_202_ACCEPTED\\n elif output > 1:\\n return str(output), status.HTTP_409_CONFLICT\\n else:\\n return str(output), status.HTTP_404_NOT_FOUND\\n except Exception as exc:\\n current_app.logger.error(\\\"Unable to reach MongoDB.\\\")\\n current_app.logger.error(exc)\\n return (\\\"Unable to reach MongoDB\\\"), status.HTTP_503_SERVICE_UNAVAILABLE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ad210ef13e5259cb53241874d57bb3\",\n \"score\": \"0.49471852\",\n \"text\": \"def test_edit_party(self):\\n response1 = self.Client.post('/api/v1/parties', data=json.dumps(self.new_party), content_type='application/json')\\n response = self.Client.put('/api/v1/parties/1', data=json.dumps(self.edit_party), content_type='application/json')\\n result = json.loads(response.data.decode())\\n self.assertEqual(result['Message'], \\\"Your party update is successful\\\")\\n self.assertEqual(200, response.status_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d850d8f04c6185ed0ba8ae849fc4dd13\",\n \"score\": \"0.4939434\",\n \"text\": \"def echo(update, context):\\r\\n update.message.reply_text(update.message.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f3d7bb954a1b2acc0a2cd57d0b62534\",\n \"score\": \"0.49321786\",\n \"text\": \"def updateCommand():\\r\\n print(\\\"Update!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6201ea341a4654efaaacc1508fa3bad\",\n \"score\": \"0.49319753\",\n \"text\": \"def test_any_word(self):\\n handler = SearchTermHandler(search_terms='alice bob carol')\\n handler.prepare()\\n \\n req = HttpRequest()\\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice+bob+carol')}\\n self.assertTrue(handler.check(req))\\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+alice')}\\n self.assertTrue(handler.check(req))\\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+alice+')}\\n self.assertTrue(handler.check(req))\\n\\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice+')}\\n self.assertTrue(handler.check(req))\\n\\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice')}\\n self.assertTrue(handler.check(req)) \\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice+++')}\\n self.assertTrue(handler.check(req))\\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice+carol')}\\n self.assertTrue(handler.check(req))\\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice++carol')}\\n self.assertTrue(handler.check(req))\\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alice+bob')}\\n self.assertTrue(handler.check(req))\\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+carol')}\\n self.assertTrue(handler.check(req))\\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+carol+')}\\n self.assertTrue(handler.check(req))\\n\\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=carol+')}\\n self.assertTrue(handler.check(req))\\n\\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+bob')}\\n self.assertTrue(handler.check(req))\\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=+bob+')}\\n self.assertTrue(handler.check(req))\\n\\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=bob+')}\\n self.assertTrue(handler.check(req))\\n\\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=bob')}\\n self.assertTrue(handler.check(req))\\n\\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=alicebob')}\\n self.assertFalse(handler.check(req))\\n \\n req.GET = {'r': urllib.quote_plus('http://www.google.co.uk/search?q=')}\\n self.assertFalse(handler.check(req))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf6a073365d36f6e49f96dafae2159da\",\n \"score\": \"0.492688\",\n \"text\": \"def update(self, request, *args, **kwargs):\\n return Response(status=status.HTTP_405_METHOD_NOT_ALLOWED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3661439b26dadb4d230d52d7b1bb533\",\n \"score\": \"0.4926483\",\n \"text\": \"def update(context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c56695614a7918c396221c17e213ccc4\",\n \"score\": \"0.49256507\",\n \"text\": \"def do_PUT(self):\\n self._route('PUT', self.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e3962703eb9712e7e9888196e56d53d\",\n \"score\": \"0.49252298\",\n \"text\": \"def module_update():\\n mid = request.form['mid']\\n name = request.form['name']\\n namespace = request.form['namespace']\\n version = request.form['version']\\n permission = request.form['permission']\\n\\n moduleservice = Module_Service(MONGO)\\n data, code, message = moduleservice.update(mid, name, namespace, version, permission)\\n return __result(data, code, message)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993476,"cells":{"query_id":{"kind":"string","value":"1776f4905487abaf8b63ef81db24fc00"},"query":{"kind":"string","value":"Returns a wrapper that enforces the presence of certain fields in a JSON body, if a body is present. Passes the JSON body as the first argument to the inner, or None if there is no body. If a field is missing or has the wrong type, returns 400."},"positive_passages":{"kind":"list like","value":[{"docid":"27c1831cfc5dd7f74980b5cf54a01a5e","score":"0.5529812","text":"def maybe_json(**jkwargs):\n\n def wrapper(func):\n @functools.wraps(func)\n def inner(*args, **kwargs):\n if request.content_length == 0:\n return func(None, *args, **kwargs)\n return expect_json(**jkwargs)(func)(*args, **kwargs)\n\n return inner\n\n return wrapper","title":""}],"string":"[\n {\n \"docid\": \"27c1831cfc5dd7f74980b5cf54a01a5e\",\n \"score\": \"0.5529812\",\n \"text\": \"def maybe_json(**jkwargs):\\n\\n def wrapper(func):\\n @functools.wraps(func)\\n def inner(*args, **kwargs):\\n if request.content_length == 0:\\n return func(None, *args, **kwargs)\\n return expect_json(**jkwargs)(func)(*args, **kwargs)\\n\\n return inner\\n\\n return wrapper\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1f56a068edabb5c94c2214a769dcb306","score":"0.7296733","text":"def expect_json(*, strict=False, optional=None, **fields):\n if optional is None:\n optional = set()\n\n def wrapper(func):\n @functools.wraps(func)\n def inner(*args, **kwargs):\n # ensure correct mimetype\n if not request.is_json:\n return error(400, \"expected json body\")\n\n # ensure body exists\n body = request.get_json(silent=True) # return None on error\n if body is None:\n return error(400, \"missing or invalid body\")\n\n # ensure fields are present\n if strict:\n required = set(fields).difference(optional) \n if not (required.issubset(body) and set(fields).issuperset(body)):\n return error(400, f\"invalid body fields: expected {list(required)}, got {list(body)}\")\n else:\n if not set(fields).difference(optional).issubset(body):\n return error(400, f\"missing body fields: {list(set(fields).difference(optional, body))}\")\n\n # check field types\n for field, e_type in fields.items():\n if field in optional and field not in body:\n continue\n if not isinstance(body[field], e_type):\n return error(400, f\"expected {field} to be {e_type.__name__}\")\n\n # everything is good!\n return func(body, *args, **kwargs)\n\n return inner\n\n return wrapper","title":""},{"docid":"7337b191745a5cfffd827c7db5195131","score":"0.7203641","text":"def required_body_field(*body_fields):\n def rest_method_wrapper(func):\n def validator(*args, **kwargs):\n # parse request argument from APIView sort of classes' methods\n try:\n request = args[1] # args: (api class, request) kwargs: {\"form\":, }\n except:\n raise Exception(\"This decorator should be used only for endpoint functions\")\n body_data = request.data\n\n if not is_valid_bodyfield(body_data, body_fields):\n exception_message = \\\n \"actual body field: {}, \\nrequired body field: {}\".format(body_data, body_fields)\n raise APIException(exception_message)\n return func(*args, **kwargs)\n return validator\n return rest_method_wrapper","title":""},{"docid":"4ffa4cc2cacb4cbc94a9329c723d4992","score":"0.6735275","text":"def json_data_required(f):\n\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if not request.get_json():\n return {\n \"status\": \"error\",\n \"data\": None,\n \"message\": MISSING_JSON_BODY_ERR_MSG,\n }, 400\n return f(*args, **kwargs)\n\n return decorated_function","title":""},{"docid":"b8be21f4f768d8523905e3ff6f262d07","score":"0.6626301","text":"def wrapper(*inner_args, **inner_kwargs):\n\n try:\n if query is not None:\n query.strict = validate\n url = furl(request.url)\n inner_kwargs['_query'] = query.load(data=url.args)\n\n if body is not None:\n body.strict = validate\n json_data = request.get_json()\n\n if json_data is None:\n # Set json_data to empty dict if body is empty, so it gets picked up by the validator\n json_data = {}\n\n inner_kwargs['_body'] = body.load(data=json_data)\n\n except ValidationError as err:\n return jsonify(err.messages), 422\n\n if output:\n data = output.dump(f(*inner_args, **inner_kwargs))\n return jsonify(data[0])\n\n return f(*inner_args, **inner_kwargs)","title":""},{"docid":"a581549f6b759408413c718d6c09f833","score":"0.6591619","text":"def validate_json(f):\n @wraps(f)\n def wrapper(*args, **kwargs):\n try:\n assert request.json\n except Exception:\n msg = \"payload must be a valid json\"\n return jsonify({\"error\": msg}), 400\n return f(*args, **kwargs)\n return wrapper","title":""},{"docid":"ab0df634133e77a64924c9c0c6799fa0","score":"0.6204612","text":"def validate_nested(field, needed_field, _, data):\n\n if 'id' in data or 'name' in data:\n return\n raise marshmallow.ValidationError(\n 'Missing data for required field if \\'%s\\' field is not '\n 'provided.' % needed_field, field\n )","title":""},{"docid":"60c9bf5812f9905ab583ec51e9f3011b","score":"0.61692417","text":"def validate_request():\n\n def real_validate_request(f):\n @wraps(f)\n def decorated(*args,**kwargs):\n if not request.json:\n return {\"status\": \"fail\",\n \"data\": {\"message\": \"Request must be a valid JSON\"}\n }, 400\n\n return f(*args, **kwargs)\n\n return decorated\n\n return real_validate_request","title":""},{"docid":"54692eb8dccab0be30c617acb3f3b9c0","score":"0.60903764","text":"def json_request_required(method):\n def wrapper(self, *args, **kwargs):\n if not self.request.content_type == 'application/json':\n raise CoreException(400, 'Request should be application/json')\n return method(self, *args, **kwargs)\n return wrapper","title":""},{"docid":"1036b562ac00b1a779dbefc55a1294e1","score":"0.6083337","text":"def test_request_body_parameter_validation_with_nullable_field(user_post_schema):\n request = RequestFactory(\n url='http://www.example.com/post/',\n content_type='application/json',\n body=json.dumps({'username': 'Test User', 'email': 'test@example.com', 'age': None}),\n method=POST,\n )\n\n validate_request(\n request=request,\n schema=user_post_schema,\n )","title":""},{"docid":"7225b6a218b5bf1516fd39da26f9533c","score":"0.601417","text":"def validate_required_fields(json, required_fields, context):\n missing_fields = [f for f in required_fields\n if not f in json.get_field_names()]\n\n if missing_fields:\n raise SwaggerError(\n \"Missing fields: %s\" % ', '.join(missing_fields), context)","title":""},{"docid":"84b67bf22a6aeffb4fe41282bfdffef2","score":"0.59481406","text":"def validate_json_request(request, required=None, optional=None):\n # Verify that the request contains a Json object\n if not request.json:\n raise NoJsonInRequest()\n obj = request.json\n # Ensure that all required elements are present\n possible_keys = []\n if not required is None:\n possible_keys = required\n for key in required:\n if not key in obj:\n raise InvalidRequest('missing element \\'' + key + '\\'')\n # Ensure that no unwanted elements are in the request body\n if not optional is None:\n possible_keys += optional\n for key in obj:\n if not key in possible_keys:\n raise InvalidRequest('unknown element \\'' + key + '\\'')\n return obj","title":""},{"docid":"019903a5087f2a34a20e3e99500e918d","score":"0.59265155","text":"def json_fields_required(*fields):\n def dec(f):\n async def res_func(request):\n try:\n r = await request.json()\n list_of_missing_fields = [field for field in fields if field not in r]\n if list_of_missing_fields:\n return web.json_response({'error': True, \n 'error_text': 'Missing fields', \n 'fields': list_of_missing_fields})\n return await f(request)\n except:\n pass\n return web.json_response({'error': True, 'error_text': 'Wrong request'})\n res_func.__doc__ = f.__doc__\n return res_func\n return dec","title":""},{"docid":"4a7bd31a65da2af7c9c3ff8024f35568","score":"0.5707317","text":"def verify_fields(function):\n @wraps(function)\n def decorated(instance, *args, **kwargs):\n \"\"\"The decorator function.\"\"\"\n data = request.get_json(force=True, silent=True)\n if not data:\n raise BadRequestException(\"No data received from request\")\n for required in instance.__model__.__table__.columns:\n if required.name in (\n instance.__model__.__table__.primary_key.columns):\n continue\n if required.name not in data:\n raise ForbiddenException('{} required'.format(required))\n return function(instance, *args, **kwargs)\n\n return decorated","title":""},{"docid":"fb9fef39c0938f221633e843c86191a4","score":"0.5684331","text":"def is_valid_json(r):\n if len(r.body) == 0:\n return\n else:\n try:\n json.loads(r.body.decode('utf-8'))\n except Exception as e:\n e\n raise exceptions.ValidationFailed('Invalid JSON string')\n else:\n return","title":""},{"docid":"70d58f990aa4311cdf82fec92f2d5146","score":"0.56408155","text":"def wrapper(*args, **kwargs):\n request = args[1]\n try:\n for parameter in required_parameters:\n assert request.args[parameter][0]\n except (KeyError, IndexError, AssertionError):\n request.setResponseCode(400)\n raise MissingParameterException(\"Parameter '%s' is \"\n \"required.\" % parameter)\n return method(*args, **kwargs)","title":""},{"docid":"310b8c17f9ecbca33096ae212fc2d917","score":"0.55955005","text":"def __init__(self):\n super(NoJsonInRequest, self).__init__('invalid request body format')","title":""},{"docid":"ab4f01c89517c0e4a9a4a6e209badb24","score":"0.5489333","text":"def test_webapi_request_fields_call_with_unexpected_arg(self):\r\n @webapi_request_fields(\r\n required={\r\n 'required_param': {\r\n 'type': int,\r\n }\r\n },\r\n )\r\n def func(request, required_param=None, extra_fields={}):\r\n func.seen = True\r\n\r\n result = func(RequestFactory().get(\r\n path='/',\r\n data={\r\n 'required_param': '42',\r\n 'optional_param': '1',\r\n }\r\n ))\r\n\r\n self.assertFalse(hasattr(func, 'seen'))\r\n self.assertEqual(result[0], INVALID_FORM_DATA)\r\n self.assertTrue('fields' in result[1])\r\n self.assertTrue('optional_param' in result[1]['fields'])","title":""},{"docid":"3403c9446a35ced5a7a56b53c09ef908","score":"0.5475372","text":"def requires_fields(*fields):\n\n def decorator(f):\n @functools.wraps(f)\n def wrapper(self, *args, **kwargs):\n fr = FieldsRequest.create(fields)\n if hasattr(self, \"populate\"):\n self.populate(fr)\n for field in fields:\n if not self.has_field(field):\n raise MissingFieldException(f\"Missing required field '{field}'\")\n return f(self, *args, **kwargs)\n\n return wrapper\n\n return decorator","title":""},{"docid":"35a18521b6fa6977cbe18366a935cb80","score":"0.544335","text":"def it_should_discard_undeclared_fields(self):\n fields = self.required\n fields.update(self.optional)\n fields['foo'] = 42\n\n res = self.test.handler.validate(json.dumps(fields))\n return res.get('foo') is None","title":""},{"docid":"2a15e356da6c53a4ea6b7f5eef387049","score":"0.5401029","text":"def webapi_request_fields(required={}, optional={}, allow_unknown=False):\r\n @webapi_decorator\r\n def _dec(view_func):\r\n @webapi_response_errors(INVALID_FORM_DATA)\r\n def _validate(*args, **kwargs):\r\n request = _find_httprequest(args)\r\n\r\n if request.method == 'GET':\r\n request_fields = request.GET\r\n else:\r\n request_fields = request.POST\r\n\r\n extra_fields = {}\r\n invalid_fields = {}\r\n supported_fields = required.copy()\r\n supported_fields.update(optional)\r\n\r\n for field_name, value in six.iteritems(request_fields):\r\n if field_name in SPECIAL_PARAMS:\r\n # These are special names and can be ignored.\r\n continue\r\n\r\n if field_name not in supported_fields:\r\n if allow_unknown:\r\n extra_fields[field_name] = value\r\n else:\r\n invalid_fields[field_name] = ['Field is not supported']\r\n\r\n for field_name, info in six.iteritems(required):\r\n temp_fields = request_fields\r\n\r\n if info['type'] == file:\r\n temp_fields = request.FILES\r\n\r\n if temp_fields.get(field_name, None) is None:\r\n invalid_fields[field_name] = ['This field is required']\r\n\r\n new_kwargs = kwargs.copy()\r\n new_kwargs['extra_fields'] = extra_fields\r\n\r\n for field_name, info in six.iteritems(supported_fields):\r\n if isinstance(info['type'], file):\r\n continue\r\n\r\n value = request_fields.get(field_name, None)\r\n\r\n if value is not None:\r\n if type(info['type']) in (list, tuple):\r\n # This is a multiple-choice. Make sure the value is\r\n # valid.\r\n choices = info['type']\r\n\r\n if value not in choices:\r\n invalid_fields[field_name] = [\r\n '\"%s\" is not a valid value. Valid values '\r\n 'are: %s' % (\r\n value,\r\n ', '.join(['\"%s\"' % choice\r\n for choice in choices])\r\n )\r\n ]\r\n else:\r\n try:\r\n if issubclass(info['type'], bool):\r\n value = value in (1, \"1\", True, \"True\", \"true\")\r\n elif issubclass(info['type'], int):\r\n try:\r\n value = int(value)\r\n except ValueError:\r\n invalid_fields[field_name] = [\r\n '\"%s\" is not an integer' % value\r\n ]\r\n except TypeError:\r\n # The field isn't a class type. This is a\r\n # coding error on the developer's side.\r\n raise TypeError('\"%s\" is not a valid field type' %\r\n info['type'])\r\n\r\n new_kwargs[field_name] = value\r\n\r\n if invalid_fields:\r\n return INVALID_FORM_DATA, {\r\n 'fields': invalid_fields,\r\n }\r\n\r\n return view_func(*args, **new_kwargs)\r\n\r\n _validate.required_fields = required.copy()\r\n _validate.optional_fields = optional.copy()\r\n\r\n if hasattr(view_func, 'required_fields'):\r\n _validate.required_fields.update(view_func.required_fields)\r\n\r\n if hasattr(view_func, 'optional_fields'):\r\n _validate.optional_fields.update(view_func.optional_fields)\r\n\r\n return _validate\r\n\r\n return _dec","title":""},{"docid":"1315fab629f3dc0a3eb4c5b28cb2eae9","score":"0.5371032","text":"def test_mandatory_fields(self, field):\n body = {\n \"first_object_id\": 1,\n \"first_object_type\": \"Control\",\n \"second_object_id\": 1,\n \"second_object_type\": \"Product\",\n }\n del body[field]\n\n response = self.client.post(self.ENDPOINT_URL,\n content_type=\"application/json\",\n data=json.dumps(body))\n\n self.assertEqual(400, response.status_code)\n\n expected_message = \"Missing mandatory attribute: %s.\" % field\n actual_message = json.loads(response.data)[\"message\"]\n self.assertEqual(expected_message, actual_message)","title":""},{"docid":"9295e9c7b73bd42ab237f9b45e52fc4b","score":"0.53606826","text":"def parse_json(view_func):\n\n @wraps(view_func)\n def _wrapped_view(request, *args, **kwargs):\n if request.method in ['POST', 'PUT', 'DELETE', 'PATCH']:\n if request.body:\n try:\n request.json = json.loads(request.body.decode())\n except ValueError:\n return json_error('Invalid JSON input')\n else:\n request.json = {}\n return view_func(request, *args, **kwargs)\n\n return _wrapped_view","title":""},{"docid":"79680c284c67fc57e3c27bedf41a36d9","score":"0.53461564","text":"def extract_json(body, schema):\n try:\n data = jsonutils.loads(body)\n except ValueError as exc:\n raise webob.exc.HTTPBadRequest(\n _('Malformed JSON: %(error)s') % {'error': exc},\n json_formatter=json_error_formatter)\n try:\n jsonschema.validate(data, schema,\n format_checker=jsonschema.FormatChecker())\n except jsonschema.ValidationError as exc:\n raise webob.exc.HTTPBadRequest(\n _('JSON does not validate: %(error)s') % {'error': exc},\n json_formatter=json_error_formatter)\n return data","title":""},{"docid":"e68d9ec6287195a9d4449529f1acf05d","score":"0.5320189","text":"def validate_input_schema(schema_dict):\n def decorator(f):\n @wraps(f)\n def wrapper(*args, **kwargs):\n try:\n validate(request.json, schema_dict)\n except ValidationError as e:\n return jsonify({\"error\": e.message}), 400\n return f(*args, **kwargs)\n return wrapper\n return decorator","title":""},{"docid":"83a873aba1b4c16b56d579a3406f6074","score":"0.5315797","text":"def test_flair_post_route_400_missing_required_field(self) -> None:\n request_body = json.dumps({\"username\": \"andy\"})\n\n response: Response = self.client.post(\n \"/v2/flair/\",\n data=request_body,\n content_type=\"application/json\",\n headers={\"Authorization\": f\"Bearer {self.jwt}\"},\n )\n response_json: dict = response.get_json()\n self.assertEqual(response.status_code, 400)\n self.assertEqual(response_json.get(\"self\"), \"/v2/flair\")\n self.assertEqual(response_json.get(\"added\"), False)\n self.assertIsNone(response_json.get(\"flair\"))\n self.assertEqual(\n response_json.get(\"error\"), \"'username' and 'flair' are required fields\"\n )","title":""},{"docid":"563e047bc0c66a005b9a3a0529423ef7","score":"0.5309327","text":"def check_request_validity(request):\n data = request.get_json() or None\n if not data:\n abort(make_response(jsonify(\n message=\"Bad request. Request data must be json formatted\"), 400))\n\n return data","title":""},{"docid":"8a7be89238b202b944122f36b25a26b7","score":"0.5308304","text":"def _validate(rh_method):\n @wraps(rh_method)\n @gen.coroutine\n def _wrapper(self, *args, **kwargs):\n # In case the specified input_schema is ``None``, we\n # don't json.loads the input, but just set it to ``None``\n # instead.\n if input_schema is not None:\n # Attempt to json.loads the input\n try:\n # TODO: Assuming UTF-8 encoding for all requests,\n # find a nice way of determining this from charset\n # in headers if provided\n encoding = \"UTF-8\"\n input_ = json.loads(self.request.body.decode(encoding))\n except ValueError as e:\n raise jsonschema.ValidationError(\n \"Input is malformed; could not decode JSON object.\"\n )\n # Validate the received input\n jsonschema.validate(\n input_,\n input_schema\n )\n else:\n input_ = None\n\n # A json.loads'd version of self.request[\"body\"] is now available\n # as self.body\n setattr(self, \"body\", input_)\n # Call the requesthandler method\n output = rh_method(self, *args, **kwargs)\n # If the rh_method returned a Future a la `raise Return(value)`\n # we grab the output.\n if isinstance(output, Future):\n output = yield output\n\n if output_schema is not None:\n # We wrap output in an object before validating in case\n # output is a string (and ergo not a validatable JSON object)\n try:\n jsonschema.validate(\n {\"result\": output},\n {\n \"type\": \"object\",\n \"properties\": {\n \"result\": output_schema\n },\n \"required\": [\"result\"]\n }\n )\n except jsonschema.ValidationError as e:\n # We essentially re-raise this as a TypeError because\n # we don't want this error data passed back to the client\n # because it's a fault on our end. The client should\n # only see a 500 - Internal Server Error.\n raise TypeError(str(e))\n\n # If no ValidationError has been raised up until here, we write\n # back output\n self.success(output)\n\n setattr(_wrapper, \"input_schema\", input_schema)\n setattr(_wrapper, \"output_schema\", output_schema)\n setattr(_wrapper, \"input_example\", input_example)\n setattr(_wrapper, \"output_example\", output_example)\n\n return _wrapper","title":""},{"docid":"2b5eb1ec585361b956158f3489c1be95","score":"0.5296139","text":"def process_body(self):\n\n body = self.request.body\n method = self.request.method\n\n if method == 'POST' or method == 'PUT':\n content_type = self.request.headers.get('Content-Type')\n\n if content_type.startswith('application/json'):\n try:\n processed_body = json.loads(\n body, object_pairs_hook=collections.OrderedDict)\n except (TypeError, ValueError) as ex:\n raise exceptions.InvalidArgument('invalid body: %s' % ex)\n else:\n processed_body = self.request.arguments\n else:\n processed_body = None\n\n self.request.body_arguments = processed_body","title":""},{"docid":"004665c472589d615f86ee2a8f322e25","score":"0.5292738","text":"def test_post_request_comments_fails_with_missing_fields_in_body(\n invalid_comment_body_with_missing_fields, client, request_headers\n):\n res = client.post(\n request_comments_url(1),\n headers=request_headers,\n json=invalid_comment_body_with_missing_fields\n )\n response = res.get_json()\n\n assert not response['success']\n assert (\n response['message']['message'][0] == 'Missing data for required field.'\n )\n assert res.status_code == 400","title":""},{"docid":"6e792dd59d992acc4928fe52e1cd9d6a","score":"0.5284317","text":"def test_webapi_request_fields_call_normalizes_params(self):\r\n @webapi_request_fields(\r\n required={\r\n 'required_param': {\r\n 'type': int,\r\n }\r\n },\r\n optional={\r\n 'optional_param': {\r\n 'type': bool,\r\n }\r\n },\r\n )\r\n def func(request, required_param=None, optional_param=None,\r\n extra_fields={}):\r\n func.seen = True\r\n self.assertTrue(isinstance(required_param, int))\r\n self.assertTrue(isinstance(optional_param, bool))\r\n self.assertEqual(required_param, 42)\r\n self.assertTrue(optional_param)\r\n self.assertFalse(extra_fields)\r\n\r\n result = func(RequestFactory().get(\r\n path='/',\r\n data={\r\n 'required_param': '42',\r\n 'optional_param': '1',\r\n }\r\n ))\r\n\r\n self.assertTrue(hasattr(func, 'seen'))\r\n self.assertEqual(result, None)","title":""},{"docid":"7cb755caa8f6b3a011f141dcbf1b9f4e","score":"0.5232812","text":"def test_HTTPrettyRequest_invalid_json_body():\n header = TEST_HEADER % {'content_type': 'application/json'}\n invalid_json = u\"{'hello', 'world','thisstringdoesntstops}\"\n request = HTTPrettyRequest(header, invalid_json)\n expect(request.parsed_body).to.equal(invalid_json)","title":""},{"docid":"be6029adc79cd3b6ae93081dfa9b956e","score":"0.5192925","text":"def test_webapi_request_fields_call_filter_special_params(self):\r\n @webapi_request_fields(\r\n required={\r\n 'required_param': {\r\n 'type': int,\r\n }\r\n },\r\n )\r\n def func(request, required_param=None, extra_fields={}):\r\n func.seen = True\r\n self.assertTrue(isinstance(required_param, int))\r\n self.assertEqual(required_param, 42)\r\n self.assertFalse(extra_fields)\r\n\r\n result = func(RequestFactory().get(\r\n path='/',\r\n data={\r\n 'required_param': '42',\r\n 'api_format': 'json',\r\n }\r\n ))\r\n\r\n self.assertTrue(hasattr(func, 'seen'))\r\n self.assertEqual(result, None)","title":""},{"docid":"d076a3e4261bf25c1dc9119a820af470","score":"0.51893365","text":"def validate_response_json(self):\n if not self.request.text:\n logging.debug(f'Body of request was empty (method={self.method})')\n\n elif 'Content-Type' not in self.request.headers:\n raise TableaurestError(f'`Content-Type` not found in request.headers {self.request.headers}')\n\n elif not self.request.headers['Content-Type'].lower().startswith('application/json'):\n raise TableaurestError(f'`Content-Type` is not set to JSON {self.request.headers}')\n\n return None","title":""},{"docid":"b2c9cde5b7c31d508156e65d114e85a5","score":"0.5169602","text":"def test_bad_get_body(self, req, includes):\n req[\"method\"] = \"GET\"\n\n with pytest.raises(exceptions.BadSchemaError):\n TRequest(req, includes)","title":""},{"docid":"7640541fa57417036e8f7adb50eed220","score":"0.5164708","text":"def parse_json_body(req):\n content_type = req.headers.get('Content-Type')\n if content_type and 'application/json' in req.headers.get('Content-Type'):\n try:\n return parse_json(req.body)\n except (TypeError, ValueError):\n pass\n return {}","title":""},{"docid":"110da85b61b895341ae026e47dc4ae31","score":"0.5130501","text":"def validate_json_payload(payload):\n\n try:\n validate(payload, payload_schema)\n except ValidationError:\n raise InvalidUsage('JSON schema mismatch: please review your input data')","title":""},{"docid":"7efb665c2cd942c741ffbbd33d2c4486","score":"0.5128083","text":"def test_request_body_parameter_validation_with_valid_value(user_post_schema):\n request = RequestFactory(\n url='http://www.example.com/post/',\n content_type='application/json',\n body=json.dumps({'username': 'Test User', 'email': 'test@example.com'}),\n method=POST,\n )\n\n validate_request(\n request=request,\n schema=user_post_schema,\n )","title":""},{"docid":"46f26b6d16797850976081810b280469","score":"0.5125526","text":"def require(fields: Iterable[str]) -> bool:\r\n return flask.request.get_json(True) and all(\r\n field in flask.request.json for field in fields)","title":""},{"docid":"60a740c334f5b5f71095e960ce246643","score":"0.5110942","text":"def test_webapi_request_fields_call_validation_int(self):\r\n @webapi_request_fields(\r\n required={\r\n 'myint': {\r\n 'type': int,\r\n }\r\n }\r\n )\r\n def func(request, myint=False, extra_fields={}):\r\n func.seen = True\r\n\r\n result = func(RequestFactory().get(\r\n path='/',\r\n data={\r\n 'myint': 'abc',\r\n }\r\n ))\r\n\r\n self.assertFalse(hasattr(func, 'seen'))\r\n self.assertEqual(result[0], INVALID_FORM_DATA)\r\n self.assertTrue('fields' in result[1])\r\n self.assertTrue('myint' in result[1]['fields'])","title":""},{"docid":"4e9d3420085a8ab03b8802fcf59a23e4","score":"0.5092112","text":"def test_bad_json_body(self):\n resp, content = fake_response(\n b\"{\", {\"status\": \"400\", \"content-type\": \"application/json\"}, reason=\"Failed\"\n )\n error = HttpError(resp, content)\n self.assertEqual(\n str(error),\n '<HttpError 400 when requesting None returned \"Failed\". Details: \"{\">',\n )","title":""},{"docid":"5c12a302b866efd2a670711928d8b95e","score":"0.50848234","text":"def requires_body(self):","title":""},{"docid":"e1ab1f892d6669f187826dbe1a8f2e25","score":"0.5076735","text":"def ensure_json_content_type(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n ctype = request.headers['Content-Type']\n if ctype != 'application/json':\n return bad_request(\"Expected 'application/json', got '%s'\" % ctype)\n return f(*args, **kwargs)\n return decorated","title":""},{"docid":"c34fc9ac70f8a06b8faf672e21acc13d","score":"0.50684637","text":"def is_valid_body_grow(json_grow):\n return isinstance(json_grow, list) and len(json_grow) == 3 and json_grow[0] == \"body\" and\\\n not isinstance(json_grow[1], bool) and isinstance(json_grow[1], int) and json_grow[1] >= 0 and\\\n not isinstance(json_grow[2], bool) and isinstance(json_grow[2], int) and json_grow[2] >= 0","title":""},{"docid":"729c97380b6d4c0e8e9d5b6e510fc83c","score":"0.50676346","text":"def validate_width(request_body: bytes) -> None:\n\n if not request_body:\n return\n\n request_text = request_body.decode()\n if 'width' not in json.loads(request_text):\n return\n\n width = json.loads(request_text).get('width')\n\n width_is_number = isinstance(width, numbers.Number)\n width_positive = width_is_number and width > 0\n\n if not width_positive:\n raise Fail(status_code=HTTPStatus.BAD_REQUEST)","title":""},{"docid":"71bfdbd298d8fc4a17fad9f2395a103f","score":"0.5066914","text":"def it_should_fail_if_required_fields_missing(self):\n try:\n self.test.handler.validate(json.dumps({\n \"surname\": \"Test\"\n }))\n except HTTPError as e:\n if e.status_code == 400:\n return True\n return False","title":""},{"docid":"0429ec9f8590f93ee247e600a706e67c","score":"0.50525695","text":"def _json_parser(self, body):\n response = body.json()\n status = response.get('status', None)\n message = response.get('message', None)\n data = response.get('data', None)\n if not data or not status or not message:\n return response\n if message:\n return body.status_code, status, data, message\n return body.status_code, status, data","title":""},{"docid":"1e989f76803fa31225622a528a9b9cfd","score":"0.5033296","text":"def _prepare_request_body(self, body, params):\r\n try:\r\n if body is None:\r\n # Initialize empty resource for setting default value\r\n body = {self._resource_name: {}}\r\n data = body[self._resource_name]\r\n except KeyError:\r\n # raise if _resource_name is not in req body.\r\n raise exc.HTTPBadRequest(_(\"Unable to find '%s' in request body\") %\r\n self._resource_name)\r\n for param in params:\r\n param_name = param['param-name']\r\n param_value = data.get(param_name)\r\n # If the parameter wasn't found and it was required, return 400\r\n if param_value is None and param['required']:\r\n msg = (_(\"Failed to parse request. \"\r\n \"Parameter '%s' not specified\") % param_name)\r\n LOG.error(msg)\r\n raise exc.HTTPBadRequest(msg)\r\n data[param_name] = param_value or param.get('default-value')\r\n return body","title":""},{"docid":"800482986673e3e892ce614e1c4729fe","score":"0.4995849","text":"def test_request_body_parameter_validation_with_invalid_value(user_post_schema):\n request = RequestFactory(\n url='http://www.example.com/post/',\n content_type='application/json',\n body=json.dumps({'username': 'Test User', 'email': 'test'}),\n method=POST,\n )\n\n with pytest.raises(ValidationError) as err:\n validate_request(\n request=request,\n schema=user_post_schema,\n )\n\n assert_message_in_errors(\n MESSAGES['format']['invalid_email'],\n err.value.detail,\n )","title":""},{"docid":"735acbc7be7ed43c65c922cdae1a92fc","score":"0.49935794","text":"def test_recieves_invalid_payload():\n res, err = JsonValidator({}).validate('{as: \"df\"}')\n assert res is None and err == {'payload': 'INVALID PAYLOAD'}","title":""},{"docid":"841b701981fa09238ea25c059b4c913c","score":"0.497959","text":"def _processBody(self, body):\n return json.loads(body)","title":""},{"docid":"2d06f1202a55345660a36e25f683ab21","score":"0.4974131","text":"def test_constrain_primitive():\n constrain = {\n 'string': {},\n 'integer': {'type': int},\n 'float': {'type': float},\n 'boolean': {'type': bool},\n 'json': {\n 'type': dict\n },\n 'list': {\n 'type': list,\n },\n }\n json = {\n 'string': 'foo',\n 'integer': 42,\n 'float': 1.10,\n 'boolean': True,\n 'json': {},\n 'list': []\n }\n json_res = {\n 'string': 'foo',\n 'integer': 42,\n 'float': 1.10,\n 'boolean': True,\n }\n res, err = JsonValidator(constrain).validate(json)\n assert res == json_res and not err","title":""},{"docid":"5647671fa0eae2d1b5894b5141af3852","score":"0.4964748","text":"def invalid_comment_body_with_missing_fields(valid_request_model):\n return {\n 'staff_id': 1,\n }","title":""},{"docid":"374bbb3db5096120a63c78d39cd97219","score":"0.4932674","text":"def assert_types(\n json: Dict[str, Any], spec: List[Union[Optional, Required]]\n) -> Dict[str, Any]:\n ret = {}\n\n def key_to_name(key: str) -> str:\n key = key.replace(\"Index\", \"Idx\") # -> flagIndex -> flag_idx\n key = key.replace(\"relatedRoundId\", \"flagRound\")\n return snake_caseify(key)\n\n for entry in spec:\n if entry.key not in json:\n if isinstance(entry, Optional):\n ret[key_to_name(entry.key)] = entry.default\n else:\n raise ValueError(\n \"Required parameter {} is missing.\".format(\n stringify_spec_entry(entry)\n )\n )\n else:\n\n val = json[entry.key]\n if val is None and isinstance(entry, Optional):\n print(\"Inserted default\")\n val = entry.default\n\n if entry.key == \"method\" and val == \"havok\":\n logger.warning(\"Ignoring Havok -- calling Havoc instead\")\n val = \"havoc\"\n check_type(entry.key, val, entry.type)\n ret[key_to_name(entry.key)] = val\n return ret","title":""},{"docid":"aed1190d0a00808f402da07e0f9efa06","score":"0.49323118","text":"def test_flair_post_route_400_empty_body(self) -> None:\n response: Response = self.client.post(\n \"/v2/flair/\", headers={\"Authorization\": f\"Bearer {self.jwt}\"}\n )\n response_json: dict = response.get_json()\n self.assertEqual(response.status_code, 400)\n self.assertEqual(response_json.get(\"self\"), \"/v2/flair\")\n self.assertEqual(response_json.get(\"added\"), False)\n self.assertIsNone(response_json.get(\"flair\"))\n self.assertEqual(response_json.get(\"error\"), \"the request body isn't populated\")","title":""},{"docid":"ca7a511635499f44479746f39adf920b","score":"0.49273345","text":"def request_data_has(data_type, keys=[]):\n def decorator(method):\n @wraps(method)\n def wrapper(self, request, *args, **kwargs):\n data = eval('request.' + data_type)\n errors = {}\n for k in keys:\n if k not in data or data[k] == None:\n error = \"This parameter is required\"\n errors[k] = error\n if errors:\n return JsonResponse(errors, status=400)\n return method(self, request, *args, **kwargs)\n return wrapper\n return decorator","title":""},{"docid":"8dfcbc3011cac8fcb497c42094b38471","score":"0.4904753","text":"async def test_convert_handler_required_params_validation(client, params):\n all_fields = ('from', 'to', 'amount')\n empty_fields = [field for field in all_fields if field not in params]\n\n response = await client.get(\n client.app.router['convert'].url_for(), params=params\n )\n json_response = await response.json()\n\n assert response.status == 422\n for field in empty_fields:\n assert field in json_response\n assert json_response[field] == ['Missing data for required field.']","title":""},{"docid":"0add8e45cb55759569e52a962225e73e","score":"0.490065","text":"def test_validator_needs_constrain():\n with pytest.raises(AttributeError):\n JsonValidator('')\n assert JsonValidator({})","title":""},{"docid":"a0caed77dd097a754cfab63956b733ec","score":"0.48713663","text":"def nestedparams():\n def decorator(view_func):\n @wraps(view_func, assigned=available_attrs(view_func))\n def _wrapped_view(request, *view_args, **view_kwargs):\n if not hasattr(request, 'PARAMS'):\n try:\n request.PARAMS = process_request(request, view_kwargs)\n\n except JSONParsingError as jsonParseErr:\n return HttpResponseBadRequest()\n\n except ParameterTypeError as paramTypeErr:\n return HttpResponseBadRequest()\n\n return view_func(request, *view_args, **view_kwargs)\n return _wrapped_view\n return decorator","title":""},{"docid":"e714aae41e59707d135d068ddf63f6e8","score":"0.48646373","text":"def validate(event):\n # get the required keys to validate the event\n required_keys = get_required_keys(event)\n for key in required_keys:\n if key not in event:\n logger.error(f\"Request event did not have parameter: {key}\")\n raise BadRequest(f\"Bad request. API body does not have the necessary parameter: {key}\")\n\n return event","title":""},{"docid":"ba449e432f84ef093e9b6a3159f1cbcc","score":"0.48372477","text":"def get_payload(cls, method, ref, params):\n payload = bottle.request.json\n if method in ('GET', 'DELETE'):\n if payload is not None:\n raise err.JSONValidationError('JSON Body is not expected here.')\n payload = {}\n else:\n if payload is None:\n raise err.JSONValidationError('JSON Body is expected here.')\n if not is_dict(payload):\n raise err.JSONValidationError('JSON Body is not in the expected format.')\n return payload","title":""},{"docid":"63b6107adeb2460a750395095b3cdbcc","score":"0.48334843","text":"def test_validate_with_json_dict(self):\r\n self.field.run_validators({'a': 1, 'b': 2})","title":""},{"docid":"bf53c7820405242a33cc707f83bd69ae","score":"0.48291922","text":"def required(cls, fields=[], dataDict={}):\n notFound = []\n if len(fields)>0:\n notFound = [i for i in fields if i not in dataDict or len(dataDict[i])<1]\n\n if (len(notFound)> 0):\n return cls.make_retun_dict( False,\"Validation error,Following fields are required: {}\".format(\", \".join(notFound)))\n \n else:\n return cls.make_retun_dict(True)","title":""},{"docid":"b0cc651a594f071c8b04379fd8917f7e","score":"0.48179507","text":"def _from_annotated_args(cls, *args: Any) -> Optional[Field]:\n for arg in args:\n if isinstance(arg, Field):\n arg._validate()\n return arg\n return None","title":""},{"docid":"45728009d53ceda05467bfe2237fbff7","score":"0.48127243","text":"def validate_all(cls, json: Dict[str, Any]) -> None:\n pass","title":""},{"docid":"395fb81abee4e2eb53b0e7429b32a3dc","score":"0.48090747","text":"def test_schema_validation_unnamed(app, client):\n openapi = OpenAPI(app)\n mock = Mock()\n\n @app.route('/', methods=['post'])\n @openapi.schema({\n 'type': 'object',\n 'required': ['spam']\n })\n def handler():\n mock()\n\n assert handler.schema == {\n 'type': 'object',\n 'required': ['spam']\n }\n with pytest.raises(ValidationError):\n client.post('/', content_type='application/json', data='{}')\n assert not mock.called","title":""},{"docid":"c0ebaffce4e487dfc81bf2eb2ccbf1b3","score":"0.4802218","text":"def json_to_object(schema, data_mandatory=True):\n json_data = request.get_json()\n if not json_data:\n return {'msg': 'No input data provided'}, 400 # bad request\n data, errors = schema.load(json_data)\n if errors:\n errors['msg'] = \"An error occured when parsing the request.\"\n return errors, 422 # unprocessable entity\n if (not data) and data_mandatory:\n return {'msg': 'No input data provided'}, 400 # bad request\n return data, 200","title":""},{"docid":"9c719d357b1a84c2f6d9f623d1d75404","score":"0.47990102","text":"def _try_decode_query_or_body(request: falcon.Request, hint: Any) -> Any:\n # An assumption is being made here, That only POST, PUT and PATCH can have bodies.\n if request.method.lower() in ['post', 'put', 'patch']:\n key = 'media'\n\n else:\n key = 'params'\n\n return TypingMiddleware._decode_or_raise_error(hint, getattr(request, key, None))","title":""},{"docid":"e9bfece13b1241dcc4dfc702bda12b32","score":"0.47969884","text":"def from_json(json_post):\n body = json_post.get('body')\n if body is None or body =='':\n raise ValidationError('post does not have a body')\n return Post(body=body)","title":""},{"docid":"f2b630ee511554b282ad4240c75ffae3","score":"0.47928947","text":"def test_schema_validation_ok(app, client):\n openapi = OpenAPI(app)\n mock = Mock()\n\n @app.route('/', methods=['post'])\n @openapi.schema({\n 'type': 'object',\n 'required': ['spam']\n })\n def handler():\n mock()\n return ''\n\n client.post('/', content_type='application/json', data='{\"spam\": \"bacon\"}')\n assert mock.called","title":""},{"docid":"eb316f57f35f68481ca155227bef3baa","score":"0.47881228","text":"def no_op(payload):\n raise BadRequestError(f\"Invalid request type.\")","title":""},{"docid":"78ed8f0dc01485b2a7d12920deabdf7c","score":"0.47844324","text":"def validate_schema(data_schema, logger=None):\n def decorator(main_func):\n\n def wrapper(*args, **kwargs):\n\n try:\n validate(kwargs.get('body'), data_schema)\n except ValidationError as e:\n if logger is not None:\n logger.error(e.message, exc_info=True)\n raise\n return main_func(*args, **kwargs)\n\n return wrapper\n\n return decorator","title":""},{"docid":"9740a46330c0ecd74a5b7dcf26636b52","score":"0.47657433","text":"def __response_validate(response: dict):\n if len(response) != 2:\n raise WrongNumberOfFieldsException()\n if 'error' not in response:\n raise MissingErrorFieldException()\n if 'result' not in response:\n raise MissingResultFieldException()","title":""},{"docid":"c537ec6111d3c5538b503e12240f76c4","score":"0.4762196","text":"def it_should_allow_all_optional_fields(self):\n\n fields = self.required\n fields.update(self.optional)\n\n self.test.handler.validate(json.dumps(fields))\n return True","title":""},{"docid":"04bc3fcb0f51b1b740f6b6d880bd472c","score":"0.47620207","text":"def test_if_request_get_json_is_none(self):\n res = self.client().post(\n '/questions/add',\n json={\n 'question': '',\n 'answer': 'Liver bites',\n 'difficulty': 1,\n 'category': 6})\n data = json.loads(res.data)\n\n self.assertEqual(res.status_code, 422)\n self.assertEqual(data['message'], 'unprocessable request')","title":""},{"docid":"0e3b2eaf195409f46156e2cbc8e7360e","score":"0.47564715","text":"def __validate_args(self, req_dict, required_args):\n\n\t\tif required_args is not None:\n\t\t\tmessage = \"missing.parameters: Requires the following request parameter(s):\"\n\t\t\tfirst_line = True\n\t\t\targs_clean = True\n\n\t\t\tfor arg in required_args:\n\t\t\t\tif first_line:\n\t\t\t\t\tfirst_line = False\n\t\t\t\telse:\n\t\t\t\t\tmessage += \",\"\n\t\t\t\tmessage += \" \"\n\t\t\t\tmessage += arg\n\t\t\t\t# blank parameters i.e. foo=&bar=1 their value in the FieldStorage\n\t\t\t\t# will be an empyt string. The req_dict is generated from a FieldStorage\n\t\t\t\t# object in the dispatcher\n\t\t\t\tif not req_dict.has_key(arg) or req_dict[arg] == '':\n\t\t\t\t\targs_clean = False\n\t\t\t\t\tmessage += ' (*)'\n\t\t\tif args_clean == False:\n\t\t\t\traise RestClientError(message)","title":""},{"docid":"912caa224ed2dd7f5177c76bbdb1e016","score":"0.47500008","text":"def validate(self, apiobj, method, api, param, safe):\n if method == \"GET\":\n self._validate_args(param, safe)\n elif method == \"PUT\":\n self._validate_put_args(param, safe)","title":""},{"docid":"04bc6cfdbc4635ecdedd5136f2fb582b","score":"0.4746554","text":"def test_missing_message_json_body(self):\n resp, content = fake_response(\n b\"{}\",\n {\"status\": \"400\", \"content-type\": \"application/json\"},\n reason=\"Failed\",\n )\n error = HttpError(resp, content)\n self.assertEqual(str(error), '<HttpError 400 \"Failed\">')","title":""},{"docid":"96f961a19ac837ff73467ddc79dd2ebb","score":"0.47428128","text":"def handle_validate(f):\n\n @functools.wraps(f)\n def wrapper(self, *func_args, **func_kwargs):\n try:\n return f(self, *func_args, **func_kwargs)\n except eowyn_exc.InvalidDataException as ida:\n flask_restful.abort(400, message=str(ida))\n return wrapper","title":""},{"docid":"561e2976897abe952a78c5fe7bf3c13a","score":"0.474216","text":"def validate_get_args(self, req_dict):\n\n\t\tif not (req_dict.has_key(\"account_number\") or req_dict.has_key(\"email_address\") or \\\n\t\t\treq_dict.has_key('user_name') or req_dict.has_key('username')):\n\t\t\traise RestClientError(\"You need at least an account_number, email_address, user_name, or username\")","title":""},{"docid":"3c6ee14fe0c7abee3b9f0b5d788195f8","score":"0.47371843","text":"def test_custom_validator_getter(app, client):\n openapi = OpenAPI(app)\n mock = Mock()\n\n @openapi.validatorgetter\n def get_validator(schema):\n return mock(schema)\n\n @app.route('/', methods=['POST'])\n @openapi.schema({'type': 'object'})\n def handler():\n return ''\n client.post('/', content_type='application/json', data='{}')\n assert mock.call_args == call({'type': 'object'})\n assert mock.return_value.validate.call_args == call({})","title":""},{"docid":"dc4a9a4c82714d77215d8812cc3b1276","score":"0.47335318","text":"def test_required_field_validation(self):\n\n resp = self.client.post(webhook_url, {})\n self.assertEqual(resp.status_code, 400)\n\n errors = json.loads(resp.content.decode())\n self.assertIn('X_GOOG_RESOURCE_STATE', errors)\n self.assertIn('X_GOOG_RESOURCE_URI', errors)\n self.assertIn('X_GOOG_RESOURCE_ID', errors)\n self.assertIn('X_GOOG_MESSAGE_NUMBER', errors)\n self.assertIn('X_GOOG_CHANNEL_ID', errors)","title":""},{"docid":"6ffea7b253ba6550061901e849c770bd","score":"0.47221944","text":"def precheck(f):\n @wraps(f)\n def wrap(*args, **kwargs):\n diary = Diary.query.get(kwargs['diaryId'])\n entry = Entry.query.get(kwargs['entryId'])\n\n if not diary:\n return jsonify({'warning': 'Diary Not Found'}), 404\n\n if not entry:\n return jsonify({'warning': 'Entry Not Found'}), 404\n\n if args[0] != entry.entryer.id:\n return jsonify({'warning': 'Not Allowed, you are not owner'}), 401\n\n return f(*args, **kwargs)\n return wrap","title":""},{"docid":"ea2d752acf996d93a8d600791c8193a4","score":"0.4717143","text":"def check_process(self, kwargs):\n try:\n exp_params = []\n check_arguments(exp_params, kwargs)\n except Exception as ex:\n return HttpErrorResponse(\"%s\" % ex)\n\n return HttpJsonResponse()","title":""},{"docid":"b79ed3669ca2701fb83f8ceb8f1ad1bf","score":"0.47066665","text":"def validate_request(req: Dict) -> bool:\n\n assert req is not None, \"no JSON payload\"\n\n # Check that the files are there\n assert \"files\" in req, \"no 'files' field in payload\"\n\n # Check that the gui flag is there\n assert \"isGui\" in req, \"no 'isGui' field in payload\"\n\n assert \"apiKey\" in req, \"no 'apiKey' field in payload\"\n\n # Check the api key is correct\n assert req[\"apiKey\"] == API_KEY, \"apiKey is incorrect\"","title":""},{"docid":"2b19bc0a440f3cb632d81587ef950e6e","score":"0.470358","text":"def assertContainsJSON(\n self, response, spec, status_code=200, msg_prefix=\"\"\n ):\n\n if status_code and (response.status_code != status_code):\n try:\n content = response.json()\n except (ValueError, JSONDecodeError):\n content = response.content\n\n msg = (\n f\"{(msg_prefix + ': ') if msg_prefix else ''}\"\n f\"Wrong status code: {response.status_code} != {status_code} : \"\n f\"(expected {status_code})\\n\"\n f\"======:\\n{content}\"\n )\n raise AssertionError(msg)\n\n # spec=None means no body expected\n if spec is None:\n if response.content:\n msg = (\n f\"{(msg_prefix + ': ') if msg_prefix else ''}\"\n f\"Content found when bodyless response specified: \"\n f\"======:\\n{response.content}\"\n )\n raise AssertionError(msg)\n else:\n # no body, status already checked, looks good!\n return\n\n try:\n # handle non-json payloads, which is an error\n content = response.json()\n except (ValueError, JSONDecodeError) as exc:\n msg = (\n f\"{(msg_prefix + ': ') if msg_prefix else ''}\"\n f\"{exc.__class__.__name__} on json decode: \"\n f\"{exc}\\n\"\n f\"======:\\n{response.content}\"\n )\n raise AssertionError(msg) from exc\n\n # do the json check\n bad_keys = check_dict_keys(content, spec)\n\n if bad_keys:\n msg = (\n f\"{(msg_prefix + ': ') if msg_prefix else ''}\"\n f\"Dict does not match spec on these keys:\\n\" +\n '\\n'.join(print_bad_keys(bad_keys))\n )\n exc = AssertionError(msg)\n exc.bad_keys = bad_keys\n raise exc","title":""},{"docid":"71db900fcb9737cc9d45d1692330364b","score":"0.47006607","text":"def valid(response: Response) -> dict:\n if response.status_code in [200, 201, 204]:\n return response.json()\n if response.status_code in [401]:\n raise RestClientUnauthorizedException(\"Incorrect login or password\")\n raise RestClientException(f\"Request failed: {response.status_code}, {response.text}\")","title":""},{"docid":"004acc421836d71d70224f100aeee063","score":"0.46991357","text":"async def validation_exception_handler(request, exc):\n return JSONResponse(\n status_code=HTTP_400_BAD_REQUEST,\n content=jsonable_encoder({\"detail\": exc.errors()}),\n )","title":""},{"docid":"302c26b00a29ec028ad6e6d3ef858ed7","score":"0.46984336","text":"def json_request_entity(fun):\n @functools.wraps(fun)\n def aux(self, request):\n content_type = request.getHeader('Content-Type')\n if content_type != PROV_MIME_TYPE:\n return respond_error(request,\n 'Entity must be in media type \"%s\".' % PROV_MIME_TYPE,\n http.UNSUPPORTED_MEDIA_TYPE)\n else:\n try:\n content = json.loads(request.content.getvalue())\n except ValueError, e:\n logger.info('Received invalid JSON document: %s', e)\n return respond_error(request, 'Invalid JSON document: %s' % e)\n else:\n return fun(self, request, content)\n return aux","title":""},{"docid":"6e055c3bf6bf1fe2fff389e68284cf94","score":"0.4694684","text":"def validate_params(required):\n\n def _validate_params(f):\n @wraps(f)\n def wrapper(*args, **kwargs):\n if not isinstance(required, (list, tuple)):\n raise TypeError, 'The params required must be list!'\n for req in required:\n if len(req) < 2:\n raise ValueError, \"The required's item length must >= 2!\"\n # Check the key is Exist:\n try:\n client_val = request_data()[req[0]]\n except KeyError, e:\n raise ParamError('Key = {} required'.format(req[0]))\n client_val_type = req[1]\n # Check teh key's type:\n if not isinstance(client_val, client_val_type):\n if isinstance(client_val, basestring):\n try:\n client_val = client_val_type(client_val)\n except ValueError, e:\n raise ParamError('Key = {} type err!'.format(req[0]))\n else:\n raise ParamError('Key = {} type err!'.format(req[0]))\n # Check the key's value is in pre-defined:\n if len(req) >= 3:\n if client_val not in req[2]:\n raise ParamError(\n 'Key = {} value = {} err!'.format(req[0], client_val))\n val = f(*args, **kwargs)\n return val\n\n return wrapper\n\n return _validate_params","title":""},{"docid":"f69664dd47063133cc3730593bc5612c","score":"0.46936616","text":"def test_lazy_validation():\n constrain = {\n 'a': {'type': int, 'gt': 10},\n 'b': {'type': int, 'lt': 100}\n }\n json = {'a': 'a', 'b': 'b'} # lazy case diferent type\n res, err = JsonValidator(constrain, lazy=True).validate(json)\n assert not res and len(err) == 1\n\n json = {'a': 9, 'b': 101} # lazy extra validation\n res, err = JsonValidator(constrain, lazy=True).validate(json)\n assert not res and len(err) == 1\n\n constrain = {\n 'birthdate': {'type': datetime, 'dformat': '%Y'},\n 'birthdate2': {'type': datetime, 'dformat': '%Y'}\n }\n json = { # lazy special type\n 'birthdate': '1990-12-24',\n 'birthdate2': '1990-12-24'\n }\n res, err = JsonValidator(constrain, lazy=True).validate(json)\n assert not res and len(err) == 1\n\n json = {} # empty field lazy error\n res, err = JsonValidator(constrain, lazy=True).validate(json)\n assert not res and len(err) == 1\n\n constrain = { # lazy dict field\n 'a': {\n 'type': dict,\n 'properties': {'b': {}}\n },\n 'c': {\n 'type': dict,\n 'properties': {'d': {}}\n },\n }\n json = {'a': {}, 'b': {}}\n res, err = JsonValidator(constrain, lazy=True).validate(json)\n assert len(err) == 1\n\n constrain = { # lazy list field\n 'a': {\n 'type': list,\n 'items': {'type': int}\n },\n 'b': {\n 'type': list,\n 'items': {'type': int}\n },\n }\n json = {'a': ['a'], 'b': ['b']}\n res, err = JsonValidator(constrain, lazy=True).validate(json)\n assert len(err) == 1","title":""},{"docid":"c590cf1246141fc84fb1b58ac801b4ef","score":"0.4690686","text":"def test_put_invalid_field(self):\n\n url = reverse('contact', args=[101])\n data = json.dumps({'first_name': 'newFirstName',\n 'secon_name': 'newSecondName'})\n\n response = self.client.put(url, data=data, content_type='application/json')\n\n expected_json_response = {}\n expected_json_response['error'] = 'Incorrect JSON format.'\n\n self.assertEqual(response.status_code, 400)\n self.assertJSONEqual(response.content.decode('utf-8'), json.dumps(expected_json_response))","title":""},{"docid":"5a75427fb3168b3930717851a5a1efa1","score":"0.46904883","text":"def api_request(func):\n\n @wraps(func)\n def decorated(*args, **kwargs):\n retv = func(*args, **kwargs)\n if \"error\" in retv:\n raise APIError(retv[\"status\"], retv[\"error_description\"])\n return retv\n return decorated","title":""},{"docid":"8064b17842e679f3f50835f2a7b2fea0","score":"0.46879628","text":"def parse_request_json(self, field):\n json_query = self.get_request_data(field=field)\n if json_query:\n return json.loads(json_query)\n else:\n return None","title":""},{"docid":"b9fc51fc4884105e7d1ab7d616fc5bd3","score":"0.46794912","text":"async def validation_exception_handler(request: Request, exc: RequestValidationError):\n return JSONResponse(\n status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,\n content=jsonable_encoder(\n {\n \"exception_message\": \"Invalid request received.\",\n \"exception_detail\": exc.errors(),\n \"request_body\": exc.body,\n }\n ),\n )","title":""},{"docid":"f1cb3278c60a9aa3f9d256696fe3f060","score":"0.46788523","text":"def require_auth(f):\n\n @wraps(f)\n def decorated(*args, **kwargs):\n #res = f(*args, **kwargs)\n headers = request.headers\n body = request.json\n if body and 'username' in body and 'password' in body:\n username = body.get('username')\n password = body.get('password')\n user = UserService.get_user_by_username(username=username)\n if not user or not user.verify_password(password):\n abort(401)\n kwargs['user_id'] = user.id\n elif 'token' in headers:\n user = UserService.verify_auth_token(headers['token'])\n if user is None:\n abort(401)\n kwargs['user_id'] = user.id\n else:\n abort(401)\n return f(*args, **kwargs)\n return decorated","title":""},{"docid":"95099ef10e718d22e4a4e9727bc7adb3","score":"0.46741766","text":"def validate_response_fields(\n response: func.HttpResponse,\n status_code: HTTPStatus = HTTPStatus.OK,\n mimetype: Optional[str] = \"application/json\",\n):\n\n assert response.status_code == status_code\n\n assert response.mimetype == mimetype","title":""},{"docid":"f5b11bc1440fa5db5d3bd1450eed9faf","score":"0.46708184","text":"def get_argument(req_handler, name, default=None):\n\n if req_handler.request.headers.get(\"Content-Type\") != APPLICATION_JSON:\n return req_handler.get_argument(name, default)\n\n try:\n parsed_body = json.loads(req_handler.request.body)\n except Exception as ex:\n raise HTTPError(log_message=\"Error decoding JSON: {}\".format(ex))\n\n if not isinstance(parsed_body, dict):\n raise HTTPError(log_message=\"Not a JSON object: {}\".format(parsed_body))\n\n return parsed_body.get(name, default)","title":""},{"docid":"a0ad103184a562ee207099753dd2c3e6","score":"0.46691513","text":"def test_invalid_type(self, field, field_type, field_number):\n body = {\n \"first_object_id\": 1,\n \"first_object_type\": \"Control\",\n \"second_object_id\": 1,\n \"second_object_type\": \"Product\",\n }\n body[field] = None\n\n response = self.client.post(self.ENDPOINT_URL,\n content_type=\"application/json\",\n data=json.dumps(body))\n\n self.assertEqual(400, response.status_code)\n\n expected_message = \"Invalid object %s for %s object.\" % (field_type,\n field_number)\n actual_message = json.loads(response.data)[\"message\"]\n self.assertEqual(expected_message, actual_message)","title":""},{"docid":"5b664a64f81285e33196b56977bc1b4e","score":"0.46658015","text":"def validate(json_dict, schema_dict):\n for item in schema_dict.get('requiredProperties', []):\n if item not in json_dict:\n raise ValidationError('Item \"{}\" required but not found.'.format(item))\n\n for item, value in json_dict.items():\n if item in schema_dict['properties'] and schema_dict['properties'][item].get('validFunc', None):\n if not schema_dict['properties'][item]['validFunc'](value):\n raise ValidationError('Item \"{}\" value invalid: \"{}\".'.format(item, value))\n elif not schema_dict.get('additionalPropertiesAllowed', True) and item not in schema_dict['properties']:\n raise ValidationError('Additional property not allowed: \"{}\"'.format(item))","title":""},{"docid":"be8ae319a4ae9fb05f9126f00e17dd17","score":"0.4664558","text":"def input_fn(request_body, request_content_type):\n \n if request_content_type == \"application/json\":\n request = json.loads(request_body)\n else:\n request = request_body\n\n return request","title":""}],"string":"[\n {\n \"docid\": \"1f56a068edabb5c94c2214a769dcb306\",\n \"score\": \"0.7296733\",\n \"text\": \"def expect_json(*, strict=False, optional=None, **fields):\\n if optional is None:\\n optional = set()\\n\\n def wrapper(func):\\n @functools.wraps(func)\\n def inner(*args, **kwargs):\\n # ensure correct mimetype\\n if not request.is_json:\\n return error(400, \\\"expected json body\\\")\\n\\n # ensure body exists\\n body = request.get_json(silent=True) # return None on error\\n if body is None:\\n return error(400, \\\"missing or invalid body\\\")\\n\\n # ensure fields are present\\n if strict:\\n required = set(fields).difference(optional) \\n if not (required.issubset(body) and set(fields).issuperset(body)):\\n return error(400, f\\\"invalid body fields: expected {list(required)}, got {list(body)}\\\")\\n else:\\n if not set(fields).difference(optional).issubset(body):\\n return error(400, f\\\"missing body fields: {list(set(fields).difference(optional, body))}\\\")\\n\\n # check field types\\n for field, e_type in fields.items():\\n if field in optional and field not in body:\\n continue\\n if not isinstance(body[field], e_type):\\n return error(400, f\\\"expected {field} to be {e_type.__name__}\\\")\\n\\n # everything is good!\\n return func(body, *args, **kwargs)\\n\\n return inner\\n\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7337b191745a5cfffd827c7db5195131\",\n \"score\": \"0.7203641\",\n \"text\": \"def required_body_field(*body_fields):\\n def rest_method_wrapper(func):\\n def validator(*args, **kwargs):\\n # parse request argument from APIView sort of classes' methods\\n try:\\n request = args[1] # args: (api class, request) kwargs: {\\\"form\\\":, }\\n except:\\n raise Exception(\\\"This decorator should be used only for endpoint functions\\\")\\n body_data = request.data\\n\\n if not is_valid_bodyfield(body_data, body_fields):\\n exception_message = \\\\\\n \\\"actual body field: {}, \\\\nrequired body field: {}\\\".format(body_data, body_fields)\\n raise APIException(exception_message)\\n return func(*args, **kwargs)\\n return validator\\n return rest_method_wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ffa4cc2cacb4cbc94a9329c723d4992\",\n \"score\": \"0.6735275\",\n \"text\": \"def json_data_required(f):\\n\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if not request.get_json():\\n return {\\n \\\"status\\\": \\\"error\\\",\\n \\\"data\\\": None,\\n \\\"message\\\": MISSING_JSON_BODY_ERR_MSG,\\n }, 400\\n return f(*args, **kwargs)\\n\\n return decorated_function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8be21f4f768d8523905e3ff6f262d07\",\n \"score\": \"0.6626301\",\n \"text\": \"def wrapper(*inner_args, **inner_kwargs):\\n\\n try:\\n if query is not None:\\n query.strict = validate\\n url = furl(request.url)\\n inner_kwargs['_query'] = query.load(data=url.args)\\n\\n if body is not None:\\n body.strict = validate\\n json_data = request.get_json()\\n\\n if json_data is None:\\n # Set json_data to empty dict if body is empty, so it gets picked up by the validator\\n json_data = {}\\n\\n inner_kwargs['_body'] = body.load(data=json_data)\\n\\n except ValidationError as err:\\n return jsonify(err.messages), 422\\n\\n if output:\\n data = output.dump(f(*inner_args, **inner_kwargs))\\n return jsonify(data[0])\\n\\n return f(*inner_args, **inner_kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a581549f6b759408413c718d6c09f833\",\n \"score\": \"0.6591619\",\n \"text\": \"def validate_json(f):\\n @wraps(f)\\n def wrapper(*args, **kwargs):\\n try:\\n assert request.json\\n except Exception:\\n msg = \\\"payload must be a valid json\\\"\\n return jsonify({\\\"error\\\": msg}), 400\\n return f(*args, **kwargs)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0df634133e77a64924c9c0c6799fa0\",\n \"score\": \"0.6204612\",\n \"text\": \"def validate_nested(field, needed_field, _, data):\\n\\n if 'id' in data or 'name' in data:\\n return\\n raise marshmallow.ValidationError(\\n 'Missing data for required field if \\\\'%s\\\\' field is not '\\n 'provided.' % needed_field, field\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60c9bf5812f9905ab583ec51e9f3011b\",\n \"score\": \"0.61692417\",\n \"text\": \"def validate_request():\\n\\n def real_validate_request(f):\\n @wraps(f)\\n def decorated(*args,**kwargs):\\n if not request.json:\\n return {\\\"status\\\": \\\"fail\\\",\\n \\\"data\\\": {\\\"message\\\": \\\"Request must be a valid JSON\\\"}\\n }, 400\\n\\n return f(*args, **kwargs)\\n\\n return decorated\\n\\n return real_validate_request\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54692eb8dccab0be30c617acb3f3b9c0\",\n \"score\": \"0.60903764\",\n \"text\": \"def json_request_required(method):\\n def wrapper(self, *args, **kwargs):\\n if not self.request.content_type == 'application/json':\\n raise CoreException(400, 'Request should be application/json')\\n return method(self, *args, **kwargs)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1036b562ac00b1a779dbefc55a1294e1\",\n \"score\": \"0.6083337\",\n \"text\": \"def test_request_body_parameter_validation_with_nullable_field(user_post_schema):\\n request = RequestFactory(\\n url='http://www.example.com/post/',\\n content_type='application/json',\\n body=json.dumps({'username': 'Test User', 'email': 'test@example.com', 'age': None}),\\n method=POST,\\n )\\n\\n validate_request(\\n request=request,\\n schema=user_post_schema,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7225b6a218b5bf1516fd39da26f9533c\",\n \"score\": \"0.601417\",\n \"text\": \"def validate_required_fields(json, required_fields, context):\\n missing_fields = [f for f in required_fields\\n if not f in json.get_field_names()]\\n\\n if missing_fields:\\n raise SwaggerError(\\n \\\"Missing fields: %s\\\" % ', '.join(missing_fields), context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84b67bf22a6aeffb4fe41282bfdffef2\",\n \"score\": \"0.59481406\",\n \"text\": \"def validate_json_request(request, required=None, optional=None):\\n # Verify that the request contains a Json object\\n if not request.json:\\n raise NoJsonInRequest()\\n obj = request.json\\n # Ensure that all required elements are present\\n possible_keys = []\\n if not required is None:\\n possible_keys = required\\n for key in required:\\n if not key in obj:\\n raise InvalidRequest('missing element \\\\'' + key + '\\\\'')\\n # Ensure that no unwanted elements are in the request body\\n if not optional is None:\\n possible_keys += optional\\n for key in obj:\\n if not key in possible_keys:\\n raise InvalidRequest('unknown element \\\\'' + key + '\\\\'')\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019903a5087f2a34a20e3e99500e918d\",\n \"score\": \"0.59265155\",\n \"text\": \"def json_fields_required(*fields):\\n def dec(f):\\n async def res_func(request):\\n try:\\n r = await request.json()\\n list_of_missing_fields = [field for field in fields if field not in r]\\n if list_of_missing_fields:\\n return web.json_response({'error': True, \\n 'error_text': 'Missing fields', \\n 'fields': list_of_missing_fields})\\n return await f(request)\\n except:\\n pass\\n return web.json_response({'error': True, 'error_text': 'Wrong request'})\\n res_func.__doc__ = f.__doc__\\n return res_func\\n return dec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a7bd31a65da2af7c9c3ff8024f35568\",\n \"score\": \"0.5707317\",\n \"text\": \"def verify_fields(function):\\n @wraps(function)\\n def decorated(instance, *args, **kwargs):\\n \\\"\\\"\\\"The decorator function.\\\"\\\"\\\"\\n data = request.get_json(force=True, silent=True)\\n if not data:\\n raise BadRequestException(\\\"No data received from request\\\")\\n for required in instance.__model__.__table__.columns:\\n if required.name in (\\n instance.__model__.__table__.primary_key.columns):\\n continue\\n if required.name not in data:\\n raise ForbiddenException('{} required'.format(required))\\n return function(instance, *args, **kwargs)\\n\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9fef39c0938f221633e843c86191a4\",\n \"score\": \"0.5684331\",\n \"text\": \"def is_valid_json(r):\\n if len(r.body) == 0:\\n return\\n else:\\n try:\\n json.loads(r.body.decode('utf-8'))\\n except Exception as e:\\n e\\n raise exceptions.ValidationFailed('Invalid JSON string')\\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d58f990aa4311cdf82fec92f2d5146\",\n \"score\": \"0.56408155\",\n \"text\": \"def wrapper(*args, **kwargs):\\n request = args[1]\\n try:\\n for parameter in required_parameters:\\n assert request.args[parameter][0]\\n except (KeyError, IndexError, AssertionError):\\n request.setResponseCode(400)\\n raise MissingParameterException(\\\"Parameter '%s' is \\\"\\n \\\"required.\\\" % parameter)\\n return method(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"310b8c17f9ecbca33096ae212fc2d917\",\n \"score\": \"0.55955005\",\n \"text\": \"def __init__(self):\\n super(NoJsonInRequest, self).__init__('invalid request body format')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab4f01c89517c0e4a9a4a6e209badb24\",\n \"score\": \"0.5489333\",\n \"text\": \"def test_webapi_request_fields_call_with_unexpected_arg(self):\\r\\n @webapi_request_fields(\\r\\n required={\\r\\n 'required_param': {\\r\\n 'type': int,\\r\\n }\\r\\n },\\r\\n )\\r\\n def func(request, required_param=None, extra_fields={}):\\r\\n func.seen = True\\r\\n\\r\\n result = func(RequestFactory().get(\\r\\n path='/',\\r\\n data={\\r\\n 'required_param': '42',\\r\\n 'optional_param': '1',\\r\\n }\\r\\n ))\\r\\n\\r\\n self.assertFalse(hasattr(func, 'seen'))\\r\\n self.assertEqual(result[0], INVALID_FORM_DATA)\\r\\n self.assertTrue('fields' in result[1])\\r\\n self.assertTrue('optional_param' in result[1]['fields'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3403c9446a35ced5a7a56b53c09ef908\",\n \"score\": \"0.5475372\",\n \"text\": \"def requires_fields(*fields):\\n\\n def decorator(f):\\n @functools.wraps(f)\\n def wrapper(self, *args, **kwargs):\\n fr = FieldsRequest.create(fields)\\n if hasattr(self, \\\"populate\\\"):\\n self.populate(fr)\\n for field in fields:\\n if not self.has_field(field):\\n raise MissingFieldException(f\\\"Missing required field '{field}'\\\")\\n return f(self, *args, **kwargs)\\n\\n return wrapper\\n\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a18521b6fa6977cbe18366a935cb80\",\n \"score\": \"0.544335\",\n \"text\": \"def it_should_discard_undeclared_fields(self):\\n fields = self.required\\n fields.update(self.optional)\\n fields['foo'] = 42\\n\\n res = self.test.handler.validate(json.dumps(fields))\\n return res.get('foo') is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a15e356da6c53a4ea6b7f5eef387049\",\n \"score\": \"0.5401029\",\n \"text\": \"def webapi_request_fields(required={}, optional={}, allow_unknown=False):\\r\\n @webapi_decorator\\r\\n def _dec(view_func):\\r\\n @webapi_response_errors(INVALID_FORM_DATA)\\r\\n def _validate(*args, **kwargs):\\r\\n request = _find_httprequest(args)\\r\\n\\r\\n if request.method == 'GET':\\r\\n request_fields = request.GET\\r\\n else:\\r\\n request_fields = request.POST\\r\\n\\r\\n extra_fields = {}\\r\\n invalid_fields = {}\\r\\n supported_fields = required.copy()\\r\\n supported_fields.update(optional)\\r\\n\\r\\n for field_name, value in six.iteritems(request_fields):\\r\\n if field_name in SPECIAL_PARAMS:\\r\\n # These are special names and can be ignored.\\r\\n continue\\r\\n\\r\\n if field_name not in supported_fields:\\r\\n if allow_unknown:\\r\\n extra_fields[field_name] = value\\r\\n else:\\r\\n invalid_fields[field_name] = ['Field is not supported']\\r\\n\\r\\n for field_name, info in six.iteritems(required):\\r\\n temp_fields = request_fields\\r\\n\\r\\n if info['type'] == file:\\r\\n temp_fields = request.FILES\\r\\n\\r\\n if temp_fields.get(field_name, None) is None:\\r\\n invalid_fields[field_name] = ['This field is required']\\r\\n\\r\\n new_kwargs = kwargs.copy()\\r\\n new_kwargs['extra_fields'] = extra_fields\\r\\n\\r\\n for field_name, info in six.iteritems(supported_fields):\\r\\n if isinstance(info['type'], file):\\r\\n continue\\r\\n\\r\\n value = request_fields.get(field_name, None)\\r\\n\\r\\n if value is not None:\\r\\n if type(info['type']) in (list, tuple):\\r\\n # This is a multiple-choice. Make sure the value is\\r\\n # valid.\\r\\n choices = info['type']\\r\\n\\r\\n if value not in choices:\\r\\n invalid_fields[field_name] = [\\r\\n '\\\"%s\\\" is not a valid value. Valid values '\\r\\n 'are: %s' % (\\r\\n value,\\r\\n ', '.join(['\\\"%s\\\"' % choice\\r\\n for choice in choices])\\r\\n )\\r\\n ]\\r\\n else:\\r\\n try:\\r\\n if issubclass(info['type'], bool):\\r\\n value = value in (1, \\\"1\\\", True, \\\"True\\\", \\\"true\\\")\\r\\n elif issubclass(info['type'], int):\\r\\n try:\\r\\n value = int(value)\\r\\n except ValueError:\\r\\n invalid_fields[field_name] = [\\r\\n '\\\"%s\\\" is not an integer' % value\\r\\n ]\\r\\n except TypeError:\\r\\n # The field isn't a class type. This is a\\r\\n # coding error on the developer's side.\\r\\n raise TypeError('\\\"%s\\\" is not a valid field type' %\\r\\n info['type'])\\r\\n\\r\\n new_kwargs[field_name] = value\\r\\n\\r\\n if invalid_fields:\\r\\n return INVALID_FORM_DATA, {\\r\\n 'fields': invalid_fields,\\r\\n }\\r\\n\\r\\n return view_func(*args, **new_kwargs)\\r\\n\\r\\n _validate.required_fields = required.copy()\\r\\n _validate.optional_fields = optional.copy()\\r\\n\\r\\n if hasattr(view_func, 'required_fields'):\\r\\n _validate.required_fields.update(view_func.required_fields)\\r\\n\\r\\n if hasattr(view_func, 'optional_fields'):\\r\\n _validate.optional_fields.update(view_func.optional_fields)\\r\\n\\r\\n return _validate\\r\\n\\r\\n return _dec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1315fab629f3dc0a3eb4c5b28cb2eae9\",\n \"score\": \"0.5371032\",\n \"text\": \"def test_mandatory_fields(self, field):\\n body = {\\n \\\"first_object_id\\\": 1,\\n \\\"first_object_type\\\": \\\"Control\\\",\\n \\\"second_object_id\\\": 1,\\n \\\"second_object_type\\\": \\\"Product\\\",\\n }\\n del body[field]\\n\\n response = self.client.post(self.ENDPOINT_URL,\\n content_type=\\\"application/json\\\",\\n data=json.dumps(body))\\n\\n self.assertEqual(400, response.status_code)\\n\\n expected_message = \\\"Missing mandatory attribute: %s.\\\" % field\\n actual_message = json.loads(response.data)[\\\"message\\\"]\\n self.assertEqual(expected_message, actual_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9295e9c7b73bd42ab237f9b45e52fc4b\",\n \"score\": \"0.53606826\",\n \"text\": \"def parse_json(view_func):\\n\\n @wraps(view_func)\\n def _wrapped_view(request, *args, **kwargs):\\n if request.method in ['POST', 'PUT', 'DELETE', 'PATCH']:\\n if request.body:\\n try:\\n request.json = json.loads(request.body.decode())\\n except ValueError:\\n return json_error('Invalid JSON input')\\n else:\\n request.json = {}\\n return view_func(request, *args, **kwargs)\\n\\n return _wrapped_view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79680c284c67fc57e3c27bedf41a36d9\",\n \"score\": \"0.53461564\",\n \"text\": \"def extract_json(body, schema):\\n try:\\n data = jsonutils.loads(body)\\n except ValueError as exc:\\n raise webob.exc.HTTPBadRequest(\\n _('Malformed JSON: %(error)s') % {'error': exc},\\n json_formatter=json_error_formatter)\\n try:\\n jsonschema.validate(data, schema,\\n format_checker=jsonschema.FormatChecker())\\n except jsonschema.ValidationError as exc:\\n raise webob.exc.HTTPBadRequest(\\n _('JSON does not validate: %(error)s') % {'error': exc},\\n json_formatter=json_error_formatter)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e68d9ec6287195a9d4449529f1acf05d\",\n \"score\": \"0.5320189\",\n \"text\": \"def validate_input_schema(schema_dict):\\n def decorator(f):\\n @wraps(f)\\n def wrapper(*args, **kwargs):\\n try:\\n validate(request.json, schema_dict)\\n except ValidationError as e:\\n return jsonify({\\\"error\\\": e.message}), 400\\n return f(*args, **kwargs)\\n return wrapper\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83a873aba1b4c16b56d579a3406f6074\",\n \"score\": \"0.5315797\",\n \"text\": \"def test_flair_post_route_400_missing_required_field(self) -> None:\\n request_body = json.dumps({\\\"username\\\": \\\"andy\\\"})\\n\\n response: Response = self.client.post(\\n \\\"/v2/flair/\\\",\\n data=request_body,\\n content_type=\\\"application/json\\\",\\n headers={\\\"Authorization\\\": f\\\"Bearer {self.jwt}\\\"},\\n )\\n response_json: dict = response.get_json()\\n self.assertEqual(response.status_code, 400)\\n self.assertEqual(response_json.get(\\\"self\\\"), \\\"/v2/flair\\\")\\n self.assertEqual(response_json.get(\\\"added\\\"), False)\\n self.assertIsNone(response_json.get(\\\"flair\\\"))\\n self.assertEqual(\\n response_json.get(\\\"error\\\"), \\\"'username' and 'flair' are required fields\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"563e047bc0c66a005b9a3a0529423ef7\",\n \"score\": \"0.5309327\",\n \"text\": \"def check_request_validity(request):\\n data = request.get_json() or None\\n if not data:\\n abort(make_response(jsonify(\\n message=\\\"Bad request. Request data must be json formatted\\\"), 400))\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a7be89238b202b944122f36b25a26b7\",\n \"score\": \"0.5308304\",\n \"text\": \"def _validate(rh_method):\\n @wraps(rh_method)\\n @gen.coroutine\\n def _wrapper(self, *args, **kwargs):\\n # In case the specified input_schema is ``None``, we\\n # don't json.loads the input, but just set it to ``None``\\n # instead.\\n if input_schema is not None:\\n # Attempt to json.loads the input\\n try:\\n # TODO: Assuming UTF-8 encoding for all requests,\\n # find a nice way of determining this from charset\\n # in headers if provided\\n encoding = \\\"UTF-8\\\"\\n input_ = json.loads(self.request.body.decode(encoding))\\n except ValueError as e:\\n raise jsonschema.ValidationError(\\n \\\"Input is malformed; could not decode JSON object.\\\"\\n )\\n # Validate the received input\\n jsonschema.validate(\\n input_,\\n input_schema\\n )\\n else:\\n input_ = None\\n\\n # A json.loads'd version of self.request[\\\"body\\\"] is now available\\n # as self.body\\n setattr(self, \\\"body\\\", input_)\\n # Call the requesthandler method\\n output = rh_method(self, *args, **kwargs)\\n # If the rh_method returned a Future a la `raise Return(value)`\\n # we grab the output.\\n if isinstance(output, Future):\\n output = yield output\\n\\n if output_schema is not None:\\n # We wrap output in an object before validating in case\\n # output is a string (and ergo not a validatable JSON object)\\n try:\\n jsonschema.validate(\\n {\\\"result\\\": output},\\n {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"result\\\": output_schema\\n },\\n \\\"required\\\": [\\\"result\\\"]\\n }\\n )\\n except jsonschema.ValidationError as e:\\n # We essentially re-raise this as a TypeError because\\n # we don't want this error data passed back to the client\\n # because it's a fault on our end. The client should\\n # only see a 500 - Internal Server Error.\\n raise TypeError(str(e))\\n\\n # If no ValidationError has been raised up until here, we write\\n # back output\\n self.success(output)\\n\\n setattr(_wrapper, \\\"input_schema\\\", input_schema)\\n setattr(_wrapper, \\\"output_schema\\\", output_schema)\\n setattr(_wrapper, \\\"input_example\\\", input_example)\\n setattr(_wrapper, \\\"output_example\\\", output_example)\\n\\n return _wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b5eb1ec585361b956158f3489c1be95\",\n \"score\": \"0.5296139\",\n \"text\": \"def process_body(self):\\n\\n body = self.request.body\\n method = self.request.method\\n\\n if method == 'POST' or method == 'PUT':\\n content_type = self.request.headers.get('Content-Type')\\n\\n if content_type.startswith('application/json'):\\n try:\\n processed_body = json.loads(\\n body, object_pairs_hook=collections.OrderedDict)\\n except (TypeError, ValueError) as ex:\\n raise exceptions.InvalidArgument('invalid body: %s' % ex)\\n else:\\n processed_body = self.request.arguments\\n else:\\n processed_body = None\\n\\n self.request.body_arguments = processed_body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"004665c472589d615f86ee2a8f322e25\",\n \"score\": \"0.5292738\",\n \"text\": \"def test_post_request_comments_fails_with_missing_fields_in_body(\\n invalid_comment_body_with_missing_fields, client, request_headers\\n):\\n res = client.post(\\n request_comments_url(1),\\n headers=request_headers,\\n json=invalid_comment_body_with_missing_fields\\n )\\n response = res.get_json()\\n\\n assert not response['success']\\n assert (\\n response['message']['message'][0] == 'Missing data for required field.'\\n )\\n assert res.status_code == 400\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e792dd59d992acc4928fe52e1cd9d6a\",\n \"score\": \"0.5284317\",\n \"text\": \"def test_webapi_request_fields_call_normalizes_params(self):\\r\\n @webapi_request_fields(\\r\\n required={\\r\\n 'required_param': {\\r\\n 'type': int,\\r\\n }\\r\\n },\\r\\n optional={\\r\\n 'optional_param': {\\r\\n 'type': bool,\\r\\n }\\r\\n },\\r\\n )\\r\\n def func(request, required_param=None, optional_param=None,\\r\\n extra_fields={}):\\r\\n func.seen = True\\r\\n self.assertTrue(isinstance(required_param, int))\\r\\n self.assertTrue(isinstance(optional_param, bool))\\r\\n self.assertEqual(required_param, 42)\\r\\n self.assertTrue(optional_param)\\r\\n self.assertFalse(extra_fields)\\r\\n\\r\\n result = func(RequestFactory().get(\\r\\n path='/',\\r\\n data={\\r\\n 'required_param': '42',\\r\\n 'optional_param': '1',\\r\\n }\\r\\n ))\\r\\n\\r\\n self.assertTrue(hasattr(func, 'seen'))\\r\\n self.assertEqual(result, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cb755caa8f6b3a011f141dcbf1b9f4e\",\n \"score\": \"0.5232812\",\n \"text\": \"def test_HTTPrettyRequest_invalid_json_body():\\n header = TEST_HEADER % {'content_type': 'application/json'}\\n invalid_json = u\\\"{'hello', 'world','thisstringdoesntstops}\\\"\\n request = HTTPrettyRequest(header, invalid_json)\\n expect(request.parsed_body).to.equal(invalid_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be6029adc79cd3b6ae93081dfa9b956e\",\n \"score\": \"0.5192925\",\n \"text\": \"def test_webapi_request_fields_call_filter_special_params(self):\\r\\n @webapi_request_fields(\\r\\n required={\\r\\n 'required_param': {\\r\\n 'type': int,\\r\\n }\\r\\n },\\r\\n )\\r\\n def func(request, required_param=None, extra_fields={}):\\r\\n func.seen = True\\r\\n self.assertTrue(isinstance(required_param, int))\\r\\n self.assertEqual(required_param, 42)\\r\\n self.assertFalse(extra_fields)\\r\\n\\r\\n result = func(RequestFactory().get(\\r\\n path='/',\\r\\n data={\\r\\n 'required_param': '42',\\r\\n 'api_format': 'json',\\r\\n }\\r\\n ))\\r\\n\\r\\n self.assertTrue(hasattr(func, 'seen'))\\r\\n self.assertEqual(result, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d076a3e4261bf25c1dc9119a820af470\",\n \"score\": \"0.51893365\",\n \"text\": \"def validate_response_json(self):\\n if not self.request.text:\\n logging.debug(f'Body of request was empty (method={self.method})')\\n\\n elif 'Content-Type' not in self.request.headers:\\n raise TableaurestError(f'`Content-Type` not found in request.headers {self.request.headers}')\\n\\n elif not self.request.headers['Content-Type'].lower().startswith('application/json'):\\n raise TableaurestError(f'`Content-Type` is not set to JSON {self.request.headers}')\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2c9cde5b7c31d508156e65d114e85a5\",\n \"score\": \"0.5169602\",\n \"text\": \"def test_bad_get_body(self, req, includes):\\n req[\\\"method\\\"] = \\\"GET\\\"\\n\\n with pytest.raises(exceptions.BadSchemaError):\\n TRequest(req, includes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7640541fa57417036e8f7adb50eed220\",\n \"score\": \"0.5164708\",\n \"text\": \"def parse_json_body(req):\\n content_type = req.headers.get('Content-Type')\\n if content_type and 'application/json' in req.headers.get('Content-Type'):\\n try:\\n return parse_json(req.body)\\n except (TypeError, ValueError):\\n pass\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"110da85b61b895341ae026e47dc4ae31\",\n \"score\": \"0.5130501\",\n \"text\": \"def validate_json_payload(payload):\\n\\n try:\\n validate(payload, payload_schema)\\n except ValidationError:\\n raise InvalidUsage('JSON schema mismatch: please review your input data')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7efb665c2cd942c741ffbbd33d2c4486\",\n \"score\": \"0.5128083\",\n \"text\": \"def test_request_body_parameter_validation_with_valid_value(user_post_schema):\\n request = RequestFactory(\\n url='http://www.example.com/post/',\\n content_type='application/json',\\n body=json.dumps({'username': 'Test User', 'email': 'test@example.com'}),\\n method=POST,\\n )\\n\\n validate_request(\\n request=request,\\n schema=user_post_schema,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f26b6d16797850976081810b280469\",\n \"score\": \"0.5125526\",\n \"text\": \"def require(fields: Iterable[str]) -> bool:\\r\\n return flask.request.get_json(True) and all(\\r\\n field in flask.request.json for field in fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a740c334f5b5f71095e960ce246643\",\n \"score\": \"0.5110942\",\n \"text\": \"def test_webapi_request_fields_call_validation_int(self):\\r\\n @webapi_request_fields(\\r\\n required={\\r\\n 'myint': {\\r\\n 'type': int,\\r\\n }\\r\\n }\\r\\n )\\r\\n def func(request, myint=False, extra_fields={}):\\r\\n func.seen = True\\r\\n\\r\\n result = func(RequestFactory().get(\\r\\n path='/',\\r\\n data={\\r\\n 'myint': 'abc',\\r\\n }\\r\\n ))\\r\\n\\r\\n self.assertFalse(hasattr(func, 'seen'))\\r\\n self.assertEqual(result[0], INVALID_FORM_DATA)\\r\\n self.assertTrue('fields' in result[1])\\r\\n self.assertTrue('myint' in result[1]['fields'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e9d3420085a8ab03b8802fcf59a23e4\",\n \"score\": \"0.5092112\",\n \"text\": \"def test_bad_json_body(self):\\n resp, content = fake_response(\\n b\\\"{\\\", {\\\"status\\\": \\\"400\\\", \\\"content-type\\\": \\\"application/json\\\"}, reason=\\\"Failed\\\"\\n )\\n error = HttpError(resp, content)\\n self.assertEqual(\\n str(error),\\n '<HttpError 400 when requesting None returned \\\"Failed\\\". Details: \\\"{\\\">',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c12a302b866efd2a670711928d8b95e\",\n \"score\": \"0.50848234\",\n \"text\": \"def requires_body(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ab1f892d6669f187826dbe1a8f2e25\",\n \"score\": \"0.5076735\",\n \"text\": \"def ensure_json_content_type(f):\\n @wraps(f)\\n def decorated(*args, **kwargs):\\n ctype = request.headers['Content-Type']\\n if ctype != 'application/json':\\n return bad_request(\\\"Expected 'application/json', got '%s'\\\" % ctype)\\n return f(*args, **kwargs)\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34fc9ac70f8a06b8faf672e21acc13d\",\n \"score\": \"0.50684637\",\n \"text\": \"def is_valid_body_grow(json_grow):\\n return isinstance(json_grow, list) and len(json_grow) == 3 and json_grow[0] == \\\"body\\\" and\\\\\\n not isinstance(json_grow[1], bool) and isinstance(json_grow[1], int) and json_grow[1] >= 0 and\\\\\\n not isinstance(json_grow[2], bool) and isinstance(json_grow[2], int) and json_grow[2] >= 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729c97380b6d4c0e8e9d5b6e510fc83c\",\n \"score\": \"0.50676346\",\n \"text\": \"def validate_width(request_body: bytes) -> None:\\n\\n if not request_body:\\n return\\n\\n request_text = request_body.decode()\\n if 'width' not in json.loads(request_text):\\n return\\n\\n width = json.loads(request_text).get('width')\\n\\n width_is_number = isinstance(width, numbers.Number)\\n width_positive = width_is_number and width > 0\\n\\n if not width_positive:\\n raise Fail(status_code=HTTPStatus.BAD_REQUEST)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71bfdbd298d8fc4a17fad9f2395a103f\",\n \"score\": \"0.5066914\",\n \"text\": \"def it_should_fail_if_required_fields_missing(self):\\n try:\\n self.test.handler.validate(json.dumps({\\n \\\"surname\\\": \\\"Test\\\"\\n }))\\n except HTTPError as e:\\n if e.status_code == 400:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0429ec9f8590f93ee247e600a706e67c\",\n \"score\": \"0.50525695\",\n \"text\": \"def _json_parser(self, body):\\n response = body.json()\\n status = response.get('status', None)\\n message = response.get('message', None)\\n data = response.get('data', None)\\n if not data or not status or not message:\\n return response\\n if message:\\n return body.status_code, status, data, message\\n return body.status_code, status, data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e989f76803fa31225622a528a9b9cfd\",\n \"score\": \"0.5033296\",\n \"text\": \"def _prepare_request_body(self, body, params):\\r\\n try:\\r\\n if body is None:\\r\\n # Initialize empty resource for setting default value\\r\\n body = {self._resource_name: {}}\\r\\n data = body[self._resource_name]\\r\\n except KeyError:\\r\\n # raise if _resource_name is not in req body.\\r\\n raise exc.HTTPBadRequest(_(\\\"Unable to find '%s' in request body\\\") %\\r\\n self._resource_name)\\r\\n for param in params:\\r\\n param_name = param['param-name']\\r\\n param_value = data.get(param_name)\\r\\n # If the parameter wasn't found and it was required, return 400\\r\\n if param_value is None and param['required']:\\r\\n msg = (_(\\\"Failed to parse request. \\\"\\r\\n \\\"Parameter '%s' not specified\\\") % param_name)\\r\\n LOG.error(msg)\\r\\n raise exc.HTTPBadRequest(msg)\\r\\n data[param_name] = param_value or param.get('default-value')\\r\\n return body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800482986673e3e892ce614e1c4729fe\",\n \"score\": \"0.4995849\",\n \"text\": \"def test_request_body_parameter_validation_with_invalid_value(user_post_schema):\\n request = RequestFactory(\\n url='http://www.example.com/post/',\\n content_type='application/json',\\n body=json.dumps({'username': 'Test User', 'email': 'test'}),\\n method=POST,\\n )\\n\\n with pytest.raises(ValidationError) as err:\\n validate_request(\\n request=request,\\n schema=user_post_schema,\\n )\\n\\n assert_message_in_errors(\\n MESSAGES['format']['invalid_email'],\\n err.value.detail,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"735acbc7be7ed43c65c922cdae1a92fc\",\n \"score\": \"0.49935794\",\n \"text\": \"def test_recieves_invalid_payload():\\n res, err = JsonValidator({}).validate('{as: \\\"df\\\"}')\\n assert res is None and err == {'payload': 'INVALID PAYLOAD'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"841b701981fa09238ea25c059b4c913c\",\n \"score\": \"0.497959\",\n \"text\": \"def _processBody(self, body):\\n return json.loads(body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d06f1202a55345660a36e25f683ab21\",\n \"score\": \"0.4974131\",\n \"text\": \"def test_constrain_primitive():\\n constrain = {\\n 'string': {},\\n 'integer': {'type': int},\\n 'float': {'type': float},\\n 'boolean': {'type': bool},\\n 'json': {\\n 'type': dict\\n },\\n 'list': {\\n 'type': list,\\n },\\n }\\n json = {\\n 'string': 'foo',\\n 'integer': 42,\\n 'float': 1.10,\\n 'boolean': True,\\n 'json': {},\\n 'list': []\\n }\\n json_res = {\\n 'string': 'foo',\\n 'integer': 42,\\n 'float': 1.10,\\n 'boolean': True,\\n }\\n res, err = JsonValidator(constrain).validate(json)\\n assert res == json_res and not err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5647671fa0eae2d1b5894b5141af3852\",\n \"score\": \"0.4964748\",\n \"text\": \"def invalid_comment_body_with_missing_fields(valid_request_model):\\n return {\\n 'staff_id': 1,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"374bbb3db5096120a63c78d39cd97219\",\n \"score\": \"0.4932674\",\n \"text\": \"def assert_types(\\n json: Dict[str, Any], spec: List[Union[Optional, Required]]\\n) -> Dict[str, Any]:\\n ret = {}\\n\\n def key_to_name(key: str) -> str:\\n key = key.replace(\\\"Index\\\", \\\"Idx\\\") # -> flagIndex -> flag_idx\\n key = key.replace(\\\"relatedRoundId\\\", \\\"flagRound\\\")\\n return snake_caseify(key)\\n\\n for entry in spec:\\n if entry.key not in json:\\n if isinstance(entry, Optional):\\n ret[key_to_name(entry.key)] = entry.default\\n else:\\n raise ValueError(\\n \\\"Required parameter {} is missing.\\\".format(\\n stringify_spec_entry(entry)\\n )\\n )\\n else:\\n\\n val = json[entry.key]\\n if val is None and isinstance(entry, Optional):\\n print(\\\"Inserted default\\\")\\n val = entry.default\\n\\n if entry.key == \\\"method\\\" and val == \\\"havok\\\":\\n logger.warning(\\\"Ignoring Havok -- calling Havoc instead\\\")\\n val = \\\"havoc\\\"\\n check_type(entry.key, val, entry.type)\\n ret[key_to_name(entry.key)] = val\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aed1190d0a00808f402da07e0f9efa06\",\n \"score\": \"0.49323118\",\n \"text\": \"def test_flair_post_route_400_empty_body(self) -> None:\\n response: Response = self.client.post(\\n \\\"/v2/flair/\\\", headers={\\\"Authorization\\\": f\\\"Bearer {self.jwt}\\\"}\\n )\\n response_json: dict = response.get_json()\\n self.assertEqual(response.status_code, 400)\\n self.assertEqual(response_json.get(\\\"self\\\"), \\\"/v2/flair\\\")\\n self.assertEqual(response_json.get(\\\"added\\\"), False)\\n self.assertIsNone(response_json.get(\\\"flair\\\"))\\n self.assertEqual(response_json.get(\\\"error\\\"), \\\"the request body isn't populated\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca7a511635499f44479746f39adf920b\",\n \"score\": \"0.49273345\",\n \"text\": \"def request_data_has(data_type, keys=[]):\\n def decorator(method):\\n @wraps(method)\\n def wrapper(self, request, *args, **kwargs):\\n data = eval('request.' + data_type)\\n errors = {}\\n for k in keys:\\n if k not in data or data[k] == None:\\n error = \\\"This parameter is required\\\"\\n errors[k] = error\\n if errors:\\n return JsonResponse(errors, status=400)\\n return method(self, request, *args, **kwargs)\\n return wrapper\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dfcbc3011cac8fcb497c42094b38471\",\n \"score\": \"0.4904753\",\n \"text\": \"async def test_convert_handler_required_params_validation(client, params):\\n all_fields = ('from', 'to', 'amount')\\n empty_fields = [field for field in all_fields if field not in params]\\n\\n response = await client.get(\\n client.app.router['convert'].url_for(), params=params\\n )\\n json_response = await response.json()\\n\\n assert response.status == 422\\n for field in empty_fields:\\n assert field in json_response\\n assert json_response[field] == ['Missing data for required field.']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0add8e45cb55759569e52a962225e73e\",\n \"score\": \"0.490065\",\n \"text\": \"def test_validator_needs_constrain():\\n with pytest.raises(AttributeError):\\n JsonValidator('')\\n assert JsonValidator({})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0caed77dd097a754cfab63956b733ec\",\n \"score\": \"0.48713663\",\n \"text\": \"def nestedparams():\\n def decorator(view_func):\\n @wraps(view_func, assigned=available_attrs(view_func))\\n def _wrapped_view(request, *view_args, **view_kwargs):\\n if not hasattr(request, 'PARAMS'):\\n try:\\n request.PARAMS = process_request(request, view_kwargs)\\n\\n except JSONParsingError as jsonParseErr:\\n return HttpResponseBadRequest()\\n\\n except ParameterTypeError as paramTypeErr:\\n return HttpResponseBadRequest()\\n\\n return view_func(request, *view_args, **view_kwargs)\\n return _wrapped_view\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e714aae41e59707d135d068ddf63f6e8\",\n \"score\": \"0.48646373\",\n \"text\": \"def validate(event):\\n # get the required keys to validate the event\\n required_keys = get_required_keys(event)\\n for key in required_keys:\\n if key not in event:\\n logger.error(f\\\"Request event did not have parameter: {key}\\\")\\n raise BadRequest(f\\\"Bad request. API body does not have the necessary parameter: {key}\\\")\\n\\n return event\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba449e432f84ef093e9b6a3159f1cbcc\",\n \"score\": \"0.48372477\",\n \"text\": \"def get_payload(cls, method, ref, params):\\n payload = bottle.request.json\\n if method in ('GET', 'DELETE'):\\n if payload is not None:\\n raise err.JSONValidationError('JSON Body is not expected here.')\\n payload = {}\\n else:\\n if payload is None:\\n raise err.JSONValidationError('JSON Body is expected here.')\\n if not is_dict(payload):\\n raise err.JSONValidationError('JSON Body is not in the expected format.')\\n return payload\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63b6107adeb2460a750395095b3cdbcc\",\n \"score\": \"0.48334843\",\n \"text\": \"def test_validate_with_json_dict(self):\\r\\n self.field.run_validators({'a': 1, 'b': 2})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf53c7820405242a33cc707f83bd69ae\",\n \"score\": \"0.48291922\",\n \"text\": \"def required(cls, fields=[], dataDict={}):\\n notFound = []\\n if len(fields)>0:\\n notFound = [i for i in fields if i not in dataDict or len(dataDict[i])<1]\\n\\n if (len(notFound)> 0):\\n return cls.make_retun_dict( False,\\\"Validation error,Following fields are required: {}\\\".format(\\\", \\\".join(notFound)))\\n \\n else:\\n return cls.make_retun_dict(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0cc651a594f071c8b04379fd8917f7e\",\n \"score\": \"0.48179507\",\n \"text\": \"def _from_annotated_args(cls, *args: Any) -> Optional[Field]:\\n for arg in args:\\n if isinstance(arg, Field):\\n arg._validate()\\n return arg\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45728009d53ceda05467bfe2237fbff7\",\n \"score\": \"0.48127243\",\n \"text\": \"def validate_all(cls, json: Dict[str, Any]) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"395fb81abee4e2eb53b0e7429b32a3dc\",\n \"score\": \"0.48090747\",\n \"text\": \"def test_schema_validation_unnamed(app, client):\\n openapi = OpenAPI(app)\\n mock = Mock()\\n\\n @app.route('/', methods=['post'])\\n @openapi.schema({\\n 'type': 'object',\\n 'required': ['spam']\\n })\\n def handler():\\n mock()\\n\\n assert handler.schema == {\\n 'type': 'object',\\n 'required': ['spam']\\n }\\n with pytest.raises(ValidationError):\\n client.post('/', content_type='application/json', data='{}')\\n assert not mock.called\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ebaffce4e487dfc81bf2eb2ccbf1b3\",\n \"score\": \"0.4802218\",\n \"text\": \"def json_to_object(schema, data_mandatory=True):\\n json_data = request.get_json()\\n if not json_data:\\n return {'msg': 'No input data provided'}, 400 # bad request\\n data, errors = schema.load(json_data)\\n if errors:\\n errors['msg'] = \\\"An error occured when parsing the request.\\\"\\n return errors, 422 # unprocessable entity\\n if (not data) and data_mandatory:\\n return {'msg': 'No input data provided'}, 400 # bad request\\n return data, 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c719d357b1a84c2f6d9f623d1d75404\",\n \"score\": \"0.47990102\",\n \"text\": \"def _try_decode_query_or_body(request: falcon.Request, hint: Any) -> Any:\\n # An assumption is being made here, That only POST, PUT and PATCH can have bodies.\\n if request.method.lower() in ['post', 'put', 'patch']:\\n key = 'media'\\n\\n else:\\n key = 'params'\\n\\n return TypingMiddleware._decode_or_raise_error(hint, getattr(request, key, None))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9bfece13b1241dcc4dfc702bda12b32\",\n \"score\": \"0.47969884\",\n \"text\": \"def from_json(json_post):\\n body = json_post.get('body')\\n if body is None or body =='':\\n raise ValidationError('post does not have a body')\\n return Post(body=body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b630ee511554b282ad4240c75ffae3\",\n \"score\": \"0.47928947\",\n \"text\": \"def test_schema_validation_ok(app, client):\\n openapi = OpenAPI(app)\\n mock = Mock()\\n\\n @app.route('/', methods=['post'])\\n @openapi.schema({\\n 'type': 'object',\\n 'required': ['spam']\\n })\\n def handler():\\n mock()\\n return ''\\n\\n client.post('/', content_type='application/json', data='{\\\"spam\\\": \\\"bacon\\\"}')\\n assert mock.called\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb316f57f35f68481ca155227bef3baa\",\n \"score\": \"0.47881228\",\n \"text\": \"def no_op(payload):\\n raise BadRequestError(f\\\"Invalid request type.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ed8f0dc01485b2a7d12920deabdf7c\",\n \"score\": \"0.47844324\",\n \"text\": \"def validate_schema(data_schema, logger=None):\\n def decorator(main_func):\\n\\n def wrapper(*args, **kwargs):\\n\\n try:\\n validate(kwargs.get('body'), data_schema)\\n except ValidationError as e:\\n if logger is not None:\\n logger.error(e.message, exc_info=True)\\n raise\\n return main_func(*args, **kwargs)\\n\\n return wrapper\\n\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9740a46330c0ecd74a5b7dcf26636b52\",\n \"score\": \"0.47657433\",\n \"text\": \"def __response_validate(response: dict):\\n if len(response) != 2:\\n raise WrongNumberOfFieldsException()\\n if 'error' not in response:\\n raise MissingErrorFieldException()\\n if 'result' not in response:\\n raise MissingResultFieldException()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c537ec6111d3c5538b503e12240f76c4\",\n \"score\": \"0.4762196\",\n \"text\": \"def it_should_allow_all_optional_fields(self):\\n\\n fields = self.required\\n fields.update(self.optional)\\n\\n self.test.handler.validate(json.dumps(fields))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04bc3fcb0f51b1b740f6b6d880bd472c\",\n \"score\": \"0.47620207\",\n \"text\": \"def test_if_request_get_json_is_none(self):\\n res = self.client().post(\\n '/questions/add',\\n json={\\n 'question': '',\\n 'answer': 'Liver bites',\\n 'difficulty': 1,\\n 'category': 6})\\n data = json.loads(res.data)\\n\\n self.assertEqual(res.status_code, 422)\\n self.assertEqual(data['message'], 'unprocessable request')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e3b2eaf195409f46156e2cbc8e7360e\",\n \"score\": \"0.47564715\",\n \"text\": \"def __validate_args(self, req_dict, required_args):\\n\\n\\t\\tif required_args is not None:\\n\\t\\t\\tmessage = \\\"missing.parameters: Requires the following request parameter(s):\\\"\\n\\t\\t\\tfirst_line = True\\n\\t\\t\\targs_clean = True\\n\\n\\t\\t\\tfor arg in required_args:\\n\\t\\t\\t\\tif first_line:\\n\\t\\t\\t\\t\\tfirst_line = False\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tmessage += \\\",\\\"\\n\\t\\t\\t\\tmessage += \\\" \\\"\\n\\t\\t\\t\\tmessage += arg\\n\\t\\t\\t\\t# blank parameters i.e. foo=&bar=1 their value in the FieldStorage\\n\\t\\t\\t\\t# will be an empyt string. The req_dict is generated from a FieldStorage\\n\\t\\t\\t\\t# object in the dispatcher\\n\\t\\t\\t\\tif not req_dict.has_key(arg) or req_dict[arg] == '':\\n\\t\\t\\t\\t\\targs_clean = False\\n\\t\\t\\t\\t\\tmessage += ' (*)'\\n\\t\\t\\tif args_clean == False:\\n\\t\\t\\t\\traise RestClientError(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"912caa224ed2dd7f5177c76bbdb1e016\",\n \"score\": \"0.47500008\",\n \"text\": \"def validate(self, apiobj, method, api, param, safe):\\n if method == \\\"GET\\\":\\n self._validate_args(param, safe)\\n elif method == \\\"PUT\\\":\\n self._validate_put_args(param, safe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04bc6cfdbc4635ecdedd5136f2fb582b\",\n \"score\": \"0.4746554\",\n \"text\": \"def test_missing_message_json_body(self):\\n resp, content = fake_response(\\n b\\\"{}\\\",\\n {\\\"status\\\": \\\"400\\\", \\\"content-type\\\": \\\"application/json\\\"},\\n reason=\\\"Failed\\\",\\n )\\n error = HttpError(resp, content)\\n self.assertEqual(str(error), '<HttpError 400 \\\"Failed\\\">')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96f961a19ac837ff73467ddc79dd2ebb\",\n \"score\": \"0.47428128\",\n \"text\": \"def handle_validate(f):\\n\\n @functools.wraps(f)\\n def wrapper(self, *func_args, **func_kwargs):\\n try:\\n return f(self, *func_args, **func_kwargs)\\n except eowyn_exc.InvalidDataException as ida:\\n flask_restful.abort(400, message=str(ida))\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"561e2976897abe952a78c5fe7bf3c13a\",\n \"score\": \"0.474216\",\n \"text\": \"def validate_get_args(self, req_dict):\\n\\n\\t\\tif not (req_dict.has_key(\\\"account_number\\\") or req_dict.has_key(\\\"email_address\\\") or \\\\\\n\\t\\t\\treq_dict.has_key('user_name') or req_dict.has_key('username')):\\n\\t\\t\\traise RestClientError(\\\"You need at least an account_number, email_address, user_name, or username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c6ee14fe0c7abee3b9f0b5d788195f8\",\n \"score\": \"0.47371843\",\n \"text\": \"def test_custom_validator_getter(app, client):\\n openapi = OpenAPI(app)\\n mock = Mock()\\n\\n @openapi.validatorgetter\\n def get_validator(schema):\\n return mock(schema)\\n\\n @app.route('/', methods=['POST'])\\n @openapi.schema({'type': 'object'})\\n def handler():\\n return ''\\n client.post('/', content_type='application/json', data='{}')\\n assert mock.call_args == call({'type': 'object'})\\n assert mock.return_value.validate.call_args == call({})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc4a9a4c82714d77215d8812cc3b1276\",\n \"score\": \"0.47335318\",\n \"text\": \"def test_required_field_validation(self):\\n\\n resp = self.client.post(webhook_url, {})\\n self.assertEqual(resp.status_code, 400)\\n\\n errors = json.loads(resp.content.decode())\\n self.assertIn('X_GOOG_RESOURCE_STATE', errors)\\n self.assertIn('X_GOOG_RESOURCE_URI', errors)\\n self.assertIn('X_GOOG_RESOURCE_ID', errors)\\n self.assertIn('X_GOOG_MESSAGE_NUMBER', errors)\\n self.assertIn('X_GOOG_CHANNEL_ID', errors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ffea7b253ba6550061901e849c770bd\",\n \"score\": \"0.47221944\",\n \"text\": \"def precheck(f):\\n @wraps(f)\\n def wrap(*args, **kwargs):\\n diary = Diary.query.get(kwargs['diaryId'])\\n entry = Entry.query.get(kwargs['entryId'])\\n\\n if not diary:\\n return jsonify({'warning': 'Diary Not Found'}), 404\\n\\n if not entry:\\n return jsonify({'warning': 'Entry Not Found'}), 404\\n\\n if args[0] != entry.entryer.id:\\n return jsonify({'warning': 'Not Allowed, you are not owner'}), 401\\n\\n return f(*args, **kwargs)\\n return wrap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea2d752acf996d93a8d600791c8193a4\",\n \"score\": \"0.4717143\",\n \"text\": \"def check_process(self, kwargs):\\n try:\\n exp_params = []\\n check_arguments(exp_params, kwargs)\\n except Exception as ex:\\n return HttpErrorResponse(\\\"%s\\\" % ex)\\n\\n return HttpJsonResponse()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b79ed3669ca2701fb83f8ceb8f1ad1bf\",\n \"score\": \"0.47066665\",\n \"text\": \"def validate_request(req: Dict) -> bool:\\n\\n assert req is not None, \\\"no JSON payload\\\"\\n\\n # Check that the files are there\\n assert \\\"files\\\" in req, \\\"no 'files' field in payload\\\"\\n\\n # Check that the gui flag is there\\n assert \\\"isGui\\\" in req, \\\"no 'isGui' field in payload\\\"\\n\\n assert \\\"apiKey\\\" in req, \\\"no 'apiKey' field in payload\\\"\\n\\n # Check the api key is correct\\n assert req[\\\"apiKey\\\"] == API_KEY, \\\"apiKey is incorrect\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b19bc0a440f3cb632d81587ef950e6e\",\n \"score\": \"0.470358\",\n \"text\": \"def assertContainsJSON(\\n self, response, spec, status_code=200, msg_prefix=\\\"\\\"\\n ):\\n\\n if status_code and (response.status_code != status_code):\\n try:\\n content = response.json()\\n except (ValueError, JSONDecodeError):\\n content = response.content\\n\\n msg = (\\n f\\\"{(msg_prefix + ': ') if msg_prefix else ''}\\\"\\n f\\\"Wrong status code: {response.status_code} != {status_code} : \\\"\\n f\\\"(expected {status_code})\\\\n\\\"\\n f\\\"======:\\\\n{content}\\\"\\n )\\n raise AssertionError(msg)\\n\\n # spec=None means no body expected\\n if spec is None:\\n if response.content:\\n msg = (\\n f\\\"{(msg_prefix + ': ') if msg_prefix else ''}\\\"\\n f\\\"Content found when bodyless response specified: \\\"\\n f\\\"======:\\\\n{response.content}\\\"\\n )\\n raise AssertionError(msg)\\n else:\\n # no body, status already checked, looks good!\\n return\\n\\n try:\\n # handle non-json payloads, which is an error\\n content = response.json()\\n except (ValueError, JSONDecodeError) as exc:\\n msg = (\\n f\\\"{(msg_prefix + ': ') if msg_prefix else ''}\\\"\\n f\\\"{exc.__class__.__name__} on json decode: \\\"\\n f\\\"{exc}\\\\n\\\"\\n f\\\"======:\\\\n{response.content}\\\"\\n )\\n raise AssertionError(msg) from exc\\n\\n # do the json check\\n bad_keys = check_dict_keys(content, spec)\\n\\n if bad_keys:\\n msg = (\\n f\\\"{(msg_prefix + ': ') if msg_prefix else ''}\\\"\\n f\\\"Dict does not match spec on these keys:\\\\n\\\" +\\n '\\\\n'.join(print_bad_keys(bad_keys))\\n )\\n exc = AssertionError(msg)\\n exc.bad_keys = bad_keys\\n raise exc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71db900fcb9737cc9d45d1692330364b\",\n \"score\": \"0.47006607\",\n \"text\": \"def valid(response: Response) -> dict:\\n if response.status_code in [200, 201, 204]:\\n return response.json()\\n if response.status_code in [401]:\\n raise RestClientUnauthorizedException(\\\"Incorrect login or password\\\")\\n raise RestClientException(f\\\"Request failed: {response.status_code}, {response.text}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"004acc421836d71d70224f100aeee063\",\n \"score\": \"0.46991357\",\n \"text\": \"async def validation_exception_handler(request, exc):\\n return JSONResponse(\\n status_code=HTTP_400_BAD_REQUEST,\\n content=jsonable_encoder({\\\"detail\\\": exc.errors()}),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302c26b00a29ec028ad6e6d3ef858ed7\",\n \"score\": \"0.46984336\",\n \"text\": \"def json_request_entity(fun):\\n @functools.wraps(fun)\\n def aux(self, request):\\n content_type = request.getHeader('Content-Type')\\n if content_type != PROV_MIME_TYPE:\\n return respond_error(request,\\n 'Entity must be in media type \\\"%s\\\".' % PROV_MIME_TYPE,\\n http.UNSUPPORTED_MEDIA_TYPE)\\n else:\\n try:\\n content = json.loads(request.content.getvalue())\\n except ValueError, e:\\n logger.info('Received invalid JSON document: %s', e)\\n return respond_error(request, 'Invalid JSON document: %s' % e)\\n else:\\n return fun(self, request, content)\\n return aux\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e055c3bf6bf1fe2fff389e68284cf94\",\n \"score\": \"0.4694684\",\n \"text\": \"def validate_params(required):\\n\\n def _validate_params(f):\\n @wraps(f)\\n def wrapper(*args, **kwargs):\\n if not isinstance(required, (list, tuple)):\\n raise TypeError, 'The params required must be list!'\\n for req in required:\\n if len(req) < 2:\\n raise ValueError, \\\"The required's item length must >= 2!\\\"\\n # Check the key is Exist:\\n try:\\n client_val = request_data()[req[0]]\\n except KeyError, e:\\n raise ParamError('Key = {} required'.format(req[0]))\\n client_val_type = req[1]\\n # Check teh key's type:\\n if not isinstance(client_val, client_val_type):\\n if isinstance(client_val, basestring):\\n try:\\n client_val = client_val_type(client_val)\\n except ValueError, e:\\n raise ParamError('Key = {} type err!'.format(req[0]))\\n else:\\n raise ParamError('Key = {} type err!'.format(req[0]))\\n # Check the key's value is in pre-defined:\\n if len(req) >= 3:\\n if client_val not in req[2]:\\n raise ParamError(\\n 'Key = {} value = {} err!'.format(req[0], client_val))\\n val = f(*args, **kwargs)\\n return val\\n\\n return wrapper\\n\\n return _validate_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f69664dd47063133cc3730593bc5612c\",\n \"score\": \"0.46936616\",\n \"text\": \"def test_lazy_validation():\\n constrain = {\\n 'a': {'type': int, 'gt': 10},\\n 'b': {'type': int, 'lt': 100}\\n }\\n json = {'a': 'a', 'b': 'b'} # lazy case diferent type\\n res, err = JsonValidator(constrain, lazy=True).validate(json)\\n assert not res and len(err) == 1\\n\\n json = {'a': 9, 'b': 101} # lazy extra validation\\n res, err = JsonValidator(constrain, lazy=True).validate(json)\\n assert not res and len(err) == 1\\n\\n constrain = {\\n 'birthdate': {'type': datetime, 'dformat': '%Y'},\\n 'birthdate2': {'type': datetime, 'dformat': '%Y'}\\n }\\n json = { # lazy special type\\n 'birthdate': '1990-12-24',\\n 'birthdate2': '1990-12-24'\\n }\\n res, err = JsonValidator(constrain, lazy=True).validate(json)\\n assert not res and len(err) == 1\\n\\n json = {} # empty field lazy error\\n res, err = JsonValidator(constrain, lazy=True).validate(json)\\n assert not res and len(err) == 1\\n\\n constrain = { # lazy dict field\\n 'a': {\\n 'type': dict,\\n 'properties': {'b': {}}\\n },\\n 'c': {\\n 'type': dict,\\n 'properties': {'d': {}}\\n },\\n }\\n json = {'a': {}, 'b': {}}\\n res, err = JsonValidator(constrain, lazy=True).validate(json)\\n assert len(err) == 1\\n\\n constrain = { # lazy list field\\n 'a': {\\n 'type': list,\\n 'items': {'type': int}\\n },\\n 'b': {\\n 'type': list,\\n 'items': {'type': int}\\n },\\n }\\n json = {'a': ['a'], 'b': ['b']}\\n res, err = JsonValidator(constrain, lazy=True).validate(json)\\n assert len(err) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c590cf1246141fc84fb1b58ac801b4ef\",\n \"score\": \"0.4690686\",\n \"text\": \"def test_put_invalid_field(self):\\n\\n url = reverse('contact', args=[101])\\n data = json.dumps({'first_name': 'newFirstName',\\n 'secon_name': 'newSecondName'})\\n\\n response = self.client.put(url, data=data, content_type='application/json')\\n\\n expected_json_response = {}\\n expected_json_response['error'] = 'Incorrect JSON format.'\\n\\n self.assertEqual(response.status_code, 400)\\n self.assertJSONEqual(response.content.decode('utf-8'), json.dumps(expected_json_response))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a75427fb3168b3930717851a5a1efa1\",\n \"score\": \"0.46904883\",\n \"text\": \"def api_request(func):\\n\\n @wraps(func)\\n def decorated(*args, **kwargs):\\n retv = func(*args, **kwargs)\\n if \\\"error\\\" in retv:\\n raise APIError(retv[\\\"status\\\"], retv[\\\"error_description\\\"])\\n return retv\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8064b17842e679f3f50835f2a7b2fea0\",\n \"score\": \"0.46879628\",\n \"text\": \"def parse_request_json(self, field):\\n json_query = self.get_request_data(field=field)\\n if json_query:\\n return json.loads(json_query)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9fc51fc4884105e7d1ab7d616fc5bd3\",\n \"score\": \"0.46794912\",\n \"text\": \"async def validation_exception_handler(request: Request, exc: RequestValidationError):\\n return JSONResponse(\\n status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,\\n content=jsonable_encoder(\\n {\\n \\\"exception_message\\\": \\\"Invalid request received.\\\",\\n \\\"exception_detail\\\": exc.errors(),\\n \\\"request_body\\\": exc.body,\\n }\\n ),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1cb3278c60a9aa3f9d256696fe3f060\",\n \"score\": \"0.46788523\",\n \"text\": \"def require_auth(f):\\n\\n @wraps(f)\\n def decorated(*args, **kwargs):\\n #res = f(*args, **kwargs)\\n headers = request.headers\\n body = request.json\\n if body and 'username' in body and 'password' in body:\\n username = body.get('username')\\n password = body.get('password')\\n user = UserService.get_user_by_username(username=username)\\n if not user or not user.verify_password(password):\\n abort(401)\\n kwargs['user_id'] = user.id\\n elif 'token' in headers:\\n user = UserService.verify_auth_token(headers['token'])\\n if user is None:\\n abort(401)\\n kwargs['user_id'] = user.id\\n else:\\n abort(401)\\n return f(*args, **kwargs)\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95099ef10e718d22e4a4e9727bc7adb3\",\n \"score\": \"0.46741766\",\n \"text\": \"def validate_response_fields(\\n response: func.HttpResponse,\\n status_code: HTTPStatus = HTTPStatus.OK,\\n mimetype: Optional[str] = \\\"application/json\\\",\\n):\\n\\n assert response.status_code == status_code\\n\\n assert response.mimetype == mimetype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5b11bc1440fa5db5d3bd1450eed9faf\",\n \"score\": \"0.46708184\",\n \"text\": \"def get_argument(req_handler, name, default=None):\\n\\n if req_handler.request.headers.get(\\\"Content-Type\\\") != APPLICATION_JSON:\\n return req_handler.get_argument(name, default)\\n\\n try:\\n parsed_body = json.loads(req_handler.request.body)\\n except Exception as ex:\\n raise HTTPError(log_message=\\\"Error decoding JSON: {}\\\".format(ex))\\n\\n if not isinstance(parsed_body, dict):\\n raise HTTPError(log_message=\\\"Not a JSON object: {}\\\".format(parsed_body))\\n\\n return parsed_body.get(name, default)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0ad103184a562ee207099753dd2c3e6\",\n \"score\": \"0.46691513\",\n \"text\": \"def test_invalid_type(self, field, field_type, field_number):\\n body = {\\n \\\"first_object_id\\\": 1,\\n \\\"first_object_type\\\": \\\"Control\\\",\\n \\\"second_object_id\\\": 1,\\n \\\"second_object_type\\\": \\\"Product\\\",\\n }\\n body[field] = None\\n\\n response = self.client.post(self.ENDPOINT_URL,\\n content_type=\\\"application/json\\\",\\n data=json.dumps(body))\\n\\n self.assertEqual(400, response.status_code)\\n\\n expected_message = \\\"Invalid object %s for %s object.\\\" % (field_type,\\n field_number)\\n actual_message = json.loads(response.data)[\\\"message\\\"]\\n self.assertEqual(expected_message, actual_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b664a64f81285e33196b56977bc1b4e\",\n \"score\": \"0.46658015\",\n \"text\": \"def validate(json_dict, schema_dict):\\n for item in schema_dict.get('requiredProperties', []):\\n if item not in json_dict:\\n raise ValidationError('Item \\\"{}\\\" required but not found.'.format(item))\\n\\n for item, value in json_dict.items():\\n if item in schema_dict['properties'] and schema_dict['properties'][item].get('validFunc', None):\\n if not schema_dict['properties'][item]['validFunc'](value):\\n raise ValidationError('Item \\\"{}\\\" value invalid: \\\"{}\\\".'.format(item, value))\\n elif not schema_dict.get('additionalPropertiesAllowed', True) and item not in schema_dict['properties']:\\n raise ValidationError('Additional property not allowed: \\\"{}\\\"'.format(item))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8ae319a4ae9fb05f9126f00e17dd17\",\n \"score\": \"0.4664558\",\n \"text\": \"def input_fn(request_body, request_content_type):\\n \\n if request_content_type == \\\"application/json\\\":\\n request = json.loads(request_body)\\n else:\\n request = request_body\\n\\n return request\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993477,"cells":{"query_id":{"kind":"string","value":"70e692868094504e23935c6af3f3608a"},"query":{"kind":"string","value":"Getter method for state, mapped from YANG variable /qos/classifiers/classifier/terms/term/state (container)"},"positive_passages":{"kind":"list like","value":[{"docid":"f22d788da20f7c4a9213740db7ac7129","score":"0.0","text":"def _get_state(self):\n return self.__state","title":""}],"string":"[\n {\n \"docid\": \"f22d788da20f7c4a9213740db7ac7129\",\n \"score\": \"0.0\",\n \"text\": \"def _get_state(self):\\n return self.__state\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f7f34e144872a5afcae6730f3513d2d6","score":"0.7400615","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"f24192a64a39b3cdbb50c4dd7e1359d2","score":"0.73903215","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"21a85cf171b561bec4962b71235e3260","score":"0.7351614","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"ad150234774d055196089a7f08157ac2","score":"0.7236827","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"fdd565a0a09bf4674799e848a78564a4","score":"0.72330064","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"0cb1c067b8cff866d279f4214248f12b","score":"0.71871597","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"84282b513394bbc6269fbbd4f3928f6c","score":"0.7090109","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"6961cf0c48d5a4c1d5e2ee790568623f","score":"0.70889825","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"c6ad7c07ad0c57d3b141920cc06e2840","score":"0.70530427","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"f17cd422f4116100cb942ccc6c7a54c9","score":"0.7049729","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"e4994975d76130dae2788ee1ff9046d1","score":"0.703751","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"78bd4adce92e426305d576e4b38d041c","score":"0.70050913","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"825b30108028dfee8e440f93844e7d53","score":"0.66303414","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"e7da43d6692f5ca24428a00773ebf7a7","score":"0.6621845","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"e244135f38ed25d608f226c8cbaba879","score":"0.65628064","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"560ec2e502a0dd5420414816fdd401c9","score":"0.63120306","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"ac9f47df4a80148c43942544eef4d823","score":"0.6309147","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"e9a9510afd21f482ed1932b18853c23f","score":"0.6230261","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"c42e2141652d026a4df98fcd7790d43e","score":"0.59345365","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"7aa5cd7e226ce785fd7e156c4d517373","score":"0.58965987","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_classifiers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_classifiers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"9c37d72f9a0299f6a26d488f411d4707","score":"0.5887326","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"1c718fdba970f8a2a9a8c11116cb6393","score":"0.57835495","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"a8a70efdd3acc7ca557be1b23a085ad0","score":"0.5742773","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"73d7d8d1caf11d86aac69cd5916293eb","score":"0.569133","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"2bb0c55033407b410e6445b3f701b864","score":"0.56678724","text":"def get_state(self, state):\n return state","title":""},{"docid":"54d3f87a1efb77391e5846c713c127cd","score":"0.56323177","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"5c8d56c0f8537d9a50120658d74dec54","score":"0.55884695","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"9821c217eea856154e39377f61555bee","score":"0.5546209","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"939d0f371b1816f7eb146b138e69dfe9","score":"0.55288804","text":"def state(self) -> \"StateEnum\":\n return StateEnum.from_value(self._attrs.get(\"state\"))","title":""},{"docid":"32169758fb6aa7a8fefea6f6ceaf62f4","score":"0.54495084","text":"def getState():\n # TODO: this isn't nearly as meaningful as it used to be","title":""},{"docid":"7bb84032b4e7d5235e1a77acdfbd20f4","score":"0.54322845","text":"def state(self) -> \"StatusState\":\n return StatusState.from_value(self._attrs.get(\"state\"))","title":""},{"docid":"583a2b949c6b626549d649b9d4933821","score":"0.5351591","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"fe1d5625b4754237c9db86d4f3f0c79e","score":"0.53392214","text":"def state(self, state):\n if state is None:\n raise ValueError(\"Invalid value for `state`, must not be `None`\") # noqa: E501\n\n self._state = state","title":""},{"docid":"aa998a741921f0b6f83ef98849b230c0","score":"0.53132385","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"afe2b21e49f963b371f1b974d0c343bc","score":"0.5305721","text":"def set_classy_state(self, state: Dict[str, Any]) -> None:\n return self.load_state_dict(state)","title":""},{"docid":"4294a6acb08e6787eb5215fb44878380","score":"0.5290656","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"000fe448ce58a5de0e4a6beff5981196","score":"0.5273497","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"bd0923327967e328ae6b1bca50b9a09d","score":"0.5265162","text":"def terminal_state(self, state):\r\n raise NotImplementedError","title":""},{"docid":"3f27ba8953b67b78b9c9428668c70fbd","score":"0.52586144","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"3bd850d00467dfe586e6b19c687bb12f","score":"0.5222108","text":"def getState(self):\n return self._state","title":""},{"docid":"3bd850d00467dfe586e6b19c687bb12f","score":"0.5222108","text":"def getState(self):\n return self._state","title":""},{"docid":"6716d37ac58492e906a672995967891d","score":"0.5196885","text":"def for_state(self, state):\n\n return self.__class__(\n self.session,\n state=state,\n term=self.term,\n order=self.order,\n direction=self.direction,\n issues=self.issues,\n categories=self.categories,\n organizations=self.organizations,\n user_ids=self.user_ids,\n group_ids=self.group_ids\n )","title":""},{"docid":"9fc0e3a9bde23b11020fd0b9aae46b33","score":"0.51938355","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"7d0426fb2c004b6c3c4cf2b94fb64fa8","score":"0.51879346","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"5acd6b07958bdcd07dcae573def2a907","score":"0.5173994","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"c15d1dcac80a35a39a47eb2849282aec","score":"0.5172066","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"c9a579245d3c0b09c63b75aa6d33788d","score":"0.51650614","text":"def get_state(self):\n return self._state","title":""},{"docid":"c9a579245d3c0b09c63b75aa6d33788d","score":"0.51650614","text":"def get_state(self):\n return self._state","title":""},{"docid":"c9a579245d3c0b09c63b75aa6d33788d","score":"0.51650614","text":"def get_state(self):\n return self._state","title":""},{"docid":"c9a579245d3c0b09c63b75aa6d33788d","score":"0.51650614","text":"def get_state(self):\n return self._state","title":""},{"docid":"c9a579245d3c0b09c63b75aa6d33788d","score":"0.51650614","text":"def get_state(self):\n return self._state","title":""},{"docid":"b2c0e55c42ed8ae1db5a82e8862fa8d0","score":"0.5156248","text":"def state(self, state):\n allowed_values = [\"PENDING\", \"FILLED\", \"TRIGGERED\", \"CANCELLED\"] # noqa: E501\n if state not in allowed_values:\n raise ValueError(\n \"Invalid value for `state` ({0}), must be one of {1}\" # noqa: E501\n .format(state, allowed_values)\n )\n\n self._state = state","title":""},{"docid":"db3c8e62032530cb424ee6fb77202b6d","score":"0.5148791","text":"def GetState(self):\r\n \r\n return self.state","title":""},{"docid":"4c147827eeefdbc2f5e1623548d826d8","score":"0.51442176","text":"def state(self, state):\n allowed_values = [\"CREATED\", \"STARTING\", \"RUNNING\", \"STOPPED\"] # noqa: E501\n if state not in allowed_values:\n raise ValueError(\n \"Invalid value for `state` ({0}), must be one of {1}\"\n .format(state, allowed_values)\n )\n\n self._state = state","title":""},{"docid":"f8fa96c5126a308599985bf0727f925d","score":"0.513713","text":"def getState(self) :\n return self.state","title":""},{"docid":"f64cee9b1d0e039fb21d4722011a1f78","score":"0.5133531","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_queues_queue_red_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_queues_queue_red_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"b2137f90e6159fbd018520838be22d6b","score":"0.5127993","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"1802372c97466ed561baa72e08333554","score":"0.51128733","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_queues_queue_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_queues_queue_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"d980b0b563593f3119a801d00055acd5","score":"0.51024497","text":"def state(self):\n _LOGGER.debug(\"[Foobar2k] State {0}\".format(self._state))\n return self._state","title":""},{"docid":"e4466b99e8840fdf889f75ac158b5ea0","score":"0.5101498","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"9bbfa57717b99d7cf68ca033dd8bb33e","score":"0.509945","text":"def get_state(self):\n return self.state","title":""},{"docid":"9bbfa57717b99d7cf68ca033dd8bb33e","score":"0.509945","text":"def get_state(self):\n return self.state","title":""},{"docid":"620c2557d426a8494814e14dcce4cb91","score":"0.50900954","text":"def get_state(self, atom):\n return self._storage.get_atom_state(atom.name)","title":""},{"docid":"2ad8e40cc4dc49c0edbcd9cac0d0e534","score":"0.50812024","text":"def getState(self):\n return self.state","title":""},{"docid":"2ad8e40cc4dc49c0edbcd9cac0d0e534","score":"0.50812024","text":"def getState(self):\n return self.state","title":""},{"docid":"2ad8e40cc4dc49c0edbcd9cac0d0e534","score":"0.50812024","text":"def getState(self):\n return self.state","title":""},{"docid":"415f59adba1d2f8236b9b2bcb68dc51d","score":"0.50796103","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_queues_queue_red_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_queues_queue_red_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"8afbedf5a1ba41150806c1cffedcd0e6","score":"0.506472","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"5523aabf09d003131fa2636f3ee3d018","score":"0.50603783","text":"def getState(self) -> None:\n return self.state","title":""},{"docid":"badeb3cd4ce5cd4609b971df59a41004","score":"0.5059022","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_queues_queue_red_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_queues_queue_red_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"39fa48e3c3fdb6c0abe578b72958b82f","score":"0.5055925","text":"def get_state(self, state):\n status = [u'noState', u'poweredOn', u'blocked', u'suspended', \n u'poweredOff', u'poweredOff', u'crashed']\n return status[int(state)]","title":""},{"docid":"3ba4d1bd09c7bafdcd11d10241017b1c","score":"0.50511235","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"1f8bae2a3500fc6ca38688f2eab33ff7","score":"0.50506425","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"08e048793592af7f7e7573c643fcf306","score":"0.5049457","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_queues_queue_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_queues_queue_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"7b7f00b57150481fbef1a8148a87c265","score":"0.50421846","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"0725ab878041f98ad62b5b47d786d537","score":"0.50381","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_queues_queue_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_queues_queue_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"f1cd09b971d366b96b15216ecb903e95","score":"0.50339377","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"73dfa27441b82685e4d5689e3ec6b375","score":"0.50266945","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"e807ce9e5e132b514e01cef960048e9d","score":"0.5023324","text":"def get_classy_state(self) -> Dict[str, Any]:\n return self.state_dict()","title":""},{"docid":"a33eda8260ed6b58e3c48c9c254ee213","score":"0.5001509","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"96f8ada63dd5b1e5af91339f78476d2f","score":"0.498534","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"7583b85a354ef90011dc6b73af124d55","score":"0.49750227","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_queues_queue_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_queues_queue_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"1269c0008b111bca59c6a01601a2b00e","score":"0.49660516","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"a07f2d217384f78c71771d9c3f2b08df","score":"0.49650818","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"f258370bcdbedf403470f4c9ba62bae0","score":"0.495828","text":"def get_state_value(self, state):\n values = self.get_all_state_action_values(state)\n policy = self.target_policy(values)\n return (values @ policy.probs.t()).item()","title":""},{"docid":"31f534a9463ec073ea8d83219c7c8c2e","score":"0.4954607","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"4120346bf78a036b5faed5d6b3e61489","score":"0.4952818","text":"def state(self) :\n\t\ttry :\n\t\t\treturn self._state\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"7d908325160fee7ceaf7f09c2467a082","score":"0.49501345","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_scheduler_policy_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"f2f4d5eef8f58489ad51e33909bca178","score":"0.49401432","text":"def state(self, state):\n allowed_values = [\"lost\", \"negotiating\", \"connected\", \"operational\", \"decommissioned\"]\n if state not in allowed_values:\n raise ValueError(\n \"Invalid value for `state`, must be one of {0}\"\n .format(allowed_values)\n )\n self._state = state","title":""},{"docid":"126aed34a4ba306c3071f0c673453aa3","score":"0.49235946","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_input_queues_queue_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_input_queues_queue_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"587de87c7c27d1480781091d545bc03a","score":"0.49203944","text":"def get_status(self, state):\n raise NotImplementedError","title":""},{"docid":"2d20270a6306240039cc1748f5b6b475","score":"0.49149233","text":"def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_output_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"state must be of a type compatible with container\"\"\",\n 'defined-type': \"container\",\n 'generated-type': \"\"\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_output_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\"state\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\"\"\",\n })\n\n self.__state = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"0308d086163fc036389f17e9c62d2670","score":"0.49109513","text":"def _set_term(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=YANGListType(\"id\",yc_term_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_terms_term, yang_name=\"term\", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='id', extensions=None), is_container='list', yang_name=\"term\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='list', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"term must be of a type compatible with list\"\"\",\n 'defined-type': \"list\",\n 'generated-type': \"\"\"YANGDynClass(base=YANGListType(\"id\",yc_term_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_terms_term, yang_name=\"term\", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='id', extensions=None), is_container='list', yang_name=\"term\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='list', is_config=True)\"\"\",\n })\n\n self.__term = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"3d36323780667ee84e5eec782a3c489b","score":"0.49035046","text":"def state(name, path=None):\n # Don't use _ensure_exists() here, it will mess with _change_state()\n\n cachekey = f\"lxc.state.{name}{path}\"\n try:\n return __context__[cachekey]\n except KeyError:\n if not exists(name, path=path):\n __context__[cachekey] = None\n else:\n cmd = \"lxc-info\"\n if path:\n cmd += f\" -P {shlex.quote(path)}\"\n cmd += f\" -n {name}\"\n ret = __salt__[\"cmd.run_all\"](cmd, python_shell=False)\n if ret[\"retcode\"] != 0:\n _clear_context()\n raise CommandExecutionError(\n f\"Unable to get state of container '{name}'\"\n )\n c_infos = ret[\"stdout\"].splitlines()\n c_state = None\n for c_info in c_infos:\n stat = c_info.split(\":\")\n if stat[0].lower() == \"state\":\n c_state = stat[1].strip().lower()\n break\n __context__[cachekey] = c_state\n return __context__[cachekey]","title":""},{"docid":"f03140f2ab91cf2c0e72bc652d50ca80","score":"0.49012795","text":"def state(self):\n print(\"getter of variable state called\")\n return self._state","title":""},{"docid":"fa842610f8ffe6138d049abf97877552","score":"0.48983452","text":"def _set_term(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=YANGListType(\"id\",yc_term_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_terms_term, yang_name=\"term\", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='id', extensions=None), is_container='list', yang_name=\"term\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='list', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"term must be of a type compatible with list\"\"\",\n 'defined-type': \"list\",\n 'generated-type': \"\"\"YANGDynClass(base=YANGListType(\"id\",yc_term_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_terms_term, yang_name=\"term\", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='id', extensions=None), is_container='list', yang_name=\"term\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='list', is_config=True)\"\"\",\n })\n\n self.__term = t\n if hasattr(self, '_set'):\n self._set()","title":""}],"string":"[\n {\n \"docid\": \"f7f34e144872a5afcae6730f3513d2d6\",\n \"score\": \"0.7400615\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24192a64a39b3cdbb50c4dd7e1359d2\",\n \"score\": \"0.73903215\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21a85cf171b561bec4962b71235e3260\",\n \"score\": \"0.7351614\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad150234774d055196089a7f08157ac2\",\n \"score\": \"0.7236827\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd565a0a09bf4674799e848a78564a4\",\n \"score\": \"0.72330064\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb1c067b8cff866d279f4214248f12b\",\n \"score\": \"0.71871597\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84282b513394bbc6269fbbd4f3928f6c\",\n \"score\": \"0.7090109\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6961cf0c48d5a4c1d5e2ee790568623f\",\n \"score\": \"0.70889825\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6ad7c07ad0c57d3b141920cc06e2840\",\n \"score\": \"0.70530427\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17cd422f4116100cb942ccc6c7a54c9\",\n \"score\": \"0.7049729\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_output_classifers_classifier_terms_term_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4994975d76130dae2788ee1ff9046d1\",\n \"score\": \"0.703751\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78bd4adce92e426305d576e4b38d041c\",\n \"score\": \"0.70050913\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_actions_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825b30108028dfee8e440f93844e7d53\",\n \"score\": \"0.66303414\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7da43d6692f5ca24428a00773ebf7a7\",\n \"score\": \"0.6621845\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e244135f38ed25d608f226c8cbaba879\",\n \"score\": \"0.65628064\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_actions_remark_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"560ec2e502a0dd5420414816fdd401c9\",\n \"score\": \"0.63120306\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac9f47df4a80148c43942544eef4d823\",\n \"score\": \"0.6309147\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9a9510afd21f482ed1932b18853c23f\",\n \"score\": \"0.6230261\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_terms_term_conditions_ipv4_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c42e2141652d026a4df98fcd7790d43e\",\n \"score\": \"0.59345365\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_classifiers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aa5cd7e226ce785fd7e156c4d517373\",\n \"score\": \"0.58965987\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_classifiers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_classifiers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c37d72f9a0299f6a26d488f411d4707\",\n \"score\": \"0.5887326\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c718fdba970f8a2a9a8c11116cb6393\",\n \"score\": \"0.57835495\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8a70efdd3acc7ca557be1b23a085ad0\",\n \"score\": \"0.5742773\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73d7d8d1caf11d86aac69cd5916293eb\",\n \"score\": \"0.569133\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bb0c55033407b410e6445b3f701b864\",\n \"score\": \"0.56678724\",\n \"text\": \"def get_state(self, state):\\n return state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54d3f87a1efb77391e5846c713c127cd\",\n \"score\": \"0.56323177\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c8d56c0f8537d9a50120658d74dec54\",\n \"score\": \"0.55884695\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9821c217eea856154e39377f61555bee\",\n \"score\": \"0.5546209\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_output_classifers_classifier_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"939d0f371b1816f7eb146b138e69dfe9\",\n \"score\": \"0.55288804\",\n \"text\": \"def state(self) -> \\\"StateEnum\\\":\\n return StateEnum.from_value(self._attrs.get(\\\"state\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32169758fb6aa7a8fefea6f6ceaf62f4\",\n \"score\": \"0.54495084\",\n \"text\": \"def getState():\\n # TODO: this isn't nearly as meaningful as it used to be\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb84032b4e7d5235e1a77acdfbd20f4\",\n \"score\": \"0.54322845\",\n \"text\": \"def state(self) -> \\\"StatusState\\\":\\n return StatusState.from_value(self._attrs.get(\\\"state\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"583a2b949c6b626549d649b9d4933821\",\n \"score\": \"0.5351591\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe1d5625b4754237c9db86d4f3f0c79e\",\n \"score\": \"0.53392214\",\n \"text\": \"def state(self, state):\\n if state is None:\\n raise ValueError(\\\"Invalid value for `state`, must not be `None`\\\") # noqa: E501\\n\\n self._state = state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa998a741921f0b6f83ef98849b230c0\",\n \"score\": \"0.53132385\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afe2b21e49f963b371f1b974d0c343bc\",\n \"score\": \"0.5305721\",\n \"text\": \"def set_classy_state(self, state: Dict[str, Any]) -> None:\\n return self.load_state_dict(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4294a6acb08e6787eb5215fb44878380\",\n \"score\": \"0.5290656\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_inputs_input_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"000fe448ce58a5de0e4a6beff5981196\",\n \"score\": \"0.5273497\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd0923327967e328ae6b1bca50b9a09d\",\n \"score\": \"0.5265162\",\n \"text\": \"def terminal_state(self, state):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f27ba8953b67b78b9c9428668c70fbd\",\n \"score\": \"0.52586144\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd850d00467dfe586e6b19c687bb12f\",\n \"score\": \"0.5222108\",\n \"text\": \"def getState(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd850d00467dfe586e6b19c687bb12f\",\n \"score\": \"0.5222108\",\n \"text\": \"def getState(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6716d37ac58492e906a672995967891d\",\n \"score\": \"0.5196885\",\n \"text\": \"def for_state(self, state):\\n\\n return self.__class__(\\n self.session,\\n state=state,\\n term=self.term,\\n order=self.order,\\n direction=self.direction,\\n issues=self.issues,\\n categories=self.categories,\\n organizations=self.organizations,\\n user_ids=self.user_ids,\\n group_ids=self.group_ids\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fc0e3a9bde23b11020fd0b9aae46b33\",\n \"score\": \"0.51938355\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d0426fb2c004b6c3c4cf2b94fb64fa8\",\n \"score\": \"0.51879346\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5acd6b07958bdcd07dcae573def2a907\",\n \"score\": \"0.5173994\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c15d1dcac80a35a39a47eb2849282aec\",\n \"score\": \"0.5172066\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a579245d3c0b09c63b75aa6d33788d\",\n \"score\": \"0.51650614\",\n \"text\": \"def get_state(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a579245d3c0b09c63b75aa6d33788d\",\n \"score\": \"0.51650614\",\n \"text\": \"def get_state(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a579245d3c0b09c63b75aa6d33788d\",\n \"score\": \"0.51650614\",\n \"text\": \"def get_state(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a579245d3c0b09c63b75aa6d33788d\",\n \"score\": \"0.51650614\",\n \"text\": \"def get_state(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a579245d3c0b09c63b75aa6d33788d\",\n \"score\": \"0.51650614\",\n \"text\": \"def get_state(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2c0e55c42ed8ae1db5a82e8862fa8d0\",\n \"score\": \"0.5156248\",\n \"text\": \"def state(self, state):\\n allowed_values = [\\\"PENDING\\\", \\\"FILLED\\\", \\\"TRIGGERED\\\", \\\"CANCELLED\\\"] # noqa: E501\\n if state not in allowed_values:\\n raise ValueError(\\n \\\"Invalid value for `state` ({0}), must be one of {1}\\\" # noqa: E501\\n .format(state, allowed_values)\\n )\\n\\n self._state = state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db3c8e62032530cb424ee6fb77202b6d\",\n \"score\": \"0.5148791\",\n \"text\": \"def GetState(self):\\r\\n \\r\\n return self.state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c147827eeefdbc2f5e1623548d826d8\",\n \"score\": \"0.51442176\",\n \"text\": \"def state(self, state):\\n allowed_values = [\\\"CREATED\\\", \\\"STARTING\\\", \\\"RUNNING\\\", \\\"STOPPED\\\"] # noqa: E501\\n if state not in allowed_values:\\n raise ValueError(\\n \\\"Invalid value for `state` ({0}), must be one of {1}\\\"\\n .format(state, allowed_values)\\n )\\n\\n self._state = state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8fa96c5126a308599985bf0727f925d\",\n \"score\": \"0.513713\",\n \"text\": \"def getState(self) :\\n return self.state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f64cee9b1d0e039fb21d4722011a1f78\",\n \"score\": \"0.5133531\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_queues_queue_red_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_queues_queue_red_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2137f90e6159fbd018520838be22d6b\",\n \"score\": \"0.5127993\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1802372c97466ed561baa72e08333554\",\n \"score\": \"0.51128733\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_queues_queue_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_queues_queue_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d980b0b563593f3119a801d00055acd5\",\n \"score\": \"0.51024497\",\n \"text\": \"def state(self):\\n _LOGGER.debug(\\\"[Foobar2k] State {0}\\\".format(self._state))\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4466b99e8840fdf889f75ac158b5ea0\",\n \"score\": \"0.5101498\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bbfa57717b99d7cf68ca033dd8bb33e\",\n \"score\": \"0.509945\",\n \"text\": \"def get_state(self):\\n return self.state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bbfa57717b99d7cf68ca033dd8bb33e\",\n \"score\": \"0.509945\",\n \"text\": \"def get_state(self):\\n return self.state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"620c2557d426a8494814e14dcce4cb91\",\n \"score\": \"0.50900954\",\n \"text\": \"def get_state(self, atom):\\n return self._storage.get_atom_state(atom.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad8e40cc4dc49c0edbcd9cac0d0e534\",\n \"score\": \"0.50812024\",\n \"text\": \"def getState(self):\\n return self.state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad8e40cc4dc49c0edbcd9cac0d0e534\",\n \"score\": \"0.50812024\",\n \"text\": \"def getState(self):\\n return self.state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad8e40cc4dc49c0edbcd9cac0d0e534\",\n \"score\": \"0.50812024\",\n \"text\": \"def getState(self):\\n return self.state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"415f59adba1d2f8236b9b2bcb68dc51d\",\n \"score\": \"0.50796103\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_queues_queue_red_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_queues_queue_red_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8afbedf5a1ba41150806c1cffedcd0e6\",\n \"score\": \"0.506472\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5523aabf09d003131fa2636f3ee3d018\",\n \"score\": \"0.50603783\",\n \"text\": \"def getState(self) -> None:\\n return self.state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"badeb3cd4ce5cd4609b971df59a41004\",\n \"score\": \"0.5059022\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_queues_queue_red_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_queues_queue_red_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39fa48e3c3fdb6c0abe578b72958b82f\",\n \"score\": \"0.5055925\",\n \"text\": \"def get_state(self, state):\\n status = [u'noState', u'poweredOn', u'blocked', u'suspended', \\n u'poweredOff', u'poweredOff', u'crashed']\\n return status[int(state)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ba4d1bd09c7bafdcd11d10241017b1c\",\n \"score\": \"0.50511235\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f8bae2a3500fc6ca38688f2eab33ff7\",\n \"score\": \"0.50506425\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08e048793592af7f7e7573c643fcf306\",\n \"score\": \"0.5049457\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_queues_queue_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_queues_queue_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b7f00b57150481fbef1a8148a87c265\",\n \"score\": \"0.50421846\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_interface_ref_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0725ab878041f98ad62b5b47d786d537\",\n \"score\": \"0.50381\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_queues_queue_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_queues_queue_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1cd09b971d366b96b15216ecb903e95\",\n \"score\": \"0.50339377\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73dfa27441b82685e4d5689e3ec6b375\",\n \"score\": \"0.50266945\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e807ce9e5e132b514e01cef960048e9d\",\n \"score\": \"0.5023324\",\n \"text\": \"def get_classy_state(self) -> Dict[str, Any]:\\n return self.state_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33eda8260ed6b58e3c48c9c254ee213\",\n \"score\": \"0.5001509\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96f8ada63dd5b1e5af91339f78476d2f\",\n \"score\": \"0.498534\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7583b85a354ef90011dc6b73af124d55\",\n \"score\": \"0.49750227\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_queues_queue_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_input_queues_queue_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1269c0008b111bca59c6a01601a2b00e\",\n \"score\": \"0.49660516\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a07f2d217384f78c71771d9c3f2b08df\",\n \"score\": \"0.49650818\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_scheduler_policies_scheduler_policy_schedulers_scheduler_output_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f258370bcdbedf403470f4c9ba62bae0\",\n \"score\": \"0.495828\",\n \"text\": \"def get_state_value(self, state):\\n values = self.get_all_state_action_values(state)\\n policy = self.target_policy(values)\\n return (values @ policy.probs.t()).item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31f534a9463ec073ea8d83219c7c8c2e\",\n \"score\": \"0.4954607\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4120346bf78a036b5faed5d6b3e61489\",\n \"score\": \"0.4952818\",\n \"text\": \"def state(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._state\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d908325160fee7ceaf7f09c2467a082\",\n \"score\": \"0.49501345\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos_elements__qos_interfaces_interface_output_scheduler_policy_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2f4d5eef8f58489ad51e33909bca178\",\n \"score\": \"0.49401432\",\n \"text\": \"def state(self, state):\\n allowed_values = [\\\"lost\\\", \\\"negotiating\\\", \\\"connected\\\", \\\"operational\\\", \\\"decommissioned\\\"]\\n if state not in allowed_values:\\n raise ValueError(\\n \\\"Invalid value for `state`, must be one of {0}\\\"\\n .format(allowed_values)\\n )\\n self._state = state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"126aed34a4ba306c3071f0c673453aa3\",\n \"score\": \"0.49235946\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_input_queues_queue_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_input_queues_queue_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"587de87c7c27d1480781091d545bc03a\",\n \"score\": \"0.49203944\",\n \"text\": \"def get_status(self, state):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d20270a6306240039cc1748f5b6b475\",\n \"score\": \"0.49149233\",\n \"text\": \"def _set_state(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=yc_state_openconfig_qos__qos_interfaces_interface_output_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"state must be of a type compatible with container\\\"\\\"\\\",\\n 'defined-type': \\\"container\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=yc_state_openconfig_qos__qos_interfaces_interface_output_scheduler_policy_schedulers_scheduler_state, is_container='container', yang_name=\\\"state\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='container', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__state = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0308d086163fc036389f17e9c62d2670\",\n \"score\": \"0.49109513\",\n \"text\": \"def _set_term(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=YANGListType(\\\"id\\\",yc_term_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_terms_term, yang_name=\\\"term\\\", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='id', extensions=None), is_container='list', yang_name=\\\"term\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='list', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"term must be of a type compatible with list\\\"\\\"\\\",\\n 'defined-type': \\\"list\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=YANGListType(\\\"id\\\",yc_term_openconfig_qos__qos_interfaces_interface_input_classifers_classifier_terms_term, yang_name=\\\"term\\\", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='id', extensions=None), is_container='list', yang_name=\\\"term\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='list', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__term = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d36323780667ee84e5eec782a3c489b\",\n \"score\": \"0.49035046\",\n \"text\": \"def state(name, path=None):\\n # Don't use _ensure_exists() here, it will mess with _change_state()\\n\\n cachekey = f\\\"lxc.state.{name}{path}\\\"\\n try:\\n return __context__[cachekey]\\n except KeyError:\\n if not exists(name, path=path):\\n __context__[cachekey] = None\\n else:\\n cmd = \\\"lxc-info\\\"\\n if path:\\n cmd += f\\\" -P {shlex.quote(path)}\\\"\\n cmd += f\\\" -n {name}\\\"\\n ret = __salt__[\\\"cmd.run_all\\\"](cmd, python_shell=False)\\n if ret[\\\"retcode\\\"] != 0:\\n _clear_context()\\n raise CommandExecutionError(\\n f\\\"Unable to get state of container '{name}'\\\"\\n )\\n c_infos = ret[\\\"stdout\\\"].splitlines()\\n c_state = None\\n for c_info in c_infos:\\n stat = c_info.split(\\\":\\\")\\n if stat[0].lower() == \\\"state\\\":\\n c_state = stat[1].strip().lower()\\n break\\n __context__[cachekey] = c_state\\n return __context__[cachekey]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f03140f2ab91cf2c0e72bc652d50ca80\",\n \"score\": \"0.49012795\",\n \"text\": \"def state(self):\\n print(\\\"getter of variable state called\\\")\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa842610f8ffe6138d049abf97877552\",\n \"score\": \"0.48983452\",\n \"text\": \"def _set_term(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=YANGListType(\\\"id\\\",yc_term_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_terms_term, yang_name=\\\"term\\\", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='id', extensions=None), is_container='list', yang_name=\\\"term\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='list', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"term must be of a type compatible with list\\\"\\\"\\\",\\n 'defined-type': \\\"list\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=YANGListType(\\\"id\\\",yc_term_openconfig_qos_elements__qos_interfaces_interface_input_classifers_classifier_terms_term, yang_name=\\\"term\\\", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='id', extensions=None), is_container='list', yang_name=\\\"term\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='list', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__term = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993478,"cells":{"query_id":{"kind":"string","value":"495b060afd563a3b0a6691f036e37e95"},"query":{"kind":"string","value":"Takes a feature and plots a primitive's time for completion (assumed to be successful) with respect to initial feature condition"},"positive_passages":{"kind":"list like","value":[{"docid":"dba72e07488e66f6febbedec16134cdc","score":"0.7870494","text":"def analyze_feature_to_time(self, feature, primitive):\n time = np.array(self.time[primitive])\n s = self.success_start[primitive]\n s = s[:, features[feature]].ravel()\n \n plt.scatter(s, time)\n plt.xlabel(feature)\n plt.ylabel('Time in seconds')\n plt.title('Time Distribution of Starting Feature ' + feature + ' in Successful Rollouts for ' + primitive + 'Primitive')\n plt.show()","title":""}],"string":"[\n {\n \"docid\": \"dba72e07488e66f6febbedec16134cdc\",\n \"score\": \"0.7870494\",\n \"text\": \"def analyze_feature_to_time(self, feature, primitive):\\n time = np.array(self.time[primitive])\\n s = self.success_start[primitive]\\n s = s[:, features[feature]].ravel()\\n \\n plt.scatter(s, time)\\n plt.xlabel(feature)\\n plt.ylabel('Time in seconds')\\n plt.title('Time Distribution of Starting Feature ' + feature + ' in Successful Rollouts for ' + primitive + 'Primitive')\\n plt.show()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0d4c94955098393f2b1123cd9107e53a","score":"0.590543","text":"def plotting_time_feat(df, features, start, stop, scaler=False):\n \n df1 = df[features]\n df2 = df1[start:stop]\n \n if scaler:\n from sklearn.preprocessing import StandardScaler\n scaler = StandardScaler()\n df_plot = scaler.fit_transform(df2)\n df_plot = pd.DataFrame(df_plot, columns=df2.columns, index=df2.index)\n else:\n df_plot = df2\n \n fig, ax = plt.subplots(figsize=(10,5))\n df_plot.plot(ax=ax);\n ax.set_title(f'From {start} to {stop}', fontsize=16)\n ax.set_xlabel('Time', fontsize=14)\n ax.tick_params(axis='x', labelrotation=30)\n if scaler:\n ax.set_title(f'From {start} to {stop} (Scaled)', fontsize=16)\n else:\n ax.set_title(f'From {start} to {stop}', fontsize=16)","title":""},{"docid":"4de91ba9a54fd1b88102c745698671e9","score":"0.5888863","text":"def analyze_2d_start_state(self, feature1, feature2, primitive):\n s = self.success_start[primitive]\n f = self.fail_start[primitive]\n\n index1 = features[feature1]\n index2 = features[feature2]\n\n s1 = s[:, index1: index1+1]\n s2 = s[:, index2: index2+1]\n\n f1 = f[:, index1: index1+1]\n f2 = f[:, index2: index2+1]\n\n colors = np.repeat(np.array([[0, 255, 0]]), s1.shape[0], axis=0)\n colors = np.vstack((colors, np.repeat(np.array([[255, 0, 0]]), f1.shape[0], axis = 0)))\n\n s1 = np.vstack((s1, f1)).ravel()\n s2 = np.vstack((s2, f2)).ravel()\n plt.scatter(s1, s2, c=colors/255)\n plt.xlabel(feature1)\n plt.ylabel(feature2)\n plt.title(' Successes and Failures for Starting Features in ' + primitive + ' Primitive')\n plt.show()","title":""},{"docid":"b241b4516f4b99c6de083451e32c13fa","score":"0.5853148","text":"def plot_step():\n pass","title":""},{"docid":"e246bda0b8e843beb89cb83db669712e","score":"0.57349163","text":"def chart_by_time_feature(feature, days):\n status_incomplete = feature.objects.filter(\n status='INCOMPLETE', incomplete_date__gte=datetime.now() - timedelta(days=days)).count()\n status_progress = feature.objects.filter(\n status='IN PROGRESS', in_progress_date__gte=datetime.now() - timedelta(days=days)).count()\n status_complete = feature.objects.filter(\n status='COMPLETE', completed_date__gte=datetime.now() - timedelta(days=days)).count()\n p_chart = pygal.Pie(print_values=True,\n legend_at_bottom_columns=3,\n legend_box_size=30,\n margin=0,\n style=custom_style,\n no_data_font_size=30,\n no_data_text='No Recorded Data',\n no_data_font_family='san-serif')\n\n if status_incomplete == 0 and status_progress == 0 and status_complete == 0:\n return 'No data has been collected for this period yet.'\n else:\n p_chart.add('INCOMPLETE', status_incomplete)\n p_chart.add('IN PROGRESS', status_progress)\n p_chart.add('COMPLETE', status_complete)\n return p_chart.render()","title":""},{"docid":"c3a1053036e50aff6dd269befc3c0813","score":"0.56702864","text":"def analyze_start_state(self, feature, primitive):\n s = self.success_start[primitive]\n f = self.fail_start[primitive]\n\n index = features[feature]\n s = s[:, index:index+1]\n f = f[:, index:index+1]\n\n # computing bin properties \n num_bin = self.histogram_bins\n start = min(np.min(s), np.min(f))\n end = max(np.max(s), np.max(f))\n bin_lims = np.linspace(start,end,num_bin+1)\n bin_centers = 0.5*(bin_lims[:-1]+bin_lims[1:])\n bin_widths = bin_lims[1:]-bin_lims[:-1]\n \n # computing histograms\n s_hist, _ = np.histogram(s, bins=bin_lims)\n f_hist, _ = np.histogram(f, bins=bin_lims)\n\n # normalizing\n norm = (s_hist + f_hist).astype(float)\n s_hist = s_hist/norm\n f_hist = f_hist/norm\n\n plt.bar(bin_centers, s_hist, width = bin_widths, align = 'center', alpha=.5)\n plt.bar(bin_centers, f_hist, width = bin_widths, align = 'center', alpha=.5)\n plt.title('Normalized Distribution of Starting Feature ' + feature.upper() + ' for ' + primitive.upper() + ' Primitive')\n plt.show()","title":""},{"docid":"c31ba70bb28bcdb441d93bf8bccb5c9f","score":"0.56102604","text":"def plot_single(args, goal_conditioned, name_task, name_plot):\n IGNORE = ['-transporter-goal-naive']\n\n # Override any parameters here.\n title_size = 40\n x_size = 38\n y_size = 38\n tick_size = 35\n legend_size = 25\n lw = 5\n ms = 12\n\n # Now make the plot.\n nrows, ncols = 1, 4\n fig, ax = plt.subplots(nrows, ncols, squeeze=True, figsize=(8.0*ncols, 9.0*nrows))\n title = f'Training Results, {name_plot}'\n supertitle = fig.suptitle(title, fontsize=title_size+2)\n\n for col, num_demos in enumerate([1, 10, 100, 1000]):\n # Extract all relevant subdirectories, and get results.\n directories, seeds = get_subdirectories_demos(name_task, num_demos)\n stats_combo = _get_results(name_task, directories, seeds, debug)\n\n # For each agent, plot. Iterate through keys of stats_combo[agent] IN ORDER.\n for agent in sorted(stats_combo.keys()):\n if agent in IGNORE:\n print(f' ignoring {agent} for demos {num_demos}')\n continue\n x_vals = []\n y_vals = []\n y_errs = [] # standard deviations\n y_stde = [] # standard errors of mean\n print(f'on agent {agent} with keys {stats_combo[agent].keys()}')\n for itr in sorted(stats_combo[agent].keys()):\n x_vals.append( itr )\n values = stats_combo[agent][itr]\n if (len(values) != 60 and name_task != 'bag-color-goal' and itr != 0):\n print(f'\\tWarning, len(values): {len(values)} for: {agent}, {itr}')\n elif (len(values) != 20 and name_task == 'bag-color-goal'):\n print(f'\\tWarning, len(values): {len(values)} for: {agent}, {itr}')\n y_vals.append( np.mean(values) )\n y_errs.append( np.std(values) )\n y_stde.append( np.std(values) / np.sqrt(len(values)) )\n x_vals = np.array(x_vals)\n y_vals = np.array(y_vals)\n y_errs = np.array(y_errs)\n y_stde = np.array(y_stde)\n\n # NOTE: we're dividing by thousands here.\n x_vals = x_vals / 1000\n\n # Remove leading dash, and then plot, while clipping stdev's to range.\n ag = agent[1:]\n agent_label = AGENT_TO_LABEL[ag]\n color = AGENT_TO_COLOR[ag]\n if standard_error:\n lower = np.clip(y_vals - y_stde, 0.0 - EPS, 1.0 + EPS)\n upper = np.clip(y_vals + y_stde, 0.0 - EPS, 1.0 + EPS)\n else:\n lower = np.clip(y_vals - y_errs, 0.0 - EPS, 1.0 + EPS)\n upper = np.clip(y_vals + y_errs, 0.0 - EPS, 1.0 + EPS)\n ax[col].plot(x_vals, y_vals, lw=lw, color=color, ms=ms, marker='o', label=agent_label)\n ax[col].fill_between(x_vals, upper, lower, color=color, alpha=0.3, linewidth=0.0)\n ax[col].legend(prop={'size': legend_size})\n\n # Bells and whistles. For y-axis label, just put it on the leftmost column.\n subplot_title = f'Train Demos: {num_demos}'\n ax[col].set_title(subplot_title, fontsize=title_size)\n ax[col].set_xlabel(xlabel, fontsize=x_size)\n ylabel = 'Success Rate'\n if not args.binary:\n if name_task in ['cloth-flat', 'cloth-flat-notarget']:\n ylabel = 'Zone Coverage'\n elif name_task in ['cable-shape', 'cable-shape-notarget', 'cable-line-notarget']:\n ylabel = 'Percent in Zone'\n if col == 0:\n ax[col].set_ylabel(ylabel, fontsize=y_size)\n ax[col].set_ylim([0.0 - EPS, 1.0 + EPS])\n ax[col].tick_params(axis='x', labelsize=tick_size)\n ax[col].tick_params(axis='y', labelsize=tick_size)\n\n # Shift subplots down due to supertitle.\n plt.tight_layout()\n supertitle.set_y(0.98)\n fig.subplots_adjust(top=0.85)\n\n # Save file.\n suffix = f'plot_demos_task_{name_task}_v02.png'\n fname = join(os.getcwd(), 'figures', suffix)\n plt.savefig(fname)\n print(f'\\nImage saved to: {fname}')","title":""},{"docid":"c5ff62f8a5a787161284c768c6b106f8","score":"0.5533698","text":"def get_PD_plot(self, feature_ind, fig_size_x = 9, fig_size_y = 6,\n x_max_lim = None, x_min_lim = None):\n \n figure, ax = plt.subplots(1,len(feature_ind), figsize= (len(feature_ind)*fig_size_x, fig_size_y))\n\n for idx,feat_id in enumerate(feature_ind):\n\n x_min = np.min(self.X[:, feat_id]) if x_min_lim is None else x_min_lim\n x_max = np.max(self.X[:, feat_id]) if x_max_lim is None else x_max_lim\n x_pi = np.linspace(x_min, x_max, 100)\n\n mean_pred_list = []\n max_pred_list = []\n min_pred_list = []\n for iii in range(x_pi.shape[0]):\n new_X_PI = self.X.copy()\n new_X_PI[:, feat_id] = x_pi[iii]\n p, c = self.gp.predict(new_X_PI, return_cov=True)\n mean_p, max_p, min_p = self.get_mean_and_std(p,c)\n\n mean_pred_list.append(mean_p)\n max_pred_list.append(max_p)\n min_pred_list.append(min_p)\n\n if len(feature_ind) > 1:\n ax[idx].plot(x_pi, mean_pred_list)\n ax[idx].fill_between(x_pi, min_pred_list, max_pred_list, alpha = 0.2)\n ax[idx].set_ylabel('Partial Dependence')\n ax[idx].set_xlabel(self.feature_names[feat_id])\n ax[idx].grid(axis='y')\n else:\n ax.plot(x_pi, mean_pred_list)\n ax.fill_between(x_pi, min_pred_list, max_pred_list, alpha = 0.2)\n ax.set_ylabel('Partial Dependence')\n ax.set_xlabel(self.feature_names[feat_id])\n ax.grid(axis='y')","title":""},{"docid":"30ebb371d7bea7899abb524f914f42ab","score":"0.5473172","text":"def display():\r\n global X, Y # pick the the features and the labels\r\n\r\n ax.clear() # clear the plot before the update\r\n\r\n # set the title, the axes and the legends\r\n ax.set_title('Passing final exams according to subject\\'s average')\r\n ax.set_xlabel('Maths')\r\n ax.set_ylabel('French')\r\n ax.set_ylim(4, 19) # only for a better view\r\n plt_legends = [mpatches.Patch(color=color[key], label=legends[key]) for key, _ in enumerate(legends)]\r\n\r\n # plot the points of the training set\r\n for i, sample in enumerate(X):\r\n ax.scatter(sample[0], sample[1], Y[i], c=color[Y[i]])\r\n\r\n if started: # waiting for start\r\n # The logistic regression is an area (infinity number of point)\r\n # So we will represent it line by line\r\n # It is a huge time-consuming process so put n as lower as possible\r\n n = 20 # number of line to plot\r\n\r\n # Create n different lines to render the logistic regression function[...]\r\n # [...] with a constant x value [...]\r\n lines = np.linspace(min(X.transpose()[0]), max(X.transpose()[0]), num=n)\r\n\r\n # [...] from the min to the max value of the second feature[...]\r\n min_y, max_y = min(X.transpose()[1]), max(X.transpose()[1])\r\n y_array = np.linspace(min_y, max_y, num=n) # from min to max eah time\r\n # [...] with the sigmoid value height according to the features.\r\n\r\n # for each line to represent\r\n for _, x in enumerate(lines):\r\n x_array = np.full(n, x) # create x constant array\r\n z_array = np.array([]) # to append with the sigmoid values\r\n\r\n for _, y in enumerate(y_array): # append with the sigmoid value\r\n z_array = np.hstack((z_array, np.array(sigmoid(weights[0] * x + weights[1] * y + bias))))\r\n\r\n # plot the line\r\n ax.plot(x_array, y_array, z_array, c='#8800FF', alpha=0.2)\r\n\r\n # add a big legend to describe the state of the logistic regression\r\n label = 'Logistic Regression :\\n'\r\n label += 'Equation : {0} * math + {1} * french + {2}\\n'. \\\r\n format(round(weights[0], 2), round(weights[1], 2), int(bias))\r\n label += 'Epoch : {0}\\n'.format(epoch)\r\n label += 'Learning Rate : {0}\\n'.format(LEARNING_RATE)\r\n label += 'Squared Error : {0}'.format(round(cost, 2))\r\n # add the created legend\r\n plt_legends.append(mpatches.Patch(color='#8800FF', label=label))\r\n\r\n # to have smaller font size\r\n font_prop = font_manager.FontProperties(fname='C:\\Windows\\Fonts\\Arial.ttf', size=6)\r\n\r\n # Put a legend above current axis\r\n plt.legend(handles=plt_legends, loc='upper center', bbox_to_anchor=(0.5, 0.1), prop=font_prop, ncol=3)\r\n\r\n # then plot everything\r\n fig.canvas.draw()","title":""},{"docid":"613fb93b342aa9ee879c0d98922f7e65","score":"0.54120535","text":"def plot_time_evolution(time_potenial,animater=False,save_animation=False):\n sns.set_style(\"darkgrid\")\n sns.set_context(\"talk\")\n fig, ax = plt.subplots(figsize=(16, 4))\n ax.set_ylim(0,1)\n ax.set_xlim(0,16)\n t=np.linspace(0,16*pi,2000)\n\n \"\"\"RHF\"\"\"\n solver =RHFSolverSystem(number_electrons,l,grid_length,num_grid_points,omega,a);\n solver.setC(np.eye(l));\n solver.solve(1e-17,25)\n C=solver.get_full_C()\n solver =GHFSolverSystem(number_electrons,l,grid_length,num_grid_points,omega,a);\n solver.setC(C);\n grid=solver.system.grid\n overlap_RHF=np.zeros(len(t))\n dipole_moment_RHF=np.zeros(len(t),dtype=np.complex128)\n energies=np.zeros(len(t))\n dt=t[1]-t[0]\n ground_state_densities_RHF=np.zeros((num_grid_points,len(t)))\n solver.init_TDHF(time_potenial)\n overlap_RHF[0]=solver.calculate_overlap()\n dipole_moment_RHF[0]=solver.getDipolemoment()\n for i,tval in enumerate(t[:-1]):\n solver.integrate(dt) #Integrate a step dt\n densities=solver.getDensity()\n ground_state_densities_RHF[:,i]=densities[0]+densities[1]\n overlap_RHF[i+1]=solver.calculate_overlap()\n dipole_moment_RHF[i+1]=solver.getDipolemoment()\n\n \"\"\"GHF\"\"\"\n solver =GHFSolverSystem(number_electrons,l,grid_length,num_grid_points,omega,a);\n solver.setC(np.eye(2*l));\n solver.solve(1e-17,5000)\n overlap_GHF=np.zeros(len(t))\n dipole_moment_GHF=np.zeros(len(t),dtype=np.complex128)\n energies=np.zeros(len(t))\n dt=t[1]-t[0]\n ground_state_densities_GHF=np.zeros((num_grid_points,len(t)))\n solver.init_TDHF(time_potenial)\n overlap_GHF[0]=solver.calculate_overlap()\n dipole_moment_GHF[0]=solver.getDipolemoment()\n for i,tval in enumerate(t[:-1]):\n solver.integrate(dt) #Integrate a step dt\n densities=solver.getDensity()\n ground_state_densities_GHF[:,i]=densities[0]+densities[1]\n overlap_GHF[i+1]=solver.calculate_overlap()\n dipole_moment_GHF[i+1]=solver.getDipolemoment()\n ax.plot(2*t/(2*pi),overlap_RHF,label=\"RHF\")\n ax.plot(t/pi,overlap_GHF,label=\"GHF\")\n\n plt.legend()\n ax.set_xlabel(r\"t $\\lambda\\omega/(2\\pi)$\")\n ax.set_ylabel(r\"Overlap $|\\langle\\Psi(t)|\\Psi(0)\\rangle|^2$\")\n plt.tight_layout()\n plt.savefig(\"../../figures/time_overlap.pdf\")\n plt.show()\n sns.set_style(\"darkgrid\")\n sns.set_context(\"talk\")\n fig, ax = plt.subplots(figsize=(16, 4))\n ax.set_xlim(0,16)\n plt.plot(t/pi,dipole_moment_RHF,label=\"RHF\",zorder=5)\n plt.plot(t/pi,dipole_moment_GHF,label=\"GHF\",zorder=3)\n plt.legend()\n plt.xlabel(r\"t $\\lambda\\omega/(2\\pi)$\")\n plt.ylabel(r\"Dipole moment\")\n plt.tight_layout()\n plt.savefig(\"../../figures/TDDipolemoment_laser.pdf\")\n plt.show()\n def animation():\n \"\"\"Animates the GHF and the RHF solution\"\"\"\n import matplotlib.animation as animation\n fig, ax = plt.subplots()\n line0, = ax.plot(grid, np.sin(grid),label=\"RHF\")\n line1, = ax.plot(grid, np.sin(grid),label=\"GHF\")\n time_text = ax.text(0.02, 0.90, '', transform=ax.transAxes)\n\n def animate(i):\n line0.set_ydata(ground_state_densities_RHF[:,i])\n line1.set_ydata(ground_state_densities_GHF[:,i])\n time_text.set_text(r'time =%.1f $\\lambda\\omega/(2\\pi)$' % (t[i]/(2*pi)))\n\n return line0,line1,time_text,\n def init():\n line0.set_ydata(np.ma.array(grid, mask=True))\n line1.set_ydata(np.ma.array(grid, mask=True))\n time_text.set_text(\"\")\n\n return line0,line1,time_text,\n\n ani = animation.FuncAnimation(fig, animate, np.arange(0,len(t)), init_func=init,\n interval=5, blit=True)\n ax.set_xlabel(\"Position [a.u.]\")\n ax.set_ylabel(r\"Total electron density $\\rho$\")\n ax.set_xlim(-10,10)\n ax.set_ylim(0,0.5)\n plt.legend()\n plt.tight_layout()\n if(save_animation):\n ani.save('../../figures/animation.gif',fps=400)\n plt.show()\n if animater:\n animation()","title":""},{"docid":"0038be2b53c9a4a350274e843e92c248","score":"0.5398446","text":"def plot_learned_traj(feature_function, train_data, objectID, feat='table'):\n\toutput = feature_function(train_data)\n\teuclidean = angles_to_coords(train_data[:, :7], feat, objectID)\n\tfig = px.scatter_3d(x=euclidean[:,0], y=euclidean[:,1], z=euclidean[:,2], color=output)\n\tfig.update_layout(title='Traces with learned function values')\n\tfig.show()","title":""},{"docid":"c034d9b540bb96fc3bad7b5ff5eb2c63","score":"0.53502405","text":"def generic_memtest_over_time(abf):\n log.debug(\"generic plot: memtest analysis\")\n assert isinstance(abf, pyabf.ABF)\n\n IhBySweep, RmBySweep, RaBySweep, CmBySweep = pyabf.memtest.step_valuesBySweep(abf)\n sweepTimesMin = np.arange(abf.sweepCount)*abf.sweepLengthSec/60\n\n plotFigNew(abf)\n\n ax1 = plt.gcf().add_subplot(2, 2, 1)\n ax1.plot(sweepTimesMin, IhBySweep, '.', color='C0')\n ax1.set_title(\"Holding Current (Ih)\")\n ax1.set_ylabel(\"Clamp Current (pA)\")\n ax1.set_xlabel(\"Experiment Time (min)\")\n ax1.margins(0,.4)\n addComments(abf, True, False)\n\n ax2 = plt.gcf().add_subplot(2, 2, 2)\n ax2.plot(sweepTimesMin, RmBySweep, '.', color='C3')\n ax2.set_title(\"Membrane Resistance (Rm)\")\n ax2.set_ylabel(\"Resistance (MOhm)\")\n ax2.set_xlabel(\"Experiment Time (min)\")\n ax2.margins(0,.4)\n ax2.axis([None,None,0,None])\n addComments(abf, True, False)\n \n ax3 = plt.gcf().add_subplot(2, 2, 3)\n ax3.plot(sweepTimesMin, RaBySweep, '.', color='C7')\n ax3.set_title(\"Access Resistance (Ra)\")\n ax3.set_ylabel(\"Resistance (MOhm)\")\n ax3.set_xlabel(\"Experiment Time (min)\")\n ax3.margins(0,.4)\n ax3.axis([None,None,0,None])\n addComments(abf, True, False)\n\n ax4 = plt.gcf().add_subplot(2, 2, 4)\n ax4.plot(sweepTimesMin, CmBySweep, '.', color='C1')\n ax4.set_title(\"Membrane Capacitance (Cm)\")\n ax4.set_ylabel(\"Capacitance (pF)\")\n ax4.set_xlabel(\"Experiment Time (min)\")\n ax4.margins(0,.4)\n ax4.axis([None,None,0,None])\n addComments(abf, True, False)\n \n plotFigSave(abf, tag=f\"generic-memtest\", labelAxes=False)\n\n return","title":""},{"docid":"9ec44cc98992ea1a25501e1012900f5d","score":"0.5346709","text":"def show_plot(plot_data, delta, title=None, single_stpe=False):\n labels = ['features', 'true_label', 'predict_label']\n markers = ['.-', 'rx', 'go']\n\n if title:\n plt.title(title)\n future = delta if delta else 0\n\n feature_time_steps = list(range(-plot_data[0].shape[0], 0))\n print('feature_time_steps: ', str(feature_time_steps))\n target_time_steps = \\\n list(np.array(range(delta, delta + plot_data[1].shape[0])) / STEP) \\\n if single_stpe else list(np.array(range(0, delta)) / STEP)\n print('target_time_steps: ', str(target_time_steps))\n for i, data in enumerate(plot_data):\n if i:\n plt.plot(target_time_steps, data, markers[i], markersize=5, \\\n label=labels[i])\n else:\n plt.plot(feature_time_steps, data, markersize=5,\n label=labels[i])\n plt.legend()\n plt.xlabel('time-step')\n plt.show()","title":""},{"docid":"4d8880a515de0a221007c4d2629e81f4","score":"0.53349674","text":"def get_VI_plot(self, feature_ind = None, num_samples = 100): \n\n \n if feature_ind == None:\n feat_ind_list = [ttt for ttt in range(len(self.feature_names))]\n else:\n feat_ind_list = feature_ind\n\n feat_res_dict = {}\n for feat_ind in feat_ind_list:\n \n ## Permute the feature\n X_VI = self.X.copy()\n feat_col = X_VI[:, feat_ind].tolist()\n random.shuffle(feat_col)\n X_VI[:, feat_ind] = feat_col\n\n store_VI = np.zeros(num_samples)\n org_pred = self.gp.sample_y(self.X, n_samples = num_samples)\n permuted_pred = self.gp.sample_y(X_VI, n_samples = num_samples)\n for uuu in range(num_samples):\n VI = self.loss_func(permuted_pred[:,uuu], self.y) - self.loss_func(org_pred[:,uuu], self.y)\n store_VI[uuu] = VI\n m_VI = np.mean(store_VI)\n std_VI = np.std(store_VI)\n feat_res_dict[feat_ind] = [m_VI, std_VI]\n\n figure, ax = plt.subplots(1,1, figsize = (8,5))\n\n y_vals = [yyy for yyy in range(len(feat_res_dict))]\n y_name = [self.feature_names[j] for j in feat_res_dict.keys()]\n x_vals = [feat_res_dict[f][0] for f in feat_res_dict.keys()]\n errs = [feat_res_dict[f][1] for f in feat_res_dict.keys()]\n ax.plot(x_vals, y_vals, 'ko')\n ax.set_yticks(y_vals)\n ax.set_yticklabels(y_name)\n ax.grid(axis='y')\n ax.errorbar(x_vals, y_vals, xerr = errs, ecolor='k', fmt='.k', linewidth = 1)\n ax.set_xlabel('VI')","title":""},{"docid":"c582c5cf59851c51a012fc5bb085398e","score":"0.531329","text":"def trajectory_plot(time_array, dt, force, initial_state, target_state, sigma1, save_path, ymin = -2, ymax = 2):\n \n \n fig, ax = plt.subplots(1, 1)\n\n \n #solve first trajectory\n vs = schemes.Euler_Maruyama_no_stop(0,initial_state, sigma1, dt=dt, dims = 1, force=force, time_array_length=len(time_array))\n \n ax.plot(time_array, vs, linewidth = 0.3, color = 'black')\n ax.plot(time_array, target_state+np.zeros(len(time_array)), linewidth = 2, linestyle = '--', color = 'C1', label = '$X_B$' )\n ax.plot(time_array, initial_state+np.zeros(len(time_array)), linewidth = 2, linestyle = '--', color = 'C0', label = '$X_A$')\n plt.text(-0.2, 1,'(b)',horizontalalignment='center', verticalalignment='center', transform = plt.gca().transAxes, fontsize=9)\n ax.set_xlabel('time [s]')\n ax.set_ylabel('x')\n ax.set_ylim((ymin,ymax))\n plt.legend()\n \n #threshold and stopping criterion\n \n if save_path is not None:\n plt.savefig(save_path)\n plt.show()","title":""},{"docid":"607eb074fb3443ca4179138ee070b9db","score":"0.53073","text":"def plot_time_profile(self,x_grid,y_grid,mag_grid,itr,num_times,pos):\n from matplotlib import cm as cm\n from matplotlib.colors import to_rgba_array\n\n color_set = cm.coolwarm(np.linspace(0,1,num_times))\n ########################################################################\n # Plot the skyrmion profile for the current time step\n ########################################################################\n self.ax_traj.contour(x_grid,y_grid,mag_grid,[0.0],linewidths=0.5, \\\n linestyles='solid',colors=to_rgba_array(color_set[itr]))\n if len(pos)>0:\n self.ax_traj.scatter(pos[0],pos[1],\\\n color=to_rgba_array(color_set[itr]), \\\n s=self.symbol_size*0.25,lw=0.01,edgecolor='black',alpha=0.5, \\\n zorder=9)\n self.ax_traj.set_aspect('equal')\n self.fig_traj.canvas.draw()\n\n return","title":""},{"docid":"4c9ee99c0f28d31fe5c8d91c9e34a024","score":"0.5306957","text":"def temporal_plot(ax, ensemble_agg, ensemble_spread, experiment):\n\n plt.sca(ax)\n iplt.plot(ensemble_agg, label=experiment, color=exp_colors[experiment])\n if ensemble_spread:\n time_values = ensemble_spread[0, ::].coord('time').points - 54567.5 \n upper_bound = ensemble_spread[0, ::].data\n lower_bound = ensemble_spread[-1, ::].data\n iplt.plt.fill_between(time_values, upper_bound, lower_bound,\n facecolor=exp_colors[experiment], alpha=0.15)","title":""},{"docid":"daab06777bf727323c42fb175c639e9d","score":"0.52701604","text":"def plot_decision_function(X, y, clf, ax):\n plot_step = 0.02\n x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n xx, yy = np.meshgrid(\n np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)\n )\n\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n ax.contourf(xx, yy, Z, alpha=0.4)\n ax.scatter(X[:, 0], X[:, 1], alpha=0.8, c=y, edgecolor=\"k\")\n ax.set_title(f\"Resampling using {clf[0].__class__.__name__}\")","title":""},{"docid":"e9d599fbed2052c3b9ae15cf650fb52a","score":"0.526962","text":"def plot_time_complexity(clf, X, y, title='Time curve'):\n import timeit\n TEST_CODE_FIT = '''\nclf.fit(X_train, y_train)'''\n TEST_CODE_PRED = '''\nclf.predict(X_test)'''\n\n training_pct = np.linspace(0.10, 1.0, 10)\n df = pd.DataFrame(columns=['train_pct', 'train_time', 'pred_time'])\n for train in training_pct:\n test_pct = 1.0 - train\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_pct, random_state=23)\n times_fit = timeit.timeit(setup='pass', stmt=TEST_CODE_FIT, number=2)\n times_fit = times_fit / 2.0\n times_pred = timeit.timeit(setup='pass', stmt=TEST_CODE_PRED, number=2)\n times_pred = times_pred / 2.0\n df.append(np.array([train, times_fit, times_pred]))\n\n train_sizes = df['train_pct'].to_numpy()\n train_times = df['train_time'].to_numpy()\n pred_time = df['pred_time'].to_numpy()\n\n # Draw lines\n plt.plot(train_sizes, train_times, '--', color=\"#111111\", label=\"Training times\")\n plt.plot(train_sizes, pred_time, color=\"#111111\", label=\"Cross-validation score\")\n\n # Create plot\n plt.title(title)\n plt.xlabel(\"Training Set Size\"), plt.ylabel(\"Accuracy Score\"), plt.legend(loc=\"best\")\n plt.tight_layout()\n\n plt.show()","title":""},{"docid":"4e684c4779d7bb7e1b26d880ae56e710","score":"0.5251226","text":"def visualize(\n trajectories_over_time: dict, reference_path: ReferencePath, params: dict\n):\n\n # global trajectory (trajectory, that is ultimately driven by the car)\n global_trajectory = []\n\n # create new figure and set its size\n fig, ax = plt.subplots()\n fig.set_size_inches(10, 6.5)\n\n # create colorbar\n colorbar = fig.colorbar(\n mappable=plt.cm.ScalarMappable(cmap=plt.cm.RdYlGn_r),\n label=\"cost of valid trajectories\",\n extend=\"both\",\n )\n colorbar.minorticks_on()\n\n # iterate through steps\n for step_counter in trajectories_over_time.keys():\n\n # extract valid trajectories of the current step\n valid_trajectories = trajectories_over_time[step_counter][\"valid\"]\n\n # extract invalid trajectories of the current step\n invalid_trajectories = trajectories_over_time[step_counter][\"invalid\"]\n\n # cost of cheapest valid trajectory\n cost_min = valid_trajectories[0].cost\n\n # cost of most expensive valid trajectory\n cost_max = valid_trajectories[-1].cost\n\n # set tick labels of color bar\n colorbar.set_ticks(ticks=np.linspace(start=0, stop=1, num=6))\n colorbar.set_ticklabels(\n ticklabels=np.linspace(\n start=np.floor(cost_min), stop=np.ceil(cost_max), num=6\n ).astype(int)\n )\n\n # clear axis\n ax.cla()\n\n # plot reference path\n ax.plot(\n reference_path.xy_rp[:, 0],\n reference_path.xy_rp[:, 1],\n \"b--\",\n label=\"reference path\",\n )\n\n # iterate through invalid trajectories\n for invalid_trajectory in invalid_trajectories:\n\n # plot invalid trajectories\n ax.plot(\n invalid_trajectory.xy[:, 0], invalid_trajectory.xy[:, 1], color=\"gray\"\n )\n\n # iterate through valid trajectories\n for valid_trajectory in valid_trajectories:\n\n # check whether there is only one valid trajectory\n if len(valid_trajectories) == 1:\n\n # assign color value\n color_value = 1\n\n else:\n\n # calculate color value\n color_value = (valid_trajectory.cost - cost_min) / (cost_max - cost_min)\n\n # plot valid trajectories\n ax.plot(\n valid_trajectory.xy[:, 0],\n valid_trajectory.xy[:, 1],\n color=plt.cm.RdYlGn_r(color_value),\n )\n\n # plot global trajectory\n global_trajectory.append(\n [valid_trajectories[0].xy[0, 0], valid_trajectories[0].xy[0, 1]]\n )\n ax.plot(\n np.array(global_trajectory)[:, 0],\n np.array(global_trajectory)[:, 1],\n \"k--\",\n label=\"driven trajectory\",\n )\n\n # plot optimal trajectory\n ax.plot(\n valid_trajectories[0].xy[:, 0],\n valid_trajectories[0].xy[:, 1],\n \"k\",\n label=\"optimal trajectory\",\n )\n\n # plot car at current position\n xy_rectangle = (\n valid_trajectories[0].xy[0, 0]\n - params[\"discretization\"][\"car_length\"]\n / 2\n * np.cos(valid_trajectories[0].theta[0])\n + params[\"discretization\"][\"car_width\"]\n / 2\n * np.sin(valid_trajectories[0].theta[0]),\n valid_trajectories[0].xy[0, 1]\n - params[\"discretization\"][\"car_length\"]\n / 2\n * np.sin(valid_trajectories[0].theta[0])\n - params[\"discretization\"][\"car_width\"]\n / 2\n * np.cos(valid_trajectories[0].theta[0]),\n )\n rectangle = plt.Rectangle(\n xy=xy_rectangle,\n width=params[\"discretization\"][\"car_length\"],\n height=params[\"discretization\"][\"car_width\"],\n angle=np.rad2deg(valid_trajectories[0].theta[0]),\n edgecolor=\"b\",\n fill=True,\n )\n ax.add_patch(p=rectangle)\n\n # plot circle at current position\n circle = plt.Circle(\n xy=(valid_trajectories[0].xy[0, 0], valid_trajectories[0].xy[0, 1]),\n radius=np.sqrt(\n (params[\"discretization\"][\"car_length\"] / 2) ** 2\n + (params[\"discretization\"][\"car_width\"] / 2) ** 2\n ),\n edgecolor=\"b\",\n fill=False,\n linestyle=\"--\",\n )\n ax.add_patch(p=circle)\n\n # plot obstacles\n ax.plot(\n params[\"xy_obstacles\"][:, 0],\n params[\"xy_obstacles\"][:, 1],\n \"rx\",\n label=\"obstacles\",\n )\n\n # add legend to plot\n handles, labels = ax.get_legend_handles_labels()\n invalid_trajectories_handle = Line2D(\n xdata=[0], ydata=[0], color=\"gray\", label=\"invalid trajectories\"\n )\n handles.insert(-1, invalid_trajectories_handle)\n rectangle_handle = mpatches.Patch(edgecolor=\"b\", fill=True, label=\"car\")\n handles.insert(-1, rectangle_handle)\n circle_handle = mpatches.Patch(\n edgecolor=\"b\", fill=False, linestyle=\"--\", label=\"over-approximation of car\"\n )\n handles.insert(-1, circle_handle)\n ax.legend(handles=handles, ncol=2)\n\n # plot settings\n ax.axis(\"equal\")\n ax.set_xlim(\n left=valid_trajectories[0].xy[0, 0] - params[\"limits\"][\"v_max\"],\n right=valid_trajectories[0].xy[0, 0]\n + params[\"discretization\"][\"t_max\"] * params[\"limits\"][\"v_max\"],\n )\n ax.set_title(\n label=r\"$t = %3.2f\\/s\\/\\/\\/,\\/\\/\\/v = %2.2f\\/\\frac{m}{s}\\/\\/\\/$\"\n % (\n step_counter * params[\"discretization\"][\"fixed_calc_time\"],\n valid_trajectories[0].v[0],\n )\n )\n ax.set_xlabel(xlabel=r\"$x$ in $m$\")\n ax.set_ylabel(ylabel=r\"$y$ in $m$\")\n ax.grid(b=True)\n\n plt.pause(interval=params[\"discretization\"][\"fixed_calc_time\"] * 0.5)\n\n plt.show()","title":""},{"docid":"5ccf20ed8468d2044ee2e791db27291a","score":"0.52504265","text":"def plot_perf():\n n = 1000\n i = 0\n max_generations = 10\n time_data = []\n generations = []\n\n while i <= max_generations:\n time_data.append(timeit.timeit(\n 'run(0.6, {}, 0.1, 1, False)'.format(i),\n number=n, setup='from tree import run'))\n generations.append(i)\n i += 1\n\n plt.plot(generations, time_data, '-bx')\n plt.ylabel('Time taken for {} iterations (sec)'.format(n))\n plt.xlabel('Number of generations of branches')\n plt.savefig(\"perf_plot.png\")\n plt.clf()","title":""},{"docid":"ef258e2951589321067a6e1494a472ee","score":"0.52486676","text":"def plot_running_times():\n fast = get_running_time(200, 'fast')\n slow = get_running_time(200, 'slow') \n #for item in fast:\n plt.plot(fast, '-r', label='fast_closest_pair')\n plt.plot(slow, '-b', label='slow_closest_pairs')\n plt.legend(loc='upper right')\n plt.title('Running times of slow_closest_pairs and fast_closest_pair')\n plt.ylabel('Milliseconds')\n plt.xlabel('Number of clusters')\n plt.show()","title":""},{"docid":"a76f3e2100d9e68479baae7fd4890177","score":"0.5244402","text":"def show_state(observation, env_id, step=0, info=\"\"):\n plt.figure(3)\n plt.clf()\n \n if len(observation)==4:\n frames = list()\n for i, val in enumerate([0.1, 0.2, 0.3, 0.4]):\n frames.append(np.multiply(observation[i], val))\n observation = np.sum(frames, axis=0)\n \n plt.imshow(observation, cmap='gray')\n plt.axis('off')\n plt.title(\"%s | Score: %s\" % (env_id, info))\n display.clear_output(wait=True)\n display.display(plt.gcf())\n plt.close()","title":""},{"docid":"9512f762da9c94eeac564e63a9eaa6fa","score":"0.52059776","text":"def run_custom_test(self, initial_X, goal, f, show_graph=True, use_observer=True, feet = True, iterations=4000):\n\n self.X = initial_X\n self.X_hat = self.X\n if use_observer:\n self.X_hat = initial_X + 0.03\n t = []\n x_l = []\n x_r = []\n v_l = []\n v_r = []\n x_l_hat = []\n x_r_hat = []\n v_l_hat = []\n v_r_hat = []\n u_l = []\n u_r = []\n \n # sep_plot_gain - 100.0\n \n if feet:\n unchanged_goal = goal * 1.0 / 3.28084\n else:\n unchanged_goal = goal \n \n for i in range(iterations):\n if not use_observer:\n self.X_hat = self.X\n if use_observer:\n if feet:\n x_l_hat.append(self.X_hat[0,0] * 3.28084)\n x_r_hat.append(self.X_hat[1,0] * 3.28084)\n v_l_hat.append(self.X_hat[2,0] * 3.28084)\n v_r_hat.append(self.X_hat[3,0] * 3.28084)\n else:\n x_l_hat.append(self.X_hat[0,0])\n x_r_hat.append(self.X_hat[1,0])\n v_l_hat.append(self.X_hat[2,0])\n v_r_hat.append(self.X_hat[3,0])\n U = f(unchanged_goal - self.X_hat)\n U = numpy.clip(U, self.minU, self.maxU)\n #torque = self.VtoT(self.X, U[0,0])\n if feet:\n x_l.append(self.X[0,0] * 3.28084)\n x_r.append(self.X[1,0] * 3.28084)\n v_l.append(self.X[2,0] * 3.28084)\n v_r.append(self.X[3,0] * 3.28084)\n else:\n x_l.append(self.X[0,0])\n x_r.append(self.X[1,0])\n v_l.append(self.X[2,0])\n v_r.append(self.X[3,0])\n if use_observer:\n self.predict_observer(U)\n #self.X = self.ddynamics(self.X, torque)\n #self.Y = self.C * self.X + self.D * U\n self.update(U)\n if use_observer:\n self.correct_observer(U)\n t.append(i * self.dt)\n u_l.append(U[0,0])\n u_r.append(U[1,0])\n \n if show_graph:\n fig_size = [12, 9]\n pyplot.rcParams[\"figure.figsize\"] = fig_size\n pyplot.subplot(2, 1, 1)\n pyplot.plot(t, x_l, label='left dist')\n pyplot.plot(t, x_r, label='right dist')\n pyplot.plot(t, v_l, label='left velocity')\n pyplot.plot(t, v_r, label='right velocity')\n print(\"Final State: \")\n if feet:\n print(self.X * 3.28084)\n else:\n print(self.X)\n print(\"\\n\")\n if use_observer:\n pyplot.plot(t, x_l_hat, label='x_hat left dist')\n pyplot.plot(t, x_r_hat, label='x_hat right dist')\n pyplot.plot(t, v_l_hat, label='x_hat left velocity')\n pyplot.plot(t, v_r_hat, label='x_hat right velocity')\n print(\"Final Estimated State: \")\n if feet:\n print(self.X_hat * 3.28084)\n else:\n print(self.X_hat)\n print(\"\\n\")\n pyplot.legend()\n \n pyplot.subplot(2, 1, 2)\n pyplot.plot(t, u_l, label='left voltage')\n pyplot.plot(t, u_r, label='right voltage')\n pyplot.legend()\n pyplot.show()","title":""},{"docid":"0e35c3351633573036ae80ba8f3643f3","score":"0.5204334","text":"def test_over_time():\n\n Cviews, Tviews = 1000, 1000\n Cconversions, Tconversions = 200, 200\n n_samples = 100000 # number of samples to use in the distribution\n\n conversions = []\n probas = []\n views = []\n for _ in xrange(5000):\n Cviews += (20*np.random.rand())\n Tviews += (20*np.random.rand())\n views.append( (Cviews, Tviews) )\n\n Cconversions += (10*np.random.rand())\n Tconversions += (10*np.random.rand())\n conversions.append( (Cconversions, Tconversions) )\n \n treatment = beta_dist(Tconversions + 1, Tviews - Tconversions + 1, n_samples)\n control = beta_dist(Cconversions + 1, Cviews - Cconversions + 1, n_samples)\n \n probas.append(np.mean(treatment > control))\n\n views = np.array(views)\n conversions = np.array(conversions)\n \n plt.plot(probas, marker=\"o\", markerfacecolor=\"purple\")\n plt.ylim((0,1))\n plt.title(\"Pr( treatement > control )\")\n\n plt.figure()\n plt.plot(views[:,0]/conversions[:,0], color=\"red\", label=\"control\")\n plt.plot(views[:,1]/conversions[:,1], color=\"steelblue\", label=\"treatment\")\n plt.title(\"conversion rate\")\n plt.legend();\n\n del views, conversions, probas\n\n return plt","title":""},{"docid":"d68aba6dc2b25275ddbcebfbcb20d67e","score":"0.51574564","text":"def generate_time_features(self):\n raise NotImplementedError('To be implemented')","title":""},{"docid":"1087d94241fbd9ad338c1ab6dfc67fe7","score":"0.5136023","text":"def plot_progress(name,sequence):\n plt.figure(figsize=(30,15))\n plt.style.use('fivethirtyeight')\n\n acceptance = []\n for i,_ in enumerate(sequence):\n try:\n if sequence[i].name is sequence[i-1].name:\n acceptance.append(0)\n else:\n acceptance.append(1)\n except Exception:\n pass\n \n acceptance.insert(0,0)\n\n acceptance = np.cumsum(np.array(acceptance)[1:])/np.arange(len(acceptance))[1:]\n\n losses = np.array([-1*s.loss for s in sequence])\n poss = np.array([s.pos for s in sequence])\n\n\n plt.subplot(211)\n plt.plot(np.arange(len(acceptance)),acceptance)\n plt.xlim(0,lim)\n plt.title(\"Acceptance Average\")\n\n plt.subplot(212)\n plt.title(\"Fitted Model Stats\")\n plt.plot(np.arange(len(acceptance)),losses ,label = 'Cross_validation MLLs')\n plt.plot(np.arange(len(acceptance)),poss ,label = 'pos estimates')\n plt.xlim(0,lim)\n plt.legend()\n name = \"progress_\"+str(name)\n plt.savefig(\"Summary/{}.png\".format(name))\n plt.close()","title":""},{"docid":"0599d76bae71f8143ad9f8eff4d78a9c","score":"0.51288545","text":"def main():\n\n import matplotlib.pyplot as plt\n import pandas as pd\n\n # The original data set\n df_0 = pd.DataFrame.from_csv('arr_dep.csv')\n # The data set with the multiplication interaction feature.\n df_times = pd.DataFrame.from_csv('arr_times_dep.csv')\n # The data set with the subtraction interaction feature.\n df_minus = pd.DataFrame.from_csv('arr_minus_dep.csv')\n\n alpha = .04\n # Show the relationships between the original features and \n # the target.\n fig = plt.figure(1)\n plt.subplot(1,2,1)\n plt.plot(df_0.index, df_0.iloc[:,1], color='red', \n marker='.', linewidth=0, alpha=alpha)\n plt.xlabel('feature 0')\n plt.ylabel('regression target')\n \n plt.subplot(1,2,2)\n plt.plot(df_0.iloc[:,0], df_0.iloc[:,1], color='blue', \n marker='.', linewidth=0, alpha=alpha)\n plt.xlabel('feature 1')\n fig.tight_layout()\n fig.savefig('original.png')\n\n # Show the relationships between the multiplicative feature and \n # the target.\n fig = plt.figure(2)\n plt.subplot(1,2,1)\n plt.plot(df_times.index, df_times.iloc[:,1], color='red', \n marker='.', linewidth=0, alpha=alpha)\n plt.xlabel('feature 0')\n plt.ylabel('regression target')\n \n plt.subplot(1,2,2)\n plt.plot(df_times.iloc[:,2], df_times.iloc[:,1], color='orange', \n marker='.', linewidth=0, alpha=alpha)\n plt.xlabel('feature 0 x feature 1')\n fig.tight_layout()\n fig.savefig('multiply.png')\n\n # Show the relationships between the subtractive feature and \n # the target.\n fig = plt.figure(3)\n plt.subplot(1,2,1)\n plt.plot(df_minus.index, df_minus.iloc[:,1], color='red', \n marker='.', linewidth=0, alpha=alpha)\n plt.xlabel('feature 0')\n plt.ylabel('regression target')\n \n alpha = .02\n plt.subplot(1,2,2)\n plt.plot(df_minus.iloc[:,2], df_minus.iloc[:,1], color='green', \n marker='.', linewidth=0, alpha=alpha)\n plt.xlabel('feature 1 - feature 0')\n fig.tight_layout()\n fig.savefig('subtraction.png')\n plt.show()","title":""},{"docid":"34947e1092a978a4b3ee8e1f702eaee0","score":"0.5124802","text":"def plot_acc(image_path, task, location, view, gender_acc, age_acc, total_acc, mode=\"train\"):\n # Use plot styling from seaborn.\n sns.set(style='darkgrid')\n\n # Increase the plot size and font size.\n sns.set(font_scale=1.5)\n plt.rcParams[\"figure.figsize\"] = (12, 6)\n\n fig = plt.gcf()\n\n if task == 'gender':\n # Plot the learning curve.\n # plt.plot(range(1, NUM_EPOCHS + 1), gender_acc, '-o', label=\"train_gender_acc\")\n plt.plot(range(1, len(gender_acc) + 1), gender_acc, '-o', label=mode + \"_gender_acc\")\n\n elif task == 'age':\n # plt.plot(range(1, NUM_EPOCHS + 1), age_acc, '-o', label=\"train_age_acc\")\n plt.plot(range(1, len(age_acc) + 1), age_acc, '-o', label=mode + \"_age_acc\")\n\n elif task == 'multi':\n # plt.plot(range(1, NUM_EPOCHS + 1), gender_acc, '-o', label=\"train_gender_acc\")\n plt.plot(range(1, len(gender_acc) + 1), gender_acc, '-o', label=mode + \"_gender_acc\")\n # plt.plot(range(1, NUM_EPOCHS + 1), age_acc, '-o', label=\"train_age_acc\")\n plt.plot(range(1, len(age_acc) + 1), age_acc, '-o', label=mode + \"_age_acc\")\n # plt.plot(range(1, NUM_EPOCHS + 1), total_acc, '-o', label=\"train_total_acc\")\n plt.plot(range(1, len(total_acc) + 1), total_acc, '-o', label=mode + \"_total_acc\")\n\n # Label the plot.\n image_name = \"Location: \" + location + \", Task: \" + task + \", View: \" + view + \", Epoch: \" + str(\n NUM_EPOCHS) + \", Shared Hidden Dim: \" + str(SHARED_HIDDEN_DIM) + \", LAMBDA1: \" + str(LAMBDA1) + \", LAMBDA1: \" \\\n + str(LAMBDA1) + \", DMVMT Acc \" + mode\n plt.title(image_name)\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Accuracy\")\n\n plt.xticks(np.arange(0, NUM_EPOCHS + 1, 5.0))\n plt.legend()\n # plt.show()\n plt.draw()\n fig.savefig(os.path.join(image_path, image_name + \".png\"), dpi=100)\n plt.close()","title":""},{"docid":"ba3be59cb6bd19e99bbbae25efae03e5","score":"0.5111923","text":"def true_plot():\n pass","title":""},{"docid":"5aa96c66999ffcf7b36b13d4c4d946e8","score":"0.5089178","text":"def task_1():\n\n fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n\n # Mesurement model\n y = np.ones(shape=(3,))/3\n x = np.arange(len(y)) - 1\n\n ax = axs[0]\n ax.set_title(r'Mesurement model $p(Z_t|X_t)$')\n bar_plot(ax, x, y, color='r')\n\n # Motion model\n p = [0, 0.75, 0.25]\n y = np.array(p)\n x = np.arange(len(y))\n\n ax = axs[1]\n ax.set_title(r'Motion model $p(X_{t+1}|X_t)$')\n bar_plot(ax, x, y, color='b')\n ax.axvline(0.25, c='k', ls='--')\n ax.axvline(-0.25, c='k', ls='--')\n\n plt.show()","title":""},{"docid":"a11f4baa5ea4612608f0e3342274f356","score":"0.5085223","text":"def convergence_plot(self, interactive=False) -> plt.plot:\n def offline_measure(df):\n return pd.Series(df['fitness'].cumsum().values /\n (df['iteration'].values+1))\n\n def online_measure(df):\n return pd.Series(df['mean_fitness'].cumsum().values /\n (df['iteration'].values+1))\n\n return NotImplementedError","title":""},{"docid":"d01b3b3443957f6212c1ac9a273c2750","score":"0.508208","text":"def plot_traj_id(\n self,\n tid,\n label=TID,\n feature=None,\n value=None,\n figsize=(10, 10),\n return_fig=True,\n save_fig=False,\n name=None,\n ):\n\n operation = begin_operation('plot_traj_id')\n\n if label not in self._data:\n self.last_operation = end_operation(operation)\n raise KeyError('%s feature not in dataframe' % label)\n\n df_ = self._data[self._data[label] == tid]\n\n if not len(df_):\n self.last_operation = end_operation(operation)\n raise IndexError(f'No trajectory with tid {tid} in dataframe')\n\n fig = plt.figure(figsize=figsize)\n\n plt.plot(\n df_.iloc[0][LONGITUDE], df_.iloc[0][LATITUDE], 'yo', markersize=23\n ) # start point\n plt.plot(\n df_.iloc[-1][LONGITUDE], df_.iloc[-1][LATITUDE], 'yX', markersize=23\n ) # end point\n\n if (not feature) or (not value) or (feature not in df_):\n plt.plot(df_[LONGITUDE], df_[LATITUDE])\n plt.plot(\n df_.loc[:, LONGITUDE], df_.loc[:, LATITUDE], 'r.', markersize=8\n )\n else:\n filter_ = df_[feature] == value\n df_nodes = df_.loc[filter_]\n df_points = df_.loc[~filter_]\n plt.plot(df_[LONGITUDE], df_[LATITUDE], linewidth=3)\n plt.plot(\n df_nodes[LONGITUDE], df_nodes[LATITUDE], 'gs', markersize=13\n )\n plt.plot(\n df_points[LONGITUDE], df_points[LATITUDE], 'r.', markersize=8\n )\n\n if save_fig:\n if not name:\n name = 'trajectory_%s.png' % tid\n plt.savefig(fname=name, fig=fig)\n\n df_ = PandasMoveDataFrame(df_)\n self.last_operation = end_operation(operation)\n\n if return_fig:\n return df_, fig\n return df_","title":""},{"docid":"f6f24656a8f389c998e6bcb33deb83b9","score":"0.5073311","text":"def plot_shape(raw, filtered, df, windowL, windowR, Fs, subj, electrode,\n plot=True):\n trial_starts = df['move_start'].astype(int).values\n N_trials = len(trial_starts)\n samps_window_lim = (windowL, windowR)\n N_samps = samps_window_lim[1] - samps_window_lim[0]\n rdsym_time_ts2 = np.ones(len(raw))*np.nan\n cycle_num = len(filtered)\n for i in range(cycle_num):\n rdsym_time_ts2[filtered['sample_lastE'][i]:filtered['sample_nextE'][i]] = filtered['rdsym_time'][i]\n\n rdsym_time2 = np.zeros((N_trials,N_samps))\n for i, t in enumerate(trial_starts):\n rdsym_time2[i] = rdsym_time_ts2[t+samps_window_lim[0]:t+samps_window_lim[1]]\n avg_rdsym_time2 = np.nanmean(rdsym_time2,axis=0)\n sem_rdsym_time2 = sp.stats.sem(rdsym_time2,axis=0)\n\n t = np.arange(samps_window_lim[0]/Fs,samps_window_lim[1]/Fs,1/Fs)\n if plot:\n plt.figure(figsize=(12,4))\n plt.plot(t,avg_rdsym_time2,'k-')\n plt.plot(t,avg_rdsym_time2-sem_rdsym_time2,'k--')\n plt.plot(t,avg_rdsym_time2+sem_rdsym_time2,'k--')\n plt.savefig(\"C:/Users/Yimeng/Documents/GitHub/LabWork/plots/\"+subj+electrode+\".png\")\n return rdsym_time2,avg_rdsym_time2,sem_rdsym_time2","title":""},{"docid":"8ad247dca9715d52257a9e028ea53b44","score":"0.5070251","text":"def livePlotData():\n\tprey = plt.plot(range(0, len(x)), x, \"GREEN\", marker=\"o\", label=\"prey\")\n\tpredator = plt.plot(range(0, len(y)), y, \"RED\", marker=\"o\", label=\"predator\")\n\tplt.legend(loc=1, bbox_to_anchor=(0.3, 1))\t\t\t\n\tplt.xlabel(\"time\")\n\tplt.ylabel(\"population\")\n\tplt.draw()\n\tplt.clf()\n\tsleep(SLEEPTIME)","title":""},{"docid":"220ca58420e7773ada23f62477ee4f1f","score":"0.50686854","text":"def generate_time_plot(state_scores, state_s_polls, burn_in, state_name, prior,\n save=False):\n n_days = state_scores.shape[0]\n x_coord = np.arange(n_days)\n burn_state_scores = state_scores[:, burn_in:]\n\n # plot dots (polls)\n poll_date = state_s_polls.date_index.as_matrix()\n poll_value = state_s_polls.p_clinton.as_matrix()\n plt.scatter(poll_date, poll_value, s=10, c='black')\n\n # plot thick blue line (median) and 90% confidence interval\n medians = np.median(burn_state_scores, axis=1)\n uppers = np.percentile(burn_state_scores, 95, axis=1)\n lowers = np.percentile(burn_state_scores, 5, axis=1)\n\n plt.title('Clinton Vote Share over Time for {0}, P(Win) = {1:.4f}'.format(\n state_name.title(), np.mean(burn_state_scores[-1] > 0.5)), fontsize=11)\n plt.ylabel('Share of Two-Party Vote')\n plt.xlabel('Day')\n plt.plot(x_coord, medians, color='blue')\n plt.plot(x_coord, lowers, color='#33CEFF')\n plt.plot(x_coord, uppers, color='#33CEFF')\n plt.fill_between(x_coord, uppers, lowers,\n interpolate=True, color='#33CEFF', alpha=.4)\n plt.axhline(y=0.5, color='black', linestyle='-')\n plt.axhline(y=prior, color='black', linestyle='--')\n\n if state_name != 'general':\n results_2016 = pd.read_csv(\n '../data/2016_results.csv', index_col=0)\n results_2016 = results_2016['dem_share_2p']\n plt.axhline(y=results_2016[state_name], color='purple', linestyle=':')\n else:\n plt.axhline(y=0.511, color='purple', linestyle=':')\n\n if save is True:\n plt.savefig('../plots/time_plots/' +\n state_name.replace(\" \", \"_\") + '.png', dpi=300)\n plt.clf()","title":""},{"docid":"e8c4c69578e10b9b75620b90d2ccda5d","score":"0.5057931","text":"def assess_concurrence(mission, show_figure=True, save_figure=False):\n\n takeoff_times = mission.launch_time_list\n\n landing_times = mission.landing_time_list\n\n mat_takeoff_times = mdates.date2num(takeoff_times)\n mat_landing_times = mdates.date2num(landing_times)\n\n # Padding sets the whitespace before and after the histogram. Currently set to 5 minutes. mat_takeoff_times is a\n # list of floats. Each float represents the takeoff time in days since 0001-01-01 00:00:00 UTC plus one.\n padding = 1.0/(24*24.0)\n\n times = np.linspace(min(mat_takeoff_times) - padding, max(mat_landing_times) + padding, 7000)\n plot_times = mdates.num2date(times)\n drones_aloft = np.zeros(len(times))\n\n # These nested loops set up the graph.The concurrent sorties graph is built as a histogram with several thousand\n # bins. Each bin has a value that corresponds to the number of UAVs flying at that time.\n for time in mat_takeoff_times:\n for jj in xrange(len(times)):\n if times[jj] >= time:\n for ii in xrange(jj, len(drones_aloft)):\n drones_aloft[ii] = drones_aloft[ii] + 1\n break\n\n for time in mat_landing_times:\n for jj in xrange(len(times)):\n if times[jj] >= time:\n for ii in xrange(jj, len(drones_aloft)):\n drones_aloft[ii] = drones_aloft[ii] - 1\n break\n\n # Calculate the width of each histogram bar\n width = ((plot_times[-1] - plot_times[0]).total_seconds())/(86400*len(plot_times))\n\n plt.figure(figsize=(11, 8))\n plt.bar(plot_times, drones_aloft, width=width, linewidth=0)\n plt.xlim(plot_times[0], plot_times[-1])\n plt.xlabel('Time of Day')\n plt.ylabel('Number of Aircraft Aloft')\n plt.title('Graph of Concurrent Sorties - %s-%s-%s' % (takeoff_times[0].month, takeoff_times[0].day, takeoff_times[0].year))\n if save_figure:\n save_path = os.path.join(mission.path,'FX%02d-M%02d_concurrent_sorties.png' % (mission.event_number, mission.mission_number))\n mission.path_dictionary['concurrent_sorties'] = save_path\n plt.savefig(save_path)\n\n if show_figure:\n plt.show()","title":""},{"docid":"e388fe358c34d2ff2afe5d83ee08efbf","score":"0.50538224","text":"def __show_each_feature(self, feature_name, feature_number):\n if feature_name==\"Text_density\":\n from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler, StandardScaler\n min_max_scaler = MinMaxScaler()\n X_train_norm = min_max_scaler.fit_transform(self.features)\n X = np.array(X_train_norm)\n y = np.array([feature[feature_number] for feature in X])\n sns.set()\n plt.title(feature_name)\n plt.xlabel('Position')\n plt.ylabel('Value')\n plt.plot(y[:], 'bo', y, 'k')\n path = PATH + feature_name+'.jpg'\n plt.savefig(path)","title":""},{"docid":"7c9b4e0182883426ac0ca002b56a625f","score":"0.5051531","text":"def generate_time_features(\n self, label_id=TRAJ_ID, label_dtype=np.float64, sort=True, inplace=True\n ):\n\n operation = begin_operation('generate_time_features')\n\n if inplace:\n data_ = self._data\n else:\n data_ = self._data.copy()\n\n try:\n print(\n '\\nCreating or updating time features seconds\\n'\n )\n\n start_time, ids, sum_size_id, size_id = self._prepare_generate_data(\n data_, sort, label_id\n )\n\n # create new feature to time\n data_[TIME_TO_PREV] = label_dtype(-1.0)\n data_[TIME_TO_NEXT] = label_dtype(-1.0)\n data_[TIME_PREV_TO_NEXT] = label_dtype(-1.0)\n\n ids = data_.index.unique()\n sum_size_id = 0\n size_id = 0\n\n for idx in progress_bar(\n ids, desc='Generating time features'\n ):\n curr_time = data_.at[idx, DATETIME].astype(label_dtype)\n\n size_id = curr_time.size\n\n if size_id <= 1:\n data_.at[idx, TIME_TO_PREV] = np.nan\n else:\n prev_time = shift(curr_time, 1)\n time_prev = (curr_time - prev_time) * (10 ** -9)\n data_.at[idx, TIME_TO_PREV] = time_prev\n\n next_time = shift(curr_time, -1)\n time_prev = (next_time - curr_time) * (10 ** -9)\n data_.at[idx, TIME_TO_NEXT] = time_prev\n\n time_prev_to_next = (next_time - prev_time) * (10 ** -9)\n data_.at[idx, TIME_PREV_TO_NEXT] = time_prev_to_next\n\n return self._return_generated_data(\n data_, start_time, operation, inplace\n )\n\n except Exception as e:\n print(\n 'label_tid:%s\\nidx:%s\\nsize_id:%s\\nsum_size_id:%s'\n % (label_id, idx, size_id, sum_size_id)\n )\n self.last_operation = end_operation(operation)\n raise e","title":""},{"docid":"e40e5361a87f522ea096215a4f6d4bac","score":"0.5044915","text":"def plot_example_cluster(cluster, x_sample, is_unhealthy, is_counterfactual, engine_num, seq_num): \r\n \r\n center_color = \"green\"\r\n label = \"factual\"\r\n if is_unhealthy and is_counterfactual:\r\n center_color = \"red\" \r\n label=\"counterfactual\"\r\n\r\n elif not is_unhealthy and is_counterfactual:\r\n center_color = \"red\"\r\n label=\"counterfactual\"\r\n\r\n elif is_unhealthy and not is_counterfactual:\r\n label=\"factual\"\r\n center_color = \"red\" \r\n \r\n \r\n #plot the members of the cluster\r\n for i in range(len(cluster)):\r\n m=cluster[[c for c in cluster.columns \r\n if c.startswith(\"ts_\")]].values[i:i+1][0]\r\n ax.plot(m, c=\"gray\", alpha=0.25)\r\n\r\n # now plot the center of the cluster\r\n center=cluster[[c for c in cluster.columns \r\n if c.startswith(\"center_\")]\r\n ].values[i:i+1][0]\r\n ax.scatter(range(0, len(center)), center, c=center_color, label=label)\r\n ax.plot(center, \"--\", c=center_color)\r\n\r\n # now plot your selected sample\r\n ax.scatter(range(0, len(x_sample)), x_sample, s=30, c=\"black\",\r\n label=\"predicted_sample\")\r\n ax.plot(x_sample, \"--\", c=\"black\")\r\n\r\n ax.set_xticks(range(0,len(center)))\r\n ax.set_yticks(np.arange(0.6, 1, 0.1))\r\n ax.legend(loc=\"lower right\", fontsize=16)\r\n\r\n # now add the important timesteps\r\n for f in local_imp_features:\r\n timestep = int(f.split(\"_\")[1])\r\n ax.axvspan(xmin=timestep-0.25,\r\n xmax=timestep+0.25,\r\n ymax=1,\r\n facecolor='green', alpha=0.2)","title":""},{"docid":"aa443a8eab066b0f33c1850764dc59a7","score":"0.50359315","text":"def plot_waveforms(time,voltage,APTimes,titlestr):\r\n \r\n plt.figure()\r\n\r\n ## Your Code Here \r\n # --------------------------------------------------------------\r\n\r\n # from homework examples:\r\n # action potentials are caught in intervals of 3 miliseconds\r\n rate = 0.003\r\n \r\n # compute the number of action potentials that are measured at each rate\r\n # this is given by the fraction between the rate and the delayed time between\r\n # each action potential\r\n timeStep = time[1]-time[0]\r\n measurements_per_rate = 2 * int( rate / timeStep ) \r\n\r\n # the x-axis varies between -3 ms to 3 ms and has steps of measurements_per_rate\r\n time_axis = np.linspace(-rate, rate, measurements_per_rate ) \r\n voltage_axis = np.zeros( measurements_per_rate )\r\n \r\n for val in range(len(APTimes)): \r\n \r\n # find index of action potential (AP) in t data array\r\n indx = plt.find( time == APTimes[val])\r\n \r\n # get data near the action potential \r\n if measurements_per_rate / 2 > indx:\r\n start_indx = 0\r\n else:\r\n start_indx = indx - measurements_per_rate / 2\r\n \r\n end_indx = indx + measurements_per_rate / 2\r\n\r\n # create a range from the starting index to the ending index\r\n action_potentials = range(start_indx, end_indx)\r\n \r\n # if there are enough points to represent the number of measurements per rate\r\n if len( action_potentials ) == measurements_per_rate: \r\n # then, get the actural voltages from the action potentials identified\r\n voltage_axis = voltage[ action_potentials ] \r\n else: \r\n # otherwise, fill the array by adding points to fill up the measurement\r\n missing = measurements_per_rate - len( action_potentials )\r\n # for poits very close to zero, fill them with zeros as well\r\n voltage_axis[ 0 : missing ] = 0 \r\n # for points not very close to zero, get the actual measurements from action potentials\r\n voltage_axis[ val:measurements_per_rate ] = voltage[ action_potentials[ 1 ] ] \r\n \r\n # plot the waveform with a blue color \r\n plt.plot(time_axis, voltage_axis, color='b', hold=True)\r\n\r\n plt.xlabel( 'Time (s)' ) # plot x-axis label\r\n plt.ylabel( 'Voltage (uV)' ) # plot y-axis label\r\n plt.title( titlestr ) # plot graph title \r\n \r\n plt.show( ) # show plot\r","title":""},{"docid":"b114d9de5d734c1d1aca9e438a2a6c50","score":"0.50341535","text":"def plot_indep_time_computation(bn,lindep, ntimes,sizes,list_args_independance=[{\"calculation_method\":\"pearson\",\"R_test\":True},{\"calculation_method\":\"pearson\"},{\"usePyAgrum\":True,\"calculation_method\":\"pearson\"}]):\n \n fig,ax = plt.subplots(nrows=len(lindep), ncols=1, sharex=True,figsize =[6.4, 2*len(lindep)])\n \n \n #define set of colors\n cmap = plt.get_cmap('gnuplot')\n colors = [cmap(i) for i in np.linspace(0.2, 1, len(list_args_independance))]\n for index, indep_relation_ship in enumerate(lindep): \n for color,test in zip(colors,list_args_independance):\n store_time,use_pyagrum=[],test.get(\"usePyAgrum\",False)\n for size in sizes:\n #we generate df temporaly with the wanted size\n gum.generateCSV(bn,os.path.join(\"databases\",\"temp_size.csv\"),size,False,with_labels=True)\n df=pd.read_csv(os.path.join(\"databases\",\"temp_size.csv\"))\n #we generate a function with the expected return\n if use_pyagrum:\n test[\"learner\"]=gum.BNLearner(os.path.join(\"databases\",\"temp_size.csv\"))\n modified_function=compute_time(indepandance(df,*indep_relation_ship,**test).realize_test)\n temps_execution=0\n for iteration in range (ntimes):\n temps_execution+=modified_function()\n store_time.append(temps_execution)\n \n ax[index].plot(sizes, store_time, label=format_args_indep(test),color=color,marker='+')\n ax[index].set_ylabel (\"Time execution\")\n ax[index].set_yscale('log')\n ax[index].set_title(format_test(indep_relation_ship)) \n \n #at the end of computation, delete temporay file created\n if os.path.exists(os.path.join(\"databases\",\"temp_size.csv\")):\n os.remove(os.path.join(\"databases\",\"temp_size.csv\")) \n plt.tick_params(axis='x',rotation=45)\n plt.xlabel (\"data size\") \n box = ax[len(lindep)-1].get_position()\n ax[len(lindep)-1].set_position([box.x0, box.y0 + box.height * 0.4,\n box.width, box.height * 0.6]) \n ax[len(lindep)-1].legend(bbox_to_anchor=(0.0, -1,1., .102), loc=3, mode=\"expand\", fancybox=True, shadow=True) \n fig.suptitle(\"Times to carry out independance tests for several sizes of datasets, each computed {} times\".format(ntimes),y=1.0,weight =\"bold\")\n fig.tight_layout()\n return fig","title":""},{"docid":"7bafa12922bb9be152d72a883db4cf51","score":"0.50142044","text":"def plot_superposed_imf(ax, imf_comp=\"Bz\", df_turn=None, stable_interval=30, \n IMF_turning=\"northward\"):\n\n if df_turn is None:\n df_turn = build_event_database(IMF_turning=IMF_turning, event_status=\"good\")\n #df_turn = df_turn.tail()\n dbdir = \"../data/sqlite3/\"\n table_name = \"IMF\"\n db_name = \"gmi_imf.sqlite\"\n\n # make a connection\n conn = sqlite3.connect(dbdir + db_name,\n detect_types = sqlite3.PARSE_DECLTYPES)\n cur = conn.cursor()\n\n dtms = pd.to_datetime(df_turn.datetime.unique())\n for dtm in dtms:\n stm = dtm - dt.timedelta(seconds=60. * stable_interval)\n etm = dtm + dt.timedelta(seconds=60. * stable_interval)\n # load data to a dataframe\n command = \"SELECT Bx, By, Bz, theta, datetime FROM {tb} \" + \\\n \"WHERE datetime BETWEEN '{stm}' AND '{etm}' \"\n command = command.format(tb=table_name, stm=stm, etm=etm)\n df = pd.read_sql(command, conn)\n dtms_tmp = pd.to_datetime(df.datetime)\n df.loc[:, \"relative_time\"] = [(x-dtm).total_seconds()/60. for x in dtms_tmp]\n label = dtm.strftime(\"%Y%m%d %H:%M\")\n ax.plot(df.relative_time, df.loc[:, imf_comp], label=label)\n ax.axvline(x=0, color=\"r\", linestyle=\"--\", linewidth=1.)\n\n #ax.legend()\n\n return","title":""},{"docid":"e0829dc09e05f2aaf2dced7c24919737","score":"0.5012225","text":"def main():\n \n teta_0 = next_0 = 1\n teta_1 = next_1 = 1\n step = 1\n min_cost = 100\n\n while step < 2000000:\n learning_rate = 1/step\n teta_0 = next_0\n teta_1 = next_1\n next_0 = teta_0 - learning_rate/len(POINTS) * calculate_teta_0(teta_0, teta_1)\n next_1 = teta_1 - learning_rate/len(POINTS) * calculate_teta_1(teta_0, teta_1)\n\n #calculate the cost for teta0 and teta1 with the job function\n cost = 1/(2*len(POINTS)) * calculate_job(teta_0, teta_1)\n \n if(cost < 0.8):\n break\n if(cost < min_cost):\n min_cost = cost\n\n step += 1\n\n plt.subplot(211)\n x, y = [elem[0] for elem in POINTS], [elem[1] for elem in POINTS]\n plt.scatter(x, y)\n\n lol = np.arange(0, 3, 0.1)\n y_chapeau = [next_0 + next_1 * elem for elem in lol]\n plt.plot(lol, y_chapeau)\n\n plt.subplot(212)\n y_lol = [0] * 30\n\n x, y = [elem[0] for elem in POINTS], [elem[1] - (next_0 + next_1 * elem[0]) for elem in POINTS]\n plt.scatter(x, y)\n plt.plot(lol, y_lol)\n plt.show()\n\n print(step)\n print(min_cost)","title":""},{"docid":"70329fd039f5ced5334b064c5ccea520","score":"0.5008261","text":"def get_ICE_plot(self, feature_ind, num_samples, fig_size_x = 9, fig_size_y = 6,\n x_max_lim = None, x_min_lim = None):\n\n ## Obtain the random rows to display\n row_inds_list = [iii for iii in range(self.X.shape[0])]\n random.shuffle(row_inds_list)\n row_inds_list = row_inds_list[0:num_samples]\n \n figure, ax = plt.subplots(1,len(feature_ind), figsize= (len(feature_ind)*fig_size_x, fig_size_y))\n\n X_PI = self.X.copy()\n for idx, feat_id in enumerate(feature_ind):\n \n \n full_list_pred = []\n for row_ind in row_inds_list:\n \n \n x_curr = X_PI[row_ind, :]\n x_curr = x_curr.reshape(1,-1)\n \n x_min = np.min(X_PI[:, feat_id]) if x_min_lim is None else x_min_lim\n x_max = np.max(X_PI[:, feat_id]) if x_max_lim is None else x_max_lim\n \n x_pi = np.linspace(x_min, x_max, 100)\n curr_pred_arr = np.zeros((3, x_pi.shape[0]))\n\n for jjj in range(x_pi.shape[0]):\n x_curr_ice = x_curr.copy()\n x_curr_ice[0,feat_id] = x_pi[jjj]\n p, c = self.gp.predict(x_curr_ice, return_cov=True)\n curr_pred_arr[0,jjj] = p\n curr_pred_arr[1,jjj] = p + np.sqrt(c)*1.96\n curr_pred_arr[2,jjj] = p - np.sqrt(c)*1.96\n full_list_pred.append(curr_pred_arr)\n \n if len(feature_ind) > 1:\n for arr in full_list_pred:\n ax[idx].plot(x_pi, arr[0,:])\n ax[idx].fill_between(x_pi, arr[1,:], arr[2,:], alpha = 0.2)\n ax[idx].set_ylabel('ICE')\n ax[idx].set_xlabel(self.feature_names[feat_id])\n ax[idx].grid(axis='y')\n else:\n for arr in full_list_pred:\n ax.plot(x_pi, arr[0,:])\n ax.fill_between(x_pi, arr[1,:], arr[2,:], alpha = 0.2)\n ax.set_ylabel('ICE')\n ax.set_xlabel(self.feature_names[feat_id])\n ax.grid(axis='y')","title":""},{"docid":"52a3dbfe0f0d9da15ecfdf0c45d38395","score":"0.5004441","text":"def plotTime(self):\n plt.figure()\n t = np.linspace(0, self.m, num=len(self.nodes_infected), endpoint=True)\n plt.title('Nodos infectados vs Tiempo')\n plt.xlabel('Instantes de tiempo')\n plt.ylabel('# de nodos infectados')\n plt.plot(t, self.nodes_infected)\n plt.show()","title":""},{"docid":"7e9060cec834d94a39fc0007adabf368","score":"0.5003","text":"def frame(I, second=5, saveable=True, name='frame', fig_idx=12836):\n if saveable is False:\n plt.ion()\n fig = plt.figure(fig_idx) # show all feature images\n\n plt.imshow(I)\n # plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick\n # plt.gca().yaxis.set_major_locator(plt.NullLocator())\n\n if saveable:\n plt.savefig(name+'.pdf',format='pdf')\n else:\n plt.draw()\n plt.pause(second)","title":""},{"docid":"56e796e03e5edfbb86383a99a1c6ab31","score":"0.5002621","text":"def visualize(sample, x, v_min, v_max, v_init, num_pumps, costs, power, pump_flow_schedule, reservoir, time, demand):\n\n print(\"\\nBuilding visualization...\")\n\n # Initialize plot\n fig, ax = plt.subplots()\n\n # Set up plot parameters (static)\n ax.set_xlim(0, 1)\n ax.set_ylim(0, v_min/2+v_max)\n ax.xaxis.set_visible(False)\n ax.set_yticks([v_min, v_max])\n ax.set_yticklabels(('','Max'))\n ax.set_title(\"Reservoir Water Level\")\n\n # Plot max/min water capacity lines (static)\n ax.plot(list(range(0, 5)), [v_max]*(5), color='#222222', label=\"Max capacity\", linewidth=1.0)\n ax.plot(list(range(0, 5)), [v_min]*(5), color='#FFA143', label=\"Min capacity\", linewidth=1.5)\n\n # Plot water as a blue bar graph (dynamic)\n barcollection = plt.bar(0.5, v_init, width=1.0, color='#2a7de1', align='center')\n\n # Blue line across top of the water (dynamic)\n water_line, = ax.plot([], [], 'b-')\n x_ax_vals = np.linspace(0, 1, 200)\n\n # Put list of pumps on plot (static)\n pumps_used = []\n for i in range(num_pumps):\n pumps_used.append(plt.figtext(0.03, 0.11+0.035*i, \"Pump \"+str(i+1), fontdict=None, color='#DDDDDD', fontsize='small'))\n \n # Put timeslot on plot (dynamic)\n time_label = ax.text(0.75, 1600, '')\n\n # Put cost for timeslot on plot (dynamic)\n cost_label = plt.figtext(0.45, 0.03, '', fontdict=None, color='k')\n\n def animate(i):\n\n # Compute minutes/hour for smooth animation over time\n m = i % (60/smoothing_factor)\n t = int( (i-m) / (60/smoothing_factor) )\n\n # Compute flow/demand per minute for smooth animation over time\n pump_min_flow = m * smoothing_factor * pump_flow_schedule[t] / 60\n demand_min = m * smoothing_factor * demand[t] / 60\n\n # Adjust water level for the given min/hour\n delta = reservoir[t] + pump_min_flow - demand_min\n y = [delta] * (len(x_ax_vals))\n for i, b in enumerate(barcollection):\n b.set_height(delta)\n water_line.set_data(x_ax_vals, y)\n\n # Adjust time/cost/pumps used text on plot for the given hour\n time_label.set_text('Time: '+str(time[t]))\n cost = 0\n for p in range(num_pumps):\n if sample[x[p][t]] == 1:\n pumps_used[p].set_color('#008c82')\n cost += sample[x[p][t]] * costs[t] * power[p] / 1000\n else:\n pumps_used[p].set_color('#DDDDDD')\n\n cost_label.set_text(\"Hourly Cost: \"+str(cost))\n\n return water_line,\n\n # Build movie visualization\n smoothing_factor = 4 # Granularity factor for animation\n anim = animation.FuncAnimation(fig, animate, repeat=False, frames=int(24*(60/smoothing_factor)), interval=2, blit=True)\n mywriter = animation.HTMLWriter(fps=30)\n anim.save('reservoir.html',writer=mywriter)\n\n print(\"\\nAnimation saved as reservoir.html.\")","title":""},{"docid":"1d0a3204c7ef658ab3107edc1b9d0ad6","score":"0.49971083","text":"def time_step(self, prob):","title":""},{"docid":"c92c45fdcd8447c6b404c14640e23cda","score":"0.49960905","text":"def chart_total_feature(feature):\n status_incomplete = feature.objects.filter(status='INCOMPLETE').count()\n status_progress = feature.objects.filter(status='IN PROGRESS').count()\n status_complete = feature.objects.filter(status='COMPLETE').count()\n p_chart = pygal.Pie(print_values=True,\n legend_at_bottom_columns=3,\n legend_box_size=30,\n margin=0,\n style=custom_style\n )\n\n p_chart.add('INCOMPLETE', status_incomplete)\n p_chart.add('IN PROGRESS', status_progress)\n p_chart.add('COMPLETE', status_complete)\n return p_chart.render()","title":""},{"docid":"13c4d70a046adf97db533d2d39763e45","score":"0.49896222","text":"def __call__(self, u, x, t, n):\n if plot_error==True:\n if n == 0:\n plt.ion()\n self.lines = plt.plot(x, u-ue(x,0), 'r-')\n plt.xlabel('x'); plt.ylabel('error')\n plt.axis([0, L, umin, umax])\n plt.legend(['t=%f' % t[n]], loc='lower left')\n else:\n self.lines[0].set_ydata(u-ue(x,t[n]))\n plt.legend(['t=%f' % t[n]], loc='lower left')\n plt.draw()\n time.sleep(2) if t[n] == 0 else time.sleep(sleep)\n plt.savefig('tmp_%04d.png' % n) # for movie making\n else:\n if n == 0:\n plt.ion()\n self.lines = plt.plot(x, u, 'r-')\n self.lines2 = plt.plot(x,ue(x,0),'b--')\n plt.xlabel('x'); plt.ylabel('error')\n plt.axis([0, L, umin, umax])\n plt.legend(['t=%f' % t[n]], loc='lower left')\n else:\n self.lines[0].set_ydata(u)\n self.lines2[0].set_ydata(ue(x,t[n]))\n plt.legend(['t=%f' % t[n]], loc='lower left')\n plt.draw()\n \n time.sleep(2) if t[n] == 0 else time.sleep(sleep)\n plt.savefig('tmp_%04d.png' % n) # for movie making","title":""},{"docid":"d91953348779907ef043f38bfa51203b","score":"0.4986097","text":"def show(self, fig=None):\n i = 0\n iterator = np.linspace(0, self.duration(), num=math.ceil(self.duration() / self.step_precision) + 1)\n tfInterp = np.zeros((4, 4, len(iterator)))\n for t in iterator:\n tfInterp[:, :, i] = self.torsoPosition(t)\n i = i + 1\n self.show_tf(fig, tfInterp, len(iterator))","title":""},{"docid":"f8497f0a636415e2cd788f6a162602c0","score":"0.49749544","text":"def plot_features(data, true_hadroness=False):\n hadro = \"mc_type\" if true_hadroness else \"reco_type\"\n\n # Energy distribution\n plt.subplot(331)\n plt.hist(data[data[hadro] < 1]['log_mc_energy'],\n histtype=u'step', bins=100,\n label=\"Gammas\")\n plt.hist(data[data[hadro] > 0]['log_mc_energy'],\n histtype=u'step', bins=100,\n label=\"Protons\")\n plt.ylabel(r'# of events', fontsize=15)\n plt.xlabel(r\"$log_{10}E$(GeV)\")\n plt.legend()\n\n # disp_ distribution\n plt.subplot(332)\n plt.hist(data[data[hadro] < 1]['disp_norm'],\n histtype=u'step', bins=100,\n label=\"Gammas\")\n plt.hist(data[data[hadro] > 0]['disp_norm'],\n histtype=u'step', bins=100,\n label=\"Protons\")\n plt.ylabel(r'# of events', fontsize=15)\n plt.xlabel(r\"disp_ (m)\")\n\n # Intensity distribution\n plt.subplot(333)\n plt.hist(data[data[hadro] < 1]['log_intensity'],\n histtype=u'step', bins=100,\n label=\"Gammas\")\n plt.hist(data[data[hadro] > 0]['log_intensity'],\n histtype=u'step', bins=100,\n label=\"Protons\")\n plt.ylabel(r'# of events', fontsize=15)\n plt.xlabel(r\"$log_{10}Intensity$\")\n\n dataforwl = data[data['log_intensity'] > np.log10(200)]\n # Width distribution\n plt.subplot(334)\n plt.hist(dataforwl[dataforwl[hadro] < 1]['width'],\n histtype=u'step', bins=100,\n label=\"Gammas\")\n plt.hist(dataforwl[dataforwl[hadro] > 0]['width'],\n histtype=u'step', bins=100,\n label=\"Protons\")\n plt.ylabel(r'# of events', fontsize=15)\n plt.xlabel(r\"Width (º)\")\n\n # Length distribution\n plt.subplot(335)\n plt.hist(dataforwl[dataforwl[hadro] < 1]['length'],\n histtype=u'step', bins=100,\n label=\"Gammas\")\n plt.hist(dataforwl[dataforwl[hadro] > 0]['length'],\n histtype=u'step', bins=100,\n label=\"Protons\")\n plt.ylabel(r'# of events', fontsize=15)\n plt.xlabel(r\"Length (º)\")\n\n # r distribution\n plt.subplot(336)\n plt.hist(data[data[hadro] < 1]['r'],\n histtype=u'step', bins=100,\n label=\"Gammas\")\n plt.hist(data[data[hadro] > 0]['r'],\n histtype=u'step', bins=100,\n label=\"Protons\")\n plt.ylabel(r'# of events', fontsize=15)\n plt.xlabel(r\"r (m)\")\n\n # psi distribution\n\n plt.subplot(337)\n plt.hist(data[data[hadro] < 1]['psi'],\n histtype=u'step', bins=100,\n label=\"Gammas\")\n plt.hist(data[data[hadro] > 0]['psi'],\n histtype=u'step', bins=100,\n label=\"Protons\")\n plt.ylabel(r'# of events', fontsize=15)\n plt.xlabel(r\"psi angle(rad)\")\n\n # psi distribution\n\n plt.subplot(338)\n plt.hist(data[data[hadro] < 1]['phi'],\n histtype=u'step', bins=100,\n label=\"Gammas\")\n plt.hist(data[data[hadro] > 0]['phi'],\n histtype=u'step', bins=100,\n label=\"Protons\")\n plt.ylabel(r'# of events', fontsize=15)\n plt.xlabel(r\"phi angle(m)\")\n\n # Time gradient\n\n plt.subplot(339)\n plt.hist(data[data[hadro] < 1]['time_gradient'],\n histtype=u'step', bins=100,\n label=\"Gammas\")\n plt.hist(data[data[hadro] > 0]['time_gradient'],\n histtype=u'step', bins=100,\n label=\"Protons\")\n plt.ylabel(r'# of events', fontsize=15)\n plt.xlabel(r\"Time gradient\")","title":""},{"docid":"5307cac80c81c7a18dfc76363b197f5e","score":"0.49647075","text":"def FeaturesMonthlyStatus():\n chart = chart_by_time_feature(Feature, 30)\n return chart","title":""},{"docid":"e1816acf57e33233d162bd99b9c1b391","score":"0.4962233","text":"def main():\n\n files = localFileList_ANITA2chamberHPol()\n\n fig = lab.figure()\n axes = []\n\n colors=[\"red\",\"blue\"]\n\n numFiles = len(files)\n index=1\n logMags = {}\n for file in files:\n if index!=1:\n axes.append(fig.add_subplot(numFiles,1,index,sharex=axes[0]))\n else:\n axes.append(fig.add_subplot(numFiles,1,index))\n \n for chan in range(0,2):\n events = importAll(file,numEvents=100,channel=chan)\n avgEvent = tf.correlateAndAverage(events)\n peak = np.argmax(np.abs(avgEvent[1])) + 500\n procAvgEvent = processAllEvents([avgEvent],totalWidth=2000,peak=peak)[0] \n logMag = tf.genLogMag(procAvgEvent[0],procAvgEvent[1]*1000)\n grpDly = tf.genGroupDelay(procAvgEvent[0]*1e9,procAvgEvent[1]*1000)\n\n offPhi = file.split(\"/\")[-1].split(\"_\")[1][1:4]\n offTheta = file.split(\"/\")[-1].split(\"_\")[2][1:4]\n print \"Phi=\"+str(offPhi)+\" Theta=\"+str(offTheta)\n \n label = str(offPhi)+\",\"+str(offTheta)\n axes[-1].plot(grpDly[0], grpDly[1], label = label, color = colors[chan])\n# axes[-1].plot(grpDly[0][1:],grpDly[1][1:],label=label,color=colors[chan])\n index += 1\n \n for ax in axes:\n ax.legend()\n fig.show()\n\n return","title":""},{"docid":"0dfc11c3590a0f010b1c907f2884db37","score":"0.49591658","text":"async def plot_last_active_learning():\n ylim = [0.3, 1.0]\n\n # start plotting\n figure = pyplot.figure()\n figure.set_size_inches(7, 5)\n\n train_sizes, train_scores, test_scores = pickle.load(open(\"al.dump\", \"rb\"))\n plot_learning_curve(figure, [1, 1, 1], train_sizes, train_scores, test_scores, title=\"LogisticRegression - C=2\",\n ylim=ylim)\n\n figure.tight_layout()\n print('ended')","title":""},{"docid":"f8cf442aaf9cc66b72d671707c3cb370","score":"0.49576867","text":"def generate_time_of_day_features(self):\n raise NotImplementedError('To be implemented')","title":""},{"docid":"abdaa270e899eb6ec4fafe47aa3d1d94","score":"0.49529967","text":"def algorithmOnlyOutcome(self):\n \n plt.figure()\n plt.plot(self.T, self.Fa, 'bo' )\n plt.xlabel(r\"t\")\n plt.ylabel(r\"F\")\n plt.title(r\"The inverse Laplace transform\")\n plt.show()","title":""},{"docid":"2e752e360106b9672e96a56d2b09e3b7","score":"0.4952189","text":"def PlotGesture(x, y, accnum, j):\n # prepare line data \n L1_x = x[0:5]\n L1_y = y[0:5]\n L2_x = [x[0]] + x[5:9]\n L2_y = [y[0]] + y[5:9]\n L3_x = x[9:13]\n L3_y = y[9:13]\n L4_x = x[13:17]\n L4_y = y[13:17]\n L5_x = [x[0]] + x[17:21]\n L5_y = [y[0]] + y[17:21]\n L6_x = x[5:18:4]\n L6_y = y[5:18:4]\n\n ax = plt.gca() # Get the current axis information\n ax.xaxis.set_ticks_position('top') # Move the X axis to the top\n ax.invert_yaxis() # Reverse Y axis\n\n plt.title(str(j)+': AccNum: ' + str(accnum))\n plt.plot(L1_x, L1_y, marker='o', mec='r', mfc='g')\n plt.plot(L2_x, L2_y, marker='o', mec='r', mfc='g')\n plt.plot(L3_x, L3_y, marker='o', mec='r', mfc='g')\n plt.plot(L4_x, L4_y, marker='o', mec='r', mfc='g')\n plt.plot(L5_x, L5_y, marker='o', mec='r', mfc='g')\n plt.plot(L6_x, L6_y, marker='o', mec='r', mfc='g')\n\n\n plt.draw()\n plt.pause(1) \n plt.close()","title":""},{"docid":"0a3af1283a69f4d177270e30b5776510","score":"0.4943097","text":"def aniplot(F, source, steps, component='Ez', num_panels=10):\n F.initialize_fields()\n\n # initialize the plot\n f, ax_list = plt.subplots(1, num_panels, figsize=(20*num_panels,20))\n Nx, Ny, _ = F.eps_r.shape\n ax_index = 0\n\n # fdtd time loop\n for t_index in range(steps):\n fields = F.forward(Jz=source(t_index))\n\n # if it's one of the num_panels panels\n if t_index % (steps // num_panels) == 0:\n\n if ax_index < num_panels: # extra safety..sometimes tries to access num_panels-th elemet of ax_list, leading to error\n\n print('working on axis {}/{} for time step {}'.format(ax_index, num_panels, t_index))\n\n # grab the axis\n ax = ax_list[ax_index]\n\n # plot the fields\n im_t = ax.pcolormesh(np.zeros((Nx, Ny)), cmap='RdBu')\n max_E = np.abs(fields[component]).max()\n im_t.set_array(fields[component][:, :, 0].ravel().T)\n im_t.set_clim([-max_E / 2.0, max_E / 2.0])\n ax.set_title('time = {} seconds'.format(F.dt*t_index))\n\n # update the axis\n ax_index += 1\n plt.show()","title":""},{"docid":"0a3af1283a69f4d177270e30b5776510","score":"0.4943097","text":"def aniplot(F, source, steps, component='Ez', num_panels=10):\n F.initialize_fields()\n\n # initialize the plot\n f, ax_list = plt.subplots(1, num_panels, figsize=(20*num_panels,20))\n Nx, Ny, _ = F.eps_r.shape\n ax_index = 0\n\n # fdtd time loop\n for t_index in range(steps):\n fields = F.forward(Jz=source(t_index))\n\n # if it's one of the num_panels panels\n if t_index % (steps // num_panels) == 0:\n\n if ax_index < num_panels: # extra safety..sometimes tries to access num_panels-th elemet of ax_list, leading to error\n\n print('working on axis {}/{} for time step {}'.format(ax_index, num_panels, t_index))\n\n # grab the axis\n ax = ax_list[ax_index]\n\n # plot the fields\n im_t = ax.pcolormesh(np.zeros((Nx, Ny)), cmap='RdBu')\n max_E = np.abs(fields[component]).max()\n im_t.set_array(fields[component][:, :, 0].ravel().T)\n im_t.set_clim([-max_E / 2.0, max_E / 2.0])\n ax.set_title('time = {} seconds'.format(F.dt*t_index))\n\n # update the axis\n ax_index += 1\n plt.show()","title":""},{"docid":"f746f7aa4feefb5ba657c04bd6bbaa7e","score":"0.4940207","text":"def displaySolutions(t,y): \n plt.figure()\n plt.plot(t,y[:,2],'b',label='C(t)')\n plt.legend(loc='best')\n plt.xlabel('t')\n plt.grid()\n plt.title('Mathilde Duverger displaySolutions')\n plt.figure()\n plt.plot(y[:,2],y[:,1],'r',label='phase plane')\n plt.legend(loc='best')\n plt.xlabel('C')\n plt.ylabel('E')\n plt.grid()\n plt.title('Mathilde Duverger displaySolutions')\n plt.show()","title":""},{"docid":"518ee2984a08139f8144d884ec2d288b","score":"0.49375466","text":"def _demo_plot():\n import matplotlib.pyplot as plt\n abfHeader=ABFheader(R\"../../../../data/17o05028_ic_steps.abf\")\n plt.figure(figsize=(8,4))\n plt.grid(alpha=.2)\n times = abfHeader.getSweepTimes()\n for sweepNumber in [0,5,10,15]:\n data = abfHeader.getSweepData(sweepNumber)\n plt.plot(times,data,lw=.7,alpha=.8,label=\"sweep %d\"%sweepNumber)\n plt.margins(0,.1)\n plt.legend(fontsize=8)\n plt.title(\"Demonstrating the ABFheader Class\")\n plt.ylabel(\"Membrane Potential (mV)\")\n plt.xlabel(\"Time (seconds)\")\n plt.tight_layout()\n plt.savefig(\"_demo.png\")\n plt.show()","title":""},{"docid":"60065c1ebf53ea90ced9473f4c81f392","score":"0.49329293","text":"def compare_features(features, names, outdir=\"plots\", diff_threshold=0.05, anchor=\"first\"):\n\n def get_baseline(vals):\n if anchor == \"mean\":\n return sum(vals) / len(vals)\n elif anchor == \"first\":\n return vals[0]\n else:\n print(f\"unknown anchor {anchor}\")\n return vals[0]\n\n def gen_plot(name, getter):\n print(f\"plotting {name}\")\n vals = [getter(f) for f in features]\n plot(names, name, vals, outdir=outdir)\n\n baseline = get_baseline(vals)\n if baseline == 0:\n baseline = 0.000001\n vals = [v + baseline for v in vals]\n\n disps = [val - baseline for val in vals]\n pc_disps = [abs(disp) / baseline for disp in disps]\n\n if not any([pc_disp >= diff_threshold for pc_disp in pc_disps]):\n return\n\n print(f\"{name}: {baseline}\")\n for name, val, pc_disp in zip(names, vals, pc_disps):\n print(f\" - {name}: {val} ({'+' if val >= baseline else '-'}{to_percent(pc_disp)})\")\n\n gen_plot(\"Total instruction counts\", lambda f: f.instructions_count_total())\n gen_plot(\"Freedom to reorder\", lambda f: f.freedom_to_reorder())\n gen_plot(\"Resource pressure\", lambda f: f.resource_pressure())\n gen_plot(\"Work items count\", lambda f: f.work_items_count())\n gen_plot(\"Granularity\", lambda f: f.granularity())\n gen_plot(\"Total barriers hit\", lambda f: f.barriers_hit_total())\n gen_plot(\"Barriers per work item\", lambda f: f.barriers_hit_per_work_item())\n gen_plot(\"Barriers per instruction\", lambda f: f.barriers_hit_per_instruction())\n gen_plot(\"Instructions per work item (mean)\", lambda f: f.instructions_count_per_work_item()[\"mean\"])\n gen_plot(\"SIMD width (mean)\", lambda f: f.simd_width()[\"mean\"])\n gen_plot(\"SIMD sum\", lambda f: f.simd_sum())\n gen_plot(\"Instructions per operand\", lambda f: f.instructions_per_operand())\n gen_plot(\"Memory access count\", lambda f: f.memory_access_count_total())\n gen_plot(\"Memory access count (unique)\", lambda f: f.memory_access_count_unique())\n gen_plot(\"Memory read count (unique)\", lambda f: f.memory_read_count_unique())\n gen_plot(\"Memory read count\", lambda f: f.memory_read_count_total())\n gen_plot(\"Memory write count (unique)\", lambda f: f.memory_write_count_unique())\n gen_plot(\"Memory write count\", lambda f: f.memory_write_count_total())\n gen_plot(\"Memory read-write ratio (unique)\", lambda f: f.memory_read_write_unique_ratio())\n gen_plot(\"Memory re-read ratio\", lambda f: f.memory_reread_ratio())\n gen_plot(\"Memory re-write ratio\", lambda f: f.memory_rewrite_ratio())\n gen_plot(\"Memory address entropy\", lambda f: f.memory_address_entropy())\n gen_plot(\"NPSL sum\", lambda f: f.normed_parallel_spacial_locality_sum())\n gen_plot(\"Global memory access %\", lambda f: f.memory_global_access_count_pc())\n gen_plot(\"Local memory access %\", lambda f: f.memory_local_access_count_pc())\n gen_plot(\"Constant memory access %\", lambda f: f.memory_constant_access_count_pc())\n gen_plot(\"Yokota branch entropy\", lambda f: f.branch_entropy_yokota())\n gen_plot(\"Average linear branch entropy\", lambda f: f.branch_entropy_average_linear())","title":""},{"docid":"79a844f1215e5dbfacb31f9a7e5be20b","score":"0.49297658","text":"def plot_results_seconds(self, on_of):\n\n time_Vector = np.linspace(0, self.active_trace.data.shape[0] / self.fm, num=self.active_trace.data.shape[0])\n print(on_of)\n # create an axis\n self.ax = self.figura_traza.add_subplot(111)\n # discards the old graph\n self.ax.cla()\n self.ax.plot(time_Vector, self.active_trace)\n self.ax.toolbar_trace = NavigationToolbar(self.canvas_traza, self)\n\n self.selector = RectangleSelector(self.ax, self.line_select_callback,\n drawtype='box', useblit=True, button=[1],\n minspanx=5, minspany=5, spancoords='pixels',\n interactive=True)\n ymin, ymax = self.ax.get_ylim()\n self.ax.vlines(on_of[:, 0], ymin, ymax, color='g', linewidth=1)\n self.ax.vlines(on_of[:, 1], ymin, ymax, color='magenta', linestyle='dashed', linewidth=1)\n\n self.ax.axvline(self.first_ticked, color='green', linestyle='solid')\n self.ax.axvline(self.last_ticked, color='magenta', linestyle='dashed')\n self.canvas_traza.draw()","title":""},{"docid":"4fc69bfb6e4f1ac4f92576a34123b616","score":"0.49267346","text":"def example_4():\n np.random.seed(3)\n\n fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(7, 4))\n n_step = 1\n strategy = BlahutArimotoTimeOptimal()\n\n f = MultiWorldFactory()\n\n w = f.simple_2agents()\n start = time.time()\n\n T = w.compute_ma_transition(w.n_a, det=1.)\n E = strategy.compute(world=w, T=T, n_step=n_step)\n print(f\"elapsed seconds: {time.time() - start:0.3f}\")\n\n # Only for plotting, positions need to be resetted\n idx = np.argsort(E)\n for j, agent in enumerate(w.agents):\n agent.set_s(w._index_to_location(idx[0], j))\n\n ax[0].pcolor(np.zeros(w.dims))\n w.plot_entities(fig, ax[0])\n ax[0].set_title(f'{n_step}-step E={E[idx[0]]:.2f} low')\n\n for j, agent in enumerate(w.agents):\n agent.set_s(w._index_to_location(idx[-1], j))\n\n ax[1].pcolor(np.zeros(w.dims))\n w.plot_entities(fig, ax[1])\n ax[1].set_title(f'{n_step}-step E={E[idx[-1]]:.2f} high')\n\n plt.show()","title":""},{"docid":"2823ffc5903c64c8ff3d0704c94b5eea","score":"0.49250218","text":"def trace_progress(values: list, true_best: float, tag: str):\n plt.figure(figsize=(19, 7))\n plt.plot(values, label=\"Tour distance\")\n plt.hlines(true_best, xmin=0, xmax=len(values), color=\"r\", label=\"True best\")\n plt.title(tag)\n plt.legend()\n plt.savefig(f\"figures/Distance_evolution_{tag}\")","title":""},{"docid":"d83d4f2d75d4d0c97ab2d5741833a52b","score":"0.49233404","text":"def plot_subset_stats(self, include_second = False, cutoff_percentile=50) :\n \n counts = self.coef_counter.loc[1, :].values\n \n if include_second : \n cutoff = self.cutoff\n cutoff_percentile = self.cutoff_percentile\n else :\n cutoff = np.percentile(counts, cutoff_percentile)\n \n n_loops = self.coef_values.shape[0]\n \n fig = plt.figure(dpi=300, figsize=(15,10))\n \n # Feature occurrence hist ----------\n ax1 = plt.subplot(231)\n h=plt.hist(counts)\n ax1.axvline(cutoff, c=\"r\", label=\"cutoff for second lasso to see\")\n plt.xlabel(\"Number of times a feature was used\")\n plt.ylabel(\"Count for frequency\")\n plt.legend()\n \n # Feature occurrence barplot ----------\n bar_colors = np.array([\"C0\"]*self.coef_counter.shape[1])\n if include_second :\n # Translate lasso_model non-zero coef features back\n # to full dimension of features that were used. \n m1 = self.original_features_kept_mask \n m2 = np.where(m1)[0][self.lasso_model.coef_!=0]\n bar_colors[m2] = \"r\"\n \n ax2=plt.subplot(232)\n self.coef_counter.loc[1, :].plot.bar(ax=ax2, color=bar_colors)\n ax2.axhline(cutoff, c=\"r\")\n plt.title(\"feature selected out out of %i\" %n_loops)\n \n # feature mean coef vs. feature occurrence scatter ----------\n ax3=plt.subplot(233)\n mean_coef_value = np.abs(self.coef_values.mean() )\n used_count = self.coef_counter.loc[1, :] \n plt.scatter( mean_coef_value, used_count, c = bar_colors)\n plt.xlabel(\"mean coef outside loop\")\n plt.ylabel(\"count used outside loop\")\n plt.title(\"feature mean coef vs times used\")\n plt.axhline(cutoff, c=\"r\")\n \n legend_elements = [Line2D([0], [0], marker='o', color='w', \n label='original feature', markerfacecolor='C0', markersize=15),\n Line2D([0], [0], marker='o', color='w', \n label='selected by second lasso', markerfacecolor='r', markersize=15)\n ]\n \n ax3.legend(handles=legend_elements, loc='best')\n \n # alpha distribution ----------\n ax4=plt.subplot(234)\n h2=plt.hist(self.outer_alphas)\n if include_second :\n plt.axvline(self.lasso_model.alpha_ , c=\"purple\", label=\"second lasso selected alpha\")\n plt.legend()\n plt.xlabel(\"LassoCV(leaveOneout).alpha_\")\n plt.ylabel(\"occurrence over external loop\")\n \n plt.subplots_adjust(hspace=0.7, wspace=0.3)\n \n # heat map of the coeficients from external loop ----------\n ax5 = plt.subplot(235)\n with sns.plotting_context(\"poster\") :\n \n sns.heatmap(self.coef_values, center=0, cmap=\"bwr\", ax=ax5)\n ax5.set(xlabel=\"\", ylabel=\"year left out\", title=\"Regression Coeficients\")","title":""},{"docid":"3d3da16edd26fb800772e1ce52664eb2","score":"0.49197158","text":"def plot_decision_funct(ax, dec_funct, index, feature=None):\n\n if isinstance(index, list):\n decision_arr = create_onset_array(dec_funct) # categorical features (e.g. strings) as values in dec_funct\n else:\n decision_arr = create_onset_array(dec_funct, feature) # create onset for specific feature\n\n y1, y2 = ax.get_ylim()\n for i in range(len(decision_arr)):\n if decision_arr[i] is not None:\n ax.vlines(decision_arr[i], y1, y2, colors='k', linestyles='dashed', label='Theoretical')\n\n if isinstance(index, list):\n # categorical features: plot a step function\n steps = np.arange(len(decision_arr))\n if len(decision_arr) < len(index):\n steps = [i for i in range(len(decision_arr)) for item in decision_arr[i]]\n ax.step(np.arange(len(steps)), steps, where=\"mid\",\n color='k', linestyle='dashed',\n label=\"\")\n else:\n #if there are holes in the onset array/decision function: remove them\n if np.any(decision_arr==None):\n y_indices = np.array(sorted(dec_funct.keys()))[np.argwhere(decision_arr!=None)]\n decision_arr = decision_arr[np.argwhere(decision_arr!=None)]\n else:\n y_indices = sorted(dec_funct.keys())\n\n #if onset array is ascending: achieve end values of each target value by shifting to the right\n if decision_arr[0] < decision_arr[1]:\n offset_arr = np.roll(decision_arr, -1)\n offset_arr[-1] = ax.get_xlim()[1] # for last value use end: ax x-limit\n\n #if descending: shift to the left\n else:\n offset_arr = np.roll(decision_arr, 1)\n offset_arr[0] = ax.get_xlim()[1]\n\n ax.hlines(y_indices, decision_arr, offset_arr, colors=\"k\", linestyles='dashed', label='')\n\n # get rid of duplicate legend values\n handles, labels = ax.get_legend_handles_labels()\n by_label = dict(zip(labels, handles))\n ax.legend(by_label.values(), by_label.keys())","title":""},{"docid":"377d8fbea0aaca996c43094b63d0866d","score":"0.49166554","text":"def plot_feature_importance(self, title='target', path=None):\r\n\r\n if type(self.validation_output).__name__ == 'CrossValidationOutput':\r\n\r\n summary = self.validation_output.feature_importance_summary\r\n\r\n summary.sort_values(CrossValidationOutput.coef_abs, axis=0, ascending=False, inplace=True)\r\n summary.drop(CrossValidationOutput.coef_abs, axis=1, inplace=True)\r\n summary.reset_index(drop=True, inplace=True)\r\n summary.iloc[:10, :].plot(kind='bar', x=BaseValidationOutput.feature_name_col, y=CrossValidationOutput.coef_mean, yerr=CrossValidationOutput.coef_std)\r\n\r\n elif type(self.validation_output).__name__ == 'HoldoutValidationOutput':\r\n\r\n summary = self.validation_output.feature_importance_summary\r\n\r\n summary.sort_values(HoldoutValidationOutput.weight_col, axis=0, ascending=False, inplace=True)\r\n summary.iloc[:10, :].plot(kind='bar', x=BaseValidationOutput.feature_name_col, y=HoldoutValidationOutput.weight_col)\r\n\r\n plt.title('Top 10 features: {0}'.format(title))\r\n if path is not None:\r\n plt.savefig(path, bbox_inches = 'tight')\r\n plt.close()\r\n else:\r\n plt.show()\r\n \r\n return summary","title":""},{"docid":"77fff25f8a5c877d244b9193cc88b685","score":"0.4902319","text":"def plot(self, robot, time=None):\n \n self.infer(robot, time=time)","title":""},{"docid":"ee269e097d579cb2a24ab667fa58037b","score":"0.49002665","text":"def plot_against_target(self, feature):\n assert feature != self._target_variable\n\n if feature in self.numeric_features:\n self._dataset[[feature, self._target_variable]].plot.scatter(x=feature,\n y=self._target_variable,\n alpha=0.1,\n title='{0} vs. target (`{1}`)'.\n format(feature,\n self._target_variable))\n else:\n title = '{0} vs. target (`{1}`)'.format(feature, self.target_variable)\n self._dataset[[feature, self._target_variable]].boxplot(by=feature)\n plt.ylabel(self.target_variable)\n plt.title(title)\n plt.suptitle(\"\")\n plt.tight_layout()","title":""},{"docid":"e32280d947805be8c6bc0575c98e5d4d","score":"0.48997205","text":"def chaperoneTime(self):\n\n fig = plt.figure(figsize=(8, 6))\n ax = fig.add_subplot(111, axisbg='#FFFFCC')\n\n x = self.data[\"time\"]\n y = self.data[\"forceX\"]\n ax.plot(x, y, '-')\n\n #def close(event):\n # plt.close('all')\n\n #axClose = plt.axes([0.4, 0.92, 0.2, 0.05])\n #closeButton = Button(axClose, 'Ok, close!')\n #closeButton.on_clicked(close)\n\n def on_click(event):\n # get the x and y coords, flip y from top to bottom\n x, y = event.x, event.y\n if event.button == 1:\n if event.inaxes is not None:\n print('data coords %f %f' % (event.xdata, event.ydata))\n self.chaperoneIndex = event.xdata\n\n plt.connect('button_press_event', on_click)\n\n # set useblit True on gtkagg for enhanced performance\n cursor = Cursor(ax, useblit=True, color='red', horizOn=False, linewidth=2)\n\n plt.show()\n self.data[\"chaperone\"] = False\n self.data.loc[self.chaperoneIndex:, \"chaperone\"] = True\n print(self.chaperoneIndex)","title":""},{"docid":"67efdc81c138c5d415ab2fae033f4579","score":"0.48972932","text":"def plot_numerical(feature):\n fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(16, 18))\n sns.kdeplot(train[feature], ax=axes[0][0], label='Train')\n sns.kdeplot(test[feature], ax=axes[0][0], label='Test')\n\n sns.kdeplot(train[train['isFraud'] == 0][feature], ax=axes[0][1], label='isFraud: 0')\n sns.kdeplot(train[train['isFraud'] == 1][feature], ax=axes[0][1], label='isFraud: 1')\n\n test[feature].index += len(train)\n axes[1][0].plot(train[feature], '.', label='Train')\n axes[1][0].plot(test[feature], '.', label='Test')\n axes[1][0].set_xlabel('row index')\n axes[1][0].legend()\n test[feature].index -= len(train)\n\n axes[1][1].plot(train[train['isFraud'] == 0][feature], '.', label='isFraud 0')\n axes[1][1].plot(train[train['isFraud'] == 1][feature], '.', label='isFraud 1')\n axes[1][1].set_xlabel('row index')\n axes[1][1].legend()\n\n pd.DataFrame({'train': [train[feature].isnull().sum()], 'test': [test[feature].isnull().sum()]}).plot(\n kind='bar', rot=0, ax=axes[2][0]\n )\n pd.DataFrame({'isFraud 0': [train[(train['isFraud'] == 0) & (train[feature].isnull())][feature].shape[0]],\n 'isFraud 1': [train[(train['isFraud'] == 1) & (train[feature].isnull())][feature].shape[0]]})\\\n .plot(kind='bar', rot=0, ax=axes[2][1])\n\n fig.suptitle(feature, fontsize=18)\n axes[0][0].set_title('Train/Test KDE distribution')\n axes[0][1].set_title('Target value KDE distribution')\n axes[1][0].set_title('Index versus value: Train/Test distribution')\n axes[1][1].set_title('Index versus value: Target distribution')\n axes[2][0].set_title('Number of NaNs');\n axes[2][1].set_title('Target value distribution among NaN values');","title":""},{"docid":"c457882640c7d05ef879241f85961573","score":"0.48962355","text":"def temporal_plot_features(ax):\n\n ax.legend(loc=2)\n ax.set_xlabel('year')\n ax.set_ylabel('interhemispheric ocean\\ntemperature difference (K)')\n ax.tick_params(top='off') \n ax.text(0.92, 0.08, '(a)', transform=ax.transAxes, fontsize=24, va='top')\n ax.axhline(y=0, color='0.5', linestyle='--', linewidth=0.5)\n ylim = ax.get_ylim()\n\n return ylim","title":""},{"docid":"9341a377d149468e1f861142b2e670f5","score":"0.48907804","text":"def draw(self, tf=1, ax=None, color=[0.0, 0.5, 1.0], plot=True):\n # First we supersample the whole curve such that there are not jumps\n # higher than a particular value\n supersampl_t, supersampl_x = misc.supersample(self, max_jump=0.01)\n # Drop all time samples after tf\n value_at_tf = self.eval(tf)\n index_tf = np.argmax(supersampl_t >= tf)\n supersampl_x = supersampl_x[:index_tf]\n supersampl_t = supersampl_t[:index_tf]\n supersampl_t = np.append(supersampl_t, tf)\n supersampl_x.append(value_at_tf.reshape(-1))\n # Reduce the set of points and times to segments and times, restricted\n # to the periodic domain.\n segments = [[supersampl_x[j], supersampl_x[j+1]]\n for j in range(len(supersampl_x)-1)]\n # Use the LineCollection class to print using segments and to assign\n # transparency or colors to each segment\n line_segments = LineCollection(segments)\n # set color\n lowest_alpha = 0.2\n color = np.array(color)\n rgb_color = np.ones((len(segments), 4))\n rgb_color[:, 0:3] = color\n rgb_color[:, 3] = np.linspace(lowest_alpha, 1, len(segments))\n line_segments.set_color(rgb_color)\n start_color = np.zeros((1, 4))\n start_color[:, 0:3] = color\n if len(segments) <= 1:\n start_color[:, 3] = 1\n else:\n start_color[:, 3] = lowest_alpha\n # plotting\n if plot:\n ax = ax or plt.gca()\n ax.add_collection(line_segments)\n ax.scatter(self.spatial_points[0, 0], self.spatial_points[0, 1],\n c=start_color, marker='x', s=0.4)\n ax.set_xlim(0, 1)\n ax.set_ylim(0, 1)\n return ax, (segments, rgb_color)\n return None, (segments, rgb_color)","title":""},{"docid":"b460b0c029f650bf3636ce731abd566e","score":"0.4890094","text":"def plot_input(u,close_i, time_scale=1):\n n_points = np.shape(u)[0]\n plt.figure(figsize=(10, 2))\n fig = plt.gcf()\n ax = plt.gca()\n t = np.linspace(0, n_points * 0.1, n_points)\n\n plt.plot(t[:close_i], u[:close_i])\n\n plt.xlabel(r'$timesteps$')\n plt.ylabel(r'$u_t$')\n plt.title('Control Input until Circling');","title":""},{"docid":"2518804e1afee137a1d08bb3f39a9636","score":"0.4885796","text":"def plot_decision_boundary(\n pred_func: Any, X: np.ndarray, y: np.ndarray, figure: Any = None\n) -> None:\n\n if figure is None: # If no figure is given, create a new one\n plt.figure()\n # Set min and max values and give it some padding\n x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n h = 0.01\n # Generate a grid of points with distance h between them\n xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n # Predict the function value for the whole grid\n Z = pred_func(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n # Plot the contour and training examples\n plt.contourf(xx, yy, Z, cmap=plt.cm.get_cmap(\"Spectral\"))\n cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cm_bright)","title":""},{"docid":"b71ee3dbdbbe152f65987f6aa02e0c86","score":"0.48842716","text":"def timestep():\n return context().reporter.timestep()","title":""},{"docid":"39cac17709c88646ba037330fdd6b1d3","score":"0.48751062","text":"def plot_est_traveltime(self, unit):\n\n fig = plt.figure(figsize=(10, 10), dpi=100)\n ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n ax.plot(self.time_grid[1:], self.est_traveltime, linewidth=1)\n plt.title('Estimated queue length')\n plt.xlabel('Time s')\n plt.ylabel('Length of queue (s)')\n\n plt.draw()","title":""},{"docid":"9a661521346833c4381d5e59f09b44ef","score":"0.48733562","text":"def doAfterTrial(self):\r\n #First need to wait for the full target display period to complete\r\n while(self.getTime() < self.targetDisplayDuration):\r\n self.wait(.010)\r\n\r\n\r\n #clear target and wait 500 milliseconds\r\n self.drawBackground(\"Black\")\r\n if(self.responseTrial == True):\r\n self.yesStimuli.draw()\r\n self.noStimuli.draw()\r\n self.task.refreshWindow()\r\n self.wait(self.standardDelay)\r\n\r\n #display feedback if this is a response trial\r\n if(self.responseTrial==True):\r\n self.drawBackground(\"Black\")\r\n if(self.lastTrialCorrect == True):\r\n self.correctStimuli.draw()\r\n elif(self.lastTrialCorrect == False):\r\n self.incorrectStimuli.draw()\r\n\r\n if(self.responseTrial == True):\r\n self.yesStimuli.draw()\r\n self.noStimuli.draw()\r\n\r\n self.task.refreshWindow()\r\n self.wait(self.displayFeedbackDuration)\r\n\r\n #clear feedback and wait 500 milliseconds\r\n self.drawBackground(\"Black\")\r\n if(self.responseTrial == True):\r\n self.yesStimuli.draw()\r\n self.noStimuli.draw()\r\n self.task.refreshWindow()\r\n self.wait(self.standardDelay)\r\n\r\n # display fixation number\r\n if(self.currentTrial.fixation_number != None):\r\n self.drawBackground(\"Black\")\r\n self.fixation.draw()\r\n if(self.responseTrial == True):\r\n self.yesStimuli.draw()\r\n self.noStimuli.draw()\r\n if(self.currentTrial.trial_number <= 3):\r\n self.demoFixationPrompt.draw()\r\n self.task.refreshWindow()\r\n self.wait(self.demoFixationDisplayDuration)\r\n\r\n #now clear fixation number\r\n self.drawBackground(\"Black\")\r\n if(self.responseTrial == True):\r\n self.yesStimuli.draw()\r\n self.noStimuli.draw()\r\n self.task.refreshWindow()","title":""},{"docid":"c6233cabe73b1bd422096dbc732dd0d0","score":"0.48646662","text":"def doAfterTrial(self):\r\n #First need to wait for the full target display period to complete\r\n while(self.getTime() < self.targetDisplayDuration):\r\n self.wait(.010)\r\n\r\n\r\n #clear target and wait 500 milliseconds\r\n self.drawBackground(\"Black\")\r\n if(self.responseTrial == True):\r\n self.yesStimuli.draw()\r\n self.noStimuli.draw()\r\n self.task.refreshWindow()\r\n self.wait(self.standardDelay)\r\n\r\n #display feedback if this is a response trial\r\n if(self.responseTrial==True):\r\n self.drawBackground(\"Black\")\r\n if(self.lastTrialCorrect == True):\r\n self.correctStimuli.draw()\r\n elif(self.lastTrialCorrect == False):\r\n self.incorrectStimuli.draw()\r\n\r\n if(self.responseTrial == True):\r\n self.yesStimuli.draw()\r\n self.noStimuli.draw()\r\n\r\n self.task.refreshWindow()\r\n self.wait(self.displayFeedbackDuration)\r\n\r\n #clear feedback\r\n self.drawBackground(\"Black\")\r\n if(self.responseTrial == True):\r\n self.yesStimuli.draw()\r\n self.noStimuli.draw()\r\n self.task.refreshWindow()","title":""},{"docid":"404892a2fba0b050f272fa703dc706ae","score":"0.48638797","text":"def plot_act(fo, leg=[], save=False, sfile=\"\"):\n\n plt.plot(fo.sumdat[0], fo.sumdat[1])\n plt.yscale('log')\n \n if len(leg)>1:\n plt.legend(leg)\n plt.xlabel(\"Time (years)\")\n plt.ylabel(\"Activity (Bq)\")\n \n if save == False:\n plt.show()\n else:\n plt.savefig(sfile)","title":""},{"docid":"19e021ce50c424474f1cfa16b7fc3170","score":"0.48590252","text":"def plot_entity(df, threshold=None, entity= \"\", savepath='./AiyuDocs/new_signalplots/'):\n accs = np.asarray(np.vstack([val for val in df.accelerations]))\n ybot, ytop = np.amax(accs), np.amin(accs)\n\n feature_names = df.columns\n display_params = feature_names[3:-1] # Skip acc, timestamp, window length\n for entityname, row in df[:2].iterrows():\n fig = plt.figure(figsize=(20,5), constrained_layout=True)\n\n x = (row['timestamps'] - row['timestamps'][0])/10**9\n y = row['accelerations']\n\n\n ax1 = fig.add_axes((0.1, 0.2, 1, 1))\n ax1.plot(x, y, zorder = 1)\n if entity == 'defect':\n ax1.set_title(\"Defect: \" + entityname)\n else:\n ax1.set_title(i)\n\n ax1.set_xlabel(\"Seconds\")\n ax1.set_ylabel(\"Amplitude\")\n ax1.set_ylim(ybot,ytop)\n ax1.margins(x=0, y=0.1)\n\n caption = ', '.join([ f\"|{feature}|: {df.loc[i, feature]}\" for feature in display_params])\n fig.text(0.1, .0005, caption, ha='left', fontsize=12)\n fig.set_size_inches(20, 5, forward=True)\n fig.savefig(savepath + entityname, bbox_inches='tight')\n plt.show()","title":""},{"docid":"8feb41ba37230f516baf20204bf8bc2c","score":"0.48534796","text":"def convergence_plot(scores):\n\n plt.figure(figsize=(10, 6))\n\n sns.set(style=\"whitegrid\")\n plt.rc('font', size=13) # controls default text sizes\n plt.rc('axes', titlesize=13) # fontsize of the axes title\n plt.rc('xtick', labelsize=13) # fontsize of the tick labels\n plt.rc('ytick', labelsize=13) # fontsize of the tick labels\n plt.rc('legend', fontsize=13)\n wg = pd.DataFrame(scores, columns=[\"score\"])\n ax = sns.lineplot(data=wg, palette=\"tab10\", linewidth=2.5)\n ax.set(xlabel=\"Iterations\")\n ax.set(ylabel=\"Score\")\n plt.show()\n\n # def plot_survival(col, clinical, labels=None, ci_show=True, show_img=True):\n # values = list(set(clinical[col]))\n # if labels == None:\n # labels = values\n # labels = [str(x) for x in values]\n # kmfs = []\n # T = []\n # labels_true = []\n # C = []\n # count = 0\n # for val in values:\n # v = str(val)\n # km = KaplanMeierFitter()\n # f = clinical[col] == val\n # t = clinical[f]['surv']\n # if t.shape[0] > 0:\n # print(\"{0} group {1} sampels\".format(labels[count], t.shape[0]))\n #\n # c = clinical[f]['event']\n # kmfs.append(km)\n # T.append(t)\n # C.append(c)\n # labels_true.append(labels[count])\n # count = count + 1\n # plt.figure(figsize=(12, 8))\n # ax = plt.subplot(111)\n # for i in range(len(C)):\n # kmf = kmfs[i]\n # t = T[i]\n # c = C[i]\n # label = labels[i]\n # kmf.fit(t, event_observed=c, label=labels_true[i])\n # if show_img:\n # kmf.plot(ci_show=ci_show, ax=ax)\n # if show_img:\n # plt.show()\n # p_vals = []\n # for i in range(len(C)):\n # for j in range(i + 1, len(C)):\n # summary = logrank_test(T[i], T[j], C[i], C[j], alpha=99)\n # v = round(-np.log10(summary.p_value), 2)\n # print(\"- log10 p-value between {0} and {1} is {2}\".format(labels[i], labels[j], v))\n # p_vals.append(v)\n # print(\"average -log10 p-value is {0}\".format(np.round(np.mean(p_vals), 2)))\n # return (np.round(np.mean(p_vals), 2))","title":""},{"docid":"15e9279b58a5c49c0fb1306e159a9076","score":"0.4844326","text":"def tick(self, process_variable, dt):","title":""},{"docid":"5cd0e1ede118266cf47680465b08bfb0","score":"0.48403832","text":"def main():\n df = read_time_data('sqlite:///../data/isrid-master.db')\n hours = df.hour.as_matrix()\n mu, kappa = estimate_distribution(hours)\n samples = radians_to_hours(np.random.vonmises(mu, kappa, pow(10, 6)))\n\n setup_environment()\n plot_histogram(hours, 'Empirical Data (N = {})'.format(len(hours)))\n plot_histogram(samples, '$10^6$ Samples from Fitted Distribution')\n plot_pdf(mu, kappa)\n\n plt.legend()\n plt.savefig('../doc/figures/incident-time-distribution/'\n 'incident-time-distribution.svg', transparent=True)\n plt.show()","title":""},{"docid":"479521daf4c8a02be51f4980e07e7b76","score":"0.4838713","text":"def plot_time(i,j):\n fig = plt.figure()\n ax = fig.add_subplot(111,projection=\"3d\")\n\n lengths = [ 5579133, 4539804, 2452883 ]\n\n ch1,ch2,ch3 = split_chromosomes(load_datasingle(i,j))\n chs_position = [ch1,ch2,ch3]\n chs_timings = get_timings()\n chs_remap = normalise2([ map_scales2(np.arange(150,len(Y)*300,300),Y,np.linspace(0,l,len(pos)),np.mean) for Y,l,pos in zip(chs_timings,lengths,chs_position) ])\n \n chs_cm = [ cm.hot(a) for a in chs_remap ]\n\n for pos,col in zip(chs_position,chs_cm):\n ax.scatter(pos[\"x\"],pos[\"y\"],pos[\"z\"],marker=\".\",c=col)\n\n plt.colorbar(cm.ScalarMappable(cmap=cm.hot))\n\n plt.show(block=False)","title":""},{"docid":"6c91bc8378a413461880d1f924450702","score":"0.48362234","text":"def univariate_plotter(feature, data, target_col, bins=10, data_test=0):\n print(' {:^100} '.format('Plots for ' + feature))\n if data[feature].dtype == 'O':\n print('Categorical feature not supported')\n else:\n cuts, grouped = get_grouped_data(input_data=data, feature=feature, target_col=target_col, bins=bins)\n has_test = type(data_test) == pd.core.frame.DataFrame\n if has_test:\n grouped_test = get_grouped_data(input_data=data_test.reset_index(drop=True), feature=feature,\n target_col=target_col, bins=bins, cuts=cuts)\n trend_corr = get_trend_correlation(grouped, grouped_test, feature, target_col)\n print(' {:^100} '.format('Train data plots'))\n\n draw_plots(input_data=grouped, feature=feature, target_col=target_col)\n print(' {:^100} '.format('Test data plots'))\n\n draw_plots(input_data=grouped_test, feature=feature, target_col=target_col, trend_correlation=trend_corr)\n else:\n draw_plots(input_data=grouped, feature=feature, target_col=target_col)\n print(\n '--------------------------------------------------------------------------------------------------------------')\n print('\\n')\n if has_test:\n return (grouped, grouped_test)\n else:\n return (grouped)","title":""},{"docid":"00923e1a1cf5b59aff870ecad4372a5e","score":"0.4831807","text":"def ctlike_speed_up(my_plotter, outpref, log_exists=False):\n if log_exists:\n aux = np.array([])\n cores = [i + 1 for i in range(my_plotter.ncsv)]\n times = pd.Series(index=cores)\n amd = pd.Series(index=cores)\n parallel_loop = pd.Series(index=cores)\n for i in xrange(my_plotter.ncsv):\n df = pd.read_csv(\n my_plotter.infile + \"_CPUs_\" + str(i + 1) + \".csv\")\n aux = select_lines(\n 'ctlike_CPUs_' + str(i + 1) + '.csv',\n df.at[0, 'TIME'],\n ['gammaspeed:real_parallel_start',\n 'gammaspeed:real_parallel_end'])\n parallel_loop[i + 1] = aux[1] - aux[0]\n\n paralleltime = parallel_loop[1]\n\n for i in xrange(my_plotter.ncsv):\n df = my_plotter.read_monitor_log(i)\n # time spent for the whole process\n times[i + 1] = df['TIME'].iget(-1) - df['TIME'].iget(0)\n amd[i + 1] = times[1] / \\\n (times[1] - paralleltime + paralleltime / (i + 1))\n\n plt.figure(21)\n my_plotter.splot(\n figtitle='Overall speed up and efficiency for ctlike',\n outfile=\"plots/\" + outpref + '_general',\n speed_frame=times,\n amdahl_frame=amd)\n\n plt.figure(20)\n my_plotter.splot(\n figtitle='Parallel speed up and efficiency for ctlike',\n outfile=\"plots/\" + outpref + '_parallel',\n speed_frame=parallel_loop,\n amdahl_frame=pd.Series(data=cores, index=cores))","title":""},{"docid":"d6bf8e6585e448d15b569a4083f6577e","score":"0.4827437","text":"def plot(self, variable=None, ax=None, show_members=False, cmap=None, x=\"time\"):\n if x == \"time\":\n x = \"valid_time\"\n assert x in [\"valid_time\", \"init\"]\n if self.kind == \"hindcast\":\n if cmap is None:\n cmap = \"viridis\"\n return plot_lead_timeseries_hindcast(\n self,\n variable=variable,\n ax=ax,\n show_members=show_members,\n cmap=cmap,\n x=x,\n )\n elif self.kind == \"perfect\":\n if cmap is None:\n cmap = \"tab10\"\n return plot_ensemble_perfect_model(\n self, variable=variable, ax=ax, show_members=show_members, cmap=cmap\n )","title":""},{"docid":"c0866f82bca8ad8ae2f6ccc41c1c749c","score":"0.48224968","text":"def subPlotStat(func, porf, time):\n\t# generate positions\n\tx, y = np.linspace(-15, 15, 600), np.linspace(-15, 15, 600)\n\tX, Y = np.meshgrid(x, y)\n\tZ = np.zeros((600, 600))\n\n\t# calculate potential/force\n\tif porf == \"potential\":\n\t\tif func == haloPot:\n\t\t\tT = func(X, Y, Z, time, 0.4, 1)[0]\n\t\telif func == cgpot:\n\t\t\tT = func(X, Y, Z, time)\n\t\telse:\n\t\t\tT = func(X, Y, Z, time)[0]\n\telif porf == \"force\":\n\t\tif func == haloPot:\n\t\t\tT = func(X, Y, Z, time, 0.5, 3)[1]\n\t\telse:\n\t\t\tT = func(X, Y, Z, time)[1]\n\n\treturn X, Y, T","title":""},{"docid":"41bdab6e90d365e038deb710ac062bc9","score":"0.48182845","text":"def helpStackPlot(dataframe, numTimeStep):\n # get info\n foodPerPreyList = dataframe[\"foodPerPrey\"]\n preyCountOverTimeList = dataframe[\"preyCountOverTime\"]\n preyPerPredList = dataframe[\"preyPerPred\"]\n\n\n # hold all alive y-arrays for this perception type, same for eaten and starved.\n aliveOverTimeLists = []\n eatenOverTimeLists = []\n starvedOverTimeLists = []\n\n # loop through runs\n for run in dataframe.index:\n # get info\n # 3 y-arrays: alive, starved, eaten.\n aliveOverTimeList, starvedOverTimeList = getNewPreyCountOverTimeList(foodPerPreyList[run], preyCountOverTimeList[run], preyPerPredList[run], numTimeStep)\n eatenOverTimeList = [aliveOverTimeList[0] - starvedOverTimeList[index] - aliveOverTimeList[index] for index in range(len(aliveOverTimeList))] # calculate number eaten.\n aliveOverTimeLists.append(aliveOverTimeList)\n eatenOverTimeLists.append(eatenOverTimeList)\n starvedOverTimeLists.append(starvedOverTimeList)\n\n # make all list lengths equal\n aliveOverTimeLists = equalizeListLengths(aliveOverTimeLists, \"alive\")\n eatenOverTimeLists = equalizeListLengths(eatenOverTimeLists, \"eaten\")\n starvedOverTimeLists = equalizeListLengths(starvedOverTimeLists, \"starved\")\n\n meanAliveOverTime = np.mean(np.array(aliveOverTimeLists), axis=0).tolist() # axis = 0 does elementwise means\n meanEatenOverTime = np.mean(np.array(eatenOverTimeLists), axis=0).tolist() \n meanStarvedOverTime = np.mean(np.array(starvedOverTimeLists), axis=0).tolist() \n return meanAliveOverTime, meanEatenOverTime, meanStarvedOverTime","title":""},{"docid":"de0d08bb8d722be14ff4043b034b7a12","score":"0.48132458","text":"def plot_np_perf():\n n = 10000\n i = 0\n max_generations = 12\n time_data = []\n generations = []\n\n while i <= max_generations:\n time_data.append(timeit.timeit(\n 'run(0.6, {}, 0.1, 1, False)'.format(i),\n number=n, setup='from tree_np import run'))\n generations.append(i)\n i += 1\n\n plt.plot(generations, time_data, '-rx', )\n plt.ylabel('Time taken for {} iterations (sec)'.format(n))\n plt.xlabel('Number of generations of branches')\n plt.legend\n plt.savefig('perf_comparison.png')","title":""},{"docid":"4c148ed3e43749dbfc62690928a2dd31","score":"0.480294","text":"def plot_fitness(self):\r\n pass","title":""},{"docid":"7de84a580df821ad6dc13090f2610da2","score":"0.47895166","text":"def show_convergence_profile(self) -> None:\n y_es = self.get_convergence_profile()\n x_es = [i for i in range(len(y_es))]\n plt.title(self.name + ' method convergence profile')\n plt.ylabel('Convergence (residual norm)')\n plt.xlabel('Iterations')\n plt.plot(x_es, y_es, 'b--')\n plt.legend(['Total iter = ' + str(self.finished_iter)])\n plt.show()","title":""},{"docid":"5e26a71fced9307a0c2421cc24f5c877","score":"0.4788722","text":"def startPlot():\r\n lbl_progress.configure(text = \"Progress: 0 %\") # reset progres\r\n #making sure that the delay paths are different\r","title":""},{"docid":"4155c1bc6fe514bbb91cd60ae4c95ac9","score":"0.47830462","text":"def plot_time(axis, df, **kwargs):\n time_range = kwargs.pop('time_range', (0, 400))\n xlabel = kwargs.pop(\"xlabel\", \"time (ps)\")\n ylabel = kwargs.pop(\"ylabel\", \"Energy Density\")\n dt = kwargs.pop('dt', 1)\n verbose = kwargs.pop('verbose', 0)\n bins = ptp(time_range) / dt\n\n if verbose > 0:\n energy_in_plot = df[(df.time > min(time_range)) & (df.time < max(time_range))].energy.sum()\n print(\"Total energy in plot is {:2.1f} %\".format(energy_in_plot * 1e2))\n #print(\"Energy in plot is :\",sum(df.energy)*1e2,\"%\")\n\n axis.hist(array(df.time), range=time_range, bins=bins,\n weights=array(df.energy), **kwargs)\n\n axis.set_xlim(time_range)\n axis.set_xlabel(xlabel)\n axis.set_ylabel(ylabel)","title":""},{"docid":"0bb176a44fd384c94a3e51447922bf71","score":"0.47813454","text":"def show_logistic_performance(results, degrees, gammas, jet):\n\n print('salut')\n acc_means = []\n for i in range(1,len(degrees)):\n acc_means_by_degree = []\n for j in range(1, len(gammas)):\n acc_means_by_degree.extend([x[4] for x in results if (x[0]==degrees[i] and x[2]==gammas[j])])\n acc_means.append(acc_means_by_degree)\n\n gammas = [round(x,5) for x in gammas]\n ax = sns.heatmap(acc_means, xticklabels=gammas, yticklabels=degrees, cmap='cubehelix_r', vmin=0.47, vmax=0.82)\n ax.invert_yaxis()\n ax.set_xlabel('gamma', labelpad=20)\n ax.set_ylabel('degree', labelpad=20)\n ax.set_title('Accuracy of data set where $PRI\\_jet\\_num$=%d using Logistic Regression\\n\\n' % jet)\n plt.savefig('./report/figures/%d_logistic_regression.pdf' % jet, bbox_inches='tight')\n plt.show()","title":""},{"docid":"6b86be0fd95ce8f063829c04020e31e1","score":"0.47779018","text":"def plot_so_gait_info(so_results_dir, gl=None):\n for fname in os.listdir(so_results_dir):\n if fname.endswith('_force.sto'):\n fpath = os.path.join(so_results_dir, fname)\n break\n\n def plot_thresholds(val):\n pl.axhline(val, c=[0.7, 0.7, 0.7])\n pl.axhline(-val, c=[0.7, 0.7, 0.7])\n\n def plot(time, y, side='right', *args, **kwargs):\n if gl != None:\n if side == 'r': side = 'right'\n elif side == 'l': side = 'left'\n plot_pgc(time, y, gl, side=side, plot_toeoff=True, *args, **kwargs)\n\n else:\n pl.plot(time, y, *args, **kwargs)\n\n legend_kwargs = {'loc': 'best', 'prop': {'size': 12}}\n\n actu = dataman.storage2numpy(fpath, 2)\n\n fig = pl.figure(figsize=(10, 12))\n grid_size = (3, 2)\n pl.subplot2grid(grid_size, (0, 0))\n pl.title('residual forces')\n for coln in ['FX', 'FY', 'FZ']:\n plot(actu['time'], actu[coln], label=coln)\n pl.axhline(0, c='k')\n pl.ylabel('force (N)')\n pl.legend(**legend_kwargs)\n pl.ylim((-40, 40))\n pl.xlim(0, 100)\n pl.grid(axis='y')\n plot_thresholds(10)\n plot_thresholds(25)\n\n pl.subplot2grid(grid_size, (0, 1))\n pl.title('residual moments')\n for coln in ['MX', 'MY', 'MZ']:\n plot(actu['time'], actu[coln], label=coln)\n pl.axhline(0, c='k')\n pl.ylabel('torque (N-m)')\n pl.legend(**legend_kwargs)\n pl.ylim((-40, 40))\n pl.xlim(0, 100)\n pl.grid(axis='y')\n plot_thresholds(30)\n\n pl.subplot2grid(grid_size, (1, 0))\n pl.title('left lower limb reserves')\n for coln in ['hip_flexion_l', 'knee_angle_l', 'ankle_angle_l']:\n plot(actu['time'], actu['reserve_%s' % coln], side=coln[-1],\n label=coln.split('_')[0])\n pl.axhline(0, c='k')\n pl.ylabel('torque (N-m)')\n pl.legend(**legend_kwargs)\n pl.xlim(0, 100)\n pl.ylim((-25, 25))\n pl.grid(axis='y')\n plot_thresholds(10)\n\n pl.subplot2grid(grid_size, (1, 1))\n pl.title('right lower limb reserves')\n for coln in ['hip_flexion_r', 'knee_angle_r', 'ankle_angle_r']:\n plot(actu['time'], actu['reserve_%s' % coln], side=coln[-1],\n label=coln.split('_')[0])\n pl.axhline(0, c='k')\n pl.ylabel('torque (N-m)')\n pl.legend(**legend_kwargs)\n pl.xlim(0, 100)\n pl.ylim((-25, 25))\n pl.grid(axis='y')\n plot_thresholds(10)\n\n pl.subplot2grid(grid_size, (2, 0))\n pl.title('lumbar rotations reserves')\n for coln in ['lumbar_bending', 'lumbar_extension', 'lumbar_rotation']:\n actu_name = 'reserve_%s' % coln\n if actu_name in actu.dtype.names:\n plot(actu['time'], actu[actu_name],\n label=coln.split('_')[-1])\n else:\n # Apoorva's model does not have lumbar muscles.\n pl.text(0, 0, 'no lumbar reserves', ha='left', va='bottom')\n pl.axhline(0, c='k')\n pl.ylabel('moment (N-m)')\n pl.xlabel('time (s)')\n pl.legend(**legend_kwargs)\n pl.xlim(0, 100)\n pl.ylim((-25, 25))\n pl.grid(axis='y')\n plot_thresholds(10)\n\n pl.subplot2grid(grid_size, (2, 1))\n pl.title('hip reserves')\n labels = ['rot_r',' rot_l', 'add_r',' add_l']\n for i, coln in enumerate(['hip_rotation_r', 'hip_rotation_l',\n 'hip_adduction_r', 'hip_adduction_l']):\n plot(actu['time'], actu['reserve_%s' % coln], side=coln[-1],\n label=labels[i])\n pl.axhline(0, c='k')\n pl.ylabel('moment (N-m)')\n pl.xlabel('time (s)')\n pl.legend(**legend_kwargs)\n pl.xlim(0, 100)\n pl.ylim((-25, 25))\n pl.grid(axis='y')\n plot_thresholds(10)\n\n pl.tight_layout()\n return fig","title":""},{"docid":"a95f3c75ba804fa3c89048c9ccb2c22d","score":"0.47778177","text":"def plotResults(recipe):\n\n r = recipe.bucky.profile.x\n\n # Plot this.\n g = recipe.bucky.profile.y\n gcalc = recipe.bucky.profile.ycalc\n diffzero = -0.8 * max(g) * numpy.ones_like(g)\n diff = g - gcalc + diffzero\n\n import pylab\n pylab.plot(r,g,'ob',label=\"G(r) Data\")\n pylab.plot(r,gcalc,'-r',label=\"G(r) Fit\")\n pylab.plot(r,diff,'-g',label=\"G(r) diff\")\n pylab.plot(r,diffzero,'-k')\n pylab.xlabel(\"$r (\\AA)$\")\n pylab.ylabel(\"$G (\\AA^{-2})$\")\n pylab.legend(loc=1)\n\n pylab.show()\n return","title":""}],"string":"[\n {\n \"docid\": \"0d4c94955098393f2b1123cd9107e53a\",\n \"score\": \"0.590543\",\n \"text\": \"def plotting_time_feat(df, features, start, stop, scaler=False):\\n \\n df1 = df[features]\\n df2 = df1[start:stop]\\n \\n if scaler:\\n from sklearn.preprocessing import StandardScaler\\n scaler = StandardScaler()\\n df_plot = scaler.fit_transform(df2)\\n df_plot = pd.DataFrame(df_plot, columns=df2.columns, index=df2.index)\\n else:\\n df_plot = df2\\n \\n fig, ax = plt.subplots(figsize=(10,5))\\n df_plot.plot(ax=ax);\\n ax.set_title(f'From {start} to {stop}', fontsize=16)\\n ax.set_xlabel('Time', fontsize=14)\\n ax.tick_params(axis='x', labelrotation=30)\\n if scaler:\\n ax.set_title(f'From {start} to {stop} (Scaled)', fontsize=16)\\n else:\\n ax.set_title(f'From {start} to {stop}', fontsize=16)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4de91ba9a54fd1b88102c745698671e9\",\n \"score\": \"0.5888863\",\n \"text\": \"def analyze_2d_start_state(self, feature1, feature2, primitive):\\n s = self.success_start[primitive]\\n f = self.fail_start[primitive]\\n\\n index1 = features[feature1]\\n index2 = features[feature2]\\n\\n s1 = s[:, index1: index1+1]\\n s2 = s[:, index2: index2+1]\\n\\n f1 = f[:, index1: index1+1]\\n f2 = f[:, index2: index2+1]\\n\\n colors = np.repeat(np.array([[0, 255, 0]]), s1.shape[0], axis=0)\\n colors = np.vstack((colors, np.repeat(np.array([[255, 0, 0]]), f1.shape[0], axis = 0)))\\n\\n s1 = np.vstack((s1, f1)).ravel()\\n s2 = np.vstack((s2, f2)).ravel()\\n plt.scatter(s1, s2, c=colors/255)\\n plt.xlabel(feature1)\\n plt.ylabel(feature2)\\n plt.title(' Successes and Failures for Starting Features in ' + primitive + ' Primitive')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b241b4516f4b99c6de083451e32c13fa\",\n \"score\": \"0.5853148\",\n \"text\": \"def plot_step():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e246bda0b8e843beb89cb83db669712e\",\n \"score\": \"0.57349163\",\n \"text\": \"def chart_by_time_feature(feature, days):\\n status_incomplete = feature.objects.filter(\\n status='INCOMPLETE', incomplete_date__gte=datetime.now() - timedelta(days=days)).count()\\n status_progress = feature.objects.filter(\\n status='IN PROGRESS', in_progress_date__gte=datetime.now() - timedelta(days=days)).count()\\n status_complete = feature.objects.filter(\\n status='COMPLETE', completed_date__gte=datetime.now() - timedelta(days=days)).count()\\n p_chart = pygal.Pie(print_values=True,\\n legend_at_bottom_columns=3,\\n legend_box_size=30,\\n margin=0,\\n style=custom_style,\\n no_data_font_size=30,\\n no_data_text='No Recorded Data',\\n no_data_font_family='san-serif')\\n\\n if status_incomplete == 0 and status_progress == 0 and status_complete == 0:\\n return 'No data has been collected for this period yet.'\\n else:\\n p_chart.add('INCOMPLETE', status_incomplete)\\n p_chart.add('IN PROGRESS', status_progress)\\n p_chart.add('COMPLETE', status_complete)\\n return p_chart.render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3a1053036e50aff6dd269befc3c0813\",\n \"score\": \"0.56702864\",\n \"text\": \"def analyze_start_state(self, feature, primitive):\\n s = self.success_start[primitive]\\n f = self.fail_start[primitive]\\n\\n index = features[feature]\\n s = s[:, index:index+1]\\n f = f[:, index:index+1]\\n\\n # computing bin properties \\n num_bin = self.histogram_bins\\n start = min(np.min(s), np.min(f))\\n end = max(np.max(s), np.max(f))\\n bin_lims = np.linspace(start,end,num_bin+1)\\n bin_centers = 0.5*(bin_lims[:-1]+bin_lims[1:])\\n bin_widths = bin_lims[1:]-bin_lims[:-1]\\n \\n # computing histograms\\n s_hist, _ = np.histogram(s, bins=bin_lims)\\n f_hist, _ = np.histogram(f, bins=bin_lims)\\n\\n # normalizing\\n norm = (s_hist + f_hist).astype(float)\\n s_hist = s_hist/norm\\n f_hist = f_hist/norm\\n\\n plt.bar(bin_centers, s_hist, width = bin_widths, align = 'center', alpha=.5)\\n plt.bar(bin_centers, f_hist, width = bin_widths, align = 'center', alpha=.5)\\n plt.title('Normalized Distribution of Starting Feature ' + feature.upper() + ' for ' + primitive.upper() + ' Primitive')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31ba70bb28bcdb441d93bf8bccb5c9f\",\n \"score\": \"0.56102604\",\n \"text\": \"def plot_single(args, goal_conditioned, name_task, name_plot):\\n IGNORE = ['-transporter-goal-naive']\\n\\n # Override any parameters here.\\n title_size = 40\\n x_size = 38\\n y_size = 38\\n tick_size = 35\\n legend_size = 25\\n lw = 5\\n ms = 12\\n\\n # Now make the plot.\\n nrows, ncols = 1, 4\\n fig, ax = plt.subplots(nrows, ncols, squeeze=True, figsize=(8.0*ncols, 9.0*nrows))\\n title = f'Training Results, {name_plot}'\\n supertitle = fig.suptitle(title, fontsize=title_size+2)\\n\\n for col, num_demos in enumerate([1, 10, 100, 1000]):\\n # Extract all relevant subdirectories, and get results.\\n directories, seeds = get_subdirectories_demos(name_task, num_demos)\\n stats_combo = _get_results(name_task, directories, seeds, debug)\\n\\n # For each agent, plot. Iterate through keys of stats_combo[agent] IN ORDER.\\n for agent in sorted(stats_combo.keys()):\\n if agent in IGNORE:\\n print(f' ignoring {agent} for demos {num_demos}')\\n continue\\n x_vals = []\\n y_vals = []\\n y_errs = [] # standard deviations\\n y_stde = [] # standard errors of mean\\n print(f'on agent {agent} with keys {stats_combo[agent].keys()}')\\n for itr in sorted(stats_combo[agent].keys()):\\n x_vals.append( itr )\\n values = stats_combo[agent][itr]\\n if (len(values) != 60 and name_task != 'bag-color-goal' and itr != 0):\\n print(f'\\\\tWarning, len(values): {len(values)} for: {agent}, {itr}')\\n elif (len(values) != 20 and name_task == 'bag-color-goal'):\\n print(f'\\\\tWarning, len(values): {len(values)} for: {agent}, {itr}')\\n y_vals.append( np.mean(values) )\\n y_errs.append( np.std(values) )\\n y_stde.append( np.std(values) / np.sqrt(len(values)) )\\n x_vals = np.array(x_vals)\\n y_vals = np.array(y_vals)\\n y_errs = np.array(y_errs)\\n y_stde = np.array(y_stde)\\n\\n # NOTE: we're dividing by thousands here.\\n x_vals = x_vals / 1000\\n\\n # Remove leading dash, and then plot, while clipping stdev's to range.\\n ag = agent[1:]\\n agent_label = AGENT_TO_LABEL[ag]\\n color = AGENT_TO_COLOR[ag]\\n if standard_error:\\n lower = np.clip(y_vals - y_stde, 0.0 - EPS, 1.0 + EPS)\\n upper = np.clip(y_vals + y_stde, 0.0 - EPS, 1.0 + EPS)\\n else:\\n lower = np.clip(y_vals - y_errs, 0.0 - EPS, 1.0 + EPS)\\n upper = np.clip(y_vals + y_errs, 0.0 - EPS, 1.0 + EPS)\\n ax[col].plot(x_vals, y_vals, lw=lw, color=color, ms=ms, marker='o', label=agent_label)\\n ax[col].fill_between(x_vals, upper, lower, color=color, alpha=0.3, linewidth=0.0)\\n ax[col].legend(prop={'size': legend_size})\\n\\n # Bells and whistles. For y-axis label, just put it on the leftmost column.\\n subplot_title = f'Train Demos: {num_demos}'\\n ax[col].set_title(subplot_title, fontsize=title_size)\\n ax[col].set_xlabel(xlabel, fontsize=x_size)\\n ylabel = 'Success Rate'\\n if not args.binary:\\n if name_task in ['cloth-flat', 'cloth-flat-notarget']:\\n ylabel = 'Zone Coverage'\\n elif name_task in ['cable-shape', 'cable-shape-notarget', 'cable-line-notarget']:\\n ylabel = 'Percent in Zone'\\n if col == 0:\\n ax[col].set_ylabel(ylabel, fontsize=y_size)\\n ax[col].set_ylim([0.0 - EPS, 1.0 + EPS])\\n ax[col].tick_params(axis='x', labelsize=tick_size)\\n ax[col].tick_params(axis='y', labelsize=tick_size)\\n\\n # Shift subplots down due to supertitle.\\n plt.tight_layout()\\n supertitle.set_y(0.98)\\n fig.subplots_adjust(top=0.85)\\n\\n # Save file.\\n suffix = f'plot_demos_task_{name_task}_v02.png'\\n fname = join(os.getcwd(), 'figures', suffix)\\n plt.savefig(fname)\\n print(f'\\\\nImage saved to: {fname}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5ff62f8a5a787161284c768c6b106f8\",\n \"score\": \"0.5533698\",\n \"text\": \"def get_PD_plot(self, feature_ind, fig_size_x = 9, fig_size_y = 6,\\n x_max_lim = None, x_min_lim = None):\\n \\n figure, ax = plt.subplots(1,len(feature_ind), figsize= (len(feature_ind)*fig_size_x, fig_size_y))\\n\\n for idx,feat_id in enumerate(feature_ind):\\n\\n x_min = np.min(self.X[:, feat_id]) if x_min_lim is None else x_min_lim\\n x_max = np.max(self.X[:, feat_id]) if x_max_lim is None else x_max_lim\\n x_pi = np.linspace(x_min, x_max, 100)\\n\\n mean_pred_list = []\\n max_pred_list = []\\n min_pred_list = []\\n for iii in range(x_pi.shape[0]):\\n new_X_PI = self.X.copy()\\n new_X_PI[:, feat_id] = x_pi[iii]\\n p, c = self.gp.predict(new_X_PI, return_cov=True)\\n mean_p, max_p, min_p = self.get_mean_and_std(p,c)\\n\\n mean_pred_list.append(mean_p)\\n max_pred_list.append(max_p)\\n min_pred_list.append(min_p)\\n\\n if len(feature_ind) > 1:\\n ax[idx].plot(x_pi, mean_pred_list)\\n ax[idx].fill_between(x_pi, min_pred_list, max_pred_list, alpha = 0.2)\\n ax[idx].set_ylabel('Partial Dependence')\\n ax[idx].set_xlabel(self.feature_names[feat_id])\\n ax[idx].grid(axis='y')\\n else:\\n ax.plot(x_pi, mean_pred_list)\\n ax.fill_between(x_pi, min_pred_list, max_pred_list, alpha = 0.2)\\n ax.set_ylabel('Partial Dependence')\\n ax.set_xlabel(self.feature_names[feat_id])\\n ax.grid(axis='y')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30ebb371d7bea7899abb524f914f42ab\",\n \"score\": \"0.5473172\",\n \"text\": \"def display():\\r\\n global X, Y # pick the the features and the labels\\r\\n\\r\\n ax.clear() # clear the plot before the update\\r\\n\\r\\n # set the title, the axes and the legends\\r\\n ax.set_title('Passing final exams according to subject\\\\'s average')\\r\\n ax.set_xlabel('Maths')\\r\\n ax.set_ylabel('French')\\r\\n ax.set_ylim(4, 19) # only for a better view\\r\\n plt_legends = [mpatches.Patch(color=color[key], label=legends[key]) for key, _ in enumerate(legends)]\\r\\n\\r\\n # plot the points of the training set\\r\\n for i, sample in enumerate(X):\\r\\n ax.scatter(sample[0], sample[1], Y[i], c=color[Y[i]])\\r\\n\\r\\n if started: # waiting for start\\r\\n # The logistic regression is an area (infinity number of point)\\r\\n # So we will represent it line by line\\r\\n # It is a huge time-consuming process so put n as lower as possible\\r\\n n = 20 # number of line to plot\\r\\n\\r\\n # Create n different lines to render the logistic regression function[...]\\r\\n # [...] with a constant x value [...]\\r\\n lines = np.linspace(min(X.transpose()[0]), max(X.transpose()[0]), num=n)\\r\\n\\r\\n # [...] from the min to the max value of the second feature[...]\\r\\n min_y, max_y = min(X.transpose()[1]), max(X.transpose()[1])\\r\\n y_array = np.linspace(min_y, max_y, num=n) # from min to max eah time\\r\\n # [...] with the sigmoid value height according to the features.\\r\\n\\r\\n # for each line to represent\\r\\n for _, x in enumerate(lines):\\r\\n x_array = np.full(n, x) # create x constant array\\r\\n z_array = np.array([]) # to append with the sigmoid values\\r\\n\\r\\n for _, y in enumerate(y_array): # append with the sigmoid value\\r\\n z_array = np.hstack((z_array, np.array(sigmoid(weights[0] * x + weights[1] * y + bias))))\\r\\n\\r\\n # plot the line\\r\\n ax.plot(x_array, y_array, z_array, c='#8800FF', alpha=0.2)\\r\\n\\r\\n # add a big legend to describe the state of the logistic regression\\r\\n label = 'Logistic Regression :\\\\n'\\r\\n label += 'Equation : {0} * math + {1} * french + {2}\\\\n'. \\\\\\r\\n format(round(weights[0], 2), round(weights[1], 2), int(bias))\\r\\n label += 'Epoch : {0}\\\\n'.format(epoch)\\r\\n label += 'Learning Rate : {0}\\\\n'.format(LEARNING_RATE)\\r\\n label += 'Squared Error : {0}'.format(round(cost, 2))\\r\\n # add the created legend\\r\\n plt_legends.append(mpatches.Patch(color='#8800FF', label=label))\\r\\n\\r\\n # to have smaller font size\\r\\n font_prop = font_manager.FontProperties(fname='C:\\\\Windows\\\\Fonts\\\\Arial.ttf', size=6)\\r\\n\\r\\n # Put a legend above current axis\\r\\n plt.legend(handles=plt_legends, loc='upper center', bbox_to_anchor=(0.5, 0.1), prop=font_prop, ncol=3)\\r\\n\\r\\n # then plot everything\\r\\n fig.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"613fb93b342aa9ee879c0d98922f7e65\",\n \"score\": \"0.54120535\",\n \"text\": \"def plot_time_evolution(time_potenial,animater=False,save_animation=False):\\n sns.set_style(\\\"darkgrid\\\")\\n sns.set_context(\\\"talk\\\")\\n fig, ax = plt.subplots(figsize=(16, 4))\\n ax.set_ylim(0,1)\\n ax.set_xlim(0,16)\\n t=np.linspace(0,16*pi,2000)\\n\\n \\\"\\\"\\\"RHF\\\"\\\"\\\"\\n solver =RHFSolverSystem(number_electrons,l,grid_length,num_grid_points,omega,a);\\n solver.setC(np.eye(l));\\n solver.solve(1e-17,25)\\n C=solver.get_full_C()\\n solver =GHFSolverSystem(number_electrons,l,grid_length,num_grid_points,omega,a);\\n solver.setC(C);\\n grid=solver.system.grid\\n overlap_RHF=np.zeros(len(t))\\n dipole_moment_RHF=np.zeros(len(t),dtype=np.complex128)\\n energies=np.zeros(len(t))\\n dt=t[1]-t[0]\\n ground_state_densities_RHF=np.zeros((num_grid_points,len(t)))\\n solver.init_TDHF(time_potenial)\\n overlap_RHF[0]=solver.calculate_overlap()\\n dipole_moment_RHF[0]=solver.getDipolemoment()\\n for i,tval in enumerate(t[:-1]):\\n solver.integrate(dt) #Integrate a step dt\\n densities=solver.getDensity()\\n ground_state_densities_RHF[:,i]=densities[0]+densities[1]\\n overlap_RHF[i+1]=solver.calculate_overlap()\\n dipole_moment_RHF[i+1]=solver.getDipolemoment()\\n\\n \\\"\\\"\\\"GHF\\\"\\\"\\\"\\n solver =GHFSolverSystem(number_electrons,l,grid_length,num_grid_points,omega,a);\\n solver.setC(np.eye(2*l));\\n solver.solve(1e-17,5000)\\n overlap_GHF=np.zeros(len(t))\\n dipole_moment_GHF=np.zeros(len(t),dtype=np.complex128)\\n energies=np.zeros(len(t))\\n dt=t[1]-t[0]\\n ground_state_densities_GHF=np.zeros((num_grid_points,len(t)))\\n solver.init_TDHF(time_potenial)\\n overlap_GHF[0]=solver.calculate_overlap()\\n dipole_moment_GHF[0]=solver.getDipolemoment()\\n for i,tval in enumerate(t[:-1]):\\n solver.integrate(dt) #Integrate a step dt\\n densities=solver.getDensity()\\n ground_state_densities_GHF[:,i]=densities[0]+densities[1]\\n overlap_GHF[i+1]=solver.calculate_overlap()\\n dipole_moment_GHF[i+1]=solver.getDipolemoment()\\n ax.plot(2*t/(2*pi),overlap_RHF,label=\\\"RHF\\\")\\n ax.plot(t/pi,overlap_GHF,label=\\\"GHF\\\")\\n\\n plt.legend()\\n ax.set_xlabel(r\\\"t $\\\\lambda\\\\omega/(2\\\\pi)$\\\")\\n ax.set_ylabel(r\\\"Overlap $|\\\\langle\\\\Psi(t)|\\\\Psi(0)\\\\rangle|^2$\\\")\\n plt.tight_layout()\\n plt.savefig(\\\"../../figures/time_overlap.pdf\\\")\\n plt.show()\\n sns.set_style(\\\"darkgrid\\\")\\n sns.set_context(\\\"talk\\\")\\n fig, ax = plt.subplots(figsize=(16, 4))\\n ax.set_xlim(0,16)\\n plt.plot(t/pi,dipole_moment_RHF,label=\\\"RHF\\\",zorder=5)\\n plt.plot(t/pi,dipole_moment_GHF,label=\\\"GHF\\\",zorder=3)\\n plt.legend()\\n plt.xlabel(r\\\"t $\\\\lambda\\\\omega/(2\\\\pi)$\\\")\\n plt.ylabel(r\\\"Dipole moment\\\")\\n plt.tight_layout()\\n plt.savefig(\\\"../../figures/TDDipolemoment_laser.pdf\\\")\\n plt.show()\\n def animation():\\n \\\"\\\"\\\"Animates the GHF and the RHF solution\\\"\\\"\\\"\\n import matplotlib.animation as animation\\n fig, ax = plt.subplots()\\n line0, = ax.plot(grid, np.sin(grid),label=\\\"RHF\\\")\\n line1, = ax.plot(grid, np.sin(grid),label=\\\"GHF\\\")\\n time_text = ax.text(0.02, 0.90, '', transform=ax.transAxes)\\n\\n def animate(i):\\n line0.set_ydata(ground_state_densities_RHF[:,i])\\n line1.set_ydata(ground_state_densities_GHF[:,i])\\n time_text.set_text(r'time =%.1f $\\\\lambda\\\\omega/(2\\\\pi)$' % (t[i]/(2*pi)))\\n\\n return line0,line1,time_text,\\n def init():\\n line0.set_ydata(np.ma.array(grid, mask=True))\\n line1.set_ydata(np.ma.array(grid, mask=True))\\n time_text.set_text(\\\"\\\")\\n\\n return line0,line1,time_text,\\n\\n ani = animation.FuncAnimation(fig, animate, np.arange(0,len(t)), init_func=init,\\n interval=5, blit=True)\\n ax.set_xlabel(\\\"Position [a.u.]\\\")\\n ax.set_ylabel(r\\\"Total electron density $\\\\rho$\\\")\\n ax.set_xlim(-10,10)\\n ax.set_ylim(0,0.5)\\n plt.legend()\\n plt.tight_layout()\\n if(save_animation):\\n ani.save('../../figures/animation.gif',fps=400)\\n plt.show()\\n if animater:\\n animation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0038be2b53c9a4a350274e843e92c248\",\n \"score\": \"0.5398446\",\n \"text\": \"def plot_learned_traj(feature_function, train_data, objectID, feat='table'):\\n\\toutput = feature_function(train_data)\\n\\teuclidean = angles_to_coords(train_data[:, :7], feat, objectID)\\n\\tfig = px.scatter_3d(x=euclidean[:,0], y=euclidean[:,1], z=euclidean[:,2], color=output)\\n\\tfig.update_layout(title='Traces with learned function values')\\n\\tfig.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c034d9b540bb96fc3bad7b5ff5eb2c63\",\n \"score\": \"0.53502405\",\n \"text\": \"def generic_memtest_over_time(abf):\\n log.debug(\\\"generic plot: memtest analysis\\\")\\n assert isinstance(abf, pyabf.ABF)\\n\\n IhBySweep, RmBySweep, RaBySweep, CmBySweep = pyabf.memtest.step_valuesBySweep(abf)\\n sweepTimesMin = np.arange(abf.sweepCount)*abf.sweepLengthSec/60\\n\\n plotFigNew(abf)\\n\\n ax1 = plt.gcf().add_subplot(2, 2, 1)\\n ax1.plot(sweepTimesMin, IhBySweep, '.', color='C0')\\n ax1.set_title(\\\"Holding Current (Ih)\\\")\\n ax1.set_ylabel(\\\"Clamp Current (pA)\\\")\\n ax1.set_xlabel(\\\"Experiment Time (min)\\\")\\n ax1.margins(0,.4)\\n addComments(abf, True, False)\\n\\n ax2 = plt.gcf().add_subplot(2, 2, 2)\\n ax2.plot(sweepTimesMin, RmBySweep, '.', color='C3')\\n ax2.set_title(\\\"Membrane Resistance (Rm)\\\")\\n ax2.set_ylabel(\\\"Resistance (MOhm)\\\")\\n ax2.set_xlabel(\\\"Experiment Time (min)\\\")\\n ax2.margins(0,.4)\\n ax2.axis([None,None,0,None])\\n addComments(abf, True, False)\\n \\n ax3 = plt.gcf().add_subplot(2, 2, 3)\\n ax3.plot(sweepTimesMin, RaBySweep, '.', color='C7')\\n ax3.set_title(\\\"Access Resistance (Ra)\\\")\\n ax3.set_ylabel(\\\"Resistance (MOhm)\\\")\\n ax3.set_xlabel(\\\"Experiment Time (min)\\\")\\n ax3.margins(0,.4)\\n ax3.axis([None,None,0,None])\\n addComments(abf, True, False)\\n\\n ax4 = plt.gcf().add_subplot(2, 2, 4)\\n ax4.plot(sweepTimesMin, CmBySweep, '.', color='C1')\\n ax4.set_title(\\\"Membrane Capacitance (Cm)\\\")\\n ax4.set_ylabel(\\\"Capacitance (pF)\\\")\\n ax4.set_xlabel(\\\"Experiment Time (min)\\\")\\n ax4.margins(0,.4)\\n ax4.axis([None,None,0,None])\\n addComments(abf, True, False)\\n \\n plotFigSave(abf, tag=f\\\"generic-memtest\\\", labelAxes=False)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ec44cc98992ea1a25501e1012900f5d\",\n \"score\": \"0.5346709\",\n \"text\": \"def show_plot(plot_data, delta, title=None, single_stpe=False):\\n labels = ['features', 'true_label', 'predict_label']\\n markers = ['.-', 'rx', 'go']\\n\\n if title:\\n plt.title(title)\\n future = delta if delta else 0\\n\\n feature_time_steps = list(range(-plot_data[0].shape[0], 0))\\n print('feature_time_steps: ', str(feature_time_steps))\\n target_time_steps = \\\\\\n list(np.array(range(delta, delta + plot_data[1].shape[0])) / STEP) \\\\\\n if single_stpe else list(np.array(range(0, delta)) / STEP)\\n print('target_time_steps: ', str(target_time_steps))\\n for i, data in enumerate(plot_data):\\n if i:\\n plt.plot(target_time_steps, data, markers[i], markersize=5, \\\\\\n label=labels[i])\\n else:\\n plt.plot(feature_time_steps, data, markersize=5,\\n label=labels[i])\\n plt.legend()\\n plt.xlabel('time-step')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d8880a515de0a221007c4d2629e81f4\",\n \"score\": \"0.53349674\",\n \"text\": \"def get_VI_plot(self, feature_ind = None, num_samples = 100): \\n\\n \\n if feature_ind == None:\\n feat_ind_list = [ttt for ttt in range(len(self.feature_names))]\\n else:\\n feat_ind_list = feature_ind\\n\\n feat_res_dict = {}\\n for feat_ind in feat_ind_list:\\n \\n ## Permute the feature\\n X_VI = self.X.copy()\\n feat_col = X_VI[:, feat_ind].tolist()\\n random.shuffle(feat_col)\\n X_VI[:, feat_ind] = feat_col\\n\\n store_VI = np.zeros(num_samples)\\n org_pred = self.gp.sample_y(self.X, n_samples = num_samples)\\n permuted_pred = self.gp.sample_y(X_VI, n_samples = num_samples)\\n for uuu in range(num_samples):\\n VI = self.loss_func(permuted_pred[:,uuu], self.y) - self.loss_func(org_pred[:,uuu], self.y)\\n store_VI[uuu] = VI\\n m_VI = np.mean(store_VI)\\n std_VI = np.std(store_VI)\\n feat_res_dict[feat_ind] = [m_VI, std_VI]\\n\\n figure, ax = plt.subplots(1,1, figsize = (8,5))\\n\\n y_vals = [yyy for yyy in range(len(feat_res_dict))]\\n y_name = [self.feature_names[j] for j in feat_res_dict.keys()]\\n x_vals = [feat_res_dict[f][0] for f in feat_res_dict.keys()]\\n errs = [feat_res_dict[f][1] for f in feat_res_dict.keys()]\\n ax.plot(x_vals, y_vals, 'ko')\\n ax.set_yticks(y_vals)\\n ax.set_yticklabels(y_name)\\n ax.grid(axis='y')\\n ax.errorbar(x_vals, y_vals, xerr = errs, ecolor='k', fmt='.k', linewidth = 1)\\n ax.set_xlabel('VI')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c582c5cf59851c51a012fc5bb085398e\",\n \"score\": \"0.531329\",\n \"text\": \"def trajectory_plot(time_array, dt, force, initial_state, target_state, sigma1, save_path, ymin = -2, ymax = 2):\\n \\n \\n fig, ax = plt.subplots(1, 1)\\n\\n \\n #solve first trajectory\\n vs = schemes.Euler_Maruyama_no_stop(0,initial_state, sigma1, dt=dt, dims = 1, force=force, time_array_length=len(time_array))\\n \\n ax.plot(time_array, vs, linewidth = 0.3, color = 'black')\\n ax.plot(time_array, target_state+np.zeros(len(time_array)), linewidth = 2, linestyle = '--', color = 'C1', label = '$X_B$' )\\n ax.plot(time_array, initial_state+np.zeros(len(time_array)), linewidth = 2, linestyle = '--', color = 'C0', label = '$X_A$')\\n plt.text(-0.2, 1,'(b)',horizontalalignment='center', verticalalignment='center', transform = plt.gca().transAxes, fontsize=9)\\n ax.set_xlabel('time [s]')\\n ax.set_ylabel('x')\\n ax.set_ylim((ymin,ymax))\\n plt.legend()\\n \\n #threshold and stopping criterion\\n \\n if save_path is not None:\\n plt.savefig(save_path)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607eb074fb3443ca4179138ee070b9db\",\n \"score\": \"0.53073\",\n \"text\": \"def plot_time_profile(self,x_grid,y_grid,mag_grid,itr,num_times,pos):\\n from matplotlib import cm as cm\\n from matplotlib.colors import to_rgba_array\\n\\n color_set = cm.coolwarm(np.linspace(0,1,num_times))\\n ########################################################################\\n # Plot the skyrmion profile for the current time step\\n ########################################################################\\n self.ax_traj.contour(x_grid,y_grid,mag_grid,[0.0],linewidths=0.5, \\\\\\n linestyles='solid',colors=to_rgba_array(color_set[itr]))\\n if len(pos)>0:\\n self.ax_traj.scatter(pos[0],pos[1],\\\\\\n color=to_rgba_array(color_set[itr]), \\\\\\n s=self.symbol_size*0.25,lw=0.01,edgecolor='black',alpha=0.5, \\\\\\n zorder=9)\\n self.ax_traj.set_aspect('equal')\\n self.fig_traj.canvas.draw()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c9ee99c0f28d31fe5c8d91c9e34a024\",\n \"score\": \"0.5306957\",\n \"text\": \"def temporal_plot(ax, ensemble_agg, ensemble_spread, experiment):\\n\\n plt.sca(ax)\\n iplt.plot(ensemble_agg, label=experiment, color=exp_colors[experiment])\\n if ensemble_spread:\\n time_values = ensemble_spread[0, ::].coord('time').points - 54567.5 \\n upper_bound = ensemble_spread[0, ::].data\\n lower_bound = ensemble_spread[-1, ::].data\\n iplt.plt.fill_between(time_values, upper_bound, lower_bound,\\n facecolor=exp_colors[experiment], alpha=0.15)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daab06777bf727323c42fb175c639e9d\",\n \"score\": \"0.52701604\",\n \"text\": \"def plot_decision_function(X, y, clf, ax):\\n plot_step = 0.02\\n x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\\n y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\\n xx, yy = np.meshgrid(\\n np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)\\n )\\n\\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\n Z = Z.reshape(xx.shape)\\n ax.contourf(xx, yy, Z, alpha=0.4)\\n ax.scatter(X[:, 0], X[:, 1], alpha=0.8, c=y, edgecolor=\\\"k\\\")\\n ax.set_title(f\\\"Resampling using {clf[0].__class__.__name__}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9d599fbed2052c3b9ae15cf650fb52a\",\n \"score\": \"0.526962\",\n \"text\": \"def plot_time_complexity(clf, X, y, title='Time curve'):\\n import timeit\\n TEST_CODE_FIT = '''\\nclf.fit(X_train, y_train)'''\\n TEST_CODE_PRED = '''\\nclf.predict(X_test)'''\\n\\n training_pct = np.linspace(0.10, 1.0, 10)\\n df = pd.DataFrame(columns=['train_pct', 'train_time', 'pred_time'])\\n for train in training_pct:\\n test_pct = 1.0 - train\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_pct, random_state=23)\\n times_fit = timeit.timeit(setup='pass', stmt=TEST_CODE_FIT, number=2)\\n times_fit = times_fit / 2.0\\n times_pred = timeit.timeit(setup='pass', stmt=TEST_CODE_PRED, number=2)\\n times_pred = times_pred / 2.0\\n df.append(np.array([train, times_fit, times_pred]))\\n\\n train_sizes = df['train_pct'].to_numpy()\\n train_times = df['train_time'].to_numpy()\\n pred_time = df['pred_time'].to_numpy()\\n\\n # Draw lines\\n plt.plot(train_sizes, train_times, '--', color=\\\"#111111\\\", label=\\\"Training times\\\")\\n plt.plot(train_sizes, pred_time, color=\\\"#111111\\\", label=\\\"Cross-validation score\\\")\\n\\n # Create plot\\n plt.title(title)\\n plt.xlabel(\\\"Training Set Size\\\"), plt.ylabel(\\\"Accuracy Score\\\"), plt.legend(loc=\\\"best\\\")\\n plt.tight_layout()\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e684c4779d7bb7e1b26d880ae56e710\",\n \"score\": \"0.5251226\",\n \"text\": \"def visualize(\\n trajectories_over_time: dict, reference_path: ReferencePath, params: dict\\n):\\n\\n # global trajectory (trajectory, that is ultimately driven by the car)\\n global_trajectory = []\\n\\n # create new figure and set its size\\n fig, ax = plt.subplots()\\n fig.set_size_inches(10, 6.5)\\n\\n # create colorbar\\n colorbar = fig.colorbar(\\n mappable=plt.cm.ScalarMappable(cmap=plt.cm.RdYlGn_r),\\n label=\\\"cost of valid trajectories\\\",\\n extend=\\\"both\\\",\\n )\\n colorbar.minorticks_on()\\n\\n # iterate through steps\\n for step_counter in trajectories_over_time.keys():\\n\\n # extract valid trajectories of the current step\\n valid_trajectories = trajectories_over_time[step_counter][\\\"valid\\\"]\\n\\n # extract invalid trajectories of the current step\\n invalid_trajectories = trajectories_over_time[step_counter][\\\"invalid\\\"]\\n\\n # cost of cheapest valid trajectory\\n cost_min = valid_trajectories[0].cost\\n\\n # cost of most expensive valid trajectory\\n cost_max = valid_trajectories[-1].cost\\n\\n # set tick labels of color bar\\n colorbar.set_ticks(ticks=np.linspace(start=0, stop=1, num=6))\\n colorbar.set_ticklabels(\\n ticklabels=np.linspace(\\n start=np.floor(cost_min), stop=np.ceil(cost_max), num=6\\n ).astype(int)\\n )\\n\\n # clear axis\\n ax.cla()\\n\\n # plot reference path\\n ax.plot(\\n reference_path.xy_rp[:, 0],\\n reference_path.xy_rp[:, 1],\\n \\\"b--\\\",\\n label=\\\"reference path\\\",\\n )\\n\\n # iterate through invalid trajectories\\n for invalid_trajectory in invalid_trajectories:\\n\\n # plot invalid trajectories\\n ax.plot(\\n invalid_trajectory.xy[:, 0], invalid_trajectory.xy[:, 1], color=\\\"gray\\\"\\n )\\n\\n # iterate through valid trajectories\\n for valid_trajectory in valid_trajectories:\\n\\n # check whether there is only one valid trajectory\\n if len(valid_trajectories) == 1:\\n\\n # assign color value\\n color_value = 1\\n\\n else:\\n\\n # calculate color value\\n color_value = (valid_trajectory.cost - cost_min) / (cost_max - cost_min)\\n\\n # plot valid trajectories\\n ax.plot(\\n valid_trajectory.xy[:, 0],\\n valid_trajectory.xy[:, 1],\\n color=plt.cm.RdYlGn_r(color_value),\\n )\\n\\n # plot global trajectory\\n global_trajectory.append(\\n [valid_trajectories[0].xy[0, 0], valid_trajectories[0].xy[0, 1]]\\n )\\n ax.plot(\\n np.array(global_trajectory)[:, 0],\\n np.array(global_trajectory)[:, 1],\\n \\\"k--\\\",\\n label=\\\"driven trajectory\\\",\\n )\\n\\n # plot optimal trajectory\\n ax.plot(\\n valid_trajectories[0].xy[:, 0],\\n valid_trajectories[0].xy[:, 1],\\n \\\"k\\\",\\n label=\\\"optimal trajectory\\\",\\n )\\n\\n # plot car at current position\\n xy_rectangle = (\\n valid_trajectories[0].xy[0, 0]\\n - params[\\\"discretization\\\"][\\\"car_length\\\"]\\n / 2\\n * np.cos(valid_trajectories[0].theta[0])\\n + params[\\\"discretization\\\"][\\\"car_width\\\"]\\n / 2\\n * np.sin(valid_trajectories[0].theta[0]),\\n valid_trajectories[0].xy[0, 1]\\n - params[\\\"discretization\\\"][\\\"car_length\\\"]\\n / 2\\n * np.sin(valid_trajectories[0].theta[0])\\n - params[\\\"discretization\\\"][\\\"car_width\\\"]\\n / 2\\n * np.cos(valid_trajectories[0].theta[0]),\\n )\\n rectangle = plt.Rectangle(\\n xy=xy_rectangle,\\n width=params[\\\"discretization\\\"][\\\"car_length\\\"],\\n height=params[\\\"discretization\\\"][\\\"car_width\\\"],\\n angle=np.rad2deg(valid_trajectories[0].theta[0]),\\n edgecolor=\\\"b\\\",\\n fill=True,\\n )\\n ax.add_patch(p=rectangle)\\n\\n # plot circle at current position\\n circle = plt.Circle(\\n xy=(valid_trajectories[0].xy[0, 0], valid_trajectories[0].xy[0, 1]),\\n radius=np.sqrt(\\n (params[\\\"discretization\\\"][\\\"car_length\\\"] / 2) ** 2\\n + (params[\\\"discretization\\\"][\\\"car_width\\\"] / 2) ** 2\\n ),\\n edgecolor=\\\"b\\\",\\n fill=False,\\n linestyle=\\\"--\\\",\\n )\\n ax.add_patch(p=circle)\\n\\n # plot obstacles\\n ax.plot(\\n params[\\\"xy_obstacles\\\"][:, 0],\\n params[\\\"xy_obstacles\\\"][:, 1],\\n \\\"rx\\\",\\n label=\\\"obstacles\\\",\\n )\\n\\n # add legend to plot\\n handles, labels = ax.get_legend_handles_labels()\\n invalid_trajectories_handle = Line2D(\\n xdata=[0], ydata=[0], color=\\\"gray\\\", label=\\\"invalid trajectories\\\"\\n )\\n handles.insert(-1, invalid_trajectories_handle)\\n rectangle_handle = mpatches.Patch(edgecolor=\\\"b\\\", fill=True, label=\\\"car\\\")\\n handles.insert(-1, rectangle_handle)\\n circle_handle = mpatches.Patch(\\n edgecolor=\\\"b\\\", fill=False, linestyle=\\\"--\\\", label=\\\"over-approximation of car\\\"\\n )\\n handles.insert(-1, circle_handle)\\n ax.legend(handles=handles, ncol=2)\\n\\n # plot settings\\n ax.axis(\\\"equal\\\")\\n ax.set_xlim(\\n left=valid_trajectories[0].xy[0, 0] - params[\\\"limits\\\"][\\\"v_max\\\"],\\n right=valid_trajectories[0].xy[0, 0]\\n + params[\\\"discretization\\\"][\\\"t_max\\\"] * params[\\\"limits\\\"][\\\"v_max\\\"],\\n )\\n ax.set_title(\\n label=r\\\"$t = %3.2f\\\\/s\\\\/\\\\/\\\\/,\\\\/\\\\/\\\\/v = %2.2f\\\\/\\\\frac{m}{s}\\\\/\\\\/\\\\/$\\\"\\n % (\\n step_counter * params[\\\"discretization\\\"][\\\"fixed_calc_time\\\"],\\n valid_trajectories[0].v[0],\\n )\\n )\\n ax.set_xlabel(xlabel=r\\\"$x$ in $m$\\\")\\n ax.set_ylabel(ylabel=r\\\"$y$ in $m$\\\")\\n ax.grid(b=True)\\n\\n plt.pause(interval=params[\\\"discretization\\\"][\\\"fixed_calc_time\\\"] * 0.5)\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ccf20ed8468d2044ee2e791db27291a\",\n \"score\": \"0.52504265\",\n \"text\": \"def plot_perf():\\n n = 1000\\n i = 0\\n max_generations = 10\\n time_data = []\\n generations = []\\n\\n while i <= max_generations:\\n time_data.append(timeit.timeit(\\n 'run(0.6, {}, 0.1, 1, False)'.format(i),\\n number=n, setup='from tree import run'))\\n generations.append(i)\\n i += 1\\n\\n plt.plot(generations, time_data, '-bx')\\n plt.ylabel('Time taken for {} iterations (sec)'.format(n))\\n plt.xlabel('Number of generations of branches')\\n plt.savefig(\\\"perf_plot.png\\\")\\n plt.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef258e2951589321067a6e1494a472ee\",\n \"score\": \"0.52486676\",\n \"text\": \"def plot_running_times():\\n fast = get_running_time(200, 'fast')\\n slow = get_running_time(200, 'slow') \\n #for item in fast:\\n plt.plot(fast, '-r', label='fast_closest_pair')\\n plt.plot(slow, '-b', label='slow_closest_pairs')\\n plt.legend(loc='upper right')\\n plt.title('Running times of slow_closest_pairs and fast_closest_pair')\\n plt.ylabel('Milliseconds')\\n plt.xlabel('Number of clusters')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a76f3e2100d9e68479baae7fd4890177\",\n \"score\": \"0.5244402\",\n \"text\": \"def show_state(observation, env_id, step=0, info=\\\"\\\"):\\n plt.figure(3)\\n plt.clf()\\n \\n if len(observation)==4:\\n frames = list()\\n for i, val in enumerate([0.1, 0.2, 0.3, 0.4]):\\n frames.append(np.multiply(observation[i], val))\\n observation = np.sum(frames, axis=0)\\n \\n plt.imshow(observation, cmap='gray')\\n plt.axis('off')\\n plt.title(\\\"%s | Score: %s\\\" % (env_id, info))\\n display.clear_output(wait=True)\\n display.display(plt.gcf())\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9512f762da9c94eeac564e63a9eaa6fa\",\n \"score\": \"0.52059776\",\n \"text\": \"def run_custom_test(self, initial_X, goal, f, show_graph=True, use_observer=True, feet = True, iterations=4000):\\n\\n self.X = initial_X\\n self.X_hat = self.X\\n if use_observer:\\n self.X_hat = initial_X + 0.03\\n t = []\\n x_l = []\\n x_r = []\\n v_l = []\\n v_r = []\\n x_l_hat = []\\n x_r_hat = []\\n v_l_hat = []\\n v_r_hat = []\\n u_l = []\\n u_r = []\\n \\n # sep_plot_gain - 100.0\\n \\n if feet:\\n unchanged_goal = goal * 1.0 / 3.28084\\n else:\\n unchanged_goal = goal \\n \\n for i in range(iterations):\\n if not use_observer:\\n self.X_hat = self.X\\n if use_observer:\\n if feet:\\n x_l_hat.append(self.X_hat[0,0] * 3.28084)\\n x_r_hat.append(self.X_hat[1,0] * 3.28084)\\n v_l_hat.append(self.X_hat[2,0] * 3.28084)\\n v_r_hat.append(self.X_hat[3,0] * 3.28084)\\n else:\\n x_l_hat.append(self.X_hat[0,0])\\n x_r_hat.append(self.X_hat[1,0])\\n v_l_hat.append(self.X_hat[2,0])\\n v_r_hat.append(self.X_hat[3,0])\\n U = f(unchanged_goal - self.X_hat)\\n U = numpy.clip(U, self.minU, self.maxU)\\n #torque = self.VtoT(self.X, U[0,0])\\n if feet:\\n x_l.append(self.X[0,0] * 3.28084)\\n x_r.append(self.X[1,0] * 3.28084)\\n v_l.append(self.X[2,0] * 3.28084)\\n v_r.append(self.X[3,0] * 3.28084)\\n else:\\n x_l.append(self.X[0,0])\\n x_r.append(self.X[1,0])\\n v_l.append(self.X[2,0])\\n v_r.append(self.X[3,0])\\n if use_observer:\\n self.predict_observer(U)\\n #self.X = self.ddynamics(self.X, torque)\\n #self.Y = self.C * self.X + self.D * U\\n self.update(U)\\n if use_observer:\\n self.correct_observer(U)\\n t.append(i * self.dt)\\n u_l.append(U[0,0])\\n u_r.append(U[1,0])\\n \\n if show_graph:\\n fig_size = [12, 9]\\n pyplot.rcParams[\\\"figure.figsize\\\"] = fig_size\\n pyplot.subplot(2, 1, 1)\\n pyplot.plot(t, x_l, label='left dist')\\n pyplot.plot(t, x_r, label='right dist')\\n pyplot.plot(t, v_l, label='left velocity')\\n pyplot.plot(t, v_r, label='right velocity')\\n print(\\\"Final State: \\\")\\n if feet:\\n print(self.X * 3.28084)\\n else:\\n print(self.X)\\n print(\\\"\\\\n\\\")\\n if use_observer:\\n pyplot.plot(t, x_l_hat, label='x_hat left dist')\\n pyplot.plot(t, x_r_hat, label='x_hat right dist')\\n pyplot.plot(t, v_l_hat, label='x_hat left velocity')\\n pyplot.plot(t, v_r_hat, label='x_hat right velocity')\\n print(\\\"Final Estimated State: \\\")\\n if feet:\\n print(self.X_hat * 3.28084)\\n else:\\n print(self.X_hat)\\n print(\\\"\\\\n\\\")\\n pyplot.legend()\\n \\n pyplot.subplot(2, 1, 2)\\n pyplot.plot(t, u_l, label='left voltage')\\n pyplot.plot(t, u_r, label='right voltage')\\n pyplot.legend()\\n pyplot.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e35c3351633573036ae80ba8f3643f3\",\n \"score\": \"0.5204334\",\n \"text\": \"def test_over_time():\\n\\n Cviews, Tviews = 1000, 1000\\n Cconversions, Tconversions = 200, 200\\n n_samples = 100000 # number of samples to use in the distribution\\n\\n conversions = []\\n probas = []\\n views = []\\n for _ in xrange(5000):\\n Cviews += (20*np.random.rand())\\n Tviews += (20*np.random.rand())\\n views.append( (Cviews, Tviews) )\\n\\n Cconversions += (10*np.random.rand())\\n Tconversions += (10*np.random.rand())\\n conversions.append( (Cconversions, Tconversions) )\\n \\n treatment = beta_dist(Tconversions + 1, Tviews - Tconversions + 1, n_samples)\\n control = beta_dist(Cconversions + 1, Cviews - Cconversions + 1, n_samples)\\n \\n probas.append(np.mean(treatment > control))\\n\\n views = np.array(views)\\n conversions = np.array(conversions)\\n \\n plt.plot(probas, marker=\\\"o\\\", markerfacecolor=\\\"purple\\\")\\n plt.ylim((0,1))\\n plt.title(\\\"Pr( treatement > control )\\\")\\n\\n plt.figure()\\n plt.plot(views[:,0]/conversions[:,0], color=\\\"red\\\", label=\\\"control\\\")\\n plt.plot(views[:,1]/conversions[:,1], color=\\\"steelblue\\\", label=\\\"treatment\\\")\\n plt.title(\\\"conversion rate\\\")\\n plt.legend();\\n\\n del views, conversions, probas\\n\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d68aba6dc2b25275ddbcebfbcb20d67e\",\n \"score\": \"0.51574564\",\n \"text\": \"def generate_time_features(self):\\n raise NotImplementedError('To be implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1087d94241fbd9ad338c1ab6dfc67fe7\",\n \"score\": \"0.5136023\",\n \"text\": \"def plot_progress(name,sequence):\\n plt.figure(figsize=(30,15))\\n plt.style.use('fivethirtyeight')\\n\\n acceptance = []\\n for i,_ in enumerate(sequence):\\n try:\\n if sequence[i].name is sequence[i-1].name:\\n acceptance.append(0)\\n else:\\n acceptance.append(1)\\n except Exception:\\n pass\\n \\n acceptance.insert(0,0)\\n\\n acceptance = np.cumsum(np.array(acceptance)[1:])/np.arange(len(acceptance))[1:]\\n\\n losses = np.array([-1*s.loss for s in sequence])\\n poss = np.array([s.pos for s in sequence])\\n\\n\\n plt.subplot(211)\\n plt.plot(np.arange(len(acceptance)),acceptance)\\n plt.xlim(0,lim)\\n plt.title(\\\"Acceptance Average\\\")\\n\\n plt.subplot(212)\\n plt.title(\\\"Fitted Model Stats\\\")\\n plt.plot(np.arange(len(acceptance)),losses ,label = 'Cross_validation MLLs')\\n plt.plot(np.arange(len(acceptance)),poss ,label = 'pos estimates')\\n plt.xlim(0,lim)\\n plt.legend()\\n name = \\\"progress_\\\"+str(name)\\n plt.savefig(\\\"Summary/{}.png\\\".format(name))\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0599d76bae71f8143ad9f8eff4d78a9c\",\n \"score\": \"0.51288545\",\n \"text\": \"def main():\\n\\n import matplotlib.pyplot as plt\\n import pandas as pd\\n\\n # The original data set\\n df_0 = pd.DataFrame.from_csv('arr_dep.csv')\\n # The data set with the multiplication interaction feature.\\n df_times = pd.DataFrame.from_csv('arr_times_dep.csv')\\n # The data set with the subtraction interaction feature.\\n df_minus = pd.DataFrame.from_csv('arr_minus_dep.csv')\\n\\n alpha = .04\\n # Show the relationships between the original features and \\n # the target.\\n fig = plt.figure(1)\\n plt.subplot(1,2,1)\\n plt.plot(df_0.index, df_0.iloc[:,1], color='red', \\n marker='.', linewidth=0, alpha=alpha)\\n plt.xlabel('feature 0')\\n plt.ylabel('regression target')\\n \\n plt.subplot(1,2,2)\\n plt.plot(df_0.iloc[:,0], df_0.iloc[:,1], color='blue', \\n marker='.', linewidth=0, alpha=alpha)\\n plt.xlabel('feature 1')\\n fig.tight_layout()\\n fig.savefig('original.png')\\n\\n # Show the relationships between the multiplicative feature and \\n # the target.\\n fig = plt.figure(2)\\n plt.subplot(1,2,1)\\n plt.plot(df_times.index, df_times.iloc[:,1], color='red', \\n marker='.', linewidth=0, alpha=alpha)\\n plt.xlabel('feature 0')\\n plt.ylabel('regression target')\\n \\n plt.subplot(1,2,2)\\n plt.plot(df_times.iloc[:,2], df_times.iloc[:,1], color='orange', \\n marker='.', linewidth=0, alpha=alpha)\\n plt.xlabel('feature 0 x feature 1')\\n fig.tight_layout()\\n fig.savefig('multiply.png')\\n\\n # Show the relationships between the subtractive feature and \\n # the target.\\n fig = plt.figure(3)\\n plt.subplot(1,2,1)\\n plt.plot(df_minus.index, df_minus.iloc[:,1], color='red', \\n marker='.', linewidth=0, alpha=alpha)\\n plt.xlabel('feature 0')\\n plt.ylabel('regression target')\\n \\n alpha = .02\\n plt.subplot(1,2,2)\\n plt.plot(df_minus.iloc[:,2], df_minus.iloc[:,1], color='green', \\n marker='.', linewidth=0, alpha=alpha)\\n plt.xlabel('feature 1 - feature 0')\\n fig.tight_layout()\\n fig.savefig('subtraction.png')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34947e1092a978a4b3ee8e1f702eaee0\",\n \"score\": \"0.5124802\",\n \"text\": \"def plot_acc(image_path, task, location, view, gender_acc, age_acc, total_acc, mode=\\\"train\\\"):\\n # Use plot styling from seaborn.\\n sns.set(style='darkgrid')\\n\\n # Increase the plot size and font size.\\n sns.set(font_scale=1.5)\\n plt.rcParams[\\\"figure.figsize\\\"] = (12, 6)\\n\\n fig = plt.gcf()\\n\\n if task == 'gender':\\n # Plot the learning curve.\\n # plt.plot(range(1, NUM_EPOCHS + 1), gender_acc, '-o', label=\\\"train_gender_acc\\\")\\n plt.plot(range(1, len(gender_acc) + 1), gender_acc, '-o', label=mode + \\\"_gender_acc\\\")\\n\\n elif task == 'age':\\n # plt.plot(range(1, NUM_EPOCHS + 1), age_acc, '-o', label=\\\"train_age_acc\\\")\\n plt.plot(range(1, len(age_acc) + 1), age_acc, '-o', label=mode + \\\"_age_acc\\\")\\n\\n elif task == 'multi':\\n # plt.plot(range(1, NUM_EPOCHS + 1), gender_acc, '-o', label=\\\"train_gender_acc\\\")\\n plt.plot(range(1, len(gender_acc) + 1), gender_acc, '-o', label=mode + \\\"_gender_acc\\\")\\n # plt.plot(range(1, NUM_EPOCHS + 1), age_acc, '-o', label=\\\"train_age_acc\\\")\\n plt.plot(range(1, len(age_acc) + 1), age_acc, '-o', label=mode + \\\"_age_acc\\\")\\n # plt.plot(range(1, NUM_EPOCHS + 1), total_acc, '-o', label=\\\"train_total_acc\\\")\\n plt.plot(range(1, len(total_acc) + 1), total_acc, '-o', label=mode + \\\"_total_acc\\\")\\n\\n # Label the plot.\\n image_name = \\\"Location: \\\" + location + \\\", Task: \\\" + task + \\\", View: \\\" + view + \\\", Epoch: \\\" + str(\\n NUM_EPOCHS) + \\\", Shared Hidden Dim: \\\" + str(SHARED_HIDDEN_DIM) + \\\", LAMBDA1: \\\" + str(LAMBDA1) + \\\", LAMBDA1: \\\" \\\\\\n + str(LAMBDA1) + \\\", DMVMT Acc \\\" + mode\\n plt.title(image_name)\\n plt.xlabel(\\\"Epoch\\\")\\n plt.ylabel(\\\"Accuracy\\\")\\n\\n plt.xticks(np.arange(0, NUM_EPOCHS + 1, 5.0))\\n plt.legend()\\n # plt.show()\\n plt.draw()\\n fig.savefig(os.path.join(image_path, image_name + \\\".png\\\"), dpi=100)\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba3be59cb6bd19e99bbbae25efae03e5\",\n \"score\": \"0.5111923\",\n \"text\": \"def true_plot():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aa96c66999ffcf7b36b13d4c4d946e8\",\n \"score\": \"0.5089178\",\n \"text\": \"def task_1():\\n\\n fig, axs = plt.subplots(1, 2, figsize=(8, 4))\\n\\n # Mesurement model\\n y = np.ones(shape=(3,))/3\\n x = np.arange(len(y)) - 1\\n\\n ax = axs[0]\\n ax.set_title(r'Mesurement model $p(Z_t|X_t)$')\\n bar_plot(ax, x, y, color='r')\\n\\n # Motion model\\n p = [0, 0.75, 0.25]\\n y = np.array(p)\\n x = np.arange(len(y))\\n\\n ax = axs[1]\\n ax.set_title(r'Motion model $p(X_{t+1}|X_t)$')\\n bar_plot(ax, x, y, color='b')\\n ax.axvline(0.25, c='k', ls='--')\\n ax.axvline(-0.25, c='k', ls='--')\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a11f4baa5ea4612608f0e3342274f356\",\n \"score\": \"0.5085223\",\n \"text\": \"def convergence_plot(self, interactive=False) -> plt.plot:\\n def offline_measure(df):\\n return pd.Series(df['fitness'].cumsum().values /\\n (df['iteration'].values+1))\\n\\n def online_measure(df):\\n return pd.Series(df['mean_fitness'].cumsum().values /\\n (df['iteration'].values+1))\\n\\n return NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d01b3b3443957f6212c1ac9a273c2750\",\n \"score\": \"0.508208\",\n \"text\": \"def plot_traj_id(\\n self,\\n tid,\\n label=TID,\\n feature=None,\\n value=None,\\n figsize=(10, 10),\\n return_fig=True,\\n save_fig=False,\\n name=None,\\n ):\\n\\n operation = begin_operation('plot_traj_id')\\n\\n if label not in self._data:\\n self.last_operation = end_operation(operation)\\n raise KeyError('%s feature not in dataframe' % label)\\n\\n df_ = self._data[self._data[label] == tid]\\n\\n if not len(df_):\\n self.last_operation = end_operation(operation)\\n raise IndexError(f'No trajectory with tid {tid} in dataframe')\\n\\n fig = plt.figure(figsize=figsize)\\n\\n plt.plot(\\n df_.iloc[0][LONGITUDE], df_.iloc[0][LATITUDE], 'yo', markersize=23\\n ) # start point\\n plt.plot(\\n df_.iloc[-1][LONGITUDE], df_.iloc[-1][LATITUDE], 'yX', markersize=23\\n ) # end point\\n\\n if (not feature) or (not value) or (feature not in df_):\\n plt.plot(df_[LONGITUDE], df_[LATITUDE])\\n plt.plot(\\n df_.loc[:, LONGITUDE], df_.loc[:, LATITUDE], 'r.', markersize=8\\n )\\n else:\\n filter_ = df_[feature] == value\\n df_nodes = df_.loc[filter_]\\n df_points = df_.loc[~filter_]\\n plt.plot(df_[LONGITUDE], df_[LATITUDE], linewidth=3)\\n plt.plot(\\n df_nodes[LONGITUDE], df_nodes[LATITUDE], 'gs', markersize=13\\n )\\n plt.plot(\\n df_points[LONGITUDE], df_points[LATITUDE], 'r.', markersize=8\\n )\\n\\n if save_fig:\\n if not name:\\n name = 'trajectory_%s.png' % tid\\n plt.savefig(fname=name, fig=fig)\\n\\n df_ = PandasMoveDataFrame(df_)\\n self.last_operation = end_operation(operation)\\n\\n if return_fig:\\n return df_, fig\\n return df_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6f24656a8f389c998e6bcb33deb83b9\",\n \"score\": \"0.5073311\",\n \"text\": \"def plot_shape(raw, filtered, df, windowL, windowR, Fs, subj, electrode,\\n plot=True):\\n trial_starts = df['move_start'].astype(int).values\\n N_trials = len(trial_starts)\\n samps_window_lim = (windowL, windowR)\\n N_samps = samps_window_lim[1] - samps_window_lim[0]\\n rdsym_time_ts2 = np.ones(len(raw))*np.nan\\n cycle_num = len(filtered)\\n for i in range(cycle_num):\\n rdsym_time_ts2[filtered['sample_lastE'][i]:filtered['sample_nextE'][i]] = filtered['rdsym_time'][i]\\n\\n rdsym_time2 = np.zeros((N_trials,N_samps))\\n for i, t in enumerate(trial_starts):\\n rdsym_time2[i] = rdsym_time_ts2[t+samps_window_lim[0]:t+samps_window_lim[1]]\\n avg_rdsym_time2 = np.nanmean(rdsym_time2,axis=0)\\n sem_rdsym_time2 = sp.stats.sem(rdsym_time2,axis=0)\\n\\n t = np.arange(samps_window_lim[0]/Fs,samps_window_lim[1]/Fs,1/Fs)\\n if plot:\\n plt.figure(figsize=(12,4))\\n plt.plot(t,avg_rdsym_time2,'k-')\\n plt.plot(t,avg_rdsym_time2-sem_rdsym_time2,'k--')\\n plt.plot(t,avg_rdsym_time2+sem_rdsym_time2,'k--')\\n plt.savefig(\\\"C:/Users/Yimeng/Documents/GitHub/LabWork/plots/\\\"+subj+electrode+\\\".png\\\")\\n return rdsym_time2,avg_rdsym_time2,sem_rdsym_time2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad247dca9715d52257a9e028ea53b44\",\n \"score\": \"0.5070251\",\n \"text\": \"def livePlotData():\\n\\tprey = plt.plot(range(0, len(x)), x, \\\"GREEN\\\", marker=\\\"o\\\", label=\\\"prey\\\")\\n\\tpredator = plt.plot(range(0, len(y)), y, \\\"RED\\\", marker=\\\"o\\\", label=\\\"predator\\\")\\n\\tplt.legend(loc=1, bbox_to_anchor=(0.3, 1))\\t\\t\\t\\n\\tplt.xlabel(\\\"time\\\")\\n\\tplt.ylabel(\\\"population\\\")\\n\\tplt.draw()\\n\\tplt.clf()\\n\\tsleep(SLEEPTIME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"220ca58420e7773ada23f62477ee4f1f\",\n \"score\": \"0.50686854\",\n \"text\": \"def generate_time_plot(state_scores, state_s_polls, burn_in, state_name, prior,\\n save=False):\\n n_days = state_scores.shape[0]\\n x_coord = np.arange(n_days)\\n burn_state_scores = state_scores[:, burn_in:]\\n\\n # plot dots (polls)\\n poll_date = state_s_polls.date_index.as_matrix()\\n poll_value = state_s_polls.p_clinton.as_matrix()\\n plt.scatter(poll_date, poll_value, s=10, c='black')\\n\\n # plot thick blue line (median) and 90% confidence interval\\n medians = np.median(burn_state_scores, axis=1)\\n uppers = np.percentile(burn_state_scores, 95, axis=1)\\n lowers = np.percentile(burn_state_scores, 5, axis=1)\\n\\n plt.title('Clinton Vote Share over Time for {0}, P(Win) = {1:.4f}'.format(\\n state_name.title(), np.mean(burn_state_scores[-1] > 0.5)), fontsize=11)\\n plt.ylabel('Share of Two-Party Vote')\\n plt.xlabel('Day')\\n plt.plot(x_coord, medians, color='blue')\\n plt.plot(x_coord, lowers, color='#33CEFF')\\n plt.plot(x_coord, uppers, color='#33CEFF')\\n plt.fill_between(x_coord, uppers, lowers,\\n interpolate=True, color='#33CEFF', alpha=.4)\\n plt.axhline(y=0.5, color='black', linestyle='-')\\n plt.axhline(y=prior, color='black', linestyle='--')\\n\\n if state_name != 'general':\\n results_2016 = pd.read_csv(\\n '../data/2016_results.csv', index_col=0)\\n results_2016 = results_2016['dem_share_2p']\\n plt.axhline(y=results_2016[state_name], color='purple', linestyle=':')\\n else:\\n plt.axhline(y=0.511, color='purple', linestyle=':')\\n\\n if save is True:\\n plt.savefig('../plots/time_plots/' +\\n state_name.replace(\\\" \\\", \\\"_\\\") + '.png', dpi=300)\\n plt.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8c4c69578e10b9b75620b90d2ccda5d\",\n \"score\": \"0.5057931\",\n \"text\": \"def assess_concurrence(mission, show_figure=True, save_figure=False):\\n\\n takeoff_times = mission.launch_time_list\\n\\n landing_times = mission.landing_time_list\\n\\n mat_takeoff_times = mdates.date2num(takeoff_times)\\n mat_landing_times = mdates.date2num(landing_times)\\n\\n # Padding sets the whitespace before and after the histogram. Currently set to 5 minutes. mat_takeoff_times is a\\n # list of floats. Each float represents the takeoff time in days since 0001-01-01 00:00:00 UTC plus one.\\n padding = 1.0/(24*24.0)\\n\\n times = np.linspace(min(mat_takeoff_times) - padding, max(mat_landing_times) + padding, 7000)\\n plot_times = mdates.num2date(times)\\n drones_aloft = np.zeros(len(times))\\n\\n # These nested loops set up the graph.The concurrent sorties graph is built as a histogram with several thousand\\n # bins. Each bin has a value that corresponds to the number of UAVs flying at that time.\\n for time in mat_takeoff_times:\\n for jj in xrange(len(times)):\\n if times[jj] >= time:\\n for ii in xrange(jj, len(drones_aloft)):\\n drones_aloft[ii] = drones_aloft[ii] + 1\\n break\\n\\n for time in mat_landing_times:\\n for jj in xrange(len(times)):\\n if times[jj] >= time:\\n for ii in xrange(jj, len(drones_aloft)):\\n drones_aloft[ii] = drones_aloft[ii] - 1\\n break\\n\\n # Calculate the width of each histogram bar\\n width = ((plot_times[-1] - plot_times[0]).total_seconds())/(86400*len(plot_times))\\n\\n plt.figure(figsize=(11, 8))\\n plt.bar(plot_times, drones_aloft, width=width, linewidth=0)\\n plt.xlim(plot_times[0], plot_times[-1])\\n plt.xlabel('Time of Day')\\n plt.ylabel('Number of Aircraft Aloft')\\n plt.title('Graph of Concurrent Sorties - %s-%s-%s' % (takeoff_times[0].month, takeoff_times[0].day, takeoff_times[0].year))\\n if save_figure:\\n save_path = os.path.join(mission.path,'FX%02d-M%02d_concurrent_sorties.png' % (mission.event_number, mission.mission_number))\\n mission.path_dictionary['concurrent_sorties'] = save_path\\n plt.savefig(save_path)\\n\\n if show_figure:\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e388fe358c34d2ff2afe5d83ee08efbf\",\n \"score\": \"0.50538224\",\n \"text\": \"def __show_each_feature(self, feature_name, feature_number):\\n if feature_name==\\\"Text_density\\\":\\n from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler, StandardScaler\\n min_max_scaler = MinMaxScaler()\\n X_train_norm = min_max_scaler.fit_transform(self.features)\\n X = np.array(X_train_norm)\\n y = np.array([feature[feature_number] for feature in X])\\n sns.set()\\n plt.title(feature_name)\\n plt.xlabel('Position')\\n plt.ylabel('Value')\\n plt.plot(y[:], 'bo', y, 'k')\\n path = PATH + feature_name+'.jpg'\\n plt.savefig(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c9b4e0182883426ac0ca002b56a625f\",\n \"score\": \"0.5051531\",\n \"text\": \"def generate_time_features(\\n self, label_id=TRAJ_ID, label_dtype=np.float64, sort=True, inplace=True\\n ):\\n\\n operation = begin_operation('generate_time_features')\\n\\n if inplace:\\n data_ = self._data\\n else:\\n data_ = self._data.copy()\\n\\n try:\\n print(\\n '\\\\nCreating or updating time features seconds\\\\n'\\n )\\n\\n start_time, ids, sum_size_id, size_id = self._prepare_generate_data(\\n data_, sort, label_id\\n )\\n\\n # create new feature to time\\n data_[TIME_TO_PREV] = label_dtype(-1.0)\\n data_[TIME_TO_NEXT] = label_dtype(-1.0)\\n data_[TIME_PREV_TO_NEXT] = label_dtype(-1.0)\\n\\n ids = data_.index.unique()\\n sum_size_id = 0\\n size_id = 0\\n\\n for idx in progress_bar(\\n ids, desc='Generating time features'\\n ):\\n curr_time = data_.at[idx, DATETIME].astype(label_dtype)\\n\\n size_id = curr_time.size\\n\\n if size_id <= 1:\\n data_.at[idx, TIME_TO_PREV] = np.nan\\n else:\\n prev_time = shift(curr_time, 1)\\n time_prev = (curr_time - prev_time) * (10 ** -9)\\n data_.at[idx, TIME_TO_PREV] = time_prev\\n\\n next_time = shift(curr_time, -1)\\n time_prev = (next_time - curr_time) * (10 ** -9)\\n data_.at[idx, TIME_TO_NEXT] = time_prev\\n\\n time_prev_to_next = (next_time - prev_time) * (10 ** -9)\\n data_.at[idx, TIME_PREV_TO_NEXT] = time_prev_to_next\\n\\n return self._return_generated_data(\\n data_, start_time, operation, inplace\\n )\\n\\n except Exception as e:\\n print(\\n 'label_tid:%s\\\\nidx:%s\\\\nsize_id:%s\\\\nsum_size_id:%s'\\n % (label_id, idx, size_id, sum_size_id)\\n )\\n self.last_operation = end_operation(operation)\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40e5361a87f522ea096215a4f6d4bac\",\n \"score\": \"0.5044915\",\n \"text\": \"def plot_example_cluster(cluster, x_sample, is_unhealthy, is_counterfactual, engine_num, seq_num): \\r\\n \\r\\n center_color = \\\"green\\\"\\r\\n label = \\\"factual\\\"\\r\\n if is_unhealthy and is_counterfactual:\\r\\n center_color = \\\"red\\\" \\r\\n label=\\\"counterfactual\\\"\\r\\n\\r\\n elif not is_unhealthy and is_counterfactual:\\r\\n center_color = \\\"red\\\"\\r\\n label=\\\"counterfactual\\\"\\r\\n\\r\\n elif is_unhealthy and not is_counterfactual:\\r\\n label=\\\"factual\\\"\\r\\n center_color = \\\"red\\\" \\r\\n \\r\\n \\r\\n #plot the members of the cluster\\r\\n for i in range(len(cluster)):\\r\\n m=cluster[[c for c in cluster.columns \\r\\n if c.startswith(\\\"ts_\\\")]].values[i:i+1][0]\\r\\n ax.plot(m, c=\\\"gray\\\", alpha=0.25)\\r\\n\\r\\n # now plot the center of the cluster\\r\\n center=cluster[[c for c in cluster.columns \\r\\n if c.startswith(\\\"center_\\\")]\\r\\n ].values[i:i+1][0]\\r\\n ax.scatter(range(0, len(center)), center, c=center_color, label=label)\\r\\n ax.plot(center, \\\"--\\\", c=center_color)\\r\\n\\r\\n # now plot your selected sample\\r\\n ax.scatter(range(0, len(x_sample)), x_sample, s=30, c=\\\"black\\\",\\r\\n label=\\\"predicted_sample\\\")\\r\\n ax.plot(x_sample, \\\"--\\\", c=\\\"black\\\")\\r\\n\\r\\n ax.set_xticks(range(0,len(center)))\\r\\n ax.set_yticks(np.arange(0.6, 1, 0.1))\\r\\n ax.legend(loc=\\\"lower right\\\", fontsize=16)\\r\\n\\r\\n # now add the important timesteps\\r\\n for f in local_imp_features:\\r\\n timestep = int(f.split(\\\"_\\\")[1])\\r\\n ax.axvspan(xmin=timestep-0.25,\\r\\n xmax=timestep+0.25,\\r\\n ymax=1,\\r\\n facecolor='green', alpha=0.2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa443a8eab066b0f33c1850764dc59a7\",\n \"score\": \"0.50359315\",\n \"text\": \"def plot_waveforms(time,voltage,APTimes,titlestr):\\r\\n \\r\\n plt.figure()\\r\\n\\r\\n ## Your Code Here \\r\\n # --------------------------------------------------------------\\r\\n\\r\\n # from homework examples:\\r\\n # action potentials are caught in intervals of 3 miliseconds\\r\\n rate = 0.003\\r\\n \\r\\n # compute the number of action potentials that are measured at each rate\\r\\n # this is given by the fraction between the rate and the delayed time between\\r\\n # each action potential\\r\\n timeStep = time[1]-time[0]\\r\\n measurements_per_rate = 2 * int( rate / timeStep ) \\r\\n\\r\\n # the x-axis varies between -3 ms to 3 ms and has steps of measurements_per_rate\\r\\n time_axis = np.linspace(-rate, rate, measurements_per_rate ) \\r\\n voltage_axis = np.zeros( measurements_per_rate )\\r\\n \\r\\n for val in range(len(APTimes)): \\r\\n \\r\\n # find index of action potential (AP) in t data array\\r\\n indx = plt.find( time == APTimes[val])\\r\\n \\r\\n # get data near the action potential \\r\\n if measurements_per_rate / 2 > indx:\\r\\n start_indx = 0\\r\\n else:\\r\\n start_indx = indx - measurements_per_rate / 2\\r\\n \\r\\n end_indx = indx + measurements_per_rate / 2\\r\\n\\r\\n # create a range from the starting index to the ending index\\r\\n action_potentials = range(start_indx, end_indx)\\r\\n \\r\\n # if there are enough points to represent the number of measurements per rate\\r\\n if len( action_potentials ) == measurements_per_rate: \\r\\n # then, get the actural voltages from the action potentials identified\\r\\n voltage_axis = voltage[ action_potentials ] \\r\\n else: \\r\\n # otherwise, fill the array by adding points to fill up the measurement\\r\\n missing = measurements_per_rate - len( action_potentials )\\r\\n # for poits very close to zero, fill them with zeros as well\\r\\n voltage_axis[ 0 : missing ] = 0 \\r\\n # for points not very close to zero, get the actual measurements from action potentials\\r\\n voltage_axis[ val:measurements_per_rate ] = voltage[ action_potentials[ 1 ] ] \\r\\n \\r\\n # plot the waveform with a blue color \\r\\n plt.plot(time_axis, voltage_axis, color='b', hold=True)\\r\\n\\r\\n plt.xlabel( 'Time (s)' ) # plot x-axis label\\r\\n plt.ylabel( 'Voltage (uV)' ) # plot y-axis label\\r\\n plt.title( titlestr ) # plot graph title \\r\\n \\r\\n plt.show( ) # show plot\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b114d9de5d734c1d1aca9e438a2a6c50\",\n \"score\": \"0.50341535\",\n \"text\": \"def plot_indep_time_computation(bn,lindep, ntimes,sizes,list_args_independance=[{\\\"calculation_method\\\":\\\"pearson\\\",\\\"R_test\\\":True},{\\\"calculation_method\\\":\\\"pearson\\\"},{\\\"usePyAgrum\\\":True,\\\"calculation_method\\\":\\\"pearson\\\"}]):\\n \\n fig,ax = plt.subplots(nrows=len(lindep), ncols=1, sharex=True,figsize =[6.4, 2*len(lindep)])\\n \\n \\n #define set of colors\\n cmap = plt.get_cmap('gnuplot')\\n colors = [cmap(i) for i in np.linspace(0.2, 1, len(list_args_independance))]\\n for index, indep_relation_ship in enumerate(lindep): \\n for color,test in zip(colors,list_args_independance):\\n store_time,use_pyagrum=[],test.get(\\\"usePyAgrum\\\",False)\\n for size in sizes:\\n #we generate df temporaly with the wanted size\\n gum.generateCSV(bn,os.path.join(\\\"databases\\\",\\\"temp_size.csv\\\"),size,False,with_labels=True)\\n df=pd.read_csv(os.path.join(\\\"databases\\\",\\\"temp_size.csv\\\"))\\n #we generate a function with the expected return\\n if use_pyagrum:\\n test[\\\"learner\\\"]=gum.BNLearner(os.path.join(\\\"databases\\\",\\\"temp_size.csv\\\"))\\n modified_function=compute_time(indepandance(df,*indep_relation_ship,**test).realize_test)\\n temps_execution=0\\n for iteration in range (ntimes):\\n temps_execution+=modified_function()\\n store_time.append(temps_execution)\\n \\n ax[index].plot(sizes, store_time, label=format_args_indep(test),color=color,marker='+')\\n ax[index].set_ylabel (\\\"Time execution\\\")\\n ax[index].set_yscale('log')\\n ax[index].set_title(format_test(indep_relation_ship)) \\n \\n #at the end of computation, delete temporay file created\\n if os.path.exists(os.path.join(\\\"databases\\\",\\\"temp_size.csv\\\")):\\n os.remove(os.path.join(\\\"databases\\\",\\\"temp_size.csv\\\")) \\n plt.tick_params(axis='x',rotation=45)\\n plt.xlabel (\\\"data size\\\") \\n box = ax[len(lindep)-1].get_position()\\n ax[len(lindep)-1].set_position([box.x0, box.y0 + box.height * 0.4,\\n box.width, box.height * 0.6]) \\n ax[len(lindep)-1].legend(bbox_to_anchor=(0.0, -1,1., .102), loc=3, mode=\\\"expand\\\", fancybox=True, shadow=True) \\n fig.suptitle(\\\"Times to carry out independance tests for several sizes of datasets, each computed {} times\\\".format(ntimes),y=1.0,weight =\\\"bold\\\")\\n fig.tight_layout()\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bafa12922bb9be152d72a883db4cf51\",\n \"score\": \"0.50142044\",\n \"text\": \"def plot_superposed_imf(ax, imf_comp=\\\"Bz\\\", df_turn=None, stable_interval=30, \\n IMF_turning=\\\"northward\\\"):\\n\\n if df_turn is None:\\n df_turn = build_event_database(IMF_turning=IMF_turning, event_status=\\\"good\\\")\\n #df_turn = df_turn.tail()\\n dbdir = \\\"../data/sqlite3/\\\"\\n table_name = \\\"IMF\\\"\\n db_name = \\\"gmi_imf.sqlite\\\"\\n\\n # make a connection\\n conn = sqlite3.connect(dbdir + db_name,\\n detect_types = sqlite3.PARSE_DECLTYPES)\\n cur = conn.cursor()\\n\\n dtms = pd.to_datetime(df_turn.datetime.unique())\\n for dtm in dtms:\\n stm = dtm - dt.timedelta(seconds=60. * stable_interval)\\n etm = dtm + dt.timedelta(seconds=60. * stable_interval)\\n # load data to a dataframe\\n command = \\\"SELECT Bx, By, Bz, theta, datetime FROM {tb} \\\" + \\\\\\n \\\"WHERE datetime BETWEEN '{stm}' AND '{etm}' \\\"\\n command = command.format(tb=table_name, stm=stm, etm=etm)\\n df = pd.read_sql(command, conn)\\n dtms_tmp = pd.to_datetime(df.datetime)\\n df.loc[:, \\\"relative_time\\\"] = [(x-dtm).total_seconds()/60. for x in dtms_tmp]\\n label = dtm.strftime(\\\"%Y%m%d %H:%M\\\")\\n ax.plot(df.relative_time, df.loc[:, imf_comp], label=label)\\n ax.axvline(x=0, color=\\\"r\\\", linestyle=\\\"--\\\", linewidth=1.)\\n\\n #ax.legend()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0829dc09e05f2aaf2dced7c24919737\",\n \"score\": \"0.5012225\",\n \"text\": \"def main():\\n \\n teta_0 = next_0 = 1\\n teta_1 = next_1 = 1\\n step = 1\\n min_cost = 100\\n\\n while step < 2000000:\\n learning_rate = 1/step\\n teta_0 = next_0\\n teta_1 = next_1\\n next_0 = teta_0 - learning_rate/len(POINTS) * calculate_teta_0(teta_0, teta_1)\\n next_1 = teta_1 - learning_rate/len(POINTS) * calculate_teta_1(teta_0, teta_1)\\n\\n #calculate the cost for teta0 and teta1 with the job function\\n cost = 1/(2*len(POINTS)) * calculate_job(teta_0, teta_1)\\n \\n if(cost < 0.8):\\n break\\n if(cost < min_cost):\\n min_cost = cost\\n\\n step += 1\\n\\n plt.subplot(211)\\n x, y = [elem[0] for elem in POINTS], [elem[1] for elem in POINTS]\\n plt.scatter(x, y)\\n\\n lol = np.arange(0, 3, 0.1)\\n y_chapeau = [next_0 + next_1 * elem for elem in lol]\\n plt.plot(lol, y_chapeau)\\n\\n plt.subplot(212)\\n y_lol = [0] * 30\\n\\n x, y = [elem[0] for elem in POINTS], [elem[1] - (next_0 + next_1 * elem[0]) for elem in POINTS]\\n plt.scatter(x, y)\\n plt.plot(lol, y_lol)\\n plt.show()\\n\\n print(step)\\n print(min_cost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70329fd039f5ced5334b064c5ccea520\",\n \"score\": \"0.5008261\",\n \"text\": \"def get_ICE_plot(self, feature_ind, num_samples, fig_size_x = 9, fig_size_y = 6,\\n x_max_lim = None, x_min_lim = None):\\n\\n ## Obtain the random rows to display\\n row_inds_list = [iii for iii in range(self.X.shape[0])]\\n random.shuffle(row_inds_list)\\n row_inds_list = row_inds_list[0:num_samples]\\n \\n figure, ax = plt.subplots(1,len(feature_ind), figsize= (len(feature_ind)*fig_size_x, fig_size_y))\\n\\n X_PI = self.X.copy()\\n for idx, feat_id in enumerate(feature_ind):\\n \\n \\n full_list_pred = []\\n for row_ind in row_inds_list:\\n \\n \\n x_curr = X_PI[row_ind, :]\\n x_curr = x_curr.reshape(1,-1)\\n \\n x_min = np.min(X_PI[:, feat_id]) if x_min_lim is None else x_min_lim\\n x_max = np.max(X_PI[:, feat_id]) if x_max_lim is None else x_max_lim\\n \\n x_pi = np.linspace(x_min, x_max, 100)\\n curr_pred_arr = np.zeros((3, x_pi.shape[0]))\\n\\n for jjj in range(x_pi.shape[0]):\\n x_curr_ice = x_curr.copy()\\n x_curr_ice[0,feat_id] = x_pi[jjj]\\n p, c = self.gp.predict(x_curr_ice, return_cov=True)\\n curr_pred_arr[0,jjj] = p\\n curr_pred_arr[1,jjj] = p + np.sqrt(c)*1.96\\n curr_pred_arr[2,jjj] = p - np.sqrt(c)*1.96\\n full_list_pred.append(curr_pred_arr)\\n \\n if len(feature_ind) > 1:\\n for arr in full_list_pred:\\n ax[idx].plot(x_pi, arr[0,:])\\n ax[idx].fill_between(x_pi, arr[1,:], arr[2,:], alpha = 0.2)\\n ax[idx].set_ylabel('ICE')\\n ax[idx].set_xlabel(self.feature_names[feat_id])\\n ax[idx].grid(axis='y')\\n else:\\n for arr in full_list_pred:\\n ax.plot(x_pi, arr[0,:])\\n ax.fill_between(x_pi, arr[1,:], arr[2,:], alpha = 0.2)\\n ax.set_ylabel('ICE')\\n ax.set_xlabel(self.feature_names[feat_id])\\n ax.grid(axis='y')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52a3dbfe0f0d9da15ecfdf0c45d38395\",\n \"score\": \"0.5004441\",\n \"text\": \"def plotTime(self):\\n plt.figure()\\n t = np.linspace(0, self.m, num=len(self.nodes_infected), endpoint=True)\\n plt.title('Nodos infectados vs Tiempo')\\n plt.xlabel('Instantes de tiempo')\\n plt.ylabel('# de nodos infectados')\\n plt.plot(t, self.nodes_infected)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e9060cec834d94a39fc0007adabf368\",\n \"score\": \"0.5003\",\n \"text\": \"def frame(I, second=5, saveable=True, name='frame', fig_idx=12836):\\n if saveable is False:\\n plt.ion()\\n fig = plt.figure(fig_idx) # show all feature images\\n\\n plt.imshow(I)\\n # plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick\\n # plt.gca().yaxis.set_major_locator(plt.NullLocator())\\n\\n if saveable:\\n plt.savefig(name+'.pdf',format='pdf')\\n else:\\n plt.draw()\\n plt.pause(second)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e796e03e5edfbb86383a99a1c6ab31\",\n \"score\": \"0.5002621\",\n \"text\": \"def visualize(sample, x, v_min, v_max, v_init, num_pumps, costs, power, pump_flow_schedule, reservoir, time, demand):\\n\\n print(\\\"\\\\nBuilding visualization...\\\")\\n\\n # Initialize plot\\n fig, ax = plt.subplots()\\n\\n # Set up plot parameters (static)\\n ax.set_xlim(0, 1)\\n ax.set_ylim(0, v_min/2+v_max)\\n ax.xaxis.set_visible(False)\\n ax.set_yticks([v_min, v_max])\\n ax.set_yticklabels(('','Max'))\\n ax.set_title(\\\"Reservoir Water Level\\\")\\n\\n # Plot max/min water capacity lines (static)\\n ax.plot(list(range(0, 5)), [v_max]*(5), color='#222222', label=\\\"Max capacity\\\", linewidth=1.0)\\n ax.plot(list(range(0, 5)), [v_min]*(5), color='#FFA143', label=\\\"Min capacity\\\", linewidth=1.5)\\n\\n # Plot water as a blue bar graph (dynamic)\\n barcollection = plt.bar(0.5, v_init, width=1.0, color='#2a7de1', align='center')\\n\\n # Blue line across top of the water (dynamic)\\n water_line, = ax.plot([], [], 'b-')\\n x_ax_vals = np.linspace(0, 1, 200)\\n\\n # Put list of pumps on plot (static)\\n pumps_used = []\\n for i in range(num_pumps):\\n pumps_used.append(plt.figtext(0.03, 0.11+0.035*i, \\\"Pump \\\"+str(i+1), fontdict=None, color='#DDDDDD', fontsize='small'))\\n \\n # Put timeslot on plot (dynamic)\\n time_label = ax.text(0.75, 1600, '')\\n\\n # Put cost for timeslot on plot (dynamic)\\n cost_label = plt.figtext(0.45, 0.03, '', fontdict=None, color='k')\\n\\n def animate(i):\\n\\n # Compute minutes/hour for smooth animation over time\\n m = i % (60/smoothing_factor)\\n t = int( (i-m) / (60/smoothing_factor) )\\n\\n # Compute flow/demand per minute for smooth animation over time\\n pump_min_flow = m * smoothing_factor * pump_flow_schedule[t] / 60\\n demand_min = m * smoothing_factor * demand[t] / 60\\n\\n # Adjust water level for the given min/hour\\n delta = reservoir[t] + pump_min_flow - demand_min\\n y = [delta] * (len(x_ax_vals))\\n for i, b in enumerate(barcollection):\\n b.set_height(delta)\\n water_line.set_data(x_ax_vals, y)\\n\\n # Adjust time/cost/pumps used text on plot for the given hour\\n time_label.set_text('Time: '+str(time[t]))\\n cost = 0\\n for p in range(num_pumps):\\n if sample[x[p][t]] == 1:\\n pumps_used[p].set_color('#008c82')\\n cost += sample[x[p][t]] * costs[t] * power[p] / 1000\\n else:\\n pumps_used[p].set_color('#DDDDDD')\\n\\n cost_label.set_text(\\\"Hourly Cost: \\\"+str(cost))\\n\\n return water_line,\\n\\n # Build movie visualization\\n smoothing_factor = 4 # Granularity factor for animation\\n anim = animation.FuncAnimation(fig, animate, repeat=False, frames=int(24*(60/smoothing_factor)), interval=2, blit=True)\\n mywriter = animation.HTMLWriter(fps=30)\\n anim.save('reservoir.html',writer=mywriter)\\n\\n print(\\\"\\\\nAnimation saved as reservoir.html.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d0a3204c7ef658ab3107edc1b9d0ad6\",\n \"score\": \"0.49971083\",\n \"text\": \"def time_step(self, prob):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92c45fdcd8447c6b404c14640e23cda\",\n \"score\": \"0.49960905\",\n \"text\": \"def chart_total_feature(feature):\\n status_incomplete = feature.objects.filter(status='INCOMPLETE').count()\\n status_progress = feature.objects.filter(status='IN PROGRESS').count()\\n status_complete = feature.objects.filter(status='COMPLETE').count()\\n p_chart = pygal.Pie(print_values=True,\\n legend_at_bottom_columns=3,\\n legend_box_size=30,\\n margin=0,\\n style=custom_style\\n )\\n\\n p_chart.add('INCOMPLETE', status_incomplete)\\n p_chart.add('IN PROGRESS', status_progress)\\n p_chart.add('COMPLETE', status_complete)\\n return p_chart.render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13c4d70a046adf97db533d2d39763e45\",\n \"score\": \"0.49896222\",\n \"text\": \"def __call__(self, u, x, t, n):\\n if plot_error==True:\\n if n == 0:\\n plt.ion()\\n self.lines = plt.plot(x, u-ue(x,0), 'r-')\\n plt.xlabel('x'); plt.ylabel('error')\\n plt.axis([0, L, umin, umax])\\n plt.legend(['t=%f' % t[n]], loc='lower left')\\n else:\\n self.lines[0].set_ydata(u-ue(x,t[n]))\\n plt.legend(['t=%f' % t[n]], loc='lower left')\\n plt.draw()\\n time.sleep(2) if t[n] == 0 else time.sleep(sleep)\\n plt.savefig('tmp_%04d.png' % n) # for movie making\\n else:\\n if n == 0:\\n plt.ion()\\n self.lines = plt.plot(x, u, 'r-')\\n self.lines2 = plt.plot(x,ue(x,0),'b--')\\n plt.xlabel('x'); plt.ylabel('error')\\n plt.axis([0, L, umin, umax])\\n plt.legend(['t=%f' % t[n]], loc='lower left')\\n else:\\n self.lines[0].set_ydata(u)\\n self.lines2[0].set_ydata(ue(x,t[n]))\\n plt.legend(['t=%f' % t[n]], loc='lower left')\\n plt.draw()\\n \\n time.sleep(2) if t[n] == 0 else time.sleep(sleep)\\n plt.savefig('tmp_%04d.png' % n) # for movie making\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d91953348779907ef043f38bfa51203b\",\n \"score\": \"0.4986097\",\n \"text\": \"def show(self, fig=None):\\n i = 0\\n iterator = np.linspace(0, self.duration(), num=math.ceil(self.duration() / self.step_precision) + 1)\\n tfInterp = np.zeros((4, 4, len(iterator)))\\n for t in iterator:\\n tfInterp[:, :, i] = self.torsoPosition(t)\\n i = i + 1\\n self.show_tf(fig, tfInterp, len(iterator))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8497f0a636415e2cd788f6a162602c0\",\n \"score\": \"0.49749544\",\n \"text\": \"def plot_features(data, true_hadroness=False):\\n hadro = \\\"mc_type\\\" if true_hadroness else \\\"reco_type\\\"\\n\\n # Energy distribution\\n plt.subplot(331)\\n plt.hist(data[data[hadro] < 1]['log_mc_energy'],\\n histtype=u'step', bins=100,\\n label=\\\"Gammas\\\")\\n plt.hist(data[data[hadro] > 0]['log_mc_energy'],\\n histtype=u'step', bins=100,\\n label=\\\"Protons\\\")\\n plt.ylabel(r'# of events', fontsize=15)\\n plt.xlabel(r\\\"$log_{10}E$(GeV)\\\")\\n plt.legend()\\n\\n # disp_ distribution\\n plt.subplot(332)\\n plt.hist(data[data[hadro] < 1]['disp_norm'],\\n histtype=u'step', bins=100,\\n label=\\\"Gammas\\\")\\n plt.hist(data[data[hadro] > 0]['disp_norm'],\\n histtype=u'step', bins=100,\\n label=\\\"Protons\\\")\\n plt.ylabel(r'# of events', fontsize=15)\\n plt.xlabel(r\\\"disp_ (m)\\\")\\n\\n # Intensity distribution\\n plt.subplot(333)\\n plt.hist(data[data[hadro] < 1]['log_intensity'],\\n histtype=u'step', bins=100,\\n label=\\\"Gammas\\\")\\n plt.hist(data[data[hadro] > 0]['log_intensity'],\\n histtype=u'step', bins=100,\\n label=\\\"Protons\\\")\\n plt.ylabel(r'# of events', fontsize=15)\\n plt.xlabel(r\\\"$log_{10}Intensity$\\\")\\n\\n dataforwl = data[data['log_intensity'] > np.log10(200)]\\n # Width distribution\\n plt.subplot(334)\\n plt.hist(dataforwl[dataforwl[hadro] < 1]['width'],\\n histtype=u'step', bins=100,\\n label=\\\"Gammas\\\")\\n plt.hist(dataforwl[dataforwl[hadro] > 0]['width'],\\n histtype=u'step', bins=100,\\n label=\\\"Protons\\\")\\n plt.ylabel(r'# of events', fontsize=15)\\n plt.xlabel(r\\\"Width (º)\\\")\\n\\n # Length distribution\\n plt.subplot(335)\\n plt.hist(dataforwl[dataforwl[hadro] < 1]['length'],\\n histtype=u'step', bins=100,\\n label=\\\"Gammas\\\")\\n plt.hist(dataforwl[dataforwl[hadro] > 0]['length'],\\n histtype=u'step', bins=100,\\n label=\\\"Protons\\\")\\n plt.ylabel(r'# of events', fontsize=15)\\n plt.xlabel(r\\\"Length (º)\\\")\\n\\n # r distribution\\n plt.subplot(336)\\n plt.hist(data[data[hadro] < 1]['r'],\\n histtype=u'step', bins=100,\\n label=\\\"Gammas\\\")\\n plt.hist(data[data[hadro] > 0]['r'],\\n histtype=u'step', bins=100,\\n label=\\\"Protons\\\")\\n plt.ylabel(r'# of events', fontsize=15)\\n plt.xlabel(r\\\"r (m)\\\")\\n\\n # psi distribution\\n\\n plt.subplot(337)\\n plt.hist(data[data[hadro] < 1]['psi'],\\n histtype=u'step', bins=100,\\n label=\\\"Gammas\\\")\\n plt.hist(data[data[hadro] > 0]['psi'],\\n histtype=u'step', bins=100,\\n label=\\\"Protons\\\")\\n plt.ylabel(r'# of events', fontsize=15)\\n plt.xlabel(r\\\"psi angle(rad)\\\")\\n\\n # psi distribution\\n\\n plt.subplot(338)\\n plt.hist(data[data[hadro] < 1]['phi'],\\n histtype=u'step', bins=100,\\n label=\\\"Gammas\\\")\\n plt.hist(data[data[hadro] > 0]['phi'],\\n histtype=u'step', bins=100,\\n label=\\\"Protons\\\")\\n plt.ylabel(r'# of events', fontsize=15)\\n plt.xlabel(r\\\"phi angle(m)\\\")\\n\\n # Time gradient\\n\\n plt.subplot(339)\\n plt.hist(data[data[hadro] < 1]['time_gradient'],\\n histtype=u'step', bins=100,\\n label=\\\"Gammas\\\")\\n plt.hist(data[data[hadro] > 0]['time_gradient'],\\n histtype=u'step', bins=100,\\n label=\\\"Protons\\\")\\n plt.ylabel(r'# of events', fontsize=15)\\n plt.xlabel(r\\\"Time gradient\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5307cac80c81c7a18dfc76363b197f5e\",\n \"score\": \"0.49647075\",\n \"text\": \"def FeaturesMonthlyStatus():\\n chart = chart_by_time_feature(Feature, 30)\\n return chart\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1816acf57e33233d162bd99b9c1b391\",\n \"score\": \"0.4962233\",\n \"text\": \"def main():\\n\\n files = localFileList_ANITA2chamberHPol()\\n\\n fig = lab.figure()\\n axes = []\\n\\n colors=[\\\"red\\\",\\\"blue\\\"]\\n\\n numFiles = len(files)\\n index=1\\n logMags = {}\\n for file in files:\\n if index!=1:\\n axes.append(fig.add_subplot(numFiles,1,index,sharex=axes[0]))\\n else:\\n axes.append(fig.add_subplot(numFiles,1,index))\\n \\n for chan in range(0,2):\\n events = importAll(file,numEvents=100,channel=chan)\\n avgEvent = tf.correlateAndAverage(events)\\n peak = np.argmax(np.abs(avgEvent[1])) + 500\\n procAvgEvent = processAllEvents([avgEvent],totalWidth=2000,peak=peak)[0] \\n logMag = tf.genLogMag(procAvgEvent[0],procAvgEvent[1]*1000)\\n grpDly = tf.genGroupDelay(procAvgEvent[0]*1e9,procAvgEvent[1]*1000)\\n\\n offPhi = file.split(\\\"/\\\")[-1].split(\\\"_\\\")[1][1:4]\\n offTheta = file.split(\\\"/\\\")[-1].split(\\\"_\\\")[2][1:4]\\n print \\\"Phi=\\\"+str(offPhi)+\\\" Theta=\\\"+str(offTheta)\\n \\n label = str(offPhi)+\\\",\\\"+str(offTheta)\\n axes[-1].plot(grpDly[0], grpDly[1], label = label, color = colors[chan])\\n# axes[-1].plot(grpDly[0][1:],grpDly[1][1:],label=label,color=colors[chan])\\n index += 1\\n \\n for ax in axes:\\n ax.legend()\\n fig.show()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dfc11c3590a0f010b1c907f2884db37\",\n \"score\": \"0.49591658\",\n \"text\": \"async def plot_last_active_learning():\\n ylim = [0.3, 1.0]\\n\\n # start plotting\\n figure = pyplot.figure()\\n figure.set_size_inches(7, 5)\\n\\n train_sizes, train_scores, test_scores = pickle.load(open(\\\"al.dump\\\", \\\"rb\\\"))\\n plot_learning_curve(figure, [1, 1, 1], train_sizes, train_scores, test_scores, title=\\\"LogisticRegression - C=2\\\",\\n ylim=ylim)\\n\\n figure.tight_layout()\\n print('ended')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8cf442aaf9cc66b72d671707c3cb370\",\n \"score\": \"0.49576867\",\n \"text\": \"def generate_time_of_day_features(self):\\n raise NotImplementedError('To be implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abdaa270e899eb6ec4fafe47aa3d1d94\",\n \"score\": \"0.49529967\",\n \"text\": \"def algorithmOnlyOutcome(self):\\n \\n plt.figure()\\n plt.plot(self.T, self.Fa, 'bo' )\\n plt.xlabel(r\\\"t\\\")\\n plt.ylabel(r\\\"F\\\")\\n plt.title(r\\\"The inverse Laplace transform\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e752e360106b9672e96a56d2b09e3b7\",\n \"score\": \"0.4952189\",\n \"text\": \"def PlotGesture(x, y, accnum, j):\\n # prepare line data \\n L1_x = x[0:5]\\n L1_y = y[0:5]\\n L2_x = [x[0]] + x[5:9]\\n L2_y = [y[0]] + y[5:9]\\n L3_x = x[9:13]\\n L3_y = y[9:13]\\n L4_x = x[13:17]\\n L4_y = y[13:17]\\n L5_x = [x[0]] + x[17:21]\\n L5_y = [y[0]] + y[17:21]\\n L6_x = x[5:18:4]\\n L6_y = y[5:18:4]\\n\\n ax = plt.gca() # Get the current axis information\\n ax.xaxis.set_ticks_position('top') # Move the X axis to the top\\n ax.invert_yaxis() # Reverse Y axis\\n\\n plt.title(str(j)+': AccNum: ' + str(accnum))\\n plt.plot(L1_x, L1_y, marker='o', mec='r', mfc='g')\\n plt.plot(L2_x, L2_y, marker='o', mec='r', mfc='g')\\n plt.plot(L3_x, L3_y, marker='o', mec='r', mfc='g')\\n plt.plot(L4_x, L4_y, marker='o', mec='r', mfc='g')\\n plt.plot(L5_x, L5_y, marker='o', mec='r', mfc='g')\\n plt.plot(L6_x, L6_y, marker='o', mec='r', mfc='g')\\n\\n\\n plt.draw()\\n plt.pause(1) \\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a3af1283a69f4d177270e30b5776510\",\n \"score\": \"0.4943097\",\n \"text\": \"def aniplot(F, source, steps, component='Ez', num_panels=10):\\n F.initialize_fields()\\n\\n # initialize the plot\\n f, ax_list = plt.subplots(1, num_panels, figsize=(20*num_panels,20))\\n Nx, Ny, _ = F.eps_r.shape\\n ax_index = 0\\n\\n # fdtd time loop\\n for t_index in range(steps):\\n fields = F.forward(Jz=source(t_index))\\n\\n # if it's one of the num_panels panels\\n if t_index % (steps // num_panels) == 0:\\n\\n if ax_index < num_panels: # extra safety..sometimes tries to access num_panels-th elemet of ax_list, leading to error\\n\\n print('working on axis {}/{} for time step {}'.format(ax_index, num_panels, t_index))\\n\\n # grab the axis\\n ax = ax_list[ax_index]\\n\\n # plot the fields\\n im_t = ax.pcolormesh(np.zeros((Nx, Ny)), cmap='RdBu')\\n max_E = np.abs(fields[component]).max()\\n im_t.set_array(fields[component][:, :, 0].ravel().T)\\n im_t.set_clim([-max_E / 2.0, max_E / 2.0])\\n ax.set_title('time = {} seconds'.format(F.dt*t_index))\\n\\n # update the axis\\n ax_index += 1\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a3af1283a69f4d177270e30b5776510\",\n \"score\": \"0.4943097\",\n \"text\": \"def aniplot(F, source, steps, component='Ez', num_panels=10):\\n F.initialize_fields()\\n\\n # initialize the plot\\n f, ax_list = plt.subplots(1, num_panels, figsize=(20*num_panels,20))\\n Nx, Ny, _ = F.eps_r.shape\\n ax_index = 0\\n\\n # fdtd time loop\\n for t_index in range(steps):\\n fields = F.forward(Jz=source(t_index))\\n\\n # if it's one of the num_panels panels\\n if t_index % (steps // num_panels) == 0:\\n\\n if ax_index < num_panels: # extra safety..sometimes tries to access num_panels-th elemet of ax_list, leading to error\\n\\n print('working on axis {}/{} for time step {}'.format(ax_index, num_panels, t_index))\\n\\n # grab the axis\\n ax = ax_list[ax_index]\\n\\n # plot the fields\\n im_t = ax.pcolormesh(np.zeros((Nx, Ny)), cmap='RdBu')\\n max_E = np.abs(fields[component]).max()\\n im_t.set_array(fields[component][:, :, 0].ravel().T)\\n im_t.set_clim([-max_E / 2.0, max_E / 2.0])\\n ax.set_title('time = {} seconds'.format(F.dt*t_index))\\n\\n # update the axis\\n ax_index += 1\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f746f7aa4feefb5ba657c04bd6bbaa7e\",\n \"score\": \"0.4940207\",\n \"text\": \"def displaySolutions(t,y): \\n plt.figure()\\n plt.plot(t,y[:,2],'b',label='C(t)')\\n plt.legend(loc='best')\\n plt.xlabel('t')\\n plt.grid()\\n plt.title('Mathilde Duverger displaySolutions')\\n plt.figure()\\n plt.plot(y[:,2],y[:,1],'r',label='phase plane')\\n plt.legend(loc='best')\\n plt.xlabel('C')\\n plt.ylabel('E')\\n plt.grid()\\n plt.title('Mathilde Duverger displaySolutions')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518ee2984a08139f8144d884ec2d288b\",\n \"score\": \"0.49375466\",\n \"text\": \"def _demo_plot():\\n import matplotlib.pyplot as plt\\n abfHeader=ABFheader(R\\\"../../../../data/17o05028_ic_steps.abf\\\")\\n plt.figure(figsize=(8,4))\\n plt.grid(alpha=.2)\\n times = abfHeader.getSweepTimes()\\n for sweepNumber in [0,5,10,15]:\\n data = abfHeader.getSweepData(sweepNumber)\\n plt.plot(times,data,lw=.7,alpha=.8,label=\\\"sweep %d\\\"%sweepNumber)\\n plt.margins(0,.1)\\n plt.legend(fontsize=8)\\n plt.title(\\\"Demonstrating the ABFheader Class\\\")\\n plt.ylabel(\\\"Membrane Potential (mV)\\\")\\n plt.xlabel(\\\"Time (seconds)\\\")\\n plt.tight_layout()\\n plt.savefig(\\\"_demo.png\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60065c1ebf53ea90ced9473f4c81f392\",\n \"score\": \"0.49329293\",\n \"text\": \"def compare_features(features, names, outdir=\\\"plots\\\", diff_threshold=0.05, anchor=\\\"first\\\"):\\n\\n def get_baseline(vals):\\n if anchor == \\\"mean\\\":\\n return sum(vals) / len(vals)\\n elif anchor == \\\"first\\\":\\n return vals[0]\\n else:\\n print(f\\\"unknown anchor {anchor}\\\")\\n return vals[0]\\n\\n def gen_plot(name, getter):\\n print(f\\\"plotting {name}\\\")\\n vals = [getter(f) for f in features]\\n plot(names, name, vals, outdir=outdir)\\n\\n baseline = get_baseline(vals)\\n if baseline == 0:\\n baseline = 0.000001\\n vals = [v + baseline for v in vals]\\n\\n disps = [val - baseline for val in vals]\\n pc_disps = [abs(disp) / baseline for disp in disps]\\n\\n if not any([pc_disp >= diff_threshold for pc_disp in pc_disps]):\\n return\\n\\n print(f\\\"{name}: {baseline}\\\")\\n for name, val, pc_disp in zip(names, vals, pc_disps):\\n print(f\\\" - {name}: {val} ({'+' if val >= baseline else '-'}{to_percent(pc_disp)})\\\")\\n\\n gen_plot(\\\"Total instruction counts\\\", lambda f: f.instructions_count_total())\\n gen_plot(\\\"Freedom to reorder\\\", lambda f: f.freedom_to_reorder())\\n gen_plot(\\\"Resource pressure\\\", lambda f: f.resource_pressure())\\n gen_plot(\\\"Work items count\\\", lambda f: f.work_items_count())\\n gen_plot(\\\"Granularity\\\", lambda f: f.granularity())\\n gen_plot(\\\"Total barriers hit\\\", lambda f: f.barriers_hit_total())\\n gen_plot(\\\"Barriers per work item\\\", lambda f: f.barriers_hit_per_work_item())\\n gen_plot(\\\"Barriers per instruction\\\", lambda f: f.barriers_hit_per_instruction())\\n gen_plot(\\\"Instructions per work item (mean)\\\", lambda f: f.instructions_count_per_work_item()[\\\"mean\\\"])\\n gen_plot(\\\"SIMD width (mean)\\\", lambda f: f.simd_width()[\\\"mean\\\"])\\n gen_plot(\\\"SIMD sum\\\", lambda f: f.simd_sum())\\n gen_plot(\\\"Instructions per operand\\\", lambda f: f.instructions_per_operand())\\n gen_plot(\\\"Memory access count\\\", lambda f: f.memory_access_count_total())\\n gen_plot(\\\"Memory access count (unique)\\\", lambda f: f.memory_access_count_unique())\\n gen_plot(\\\"Memory read count (unique)\\\", lambda f: f.memory_read_count_unique())\\n gen_plot(\\\"Memory read count\\\", lambda f: f.memory_read_count_total())\\n gen_plot(\\\"Memory write count (unique)\\\", lambda f: f.memory_write_count_unique())\\n gen_plot(\\\"Memory write count\\\", lambda f: f.memory_write_count_total())\\n gen_plot(\\\"Memory read-write ratio (unique)\\\", lambda f: f.memory_read_write_unique_ratio())\\n gen_plot(\\\"Memory re-read ratio\\\", lambda f: f.memory_reread_ratio())\\n gen_plot(\\\"Memory re-write ratio\\\", lambda f: f.memory_rewrite_ratio())\\n gen_plot(\\\"Memory address entropy\\\", lambda f: f.memory_address_entropy())\\n gen_plot(\\\"NPSL sum\\\", lambda f: f.normed_parallel_spacial_locality_sum())\\n gen_plot(\\\"Global memory access %\\\", lambda f: f.memory_global_access_count_pc())\\n gen_plot(\\\"Local memory access %\\\", lambda f: f.memory_local_access_count_pc())\\n gen_plot(\\\"Constant memory access %\\\", lambda f: f.memory_constant_access_count_pc())\\n gen_plot(\\\"Yokota branch entropy\\\", lambda f: f.branch_entropy_yokota())\\n gen_plot(\\\"Average linear branch entropy\\\", lambda f: f.branch_entropy_average_linear())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a844f1215e5dbfacb31f9a7e5be20b\",\n \"score\": \"0.49297658\",\n \"text\": \"def plot_results_seconds(self, on_of):\\n\\n time_Vector = np.linspace(0, self.active_trace.data.shape[0] / self.fm, num=self.active_trace.data.shape[0])\\n print(on_of)\\n # create an axis\\n self.ax = self.figura_traza.add_subplot(111)\\n # discards the old graph\\n self.ax.cla()\\n self.ax.plot(time_Vector, self.active_trace)\\n self.ax.toolbar_trace = NavigationToolbar(self.canvas_traza, self)\\n\\n self.selector = RectangleSelector(self.ax, self.line_select_callback,\\n drawtype='box', useblit=True, button=[1],\\n minspanx=5, minspany=5, spancoords='pixels',\\n interactive=True)\\n ymin, ymax = self.ax.get_ylim()\\n self.ax.vlines(on_of[:, 0], ymin, ymax, color='g', linewidth=1)\\n self.ax.vlines(on_of[:, 1], ymin, ymax, color='magenta', linestyle='dashed', linewidth=1)\\n\\n self.ax.axvline(self.first_ticked, color='green', linestyle='solid')\\n self.ax.axvline(self.last_ticked, color='magenta', linestyle='dashed')\\n self.canvas_traza.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc69bfb6e4f1ac4f92576a34123b616\",\n \"score\": \"0.49267346\",\n \"text\": \"def example_4():\\n np.random.seed(3)\\n\\n fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(7, 4))\\n n_step = 1\\n strategy = BlahutArimotoTimeOptimal()\\n\\n f = MultiWorldFactory()\\n\\n w = f.simple_2agents()\\n start = time.time()\\n\\n T = w.compute_ma_transition(w.n_a, det=1.)\\n E = strategy.compute(world=w, T=T, n_step=n_step)\\n print(f\\\"elapsed seconds: {time.time() - start:0.3f}\\\")\\n\\n # Only for plotting, positions need to be resetted\\n idx = np.argsort(E)\\n for j, agent in enumerate(w.agents):\\n agent.set_s(w._index_to_location(idx[0], j))\\n\\n ax[0].pcolor(np.zeros(w.dims))\\n w.plot_entities(fig, ax[0])\\n ax[0].set_title(f'{n_step}-step E={E[idx[0]]:.2f} low')\\n\\n for j, agent in enumerate(w.agents):\\n agent.set_s(w._index_to_location(idx[-1], j))\\n\\n ax[1].pcolor(np.zeros(w.dims))\\n w.plot_entities(fig, ax[1])\\n ax[1].set_title(f'{n_step}-step E={E[idx[-1]]:.2f} high')\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2823ffc5903c64c8ff3d0704c94b5eea\",\n \"score\": \"0.49250218\",\n \"text\": \"def trace_progress(values: list, true_best: float, tag: str):\\n plt.figure(figsize=(19, 7))\\n plt.plot(values, label=\\\"Tour distance\\\")\\n plt.hlines(true_best, xmin=0, xmax=len(values), color=\\\"r\\\", label=\\\"True best\\\")\\n plt.title(tag)\\n plt.legend()\\n plt.savefig(f\\\"figures/Distance_evolution_{tag}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d83d4f2d75d4d0c97ab2d5741833a52b\",\n \"score\": \"0.49233404\",\n \"text\": \"def plot_subset_stats(self, include_second = False, cutoff_percentile=50) :\\n \\n counts = self.coef_counter.loc[1, :].values\\n \\n if include_second : \\n cutoff = self.cutoff\\n cutoff_percentile = self.cutoff_percentile\\n else :\\n cutoff = np.percentile(counts, cutoff_percentile)\\n \\n n_loops = self.coef_values.shape[0]\\n \\n fig = plt.figure(dpi=300, figsize=(15,10))\\n \\n # Feature occurrence hist ----------\\n ax1 = plt.subplot(231)\\n h=plt.hist(counts)\\n ax1.axvline(cutoff, c=\\\"r\\\", label=\\\"cutoff for second lasso to see\\\")\\n plt.xlabel(\\\"Number of times a feature was used\\\")\\n plt.ylabel(\\\"Count for frequency\\\")\\n plt.legend()\\n \\n # Feature occurrence barplot ----------\\n bar_colors = np.array([\\\"C0\\\"]*self.coef_counter.shape[1])\\n if include_second :\\n # Translate lasso_model non-zero coef features back\\n # to full dimension of features that were used. \\n m1 = self.original_features_kept_mask \\n m2 = np.where(m1)[0][self.lasso_model.coef_!=0]\\n bar_colors[m2] = \\\"r\\\"\\n \\n ax2=plt.subplot(232)\\n self.coef_counter.loc[1, :].plot.bar(ax=ax2, color=bar_colors)\\n ax2.axhline(cutoff, c=\\\"r\\\")\\n plt.title(\\\"feature selected out out of %i\\\" %n_loops)\\n \\n # feature mean coef vs. feature occurrence scatter ----------\\n ax3=plt.subplot(233)\\n mean_coef_value = np.abs(self.coef_values.mean() )\\n used_count = self.coef_counter.loc[1, :] \\n plt.scatter( mean_coef_value, used_count, c = bar_colors)\\n plt.xlabel(\\\"mean coef outside loop\\\")\\n plt.ylabel(\\\"count used outside loop\\\")\\n plt.title(\\\"feature mean coef vs times used\\\")\\n plt.axhline(cutoff, c=\\\"r\\\")\\n \\n legend_elements = [Line2D([0], [0], marker='o', color='w', \\n label='original feature', markerfacecolor='C0', markersize=15),\\n Line2D([0], [0], marker='o', color='w', \\n label='selected by second lasso', markerfacecolor='r', markersize=15)\\n ]\\n \\n ax3.legend(handles=legend_elements, loc='best')\\n \\n # alpha distribution ----------\\n ax4=plt.subplot(234)\\n h2=plt.hist(self.outer_alphas)\\n if include_second :\\n plt.axvline(self.lasso_model.alpha_ , c=\\\"purple\\\", label=\\\"second lasso selected alpha\\\")\\n plt.legend()\\n plt.xlabel(\\\"LassoCV(leaveOneout).alpha_\\\")\\n plt.ylabel(\\\"occurrence over external loop\\\")\\n \\n plt.subplots_adjust(hspace=0.7, wspace=0.3)\\n \\n # heat map of the coeficients from external loop ----------\\n ax5 = plt.subplot(235)\\n with sns.plotting_context(\\\"poster\\\") :\\n \\n sns.heatmap(self.coef_values, center=0, cmap=\\\"bwr\\\", ax=ax5)\\n ax5.set(xlabel=\\\"\\\", ylabel=\\\"year left out\\\", title=\\\"Regression Coeficients\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d3da16edd26fb800772e1ce52664eb2\",\n \"score\": \"0.49197158\",\n \"text\": \"def plot_decision_funct(ax, dec_funct, index, feature=None):\\n\\n if isinstance(index, list):\\n decision_arr = create_onset_array(dec_funct) # categorical features (e.g. strings) as values in dec_funct\\n else:\\n decision_arr = create_onset_array(dec_funct, feature) # create onset for specific feature\\n\\n y1, y2 = ax.get_ylim()\\n for i in range(len(decision_arr)):\\n if decision_arr[i] is not None:\\n ax.vlines(decision_arr[i], y1, y2, colors='k', linestyles='dashed', label='Theoretical')\\n\\n if isinstance(index, list):\\n # categorical features: plot a step function\\n steps = np.arange(len(decision_arr))\\n if len(decision_arr) < len(index):\\n steps = [i for i in range(len(decision_arr)) for item in decision_arr[i]]\\n ax.step(np.arange(len(steps)), steps, where=\\\"mid\\\",\\n color='k', linestyle='dashed',\\n label=\\\"\\\")\\n else:\\n #if there are holes in the onset array/decision function: remove them\\n if np.any(decision_arr==None):\\n y_indices = np.array(sorted(dec_funct.keys()))[np.argwhere(decision_arr!=None)]\\n decision_arr = decision_arr[np.argwhere(decision_arr!=None)]\\n else:\\n y_indices = sorted(dec_funct.keys())\\n\\n #if onset array is ascending: achieve end values of each target value by shifting to the right\\n if decision_arr[0] < decision_arr[1]:\\n offset_arr = np.roll(decision_arr, -1)\\n offset_arr[-1] = ax.get_xlim()[1] # for last value use end: ax x-limit\\n\\n #if descending: shift to the left\\n else:\\n offset_arr = np.roll(decision_arr, 1)\\n offset_arr[0] = ax.get_xlim()[1]\\n\\n ax.hlines(y_indices, decision_arr, offset_arr, colors=\\\"k\\\", linestyles='dashed', label='')\\n\\n # get rid of duplicate legend values\\n handles, labels = ax.get_legend_handles_labels()\\n by_label = dict(zip(labels, handles))\\n ax.legend(by_label.values(), by_label.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"377d8fbea0aaca996c43094b63d0866d\",\n \"score\": \"0.49166554\",\n \"text\": \"def plot_feature_importance(self, title='target', path=None):\\r\\n\\r\\n if type(self.validation_output).__name__ == 'CrossValidationOutput':\\r\\n\\r\\n summary = self.validation_output.feature_importance_summary\\r\\n\\r\\n summary.sort_values(CrossValidationOutput.coef_abs, axis=0, ascending=False, inplace=True)\\r\\n summary.drop(CrossValidationOutput.coef_abs, axis=1, inplace=True)\\r\\n summary.reset_index(drop=True, inplace=True)\\r\\n summary.iloc[:10, :].plot(kind='bar', x=BaseValidationOutput.feature_name_col, y=CrossValidationOutput.coef_mean, yerr=CrossValidationOutput.coef_std)\\r\\n\\r\\n elif type(self.validation_output).__name__ == 'HoldoutValidationOutput':\\r\\n\\r\\n summary = self.validation_output.feature_importance_summary\\r\\n\\r\\n summary.sort_values(HoldoutValidationOutput.weight_col, axis=0, ascending=False, inplace=True)\\r\\n summary.iloc[:10, :].plot(kind='bar', x=BaseValidationOutput.feature_name_col, y=HoldoutValidationOutput.weight_col)\\r\\n\\r\\n plt.title('Top 10 features: {0}'.format(title))\\r\\n if path is not None:\\r\\n plt.savefig(path, bbox_inches = 'tight')\\r\\n plt.close()\\r\\n else:\\r\\n plt.show()\\r\\n \\r\\n return summary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77fff25f8a5c877d244b9193cc88b685\",\n \"score\": \"0.4902319\",\n \"text\": \"def plot(self, robot, time=None):\\n \\n self.infer(robot, time=time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee269e097d579cb2a24ab667fa58037b\",\n \"score\": \"0.49002665\",\n \"text\": \"def plot_against_target(self, feature):\\n assert feature != self._target_variable\\n\\n if feature in self.numeric_features:\\n self._dataset[[feature, self._target_variable]].plot.scatter(x=feature,\\n y=self._target_variable,\\n alpha=0.1,\\n title='{0} vs. target (`{1}`)'.\\n format(feature,\\n self._target_variable))\\n else:\\n title = '{0} vs. target (`{1}`)'.format(feature, self.target_variable)\\n self._dataset[[feature, self._target_variable]].boxplot(by=feature)\\n plt.ylabel(self.target_variable)\\n plt.title(title)\\n plt.suptitle(\\\"\\\")\\n plt.tight_layout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e32280d947805be8c6bc0575c98e5d4d\",\n \"score\": \"0.48997205\",\n \"text\": \"def chaperoneTime(self):\\n\\n fig = plt.figure(figsize=(8, 6))\\n ax = fig.add_subplot(111, axisbg='#FFFFCC')\\n\\n x = self.data[\\\"time\\\"]\\n y = self.data[\\\"forceX\\\"]\\n ax.plot(x, y, '-')\\n\\n #def close(event):\\n # plt.close('all')\\n\\n #axClose = plt.axes([0.4, 0.92, 0.2, 0.05])\\n #closeButton = Button(axClose, 'Ok, close!')\\n #closeButton.on_clicked(close)\\n\\n def on_click(event):\\n # get the x and y coords, flip y from top to bottom\\n x, y = event.x, event.y\\n if event.button == 1:\\n if event.inaxes is not None:\\n print('data coords %f %f' % (event.xdata, event.ydata))\\n self.chaperoneIndex = event.xdata\\n\\n plt.connect('button_press_event', on_click)\\n\\n # set useblit True on gtkagg for enhanced performance\\n cursor = Cursor(ax, useblit=True, color='red', horizOn=False, linewidth=2)\\n\\n plt.show()\\n self.data[\\\"chaperone\\\"] = False\\n self.data.loc[self.chaperoneIndex:, \\\"chaperone\\\"] = True\\n print(self.chaperoneIndex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67efdc81c138c5d415ab2fae033f4579\",\n \"score\": \"0.48972932\",\n \"text\": \"def plot_numerical(feature):\\n fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(16, 18))\\n sns.kdeplot(train[feature], ax=axes[0][0], label='Train')\\n sns.kdeplot(test[feature], ax=axes[0][0], label='Test')\\n\\n sns.kdeplot(train[train['isFraud'] == 0][feature], ax=axes[0][1], label='isFraud: 0')\\n sns.kdeplot(train[train['isFraud'] == 1][feature], ax=axes[0][1], label='isFraud: 1')\\n\\n test[feature].index += len(train)\\n axes[1][0].plot(train[feature], '.', label='Train')\\n axes[1][0].plot(test[feature], '.', label='Test')\\n axes[1][0].set_xlabel('row index')\\n axes[1][0].legend()\\n test[feature].index -= len(train)\\n\\n axes[1][1].plot(train[train['isFraud'] == 0][feature], '.', label='isFraud 0')\\n axes[1][1].plot(train[train['isFraud'] == 1][feature], '.', label='isFraud 1')\\n axes[1][1].set_xlabel('row index')\\n axes[1][1].legend()\\n\\n pd.DataFrame({'train': [train[feature].isnull().sum()], 'test': [test[feature].isnull().sum()]}).plot(\\n kind='bar', rot=0, ax=axes[2][0]\\n )\\n pd.DataFrame({'isFraud 0': [train[(train['isFraud'] == 0) & (train[feature].isnull())][feature].shape[0]],\\n 'isFraud 1': [train[(train['isFraud'] == 1) & (train[feature].isnull())][feature].shape[0]]})\\\\\\n .plot(kind='bar', rot=0, ax=axes[2][1])\\n\\n fig.suptitle(feature, fontsize=18)\\n axes[0][0].set_title('Train/Test KDE distribution')\\n axes[0][1].set_title('Target value KDE distribution')\\n axes[1][0].set_title('Index versus value: Train/Test distribution')\\n axes[1][1].set_title('Index versus value: Target distribution')\\n axes[2][0].set_title('Number of NaNs');\\n axes[2][1].set_title('Target value distribution among NaN values');\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c457882640c7d05ef879241f85961573\",\n \"score\": \"0.48962355\",\n \"text\": \"def temporal_plot_features(ax):\\n\\n ax.legend(loc=2)\\n ax.set_xlabel('year')\\n ax.set_ylabel('interhemispheric ocean\\\\ntemperature difference (K)')\\n ax.tick_params(top='off') \\n ax.text(0.92, 0.08, '(a)', transform=ax.transAxes, fontsize=24, va='top')\\n ax.axhline(y=0, color='0.5', linestyle='--', linewidth=0.5)\\n ylim = ax.get_ylim()\\n\\n return ylim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9341a377d149468e1f861142b2e670f5\",\n \"score\": \"0.48907804\",\n \"text\": \"def draw(self, tf=1, ax=None, color=[0.0, 0.5, 1.0], plot=True):\\n # First we supersample the whole curve such that there are not jumps\\n # higher than a particular value\\n supersampl_t, supersampl_x = misc.supersample(self, max_jump=0.01)\\n # Drop all time samples after tf\\n value_at_tf = self.eval(tf)\\n index_tf = np.argmax(supersampl_t >= tf)\\n supersampl_x = supersampl_x[:index_tf]\\n supersampl_t = supersampl_t[:index_tf]\\n supersampl_t = np.append(supersampl_t, tf)\\n supersampl_x.append(value_at_tf.reshape(-1))\\n # Reduce the set of points and times to segments and times, restricted\\n # to the periodic domain.\\n segments = [[supersampl_x[j], supersampl_x[j+1]]\\n for j in range(len(supersampl_x)-1)]\\n # Use the LineCollection class to print using segments and to assign\\n # transparency or colors to each segment\\n line_segments = LineCollection(segments)\\n # set color\\n lowest_alpha = 0.2\\n color = np.array(color)\\n rgb_color = np.ones((len(segments), 4))\\n rgb_color[:, 0:3] = color\\n rgb_color[:, 3] = np.linspace(lowest_alpha, 1, len(segments))\\n line_segments.set_color(rgb_color)\\n start_color = np.zeros((1, 4))\\n start_color[:, 0:3] = color\\n if len(segments) <= 1:\\n start_color[:, 3] = 1\\n else:\\n start_color[:, 3] = lowest_alpha\\n # plotting\\n if plot:\\n ax = ax or plt.gca()\\n ax.add_collection(line_segments)\\n ax.scatter(self.spatial_points[0, 0], self.spatial_points[0, 1],\\n c=start_color, marker='x', s=0.4)\\n ax.set_xlim(0, 1)\\n ax.set_ylim(0, 1)\\n return ax, (segments, rgb_color)\\n return None, (segments, rgb_color)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b460b0c029f650bf3636ce731abd566e\",\n \"score\": \"0.4890094\",\n \"text\": \"def plot_input(u,close_i, time_scale=1):\\n n_points = np.shape(u)[0]\\n plt.figure(figsize=(10, 2))\\n fig = plt.gcf()\\n ax = plt.gca()\\n t = np.linspace(0, n_points * 0.1, n_points)\\n\\n plt.plot(t[:close_i], u[:close_i])\\n\\n plt.xlabel(r'$timesteps$')\\n plt.ylabel(r'$u_t$')\\n plt.title('Control Input until Circling');\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2518804e1afee137a1d08bb3f39a9636\",\n \"score\": \"0.4885796\",\n \"text\": \"def plot_decision_boundary(\\n pred_func: Any, X: np.ndarray, y: np.ndarray, figure: Any = None\\n) -> None:\\n\\n if figure is None: # If no figure is given, create a new one\\n plt.figure()\\n # Set min and max values and give it some padding\\n x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\\n y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\\n h = 0.01\\n # Generate a grid of points with distance h between them\\n xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\\n # Predict the function value for the whole grid\\n Z = pred_func(np.c_[xx.ravel(), yy.ravel()])\\n Z = Z.reshape(xx.shape)\\n # Plot the contour and training examples\\n plt.contourf(xx, yy, Z, cmap=plt.cm.get_cmap(\\\"Spectral\\\"))\\n cm_bright = ListedColormap([\\\"#FF0000\\\", \\\"#0000FF\\\"])\\n plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cm_bright)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b71ee3dbdbbe152f65987f6aa02e0c86\",\n \"score\": \"0.48842716\",\n \"text\": \"def timestep():\\n return context().reporter.timestep()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39cac17709c88646ba037330fdd6b1d3\",\n \"score\": \"0.48751062\",\n \"text\": \"def plot_est_traveltime(self, unit):\\n\\n fig = plt.figure(figsize=(10, 10), dpi=100)\\n ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\\n ax.plot(self.time_grid[1:], self.est_traveltime, linewidth=1)\\n plt.title('Estimated queue length')\\n plt.xlabel('Time s')\\n plt.ylabel('Length of queue (s)')\\n\\n plt.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a661521346833c4381d5e59f09b44ef\",\n \"score\": \"0.48733562\",\n \"text\": \"def doAfterTrial(self):\\r\\n #First need to wait for the full target display period to complete\\r\\n while(self.getTime() < self.targetDisplayDuration):\\r\\n self.wait(.010)\\r\\n\\r\\n\\r\\n #clear target and wait 500 milliseconds\\r\\n self.drawBackground(\\\"Black\\\")\\r\\n if(self.responseTrial == True):\\r\\n self.yesStimuli.draw()\\r\\n self.noStimuli.draw()\\r\\n self.task.refreshWindow()\\r\\n self.wait(self.standardDelay)\\r\\n\\r\\n #display feedback if this is a response trial\\r\\n if(self.responseTrial==True):\\r\\n self.drawBackground(\\\"Black\\\")\\r\\n if(self.lastTrialCorrect == True):\\r\\n self.correctStimuli.draw()\\r\\n elif(self.lastTrialCorrect == False):\\r\\n self.incorrectStimuli.draw()\\r\\n\\r\\n if(self.responseTrial == True):\\r\\n self.yesStimuli.draw()\\r\\n self.noStimuli.draw()\\r\\n\\r\\n self.task.refreshWindow()\\r\\n self.wait(self.displayFeedbackDuration)\\r\\n\\r\\n #clear feedback and wait 500 milliseconds\\r\\n self.drawBackground(\\\"Black\\\")\\r\\n if(self.responseTrial == True):\\r\\n self.yesStimuli.draw()\\r\\n self.noStimuli.draw()\\r\\n self.task.refreshWindow()\\r\\n self.wait(self.standardDelay)\\r\\n\\r\\n # display fixation number\\r\\n if(self.currentTrial.fixation_number != None):\\r\\n self.drawBackground(\\\"Black\\\")\\r\\n self.fixation.draw()\\r\\n if(self.responseTrial == True):\\r\\n self.yesStimuli.draw()\\r\\n self.noStimuli.draw()\\r\\n if(self.currentTrial.trial_number <= 3):\\r\\n self.demoFixationPrompt.draw()\\r\\n self.task.refreshWindow()\\r\\n self.wait(self.demoFixationDisplayDuration)\\r\\n\\r\\n #now clear fixation number\\r\\n self.drawBackground(\\\"Black\\\")\\r\\n if(self.responseTrial == True):\\r\\n self.yesStimuli.draw()\\r\\n self.noStimuli.draw()\\r\\n self.task.refreshWindow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6233cabe73b1bd422096dbc732dd0d0\",\n \"score\": \"0.48646662\",\n \"text\": \"def doAfterTrial(self):\\r\\n #First need to wait for the full target display period to complete\\r\\n while(self.getTime() < self.targetDisplayDuration):\\r\\n self.wait(.010)\\r\\n\\r\\n\\r\\n #clear target and wait 500 milliseconds\\r\\n self.drawBackground(\\\"Black\\\")\\r\\n if(self.responseTrial == True):\\r\\n self.yesStimuli.draw()\\r\\n self.noStimuli.draw()\\r\\n self.task.refreshWindow()\\r\\n self.wait(self.standardDelay)\\r\\n\\r\\n #display feedback if this is a response trial\\r\\n if(self.responseTrial==True):\\r\\n self.drawBackground(\\\"Black\\\")\\r\\n if(self.lastTrialCorrect == True):\\r\\n self.correctStimuli.draw()\\r\\n elif(self.lastTrialCorrect == False):\\r\\n self.incorrectStimuli.draw()\\r\\n\\r\\n if(self.responseTrial == True):\\r\\n self.yesStimuli.draw()\\r\\n self.noStimuli.draw()\\r\\n\\r\\n self.task.refreshWindow()\\r\\n self.wait(self.displayFeedbackDuration)\\r\\n\\r\\n #clear feedback\\r\\n self.drawBackground(\\\"Black\\\")\\r\\n if(self.responseTrial == True):\\r\\n self.yesStimuli.draw()\\r\\n self.noStimuli.draw()\\r\\n self.task.refreshWindow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"404892a2fba0b050f272fa703dc706ae\",\n \"score\": \"0.48638797\",\n \"text\": \"def plot_act(fo, leg=[], save=False, sfile=\\\"\\\"):\\n\\n plt.plot(fo.sumdat[0], fo.sumdat[1])\\n plt.yscale('log')\\n \\n if len(leg)>1:\\n plt.legend(leg)\\n plt.xlabel(\\\"Time (years)\\\")\\n plt.ylabel(\\\"Activity (Bq)\\\")\\n \\n if save == False:\\n plt.show()\\n else:\\n plt.savefig(sfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e021ce50c424474f1cfa16b7fc3170\",\n \"score\": \"0.48590252\",\n \"text\": \"def plot_entity(df, threshold=None, entity= \\\"\\\", savepath='./AiyuDocs/new_signalplots/'):\\n accs = np.asarray(np.vstack([val for val in df.accelerations]))\\n ybot, ytop = np.amax(accs), np.amin(accs)\\n\\n feature_names = df.columns\\n display_params = feature_names[3:-1] # Skip acc, timestamp, window length\\n for entityname, row in df[:2].iterrows():\\n fig = plt.figure(figsize=(20,5), constrained_layout=True)\\n\\n x = (row['timestamps'] - row['timestamps'][0])/10**9\\n y = row['accelerations']\\n\\n\\n ax1 = fig.add_axes((0.1, 0.2, 1, 1))\\n ax1.plot(x, y, zorder = 1)\\n if entity == 'defect':\\n ax1.set_title(\\\"Defect: \\\" + entityname)\\n else:\\n ax1.set_title(i)\\n\\n ax1.set_xlabel(\\\"Seconds\\\")\\n ax1.set_ylabel(\\\"Amplitude\\\")\\n ax1.set_ylim(ybot,ytop)\\n ax1.margins(x=0, y=0.1)\\n\\n caption = ', '.join([ f\\\"|{feature}|: {df.loc[i, feature]}\\\" for feature in display_params])\\n fig.text(0.1, .0005, caption, ha='left', fontsize=12)\\n fig.set_size_inches(20, 5, forward=True)\\n fig.savefig(savepath + entityname, bbox_inches='tight')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8feb41ba37230f516baf20204bf8bc2c\",\n \"score\": \"0.48534796\",\n \"text\": \"def convergence_plot(scores):\\n\\n plt.figure(figsize=(10, 6))\\n\\n sns.set(style=\\\"whitegrid\\\")\\n plt.rc('font', size=13) # controls default text sizes\\n plt.rc('axes', titlesize=13) # fontsize of the axes title\\n plt.rc('xtick', labelsize=13) # fontsize of the tick labels\\n plt.rc('ytick', labelsize=13) # fontsize of the tick labels\\n plt.rc('legend', fontsize=13)\\n wg = pd.DataFrame(scores, columns=[\\\"score\\\"])\\n ax = sns.lineplot(data=wg, palette=\\\"tab10\\\", linewidth=2.5)\\n ax.set(xlabel=\\\"Iterations\\\")\\n ax.set(ylabel=\\\"Score\\\")\\n plt.show()\\n\\n # def plot_survival(col, clinical, labels=None, ci_show=True, show_img=True):\\n # values = list(set(clinical[col]))\\n # if labels == None:\\n # labels = values\\n # labels = [str(x) for x in values]\\n # kmfs = []\\n # T = []\\n # labels_true = []\\n # C = []\\n # count = 0\\n # for val in values:\\n # v = str(val)\\n # km = KaplanMeierFitter()\\n # f = clinical[col] == val\\n # t = clinical[f]['surv']\\n # if t.shape[0] > 0:\\n # print(\\\"{0} group {1} sampels\\\".format(labels[count], t.shape[0]))\\n #\\n # c = clinical[f]['event']\\n # kmfs.append(km)\\n # T.append(t)\\n # C.append(c)\\n # labels_true.append(labels[count])\\n # count = count + 1\\n # plt.figure(figsize=(12, 8))\\n # ax = plt.subplot(111)\\n # for i in range(len(C)):\\n # kmf = kmfs[i]\\n # t = T[i]\\n # c = C[i]\\n # label = labels[i]\\n # kmf.fit(t, event_observed=c, label=labels_true[i])\\n # if show_img:\\n # kmf.plot(ci_show=ci_show, ax=ax)\\n # if show_img:\\n # plt.show()\\n # p_vals = []\\n # for i in range(len(C)):\\n # for j in range(i + 1, len(C)):\\n # summary = logrank_test(T[i], T[j], C[i], C[j], alpha=99)\\n # v = round(-np.log10(summary.p_value), 2)\\n # print(\\\"- log10 p-value between {0} and {1} is {2}\\\".format(labels[i], labels[j], v))\\n # p_vals.append(v)\\n # print(\\\"average -log10 p-value is {0}\\\".format(np.round(np.mean(p_vals), 2)))\\n # return (np.round(np.mean(p_vals), 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15e9279b58a5c49c0fb1306e159a9076\",\n \"score\": \"0.4844326\",\n \"text\": \"def tick(self, process_variable, dt):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cd0e1ede118266cf47680465b08bfb0\",\n \"score\": \"0.48403832\",\n \"text\": \"def main():\\n df = read_time_data('sqlite:///../data/isrid-master.db')\\n hours = df.hour.as_matrix()\\n mu, kappa = estimate_distribution(hours)\\n samples = radians_to_hours(np.random.vonmises(mu, kappa, pow(10, 6)))\\n\\n setup_environment()\\n plot_histogram(hours, 'Empirical Data (N = {})'.format(len(hours)))\\n plot_histogram(samples, '$10^6$ Samples from Fitted Distribution')\\n plot_pdf(mu, kappa)\\n\\n plt.legend()\\n plt.savefig('../doc/figures/incident-time-distribution/'\\n 'incident-time-distribution.svg', transparent=True)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"479521daf4c8a02be51f4980e07e7b76\",\n \"score\": \"0.4838713\",\n \"text\": \"def plot_time(i,j):\\n fig = plt.figure()\\n ax = fig.add_subplot(111,projection=\\\"3d\\\")\\n\\n lengths = [ 5579133, 4539804, 2452883 ]\\n\\n ch1,ch2,ch3 = split_chromosomes(load_datasingle(i,j))\\n chs_position = [ch1,ch2,ch3]\\n chs_timings = get_timings()\\n chs_remap = normalise2([ map_scales2(np.arange(150,len(Y)*300,300),Y,np.linspace(0,l,len(pos)),np.mean) for Y,l,pos in zip(chs_timings,lengths,chs_position) ])\\n \\n chs_cm = [ cm.hot(a) for a in chs_remap ]\\n\\n for pos,col in zip(chs_position,chs_cm):\\n ax.scatter(pos[\\\"x\\\"],pos[\\\"y\\\"],pos[\\\"z\\\"],marker=\\\".\\\",c=col)\\n\\n plt.colorbar(cm.ScalarMappable(cmap=cm.hot))\\n\\n plt.show(block=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c91bc8378a413461880d1f924450702\",\n \"score\": \"0.48362234\",\n \"text\": \"def univariate_plotter(feature, data, target_col, bins=10, data_test=0):\\n print(' {:^100} '.format('Plots for ' + feature))\\n if data[feature].dtype == 'O':\\n print('Categorical feature not supported')\\n else:\\n cuts, grouped = get_grouped_data(input_data=data, feature=feature, target_col=target_col, bins=bins)\\n has_test = type(data_test) == pd.core.frame.DataFrame\\n if has_test:\\n grouped_test = get_grouped_data(input_data=data_test.reset_index(drop=True), feature=feature,\\n target_col=target_col, bins=bins, cuts=cuts)\\n trend_corr = get_trend_correlation(grouped, grouped_test, feature, target_col)\\n print(' {:^100} '.format('Train data plots'))\\n\\n draw_plots(input_data=grouped, feature=feature, target_col=target_col)\\n print(' {:^100} '.format('Test data plots'))\\n\\n draw_plots(input_data=grouped_test, feature=feature, target_col=target_col, trend_correlation=trend_corr)\\n else:\\n draw_plots(input_data=grouped, feature=feature, target_col=target_col)\\n print(\\n '--------------------------------------------------------------------------------------------------------------')\\n print('\\\\n')\\n if has_test:\\n return (grouped, grouped_test)\\n else:\\n return (grouped)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00923e1a1cf5b59aff870ecad4372a5e\",\n \"score\": \"0.4831807\",\n \"text\": \"def ctlike_speed_up(my_plotter, outpref, log_exists=False):\\n if log_exists:\\n aux = np.array([])\\n cores = [i + 1 for i in range(my_plotter.ncsv)]\\n times = pd.Series(index=cores)\\n amd = pd.Series(index=cores)\\n parallel_loop = pd.Series(index=cores)\\n for i in xrange(my_plotter.ncsv):\\n df = pd.read_csv(\\n my_plotter.infile + \\\"_CPUs_\\\" + str(i + 1) + \\\".csv\\\")\\n aux = select_lines(\\n 'ctlike_CPUs_' + str(i + 1) + '.csv',\\n df.at[0, 'TIME'],\\n ['gammaspeed:real_parallel_start',\\n 'gammaspeed:real_parallel_end'])\\n parallel_loop[i + 1] = aux[1] - aux[0]\\n\\n paralleltime = parallel_loop[1]\\n\\n for i in xrange(my_plotter.ncsv):\\n df = my_plotter.read_monitor_log(i)\\n # time spent for the whole process\\n times[i + 1] = df['TIME'].iget(-1) - df['TIME'].iget(0)\\n amd[i + 1] = times[1] / \\\\\\n (times[1] - paralleltime + paralleltime / (i + 1))\\n\\n plt.figure(21)\\n my_plotter.splot(\\n figtitle='Overall speed up and efficiency for ctlike',\\n outfile=\\\"plots/\\\" + outpref + '_general',\\n speed_frame=times,\\n amdahl_frame=amd)\\n\\n plt.figure(20)\\n my_plotter.splot(\\n figtitle='Parallel speed up and efficiency for ctlike',\\n outfile=\\\"plots/\\\" + outpref + '_parallel',\\n speed_frame=parallel_loop,\\n amdahl_frame=pd.Series(data=cores, index=cores))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6bf8e6585e448d15b569a4083f6577e\",\n \"score\": \"0.4827437\",\n \"text\": \"def plot(self, variable=None, ax=None, show_members=False, cmap=None, x=\\\"time\\\"):\\n if x == \\\"time\\\":\\n x = \\\"valid_time\\\"\\n assert x in [\\\"valid_time\\\", \\\"init\\\"]\\n if self.kind == \\\"hindcast\\\":\\n if cmap is None:\\n cmap = \\\"viridis\\\"\\n return plot_lead_timeseries_hindcast(\\n self,\\n variable=variable,\\n ax=ax,\\n show_members=show_members,\\n cmap=cmap,\\n x=x,\\n )\\n elif self.kind == \\\"perfect\\\":\\n if cmap is None:\\n cmap = \\\"tab10\\\"\\n return plot_ensemble_perfect_model(\\n self, variable=variable, ax=ax, show_members=show_members, cmap=cmap\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0866f82bca8ad8ae2f6ccc41c1c749c\",\n \"score\": \"0.48224968\",\n \"text\": \"def subPlotStat(func, porf, time):\\n\\t# generate positions\\n\\tx, y = np.linspace(-15, 15, 600), np.linspace(-15, 15, 600)\\n\\tX, Y = np.meshgrid(x, y)\\n\\tZ = np.zeros((600, 600))\\n\\n\\t# calculate potential/force\\n\\tif porf == \\\"potential\\\":\\n\\t\\tif func == haloPot:\\n\\t\\t\\tT = func(X, Y, Z, time, 0.4, 1)[0]\\n\\t\\telif func == cgpot:\\n\\t\\t\\tT = func(X, Y, Z, time)\\n\\t\\telse:\\n\\t\\t\\tT = func(X, Y, Z, time)[0]\\n\\telif porf == \\\"force\\\":\\n\\t\\tif func == haloPot:\\n\\t\\t\\tT = func(X, Y, Z, time, 0.5, 3)[1]\\n\\t\\telse:\\n\\t\\t\\tT = func(X, Y, Z, time)[1]\\n\\n\\treturn X, Y, T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41bdab6e90d365e038deb710ac062bc9\",\n \"score\": \"0.48182845\",\n \"text\": \"def helpStackPlot(dataframe, numTimeStep):\\n # get info\\n foodPerPreyList = dataframe[\\\"foodPerPrey\\\"]\\n preyCountOverTimeList = dataframe[\\\"preyCountOverTime\\\"]\\n preyPerPredList = dataframe[\\\"preyPerPred\\\"]\\n\\n\\n # hold all alive y-arrays for this perception type, same for eaten and starved.\\n aliveOverTimeLists = []\\n eatenOverTimeLists = []\\n starvedOverTimeLists = []\\n\\n # loop through runs\\n for run in dataframe.index:\\n # get info\\n # 3 y-arrays: alive, starved, eaten.\\n aliveOverTimeList, starvedOverTimeList = getNewPreyCountOverTimeList(foodPerPreyList[run], preyCountOverTimeList[run], preyPerPredList[run], numTimeStep)\\n eatenOverTimeList = [aliveOverTimeList[0] - starvedOverTimeList[index] - aliveOverTimeList[index] for index in range(len(aliveOverTimeList))] # calculate number eaten.\\n aliveOverTimeLists.append(aliveOverTimeList)\\n eatenOverTimeLists.append(eatenOverTimeList)\\n starvedOverTimeLists.append(starvedOverTimeList)\\n\\n # make all list lengths equal\\n aliveOverTimeLists = equalizeListLengths(aliveOverTimeLists, \\\"alive\\\")\\n eatenOverTimeLists = equalizeListLengths(eatenOverTimeLists, \\\"eaten\\\")\\n starvedOverTimeLists = equalizeListLengths(starvedOverTimeLists, \\\"starved\\\")\\n\\n meanAliveOverTime = np.mean(np.array(aliveOverTimeLists), axis=0).tolist() # axis = 0 does elementwise means\\n meanEatenOverTime = np.mean(np.array(eatenOverTimeLists), axis=0).tolist() \\n meanStarvedOverTime = np.mean(np.array(starvedOverTimeLists), axis=0).tolist() \\n return meanAliveOverTime, meanEatenOverTime, meanStarvedOverTime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de0d08bb8d722be14ff4043b034b7a12\",\n \"score\": \"0.48132458\",\n \"text\": \"def plot_np_perf():\\n n = 10000\\n i = 0\\n max_generations = 12\\n time_data = []\\n generations = []\\n\\n while i <= max_generations:\\n time_data.append(timeit.timeit(\\n 'run(0.6, {}, 0.1, 1, False)'.format(i),\\n number=n, setup='from tree_np import run'))\\n generations.append(i)\\n i += 1\\n\\n plt.plot(generations, time_data, '-rx', )\\n plt.ylabel('Time taken for {} iterations (sec)'.format(n))\\n plt.xlabel('Number of generations of branches')\\n plt.legend\\n plt.savefig('perf_comparison.png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c148ed3e43749dbfc62690928a2dd31\",\n \"score\": \"0.480294\",\n \"text\": \"def plot_fitness(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7de84a580df821ad6dc13090f2610da2\",\n \"score\": \"0.47895166\",\n \"text\": \"def show_convergence_profile(self) -> None:\\n y_es = self.get_convergence_profile()\\n x_es = [i for i in range(len(y_es))]\\n plt.title(self.name + ' method convergence profile')\\n plt.ylabel('Convergence (residual norm)')\\n plt.xlabel('Iterations')\\n plt.plot(x_es, y_es, 'b--')\\n plt.legend(['Total iter = ' + str(self.finished_iter)])\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e26a71fced9307a0c2421cc24f5c877\",\n \"score\": \"0.4788722\",\n \"text\": \"def startPlot():\\r\\n lbl_progress.configure(text = \\\"Progress: 0 %\\\") # reset progres\\r\\n #making sure that the delay paths are different\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4155c1bc6fe514bbb91cd60ae4c95ac9\",\n \"score\": \"0.47830462\",\n \"text\": \"def plot_time(axis, df, **kwargs):\\n time_range = kwargs.pop('time_range', (0, 400))\\n xlabel = kwargs.pop(\\\"xlabel\\\", \\\"time (ps)\\\")\\n ylabel = kwargs.pop(\\\"ylabel\\\", \\\"Energy Density\\\")\\n dt = kwargs.pop('dt', 1)\\n verbose = kwargs.pop('verbose', 0)\\n bins = ptp(time_range) / dt\\n\\n if verbose > 0:\\n energy_in_plot = df[(df.time > min(time_range)) & (df.time < max(time_range))].energy.sum()\\n print(\\\"Total energy in plot is {:2.1f} %\\\".format(energy_in_plot * 1e2))\\n #print(\\\"Energy in plot is :\\\",sum(df.energy)*1e2,\\\"%\\\")\\n\\n axis.hist(array(df.time), range=time_range, bins=bins,\\n weights=array(df.energy), **kwargs)\\n\\n axis.set_xlim(time_range)\\n axis.set_xlabel(xlabel)\\n axis.set_ylabel(ylabel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bb176a44fd384c94a3e51447922bf71\",\n \"score\": \"0.47813454\",\n \"text\": \"def show_logistic_performance(results, degrees, gammas, jet):\\n\\n print('salut')\\n acc_means = []\\n for i in range(1,len(degrees)):\\n acc_means_by_degree = []\\n for j in range(1, len(gammas)):\\n acc_means_by_degree.extend([x[4] for x in results if (x[0]==degrees[i] and x[2]==gammas[j])])\\n acc_means.append(acc_means_by_degree)\\n\\n gammas = [round(x,5) for x in gammas]\\n ax = sns.heatmap(acc_means, xticklabels=gammas, yticklabels=degrees, cmap='cubehelix_r', vmin=0.47, vmax=0.82)\\n ax.invert_yaxis()\\n ax.set_xlabel('gamma', labelpad=20)\\n ax.set_ylabel('degree', labelpad=20)\\n ax.set_title('Accuracy of data set where $PRI\\\\_jet\\\\_num$=%d using Logistic Regression\\\\n\\\\n' % jet)\\n plt.savefig('./report/figures/%d_logistic_regression.pdf' % jet, bbox_inches='tight')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b86be0fd95ce8f063829c04020e31e1\",\n \"score\": \"0.47779018\",\n \"text\": \"def plot_so_gait_info(so_results_dir, gl=None):\\n for fname in os.listdir(so_results_dir):\\n if fname.endswith('_force.sto'):\\n fpath = os.path.join(so_results_dir, fname)\\n break\\n\\n def plot_thresholds(val):\\n pl.axhline(val, c=[0.7, 0.7, 0.7])\\n pl.axhline(-val, c=[0.7, 0.7, 0.7])\\n\\n def plot(time, y, side='right', *args, **kwargs):\\n if gl != None:\\n if side == 'r': side = 'right'\\n elif side == 'l': side = 'left'\\n plot_pgc(time, y, gl, side=side, plot_toeoff=True, *args, **kwargs)\\n\\n else:\\n pl.plot(time, y, *args, **kwargs)\\n\\n legend_kwargs = {'loc': 'best', 'prop': {'size': 12}}\\n\\n actu = dataman.storage2numpy(fpath, 2)\\n\\n fig = pl.figure(figsize=(10, 12))\\n grid_size = (3, 2)\\n pl.subplot2grid(grid_size, (0, 0))\\n pl.title('residual forces')\\n for coln in ['FX', 'FY', 'FZ']:\\n plot(actu['time'], actu[coln], label=coln)\\n pl.axhline(0, c='k')\\n pl.ylabel('force (N)')\\n pl.legend(**legend_kwargs)\\n pl.ylim((-40, 40))\\n pl.xlim(0, 100)\\n pl.grid(axis='y')\\n plot_thresholds(10)\\n plot_thresholds(25)\\n\\n pl.subplot2grid(grid_size, (0, 1))\\n pl.title('residual moments')\\n for coln in ['MX', 'MY', 'MZ']:\\n plot(actu['time'], actu[coln], label=coln)\\n pl.axhline(0, c='k')\\n pl.ylabel('torque (N-m)')\\n pl.legend(**legend_kwargs)\\n pl.ylim((-40, 40))\\n pl.xlim(0, 100)\\n pl.grid(axis='y')\\n plot_thresholds(30)\\n\\n pl.subplot2grid(grid_size, (1, 0))\\n pl.title('left lower limb reserves')\\n for coln in ['hip_flexion_l', 'knee_angle_l', 'ankle_angle_l']:\\n plot(actu['time'], actu['reserve_%s' % coln], side=coln[-1],\\n label=coln.split('_')[0])\\n pl.axhline(0, c='k')\\n pl.ylabel('torque (N-m)')\\n pl.legend(**legend_kwargs)\\n pl.xlim(0, 100)\\n pl.ylim((-25, 25))\\n pl.grid(axis='y')\\n plot_thresholds(10)\\n\\n pl.subplot2grid(grid_size, (1, 1))\\n pl.title('right lower limb reserves')\\n for coln in ['hip_flexion_r', 'knee_angle_r', 'ankle_angle_r']:\\n plot(actu['time'], actu['reserve_%s' % coln], side=coln[-1],\\n label=coln.split('_')[0])\\n pl.axhline(0, c='k')\\n pl.ylabel('torque (N-m)')\\n pl.legend(**legend_kwargs)\\n pl.xlim(0, 100)\\n pl.ylim((-25, 25))\\n pl.grid(axis='y')\\n plot_thresholds(10)\\n\\n pl.subplot2grid(grid_size, (2, 0))\\n pl.title('lumbar rotations reserves')\\n for coln in ['lumbar_bending', 'lumbar_extension', 'lumbar_rotation']:\\n actu_name = 'reserve_%s' % coln\\n if actu_name in actu.dtype.names:\\n plot(actu['time'], actu[actu_name],\\n label=coln.split('_')[-1])\\n else:\\n # Apoorva's model does not have lumbar muscles.\\n pl.text(0, 0, 'no lumbar reserves', ha='left', va='bottom')\\n pl.axhline(0, c='k')\\n pl.ylabel('moment (N-m)')\\n pl.xlabel('time (s)')\\n pl.legend(**legend_kwargs)\\n pl.xlim(0, 100)\\n pl.ylim((-25, 25))\\n pl.grid(axis='y')\\n plot_thresholds(10)\\n\\n pl.subplot2grid(grid_size, (2, 1))\\n pl.title('hip reserves')\\n labels = ['rot_r',' rot_l', 'add_r',' add_l']\\n for i, coln in enumerate(['hip_rotation_r', 'hip_rotation_l',\\n 'hip_adduction_r', 'hip_adduction_l']):\\n plot(actu['time'], actu['reserve_%s' % coln], side=coln[-1],\\n label=labels[i])\\n pl.axhline(0, c='k')\\n pl.ylabel('moment (N-m)')\\n pl.xlabel('time (s)')\\n pl.legend(**legend_kwargs)\\n pl.xlim(0, 100)\\n pl.ylim((-25, 25))\\n pl.grid(axis='y')\\n plot_thresholds(10)\\n\\n pl.tight_layout()\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95f3c75ba804fa3c89048c9ccb2c22d\",\n \"score\": \"0.47778177\",\n \"text\": \"def plotResults(recipe):\\n\\n r = recipe.bucky.profile.x\\n\\n # Plot this.\\n g = recipe.bucky.profile.y\\n gcalc = recipe.bucky.profile.ycalc\\n diffzero = -0.8 * max(g) * numpy.ones_like(g)\\n diff = g - gcalc + diffzero\\n\\n import pylab\\n pylab.plot(r,g,'ob',label=\\\"G(r) Data\\\")\\n pylab.plot(r,gcalc,'-r',label=\\\"G(r) Fit\\\")\\n pylab.plot(r,diff,'-g',label=\\\"G(r) diff\\\")\\n pylab.plot(r,diffzero,'-k')\\n pylab.xlabel(\\\"$r (\\\\AA)$\\\")\\n pylab.ylabel(\\\"$G (\\\\AA^{-2})$\\\")\\n pylab.legend(loc=1)\\n\\n pylab.show()\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993479,"cells":{"query_id":{"kind":"string","value":"acd20aee05a7edf794a69b3f3aa3bf3c"},"query":{"kind":"string","value":"decompose the vector field (u,v) into potential (up,vp) and solenoidal (us,vs) fields using 2D FT a la Smith JPO 2008"},"positive_passages":{"kind":"list like","value":[{"docid":"f7f32f5f034ff0911e688bdd88cc3cd3","score":"0.64122146","text":"def ps(u,v,dx,dy):\n\n ix,jx,kx = u.shape\n dl = 1./(ix*dy)\n dk = 1./(jx*dx)\n kNy = 1./(2*dx)\n lNy = 1./(2*dy)\n k = np.arange(-kNy,kNy,dk)\n k = np.fft.fftshift(k)\n l = np.arange(-lNy,lNy,dl)\n l = np.fft.fftshift(l)\n K,L = np.meshgrid(k,l)\n THETA = (np.arctan2(L,K))\n THETA = np.repeat(THETA,kx).reshape(ix,jx,kx)\n\n U = np.fft.fft2(u,axes=(0,1))\n V = np.fft.fft2(v,axes=(0,1))\n\n P = U*np.cos(THETA) + V*np.sin(THETA)\n S = -U*np.sin(THETA) + V*np.cos(THETA)\n\n # back to physical space\n up = np.real(np.fft.ifft2(P*np.cos(THETA),axes=(0,1)))\n vp = np.real(np.fft.ifft2(P*np.sin(THETA),axes=(0,1)))\n\n us = np.real(np.fft.ifft2(-S*np.sin(THETA),axes=(0,1)))\n vs = np.real(np.fft.ifft2(S*np.cos(THETA),axes=(0,1)))\n\n return up,vp,us,vs","title":""}],"string":"[\n {\n \"docid\": \"f7f32f5f034ff0911e688bdd88cc3cd3\",\n \"score\": \"0.64122146\",\n \"text\": \"def ps(u,v,dx,dy):\\n\\n ix,jx,kx = u.shape\\n dl = 1./(ix*dy)\\n dk = 1./(jx*dx)\\n kNy = 1./(2*dx)\\n lNy = 1./(2*dy)\\n k = np.arange(-kNy,kNy,dk)\\n k = np.fft.fftshift(k)\\n l = np.arange(-lNy,lNy,dl)\\n l = np.fft.fftshift(l)\\n K,L = np.meshgrid(k,l)\\n THETA = (np.arctan2(L,K))\\n THETA = np.repeat(THETA,kx).reshape(ix,jx,kx)\\n\\n U = np.fft.fft2(u,axes=(0,1))\\n V = np.fft.fft2(v,axes=(0,1))\\n\\n P = U*np.cos(THETA) + V*np.sin(THETA)\\n S = -U*np.sin(THETA) + V*np.cos(THETA)\\n\\n # back to physical space\\n up = np.real(np.fft.ifft2(P*np.cos(THETA),axes=(0,1)))\\n vp = np.real(np.fft.ifft2(P*np.sin(THETA),axes=(0,1)))\\n\\n us = np.real(np.fft.ifft2(-S*np.sin(THETA),axes=(0,1)))\\n vs = np.real(np.fft.ifft2(S*np.cos(THETA),axes=(0,1)))\\n\\n return up,vp,us,vs\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3e69e3b72302484e63106a940fe1501a","score":"0.68711233","text":"def vectorfield(w, t, p):\n x1, y1, x2, y2 = w\n t1, t2, m, l = p\n\n # Create f = (x1',y1',x2',y2'):\n f = [y1,\n ((2./3.)*((t1/(0.5*m*l**2)+(np.sin(x2)*y2*(2*y1+y2))))-(((2./3.)+(np.cos(x2))*((t2/0.5*m*l**2))-np.sin(x2)*y1**2)))/((16./9.)-(np.cos(x2))**2),\n y2,\n -(((2./3.)+np.cos(x2))*((t1/(0.5*m*l**2))+np.sin(x2)*y2*(2*y1+y2))+2*(((5./3.)+np.cos(x2))*(t2/0.5*m*l**2)-np.sin(x2)*y1**2))/((16./9.)-(np.cos(x2))**2)]\n return f","title":""},{"docid":"854db4ee6ef02459ebb7d7c482b70acc","score":"0.66949934","text":"def vectorfield(w, t, p):\n x1, y1, x2, y2 = w\n m1, m2, k1, k2, L1, L2, b1, b2 = p\n\n # Create f = (x1',y1',x2',y2'):\n f = [y1,\n (-b1 * y1 - k1 * (x1 - L1) + k2 * (x2 - x1 - L2)) / m1,\n y2,\n (-b2 * y2 - k2 * (x2 - x1 - L2)) / m2]\n return f","title":""},{"docid":"854db4ee6ef02459ebb7d7c482b70acc","score":"0.66949934","text":"def vectorfield(w, t, p):\n x1, y1, x2, y2 = w\n m1, m2, k1, k2, L1, L2, b1, b2 = p\n\n # Create f = (x1',y1',x2',y2'):\n f = [y1,\n (-b1 * y1 - k1 * (x1 - L1) + k2 * (x2 - x1 - L2)) / m1,\n y2,\n (-b2 * y2 - k2 * (x2 - x1 - L2)) / m2]\n return f","title":""},{"docid":"13aace7a408ad75e58d5de582fdd7bc1","score":"0.63444346","text":"def smith2007_decomposition(u, v, dx, dy):\n\n ny,nx = u.shape\n k = np.fft.fftfreq(nx, dx)\n k = np.fft.fftshift(k)\n l = np.fft.fftfreq(ny, dy)\n l = np.fft.fftshift(l)\n\n kk,ll = np.meshgrid(k, l)\n THETA = (np.arctan2(ll, kk))\n\n U = np.fft.fft2(u,axes=(0, 1))\n V = np.fft.fft2(v,axes=(0, 1))\n\n P = U*np.cos(THETA) + V*np.sin(THETA)\n S = -U*np.sin(THETA) + V*np.cos(THETA)\n\n # back to physical space\n u_phi = np.real(np.fft.ifft2(P*np.cos(THETA),axes=(0,1)))\n v_phi = np.real(np.fft.ifft2(P*np.sin(THETA),axes=(0,1)))\n\n u_psi = np.real(np.fft.ifft2(-S*np.sin(THETA),axes=(0,1)))\n v_psi = np.real(np.fft.ifft2(S*np.cos(THETA),axes=(0,1)))\n\n assert np.allclose(u, (u_phi+u_psi), rtol=1e-5, atol=1e-5), \"Decomposition error: rotational\\\n and divergent u-components are not orthogonal\"\n assert np.allclose(v, (v_phi+v_psi), rtol=1e-5, atol=1e-5), \"Decomposition error: rotational\\\n and divergent v-components are not orthogonal\"\n\n return u_phi, v_phi, u_psi, v_psi","title":""},{"docid":"30aa2ad9ccbdf06331aaf99a4fbbe5c0","score":"0.6306826","text":"def getGridVelocity(fieldset, vector, method=\"data\"):\n if len(vector) == 6:\n [lons, lats, xs, ys, depth, time] = vector\n else:\n print \"getGridVelocity(): vector not in correct shape. Expected 6, got\", len(vector)\n\n res0 = np.size(lons)\n res1 = np.size(lats)\n res2 = np.size(xs)\n res3 = np.size(ys)\n if res0 == res1 == res2 == res3:\n res = res0\n else:\n print \"getGridVelocity(): Grid is not square, i.e. len(lons) != len(lats)\"\n print \"getGridVelocity(): recieved lons:\", lons\n print \"getGridVelocity(): recieved lats:\", lats\n return\n\n u = np.zeros((res, res))\n v = np.zeros((res, res))\n\n if method == \"data\":\n for i in range(res):\n for j in range(res):\n u[i][j] = fieldset.U.data[0, ys[i], xs[j]]\n v[i][j] = fieldset.V.data[0, ys[i], xs[j]]\n elif method == \"eval\":\n for i in range(res):\n for j in range(res):\n u[j][i] = fieldset.U.eval(time, lons[i], lats[j], depth)\n v[j][i] = fieldset.V.eval(time, lons[i], lats[j], depth)\n else:\n print \"getGridVelocity(): method not found, use \\\"data\\\" or \\\"eval\\\".\"\n return\n\n return [u, v, lons, lats, depth, time]","title":""},{"docid":"38c358e5cc42ea84b7a67a205ca9aaab","score":"0.62549984","text":"def _calc_phase_plane(self):\n svx_ind = self.model.state_variables.index(self.svx)\n svy_ind = self.model.state_variables.index(self.svy)\n\n #Calculate the vector field discretely sampled at a grid of points\n grid_point = self.default_sv.copy()\n self.U = numpy.zeros((NUMBEROFGRIDPOINTS, NUMBEROFGRIDPOINTS, self.model.number_of_modes))\n self.V = numpy.zeros((NUMBEROFGRIDPOINTS, NUMBEROFGRIDPOINTS, self.model.number_of_modes))\n for ii in xrange(NUMBEROFGRIDPOINTS):\n grid_point[svy_ind] = self.Y[ii]\n for jj in xrange(NUMBEROFGRIDPOINTS):\n #import pdb; pdb.set_trace()\n grid_point[svx_ind] = self.X[jj]\n d = self.model.dfun(grid_point, self.no_coupling)\n\n for kk in range(self.model.number_of_modes):\n self.U[ii, jj, kk] = d[svx_ind, 0, kk]\n self.V[ii, jj, kk] = d[svy_ind, 0, kk]\n\n #self.UVmag = numpy.sqrt(self.U**2 + self.V**2)\n #import pdb; pdb.set_trace()\n if numpy.isnan(self.U).any() or numpy.isnan(self.V).any():\n self.log.error(\"NaN\")","title":""},{"docid":"6609cc822daf543074721126a9832e27","score":"0.6222254","text":"def computeUV(mdl,xx,yy,uu,vv):\n dshape=uu.shape\n uu=uu.flatten()\n vv=vv.flatten()\n corr=np.zeros(uu.shape,dtype=complex)\n for ind in range(uu.shape[0]):\n corr[ind]=idft2d(mdl,xx,yy,uu[ind],vv[ind],norm=mdl.size)\n return np.reshape(corr,dshape)","title":""},{"docid":"08ebd112638a7371714b03e992c3c935","score":"0.61830056","text":"def merid_advec_upwind(field, v, lat, radius, vec_field=False):\n lat = to_radians(lat)\n # Assume latitude and longitude are last two axes.\n if field.ndim == 2:\n lat = lat[np.newaxis,:]\n if field.ndim == 3:\n lat = lat[np.newaxis,:,np.newaxis]\n elif field.ndim == 4:\n lat = lat[np.newaxis,:,np.newaxis,np.newaxis]\n f = field.T\n prefactor = 1. / radius\n # Del operator differs in spherical coords for scalar v. vector fields.\n if vec_field:\n f *= np.cos(lat)\n prefactor /= np.cos(lat)\n prefactor = prefactor.T\n # Roll lat to be leading axis for broadcasting.\n f = np.rollaxis(f, 1, 0)\n lat = np.rollaxis(lat, 1, 0)\n # Create arrays holding positive and negative values, each with forward\n # differencing at south pole and backward differencing at north pole.\n # df_fwd_except_np = fwd_diff2(f, lat)\n # df_bwd_except_sp = fwd_diff2(f[::-1], lat[::-1])[::-1]\n # df_fwd_adj_np = fwd_diff1(f[-2:], lat[-2:])\n # df_bwd_adj_sp = fwd_diff1(f[1::-1], lat[1::-1])[::-1]\n # df_fwd = np.ma.concatenate((df_fwd_except_np, df_fwd_adj_np,\n # df_bwd_except_sp[-1:]), axis=0)\n # df_bwd = np.ma.concatenate((df_fwd_except_np[:1], df_bwd_adj_sp,\n # df_bwd_except_sp), axis=0)\n df_fwd_except_np = fwd_diff1(f, lat)\n df_bwd_except_sp = fwd_diff1(f[::-1], lat[::-1])[::-1]\n df_fwd = np.ma.concatenate((df_fwd_except_np,\n df_bwd_except_sp[-1:]), axis=0)\n df_bwd = np.ma.concatenate((df_fwd_except_np[:1],\n df_bwd_except_sp), axis=0)\n # Roll axis and transpose again to regain original axis order.\n df_fwd = np.rollaxis(df_fwd, 0, 2).T\n df_bwd = np.rollaxis(df_bwd, 0, 2).T\n return prefactor*upwind_scheme(df_fwd, df_bwd, v)","title":""},{"docid":"397131f7cbae93ffd94cf2e87f9cf0b9","score":"0.61514723","text":"def test_pde_vector():\n eq = PDE({\"u\": \"vector_laplace(u) + exp(-t)\"})\n assert eq.explicit_time_dependence\n assert not eq.complex_valued\n grid = grids.UnitGrid([8, 8])\n field = VectorField.random_normal(grid)\n\n res_a = eq.solve(field, t_range=1, dt=0.01, backend=\"numpy\", tracker=None)\n res_b = eq.solve(field, t_range=1, dt=0.01, backend=\"numba\", tracker=None)\n\n res_a.assert_field_compatible(res_b)\n np.testing.assert_allclose(res_a.data, res_b.data)","title":""},{"docid":"6d39bbf5f02bec67aef5de7d686610a7","score":"0.6083987","text":"def u(self,t, pars, variables_from_optimizer=None):\n return pars['v']","title":""},{"docid":"6d39bbf5f02bec67aef5de7d686610a7","score":"0.6083987","text":"def u(self,t, pars, variables_from_optimizer=None):\n return pars['v']","title":""},{"docid":"6e5068203c208a9f893e0e25ff72f8c0","score":"0.606343","text":"def uvvis(t,l,f):\n\n #l = l[:25]\n #f = f[:25]\n ###CONSTANTS####\n NA=6.02214199*10**23 #avogadros number\n c=299792458 #speed of light\n e=1.60217662*10**(-19) #electron charge\n me=9.10938*10**(-31) #electron mass\n pi=math.pi\n epsvac=8.8541878176*10**(-12)\n ### \n sigmaeV=0.3\n sigmacm=sigmaeV*8065.544\n ### \n\n k=(NA*e**2)/(np.log(10)*2*me*c**2*epsvac)*np.sqrt(np.log(2)/pi)*10**(-1)\n\n lambda1=np.zeros(len(l))\n lambda_tot=np.zeros(len(t))\n for x in range(1,len(t)):\n for i in range(0,len(l)):\n lambda1[i]=(k/sigmacm)*f[i]*np.exp(-4*np.log(2)*((1/t[x]-1/l[i])/(10**(-7)*sigmacm))**2)\n lambda_tot[x]=sum(lambda1)\n return np.array(lambda_tot)*10**-4","title":""},{"docid":"9c264e995779048509dae5683101caf3","score":"0.5993902","text":"def form_stokesV(self):\r\n uvxy = UVData()\r\n uvxy.read_miriad(self.files['xy'])\r\n uvyx = UVData()\r\n uvyx.read_miriad(self.files['yx'])\r\n\r\n # compute redundancy information if it hasn't been done yet\r\n if self.caldata == tuple():\r\n self.calculate_caldata(uvxy)\r\n\r\n # get metadata\r\n info = {}\r\n # convert from Hz -> GHz\r\n info['freqs'] = uvxy.freq_array[0, :] / 1e9\r\n info['times'] = np.unique(uvxy.time_array)\r\n\r\n # get data and flags\r\n dV = {}\r\n fV = {}\r\n for key, dxy in uvxy.antpairpol_iter():\r\n # ignore auto correlations\r\n if key[0] == key[1]:\r\n continue\r\n ind1, ind2, ipol = uvxy._key2inds(key)\r\n for ind in [ind1, ind2]:\r\n if len(ind) == 0:\r\n continue\r\n # assumes that xy and yx files only have a single polarization in them\r\n dyx = uvyx.data_array[ind, 0, :, 0]\r\n fxy = uvxy.flag_array[ind, 0, :, 0]\r\n fyx = uvyx.flag_array[ind, 0, :, 0]\r\n\r\n antkey = key[:2]\r\n dV[antkey] = {'V': -1j * dxy + 1j * dyx}\r\n fV[antkey] = {'V': fxy + fyx}\r\n\r\n # clean up\r\n del uvxy, uvyx\r\n\r\n # assign to object variables\r\n self.info = info.copy()\r\n self.data = dV.copy()\r\n self.flags = fV.copy()","title":""},{"docid":"8bf5341032765c39b8d11c3f09d11205","score":"0.59200287","text":"def svd(pos, vec, E, nu, mesh_size, lanczos_exp):\n grid_mat, u, i_max, j_max, i_bound_size, j_bound_size = interp_vec2grid(pos, vec, mesh_size)\n kx, ky, lanczosx, lanczosy = calculate_fourier_modes(mesh_size, i_max, j_max, lanczos_exp)\n GFt = calculate_greens_function(E, nu, kx, ky)\n\n # Deleate large data object\n del grid_mat, kx, ky\n\n Ftu = np.fft.fft2(u).reshape(3, -1).T\n shape = GFt[0, 0].shape\n\n # Perform flattening in Fourier mode space along the way\n U_h = np.empty((shape[0] * shape[1], 3, 3), dtype=np.complex128)\n s_h = np.empty((shape[0] * shape[1], 3))\n for i in range(shape[0]):\n for j in range(shape[1]):\n idx = i * shape[1] + j\n U_h[idx, :], s_h[idx, :], _V = np.linalg.svd(GFt[:, :, i, j])\n\n # Convert to Output format.\n b = Ftu.flatten() # = [u(k,l).T]\n s = s_h.flatten() # = [s(k,l).T]\n # sparseU = block_diag(U_h, format=\"csr\")\n return U_h, s, b","title":""},{"docid":"e31b9ab585bfebdb32e4b91ad16b3b5a","score":"0.5896861","text":"def v_pairing_uu(i,j,n,g=1.0,d=[0,0,0],channel=\"ee\"):\n v = interaction()\n if channel==\"ee\": # ee channel\n v.a = element(i,n,[0,3],d=4,j=j) # cc\n v.b = element(j,n,[3,0],d=4,j=i) # cdcd\n elif channel==\"hh\":\n v.a = element(i,n,[3,0],d=4,j=j) # cc\n v.b = element(j,n,[0,3],d=4,j=i) # cdcd\n else: raise\n v.dir = d # direction of the interaction \n v.g = g\n v.contribution = \"A\"\n v.i = i\n v.j = j\n return v","title":""},{"docid":"5682fc75424692d6503578a0391109ab","score":"0.58859694","text":"def u(self, t, pars, variables_from_optimizer=None):\n return pars['v']","title":""},{"docid":"698be2d669bfc45776a7a2a8b4d57999","score":"0.5860657","text":"def du(t,u):\n\t\n\tu_p = np.zeros(2)\n\tu_p[0] = u[1]\n\tu_p[1] = -g + 1/(m_s + m_p(t))*(dm_p(t)*v_e - 0.5*rho*u[1]*np.abs(u[1])*A*c_d)\n\t\n\treturn u_p","title":""},{"docid":"7ba7e187399f1ee6668b728fc34108fe","score":"0.584209","text":"def vector_transport(\n self, u: jnp.ndarray, vec1: jnp.ndarray, vec2: jnp.ndarray\n ) -> jnp.ndarray:\n\n new_u = self.retraction(u, vec2)\n return self.proj(new_u, vec1)","title":""},{"docid":"39096c5a1f4fdf04e09ff5b4801975cb","score":"0.58269244","text":"def get_rotaries_from_2d_field( t, U_in, F, Amin=0, Amax=2, dA=.01, Pmin=0, Pmax=2*np.pi, dP=.1):\n \n # find time dimension\n dim = np.asarray([0,1])[ np.asarray(U_in.shape)==t.size ]\n if dim[0] == 0: # if time is first dimension -> transpose\n U_in = U_in.T\n\n dimz = U_in.shape[0]\n dimF = F.size\n W_ac = np.zeros((dimz, dimF))\n W_c = np.zeros((dimz, dimF))\n Phi_ac = np.zeros((dimz, dimF))\n Phi_c = np.zeros((dimz, dimF))\n\n for i in range(dimz):\n W_ac[i,:], W_c[i,:], Phi_ac[i,:], Phi_c[i,:] = get_rotaries_from_time_series( \n t, U_in[i,:], F, Amin=0, Amax=2, dA=.01, Pmin=0, Pmax=2*np.pi, dP=.1)\n\n\n return W_ac, W_c, Phi_ac, Phi_c","title":""},{"docid":"d460b509695bc4af16bae0c8f9fdbdd8","score":"0.5807354","text":"def dftuv(M, N):\n\n # set up range of values\n u = np.arange(0, M)\n v = np.arange(0, N)\n\n # compute the indices for use in meshgrid corresponding to the positive\n # frequency points in a 2D FT\n\n idx = u > M / 2\n u[idx] = u[idx] - M\n\n idy = v > N / 2\n v[idy] = v[idy] - N\n\n # compute the meshgrid arrays\n U, V = np.meshgrid(u, v)\n\n return U, V","title":""},{"docid":"af973f52d83e1a3ab8527efb56a46683","score":"0.58029777","text":"def vector_transformation(peff,latitude_out,longitude_out, B0,field_x_cea,\n field_y_cea, field_z_cea, lat_in_rad=False):\n\n nlat_out = len(latitude_out)\n nlon_out = len(longitude_out)\n\n PP = peff\n if lat_in_rad is False:\n BB = latitude_out[None, 0:nlat_out] * np.pi / 180.0\n LL = longitude_out[0:nlon_out, None] * np.pi / 180.0\n else:\n BB = latitude_out\n LL = longitude_out\n L0 = 0.0 # We use central meridian\n Ldif = LL - L0\n\n a11 = -np.sin(B0)*np.sin(PP)*np.sin(Ldif)+np.cos(PP)*np.cos(Ldif)\n a12 = np.sin(B0)*np.cos(PP)*np.sin(Ldif)+np.sin(PP)*np.cos(Ldif)\n a13 = -np.cos(B0)*np.sin(Ldif)\n a21 = -np.sin(BB)*(np.sin(B0)*np.sin(PP)*np.cos(Ldif)+np.cos(PP)*np.sin(Ldif))-np.cos(BB)*np.cos(B0)*np.sin(PP)\n a22 = np.sin(BB)*(np.sin(B0)*np.cos(PP)*np.cos(Ldif)-np.sin(PP)*np.sin(Ldif))+np.cos(BB)*np.cos(B0)*np.cos(PP)\n a23 = -np.cos(B0)*np.sin(BB)*np.cos(Ldif)+np.sin(B0)*np.cos(BB)\n a31 = np.cos(BB)*(np.sin(B0)*np.sin(PP)*np.cos(Ldif)+np.cos(PP)*np.sin(Ldif))-np.sin(BB)*np.cos(B0)*np.sin(PP)\n a32 = -np.cos(BB)*(np.sin(B0)*np.cos(PP)*np.cos(Ldif)-np.sin(PP)*np.sin(Ldif))+np.sin(BB)*np.cos(B0)*np.cos(PP)\n a33 = np.cos(BB)*np.cos(B0)*np.cos(Ldif)+np.sin(BB)*np.sin(B0)\n\n field_x_h = a11 * field_x_cea + a12 * field_y_cea + a13 * field_z_cea\n field_y_h = a21 * field_x_cea + a22 * field_y_cea + a23 * field_z_cea\n field_z_h = a31 * field_x_cea + a32 * field_y_cea + a33 * field_z_cea\n\n # field_z_h positive towards earth\n # field_y_h positive towards south (-field_y_h = Bt_cea)\n # field_x_h positive towards west\n\n field_y_h *= -1.0\n\n return field_x_h, field_y_h, field_z_h","title":""},{"docid":"7184a29339231d4daf5e1d70bc2b8f79","score":"0.57713467","text":"def STUV(E,g,F,G):\n\tM = 1 + E*np.conj(g)\n\tS = (E+g)/M\n\tT = (F - np.conj(E)*G)/np.conj(M)\n\tU = (F - E*np.conj(F))/M\n\tV = (G - E*F)/M\n\t\n\treturn S,T,U,V","title":""},{"docid":"91ecdac759ef1834876d29444c9275d4","score":"0.57539415","text":"def __fvec(t, x, u, ux, uxx, fval, npde, vnpts):\r\n\r\n for i in range(int(npde)):\r\n l = i*vnpts\r\n r = (i+1)*vnpts\r\n\r\n u_sliced[i] = u[l:r]\r\n ux_sliced[i] = ux[l:r]\r\n uxx_sliced[i] = uxx[l:r]\r\n fval_sliced[i] = fval[l:r]\r\n\r\n\r\n # Make vectorized call with f.\r\n problem_definition.f(t, x, u_sliced, ux_sliced, \r\n uxx_sliced, fval_sliced)\r\n\r\n for i in range(int(npde)):\r\n fval[i*vnpts:(i+1)*vnpts] = fval_sliced[i]\r\n\r\n # Return fval, array will have been modified with this call.\r\n return fval","title":""},{"docid":"8d44a66d2d365c340ddc5790ace2df0b","score":"0.5750002","text":"def form_stokesU(self):\r\n uvxy = UVData()\r\n uvxy.read_miriad(self.files['xy'])\r\n uvyx = UVData()\r\n uvyx.read_miriad(self.files['yx'])\r\n\r\n # compute redundancy information if it hasn't been done yet\r\n if self.caldata == tuple():\r\n self.calculate_caldata(uvxy)\r\n\r\n # get metadata\r\n info = {}\r\n # convert from Hz -> GHz\r\n info['freqs'] = uvxy.freq_array[0, :] / 1e9\r\n info['times'] = np.unique(uvxy.time_array)\r\n\r\n # get data and flags\r\n dU = {}\r\n fU = {}\r\n for key, dxy in uvxy.antpairpol_iter():\r\n # ignore auto correlations\r\n if key[0] == key[1]:\r\n continue\r\n ind1, ind2, ipol = uvxy._key2inds(key)\r\n for ind in [ind1, ind2]:\r\n if len(ind) == 0:\r\n continue\r\n # assumes that xy and yx files only have a single polarization in them\r\n dyx = uvyx.data_array[ind, 0, :, 0]\r\n fxy = uvxy.flag_array[ind, 0, :, 0]\r\n fyx = uvyx.flag_array[ind, 0, :, 0]\r\n\r\n antkey = key[:2]\r\n dU[antkey] = {'U': dxy + dyx}\r\n fU[antkey] = {'U': fxy + fyx}\r\n\r\n # clean up\r\n del uvxy, uvyx\r\n\r\n # assign to object variables\r\n self.info = info.copy()\r\n self.data = dU.copy()\r\n self.flags = fU.copy()","title":""},{"docid":"a474e06c4adacd3443671a5fa9a378c2","score":"0.5716817","text":"def extract_hc_uv_spatial(sshfile):\n ssh = sshdriver.load_ssh_clim(sshfile)\n \n # Step 1: compute the spatial gradients of eta\n omega = ssh._ds['omega'].values\n f_cor = calc_coriolis(ssh.yv)\n\n ntide = omega.shape[0]\n\n # Load the full matrix\n eta_re = ssh._ds['SSH_BC_Aa'][...].values\n eta_im = ssh._ds['SSH_BC_Ba'][...].values\n\n # Calculate the coriolis\n f_cor = calc_coriolis(ssh.yv)\n\n u = np.zeros((ntide,ssh.Nc), np.complex128)\n v = np.zeros((ntide,ssh.Nc), np.complex128)\n\n for ii in range(ntide):\n eta_re_dx, eta_re_dy = ssh.calc_grad(eta_re[ii,:])\n eta_im_dx, eta_im_dy = ssh.calc_grad(eta_im[ii,:])\n\n u[ii,:] = calc_u_complex(eta_re_dx+1j*eta_im_dx, eta_re_dy+1j*eta_im_dy, omega[ii], f_cor)\n v[ii,:] = calc_v_complex(eta_re_dx+1j*eta_im_dx, eta_re_dy+1j*eta_im_dy, omega[ii], f_cor)\n \n return u, v, omega","title":""},{"docid":"aa75c648c16295e7717f04fa242600e7","score":"0.56884915","text":"def get_input_velocities_at_segments(self):\n\n # define total velocity\n if self.zeta_dot is not None:\n u_tot = self.u_ext - self.zeta_dot\n else:\n u_tot = self.u_ext\n\n # Include rotation\n for i_m in range(self.maps.M + 1):\n for i_n in range(self.maps.N + 1):\n u_tot[:, i_m, i_n] -= np.cross(self.omega, self.zeta[:, i_m, i_n]) + self.for_vel_tra\n\n M, N = self.maps.M, self.maps.N\n self.u_input_seg = np.empty((3, 4, M, N))\n\n # indiced as per self.maps\n dmver = [0, 1, 1, 0] # delta to go from (m,n) panel to (m,n) vertices\n dnver = [0, 0, 1, 1]\n svec = [0, 1, 2, 3] # seg. no.\n avec = [0, 1, 2, 3] # 1st vertex no.\n bvec = [1, 2, 3, 0] # 2nd vertex no.\n\n ##### panel (0,0): compute all\n mm, nn = 0, 0\n for ss, aa, bb in zip(svec, avec, bvec):\n uA = u_tot[:, mm + dmver[aa], nn + dnver[aa]]\n uB = u_tot[:, mm + dmver[bb], nn + dnver[bb]]\n self.u_input_seg[:, ss, mm, nn] = .5 * (uA + uB)\n\n ##### panels n=0: copy seg.3\n nn = 0\n svec = [0, 1, 2] # seg. no.\n avec = [0, 1, 2] # 1st vertex no.\n bvec = [1, 2, 3] # 2nd vertex no.\n for mm in range(1, M):\n for ss, aa, bb in zip(svec, avec, bvec):\n uA = u_tot[:, mm + dmver[aa], nn + dnver[aa]]\n uB = u_tot[:, mm + dmver[bb], nn + dnver[bb]]\n self.u_input_seg[:, ss, mm, nn] = .5 * (uA + uB)\n self.u_input_seg[:, 3, mm, nn] = self.u_input_seg[:, 1, mm - 1, nn]\n ##### panels m=0: copy seg.0\n mm = 0\n svec = [1, 2, 3] # seg. number\n avec = [1, 2, 3] # 1st vertex of seg.\n bvec = [2, 3, 0] # 2nd vertex of seg.\n for nn in range(1, N):\n for ss, aa, bb in zip(svec, avec, bvec):\n uA = u_tot[:, mm + dmver[aa], nn + dnver[aa]]\n uB = u_tot[:, mm + dmver[bb], nn + dnver[bb]]\n self.u_input_seg[:, ss, mm, nn] = .5 * (uA + uB)\n self.u_input_seg[:, 0, mm, nn] = self.u_input_seg[:, 2, mm, nn - 1]\n ##### all others: copy seg. 0 and 3\n svec = [1, 2] # seg. number\n avec = [1, 2] # 1st vertex of seg.\n bvec = [2, 3] # 2nd vertex of seg.\n for pp in itertools.product(range(1, M), range(1, N)):\n mm, nn = pp\n for ss, aa, bb in zip(svec, avec, bvec):\n uA = u_tot[:, mm + dmver[aa], nn + dnver[aa]]\n uB = u_tot[:, mm + dmver[bb], nn + dnver[bb]]\n self.u_input_seg[:, ss, mm, nn] = .5 * (uA + uB)\n self.u_input_seg[:, 0, mm, nn] = self.u_input_seg[:, 2, mm, nn - 1]\n self.u_input_seg[:, 3, mm, nn] = self.u_input_seg[:, 1, mm - 1, nn]\n\n return self","title":""},{"docid":"a0b30f0722eeb4d508473ce58b953f1c","score":"0.5681747","text":"def v2v3(u):\n if len(u) != 3:\n raise RuntimeError('Not a vector')\n norm = np.sqrt(u[0]**2 + u[1]**2 + u[2]**2) # Works for list or array\n v2 = 3600*np.rad2deg(np.arctan2(u[1], u[0])) # atan2 puts it in the correct quadrant\n v3 = 3600*np.rad2deg(np.arcsin(u[2]/norm))\n return v2, v3","title":""},{"docid":"a3a1030e1b76fc8859135985bcd1f6e8","score":"0.5664697","text":"def extract_uv_dff(sshfile, xlims, ylims, dx, \n thetalow, thetahigh):\n ssh = sshdriver.load_ssh_clim(sshfile)\n\n # Interpolate the amplitude onto a grid prior to DFF\n xgrd = np.arange(xlims[0], xlims[1]+dx, dx)\n ygrd = np.arange(ylims[0], ylims[1]+dx, dx)\n X,Y = np.meshgrid(xgrd, ygrd)\n My, Mx = X.shape\n \n u, v, omega = extract_hc_uv_spatial(ssh)\n ntide, Nc = u.shape\n \n # Interpolate\n aain = np.zeros((Nc,))\n _, u_re, u_im, _ = sshdriver.extract_amp_xy(ssh, X, Y, aain, np.real(u), np.imag(u), kind='linear')\n _, v_re, v_im, _ = sshdriver.extract_amp_xy(ssh, X, Y, aain, np.real(v), np.imag(v), kind='linear')\n \n u_re_f, u_im_f = sshdriver.extract_dff(u_re, u_im, X, Y, dx, thetalow, thetahigh)\n v_re_f, v_im_f = sshdriver.extract_dff(v_re, v_im, X, Y, dx, thetalow, thetahigh)\n\n return u_re_f, u_im_f, v_re_f, v_im_f, X, Y, omega","title":""},{"docid":"9243ff089559cff8a3e877895409a922","score":"0.56461966","text":"def trans_uvd(kp_3d):\n uvd = np.mat(lr_k) * np.mat(kp_3d)\n uvd[:2, :] = uvd[:2, :] / uvd[2:, :]\n # uvd 3*21\n pts = uvd.T\n pts = np.array(pts)\n return pts","title":""},{"docid":"37c4711fec3d3258a33c76df81fe16b5","score":"0.56442404","text":"def ecef2enuv(u, v, w, lat0, lon0, deg: bool = True) -> tuple:\n if deg:\n lat0 = radians(lat0)\n lon0 = radians(lon0)\n\n t = cos(lon0) * u + sin(lon0) * v\n uEast = -sin(lon0) * u + cos(lon0) * v\n wUp = cos(lat0) * t + sin(lat0) * w\n vNorth = -sin(lat0) * t + cos(lat0) * w\n\n return uEast, vNorth, wUp","title":""},{"docid":"07bc879219088a17052248fbfaaf0c3c","score":"0.56402993","text":"def test_pde_vector_scalar():\n eq = PDE({\"u\": \"vector_laplace(u) - u + gradient(v)\", \"v\": \"- divergence(u)\"})\n assert not eq.explicit_time_dependence\n assert not eq.complex_valued\n grid = grids.UnitGrid([8, 8])\n field = FieldCollection(\n [VectorField.random_uniform(grid), ScalarField.random_uniform(grid)]\n )\n\n res_a = eq.solve(field, t_range=1, dt=0.01, backend=\"numpy\", tracker=None)\n res_b = eq.solve(field, t_range=1, dt=0.01, backend=\"numba\", tracker=None)\n\n res_a.assert_field_compatible(res_b)\n np.testing.assert_allclose(res_a.data, res_b.data)","title":""},{"docid":"219064bf9197825739fb7dd08565e77b","score":"0.563774","text":"def reconstruct_displacement_field(GFt, Ftfx, Ftfy, Ftfz, lanczosx, lanczosy):\n \n def _comp_i(Ftfx, Ftfy, Ftfz, GFt_i):\n GFt_ix, GFt_iy, GFt_iz = GFt_i\n return GFt_ix * Ftfx + GFt_iy * Ftfy + GFt_iz * Ftfz\n\n Ftux_rec = _comp_i(Ftfx, Ftfy, Ftfz, GFt[0])\n Ftuy_rec = _comp_i(Ftfx, Ftfy, Ftfz, GFt[1])\n Ftuz_rec = _comp_i(Ftfx, Ftfy, Ftfz, GFt[2])\n ux_rec = np.fft.ifft2(lanczosx * Ftux_rec)\n uy_rec = np.fft.ifft2(lanczosy * Ftuy_rec)\n uz_rec = np.fft.ifft2(Ftuz_rec)\n urec = np.array([np.real(ux_rec), np.real(uy_rec), np.real(uz_rec)])\n Fturec = np.array([Ftux_rec, Ftuy_rec, Ftuz_rec])\n return urec, Fturec","title":""},{"docid":"4ec4044c1398e875af45b100862ad327","score":"0.56371","text":"def decompose_e(E, K, data1, data2):\n # _, r, t, _ = cv2.recoverPose(E, data1[:2, :].T, data2[:2, :].T, K)\n # print(r, t)\n\n\n data1,data2=data1[:,:],data2[:,:]\n x1,x2=data1[:,1],data2[:,1]\n U, S, Vh = np.linalg.svd(E)\n W=np.array([[0,-1,0],[1,0,0],[0,0,1]])\n t=(U[:,-1]).reshape(U.shape[0],1)\n def check(r,t):\n if(np.linalg.det(r)<0):\n r=-r\n p2 = K @ np.concatenate([r, t], axis=1)\n p1 = K @ np.concatenate([np.eye(3), np.zeros_like(t)], axis=1)\n A=np.array([x1[0]*p1[2]-p1[0],x1[1]*p1[2]-p1[1],x2[0]*p2[2]-p2[0],x2[1]*p2[2]-p2[1]])\n U,s,Vh=np.linalg.svd(A)\n X=Vh[-1, :]\n X[:3]=X[:3]/X[3]\n X[3]=1\n z=X[2]/X[3]\n trans = np.concatenate([r, t], axis=1)\n camerapose=np.concatenate([trans,np.array([[0,0,0,1]])],axis=0)\n return z>0 and (camerapose@X)[2]>0, trans\n\n bol, trans = check(U @ W @ Vh, t)\n if (bol): return trans\n bol, trans = check(U @ W @ Vh, -t)\n if (bol): return trans\n bol, trans = check(U @ W.T @ Vh, -t)\n if (bol): return trans\n bol,trans= check(U @ W.T @ Vh, t)\n if(bol):\n #print(trans)\n return trans\n \"\"\"YOUR CODE STARTS HERE\"\"\"\n\n \"\"\"YOUR CODE ENDS HERE\"\"\"","title":""},{"docid":"49313ebbee3c76d28e6f9472b76720d1","score":"0.56328714","text":"def f(u):\n\n v = u[0]\n theta = u[1]\n x = u[2]\n y = u[3]\n return np.array([-g * sin(theta) - C_D / C_L * g / v_t ** 2 * v ** 2,\n -g * cos(theta) / v + g / v_t ** 2 * v,\n v * cos(theta),\n v * sin(theta)])","title":""},{"docid":"46fc3eb1537d52f0a6c652e62ea60034","score":"0.56305045","text":"def f(u):\n \n #define locations of inputs in u matrix\n v = u[0]\n theta = u[1]\n x = u[2]\n y = u[3]\n \n return np.array([-g*sin(theta) - (C_D/C_L)*(g/(v_t**2))*v**2,\\\n (-g*cos(theta))/v + (g/(v_t**2))*v, v*cos(theta), v*sin(theta)])","title":""},{"docid":"8f3066fb4759a2ca5736241230f36a4a","score":"0.5628934","text":"def vert_advec_upwind(field, omega, p):\n # Assume pressure is 3rd to last axis: ([time,] p, lat, lon)\n f = field.T\n p = to_pascal(p)\n # Amend array dimensions as necessary for broadcasting purposes.\n if p.ndim == 1:\n p = p[np.newaxis,np.newaxis,:,np.newaxis]\n elif p.ndim == 3:\n p = p[np.newaxis,:,:,:].T\n else:\n p = p.T\n # Roll pressure to be leading axis for broadcasting.\n f = np.rollaxis(f, 2, 0)\n p = np.rollaxis(p, 2, 0)\n # Create arrays holding positive and negative values, each with forward\n # differencing at south pole and backward differencing at north pole.\n df_fwd_partial = fwd_diff1(f, p)\n df_bwd_partial = fwd_diff1(f[::-1], p[::-1])[::-1]\n df_fwd = np.ma.concatenate((df_fwd_partial, df_bwd_partial[-1:]), axis=0)\n df_bwd = np.ma.concatenate((df_fwd_partial[:1], df_bwd_partial), axis=0)\n # Roll axis and transpose again to regain original axis order.\n df_fwd = np.rollaxis(df_fwd, 0, -1).T\n df_bwd = np.rollaxis(df_bwd, 0, -1).T\n return upwind_scheme(df_fwd, df_bwd, omega)","title":""},{"docid":"45d8784aa2a1cea0e146cd4e5a3655be","score":"0.5619489","text":"def InducedVelocities(geometry):\n\n # Combining multiple rotor system\n rotors = geometry.WT_lst\n N_pts = 0\n N_hs = 0\n ctrl_pts = []\n hs_vortex = []\n idx_lst = [0]\n # Loop over rotors\n for t in range(len(rotors)):\n N_pts += rotors['WT'+str(t+1)]['N_pts'] #Total number of control points of system\n N_hs += rotors['WT'+str(t+1)]['N_hs'] #Total number of vortices of system\n idx_lst.append(N_hs) #Index of where circulation switches rotors\n ctrl_pts = ctrl_pts + rotors['WT'+str(t+1)]['blades']['control_pts'] #Stacking control points\n hs_vortex = hs_vortex + rotors['WT'+str(t+1)]['blades']['HS_vortex'] #Stacking vortices\n\n # Empty induced velocity matrices\n u_ind_mat = np.zeros((N_pts,N_hs))\n v_ind_mat = np.zeros((N_pts,N_hs))\n w_ind_mat = np.zeros((N_pts,N_hs))\n\n # For every control point\n for i in range(N_pts):\n coords = ctrl_pts[i]['coords']\n\n # For every HS vortex\n for j in range(N_hs):\n hs = hs_vortex[j]\n u_ind = 0\n v_ind = 0\n w_ind = 0\n # For ever filament of HS vortex\n for k in range(len(hs)):\n fil = hs[k]\n\n # Compute induced velocity\n w = SingleFilament(coords,fil['pos1'],fil['pos2'],fil['Gamma'])\n # Sum effect of all filaments at control point for 1 vortex\n u_ind+=w[0]\n v_ind+=w[1]\n w_ind+=w[2]\n\n # Store induced velocities per control point and HS vortex\n u_ind_mat[i,j] = u_ind\n v_ind_mat[i,j] = v_ind\n w_ind_mat[i,j] = w_ind\n\n return u_ind_mat, v_ind_mat, w_ind_mat, idx_lst","title":""},{"docid":"9ac0ae16aa79f8f2813dffdf5953d516","score":"0.56009007","text":"def create_vectorfield(grid_U, grid_V, variable, variable_lvl, unit, model, vmin, vmax, lats_labels, lons_labels, df_importances, location):\n\n # Convert string labels to floats\n lats = [int(lat)/100.0 for lat in lats_labels]\n lons = [int(lon)/100.0 for lon in lons_labels]\n\n # Create mapfigure and set title (if wanted)\n mf = Mapfigure(lon=np.array(lons), lat=np.array(lats))\n mf.drawmap(nbrem=1, nbrep=1)\n # plt.title(f\"{variable} at {variable_lvl} hPa for {model} data\")\n\n # Calculate norm of U and V vectors\n grid_ges = np.sqrt(grid_U**2+grid_V**2)\n\n # Set correct colorbar intervals in plot\n norm = matplotlib.colors.Normalize()\n norm.autoscale(grid_ges.flatten())\n cm = plt.cm.get_cmap('rocket_r')\n sm = plt.cm.ScalarMappable(cmap=cm)\n sm.set_array(grid_ges.flatten())\n \n # Make vector plot\n plt.quiver(lons, lats, grid_U, grid_V, scale_units=\"xy\", color=cm(norm(grid_ges.flatten())))\n \n # Format colorbar nicely\n ticks = np.arange(vmin, vmax, 1)\n cbar = plt.colorbar(sm,\n boundaries=ticks,\n ticks=ticks,\n extend=\"both\", fraction=0.0188, pad=0.03)\n cbar.set_label(unit, rotation=90, labelpad=10)\n\n # Plot the feature importances on the contour plot\n for i in df_importances.index:\n plt.plot(df_importances.loc[i, \"lon1\"],\n df_importances.loc[i, \"lat1\"],\n \"bx\",\n markersize=12*df_importances.loc[i, \"importance\"]/df_importances[\"importance\"].max(),\n mew=4)\n\n \n \n # Plot station location (Altdorf 8.64441, 46.88042, Lugano: 8.96004, 46.01008)\n if location == \"ALT\":\n plt.plot(8.64441, 46.88042, 'o', color=\"#00FF00\",markersize=8)\n elif location == \"LUG\":\n plt.plot(8.96004, 46.01008, 'o', color=\"#00FF00\",markersize=8)\n \n # Uncomment if you want to save the figure solely\n # plt.savefig(f'weathermap_{variable}_{variable_lvl}_{model}.pdf', bbox_inches='tight', dpi=200)\n # print(f\"Saved figure at: weathermap_{variable}_{variable_lvl}_{model}.pdf\")","title":""},{"docid":"1a049f735dd34be5c3476617e7a0f0ca","score":"0.5577877","text":"def sv2elts(state, mu):\n\tr = state[0:3]\n\tv = state[3:6]\n\thvec = np.cross(r,v)\n\tincl = get_inclination(state)\n\traan = get_raan(state)\n\tecc = get_eccentricity(state, mu)\n\tomega = get_omega(state, mu)\n\ttheta = get_trueanom(state, mu)\n\tperiod = get_period(state, mu)\n\ta = get_semiMajorAxis(state, mu)\n\n\t# create dictionary to return\n\telts = {\n\t\t\"hvec\": hvec,\n\t\t\"incl\": incl,\n\t\t\"evec\": ecc,\n\t\t\"raan\": raan, \n\t\t\"omega\": omega, \n\t\t\"theta\": theta,\n\t\t\"period\": period,\n\t\t\"semiMajorAxis\": a\n\t}\n\treturn elts","title":""},{"docid":"8dedf0c5ed7aa5d818f33bf42fe0c859","score":"0.55514354","text":"def u_v(vel_csv_file):\n\n\t# vel_csv_file is the output from Streams. The 1st row needs to be skipped\n\tvelocity = pd.read_csv(vel_csv_file, index_col = [0], skiprows=[0], na_values =' NaN')\n\n\t# convert all NaN to 0s.\n\tvelocity.fillna(0, inplace=True)\n\n\t# total number of rows with valid data for each velocity component\n\ttotal_rows = nb_matrix * nb_y_rows\n\n\t# initiate numpy arrays to store velocity data from velocity dataframe\n\tu_velocity = np.zeros((nb_matrix, nb_y_rows, nb_x_cols))\n\tv_velocity = np.zeros((nb_matrix, nb_y_rows, nb_x_cols))\n\n\tfor i in range(nb_matrix):\n\t\tu_velocity[i] = velocity.values[i*nb_y_rows:(i+1)*nb_y_rows , :]\n\t\tv_velocity[i] = velocity.values[total_rows+i*nb_y_rows:total_rows+(i+1)*nb_y_rows, :]\n\n\treturn u_velocity, v_velocity","title":""},{"docid":"93100a9c70616b32c5c88a9b6ae1e160","score":"0.5546929","text":"def diffun(T, u):\n u = u.reshape([(nr + 1), NION, nz])\n \n C = u[LIQUID, :, :]\n q = u[RESIN:, :, :]\n q_s = u[SURF, :, :]\n \n u[LIQUID, :, 0] = get_Cin(T) # XXX: SLOW\n \n # Calculate total concentrations along column\n CT = u[LIQUID, :, :].sum(axis=0)\n \n # update Ceq\n Ceq = calc_Ceq(q_s, CT)\n \n # Calculate flux terms\n J = - kL * (C - Ceq) # mass flux \n Jas = J * 3/rb # mass flux * specific surface area of bead\n\n # Initialize arrays for derivatives\n du_dT = np.zeros([nr+1, NION, nz])\n dq_dT = np.zeros([nr, NION, nz])\n dC_dT = np.zeros([NION, nz]) \n dq_dT_w = np.zeros([NION, nz])\n \n # Liquid phase\n C_swap = np.swapaxes(C, 0, 1)\n Az_C = np.swapaxes(np.matmul(Az, C_swap), 0, 1)\n dC_dT = (-v/L*(Az_C) + (1-EBED)*Jas)/EBED * t_half\n \n # diffusion in bead HSDMIX\n q_swap = np.swapaxes(q, 0, 1)\n Br_q = np.swapaxes(np.matmul(Br, q_swap), 0, 1)\n dq_dT = Ds * t_half / rb**2 * Br_q \n\n # intermediate term for dq_dT at bead surface HSDMIX \n dq_dT_swap = np.swapaxes(dq_dT[:SURF, :, :],0,1)\n dq_dT_w = np.matmul(Wr[:-1], dq_dT_swap)\n \n # Fill out du_dT \n du_dT[LIQUID, :, :] = dC_dT\n du_dT[LIQUID, :, 0] = 0 # Inlet\n du_dT[RESIN:, :, :] = dq_dT\n du_dT[SURF, :, :] = (-t_half / rb * J - dq_dT_w)/Wr[-1] \n \n du_dT = du_dT.reshape(NEQ) # solve_ivp requires this to be a vector\n \n return du_dT","title":""},{"docid":"994b15f0070369ecb258cdc7e5bdd275","score":"0.55365163","text":"def kuramoto_sivachinsky(u, d):\n\n tmax = 1000\n N = u.shape[0]\n v = fft(u)\n\n # Precompute various ETDRK4 scalar quantities:\n h = 0.25 # time step\n # nplt = np.floor((tmax/100)/h)\n nplt = 1\n\n k = fftfreq(N, d=d)\n # k = [0:N/2-1 0 -N/2+1:-1]’/16 # wave numbers\n\n L = k**2 - k**4 # Fourier multipliers\n E = exp(h * L)\n E2 = exp(h * L / 2)\n M = 16 # no. of points for complex means\n r = exp(1j * pi * (np.arange(1, M + 1) - .5) / M) # roots of unity\n LR = h * L[:, None] + r[None, :]\n\n Q = h * np.mean((exp(LR / 2) - 1) / LR, axis=1).real\n f1 = h * np.mean(\n (-4 - LR + exp(LR) * (4 - 3 * LR + LR**2)) / LR**3, axis=1).real\n f2 = h * np.mean((2 + LR + exp(LR) * (-2 + LR)) / LR**3, axis=1).real\n f3 = h * np.mean(\n (-4 - 3 * LR - LR**2 + exp(LR) * (4 - LR)) / LR**3, axis=1).real\n\n # Main time-stepping loop:\n uu = []\n tt = []\n\n nmax = round(tmax / h)\n\n g = -0.5j * k\n for n in range(nmax):\n t = n * h\n Nv = g * fft(np.real(ifft(v))**2)\n a = E2 * v + Q * Nv\n Na = g * fft(np.real(ifft(a))**2)\n b = E2 * v + Q * Na\n Nb = g * fft(np.real(ifft(b))**2)\n c = E2 * a + Q * (2 * Nb - Nv)\n Nc = g * fft(np.real(ifft(c))**2)\n v = E * v + Nv * f1 + 2 * (Na + Nb) * f2 + Nc * f3\n\n if n % nplt == 0:\n u = ifft(v).real\n uu.append(u)\n tt.append(t)\n\n return np.stack(tt), np.stack(uu)","title":""},{"docid":"a579c1c9bc6f9e0c3ad3b11de5d016ae","score":"0.5533154","text":"def u_exact(self, t):\n\n me = mesh(self.nvars)\n me.values[0, :, :] = np.sin(2 * np.pi * self.xc) * np.sin(2 * np.pi * self.yc)\n # me.values[1,:,:] = np.sin(2*np.pi*self.xc)#*np.sin(2*np.pi*self.yc)\n\n return me","title":""},{"docid":"6b6112925d79a016502b2c55a73d0014","score":"0.553298","text":"def process_zvi(tr,dz):\n z = np.array(tr)\n t = np.array(tr)\n z[0] = 0\n t[0] = 0\n for i in range(1,tr.size):\n z[i] = i * dz\n t[i] = t[i-1] +(dz / tr[i]) * 2000.0 # to make t2w in ms\n return z,t","title":""},{"docid":"760b5e6318295cbf2fceba6889d1f674","score":"0.5531234","text":"def spd_dir2uv(spd,dir):\r\n import numpy as np\r\n\r\n dir2 = (np.copy(dir) + 180.0)*np.pi/180.0\r\n s = np.sin(dir2)\r\n c = np.cos(dir2)\r\n v = -spd*s\r\n u = -spd*c\r\n\r\n return (u, v)","title":""},{"docid":"0a9829b11b46f05131536f8460c370a6","score":"0.5526411","text":"def horiz_advec_sfc_pressure(ps, u, v, lat, lon, radius, p, vec_field=False):\n # Pressure data indexing is surface to TOA; sigma data is opposite.\n if p.shape[-1] == 1:\n sfc_index = 0\n else:\n sfc_index = -1\n print sfc_index, u[:,sfc_index,45,0]\n u_sfc = (u[:,sfc_index])[:,np.newaxis,:,:]\n v_sfc = (v[:,sfc_index])[:,np.newaxis,:,:]\n return horiz_advec(ps, u_sfc, v_sfc, lat, lon, radius,\n vec_field=vec_field) * (1./grav)","title":""},{"docid":"5647aa1b97b80bc8522377ece765bb58","score":"0.55243546","text":"def _velocity_3D_from_vortex_filament(self, cp_i, core):\n\n r_gamma = self.geo.filaments[-1] # vortex strength of each filament\n x1 = self.geo.filaments[0]\n y1 = self.geo.filaments[1]\n z1 = self.geo.filaments[2]\n x2 = self.geo.filaments[3]\n y2 = self.geo.filaments[4]\n z2 = self.geo.filaments[5]\n xc, yc, zc = cp_i\n\n R1 = np.sqrt((xc - x1) ** 2 + (yc - y1) ** 2 + (zc - z1) ** 2)\n R2 = np.sqrt((xc - x2) ** 2 + (yc - y2) ** 2 + (zc - z2) ** 2)\n\n R12_xx = ((yc - y1) * (zc - z2)) - ((zc - z1) * (yc - y2))\n R12_xy = -((xc - x1) * (zc - z2)) + ((zc - z1) * (xc - x2))\n R12_xz = ((xc - x1) * (yc - y2)) - ((yc - y1) * (xc - x2))\n\n R12_sq = (R12_xx ** 2) + (R12_xy ** 2) + (R12_xz ** 2)\n\n R01 = ((x2 - x1) * (xc - x1)) + ((y2 - y1) * (yc - y1)) + ((z2 - z1) * (zc - z1))\n R02 = ((x2 - x1) * (xc - x2)) + ((y2 - y1) * (yc - y2)) + ((z2 - z1) * (zc - z2))\n\n # check if target point is in the vortex filament core,\n # and modify to solid body rotation\n R12_sq = np.where(R12_sq < core ** 2, core ** 2, R12_sq)\n R1 = np.where(R1 < core, core, R1)\n R2 = np.where(R1 < core, core, R2)\n\n K = (r_gamma / (4 * np.pi * R12_sq)) * ((R01 / R1) - (R02 / R2))\n\n U = np.sum(K * R12_xx, axis=1)\n V = np.sum(K * R12_xy, axis=1)\n W = np.sum(K * R12_xz, axis=1)\n\n return np.array([U, V, W])","title":""},{"docid":"fcf7f821af38b9834b46dbc2f25a6baa","score":"0.55185246","text":"def gnss_velocity_fields(dset: \"Dataset\") -> None:\n \n # Add velocity in topocentric coordinates\n lat, lon, height = dset.site_pos.pos.llh.T\n vel_enu = np.squeeze(rotation.trs2enu(lat, lon) @ dset.site_vel[:,:,None])\n dset.add_float(\"site_vel_east\", val=vel_enu[:,0], unit=\"meter/second\", write_level=\"operational\")\n dset.add_float(\"site_vel_north\", val=vel_enu[:,1], unit=\"meter/second\", write_level=\"operational\")\n dset.add_float(\"site_vel_up\", val=vel_enu[:,2], unit=\"meter/second\", write_level=\"operational\")\n \n # Add horizontal velocity (HV), vertical velocity (VV) and 3D velocity\n dset.add_float(\n \"site_vel_h\", \n val=np.sqrt(vel_enu[:,0] ** 2 + vel_enu[:,1] ** 2),\n unit=\"meter/second\",\n write_level=\"operational\",\n )\n dset.add_float(\"site_vel_v\", val=np.absolute(vel_enu[:,2]), unit=\"meter/second\")\n dset.add_float(\n \"site_vel_3d\", \n val=np.sqrt(dset.site_vel[:,0] ** 2 + dset.site_vel[:,1] ** 2 + dset.site_vel[:,2] ** 2),\n #val=np.sqrt(vel_enu[:,0] ** 2 + vel_enu[:,1] ** 2 + vel_enu[:,2] ** 2),\n unit=\"meter/second\",\n write_level=\"operational\",\n \n )\n\n # Add site velocity and standard deviation of site velocity coordinates\n dset.add_float(\"site_vel_x\", val=dset.site_vel[:, 0], unit=\"meter/second\", write_level=\"analysis\")\n dset.add_float(\"site_vel_y\", val=dset.site_vel[:, 1], unit=\"meter/second\", write_level=\"analysis\")\n dset.add_float(\"site_vel_z\", val=dset.site_vel[:, 2], unit=\"meter/second\", write_level=\"analysis\")\n dset.add_float(\n \"site_vel_sigma_x\", \n val=np.sqrt(dset.estimate_cov_site_vel_xx), \n unit=\"meter/second\",\n write_level=\"analysis\",\n )\n dset.add_float(\n \"site_vel_sigma_y\", \n val=np.sqrt(dset.estimate_cov_site_vel_yy), \n unit=\"meter/second\",\n write_level=\"analysis\",\n )\n dset.add_float(\n \"site_vel_sigma_z\", \n val=np.sqrt(dset.estimate_cov_site_vel_zz), \n unit=\"meter/second\",\n write_level=\"analysis\",\n )","title":""},{"docid":"7fd4308157d20c55e6af05926581cca2","score":"0.5501057","text":"def solver(I, V, f, c, L, dt, C, T, user_action=None,\n version='vectorized', bc_left='dudx=0'):\n Nt = int(round(T/dt))\n t = linspace(0, Nt*dt, Nt+1) # Mesh points in time\n dx = dt*c/float(C)\n Nx = int(round(L/dx))\n x = linspace(0, L, Nx+1) # Mesh points in space\n dx = x[1] - x[0]\n C2 = C**2 # Help variable in the scheme\n if f is None or f == 0:\n f = (lambda x, t: 0) if version == 'scalar' else \\\n lambda x, t: zeros(x.shape)\n if V is None or V == 0:\n V = (lambda x: 0) if version == 'scalar' else \\\n lambda x: zeros(x.shape)\n\n u = zeros(Nx+1) # Solution array at new time level\n u_1 = zeros(Nx+1) # Solution at 1 time level back\n u_2 = zeros(Nx+1) # Solution at 2 time levels back\n\n import time; t0 = time.clock() # for measuring CPU time\n\n global periodic\n periodic = False # True: periodic condition at x=0, False: du/dn=0\n\n # Load initial condition into u_1\n for i in range(0,Nx+1):\n u_1[i] = I(x[i])\n\n if user_action is not None:\n user_action(u_1, x, t, 0)\n\n # Special formula for first time step\n n = 0\n for i in range(1, Nx):\n u[i] = u_1[i] + dt*V(x[i]) + \\\n 0.5*C2*(u_1[i-1] - 2*u_1[i] + u_1[i+1]) + \\\n 0.5*dt**2*f(x[i], t[n])\n i = 0\n if bc_left == 'dudx=0':\n u[i] = u_1[i] + dt*V(x[i]) + \\\n 0.5*C2*(u_1[i+1] - 2*u_1[i] + u_1[i+1]) + \\\n 0.5*dt**2*f(x[i], t[n])\n else: # open boundary condition\n u[i] = u_1[i] + C*(u_1[i+1] - u_1[i])\n\n u[Nx] = 0\n\n if user_action is not None:\n user_action(u, x, t, 1)\n\n # Switch variables before next step\n u_2[:], u_1[:] = u_1, u\n\n for n in range(1, Nt):\n # Update all inner points at time t[n+1]\n\n # Turn to periodic conditions when initial disturbance\n # at x=0 has died out\n if not periodic and u[Nx] > 0.00001:\n periodic = True\n\n if version == 'scalar':\n for i in range(1, Nx):\n u[i] = - u_2[i] + 2*u_1[i] + \\\n C2*(u_1[i-1] - 2*u_1[i] + u_1[i+1]) + \\\n dt**2*f(x[i], t[n])\n elif version == 'vectorized': # (1:-1 slice style)\n f_a = f(x, t[n]) # Precompute in array\n u[1:-1] = - u_2[1:-1] + 2*u_1[1:-1] + \\\n C2*(u_1[0:-2] - 2*u_1[1:-1] + u_1[2:]) + \\\n dt**2*f_a[1:-1]\n\n # Insert boundary conditions\n u[Nx] = u_1[Nx] - C*(u_1[Nx] - u_1[Nx-1]) # open condition\n if periodic:\n u[0] = u[Nx]\n else:\n i = 0\n if bc_left == 'dudx=0':\n u[i] = - u_2[i] + 2*u_1[i] + \\\n C2*(u_1[i+1] - 2*u_1[i] + u_1[i+1]) + \\\n dt**2*f(x[i], t[n])\n else: # open boundary condition\n u[i] = u_1[i] + C*(u_1[i+1] - u_1[i])\n\n if user_action is not None:\n if user_action(u, x, t, n+1):\n break\n\n # Switch variables before next step\n u_2[:], u_1[:] = u_1, u\n\n cpu_time = t0 - time.clock()\n return u, x, t, cpu_time","title":""},{"docid":"8dc5f8b98d1afd9182f9ae205aa6e444","score":"0.55007595","text":"def test_pde_2scalar():\n eq = PDE({\"u\": \"laplace(u) - u\", \"v\": \"- u * v\"})\n assert not eq.explicit_time_dependence\n assert not eq.complex_valued\n grid = grids.UnitGrid([8])\n field = FieldCollection.scalar_random_uniform(2, grid)\n\n res_a = eq.solve(field, t_range=1, dt=0.01, backend=\"numpy\", tracker=None)\n res_b = eq.solve(field, t_range=1, dt=0.01, backend=\"numba\", tracker=None)\n\n res_a.assert_field_compatible(res_b)\n np.testing.assert_allclose(res_a.data, res_b.data)","title":""},{"docid":"aa12da5e7b2779543571150a9dff15ab","score":"0.5486525","text":"def dvr_feh():\n \n t = Table.read('../data/master_catalog.fits')\n ind = (-t['lnL'] < 2.5E3+t['SNR']**2.4) & (t['SNR']>3)\n t = t[ind]\n mem = get_members(t)\n t = t[mem]\n \n ind_mpoor = t['FeH']<-2.15\n ind_mpoor = t['FeH']<-2.3\n t = t[ind_mpoor]\n #print(np.min(t['FeH']), np.max(t['FeH']))\n \n#def br():\n spur = (t['field']==2) | (t['field']==4) | (t['field']==5) | (t['field']==6)\n stream = ~spur\n \n # Load orbit\n pkl = pickle.load(open('../data/orbit_vr_interp.pkl', 'rb'))\n qpoly = pkl['f']\n \n # medians\n phi1_med = np.zeros(8)\n vr_med = np.zeros(8)\n vr_sig = np.zeros(8)\n vr_std = np.zeros(8)\n \n for e, ind in enumerate([stream, spur]):\n #plt.sca(ax[2])\n fields = np.unique(t['field'][ind])\n \n for ee, f in enumerate(fields):\n ifield = t['field']==f\n vr = qpoly(t['phi1'][ind & ifield])\n #plt.errorbar(np.median(t['phi1'][ind & ifield]), np.median(t['Vrad'][ind & ifield] - vr), yerr=np.std(t['Vrad'][ind & ifield]), fmt='none', color='k', lw=2, zorder=ee+2)\n #plt.plot(np.median(t['phi1'][ind & ifield]), np.median(t['Vrad'][ind & ifield] - vr), marker=markers[e], color=colors[e], ms=sizes[e], mec='k', mew=2, zorder=ee+3+e)\n \n phi1_med[f-1] = np.median(t['phi1'][ind & ifield])\n vr_med[f-1] = np.median(t['Vrad'][ind & ifield] - vr)\n vr_sig[f-1] = np.std(t['Vrad'][ind & ifield])\n vr_std[f-1] = np.median(t['std_Vrad'][ind & ifield])\n \n print(vr_med[0] - vr_med[5], vr_std[0], vr_std[5])\n print(vr_med[2] - vr_med[1], vr_std[2], vr_std[1])","title":""},{"docid":"211a88eae0c5d01f69d1c12db5708e38","score":"0.5481848","text":"def velfield(params, shape, oversample=1):\n\n #This is the 'angular eccentricity'\n #Shapes the flattening of the elliptical coordinates\n #cos(theta) is just b/a for the ellipse\n #sin(theta) is sqrt(1-b**2/a**2), or the eccentricity e\n #Should limit a=5b for reasonable galaxies\n\n assert type(oversample)==int, 'Oversample must be an integer'\n\n\n PA=params['PA']\n xc=params['xc']\n yc=params['yc']\n v0=params['v0']\n PA_rad=PA*np.pi/180.\n\n\n #Get coordinate axes\n max_shift=settings.max_centre_shift\n ys=np.linspace(0-max_shift, shape[0]+max_shift, oversample*(shape[0]+2*max_shift))\n xs=np.linspace(0-max_shift, shape[1]+max_shift, oversample*(shape[1]+2*max_shift))\n X_0, Y_0=np.meshgrid(xs, ys)\n\n\n #Shift things to the centre, rotate them by PA, then shift back\n centre_x=shape[0]/2.0\n centre_y=shape[1]/2.0\n\n X_r, Y_r=rotate_coordinates(X_0-centre_x, Y_0-centre_y, PA_rad)\n\n X=X_r+centre_x\n Y=Y_r+centre_y\n\n #Intrinisc viewing angle of the disk\n theta=params['theta']\n theta_rad=theta*np.pi/180.\n\n \n\n #Get the simple axisymetric velfield, then scale by (X-centre_of_array)/R)\n R = np.sqrt((X-centre_x)**2 + ((Y-centre_y)/np.cos(theta_rad))**2)\n velfield= v_circ_exp_quick(R, params)*(X-centre_x)/(R*np.sin(theta_rad))\n\n #Shift the velfield to where it should be\n shift=np.array([yc-centre_y, xc-centre_x])*oversample\n velfield=ndi.shift(velfield, shift, order=0, mode='nearest', cval=np.nan)\n\n #Crop away everything which we don't need- larger than the original data\n m_x=(X_0>0)&(X_0<shape[1])\n m_y=(Y_0>0)&(Y_0<shape[0])\n m_t=(m_x)&(m_y)\n\n \n\n velfield_final=velfield[m_t].reshape(oversample*shape[0], oversample*shape[1])+v0\n\n # import matplotlib.pyplot as plt \n # import ipdb; ipdb.set_trace()\n\n return velfield_final","title":""},{"docid":"eac7cd78edce67e2bfe3691facaa3200","score":"0.5464672","text":"def demag_field_factor(self, vec_1, vec_2):\n x = np.linspace(-self.parameters.thickness()/2, self.parameters.thickness()/2, 50)\n norm_vec = np.cosh(np.linalg.norm([vec_1 + vec_2, self.bloch_vec_perp]) * x[:, np.newaxis])\n tmp = np.trapz(norm_vec, x, axis=0) / self.parameters.thickness()\n #print(tmp*sin(radians(self.parameters.perpendicular_bloch_vector()[0]))**2 / 2)\n return tmp","title":""},{"docid":"bea3c897a1047808c0454c72ade13aab","score":"0.5461648","text":"def calculateFlux(vector):\n if len(vector) == 6:\n [u, v, lons, lats, depth, time] = vector\n else:\n print \"calculateFlux(): vector not in correct shape. Expected 6, got\", len(vector)\n\n res0 = np.size(lons)\n res1 = np.size(lats)\n if res0 == res1:\n res = res0\n else:\n print \"calculateFlux(): Grid is not square, i.e. len(lons) != len(lats)\"\n return\n\n if res == 2:\n [lon_l, lon_r] = lons\n [lat_u, lat_l] = lats\n else:\n half = int(np.round(res/2))\n [lon_l, lon_r] = lons[half-1 : half+1]\n [lat_u, lat_l] = lats[half-1 : half+1]\n u = u[half-1 : half+1, half-1 : half+1]\n v = v[half-1 : half+1, half-1 : half+1]\n\n u = u.flatten()\n v = v.flatten()\n\n d_north = globalDistance([lon_l, lat_u], [lon_r, lat_u])\n d_south = globalDistance([lon_l, lat_l], [lon_r, lat_l])\n d_east = globalDistance([lon_r, lat_u], [lon_r, lat_l])\n d_west = globalDistance([lon_l, lat_u], [lon_l, lat_l])\n\n # using average velocities\n v_north = 1/2 * (v[0] + v[1])\n v_south = 1/2 * (v[2] + v[3])\n u_east = 1/2 * (u[1] + u[3])\n u_west = 1/2 * (u[0] + u[2])\n\n f_north = d_north * v_north\n f_south = d_south * v_south\n f_east = d_east * u_east\n f_west = d_west * u_west\n\n return [f_north, f_south, f_east, f_west]","title":""},{"docid":"3b314fbfa3de07ce5fc841559eb41fa2","score":"0.5456559","text":"def v_mer(XYZ,t):\n k=kvag(w)\n x=XYZ[0]\n z=XYZ[2]\n vx=h0*w*np.exp(k*z)*np.cos(w*t-k*x)\n vz=-h0*w*np.exp(k*z)*np.sin(w*t-k*x)\n return [vx,0,vz]","title":""},{"docid":"839234417bbc734a34fc2c3c94474f4d","score":"0.54562557","text":"def non_unitary_pairing(v):\n vc = [np.conjugate(v[i]) for i in range(3)]\n ux = v[1]*vc[2] - v[2]*vc[1]\n uy = v[2]*vc[0] - v[0]*vc[2]\n uz = v[0]*vc[1] - v[1]*vc[0]\n return (1j*np.array([ux,uy,uz])).real","title":""},{"docid":"c22e0a4d8e3164ab443baf6c5abc7d2c","score":"0.5444358","text":"def process_zva(tr,dz):\n z = np.array(tr)\n t = np.array(tr)\n z[0] = 0\n t[0] = 0\n for i in range(1,tr.size):\n z[i] = i * dz\n t[i] = z[i] / tr[i] * 2000.0\n return z,t","title":""},{"docid":"c565ea7355fd88d37fbcf4f0d098b305","score":"0.5437116","text":"def transportdelp_update_vorticity_and_kineticenergy(\n delp: FloatField,\n pt: FloatField,\n utc: FloatField,\n vtc: FloatField,\n w: FloatField,\n rarea: FloatFieldIJ,\n delpc: FloatField,\n ptc: FloatField,\n wc: FloatField,\n ke: FloatField,\n vort: FloatField,\n ua: FloatField,\n va: FloatField,\n uc: FloatField,\n vc: FloatField,\n u: FloatField,\n v: FloatField,\n fx: FloatField,\n fx1: FloatField,\n fx2: FloatField,\n sin_sg1: FloatFieldIJ,\n cos_sg1: FloatFieldIJ,\n sin_sg2: FloatFieldIJ,\n cos_sg2: FloatFieldIJ,\n sin_sg3: FloatFieldIJ,\n cos_sg3: FloatFieldIJ,\n sin_sg4: FloatFieldIJ,\n cos_sg4: FloatFieldIJ,\n dt2: float,\n):\n\n from __externals__ import grid_type, i_end, i_start, j_end, j_start\n\n with computation(PARALLEL), interval(...):\n compile_assert(grid_type < 3)\n # additional assumption (not grid.nested)\n # corresponds to x fluxes function, but for y-direction\n fy1 = delp[0, -1, 0] if vtc > 0.0 else delp\n fy = pt[0, -1, 0] if vtc > 0.0 else pt\n fy2 = w[0, -1, 0] if vtc > 0.0 else w\n fy1 = vtc * fy1\n fy = fy1 * fy\n fy2 = fy1 * fy2\n\n delpc = delp + (fx1 - fx1[1, 0, 0] + fy1 - fy1[0, 1, 0]) * rarea\n ptc = (pt * delp + (fx - fx[1, 0, 0] + fy - fy[0, 1, 0]) * rarea) / delpc\n wc = (w * delp + (fx2 - fx2[1, 0, 0] + fy2 - fy2[0, 1, 0]) * rarea) / delpc\n\n with computation(PARALLEL), interval(...):\n # update vorticity and kinetic energy\n compile_assert(grid_type < 3)\n\n ke = uc if ua > 0.0 else uc[1, 0, 0]\n vort = vc if va > 0.0 else vc[0, 1, 0]\n\n with horizontal(region[:, j_start - 1], region[:, j_end]):\n vort = vort * sin_sg4 + u[0, 1, 0] * cos_sg4 if va <= 0.0 else vort\n with horizontal(region[:, j_start], region[:, j_end + 1]):\n vort = vort * sin_sg2 + u * cos_sg2 if va > 0.0 else vort\n\n with horizontal(region[i_end, :], region[i_start - 1, :]):\n ke = ke * sin_sg3 + v[1, 0, 0] * cos_sg3 if ua <= 0.0 else ke\n with horizontal(region[i_end + 1, :], region[i_start, :]):\n ke = ke * sin_sg1 + v * cos_sg1 if ua > 0.0 else ke\n\n ke = 0.5 * dt2 * (ua * ke + va * vort)","title":""},{"docid":"2be6c27424445d25e6aa8d0075c9b35b","score":"0.54212666","text":"def uv2spd_dir(u,v):\r\n import numpy as np\r\n\r\n spd = np.zeros_like(u)\r\n dir = np.zeros_like(u)\r\n\r\n spd = np.sqrt(u**2 + v**2)\r\n dir = np.arctan2(v, u)*180.0/np.pi\r\n\r\n return (spd, dir)","title":""},{"docid":"d094a520154ca1da5cf9d7fc57245a0e","score":"0.54210436","text":"def decele(self):\n ## F = m a\n a__ = (self.lift_and_drag_forces()[1] / self.body_m).to(u.cm/u.s**2)\n v_f = self.v_sf - self.t_dec * a__\n \n return v_f","title":""},{"docid":"cca85664ad5b760e1f5b5c437838899c","score":"0.54206795","text":"def mfg_coeffs2(params, afs, dz, fg, mfgin, rhogin, Sgs, Smgp, Smgs, Tg, ug, ugin, v):\n D = params['D']\n N = params['N']\n R = params['R']\n Mgin = params['Mgin']\n ef0 = params['ef0']\n g = params['g']\n rhop = params['rhop']\n\n ef = ef0\n rhog = rhogin\n rhogb = rhogin\n\n Mg = np.full(N, Mgin)\n P = ((R * rhog * Tg) / Mg) * 1e3\n\n dp = np.zeros(len(P))\n dp[:-1] = np.diff(P)\n\n a = np.concatenate(([ugin], ug[0:N - 1]))\n\n b1 = ug[0] - ugin - ((2 * dz) / rhogb) * (afs[0] * Smgs[0] - Smgp[0] + Sgs)\n binner = ug[1:N] - ug[0:N - 1] - ((2 * dz) / rhogb) * (afs[1:N] * Smgs[1:N] - Smgp[1:N] + Sgs)\n b = np.concatenate(([b1], binner))\n\n c = ug\n b[N - 1] = b[N - 1] + c[N - 1]\n\n x = g * (ef * (1 - ef) * rhop - rhogb)\n y = ((2 * fg * rhogb) / D) * ug * abs(ug)\n d = 2 * dz * (x + (afs * Smgs * v) - y - ef * (dp / dz))\n d[0] = d[0] + ugin * mfgin\n\n return a, b, c, d","title":""},{"docid":"00e07c539bfb06409b118c35cda20b34","score":"0.5420611","text":"def __undepart_synthesis__(self):\n\n n = self.numberUV[0]\n m = int(n / 2)\n w0 = np.cosh(np.arccosh(self.R0) / (n - 1))\n\n cheby_coef = np.zeros(n, dtype=float)\n cheby_coef[-1] = 1\n\n odd = self.oddU\n\n # odd or even\n val = 1 if odd else 0.5\n val2 = 2 if odd else 1\n val_current_coef = 4.0 if odd else 1\n pq = np.arange(1, m + val2, 1)\n\n cos_base = np.cos((pq - val) * np.pi / n)\n x0 = w0 * np.einsum(\"i,j->ij\", cos_base, cos_base)\n cheby_value = np.polynomial.chebyshev.chebval(x0, cheby_coef)\n cheby_value[0, 0] = cheby_value[0, 0] / val_current_coef\n\n zmc = np.arange(1, m + val2, 1)\n cos_inner = np.cos(np.einsum(\"i,j->ij\", (zmc - val), (pq - val)) * 2 * np.pi / n)\n current = (4 / n) ** 2 * np.einsum(\"mp,nq,pq->mn\", cos_inner, cos_inner, cheby_value)\n\n self.current = current / np.max(current)","title":""},{"docid":"71f7cf74bf71616122bee2ac1997dd7d","score":"0.54186696","text":"def __eval_fimpl(self, u, t):\n\n fimpl = mesh(self.nvars, val=0.0)\n # fimpl.values = self.A.dot(u.values)\n\n return fimpl","title":""},{"docid":"c598251d8d831447c7481f4ab4461f91","score":"0.54177445","text":"def calculate_stress_field(Ftfx, Ftfy, Ftfz, lanczosx, lanczosy, grid_mat, u, i_max, j_max, \\\n i_bound_size, j_bound_size, pix_per_mu, mesh_size):\n \n fx = np.fft.ifft2(lanczosx * Ftfx)\n fy = np.fft.ifft2(lanczosy * Ftfy)\n fz = np.fft.ifft2(Ftfz)\n\n pos = np.array([np.reshape(grid_mat[0], (i_max * j_max)), \\\n np.reshape(grid_mat[1], (i_max * j_max))])\n vec = np.array([np.reshape(u[0], (i_max * j_max)),\n np.reshape(u[1], (i_max * j_max)),\n np.reshape(u[2], (i_max * j_max))])\n\n f = np.array([np.real(fx), np.real(fy), np.real(fz)])\n fnorm = (f[0] ** 2 + f[1] ** 2 + f[2] ** 2) ** 0.5\n if j_bound_size > 0 and i_bound_size > 0:\n energy = calculate_energy(u[:, j_bound_size:-j_bound_size, i_bound_size:-i_bound_size], \\\n f[:, j_bound_size:-j_bound_size, i_bound_size:-i_bound_size], pix_per_mu, mesh_size)\n force = calculate_total_force(fnorm[j_bound_size:-j_bound_size, i_bound_size:-i_bound_size], pix_per_mu,\n mesh_size)\n else:\n energy = calculate_energy(u, f, pix_per_mu, mesh_size)\n force = calculate_total_force(fnorm, pix_per_mu, mesh_size)\n return pos, vec, fnorm, f, energy, force","title":""},{"docid":"81bab433073268c51784711cbc856bab","score":"0.54106164","text":"def derivatives(self, u=-1, v=-1, order=0):\n # Check all parameters are set before the surface evaluation\n self._check_variables()\n # Check u and v parameters are correct\n utilities.check_uv(u, v)\n\n # Algorithm A3.6\n du = min(self._degree_u, order)\n dv = min(self._degree_v, order)\n\n SKL = [[[0.0 for _ in range(self._dimension)] for _ in range(dv + 1)] for _ in range(du + 1)]\n\n span_u = helpers.find_span(self.knotvector_u, self._control_points_size_u, u)\n bfunsders_u = helpers.basis_function_ders(self._degree_u, self._knot_vector_u, span_u, u, du)\n span_v = helpers.find_span(self.knotvector_v, self._control_points_size_v, v)\n bfunsders_v = helpers.basis_function_ders(self._degree_v, self._knot_vector_v, span_v, v, dv)\n\n for k in range(0, du + 1):\n temp = [[] for _ in range(self._degree_v + 1)]\n for s in range(0, self._degree_v + 1):\n temp[s] = [0.0 for _ in range(self._dimension)]\n for r in range(0, self._degree_u + 1):\n cu = span_u - self._degree_u + r\n cv = span_v - self._degree_v + s\n temp[s][:] = [tmp + (bfunsders_u[k][r] * cp) for tmp, cp in\n zip(temp[s], self._control_points2D[cu][cv])]\n\n dd = min(order - k, dv)\n for l in range(0, dd + 1):\n for s in range(0, self._degree_v + 1):\n SKL[k][l][:] = [elem + (bfunsders_v[l][s] * tmp) for elem, tmp in zip(SKL[k][l], temp[s])]\n\n # Return the derivatives\n return SKL","title":""},{"docid":"f60a797a97a872c4aa61f74dfe946141","score":"0.5405509","text":"def _vmulv(u,v):\n #M=array('d')\n M=[]\n for i in xrange(len(u)):\n for j in xrange(len(v)):\n M.append(u[i]*v[j])\n return M","title":""},{"docid":"34fd9eca8cb9e8680afe8309895e9372","score":"0.540538","text":"def deflection_vector(h_field):\n # The three components of the vector field are:\n grad0, grad1, grad2 = np.gradient(h_field)\n\n # We want to normalize it (which is not necessary, but we will do it here):\n norm = np.sqrt(grad0**2+grad1**2+grad2**2)\n norm[norm==0] = 1e10\n grad0 *= 1/norm\n grad1 *= 1/norm\n grad2 *= 1/norm\n \n return grad0, grad1, grad2","title":""},{"docid":"dc980247912a83d3f4a4901e17b93d31","score":"0.5404097","text":"def process_tvi(tr,dz):\n\n z = np.array(tr)\n t = np.array(tr)\n dzsec = dz / 2000.0 #dz in sec t1w\n z[0] = 0\n t[0] = 0\n for i in range(1,tr.size):\n z[i] = z[i-1] + dzsec * tr[i]\n t[i] = i * dz # in t2w ms\n return z,t","title":""},{"docid":"c189523597af0f919dae64992ca3f359","score":"0.53906476","text":"def project_ecef_vector_onto_basis(x, y, z, xx, xy, xz, yx, yy, yz, zx, zy, zz):\n \n out_x = x*xx + y*xy + z*xz\n out_y = x*yx + y*yy + z*yz\n out_z = x*zx + y*zy + z*zz\n \n return out_x, out_y, out_z","title":""},{"docid":"c861aa2c204a5ae956344f1a3b0faff9","score":"0.5389964","text":"def fov(data):\n \n #rospy.loginfo(rospy.get_caller_id() + \"I heard %s\", data.data)\n pos_x = data.position.x\n pos_y = data.position.y\n pos_z = data.position.z\n q_0 = data.orientation.x\n q_1 = data.orientation.y\n q_2 = data.orientation.z\n q_3 = data.orientation.w\n #Currently considering orientation with a 3D axis but available data is for only 2D\n '''T_AL = [[q_0**2+q_1**2-q_2**2-q_3**2 ,2*((q_1*q_2)- (q_0*q_3)) ,2*((q_0*q_2)+ (q_1*q_3)) ,pos_x],\n [2*((q_0*q_3)+ (q_1*q_2)) ,q_0**2-q_1**2+q_2**2-q_3**2 ,2*((q_2*q_3)- (q_0*q_1)) ,pos_y ],\n [2*((q_1*q_3)- (q_0*q_2)) ,2*((q_0*q_1)+ (q_2*q_3)) , q_0**2-q_1**2-q_2**2+q_3**2 ,pos_z],\n [0 ,0 ,0 ,1]]'''\n #Transpose of the above matrix, only the rotation matrix part of the Translation matrix\n T_AL = [[q_0**2+q_1**2-q_2**2-q_3**2 , 2*((q_0*q_3)+ (q_1*q_2)),2*((q_1*q_3)- (q_0*q_2)) ,pos_x],\n [2*((q_1*q_2)- (q_0*q_3)) ,q_0**2-q_1**2+q_2**2-q_3**2 ,2*((q_0*q_1)+ (q_2*q_3)) ,pos_y ],\n [2*((q_0*q_2)+ (q_1*q_3)) ,2*((q_2*q_3)- (q_0*q_1)) , q_0**2-q_1**2-q_2**2+q_3**2 ,pos_z],\n [0 ,0 ,0 ,1]]\n #Now will need to multiply Transform Matrix with 5 points of 4 Pyramids of FOV\n #Matrix Multiplication of first pyramid for global coordinates\n pog_1 = np.matmul(T_AL ,po_1)\n pag_1 = np.matmul(T_AL ,pa_1)\n pbg_1 = np.matmul(T_AL ,pb_1)\n pcg_1 = np.matmul(T_AL ,pc_1)\n pdg_1 = np.matmul(T_AL ,pd_1)\n psg_1 = np.matmul(T_AL ,ps_1)\n \n #Matrix Multiplication of first pyramid's top plane normal and d value\n pvtop_1 = np.subtract(pog_1,pag_1)\n pvtop_2 = np.subtract(pog_1,pbg_1) \n norm_1 = np.cross(pvtop_1[0:3], pvtop_2[0:3]) \n d_1 = np.matmul(norm_1.T ,pog_1[0:3])#check if this method gives a scalar value\n pvside_1 = np.subtract(pog_1,pcg_1)\n pvside_2 = np.subtract(pog_1,pbg_1)\n norm_2 = np.cross(pvside_1[0:3], pvside_2[0:3]) \n d_2 = np.matmul(norm_2.T ,pog_1[0:3])\n\n pvbot_1 = np.subtract(pog_1,pcg_1)\n pvbot_2 = np.subtract(pog_1,pdg_1)\n norm_3 = np.cross(pvbot_1[0:3], pvbot_2[0:3]) \n d_3 = np.matmul(norm_3.T ,pog_1[0:3])\n\n pvside_11 = np.subtract(pog_1,pag_1)\n pvside_22 = np.subtract(pog_1,pdg_1)\n norm_4 = np.cross(pvside_11[0:3], pvside_22[0:3]) \n d_4 = np.matmul(norm_4.T ,pog_1[0:3])\n\n pvfront_1 = np.subtract(pbg_1,pag_1)\n pvfront_2 = np.subtract(pbg_1,pcg_1)\n norm_5 = np.cross(pvfront_1[0:3], pvfront_2[0:3]) \n d_5 = np.matmul(norm_5.T ,pag_1[0:3])\n \n\n \n ##################################################################\n ##################################################################\n #Matrix Multiplication of 2nd pyramid\n pog_2 = np.matmul(T_AL ,po_2)\n pag_2 = np.matmul(T_AL ,pa_2)\n pbg_2 = np.matmul(T_AL ,pb_2)\n pcg_2 = np.matmul(T_AL ,pc_2)\n pdg_2 = np.matmul(T_AL ,pd_2)\n psg_2 = np.matmul(T_AL ,ps_2)\n #Matrix Multiplication of second pyramid's top plane normal and d value\n pvtop_2_1 = np.subtract(pog_2,pag_2)\n pvtop_2_2 = np.subtract(pog_2,pbg_2) \n norm_2_1 = np.cross(pvtop_2_1[0:3], pvtop_2_2[0:3]) \n d_2_1 = np.matmul(norm_2_1.T ,pog_2[0:3])\n \n pvside_2_1 = np.subtract(pog_2,pcg_2)\n pvside_2_2 = np.subtract(pog_2,pbg_2)\n norm_2_2 = np.cross(pvside_2_1[0:3], pvside_2_2[0:3]) \n d_2_2 = np.matmul(norm_2_2.T ,pog_2[0:3])\n\n pvbot_2_1 = np.subtract(pog_2,pcg_2) \n pvbot_2_2 = np.subtract(pog_2,pdg_2) \n norm_2_3 = np.cross(pvbot_2_1[0:3], pvbot_2_2[0:3]) \n d_2_3 = np.matmul(norm_2_3.T ,pog_2[0:3])\n\n pvside_2_11 = np.subtract(pog_2,pag_2) \n pvside_2_22 = np.subtract(pog_2,pdg_2) \n norm_2_4 = np.cross(pvside_2_11[0:3], pvside_2_22[0:3]) \n d_2_4 = np.matmul(norm_2_4.T ,pog_2[0:3])\n\n pvfront_2_1 = np.subtract(pbg_2,pag_2)\n pvfront_2_2 = np.subtract(pbg_2,pcg_2)\n norm_2_5 = np.cross(pvfront_2_1[0:3], pvfront_2_2[0:3]) \n d_2_5 = np.matmul(norm_2_5.T ,pag_2[0:3])\n ##################################################################\n ##################################################################\n #Matrix Multiplication of 3rd pyramid\n pog_3 = np.matmul(T_AL ,po_3)\n pag_3 = np.matmul(T_AL ,pa_3)\n pbg_3 = np.matmul(T_AL ,pb_3)\n pcg_3 = np.matmul(T_AL ,pc_3)\n pdg_3 = np.matmul(T_AL ,pd_3)\n psg_3 = np.matmul(T_AL ,ps_3)\n #Matrix Multiplication of third pyramid's top plane normal and d value\n pvtop_3_1 = np.subtract(pog_3,pag_3) \n pvtop_3_2 = np.subtract(pog_3,pbg_3)\n norm_3_1 = np.cross(pvtop_3_1[0:3], pvtop_3_2[0:3]) \n d_3_1 = np.matmul(norm_3_1.T,pog_3[0:3])\n \n pvside_3_1 = np.subtract(pog_3,pcg_3) \n pvside_3_2 = np.subtract(pog_3,pbg_3)\n norm_3_2 = np.cross(pvside_3_1[0:3], pvside_3_2[0:3]) \n d_3_2 = np.matmul(norm_3_2.T ,pog_3[0:3])\n\n pvbot_3_1 = np.subtract(pog_3,pcg_3)\n pvbot_3_2 = np.subtract(pog_3,pdg_3)\n norm_3_3 = np.cross(pvbot_3_1[0:3], pvbot_3_2[0:3]) \n d_3_3 = np.matmul(norm_3_3.T ,pog_3[0:3])\n\n pvside_3_11 = np.subtract(pog_3,pag_3) \n pvside_3_22 = np.subtract(pog_3,pdg_3)\n norm_3_4 = np.cross(pvside_3_11[0:3], pvside_3_22[0:3]) \n d_3_4 = np.matmul(norm_3_4.T ,pog_3[0:3])\n\n pvfront_3_1 = np.subtract(pbg_3,pag_3) \n pvfront_3_2 = np.subtract(pbg_3,pcg_3)\n norm_3_5 = np.cross(pvfront_3_1[0:3], pvfront_3_2[0:3]) \n d_3_5 = np.matmul(norm_3_5.T ,pag_3[0:3])\n ##################################################################\n ##################################################################\n #Matrix Multiplication of 4th pyramid\n pog_4 = np.matmul(T_AL ,po_4)\n pag_4 = np.matmul(T_AL ,pa_4)\n pbg_4 = np.matmul(T_AL ,pb_4)\n pcg_4 = np.matmul(T_AL ,pc_4)\n pdg_4 = np.matmul(T_AL ,pd_4)\n psg_4 = np.matmul(T_AL ,ps_4)\n #Matrix Multiplication of fourth pyramid's top plane normal and d value\n pvtop_4_1 = np.subtract(pog_4,pag_4)\n pvtop_4_2 = np.subtract(pog_4,pbg_4)\n norm_4_1 = np.cross(pvtop_4_1[0:3], pvtop_4_2[0:3]) \n d_4_1 = np.matmul(norm_4_1.T ,pog_4[0:3])\n \n pvside_4_1 = np.subtract(pog_4,pcg_4)\n pvside_4_2 = np.subtract(pog_4,pbg_4)\n norm_4_2 = np.cross(pvside_4_1[0:3], pvside_4_2[0:3]) \n d_4_2 = np.matmul(norm_4_2.T ,pog_4[0:3])\n\n pvbot_4_1 = np.subtract(pog_4,pcg_4)\n pvbot_4_2 = np.subtract(pog_4,pdg_4) \n norm_4_3 = np.cross(pvbot_4_1[0:3], pvbot_4_2[0:3]) \n d_4_3 = np.matmul(norm_4_3.T ,pog_4[0:3])\n\n pvside_4_11 = np.subtract(pog_4,pag_4)\n pvside_4_22 = np.subtract(pog_4,pdg_4) \n norm_4_4 = np.cross(pvside_4_11[0:3], pvside_4_22[0:3]) \n d_4_4 = np.matmul(norm_4_4.T ,pog_4[0:3])\n\n pvfront_4_1 = np.subtract(pbg_4,pag_4) \n pvfront_4_2 = np.subtract(pbg_4,pcg_4)\n norm_4_5 = np.cross(pvfront_4_1[0:3], pvfront_4_2[0:3]) \n d_4_5 = np.matmul(norm_4_5.T ,pag_4[0:3])\n ##################################################################\n ##Normal direction verification for sample point in pyramid 1\n num_s = norm_1[0]*psg_1[0] + norm_1[1]*psg_1[1] + norm_1[2]*psg_1[2] - d_1\n den_s = math.sqrt(norm_1[0]**2+norm_1[1]**2+norm_1[2]**2)\n D_1_2_s[0] = num_s/den_s\n num_2_s = norm_2[0]*psg_1[0] + norm_2[1]*psg_1[1] + norm_2[2]*psg_1[2] - d_2\n den_2_s = math.sqrt(norm_2[0]**2+norm_2[1]**2+norm_2[2]**2)\n D_1_2_s[1]= num_2_s/den_2_s\n num_3_s = norm_3[0]*psg_1[0] + norm_3[1]*psg_1[1] + norm_3[2]*psg_1[2] - d_3\n den_3_s = math.sqrt(norm_3[0]**2+norm_3[1]**2+norm_3[2]**2)\n D_1_2_s[2] = num_3_s/den_3_s\n num_4_s = norm_4[0]*psg_1[0] + norm_4[1]*psg_1[1] + norm_4[2]*psg_1[2] - d_4\n den_4_s = math.sqrt(norm_4[0]**2+norm_4[1]**2+norm_4[2]**2)\n D_1_2_s[3] = num_4_s/den_4_s\n num_5_s = norm_5[0]*psg_1[0] + norm_5[1]*psg_1[1] + norm_5[2]*psg_1[2] - d_5\n den_5_s = math.sqrt(norm_5[0]**2+norm_5[1]**2+norm_5[2]**2)\n D_1_2_s[4] = num_5_s/den_5_s\n ##Normal direction verification for sample point in pyramid 2\n num_2_s = norm_2_1[0]*psg_2[0] + norm_2_1[1]*psg_2[1] + norm_2_1[2]*psg_2[2] - d_2_1\n den_2_s = math.sqrt(norm_2_1[0]**2+norm_2_1[1]**2+norm_2_1[2]**2)\n D_2_2_s[0] = num_2_s/den_2_s\n num_2_2_s = norm_2_2[0]*psg_2[0] + norm_2_2[1]*psg_2[1] + norm_2_2[2]*psg_2[2] - d_2_2\n den_2_2_s = math.sqrt(norm_2_2[0]**2+norm_2_2[1]**2+norm_2_2[2]**2)\n D_2_2_s[1]= num_2_2_s/den_2_2_s\n num_2_3_s = norm_2_3[0]*psg_2[0] + norm_2_3[1]*psg_2[1] + norm_2_3[2]*psg_2[2] - d_2_3\n den_2_3_s = math.sqrt(norm_2_3[0]**2+norm_2_3[1]**2+norm_2_3[2]**2)\n D_2_2_s[2] = num_2_3_s/den_2_3_s\n num_2_4_s = norm_2_4[0]*psg_2[0] + norm_2_4[1]*psg_2[1] + norm_2_4[2]*psg_2[2] - d_2_4\n den_2_4_s = math.sqrt(norm_2_4[0]**2+norm_2_4[1]**2+norm_2_4[2]**2)\n D_2_2_s[3] = num_2_4_s/den_2_4_s\n num_2_5_s = norm_2_5[0]*psg_2[0] + norm_2_5[1]*psg_2[1] + norm_2_5[2]*psg_2[2] - d_2_5\n den_2_5_s = math.sqrt(norm_2_5[0]**2+norm_2_5[1]**2+norm_2_5[2]**2)\n D_2_2_s[4] = num_2_5_s/den_2_5_s\n ##Normal direction verification for sample point in pyramid 3\n num_3_s = norm_3_1[0]*psg_3[0] + norm_3_1[1]*psg_3[1] + norm_3_1[2]*psg_3[2] - d_3_1\n den_3_s = math.sqrt(norm_3_1[0]**2+norm_3_1[1]**2+norm_3_1[2]**2)\n D_3_2_s[0] = num_3_s/den_3_s\n num_3_2_s = norm_3_2[0]*psg_3[0] + norm_3_2[1]*psg_3[1] + norm_3_2[2]*psg_3[2] - d_3_2\n den_3_2_s = math.sqrt(norm_3_2[0]**2+norm_3_2[1]**2+norm_3_2[2]**2)\n D_3_2_s[1]= num_3_2_s/den_3_2_s\n num_3_3_s = norm_3_3[0]*psg_3[0] + norm_3_3[1]*psg_3[1] + norm_3_3[2]*psg_3[2] - d_3_3\n den_3_3_s = math.sqrt(norm_3_3[0]**2+norm_3_3[1]**2+norm_3_3[2]**2)\n D_3_2_s[2] = num_3_3_s/den_3_3_s\n num_3_4_s = norm_3_4[0]*psg_3[0] + norm_3_4[1]*psg_3[1] + norm_3_4[2]*psg_3[2] - d_3_4\n den_3_4_s = math.sqrt(norm_3_4[0]**2+norm_3_4[1]**2+norm_3_4[2]**2)\n D_3_2_s[3] = num_3_4_s/den_3_4_s\n num_3_5_s = norm_3_5[0]*psg_3[0] + norm_3_5[1]*psg_3[1] + norm_3_5[2]*psg_3[2] - d_3_5\n den_3_5_s = math.sqrt(norm_3_5[0]**2+norm_3_5[1]**2+norm_3_5[2]**2)\n D_3_2_s[4] = num_3_5_s/den_3_5_s\n ##Normal direction verification for sample point in pyramid 4\n num_4_s = norm_4_1[0]*psg_4[0] + norm_4_1[1]*psg_4[1] + norm_4_1[2]*psg_4[2] - d_4_1\n den_4_s = math.sqrt(norm_4_1[0]**2+norm_4_1[1]**2+norm_4_1[2]**2)\n D_4_2_s[0] = num_4_s/den_4_s\n num_4_2_s = norm_4_2[0]*psg_4[0] + norm_4_2[1]*psg_4[1] + norm_4_2[2]*psg_4[2] - d_4_2\n den_4_2_s = math.sqrt(norm_4_2[0]**2+norm_4_2[1]**2+norm_4_2[2]**2)\n D_4_2_s[1]= num_4_2_s/den_4_2_s\n num_4_3_s = norm_4_3[0]*psg_4[0] + norm_4_3[1]*psg_4[1] + norm_4_3[2]*psg_4[2] - d_4_3\n den_4_3_s = math.sqrt(norm_4_3[0]**2+norm_4_3[1]**2+norm_4_3[2]**2)\n D_4_2_s[2] = num_4_3_s/den_4_3_s\n num_4_4_s = norm_4_4[0]*psg_4[0] + norm_4_4[1]*psg_4[1] + norm_4_4[2]*psg_4[2] - d_4_4\n den_4_4_s = math.sqrt(norm_4_4[0]**2+norm_4_4[1]**2+norm_4_4[2]**2)\n D_4_2_s[3] = num_4_4_s/den_4_4_s\n num_4_5_s = norm_4_5[0]*psg_4[0] + norm_4_5[1]*psg_4[1] + norm_4_5[2]*psg_4[2]- d_4_5\n den_4_5_s = math.sqrt(norm_4_5[0]**2+norm_4_5[1]**2+norm_4_5[2]**2)\n D_4_2_s[4] = num_4_5_s/den_4_5_s\n ##################################################################\n ##Begining of DRONE 2,3,4 Inclusion Test using a for Loop\n ##Distance from Pyramids' points comparison dist_p1_d2 is distance of pyramid 1 points from d2, drone 2\n # make this into a loop for each neighboring agent\n for a in range(agents-1):#looping with range method ex: rang(2) -> output is 0,1\n #pub_incl = rospy.Publisher('/%s_%d/incl_test'(\"guidance\", a+2), Int32, queue_size=40)#not sure if this will work, still need to check\n pyr_incl = np.zeros(4)\n dist_p1_d2[0] = math.sqrt((pos_list[a,0] - pag_1[0] )**2 + (pos_list[a,1] - pag_1[1])**2 + ( pos_list[a,2] - pag_1[2])**2 )\n dist_p1_d2[1] = math.sqrt((pos_list[a,0] - pbg_1[0] )**2 + (pos_list[a,1] - pbg_1[1])**2 + ( pos_list[a,2] - pbg_1[2])**2)\n dist_p1_d2[2] = math.sqrt((pos_list[a,0] - pcg_1[0] )**2 + (pos_list[a,1] - pcg_1[1])**2 + ( pos_list[a,2] - pcg_1[2])**2)\n dist_p1_d2[3] = math.sqrt((pos_list[a,0] - pdg_1[0] )**2 + (pos_list[a,1] - pdg_1[1])**2 + ( pos_list[a,2] - pdg_1[2])**2)\n dist_comp[0] = min(dist_p1_d2)\n ##Distance from Pyramids' points comparison dist_p1_d2 is distance of pyramid 2 points from d2, drone 2\n dist_p2_d2[0] = math.sqrt((pos_list[a,0] - pag_2[0] )**2 + (pos_list[a,1] - pag_2[1])**2 + ( pos_list[a,2] - pag_2[2])**2)\n dist_p2_d2[1] = math.sqrt((pos_list[a,0] - pbg_2[0] )**2 + (pos_list[a,1] - pbg_2[1])**2 + ( pos_list[a,2] - pbg_2[2])**2)\n dist_p2_d2[2] = math.sqrt((pos_list[a,0] - pcg_2[0] )**2 + (pos_list[a,1] - pcg_2[1])**2 + ( pos_list[a,2] - pcg_2[2])**2)\n dist_p2_d2[3] = math.sqrt((pos_list[a,0] - pdg_2[0] )**2 + (pos_list[a,1] - pdg_2[1])**2 + ( pos_list[a,2] - pdg_2[2])**2)\n dist_comp[1] = min(dist_p2_d2)\n ##Distance from Pyramids' points comparison dist_p1_d2 is distance of pyramid 3 points from d2, drone 2\n dist_p3_d2[0] = math.sqrt((pos_list[a,0] - pag_3[0] )**2 + (pos_list[a,1]- pag_3[1])**2 + ( pos_list[a,2] - pag_3[2])**2)\n dist_p3_d2[1] = math.sqrt((pos_list[a,0] - pbg_3[0] )**2 + (pos_list[a,1] - pbg_3[1])**2 + ( pos_list[a,2] - pbg_3[2])**2)\n dist_p3_d2[2] = math.sqrt((pos_list[a,0] - pcg_3[0] )**2 + (pos_list[a,1] - pcg_3[1])**2 + ( pos_list[a,2] - pcg_3[2])**2)\n dist_p3_d2[3] = math.sqrt((pos_list[a,0] - pdg_3[0] )**2 + (pos_list[a,1] - pdg_3[1])**2 + ( pos_list[a,2] - pdg_3[2])**2)\n dist_comp[2] = min(dist_p3_d2)\n ##Distance from Pyramids' points comparison dist_p1_d2 is distance of pyramid 4 points from d2, drone 2\n dist_p4_d2[0] = math.sqrt((pos_list[a,0] - pag_4[0] )**2 + (pos_list[a,1] - pag_4[1])**2 + ( pos_list[a,2] - pag_4[2])**2)\n dist_p4_d2[1] = math.sqrt((pos_list[a,0] - pbg_4[0] )**2 + (pos_list[a,1] - pbg_4[1])**2 + ( pos_list[a,2] - pbg_4[2])**2)\n dist_p4_d2[2] = math.sqrt((pos_list[a,0] - pcg_4[0] )**2 + (pos_list[a,1] - pcg_4[1])**2 + ( pos_list[a,2] - pcg_4[2])**2)\n dist_p4_d2[3] = math.sqrt((pos_list[a,0] - pdg_4[0] )**2 + (pos_list[a,1] - pdg_4[1])**2 + ( pos_list[a,2] - pdg_4[2])**2)\n dist_comp[3] = min(dist_p4_d2)\n ##Decide which pyramid is Drone 2 closest to\n if (pos_list[a,0] != 0 and pos_list[a,1] != 0 and pos_list[a,2] != 0): #To check if drone even exists in the x-y-z- grid because x=y=z=0 is not possible\n minni= min(dist_comp)\n index_dist_comp = dist_comp.tolist().index(minni)#WHAT HAPPENS IF THE DISTANCE IS EQUAL FROM TWO PYRAMIDS\n pyramid_no = index_dist_comp + 1\n ##for drone 2 and 1st pyramid inclusion test\n #if pyramid_no == 1:\n num = norm_1[0]*pos_list[a,0] + norm_1[1]*pos_list[a,1] + norm_1[2]*pos_list[a,2] - d_1\n den = math.sqrt(norm_1[0]**2+norm_1[1]**2+norm_1[2]**2)\n D_1_2[0] = num/den\n num_2 = norm_2[0]*pos_list[a,0] + norm_2[1]*pos_list[a,1] + norm_2[2]*pos_list[a,2] - d_2\n den_2 = math.sqrt(norm_2[0]**2+norm_2[1]**2+norm_2[2]**2)\n D_1_2[1]= num_2/den_2\n num_3 = norm_3[0]*pos_list[a,0] + norm_3[1]*pos_list[a,1] + norm_3[2]*pos_list[a,2] - d_3\n den_3 = math.sqrt(norm_3[0]**2+norm_3[1]**2+norm_3[2]**2)\n D_1_2[2] = num_3/den_3\n num_4 = norm_4[0]*pos_list[a,0] + norm_4[1]*pos_list[a,1] + norm_4[2]*pos_list[a,2] - d_4\n den_4 = math.sqrt(norm_4[0]**2+norm_4[1]**2+norm_4[2]**2)\n D_1_2[3] = num_4/den_4\n num_5 = norm_5[0]*pos_list[a,0] + norm_5[1]*pos_list[a,1] + norm_5[2]*pos_list[a,2] - d_5\n den_5 = math.sqrt(norm_5[0]**2+norm_5[1]**2+norm_5[2]**2)\n D_1_2[4] = num_5/den_5\n #print(num_5)\n #print(den_5)\n for i in range(len(D_1_2)):\n if D_1_2[i]/abs(D_1_2[i]) == D_1_2_s[i]/abs(D_1_2_s[i]):\n #print(D_1_2[i])\n #print( D_1_2_s[i])\n bool_data[i] = 1\n else:\n #print(D_1_2[i])\n #print( D_1_2_s[i])\n bool_data[i] = 0\n # Calculate Euclidean distance of Drone Position with respect to sample point psg_1 for pyramid 1 for x-y coordinate system as Pozyx is running on 2D algorithm\n euc_dist = math.sqrt((pos_list[a,0] - psg_1[0] )**2 + (pos_list[a,1]- psg_1[1])**2 + (pos_list[a,2]- psg_1[2])**2) \n if min(bool_data) == 0:\n #print('out_1')\n #print(a+2)\n #adj[a+1] = 0 #This will remain zero, which means nhbd agent is not in FOV\n pyr_incl[0] = 0\n #pub_incl.publish(0)\n else:\n #print('in_1')\n #print(a+2)\n pyr_incl[0] = euc_dist #it will be euc distance instead of 0\n #pub_incl.publish(1)\n \n \n #if pyramid_no == 2:\n num_2 = norm_2_1[0]*pos_list[a,0] + norm_2_1[1]*pos_list[a,1] + norm_2_1[2]*pos_list[a,2] - d_2_1\n den_2 = math.sqrt(norm_2_1[0]**2+norm_2_1[1]**2+norm_2_1[2]**2)\n D_2_2[0] = num_2/den_2\n num_2_2 = norm_2_2[0]*pos_list[a,0] + norm_2_2[1]*pos_list[a,1] + norm_2_2[2]*pos_list[a,2] - d_2_2\n den_2_2 = math.sqrt(norm_2_2[0]**2+norm_2_2[1]**2+norm_2_2[2]**2)\n D_2_2[1]= num_2_2/den_2_2\n num_2_3 = norm_2_3[0]*pos_list[a,0] + norm_2_3[1]*pos_list[a,1] + norm_2_3[2]*pos_list[a,2] - d_2_3\n den_2_3 = math.sqrt(norm_2_3[0]**2+norm_2_3[1]**2+norm_2_3[2]**2)\n D_2_2[2] = num_2_3/den_2_3\n num_2_4 = norm_2_4[0]*pos_list[a,0] + norm_2_4[1]*pos_list[a,1] + norm_2_4[2]*pos_list[a,2] - d_2_4\n den_2_4 = math.sqrt(norm_2_4[0]**2+norm_2_4[1]**2+norm_2_4[2]**2)\n D_2_2[3] = num_2_4/den_2_4\n num_2_5 = norm_2_5[0]*pos_list[a,0] + norm_2_5[1]*pos_list[a,1] + norm_2_5[2]*pos_list[a,2] - d_2_5\n den_2_5 = math.sqrt(norm_2_5[0]**2+norm_2_5[1]**2+norm_2_5[2]**2)\n D_2_2[4] = num_2_5/den_2_5\n for i in range(len(D_2_2)):\n if D_2_2[i]/abs(D_2_2[i]) == D_2_2_s[i]/abs(D_2_2_s[i]):\n bool_data[i] = 1\n \n else:\n bool_data[i] = 0\n euc_dist = math.sqrt((pos_list[a,0] - psg_2[0] )**2 + (pos_list[a,1]- psg_2[1])**2 + (pos_list[a,2]- psg_2[2])**2 ) \n if min(bool_data) == 0:\n #print('out_2')\n #print(a+2)\n pyr_incl[1] = 0\n #print(adj)\n #pub_incl.publish(0)\n else:\n #print('in_2')\n #print(a+2)\n pyr_incl[1] = euc_dist #it will be euc distance instead of 0\n #pub_incl.publish(1)\n \n #if pyramid_no == 3:\n num_3 = norm_3_1[0]*pos_list[a,0] + norm_3_1[1]*pos_list[a,1] + norm_3_1[2]*pos_list[a,2] - d_3_1\n den_3 = math.sqrt(norm_3_1[0]**2+norm_3_1[1]**2+norm_3_1[2]**2)\n D_3_2[0] = num_3/den_3\n num_3_2 = norm_3_2[0]*pos_list[a,0] + norm_3_2[1]*pos_list[a,1] + norm_3_2[2]*pos_list[a,2] - d_3_2\n den_3_2 = math.sqrt(norm_3_2[0]**2+norm_3_2[1]**2+norm_3_2[2]**2)\n D_3_2[1]= num_3_2/den_3_2\n num_3_3 = norm_3_3[0]*pos_list[a,0] + norm_3_3[1]*pos_list[a,1] + norm_3_3[2]*pos_list[a,2] - d_3_3\n den_3_3 = math.sqrt(norm_3_3[0]**2+norm_3_3[1]**2+norm_3_3[2]**2)\n D_3_2[2] = num_3_3/den_3_3\n num_3_4 = norm_3_4[0]*pos_list[a,0] + norm_3_4[1]*pos_list[a,1] + norm_3_4[2]*pos_list[a,2] - d_3_4\n den_3_4 = math.sqrt(norm_3_4[0]**2+norm_3_4[1]**2+norm_3_4[2]**2)\n D_3_2[3] = num_3_4/den_3_4\n num_3_5 = norm_3_5[0]*pos_list[a,0] + norm_3_5[1]*pos_list[a,1] + norm_3_5[2]*pos_list[a,2] - d_3_5\n den_3_5 = math.sqrt(norm_3_5[0]**2+norm_3_5[1]**2+norm_3_5[2]**2)\n D_3_2[4] = num_3_5/den_3_5\n for i in range(len(D_3_2)):\n if D_3_2[i]/abs(D_3_2[i]) == D_3_2_s[i]/abs(D_3_2_s[i]):\n bool_data[i] = 1\n \n else:\n bool_data[i] = 0\n euc_dist = math.sqrt((pos_list[a,0] - psg_3[0] )**2 + (pos_list[a,1]- psg_3[1])**2 + (pos_list[a,2]- psg_3[2])**2) \n if min(bool_data) == 0:\n #print('out_3')\n #print (a+2)\n pyr_incl[2] = 0\n #pub_incl.publish(0)\n else:\n #print('in_3')\n #print(a+2)\n pyr_incl[2] = euc_dist #it will be euc distance instead of 0\n #pub_incl.publish(1)\n \n #if pyramid_no == 4:\n num_4 = norm_4_1[0]*pos_list[a,0] + norm_4_1[1]*pos_list[a,1] + norm_4_1[2]*pos_list[a,2] - d_4_1\n den_4 = math.sqrt(norm_4_1[0]**2+norm_4_1[1]**2+norm_4_1[2]**2)\n D_4_2[0] = num_4/den_4\n num_4_2 = norm_4_2[0]*pos_list[a,0] + norm_4_2[1]*pos_list[a,1] + norm_4_2[2]*pos_list[a,2] - d_4_2\n den_4_2 = math.sqrt(norm_4_2[0]**2+norm_4_2[1]**2+norm_4_2[2]**2)\n D_4_2[1]= num_4_2/den_4_2\n num_4_3 = norm_4_3[0]*pos_list[a,0] + norm_4_3[1]*pos_list[a,1] + norm_4_3[2]*pos_list[a,2] - d_4_3\n den_4_3 = math.sqrt(norm_4_3[0]**2+norm_4_3[1]**2+norm_4_3[2]**2)\n D_4_2[2] = num_4_3/den_4_3\n num_4_4 = norm_4_4[0]*pos_list[a,0] + norm_4_4[1]*pos_list[a,1] + norm_4_4[2]*pos_list[a,2] - d_4_4\n den_4_4 = math.sqrt(norm_4_4[0]**2+norm_4_4[1]**2+norm_4_4[2]**2)\n D_4_2[3] = num_4_4/den_4_4\n num_4_5 = norm_4_5[0]*pos_list[a,0] + norm_4_5[1]*pos_list[a,1] + norm_4_5[2]*pos_list[a,2] - d_4_5\n den_4_5 = math.sqrt(norm_4_5[0]**2+norm_4_5[1]**2+norm_4_5[2]**2)\n D_4_2[4] = num_4_5/den_4_5\n for i in range(len(D_4_2)):\n if D_4_2[i]/abs(D_4_2[i]) == D_4_2_s[i]/abs(D_4_2_s[i]):\n bool_data[i] = 1\n else:\n bool_data[i] = 0\n euc_dist = math.sqrt((pos_list[a,0] - psg_4[0] )**2 + (pos_list[a,1]- psg_4[1])**2 + (pos_list[a,2]- psg_4[2])**2 ) \n if min(bool_data) == 0:\n #print('out_4')\n #print(a+2)\n pyr_incl[3] = 0 \n else:\n #print('in_4')\n #print(a+2)\n pyr_incl[3] = euc_dist #it will be euc distance instead of 0\n adj[a+1] = max(pyr_incl)\n \n ##END of DRONE Inclusion Test","title":""},{"docid":"82a00b5d1875f7a99a27e7038f7c6c26","score":"0.5384236","text":"def ibvs_run_ur5(self,xo,xd,uvm,z,q,info,flag):\n xp=self.get_fov_desire_uv(uvm[0],xo,xd,flag)\n print \"xp\",xp\n \"\"\"\n Second,caculating cam vodt and deta joint speed\n desireuv=xp\n \"\"\"\n # cam_vdot=self.get_cam_vdot_wz(uvm,z,xp,uvm[0])\n\n joint_speed_dot=self.get_joint_speed(uvm, z, xp, uvm[0], q, info)\n print \"joint_speed_dot\",joint_speed_dot\n \"\"\"\n Third,caculating deta joint speed\n \"\"\"\n deta_joint_angular=self.get_deta_joint_angular(joint_speed_dot)\n print \"deta_joint_angular\",deta_joint_angular\n \"\"\"\n Fourth,get joint angular\n \"\"\"\n pub_joint=self.get_joint_angular(q, deta_joint_angular)\n print \"pub_joint\",pub_joint\n return pub_joint","title":""},{"docid":"90d9f13474d8dfa3ca748b45bbaeb076","score":"0.5368649","text":"def write_fieldgeneral(self,u,d=None,filename='field_general.out',N=100,s=1.0):\n if d is None:\n d=np.zeros(2*self.D,dtype=complex)\n\n try:\n ex=self.ex\n if s==1.0:\n x=np.linspace(-0.5,0.5,N)\n xl=sub.t_dir(x,ex)\n else:\n xl=np.linspace(-s*0.5,s*0.5,N)\n x=sub.t_inv(xl,ex)\n except AttributeError:\n x=np.linspace(-0.5,0.5,N)\n xl=np.linspace(-0.5,0.5,N)\n try:\n ey=self.ey\n if s==1.0:\n y=np.linspace(-0.5,0.5,N)\n yl=sub.t_dir(y,ey)\n else:\n yl=np.linspace(-s*0.5,s*0.5,N)\n y=sub.t_inv(yl,ey)\n except AttributeError:\n y=np.linspace(-0.5,0.5,N)\n yl=np.linspace(-0.5,0.5,N)\n\n [X,Y]=np.meshgrid(x,y,indexing='ij')\n Ef=np.dot(self.V,u+d)\n [WEx,WEy]=np.split(Ef,2)\n #[WEx,WEy]=np.split(self.V[:,j],2)\n #[WHx,WHy]=np.split(self.VH[:,j],2)\n Ex,Ey=np.zeros((N,N),dtype='complex'),np.zeros((N,N),dtype='complex')\n for i in range(self.D):\n EXP=np.exp((0+2j)*np.pi*((self.G[i][0]+self.kx)*X+(self.G[i][1]+self.ky)*Y))\n# Ex+=WEx[i]*EXP\n# Ey+=WEy[i]*EXP\n Ex=np.add(Ex,np.dot(WEx[i],EXP))\n Ey=np.add(Ey,np.dot(WEy[i],EXP))\n f=open(filename,'w')\n f.write('# x y ReEx ImEx AbsEx ReEy ImEy AbsEy \\n')\n for i in range(N): \n for j in range(N):\n f.write(10*'%12.6f' % (x[i],xl[i],y[j]*self.Nyx,yl[j]*self.Nyx,Ex[i,j].real,Ex[i,j].imag,abs(Ex[i,j]),Ey[i,j].real,Ey[i,j].imag,abs(Ey[i,j])) + '\\n')\n f.write('\\n')\n f.close()","title":""},{"docid":"1e7a42489292bee3e172bdb61353b366","score":"0.53680575","text":"def tri(veci, f, q, number, point_numbers, poly_array, v, normal, face_normal_no, vn, dxbuilding, behind):\n size = 3\n g = np.zeros((size, 2)) # (3,2)\n # inits\n nc = 0\n behind = 0\n normal = vn[poly_array[q, 1], :]\n d = -np.dot(normal, ValueError(poly_array[q, 2]))\n v_d = np.dot(normal, f)\n if v_d >= 0.0:\n dxbuilding = HUGE\n v_0 = -(np.dot(normal, veci)+d)\n t = v_0/v_d\n if t < 0.0:\n dxbuilding = HUGE\n behind = 1\n # Stage 1\n intersection = veci + f*t\n maximum = max(abs(normal))\n # G: What if two normal values are the same? Anyway:\n if maximum == abs(normal[0]):\n for P in range(size):\n g[P, :] = (intersection[1]-v[int(poly_array[q, 1+P]), 1], intersection[2]-v[int(poly_array[q, 1+P]), 2])\n elif maximum == normal[1]:\n for P in range(size):\n g[P, :] = (intersection[0]-v[int(poly_array[q, 1+P]), 0], intersection[2]-v[int(poly_array[q, 1+P]), 2])\n elif maximum == normal[2]:\n for P in range(size):\n g[P, :] = (intersection[0]-v[int(poly_array[q, 1+P]), 0], intersection[1]-v[int(poly_array[q, 1+P]), 1])\n # Stage 2\n for P in range(size):\n if P == size:\n if g[P, 1] < 0.0:\n sh = -1\n else:\n sh = 1\n elif (g[P, 0] > 0.0) or (g[P+1, 1] > 0.0):\n if (g[P, 0]-(g[P, 1]*(g[P+1, 0]-g[P, 0])/(g[P+1, 1]-g[P, 1]))) > 0.0:\n nc += 1\n odd = nc % 2 # get remainder to find if odd or not\n # This was this way in original fortran\n if odd:\n dxbuilding = t\n else:\n dxbuilding = HUGE\n return dxbuilding, behind","title":""},{"docid":"18eee549a824bcffbb807627934387d8","score":"0.53669924","text":"def eval_f(self, u, t):\n me = self.dtype_f(self.init, val=0.0)\n\n # compute the acceleration due to gravitational forces\n # ... only with respect to the sun\n if self.sun_only:\n for i in range(1, self.init[0][-1]):\n dx = u.pos[:, i] - u.pos[:, 0]\n r = np.sqrt(np.dot(dx, dx))\n df = self.G * dx / (r**3)\n me[:, i] -= u.m[0] * df\n\n # ... or with all planets involved\n else:\n for i in range(self.init[0][-1]):\n for j in range(i):\n dx = u.pos[:, i] - u.pos[:, j]\n r = np.sqrt(np.dot(dx, dx))\n df = self.G * dx / (r**3)\n me[:, i] -= u.m[j] * df\n me[:, j] += u.m[i] * df\n\n return me","title":""},{"docid":"659688c88c8b1baa84e8d0a392a6737f","score":"0.536049","text":"def uprime(u, CL, CD, g, vt):\n v, theta, x, y = u\n uprimevector = numpy.array([-g * math.sin(theta) - CD/CL * g / vt**2 * v**2, -g * math.cos(theta)/v+g/vt**2 * v, v*math.cos(theta),v*math.sin(theta)])\n\n return uprimevector","title":""},{"docid":"3d1ec2fdb3ceca11318f65158241309b","score":"0.53565365","text":"def calc_uvw(b, tobs, src_epoch, src_lon, src_lat):\n if type(tobs)!=np.ndarray: tobs = np.array(tobs)\n if tobs.ndim < 1: tobs = tobs[np.newaxis]\n nt = tobs.shape[0] \n if type(b)==list: b = np.array(b)\n if b.ndim < 2: b = b[np.newaxis,:]\n nb = b.shape[0] \n \n bu = np.zeros((nt,nb))\n bv = np.zeros((nt,nb))\n bw = np.zeros((nt,nb))\n \n # Define the reference frame\n me = cc.measures.measures()\n qa = cc.quanta.quanta()\n me.doframe(me.observatory('OVRO_MMA'))\n me.doframe(me.direction(src_epoch, \n qa.quantity(src_lon.to_value(u.deg),'deg'), \n qa.quantity(src_lat.to_value(u.deg),'deg')))\n\n contains_nans=False\n\n for i in range(nt):\n me.doframe(me.epoch('UTC',qa.quantity(tobs[i],'d'))) \n for j in range(nb):\n bl = me.baseline('itrf',qa.quantity(b[j,0],'m'),\n qa.quantity(b[j,1],'m'),qa.quantity(b[j,2],'m'))\n # Get the uvw coordinates\n try:\n uvw=me.touvw(bl)[1]['value']\n bu[i,j],bv[i,j],bw[i,j]=uvw[0],uvw[1],uvw[2]\n except KeyError:\n contains_nans=True\n bu[i,j],bv[i,j],bw[i,j]=np.nan,np.nan,np.nan\n if contains_nans:\n print('Warning: some solutions not found for u,v,w coordinates')\n \n return bu.T,bv.T,bw.T","title":""},{"docid":"d39830f1bc4976cb01d26d8e360fd08e","score":"0.5354262","text":"def dfdV(self, j):\n dfdV = np.zeros((self.num_dim_2d, self.num_factors))\n a = self.angles[j]\n\n U_x = self.U[self.x_portion_3d,:]\n U_y = self.U[self.y_portion_3d,:]\n U_z = self.U[self.z_portion_3d,:]\n\n for i in xrange(self.num_joints):\n u_x_coord = i\n u_y_coord = i + self.num_joints\n\n dfdV[u_x_coord,:] = U_x[i,:] * np.cos(a) + U_z[i,:] * np.sin(a)\n dfdV[u_y_coord,:] = U_y[i,:]\n\n return dfdV","title":""},{"docid":"a96c1cb57427d0d51f9c9c2a192f77cf","score":"0.53467023","text":"def vntF(self):\n return [self.ntFx, self.ntFy] + ([] if self.dim == 2 else [self.ntFz])","title":""},{"docid":"5fb55516b2b9756e869e92ce73502dd4","score":"0.53442144","text":"def orbit_grapher3(p, v):\n \n assert len(p) == 3, \"Position vector not 3-dimensional\"\n assert len(v) == 3, \"Velocity vector not 3-dimensional\"\n \n for interval in dt:\n\n t_array = np.arange(0, t_max, interval)\n\n # initialise empty lists to record trajectories. will end up in a list of tuples.\n p_list = []\n v_list = []\n \n p_list.append(p)\n v_list.append(v)\n r = np.linalg.norm(p)\n r_unit = (p[0]/r, p[1]/r, p[2]/r)\n F_mag = (G*M*m)/(r**2)\n F = (r_unit[0]*F_mag, r_unit[1]*F_mag, r_unit[2]*F_mag)\n a = (-F[0]/m, -F[1]/m, -F[2]/m)\n p_x = p[0] + interval * v[0]\n p_y = p[1] + interval * v[1]\n p_z = p[2] + interval * v[2]\n p = (p_x, p_y, p_z)\n v_x = v[0] + interval * a[0]\n v_y = v[1] + interval * a[1]\n v_z = v[2] + interval * a[2]\n v = (v_x, v_y, v_z)\n p_list.append(p)\n v_list.append(v)\n \n # Verlet integration\n for i in range(len(t_array)-2):\n\n # calculate new position and velocity\n r = np.linalg.norm(p)\n r_unit = (p[0]/r, p[1]/r, p[2]/r)\n F_mag = (G*M*m)/(r**2)\n F = (r_unit[0]*F_mag, r_unit[1]*F_mag, r_unit[2]*F_mag)\n a = (-F[0]/m, -F[1]/m, -F[2]/m)\n p_x = 2*(p_list[-1][0]) - (p_list[-2][0]) + (interval ** 2) * a[0]\n p_y = 2*(p_list[-1][1]) - (p_list[-2][1]) + (interval ** 2) * a[1]\n p_z = 2*(p_list[-1][2]) - (p_list[-2][2]) + (interval ** 2) * a[2]\n p = (p_x, p_y, p_z)\n v_x = (p_x - (p_list[-2][0])) / (2*interval)\n v_y = (p_y - (p_list[-2][1])) / (2*interval)\n v_z = (p_z - (p_list[-2][2])) / (2*interval)\n v = (v_x, v_y, v_z)\n \n p_list.append(p)\n v_list.append(v)\n assert len(p_list) == len(v_list) #check if position list and velocity list have equal number of terms\n p_x_list = []\n p_y_list = []\n p_z_list = []\n for position_vector in p_list:\n p_x_list.append(position_vector[0])\n p_y_list.append(position_vector[1])\n p_z_list.append(position_vector[2])\n \n v_x_list = []\n v_y_list = []\n v_z_list = []\n for velocity_vector in v_list:\n v_x_list.append(velocity_vector[0])\n v_y_list.append(velocity_vector[1])\n v_z_list.append(velocity_vector[2])\n\n # convert trajectory lists into arrays, so they can be indexed more easily\n p_x_array = np.array(p_x_list)\n p_y_array = np.array(p_y_list)\n p_z_array = np.array(p_z_list)\n \n v_x_array = np.array(v_x_list)\n v_y_array = np.array(v_y_list)\n v_z_array = np.array(v_z_list)\n\n # plot the orbital graph in 3-D\n plt.figure(1)\n plt.clf()\n plt.xlabel('t')\n plt.ylabel ('y')\n plt.title('For dt=' + str(interval))\n plt.grid()\n plt.plot(t_array, p_y_array)\n# plt.plot(t_array, v_array, label='v (m/s)')\n plt.legend()\n plt.show()\n return 0","title":""},{"docid":"a92e564caba7871cec8e713a67b3a1a2","score":"0.5338723","text":"def weingarten(self, X, U, V):","title":""},{"docid":"acdd545de83b583bf1c82dc34c8e248a","score":"0.5330194","text":"def svd_flip(u, v, u_based_decision=...):\n ...","title":""},{"docid":"7139544e6931bebc6348551108253c08","score":"0.53260297","text":"def main(data, setup):\n # input check\n varnames = ('vm_raw_c', 'vm_raw_c_theo', 'ze', 'ze_theo')\n for varname in varnames:\n if not varname in data.keys():\n raise LookupError('data must contain variable %s.' % varname)\n\n # display info message\n chrono = setup['chrono']\n chrono.issue('target velocity: correct for finite beam width...')\n\n # retrieve variabbles\n vm_raw_c = data['vm_raw_c']\n vm_raw_c_theo = data['vm_raw_c_theo']\n vnys = data['nqv']\n\n # ========== main =================================== #\n # get mask\n mask = get_correction_mask(data, setup)\n\n alternative = False\n if alternative:\n # get factor\n factor = get_correction_factor(data, setup)\n v_out = np.nan * np.zeros_like(vm_raw_c)\n v_out[~mask] = vm_raw_c[~mask]\n\n v_meas = vm_raw_c[mask]\n v_sfc = vm_raw_c_theo[mask]\n f = factor[mask]\n\n with warnings.catch_warnings():\n warnings.simplefilter('ignore', RuntimeWarning)\n v_atm = (v_meas - f * v_sfc) / (1. - f)\n v_atm[f>=1] = v_sfc[f>=1]\n v_out[mask] = v_atm\n else:\n # correct masked values\n v_out = np.nan * np.zeros_like(vm_raw_c)\n v_out[~mask] = vm_raw_c[~mask]\n v_out[mask] = vm_raw_c[mask] - vm_raw_c_theo[mask]\n\n # mod\n v_out= symmod(v_out, 2*vnys)\n # ==================================================== #\n\n # output\n data['vm_c_bw-corrected'] = v_out\n return data","title":""},{"docid":"2aa9a443afa73649f6a829d7870ce7c9","score":"0.5317693","text":"def f2(y, t):\n\tb = 0.002\n\tg = -9.8\n\ty = y[0] # Position\n\tv = y[1] # Velocity\n\n\tdydt = v\n\tdvdt = -b * v * abs(v)/m\n\treturn [dydt, dvdt]","title":""},{"docid":"1750feef0a59ab12efddc65619b33406","score":"0.53129923","text":"def get_perp_vec(u1, u2, direction=1):\n\n x1, y1 = u1\n x2, y2 = u2\n vx, vy = x2-x1, y2-y1\n v = np.linalg.norm((vx, vy))\n wx, wy = -vy/v * direction, vx/v * direction\n return wx, wy","title":""},{"docid":"0ad0ffbb9f640d3146e846dafda81496","score":"0.5311321","text":"def wrf_vort( U, V, dx ):\n assert U.shape == V.shape, 'Arrays are different shapes. They must be the same shape.'\n dy = dx\n du = numpy.gradient( U )\n dv = numpy.gradient( V )\n return ( dv[-1]/dx - du[-2]/dy )","title":""},{"docid":"c55a91c89ba245a9e95e7418f0cfb4a8","score":"0.53054804","text":"def zonal_advec_upwind(field, u, lat, lon, radius):\n prefactor = latlon_deriv_prefactor(lat, radius)[:,np.newaxis]\n # Assume latitude and longitude are last two axes.\n # Transpose the arrays: lon is 1st axis, lat is 2nd.\n f = field.T\n # Wrap around at 0/360 degrees longitude.\n # Assumes longitude array starts at 0 and goes to 360.\n # f = np.ma.concatenate((f, f[:2]), axis=0)\n # lon = to_radians(lon)[:,np.newaxis,np.newaxis,np.newaxis]\n # lon = np.ma.concatenate((lon, 2*np.pi + lon[:2]), axis=0)\n # df_fwd = fwd_diff2(f, lon)\n # df_bwd = np.ma.concatenate((df_fwd[-2:], df_fwd[:-2]), axis=0)\n f = np.ma.concatenate((f, f[:1]), axis=0)\n lon = to_radians(lon)[:,np.newaxis,np.newaxis,np.newaxis]\n lon = np.ma.concatenate((lon, 2*np.pi + lon[:1]), axis=0)\n df_fwd = fwd_diff1(f, lon)\n df_bwd = np.ma.concatenate((df_fwd[-1:], df_fwd[:-1]), axis=0)\n # Transpose again to regain original axis order.\n return prefactor*upwind_scheme(df_fwd.T, df_bwd.T, u)","title":""},{"docid":"e00ce1852f03e5a4fb721e445ac19fab","score":"0.5301055","text":"def make_tet(v0,v1,v2):\n a,b,c = v0.length(), v1.length(), v2.length()\n d,e,f = (v0-v1).length(), (v1-v2).length(), (v2-v0).length()\n edges = a,b,c,d,e,f\n return ivm_volume(edges), xyz_volume(edges)","title":""},{"docid":"6463561cd3289762d24647ce050b535a","score":"0.5298506","text":"def v2t(v):\r\n c = cos(v[2, 0])\r\n s = sin(v[2, 0])\r\n T = np.array([[c, -s, v[0, 0]], [s, c, v[1, 0]], [0, 0, 1]])\r\n return T","title":""},{"docid":"7ba1ec17c2fd6b28d136fd2635a25e2a","score":"0.52970964","text":"def BruneModel(F_vec, Omega, f_c , tstar, t='velocity'):\n \n U = zeros(shape(F_vec))\n if t == 'displacement': # displacement spectrum\n U = Omega/(1 + power(F_vec/f_c,2))*exp(-np.pi*F_vec*tstar)\n elif t == 'velocity': # velocity spectrum\n U = (2*pi*F_vec) * Omega/(1 + power(F_vec/f_c,2))*exp(-np.pi*F_vec*tstar)\n elif t == 'acceleration': # accelaration spectrum\n U = (2*pi*F_vec)**2 * Omega/(1 + power(F_vec/f_c,2))*exp(-np.pi*F_vec*tstar)\n else:\n error('unrecognized spectrum option')\n\n return U","title":""},{"docid":"f753b896038d40dd5409c4fd6ede0248","score":"0.52957594","text":"def dynamics(self,eta,nu,u_actual,u_control,sampleTime): \n\n # Input vector\n n = u_actual \n\n # Current velocities\n u_c = self.V_c * math.cos(self.beta_c - eta[5]) # current surge velocity\n v_c = self.V_c * math.sin(self.beta_c - eta[5]) # current sway velocity\n\n nu_c = np.array([u_c,v_c,0,0,0,0],float) # current velocity vector\n nu_r = nu - nu_c # relative velocity vector\n \n # Control forces and moments with propeller saturation \n n_squared = np.zeros(self.dimU)\n for i in range(0,self.dimU): \n n[i] = sat(n[i],-self.n_max[i],self.n_max[i]) # saturation limits\n n_squared[i] = abs( n[i] ) * n[i] \n\n tau3 = np.matmul(self.B, n_squared)\n tau = np.array([ tau3[0], tau3[1], 0, 0, 0, tau3[2]],float)\n \n # 6-DOF semisub model\n nu_dot = np.matmul( self.Minv, tau \n - np.matmul(self.D, nu_r) - np.matmul(self.G, eta)) \n n_dot = (u_control - u_actual) / self.T_n\n\n # Forward Euler integration\n nu = nu + sampleTime * nu_dot\n n = n + sampleTime * n_dot \n\n u_actual = np.array(n,float) \n \n return nu, u_actual","title":""},{"docid":"7672889fec840d335b7affbd5425b1cc","score":"0.5291176","text":"def _vtk_check(self, filename: str, obj: \"StokesFlowObj\", ref_slt: \"slt\" = None, INDEX=''):\n comm = PETSc.COMM_WORLD.tompi4py()\n rank = comm.Get_rank()\n\n obj_u = self.solve_obj_u(obj, INDEX)\n obj.set_re_velocity(obj_u)\n if ref_slt is None:\n u_exact = obj.get_velocity()\n else:\n u_exact = ref_slt.get_solution(obj.get_u_geo())\n\n if rank == 0:\n velocity_x = obj_u[0::3].copy()\n velocity_y = obj_u[1::3].copy()\n velocity_z = obj_u[2::3].copy()\n velocity_err = u_exact - obj_u\n velocity_err_x = velocity_err[0::3].copy()\n velocity_err_y = velocity_err[1::3].copy()\n velocity_err_z = velocity_err[2::3].copy()\n rel_err_x = np.abs(velocity_err_x / velocity_x)\n rel_err_y = np.abs(velocity_err_y / velocity_y)\n rel_err_z = np.abs(velocity_err_z / velocity_z)\n nodes = obj.get_u_nodes()\n pointsToVTK(filename, nodes[:, 0].copy(), nodes[:, 1].copy(), nodes[:, 2].copy(),\n data={\"velocity_err\": (velocity_err_x, velocity_err_y, velocity_err_z),\n \"velocity\": (velocity_x, velocity_y, velocity_z),\n \"rel_err\": (rel_err_x, rel_err_y, rel_err_z)})\n\n errorall = np.sqrt(np.sum((obj_u - u_exact) ** 2) / np.sum(u_exact ** 2))\n errorx = np.sqrt(np.sum((obj_u[0::3] - u_exact[0::3]) ** 2) / np.sum(u_exact[0::3] ** 2))\n errory = np.sqrt(np.sum((obj_u[1::3] - u_exact[1::3]) ** 2) / np.sum(u_exact[1::3] ** 2))\n errorz = np.sqrt(np.sum((obj_u[2::3] - u_exact[2::3]) ** 2) / np.sum(u_exact[2::3] ** 2))\n error = np.hstack((errorall, errorx, errory, errorz))\n\n return error","title":""},{"docid":"857702b6fc71867a5e6da8aa8ee02db4","score":"0.5288923","text":"def faltung2(v, f, t):\n dx = (t[-1]-t[0])/(len(t)-1)\n g = np.zeros(len(t))\n # herausfinden wo t = 0\n for i in np.arange(len(t)):\n if t[i] >= 0:\n t_0 = i\n break\n # Faltung ab t = 0\n for i in np.arange(len(t)):\n g[0+i]= np.sum(v[0:i+1]*np.flip(f[0:i+1])*dx)\n return g","title":""},{"docid":"1494498a54df1b041411e3fddc3b729a","score":"0.52836055","text":"def _update_v_fwd1(\n self,\n step: int,\n state: State,\n training: bool = True,\n ) -> tuple[State, Tensor]:\n eps = sigmoid(tf.math.log(self.veps[step]))\n # force = tf.cast(tf.reshape(force, state.v.shape), state.v.dtype)\n # exp_s = tf.cast(\n # tf.reshape(tf.exp(logjac), state.v.shape),\n # state.v.dtype\n # )\n # exp_q = tf.cast(\n # tf.reshape(tf.exp(tf.multiply(eps, q)), state.v.shape),\n # state.v.dtype\n # )\n # t = tf.cast(tf.reshape(t, state.v.shape), state.v.dtype)\n # vf = exp_s * state.v - 0.5 * eps * (force * exp_q + t)\n # vf = tf.multiply(exp_s, state.v) - 0.5 * eps * (force * exp_q + t)\n # halfeps = tf.cast(0.5 * eps, state.x.dtype)\n # vf = (\n # tf.multiply(exp_s, state.v)\n # - halfeps * (tf.multiply(force, exp_q) + t)\n # )\n force = self.grad_potential(state.x, state.beta)\n # vNet: (x, F) --> (s, t, q)\n s, t, q = self._call_vnet(step, (state.x, force), training=training)\n eps = tf.cast(eps, s.dtype)\n halfeps = tf.scalar_mul(0.5, eps)\n assert eps is not None and isinstance(eps, tf.Tensor)\n logjac = tf.scalar_mul(halfeps, s)\n logdet = tf.reduce_sum(self.flatten(logjac), axis=1)\n force = tf.cast(tf.reshape(force, state.v.shape), s.dtype)\n exp_s = tf.reshape(tf.exp(logjac), state.v.shape)\n exp_q = tf.reshape(tf.exp(tf.scalar_mul(eps, q)), state.v.shape)\n t = tf.reshape(t, state.v.shape)\n v = tf.cast(state.v, exp_s.dtype)\n vf = exp_s * v - tf.scalar_mul(halfeps, (force * exp_q + t))\n\n return State(state.x, vf, state.beta), logdet","title":""},{"docid":"d646b910e579db9755cda13c0c858840","score":"0.5281493","text":"def calcUpVector(self):\n self._up = np.cross(self._transverse, self._fv)","title":""},{"docid":"5d3f753d1bddb76effdc3b0dc8287023","score":"0.528143","text":"def project(v, u):\n return (np.dot(v.T, u) / np.dot(u.T, u)) * u","title":""},{"docid":"c558600f5c729195c56974437a1bcadd","score":"0.52807224","text":"def probe_at_dx(\n du=0.001,\n velocity_file=None,\n stlfile=\"c0006.stl\",\n output_fn=\"surface.vtp\",\n velocity_name=\"v [m/s]\",\n write=False,\n mu=1.0,\n move_mesh=False,\n sign=1,\n):\n\n if type(stlfile) is str:\n stl = read_vtk(stlfile)\n else:\n stl = stlfile\n\n vertices = numpy_support.vtk_to_numpy(stl.GetPoints().GetData())\n # indices = numpy_support.vtk_to_numpy(stl.GetPolys().GetData()).reshape(-1, 4)[:, 1:4]\n merged = vtk.vtkPolyData()\n merged.DeepCopy(stl)\n\n vel_data = read_vtk(velocity_file)\n\n # Compute normals to vertices\n normalGenerator = vtk.vtkPolyDataNormals()\n normalGenerator.SetInputData(merged)\n normalGenerator.ComputePointNormalsOn()\n normalGenerator.ComputeCellNormalsOff()\n normalGenerator.SetSplitting(0)\n normalGenerator.SetConsistency(0)\n normalGenerator.Update()\n\n merged = normalGenerator.GetOutput()\n normals = numpy_support.vtk_to_numpy(merged.GetPointData().GetNormals())\n\n points = vtk.vtkPoints()\n pointsPolyData = vtk.vtkPolyData()\n\n for normal, pos in zip(normals, vertices):\n points.InsertNextPoint(pos + normal * (-sign * du))\n\n pointsPolyData.SetPoints(points)\n probe_filter = vtk.vtkProbeFilter()\n probe_filter.SetInputData(pointsPolyData)\n probe_filter.SetSourceData(vel_data)\n probe_filter.GetOutputPort()\n probe_filter.Update()\n\n probed_data = probe_filter.GetOutput().GetPointData()\n\n if isinstance(velocity_name, str):\n v_vec = numpy_support.vtk_to_numpy(probed_data.GetArray(velocity_name))\n elif isinstance(velocity_name, list):\n vx = numpy_support.vtk_to_numpy(probed_data.GetArray(velocity_name[0]))\n vy = numpy_support.vtk_to_numpy(probed_data.GetArray(velocity_name[1]))\n vz = numpy_support.vtk_to_numpy(probed_data.GetArray(velocity_name[2]))\n v_vec = np.stack((vx, vy, vz), 1).reshape((-1, 3))\n else:\n raise NotImplementedError\n\n logging.debug(v_vec.shape)\n\n velocity = vtk.vtkFloatArray()\n velocity.SetNumberOfComponents(1)\n velocity.SetName(\"X_at_epsilon\")\n\n for v in v_vec:\n # if np.max(v) > 1e33 or np.min(v) < -1e33:\n # s = np.array([0.00])\n\n velocity.InsertNextTypedTuple([v])\n\n merged.GetPointData().AddArray(velocity)\n\n if move_mesh:\n merged.SetPoints(points)\n logging.debug(\"moving point by dx inside\")\n\n merged.Modified()\n if write:\n save_vtp(merged, output_fn)\n\n return merged","title":""},{"docid":"8e62f171cf3bfc0b3d9595d83372b1ce","score":"0.5278805","text":"def convert_w_2D(w, w_):\n\n rhot = w[w_['h']] + w[w_['rhob']]\n\n sac_gdf_output = {}\n\n sac_gdf_output['density_pert'] = {\n 'field': w[w_['h']] * u.Unit('kg/m^3'),\n 'field_name': 'perturbation density',\n 'staggering': 0\n }\n sac_gdf_output['density_bg'] = {\n 'field': w[w_['rhob']] * u.Unit('kg/m^3'),\n 'field_name':'background density',\n 'staggering':0\n }\n sac_gdf_output['velocity_x'] = {\n 'field': w[w_['m2']]/rhot * u.Unit('m/s'),\n 'field_name': 'velocity x component',\n 'staggering': 0\n }\n sac_gdf_output['velocity_z'] = {\n 'field': w[w_['m1']]/rhot * u.Unit('m/s'),\n 'field_name':'velocity z component',\n 'staggering':0\n }\n sac_gdf_output['mag_field_x_pert'] = {\n 'field':u.Quantity(w[w_['b2']], unit=u.T),\n 'field_name':'perturbation magnetic field x component',\n 'staggering':0\n }\n sac_gdf_output['mag_field_z_pert'] = {\n 'field': w[w_['b1']] * u.T,\n 'field_name': 'perturbation magnetic field z component',\n 'staggering': 0\n }\n sac_gdf_output['mag_field_x_bg'] = {\n 'field': w[w_['bg2']] * u.T,\n 'field_name':'background magnetic field x component',\n 'staggering':0\n }\n sac_gdf_output['mag_field_z_bg'] = {\n 'field': w[w_['bg1']] * u.T,\n 'field_name': 'background magnetic field z component',\n 'staggering': 0\n }\n sac_gdf_output['internal_energy_pert'] = {\n 'field': w[w_['e']] * u.Pa,\n 'field_name':'perturbation internal energy',\n 'staggering':0\n }\n sac_gdf_output['internal_energy_bg'] = {\n 'field': w[w_['eb']] * u.Pa,\n 'field_name':'background internal energy',\n 'staggering':0\n }\n\n return sac_gdf_output","title":""},{"docid":"938bbba70456a26d75abc404eea873ee","score":"0.5277239","text":"def Jtvec(self, m, v, f=None):\n Jtvec = self.G.T @ v.astype(np.float32)\n return np.asarray(self.rhoDeriv.T @ Jtvec)","title":""},{"docid":"abfc585fcf0a66f96a6f553db436c272","score":"0.52707845","text":"def model(t, x, u, p):\n # Unpack parameters\n k, e, w = p\n # Unpack initial conditions\n x0, x1 = x\n # Calculate derivatives\n dx = np.zeros(2)\n dx[0] = x1\n dx[1] = k*w**2*u - (2*e*w*x1 + w**2*x0)\n return dx","title":""},{"docid":"e8b3f8306bf83d1265f0d3dffcd65db1","score":"0.5266392","text":"def test_pde_product_operators():\n eq = PDE(\n {\"p\": \"gradient(dot(p, p) + inner(p, p)) + tensor_divergence(outer(p, p))\"}\n )\n assert not eq.explicit_time_dependence\n assert not eq.complex_valued\n field = VectorField(grids.UnitGrid([4]), 1)\n res = eq.solve(field, t_range=1, dt=0.1, backend=\"numpy\", tracker=None)\n np.testing.assert_allclose(res.data, field.data)","title":""}],"string":"[\n {\n \"docid\": \"3e69e3b72302484e63106a940fe1501a\",\n \"score\": \"0.68711233\",\n \"text\": \"def vectorfield(w, t, p):\\n x1, y1, x2, y2 = w\\n t1, t2, m, l = p\\n\\n # Create f = (x1',y1',x2',y2'):\\n f = [y1,\\n ((2./3.)*((t1/(0.5*m*l**2)+(np.sin(x2)*y2*(2*y1+y2))))-(((2./3.)+(np.cos(x2))*((t2/0.5*m*l**2))-np.sin(x2)*y1**2)))/((16./9.)-(np.cos(x2))**2),\\n y2,\\n -(((2./3.)+np.cos(x2))*((t1/(0.5*m*l**2))+np.sin(x2)*y2*(2*y1+y2))+2*(((5./3.)+np.cos(x2))*(t2/0.5*m*l**2)-np.sin(x2)*y1**2))/((16./9.)-(np.cos(x2))**2)]\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854db4ee6ef02459ebb7d7c482b70acc\",\n \"score\": \"0.66949934\",\n \"text\": \"def vectorfield(w, t, p):\\n x1, y1, x2, y2 = w\\n m1, m2, k1, k2, L1, L2, b1, b2 = p\\n\\n # Create f = (x1',y1',x2',y2'):\\n f = [y1,\\n (-b1 * y1 - k1 * (x1 - L1) + k2 * (x2 - x1 - L2)) / m1,\\n y2,\\n (-b2 * y2 - k2 * (x2 - x1 - L2)) / m2]\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854db4ee6ef02459ebb7d7c482b70acc\",\n \"score\": \"0.66949934\",\n \"text\": \"def vectorfield(w, t, p):\\n x1, y1, x2, y2 = w\\n m1, m2, k1, k2, L1, L2, b1, b2 = p\\n\\n # Create f = (x1',y1',x2',y2'):\\n f = [y1,\\n (-b1 * y1 - k1 * (x1 - L1) + k2 * (x2 - x1 - L2)) / m1,\\n y2,\\n (-b2 * y2 - k2 * (x2 - x1 - L2)) / m2]\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13aace7a408ad75e58d5de582fdd7bc1\",\n \"score\": \"0.63444346\",\n \"text\": \"def smith2007_decomposition(u, v, dx, dy):\\n\\n ny,nx = u.shape\\n k = np.fft.fftfreq(nx, dx)\\n k = np.fft.fftshift(k)\\n l = np.fft.fftfreq(ny, dy)\\n l = np.fft.fftshift(l)\\n\\n kk,ll = np.meshgrid(k, l)\\n THETA = (np.arctan2(ll, kk))\\n\\n U = np.fft.fft2(u,axes=(0, 1))\\n V = np.fft.fft2(v,axes=(0, 1))\\n\\n P = U*np.cos(THETA) + V*np.sin(THETA)\\n S = -U*np.sin(THETA) + V*np.cos(THETA)\\n\\n # back to physical space\\n u_phi = np.real(np.fft.ifft2(P*np.cos(THETA),axes=(0,1)))\\n v_phi = np.real(np.fft.ifft2(P*np.sin(THETA),axes=(0,1)))\\n\\n u_psi = np.real(np.fft.ifft2(-S*np.sin(THETA),axes=(0,1)))\\n v_psi = np.real(np.fft.ifft2(S*np.cos(THETA),axes=(0,1)))\\n\\n assert np.allclose(u, (u_phi+u_psi), rtol=1e-5, atol=1e-5), \\\"Decomposition error: rotational\\\\\\n and divergent u-components are not orthogonal\\\"\\n assert np.allclose(v, (v_phi+v_psi), rtol=1e-5, atol=1e-5), \\\"Decomposition error: rotational\\\\\\n and divergent v-components are not orthogonal\\\"\\n\\n return u_phi, v_phi, u_psi, v_psi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30aa2ad9ccbdf06331aaf99a4fbbe5c0\",\n \"score\": \"0.6306826\",\n \"text\": \"def getGridVelocity(fieldset, vector, method=\\\"data\\\"):\\n if len(vector) == 6:\\n [lons, lats, xs, ys, depth, time] = vector\\n else:\\n print \\\"getGridVelocity(): vector not in correct shape. Expected 6, got\\\", len(vector)\\n\\n res0 = np.size(lons)\\n res1 = np.size(lats)\\n res2 = np.size(xs)\\n res3 = np.size(ys)\\n if res0 == res1 == res2 == res3:\\n res = res0\\n else:\\n print \\\"getGridVelocity(): Grid is not square, i.e. len(lons) != len(lats)\\\"\\n print \\\"getGridVelocity(): recieved lons:\\\", lons\\n print \\\"getGridVelocity(): recieved lats:\\\", lats\\n return\\n\\n u = np.zeros((res, res))\\n v = np.zeros((res, res))\\n\\n if method == \\\"data\\\":\\n for i in range(res):\\n for j in range(res):\\n u[i][j] = fieldset.U.data[0, ys[i], xs[j]]\\n v[i][j] = fieldset.V.data[0, ys[i], xs[j]]\\n elif method == \\\"eval\\\":\\n for i in range(res):\\n for j in range(res):\\n u[j][i] = fieldset.U.eval(time, lons[i], lats[j], depth)\\n v[j][i] = fieldset.V.eval(time, lons[i], lats[j], depth)\\n else:\\n print \\\"getGridVelocity(): method not found, use \\\\\\\"data\\\\\\\" or \\\\\\\"eval\\\\\\\".\\\"\\n return\\n\\n return [u, v, lons, lats, depth, time]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c358e5cc42ea84b7a67a205ca9aaab\",\n \"score\": \"0.62549984\",\n \"text\": \"def _calc_phase_plane(self):\\n svx_ind = self.model.state_variables.index(self.svx)\\n svy_ind = self.model.state_variables.index(self.svy)\\n\\n #Calculate the vector field discretely sampled at a grid of points\\n grid_point = self.default_sv.copy()\\n self.U = numpy.zeros((NUMBEROFGRIDPOINTS, NUMBEROFGRIDPOINTS, self.model.number_of_modes))\\n self.V = numpy.zeros((NUMBEROFGRIDPOINTS, NUMBEROFGRIDPOINTS, self.model.number_of_modes))\\n for ii in xrange(NUMBEROFGRIDPOINTS):\\n grid_point[svy_ind] = self.Y[ii]\\n for jj in xrange(NUMBEROFGRIDPOINTS):\\n #import pdb; pdb.set_trace()\\n grid_point[svx_ind] = self.X[jj]\\n d = self.model.dfun(grid_point, self.no_coupling)\\n\\n for kk in range(self.model.number_of_modes):\\n self.U[ii, jj, kk] = d[svx_ind, 0, kk]\\n self.V[ii, jj, kk] = d[svy_ind, 0, kk]\\n\\n #self.UVmag = numpy.sqrt(self.U**2 + self.V**2)\\n #import pdb; pdb.set_trace()\\n if numpy.isnan(self.U).any() or numpy.isnan(self.V).any():\\n self.log.error(\\\"NaN\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6609cc822daf543074721126a9832e27\",\n \"score\": \"0.6222254\",\n \"text\": \"def computeUV(mdl,xx,yy,uu,vv):\\n dshape=uu.shape\\n uu=uu.flatten()\\n vv=vv.flatten()\\n corr=np.zeros(uu.shape,dtype=complex)\\n for ind in range(uu.shape[0]):\\n corr[ind]=idft2d(mdl,xx,yy,uu[ind],vv[ind],norm=mdl.size)\\n return np.reshape(corr,dshape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08ebd112638a7371714b03e992c3c935\",\n \"score\": \"0.61830056\",\n \"text\": \"def merid_advec_upwind(field, v, lat, radius, vec_field=False):\\n lat = to_radians(lat)\\n # Assume latitude and longitude are last two axes.\\n if field.ndim == 2:\\n lat = lat[np.newaxis,:]\\n if field.ndim == 3:\\n lat = lat[np.newaxis,:,np.newaxis]\\n elif field.ndim == 4:\\n lat = lat[np.newaxis,:,np.newaxis,np.newaxis]\\n f = field.T\\n prefactor = 1. / radius\\n # Del operator differs in spherical coords for scalar v. vector fields.\\n if vec_field:\\n f *= np.cos(lat)\\n prefactor /= np.cos(lat)\\n prefactor = prefactor.T\\n # Roll lat to be leading axis for broadcasting.\\n f = np.rollaxis(f, 1, 0)\\n lat = np.rollaxis(lat, 1, 0)\\n # Create arrays holding positive and negative values, each with forward\\n # differencing at south pole and backward differencing at north pole.\\n # df_fwd_except_np = fwd_diff2(f, lat)\\n # df_bwd_except_sp = fwd_diff2(f[::-1], lat[::-1])[::-1]\\n # df_fwd_adj_np = fwd_diff1(f[-2:], lat[-2:])\\n # df_bwd_adj_sp = fwd_diff1(f[1::-1], lat[1::-1])[::-1]\\n # df_fwd = np.ma.concatenate((df_fwd_except_np, df_fwd_adj_np,\\n # df_bwd_except_sp[-1:]), axis=0)\\n # df_bwd = np.ma.concatenate((df_fwd_except_np[:1], df_bwd_adj_sp,\\n # df_bwd_except_sp), axis=0)\\n df_fwd_except_np = fwd_diff1(f, lat)\\n df_bwd_except_sp = fwd_diff1(f[::-1], lat[::-1])[::-1]\\n df_fwd = np.ma.concatenate((df_fwd_except_np,\\n df_bwd_except_sp[-1:]), axis=0)\\n df_bwd = np.ma.concatenate((df_fwd_except_np[:1],\\n df_bwd_except_sp), axis=0)\\n # Roll axis and transpose again to regain original axis order.\\n df_fwd = np.rollaxis(df_fwd, 0, 2).T\\n df_bwd = np.rollaxis(df_bwd, 0, 2).T\\n return prefactor*upwind_scheme(df_fwd, df_bwd, v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"397131f7cbae93ffd94cf2e87f9cf0b9\",\n \"score\": \"0.61514723\",\n \"text\": \"def test_pde_vector():\\n eq = PDE({\\\"u\\\": \\\"vector_laplace(u) + exp(-t)\\\"})\\n assert eq.explicit_time_dependence\\n assert not eq.complex_valued\\n grid = grids.UnitGrid([8, 8])\\n field = VectorField.random_normal(grid)\\n\\n res_a = eq.solve(field, t_range=1, dt=0.01, backend=\\\"numpy\\\", tracker=None)\\n res_b = eq.solve(field, t_range=1, dt=0.01, backend=\\\"numba\\\", tracker=None)\\n\\n res_a.assert_field_compatible(res_b)\\n np.testing.assert_allclose(res_a.data, res_b.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d39bbf5f02bec67aef5de7d686610a7\",\n \"score\": \"0.6083987\",\n \"text\": \"def u(self,t, pars, variables_from_optimizer=None):\\n return pars['v']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d39bbf5f02bec67aef5de7d686610a7\",\n \"score\": \"0.6083987\",\n \"text\": \"def u(self,t, pars, variables_from_optimizer=None):\\n return pars['v']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e5068203c208a9f893e0e25ff72f8c0\",\n \"score\": \"0.606343\",\n \"text\": \"def uvvis(t,l,f):\\n\\n #l = l[:25]\\n #f = f[:25]\\n ###CONSTANTS####\\n NA=6.02214199*10**23 #avogadros number\\n c=299792458 #speed of light\\n e=1.60217662*10**(-19) #electron charge\\n me=9.10938*10**(-31) #electron mass\\n pi=math.pi\\n epsvac=8.8541878176*10**(-12)\\n ### \\n sigmaeV=0.3\\n sigmacm=sigmaeV*8065.544\\n ### \\n\\n k=(NA*e**2)/(np.log(10)*2*me*c**2*epsvac)*np.sqrt(np.log(2)/pi)*10**(-1)\\n\\n lambda1=np.zeros(len(l))\\n lambda_tot=np.zeros(len(t))\\n for x in range(1,len(t)):\\n for i in range(0,len(l)):\\n lambda1[i]=(k/sigmacm)*f[i]*np.exp(-4*np.log(2)*((1/t[x]-1/l[i])/(10**(-7)*sigmacm))**2)\\n lambda_tot[x]=sum(lambda1)\\n return np.array(lambda_tot)*10**-4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c264e995779048509dae5683101caf3\",\n \"score\": \"0.5993902\",\n \"text\": \"def form_stokesV(self):\\r\\n uvxy = UVData()\\r\\n uvxy.read_miriad(self.files['xy'])\\r\\n uvyx = UVData()\\r\\n uvyx.read_miriad(self.files['yx'])\\r\\n\\r\\n # compute redundancy information if it hasn't been done yet\\r\\n if self.caldata == tuple():\\r\\n self.calculate_caldata(uvxy)\\r\\n\\r\\n # get metadata\\r\\n info = {}\\r\\n # convert from Hz -> GHz\\r\\n info['freqs'] = uvxy.freq_array[0, :] / 1e9\\r\\n info['times'] = np.unique(uvxy.time_array)\\r\\n\\r\\n # get data and flags\\r\\n dV = {}\\r\\n fV = {}\\r\\n for key, dxy in uvxy.antpairpol_iter():\\r\\n # ignore auto correlations\\r\\n if key[0] == key[1]:\\r\\n continue\\r\\n ind1, ind2, ipol = uvxy._key2inds(key)\\r\\n for ind in [ind1, ind2]:\\r\\n if len(ind) == 0:\\r\\n continue\\r\\n # assumes that xy and yx files only have a single polarization in them\\r\\n dyx = uvyx.data_array[ind, 0, :, 0]\\r\\n fxy = uvxy.flag_array[ind, 0, :, 0]\\r\\n fyx = uvyx.flag_array[ind, 0, :, 0]\\r\\n\\r\\n antkey = key[:2]\\r\\n dV[antkey] = {'V': -1j * dxy + 1j * dyx}\\r\\n fV[antkey] = {'V': fxy + fyx}\\r\\n\\r\\n # clean up\\r\\n del uvxy, uvyx\\r\\n\\r\\n # assign to object variables\\r\\n self.info = info.copy()\\r\\n self.data = dV.copy()\\r\\n self.flags = fV.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bf5341032765c39b8d11c3f09d11205\",\n \"score\": \"0.59200287\",\n \"text\": \"def svd(pos, vec, E, nu, mesh_size, lanczos_exp):\\n grid_mat, u, i_max, j_max, i_bound_size, j_bound_size = interp_vec2grid(pos, vec, mesh_size)\\n kx, ky, lanczosx, lanczosy = calculate_fourier_modes(mesh_size, i_max, j_max, lanczos_exp)\\n GFt = calculate_greens_function(E, nu, kx, ky)\\n\\n # Deleate large data object\\n del grid_mat, kx, ky\\n\\n Ftu = np.fft.fft2(u).reshape(3, -1).T\\n shape = GFt[0, 0].shape\\n\\n # Perform flattening in Fourier mode space along the way\\n U_h = np.empty((shape[0] * shape[1], 3, 3), dtype=np.complex128)\\n s_h = np.empty((shape[0] * shape[1], 3))\\n for i in range(shape[0]):\\n for j in range(shape[1]):\\n idx = i * shape[1] + j\\n U_h[idx, :], s_h[idx, :], _V = np.linalg.svd(GFt[:, :, i, j])\\n\\n # Convert to Output format.\\n b = Ftu.flatten() # = [u(k,l).T]\\n s = s_h.flatten() # = [s(k,l).T]\\n # sparseU = block_diag(U_h, format=\\\"csr\\\")\\n return U_h, s, b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e31b9ab585bfebdb32e4b91ad16b3b5a\",\n \"score\": \"0.5896861\",\n \"text\": \"def v_pairing_uu(i,j,n,g=1.0,d=[0,0,0],channel=\\\"ee\\\"):\\n v = interaction()\\n if channel==\\\"ee\\\": # ee channel\\n v.a = element(i,n,[0,3],d=4,j=j) # cc\\n v.b = element(j,n,[3,0],d=4,j=i) # cdcd\\n elif channel==\\\"hh\\\":\\n v.a = element(i,n,[3,0],d=4,j=j) # cc\\n v.b = element(j,n,[0,3],d=4,j=i) # cdcd\\n else: raise\\n v.dir = d # direction of the interaction \\n v.g = g\\n v.contribution = \\\"A\\\"\\n v.i = i\\n v.j = j\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5682fc75424692d6503578a0391109ab\",\n \"score\": \"0.58859694\",\n \"text\": \"def u(self, t, pars, variables_from_optimizer=None):\\n return pars['v']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"698be2d669bfc45776a7a2a8b4d57999\",\n \"score\": \"0.5860657\",\n \"text\": \"def du(t,u):\\n\\t\\n\\tu_p = np.zeros(2)\\n\\tu_p[0] = u[1]\\n\\tu_p[1] = -g + 1/(m_s + m_p(t))*(dm_p(t)*v_e - 0.5*rho*u[1]*np.abs(u[1])*A*c_d)\\n\\t\\n\\treturn u_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba7e187399f1ee6668b728fc34108fe\",\n \"score\": \"0.584209\",\n \"text\": \"def vector_transport(\\n self, u: jnp.ndarray, vec1: jnp.ndarray, vec2: jnp.ndarray\\n ) -> jnp.ndarray:\\n\\n new_u = self.retraction(u, vec2)\\n return self.proj(new_u, vec1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39096c5a1f4fdf04e09ff5b4801975cb\",\n \"score\": \"0.58269244\",\n \"text\": \"def get_rotaries_from_2d_field( t, U_in, F, Amin=0, Amax=2, dA=.01, Pmin=0, Pmax=2*np.pi, dP=.1):\\n \\n # find time dimension\\n dim = np.asarray([0,1])[ np.asarray(U_in.shape)==t.size ]\\n if dim[0] == 0: # if time is first dimension -> transpose\\n U_in = U_in.T\\n\\n dimz = U_in.shape[0]\\n dimF = F.size\\n W_ac = np.zeros((dimz, dimF))\\n W_c = np.zeros((dimz, dimF))\\n Phi_ac = np.zeros((dimz, dimF))\\n Phi_c = np.zeros((dimz, dimF))\\n\\n for i in range(dimz):\\n W_ac[i,:], W_c[i,:], Phi_ac[i,:], Phi_c[i,:] = get_rotaries_from_time_series( \\n t, U_in[i,:], F, Amin=0, Amax=2, dA=.01, Pmin=0, Pmax=2*np.pi, dP=.1)\\n\\n\\n return W_ac, W_c, Phi_ac, Phi_c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d460b509695bc4af16bae0c8f9fdbdd8\",\n \"score\": \"0.5807354\",\n \"text\": \"def dftuv(M, N):\\n\\n # set up range of values\\n u = np.arange(0, M)\\n v = np.arange(0, N)\\n\\n # compute the indices for use in meshgrid corresponding to the positive\\n # frequency points in a 2D FT\\n\\n idx = u > M / 2\\n u[idx] = u[idx] - M\\n\\n idy = v > N / 2\\n v[idy] = v[idy] - N\\n\\n # compute the meshgrid arrays\\n U, V = np.meshgrid(u, v)\\n\\n return U, V\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af973f52d83e1a3ab8527efb56a46683\",\n \"score\": \"0.58029777\",\n \"text\": \"def vector_transformation(peff,latitude_out,longitude_out, B0,field_x_cea,\\n field_y_cea, field_z_cea, lat_in_rad=False):\\n\\n nlat_out = len(latitude_out)\\n nlon_out = len(longitude_out)\\n\\n PP = peff\\n if lat_in_rad is False:\\n BB = latitude_out[None, 0:nlat_out] * np.pi / 180.0\\n LL = longitude_out[0:nlon_out, None] * np.pi / 180.0\\n else:\\n BB = latitude_out\\n LL = longitude_out\\n L0 = 0.0 # We use central meridian\\n Ldif = LL - L0\\n\\n a11 = -np.sin(B0)*np.sin(PP)*np.sin(Ldif)+np.cos(PP)*np.cos(Ldif)\\n a12 = np.sin(B0)*np.cos(PP)*np.sin(Ldif)+np.sin(PP)*np.cos(Ldif)\\n a13 = -np.cos(B0)*np.sin(Ldif)\\n a21 = -np.sin(BB)*(np.sin(B0)*np.sin(PP)*np.cos(Ldif)+np.cos(PP)*np.sin(Ldif))-np.cos(BB)*np.cos(B0)*np.sin(PP)\\n a22 = np.sin(BB)*(np.sin(B0)*np.cos(PP)*np.cos(Ldif)-np.sin(PP)*np.sin(Ldif))+np.cos(BB)*np.cos(B0)*np.cos(PP)\\n a23 = -np.cos(B0)*np.sin(BB)*np.cos(Ldif)+np.sin(B0)*np.cos(BB)\\n a31 = np.cos(BB)*(np.sin(B0)*np.sin(PP)*np.cos(Ldif)+np.cos(PP)*np.sin(Ldif))-np.sin(BB)*np.cos(B0)*np.sin(PP)\\n a32 = -np.cos(BB)*(np.sin(B0)*np.cos(PP)*np.cos(Ldif)-np.sin(PP)*np.sin(Ldif))+np.sin(BB)*np.cos(B0)*np.cos(PP)\\n a33 = np.cos(BB)*np.cos(B0)*np.cos(Ldif)+np.sin(BB)*np.sin(B0)\\n\\n field_x_h = a11 * field_x_cea + a12 * field_y_cea + a13 * field_z_cea\\n field_y_h = a21 * field_x_cea + a22 * field_y_cea + a23 * field_z_cea\\n field_z_h = a31 * field_x_cea + a32 * field_y_cea + a33 * field_z_cea\\n\\n # field_z_h positive towards earth\\n # field_y_h positive towards south (-field_y_h = Bt_cea)\\n # field_x_h positive towards west\\n\\n field_y_h *= -1.0\\n\\n return field_x_h, field_y_h, field_z_h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7184a29339231d4daf5e1d70bc2b8f79\",\n \"score\": \"0.57713467\",\n \"text\": \"def STUV(E,g,F,G):\\n\\tM = 1 + E*np.conj(g)\\n\\tS = (E+g)/M\\n\\tT = (F - np.conj(E)*G)/np.conj(M)\\n\\tU = (F - E*np.conj(F))/M\\n\\tV = (G - E*F)/M\\n\\t\\n\\treturn S,T,U,V\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ecdac759ef1834876d29444c9275d4\",\n \"score\": \"0.57539415\",\n \"text\": \"def __fvec(t, x, u, ux, uxx, fval, npde, vnpts):\\r\\n\\r\\n for i in range(int(npde)):\\r\\n l = i*vnpts\\r\\n r = (i+1)*vnpts\\r\\n\\r\\n u_sliced[i] = u[l:r]\\r\\n ux_sliced[i] = ux[l:r]\\r\\n uxx_sliced[i] = uxx[l:r]\\r\\n fval_sliced[i] = fval[l:r]\\r\\n\\r\\n\\r\\n # Make vectorized call with f.\\r\\n problem_definition.f(t, x, u_sliced, ux_sliced, \\r\\n uxx_sliced, fval_sliced)\\r\\n\\r\\n for i in range(int(npde)):\\r\\n fval[i*vnpts:(i+1)*vnpts] = fval_sliced[i]\\r\\n\\r\\n # Return fval, array will have been modified with this call.\\r\\n return fval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d44a66d2d365c340ddc5790ace2df0b\",\n \"score\": \"0.5750002\",\n \"text\": \"def form_stokesU(self):\\r\\n uvxy = UVData()\\r\\n uvxy.read_miriad(self.files['xy'])\\r\\n uvyx = UVData()\\r\\n uvyx.read_miriad(self.files['yx'])\\r\\n\\r\\n # compute redundancy information if it hasn't been done yet\\r\\n if self.caldata == tuple():\\r\\n self.calculate_caldata(uvxy)\\r\\n\\r\\n # get metadata\\r\\n info = {}\\r\\n # convert from Hz -> GHz\\r\\n info['freqs'] = uvxy.freq_array[0, :] / 1e9\\r\\n info['times'] = np.unique(uvxy.time_array)\\r\\n\\r\\n # get data and flags\\r\\n dU = {}\\r\\n fU = {}\\r\\n for key, dxy in uvxy.antpairpol_iter():\\r\\n # ignore auto correlations\\r\\n if key[0] == key[1]:\\r\\n continue\\r\\n ind1, ind2, ipol = uvxy._key2inds(key)\\r\\n for ind in [ind1, ind2]:\\r\\n if len(ind) == 0:\\r\\n continue\\r\\n # assumes that xy and yx files only have a single polarization in them\\r\\n dyx = uvyx.data_array[ind, 0, :, 0]\\r\\n fxy = uvxy.flag_array[ind, 0, :, 0]\\r\\n fyx = uvyx.flag_array[ind, 0, :, 0]\\r\\n\\r\\n antkey = key[:2]\\r\\n dU[antkey] = {'U': dxy + dyx}\\r\\n fU[antkey] = {'U': fxy + fyx}\\r\\n\\r\\n # clean up\\r\\n del uvxy, uvyx\\r\\n\\r\\n # assign to object variables\\r\\n self.info = info.copy()\\r\\n self.data = dU.copy()\\r\\n self.flags = fU.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a474e06c4adacd3443671a5fa9a378c2\",\n \"score\": \"0.5716817\",\n \"text\": \"def extract_hc_uv_spatial(sshfile):\\n ssh = sshdriver.load_ssh_clim(sshfile)\\n \\n # Step 1: compute the spatial gradients of eta\\n omega = ssh._ds['omega'].values\\n f_cor = calc_coriolis(ssh.yv)\\n\\n ntide = omega.shape[0]\\n\\n # Load the full matrix\\n eta_re = ssh._ds['SSH_BC_Aa'][...].values\\n eta_im = ssh._ds['SSH_BC_Ba'][...].values\\n\\n # Calculate the coriolis\\n f_cor = calc_coriolis(ssh.yv)\\n\\n u = np.zeros((ntide,ssh.Nc), np.complex128)\\n v = np.zeros((ntide,ssh.Nc), np.complex128)\\n\\n for ii in range(ntide):\\n eta_re_dx, eta_re_dy = ssh.calc_grad(eta_re[ii,:])\\n eta_im_dx, eta_im_dy = ssh.calc_grad(eta_im[ii,:])\\n\\n u[ii,:] = calc_u_complex(eta_re_dx+1j*eta_im_dx, eta_re_dy+1j*eta_im_dy, omega[ii], f_cor)\\n v[ii,:] = calc_v_complex(eta_re_dx+1j*eta_im_dx, eta_re_dy+1j*eta_im_dy, omega[ii], f_cor)\\n \\n return u, v, omega\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa75c648c16295e7717f04fa242600e7\",\n \"score\": \"0.56884915\",\n \"text\": \"def get_input_velocities_at_segments(self):\\n\\n # define total velocity\\n if self.zeta_dot is not None:\\n u_tot = self.u_ext - self.zeta_dot\\n else:\\n u_tot = self.u_ext\\n\\n # Include rotation\\n for i_m in range(self.maps.M + 1):\\n for i_n in range(self.maps.N + 1):\\n u_tot[:, i_m, i_n] -= np.cross(self.omega, self.zeta[:, i_m, i_n]) + self.for_vel_tra\\n\\n M, N = self.maps.M, self.maps.N\\n self.u_input_seg = np.empty((3, 4, M, N))\\n\\n # indiced as per self.maps\\n dmver = [0, 1, 1, 0] # delta to go from (m,n) panel to (m,n) vertices\\n dnver = [0, 0, 1, 1]\\n svec = [0, 1, 2, 3] # seg. no.\\n avec = [0, 1, 2, 3] # 1st vertex no.\\n bvec = [1, 2, 3, 0] # 2nd vertex no.\\n\\n ##### panel (0,0): compute all\\n mm, nn = 0, 0\\n for ss, aa, bb in zip(svec, avec, bvec):\\n uA = u_tot[:, mm + dmver[aa], nn + dnver[aa]]\\n uB = u_tot[:, mm + dmver[bb], nn + dnver[bb]]\\n self.u_input_seg[:, ss, mm, nn] = .5 * (uA + uB)\\n\\n ##### panels n=0: copy seg.3\\n nn = 0\\n svec = [0, 1, 2] # seg. no.\\n avec = [0, 1, 2] # 1st vertex no.\\n bvec = [1, 2, 3] # 2nd vertex no.\\n for mm in range(1, M):\\n for ss, aa, bb in zip(svec, avec, bvec):\\n uA = u_tot[:, mm + dmver[aa], nn + dnver[aa]]\\n uB = u_tot[:, mm + dmver[bb], nn + dnver[bb]]\\n self.u_input_seg[:, ss, mm, nn] = .5 * (uA + uB)\\n self.u_input_seg[:, 3, mm, nn] = self.u_input_seg[:, 1, mm - 1, nn]\\n ##### panels m=0: copy seg.0\\n mm = 0\\n svec = [1, 2, 3] # seg. number\\n avec = [1, 2, 3] # 1st vertex of seg.\\n bvec = [2, 3, 0] # 2nd vertex of seg.\\n for nn in range(1, N):\\n for ss, aa, bb in zip(svec, avec, bvec):\\n uA = u_tot[:, mm + dmver[aa], nn + dnver[aa]]\\n uB = u_tot[:, mm + dmver[bb], nn + dnver[bb]]\\n self.u_input_seg[:, ss, mm, nn] = .5 * (uA + uB)\\n self.u_input_seg[:, 0, mm, nn] = self.u_input_seg[:, 2, mm, nn - 1]\\n ##### all others: copy seg. 0 and 3\\n svec = [1, 2] # seg. number\\n avec = [1, 2] # 1st vertex of seg.\\n bvec = [2, 3] # 2nd vertex of seg.\\n for pp in itertools.product(range(1, M), range(1, N)):\\n mm, nn = pp\\n for ss, aa, bb in zip(svec, avec, bvec):\\n uA = u_tot[:, mm + dmver[aa], nn + dnver[aa]]\\n uB = u_tot[:, mm + dmver[bb], nn + dnver[bb]]\\n self.u_input_seg[:, ss, mm, nn] = .5 * (uA + uB)\\n self.u_input_seg[:, 0, mm, nn] = self.u_input_seg[:, 2, mm, nn - 1]\\n self.u_input_seg[:, 3, mm, nn] = self.u_input_seg[:, 1, mm - 1, nn]\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0b30f0722eeb4d508473ce58b953f1c\",\n \"score\": \"0.5681747\",\n \"text\": \"def v2v3(u):\\n if len(u) != 3:\\n raise RuntimeError('Not a vector')\\n norm = np.sqrt(u[0]**2 + u[1]**2 + u[2]**2) # Works for list or array\\n v2 = 3600*np.rad2deg(np.arctan2(u[1], u[0])) # atan2 puts it in the correct quadrant\\n v3 = 3600*np.rad2deg(np.arcsin(u[2]/norm))\\n return v2, v3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a1030e1b76fc8859135985bcd1f6e8\",\n \"score\": \"0.5664697\",\n \"text\": \"def extract_uv_dff(sshfile, xlims, ylims, dx, \\n thetalow, thetahigh):\\n ssh = sshdriver.load_ssh_clim(sshfile)\\n\\n # Interpolate the amplitude onto a grid prior to DFF\\n xgrd = np.arange(xlims[0], xlims[1]+dx, dx)\\n ygrd = np.arange(ylims[0], ylims[1]+dx, dx)\\n X,Y = np.meshgrid(xgrd, ygrd)\\n My, Mx = X.shape\\n \\n u, v, omega = extract_hc_uv_spatial(ssh)\\n ntide, Nc = u.shape\\n \\n # Interpolate\\n aain = np.zeros((Nc,))\\n _, u_re, u_im, _ = sshdriver.extract_amp_xy(ssh, X, Y, aain, np.real(u), np.imag(u), kind='linear')\\n _, v_re, v_im, _ = sshdriver.extract_amp_xy(ssh, X, Y, aain, np.real(v), np.imag(v), kind='linear')\\n \\n u_re_f, u_im_f = sshdriver.extract_dff(u_re, u_im, X, Y, dx, thetalow, thetahigh)\\n v_re_f, v_im_f = sshdriver.extract_dff(v_re, v_im, X, Y, dx, thetalow, thetahigh)\\n\\n return u_re_f, u_im_f, v_re_f, v_im_f, X, Y, omega\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9243ff089559cff8a3e877895409a922\",\n \"score\": \"0.56461966\",\n \"text\": \"def trans_uvd(kp_3d):\\n uvd = np.mat(lr_k) * np.mat(kp_3d)\\n uvd[:2, :] = uvd[:2, :] / uvd[2:, :]\\n # uvd 3*21\\n pts = uvd.T\\n pts = np.array(pts)\\n return pts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c4711fec3d3258a33c76df81fe16b5\",\n \"score\": \"0.56442404\",\n \"text\": \"def ecef2enuv(u, v, w, lat0, lon0, deg: bool = True) -> tuple:\\n if deg:\\n lat0 = radians(lat0)\\n lon0 = radians(lon0)\\n\\n t = cos(lon0) * u + sin(lon0) * v\\n uEast = -sin(lon0) * u + cos(lon0) * v\\n wUp = cos(lat0) * t + sin(lat0) * w\\n vNorth = -sin(lat0) * t + cos(lat0) * w\\n\\n return uEast, vNorth, wUp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07bc879219088a17052248fbfaaf0c3c\",\n \"score\": \"0.56402993\",\n \"text\": \"def test_pde_vector_scalar():\\n eq = PDE({\\\"u\\\": \\\"vector_laplace(u) - u + gradient(v)\\\", \\\"v\\\": \\\"- divergence(u)\\\"})\\n assert not eq.explicit_time_dependence\\n assert not eq.complex_valued\\n grid = grids.UnitGrid([8, 8])\\n field = FieldCollection(\\n [VectorField.random_uniform(grid), ScalarField.random_uniform(grid)]\\n )\\n\\n res_a = eq.solve(field, t_range=1, dt=0.01, backend=\\\"numpy\\\", tracker=None)\\n res_b = eq.solve(field, t_range=1, dt=0.01, backend=\\\"numba\\\", tracker=None)\\n\\n res_a.assert_field_compatible(res_b)\\n np.testing.assert_allclose(res_a.data, res_b.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"219064bf9197825739fb7dd08565e77b\",\n \"score\": \"0.563774\",\n \"text\": \"def reconstruct_displacement_field(GFt, Ftfx, Ftfy, Ftfz, lanczosx, lanczosy):\\n \\n def _comp_i(Ftfx, Ftfy, Ftfz, GFt_i):\\n GFt_ix, GFt_iy, GFt_iz = GFt_i\\n return GFt_ix * Ftfx + GFt_iy * Ftfy + GFt_iz * Ftfz\\n\\n Ftux_rec = _comp_i(Ftfx, Ftfy, Ftfz, GFt[0])\\n Ftuy_rec = _comp_i(Ftfx, Ftfy, Ftfz, GFt[1])\\n Ftuz_rec = _comp_i(Ftfx, Ftfy, Ftfz, GFt[2])\\n ux_rec = np.fft.ifft2(lanczosx * Ftux_rec)\\n uy_rec = np.fft.ifft2(lanczosy * Ftuy_rec)\\n uz_rec = np.fft.ifft2(Ftuz_rec)\\n urec = np.array([np.real(ux_rec), np.real(uy_rec), np.real(uz_rec)])\\n Fturec = np.array([Ftux_rec, Ftuy_rec, Ftuz_rec])\\n return urec, Fturec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec4044c1398e875af45b100862ad327\",\n \"score\": \"0.56371\",\n \"text\": \"def decompose_e(E, K, data1, data2):\\n # _, r, t, _ = cv2.recoverPose(E, data1[:2, :].T, data2[:2, :].T, K)\\n # print(r, t)\\n\\n\\n data1,data2=data1[:,:],data2[:,:]\\n x1,x2=data1[:,1],data2[:,1]\\n U, S, Vh = np.linalg.svd(E)\\n W=np.array([[0,-1,0],[1,0,0],[0,0,1]])\\n t=(U[:,-1]).reshape(U.shape[0],1)\\n def check(r,t):\\n if(np.linalg.det(r)<0):\\n r=-r\\n p2 = K @ np.concatenate([r, t], axis=1)\\n p1 = K @ np.concatenate([np.eye(3), np.zeros_like(t)], axis=1)\\n A=np.array([x1[0]*p1[2]-p1[0],x1[1]*p1[2]-p1[1],x2[0]*p2[2]-p2[0],x2[1]*p2[2]-p2[1]])\\n U,s,Vh=np.linalg.svd(A)\\n X=Vh[-1, :]\\n X[:3]=X[:3]/X[3]\\n X[3]=1\\n z=X[2]/X[3]\\n trans = np.concatenate([r, t], axis=1)\\n camerapose=np.concatenate([trans,np.array([[0,0,0,1]])],axis=0)\\n return z>0 and (camerapose@X)[2]>0, trans\\n\\n bol, trans = check(U @ W @ Vh, t)\\n if (bol): return trans\\n bol, trans = check(U @ W @ Vh, -t)\\n if (bol): return trans\\n bol, trans = check(U @ W.T @ Vh, -t)\\n if (bol): return trans\\n bol,trans= check(U @ W.T @ Vh, t)\\n if(bol):\\n #print(trans)\\n return trans\\n \\\"\\\"\\\"YOUR CODE STARTS HERE\\\"\\\"\\\"\\n\\n \\\"\\\"\\\"YOUR CODE ENDS HERE\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49313ebbee3c76d28e6f9472b76720d1\",\n \"score\": \"0.56328714\",\n \"text\": \"def f(u):\\n\\n v = u[0]\\n theta = u[1]\\n x = u[2]\\n y = u[3]\\n return np.array([-g * sin(theta) - C_D / C_L * g / v_t ** 2 * v ** 2,\\n -g * cos(theta) / v + g / v_t ** 2 * v,\\n v * cos(theta),\\n v * sin(theta)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46fc3eb1537d52f0a6c652e62ea60034\",\n \"score\": \"0.56305045\",\n \"text\": \"def f(u):\\n \\n #define locations of inputs in u matrix\\n v = u[0]\\n theta = u[1]\\n x = u[2]\\n y = u[3]\\n \\n return np.array([-g*sin(theta) - (C_D/C_L)*(g/(v_t**2))*v**2,\\\\\\n (-g*cos(theta))/v + (g/(v_t**2))*v, v*cos(theta), v*sin(theta)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f3066fb4759a2ca5736241230f36a4a\",\n \"score\": \"0.5628934\",\n \"text\": \"def vert_advec_upwind(field, omega, p):\\n # Assume pressure is 3rd to last axis: ([time,] p, lat, lon)\\n f = field.T\\n p = to_pascal(p)\\n # Amend array dimensions as necessary for broadcasting purposes.\\n if p.ndim == 1:\\n p = p[np.newaxis,np.newaxis,:,np.newaxis]\\n elif p.ndim == 3:\\n p = p[np.newaxis,:,:,:].T\\n else:\\n p = p.T\\n # Roll pressure to be leading axis for broadcasting.\\n f = np.rollaxis(f, 2, 0)\\n p = np.rollaxis(p, 2, 0)\\n # Create arrays holding positive and negative values, each with forward\\n # differencing at south pole and backward differencing at north pole.\\n df_fwd_partial = fwd_diff1(f, p)\\n df_bwd_partial = fwd_diff1(f[::-1], p[::-1])[::-1]\\n df_fwd = np.ma.concatenate((df_fwd_partial, df_bwd_partial[-1:]), axis=0)\\n df_bwd = np.ma.concatenate((df_fwd_partial[:1], df_bwd_partial), axis=0)\\n # Roll axis and transpose again to regain original axis order.\\n df_fwd = np.rollaxis(df_fwd, 0, -1).T\\n df_bwd = np.rollaxis(df_bwd, 0, -1).T\\n return upwind_scheme(df_fwd, df_bwd, omega)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d8784aa2a1cea0e146cd4e5a3655be\",\n \"score\": \"0.5619489\",\n \"text\": \"def InducedVelocities(geometry):\\n\\n # Combining multiple rotor system\\n rotors = geometry.WT_lst\\n N_pts = 0\\n N_hs = 0\\n ctrl_pts = []\\n hs_vortex = []\\n idx_lst = [0]\\n # Loop over rotors\\n for t in range(len(rotors)):\\n N_pts += rotors['WT'+str(t+1)]['N_pts'] #Total number of control points of system\\n N_hs += rotors['WT'+str(t+1)]['N_hs'] #Total number of vortices of system\\n idx_lst.append(N_hs) #Index of where circulation switches rotors\\n ctrl_pts = ctrl_pts + rotors['WT'+str(t+1)]['blades']['control_pts'] #Stacking control points\\n hs_vortex = hs_vortex + rotors['WT'+str(t+1)]['blades']['HS_vortex'] #Stacking vortices\\n\\n # Empty induced velocity matrices\\n u_ind_mat = np.zeros((N_pts,N_hs))\\n v_ind_mat = np.zeros((N_pts,N_hs))\\n w_ind_mat = np.zeros((N_pts,N_hs))\\n\\n # For every control point\\n for i in range(N_pts):\\n coords = ctrl_pts[i]['coords']\\n\\n # For every HS vortex\\n for j in range(N_hs):\\n hs = hs_vortex[j]\\n u_ind = 0\\n v_ind = 0\\n w_ind = 0\\n # For ever filament of HS vortex\\n for k in range(len(hs)):\\n fil = hs[k]\\n\\n # Compute induced velocity\\n w = SingleFilament(coords,fil['pos1'],fil['pos2'],fil['Gamma'])\\n # Sum effect of all filaments at control point for 1 vortex\\n u_ind+=w[0]\\n v_ind+=w[1]\\n w_ind+=w[2]\\n\\n # Store induced velocities per control point and HS vortex\\n u_ind_mat[i,j] = u_ind\\n v_ind_mat[i,j] = v_ind\\n w_ind_mat[i,j] = w_ind\\n\\n return u_ind_mat, v_ind_mat, w_ind_mat, idx_lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ac0ae16aa79f8f2813dffdf5953d516\",\n \"score\": \"0.56009007\",\n \"text\": \"def create_vectorfield(grid_U, grid_V, variable, variable_lvl, unit, model, vmin, vmax, lats_labels, lons_labels, df_importances, location):\\n\\n # Convert string labels to floats\\n lats = [int(lat)/100.0 for lat in lats_labels]\\n lons = [int(lon)/100.0 for lon in lons_labels]\\n\\n # Create mapfigure and set title (if wanted)\\n mf = Mapfigure(lon=np.array(lons), lat=np.array(lats))\\n mf.drawmap(nbrem=1, nbrep=1)\\n # plt.title(f\\\"{variable} at {variable_lvl} hPa for {model} data\\\")\\n\\n # Calculate norm of U and V vectors\\n grid_ges = np.sqrt(grid_U**2+grid_V**2)\\n\\n # Set correct colorbar intervals in plot\\n norm = matplotlib.colors.Normalize()\\n norm.autoscale(grid_ges.flatten())\\n cm = plt.cm.get_cmap('rocket_r')\\n sm = plt.cm.ScalarMappable(cmap=cm)\\n sm.set_array(grid_ges.flatten())\\n \\n # Make vector plot\\n plt.quiver(lons, lats, grid_U, grid_V, scale_units=\\\"xy\\\", color=cm(norm(grid_ges.flatten())))\\n \\n # Format colorbar nicely\\n ticks = np.arange(vmin, vmax, 1)\\n cbar = plt.colorbar(sm,\\n boundaries=ticks,\\n ticks=ticks,\\n extend=\\\"both\\\", fraction=0.0188, pad=0.03)\\n cbar.set_label(unit, rotation=90, labelpad=10)\\n\\n # Plot the feature importances on the contour plot\\n for i in df_importances.index:\\n plt.plot(df_importances.loc[i, \\\"lon1\\\"],\\n df_importances.loc[i, \\\"lat1\\\"],\\n \\\"bx\\\",\\n markersize=12*df_importances.loc[i, \\\"importance\\\"]/df_importances[\\\"importance\\\"].max(),\\n mew=4)\\n\\n \\n \\n # Plot station location (Altdorf 8.64441, 46.88042, Lugano: 8.96004, 46.01008)\\n if location == \\\"ALT\\\":\\n plt.plot(8.64441, 46.88042, 'o', color=\\\"#00FF00\\\",markersize=8)\\n elif location == \\\"LUG\\\":\\n plt.plot(8.96004, 46.01008, 'o', color=\\\"#00FF00\\\",markersize=8)\\n \\n # Uncomment if you want to save the figure solely\\n # plt.savefig(f'weathermap_{variable}_{variable_lvl}_{model}.pdf', bbox_inches='tight', dpi=200)\\n # print(f\\\"Saved figure at: weathermap_{variable}_{variable_lvl}_{model}.pdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a049f735dd34be5c3476617e7a0f0ca\",\n \"score\": \"0.5577877\",\n \"text\": \"def sv2elts(state, mu):\\n\\tr = state[0:3]\\n\\tv = state[3:6]\\n\\thvec = np.cross(r,v)\\n\\tincl = get_inclination(state)\\n\\traan = get_raan(state)\\n\\tecc = get_eccentricity(state, mu)\\n\\tomega = get_omega(state, mu)\\n\\ttheta = get_trueanom(state, mu)\\n\\tperiod = get_period(state, mu)\\n\\ta = get_semiMajorAxis(state, mu)\\n\\n\\t# create dictionary to return\\n\\telts = {\\n\\t\\t\\\"hvec\\\": hvec,\\n\\t\\t\\\"incl\\\": incl,\\n\\t\\t\\\"evec\\\": ecc,\\n\\t\\t\\\"raan\\\": raan, \\n\\t\\t\\\"omega\\\": omega, \\n\\t\\t\\\"theta\\\": theta,\\n\\t\\t\\\"period\\\": period,\\n\\t\\t\\\"semiMajorAxis\\\": a\\n\\t}\\n\\treturn elts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dedf0c5ed7aa5d818f33bf42fe0c859\",\n \"score\": \"0.55514354\",\n \"text\": \"def u_v(vel_csv_file):\\n\\n\\t# vel_csv_file is the output from Streams. The 1st row needs to be skipped\\n\\tvelocity = pd.read_csv(vel_csv_file, index_col = [0], skiprows=[0], na_values =' NaN')\\n\\n\\t# convert all NaN to 0s.\\n\\tvelocity.fillna(0, inplace=True)\\n\\n\\t# total number of rows with valid data for each velocity component\\n\\ttotal_rows = nb_matrix * nb_y_rows\\n\\n\\t# initiate numpy arrays to store velocity data from velocity dataframe\\n\\tu_velocity = np.zeros((nb_matrix, nb_y_rows, nb_x_cols))\\n\\tv_velocity = np.zeros((nb_matrix, nb_y_rows, nb_x_cols))\\n\\n\\tfor i in range(nb_matrix):\\n\\t\\tu_velocity[i] = velocity.values[i*nb_y_rows:(i+1)*nb_y_rows , :]\\n\\t\\tv_velocity[i] = velocity.values[total_rows+i*nb_y_rows:total_rows+(i+1)*nb_y_rows, :]\\n\\n\\treturn u_velocity, v_velocity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93100a9c70616b32c5c88a9b6ae1e160\",\n \"score\": \"0.5546929\",\n \"text\": \"def diffun(T, u):\\n u = u.reshape([(nr + 1), NION, nz])\\n \\n C = u[LIQUID, :, :]\\n q = u[RESIN:, :, :]\\n q_s = u[SURF, :, :]\\n \\n u[LIQUID, :, 0] = get_Cin(T) # XXX: SLOW\\n \\n # Calculate total concentrations along column\\n CT = u[LIQUID, :, :].sum(axis=0)\\n \\n # update Ceq\\n Ceq = calc_Ceq(q_s, CT)\\n \\n # Calculate flux terms\\n J = - kL * (C - Ceq) # mass flux \\n Jas = J * 3/rb # mass flux * specific surface area of bead\\n\\n # Initialize arrays for derivatives\\n du_dT = np.zeros([nr+1, NION, nz])\\n dq_dT = np.zeros([nr, NION, nz])\\n dC_dT = np.zeros([NION, nz]) \\n dq_dT_w = np.zeros([NION, nz])\\n \\n # Liquid phase\\n C_swap = np.swapaxes(C, 0, 1)\\n Az_C = np.swapaxes(np.matmul(Az, C_swap), 0, 1)\\n dC_dT = (-v/L*(Az_C) + (1-EBED)*Jas)/EBED * t_half\\n \\n # diffusion in bead HSDMIX\\n q_swap = np.swapaxes(q, 0, 1)\\n Br_q = np.swapaxes(np.matmul(Br, q_swap), 0, 1)\\n dq_dT = Ds * t_half / rb**2 * Br_q \\n\\n # intermediate term for dq_dT at bead surface HSDMIX \\n dq_dT_swap = np.swapaxes(dq_dT[:SURF, :, :],0,1)\\n dq_dT_w = np.matmul(Wr[:-1], dq_dT_swap)\\n \\n # Fill out du_dT \\n du_dT[LIQUID, :, :] = dC_dT\\n du_dT[LIQUID, :, 0] = 0 # Inlet\\n du_dT[RESIN:, :, :] = dq_dT\\n du_dT[SURF, :, :] = (-t_half / rb * J - dq_dT_w)/Wr[-1] \\n \\n du_dT = du_dT.reshape(NEQ) # solve_ivp requires this to be a vector\\n \\n return du_dT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"994b15f0070369ecb258cdc7e5bdd275\",\n \"score\": \"0.55365163\",\n \"text\": \"def kuramoto_sivachinsky(u, d):\\n\\n tmax = 1000\\n N = u.shape[0]\\n v = fft(u)\\n\\n # Precompute various ETDRK4 scalar quantities:\\n h = 0.25 # time step\\n # nplt = np.floor((tmax/100)/h)\\n nplt = 1\\n\\n k = fftfreq(N, d=d)\\n # k = [0:N/2-1 0 -N/2+1:-1]’/16 # wave numbers\\n\\n L = k**2 - k**4 # Fourier multipliers\\n E = exp(h * L)\\n E2 = exp(h * L / 2)\\n M = 16 # no. of points for complex means\\n r = exp(1j * pi * (np.arange(1, M + 1) - .5) / M) # roots of unity\\n LR = h * L[:, None] + r[None, :]\\n\\n Q = h * np.mean((exp(LR / 2) - 1) / LR, axis=1).real\\n f1 = h * np.mean(\\n (-4 - LR + exp(LR) * (4 - 3 * LR + LR**2)) / LR**3, axis=1).real\\n f2 = h * np.mean((2 + LR + exp(LR) * (-2 + LR)) / LR**3, axis=1).real\\n f3 = h * np.mean(\\n (-4 - 3 * LR - LR**2 + exp(LR) * (4 - LR)) / LR**3, axis=1).real\\n\\n # Main time-stepping loop:\\n uu = []\\n tt = []\\n\\n nmax = round(tmax / h)\\n\\n g = -0.5j * k\\n for n in range(nmax):\\n t = n * h\\n Nv = g * fft(np.real(ifft(v))**2)\\n a = E2 * v + Q * Nv\\n Na = g * fft(np.real(ifft(a))**2)\\n b = E2 * v + Q * Na\\n Nb = g * fft(np.real(ifft(b))**2)\\n c = E2 * a + Q * (2 * Nb - Nv)\\n Nc = g * fft(np.real(ifft(c))**2)\\n v = E * v + Nv * f1 + 2 * (Na + Nb) * f2 + Nc * f3\\n\\n if n % nplt == 0:\\n u = ifft(v).real\\n uu.append(u)\\n tt.append(t)\\n\\n return np.stack(tt), np.stack(uu)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a579c1c9bc6f9e0c3ad3b11de5d016ae\",\n \"score\": \"0.5533154\",\n \"text\": \"def u_exact(self, t):\\n\\n me = mesh(self.nvars)\\n me.values[0, :, :] = np.sin(2 * np.pi * self.xc) * np.sin(2 * np.pi * self.yc)\\n # me.values[1,:,:] = np.sin(2*np.pi*self.xc)#*np.sin(2*np.pi*self.yc)\\n\\n return me\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b6112925d79a016502b2c55a73d0014\",\n \"score\": \"0.553298\",\n \"text\": \"def process_zvi(tr,dz):\\n z = np.array(tr)\\n t = np.array(tr)\\n z[0] = 0\\n t[0] = 0\\n for i in range(1,tr.size):\\n z[i] = i * dz\\n t[i] = t[i-1] +(dz / tr[i]) * 2000.0 # to make t2w in ms\\n return z,t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760b5e6318295cbf2fceba6889d1f674\",\n \"score\": \"0.5531234\",\n \"text\": \"def spd_dir2uv(spd,dir):\\r\\n import numpy as np\\r\\n\\r\\n dir2 = (np.copy(dir) + 180.0)*np.pi/180.0\\r\\n s = np.sin(dir2)\\r\\n c = np.cos(dir2)\\r\\n v = -spd*s\\r\\n u = -spd*c\\r\\n\\r\\n return (u, v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a9829b11b46f05131536f8460c370a6\",\n \"score\": \"0.5526411\",\n \"text\": \"def horiz_advec_sfc_pressure(ps, u, v, lat, lon, radius, p, vec_field=False):\\n # Pressure data indexing is surface to TOA; sigma data is opposite.\\n if p.shape[-1] == 1:\\n sfc_index = 0\\n else:\\n sfc_index = -1\\n print sfc_index, u[:,sfc_index,45,0]\\n u_sfc = (u[:,sfc_index])[:,np.newaxis,:,:]\\n v_sfc = (v[:,sfc_index])[:,np.newaxis,:,:]\\n return horiz_advec(ps, u_sfc, v_sfc, lat, lon, radius,\\n vec_field=vec_field) * (1./grav)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5647aa1b97b80bc8522377ece765bb58\",\n \"score\": \"0.55243546\",\n \"text\": \"def _velocity_3D_from_vortex_filament(self, cp_i, core):\\n\\n r_gamma = self.geo.filaments[-1] # vortex strength of each filament\\n x1 = self.geo.filaments[0]\\n y1 = self.geo.filaments[1]\\n z1 = self.geo.filaments[2]\\n x2 = self.geo.filaments[3]\\n y2 = self.geo.filaments[4]\\n z2 = self.geo.filaments[5]\\n xc, yc, zc = cp_i\\n\\n R1 = np.sqrt((xc - x1) ** 2 + (yc - y1) ** 2 + (zc - z1) ** 2)\\n R2 = np.sqrt((xc - x2) ** 2 + (yc - y2) ** 2 + (zc - z2) ** 2)\\n\\n R12_xx = ((yc - y1) * (zc - z2)) - ((zc - z1) * (yc - y2))\\n R12_xy = -((xc - x1) * (zc - z2)) + ((zc - z1) * (xc - x2))\\n R12_xz = ((xc - x1) * (yc - y2)) - ((yc - y1) * (xc - x2))\\n\\n R12_sq = (R12_xx ** 2) + (R12_xy ** 2) + (R12_xz ** 2)\\n\\n R01 = ((x2 - x1) * (xc - x1)) + ((y2 - y1) * (yc - y1)) + ((z2 - z1) * (zc - z1))\\n R02 = ((x2 - x1) * (xc - x2)) + ((y2 - y1) * (yc - y2)) + ((z2 - z1) * (zc - z2))\\n\\n # check if target point is in the vortex filament core,\\n # and modify to solid body rotation\\n R12_sq = np.where(R12_sq < core ** 2, core ** 2, R12_sq)\\n R1 = np.where(R1 < core, core, R1)\\n R2 = np.where(R1 < core, core, R2)\\n\\n K = (r_gamma / (4 * np.pi * R12_sq)) * ((R01 / R1) - (R02 / R2))\\n\\n U = np.sum(K * R12_xx, axis=1)\\n V = np.sum(K * R12_xy, axis=1)\\n W = np.sum(K * R12_xz, axis=1)\\n\\n return np.array([U, V, W])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcf7f821af38b9834b46dbc2f25a6baa\",\n \"score\": \"0.55185246\",\n \"text\": \"def gnss_velocity_fields(dset: \\\"Dataset\\\") -> None:\\n \\n # Add velocity in topocentric coordinates\\n lat, lon, height = dset.site_pos.pos.llh.T\\n vel_enu = np.squeeze(rotation.trs2enu(lat, lon) @ dset.site_vel[:,:,None])\\n dset.add_float(\\\"site_vel_east\\\", val=vel_enu[:,0], unit=\\\"meter/second\\\", write_level=\\\"operational\\\")\\n dset.add_float(\\\"site_vel_north\\\", val=vel_enu[:,1], unit=\\\"meter/second\\\", write_level=\\\"operational\\\")\\n dset.add_float(\\\"site_vel_up\\\", val=vel_enu[:,2], unit=\\\"meter/second\\\", write_level=\\\"operational\\\")\\n \\n # Add horizontal velocity (HV), vertical velocity (VV) and 3D velocity\\n dset.add_float(\\n \\\"site_vel_h\\\", \\n val=np.sqrt(vel_enu[:,0] ** 2 + vel_enu[:,1] ** 2),\\n unit=\\\"meter/second\\\",\\n write_level=\\\"operational\\\",\\n )\\n dset.add_float(\\\"site_vel_v\\\", val=np.absolute(vel_enu[:,2]), unit=\\\"meter/second\\\")\\n dset.add_float(\\n \\\"site_vel_3d\\\", \\n val=np.sqrt(dset.site_vel[:,0] ** 2 + dset.site_vel[:,1] ** 2 + dset.site_vel[:,2] ** 2),\\n #val=np.sqrt(vel_enu[:,0] ** 2 + vel_enu[:,1] ** 2 + vel_enu[:,2] ** 2),\\n unit=\\\"meter/second\\\",\\n write_level=\\\"operational\\\",\\n \\n )\\n\\n # Add site velocity and standard deviation of site velocity coordinates\\n dset.add_float(\\\"site_vel_x\\\", val=dset.site_vel[:, 0], unit=\\\"meter/second\\\", write_level=\\\"analysis\\\")\\n dset.add_float(\\\"site_vel_y\\\", val=dset.site_vel[:, 1], unit=\\\"meter/second\\\", write_level=\\\"analysis\\\")\\n dset.add_float(\\\"site_vel_z\\\", val=dset.site_vel[:, 2], unit=\\\"meter/second\\\", write_level=\\\"analysis\\\")\\n dset.add_float(\\n \\\"site_vel_sigma_x\\\", \\n val=np.sqrt(dset.estimate_cov_site_vel_xx), \\n unit=\\\"meter/second\\\",\\n write_level=\\\"analysis\\\",\\n )\\n dset.add_float(\\n \\\"site_vel_sigma_y\\\", \\n val=np.sqrt(dset.estimate_cov_site_vel_yy), \\n unit=\\\"meter/second\\\",\\n write_level=\\\"analysis\\\",\\n )\\n dset.add_float(\\n \\\"site_vel_sigma_z\\\", \\n val=np.sqrt(dset.estimate_cov_site_vel_zz), \\n unit=\\\"meter/second\\\",\\n write_level=\\\"analysis\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fd4308157d20c55e6af05926581cca2\",\n \"score\": \"0.5501057\",\n \"text\": \"def solver(I, V, f, c, L, dt, C, T, user_action=None,\\n version='vectorized', bc_left='dudx=0'):\\n Nt = int(round(T/dt))\\n t = linspace(0, Nt*dt, Nt+1) # Mesh points in time\\n dx = dt*c/float(C)\\n Nx = int(round(L/dx))\\n x = linspace(0, L, Nx+1) # Mesh points in space\\n dx = x[1] - x[0]\\n C2 = C**2 # Help variable in the scheme\\n if f is None or f == 0:\\n f = (lambda x, t: 0) if version == 'scalar' else \\\\\\n lambda x, t: zeros(x.shape)\\n if V is None or V == 0:\\n V = (lambda x: 0) if version == 'scalar' else \\\\\\n lambda x: zeros(x.shape)\\n\\n u = zeros(Nx+1) # Solution array at new time level\\n u_1 = zeros(Nx+1) # Solution at 1 time level back\\n u_2 = zeros(Nx+1) # Solution at 2 time levels back\\n\\n import time; t0 = time.clock() # for measuring CPU time\\n\\n global periodic\\n periodic = False # True: periodic condition at x=0, False: du/dn=0\\n\\n # Load initial condition into u_1\\n for i in range(0,Nx+1):\\n u_1[i] = I(x[i])\\n\\n if user_action is not None:\\n user_action(u_1, x, t, 0)\\n\\n # Special formula for first time step\\n n = 0\\n for i in range(1, Nx):\\n u[i] = u_1[i] + dt*V(x[i]) + \\\\\\n 0.5*C2*(u_1[i-1] - 2*u_1[i] + u_1[i+1]) + \\\\\\n 0.5*dt**2*f(x[i], t[n])\\n i = 0\\n if bc_left == 'dudx=0':\\n u[i] = u_1[i] + dt*V(x[i]) + \\\\\\n 0.5*C2*(u_1[i+1] - 2*u_1[i] + u_1[i+1]) + \\\\\\n 0.5*dt**2*f(x[i], t[n])\\n else: # open boundary condition\\n u[i] = u_1[i] + C*(u_1[i+1] - u_1[i])\\n\\n u[Nx] = 0\\n\\n if user_action is not None:\\n user_action(u, x, t, 1)\\n\\n # Switch variables before next step\\n u_2[:], u_1[:] = u_1, u\\n\\n for n in range(1, Nt):\\n # Update all inner points at time t[n+1]\\n\\n # Turn to periodic conditions when initial disturbance\\n # at x=0 has died out\\n if not periodic and u[Nx] > 0.00001:\\n periodic = True\\n\\n if version == 'scalar':\\n for i in range(1, Nx):\\n u[i] = - u_2[i] + 2*u_1[i] + \\\\\\n C2*(u_1[i-1] - 2*u_1[i] + u_1[i+1]) + \\\\\\n dt**2*f(x[i], t[n])\\n elif version == 'vectorized': # (1:-1 slice style)\\n f_a = f(x, t[n]) # Precompute in array\\n u[1:-1] = - u_2[1:-1] + 2*u_1[1:-1] + \\\\\\n C2*(u_1[0:-2] - 2*u_1[1:-1] + u_1[2:]) + \\\\\\n dt**2*f_a[1:-1]\\n\\n # Insert boundary conditions\\n u[Nx] = u_1[Nx] - C*(u_1[Nx] - u_1[Nx-1]) # open condition\\n if periodic:\\n u[0] = u[Nx]\\n else:\\n i = 0\\n if bc_left == 'dudx=0':\\n u[i] = - u_2[i] + 2*u_1[i] + \\\\\\n C2*(u_1[i+1] - 2*u_1[i] + u_1[i+1]) + \\\\\\n dt**2*f(x[i], t[n])\\n else: # open boundary condition\\n u[i] = u_1[i] + C*(u_1[i+1] - u_1[i])\\n\\n if user_action is not None:\\n if user_action(u, x, t, n+1):\\n break\\n\\n # Switch variables before next step\\n u_2[:], u_1[:] = u_1, u\\n\\n cpu_time = t0 - time.clock()\\n return u, x, t, cpu_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dc5f8b98d1afd9182f9ae205aa6e444\",\n \"score\": \"0.55007595\",\n \"text\": \"def test_pde_2scalar():\\n eq = PDE({\\\"u\\\": \\\"laplace(u) - u\\\", \\\"v\\\": \\\"- u * v\\\"})\\n assert not eq.explicit_time_dependence\\n assert not eq.complex_valued\\n grid = grids.UnitGrid([8])\\n field = FieldCollection.scalar_random_uniform(2, grid)\\n\\n res_a = eq.solve(field, t_range=1, dt=0.01, backend=\\\"numpy\\\", tracker=None)\\n res_b = eq.solve(field, t_range=1, dt=0.01, backend=\\\"numba\\\", tracker=None)\\n\\n res_a.assert_field_compatible(res_b)\\n np.testing.assert_allclose(res_a.data, res_b.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa12da5e7b2779543571150a9dff15ab\",\n \"score\": \"0.5486525\",\n \"text\": \"def dvr_feh():\\n \\n t = Table.read('../data/master_catalog.fits')\\n ind = (-t['lnL'] < 2.5E3+t['SNR']**2.4) & (t['SNR']>3)\\n t = t[ind]\\n mem = get_members(t)\\n t = t[mem]\\n \\n ind_mpoor = t['FeH']<-2.15\\n ind_mpoor = t['FeH']<-2.3\\n t = t[ind_mpoor]\\n #print(np.min(t['FeH']), np.max(t['FeH']))\\n \\n#def br():\\n spur = (t['field']==2) | (t['field']==4) | (t['field']==5) | (t['field']==6)\\n stream = ~spur\\n \\n # Load orbit\\n pkl = pickle.load(open('../data/orbit_vr_interp.pkl', 'rb'))\\n qpoly = pkl['f']\\n \\n # medians\\n phi1_med = np.zeros(8)\\n vr_med = np.zeros(8)\\n vr_sig = np.zeros(8)\\n vr_std = np.zeros(8)\\n \\n for e, ind in enumerate([stream, spur]):\\n #plt.sca(ax[2])\\n fields = np.unique(t['field'][ind])\\n \\n for ee, f in enumerate(fields):\\n ifield = t['field']==f\\n vr = qpoly(t['phi1'][ind & ifield])\\n #plt.errorbar(np.median(t['phi1'][ind & ifield]), np.median(t['Vrad'][ind & ifield] - vr), yerr=np.std(t['Vrad'][ind & ifield]), fmt='none', color='k', lw=2, zorder=ee+2)\\n #plt.plot(np.median(t['phi1'][ind & ifield]), np.median(t['Vrad'][ind & ifield] - vr), marker=markers[e], color=colors[e], ms=sizes[e], mec='k', mew=2, zorder=ee+3+e)\\n \\n phi1_med[f-1] = np.median(t['phi1'][ind & ifield])\\n vr_med[f-1] = np.median(t['Vrad'][ind & ifield] - vr)\\n vr_sig[f-1] = np.std(t['Vrad'][ind & ifield])\\n vr_std[f-1] = np.median(t['std_Vrad'][ind & ifield])\\n \\n print(vr_med[0] - vr_med[5], vr_std[0], vr_std[5])\\n print(vr_med[2] - vr_med[1], vr_std[2], vr_std[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"211a88eae0c5d01f69d1c12db5708e38\",\n \"score\": \"0.5481848\",\n \"text\": \"def velfield(params, shape, oversample=1):\\n\\n #This is the 'angular eccentricity'\\n #Shapes the flattening of the elliptical coordinates\\n #cos(theta) is just b/a for the ellipse\\n #sin(theta) is sqrt(1-b**2/a**2), or the eccentricity e\\n #Should limit a=5b for reasonable galaxies\\n\\n assert type(oversample)==int, 'Oversample must be an integer'\\n\\n\\n PA=params['PA']\\n xc=params['xc']\\n yc=params['yc']\\n v0=params['v0']\\n PA_rad=PA*np.pi/180.\\n\\n\\n #Get coordinate axes\\n max_shift=settings.max_centre_shift\\n ys=np.linspace(0-max_shift, shape[0]+max_shift, oversample*(shape[0]+2*max_shift))\\n xs=np.linspace(0-max_shift, shape[1]+max_shift, oversample*(shape[1]+2*max_shift))\\n X_0, Y_0=np.meshgrid(xs, ys)\\n\\n\\n #Shift things to the centre, rotate them by PA, then shift back\\n centre_x=shape[0]/2.0\\n centre_y=shape[1]/2.0\\n\\n X_r, Y_r=rotate_coordinates(X_0-centre_x, Y_0-centre_y, PA_rad)\\n\\n X=X_r+centre_x\\n Y=Y_r+centre_y\\n\\n #Intrinisc viewing angle of the disk\\n theta=params['theta']\\n theta_rad=theta*np.pi/180.\\n\\n \\n\\n #Get the simple axisymetric velfield, then scale by (X-centre_of_array)/R)\\n R = np.sqrt((X-centre_x)**2 + ((Y-centre_y)/np.cos(theta_rad))**2)\\n velfield= v_circ_exp_quick(R, params)*(X-centre_x)/(R*np.sin(theta_rad))\\n\\n #Shift the velfield to where it should be\\n shift=np.array([yc-centre_y, xc-centre_x])*oversample\\n velfield=ndi.shift(velfield, shift, order=0, mode='nearest', cval=np.nan)\\n\\n #Crop away everything which we don't need- larger than the original data\\n m_x=(X_0>0)&(X_0<shape[1])\\n m_y=(Y_0>0)&(Y_0<shape[0])\\n m_t=(m_x)&(m_y)\\n\\n \\n\\n velfield_final=velfield[m_t].reshape(oversample*shape[0], oversample*shape[1])+v0\\n\\n # import matplotlib.pyplot as plt \\n # import ipdb; ipdb.set_trace()\\n\\n return velfield_final\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eac7cd78edce67e2bfe3691facaa3200\",\n \"score\": \"0.5464672\",\n \"text\": \"def demag_field_factor(self, vec_1, vec_2):\\n x = np.linspace(-self.parameters.thickness()/2, self.parameters.thickness()/2, 50)\\n norm_vec = np.cosh(np.linalg.norm([vec_1 + vec_2, self.bloch_vec_perp]) * x[:, np.newaxis])\\n tmp = np.trapz(norm_vec, x, axis=0) / self.parameters.thickness()\\n #print(tmp*sin(radians(self.parameters.perpendicular_bloch_vector()[0]))**2 / 2)\\n return tmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bea3c897a1047808c0454c72ade13aab\",\n \"score\": \"0.5461648\",\n \"text\": \"def calculateFlux(vector):\\n if len(vector) == 6:\\n [u, v, lons, lats, depth, time] = vector\\n else:\\n print \\\"calculateFlux(): vector not in correct shape. Expected 6, got\\\", len(vector)\\n\\n res0 = np.size(lons)\\n res1 = np.size(lats)\\n if res0 == res1:\\n res = res0\\n else:\\n print \\\"calculateFlux(): Grid is not square, i.e. len(lons) != len(lats)\\\"\\n return\\n\\n if res == 2:\\n [lon_l, lon_r] = lons\\n [lat_u, lat_l] = lats\\n else:\\n half = int(np.round(res/2))\\n [lon_l, lon_r] = lons[half-1 : half+1]\\n [lat_u, lat_l] = lats[half-1 : half+1]\\n u = u[half-1 : half+1, half-1 : half+1]\\n v = v[half-1 : half+1, half-1 : half+1]\\n\\n u = u.flatten()\\n v = v.flatten()\\n\\n d_north = globalDistance([lon_l, lat_u], [lon_r, lat_u])\\n d_south = globalDistance([lon_l, lat_l], [lon_r, lat_l])\\n d_east = globalDistance([lon_r, lat_u], [lon_r, lat_l])\\n d_west = globalDistance([lon_l, lat_u], [lon_l, lat_l])\\n\\n # using average velocities\\n v_north = 1/2 * (v[0] + v[1])\\n v_south = 1/2 * (v[2] + v[3])\\n u_east = 1/2 * (u[1] + u[3])\\n u_west = 1/2 * (u[0] + u[2])\\n\\n f_north = d_north * v_north\\n f_south = d_south * v_south\\n f_east = d_east * u_east\\n f_west = d_west * u_west\\n\\n return [f_north, f_south, f_east, f_west]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b314fbfa3de07ce5fc841559eb41fa2\",\n \"score\": \"0.5456559\",\n \"text\": \"def v_mer(XYZ,t):\\n k=kvag(w)\\n x=XYZ[0]\\n z=XYZ[2]\\n vx=h0*w*np.exp(k*z)*np.cos(w*t-k*x)\\n vz=-h0*w*np.exp(k*z)*np.sin(w*t-k*x)\\n return [vx,0,vz]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"839234417bbc734a34fc2c3c94474f4d\",\n \"score\": \"0.54562557\",\n \"text\": \"def non_unitary_pairing(v):\\n vc = [np.conjugate(v[i]) for i in range(3)]\\n ux = v[1]*vc[2] - v[2]*vc[1]\\n uy = v[2]*vc[0] - v[0]*vc[2]\\n uz = v[0]*vc[1] - v[1]*vc[0]\\n return (1j*np.array([ux,uy,uz])).real\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c22e0a4d8e3164ab443baf6c5abc7d2c\",\n \"score\": \"0.5444358\",\n \"text\": \"def process_zva(tr,dz):\\n z = np.array(tr)\\n t = np.array(tr)\\n z[0] = 0\\n t[0] = 0\\n for i in range(1,tr.size):\\n z[i] = i * dz\\n t[i] = z[i] / tr[i] * 2000.0\\n return z,t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c565ea7355fd88d37fbcf4f0d098b305\",\n \"score\": \"0.5437116\",\n \"text\": \"def transportdelp_update_vorticity_and_kineticenergy(\\n delp: FloatField,\\n pt: FloatField,\\n utc: FloatField,\\n vtc: FloatField,\\n w: FloatField,\\n rarea: FloatFieldIJ,\\n delpc: FloatField,\\n ptc: FloatField,\\n wc: FloatField,\\n ke: FloatField,\\n vort: FloatField,\\n ua: FloatField,\\n va: FloatField,\\n uc: FloatField,\\n vc: FloatField,\\n u: FloatField,\\n v: FloatField,\\n fx: FloatField,\\n fx1: FloatField,\\n fx2: FloatField,\\n sin_sg1: FloatFieldIJ,\\n cos_sg1: FloatFieldIJ,\\n sin_sg2: FloatFieldIJ,\\n cos_sg2: FloatFieldIJ,\\n sin_sg3: FloatFieldIJ,\\n cos_sg3: FloatFieldIJ,\\n sin_sg4: FloatFieldIJ,\\n cos_sg4: FloatFieldIJ,\\n dt2: float,\\n):\\n\\n from __externals__ import grid_type, i_end, i_start, j_end, j_start\\n\\n with computation(PARALLEL), interval(...):\\n compile_assert(grid_type < 3)\\n # additional assumption (not grid.nested)\\n # corresponds to x fluxes function, but for y-direction\\n fy1 = delp[0, -1, 0] if vtc > 0.0 else delp\\n fy = pt[0, -1, 0] if vtc > 0.0 else pt\\n fy2 = w[0, -1, 0] if vtc > 0.0 else w\\n fy1 = vtc * fy1\\n fy = fy1 * fy\\n fy2 = fy1 * fy2\\n\\n delpc = delp + (fx1 - fx1[1, 0, 0] + fy1 - fy1[0, 1, 0]) * rarea\\n ptc = (pt * delp + (fx - fx[1, 0, 0] + fy - fy[0, 1, 0]) * rarea) / delpc\\n wc = (w * delp + (fx2 - fx2[1, 0, 0] + fy2 - fy2[0, 1, 0]) * rarea) / delpc\\n\\n with computation(PARALLEL), interval(...):\\n # update vorticity and kinetic energy\\n compile_assert(grid_type < 3)\\n\\n ke = uc if ua > 0.0 else uc[1, 0, 0]\\n vort = vc if va > 0.0 else vc[0, 1, 0]\\n\\n with horizontal(region[:, j_start - 1], region[:, j_end]):\\n vort = vort * sin_sg4 + u[0, 1, 0] * cos_sg4 if va <= 0.0 else vort\\n with horizontal(region[:, j_start], region[:, j_end + 1]):\\n vort = vort * sin_sg2 + u * cos_sg2 if va > 0.0 else vort\\n\\n with horizontal(region[i_end, :], region[i_start - 1, :]):\\n ke = ke * sin_sg3 + v[1, 0, 0] * cos_sg3 if ua <= 0.0 else ke\\n with horizontal(region[i_end + 1, :], region[i_start, :]):\\n ke = ke * sin_sg1 + v * cos_sg1 if ua > 0.0 else ke\\n\\n ke = 0.5 * dt2 * (ua * ke + va * vort)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2be6c27424445d25e6aa8d0075c9b35b\",\n \"score\": \"0.54212666\",\n \"text\": \"def uv2spd_dir(u,v):\\r\\n import numpy as np\\r\\n\\r\\n spd = np.zeros_like(u)\\r\\n dir = np.zeros_like(u)\\r\\n\\r\\n spd = np.sqrt(u**2 + v**2)\\r\\n dir = np.arctan2(v, u)*180.0/np.pi\\r\\n\\r\\n return (spd, dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d094a520154ca1da5cf9d7fc57245a0e\",\n \"score\": \"0.54210436\",\n \"text\": \"def decele(self):\\n ## F = m a\\n a__ = (self.lift_and_drag_forces()[1] / self.body_m).to(u.cm/u.s**2)\\n v_f = self.v_sf - self.t_dec * a__\\n \\n return v_f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cca85664ad5b760e1f5b5c437838899c\",\n \"score\": \"0.54206795\",\n \"text\": \"def mfg_coeffs2(params, afs, dz, fg, mfgin, rhogin, Sgs, Smgp, Smgs, Tg, ug, ugin, v):\\n D = params['D']\\n N = params['N']\\n R = params['R']\\n Mgin = params['Mgin']\\n ef0 = params['ef0']\\n g = params['g']\\n rhop = params['rhop']\\n\\n ef = ef0\\n rhog = rhogin\\n rhogb = rhogin\\n\\n Mg = np.full(N, Mgin)\\n P = ((R * rhog * Tg) / Mg) * 1e3\\n\\n dp = np.zeros(len(P))\\n dp[:-1] = np.diff(P)\\n\\n a = np.concatenate(([ugin], ug[0:N - 1]))\\n\\n b1 = ug[0] - ugin - ((2 * dz) / rhogb) * (afs[0] * Smgs[0] - Smgp[0] + Sgs)\\n binner = ug[1:N] - ug[0:N - 1] - ((2 * dz) / rhogb) * (afs[1:N] * Smgs[1:N] - Smgp[1:N] + Sgs)\\n b = np.concatenate(([b1], binner))\\n\\n c = ug\\n b[N - 1] = b[N - 1] + c[N - 1]\\n\\n x = g * (ef * (1 - ef) * rhop - rhogb)\\n y = ((2 * fg * rhogb) / D) * ug * abs(ug)\\n d = 2 * dz * (x + (afs * Smgs * v) - y - ef * (dp / dz))\\n d[0] = d[0] + ugin * mfgin\\n\\n return a, b, c, d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e07c539bfb06409b118c35cda20b34\",\n \"score\": \"0.5420611\",\n \"text\": \"def __undepart_synthesis__(self):\\n\\n n = self.numberUV[0]\\n m = int(n / 2)\\n w0 = np.cosh(np.arccosh(self.R0) / (n - 1))\\n\\n cheby_coef = np.zeros(n, dtype=float)\\n cheby_coef[-1] = 1\\n\\n odd = self.oddU\\n\\n # odd or even\\n val = 1 if odd else 0.5\\n val2 = 2 if odd else 1\\n val_current_coef = 4.0 if odd else 1\\n pq = np.arange(1, m + val2, 1)\\n\\n cos_base = np.cos((pq - val) * np.pi / n)\\n x0 = w0 * np.einsum(\\\"i,j->ij\\\", cos_base, cos_base)\\n cheby_value = np.polynomial.chebyshev.chebval(x0, cheby_coef)\\n cheby_value[0, 0] = cheby_value[0, 0] / val_current_coef\\n\\n zmc = np.arange(1, m + val2, 1)\\n cos_inner = np.cos(np.einsum(\\\"i,j->ij\\\", (zmc - val), (pq - val)) * 2 * np.pi / n)\\n current = (4 / n) ** 2 * np.einsum(\\\"mp,nq,pq->mn\\\", cos_inner, cos_inner, cheby_value)\\n\\n self.current = current / np.max(current)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71f7cf74bf71616122bee2ac1997dd7d\",\n \"score\": \"0.54186696\",\n \"text\": \"def __eval_fimpl(self, u, t):\\n\\n fimpl = mesh(self.nvars, val=0.0)\\n # fimpl.values = self.A.dot(u.values)\\n\\n return fimpl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c598251d8d831447c7481f4ab4461f91\",\n \"score\": \"0.54177445\",\n \"text\": \"def calculate_stress_field(Ftfx, Ftfy, Ftfz, lanczosx, lanczosy, grid_mat, u, i_max, j_max, \\\\\\n i_bound_size, j_bound_size, pix_per_mu, mesh_size):\\n \\n fx = np.fft.ifft2(lanczosx * Ftfx)\\n fy = np.fft.ifft2(lanczosy * Ftfy)\\n fz = np.fft.ifft2(Ftfz)\\n\\n pos = np.array([np.reshape(grid_mat[0], (i_max * j_max)), \\\\\\n np.reshape(grid_mat[1], (i_max * j_max))])\\n vec = np.array([np.reshape(u[0], (i_max * j_max)),\\n np.reshape(u[1], (i_max * j_max)),\\n np.reshape(u[2], (i_max * j_max))])\\n\\n f = np.array([np.real(fx), np.real(fy), np.real(fz)])\\n fnorm = (f[0] ** 2 + f[1] ** 2 + f[2] ** 2) ** 0.5\\n if j_bound_size > 0 and i_bound_size > 0:\\n energy = calculate_energy(u[:, j_bound_size:-j_bound_size, i_bound_size:-i_bound_size], \\\\\\n f[:, j_bound_size:-j_bound_size, i_bound_size:-i_bound_size], pix_per_mu, mesh_size)\\n force = calculate_total_force(fnorm[j_bound_size:-j_bound_size, i_bound_size:-i_bound_size], pix_per_mu,\\n mesh_size)\\n else:\\n energy = calculate_energy(u, f, pix_per_mu, mesh_size)\\n force = calculate_total_force(fnorm, pix_per_mu, mesh_size)\\n return pos, vec, fnorm, f, energy, force\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81bab433073268c51784711cbc856bab\",\n \"score\": \"0.54106164\",\n \"text\": \"def derivatives(self, u=-1, v=-1, order=0):\\n # Check all parameters are set before the surface evaluation\\n self._check_variables()\\n # Check u and v parameters are correct\\n utilities.check_uv(u, v)\\n\\n # Algorithm A3.6\\n du = min(self._degree_u, order)\\n dv = min(self._degree_v, order)\\n\\n SKL = [[[0.0 for _ in range(self._dimension)] for _ in range(dv + 1)] for _ in range(du + 1)]\\n\\n span_u = helpers.find_span(self.knotvector_u, self._control_points_size_u, u)\\n bfunsders_u = helpers.basis_function_ders(self._degree_u, self._knot_vector_u, span_u, u, du)\\n span_v = helpers.find_span(self.knotvector_v, self._control_points_size_v, v)\\n bfunsders_v = helpers.basis_function_ders(self._degree_v, self._knot_vector_v, span_v, v, dv)\\n\\n for k in range(0, du + 1):\\n temp = [[] for _ in range(self._degree_v + 1)]\\n for s in range(0, self._degree_v + 1):\\n temp[s] = [0.0 for _ in range(self._dimension)]\\n for r in range(0, self._degree_u + 1):\\n cu = span_u - self._degree_u + r\\n cv = span_v - self._degree_v + s\\n temp[s][:] = [tmp + (bfunsders_u[k][r] * cp) for tmp, cp in\\n zip(temp[s], self._control_points2D[cu][cv])]\\n\\n dd = min(order - k, dv)\\n for l in range(0, dd + 1):\\n for s in range(0, self._degree_v + 1):\\n SKL[k][l][:] = [elem + (bfunsders_v[l][s] * tmp) for elem, tmp in zip(SKL[k][l], temp[s])]\\n\\n # Return the derivatives\\n return SKL\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60a797a97a872c4aa61f74dfe946141\",\n \"score\": \"0.5405509\",\n \"text\": \"def _vmulv(u,v):\\n #M=array('d')\\n M=[]\\n for i in xrange(len(u)):\\n for j in xrange(len(v)):\\n M.append(u[i]*v[j])\\n return M\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34fd9eca8cb9e8680afe8309895e9372\",\n \"score\": \"0.540538\",\n \"text\": \"def deflection_vector(h_field):\\n # The three components of the vector field are:\\n grad0, grad1, grad2 = np.gradient(h_field)\\n\\n # We want to normalize it (which is not necessary, but we will do it here):\\n norm = np.sqrt(grad0**2+grad1**2+grad2**2)\\n norm[norm==0] = 1e10\\n grad0 *= 1/norm\\n grad1 *= 1/norm\\n grad2 *= 1/norm\\n \\n return grad0, grad1, grad2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc980247912a83d3f4a4901e17b93d31\",\n \"score\": \"0.5404097\",\n \"text\": \"def process_tvi(tr,dz):\\n\\n z = np.array(tr)\\n t = np.array(tr)\\n dzsec = dz / 2000.0 #dz in sec t1w\\n z[0] = 0\\n t[0] = 0\\n for i in range(1,tr.size):\\n z[i] = z[i-1] + dzsec * tr[i]\\n t[i] = i * dz # in t2w ms\\n return z,t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c189523597af0f919dae64992ca3f359\",\n \"score\": \"0.53906476\",\n \"text\": \"def project_ecef_vector_onto_basis(x, y, z, xx, xy, xz, yx, yy, yz, zx, zy, zz):\\n \\n out_x = x*xx + y*xy + z*xz\\n out_y = x*yx + y*yy + z*yz\\n out_z = x*zx + y*zy + z*zz\\n \\n return out_x, out_y, out_z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c861aa2c204a5ae956344f1a3b0faff9\",\n \"score\": \"0.5389964\",\n \"text\": \"def fov(data):\\n \\n #rospy.loginfo(rospy.get_caller_id() + \\\"I heard %s\\\", data.data)\\n pos_x = data.position.x\\n pos_y = data.position.y\\n pos_z = data.position.z\\n q_0 = data.orientation.x\\n q_1 = data.orientation.y\\n q_2 = data.orientation.z\\n q_3 = data.orientation.w\\n #Currently considering orientation with a 3D axis but available data is for only 2D\\n '''T_AL = [[q_0**2+q_1**2-q_2**2-q_3**2 ,2*((q_1*q_2)- (q_0*q_3)) ,2*((q_0*q_2)+ (q_1*q_3)) ,pos_x],\\n [2*((q_0*q_3)+ (q_1*q_2)) ,q_0**2-q_1**2+q_2**2-q_3**2 ,2*((q_2*q_3)- (q_0*q_1)) ,pos_y ],\\n [2*((q_1*q_3)- (q_0*q_2)) ,2*((q_0*q_1)+ (q_2*q_3)) , q_0**2-q_1**2-q_2**2+q_3**2 ,pos_z],\\n [0 ,0 ,0 ,1]]'''\\n #Transpose of the above matrix, only the rotation matrix part of the Translation matrix\\n T_AL = [[q_0**2+q_1**2-q_2**2-q_3**2 , 2*((q_0*q_3)+ (q_1*q_2)),2*((q_1*q_3)- (q_0*q_2)) ,pos_x],\\n [2*((q_1*q_2)- (q_0*q_3)) ,q_0**2-q_1**2+q_2**2-q_3**2 ,2*((q_0*q_1)+ (q_2*q_3)) ,pos_y ],\\n [2*((q_0*q_2)+ (q_1*q_3)) ,2*((q_2*q_3)- (q_0*q_1)) , q_0**2-q_1**2-q_2**2+q_3**2 ,pos_z],\\n [0 ,0 ,0 ,1]]\\n #Now will need to multiply Transform Matrix with 5 points of 4 Pyramids of FOV\\n #Matrix Multiplication of first pyramid for global coordinates\\n pog_1 = np.matmul(T_AL ,po_1)\\n pag_1 = np.matmul(T_AL ,pa_1)\\n pbg_1 = np.matmul(T_AL ,pb_1)\\n pcg_1 = np.matmul(T_AL ,pc_1)\\n pdg_1 = np.matmul(T_AL ,pd_1)\\n psg_1 = np.matmul(T_AL ,ps_1)\\n \\n #Matrix Multiplication of first pyramid's top plane normal and d value\\n pvtop_1 = np.subtract(pog_1,pag_1)\\n pvtop_2 = np.subtract(pog_1,pbg_1) \\n norm_1 = np.cross(pvtop_1[0:3], pvtop_2[0:3]) \\n d_1 = np.matmul(norm_1.T ,pog_1[0:3])#check if this method gives a scalar value\\n pvside_1 = np.subtract(pog_1,pcg_1)\\n pvside_2 = np.subtract(pog_1,pbg_1)\\n norm_2 = np.cross(pvside_1[0:3], pvside_2[0:3]) \\n d_2 = np.matmul(norm_2.T ,pog_1[0:3])\\n\\n pvbot_1 = np.subtract(pog_1,pcg_1)\\n pvbot_2 = np.subtract(pog_1,pdg_1)\\n norm_3 = np.cross(pvbot_1[0:3], pvbot_2[0:3]) \\n d_3 = np.matmul(norm_3.T ,pog_1[0:3])\\n\\n pvside_11 = np.subtract(pog_1,pag_1)\\n pvside_22 = np.subtract(pog_1,pdg_1)\\n norm_4 = np.cross(pvside_11[0:3], pvside_22[0:3]) \\n d_4 = np.matmul(norm_4.T ,pog_1[0:3])\\n\\n pvfront_1 = np.subtract(pbg_1,pag_1)\\n pvfront_2 = np.subtract(pbg_1,pcg_1)\\n norm_5 = np.cross(pvfront_1[0:3], pvfront_2[0:3]) \\n d_5 = np.matmul(norm_5.T ,pag_1[0:3])\\n \\n\\n \\n ##################################################################\\n ##################################################################\\n #Matrix Multiplication of 2nd pyramid\\n pog_2 = np.matmul(T_AL ,po_2)\\n pag_2 = np.matmul(T_AL ,pa_2)\\n pbg_2 = np.matmul(T_AL ,pb_2)\\n pcg_2 = np.matmul(T_AL ,pc_2)\\n pdg_2 = np.matmul(T_AL ,pd_2)\\n psg_2 = np.matmul(T_AL ,ps_2)\\n #Matrix Multiplication of second pyramid's top plane normal and d value\\n pvtop_2_1 = np.subtract(pog_2,pag_2)\\n pvtop_2_2 = np.subtract(pog_2,pbg_2) \\n norm_2_1 = np.cross(pvtop_2_1[0:3], pvtop_2_2[0:3]) \\n d_2_1 = np.matmul(norm_2_1.T ,pog_2[0:3])\\n \\n pvside_2_1 = np.subtract(pog_2,pcg_2)\\n pvside_2_2 = np.subtract(pog_2,pbg_2)\\n norm_2_2 = np.cross(pvside_2_1[0:3], pvside_2_2[0:3]) \\n d_2_2 = np.matmul(norm_2_2.T ,pog_2[0:3])\\n\\n pvbot_2_1 = np.subtract(pog_2,pcg_2) \\n pvbot_2_2 = np.subtract(pog_2,pdg_2) \\n norm_2_3 = np.cross(pvbot_2_1[0:3], pvbot_2_2[0:3]) \\n d_2_3 = np.matmul(norm_2_3.T ,pog_2[0:3])\\n\\n pvside_2_11 = np.subtract(pog_2,pag_2) \\n pvside_2_22 = np.subtract(pog_2,pdg_2) \\n norm_2_4 = np.cross(pvside_2_11[0:3], pvside_2_22[0:3]) \\n d_2_4 = np.matmul(norm_2_4.T ,pog_2[0:3])\\n\\n pvfront_2_1 = np.subtract(pbg_2,pag_2)\\n pvfront_2_2 = np.subtract(pbg_2,pcg_2)\\n norm_2_5 = np.cross(pvfront_2_1[0:3], pvfront_2_2[0:3]) \\n d_2_5 = np.matmul(norm_2_5.T ,pag_2[0:3])\\n ##################################################################\\n ##################################################################\\n #Matrix Multiplication of 3rd pyramid\\n pog_3 = np.matmul(T_AL ,po_3)\\n pag_3 = np.matmul(T_AL ,pa_3)\\n pbg_3 = np.matmul(T_AL ,pb_3)\\n pcg_3 = np.matmul(T_AL ,pc_3)\\n pdg_3 = np.matmul(T_AL ,pd_3)\\n psg_3 = np.matmul(T_AL ,ps_3)\\n #Matrix Multiplication of third pyramid's top plane normal and d value\\n pvtop_3_1 = np.subtract(pog_3,pag_3) \\n pvtop_3_2 = np.subtract(pog_3,pbg_3)\\n norm_3_1 = np.cross(pvtop_3_1[0:3], pvtop_3_2[0:3]) \\n d_3_1 = np.matmul(norm_3_1.T,pog_3[0:3])\\n \\n pvside_3_1 = np.subtract(pog_3,pcg_3) \\n pvside_3_2 = np.subtract(pog_3,pbg_3)\\n norm_3_2 = np.cross(pvside_3_1[0:3], pvside_3_2[0:3]) \\n d_3_2 = np.matmul(norm_3_2.T ,pog_3[0:3])\\n\\n pvbot_3_1 = np.subtract(pog_3,pcg_3)\\n pvbot_3_2 = np.subtract(pog_3,pdg_3)\\n norm_3_3 = np.cross(pvbot_3_1[0:3], pvbot_3_2[0:3]) \\n d_3_3 = np.matmul(norm_3_3.T ,pog_3[0:3])\\n\\n pvside_3_11 = np.subtract(pog_3,pag_3) \\n pvside_3_22 = np.subtract(pog_3,pdg_3)\\n norm_3_4 = np.cross(pvside_3_11[0:3], pvside_3_22[0:3]) \\n d_3_4 = np.matmul(norm_3_4.T ,pog_3[0:3])\\n\\n pvfront_3_1 = np.subtract(pbg_3,pag_3) \\n pvfront_3_2 = np.subtract(pbg_3,pcg_3)\\n norm_3_5 = np.cross(pvfront_3_1[0:3], pvfront_3_2[0:3]) \\n d_3_5 = np.matmul(norm_3_5.T ,pag_3[0:3])\\n ##################################################################\\n ##################################################################\\n #Matrix Multiplication of 4th pyramid\\n pog_4 = np.matmul(T_AL ,po_4)\\n pag_4 = np.matmul(T_AL ,pa_4)\\n pbg_4 = np.matmul(T_AL ,pb_4)\\n pcg_4 = np.matmul(T_AL ,pc_4)\\n pdg_4 = np.matmul(T_AL ,pd_4)\\n psg_4 = np.matmul(T_AL ,ps_4)\\n #Matrix Multiplication of fourth pyramid's top plane normal and d value\\n pvtop_4_1 = np.subtract(pog_4,pag_4)\\n pvtop_4_2 = np.subtract(pog_4,pbg_4)\\n norm_4_1 = np.cross(pvtop_4_1[0:3], pvtop_4_2[0:3]) \\n d_4_1 = np.matmul(norm_4_1.T ,pog_4[0:3])\\n \\n pvside_4_1 = np.subtract(pog_4,pcg_4)\\n pvside_4_2 = np.subtract(pog_4,pbg_4)\\n norm_4_2 = np.cross(pvside_4_1[0:3], pvside_4_2[0:3]) \\n d_4_2 = np.matmul(norm_4_2.T ,pog_4[0:3])\\n\\n pvbot_4_1 = np.subtract(pog_4,pcg_4)\\n pvbot_4_2 = np.subtract(pog_4,pdg_4) \\n norm_4_3 = np.cross(pvbot_4_1[0:3], pvbot_4_2[0:3]) \\n d_4_3 = np.matmul(norm_4_3.T ,pog_4[0:3])\\n\\n pvside_4_11 = np.subtract(pog_4,pag_4)\\n pvside_4_22 = np.subtract(pog_4,pdg_4) \\n norm_4_4 = np.cross(pvside_4_11[0:3], pvside_4_22[0:3]) \\n d_4_4 = np.matmul(norm_4_4.T ,pog_4[0:3])\\n\\n pvfront_4_1 = np.subtract(pbg_4,pag_4) \\n pvfront_4_2 = np.subtract(pbg_4,pcg_4)\\n norm_4_5 = np.cross(pvfront_4_1[0:3], pvfront_4_2[0:3]) \\n d_4_5 = np.matmul(norm_4_5.T ,pag_4[0:3])\\n ##################################################################\\n ##Normal direction verification for sample point in pyramid 1\\n num_s = norm_1[0]*psg_1[0] + norm_1[1]*psg_1[1] + norm_1[2]*psg_1[2] - d_1\\n den_s = math.sqrt(norm_1[0]**2+norm_1[1]**2+norm_1[2]**2)\\n D_1_2_s[0] = num_s/den_s\\n num_2_s = norm_2[0]*psg_1[0] + norm_2[1]*psg_1[1] + norm_2[2]*psg_1[2] - d_2\\n den_2_s = math.sqrt(norm_2[0]**2+norm_2[1]**2+norm_2[2]**2)\\n D_1_2_s[1]= num_2_s/den_2_s\\n num_3_s = norm_3[0]*psg_1[0] + norm_3[1]*psg_1[1] + norm_3[2]*psg_1[2] - d_3\\n den_3_s = math.sqrt(norm_3[0]**2+norm_3[1]**2+norm_3[2]**2)\\n D_1_2_s[2] = num_3_s/den_3_s\\n num_4_s = norm_4[0]*psg_1[0] + norm_4[1]*psg_1[1] + norm_4[2]*psg_1[2] - d_4\\n den_4_s = math.sqrt(norm_4[0]**2+norm_4[1]**2+norm_4[2]**2)\\n D_1_2_s[3] = num_4_s/den_4_s\\n num_5_s = norm_5[0]*psg_1[0] + norm_5[1]*psg_1[1] + norm_5[2]*psg_1[2] - d_5\\n den_5_s = math.sqrt(norm_5[0]**2+norm_5[1]**2+norm_5[2]**2)\\n D_1_2_s[4] = num_5_s/den_5_s\\n ##Normal direction verification for sample point in pyramid 2\\n num_2_s = norm_2_1[0]*psg_2[0] + norm_2_1[1]*psg_2[1] + norm_2_1[2]*psg_2[2] - d_2_1\\n den_2_s = math.sqrt(norm_2_1[0]**2+norm_2_1[1]**2+norm_2_1[2]**2)\\n D_2_2_s[0] = num_2_s/den_2_s\\n num_2_2_s = norm_2_2[0]*psg_2[0] + norm_2_2[1]*psg_2[1] + norm_2_2[2]*psg_2[2] - d_2_2\\n den_2_2_s = math.sqrt(norm_2_2[0]**2+norm_2_2[1]**2+norm_2_2[2]**2)\\n D_2_2_s[1]= num_2_2_s/den_2_2_s\\n num_2_3_s = norm_2_3[0]*psg_2[0] + norm_2_3[1]*psg_2[1] + norm_2_3[2]*psg_2[2] - d_2_3\\n den_2_3_s = math.sqrt(norm_2_3[0]**2+norm_2_3[1]**2+norm_2_3[2]**2)\\n D_2_2_s[2] = num_2_3_s/den_2_3_s\\n num_2_4_s = norm_2_4[0]*psg_2[0] + norm_2_4[1]*psg_2[1] + norm_2_4[2]*psg_2[2] - d_2_4\\n den_2_4_s = math.sqrt(norm_2_4[0]**2+norm_2_4[1]**2+norm_2_4[2]**2)\\n D_2_2_s[3] = num_2_4_s/den_2_4_s\\n num_2_5_s = norm_2_5[0]*psg_2[0] + norm_2_5[1]*psg_2[1] + norm_2_5[2]*psg_2[2] - d_2_5\\n den_2_5_s = math.sqrt(norm_2_5[0]**2+norm_2_5[1]**2+norm_2_5[2]**2)\\n D_2_2_s[4] = num_2_5_s/den_2_5_s\\n ##Normal direction verification for sample point in pyramid 3\\n num_3_s = norm_3_1[0]*psg_3[0] + norm_3_1[1]*psg_3[1] + norm_3_1[2]*psg_3[2] - d_3_1\\n den_3_s = math.sqrt(norm_3_1[0]**2+norm_3_1[1]**2+norm_3_1[2]**2)\\n D_3_2_s[0] = num_3_s/den_3_s\\n num_3_2_s = norm_3_2[0]*psg_3[0] + norm_3_2[1]*psg_3[1] + norm_3_2[2]*psg_3[2] - d_3_2\\n den_3_2_s = math.sqrt(norm_3_2[0]**2+norm_3_2[1]**2+norm_3_2[2]**2)\\n D_3_2_s[1]= num_3_2_s/den_3_2_s\\n num_3_3_s = norm_3_3[0]*psg_3[0] + norm_3_3[1]*psg_3[1] + norm_3_3[2]*psg_3[2] - d_3_3\\n den_3_3_s = math.sqrt(norm_3_3[0]**2+norm_3_3[1]**2+norm_3_3[2]**2)\\n D_3_2_s[2] = num_3_3_s/den_3_3_s\\n num_3_4_s = norm_3_4[0]*psg_3[0] + norm_3_4[1]*psg_3[1] + norm_3_4[2]*psg_3[2] - d_3_4\\n den_3_4_s = math.sqrt(norm_3_4[0]**2+norm_3_4[1]**2+norm_3_4[2]**2)\\n D_3_2_s[3] = num_3_4_s/den_3_4_s\\n num_3_5_s = norm_3_5[0]*psg_3[0] + norm_3_5[1]*psg_3[1] + norm_3_5[2]*psg_3[2] - d_3_5\\n den_3_5_s = math.sqrt(norm_3_5[0]**2+norm_3_5[1]**2+norm_3_5[2]**2)\\n D_3_2_s[4] = num_3_5_s/den_3_5_s\\n ##Normal direction verification for sample point in pyramid 4\\n num_4_s = norm_4_1[0]*psg_4[0] + norm_4_1[1]*psg_4[1] + norm_4_1[2]*psg_4[2] - d_4_1\\n den_4_s = math.sqrt(norm_4_1[0]**2+norm_4_1[1]**2+norm_4_1[2]**2)\\n D_4_2_s[0] = num_4_s/den_4_s\\n num_4_2_s = norm_4_2[0]*psg_4[0] + norm_4_2[1]*psg_4[1] + norm_4_2[2]*psg_4[2] - d_4_2\\n den_4_2_s = math.sqrt(norm_4_2[0]**2+norm_4_2[1]**2+norm_4_2[2]**2)\\n D_4_2_s[1]= num_4_2_s/den_4_2_s\\n num_4_3_s = norm_4_3[0]*psg_4[0] + norm_4_3[1]*psg_4[1] + norm_4_3[2]*psg_4[2] - d_4_3\\n den_4_3_s = math.sqrt(norm_4_3[0]**2+norm_4_3[1]**2+norm_4_3[2]**2)\\n D_4_2_s[2] = num_4_3_s/den_4_3_s\\n num_4_4_s = norm_4_4[0]*psg_4[0] + norm_4_4[1]*psg_4[1] + norm_4_4[2]*psg_4[2] - d_4_4\\n den_4_4_s = math.sqrt(norm_4_4[0]**2+norm_4_4[1]**2+norm_4_4[2]**2)\\n D_4_2_s[3] = num_4_4_s/den_4_4_s\\n num_4_5_s = norm_4_5[0]*psg_4[0] + norm_4_5[1]*psg_4[1] + norm_4_5[2]*psg_4[2]- d_4_5\\n den_4_5_s = math.sqrt(norm_4_5[0]**2+norm_4_5[1]**2+norm_4_5[2]**2)\\n D_4_2_s[4] = num_4_5_s/den_4_5_s\\n ##################################################################\\n ##Begining of DRONE 2,3,4 Inclusion Test using a for Loop\\n ##Distance from Pyramids' points comparison dist_p1_d2 is distance of pyramid 1 points from d2, drone 2\\n # make this into a loop for each neighboring agent\\n for a in range(agents-1):#looping with range method ex: rang(2) -> output is 0,1\\n #pub_incl = rospy.Publisher('/%s_%d/incl_test'(\\\"guidance\\\", a+2), Int32, queue_size=40)#not sure if this will work, still need to check\\n pyr_incl = np.zeros(4)\\n dist_p1_d2[0] = math.sqrt((pos_list[a,0] - pag_1[0] )**2 + (pos_list[a,1] - pag_1[1])**2 + ( pos_list[a,2] - pag_1[2])**2 )\\n dist_p1_d2[1] = math.sqrt((pos_list[a,0] - pbg_1[0] )**2 + (pos_list[a,1] - pbg_1[1])**2 + ( pos_list[a,2] - pbg_1[2])**2)\\n dist_p1_d2[2] = math.sqrt((pos_list[a,0] - pcg_1[0] )**2 + (pos_list[a,1] - pcg_1[1])**2 + ( pos_list[a,2] - pcg_1[2])**2)\\n dist_p1_d2[3] = math.sqrt((pos_list[a,0] - pdg_1[0] )**2 + (pos_list[a,1] - pdg_1[1])**2 + ( pos_list[a,2] - pdg_1[2])**2)\\n dist_comp[0] = min(dist_p1_d2)\\n ##Distance from Pyramids' points comparison dist_p1_d2 is distance of pyramid 2 points from d2, drone 2\\n dist_p2_d2[0] = math.sqrt((pos_list[a,0] - pag_2[0] )**2 + (pos_list[a,1] - pag_2[1])**2 + ( pos_list[a,2] - pag_2[2])**2)\\n dist_p2_d2[1] = math.sqrt((pos_list[a,0] - pbg_2[0] )**2 + (pos_list[a,1] - pbg_2[1])**2 + ( pos_list[a,2] - pbg_2[2])**2)\\n dist_p2_d2[2] = math.sqrt((pos_list[a,0] - pcg_2[0] )**2 + (pos_list[a,1] - pcg_2[1])**2 + ( pos_list[a,2] - pcg_2[2])**2)\\n dist_p2_d2[3] = math.sqrt((pos_list[a,0] - pdg_2[0] )**2 + (pos_list[a,1] - pdg_2[1])**2 + ( pos_list[a,2] - pdg_2[2])**2)\\n dist_comp[1] = min(dist_p2_d2)\\n ##Distance from Pyramids' points comparison dist_p1_d2 is distance of pyramid 3 points from d2, drone 2\\n dist_p3_d2[0] = math.sqrt((pos_list[a,0] - pag_3[0] )**2 + (pos_list[a,1]- pag_3[1])**2 + ( pos_list[a,2] - pag_3[2])**2)\\n dist_p3_d2[1] = math.sqrt((pos_list[a,0] - pbg_3[0] )**2 + (pos_list[a,1] - pbg_3[1])**2 + ( pos_list[a,2] - pbg_3[2])**2)\\n dist_p3_d2[2] = math.sqrt((pos_list[a,0] - pcg_3[0] )**2 + (pos_list[a,1] - pcg_3[1])**2 + ( pos_list[a,2] - pcg_3[2])**2)\\n dist_p3_d2[3] = math.sqrt((pos_list[a,0] - pdg_3[0] )**2 + (pos_list[a,1] - pdg_3[1])**2 + ( pos_list[a,2] - pdg_3[2])**2)\\n dist_comp[2] = min(dist_p3_d2)\\n ##Distance from Pyramids' points comparison dist_p1_d2 is distance of pyramid 4 points from d2, drone 2\\n dist_p4_d2[0] = math.sqrt((pos_list[a,0] - pag_4[0] )**2 + (pos_list[a,1] - pag_4[1])**2 + ( pos_list[a,2] - pag_4[2])**2)\\n dist_p4_d2[1] = math.sqrt((pos_list[a,0] - pbg_4[0] )**2 + (pos_list[a,1] - pbg_4[1])**2 + ( pos_list[a,2] - pbg_4[2])**2)\\n dist_p4_d2[2] = math.sqrt((pos_list[a,0] - pcg_4[0] )**2 + (pos_list[a,1] - pcg_4[1])**2 + ( pos_list[a,2] - pcg_4[2])**2)\\n dist_p4_d2[3] = math.sqrt((pos_list[a,0] - pdg_4[0] )**2 + (pos_list[a,1] - pdg_4[1])**2 + ( pos_list[a,2] - pdg_4[2])**2)\\n dist_comp[3] = min(dist_p4_d2)\\n ##Decide which pyramid is Drone 2 closest to\\n if (pos_list[a,0] != 0 and pos_list[a,1] != 0 and pos_list[a,2] != 0): #To check if drone even exists in the x-y-z- grid because x=y=z=0 is not possible\\n minni= min(dist_comp)\\n index_dist_comp = dist_comp.tolist().index(minni)#WHAT HAPPENS IF THE DISTANCE IS EQUAL FROM TWO PYRAMIDS\\n pyramid_no = index_dist_comp + 1\\n ##for drone 2 and 1st pyramid inclusion test\\n #if pyramid_no == 1:\\n num = norm_1[0]*pos_list[a,0] + norm_1[1]*pos_list[a,1] + norm_1[2]*pos_list[a,2] - d_1\\n den = math.sqrt(norm_1[0]**2+norm_1[1]**2+norm_1[2]**2)\\n D_1_2[0] = num/den\\n num_2 = norm_2[0]*pos_list[a,0] + norm_2[1]*pos_list[a,1] + norm_2[2]*pos_list[a,2] - d_2\\n den_2 = math.sqrt(norm_2[0]**2+norm_2[1]**2+norm_2[2]**2)\\n D_1_2[1]= num_2/den_2\\n num_3 = norm_3[0]*pos_list[a,0] + norm_3[1]*pos_list[a,1] + norm_3[2]*pos_list[a,2] - d_3\\n den_3 = math.sqrt(norm_3[0]**2+norm_3[1]**2+norm_3[2]**2)\\n D_1_2[2] = num_3/den_3\\n num_4 = norm_4[0]*pos_list[a,0] + norm_4[1]*pos_list[a,1] + norm_4[2]*pos_list[a,2] - d_4\\n den_4 = math.sqrt(norm_4[0]**2+norm_4[1]**2+norm_4[2]**2)\\n D_1_2[3] = num_4/den_4\\n num_5 = norm_5[0]*pos_list[a,0] + norm_5[1]*pos_list[a,1] + norm_5[2]*pos_list[a,2] - d_5\\n den_5 = math.sqrt(norm_5[0]**2+norm_5[1]**2+norm_5[2]**2)\\n D_1_2[4] = num_5/den_5\\n #print(num_5)\\n #print(den_5)\\n for i in range(len(D_1_2)):\\n if D_1_2[i]/abs(D_1_2[i]) == D_1_2_s[i]/abs(D_1_2_s[i]):\\n #print(D_1_2[i])\\n #print( D_1_2_s[i])\\n bool_data[i] = 1\\n else:\\n #print(D_1_2[i])\\n #print( D_1_2_s[i])\\n bool_data[i] = 0\\n # Calculate Euclidean distance of Drone Position with respect to sample point psg_1 for pyramid 1 for x-y coordinate system as Pozyx is running on 2D algorithm\\n euc_dist = math.sqrt((pos_list[a,0] - psg_1[0] )**2 + (pos_list[a,1]- psg_1[1])**2 + (pos_list[a,2]- psg_1[2])**2) \\n if min(bool_data) == 0:\\n #print('out_1')\\n #print(a+2)\\n #adj[a+1] = 0 #This will remain zero, which means nhbd agent is not in FOV\\n pyr_incl[0] = 0\\n #pub_incl.publish(0)\\n else:\\n #print('in_1')\\n #print(a+2)\\n pyr_incl[0] = euc_dist #it will be euc distance instead of 0\\n #pub_incl.publish(1)\\n \\n \\n #if pyramid_no == 2:\\n num_2 = norm_2_1[0]*pos_list[a,0] + norm_2_1[1]*pos_list[a,1] + norm_2_1[2]*pos_list[a,2] - d_2_1\\n den_2 = math.sqrt(norm_2_1[0]**2+norm_2_1[1]**2+norm_2_1[2]**2)\\n D_2_2[0] = num_2/den_2\\n num_2_2 = norm_2_2[0]*pos_list[a,0] + norm_2_2[1]*pos_list[a,1] + norm_2_2[2]*pos_list[a,2] - d_2_2\\n den_2_2 = math.sqrt(norm_2_2[0]**2+norm_2_2[1]**2+norm_2_2[2]**2)\\n D_2_2[1]= num_2_2/den_2_2\\n num_2_3 = norm_2_3[0]*pos_list[a,0] + norm_2_3[1]*pos_list[a,1] + norm_2_3[2]*pos_list[a,2] - d_2_3\\n den_2_3 = math.sqrt(norm_2_3[0]**2+norm_2_3[1]**2+norm_2_3[2]**2)\\n D_2_2[2] = num_2_3/den_2_3\\n num_2_4 = norm_2_4[0]*pos_list[a,0] + norm_2_4[1]*pos_list[a,1] + norm_2_4[2]*pos_list[a,2] - d_2_4\\n den_2_4 = math.sqrt(norm_2_4[0]**2+norm_2_4[1]**2+norm_2_4[2]**2)\\n D_2_2[3] = num_2_4/den_2_4\\n num_2_5 = norm_2_5[0]*pos_list[a,0] + norm_2_5[1]*pos_list[a,1] + norm_2_5[2]*pos_list[a,2] - d_2_5\\n den_2_5 = math.sqrt(norm_2_5[0]**2+norm_2_5[1]**2+norm_2_5[2]**2)\\n D_2_2[4] = num_2_5/den_2_5\\n for i in range(len(D_2_2)):\\n if D_2_2[i]/abs(D_2_2[i]) == D_2_2_s[i]/abs(D_2_2_s[i]):\\n bool_data[i] = 1\\n \\n else:\\n bool_data[i] = 0\\n euc_dist = math.sqrt((pos_list[a,0] - psg_2[0] )**2 + (pos_list[a,1]- psg_2[1])**2 + (pos_list[a,2]- psg_2[2])**2 ) \\n if min(bool_data) == 0:\\n #print('out_2')\\n #print(a+2)\\n pyr_incl[1] = 0\\n #print(adj)\\n #pub_incl.publish(0)\\n else:\\n #print('in_2')\\n #print(a+2)\\n pyr_incl[1] = euc_dist #it will be euc distance instead of 0\\n #pub_incl.publish(1)\\n \\n #if pyramid_no == 3:\\n num_3 = norm_3_1[0]*pos_list[a,0] + norm_3_1[1]*pos_list[a,1] + norm_3_1[2]*pos_list[a,2] - d_3_1\\n den_3 = math.sqrt(norm_3_1[0]**2+norm_3_1[1]**2+norm_3_1[2]**2)\\n D_3_2[0] = num_3/den_3\\n num_3_2 = norm_3_2[0]*pos_list[a,0] + norm_3_2[1]*pos_list[a,1] + norm_3_2[2]*pos_list[a,2] - d_3_2\\n den_3_2 = math.sqrt(norm_3_2[0]**2+norm_3_2[1]**2+norm_3_2[2]**2)\\n D_3_2[1]= num_3_2/den_3_2\\n num_3_3 = norm_3_3[0]*pos_list[a,0] + norm_3_3[1]*pos_list[a,1] + norm_3_3[2]*pos_list[a,2] - d_3_3\\n den_3_3 = math.sqrt(norm_3_3[0]**2+norm_3_3[1]**2+norm_3_3[2]**2)\\n D_3_2[2] = num_3_3/den_3_3\\n num_3_4 = norm_3_4[0]*pos_list[a,0] + norm_3_4[1]*pos_list[a,1] + norm_3_4[2]*pos_list[a,2] - d_3_4\\n den_3_4 = math.sqrt(norm_3_4[0]**2+norm_3_4[1]**2+norm_3_4[2]**2)\\n D_3_2[3] = num_3_4/den_3_4\\n num_3_5 = norm_3_5[0]*pos_list[a,0] + norm_3_5[1]*pos_list[a,1] + norm_3_5[2]*pos_list[a,2] - d_3_5\\n den_3_5 = math.sqrt(norm_3_5[0]**2+norm_3_5[1]**2+norm_3_5[2]**2)\\n D_3_2[4] = num_3_5/den_3_5\\n for i in range(len(D_3_2)):\\n if D_3_2[i]/abs(D_3_2[i]) == D_3_2_s[i]/abs(D_3_2_s[i]):\\n bool_data[i] = 1\\n \\n else:\\n bool_data[i] = 0\\n euc_dist = math.sqrt((pos_list[a,0] - psg_3[0] )**2 + (pos_list[a,1]- psg_3[1])**2 + (pos_list[a,2]- psg_3[2])**2) \\n if min(bool_data) == 0:\\n #print('out_3')\\n #print (a+2)\\n pyr_incl[2] = 0\\n #pub_incl.publish(0)\\n else:\\n #print('in_3')\\n #print(a+2)\\n pyr_incl[2] = euc_dist #it will be euc distance instead of 0\\n #pub_incl.publish(1)\\n \\n #if pyramid_no == 4:\\n num_4 = norm_4_1[0]*pos_list[a,0] + norm_4_1[1]*pos_list[a,1] + norm_4_1[2]*pos_list[a,2] - d_4_1\\n den_4 = math.sqrt(norm_4_1[0]**2+norm_4_1[1]**2+norm_4_1[2]**2)\\n D_4_2[0] = num_4/den_4\\n num_4_2 = norm_4_2[0]*pos_list[a,0] + norm_4_2[1]*pos_list[a,1] + norm_4_2[2]*pos_list[a,2] - d_4_2\\n den_4_2 = math.sqrt(norm_4_2[0]**2+norm_4_2[1]**2+norm_4_2[2]**2)\\n D_4_2[1]= num_4_2/den_4_2\\n num_4_3 = norm_4_3[0]*pos_list[a,0] + norm_4_3[1]*pos_list[a,1] + norm_4_3[2]*pos_list[a,2] - d_4_3\\n den_4_3 = math.sqrt(norm_4_3[0]**2+norm_4_3[1]**2+norm_4_3[2]**2)\\n D_4_2[2] = num_4_3/den_4_3\\n num_4_4 = norm_4_4[0]*pos_list[a,0] + norm_4_4[1]*pos_list[a,1] + norm_4_4[2]*pos_list[a,2] - d_4_4\\n den_4_4 = math.sqrt(norm_4_4[0]**2+norm_4_4[1]**2+norm_4_4[2]**2)\\n D_4_2[3] = num_4_4/den_4_4\\n num_4_5 = norm_4_5[0]*pos_list[a,0] + norm_4_5[1]*pos_list[a,1] + norm_4_5[2]*pos_list[a,2] - d_4_5\\n den_4_5 = math.sqrt(norm_4_5[0]**2+norm_4_5[1]**2+norm_4_5[2]**2)\\n D_4_2[4] = num_4_5/den_4_5\\n for i in range(len(D_4_2)):\\n if D_4_2[i]/abs(D_4_2[i]) == D_4_2_s[i]/abs(D_4_2_s[i]):\\n bool_data[i] = 1\\n else:\\n bool_data[i] = 0\\n euc_dist = math.sqrt((pos_list[a,0] - psg_4[0] )**2 + (pos_list[a,1]- psg_4[1])**2 + (pos_list[a,2]- psg_4[2])**2 ) \\n if min(bool_data) == 0:\\n #print('out_4')\\n #print(a+2)\\n pyr_incl[3] = 0 \\n else:\\n #print('in_4')\\n #print(a+2)\\n pyr_incl[3] = euc_dist #it will be euc distance instead of 0\\n adj[a+1] = max(pyr_incl)\\n \\n ##END of DRONE Inclusion Test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a00b5d1875f7a99a27e7038f7c6c26\",\n \"score\": \"0.5384236\",\n \"text\": \"def ibvs_run_ur5(self,xo,xd,uvm,z,q,info,flag):\\n xp=self.get_fov_desire_uv(uvm[0],xo,xd,flag)\\n print \\\"xp\\\",xp\\n \\\"\\\"\\\"\\n Second,caculating cam vodt and deta joint speed\\n desireuv=xp\\n \\\"\\\"\\\"\\n # cam_vdot=self.get_cam_vdot_wz(uvm,z,xp,uvm[0])\\n\\n joint_speed_dot=self.get_joint_speed(uvm, z, xp, uvm[0], q, info)\\n print \\\"joint_speed_dot\\\",joint_speed_dot\\n \\\"\\\"\\\"\\n Third,caculating deta joint speed\\n \\\"\\\"\\\"\\n deta_joint_angular=self.get_deta_joint_angular(joint_speed_dot)\\n print \\\"deta_joint_angular\\\",deta_joint_angular\\n \\\"\\\"\\\"\\n Fourth,get joint angular\\n \\\"\\\"\\\"\\n pub_joint=self.get_joint_angular(q, deta_joint_angular)\\n print \\\"pub_joint\\\",pub_joint\\n return pub_joint\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90d9f13474d8dfa3ca748b45bbaeb076\",\n \"score\": \"0.5368649\",\n \"text\": \"def write_fieldgeneral(self,u,d=None,filename='field_general.out',N=100,s=1.0):\\n if d is None:\\n d=np.zeros(2*self.D,dtype=complex)\\n\\n try:\\n ex=self.ex\\n if s==1.0:\\n x=np.linspace(-0.5,0.5,N)\\n xl=sub.t_dir(x,ex)\\n else:\\n xl=np.linspace(-s*0.5,s*0.5,N)\\n x=sub.t_inv(xl,ex)\\n except AttributeError:\\n x=np.linspace(-0.5,0.5,N)\\n xl=np.linspace(-0.5,0.5,N)\\n try:\\n ey=self.ey\\n if s==1.0:\\n y=np.linspace(-0.5,0.5,N)\\n yl=sub.t_dir(y,ey)\\n else:\\n yl=np.linspace(-s*0.5,s*0.5,N)\\n y=sub.t_inv(yl,ey)\\n except AttributeError:\\n y=np.linspace(-0.5,0.5,N)\\n yl=np.linspace(-0.5,0.5,N)\\n\\n [X,Y]=np.meshgrid(x,y,indexing='ij')\\n Ef=np.dot(self.V,u+d)\\n [WEx,WEy]=np.split(Ef,2)\\n #[WEx,WEy]=np.split(self.V[:,j],2)\\n #[WHx,WHy]=np.split(self.VH[:,j],2)\\n Ex,Ey=np.zeros((N,N),dtype='complex'),np.zeros((N,N),dtype='complex')\\n for i in range(self.D):\\n EXP=np.exp((0+2j)*np.pi*((self.G[i][0]+self.kx)*X+(self.G[i][1]+self.ky)*Y))\\n# Ex+=WEx[i]*EXP\\n# Ey+=WEy[i]*EXP\\n Ex=np.add(Ex,np.dot(WEx[i],EXP))\\n Ey=np.add(Ey,np.dot(WEy[i],EXP))\\n f=open(filename,'w')\\n f.write('# x y ReEx ImEx AbsEx ReEy ImEy AbsEy \\\\n')\\n for i in range(N): \\n for j in range(N):\\n f.write(10*'%12.6f' % (x[i],xl[i],y[j]*self.Nyx,yl[j]*self.Nyx,Ex[i,j].real,Ex[i,j].imag,abs(Ex[i,j]),Ey[i,j].real,Ey[i,j].imag,abs(Ey[i,j])) + '\\\\n')\\n f.write('\\\\n')\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7a42489292bee3e172bdb61353b366\",\n \"score\": \"0.53680575\",\n \"text\": \"def tri(veci, f, q, number, point_numbers, poly_array, v, normal, face_normal_no, vn, dxbuilding, behind):\\n size = 3\\n g = np.zeros((size, 2)) # (3,2)\\n # inits\\n nc = 0\\n behind = 0\\n normal = vn[poly_array[q, 1], :]\\n d = -np.dot(normal, ValueError(poly_array[q, 2]))\\n v_d = np.dot(normal, f)\\n if v_d >= 0.0:\\n dxbuilding = HUGE\\n v_0 = -(np.dot(normal, veci)+d)\\n t = v_0/v_d\\n if t < 0.0:\\n dxbuilding = HUGE\\n behind = 1\\n # Stage 1\\n intersection = veci + f*t\\n maximum = max(abs(normal))\\n # G: What if two normal values are the same? Anyway:\\n if maximum == abs(normal[0]):\\n for P in range(size):\\n g[P, :] = (intersection[1]-v[int(poly_array[q, 1+P]), 1], intersection[2]-v[int(poly_array[q, 1+P]), 2])\\n elif maximum == normal[1]:\\n for P in range(size):\\n g[P, :] = (intersection[0]-v[int(poly_array[q, 1+P]), 0], intersection[2]-v[int(poly_array[q, 1+P]), 2])\\n elif maximum == normal[2]:\\n for P in range(size):\\n g[P, :] = (intersection[0]-v[int(poly_array[q, 1+P]), 0], intersection[1]-v[int(poly_array[q, 1+P]), 1])\\n # Stage 2\\n for P in range(size):\\n if P == size:\\n if g[P, 1] < 0.0:\\n sh = -1\\n else:\\n sh = 1\\n elif (g[P, 0] > 0.0) or (g[P+1, 1] > 0.0):\\n if (g[P, 0]-(g[P, 1]*(g[P+1, 0]-g[P, 0])/(g[P+1, 1]-g[P, 1]))) > 0.0:\\n nc += 1\\n odd = nc % 2 # get remainder to find if odd or not\\n # This was this way in original fortran\\n if odd:\\n dxbuilding = t\\n else:\\n dxbuilding = HUGE\\n return dxbuilding, behind\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18eee549a824bcffbb807627934387d8\",\n \"score\": \"0.53669924\",\n \"text\": \"def eval_f(self, u, t):\\n me = self.dtype_f(self.init, val=0.0)\\n\\n # compute the acceleration due to gravitational forces\\n # ... only with respect to the sun\\n if self.sun_only:\\n for i in range(1, self.init[0][-1]):\\n dx = u.pos[:, i] - u.pos[:, 0]\\n r = np.sqrt(np.dot(dx, dx))\\n df = self.G * dx / (r**3)\\n me[:, i] -= u.m[0] * df\\n\\n # ... or with all planets involved\\n else:\\n for i in range(self.init[0][-1]):\\n for j in range(i):\\n dx = u.pos[:, i] - u.pos[:, j]\\n r = np.sqrt(np.dot(dx, dx))\\n df = self.G * dx / (r**3)\\n me[:, i] -= u.m[j] * df\\n me[:, j] += u.m[i] * df\\n\\n return me\",\n \"title\": \"\"\n },\n {\n \"docid\": \"659688c88c8b1baa84e8d0a392a6737f\",\n \"score\": \"0.536049\",\n \"text\": \"def uprime(u, CL, CD, g, vt):\\n v, theta, x, y = u\\n uprimevector = numpy.array([-g * math.sin(theta) - CD/CL * g / vt**2 * v**2, -g * math.cos(theta)/v+g/vt**2 * v, v*math.cos(theta),v*math.sin(theta)])\\n\\n return uprimevector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d1ec2fdb3ceca11318f65158241309b\",\n \"score\": \"0.53565365\",\n \"text\": \"def calc_uvw(b, tobs, src_epoch, src_lon, src_lat):\\n if type(tobs)!=np.ndarray: tobs = np.array(tobs)\\n if tobs.ndim < 1: tobs = tobs[np.newaxis]\\n nt = tobs.shape[0] \\n if type(b)==list: b = np.array(b)\\n if b.ndim < 2: b = b[np.newaxis,:]\\n nb = b.shape[0] \\n \\n bu = np.zeros((nt,nb))\\n bv = np.zeros((nt,nb))\\n bw = np.zeros((nt,nb))\\n \\n # Define the reference frame\\n me = cc.measures.measures()\\n qa = cc.quanta.quanta()\\n me.doframe(me.observatory('OVRO_MMA'))\\n me.doframe(me.direction(src_epoch, \\n qa.quantity(src_lon.to_value(u.deg),'deg'), \\n qa.quantity(src_lat.to_value(u.deg),'deg')))\\n\\n contains_nans=False\\n\\n for i in range(nt):\\n me.doframe(me.epoch('UTC',qa.quantity(tobs[i],'d'))) \\n for j in range(nb):\\n bl = me.baseline('itrf',qa.quantity(b[j,0],'m'),\\n qa.quantity(b[j,1],'m'),qa.quantity(b[j,2],'m'))\\n # Get the uvw coordinates\\n try:\\n uvw=me.touvw(bl)[1]['value']\\n bu[i,j],bv[i,j],bw[i,j]=uvw[0],uvw[1],uvw[2]\\n except KeyError:\\n contains_nans=True\\n bu[i,j],bv[i,j],bw[i,j]=np.nan,np.nan,np.nan\\n if contains_nans:\\n print('Warning: some solutions not found for u,v,w coordinates')\\n \\n return bu.T,bv.T,bw.T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d39830f1bc4976cb01d26d8e360fd08e\",\n \"score\": \"0.5354262\",\n \"text\": \"def dfdV(self, j):\\n dfdV = np.zeros((self.num_dim_2d, self.num_factors))\\n a = self.angles[j]\\n\\n U_x = self.U[self.x_portion_3d,:]\\n U_y = self.U[self.y_portion_3d,:]\\n U_z = self.U[self.z_portion_3d,:]\\n\\n for i in xrange(self.num_joints):\\n u_x_coord = i\\n u_y_coord = i + self.num_joints\\n\\n dfdV[u_x_coord,:] = U_x[i,:] * np.cos(a) + U_z[i,:] * np.sin(a)\\n dfdV[u_y_coord,:] = U_y[i,:]\\n\\n return dfdV\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a96c1cb57427d0d51f9c9c2a192f77cf\",\n \"score\": \"0.53467023\",\n \"text\": \"def vntF(self):\\n return [self.ntFx, self.ntFy] + ([] if self.dim == 2 else [self.ntFz])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb55516b2b9756e869e92ce73502dd4\",\n \"score\": \"0.53442144\",\n \"text\": \"def orbit_grapher3(p, v):\\n \\n assert len(p) == 3, \\\"Position vector not 3-dimensional\\\"\\n assert len(v) == 3, \\\"Velocity vector not 3-dimensional\\\"\\n \\n for interval in dt:\\n\\n t_array = np.arange(0, t_max, interval)\\n\\n # initialise empty lists to record trajectories. will end up in a list of tuples.\\n p_list = []\\n v_list = []\\n \\n p_list.append(p)\\n v_list.append(v)\\n r = np.linalg.norm(p)\\n r_unit = (p[0]/r, p[1]/r, p[2]/r)\\n F_mag = (G*M*m)/(r**2)\\n F = (r_unit[0]*F_mag, r_unit[1]*F_mag, r_unit[2]*F_mag)\\n a = (-F[0]/m, -F[1]/m, -F[2]/m)\\n p_x = p[0] + interval * v[0]\\n p_y = p[1] + interval * v[1]\\n p_z = p[2] + interval * v[2]\\n p = (p_x, p_y, p_z)\\n v_x = v[0] + interval * a[0]\\n v_y = v[1] + interval * a[1]\\n v_z = v[2] + interval * a[2]\\n v = (v_x, v_y, v_z)\\n p_list.append(p)\\n v_list.append(v)\\n \\n # Verlet integration\\n for i in range(len(t_array)-2):\\n\\n # calculate new position and velocity\\n r = np.linalg.norm(p)\\n r_unit = (p[0]/r, p[1]/r, p[2]/r)\\n F_mag = (G*M*m)/(r**2)\\n F = (r_unit[0]*F_mag, r_unit[1]*F_mag, r_unit[2]*F_mag)\\n a = (-F[0]/m, -F[1]/m, -F[2]/m)\\n p_x = 2*(p_list[-1][0]) - (p_list[-2][0]) + (interval ** 2) * a[0]\\n p_y = 2*(p_list[-1][1]) - (p_list[-2][1]) + (interval ** 2) * a[1]\\n p_z = 2*(p_list[-1][2]) - (p_list[-2][2]) + (interval ** 2) * a[2]\\n p = (p_x, p_y, p_z)\\n v_x = (p_x - (p_list[-2][0])) / (2*interval)\\n v_y = (p_y - (p_list[-2][1])) / (2*interval)\\n v_z = (p_z - (p_list[-2][2])) / (2*interval)\\n v = (v_x, v_y, v_z)\\n \\n p_list.append(p)\\n v_list.append(v)\\n assert len(p_list) == len(v_list) #check if position list and velocity list have equal number of terms\\n p_x_list = []\\n p_y_list = []\\n p_z_list = []\\n for position_vector in p_list:\\n p_x_list.append(position_vector[0])\\n p_y_list.append(position_vector[1])\\n p_z_list.append(position_vector[2])\\n \\n v_x_list = []\\n v_y_list = []\\n v_z_list = []\\n for velocity_vector in v_list:\\n v_x_list.append(velocity_vector[0])\\n v_y_list.append(velocity_vector[1])\\n v_z_list.append(velocity_vector[2])\\n\\n # convert trajectory lists into arrays, so they can be indexed more easily\\n p_x_array = np.array(p_x_list)\\n p_y_array = np.array(p_y_list)\\n p_z_array = np.array(p_z_list)\\n \\n v_x_array = np.array(v_x_list)\\n v_y_array = np.array(v_y_list)\\n v_z_array = np.array(v_z_list)\\n\\n # plot the orbital graph in 3-D\\n plt.figure(1)\\n plt.clf()\\n plt.xlabel('t')\\n plt.ylabel ('y')\\n plt.title('For dt=' + str(interval))\\n plt.grid()\\n plt.plot(t_array, p_y_array)\\n# plt.plot(t_array, v_array, label='v (m/s)')\\n plt.legend()\\n plt.show()\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a92e564caba7871cec8e713a67b3a1a2\",\n \"score\": \"0.5338723\",\n \"text\": \"def weingarten(self, X, U, V):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acdd545de83b583bf1c82dc34c8e248a\",\n \"score\": \"0.5330194\",\n \"text\": \"def svd_flip(u, v, u_based_decision=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7139544e6931bebc6348551108253c08\",\n \"score\": \"0.53260297\",\n \"text\": \"def main(data, setup):\\n # input check\\n varnames = ('vm_raw_c', 'vm_raw_c_theo', 'ze', 'ze_theo')\\n for varname in varnames:\\n if not varname in data.keys():\\n raise LookupError('data must contain variable %s.' % varname)\\n\\n # display info message\\n chrono = setup['chrono']\\n chrono.issue('target velocity: correct for finite beam width...')\\n\\n # retrieve variabbles\\n vm_raw_c = data['vm_raw_c']\\n vm_raw_c_theo = data['vm_raw_c_theo']\\n vnys = data['nqv']\\n\\n # ========== main =================================== #\\n # get mask\\n mask = get_correction_mask(data, setup)\\n\\n alternative = False\\n if alternative:\\n # get factor\\n factor = get_correction_factor(data, setup)\\n v_out = np.nan * np.zeros_like(vm_raw_c)\\n v_out[~mask] = vm_raw_c[~mask]\\n\\n v_meas = vm_raw_c[mask]\\n v_sfc = vm_raw_c_theo[mask]\\n f = factor[mask]\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter('ignore', RuntimeWarning)\\n v_atm = (v_meas - f * v_sfc) / (1. - f)\\n v_atm[f>=1] = v_sfc[f>=1]\\n v_out[mask] = v_atm\\n else:\\n # correct masked values\\n v_out = np.nan * np.zeros_like(vm_raw_c)\\n v_out[~mask] = vm_raw_c[~mask]\\n v_out[mask] = vm_raw_c[mask] - vm_raw_c_theo[mask]\\n\\n # mod\\n v_out= symmod(v_out, 2*vnys)\\n # ==================================================== #\\n\\n # output\\n data['vm_c_bw-corrected'] = v_out\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aa9a443afa73649f6a829d7870ce7c9\",\n \"score\": \"0.5317693\",\n \"text\": \"def f2(y, t):\\n\\tb = 0.002\\n\\tg = -9.8\\n\\ty = y[0] # Position\\n\\tv = y[1] # Velocity\\n\\n\\tdydt = v\\n\\tdvdt = -b * v * abs(v)/m\\n\\treturn [dydt, dvdt]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1750feef0a59ab12efddc65619b33406\",\n \"score\": \"0.53129923\",\n \"text\": \"def get_perp_vec(u1, u2, direction=1):\\n\\n x1, y1 = u1\\n x2, y2 = u2\\n vx, vy = x2-x1, y2-y1\\n v = np.linalg.norm((vx, vy))\\n wx, wy = -vy/v * direction, vx/v * direction\\n return wx, wy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ad0ffbb9f640d3146e846dafda81496\",\n \"score\": \"0.5311321\",\n \"text\": \"def wrf_vort( U, V, dx ):\\n assert U.shape == V.shape, 'Arrays are different shapes. They must be the same shape.'\\n dy = dx\\n du = numpy.gradient( U )\\n dv = numpy.gradient( V )\\n return ( dv[-1]/dx - du[-2]/dy )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c55a91c89ba245a9e95e7418f0cfb4a8\",\n \"score\": \"0.53054804\",\n \"text\": \"def zonal_advec_upwind(field, u, lat, lon, radius):\\n prefactor = latlon_deriv_prefactor(lat, radius)[:,np.newaxis]\\n # Assume latitude and longitude are last two axes.\\n # Transpose the arrays: lon is 1st axis, lat is 2nd.\\n f = field.T\\n # Wrap around at 0/360 degrees longitude.\\n # Assumes longitude array starts at 0 and goes to 360.\\n # f = np.ma.concatenate((f, f[:2]), axis=0)\\n # lon = to_radians(lon)[:,np.newaxis,np.newaxis,np.newaxis]\\n # lon = np.ma.concatenate((lon, 2*np.pi + lon[:2]), axis=0)\\n # df_fwd = fwd_diff2(f, lon)\\n # df_bwd = np.ma.concatenate((df_fwd[-2:], df_fwd[:-2]), axis=0)\\n f = np.ma.concatenate((f, f[:1]), axis=0)\\n lon = to_radians(lon)[:,np.newaxis,np.newaxis,np.newaxis]\\n lon = np.ma.concatenate((lon, 2*np.pi + lon[:1]), axis=0)\\n df_fwd = fwd_diff1(f, lon)\\n df_bwd = np.ma.concatenate((df_fwd[-1:], df_fwd[:-1]), axis=0)\\n # Transpose again to regain original axis order.\\n return prefactor*upwind_scheme(df_fwd.T, df_bwd.T, u)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e00ce1852f03e5a4fb721e445ac19fab\",\n \"score\": \"0.5301055\",\n \"text\": \"def make_tet(v0,v1,v2):\\n a,b,c = v0.length(), v1.length(), v2.length()\\n d,e,f = (v0-v1).length(), (v1-v2).length(), (v2-v0).length()\\n edges = a,b,c,d,e,f\\n return ivm_volume(edges), xyz_volume(edges)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6463561cd3289762d24647ce050b535a\",\n \"score\": \"0.5298506\",\n \"text\": \"def v2t(v):\\r\\n c = cos(v[2, 0])\\r\\n s = sin(v[2, 0])\\r\\n T = np.array([[c, -s, v[0, 0]], [s, c, v[1, 0]], [0, 0, 1]])\\r\\n return T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba1ec17c2fd6b28d136fd2635a25e2a\",\n \"score\": \"0.52970964\",\n \"text\": \"def BruneModel(F_vec, Omega, f_c , tstar, t='velocity'):\\n \\n U = zeros(shape(F_vec))\\n if t == 'displacement': # displacement spectrum\\n U = Omega/(1 + power(F_vec/f_c,2))*exp(-np.pi*F_vec*tstar)\\n elif t == 'velocity': # velocity spectrum\\n U = (2*pi*F_vec) * Omega/(1 + power(F_vec/f_c,2))*exp(-np.pi*F_vec*tstar)\\n elif t == 'acceleration': # accelaration spectrum\\n U = (2*pi*F_vec)**2 * Omega/(1 + power(F_vec/f_c,2))*exp(-np.pi*F_vec*tstar)\\n else:\\n error('unrecognized spectrum option')\\n\\n return U\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f753b896038d40dd5409c4fd6ede0248\",\n \"score\": \"0.52957594\",\n \"text\": \"def dynamics(self,eta,nu,u_actual,u_control,sampleTime): \\n\\n # Input vector\\n n = u_actual \\n\\n # Current velocities\\n u_c = self.V_c * math.cos(self.beta_c - eta[5]) # current surge velocity\\n v_c = self.V_c * math.sin(self.beta_c - eta[5]) # current sway velocity\\n\\n nu_c = np.array([u_c,v_c,0,0,0,0],float) # current velocity vector\\n nu_r = nu - nu_c # relative velocity vector\\n \\n # Control forces and moments with propeller saturation \\n n_squared = np.zeros(self.dimU)\\n for i in range(0,self.dimU): \\n n[i] = sat(n[i],-self.n_max[i],self.n_max[i]) # saturation limits\\n n_squared[i] = abs( n[i] ) * n[i] \\n\\n tau3 = np.matmul(self.B, n_squared)\\n tau = np.array([ tau3[0], tau3[1], 0, 0, 0, tau3[2]],float)\\n \\n # 6-DOF semisub model\\n nu_dot = np.matmul( self.Minv, tau \\n - np.matmul(self.D, nu_r) - np.matmul(self.G, eta)) \\n n_dot = (u_control - u_actual) / self.T_n\\n\\n # Forward Euler integration\\n nu = nu + sampleTime * nu_dot\\n n = n + sampleTime * n_dot \\n\\n u_actual = np.array(n,float) \\n \\n return nu, u_actual\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7672889fec840d335b7affbd5425b1cc\",\n \"score\": \"0.5291176\",\n \"text\": \"def _vtk_check(self, filename: str, obj: \\\"StokesFlowObj\\\", ref_slt: \\\"slt\\\" = None, INDEX=''):\\n comm = PETSc.COMM_WORLD.tompi4py()\\n rank = comm.Get_rank()\\n\\n obj_u = self.solve_obj_u(obj, INDEX)\\n obj.set_re_velocity(obj_u)\\n if ref_slt is None:\\n u_exact = obj.get_velocity()\\n else:\\n u_exact = ref_slt.get_solution(obj.get_u_geo())\\n\\n if rank == 0:\\n velocity_x = obj_u[0::3].copy()\\n velocity_y = obj_u[1::3].copy()\\n velocity_z = obj_u[2::3].copy()\\n velocity_err = u_exact - obj_u\\n velocity_err_x = velocity_err[0::3].copy()\\n velocity_err_y = velocity_err[1::3].copy()\\n velocity_err_z = velocity_err[2::3].copy()\\n rel_err_x = np.abs(velocity_err_x / velocity_x)\\n rel_err_y = np.abs(velocity_err_y / velocity_y)\\n rel_err_z = np.abs(velocity_err_z / velocity_z)\\n nodes = obj.get_u_nodes()\\n pointsToVTK(filename, nodes[:, 0].copy(), nodes[:, 1].copy(), nodes[:, 2].copy(),\\n data={\\\"velocity_err\\\": (velocity_err_x, velocity_err_y, velocity_err_z),\\n \\\"velocity\\\": (velocity_x, velocity_y, velocity_z),\\n \\\"rel_err\\\": (rel_err_x, rel_err_y, rel_err_z)})\\n\\n errorall = np.sqrt(np.sum((obj_u - u_exact) ** 2) / np.sum(u_exact ** 2))\\n errorx = np.sqrt(np.sum((obj_u[0::3] - u_exact[0::3]) ** 2) / np.sum(u_exact[0::3] ** 2))\\n errory = np.sqrt(np.sum((obj_u[1::3] - u_exact[1::3]) ** 2) / np.sum(u_exact[1::3] ** 2))\\n errorz = np.sqrt(np.sum((obj_u[2::3] - u_exact[2::3]) ** 2) / np.sum(u_exact[2::3] ** 2))\\n error = np.hstack((errorall, errorx, errory, errorz))\\n\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"857702b6fc71867a5e6da8aa8ee02db4\",\n \"score\": \"0.5288923\",\n \"text\": \"def faltung2(v, f, t):\\n dx = (t[-1]-t[0])/(len(t)-1)\\n g = np.zeros(len(t))\\n # herausfinden wo t = 0\\n for i in np.arange(len(t)):\\n if t[i] >= 0:\\n t_0 = i\\n break\\n # Faltung ab t = 0\\n for i in np.arange(len(t)):\\n g[0+i]= np.sum(v[0:i+1]*np.flip(f[0:i+1])*dx)\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1494498a54df1b041411e3fddc3b729a\",\n \"score\": \"0.52836055\",\n \"text\": \"def _update_v_fwd1(\\n self,\\n step: int,\\n state: State,\\n training: bool = True,\\n ) -> tuple[State, Tensor]:\\n eps = sigmoid(tf.math.log(self.veps[step]))\\n # force = tf.cast(tf.reshape(force, state.v.shape), state.v.dtype)\\n # exp_s = tf.cast(\\n # tf.reshape(tf.exp(logjac), state.v.shape),\\n # state.v.dtype\\n # )\\n # exp_q = tf.cast(\\n # tf.reshape(tf.exp(tf.multiply(eps, q)), state.v.shape),\\n # state.v.dtype\\n # )\\n # t = tf.cast(tf.reshape(t, state.v.shape), state.v.dtype)\\n # vf = exp_s * state.v - 0.5 * eps * (force * exp_q + t)\\n # vf = tf.multiply(exp_s, state.v) - 0.5 * eps * (force * exp_q + t)\\n # halfeps = tf.cast(0.5 * eps, state.x.dtype)\\n # vf = (\\n # tf.multiply(exp_s, state.v)\\n # - halfeps * (tf.multiply(force, exp_q) + t)\\n # )\\n force = self.grad_potential(state.x, state.beta)\\n # vNet: (x, F) --> (s, t, q)\\n s, t, q = self._call_vnet(step, (state.x, force), training=training)\\n eps = tf.cast(eps, s.dtype)\\n halfeps = tf.scalar_mul(0.5, eps)\\n assert eps is not None and isinstance(eps, tf.Tensor)\\n logjac = tf.scalar_mul(halfeps, s)\\n logdet = tf.reduce_sum(self.flatten(logjac), axis=1)\\n force = tf.cast(tf.reshape(force, state.v.shape), s.dtype)\\n exp_s = tf.reshape(tf.exp(logjac), state.v.shape)\\n exp_q = tf.reshape(tf.exp(tf.scalar_mul(eps, q)), state.v.shape)\\n t = tf.reshape(t, state.v.shape)\\n v = tf.cast(state.v, exp_s.dtype)\\n vf = exp_s * v - tf.scalar_mul(halfeps, (force * exp_q + t))\\n\\n return State(state.x, vf, state.beta), logdet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d646b910e579db9755cda13c0c858840\",\n \"score\": \"0.5281493\",\n \"text\": \"def calcUpVector(self):\\n self._up = np.cross(self._transverse, self._fv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d3f753d1bddb76effdc3b0dc8287023\",\n \"score\": \"0.528143\",\n \"text\": \"def project(v, u):\\n return (np.dot(v.T, u) / np.dot(u.T, u)) * u\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c558600f5c729195c56974437a1bcadd\",\n \"score\": \"0.52807224\",\n \"text\": \"def probe_at_dx(\\n du=0.001,\\n velocity_file=None,\\n stlfile=\\\"c0006.stl\\\",\\n output_fn=\\\"surface.vtp\\\",\\n velocity_name=\\\"v [m/s]\\\",\\n write=False,\\n mu=1.0,\\n move_mesh=False,\\n sign=1,\\n):\\n\\n if type(stlfile) is str:\\n stl = read_vtk(stlfile)\\n else:\\n stl = stlfile\\n\\n vertices = numpy_support.vtk_to_numpy(stl.GetPoints().GetData())\\n # indices = numpy_support.vtk_to_numpy(stl.GetPolys().GetData()).reshape(-1, 4)[:, 1:4]\\n merged = vtk.vtkPolyData()\\n merged.DeepCopy(stl)\\n\\n vel_data = read_vtk(velocity_file)\\n\\n # Compute normals to vertices\\n normalGenerator = vtk.vtkPolyDataNormals()\\n normalGenerator.SetInputData(merged)\\n normalGenerator.ComputePointNormalsOn()\\n normalGenerator.ComputeCellNormalsOff()\\n normalGenerator.SetSplitting(0)\\n normalGenerator.SetConsistency(0)\\n normalGenerator.Update()\\n\\n merged = normalGenerator.GetOutput()\\n normals = numpy_support.vtk_to_numpy(merged.GetPointData().GetNormals())\\n\\n points = vtk.vtkPoints()\\n pointsPolyData = vtk.vtkPolyData()\\n\\n for normal, pos in zip(normals, vertices):\\n points.InsertNextPoint(pos + normal * (-sign * du))\\n\\n pointsPolyData.SetPoints(points)\\n probe_filter = vtk.vtkProbeFilter()\\n probe_filter.SetInputData(pointsPolyData)\\n probe_filter.SetSourceData(vel_data)\\n probe_filter.GetOutputPort()\\n probe_filter.Update()\\n\\n probed_data = probe_filter.GetOutput().GetPointData()\\n\\n if isinstance(velocity_name, str):\\n v_vec = numpy_support.vtk_to_numpy(probed_data.GetArray(velocity_name))\\n elif isinstance(velocity_name, list):\\n vx = numpy_support.vtk_to_numpy(probed_data.GetArray(velocity_name[0]))\\n vy = numpy_support.vtk_to_numpy(probed_data.GetArray(velocity_name[1]))\\n vz = numpy_support.vtk_to_numpy(probed_data.GetArray(velocity_name[2]))\\n v_vec = np.stack((vx, vy, vz), 1).reshape((-1, 3))\\n else:\\n raise NotImplementedError\\n\\n logging.debug(v_vec.shape)\\n\\n velocity = vtk.vtkFloatArray()\\n velocity.SetNumberOfComponents(1)\\n velocity.SetName(\\\"X_at_epsilon\\\")\\n\\n for v in v_vec:\\n # if np.max(v) > 1e33 or np.min(v) < -1e33:\\n # s = np.array([0.00])\\n\\n velocity.InsertNextTypedTuple([v])\\n\\n merged.GetPointData().AddArray(velocity)\\n\\n if move_mesh:\\n merged.SetPoints(points)\\n logging.debug(\\\"moving point by dx inside\\\")\\n\\n merged.Modified()\\n if write:\\n save_vtp(merged, output_fn)\\n\\n return merged\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e62f171cf3bfc0b3d9595d83372b1ce\",\n \"score\": \"0.5278805\",\n \"text\": \"def convert_w_2D(w, w_):\\n\\n rhot = w[w_['h']] + w[w_['rhob']]\\n\\n sac_gdf_output = {}\\n\\n sac_gdf_output['density_pert'] = {\\n 'field': w[w_['h']] * u.Unit('kg/m^3'),\\n 'field_name': 'perturbation density',\\n 'staggering': 0\\n }\\n sac_gdf_output['density_bg'] = {\\n 'field': w[w_['rhob']] * u.Unit('kg/m^3'),\\n 'field_name':'background density',\\n 'staggering':0\\n }\\n sac_gdf_output['velocity_x'] = {\\n 'field': w[w_['m2']]/rhot * u.Unit('m/s'),\\n 'field_name': 'velocity x component',\\n 'staggering': 0\\n }\\n sac_gdf_output['velocity_z'] = {\\n 'field': w[w_['m1']]/rhot * u.Unit('m/s'),\\n 'field_name':'velocity z component',\\n 'staggering':0\\n }\\n sac_gdf_output['mag_field_x_pert'] = {\\n 'field':u.Quantity(w[w_['b2']], unit=u.T),\\n 'field_name':'perturbation magnetic field x component',\\n 'staggering':0\\n }\\n sac_gdf_output['mag_field_z_pert'] = {\\n 'field': w[w_['b1']] * u.T,\\n 'field_name': 'perturbation magnetic field z component',\\n 'staggering': 0\\n }\\n sac_gdf_output['mag_field_x_bg'] = {\\n 'field': w[w_['bg2']] * u.T,\\n 'field_name':'background magnetic field x component',\\n 'staggering':0\\n }\\n sac_gdf_output['mag_field_z_bg'] = {\\n 'field': w[w_['bg1']] * u.T,\\n 'field_name': 'background magnetic field z component',\\n 'staggering': 0\\n }\\n sac_gdf_output['internal_energy_pert'] = {\\n 'field': w[w_['e']] * u.Pa,\\n 'field_name':'perturbation internal energy',\\n 'staggering':0\\n }\\n sac_gdf_output['internal_energy_bg'] = {\\n 'field': w[w_['eb']] * u.Pa,\\n 'field_name':'background internal energy',\\n 'staggering':0\\n }\\n\\n return sac_gdf_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"938bbba70456a26d75abc404eea873ee\",\n \"score\": \"0.5277239\",\n \"text\": \"def Jtvec(self, m, v, f=None):\\n Jtvec = self.G.T @ v.astype(np.float32)\\n return np.asarray(self.rhoDeriv.T @ Jtvec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abfc585fcf0a66f96a6f553db436c272\",\n \"score\": \"0.52707845\",\n \"text\": \"def model(t, x, u, p):\\n # Unpack parameters\\n k, e, w = p\\n # Unpack initial conditions\\n x0, x1 = x\\n # Calculate derivatives\\n dx = np.zeros(2)\\n dx[0] = x1\\n dx[1] = k*w**2*u - (2*e*w*x1 + w**2*x0)\\n return dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8b3f8306bf83d1265f0d3dffcd65db1\",\n \"score\": \"0.5266392\",\n \"text\": \"def test_pde_product_operators():\\n eq = PDE(\\n {\\\"p\\\": \\\"gradient(dot(p, p) + inner(p, p)) + tensor_divergence(outer(p, p))\\\"}\\n )\\n assert not eq.explicit_time_dependence\\n assert not eq.complex_valued\\n field = VectorField(grids.UnitGrid([4]), 1)\\n res = eq.solve(field, t_range=1, dt=0.1, backend=\\\"numpy\\\", tracker=None)\\n np.testing.assert_allclose(res.data, field.data)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993480,"cells":{"query_id":{"kind":"string","value":"53a18a58bf82ca82cde3df84f4b10779"},"query":{"kind":"string","value":"This method unbinds the skin for all the meshes in the given list, if any."},"positive_passages":{"kind":"list like","value":[{"docid":"57e74bb8b7ccaf11d9be63c75b70cbbf","score":"0.69904643","text":"def detachSkinFromMesh(meshes):\n\n pass","title":""}],"string":"[\n {\n \"docid\": \"57e74bb8b7ccaf11d9be63c75b70cbbf\",\n \"score\": \"0.69904643\",\n \"text\": \"def detachSkinFromMesh(meshes):\\n\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ab9be50819a072e20e9f8437cb740c4d","score":"0.63012815","text":"def unregister_layers(self, names, out):\n skinstool = getToolByName(self, 'portal_skins')\n\n # remove this layer from all known skins\n for skinName in skinstool.getSkinSelections():\n path = skinstool.getSkinPath(skinName) \n path = [i.strip() for i in path.split(',')]\n\n opath = list(path)\n for name in names:\n if name in path:\n path.remove(name)\n\n if opath != path:\n path = ','.join(path)\n skinstool.addSkinSelection(skinName, path)\n\n print >>out, \"removed layers '%s' from '%s'\" % (', '.join(names), skinName)\n self.changeSkin(None)","title":""},{"docid":"156b171532e4875a9e53f82487a3c657","score":"0.59158087","text":"def DoCopyMeshesToVisual(skinnedObject, meshList):\n if skinnedObject is None or skinnedObject.visualModel is None:\n return\n skinnedObject.visualModel.meshes.removeAt(-1)\n for mesh in meshList:\n skinnedObject.visualModel.meshes.append(mesh)","title":""},{"docid":"1a2e5a9a4b70e61783a01a9c874f1c41","score":"0.5870197","text":"def unbind_all(self, values):\n for k in values:\n self.unbind(k)","title":""},{"docid":"7e01b17b4d623e5d88ad9fa868953ce3","score":"0.5649311","text":"def undraw(itemlist):\n pf.canvas.remove(itemlist)\n pf.canvas.update()\n pf.app.processEvents()","title":""},{"docid":"8901d8c1c2b91b5243f13fb0c9c99f8e","score":"0.5614368","text":"def unbind_on_symbols(self):\n self.zbarcam.unbind(symbols=self.on_symbols)","title":""},{"docid":"459dec8ae22734aa580e689393b3b654","score":"0.5508435","text":"def resetBoundMeshes(self):\n for mName in self.getBoundMeshes():\n if mName != self.getSeedMesh().name:\n self.removeBoundMesh(mName)","title":""},{"docid":"12958655c0d9ffb03aa8cdcac74bf383","score":"0.5473903","text":"def _remove_meshes(self, mesh_names: [str] = None) -> None:\n if mesh_names is None:\n mesh_names = self.meshes.keys()\n\n print(\"Removing all {} from simulation\".format(mesh_names))\n for mesh_name in mesh_names:\n meshes = self.meshes[mesh_name]\n\n i = len(meshes) - 1\n\n # Deletion is expensive, so iterate backwards in list\n while i >= 0:\n # Remove from simulation and delete from list of meshes we're tracking.\n self.bt.removeBody(meshes[i])\n del meshes[i]\n\n i -= 1\n\n self.bt.removeBody(self.robot)","title":""},{"docid":"96e832996f9bf14742361688baca8cea","score":"0.5429389","text":"def clear(self):\n for animal in self.animals:\n animal.undraw()\n self.canvas.delete('slime')","title":""},{"docid":"9ba41b49853b18c89089f120518d9186","score":"0.5421912","text":"def unbind(self):\n glBindTexture(GL_TEXTURE_2D, 0)","title":""},{"docid":"3b0bc676a345f9c6b88041b52601b92a","score":"0.54160416","text":"def _delete_bubbles(self, bubbles):\n for bubble in bubbles:\n self.board[bubble[0]][bubble[1]].color = None","title":""},{"docid":"b0e1130b6c07e6706e6ced99b9006799","score":"0.54046196","text":"def stop_anim(self):\n\n self.stop_animation = True\n self.start.configure(state=NORMAL)\n self.stop.configure(state=DISABLED)\n self.update()\n\n for i in range(len(self.particlelist)):\n xlist = self.particlelist[i]\n for particle in xlist:\n particle.delete()\n self.particlelist[i] = []\n\n self.init()","title":""},{"docid":"8f96addcce04384f39de6a0d3a0856dc","score":"0.5404546","text":"def remove_regions(self):\n for regions in self.hulls:\n for region in regions:\n for vb in [self.view3D, self.view2D]:\n if region in vb.items:\n vb.removeItem(region)","title":""},{"docid":"6c382e30a56ff26aecba4a1aec40478e","score":"0.5381617","text":"def destroy_all(self):\r\n\r\n [widget.destroy() for widget in self.widgets]","title":""},{"docid":"d5fb1be24eaa537332b82951ebe25e03","score":"0.53641725","text":"def tear_off():\r\n s = mampy.selected()\r\n\r\n for comp in s.itercomps():\r\n if not comp.is_face():\r\n continue\r\n\r\n edges = comp.to_edge(border=True)\r\n cmds.polyMapCut(list(edges))\r\n cmds.select(list(s))","title":""},{"docid":"8dffe1a8e8ed7d4340a620367a1dc40b","score":"0.5351901","text":"def unbind(self: T, dim: int = 0) -> T:\n assert dim != -1 and dim != self.tensor.dim() - 1\n boxes_list = self.tensor.unbind(dim=dim)\n return [type(self)(boxes, clone=False) for boxes in boxes_list]","title":""},{"docid":"fa83abe46bc5655d32b74c3541f2aeae","score":"0.52715826","text":"def set_light_off(self, names):\n for light_name in self.lights:\n if light_name in names:\n self.put(f\"api/{self.username}/lights/{self.lights[light_name]}/state\",\n data={\"on\": False})","title":""},{"docid":"28968065d942451a157df69495ef2ea7","score":"0.5212518","text":"def make_all_skin():\n human = get_human_mesh()\n generic_slot = get_mat_slot(human, 'MBlab_generic')\n skin_material = node.material('MBlab_human_skin')\n\n generic_slot.material = skin_material","title":""},{"docid":"a37e32e73e06b5e559537db3ee2e3375","score":"0.5206416","text":"def remove_all_components(self):\n self._js(f\"\"\"this.stage.removeAllComponents()\"\"\")\n i = 0\n while hasattr(self, f'component_{i}'):\n delattr(self, f'component_{i}')\n i += 1\n self._ngl_component_ids = []","title":""},{"docid":"5cfff6ba5d7a383838d71030cc9cec57","score":"0.5202846","text":"def deactivateAll(self):\n for piece in self._pieces:\n piece.deactivate()","title":""},{"docid":"5cfff6ba5d7a383838d71030cc9cec57","score":"0.5202846","text":"def deactivateAll(self):\n for piece in self._pieces:\n piece.deactivate()","title":""},{"docid":"5036f5f4aa8e555b5ca3f3edd411ab5f","score":"0.5202561","text":"def CopyMeshesToVisual(self, meshList):\n SkinLightmapRenderer.DoCopyMeshesToVisual(self.skinnedObject, meshList)","title":""},{"docid":"8f0ab51d6aa90be74dda03066c6fae51","score":"0.5198341","text":"def detach(self):\r\n for h in self._all:\r\n if h is not None:\r\n h.detach_()","title":""},{"docid":"095fb9e7fcd3dab592f37f90ee45cb40","score":"0.5194524","text":"def removeStyles( self, styleLabels ) :\n\n self.__delayedNeutrons.removeStyles( styleLabels )\n self.__fissionEnergyReleases.removeStyles( styleLabels )\n self.__productYields.removeStyles( styleLabels )","title":""},{"docid":"2b14978b75ab2988e5b6d3cd1e4e8d94","score":"0.51754344","text":"def unlight(self, **kwargs):\n del kwargs\n self.log.debug(\"Turning off Lights\")\n for light in self.machine.lights.items_tagged(\n self.config['lights_tag']):\n light.off()","title":""},{"docid":"6a818df2fb8a96f3fc3c8eac563fbb6c","score":"0.51716644","text":"def remove_drawn_items(self, search_list):\r\n\r\n if self.debug: self.debug_log('remove_drawn_items %s' %search_list)\r\n\r\n for item in search_list: self.canvas.delete(int(item)) \r\n search_list.clear()","title":""},{"docid":"75741fec58af3f719bb170ad79be9a33","score":"0.5164895","text":"def remove_all_materials():\n\n allGeo = [x.getTransform() for x in pm.ls(type=['mesh', 'nurbsSurface'])]\n # restore the user's previous selection after running this.\n # The MEL command requires a selection I think.\n oldSel = pm.selected()\n pm.select(allGeo)\n pm.hyperShade(assign='lambert1')\n #TODO: debug this to make sure it is always \"hyperShadePanel1\"\n pm.mel.hyperShadePanelMenuCommand(\"hyperShadePanel1\", \"deleteUnusedNodes\")\n pm.select(oldSel)","title":""},{"docid":"e63a53944257a2a0bbc6ae455ed8d408","score":"0.5144274","text":"def remove_all(cls):\n for sprite in cls.all_attacks:\n sprite.remove_from_sprite_lists()","title":""},{"docid":"076d6a982cbfe03559206ae55a5f5574","score":"0.512024","text":"def deactivateAll(self):\n for piece in self._pieces:\n piece.deactivate()\n piece.faceDown()\n piece.notReady()\n for button in self._buttons:\n button.deactivate()\n self._panel.deselect(self._currentPlayer)","title":""},{"docid":"6f072b815987bd1d999626cce254b27f","score":"0.51179564","text":"def clear_scene():\n\tdelete_list = [item.name for item in bpy.data.objects if item.type == \"MESH\" or item.type == \"LAMP\"]\n\n\tfor obj in delete_list:\n\t bpy.data.objects[obj].select = True\n\n\tbpy.ops.object.delete()\n\n\tfor item in bpy.data.meshes:\n\t bpy.data.meshes.remove(item)","title":""},{"docid":"8e7f5925d7227cdd090295da47d3bd09","score":"0.51095647","text":"def RestoreShaders(self, meshes = None):\n if meshes is None:\n meshes = self.meshes\n SkinLightmapRenderer.DoRestoreShaders(meshes)","title":""},{"docid":"132834e20057d2e81b0ecceac6719fa5","score":"0.5108547","text":"def _remove(self, nameList, **kwargs):\n for name in nameList:\n for item in pm.lsUI():\n if item.endswith(name):\n pm.deleteUI(item)","title":""},{"docid":"24f17d0b9cec9d19d9d9ca729f55c7a5","score":"0.51045203","text":"def unregister_all(db):\n logging.info(\"Leaving network mesh...\")\n try:\n [node.client.unregister(config.ST_OWN_NAME) for node in get_all(db)]\n # only server errors are possible, ignore them since we're shutting down\n except Exception:\n pass","title":""},{"docid":"218c76d292dee304ddd139d9aa10a8db","score":"0.5103793","text":"def erase_hand(self):\n self._hand = []","title":""},{"docid":"789747fd7458111f4853bdf907c5a9e7","score":"0.5090994","text":"def unload(self):\n for asset in self.assets:\n if asset[0].loaded:\n asset[0].unload()","title":""},{"docid":"f1d22ff857d5e753e28496a0b0493d15","score":"0.50875366","text":"def OnBeforeUnwrap(self):\n self.ChangeEffect('lightmapRenderEffect')\n scene = SkinLightmapRenderer.Scene()\n if hasattr(scene, 'updateShadowCubeMap'):\n scene.updateShadowCubeMap = False\n if self.skinnedObject is not None and self.skinnedObject.visualModel is not None:\n self.savedMeshes = self.skinnedObject.visualModel.meshes[:]\n filteredMeshes = [ ref.object for ref in self.meshes.iterkeys() if ref.object is not None ]\n self.CopyMeshesToVisual(filteredMeshes)\n for meshData in self.meshes.itervalues():\n for list in meshData.liveParameters:\n for params in list:\n params[1].value = params[0].value\n params[2].value = params[0].value\n\n scene.useFilterList = True\n trinity.settings.SetValue('apexVisualizeOnly', True)","title":""},{"docid":"80fa13b9422289bb4ff380604a6323d3","score":"0.50841707","text":"def remove_all_widgets():\r\n global win\r\n for widget in win.winfo_children():\r\n widget.grid_remove()","title":""},{"docid":"33652e129165d2a4bb7e1a1b6ebdb844","score":"0.5062499","text":"def unbind(self, name):\n self.keys[name].pop()","title":""},{"docid":"a719847707730ab8ca970ef85f773b82","score":"0.50543344","text":"def reset_word_list(self):\n for word in self._word_found_frame.winfo_children():\n word.destroy()","title":""},{"docid":"dbf05dc370c512b8881db7ef53e4df15","score":"0.50513124","text":"def remove_hooks(hooks):\n for hook in hooks:\n hook.remove()","title":""},{"docid":"e408a9cbc2448ffe91fe394351598864","score":"0.5031984","text":"def strip_visuals(self):\n from ..visual.color import ColorVisuals\n for geometry in self.geometry.values():\n if util.is_instance_named(geometry, 'Trimesh'):\n geometry.visual = ColorVisuals(mesh=geometry)","title":""},{"docid":"c9e59a6db51e0e6f50fc437ea8bbad01","score":"0.5026598","text":"def unmask_all(self):\n masked_properties = reversed(self._masked_properties)\n for masked_property in masked_properties:\n self.unmask(**masked_property, raise_exception=False)\n self._masked_properties.clear()","title":""},{"docid":"0397e158c52b4561c8b1515b81df4b87","score":"0.50064933","text":"def clear_strip(self):\n\n\t\tfor led in range(self.num_led):\n\t\t\tself.set_pixel(led, 0, 0, 0)\n\t\tself.show()","title":""},{"docid":"262201f6d2a40a166ff3a3c1e245f396","score":"0.5004906","text":"def destroy_all_handles(self):\n to_remove = self.handles_d.keys()\n for name in to_remove:\n self.destroy_handle(name)","title":""},{"docid":"a3f7b7698be1f8a2551235d38f53cfad","score":"0.49976552","text":"def xontribs_unload(\n names: Annotated[\n tp.Sequence[str],\n Arg(nargs=\"+\", completer=xontrib_unload_completer),\n ] = (),\n verbose=False,\n):\n for name in names:\n if verbose:\n print(f\"unloading xontrib {name!r}\")\n\n spec = find_xontrib(name)\n try:\n if spec and spec.name in sys.modules:\n module = sys.modules[spec.name]\n unloader = getattr(module, \"_unload_xontrib_\", None)\n if unloader is not None:\n unloader(XSH)\n del sys.modules[spec.name]\n except Exception as ex:\n print_exception(f\"Failed to unload xontrib {name} ({ex})\")","title":""},{"docid":"f59f8d3e0682a7543f217e6a2a5eca2a","score":"0.49950922","text":"def DetachSizer(self):","title":""},{"docid":"6cacdafc0cdd4167e2173a03514dea7e","score":"0.49881887","text":"def empty_layers(self):\r\n for l in self.layers:\r\n l.set_stage(None)\r\n del self.layers[:]\r\n del self.__dialogs[:]\r\n self.set_prerender_buffer(None)\r\n self.redraw()","title":""},{"docid":"74bc959d636018518b7194fb0ca1e87d","score":"0.498759","text":"def OnAfterUnwrap(self):\n scene = SkinLightmapRenderer.Scene()\n if hasattr(scene, 'updateShadowCubeMap'):\n scene.updateShadowCubeMap = True\n if hasattr(self, 'savedMeshes'):\n self.CopyMeshesToVisual(self.savedMeshes)\n del self.savedMeshes\n scene.useFilterList = False\n trinity.settings.SetValue('apexVisualizeOnly', False)","title":""},{"docid":"16ca3abbbe9cdfa0cb7daff89dd5d0d2","score":"0.49831438","text":"def removeFaceColors(*args, **kwargs):\n \n pass","title":""},{"docid":"637da223bb6fd24df52709f8a3ea20ad","score":"0.49548858","text":"def RemoveAllShapes(self):\n self._shapeList = []","title":""},{"docid":"66e1b954b65ecc3782e5ee9cb93118be","score":"0.4947762","text":"def UnSetColorSurf(self, *args):\n return _XCAFPrs.XCAFPrs_Style_UnSetColorSurf(self, *args)","title":""},{"docid":"c586cfa77a029952eeb7aee81cab186c","score":"0.4936064","text":"def SmartUnsetAll(self):\n self.stubs.reverse()\n\n for args in self.stubs:\n setattr(*args)\n\n self.stubs = []","title":""},{"docid":"0a0bc2df709d920823ed7b78aabaa1eb","score":"0.4927447","text":"def clear_all_surfaces(self):\n self.end_all_painters()\n self.fill_all_default()\n self.set_all_painters()\n self.paths = GroupOfPaths(path=Path()) # TODO: Why [Interessant]: Wenn path=Path() weggelassen wird: Alter Path wird neu referenziert\n self.update()","title":""},{"docid":"e9169b164d3ff67c7fa2c701bce212c1","score":"0.49066675","text":"def sprite_reset(self):\r\n for x in obstacle_group:\r\n x.kill()\r\n for x in tree_and_flag_group:\r\n x.kill()\r\n for x in tree_group:\r\n x.kill()\r\n for x in flag_group:\r\n x.kill()\r\n for x in snowball_group:\r\n x.kill()","title":""},{"docid":"703166496c9a180de52e9e2833b99a58","score":"0.49017778","text":"def remove(self, *groups):\n\t\tspyral.sprite.Sprite.remove(self, *groups);\n\t\tif self.face:\n\t\t\tself.face.remove(*groups);\n\t\t\tself.face= None;","title":""},{"docid":"fc1c16aeafb41959a91d2080a1322f95","score":"0.49015778","text":"def remove_all_enemies(cls) -> None:\n for enemy in cls.all_enemies:\n enemy.remove_from_sprite_lists()\n cls.stronger_enemies = []","title":""},{"docid":"fd3dc2e848065f868304276da2d92b4d","score":"0.48995638","text":"def remove_attached_tool(self):\n self.ensure_client()\n\n # robot has 0 or more tools, each tool has a list of attached meshes\n for tool_mesh_list in self.robot.get_attached_tool_collision_meshes():\n for attached_collision_mesh in tool_mesh_list:\n self.remove_attached_collision_mesh(attached_collision_mesh.collision_mesh.id)\n self.remove_collision_mesh(attached_collision_mesh.collision_mesh.id)\n self.robot.detach_tool()","title":""},{"docid":"80ab06972f68ade0f50629299e26b051","score":"0.48867112","text":"def dellib(self, i, j):\n self.liberties.remove((i, j))\n if not len(self.liberties):\n disp_info('{} player has captured {} stone(s)'.format( \\\n 'White' if self.player == 1 else 'Black', self.stones))\n self.captured()\n self.empty()","title":""},{"docid":"ac3cc992c2fec92d8e8a6c4687865bba","score":"0.48817056","text":"def _clearAll(self):\n np = self.lb.size()\n for _ in six.moves.range(np):\n self.lb.select_clear(0, self.lb.size())\n self.lb.select_set(0)\n self._removeButtonClicked()","title":""},{"docid":"fded347ee183faa9fecb7639dc9fb552","score":"0.488129","text":"def _handle_let(self, varlist, bdy):\n for k in varlist:\n self.cache.unbind(k)\n return bdy","title":""},{"docid":"b0bd90d20d7d823f82aecad568c17ddc","score":"0.48790768","text":"def destroy_all_stefans(self):\n self.stefans = []","title":""},{"docid":"4b0b1e84b603fe7bbf493fdd4f60581e","score":"0.4869532","text":"def unwatch(models=None):\n if models:\n if not isinstance(models, (tuple, list)):\n models = (models,)\n for model in models:\n if not hasattr(model, \"_wandb_hook_names\"):\n wandb.termwarn(\"%s model has not been watched\" % model)\n else:\n for name in model._wandb_hook_names:\n wandb.run.history.torch.unhook(name)\n delattr(model, \"_wandb_hook_names\")\n # TODO: we should also remove recursively model._wandb_watch_called\n\n else:\n wandb.run.history.torch.unhook_all()","title":""},{"docid":"106ab94f3932fd3e95baa61a7494d668","score":"0.48382485","text":"def unrefreshed_images_blit(self):\n # apply all maze blocks sprites on the game screen\n GameDisplay.maze_blocks_sprites.draw(self.game_screen)\n # apply all item to collect sprites on the game screen\n GameDisplay.items_sprites.draw(self.game_screen)\n # apply guardian sprite on the game screen\n GameDisplay.guardian_sprite.draw(self.game_screen)","title":""},{"docid":"2cf8a6b66061a5e73e91314a0b836be5","score":"0.4835862","text":"def remove_all_bound_printers(self):\n apiutility = APIUtility()\n apiutility.unbind_all_printers()","title":""},{"docid":"8a2319f4680a914b661ef6b8b397e67b","score":"0.4834458","text":"def delete_all():\n bpy.ops.object.select_all(action='SELECT')\n bpy.ops.object.delete() \n for material in bpy.data.materials:\n material.user_clear()\n bpy.data.materials.remove(material)","title":""},{"docid":"2996c8e2158f7d3df3b630ba8e0dfb49","score":"0.4813211","text":"async def unregister_all_websockets(self) -> None:\n for web_socket_handle in self._web_sockets.values():\n await web_socket_handle.clear_websocket()\n self._web_sockets.clear()","title":""},{"docid":"8eca672c840422d90420fd75e3a0c64c","score":"0.48082244","text":"def _remove_decoder(self, l, byList):\n\n for i in xrange(l):\n try:\n d = DECODERTYPEXREF[byList[i]][0]\n try:\n del self.decoders[self.decoders.index(d)]\n except ValueError:\n continue\n except KeyError:\n continue\n continue","title":""},{"docid":"12a354e31fc8b6acdd983da4377c8273","score":"0.4795117","text":"def load_weights(weight_dir, geo_list=[], joint_list=[]):\n\n # weights folder\n\n weight_files = os.listdir(weight_dir)\n\n # load skin weights\n # alternatively, if you don't want such a long line of code:\n\n for geo in geo_list:\n kwargs = {\n 'toSelectedBones': True,\n 'bindMethod': 0,\n 'skinMethod': 2,\n 'name': geo.replace('geo', 'scls'),\n 'normalizeWeights': 1,\n 'maximumInfluences': 4\n }\n try:\n scls = mc.skinCluster(joint_list, geo, **kwargs)[0]\n except RuntimeError:\n print 'bad bind' +str(RuntimeError)\n\n for wt_file in weight_files:\n\n ext_res = os.path.splitext(wt_file)\n\n # check extension format\n if not ext_res > 1:\n continue\n\n # check skin weight file\n if not ext_res[1] == '.json':\n continue\n\n # check geometry list\n if geo_list and not ext_res[0] in geo_list:\n continue\n\n # check if objects exist\n if not mc.objExists(ext_res[0]):\n continue\n\n fullpath_weight_file = os.path.join(weight_dir, wt_file)\n\n if \".json\" in ext_res[1]:\n with open(fullpath_weight_file, 'r') as f:\n data = f.read()\n\n #with open(fullpath_weight_file) as json_file:\n #data = json.load(json_file)\n importer = JsonImporter()\n layerData = importer.process(data)\n try:\n layerData.saveTo(wt_file.replace('.json', 'Shape'))\n except Exception as e:\n print 'warning:' + str(e)\n\n #bSkinSaver2.bLoadSkinValues(loadOnSelection=False, inputFile=fullpath_weight_file)","title":""},{"docid":"e1f5b4f5b667a00adab1a97bed63c1f1","score":"0.47942844","text":"def unbind(self) -> None:\n self._window = None","title":""},{"docid":"9d6d36b4f3aa91f58d4587844f10e495","score":"0.47920412","text":"def unlockFaceVertexNormals(*args, **kwargs):\n \n pass","title":""},{"docid":"6637197d4262177657d23d0394d6cb6f","score":"0.47918147","text":"def unbind(input, dim=0):\n return tuple(\n Cplx(re, im)\n for re, im in zip(\n torch.unbind(input.real, dim),\n torch.unbind(input.imag, dim),\n )\n )","title":""},{"docid":"35e829db6ea3f72ab7e42f8bcb224002","score":"0.47871253","text":"def cleanup_medkits():\n for medkit_heal in list(medkit_heals):\n if medkit_heal.y == medkit_heal.target_y:\n medkit_heals.remove(medkit_heal)","title":""},{"docid":"e0d78d40e15ccd87827b92ce94790dce","score":"0.47783932","text":"def remove_mask_from_list(self, mask, lst):\n if mask is None:\n raise InsufficentParamsError('MODE')\n else:\n try:\n lst.remove(mask)\n except ValueError:\n pass # No message to be relayed as per spec.","title":""},{"docid":"65a0da4a6b74964be550e23ad3211559","score":"0.47779578","text":"def noneAdmix(self):\n while len(self.admixSampleList) > 0 :\n for box in self.admixSampleList:\n box.hide()\n self.admixSampleList.remove(box)","title":""},{"docid":"bee506aa479bbfedac56bb9a5bdb9196","score":"0.47732332","text":"def set_textures(self, texture_paths={}):\r\n for i in range(0, 6):\r\n diffuse = sandbox.base.loader.loadTexture(texture_paths['diffuse'].replace('#',\r\n str(i)))\r\n diffuse.setMinfilter(Texture.FTLinearMipmapLinear)\r\n diffuse.setAnisotropicDegree(4)\r\n self.sides[i].set_texture(diffuse)\r\n continue\r\n normal = sandbox.base.loader.loadTexture('Data/Textures/EmptyNormalTexture.png')\r\n normalts = TextureStage('normalts')\r\n self.sides[i].set_texture(normalts, normal)\r\n if 'specular' in texture_paths:\r\n specular = sandbox.base.loader.loadTexture(texture_paths['specular'].replace('#',\r\n str(i)))\r\n else:\r\n specular = sandbox.base.loader.loadTexture('Data/Textures/EmptySpecularTexture.png')\r\n spects = TextureStage('spects')\r\n self.sides[i].set_texture(spects, specular)\r\n roughness = sandbox.base.loader.loadTexture('Data/Textures/EmptyRoughnessTexture.png')\r\n roughts= TextureStage('roughts')\r\n self.sides[i].set_texture(roughts, roughness)\r\n '''if 'night' in texture_paths:\r\n self.sides[i].set_shader_input('nightTexture',\r\n sandbox.base.loader.loadTexture(\r\n texture_paths['night'].replace('#',\r\n str(i))))'''\r\n '''self.sides[i].set_shader_input('colorTexture', texture)\r\n if night_path:\r\n self.sides[i].set_shader_input('nightTesture',\r\n loader.loadTexture(\r\n night_path.replace('#',\r\n str(i))))\r\n if gloss_path:\r\n self.sides[i].set_shader_input('glossTexture',\r\n loader.loadTexture(\r\n gloss_path.replace('#',\r\n str(i))))'''\r\n self.load_shaders()","title":""},{"docid":"050ac431997ae9b6afbe14d55f5af7a4","score":"0.477187","text":"def clear_hands(self):\n self.hands = []","title":""},{"docid":"d098cd04a5347dcb232f50170a50185a","score":"0.47709107","text":"def reset(self):\n for turtle in self._turtles:\n turtle._setmode(self._mode)\n turtle.reset()","title":""},{"docid":"cf4f2fb81d816a60029ed08243d334eb","score":"0.4770335","text":"def remove(self, names):\n for name in names:\n try:\n component = self._components.pop(name)\n self._remaining.discard(component)\n self._instantiated.discard(component)\n except KeyError:\n _logger.warning(\"Unknown component: %s\", name)","title":""},{"docid":"02df66680481fa36942cc87c421b22ed","score":"0.47657114","text":"def eraseScreen(self):\n if self.hasjoined and self.hasjoinresponse:\n self.changedrects = []\n for obj in self.deadobjectlist:\n self.changedrects.extend(obj.eraseObject(self.screen))\n self.deadobjectlist = []\n for obj in self.objectlist.values()+self.staticobjectlist:\n self.changedrects.extend(obj.eraseObject(self.screen))","title":""},{"docid":"9ac466c3eb01d9f81bb832b9e01f1ff2","score":"0.47639903","text":"def removeAll( self ):\n for layer in self.layers: \n QgsMapLayerRegistry.instance().removeMapLayer( layer.layer().id())\n self.clear()","title":""},{"docid":"4e2c3091771e05e044b51ca32674392a","score":"0.47558662","text":"def _unbind_observers(self):\n workbench = self.workbench\n point = workbench.get_extension_point(STATES_POINT)\n point.unobserve('extensions', self._on_states_updated)\n\n #TODO also unbind dynamic observers here in case there is any remaining","title":""},{"docid":"69f97417f83c63318d9654afd4a95e7e","score":"0.47558016","text":"def _setAllBBoxUpdatesOff():\r\n for each in cmds.ls(type = 'core_archive'):\r\n cmds.setAttr('%s.auto_bbox_update' % each, 0)","title":""},{"docid":"4b2788138b8b3da16eb27a76a17429ce","score":"0.47542983","text":"def clear(self):\n for block in bpy.data.meshes:\n if block.users == 0:\n bpy.data.meshes.remove(block)\n for block in bpy.data.materials:\n if block.users == 0:\n bpy.data.materials.remove(block)\n for block in bpy.data.textures:\n if block.users == 0:\n bpy.data.textures.remove(block)\n for block in bpy.data.images:\n if block.users == 0:\n bpy.data.images.remove(block)\n bpy.ops.object.mode_set(mode='OBJECT')\n bpy.ops.object.select_all(action='SELECT')\n bpy.ops.object.delete()","title":""},{"docid":"e6615fb0b543fb2605950683775c2f87","score":"0.4750799","text":"def removeAllPlots(self):\n for function in self._allFunctions:\n self._canvas.delete(function['canvasObj'])","title":""},{"docid":"085ca564462294a5c5a2e66646234173","score":"0.47453618","text":"def clear(self):\n for i in self.__bindings.keys():\n self.__bindings[i].clear()","title":""},{"docid":"323e650e7883947e74d838f504d4d0be","score":"0.47398314","text":"def reset(self):\n for b in self.blocklist:\n b.reset()","title":""},{"docid":"08ec9ade245b8e925e253f51e3f49c3f","score":"0.47361177","text":"def destroy(self):\n self.__destroyTextures()","title":""},{"docid":"ed31c77c02cab49a0058c77fef01fc16","score":"0.47288817","text":"def remove(self):\r\n for _, scatter in self.scatters:\r\n scatter.remove()","title":""},{"docid":"687edfdffc72b50edd1d202b81fe316b","score":"0.4717254","text":"def DeleteAllShapes(self):\n for shape in self._shapeList[:]:\n if not shape.GetParent():\n self.RemoveShape(shape)\n shape.Delete()","title":""},{"docid":"a60961fc01c2c09ded69d8c5a26e137b","score":"0.4714101","text":"def _setupSkins(context, skin_prefix, definition=None):\n portal = context.getSite()\n ps = portal.portal_skins\n skins = ps._getSelections()\n\n if definition is None:\n definition = _defaultSkinDefinition(skin_prefix)\n\n for skin, layers in definition.items():\n if not skin in skins: continue\n new_layers = []\n current_layers = skins[skin].split(',')\n for current_layer in current_layers:\n if current_layer.startswith(skin_prefix) and \\\n (current_layer not in layers):\n # This layer must be removed from the skin\n pass\n else:\n new_layers.append(current_layer)\n\n # Change the layer order\n layers.reverse()\n for layer in layers:\n if layer in new_layers:\n new_layers.remove(layer)\n new_layers.insert(1, layer)\n else:\n new_layers.insert(1, layer)\n\n skins[skin] = ','.join(new_layers)","title":""},{"docid":"d9ea942d736a043f62d01d9211313bd4","score":"0.47128737","text":"def delattrs(self, lst):\n for key in lst:\n if key in self.__dict__:\n del self.__dict__[key]","title":""},{"docid":"1be871a7d48074e855af88a27e98190a","score":"0.468809","text":"def ledsOff():\n [pyb.LED(i).off() for i in range(1,5)]","title":""},{"docid":"a98525626bfaeff0e3c99f5208f8965d","score":"0.46870944","text":"def matsPurgeout():\n bpy.ops.object.mode_set(mode='OBJECT', toggle=False)\n for ob in bpy.context.selected_objects:\n bpy.context.scene.objects.active = ob\n if ob.type != 'MESH':\n continue\n mat_slots = {}\n for p in ob.data.polygons:\n mat_slots[p.material_index] = 1\n mat_slots = mat_slots.keys()\n for i in range(len(ob.material_slots) - 1, -1, -1):\n if i not in mat_slots:\n bpy.context.object.active_material_index = i\n bpy.ops.object.material_slot_remove()","title":""},{"docid":"ab983ea1a397a1df2304d2da00dba413","score":"0.46860063","text":"def hide_all(self):\n for object_1 in self.objects:\n object_1.setAutoDraw(False)","title":""},{"docid":"9708d9628b717c0ded4adbbdd2f67907","score":"0.46845552","text":"def disable(self):\n self.active = False\n\n for skill in self.skills:\n skill.disable()","title":""},{"docid":"edab5144852269007fee67b8c02714a0","score":"0.46825266","text":"def unbind(self):\n if not self.binded():\n return\n\n self._unbind() and self._mkstat and aux.remove_dirs(self._newdir)","title":""},{"docid":"6759e495158ea8c4ea17111d3939a83d","score":"0.46823573","text":"def clear_all(self):\r\n for control in self.controls:\r\n control.clear()","title":""},{"docid":"6759e495158ea8c4ea17111d3939a83d","score":"0.46823573","text":"def clear_all(self):\r\n for control in self.controls:\r\n control.clear()","title":""},{"docid":"bce1c5d176d824f40d9a5d1846f4f803","score":"0.46802637","text":"def clear_all(self):\r\n aant = self.nb.count()\r\n widgets = [self.nb.widget(x) for x in range(aant)]\r\n self.nb.clear()\r\n for wdg in widgets:\r\n wdg.destroy()","title":""},{"docid":"1f5909b99590359ee5b57862e93084e8","score":"0.46798316","text":"def clear_presets(uid):\n PRESETS[uid].clear()","title":""},{"docid":"b2d668fae9b28557d43951705e0ee12b","score":"0.46754968","text":"def remove(self,w):\n self.blur(w)\n self.widgets.remove(w)\n #self.repaint()\n self.chsize()","title":""}],"string":"[\n {\n \"docid\": \"ab9be50819a072e20e9f8437cb740c4d\",\n \"score\": \"0.63012815\",\n \"text\": \"def unregister_layers(self, names, out):\\n skinstool = getToolByName(self, 'portal_skins')\\n\\n # remove this layer from all known skins\\n for skinName in skinstool.getSkinSelections():\\n path = skinstool.getSkinPath(skinName) \\n path = [i.strip() for i in path.split(',')]\\n\\n opath = list(path)\\n for name in names:\\n if name in path:\\n path.remove(name)\\n\\n if opath != path:\\n path = ','.join(path)\\n skinstool.addSkinSelection(skinName, path)\\n\\n print >>out, \\\"removed layers '%s' from '%s'\\\" % (', '.join(names), skinName)\\n self.changeSkin(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"156b171532e4875a9e53f82487a3c657\",\n \"score\": \"0.59158087\",\n \"text\": \"def DoCopyMeshesToVisual(skinnedObject, meshList):\\n if skinnedObject is None or skinnedObject.visualModel is None:\\n return\\n skinnedObject.visualModel.meshes.removeAt(-1)\\n for mesh in meshList:\\n skinnedObject.visualModel.meshes.append(mesh)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a2e5a9a4b70e61783a01a9c874f1c41\",\n \"score\": \"0.5870197\",\n \"text\": \"def unbind_all(self, values):\\n for k in values:\\n self.unbind(k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e01b17b4d623e5d88ad9fa868953ce3\",\n \"score\": \"0.5649311\",\n \"text\": \"def undraw(itemlist):\\n pf.canvas.remove(itemlist)\\n pf.canvas.update()\\n pf.app.processEvents()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8901d8c1c2b91b5243f13fb0c9c99f8e\",\n \"score\": \"0.5614368\",\n \"text\": \"def unbind_on_symbols(self):\\n self.zbarcam.unbind(symbols=self.on_symbols)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"459dec8ae22734aa580e689393b3b654\",\n \"score\": \"0.5508435\",\n \"text\": \"def resetBoundMeshes(self):\\n for mName in self.getBoundMeshes():\\n if mName != self.getSeedMesh().name:\\n self.removeBoundMesh(mName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12958655c0d9ffb03aa8cdcac74bf383\",\n \"score\": \"0.5473903\",\n \"text\": \"def _remove_meshes(self, mesh_names: [str] = None) -> None:\\n if mesh_names is None:\\n mesh_names = self.meshes.keys()\\n\\n print(\\\"Removing all {} from simulation\\\".format(mesh_names))\\n for mesh_name in mesh_names:\\n meshes = self.meshes[mesh_name]\\n\\n i = len(meshes) - 1\\n\\n # Deletion is expensive, so iterate backwards in list\\n while i >= 0:\\n # Remove from simulation and delete from list of meshes we're tracking.\\n self.bt.removeBody(meshes[i])\\n del meshes[i]\\n\\n i -= 1\\n\\n self.bt.removeBody(self.robot)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e832996f9bf14742361688baca8cea\",\n \"score\": \"0.5429389\",\n \"text\": \"def clear(self):\\n for animal in self.animals:\\n animal.undraw()\\n self.canvas.delete('slime')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba41b49853b18c89089f120518d9186\",\n \"score\": \"0.5421912\",\n \"text\": \"def unbind(self):\\n glBindTexture(GL_TEXTURE_2D, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b0bc676a345f9c6b88041b52601b92a\",\n \"score\": \"0.54160416\",\n \"text\": \"def _delete_bubbles(self, bubbles):\\n for bubble in bubbles:\\n self.board[bubble[0]][bubble[1]].color = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0e1130b6c07e6706e6ced99b9006799\",\n \"score\": \"0.54046196\",\n \"text\": \"def stop_anim(self):\\n\\n self.stop_animation = True\\n self.start.configure(state=NORMAL)\\n self.stop.configure(state=DISABLED)\\n self.update()\\n\\n for i in range(len(self.particlelist)):\\n xlist = self.particlelist[i]\\n for particle in xlist:\\n particle.delete()\\n self.particlelist[i] = []\\n\\n self.init()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f96addcce04384f39de6a0d3a0856dc\",\n \"score\": \"0.5404546\",\n \"text\": \"def remove_regions(self):\\n for regions in self.hulls:\\n for region in regions:\\n for vb in [self.view3D, self.view2D]:\\n if region in vb.items:\\n vb.removeItem(region)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c382e30a56ff26aecba4a1aec40478e\",\n \"score\": \"0.5381617\",\n \"text\": \"def destroy_all(self):\\r\\n\\r\\n [widget.destroy() for widget in self.widgets]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5fb1be24eaa537332b82951ebe25e03\",\n \"score\": \"0.53641725\",\n \"text\": \"def tear_off():\\r\\n s = mampy.selected()\\r\\n\\r\\n for comp in s.itercomps():\\r\\n if not comp.is_face():\\r\\n continue\\r\\n\\r\\n edges = comp.to_edge(border=True)\\r\\n cmds.polyMapCut(list(edges))\\r\\n cmds.select(list(s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dffe1a8e8ed7d4340a620367a1dc40b\",\n \"score\": \"0.5351901\",\n \"text\": \"def unbind(self: T, dim: int = 0) -> T:\\n assert dim != -1 and dim != self.tensor.dim() - 1\\n boxes_list = self.tensor.unbind(dim=dim)\\n return [type(self)(boxes, clone=False) for boxes in boxes_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa83abe46bc5655d32b74c3541f2aeae\",\n \"score\": \"0.52715826\",\n \"text\": \"def set_light_off(self, names):\\n for light_name in self.lights:\\n if light_name in names:\\n self.put(f\\\"api/{self.username}/lights/{self.lights[light_name]}/state\\\",\\n data={\\\"on\\\": False})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28968065d942451a157df69495ef2ea7\",\n \"score\": \"0.5212518\",\n \"text\": \"def make_all_skin():\\n human = get_human_mesh()\\n generic_slot = get_mat_slot(human, 'MBlab_generic')\\n skin_material = node.material('MBlab_human_skin')\\n\\n generic_slot.material = skin_material\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a37e32e73e06b5e559537db3ee2e3375\",\n \"score\": \"0.5206416\",\n \"text\": \"def remove_all_components(self):\\n self._js(f\\\"\\\"\\\"this.stage.removeAllComponents()\\\"\\\"\\\")\\n i = 0\\n while hasattr(self, f'component_{i}'):\\n delattr(self, f'component_{i}')\\n i += 1\\n self._ngl_component_ids = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cfff6ba5d7a383838d71030cc9cec57\",\n \"score\": \"0.5202846\",\n \"text\": \"def deactivateAll(self):\\n for piece in self._pieces:\\n piece.deactivate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cfff6ba5d7a383838d71030cc9cec57\",\n \"score\": \"0.5202846\",\n \"text\": \"def deactivateAll(self):\\n for piece in self._pieces:\\n piece.deactivate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5036f5f4aa8e555b5ca3f3edd411ab5f\",\n \"score\": \"0.5202561\",\n \"text\": \"def CopyMeshesToVisual(self, meshList):\\n SkinLightmapRenderer.DoCopyMeshesToVisual(self.skinnedObject, meshList)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f0ab51d6aa90be74dda03066c6fae51\",\n \"score\": \"0.5198341\",\n \"text\": \"def detach(self):\\r\\n for h in self._all:\\r\\n if h is not None:\\r\\n h.detach_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"095fb9e7fcd3dab592f37f90ee45cb40\",\n \"score\": \"0.5194524\",\n \"text\": \"def removeStyles( self, styleLabels ) :\\n\\n self.__delayedNeutrons.removeStyles( styleLabels )\\n self.__fissionEnergyReleases.removeStyles( styleLabels )\\n self.__productYields.removeStyles( styleLabels )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b14978b75ab2988e5b6d3cd1e4e8d94\",\n \"score\": \"0.51754344\",\n \"text\": \"def unlight(self, **kwargs):\\n del kwargs\\n self.log.debug(\\\"Turning off Lights\\\")\\n for light in self.machine.lights.items_tagged(\\n self.config['lights_tag']):\\n light.off()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a818df2fb8a96f3fc3c8eac563fbb6c\",\n \"score\": \"0.51716644\",\n \"text\": \"def remove_drawn_items(self, search_list):\\r\\n\\r\\n if self.debug: self.debug_log('remove_drawn_items %s' %search_list)\\r\\n\\r\\n for item in search_list: self.canvas.delete(int(item)) \\r\\n search_list.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75741fec58af3f719bb170ad79be9a33\",\n \"score\": \"0.5164895\",\n \"text\": \"def remove_all_materials():\\n\\n allGeo = [x.getTransform() for x in pm.ls(type=['mesh', 'nurbsSurface'])]\\n # restore the user's previous selection after running this.\\n # The MEL command requires a selection I think.\\n oldSel = pm.selected()\\n pm.select(allGeo)\\n pm.hyperShade(assign='lambert1')\\n #TODO: debug this to make sure it is always \\\"hyperShadePanel1\\\"\\n pm.mel.hyperShadePanelMenuCommand(\\\"hyperShadePanel1\\\", \\\"deleteUnusedNodes\\\")\\n pm.select(oldSel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63a53944257a2a0bbc6ae455ed8d408\",\n \"score\": \"0.5144274\",\n \"text\": \"def remove_all(cls):\\n for sprite in cls.all_attacks:\\n sprite.remove_from_sprite_lists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"076d6a982cbfe03559206ae55a5f5574\",\n \"score\": \"0.512024\",\n \"text\": \"def deactivateAll(self):\\n for piece in self._pieces:\\n piece.deactivate()\\n piece.faceDown()\\n piece.notReady()\\n for button in self._buttons:\\n button.deactivate()\\n self._panel.deselect(self._currentPlayer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f072b815987bd1d999626cce254b27f\",\n \"score\": \"0.51179564\",\n \"text\": \"def clear_scene():\\n\\tdelete_list = [item.name for item in bpy.data.objects if item.type == \\\"MESH\\\" or item.type == \\\"LAMP\\\"]\\n\\n\\tfor obj in delete_list:\\n\\t bpy.data.objects[obj].select = True\\n\\n\\tbpy.ops.object.delete()\\n\\n\\tfor item in bpy.data.meshes:\\n\\t bpy.data.meshes.remove(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e7f5925d7227cdd090295da47d3bd09\",\n \"score\": \"0.51095647\",\n \"text\": \"def RestoreShaders(self, meshes = None):\\n if meshes is None:\\n meshes = self.meshes\\n SkinLightmapRenderer.DoRestoreShaders(meshes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132834e20057d2e81b0ecceac6719fa5\",\n \"score\": \"0.5108547\",\n \"text\": \"def _remove(self, nameList, **kwargs):\\n for name in nameList:\\n for item in pm.lsUI():\\n if item.endswith(name):\\n pm.deleteUI(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24f17d0b9cec9d19d9d9ca729f55c7a5\",\n \"score\": \"0.51045203\",\n \"text\": \"def unregister_all(db):\\n logging.info(\\\"Leaving network mesh...\\\")\\n try:\\n [node.client.unregister(config.ST_OWN_NAME) for node in get_all(db)]\\n # only server errors are possible, ignore them since we're shutting down\\n except Exception:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"218c76d292dee304ddd139d9aa10a8db\",\n \"score\": \"0.5103793\",\n \"text\": \"def erase_hand(self):\\n self._hand = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789747fd7458111f4853bdf907c5a9e7\",\n \"score\": \"0.5090994\",\n \"text\": \"def unload(self):\\n for asset in self.assets:\\n if asset[0].loaded:\\n asset[0].unload()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1d22ff857d5e753e28496a0b0493d15\",\n \"score\": \"0.50875366\",\n \"text\": \"def OnBeforeUnwrap(self):\\n self.ChangeEffect('lightmapRenderEffect')\\n scene = SkinLightmapRenderer.Scene()\\n if hasattr(scene, 'updateShadowCubeMap'):\\n scene.updateShadowCubeMap = False\\n if self.skinnedObject is not None and self.skinnedObject.visualModel is not None:\\n self.savedMeshes = self.skinnedObject.visualModel.meshes[:]\\n filteredMeshes = [ ref.object for ref in self.meshes.iterkeys() if ref.object is not None ]\\n self.CopyMeshesToVisual(filteredMeshes)\\n for meshData in self.meshes.itervalues():\\n for list in meshData.liveParameters:\\n for params in list:\\n params[1].value = params[0].value\\n params[2].value = params[0].value\\n\\n scene.useFilterList = True\\n trinity.settings.SetValue('apexVisualizeOnly', True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80fa13b9422289bb4ff380604a6323d3\",\n \"score\": \"0.50841707\",\n \"text\": \"def remove_all_widgets():\\r\\n global win\\r\\n for widget in win.winfo_children():\\r\\n widget.grid_remove()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33652e129165d2a4bb7e1a1b6ebdb844\",\n \"score\": \"0.5062499\",\n \"text\": \"def unbind(self, name):\\n self.keys[name].pop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a719847707730ab8ca970ef85f773b82\",\n \"score\": \"0.50543344\",\n \"text\": \"def reset_word_list(self):\\n for word in self._word_found_frame.winfo_children():\\n word.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbf05dc370c512b8881db7ef53e4df15\",\n \"score\": \"0.50513124\",\n \"text\": \"def remove_hooks(hooks):\\n for hook in hooks:\\n hook.remove()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e408a9cbc2448ffe91fe394351598864\",\n \"score\": \"0.5031984\",\n \"text\": \"def strip_visuals(self):\\n from ..visual.color import ColorVisuals\\n for geometry in self.geometry.values():\\n if util.is_instance_named(geometry, 'Trimesh'):\\n geometry.visual = ColorVisuals(mesh=geometry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9e59a6db51e0e6f50fc437ea8bbad01\",\n \"score\": \"0.5026598\",\n \"text\": \"def unmask_all(self):\\n masked_properties = reversed(self._masked_properties)\\n for masked_property in masked_properties:\\n self.unmask(**masked_property, raise_exception=False)\\n self._masked_properties.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0397e158c52b4561c8b1515b81df4b87\",\n \"score\": \"0.50064933\",\n \"text\": \"def clear_strip(self):\\n\\n\\t\\tfor led in range(self.num_led):\\n\\t\\t\\tself.set_pixel(led, 0, 0, 0)\\n\\t\\tself.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"262201f6d2a40a166ff3a3c1e245f396\",\n \"score\": \"0.5004906\",\n \"text\": \"def destroy_all_handles(self):\\n to_remove = self.handles_d.keys()\\n for name in to_remove:\\n self.destroy_handle(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3f7b7698be1f8a2551235d38f53cfad\",\n \"score\": \"0.49976552\",\n \"text\": \"def xontribs_unload(\\n names: Annotated[\\n tp.Sequence[str],\\n Arg(nargs=\\\"+\\\", completer=xontrib_unload_completer),\\n ] = (),\\n verbose=False,\\n):\\n for name in names:\\n if verbose:\\n print(f\\\"unloading xontrib {name!r}\\\")\\n\\n spec = find_xontrib(name)\\n try:\\n if spec and spec.name in sys.modules:\\n module = sys.modules[spec.name]\\n unloader = getattr(module, \\\"_unload_xontrib_\\\", None)\\n if unloader is not None:\\n unloader(XSH)\\n del sys.modules[spec.name]\\n except Exception as ex:\\n print_exception(f\\\"Failed to unload xontrib {name} ({ex})\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f59f8d3e0682a7543f217e6a2a5eca2a\",\n \"score\": \"0.49950922\",\n \"text\": \"def DetachSizer(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cacdafc0cdd4167e2173a03514dea7e\",\n \"score\": \"0.49881887\",\n \"text\": \"def empty_layers(self):\\r\\n for l in self.layers:\\r\\n l.set_stage(None)\\r\\n del self.layers[:]\\r\\n del self.__dialogs[:]\\r\\n self.set_prerender_buffer(None)\\r\\n self.redraw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74bc959d636018518b7194fb0ca1e87d\",\n \"score\": \"0.498759\",\n \"text\": \"def OnAfterUnwrap(self):\\n scene = SkinLightmapRenderer.Scene()\\n if hasattr(scene, 'updateShadowCubeMap'):\\n scene.updateShadowCubeMap = True\\n if hasattr(self, 'savedMeshes'):\\n self.CopyMeshesToVisual(self.savedMeshes)\\n del self.savedMeshes\\n scene.useFilterList = False\\n trinity.settings.SetValue('apexVisualizeOnly', False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ca3abbbe9cdfa0cb7daff89dd5d0d2\",\n \"score\": \"0.49831438\",\n \"text\": \"def removeFaceColors(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637da223bb6fd24df52709f8a3ea20ad\",\n \"score\": \"0.49548858\",\n \"text\": \"def RemoveAllShapes(self):\\n self._shapeList = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66e1b954b65ecc3782e5ee9cb93118be\",\n \"score\": \"0.4947762\",\n \"text\": \"def UnSetColorSurf(self, *args):\\n return _XCAFPrs.XCAFPrs_Style_UnSetColorSurf(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c586cfa77a029952eeb7aee81cab186c\",\n \"score\": \"0.4936064\",\n \"text\": \"def SmartUnsetAll(self):\\n self.stubs.reverse()\\n\\n for args in self.stubs:\\n setattr(*args)\\n\\n self.stubs = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a0bc2df709d920823ed7b78aabaa1eb\",\n \"score\": \"0.4927447\",\n \"text\": \"def clear_all_surfaces(self):\\n self.end_all_painters()\\n self.fill_all_default()\\n self.set_all_painters()\\n self.paths = GroupOfPaths(path=Path()) # TODO: Why [Interessant]: Wenn path=Path() weggelassen wird: Alter Path wird neu referenziert\\n self.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9169b164d3ff67c7fa2c701bce212c1\",\n \"score\": \"0.49066675\",\n \"text\": \"def sprite_reset(self):\\r\\n for x in obstacle_group:\\r\\n x.kill()\\r\\n for x in tree_and_flag_group:\\r\\n x.kill()\\r\\n for x in tree_group:\\r\\n x.kill()\\r\\n for x in flag_group:\\r\\n x.kill()\\r\\n for x in snowball_group:\\r\\n x.kill()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"703166496c9a180de52e9e2833b99a58\",\n \"score\": \"0.49017778\",\n \"text\": \"def remove(self, *groups):\\n\\t\\tspyral.sprite.Sprite.remove(self, *groups);\\n\\t\\tif self.face:\\n\\t\\t\\tself.face.remove(*groups);\\n\\t\\t\\tself.face= None;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc1c16aeafb41959a91d2080a1322f95\",\n \"score\": \"0.49015778\",\n \"text\": \"def remove_all_enemies(cls) -> None:\\n for enemy in cls.all_enemies:\\n enemy.remove_from_sprite_lists()\\n cls.stronger_enemies = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd3dc2e848065f868304276da2d92b4d\",\n \"score\": \"0.48995638\",\n \"text\": \"def remove_attached_tool(self):\\n self.ensure_client()\\n\\n # robot has 0 or more tools, each tool has a list of attached meshes\\n for tool_mesh_list in self.robot.get_attached_tool_collision_meshes():\\n for attached_collision_mesh in tool_mesh_list:\\n self.remove_attached_collision_mesh(attached_collision_mesh.collision_mesh.id)\\n self.remove_collision_mesh(attached_collision_mesh.collision_mesh.id)\\n self.robot.detach_tool()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ab06972f68ade0f50629299e26b051\",\n \"score\": \"0.48867112\",\n \"text\": \"def dellib(self, i, j):\\n self.liberties.remove((i, j))\\n if not len(self.liberties):\\n disp_info('{} player has captured {} stone(s)'.format( \\\\\\n 'White' if self.player == 1 else 'Black', self.stones))\\n self.captured()\\n self.empty()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac3cc992c2fec92d8e8a6c4687865bba\",\n \"score\": \"0.48817056\",\n \"text\": \"def _clearAll(self):\\n np = self.lb.size()\\n for _ in six.moves.range(np):\\n self.lb.select_clear(0, self.lb.size())\\n self.lb.select_set(0)\\n self._removeButtonClicked()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fded347ee183faa9fecb7639dc9fb552\",\n \"score\": \"0.488129\",\n \"text\": \"def _handle_let(self, varlist, bdy):\\n for k in varlist:\\n self.cache.unbind(k)\\n return bdy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0bd90d20d7d823f82aecad568c17ddc\",\n \"score\": \"0.48790768\",\n \"text\": \"def destroy_all_stefans(self):\\n self.stefans = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b0b1e84b603fe7bbf493fdd4f60581e\",\n \"score\": \"0.4869532\",\n \"text\": \"def unwatch(models=None):\\n if models:\\n if not isinstance(models, (tuple, list)):\\n models = (models,)\\n for model in models:\\n if not hasattr(model, \\\"_wandb_hook_names\\\"):\\n wandb.termwarn(\\\"%s model has not been watched\\\" % model)\\n else:\\n for name in model._wandb_hook_names:\\n wandb.run.history.torch.unhook(name)\\n delattr(model, \\\"_wandb_hook_names\\\")\\n # TODO: we should also remove recursively model._wandb_watch_called\\n\\n else:\\n wandb.run.history.torch.unhook_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"106ab94f3932fd3e95baa61a7494d668\",\n \"score\": \"0.48382485\",\n \"text\": \"def unrefreshed_images_blit(self):\\n # apply all maze blocks sprites on the game screen\\n GameDisplay.maze_blocks_sprites.draw(self.game_screen)\\n # apply all item to collect sprites on the game screen\\n GameDisplay.items_sprites.draw(self.game_screen)\\n # apply guardian sprite on the game screen\\n GameDisplay.guardian_sprite.draw(self.game_screen)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cf8a6b66061a5e73e91314a0b836be5\",\n \"score\": \"0.4835862\",\n \"text\": \"def remove_all_bound_printers(self):\\n apiutility = APIUtility()\\n apiutility.unbind_all_printers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a2319f4680a914b661ef6b8b397e67b\",\n \"score\": \"0.4834458\",\n \"text\": \"def delete_all():\\n bpy.ops.object.select_all(action='SELECT')\\n bpy.ops.object.delete() \\n for material in bpy.data.materials:\\n material.user_clear()\\n bpy.data.materials.remove(material)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2996c8e2158f7d3df3b630ba8e0dfb49\",\n \"score\": \"0.4813211\",\n \"text\": \"async def unregister_all_websockets(self) -> None:\\n for web_socket_handle in self._web_sockets.values():\\n await web_socket_handle.clear_websocket()\\n self._web_sockets.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eca672c840422d90420fd75e3a0c64c\",\n \"score\": \"0.48082244\",\n \"text\": \"def _remove_decoder(self, l, byList):\\n\\n for i in xrange(l):\\n try:\\n d = DECODERTYPEXREF[byList[i]][0]\\n try:\\n del self.decoders[self.decoders.index(d)]\\n except ValueError:\\n continue\\n except KeyError:\\n continue\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12a354e31fc8b6acdd983da4377c8273\",\n \"score\": \"0.4795117\",\n \"text\": \"def load_weights(weight_dir, geo_list=[], joint_list=[]):\\n\\n # weights folder\\n\\n weight_files = os.listdir(weight_dir)\\n\\n # load skin weights\\n # alternatively, if you don't want such a long line of code:\\n\\n for geo in geo_list:\\n kwargs = {\\n 'toSelectedBones': True,\\n 'bindMethod': 0,\\n 'skinMethod': 2,\\n 'name': geo.replace('geo', 'scls'),\\n 'normalizeWeights': 1,\\n 'maximumInfluences': 4\\n }\\n try:\\n scls = mc.skinCluster(joint_list, geo, **kwargs)[0]\\n except RuntimeError:\\n print 'bad bind' +str(RuntimeError)\\n\\n for wt_file in weight_files:\\n\\n ext_res = os.path.splitext(wt_file)\\n\\n # check extension format\\n if not ext_res > 1:\\n continue\\n\\n # check skin weight file\\n if not ext_res[1] == '.json':\\n continue\\n\\n # check geometry list\\n if geo_list and not ext_res[0] in geo_list:\\n continue\\n\\n # check if objects exist\\n if not mc.objExists(ext_res[0]):\\n continue\\n\\n fullpath_weight_file = os.path.join(weight_dir, wt_file)\\n\\n if \\\".json\\\" in ext_res[1]:\\n with open(fullpath_weight_file, 'r') as f:\\n data = f.read()\\n\\n #with open(fullpath_weight_file) as json_file:\\n #data = json.load(json_file)\\n importer = JsonImporter()\\n layerData = importer.process(data)\\n try:\\n layerData.saveTo(wt_file.replace('.json', 'Shape'))\\n except Exception as e:\\n print 'warning:' + str(e)\\n\\n #bSkinSaver2.bLoadSkinValues(loadOnSelection=False, inputFile=fullpath_weight_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f5b4f5b667a00adab1a97bed63c1f1\",\n \"score\": \"0.47942844\",\n \"text\": \"def unbind(self) -> None:\\n self._window = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d6d36b4f3aa91f58d4587844f10e495\",\n \"score\": \"0.47920412\",\n \"text\": \"def unlockFaceVertexNormals(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6637197d4262177657d23d0394d6cb6f\",\n \"score\": \"0.47918147\",\n \"text\": \"def unbind(input, dim=0):\\n return tuple(\\n Cplx(re, im)\\n for re, im in zip(\\n torch.unbind(input.real, dim),\\n torch.unbind(input.imag, dim),\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e829db6ea3f72ab7e42f8bcb224002\",\n \"score\": \"0.47871253\",\n \"text\": \"def cleanup_medkits():\\n for medkit_heal in list(medkit_heals):\\n if medkit_heal.y == medkit_heal.target_y:\\n medkit_heals.remove(medkit_heal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d78d40e15ccd87827b92ce94790dce\",\n \"score\": \"0.47783932\",\n \"text\": \"def remove_mask_from_list(self, mask, lst):\\n if mask is None:\\n raise InsufficentParamsError('MODE')\\n else:\\n try:\\n lst.remove(mask)\\n except ValueError:\\n pass # No message to be relayed as per spec.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65a0da4a6b74964be550e23ad3211559\",\n \"score\": \"0.47779578\",\n \"text\": \"def noneAdmix(self):\\n while len(self.admixSampleList) > 0 :\\n for box in self.admixSampleList:\\n box.hide()\\n self.admixSampleList.remove(box)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee506aa479bbfedac56bb9a5bdb9196\",\n \"score\": \"0.47732332\",\n \"text\": \"def set_textures(self, texture_paths={}):\\r\\n for i in range(0, 6):\\r\\n diffuse = sandbox.base.loader.loadTexture(texture_paths['diffuse'].replace('#',\\r\\n str(i)))\\r\\n diffuse.setMinfilter(Texture.FTLinearMipmapLinear)\\r\\n diffuse.setAnisotropicDegree(4)\\r\\n self.sides[i].set_texture(diffuse)\\r\\n continue\\r\\n normal = sandbox.base.loader.loadTexture('Data/Textures/EmptyNormalTexture.png')\\r\\n normalts = TextureStage('normalts')\\r\\n self.sides[i].set_texture(normalts, normal)\\r\\n if 'specular' in texture_paths:\\r\\n specular = sandbox.base.loader.loadTexture(texture_paths['specular'].replace('#',\\r\\n str(i)))\\r\\n else:\\r\\n specular = sandbox.base.loader.loadTexture('Data/Textures/EmptySpecularTexture.png')\\r\\n spects = TextureStage('spects')\\r\\n self.sides[i].set_texture(spects, specular)\\r\\n roughness = sandbox.base.loader.loadTexture('Data/Textures/EmptyRoughnessTexture.png')\\r\\n roughts= TextureStage('roughts')\\r\\n self.sides[i].set_texture(roughts, roughness)\\r\\n '''if 'night' in texture_paths:\\r\\n self.sides[i].set_shader_input('nightTexture',\\r\\n sandbox.base.loader.loadTexture(\\r\\n texture_paths['night'].replace('#',\\r\\n str(i))))'''\\r\\n '''self.sides[i].set_shader_input('colorTexture', texture)\\r\\n if night_path:\\r\\n self.sides[i].set_shader_input('nightTesture',\\r\\n loader.loadTexture(\\r\\n night_path.replace('#',\\r\\n str(i))))\\r\\n if gloss_path:\\r\\n self.sides[i].set_shader_input('glossTexture',\\r\\n loader.loadTexture(\\r\\n gloss_path.replace('#',\\r\\n str(i))))'''\\r\\n self.load_shaders()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"050ac431997ae9b6afbe14d55f5af7a4\",\n \"score\": \"0.477187\",\n \"text\": \"def clear_hands(self):\\n self.hands = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d098cd04a5347dcb232f50170a50185a\",\n \"score\": \"0.47709107\",\n \"text\": \"def reset(self):\\n for turtle in self._turtles:\\n turtle._setmode(self._mode)\\n turtle.reset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf4f2fb81d816a60029ed08243d334eb\",\n \"score\": \"0.4770335\",\n \"text\": \"def remove(self, names):\\n for name in names:\\n try:\\n component = self._components.pop(name)\\n self._remaining.discard(component)\\n self._instantiated.discard(component)\\n except KeyError:\\n _logger.warning(\\\"Unknown component: %s\\\", name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02df66680481fa36942cc87c421b22ed\",\n \"score\": \"0.47657114\",\n \"text\": \"def eraseScreen(self):\\n if self.hasjoined and self.hasjoinresponse:\\n self.changedrects = []\\n for obj in self.deadobjectlist:\\n self.changedrects.extend(obj.eraseObject(self.screen))\\n self.deadobjectlist = []\\n for obj in self.objectlist.values()+self.staticobjectlist:\\n self.changedrects.extend(obj.eraseObject(self.screen))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ac466c3eb01d9f81bb832b9e01f1ff2\",\n \"score\": \"0.47639903\",\n \"text\": \"def removeAll( self ):\\n for layer in self.layers: \\n QgsMapLayerRegistry.instance().removeMapLayer( layer.layer().id())\\n self.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e2c3091771e05e044b51ca32674392a\",\n \"score\": \"0.47558662\",\n \"text\": \"def _unbind_observers(self):\\n workbench = self.workbench\\n point = workbench.get_extension_point(STATES_POINT)\\n point.unobserve('extensions', self._on_states_updated)\\n\\n #TODO also unbind dynamic observers here in case there is any remaining\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69f97417f83c63318d9654afd4a95e7e\",\n \"score\": \"0.47558016\",\n \"text\": \"def _setAllBBoxUpdatesOff():\\r\\n for each in cmds.ls(type = 'core_archive'):\\r\\n cmds.setAttr('%s.auto_bbox_update' % each, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b2788138b8b3da16eb27a76a17429ce\",\n \"score\": \"0.47542983\",\n \"text\": \"def clear(self):\\n for block in bpy.data.meshes:\\n if block.users == 0:\\n bpy.data.meshes.remove(block)\\n for block in bpy.data.materials:\\n if block.users == 0:\\n bpy.data.materials.remove(block)\\n for block in bpy.data.textures:\\n if block.users == 0:\\n bpy.data.textures.remove(block)\\n for block in bpy.data.images:\\n if block.users == 0:\\n bpy.data.images.remove(block)\\n bpy.ops.object.mode_set(mode='OBJECT')\\n bpy.ops.object.select_all(action='SELECT')\\n bpy.ops.object.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6615fb0b543fb2605950683775c2f87\",\n \"score\": \"0.4750799\",\n \"text\": \"def removeAllPlots(self):\\n for function in self._allFunctions:\\n self._canvas.delete(function['canvasObj'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"085ca564462294a5c5a2e66646234173\",\n \"score\": \"0.47453618\",\n \"text\": \"def clear(self):\\n for i in self.__bindings.keys():\\n self.__bindings[i].clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"323e650e7883947e74d838f504d4d0be\",\n \"score\": \"0.47398314\",\n \"text\": \"def reset(self):\\n for b in self.blocklist:\\n b.reset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08ec9ade245b8e925e253f51e3f49c3f\",\n \"score\": \"0.47361177\",\n \"text\": \"def destroy(self):\\n self.__destroyTextures()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed31c77c02cab49a0058c77fef01fc16\",\n \"score\": \"0.47288817\",\n \"text\": \"def remove(self):\\r\\n for _, scatter in self.scatters:\\r\\n scatter.remove()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"687edfdffc72b50edd1d202b81fe316b\",\n \"score\": \"0.4717254\",\n \"text\": \"def DeleteAllShapes(self):\\n for shape in self._shapeList[:]:\\n if not shape.GetParent():\\n self.RemoveShape(shape)\\n shape.Delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a60961fc01c2c09ded69d8c5a26e137b\",\n \"score\": \"0.4714101\",\n \"text\": \"def _setupSkins(context, skin_prefix, definition=None):\\n portal = context.getSite()\\n ps = portal.portal_skins\\n skins = ps._getSelections()\\n\\n if definition is None:\\n definition = _defaultSkinDefinition(skin_prefix)\\n\\n for skin, layers in definition.items():\\n if not skin in skins: continue\\n new_layers = []\\n current_layers = skins[skin].split(',')\\n for current_layer in current_layers:\\n if current_layer.startswith(skin_prefix) and \\\\\\n (current_layer not in layers):\\n # This layer must be removed from the skin\\n pass\\n else:\\n new_layers.append(current_layer)\\n\\n # Change the layer order\\n layers.reverse()\\n for layer in layers:\\n if layer in new_layers:\\n new_layers.remove(layer)\\n new_layers.insert(1, layer)\\n else:\\n new_layers.insert(1, layer)\\n\\n skins[skin] = ','.join(new_layers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9ea942d736a043f62d01d9211313bd4\",\n \"score\": \"0.47128737\",\n \"text\": \"def delattrs(self, lst):\\n for key in lst:\\n if key in self.__dict__:\\n del self.__dict__[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1be871a7d48074e855af88a27e98190a\",\n \"score\": \"0.468809\",\n \"text\": \"def ledsOff():\\n [pyb.LED(i).off() for i in range(1,5)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a98525626bfaeff0e3c99f5208f8965d\",\n \"score\": \"0.46870944\",\n \"text\": \"def matsPurgeout():\\n bpy.ops.object.mode_set(mode='OBJECT', toggle=False)\\n for ob in bpy.context.selected_objects:\\n bpy.context.scene.objects.active = ob\\n if ob.type != 'MESH':\\n continue\\n mat_slots = {}\\n for p in ob.data.polygons:\\n mat_slots[p.material_index] = 1\\n mat_slots = mat_slots.keys()\\n for i in range(len(ob.material_slots) - 1, -1, -1):\\n if i not in mat_slots:\\n bpy.context.object.active_material_index = i\\n bpy.ops.object.material_slot_remove()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab983ea1a397a1df2304d2da00dba413\",\n \"score\": \"0.46860063\",\n \"text\": \"def hide_all(self):\\n for object_1 in self.objects:\\n object_1.setAutoDraw(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9708d9628b717c0ded4adbbdd2f67907\",\n \"score\": \"0.46845552\",\n \"text\": \"def disable(self):\\n self.active = False\\n\\n for skill in self.skills:\\n skill.disable()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edab5144852269007fee67b8c02714a0\",\n \"score\": \"0.46825266\",\n \"text\": \"def unbind(self):\\n if not self.binded():\\n return\\n\\n self._unbind() and self._mkstat and aux.remove_dirs(self._newdir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6759e495158ea8c4ea17111d3939a83d\",\n \"score\": \"0.46823573\",\n \"text\": \"def clear_all(self):\\r\\n for control in self.controls:\\r\\n control.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6759e495158ea8c4ea17111d3939a83d\",\n \"score\": \"0.46823573\",\n \"text\": \"def clear_all(self):\\r\\n for control in self.controls:\\r\\n control.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bce1c5d176d824f40d9a5d1846f4f803\",\n \"score\": \"0.46802637\",\n \"text\": \"def clear_all(self):\\r\\n aant = self.nb.count()\\r\\n widgets = [self.nb.widget(x) for x in range(aant)]\\r\\n self.nb.clear()\\r\\n for wdg in widgets:\\r\\n wdg.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5909b99590359ee5b57862e93084e8\",\n \"score\": \"0.46798316\",\n \"text\": \"def clear_presets(uid):\\n PRESETS[uid].clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d668fae9b28557d43951705e0ee12b\",\n \"score\": \"0.46754968\",\n \"text\": \"def remove(self,w):\\n self.blur(w)\\n self.widgets.remove(w)\\n #self.repaint()\\n self.chsize()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993481,"cells":{"query_id":{"kind":"string","value":"675c230b3f4344b7d3447c81c8c65558"},"query":{"kind":"string","value":"Send valid get request to github_callback and receive no access_token."},"positive_passages":{"kind":"list like","value":[{"docid":"a69334d39c6abec677166da4651c9076","score":"0.7379556","text":"def test_github_callback_incomplete_get(self, mock_post, mock_g, mock_user_model):\n mock_post.return_value.json.return_value = {}\n\n with self.app.test_client() as client:\n response = client.get(\"/account/github_callback\", query_string={'code': 'secret'})\n\n self.assertEqual(response.status_code, 302)\n mock_post.assert_called_once()\n mock_user_model.query.filter.assert_called_once()\n mock_g.db.commit.assert_not_called()\n mock_g.log.error.assert_called_once_with(\"GitHub didn't return an access token\")","title":""}],"string":"[\n {\n \"docid\": \"a69334d39c6abec677166da4651c9076\",\n \"score\": \"0.7379556\",\n \"text\": \"def test_github_callback_incomplete_get(self, mock_post, mock_g, mock_user_model):\\n mock_post.return_value.json.return_value = {}\\n\\n with self.app.test_client() as client:\\n response = client.get(\\\"/account/github_callback\\\", query_string={'code': 'secret'})\\n\\n self.assertEqual(response.status_code, 302)\\n mock_post.assert_called_once()\\n mock_user_model.query.filter.assert_called_once()\\n mock_g.db.commit.assert_not_called()\\n mock_g.log.error.assert_called_once_with(\\\"GitHub didn't return an access token\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"bc74d9d65ec4b98ac2716efa2f8a1873","score":"0.6934215","text":"def test_github_callback_empty_get(self, mock_post):\n with self.app.test_client() as client:\n response = client.get(\"/account/github_callback\")\n\n self.assertEqual(response.status_code, 400)\n mock_post.assert_not_called()","title":""},{"docid":"b5723fc53fb96563c151d493cef1b4a1","score":"0.6637549","text":"def test_github_callback_valid_get(self, mock_post, mock_g, mock_user_model):\n mock_post.return_value.json.return_value = {'access_token': 'test'}\n\n with self.app.test_client() as client:\n response = client.get(\"/account/github_callback\", query_string={'code': 'secret'})\n\n self.assertEqual(response.status_code, 302)\n mock_post.assert_called_once()\n self.assertEqual(2, mock_user_model.query.filter.call_count)\n mock_user_model.query.filter.assert_called_with(mock_user_model.id == mock_g.user.id)\n mock_g.db.commit.assert_called_once()\n mock_g.log.error.assert_not_called()","title":""},{"docid":"f1f03a15a8e6cc68ef7aea431c331c90","score":"0.640843","text":"def githubretrievetoken(code):\n gitrequest = urllib.request.Request(URL_GITHUBRETRIEVETOKEN)\n gitrequest.add_header('Accept', 'application/json')\n parameters = {'client_id':github_client_id(),\n 'client_secret':github_client_secret(),\n 'code':code,\n 'redirect_uri':getredirecturl()}\n data = urllib.parse.urlencode(parameters)\n data = data.encode('utf-8')\n response = urllib.request.urlopen(gitrequest, data)\n jsresponse = json.loads(response.read().decode(\"utf-8\"))\n if 'access_token' in jsresponse:\n session[SESSION_ACCESSTOKEN] = jsresponse.get('access_token')","title":""},{"docid":"b24a446ad3a58ccd6022f14c4a790437","score":"0.6049778","text":"def github_oauth(code):\n params = {}\n params['client_id'] = app.github.get('client_id')\n params['client_secret'] = app.github.get('client_secret')\n params['code'] = code\n\n headers={'Accept': 'application/json'}\n\n gh_oauth_get_token = 'https://github.com/login/oauth/access_token'\n r = requests.post(gh_oauth_get_token, params=params, headers=headers)\n if r.status_code == requests.codes.OK:\n return r.json()['access_token']\n else:\n return 'unavailable'","title":""},{"docid":"2dd13ae048eb13d0dfbe5c54fd5f98f8","score":"0.60312855","text":"def githubrequest(user, repo, path, method='GET'):\n url = \"https://api.github.com/repos/{0}/{1}/contents/{2}\".format(user, repo, path)\n return finishrequestsetup(url, method)","title":""},{"docid":"904b096d20aa81a9c479195efa28504b","score":"0.60227907","text":"async def _get(self, ctx: commands.Context, url: str, private=False):\n if not private:\n match = re.fullmatch(self.githubURL, url)\n if match is None:\n return await ctx.send(self.invalid)\n\n url = f\"https://github.com/{match.group(1)}/{match.group(2)}/commits.atom\"\n\n async with aiohttp.ClientSession() as session:\n async with session.get(url) as resp:\n html = await resp.read()\n if resp.status != 200:\n return await ctx.send(self.invalid)\n entry = feedparser.parse(html).entries[0]\n e = await self.commit_embed(entry, feedparser.parse(html).feed.link)\n await ctx.send(embed=e)","title":""},{"docid":"f15fb0099ce1e761d9bd48d123a7a3b8","score":"0.6009821","text":"def callback():\n #form = LoginForm()\n #if form.validate_on_submit():\n\n \"\"\" Step 3: Retrieving an access token.\n\n The user has been redirected back from the provider to your registered\n callback URL. With this redirection comes an authorization code included\n in the redirect URL. We will use that to obtain an access token.\n \"\"\"\n\n #check if repo is valid repo\n github = OAuth2Session(client_id, state=session['oauth_state'])\n token = github.fetch_token(token_url, client_secret=client_secret, authorization_response=request.url)\n\n session['token'] = token\n session['token'].permanent = True\n\n return redirect(url_for('form'))","title":""},{"docid":"a559cbfd49c1581f7fbe5e84c6897157","score":"0.59529126","text":"def _get_access_token():\n token = db_config.get_value('oss_fuzz_robot_github_personal_access_token')\n if not token:\n raise RuntimeError('Unable to access GitHub account to '\n 'file/close the issue.')\n return github.Github(token)","title":""},{"docid":"a2566717198c2a440f04025ce20c2f43","score":"0.59377015","text":"def callback(request):\n github_oauth_session = OAuth2Session(client_id=CLIENT_ID,\n state=request.session[OAUTH_STATE])\n access_token_info = github_oauth_session.fetch_token(token_url=TOKEN_URL,\n client_secret=CLIENT_SECRET,\n authorization_response=request.get_full_path())\n request.session[OAUTH_TOKEN] = access_token_info\n return redirect(dashboard)","title":""},{"docid":"efe1425d871ae4a8fcdbf9603fec6d2c","score":"0.59277016","text":"def do_oauth():\n token = session.get('token', None)\n if not token or token=='unavailable':\n # code parameter will be returned by GitHub in Step 1 of OAuth2 authentication\n code = request.args.get('code', None)\n if not code:\n redirect_uri = 'https://github.com/login/oauth/authorize?client_id={}&scope=repo'.format(app.github.get('client_id'))\n return redirect(redirect_uri)\n else:\n token = github_oauth(code)\n session['token'] = token\n return redirect('/')","title":""},{"docid":"51e9e52a5295117af01d64f93f06d941","score":"0.59064966","text":"def _handle_response():\n params = {\n 'code': request.args.get('code'),\n 'client_id': Config.GITHUB_CLIENT_ID,\n 'client_secret': Config.GITHUB_CLIENT_SECRET\n }\n url = \"https://github.com/login/oauth/access_token\"\n response = requests.post(url, data=params)\n data = parse_qs(response.content)\n for k, v in data.items():\n if len(v) == 1:\n data[k] = v[0]\n token = data.get(b'access_token', None)\n if token is not None:\n token = token.decode('ascii')\n return token","title":""},{"docid":"38c1b655dd81d956af579a4e95155515","score":"0.58930653","text":"def get_url(url,api_key=os.getenv('GH_APIKEY')):\n \n headers = {\n \"Authorization\": f\"Bearer {api_key}\"\n }\n\n res = requests.get(url, headers=headers)\n #res = requests.get(url, params=query_params, headers=headers)\n \n data = res.json()\n if res.status_code != 200:\n \n raise ValueError(f'Invalid Github API call: {data[\"message\"]}\\nSee more in {data[\"documentation_url\"]}')\n \n else:\n print(f\"Requested data to {url}; status_code:{res.status_code}\")\n \n return res","title":""},{"docid":"d9657a8c64940d78e4d6cbe6a2e0976b","score":"0.58899975","text":"def test_basic_fetch(self):\n self.gh_client.fetch(\"https://api.github.com/url\")\n self.assertTrue(self.http_client.fetch.called)","title":""},{"docid":"b382411abfb77aacb2aebda234a5d572","score":"0.58876705","text":"async def test_get_access_token_none(self):\n request = MockRequest()\n access_token = await get_access_token(request)\n self.assertEqual(None, access_token)","title":""},{"docid":"038cc3c114984128f101389bbc2ec9a6","score":"0.587748","text":"def test_github_callback_empty_post(self, mock_post):\n with self.app.test_client() as client:\n response = client.post(\"/account/github_callback\")\n\n self.assertEqual(response.status_code, 400)\n mock_post.assert_not_called()","title":""},{"docid":"29757b3aed12ea6c499f8de79ae3a6b2","score":"0.58709323","text":"def make_gh_request(url, err=True, msg=None):\n if not msg:\n msg = (\n \"Unable to connect. If your network is working properly, you might\"\n \" have reached Github's API request limit. Try adding a Github API\"\n \" token to the 'POPPER_GITHUB_API_TOKEN' variable.\"\n )\n\n response = requests.get(url, headers=get_gh_headers())\n if err and response.status_code != 200:\n log.fail(msg)\n else:\n return response","title":""},{"docid":"3f1f37740b300645e4e608675ce6dc84","score":"0.5802419","text":"def _do_get_request(self, url, token=None, exit_on_error=True):\n headers = {}\n\n if token is not None:\n headers[\"Authorization\"] = \"Bearer {}\".format(token)\n\n result = get(self.base_url+url, headers=headers)\n\n if not 199 < result.status_code < 300:\n print(\"Could not contact {} - Error: {}\".format(self.base_url+url,\n result.status_code))\n if exit_on_error:\n sys.exit(4)\n\n return result.json()","title":""},{"docid":"183806d6703518cf14e6840688d4cfef","score":"0.5770529","text":"def githubauthurl():\n url = URL_GITHUBAUTHORIZE + \"?\"\n url += \"client_id=\"+github_client_id()\n url += \"&redirect_uri=\"+getredirecturl()\n url += \"&scope=repo gist&state=\"+newgithubstate()\n print(url)\n return url","title":""},{"docid":"44479b32873faf35c388f7c55920fb58","score":"0.57642317","text":"async def auth_github(request):\n if 'error' in request.query:\n logger.error(request.query)\n web.HTTPUnauthorized(body=\"Encountered an error. \")\n\n session = await get_session(request)\n nexturl = request.query.get('next') or '/'\n baseurl = BOT_BASEURL + \"/auth/github?next=\" + nexturl\n try:\n ghappapi = request.app['ghappapi']\n ghapi = await ghappapi.oauth_github_user(baseurl, session, request.query)\n if ghapi.username:\n await remember(request, web.HTTPFound(nexturl), ghapi.token)\n return web.HTTPFound(nexturl)\n except web.HTTPFound:\n raise\n except Exception as exc:\n logger.exception(\"failed to auth\")\n return web.HTTPUnauthorized(body=\"Could not authenticate your Github account\")","title":""},{"docid":"5ec3d15ede9ecf2e088b79e70a6d466a","score":"0.5759575","text":"async def login(request):\n return web.HTTPFound('/auth/github')","title":""},{"docid":"762b37972a8b4cb9dbb95a15faa2b10e","score":"0.57585263","text":"async def callback(self, request: web.Request):\r\n code = request.query[\"code\"]\r\n token = await self.fetch_token(code)\r\n raise web.HTTPFound(f'/me?token={token}')","title":""},{"docid":"46da20900ada1dac6001ab11964b3f66","score":"0.5700217","text":"async def fetch_token(self, code):\n web_client = self.request.app['web_client']\n token_url = 'https://github.com/login/oauth/access_token'\n data = {\n 'client_id': settings.CLIENT_ID,\n 'client_secret': settings.CLIENT_SECRET,\n 'code': code\n }\n headers = {'Accept': 'application/json'}\n token = await web_client.post(token_url, data=data, headers=headers)\n return token['access_token']","title":""},{"docid":"1c18f89d432ed70601f262bc9d970d14","score":"0.5684983","text":"def githubforgetauth():\n session.pop(SESSION_ACCESSTOKEN, None)","title":""},{"docid":"0dcf6b617d87042250c8843111fd6abf","score":"0.56592345","text":"def get(self, request, *args, **kwargs):\n if not request.GET.get('state'):\n return self.response_error(request, 'Missing state property.')\n elif not request.GET.get('code'):\n return self.response_error(request, 'Missing code property.')\n\n state = request.GET['state']\n code = request.GET['code']\n\n if state != request.session.get('github_state'):\n return self.response_error(request, 'Invalid state.')\n\n github.redirect_uri = absolute_url(request, 'accounts:github_sign_in')\n github.scope = '' # need to reset scope to '', because otherwise we'll get trouble\n\n try:\n github.fetch_token(self.URL_GET_TOKEN,\n code=code,\n client_secret=settings.GITHUB_CLIENT_SECRET)\n except Exception:\n return self.response_error(request, 'An error occurred while verifying response.')\n\n response = github.get(self.URL_USER_PROFILE)\n\n try:\n data = json.loads(response.content.decode('utf-8'))\n except ValueError:\n return self.response_error(request, 'Invalid response format.')\n\n email_address = None\n\n if isinstance(data, dict) and data.get('email'):\n email_address = data['email']\n\n if not email_address:\n return self.response_error(request, 'No valid account-based email address found.')\n\n # Now check if the account exists and login\n try:\n user = User.objects.get(email__iexact=email_address)\n except User.DoesNotExist:\n user = self.create_user(email_address)\n\n if not user.is_active:\n return self.response_error(request, 'Your account is inactive.')\n elif user.type != UserType.GITHUB:\n return self.response_error(request, 'Account type mismatch.')\n else:\n return self.login(request, user)","title":""},{"docid":"9a1e7feefd8760f2426fb188e9cc4411","score":"0.5651078","text":"def github() -> None:\n pass","title":""},{"docid":"c10fa3ff8b63c7c4cd586a2d873f64e5","score":"0.555293","text":"def __post_init__(self):\n self.connect = github.Github(self.token)\n\n try:\n self.repo_obj = self.connect.get_repo(\n f'{self.organization}/{self.repository}'\n )\n except (github.BadCredentialsException,\n github.BadAttributeException) as error:\n print(error)\n sys.exit(1)","title":""},{"docid":"dc9dc49604a6257f9e5162200845d8f7","score":"0.5523924","text":"def get_access_token(self):\n logger.debug('Do nothing to get access token')","title":""},{"docid":"7cc48edfe2db83688878f9091b7005f1","score":"0.55082214","text":"def github():\n pass","title":""},{"docid":"59587c2a2e6351215bd789cbc0e29851","score":"0.5459952","text":"def bpasteclicked(self):\n url = self.leback.text()\n # add a s so we have https hotfix for request_oAuthlib becouse it's make it only with https\n firstpart = url[0:4]\n secondpart = url[4:len(url)]\n completeurl = firstpart + 's' + secondpart\n self.github.fetch_token(token_url, client_secret=client_secret,\n authorization_response=completeurl)\n # hide login button so user not try to relogin and an exception is create\n self.bpaste.hide()\n self.cbown.show()","title":""},{"docid":"8d7a3f619eb76239267fdb0a2fa091ae","score":"0.5432267","text":"def __handle_github(self, code):\n # request token\n params = self.services[\"github\"]\n response = requests.post(\n params[\"token_url\"],\n data={\n \"code\": code,\n \"client_id\": params[\"client_id\"],\n \"client_secret\": params[\"secret\"],\n },\n headers={\"Accept\": \"application/json\"},\n ).json()\n # if all is well, we hava a token\n token = response.get(\"access_token\", \"\")\n # get a user profile\n response = requests.get(\n \"https://api.github.com/user\",\n headers={\"Authorization\": f\"Bearer {token}\", \"Accept\": \"application/json\"},\n )\n response = response.json()\n id = response[\"id\"]\n email = response.get(\"email\", \"email-unknown\")\n name = response[\"name\"] or response[\"login\"] or response[\"id\"]\n return (id, email, name)","title":""},{"docid":"7222de3f88dcf99d1b945cd9f9ef9d13","score":"0.5414896","text":"def _get_token_endpoint(self):\n return 'https://github.com/login/oauth/access_token'","title":""},{"docid":"c46ffcb575eb8948574f593805338494","score":"0.5390511","text":"def make_gh_request(url, err=True, msg=None):\n if not msg:\n msg = (\n \"Unable to connect. Please check your network connection.\"\n )\n\n response = requests.get(url)\n if err and response.status_code != 200:\n log.fail(msg)\n else:\n return response","title":""},{"docid":"1d39b067ea2188c1f776092970fbd5a3","score":"0.53803855","text":"def request_github_events(user=None):\n print \"Getting Recent GitHub Events\"\n if user:\n response = requests.get('https://api.github.com/users/' + str(user) + '/events')\n else:\n response = requests.get('https://api.github.com/events')\n response_json = response.json()\n print response_json","title":""},{"docid":"b423babba74e00bdf2610dca84fee58c","score":"0.5375007","text":"def call_github(email):\n\n url = 'https://api.github.com/search/users'\n values = {'q' : '{0} in:email type:user repos:>0'.format(email) }\n\n # TODO Insert Github credentials here. You will be limited to 30 requests per minute.\n auth_info = '{0}:{1}'.format('','')\n\n basic = base64.b64encode(auth_info)\n headers = { 'Authorization' : 'Basic ' + basic }\n params = urllib.urlencode(values)\n req = urllib2.Request('{0}?{1}'.format(url,params), headers=headers)\n response = json.loads(urllib2.urlopen(req).read())\n\n if response and response['total_count'] == 1:\n return response['items'][0]['html_url']\n else:\n return None","title":""},{"docid":"33bfc0291dc080cf0bda7db941fbccd9","score":"0.5358323","text":"def post(self, request, *args, **kwargs):\n github.redirect_uri = absolute_url(request, 'accounts:github_sign_in')\n github.scope = None\n\n authorization_url, state = github.authorization_url('https://github.com/login/oauth/authorize')\n\n request.session['github_state'] = state\n\n return self.redirect(authorization_url)","title":""},{"docid":"88a2f4af1deefd8dff522a91438cc91c","score":"0.53529894","text":"def test_fetch_params(self):\n params = {\"unique_param_name\": 1}\n self.gh_client.fetch(\"https://api.github.com/url\", params=params)\n url = self._get_url()\n self.assertTrue(\"unique_param_name\" in url)","title":""},{"docid":"43f8865ca4b35e0a6651cc5d4889d864","score":"0.5331069","text":"async def github(self, ctx):\n await ctx.reply(self.github_link)","title":""},{"docid":"6c73cdc749afd0bfde26038726aab449","score":"0.5329995","text":"def request_github_token(github_username, github_password, twofactor=None):\n note = 'nbreport for {user}@{machine} on {time}'.format(\n user=getuser(),\n machine=gethostname(),\n time=datetime.datetime.now().isoformat()\n )\n request_data = {\n 'scopes': ['read:user', 'read:org'],\n 'note': note,\n 'note_url': 'https://nbreport.lsst.io'\n }\n headers = {\n 'Accept': 'application/vnd.github.v3+json'\n }\n if twofactor is not None:\n headers['X-GitHub-OTP'] = str(twofactor)\n response = requests.post(\n 'https://api.github.com/authorizations',\n auth=(github_username, github_password),\n headers=headers,\n json=request_data\n )\n\n # Handle two-factor authentication\n if response.status_code == 401 and 'X-GitHub-OTP' in response.headers:\n raise GitHubTwoFactorRequired\n # Handle other errors\n response.raise_for_status()\n\n return response.json()","title":""},{"docid":"ff698dcb9eaa2debb406d4275669310d","score":"0.5319272","text":"def github_client(token: Optional[str] = None) -> Github:\n if token:\n # Authenticate as a user\n return Github(token)\n elif GITHUB_API_CREDENTIALS:\n # Authenticate as a OAuth client application\n # No extra permissions, just higher rate limits\n client_id, client_secret = GITHUB_API_CREDENTIALS.split(\":\")\n return Github(client_id, client_secret)\n else:\n # Unauthenticated access\n return Github()","title":""},{"docid":"e01a4d72f454bc26f00700c5e562f161","score":"0.53015566","text":"def test_token_missing(self):\n self._do_request(set_token=False, set_target_screen_name=True, expected_status=500,\n expected_error_msg=\"Missing OAuth Token\")","title":""},{"docid":"137cab6ee887217e30c6c2de3048071e","score":"0.52993137","text":"def __unsigned_request(self, url, additional_params={}, err_msg=\"Error\"):\n\n response = requests.get(url, params=additional_params).json()\n \n # Throw BinanceException if returned JSON is an error code.\n if 'code' in response:\n raise BinanceException(f\"{err_msg}: code {response['code']}: {response['msg']}\")\n else:\n return response","title":""},{"docid":"3d55d8fb98dde149694c80f4d80898ce","score":"0.52960086","text":"def do_GET(self):\n self.set_headers()\n\n # Call get_callback argument\n if get_callback is not None:\n get_callback(self, None, **callback_args)","title":""},{"docid":"f81a5d5878e3bcf536872450eda08c83","score":"0.5277354","text":"def test_get(self):\n token = self.api.get(f'{self.url_prefix}/token').json['token']\n response = self.api.get(\n f'{self.url_prefix}/person', headers={'Authorization': token})\n self.assertIsNotNone(response)\n self.assertAlmostEqual(response.status_code, 200)","title":""},{"docid":"f20386df26b975df4087728ad69764a9","score":"0.5255422","text":"def test_RESOURCE_api_WITH_no_access_token_EXPECT_request_denied() -> None:\n endpoint = MainStack.get_output(MainStack.API_ENDPOINT_KEY)\n\n http = urllib3.PoolManager()\n\n response: HTTPResponse = http.request(\n method='GET',\n url=endpoint,\n headers={},\n )\n\n assert response.status == 401","title":""},{"docid":"d3aec0b2e74c9eb1846da2986b689b08","score":"0.5253127","text":"def test_com_adobe_granite_auth_oauth_impl_github_provider_impl(self):\n pass","title":""},{"docid":"3bd29a7574805c37323d9166fb201b09","score":"0.5250345","text":"def fetch(self):\n api = f\"https://api.github.com/users/{self.username}\"\n\n token = \"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\"\n\n headersDic = {\n \"Authorization\": f\"token {token}\"\n }\n resp = get(api, headers=headersDic).json()\n return resp","title":""},{"docid":"77f189fb74cfdcfc0d95a3bd099eb82b","score":"0.52434766","text":"def test_returns_401_if_no_token(self):\n # Act\n response = self.client.get(self.url)\n # Assert\n self.assertEqual(response.status_code, 401)","title":""},{"docid":"9ea8a523195e47a0d03f7e467b8f1428","score":"0.52408314","text":"def github_html_response(response):\n if response.code != 200:\n logging.warning(\"GitHub URL response invalid: %s\",\n response.code)\n return\n atom_link = get_commit_atom_link(response.body)\n self._client.fetch(atom_link, callback=github_commit_response)","title":""},{"docid":"572317e803c0a9a6f0454da111788c63","score":"0.52407706","text":"def githubretrievegist(gistid):\n requestcontext = Context('', '', gistid)\n gitrequest, token = gistrequest(gistid)\n return finishrequest(requestcontext, \n gitrequest, \n token, \n lambda x: x['files'][list(x['files'].keys())[0]]['content'].encode('utf-8'), # content\n lambda x: list(x['files'].keys())[0]) # file name","title":""},{"docid":"c6f9763ba62ee2749b20ff6bb42e4217","score":"0.52229315","text":"def test_get_request_fails(self):\n self.service_request(method='GET', path='/tokens', assert_status=404)","title":""},{"docid":"2ed7e6f4e168e1a2e2aeb5d1cfbc6701","score":"0.52150613","text":"def http_request(self, *args, **kwargs):\n token = self.get_access_token()\n headers = {\n 'Authorization': f'Bearer {token}',\n 'Content-Type': 'application/json',\n }\n return super()._http_request(*args, headers=headers, **kwargs) # type: ignore[misc]","title":""},{"docid":"ebbdf4f49c8524aae97ccfda5a4049e6","score":"0.5213798","text":"def _api_get(self, url):\n try:\n response = self.client.http_get(\n url,\n headers={\n 'PRIVATE-TOKEN': self._get_private_token(),\n })\n\n return response.data, response.headers\n except HTTPError as e:\n if e.code == 401:\n raise AuthorizationError(\n ugettext('The username or password is incorrect.'))\n elif e.code == 404:\n raise\n else:\n logger.exception('Failed to execute a GET request at %s: %s',\n url, e)\n raise","title":""},{"docid":"9f528300ef1fe55f8527e1c0989ea493","score":"0.52003086","text":"def gconnect():\n # Validate state token\n if request.args.get('state') != login_session['state']:\n response = make_response(json.dumps('Invalid state parameter'), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Obtain authorization code\n code = request.data\n\n try:\n # Upgrade the authorization code to a credentials object\n if not request.headers.get('X-Requested-With'):\n abort(403)\n\n flow = flow_from_clientsecrets(os.path.abspath(os.path.join(\n os.path.dirname(__file__), CLIENT_SECRET_FILE)), scope='profile')\n flow.redirect_uri = 'postmessage'\n # Exchange auth code for access token, refresh token, and ID token\n credentials = flow.step2_exchange(code)\n except FlowExchangeError:\n response = make_response(json.dumps(\n \"Failed to upgrade the authorization code.\"), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Check that the access token is valid\n access_token = credentials.access_token\n url = (\n 'https://www.googleapis.com/oauth2/v1/tokeninfo?access_token=%s'\n % access_token)\n h = httplib2.Http()\n result = json.loads(h.request(url, 'GET')[1])\n # If there was an error in the access token info, abort\n if result.get('error') is not None:\n response = make_response(json.dumps(result.get('error')), 500)\n respose.headers['Content-Type'] = 'application/json'\n return response\n\n # Verify that the access token is used for the intended user\n # Get profile info from ID token\n userid = credentials.id_token['sub']\n email = credentials.id_token['email']\n\n if result['user_id'] != userid:\n response = make_response(json.dumps(\n \"Token's user ID doesn't match the given user ID.\"), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Verify that the access token is valid for this app\n if result['issued_to'] != CLIENT_ID:\n response = make_response(json.dumps(\n \"Token's client ID does not match app's\"), 401)\n print \"Token's client ID does not match app's.\"\n response.headers['Content-Type'] = 'application/json'\n return response\n\n stored_access_token = login_session.get('access_token')\n stored_userid = login_session.get('userid')\n if stored_access_token is not None and userid == stored_userid:\n login_session['access_token'] = credentials.access_token\n response = make_response(json.dumps(\n 'Current user is already connected'), 200)\n flash(\"Current user is already connected!\")\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Store the access token in the session for later use\n login_session['access_token'] = credentials.access_token\n login_session['userid'] = userid\n\n # Get user info\n userinfo_url = \"https://www.googleapis.com/oauth2/v1/userinfo\"\n params = {'access_token': credentials.access_token, 'alt': 'json'}\n answer = requests.get(userinfo_url, params=params)\n\n data = answer.json()\n\n login_session['username'] = data['name']\n login_session['picture'] = data['picture']\n login_session['email'] = data['email']\n\n # Check if user is already in the database, and if not, creates a new user\n # object\n user_id = getUserID(login_session['email'])\n if not user_id:\n user_id = createUser(login_session)\n login_session['user_id'] = user_id\n\n output = ''\n output += '<h2>Welcome, '\n output += login_session['username']\n output += '!</h2>'\n output += '<img src=\"'\n output += login_session['picture']\n output += '\"class=\"signin-pic\" style = \"width: 100px; \\\n height: 100px;border-radius: 50px;\"> '\n flash(\"You are now logged in as %s\" % login_session['username'])\n print \"done!\"\n return output","title":""},{"docid":"a9ed39de2e71d12a0a53e99b3066ddb9","score":"0.52002305","text":"def gistrequest(gistid, method='GET'):\n url = \"https://api.github.com/gists/{0}\".format(gistid)\n return finishrequestsetup(url, method)","title":""},{"docid":"565b4a23dd11a091209d401f69f5b496","score":"0.51997966","text":"def testSanityGet(self, mock_get_current_user):\n mock_get_current_user.return_value = users.User(\n email='bob@test.com', _auth_domain='gmail.com')\n\n response = self.testapp.get(oauth_playground.URL_BASE)\n self.assertEqual(200, response.status_int)","title":""},{"docid":"856b4151c3bcd1b26eb280a7a310dd95","score":"0.51993173","text":"def request_no_redirect():\n\n # Redirects by default\n r = requests.get('http://github.com') # default Requests allow redirect\n print r.url # https://github.com/\n print r.status_code # 200\n print r.history #[<Response [301]>] # Shows history of a redirect\n\n # Don't allow redirect\n r = requests.get('http://github.com', allow_redirects=False)\n print r.status_code # 301\n print r.history # []","title":""},{"docid":"bd3ec185e7d24112699503a272a892f6","score":"0.51975936","text":"def githubloggedin():\n github_token = session.get(SESSION_ACCESSTOKEN)\n return True if github_token else False","title":""},{"docid":"3f5ccf7664cea1dab8d8b0807693603d","score":"0.5187894","text":"def test_cant_get_branch_missing_params(self):\n json_response = api_call(self, \"GET\", '/branch/get', dict(), 200, True)\n self.assertEqual(json_response[\"status_code\"], 400)\n self.assertEqual(json_response[\"message\"], 'Invalid request')\n\n json_response = api_call(self, \"GET\", '/branch/get', dict(id=None), 200, True)\n self.assertEqual(json_response[\"status_code\"], 400)\n self.assertEqual(json_response[\"message\"], 'Invalid request')","title":""},{"docid":"41c6c16f8008e0d12729348c3f1a79dd","score":"0.51752985","text":"def get_github_client(token):\n\n gh = github3.GitHub()\n\n betamax_library_dir = os.environ.get('BETAMAX_LIBRARY_DIR')\n betamax_cassette = os.environ.get('BETAMAX_CASSETTE')\n\n if betamax_library_dir and betamax_cassette:\n # Delay import because only needed for testing.\n import betamax\n\n with betamax.Betamax.configure() as config:\n config.cassette_library_dir = betamax_library_dir\n\n # We don't want requests hitting the network at all.\n config.default_cassette_options['record_mode'] = 'none'\n\n recorder = betamax.Betamax(gh._session)\n recorder.use_cassette(betamax_cassette)\n recorder.start()\n\n gh.login(token=token)\n\n return gh","title":""},{"docid":"be8e6b30536db24c97e7669753de5cad","score":"0.5170267","text":"async def get(self, request: Request) -> Response:\n hass = request.app[\"hass\"]\n\n def check_get(param):\n if param not in request.query:\n _LOGGER.error(\"State missing in request.\")\n raise HTTPBadRequest(text=f\"Parameter {param} not found\")\n return request.query[param]\n\n state = check_get(\"state\")\n code = check_get(\"code\")\n\n if state not in self._flows:\n _LOGGER.error(\"State unexpected %s\", state)\n raise HTTPBadRequest(text=\"State can not be translated into flow\")\n\n flow_id = self._flows[state]\n _LOGGER.debug(\"Received auth request for flow %s\", flow_id)\n\n try:\n await hass.config_entries.flow.async_configure(flow_id, {\"code\": code})\n\n return Response(\n headers={\"content-type\": \"text/html\"},\n text=\"<script>window.close()</script>\",\n )\n except data_entry_flow.UnknownFlow:\n raise HTTPBadRequest(text=\"Unknown flow\")","title":""},{"docid":"74ee81e88d3a2dd5fe5c063214e9fd74","score":"0.51567596","text":"def test_missing_access_token(self):\n data = {\"provider\":\"facebook\"}\n response = self.client.post(self.oauth_url, data=data)\n\n self.assertEqual(response.status_code, 400)","title":""},{"docid":"1896fbd0dc815ad0ef004c3930b92b44","score":"0.514684","text":"def test_fetch_github_own(self, github_setup):\n data = github_setup\n repo_owner_type, repo_type = data[0], data[1]\n os.chdir(FETCH_TARGET)\n subprocess.run(\n [\n sys.executable,\n PYREPOMAN_MAIN_PATH,\n ACTION_IDENTIFIER,\n \"github\",\n repo_owner_type,\n \"--repo-type\",\n repo_type,\n ]\n )\n os.chdir(\"..\")\n dcmp = filecmp.dircmp(FETCH_TARGET, MODEL_TARGET)\n diff = Diff(dcmp)\n assert diff.run() is False","title":""},{"docid":"5f071e28f7e4da85728d6d9f4bbc0108","score":"0.5146166","text":"def test_exp_init_get(self):\n query_string = [('gitrepo', 'gitrepo_example')]\n response = self.client.open('/v1/exp/init',\n method='GET',\n query_string=query_string)\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","title":""},{"docid":"92c026688f4edcfbd2e8223ef4091905","score":"0.514316","text":"def login_with_oauth_code(request):\n\n # Get the value of the redirect returned from the TTN site\n # token_type = request.GET.get('token_type', '')\n # access_token = request.GET.get('access_token', '')\n # expires_in = request.GET.get('expires_in')\n\n # We can now check the access_token for its info\n return JsonResponse({})","title":""},{"docid":"7d651821c8ca35ee72a6d4ccc0076c53","score":"0.5137772","text":"def get_g():\n with open(SCRUM_PATH, \"r\") as dot_scrum_file:\n dot_scrum = dot_scrum_file.read()\n dot_scrum = loads(dot_scrum)\n return Github(dot_scrum[\"api_key\"], timeout=600000)","title":""},{"docid":"2ba57aa41d80e5d60619364a9a47bb2c","score":"0.51356155","text":"def authorize(request):\n github_oauth_session = OAuth2Session(client_id=CLIENT_ID,)\n authorization_url, state = github_oauth_session.authorization_url(AUTHORIZATION_BASE_URL+\"?scope=repo\")\n\n request.session[OAUTH_STATE] = state\n\n return redirect(authorization_url)","title":""},{"docid":"dadadef7948f33976def11eca9666687","score":"0.5131079","text":"def oauth_request(self, token, url, params=None, method=\"GET\"):\n return self.request(\n url, method=method, params=params, auth=self.oauth_auth(token)\n )","title":""},{"docid":"ceca7a6bbb62e103ee11d2ab8ebd6a14","score":"0.51287526","text":"def test_fetch_github_other(self, github_setup):\n data = github_setup\n username = configholder.get_config_value(\"GITHUB_USERNAME\")\n repo_owner_type, repo_type = data[0], data[1]\n os.chdir(FETCH_TARGET)\n subprocess.run(\n [\n sys.executable,\n PYREPOMAN_MAIN_PATH,\n ACTION_IDENTIFIER,\n \"github\",\n repo_owner_type,\n username,\n \"--repo-type\",\n repo_type,\n ]\n )\n os.chdir(\"..\")\n dcmp = filecmp.dircmp(FETCH_TARGET, MODEL_TARGET)\n diff = Diff(dcmp)\n assert diff.run() is False","title":""},{"docid":"19ec5ed3242bb55def4ed9a36c9487e4","score":"0.51242167","text":"def _call_api(self, verb, url, check=True, **request_kwargs):\n api = 'https://api.github.com{}'.format(url)\n auth_headers = {'Authorization': 'token {}'.format(self.api_token)}\n headers = {**auth_headers, **request_kwargs.pop('headers', {})}\n resp = getattr(requests, verb)(api, headers=headers, **request_kwargs)\n if check:\n resp.raise_for_status()\n return resp","title":""},{"docid":"bb6204117233eff2edff00f9caaa4cd9","score":"0.5123553","text":"def get_access_token(self):\n request = self.serverInterface().requestHandler()\n params = request.parameterMap()\n # 1: search in the bearer\n auth_header = params.get('HTTP_AUTHORIZATION', '')\n if auth_header.find('Bearer ') == 0:\n access_token = auth_header[6:]\n self.log('get_access_token() Got HTTP_AUTHORIZATION bearer: %s' % access_token)\n # Search in cache\n if self.token_storage.get(access_token):\n self.log('get_access_token() HTTP_AUTHORIZATION bearer is verified!')\n return access_token # is valid!\n else:\n self.log('get_access_token() HTTP_AUTHORIZATION bearer is NOT verified!')\n raise OAuthException('access_token in request is NOT verified! %s' % access_token)\n # 2: search in the query string ...\n # or search in the POST body\n access_token = params.get('ACCESS_TOKEN', None)\n if access_token is not None:\n if self.token_storage.get(access_token):\n self.log('get_access_token() access_token in request is verified!')\n return access_token # is valid!\n else:\n self.log('get_access_token() access_token in request is NOT verified! %s' % access_token)\n # TODO: re-validate the token against Twitter endpoint\n raise OAuthException('access_token in request is NOT verified!! %s' % access_token)\n # 4: Search for token from verify step\n # NOTE: this is not the access_token but a request_token!\n request_token = params.get('OAUTH_TOKEN', None)\n verifier_token = params.get('OAUTH_VERIFIER', None)\n if verifier_token is not None and request_token is not None:\n try:\n access_token = self.authenticated(request_token, verifier_token)\n # Redirect\n url = self.get_current_url()\n # Remove OAUTH_TOKEN and OAUTH_VERIFIER (lower case)\n scheme, domain, path, params, query, fragment = urlparse.urlparse(url)\n query_params = dict(urlparse.parse_qsl(query))\n del query_params['oauth_token']\n del query_params['oauth_verifier']\n # Add access_token to the url\n query_params['access_token'] = access_token\n query = '&'.join([\"%s=%s\" % (k, v) for k, v in query_params.items()])\n url = urlparse.urlunparse((scheme, domain, path, params, query, fragment))\n self.redirect_url = url\n return None\n except Exception as e:\n self.log('get_access_token() Cannot verify access_token: %s' % e)\n raise OAuthException('get_access_token() Cannot verify access_token!')\n return None","title":""},{"docid":"3dc901864991b1d377b3877545fe3742","score":"0.5117407","text":"def _generate_get_request(self, url):\n try:\n request = requests.get(\n url,\n auth=HTTPBasicAuth(self.account.email, self.account.password),\n verify=True\n )\n return request\n except Exception:\n print(\"Unknown exception occured. Check your internet connection?\")\n return None","title":""},{"docid":"c488af3cf216f3823c06c3f4470b0787","score":"0.5113194","text":"def fetch(self, request, callback=..., raise_error=..., **kwargs):\n ...","title":""},{"docid":"200866c78c9d38c2c109b60dac507d7f","score":"0.5101486","text":"def request_from_github(abort_code: int = 418) -> Callable:\n def decorator(f):\n \"\"\"Decorate the function to check if a request is a GitHub hook request.\"\"\"\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if request.method != 'POST':\n return 'OK'\n\n request_ip = ip_address(f\"{request.remote_addr}\")\n if not is_github_web_hook_ip(request_ip):\n g.log.warning(f\"Unauthorized attempt to deploy by IP {request_ip}\")\n abort(abort_code)\n\n for header in ['X-GitHub-Event', 'X-GitHub-Delivery', 'X-Hub-Signature', 'User-Agent']:\n if header not in request.headers:\n g.log.critical(f\"{header} not in headers!\")\n abort(abort_code)\n\n ua = request.headers.get('User-Agent')\n if not ua.startswith('GitHub-Hookshot/'):\n g.log.critical(\"User-Agent does not begin with GitHub-Hookshot/!\")\n abort(abort_code)\n\n if not request.is_json:\n g.log.critical(\"Request is not JSON!\")\n abort(abort_code)\n\n return f(*args, **kwargs)\n return decorated_function\n return decorator","title":""},{"docid":"39b782be4b19568f87ca1355dd021346","score":"0.5095502","text":"def _get_request(self, path):\n\n url = self.base_url + path\n headers = {'Content-Type': 'application/json'}\n response = requests.get(url, headers=headers)\n if response.status_code == 200:\n return response\n else:\n raise APIErrorException('Api Not working')","title":""},{"docid":"64d2ce925267229c4654b10f7013571f","score":"0.5091288","text":"def test_missing_parameters(self):\n response = self._auth_request('get')\n\n self.assertEqual(response.status_code, 200)\n self.assertEqual(bool(response.content), True)","title":""},{"docid":"a5a53853f2dbb33369c7913988209cde","score":"0.5088976","text":"def __request_get(self, url, auth=None, data=None, token=None, parameters = None):\n headers = {}\n\n if token:\n headers['Authorization'] = \"Bearer {}\".format(token)\n\n try:\n r = requests.get(url, auth=auth, headers=headers, data=data, params=parameters,verify=False)\n rsp = r.json()\n except requests.exceptions.RequestException as err:\n return self.__response_message(err, 0)\n\n return self.__response_message(rsp, r.status_code)","title":""},{"docid":"3822529d9b2aaf3789c545404e0af897","score":"0.50873274","text":"def test_build_new_review_request_with_private_github_repository(self):\n repository = self._create_repository(github=True,\n repository_plan='private')\n review_request = self.create_review_request(repository=repository)\n diffset = self.create_diffset(review_request=review_request)\n diffset.base_commit_id = '8fd69d70f07b57c21ad8733c1c04ae604d21493f'\n diffset.save()\n\n self._create_config()\n self.integration.enable_integration()\n\n data = self._spy_on_urlopen()\n\n review_request.publish(review_request.submitter)\n\n self.assertTrue(urlopen.spy.called)\n self.assertEqual(data['url'],\n 'https://circleci.com/api/v1.1/project/github/'\n 'myuser/myprivaterepo/tree/review-requests?'\n 'circle-token=test-api-token')","title":""},{"docid":"d2f64d11a88737b148e3ab4bad5efa35","score":"0.5081073","text":"def _request(self, url, method = u\"get\", data = None, headers=None, **kwargs):\n # if self.access_token:\n # auth_header = {\n # u\"Authorization\": \"Bearer %s\" % (self.access_token)\n # }\n # headers.update(auth_header)\n #basic auth\n msg = \"method: %s url:%s\\nheaders:%s\\ndata:%s\" % (\n method, url, headers, data)\n #print msg\n if not self.use_oauth:\n auth = (self.sk_user, self.sk_pw)\n if not self.client:\n self.client = requests.session()\n r = self.client.request(method, url, headers=headers, data=data, auth=auth,**kwargs)\n else:\n if not self.client:\n self.client = requests.session(hooks={'pre_request': oauth_hook})\n r = self.client.request(method, url, headers=headers, data=data,**kwargs)\n return r","title":""},{"docid":"e334846e7415a418e3438bd2d8c98543","score":"0.50765806","text":"async def lazy_get(url: str,\n callback: Callable,\n headers: Optional[dict]=None,\n timeout: Optional[timedelta]=timedelta(seconds=120),\n interval: Optional[timedelta]=timedelta(seconds=10)):\n url = BASE_URL + url\n response = requests.get(url, headers=headers, timeout=3000)\n\n # Wait and re-request to allow github to process query\n while response.status_code == 202 and timeout.total_seconds() > 0:\n time.sleep(interval.total_seconds())\n timeout -= interval\n response = requests.get(url, headers=headers, timeout=3000)\n\n await callback(response.json())","title":""},{"docid":"8e1ac09fed452f678da61ec5d3ad4b08","score":"0.50751555","text":"def _get(self, url_key):\n url = self.bitbucket.url(url_key)\n return self.bitbucket.dispatch('GET', url, auth=self.bitbucket.auth)","title":""},{"docid":"7b62bf217c0eff4b56802b069595b4a2","score":"0.50718045","text":"def get_session():\n if not os.path.isfile(TOKEN_FILE):\n sys.stderr.write('Please write a GitHub token to .token\\n')\n sys.exit(1)\n\n with open(TOKEN_FILE) as fd:\n token = fd.readline().strip()\n\n gh = GitHub(token=token)\n try: # Test connection before starting\n gh.is_starred('github', 'gitignore')\n return gh\n except GitHubError as exc:\n sys.stderr.write('Invalid token found\\n')\n sys.exit(1)","title":""},{"docid":"46976c0dd457660cff1ebea994e8eb69","score":"0.50716704","text":"def githubretrievefile(user, repo, path):\n requestcontext = Context(user, repo, path)\n gitrequest, token = githubrequest(user, repo, path)\n return finishrequest(requestcontext, \n gitrequest, \n token, \n lambda x: base64.b64decode(x['content'].encode('utf-8')))","title":""},{"docid":"420e735bc66df08209ce75210db854ff","score":"0.50701255","text":"def test_get_other(self):\n response = self.get_json('/users/2/tokens/3', expect_errors=True)\n self.assertEqual(200, response.status_code)\n self.assertEqual(2, response.json['user_id'])\n self.assertEqual('valid_user_token', response.json['access_token'])","title":""},{"docid":"d69a8c811ef95cfc4903453e0a06721a","score":"0.50598073","text":"async def get(\n self,\n endpoint: str,\n returnjson: bool = True,\n headers: dict or None = None,\n params: dict or None = None,\n ) -> AIOGitHubAPIResponse:\n url = f\"{BASE_API_URL}{endpoint}\"\n response = await async_call_api(\n session=self.session,\n method=\"GET\",\n url=url,\n returnjson=returnjson,\n headers=headers,\n params=params,\n )\n self.ratelimits.load_from_response_headers(response.headers)\n return response.data","title":""},{"docid":"eec58ea443e8d87f5299d4a34b57441f","score":"0.5057483","text":"def _github(self, url, payload=None, ignore_errors=False, nb_tries=2):\n for repo in self:\n if not repo.token:\n return\n match_object = re.search('([^/]+)/([^/]+)/([^/.]+(.git)?)', repo.base)\n if match_object:\n url = url.replace(':owner', match_object.group(2))\n url = url.replace(':repo', match_object.group(3))\n url = 'https://api.%s%s' % (match_object.group(1), url)\n session = requests.Session()\n session.auth = (repo.token, 'x-oauth-basic')\n session.headers.update({'Accept': 'application/vnd.github.she-hulk-preview+json'})\n try_count = 0\n while try_count < nb_tries:\n try:\n if payload:\n response = session.post(url, data=json.dumps(payload))\n else:\n response = session.get(url)\n response.raise_for_status()\n if try_count > 0:\n _logger.info('Success after %s tries' % (try_count + 1))\n return response.json()\n except Exception as e:\n try_count += 1\n if try_count < nb_tries:\n time.sleep(2)\n else:\n if ignore_errors:\n _logger.exception('Ignored github error %s %r (try %s/%s)' % (url, payload, try_count + 1, nb_tries))\n else:\n raise","title":""},{"docid":"35434e0ff6def0cc3fbc3989846c10ff","score":"0.50543314","text":"def test_get_profile_no_token(self):\n\n with self.client:\n response = self.client.get(\n '/auth/profile',\n )\n data = json.loads(response.data.decode())\n self.assertEqual(data['status'], 'error')\n self.assertEqual(\n data['message'],\n 'No token provided.'\n )\n self.assert403(response)","title":""},{"docid":"15302bc76e0b2a9735a755ac486da61b","score":"0.5052027","text":"def on_callback():\n print \"redirect\"\n code = request.values.get(\"code\")\n if not code:\n return 'Missing code'\n try:\n instagram_client = client.InstagramAPI(client_id=CLIENT_ID, client_secret=CLIENT_SECRET, redirect_uri=REDIRECT_URI)\n access_token, instagram_user = instagram_client.exchange_code_for_access_token(code)\n print \"access token= \" + access_token\n if not access_token:\n return Response('Could not get access token')\n\n except Exception, e:\n logging.getLogger(__name__).error(e)\n return Response(\"Didn't get the access token.\")\n\n return Response(\"ok\")","title":""},{"docid":"686e605b6055e2a4b7fd4e13ba9fbe74","score":"0.504534","text":"def get_auth_code():\n oauth_url = 'https://accounts.google.com/o/oauth2/token'\n # create post data\n data = dict(\n refresh_token=settings.YOUTUBE_REFRESH_TOKEN, grant_type='refresh_token',\n client_id=settings.YOUTUBE_CLIENT_ID, client_secret=settings.YOUTUBE_CLIENT_SECRET, )\n headers = {'Content-Type': 'application/x-www-form-urlencoded', 'Accept': 'application/json'}\n data = urllib.parse.urlencode(data).encode('utf-8')\n # make request and take response\n request = urllib.request.Request(oauth_url, data=data, headers=headers)\n response = urllib.request.urlopen(request)\n # get access_token from response\n response = json.loads(response.read().decode('utf-8'))\n return response['access_token']","title":""},{"docid":"b3e3ce680de42e6d9c1e53c5a21362c7","score":"0.50431806","text":"def test_get_required(self):\n resp = self.client.post(\n self.url,\n {\n 'data': 'test'\n },\n HTTP_X_REQUESTED_WITH='XMLHttpRequest'\n )\n\n self.assertEqual(resp.status_code, 400)\n resp_data = json.loads(resp.content)\n\n self.assertEqual(resp_data['status'], 'error')\n self.assertEqual(\n resp_data['error_message'],\n 'Only GET requests are allowed'\n )","title":""},{"docid":"b5dcbcf1336f46e6cf096ab835c93359","score":"0.5029519","text":"def get_response():\r\n url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'\r\n r = requests.get(url)\r\n return r","title":""},{"docid":"3239d880217ea4660c09dbe21d87c17a","score":"0.5024433","text":"def test_github_auth_headers():\n github_access_token = 'access'\n assert github_auth_headers(github_access_token) == {\n \"Authorization\": \"Bearer {}\".format(github_access_token),\n \"Accept\": \"application/vnd.github.v3+json\",\n }","title":""},{"docid":"f045afec0539b937466abcc6a3f223fb","score":"0.5023508","text":"def should_be_callable_when_anonymous(self, url=None):\r\n if not url:\r\n url = self.get_url()\r\n resp = self.client.get(url, data=self.get_data_payload())\r\n self.assertEqual(resp.status_code, 200)\r\n return resp","title":""},{"docid":"eeb0b7c296a8405e71497c3afdff0dc7","score":"0.5020442","text":"async def github(self, ctx, repo):\n url = f\"https://api.github.com/repos/{repo}\"\n async with self.bot.session.get(url) as response:\n if response.status != 200:\n return await ctx.send(\"Could not find repo.\")\n # We wrap it inside a Map so we can use dot notation\n data = Map(await response.json())\n embed = discord.Embed(title=data.full_name, description=data.description, url = data.html_url)\n embed.set_author(name=data.owner.login, icon_url=data.owner.avatar_url)\n if data.license and data.license.name:\n embed.add_field(name=\"License\", value=data.license.name)\n desc = \"\\n\\n\"\n desc += f\"🌍 **Language:** {data.language}\\n\" if data.language else \"\"\n desc += f\"⭐ **Stargazers:** {data.stargazers_count}\\n\"\n desc += f\"👀 **Watchers:** {data.watchers}\\n\"\n desc += f\"🍴 **Forks:** {data.forks_count}\\n\"\n desc += f\"❔ **Open Issues:** {data.open_issues}\\n\"\n desc += f\"🏠 **Home Page:** {data.homepage}\\n\" if data.homepage else \"\"\n embed.description += desc\n await ctx.send(embed=embed)","title":""},{"docid":"cbdca867aafb5e0241149486e720f22f","score":"0.502032","text":"def oauth2callback(request):\n logger.debug('Entering oauth2 code_consumer')\n\n oauth_state = request.GET.get('state')\n oauth_code = request.GET.get('code')\n if oauth_state != request.session['oauth_state']:\n logger.error('oauth_state err: could not get authorization code.')\n logger.debug('Got code %s' % oauth_code)\n body = \"grant_type=%s&client_id=%s&client_secret=%s&code=%s&redirect_uri=%s&scope=%s\" %(\n settings.OAUTH2_CONFIG['oauth_client']['grant_type'],\n settings.OAUTH2_CONFIG['oauth_client']['client_id'],\n settings.OAUTH2_CONFIG['oauth_client']['client_secret'],\n oauth_code,\n settings.OAUTH2_CONFIG['oauth_client']['callback_url'],\n urllib.quote(settings.OAUTH2_CONFIG['oauth_client']['scope'], safe=\"%=&?~#!$,;'@()*[]\"),\n )\n logger.debug('Body: %s ' % body)\n token_url = '%s/r/access_token/request' % settings.OAUTH2_CONFIG['oauth_authz_server']['auth_endpoint']\n client_cert = settings.OAUTH2_CONFIG['oauth_client']['client_cert']\n client_key = settings.OAUTH2_CONFIG['oauth_client']['client_key']\n resp = requests.post(\n url=token_url,\n data=body,\n headers={'Content-Type': 'application/x-www-form-urlencoded'},\n cert=(client_cert, client_key),\n verify=False\n )\n access_token=str(json.loads(resp._content).get('access_token'))\n logger.debug('Got access_token: %s' % access_token)\n request.session['oauth_access_token'] = access_token\n delegatedCert = DelegatedCertificate()\n cert=delegatedCert.get_cert(access_token,settings.OAUTH2_CONFIG['ca_server']['url'], client_cert, client_key)\n delegatedCert.write_key_to_file()\n #res = myproxy_client.logon(request.user.id, \"nopassword\", settings.ONLINE_CA_URI,ssl_ctx=ssl_ctx)\n #pem_out = res.read()\n #cert = crypto.load_certificate(crypto.FILETYPE_PEM, pem_out)\n request.session['pkey_file_path']=delegatedCert.pkey_file_path\n request.session['certificate']=cert\n logger.debug(\"Key path: %s \" % request.session['pkey_file_path'] )\n #request.session['certificate']= DelegatedCertificate(cert,pem_out)\n logger.debug(\"User has cert %s \" % request.user.certificate )\n return HttpResponseRedirect(profile_url(request.user))","title":""},{"docid":"8fa85274fbf928c9e5ef74f5dd765f5d","score":"0.501468","text":"def get_conn(self) -> GithubClient:\n if self.client is not None:\n return self.client\n\n conn = self.get_connection(self.github_conn_id)\n access_token = conn.password\n host = conn.host\n\n # Currently the only method of authenticating to GitHub in Airflow is via a token. This is not the\n # only means available, but raising an exception to enforce this method for now.\n # TODO: When/If other auth methods are implemented this exception should be removed/modified.\n if not access_token:\n raise AirflowException(\"An access token is required to authenticate to GitHub.\")\n\n if not host:\n self.client = GithubClient(login_or_token=access_token)\n else:\n self.client = GithubClient(login_or_token=access_token, base_url=host)\n\n return self.client","title":""},{"docid":"ae648e97bb3ca0b0ffae33753f8d0388","score":"0.5014141","text":"def test_get_request_with_wrong_token(self):\n\n details = \"Invalid link!\"\n token = get_user_token(user_id=User.objects.first().id)[:-4]\n\n url = reverse(\"confirm_reset\", None, {token})\n response = self.client.get(url, format=\"json\")\n\n assert response.status_code == status.HTTP_400_BAD_REQUEST\n assert response.data[\"details\"] == details","title":""},{"docid":"eabb54181f7c08bfdcb182aba99f7953","score":"0.5006312","text":"def get(self, request, *args, **kwargs):\n if not request.GET.get('state'):\n return self.response_error(request, 'Missing state property.')\n elif not request.GET.get('code'):\n return self.response_error(request, 'Missing code property.')\n\n state = request.GET['state']\n code = request.GET['code']\n\n if state != request.session.get('gapi_state'):\n return self.response_error(request, 'Invalid state.')\n\n google.redirect_uri = absolute_url(request, 'accounts:google_sign_in')\n google.scope = '' # need to reset scope to '', because otherwise we'll get trouble\n\n try:\n google.fetch_token(self.URL_GET_TOKEN,\n code=code,\n client_secret=settings.GAPI_CLIENT_SECRET)\n except Exception:\n return self.response_error(request, 'An error occurred while verifying response.')\n\n response = google.get(self.URL_PLUS_API_PEOPLE_GET)\n\n try:\n data = json.loads(response.content.decode('utf-8'))\n except ValueError:\n return self.response_error(request, 'Invalid response format.')\n\n email_address = None\n\n if isinstance(data, dict) and data.get('emails'):\n for i in data['emails']:\n if i['type'] == 'account':\n email_address = i['value']\n break\n\n if not email_address:\n return self.response_error(request, 'No valid account-based email address found.')\n\n # Now check if the account exists and login\n user = None\n\n try:\n user = User.objects.get(email__iexact=email_address)\n except User.DoesNotExist:\n user = self.create_user(email_address)\n\n if not user.is_active:\n return self.response_error(request, 'Your account is inactive.')\n elif user.type != UserType.GOOGLE:\n return self.response_error(request, 'Account type mismatch.')\n else:\n return self.login(request, user)","title":""},{"docid":"9fb8db38b56223c0443cf1b2869244a5","score":"0.5001218","text":"def getOAuthToken():\n oauth_token = input('Enter your OAuth Token: ')\n return oauth_token","title":""},{"docid":"3bec94fbf13695ebb1ac9e380d9edfaa","score":"0.49979693","text":"def GET_request(self, url):\n self.response = requests.request('GET', url=url, headers=self.headers, verify=False)","title":""},{"docid":"03568af1e41090f65d5e0a58b577ab7a","score":"0.4994307","text":"def _get(self, **kwargs) -> json:\n url = self._prepare_url(**kwargs)\n self.logger.debug(url)\n result = requests.request('GET', url.url)\n\n if result.status_code == 200:\n return result.json()\n self.logger.error(f\"URL {url} |----| {result.content}\")\n raise PyAFCommunicationError(\"Data was not received\")","title":""}],"string":"[\n {\n \"docid\": \"bc74d9d65ec4b98ac2716efa2f8a1873\",\n \"score\": \"0.6934215\",\n \"text\": \"def test_github_callback_empty_get(self, mock_post):\\n with self.app.test_client() as client:\\n response = client.get(\\\"/account/github_callback\\\")\\n\\n self.assertEqual(response.status_code, 400)\\n mock_post.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5723fc53fb96563c151d493cef1b4a1\",\n \"score\": \"0.6637549\",\n \"text\": \"def test_github_callback_valid_get(self, mock_post, mock_g, mock_user_model):\\n mock_post.return_value.json.return_value = {'access_token': 'test'}\\n\\n with self.app.test_client() as client:\\n response = client.get(\\\"/account/github_callback\\\", query_string={'code': 'secret'})\\n\\n self.assertEqual(response.status_code, 302)\\n mock_post.assert_called_once()\\n self.assertEqual(2, mock_user_model.query.filter.call_count)\\n mock_user_model.query.filter.assert_called_with(mock_user_model.id == mock_g.user.id)\\n mock_g.db.commit.assert_called_once()\\n mock_g.log.error.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f03a15a8e6cc68ef7aea431c331c90\",\n \"score\": \"0.640843\",\n \"text\": \"def githubretrievetoken(code):\\n gitrequest = urllib.request.Request(URL_GITHUBRETRIEVETOKEN)\\n gitrequest.add_header('Accept', 'application/json')\\n parameters = {'client_id':github_client_id(),\\n 'client_secret':github_client_secret(),\\n 'code':code,\\n 'redirect_uri':getredirecturl()}\\n data = urllib.parse.urlencode(parameters)\\n data = data.encode('utf-8')\\n response = urllib.request.urlopen(gitrequest, data)\\n jsresponse = json.loads(response.read().decode(\\\"utf-8\\\"))\\n if 'access_token' in jsresponse:\\n session[SESSION_ACCESSTOKEN] = jsresponse.get('access_token')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b24a446ad3a58ccd6022f14c4a790437\",\n \"score\": \"0.6049778\",\n \"text\": \"def github_oauth(code):\\n params = {}\\n params['client_id'] = app.github.get('client_id')\\n params['client_secret'] = app.github.get('client_secret')\\n params['code'] = code\\n\\n headers={'Accept': 'application/json'}\\n\\n gh_oauth_get_token = 'https://github.com/login/oauth/access_token'\\n r = requests.post(gh_oauth_get_token, params=params, headers=headers)\\n if r.status_code == requests.codes.OK:\\n return r.json()['access_token']\\n else:\\n return 'unavailable'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd13ae048eb13d0dfbe5c54fd5f98f8\",\n \"score\": \"0.60312855\",\n \"text\": \"def githubrequest(user, repo, path, method='GET'):\\n url = \\\"https://api.github.com/repos/{0}/{1}/contents/{2}\\\".format(user, repo, path)\\n return finishrequestsetup(url, method)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"904b096d20aa81a9c479195efa28504b\",\n \"score\": \"0.60227907\",\n \"text\": \"async def _get(self, ctx: commands.Context, url: str, private=False):\\n if not private:\\n match = re.fullmatch(self.githubURL, url)\\n if match is None:\\n return await ctx.send(self.invalid)\\n\\n url = f\\\"https://github.com/{match.group(1)}/{match.group(2)}/commits.atom\\\"\\n\\n async with aiohttp.ClientSession() as session:\\n async with session.get(url) as resp:\\n html = await resp.read()\\n if resp.status != 200:\\n return await ctx.send(self.invalid)\\n entry = feedparser.parse(html).entries[0]\\n e = await self.commit_embed(entry, feedparser.parse(html).feed.link)\\n await ctx.send(embed=e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15fb0099ce1e761d9bd48d123a7a3b8\",\n \"score\": \"0.6009821\",\n \"text\": \"def callback():\\n #form = LoginForm()\\n #if form.validate_on_submit():\\n\\n \\\"\\\"\\\" Step 3: Retrieving an access token.\\n\\n The user has been redirected back from the provider to your registered\\n callback URL. With this redirection comes an authorization code included\\n in the redirect URL. We will use that to obtain an access token.\\n \\\"\\\"\\\"\\n\\n #check if repo is valid repo\\n github = OAuth2Session(client_id, state=session['oauth_state'])\\n token = github.fetch_token(token_url, client_secret=client_secret, authorization_response=request.url)\\n\\n session['token'] = token\\n session['token'].permanent = True\\n\\n return redirect(url_for('form'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a559cbfd49c1581f7fbe5e84c6897157\",\n \"score\": \"0.59529126\",\n \"text\": \"def _get_access_token():\\n token = db_config.get_value('oss_fuzz_robot_github_personal_access_token')\\n if not token:\\n raise RuntimeError('Unable to access GitHub account to '\\n 'file/close the issue.')\\n return github.Github(token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2566717198c2a440f04025ce20c2f43\",\n \"score\": \"0.59377015\",\n \"text\": \"def callback(request):\\n github_oauth_session = OAuth2Session(client_id=CLIENT_ID,\\n state=request.session[OAUTH_STATE])\\n access_token_info = github_oauth_session.fetch_token(token_url=TOKEN_URL,\\n client_secret=CLIENT_SECRET,\\n authorization_response=request.get_full_path())\\n request.session[OAUTH_TOKEN] = access_token_info\\n return redirect(dashboard)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efe1425d871ae4a8fcdbf9603fec6d2c\",\n \"score\": \"0.59277016\",\n \"text\": \"def do_oauth():\\n token = session.get('token', None)\\n if not token or token=='unavailable':\\n # code parameter will be returned by GitHub in Step 1 of OAuth2 authentication\\n code = request.args.get('code', None)\\n if not code:\\n redirect_uri = 'https://github.com/login/oauth/authorize?client_id={}&scope=repo'.format(app.github.get('client_id'))\\n return redirect(redirect_uri)\\n else:\\n token = github_oauth(code)\\n session['token'] = token\\n return redirect('/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51e9e52a5295117af01d64f93f06d941\",\n \"score\": \"0.59064966\",\n \"text\": \"def _handle_response():\\n params = {\\n 'code': request.args.get('code'),\\n 'client_id': Config.GITHUB_CLIENT_ID,\\n 'client_secret': Config.GITHUB_CLIENT_SECRET\\n }\\n url = \\\"https://github.com/login/oauth/access_token\\\"\\n response = requests.post(url, data=params)\\n data = parse_qs(response.content)\\n for k, v in data.items():\\n if len(v) == 1:\\n data[k] = v[0]\\n token = data.get(b'access_token', None)\\n if token is not None:\\n token = token.decode('ascii')\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c1b655dd81d956af579a4e95155515\",\n \"score\": \"0.58930653\",\n \"text\": \"def get_url(url,api_key=os.getenv('GH_APIKEY')):\\n \\n headers = {\\n \\\"Authorization\\\": f\\\"Bearer {api_key}\\\"\\n }\\n\\n res = requests.get(url, headers=headers)\\n #res = requests.get(url, params=query_params, headers=headers)\\n \\n data = res.json()\\n if res.status_code != 200:\\n \\n raise ValueError(f'Invalid Github API call: {data[\\\"message\\\"]}\\\\nSee more in {data[\\\"documentation_url\\\"]}')\\n \\n else:\\n print(f\\\"Requested data to {url}; status_code:{res.status_code}\\\")\\n \\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9657a8c64940d78e4d6cbe6a2e0976b\",\n \"score\": \"0.58899975\",\n \"text\": \"def test_basic_fetch(self):\\n self.gh_client.fetch(\\\"https://api.github.com/url\\\")\\n self.assertTrue(self.http_client.fetch.called)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b382411abfb77aacb2aebda234a5d572\",\n \"score\": \"0.58876705\",\n \"text\": \"async def test_get_access_token_none(self):\\n request = MockRequest()\\n access_token = await get_access_token(request)\\n self.assertEqual(None, access_token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"038cc3c114984128f101389bbc2ec9a6\",\n \"score\": \"0.587748\",\n \"text\": \"def test_github_callback_empty_post(self, mock_post):\\n with self.app.test_client() as client:\\n response = client.post(\\\"/account/github_callback\\\")\\n\\n self.assertEqual(response.status_code, 400)\\n mock_post.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29757b3aed12ea6c499f8de79ae3a6b2\",\n \"score\": \"0.58709323\",\n \"text\": \"def make_gh_request(url, err=True, msg=None):\\n if not msg:\\n msg = (\\n \\\"Unable to connect. If your network is working properly, you might\\\"\\n \\\" have reached Github's API request limit. Try adding a Github API\\\"\\n \\\" token to the 'POPPER_GITHUB_API_TOKEN' variable.\\\"\\n )\\n\\n response = requests.get(url, headers=get_gh_headers())\\n if err and response.status_code != 200:\\n log.fail(msg)\\n else:\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f1f37740b300645e4e608675ce6dc84\",\n \"score\": \"0.5802419\",\n \"text\": \"def _do_get_request(self, url, token=None, exit_on_error=True):\\n headers = {}\\n\\n if token is not None:\\n headers[\\\"Authorization\\\"] = \\\"Bearer {}\\\".format(token)\\n\\n result = get(self.base_url+url, headers=headers)\\n\\n if not 199 < result.status_code < 300:\\n print(\\\"Could not contact {} - Error: {}\\\".format(self.base_url+url,\\n result.status_code))\\n if exit_on_error:\\n sys.exit(4)\\n\\n return result.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"183806d6703518cf14e6840688d4cfef\",\n \"score\": \"0.5770529\",\n \"text\": \"def githubauthurl():\\n url = URL_GITHUBAUTHORIZE + \\\"?\\\"\\n url += \\\"client_id=\\\"+github_client_id()\\n url += \\\"&redirect_uri=\\\"+getredirecturl()\\n url += \\\"&scope=repo gist&state=\\\"+newgithubstate()\\n print(url)\\n return url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44479b32873faf35c388f7c55920fb58\",\n \"score\": \"0.57642317\",\n \"text\": \"async def auth_github(request):\\n if 'error' in request.query:\\n logger.error(request.query)\\n web.HTTPUnauthorized(body=\\\"Encountered an error. \\\")\\n\\n session = await get_session(request)\\n nexturl = request.query.get('next') or '/'\\n baseurl = BOT_BASEURL + \\\"/auth/github?next=\\\" + nexturl\\n try:\\n ghappapi = request.app['ghappapi']\\n ghapi = await ghappapi.oauth_github_user(baseurl, session, request.query)\\n if ghapi.username:\\n await remember(request, web.HTTPFound(nexturl), ghapi.token)\\n return web.HTTPFound(nexturl)\\n except web.HTTPFound:\\n raise\\n except Exception as exc:\\n logger.exception(\\\"failed to auth\\\")\\n return web.HTTPUnauthorized(body=\\\"Could not authenticate your Github account\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ec3d15ede9ecf2e088b79e70a6d466a\",\n \"score\": \"0.5759575\",\n \"text\": \"async def login(request):\\n return web.HTTPFound('/auth/github')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"762b37972a8b4cb9dbb95a15faa2b10e\",\n \"score\": \"0.57585263\",\n \"text\": \"async def callback(self, request: web.Request):\\r\\n code = request.query[\\\"code\\\"]\\r\\n token = await self.fetch_token(code)\\r\\n raise web.HTTPFound(f'/me?token={token}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46da20900ada1dac6001ab11964b3f66\",\n \"score\": \"0.5700217\",\n \"text\": \"async def fetch_token(self, code):\\n web_client = self.request.app['web_client']\\n token_url = 'https://github.com/login/oauth/access_token'\\n data = {\\n 'client_id': settings.CLIENT_ID,\\n 'client_secret': settings.CLIENT_SECRET,\\n 'code': code\\n }\\n headers = {'Accept': 'application/json'}\\n token = await web_client.post(token_url, data=data, headers=headers)\\n return token['access_token']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c18f89d432ed70601f262bc9d970d14\",\n \"score\": \"0.5684983\",\n \"text\": \"def githubforgetauth():\\n session.pop(SESSION_ACCESSTOKEN, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dcf6b617d87042250c8843111fd6abf\",\n \"score\": \"0.56592345\",\n \"text\": \"def get(self, request, *args, **kwargs):\\n if not request.GET.get('state'):\\n return self.response_error(request, 'Missing state property.')\\n elif not request.GET.get('code'):\\n return self.response_error(request, 'Missing code property.')\\n\\n state = request.GET['state']\\n code = request.GET['code']\\n\\n if state != request.session.get('github_state'):\\n return self.response_error(request, 'Invalid state.')\\n\\n github.redirect_uri = absolute_url(request, 'accounts:github_sign_in')\\n github.scope = '' # need to reset scope to '', because otherwise we'll get trouble\\n\\n try:\\n github.fetch_token(self.URL_GET_TOKEN,\\n code=code,\\n client_secret=settings.GITHUB_CLIENT_SECRET)\\n except Exception:\\n return self.response_error(request, 'An error occurred while verifying response.')\\n\\n response = github.get(self.URL_USER_PROFILE)\\n\\n try:\\n data = json.loads(response.content.decode('utf-8'))\\n except ValueError:\\n return self.response_error(request, 'Invalid response format.')\\n\\n email_address = None\\n\\n if isinstance(data, dict) and data.get('email'):\\n email_address = data['email']\\n\\n if not email_address:\\n return self.response_error(request, 'No valid account-based email address found.')\\n\\n # Now check if the account exists and login\\n try:\\n user = User.objects.get(email__iexact=email_address)\\n except User.DoesNotExist:\\n user = self.create_user(email_address)\\n\\n if not user.is_active:\\n return self.response_error(request, 'Your account is inactive.')\\n elif user.type != UserType.GITHUB:\\n return self.response_error(request, 'Account type mismatch.')\\n else:\\n return self.login(request, user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a1e7feefd8760f2426fb188e9cc4411\",\n \"score\": \"0.5651078\",\n \"text\": \"def github() -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10fa3ff8b63c7c4cd586a2d873f64e5\",\n \"score\": \"0.555293\",\n \"text\": \"def __post_init__(self):\\n self.connect = github.Github(self.token)\\n\\n try:\\n self.repo_obj = self.connect.get_repo(\\n f'{self.organization}/{self.repository}'\\n )\\n except (github.BadCredentialsException,\\n github.BadAttributeException) as error:\\n print(error)\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9dc49604a6257f9e5162200845d8f7\",\n \"score\": \"0.5523924\",\n \"text\": \"def get_access_token(self):\\n logger.debug('Do nothing to get access token')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc48edfe2db83688878f9091b7005f1\",\n \"score\": \"0.55082214\",\n \"text\": \"def github():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59587c2a2e6351215bd789cbc0e29851\",\n \"score\": \"0.5459952\",\n \"text\": \"def bpasteclicked(self):\\n url = self.leback.text()\\n # add a s so we have https hotfix for request_oAuthlib becouse it's make it only with https\\n firstpart = url[0:4]\\n secondpart = url[4:len(url)]\\n completeurl = firstpart + 's' + secondpart\\n self.github.fetch_token(token_url, client_secret=client_secret,\\n authorization_response=completeurl)\\n # hide login button so user not try to relogin and an exception is create\\n self.bpaste.hide()\\n self.cbown.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7a3f619eb76239267fdb0a2fa091ae\",\n \"score\": \"0.5432267\",\n \"text\": \"def __handle_github(self, code):\\n # request token\\n params = self.services[\\\"github\\\"]\\n response = requests.post(\\n params[\\\"token_url\\\"],\\n data={\\n \\\"code\\\": code,\\n \\\"client_id\\\": params[\\\"client_id\\\"],\\n \\\"client_secret\\\": params[\\\"secret\\\"],\\n },\\n headers={\\\"Accept\\\": \\\"application/json\\\"},\\n ).json()\\n # if all is well, we hava a token\\n token = response.get(\\\"access_token\\\", \\\"\\\")\\n # get a user profile\\n response = requests.get(\\n \\\"https://api.github.com/user\\\",\\n headers={\\\"Authorization\\\": f\\\"Bearer {token}\\\", \\\"Accept\\\": \\\"application/json\\\"},\\n )\\n response = response.json()\\n id = response[\\\"id\\\"]\\n email = response.get(\\\"email\\\", \\\"email-unknown\\\")\\n name = response[\\\"name\\\"] or response[\\\"login\\\"] or response[\\\"id\\\"]\\n return (id, email, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7222de3f88dcf99d1b945cd9f9ef9d13\",\n \"score\": \"0.5414896\",\n \"text\": \"def _get_token_endpoint(self):\\n return 'https://github.com/login/oauth/access_token'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c46ffcb575eb8948574f593805338494\",\n \"score\": \"0.5390511\",\n \"text\": \"def make_gh_request(url, err=True, msg=None):\\n if not msg:\\n msg = (\\n \\\"Unable to connect. Please check your network connection.\\\"\\n )\\n\\n response = requests.get(url)\\n if err and response.status_code != 200:\\n log.fail(msg)\\n else:\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d39b067ea2188c1f776092970fbd5a3\",\n \"score\": \"0.53803855\",\n \"text\": \"def request_github_events(user=None):\\n print \\\"Getting Recent GitHub Events\\\"\\n if user:\\n response = requests.get('https://api.github.com/users/' + str(user) + '/events')\\n else:\\n response = requests.get('https://api.github.com/events')\\n response_json = response.json()\\n print response_json\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b423babba74e00bdf2610dca84fee58c\",\n \"score\": \"0.5375007\",\n \"text\": \"def call_github(email):\\n\\n url = 'https://api.github.com/search/users'\\n values = {'q' : '{0} in:email type:user repos:>0'.format(email) }\\n\\n # TODO Insert Github credentials here. You will be limited to 30 requests per minute.\\n auth_info = '{0}:{1}'.format('','')\\n\\n basic = base64.b64encode(auth_info)\\n headers = { 'Authorization' : 'Basic ' + basic }\\n params = urllib.urlencode(values)\\n req = urllib2.Request('{0}?{1}'.format(url,params), headers=headers)\\n response = json.loads(urllib2.urlopen(req).read())\\n\\n if response and response['total_count'] == 1:\\n return response['items'][0]['html_url']\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33bfc0291dc080cf0bda7db941fbccd9\",\n \"score\": \"0.5358323\",\n \"text\": \"def post(self, request, *args, **kwargs):\\n github.redirect_uri = absolute_url(request, 'accounts:github_sign_in')\\n github.scope = None\\n\\n authorization_url, state = github.authorization_url('https://github.com/login/oauth/authorize')\\n\\n request.session['github_state'] = state\\n\\n return self.redirect(authorization_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88a2f4af1deefd8dff522a91438cc91c\",\n \"score\": \"0.53529894\",\n \"text\": \"def test_fetch_params(self):\\n params = {\\\"unique_param_name\\\": 1}\\n self.gh_client.fetch(\\\"https://api.github.com/url\\\", params=params)\\n url = self._get_url()\\n self.assertTrue(\\\"unique_param_name\\\" in url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f8865ca4b35e0a6651cc5d4889d864\",\n \"score\": \"0.5331069\",\n \"text\": \"async def github(self, ctx):\\n await ctx.reply(self.github_link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c73cdc749afd0bfde26038726aab449\",\n \"score\": \"0.5329995\",\n \"text\": \"def request_github_token(github_username, github_password, twofactor=None):\\n note = 'nbreport for {user}@{machine} on {time}'.format(\\n user=getuser(),\\n machine=gethostname(),\\n time=datetime.datetime.now().isoformat()\\n )\\n request_data = {\\n 'scopes': ['read:user', 'read:org'],\\n 'note': note,\\n 'note_url': 'https://nbreport.lsst.io'\\n }\\n headers = {\\n 'Accept': 'application/vnd.github.v3+json'\\n }\\n if twofactor is not None:\\n headers['X-GitHub-OTP'] = str(twofactor)\\n response = requests.post(\\n 'https://api.github.com/authorizations',\\n auth=(github_username, github_password),\\n headers=headers,\\n json=request_data\\n )\\n\\n # Handle two-factor authentication\\n if response.status_code == 401 and 'X-GitHub-OTP' in response.headers:\\n raise GitHubTwoFactorRequired\\n # Handle other errors\\n response.raise_for_status()\\n\\n return response.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff698dcb9eaa2debb406d4275669310d\",\n \"score\": \"0.5319272\",\n \"text\": \"def github_client(token: Optional[str] = None) -> Github:\\n if token:\\n # Authenticate as a user\\n return Github(token)\\n elif GITHUB_API_CREDENTIALS:\\n # Authenticate as a OAuth client application\\n # No extra permissions, just higher rate limits\\n client_id, client_secret = GITHUB_API_CREDENTIALS.split(\\\":\\\")\\n return Github(client_id, client_secret)\\n else:\\n # Unauthenticated access\\n return Github()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e01a4d72f454bc26f00700c5e562f161\",\n \"score\": \"0.53015566\",\n \"text\": \"def test_token_missing(self):\\n self._do_request(set_token=False, set_target_screen_name=True, expected_status=500,\\n expected_error_msg=\\\"Missing OAuth Token\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"137cab6ee887217e30c6c2de3048071e\",\n \"score\": \"0.52993137\",\n \"text\": \"def __unsigned_request(self, url, additional_params={}, err_msg=\\\"Error\\\"):\\n\\n response = requests.get(url, params=additional_params).json()\\n \\n # Throw BinanceException if returned JSON is an error code.\\n if 'code' in response:\\n raise BinanceException(f\\\"{err_msg}: code {response['code']}: {response['msg']}\\\")\\n else:\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d55d8fb98dde149694c80f4d80898ce\",\n \"score\": \"0.52960086\",\n \"text\": \"def do_GET(self):\\n self.set_headers()\\n\\n # Call get_callback argument\\n if get_callback is not None:\\n get_callback(self, None, **callback_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f81a5d5878e3bcf536872450eda08c83\",\n \"score\": \"0.5277354\",\n \"text\": \"def test_get(self):\\n token = self.api.get(f'{self.url_prefix}/token').json['token']\\n response = self.api.get(\\n f'{self.url_prefix}/person', headers={'Authorization': token})\\n self.assertIsNotNone(response)\\n self.assertAlmostEqual(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f20386df26b975df4087728ad69764a9\",\n \"score\": \"0.5255422\",\n \"text\": \"def test_RESOURCE_api_WITH_no_access_token_EXPECT_request_denied() -> None:\\n endpoint = MainStack.get_output(MainStack.API_ENDPOINT_KEY)\\n\\n http = urllib3.PoolManager()\\n\\n response: HTTPResponse = http.request(\\n method='GET',\\n url=endpoint,\\n headers={},\\n )\\n\\n assert response.status == 401\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3aec0b2e74c9eb1846da2986b689b08\",\n \"score\": \"0.5253127\",\n \"text\": \"def test_com_adobe_granite_auth_oauth_impl_github_provider_impl(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd29a7574805c37323d9166fb201b09\",\n \"score\": \"0.5250345\",\n \"text\": \"def fetch(self):\\n api = f\\\"https://api.github.com/users/{self.username}\\\"\\n\\n token = \\\"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\\\"\\n\\n headersDic = {\\n \\\"Authorization\\\": f\\\"token {token}\\\"\\n }\\n resp = get(api, headers=headersDic).json()\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f189fb74cfdcfc0d95a3bd099eb82b\",\n \"score\": \"0.52434766\",\n \"text\": \"def test_returns_401_if_no_token(self):\\n # Act\\n response = self.client.get(self.url)\\n # Assert\\n self.assertEqual(response.status_code, 401)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ea8a523195e47a0d03f7e467b8f1428\",\n \"score\": \"0.52408314\",\n \"text\": \"def github_html_response(response):\\n if response.code != 200:\\n logging.warning(\\\"GitHub URL response invalid: %s\\\",\\n response.code)\\n return\\n atom_link = get_commit_atom_link(response.body)\\n self._client.fetch(atom_link, callback=github_commit_response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"572317e803c0a9a6f0454da111788c63\",\n \"score\": \"0.52407706\",\n \"text\": \"def githubretrievegist(gistid):\\n requestcontext = Context('', '', gistid)\\n gitrequest, token = gistrequest(gistid)\\n return finishrequest(requestcontext, \\n gitrequest, \\n token, \\n lambda x: x['files'][list(x['files'].keys())[0]]['content'].encode('utf-8'), # content\\n lambda x: list(x['files'].keys())[0]) # file name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6f9763ba62ee2749b20ff6bb42e4217\",\n \"score\": \"0.52229315\",\n \"text\": \"def test_get_request_fails(self):\\n self.service_request(method='GET', path='/tokens', assert_status=404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ed7e6f4e168e1a2e2aeb5d1cfbc6701\",\n \"score\": \"0.52150613\",\n \"text\": \"def http_request(self, *args, **kwargs):\\n token = self.get_access_token()\\n headers = {\\n 'Authorization': f'Bearer {token}',\\n 'Content-Type': 'application/json',\\n }\\n return super()._http_request(*args, headers=headers, **kwargs) # type: ignore[misc]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebbdf4f49c8524aae97ccfda5a4049e6\",\n \"score\": \"0.5213798\",\n \"text\": \"def _api_get(self, url):\\n try:\\n response = self.client.http_get(\\n url,\\n headers={\\n 'PRIVATE-TOKEN': self._get_private_token(),\\n })\\n\\n return response.data, response.headers\\n except HTTPError as e:\\n if e.code == 401:\\n raise AuthorizationError(\\n ugettext('The username or password is incorrect.'))\\n elif e.code == 404:\\n raise\\n else:\\n logger.exception('Failed to execute a GET request at %s: %s',\\n url, e)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f528300ef1fe55f8527e1c0989ea493\",\n \"score\": \"0.52003086\",\n \"text\": \"def gconnect():\\n # Validate state token\\n if request.args.get('state') != login_session['state']:\\n response = make_response(json.dumps('Invalid state parameter'), 401)\\n response.headers['Content-Type'] = 'application/json'\\n return response\\n\\n # Obtain authorization code\\n code = request.data\\n\\n try:\\n # Upgrade the authorization code to a credentials object\\n if not request.headers.get('X-Requested-With'):\\n abort(403)\\n\\n flow = flow_from_clientsecrets(os.path.abspath(os.path.join(\\n os.path.dirname(__file__), CLIENT_SECRET_FILE)), scope='profile')\\n flow.redirect_uri = 'postmessage'\\n # Exchange auth code for access token, refresh token, and ID token\\n credentials = flow.step2_exchange(code)\\n except FlowExchangeError:\\n response = make_response(json.dumps(\\n \\\"Failed to upgrade the authorization code.\\\"), 401)\\n response.headers['Content-Type'] = 'application/json'\\n return response\\n\\n # Check that the access token is valid\\n access_token = credentials.access_token\\n url = (\\n 'https://www.googleapis.com/oauth2/v1/tokeninfo?access_token=%s'\\n % access_token)\\n h = httplib2.Http()\\n result = json.loads(h.request(url, 'GET')[1])\\n # If there was an error in the access token info, abort\\n if result.get('error') is not None:\\n response = make_response(json.dumps(result.get('error')), 500)\\n respose.headers['Content-Type'] = 'application/json'\\n return response\\n\\n # Verify that the access token is used for the intended user\\n # Get profile info from ID token\\n userid = credentials.id_token['sub']\\n email = credentials.id_token['email']\\n\\n if result['user_id'] != userid:\\n response = make_response(json.dumps(\\n \\\"Token's user ID doesn't match the given user ID.\\\"), 401)\\n response.headers['Content-Type'] = 'application/json'\\n return response\\n\\n # Verify that the access token is valid for this app\\n if result['issued_to'] != CLIENT_ID:\\n response = make_response(json.dumps(\\n \\\"Token's client ID does not match app's\\\"), 401)\\n print \\\"Token's client ID does not match app's.\\\"\\n response.headers['Content-Type'] = 'application/json'\\n return response\\n\\n stored_access_token = login_session.get('access_token')\\n stored_userid = login_session.get('userid')\\n if stored_access_token is not None and userid == stored_userid:\\n login_session['access_token'] = credentials.access_token\\n response = make_response(json.dumps(\\n 'Current user is already connected'), 200)\\n flash(\\\"Current user is already connected!\\\")\\n response.headers['Content-Type'] = 'application/json'\\n return response\\n\\n # Store the access token in the session for later use\\n login_session['access_token'] = credentials.access_token\\n login_session['userid'] = userid\\n\\n # Get user info\\n userinfo_url = \\\"https://www.googleapis.com/oauth2/v1/userinfo\\\"\\n params = {'access_token': credentials.access_token, 'alt': 'json'}\\n answer = requests.get(userinfo_url, params=params)\\n\\n data = answer.json()\\n\\n login_session['username'] = data['name']\\n login_session['picture'] = data['picture']\\n login_session['email'] = data['email']\\n\\n # Check if user is already in the database, and if not, creates a new user\\n # object\\n user_id = getUserID(login_session['email'])\\n if not user_id:\\n user_id = createUser(login_session)\\n login_session['user_id'] = user_id\\n\\n output = ''\\n output += '<h2>Welcome, '\\n output += login_session['username']\\n output += '!</h2>'\\n output += '<img src=\\\"'\\n output += login_session['picture']\\n output += '\\\"class=\\\"signin-pic\\\" style = \\\"width: 100px; \\\\\\n height: 100px;border-radius: 50px;\\\"> '\\n flash(\\\"You are now logged in as %s\\\" % login_session['username'])\\n print \\\"done!\\\"\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9ed39de2e71d12a0a53e99b3066ddb9\",\n \"score\": \"0.52002305\",\n \"text\": \"def gistrequest(gistid, method='GET'):\\n url = \\\"https://api.github.com/gists/{0}\\\".format(gistid)\\n return finishrequestsetup(url, method)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"565b4a23dd11a091209d401f69f5b496\",\n \"score\": \"0.51997966\",\n \"text\": \"def testSanityGet(self, mock_get_current_user):\\n mock_get_current_user.return_value = users.User(\\n email='bob@test.com', _auth_domain='gmail.com')\\n\\n response = self.testapp.get(oauth_playground.URL_BASE)\\n self.assertEqual(200, response.status_int)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856b4151c3bcd1b26eb280a7a310dd95\",\n \"score\": \"0.51993173\",\n \"text\": \"def request_no_redirect():\\n\\n # Redirects by default\\n r = requests.get('http://github.com') # default Requests allow redirect\\n print r.url # https://github.com/\\n print r.status_code # 200\\n print r.history #[<Response [301]>] # Shows history of a redirect\\n\\n # Don't allow redirect\\n r = requests.get('http://github.com', allow_redirects=False)\\n print r.status_code # 301\\n print r.history # []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd3ec185e7d24112699503a272a892f6\",\n \"score\": \"0.51975936\",\n \"text\": \"def githubloggedin():\\n github_token = session.get(SESSION_ACCESSTOKEN)\\n return True if github_token else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5ccf7664cea1dab8d8b0807693603d\",\n \"score\": \"0.5187894\",\n \"text\": \"def test_cant_get_branch_missing_params(self):\\n json_response = api_call(self, \\\"GET\\\", '/branch/get', dict(), 200, True)\\n self.assertEqual(json_response[\\\"status_code\\\"], 400)\\n self.assertEqual(json_response[\\\"message\\\"], 'Invalid request')\\n\\n json_response = api_call(self, \\\"GET\\\", '/branch/get', dict(id=None), 200, True)\\n self.assertEqual(json_response[\\\"status_code\\\"], 400)\\n self.assertEqual(json_response[\\\"message\\\"], 'Invalid request')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41c6c16f8008e0d12729348c3f1a79dd\",\n \"score\": \"0.51752985\",\n \"text\": \"def get_github_client(token):\\n\\n gh = github3.GitHub()\\n\\n betamax_library_dir = os.environ.get('BETAMAX_LIBRARY_DIR')\\n betamax_cassette = os.environ.get('BETAMAX_CASSETTE')\\n\\n if betamax_library_dir and betamax_cassette:\\n # Delay import because only needed for testing.\\n import betamax\\n\\n with betamax.Betamax.configure() as config:\\n config.cassette_library_dir = betamax_library_dir\\n\\n # We don't want requests hitting the network at all.\\n config.default_cassette_options['record_mode'] = 'none'\\n\\n recorder = betamax.Betamax(gh._session)\\n recorder.use_cassette(betamax_cassette)\\n recorder.start()\\n\\n gh.login(token=token)\\n\\n return gh\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8e6b30536db24c97e7669753de5cad\",\n \"score\": \"0.5170267\",\n \"text\": \"async def get(self, request: Request) -> Response:\\n hass = request.app[\\\"hass\\\"]\\n\\n def check_get(param):\\n if param not in request.query:\\n _LOGGER.error(\\\"State missing in request.\\\")\\n raise HTTPBadRequest(text=f\\\"Parameter {param} not found\\\")\\n return request.query[param]\\n\\n state = check_get(\\\"state\\\")\\n code = check_get(\\\"code\\\")\\n\\n if state not in self._flows:\\n _LOGGER.error(\\\"State unexpected %s\\\", state)\\n raise HTTPBadRequest(text=\\\"State can not be translated into flow\\\")\\n\\n flow_id = self._flows[state]\\n _LOGGER.debug(\\\"Received auth request for flow %s\\\", flow_id)\\n\\n try:\\n await hass.config_entries.flow.async_configure(flow_id, {\\\"code\\\": code})\\n\\n return Response(\\n headers={\\\"content-type\\\": \\\"text/html\\\"},\\n text=\\\"<script>window.close()</script>\\\",\\n )\\n except data_entry_flow.UnknownFlow:\\n raise HTTPBadRequest(text=\\\"Unknown flow\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74ee81e88d3a2dd5fe5c063214e9fd74\",\n \"score\": \"0.51567596\",\n \"text\": \"def test_missing_access_token(self):\\n data = {\\\"provider\\\":\\\"facebook\\\"}\\n response = self.client.post(self.oauth_url, data=data)\\n\\n self.assertEqual(response.status_code, 400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1896fbd0dc815ad0ef004c3930b92b44\",\n \"score\": \"0.514684\",\n \"text\": \"def test_fetch_github_own(self, github_setup):\\n data = github_setup\\n repo_owner_type, repo_type = data[0], data[1]\\n os.chdir(FETCH_TARGET)\\n subprocess.run(\\n [\\n sys.executable,\\n PYREPOMAN_MAIN_PATH,\\n ACTION_IDENTIFIER,\\n \\\"github\\\",\\n repo_owner_type,\\n \\\"--repo-type\\\",\\n repo_type,\\n ]\\n )\\n os.chdir(\\\"..\\\")\\n dcmp = filecmp.dircmp(FETCH_TARGET, MODEL_TARGET)\\n diff = Diff(dcmp)\\n assert diff.run() is False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f071e28f7e4da85728d6d9f4bbc0108\",\n \"score\": \"0.5146166\",\n \"text\": \"def test_exp_init_get(self):\\n query_string = [('gitrepo', 'gitrepo_example')]\\n response = self.client.open('/v1/exp/init',\\n method='GET',\\n query_string=query_string)\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92c026688f4edcfbd2e8223ef4091905\",\n \"score\": \"0.514316\",\n \"text\": \"def login_with_oauth_code(request):\\n\\n # Get the value of the redirect returned from the TTN site\\n # token_type = request.GET.get('token_type', '')\\n # access_token = request.GET.get('access_token', '')\\n # expires_in = request.GET.get('expires_in')\\n\\n # We can now check the access_token for its info\\n return JsonResponse({})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d651821c8ca35ee72a6d4ccc0076c53\",\n \"score\": \"0.5137772\",\n \"text\": \"def get_g():\\n with open(SCRUM_PATH, \\\"r\\\") as dot_scrum_file:\\n dot_scrum = dot_scrum_file.read()\\n dot_scrum = loads(dot_scrum)\\n return Github(dot_scrum[\\\"api_key\\\"], timeout=600000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ba57aa41d80e5d60619364a9a47bb2c\",\n \"score\": \"0.51356155\",\n \"text\": \"def authorize(request):\\n github_oauth_session = OAuth2Session(client_id=CLIENT_ID,)\\n authorization_url, state = github_oauth_session.authorization_url(AUTHORIZATION_BASE_URL+\\\"?scope=repo\\\")\\n\\n request.session[OAUTH_STATE] = state\\n\\n return redirect(authorization_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dadadef7948f33976def11eca9666687\",\n \"score\": \"0.5131079\",\n \"text\": \"def oauth_request(self, token, url, params=None, method=\\\"GET\\\"):\\n return self.request(\\n url, method=method, params=params, auth=self.oauth_auth(token)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceca7a6bbb62e103ee11d2ab8ebd6a14\",\n \"score\": \"0.51287526\",\n \"text\": \"def test_fetch_github_other(self, github_setup):\\n data = github_setup\\n username = configholder.get_config_value(\\\"GITHUB_USERNAME\\\")\\n repo_owner_type, repo_type = data[0], data[1]\\n os.chdir(FETCH_TARGET)\\n subprocess.run(\\n [\\n sys.executable,\\n PYREPOMAN_MAIN_PATH,\\n ACTION_IDENTIFIER,\\n \\\"github\\\",\\n repo_owner_type,\\n username,\\n \\\"--repo-type\\\",\\n repo_type,\\n ]\\n )\\n os.chdir(\\\"..\\\")\\n dcmp = filecmp.dircmp(FETCH_TARGET, MODEL_TARGET)\\n diff = Diff(dcmp)\\n assert diff.run() is False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19ec5ed3242bb55def4ed9a36c9487e4\",\n \"score\": \"0.51242167\",\n \"text\": \"def _call_api(self, verb, url, check=True, **request_kwargs):\\n api = 'https://api.github.com{}'.format(url)\\n auth_headers = {'Authorization': 'token {}'.format(self.api_token)}\\n headers = {**auth_headers, **request_kwargs.pop('headers', {})}\\n resp = getattr(requests, verb)(api, headers=headers, **request_kwargs)\\n if check:\\n resp.raise_for_status()\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb6204117233eff2edff00f9caaa4cd9\",\n \"score\": \"0.5123553\",\n \"text\": \"def get_access_token(self):\\n request = self.serverInterface().requestHandler()\\n params = request.parameterMap()\\n # 1: search in the bearer\\n auth_header = params.get('HTTP_AUTHORIZATION', '')\\n if auth_header.find('Bearer ') == 0:\\n access_token = auth_header[6:]\\n self.log('get_access_token() Got HTTP_AUTHORIZATION bearer: %s' % access_token)\\n # Search in cache\\n if self.token_storage.get(access_token):\\n self.log('get_access_token() HTTP_AUTHORIZATION bearer is verified!')\\n return access_token # is valid!\\n else:\\n self.log('get_access_token() HTTP_AUTHORIZATION bearer is NOT verified!')\\n raise OAuthException('access_token in request is NOT verified! %s' % access_token)\\n # 2: search in the query string ...\\n # or search in the POST body\\n access_token = params.get('ACCESS_TOKEN', None)\\n if access_token is not None:\\n if self.token_storage.get(access_token):\\n self.log('get_access_token() access_token in request is verified!')\\n return access_token # is valid!\\n else:\\n self.log('get_access_token() access_token in request is NOT verified! %s' % access_token)\\n # TODO: re-validate the token against Twitter endpoint\\n raise OAuthException('access_token in request is NOT verified!! %s' % access_token)\\n # 4: Search for token from verify step\\n # NOTE: this is not the access_token but a request_token!\\n request_token = params.get('OAUTH_TOKEN', None)\\n verifier_token = params.get('OAUTH_VERIFIER', None)\\n if verifier_token is not None and request_token is not None:\\n try:\\n access_token = self.authenticated(request_token, verifier_token)\\n # Redirect\\n url = self.get_current_url()\\n # Remove OAUTH_TOKEN and OAUTH_VERIFIER (lower case)\\n scheme, domain, path, params, query, fragment = urlparse.urlparse(url)\\n query_params = dict(urlparse.parse_qsl(query))\\n del query_params['oauth_token']\\n del query_params['oauth_verifier']\\n # Add access_token to the url\\n query_params['access_token'] = access_token\\n query = '&'.join([\\\"%s=%s\\\" % (k, v) for k, v in query_params.items()])\\n url = urlparse.urlunparse((scheme, domain, path, params, query, fragment))\\n self.redirect_url = url\\n return None\\n except Exception as e:\\n self.log('get_access_token() Cannot verify access_token: %s' % e)\\n raise OAuthException('get_access_token() Cannot verify access_token!')\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc901864991b1d377b3877545fe3742\",\n \"score\": \"0.5117407\",\n \"text\": \"def _generate_get_request(self, url):\\n try:\\n request = requests.get(\\n url,\\n auth=HTTPBasicAuth(self.account.email, self.account.password),\\n verify=True\\n )\\n return request\\n except Exception:\\n print(\\\"Unknown exception occured. Check your internet connection?\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c488af3cf216f3823c06c3f4470b0787\",\n \"score\": \"0.5113194\",\n \"text\": \"def fetch(self, request, callback=..., raise_error=..., **kwargs):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"200866c78c9d38c2c109b60dac507d7f\",\n \"score\": \"0.5101486\",\n \"text\": \"def request_from_github(abort_code: int = 418) -> Callable:\\n def decorator(f):\\n \\\"\\\"\\\"Decorate the function to check if a request is a GitHub hook request.\\\"\\\"\\\"\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if request.method != 'POST':\\n return 'OK'\\n\\n request_ip = ip_address(f\\\"{request.remote_addr}\\\")\\n if not is_github_web_hook_ip(request_ip):\\n g.log.warning(f\\\"Unauthorized attempt to deploy by IP {request_ip}\\\")\\n abort(abort_code)\\n\\n for header in ['X-GitHub-Event', 'X-GitHub-Delivery', 'X-Hub-Signature', 'User-Agent']:\\n if header not in request.headers:\\n g.log.critical(f\\\"{header} not in headers!\\\")\\n abort(abort_code)\\n\\n ua = request.headers.get('User-Agent')\\n if not ua.startswith('GitHub-Hookshot/'):\\n g.log.critical(\\\"User-Agent does not begin with GitHub-Hookshot/!\\\")\\n abort(abort_code)\\n\\n if not request.is_json:\\n g.log.critical(\\\"Request is not JSON!\\\")\\n abort(abort_code)\\n\\n return f(*args, **kwargs)\\n return decorated_function\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39b782be4b19568f87ca1355dd021346\",\n \"score\": \"0.5095502\",\n \"text\": \"def _get_request(self, path):\\n\\n url = self.base_url + path\\n headers = {'Content-Type': 'application/json'}\\n response = requests.get(url, headers=headers)\\n if response.status_code == 200:\\n return response\\n else:\\n raise APIErrorException('Api Not working')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64d2ce925267229c4654b10f7013571f\",\n \"score\": \"0.5091288\",\n \"text\": \"def test_missing_parameters(self):\\n response = self._auth_request('get')\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(bool(response.content), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a53853f2dbb33369c7913988209cde\",\n \"score\": \"0.5088976\",\n \"text\": \"def __request_get(self, url, auth=None, data=None, token=None, parameters = None):\\n headers = {}\\n\\n if token:\\n headers['Authorization'] = \\\"Bearer {}\\\".format(token)\\n\\n try:\\n r = requests.get(url, auth=auth, headers=headers, data=data, params=parameters,verify=False)\\n rsp = r.json()\\n except requests.exceptions.RequestException as err:\\n return self.__response_message(err, 0)\\n\\n return self.__response_message(rsp, r.status_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3822529d9b2aaf3789c545404e0af897\",\n \"score\": \"0.50873274\",\n \"text\": \"def test_build_new_review_request_with_private_github_repository(self):\\n repository = self._create_repository(github=True,\\n repository_plan='private')\\n review_request = self.create_review_request(repository=repository)\\n diffset = self.create_diffset(review_request=review_request)\\n diffset.base_commit_id = '8fd69d70f07b57c21ad8733c1c04ae604d21493f'\\n diffset.save()\\n\\n self._create_config()\\n self.integration.enable_integration()\\n\\n data = self._spy_on_urlopen()\\n\\n review_request.publish(review_request.submitter)\\n\\n self.assertTrue(urlopen.spy.called)\\n self.assertEqual(data['url'],\\n 'https://circleci.com/api/v1.1/project/github/'\\n 'myuser/myprivaterepo/tree/review-requests?'\\n 'circle-token=test-api-token')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2f64d11a88737b148e3ab4bad5efa35\",\n \"score\": \"0.5081073\",\n \"text\": \"def _request(self, url, method = u\\\"get\\\", data = None, headers=None, **kwargs):\\n # if self.access_token:\\n # auth_header = {\\n # u\\\"Authorization\\\": \\\"Bearer %s\\\" % (self.access_token)\\n # }\\n # headers.update(auth_header)\\n #basic auth\\n msg = \\\"method: %s url:%s\\\\nheaders:%s\\\\ndata:%s\\\" % (\\n method, url, headers, data)\\n #print msg\\n if not self.use_oauth:\\n auth = (self.sk_user, self.sk_pw)\\n if not self.client:\\n self.client = requests.session()\\n r = self.client.request(method, url, headers=headers, data=data, auth=auth,**kwargs)\\n else:\\n if not self.client:\\n self.client = requests.session(hooks={'pre_request': oauth_hook})\\n r = self.client.request(method, url, headers=headers, data=data,**kwargs)\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e334846e7415a418e3438bd2d8c98543\",\n \"score\": \"0.50765806\",\n \"text\": \"async def lazy_get(url: str,\\n callback: Callable,\\n headers: Optional[dict]=None,\\n timeout: Optional[timedelta]=timedelta(seconds=120),\\n interval: Optional[timedelta]=timedelta(seconds=10)):\\n url = BASE_URL + url\\n response = requests.get(url, headers=headers, timeout=3000)\\n\\n # Wait and re-request to allow github to process query\\n while response.status_code == 202 and timeout.total_seconds() > 0:\\n time.sleep(interval.total_seconds())\\n timeout -= interval\\n response = requests.get(url, headers=headers, timeout=3000)\\n\\n await callback(response.json())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e1ac09fed452f678da61ec5d3ad4b08\",\n \"score\": \"0.50751555\",\n \"text\": \"def _get(self, url_key):\\n url = self.bitbucket.url(url_key)\\n return self.bitbucket.dispatch('GET', url, auth=self.bitbucket.auth)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b62bf217c0eff4b56802b069595b4a2\",\n \"score\": \"0.50718045\",\n \"text\": \"def get_session():\\n if not os.path.isfile(TOKEN_FILE):\\n sys.stderr.write('Please write a GitHub token to .token\\\\n')\\n sys.exit(1)\\n\\n with open(TOKEN_FILE) as fd:\\n token = fd.readline().strip()\\n\\n gh = GitHub(token=token)\\n try: # Test connection before starting\\n gh.is_starred('github', 'gitignore')\\n return gh\\n except GitHubError as exc:\\n sys.stderr.write('Invalid token found\\\\n')\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46976c0dd457660cff1ebea994e8eb69\",\n \"score\": \"0.50716704\",\n \"text\": \"def githubretrievefile(user, repo, path):\\n requestcontext = Context(user, repo, path)\\n gitrequest, token = githubrequest(user, repo, path)\\n return finishrequest(requestcontext, \\n gitrequest, \\n token, \\n lambda x: base64.b64decode(x['content'].encode('utf-8')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"420e735bc66df08209ce75210db854ff\",\n \"score\": \"0.50701255\",\n \"text\": \"def test_get_other(self):\\n response = self.get_json('/users/2/tokens/3', expect_errors=True)\\n self.assertEqual(200, response.status_code)\\n self.assertEqual(2, response.json['user_id'])\\n self.assertEqual('valid_user_token', response.json['access_token'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d69a8c811ef95cfc4903453e0a06721a\",\n \"score\": \"0.50598073\",\n \"text\": \"async def get(\\n self,\\n endpoint: str,\\n returnjson: bool = True,\\n headers: dict or None = None,\\n params: dict or None = None,\\n ) -> AIOGitHubAPIResponse:\\n url = f\\\"{BASE_API_URL}{endpoint}\\\"\\n response = await async_call_api(\\n session=self.session,\\n method=\\\"GET\\\",\\n url=url,\\n returnjson=returnjson,\\n headers=headers,\\n params=params,\\n )\\n self.ratelimits.load_from_response_headers(response.headers)\\n return response.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eec58ea443e8d87f5299d4a34b57441f\",\n \"score\": \"0.5057483\",\n \"text\": \"def _github(self, url, payload=None, ignore_errors=False, nb_tries=2):\\n for repo in self:\\n if not repo.token:\\n return\\n match_object = re.search('([^/]+)/([^/]+)/([^/.]+(.git)?)', repo.base)\\n if match_object:\\n url = url.replace(':owner', match_object.group(2))\\n url = url.replace(':repo', match_object.group(3))\\n url = 'https://api.%s%s' % (match_object.group(1), url)\\n session = requests.Session()\\n session.auth = (repo.token, 'x-oauth-basic')\\n session.headers.update({'Accept': 'application/vnd.github.she-hulk-preview+json'})\\n try_count = 0\\n while try_count < nb_tries:\\n try:\\n if payload:\\n response = session.post(url, data=json.dumps(payload))\\n else:\\n response = session.get(url)\\n response.raise_for_status()\\n if try_count > 0:\\n _logger.info('Success after %s tries' % (try_count + 1))\\n return response.json()\\n except Exception as e:\\n try_count += 1\\n if try_count < nb_tries:\\n time.sleep(2)\\n else:\\n if ignore_errors:\\n _logger.exception('Ignored github error %s %r (try %s/%s)' % (url, payload, try_count + 1, nb_tries))\\n else:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35434e0ff6def0cc3fbc3989846c10ff\",\n \"score\": \"0.50543314\",\n \"text\": \"def test_get_profile_no_token(self):\\n\\n with self.client:\\n response = self.client.get(\\n '/auth/profile',\\n )\\n data = json.loads(response.data.decode())\\n self.assertEqual(data['status'], 'error')\\n self.assertEqual(\\n data['message'],\\n 'No token provided.'\\n )\\n self.assert403(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15302bc76e0b2a9735a755ac486da61b\",\n \"score\": \"0.5052027\",\n \"text\": \"def on_callback():\\n print \\\"redirect\\\"\\n code = request.values.get(\\\"code\\\")\\n if not code:\\n return 'Missing code'\\n try:\\n instagram_client = client.InstagramAPI(client_id=CLIENT_ID, client_secret=CLIENT_SECRET, redirect_uri=REDIRECT_URI)\\n access_token, instagram_user = instagram_client.exchange_code_for_access_token(code)\\n print \\\"access token= \\\" + access_token\\n if not access_token:\\n return Response('Could not get access token')\\n\\n except Exception, e:\\n logging.getLogger(__name__).error(e)\\n return Response(\\\"Didn't get the access token.\\\")\\n\\n return Response(\\\"ok\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"686e605b6055e2a4b7fd4e13ba9fbe74\",\n \"score\": \"0.504534\",\n \"text\": \"def get_auth_code():\\n oauth_url = 'https://accounts.google.com/o/oauth2/token'\\n # create post data\\n data = dict(\\n refresh_token=settings.YOUTUBE_REFRESH_TOKEN, grant_type='refresh_token',\\n client_id=settings.YOUTUBE_CLIENT_ID, client_secret=settings.YOUTUBE_CLIENT_SECRET, )\\n headers = {'Content-Type': 'application/x-www-form-urlencoded', 'Accept': 'application/json'}\\n data = urllib.parse.urlencode(data).encode('utf-8')\\n # make request and take response\\n request = urllib.request.Request(oauth_url, data=data, headers=headers)\\n response = urllib.request.urlopen(request)\\n # get access_token from response\\n response = json.loads(response.read().decode('utf-8'))\\n return response['access_token']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3e3ce680de42e6d9c1e53c5a21362c7\",\n \"score\": \"0.50431806\",\n \"text\": \"def test_get_required(self):\\n resp = self.client.post(\\n self.url,\\n {\\n 'data': 'test'\\n },\\n HTTP_X_REQUESTED_WITH='XMLHttpRequest'\\n )\\n\\n self.assertEqual(resp.status_code, 400)\\n resp_data = json.loads(resp.content)\\n\\n self.assertEqual(resp_data['status'], 'error')\\n self.assertEqual(\\n resp_data['error_message'],\\n 'Only GET requests are allowed'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5dcbcf1336f46e6cf096ab835c93359\",\n \"score\": \"0.5029519\",\n \"text\": \"def get_response():\\r\\n url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'\\r\\n r = requests.get(url)\\r\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3239d880217ea4660c09dbe21d87c17a\",\n \"score\": \"0.5024433\",\n \"text\": \"def test_github_auth_headers():\\n github_access_token = 'access'\\n assert github_auth_headers(github_access_token) == {\\n \\\"Authorization\\\": \\\"Bearer {}\\\".format(github_access_token),\\n \\\"Accept\\\": \\\"application/vnd.github.v3+json\\\",\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f045afec0539b937466abcc6a3f223fb\",\n \"score\": \"0.5023508\",\n \"text\": \"def should_be_callable_when_anonymous(self, url=None):\\r\\n if not url:\\r\\n url = self.get_url()\\r\\n resp = self.client.get(url, data=self.get_data_payload())\\r\\n self.assertEqual(resp.status_code, 200)\\r\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeb0b7c296a8405e71497c3afdff0dc7\",\n \"score\": \"0.5020442\",\n \"text\": \"async def github(self, ctx, repo):\\n url = f\\\"https://api.github.com/repos/{repo}\\\"\\n async with self.bot.session.get(url) as response:\\n if response.status != 200:\\n return await ctx.send(\\\"Could not find repo.\\\")\\n # We wrap it inside a Map so we can use dot notation\\n data = Map(await response.json())\\n embed = discord.Embed(title=data.full_name, description=data.description, url = data.html_url)\\n embed.set_author(name=data.owner.login, icon_url=data.owner.avatar_url)\\n if data.license and data.license.name:\\n embed.add_field(name=\\\"License\\\", value=data.license.name)\\n desc = \\\"\\\\n\\\\n\\\"\\n desc += f\\\"🌍 **Language:** {data.language}\\\\n\\\" if data.language else \\\"\\\"\\n desc += f\\\"⭐ **Stargazers:** {data.stargazers_count}\\\\n\\\"\\n desc += f\\\"👀 **Watchers:** {data.watchers}\\\\n\\\"\\n desc += f\\\"🍴 **Forks:** {data.forks_count}\\\\n\\\"\\n desc += f\\\"❔ **Open Issues:** {data.open_issues}\\\\n\\\"\\n desc += f\\\"🏠 **Home Page:** {data.homepage}\\\\n\\\" if data.homepage else \\\"\\\"\\n embed.description += desc\\n await ctx.send(embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbdca867aafb5e0241149486e720f22f\",\n \"score\": \"0.502032\",\n \"text\": \"def oauth2callback(request):\\n logger.debug('Entering oauth2 code_consumer')\\n\\n oauth_state = request.GET.get('state')\\n oauth_code = request.GET.get('code')\\n if oauth_state != request.session['oauth_state']:\\n logger.error('oauth_state err: could not get authorization code.')\\n logger.debug('Got code %s' % oauth_code)\\n body = \\\"grant_type=%s&client_id=%s&client_secret=%s&code=%s&redirect_uri=%s&scope=%s\\\" %(\\n settings.OAUTH2_CONFIG['oauth_client']['grant_type'],\\n settings.OAUTH2_CONFIG['oauth_client']['client_id'],\\n settings.OAUTH2_CONFIG['oauth_client']['client_secret'],\\n oauth_code,\\n settings.OAUTH2_CONFIG['oauth_client']['callback_url'],\\n urllib.quote(settings.OAUTH2_CONFIG['oauth_client']['scope'], safe=\\\"%=&?~#!$,;'@()*[]\\\"),\\n )\\n logger.debug('Body: %s ' % body)\\n token_url = '%s/r/access_token/request' % settings.OAUTH2_CONFIG['oauth_authz_server']['auth_endpoint']\\n client_cert = settings.OAUTH2_CONFIG['oauth_client']['client_cert']\\n client_key = settings.OAUTH2_CONFIG['oauth_client']['client_key']\\n resp = requests.post(\\n url=token_url,\\n data=body,\\n headers={'Content-Type': 'application/x-www-form-urlencoded'},\\n cert=(client_cert, client_key),\\n verify=False\\n )\\n access_token=str(json.loads(resp._content).get('access_token'))\\n logger.debug('Got access_token: %s' % access_token)\\n request.session['oauth_access_token'] = access_token\\n delegatedCert = DelegatedCertificate()\\n cert=delegatedCert.get_cert(access_token,settings.OAUTH2_CONFIG['ca_server']['url'], client_cert, client_key)\\n delegatedCert.write_key_to_file()\\n #res = myproxy_client.logon(request.user.id, \\\"nopassword\\\", settings.ONLINE_CA_URI,ssl_ctx=ssl_ctx)\\n #pem_out = res.read()\\n #cert = crypto.load_certificate(crypto.FILETYPE_PEM, pem_out)\\n request.session['pkey_file_path']=delegatedCert.pkey_file_path\\n request.session['certificate']=cert\\n logger.debug(\\\"Key path: %s \\\" % request.session['pkey_file_path'] )\\n #request.session['certificate']= DelegatedCertificate(cert,pem_out)\\n logger.debug(\\\"User has cert %s \\\" % request.user.certificate )\\n return HttpResponseRedirect(profile_url(request.user))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fa85274fbf928c9e5ef74f5dd765f5d\",\n \"score\": \"0.501468\",\n \"text\": \"def get_conn(self) -> GithubClient:\\n if self.client is not None:\\n return self.client\\n\\n conn = self.get_connection(self.github_conn_id)\\n access_token = conn.password\\n host = conn.host\\n\\n # Currently the only method of authenticating to GitHub in Airflow is via a token. This is not the\\n # only means available, but raising an exception to enforce this method for now.\\n # TODO: When/If other auth methods are implemented this exception should be removed/modified.\\n if not access_token:\\n raise AirflowException(\\\"An access token is required to authenticate to GitHub.\\\")\\n\\n if not host:\\n self.client = GithubClient(login_or_token=access_token)\\n else:\\n self.client = GithubClient(login_or_token=access_token, base_url=host)\\n\\n return self.client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae648e97bb3ca0b0ffae33753f8d0388\",\n \"score\": \"0.5014141\",\n \"text\": \"def test_get_request_with_wrong_token(self):\\n\\n details = \\\"Invalid link!\\\"\\n token = get_user_token(user_id=User.objects.first().id)[:-4]\\n\\n url = reverse(\\\"confirm_reset\\\", None, {token})\\n response = self.client.get(url, format=\\\"json\\\")\\n\\n assert response.status_code == status.HTTP_400_BAD_REQUEST\\n assert response.data[\\\"details\\\"] == details\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabb54181f7c08bfdcb182aba99f7953\",\n \"score\": \"0.5006312\",\n \"text\": \"def get(self, request, *args, **kwargs):\\n if not request.GET.get('state'):\\n return self.response_error(request, 'Missing state property.')\\n elif not request.GET.get('code'):\\n return self.response_error(request, 'Missing code property.')\\n\\n state = request.GET['state']\\n code = request.GET['code']\\n\\n if state != request.session.get('gapi_state'):\\n return self.response_error(request, 'Invalid state.')\\n\\n google.redirect_uri = absolute_url(request, 'accounts:google_sign_in')\\n google.scope = '' # need to reset scope to '', because otherwise we'll get trouble\\n\\n try:\\n google.fetch_token(self.URL_GET_TOKEN,\\n code=code,\\n client_secret=settings.GAPI_CLIENT_SECRET)\\n except Exception:\\n return self.response_error(request, 'An error occurred while verifying response.')\\n\\n response = google.get(self.URL_PLUS_API_PEOPLE_GET)\\n\\n try:\\n data = json.loads(response.content.decode('utf-8'))\\n except ValueError:\\n return self.response_error(request, 'Invalid response format.')\\n\\n email_address = None\\n\\n if isinstance(data, dict) and data.get('emails'):\\n for i in data['emails']:\\n if i['type'] == 'account':\\n email_address = i['value']\\n break\\n\\n if not email_address:\\n return self.response_error(request, 'No valid account-based email address found.')\\n\\n # Now check if the account exists and login\\n user = None\\n\\n try:\\n user = User.objects.get(email__iexact=email_address)\\n except User.DoesNotExist:\\n user = self.create_user(email_address)\\n\\n if not user.is_active:\\n return self.response_error(request, 'Your account is inactive.')\\n elif user.type != UserType.GOOGLE:\\n return self.response_error(request, 'Account type mismatch.')\\n else:\\n return self.login(request, user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb8db38b56223c0443cf1b2869244a5\",\n \"score\": \"0.5001218\",\n \"text\": \"def getOAuthToken():\\n oauth_token = input('Enter your OAuth Token: ')\\n return oauth_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bec94fbf13695ebb1ac9e380d9edfaa\",\n \"score\": \"0.49979693\",\n \"text\": \"def GET_request(self, url):\\n self.response = requests.request('GET', url=url, headers=self.headers, verify=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03568af1e41090f65d5e0a58b577ab7a\",\n \"score\": \"0.4994307\",\n \"text\": \"def _get(self, **kwargs) -> json:\\n url = self._prepare_url(**kwargs)\\n self.logger.debug(url)\\n result = requests.request('GET', url.url)\\n\\n if result.status_code == 200:\\n return result.json()\\n self.logger.error(f\\\"URL {url} |----| {result.content}\\\")\\n raise PyAFCommunicationError(\\\"Data was not received\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993482,"cells":{"query_id":{"kind":"string","value":"6f9db51ad2eab65a0c229c057a3eef2d"},"query":{"kind":"string","value":"Add gaussian heatmap for character bbox to the image"},"positive_passages":{"kind":"list like","value":[{"docid":"507a93ec78ea3d50b2bc339483425447","score":"0.6403565","text":"def add_character(image, bbox):\n\n\tif not Polygon(bbox.reshape([4, 2]).astype(np.int32)).is_valid:\n\t\treturn image\n\n\ttop_left = np.array([np.min(bbox[:, 0]), np.min(bbox[:, 1])]).astype(np.int32)\n\ttop_right = np.array([np.max(bbox[:, 0]), np.max(bbox[:, 1])]).astype(np.int32)\n\n\tif top_right[1] < 0 or top_right[0] < 0:\n\t\treturn image\n\tif top_left[1] > image.shape[0] or top_left[0] > image.shape[1]:\n\t\treturn image\n\tbbox -= top_left[None, :]\n\ttransformed = four_point_transform(gaussian_heatmap.copy(), bbox.astype(np.float32))\n\n\tstart_row = max(top_left[1], 0) - top_left[1]\n\tstart_col = max(top_left[0], 0) - top_left[0]\n\tend_row = min(top_left[1] + transformed.shape[0], image.shape[0])\n\tend_col = min(top_left[0] + transformed.shape[1], image.shape[1])\n\n\timage[max(top_left[1], 0):end_row, max(top_left[0], 0):end_col] += \\\n\t\ttransformed[\n\t\tstart_row:end_row - top_left[1],\n\t\tstart_col:end_col - top_left[0]]\n\n\treturn image","title":""}],"string":"[\n {\n \"docid\": \"507a93ec78ea3d50b2bc339483425447\",\n \"score\": \"0.6403565\",\n \"text\": \"def add_character(image, bbox):\\n\\n\\tif not Polygon(bbox.reshape([4, 2]).astype(np.int32)).is_valid:\\n\\t\\treturn image\\n\\n\\ttop_left = np.array([np.min(bbox[:, 0]), np.min(bbox[:, 1])]).astype(np.int32)\\n\\ttop_right = np.array([np.max(bbox[:, 0]), np.max(bbox[:, 1])]).astype(np.int32)\\n\\n\\tif top_right[1] < 0 or top_right[0] < 0:\\n\\t\\treturn image\\n\\tif top_left[1] > image.shape[0] or top_left[0] > image.shape[1]:\\n\\t\\treturn image\\n\\tbbox -= top_left[None, :]\\n\\ttransformed = four_point_transform(gaussian_heatmap.copy(), bbox.astype(np.float32))\\n\\n\\tstart_row = max(top_left[1], 0) - top_left[1]\\n\\tstart_col = max(top_left[0], 0) - top_left[0]\\n\\tend_row = min(top_left[1] + transformed.shape[0], image.shape[0])\\n\\tend_col = min(top_left[0] + transformed.shape[1], image.shape[1])\\n\\n\\timage[max(top_left[1], 0):end_row, max(top_left[0], 0):end_col] += \\\\\\n\\t\\ttransformed[\\n\\t\\tstart_row:end_row - top_left[1],\\n\\t\\tstart_col:end_col - top_left[0]]\\n\\n\\treturn image\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f243def6ba73436b53aeb3f0168fde06","score":"0.63595766","text":"def process_image(img):\n avg_heat = np.zeros_like(image[:, :, 0]).astype(np.float)\n out_img, out_boxes, heat_map = find_cars(img, scale)\n\n # -----\n heat = np.zeros_like(image[:, :, 0]).astype(np.float)\n # Add heat to each box in box list\n heat = add_heat(heat, out_boxes)\n\n # Apply threshold to help remove false positives\n heat = apply_threshold(heat, 1)\n # heat = apply_threshold(heat, 2)\n\n avg_heat = cv2.addWeighted(avg_heat, 0.8, heat, 0.2, 0.)\n\n # Apply threshold to help remove near-zero noise\n heatmap = apply_threshold(cv2.blur(avg_heat, (15, 15)), 0.5)\n\n # Visualize the heatmap when displaying\n heatmap = np.clip(heatmap, 0, 255) # limit the values in the heatmap array\n\n # -----\n # Find final boxes from heatmap using label function\n labels = label(heat_map)\n # Draw bounding boxes on a copy of the image\n output_img = draw_labeled_bboxes(np.copy(img), labels)\n\n # -----\n r = 377.0 / avg_heat.shape[1] # calculate height\n dim = (377, int(avg_heat.shape[0] * r)) # width, height\n resized = cv2.resize(avg_heat, dim, interpolation=cv2.INTER_AREA)\n # add to output_img\n output_img[0:0 + resized.shape[0], 0:0 + resized.shape[1]] = np.repeat(resized[:, :, np.newaxis], 3,\n axis=2) * 255\n output_img = cv2.putText(output_img, \"Heatmap\", (34, 34), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2,\n cv2.LINE_AA)\n\n # -----\n return output_img","title":""},{"docid":"a9140b69aa7a98a7f531328fe38959b0","score":"0.63407224","text":"def learnGauss(metricArray): \n fit = gaussReg() \n n= 100 #You should probably change this...\n overlap = 0.3 \n imageSize = [100,400]\n densMap(fit, metricArray, n, overlap, imageSize )\n overlayMap('SmallTile.jpg', 'ContourPlot.jpg') \n # Final map is saved as OverlayMap.jpg","title":""},{"docid":"952114e6abc2bbbcfbca555055f473e2","score":"0.629014","text":"def add_heat(img, bbox_list):\n heatmap = np.zeros_like(img[:,:,0]).astype(np.float)\n for box in bbox_list:\n # Assuming each \"box\" takes the form ((x1, y1), (x2, y2))\n heatmap[box[0][0]:box[0][1], box[1][0]:box[1][1]] += 1\n #heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1\n return heatmap","title":""},{"docid":"c47ebbf628fd6d7788f1c05fbaef245c","score":"0.6085818","text":"def generate_heatmap(self):\n data = []\n for j in range(len(self.generations[0])):\n row = []\n for i in range(len(self.generations)):\n row.append(self.generations[i][j].fitness)\n data.append(row)\n data = np.array(data)\n\n # Display log error in colorbar.\n tick_range = range(\n math.floor(math.log10(data.min().min())),\n 1 + math.ceil(math.log10(data.max().max())))\n cbar_ticks = [math.pow(10, i) for i in tick_range]\n log_norm = LogNorm(vmin=data.min().min(), vmax=data.max().max())\n\n plt.figure(figsize=(10, 5))\n ax = sns.heatmap(\n data,\n cmap='viridis',\n xticklabels=2,\n yticklabels=2,\n norm=log_norm,\n cbar_kws={'ticks': cbar_ticks, 'aspect': 15})\n\n hfont = {'fontname': 'Helvetica'}\n plt.xlabel('Generation', **hfont)\n plt.ylabel('Individual', **hfont)\n plt.xticks(\n [i for i in range(0, self.config.max_generations, 5)],\n [i for i in range(0, self.config.max_generations, 5)])\n plt.yticks(\n [i for i in range(0, self.config.population_size, 5)],\n [i for i in range(0, self.config.population_size, 5)])\n\n ax.invert_yaxis()\n ax.collections[0].colorbar.set_label('Error')\n ax.spines['right'].set_visible(False)\n ax.spines['top'].set_visible(False)\n plt.savefig('figures/Parameter Tuning Figure/heatmap.svg')","title":""},{"docid":"cee4d8d72785e8ba316194a9e7e0a123","score":"0.59814197","text":"def add_character_others(image, weight_map, weight_val, bbox):\n\n\tif not Polygon(bbox.reshape([4, 2]).astype(np.int32)).is_valid:\n\t\treturn image\n\n\ttop_left = np.array([np.min(bbox[:, 0]), np.min(bbox[:, 1])]).astype(np.int32)\n\tif top_left[1] > image.shape[0] or top_left[0] > image.shape[1]:\n\t\treturn image, weight_map\n\tbbox -= top_left[None, :]\n\ttransformed = four_point_transform(gaussian_heatmap.copy(), bbox.astype(np.float32))\n\n\tstart_row = max(top_left[1], 0) - top_left[1]\n\tstart_col = max(top_left[0], 0) - top_left[0]\n\tend_row = min(top_left[1] + transformed.shape[0], image.shape[0])\n\tend_col = min(top_left[0] + transformed.shape[1], image.shape[1])\n\timage[max(top_left[1], 0):end_row, max(top_left[0], 0):end_col] += \\\n\t\ttransformed[\n\t\tstart_row:end_row - top_left[1],\n\t\tstart_col:end_col - top_left[0]]\n\n\tweight_map[max(top_left[1], 0):end_row, max(top_left[0], 0):end_col] += \\\n\t\tnp.float32(transformed[\n\t\t\tstart_row:end_row - top_left[1],\n\t\t\tstart_col:end_col - top_left[0]] != 0)*weight_val\n\n\treturn image, weight_map","title":""},{"docid":"ba45dae9b86cf41dc4e87f9217ec3381","score":"0.59801453","text":"def add_heat(heatmap, bboxes, amount):\n for bbox in bboxes:\n window = heatmap[bbox[0][1]:bbox[1][1], bbox[0][0]:bbox[1][0]]\n window[window < 256 - amount] += amount\n return heatmap","title":""},{"docid":"0ef2603db3b8dbde282dd6bdbaa4326a","score":"0.5906126","text":"def generate_heatmap(height, width, landmarks, sigma=3):\n num_landmarks = landmarks.shape[0]\n hm = np.zeros((height, width, num_landmarks), dtype=np.float32)\n for i in range(num_landmarks):\n if not np.array_equal(landmarks[i], [-1, -1]):\n\n hm[:, :, i] = gaussian_k(landmarks[i][0] * height,\n landmarks[i][1] * width,\n sigma,\n height, width)\n else:\n hm[:, :, i] = np.zeros((height, width))\n\n return hm","title":""},{"docid":"9d1f30de35f51410811e5d59100b989b","score":"0.5886273","text":"def draw_heat_map(image, heat_map):\n return image","title":""},{"docid":"c6a30ab63d733f76be034cc57ce061c6","score":"0.5851643","text":"def add_heat(heatmap, bbox_list):\n # Iterate through list of bboxes\n for box in bbox_list:\n # Add += 1 for all pixels inside each bbox\n # Assuming each \"box\" takes the form ((x1, y1), (x2, y2))\n heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1\n\n # Return updated heatmap\n return heatmap # Iterate through list of bboxes","title":""},{"docid":"c830000d22e81a20333e22f42d17c049","score":"0.5835045","text":"def getHeatMap(size_w, size_h, boxes):\n kernel = np.array([[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]]).astype(np.uint8)\n heat = torch.zeros(size=(size_h, size_w))\n for box in boxes:\n sx, sy, ex, ey = box\n sx, sy, ex, ey = int(sx), int(sy), int(ex), int(ey)\n heat[sy:ey, sx:ex] += 1\n heat = cv2.dilate(np.float32(heat), kernel= kernel, iterations=1)\n heat = cv2.GaussianBlur(np.array(heat), (5, 5), sigmaX=0)\n return heat","title":""},{"docid":"82268b91df7f1d27dd09a6236db5f55b","score":"0.58092785","text":"def _gaussian_blur(heatmaps, kernel=11):\n assert kernel % 2 == 1\n border = (kernel - 1) // 2\n batch_size = heatmaps.shape[0]\n num_joints = heatmaps.shape[1]\n height = heatmaps.shape[2]\n width = heatmaps.shape[3]\n for i in range(batch_size):\n for j in range(num_joints):\n origin_max = np.max(heatmaps[i, j])\n dr = np.zeros((height + 2 * border, width + 2 * border), dtype=np.float32)\n dr[border:-border, border:-border] = heatmaps[i, j].copy()\n dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)\n heatmaps[i, j] = dr[border:-border, border:-border].copy()\n heatmaps[i, j] *= origin_max / np.max(heatmaps[i, j])\n return heatmaps","title":""},{"docid":"cb93b3dc853f0f7b657d17a9fbe4ec26","score":"0.5804239","text":"def add_heat(heatmap, bbox_list):\n for box in bbox_list:\n # Add += 1 for all pixels inside each bbox\n # Assuming each \"box\" takes the form ((x1, y1), (x2, y2))\n heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1\n\n # Return updated heatmap\n return heatmap","title":""},{"docid":"045b11eb12811396ec5ebf3f66f8565e","score":"0.5801142","text":"def _gaussian_weighted_map_const_multi(\n y_grid, x_grid, heatmap, points_y, points_x, boxes,\n gaussian_denom_ratio):\n num_instances, _ = _get_shape(boxes, 2)\n height, width, num_keypoints = _get_shape(heatmap, 3)\n\n # [height, width, num_instances, num_keypoints].\n # Note that we intentionally avoid using tf.newaxis as TfLite converter\n # doesn't like it.\n y_diff = (\n tf.reshape(y_grid, [height, width, 1, 1]) -\n tf.reshape(points_y, [1, 1, num_instances, num_keypoints]))\n x_diff = (\n tf.reshape(x_grid, [height, width, 1, 1]) -\n tf.reshape(points_x, [1, 1, num_instances, num_keypoints]))\n distance_square = y_diff * y_diff + x_diff * x_diff\n\n y_min, x_min, y_max, x_max = tf.split(boxes, 4, axis=1)\n\n # Make the mask with all 1.0 in the box regions.\n # Shape: [height, width, num_instances]\n in_boxes = tf.math.logical_and(\n tf.math.logical_and(\n tf.reshape(y_grid, [height, width, 1]) >= tf.reshape(\n y_min, [1, 1, num_instances]),\n tf.reshape(y_grid, [height, width, 1]) < tf.reshape(\n y_max, [1, 1, num_instances])),\n tf.math.logical_and(\n tf.reshape(x_grid, [height, width, 1]) >= tf.reshape(\n x_min, [1, 1, num_instances]),\n tf.reshape(x_grid, [height, width, 1]) < tf.reshape(\n x_max, [1, 1, num_instances])))\n in_boxes = tf.cast(in_boxes, dtype=tf.float32)\n\n gaussian_denom = tf.cast(\n tf.minimum(height, width), dtype=tf.float32) * gaussian_denom_ratio\n # shape: [height, width, num_instances, num_keypoints]\n gaussian_map = tf.exp((-1 * distance_square) / gaussian_denom)\n return tf.expand_dims(heatmap, axis=2) * gaussian_map * tf.reshape(\n in_boxes, [height, width, num_instances, 1])","title":""},{"docid":"527520b3d95728439eb344fea96f62e9","score":"0.5798712","text":"def generate_heatmap(self):\n data = []\n for j in range(len(self.generations[0])):\n row = []\n for i in range(len(self.generations)):\n row.append(self.generations[i][j].fitness)\n data.append(row)\n data = np.array(data)\n\n plt.figure()\n ax = sns.heatmap(\n data,\n cmap='RdBu',\n xticklabels=2,\n yticklabels=2)\n\n hfont = {'fontname': 'Helvetica'}\n plt.xlabel('Generation', **hfont)\n plt.ylabel('Individual', **hfont)\n ax.invert_yaxis()\n ax.axhline(linewidth=4, color='black')\n ax.axvline(linewidth=4, color='black')\n ax.collections[0].colorbar.set_label('Fitness')\n plt.savefig('figures/Voltage Clamp Figure/Single VC Optimization/'\n 'heatmap.svg')","title":""},{"docid":"30d3d2ed122b04a2d58839f205bd0a30","score":"0.57727194","text":"def generate_heatmap(masked_index_image,heatmap_filename):\n plt.figure(figsize = (2*7.2,12.8))\n cmap = plt.cm.RdYlGn_r\n cmap.set_bad(color='black')\n norm = cl.Normalize(vmin=0, vmax=masked_index_image.max())\n imgplot = plt.imshow(masked_index_image,cmap=cmap,norm=norm)\n plt.colorbar()\n plt.savefig(heatmap_filename,dpi=100)\n plt.close()","title":""},{"docid":"b072ec06ef0b3aa50eb88e617fda502f","score":"0.5737168","text":"def fancy_heatmap(heatmap, threshold):\n # get some dimensions of the image, size and strength to write the text in etc.\n y = int(np.ceil(heatmap.shape[0]*.1))\n x = int(np.ceil(heatmap.shape[1]*.05))\n size = int(np.ceil(heatmap.shape[0]*.002))\n strength = int(np.ceil(heatmap.shape[0]*.004))\n # Maximum value of heatmap\n m = max(heatmap.ravel())\n # Normalized threshold (color values need to be in range 0..1)\n norm_threshold = 0 if m == 0 else threshold/m\n # Red channel will contain the heatmap, but normalized\n r = normalize(heatmap, norm='max')\n # Green and blue will also contain the heatmap, but values below (normalized) threshold set to zero\n gb = np.copy(r)\n gb[gb <= norm_threshold] = 0\n # Resulting image with all three channels merged and added text\n result = cv2.merge([r, gb, gb])\n cv2.putText(result, \"Max: {:.2f}\".format(m), (x, y), cv2.FONT_HERSHEY_SIMPLEX, size, (1.,1.,1.), strength)\n return result","title":""},{"docid":"489b5eba9008b80019c9e6e5d7b48dcf","score":"0.5697161","text":"def generate_2d_guassian(height, width, y0, x0, visibility=2, sigma=1, scale=12):\n heatmap = tf.zeros((height, width))\n\n # this gaussian patch is 7x7, let's get four corners of it first\n xmin = x0 - 3 * sigma\n ymin = y0 - 3 * sigma\n xmax = x0 + 3 * sigma\n ymax = y0 + 3 * sigma\n # if the patch is out of image boundary we simply return nothing according to the source code\n # [1]\"In these cases the joint is either truncated or severely occluded, so for\n # supervision a ground truth heatmap of all zeros is provided.\"\n if xmin >= width or ymin >= height or xmax < 0 or ymax < 0 or visibility == 0:\n return heatmap\n\n size = 6 * sigma + 1\n x, y = tf.meshgrid(tf.range(0, 6 * sigma + 1, 1), tf.range(0, 6 * sigma + 1, 1), indexing='xy')\n\n # the center of the gaussian patch should be 1\n center_x = size // 2\n center_y = size // 2\n\n # generate this 7x7 gaussian patch\n gaussian_patch = tf.cast(tf.math.exp(\n -(tf.square(x - center_x) + tf.math.square(y - center_y)) / (tf.math.square(sigma) * 2)) * scale,\n dtype=tf.float32)\n\n # part of the patch could be out of the boundary, so we need to determine the valid range\n # if xmin = -2, it means the 2 left-most columns are invalid, which is max(0, -(-2)) = 2\n patch_xmin = tf.math.maximum(0, -xmin)\n patch_ymin = tf.math.maximum(0, -ymin)\n # if xmin = 59, xmax = 66, but our output is 64x64, then we should discard 2 right-most columns\n # which is min(64, 66) - 59 = 5, and column 6 and 7 are discarded\n patch_xmax = tf.math.minimum(xmax, width) - xmin\n patch_ymax = tf.math.minimum(ymax, height) - ymin\n\n # also, we need to determine where to put this patch in the whole heatmap\n heatmap_xmin = tf.math.maximum(0, xmin)\n heatmap_ymin = tf.math.maximum(0, ymin)\n heatmap_xmax = tf.math.minimum(xmax, width)\n heatmap_ymax = tf.math.minimum(ymax, height)\n\n # finally, insert this patch into the heatmap\n indices = tf.TensorArray(tf.int32, 1, dynamic_size=True)\n updates = tf.TensorArray(tf.float32, 1, dynamic_size=True)\n\n count = 0\n\n for j in tf.range(patch_ymin, patch_ymax):\n for i in tf.range(patch_xmin, patch_xmax):\n indices = indices.write(count, [heatmap_ymin + j, heatmap_xmin + i])\n updates = updates.write(count, gaussian_patch[j][i])\n count += 1\n\n heatmap = tf.tensor_scatter_nd_update(heatmap, indices.stack(), updates.stack())\n\n # unfortunately, the code below doesn't work because\n # tensorflow.python.framework.ops.EagerTensor' object does not support item assignment\n # heatmap[heatmap_ymin:heatmap_ymax, heatmap_xmin:heatmap_xmax] = gaussian_patch[patch_ymin:patch_ymax,patch_xmin:patch_xmax]\n\n return heatmap","title":""},{"docid":"afb96ceaa06b60c8818417f4ba7ea769","score":"0.5645221","text":"def find_cars(img, scale):\n img_boxes = [] # Clears img_boxes so we don't keep unwanted heatmap history\n count = 0\n draw_img = np.copy(img)\n\n # Make a heatmap of zeros\n heatmap = np.zeros_like(img[:, :, 0])\n\n # IMPORTANT : reading *.jpeg's (scaled 0-255, aka scaling needed), but\n # # trained on *.png's (scaled 0-1, aka scaling not needed)\n if img.dtype == 'uint8':\n img = img.astype(np.float32) / 255 # aka scaling needed\n\n img_tosearch = img[ystart:ystop, :, :]\n ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')\n\n if scale != 1: # resize whole image instead of separate windows\n imshape = ctrans_tosearch.shape\n ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1] / scale), np.int(imshape[0] / scale)))\n\n ch1 = ctrans_tosearch[:, :, 0]\n ch2 = ctrans_tosearch[:, :, 1]\n ch3 = ctrans_tosearch[:, :, 2]\n\n # Define blocks and steps as above\n # These hold the number of HOG cells\n nxblocks = (ch1.shape[1] // pix_per_cell) - 1 # Note : '//' causes integers to be result, instead of floats\n nyblocks = (ch1.shape[0] // pix_per_cell) - 1\n # How many features per block are we going to be extracting\n nfeat_per_block = orient * cell_per_block ** 2\n window = 64\n nblocks_per_window = (window // pix_per_cell) - 1\n # aka 75% overlap between cells\n cells_per_step = 2 # Instead of overlap, define how many cells to step\n nxsteps = (nxblocks - nblocks_per_window) // cells_per_step\n nysteps = (nyblocks - nblocks_per_window) // cells_per_step\n\n # Compute individual channel HOG features for the entire image\n hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\n hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)\n hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)\n\n for xb in range(nxsteps):\n for yb in range(nysteps):\n count += 1\n ypos = yb * cells_per_step\n xpos = xb * cells_per_step\n # Extract HOG for this patch\n hog_feat1 = hog1[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\n hog_feat2 = hog2[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\n hog_feat3 = hog3[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\n hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\n\n xleft = xpos * pix_per_cell\n ytop = ypos * pix_per_cell\n\n # Extract the image patch\n subimg = cv2.resize(ctrans_tosearch[ytop:ytop + window, xleft:xleft + window], (64, 64))\n\n # Get colour features\n spatial_features = bin_spatial(subimg, size=spatial_size)\n hist_features = color_hist(subimg, nbins=hist_bins)\n\n # Scale features and make a prediction\n test_features = X_scaler.transform(\n np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))\n test_prediction = svc.predict((test_features))\n\n if test_prediction == 1:\n xbox_left = np.int(xleft * scale)\n ytop_draw = np.int(ytop * scale)\n win_draw = np.int(window * scale)\n cv2.rectangle(draw_img, (xbox_left, ytop_draw + ystart),\n (xbox_left + win_draw, ytop_draw + win_draw + ystart), (0, 0, 255))\n img_boxes.append(\n ((xbox_left, ytop_draw + ystart), (xbox_left + win_draw, ytop_draw + win_draw + ystart)))\n heatmap[ytop_draw + ystart:ytop_draw + win_draw + ystart, xbox_left:xbox_left + win_draw] += 1\n\n return draw_img, img_boxes, heatmap","title":""},{"docid":"5b8291a766e94e155a923290efacbd87","score":"0.5586054","text":"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n plt.title('epsilon')\n plt.ylabel('gamma')\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)\n ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"9256a6340d01503dedc6f182c2ee569c","score":"0.5572046","text":"def fit_gaussian2d(image):\n\n # Estimate center of target\n y_mean, x_mean = np.array(image.shape) // 2 # Center guess\n\n # Create model to fit\n model = models.Gaussian2D(amplitude=image.max(),\n x_mean=x_mean,\n y_mean=y_mean,\n fixed={}\n )\n\n # Fit model to grid\n fitted_model, fit = fit_model(image, model)\n\n return fitted_model","title":""},{"docid":"bb7e3c192d3f6ad425c8c72436c5f265","score":"0.5538218","text":"def visualize_heatmap_activation(model, test_img):\n preds = model.predict(test_img)\n\n # Setting up the Grad-CAM algorithm\n img_output = model.output[:, np.argmax(preds[0])]\n last_conv_layer = model.get_layer('conv2d_3')\n grads = backend.gradients(img_output, last_conv_layer.output)[0]\n pooled_grads = backend.mean(grads, axis=(0, 1, 2))\n\n iterate = backend.function([model.input], [pooled_grads, last_conv_layer.output[0]])\n pooled_grads_value, conv_layer_output_value = iterate([test_img])\n\n for i in range(len(pooled_grads_value)):\n conv_layer_output_value[:, :, i] *= pooled_grads_value[i]\n\n heatmap = np.mean(conv_layer_output_value, axis=-1)\n\n # Heatmap post-processing\n heatmap = np.maximum(heatmap, 0)\n heatmap /= np.max(heatmap)\n\n # Display the test image alongside the heatmap\n plt.pcolormesh(test_img[0, :, :, 0])\n plt.figure()\n plt.pcolormesh(heatmap, cmap=\"magma\")\n plt.show()","title":""},{"docid":"00d7479a009b11cd91cc27c94b00998f","score":"0.55210364","text":"def _make_gaussian_maps(x, gaussians, size=None, scaling=6.):\n if size is None:\n size = x.shape[-2:]\n bs = x.shape[0]\n else:\n size = [size] * 2\n bs = 1\n dtype = x.dtype\n device = x.device\n\n gaussian_maps = []\n map_template = torch.ones(*size, dtype=dtype, device=device)\n meshgrids = torch.meshgrid(\n [torch.linspace(0, 1, size[0], dtype=dtype, device=device),\n torch.linspace(0, 1, size[1], dtype=dtype, device=device),])\n\n for gaussian_idx, yx_mu_logstd in enumerate(torch.unbind(gaussians)):\n map = map_template.clone()\n for mu_logstd, mgrid in zip(yx_mu_logstd, meshgrids):\n mu = mu_logstd[0]\n std = torch.exp(mu_logstd[1])\n map *= torch.exp(-((mgrid - mu) / std) ** 2 / 2)\n\n map *= scaling\n gaussian_maps.append(map)\n\n gaussian_maps = torch.stack(gaussian_maps)\n gaussian_maps = gaussian_maps.unsqueeze(0).expand(bs, -1, -1, -1)\n return gaussian_maps","title":""},{"docid":"a2973662162cb8121f4e4e7cbdf27b55","score":"0.55196005","text":"def add_heat(heatmap, bbox_list):\n for box in bbox_list:\n\n # Assuming each \"box\" takes the form ((x1, y1), (x2, y2))\n heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1\n\n return heatmap","title":""},{"docid":"8bb65a49638361c188b3be3f571259d5","score":"0.55166715","text":"def create_heatmap(num_maps, height, width, all_joints, sigma, stride):\n heatmap = np.zeros((height, width, num_maps), dtype=np.float64)\n\n for joints in all_joints:\n for plane_idx, joint in enumerate(joints):\n if joint:\n _put_heatmap_on_plane(heatmap, plane_idx, joint, sigma, height, width, stride)\n\n # background\n heatmap[:, :, -1] = np.clip(1.0 - np.amax(heatmap, axis=2), 0.0, 1.0)\n\n return heatmap","title":""},{"docid":"07c587c4fb31a735b55ddf5104fcf011","score":"0.54902524","text":"def add_1d_heatmaps(self,\n maps: dict,\n background: np.ndarray,\n map2d_size: Union[tuple, list],\n K: int,\n interval: int = 10):\n y_startpoint, x_startpoint = [int(1.1*map2d_size[1]),\n int(0.1*map2d_size[0])],\\\n [int(0.1*map2d_size[1]),\n int(1.1*map2d_size[0])]\n x_startpoint[1] += interval * 2\n y_startpoint[0] += interval * 2\n add = interval + 10\n for i in range(K):\n self.image_cover(background, maps['x'][i], x_startpoint[0],\n x_startpoint[1])\n cv.putText(background, str(i),\n (x_startpoint[0] - 30, x_startpoint[1] + 10),\n cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)\n self.image_cover(background, maps['y'][i], y_startpoint[0],\n y_startpoint[1])\n cv.putText(background, str(i),\n (y_startpoint[0], y_startpoint[1] - 5),\n cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)\n x_startpoint[1] += add\n y_startpoint[0] += add\n return background[:x_startpoint[1] + y_startpoint[1] +\n 1, :y_startpoint[0] + x_startpoint[0] + 1]","title":""},{"docid":"9d7dabf0a71c1ac94db05462ae1ef82c","score":"0.54839283","text":"def make_gaussian_label_image(mu_x, mu_y, num_x_px, num_y_px, var=0.05):\n x = np.linspace(0, 1, num_x_px)\n y = np.linspace(0, 1, num_y_px)\n xs, ys = np.meshgrid(x, y)\n locs = np.concatenate([xs[:, :, np.newaxis], ys[:, :, np.newaxis]], axis=2)\n locs_reshaped = locs.reshape(np.prod(locs.shape[:-1]), locs.shape[-1])\n\n rv = multivariate_normal([mu_x, mu_y], [[var, 0], [0, var]])\n return rv.pdf(locs_reshaped).reshape(locs.shape[:-1])","title":""},{"docid":"08ed450750248f0963fa8c9d05ea2310","score":"0.5466378","text":"def gaussian_proba_map(img):\n method = 'cv2.TM_CCOEFF_NORMED'\n sigmas = [41,31,21,11]\n out = np.zeros(img.shape)\n for sigma in sigmas:\n size=3*sigma\n template = gaussian(size,sigma)\n template/=template.max()\n template*=255\n template = template.astype(np.uint8)\n \n img2 = img.copy()\n meth = eval(method)\n # Apply template Matching\n res = cv2.matchTemplate(img2,template,meth)\n res = np.pad(res,size/2,mode='constant')\n to_replace = res>out\n out[to_replace] = res[to_replace]\n return out","title":""},{"docid":"6f81771501de9795414ad3e296002f42","score":"0.5454012","text":"def create_heat_map(self, ax=None, block=True):\n plt.figure()\n ax = sns.heatmap(data=self.data, fmt=\"\", cmap='RdYlGn', linewidths=0.3, ax=ax)\n ax.invert_yaxis()\n ax.set(xlabel='Books index', ylabel='Books values over iterations', title='Heat map for the prediction result'\n ' of each book over iterations')\n\n # plt.show(block=block)","title":""},{"docid":"65cd6f6bdcb772f058737d6f0b7668d7","score":"0.54503685","text":"def heatmap(args):\n p = OptionParser(heatmap.__doc__)\n p.add_option(\"--stacks\",\n default=\"Exons,Introns,DNA_transposons,Retrotransposons\",\n help=\"Features to plot in stackplot [default: %default]\")\n p.add_option(\"--heatmaps\",\n default=\"Copia,Gypsy,hAT,Helitron,Introns,Exons\",\n help=\"Features to plot in heatmaps [default: %default]\")\n p.add_option(\"--meres\", default=None,\n help=\"Extra centromere / telomere features [default: %default]\")\n add_window_options(p)\n opts, args, iopts = p.set_image_options(args, figsize=\"8x5\")\n\n if len(args) != 2:\n sys.exit(not p.print_help())\n\n fastafile, chr = args\n window, shift, subtract = check_window_options(opts)\n\n stacks = opts.stacks.split(\",\")\n heatmaps = opts.heatmaps.split(\",\")\n stackbeds = get_beds(stacks)\n heatmapbeds = get_beds(heatmaps)\n stackbins = get_binfiles(stackbeds, fastafile, shift, subtract=subtract)\n heatmapbins = get_binfiles(heatmapbeds, fastafile, shift, subtract=subtract)\n\n margin = .06\n inner = .015\n clen = Sizes(fastafile).mapping[chr]\n\n fig = plt.figure(1, (iopts.w, iopts.h))\n root = fig.add_axes([0, 0, 1, 1])\n\n # Gauge\n ratio = draw_gauge(root, margin, clen, rightmargin=4 * margin)\n yinterval = .3\n xx = margin\n yy = 1 - margin\n yy -= yinterval\n xlen = clen / ratio\n cc = chr\n if \"_\" in chr:\n ca, cb = chr.split(\"_\")\n cc = ca[0].upper() + cb\n\n root.add_patch(Rectangle((xx, yy), xlen, yinterval - inner, color=gray))\n ax = fig.add_axes([xx, yy, xlen, yinterval - inner])\n\n nbins = get_nbins(clen, shift)\n\n owindow = clen / 100\n if owindow > window:\n window = owindow / shift * shift\n\n stackplot(ax, stackbins, nbins, palette, chr, window, shift)\n ax.text(.1, .9, cc, va=\"top\", zorder=100, transform=ax.transAxes,\n bbox=dict(boxstyle=\"round\", fc=\"w\", alpha=.5))\n\n # Legends\n xx += xlen + .01\n yspace = (yinterval - inner) / (len(stackbins) + 1)\n yy = 1 - margin - yinterval\n for s, p in zip(stacks, palette):\n s = s.replace(\"_\", \" \")\n s = Registration.get(s, s)\n\n yy += yspace\n root.add_patch(Rectangle((xx, yy), inner, inner, color=p, lw=0))\n root.text(xx + 1.5 * inner, yy, s, size=10)\n\n yh = .05 # Heatmap height\n # Heatmaps\n xx = margin\n yy = 1 - margin - yinterval - inner\n for s, p in zip(heatmaps, heatmapbins):\n s = s.replace(\"_\", \" \")\n s = Registration.get(s, s)\n\n yy -= yh\n m = stackarray(p, chr, window, shift)\n\n Y = np.array([m, m])\n root.imshow(Y, extent=(xx, xx + xlen, yy, yy + yh - inner),\n interpolation=\"nearest\", aspect=\"auto\")\n root.text(xx + xlen + .01, yy, s, size=10)\n\n yy -= yh\n\n meres = opts.meres\n if meres:\n bed = Bed(meres)\n for b in bed:\n if b.seqid != chr:\n continue\n pos = (b.start + b.end) / 2\n cpos = pos / ratio\n xx = margin + cpos\n accn = b.accn.capitalize()\n root.add_patch(CirclePolygon((xx, yy), radius=.01, fc=\"m\", ec=\"m\"))\n root.text(xx + .014, yy, accn, va=\"center\", color=\"m\")\n\n root.set_xlim(0, 1)\n root.set_ylim(0, 1)\n root.set_axis_off()\n\n image_name = chr + \".\" + iopts.format\n savefig(image_name, dpi=iopts.dpi, iopts=iopts)","title":""},{"docid":"2b545016a27d205133e3e90546ecc147","score":"0.5447263","text":"def generateHeatMask( im ):\n featref = computeFeatures(im)\n h,w,nbp = im.shape\n print(\"w:%d,h:%d\"%(w,h))\n heatMap = np.zeros((h,w,1), dtype=np.int8)\n black = [0,0,0]\n white = [255,255,255]\n rMaxDiff = 0\n for j in range(h):\n print(\"j:%d\" % j )\n for i in range(w):\n #~ print(\"i:%d\" % i )\n arDiff = []\n for color in [black,white]:\n #~ print(\"color:%s\" % color)\n imt = np.copy(im)\n \n # quite same time on my tablet !!! \n # (would have think the [] would be far fastest!)\n if 0:\n cv2.circle(imt, (i,j), 1, color )\n else:\n imt[j,i]=color\n if 0:\n cv2.imshow(\"imt\",imt)\n cv2.waitKey(1)\n #~ feat = computeFeatures(imt)\n #~ rDiff = diffFeatures(featref,feat)\n rDiff = mseFloat(im,imt)\n arDiff.append(rDiff)\n #~ print(rDiff)\n rDiff = max(arDiff)\n if rDiff > rMaxDiff:\n rMaxDiff = rDiff\n heatMap[j,i] = rDiff*10\n print(\"rMaxDiff: %5.3f\" % rMaxDiff )\n #~ print(dir(cv2))\n #~ heatMap = cv2.resize(heatMap,(w*2,h*2))\n cv2.namedWindow(\"heat\",cv2.CV_WINDOW_AUTOSIZE|cv2.WINDOW_NORMAL)\n cv2.imshow(\"heat\",heatMap)\n cv2.resizeWindow(\"heat\",600,480)\n cv2.waitKey(0)","title":""},{"docid":"d1ea515f2a909fe0027da3d4fcabbf1f","score":"0.542023","text":"def heatmap(mat, x_label=None, y_label=None, axes=None,\n title=None, save=False):\n sns.heatmap(mat)\n plt.show()","title":""},{"docid":"49df70c1282bafe379f4145ad7b9c40e","score":"0.5408016","text":"def find_cars(img,\n clf,\n scaler,\n color_space,\n spatial_size,\n hist_bins,\n scale,\n cells_per_step,\n x_start_stop,\n y_start_stop,\n orient,\n pix_per_cell,\n cell_per_block):\n draw_img = np.copy(img)\n\n heatmap = np.zeros_like(img[:, :, 0])\n\n img = img.astype(np.float32)/255\n\n img_to_search = img[y_start_stop[0]:y_start_stop[1], x_start_stop[0]:x_start_stop[1], :]\n\n # color transformed image\n ctrans_to_search = change_color_space(img_to_search, colorspace=color_space)\n\n if scale != 1:\n imshape = ctrans_to_search.shape\n ctrans_to_search = cv2.resize(ctrans_to_search, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))\n\n ch1 = ctrans_to_search[:, :, 0]\n ch2 = ctrans_to_search[:, :, 1]\n ch3 = ctrans_to_search[:, :, 2]\n\n nxblocks = (ch1.shape[1] // pix_per_cell) - 1 # number of hog cells\n nyblocks = (ch1.shape[0] // pix_per_cell) - 1\n nfeat_per_block = orient*cell_per_block**2\n window = 64\n nblocks_per_window = (window // pix_per_cell) - 1\n\n nxsteps = (nxblocks - nblocks_per_window) // cells_per_step\n nysteps = (nyblocks - nblocks_per_window) // cells_per_step\n\n # compute individual channel HOG features for the intire image\n hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\n hog2 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\n hog3 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\n\n for xb in range(nxsteps):\n for yb in range(nysteps):\n ypos = yb*cells_per_step\n xpos = xb*cells_per_step\n # extract hog features for this patch\n hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()\n hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()\n hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()\n hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\n\n xleft = xpos*pix_per_cell\n ytop = ypos*pix_per_cell\n\n # extract the image path\n subimg = cv2.resize(ctrans_to_search[ytop:ytop+window, xleft:xleft+window], (64,64))\n\n # get color features\n spatial_features = get_bin_spatial(subimg, size=spatial_size)\n hist_features = get_color_hist(subimg, nbins=hist_bins)\n\n # scale features and make prediction\n test_features = scaler.transform(np.hstack((spatial_features, hist_features, hog_features)))\n\n test_prediction = clf.predict(test_features)\n\n if test_prediction == 1:\n xbox_left = np.int(xleft*scale)\n ytop_draw = np.int(ytop*scale)\n win_draw = np.int(window*scale)\n cv2.rectangle(draw_img, (xbox_left+x_start_stop[0], ytop_draw+y_start_stop[0]), (xbox_left+win_draw+x_start_stop[0], ytop_draw+win_draw+y_start_stop[0]), (0,0,255), 6)\n heatmap[ytop_draw+y_start_stop[0]:ytop_draw+win_draw+y_start_stop[0], xbox_left+x_start_stop[0]:xbox_left+win_draw+x_start_stop[0]] += 1\n\n return draw_img, heatmap","title":""},{"docid":"adcffd6c5cc1b2376463c6852f50f883","score":"0.5385292","text":"def gaussExpand(img: np.ndarray, gs_k: np.ndarray) -> np.ndarray:\r\n gs_k = (gs_k / gs_k.sum()) * 4\r\n if img.ndim == 3:\r\n h, w, d = img.shape[:3]\r\n newImg = np.zeros((2 * h, 2 * w, d))\r\n else:\r\n h, w = img.shape[:2]\r\n newImg = np.zeros((2 * h, 2 * w))\r\n newImg[::2, ::2] = img\r\n image = cv2.filter2D(newImg, -1, gs_k, cv2.BORDER_REPLICATE)\r\n return image","title":""},{"docid":"0d9b9fba0544b38e74f62691507b0978","score":"0.53726906","text":"def add_heat(heatmap, bbox_list, heatmap_history):\n # Iterate through list of bboxes\n for bounding_box in bbox_list:\n box = bounding_box.box\n # Add += 1 for all pixels inside each bbox\n # Assuming each \"box\" takes the form ((x1, y1), (x2, y2))\n prob = np.full_like(heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]], bounding_box.probability, dtype=np.float)\n heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] = np.maximum(heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]],\n prob)\n\n total_heatmap = heatmap.copy()\n for idx in range(len(heatmap_history)):\n total_heatmap = np.add(total_heatmap, heatmap_history[idx])\n # Return updated heatmap\n return total_heatmap, heatmap","title":""},{"docid":"a7e5b56ad9af517c0b6548d5bec138b0","score":"0.53710204","text":"def make_lungmask(img, display=False):\n row_size= img.shape[0]\n col_size = img.shape[1]\n \n mean = np.mean(img)\n std = np.std(img)\n img = img-mean\n img = img/std\n\n # uses hounsfield values near lungs to normalize images\n\n middle = img[int(col_size/5):int(col_size/5*4),int(row_size/5):int(row_size/5*4)] \n mean = np.mean(middle) \n max = np.max(img)\n min = np.min(img)\n img[img==max]=mean\n img[img==min]=mean\n \n # uses kmeans to separate foreground (soft tissue / bone) and background (lung/air)\n\n kmeans = KMeans(n_clusters=2).fit(np.reshape(middle,[np.prod(middle.shape),1]))\n centers = sorted(kmeans.cluster_centers_.flatten())\n threshold = np.mean(centers)\n thresh_img = np.where(img<threshold,1.0,0.0)\n\n # performs erosion and dilation\n\n eroded = morphology.erosion(thresh_img,np.ones([3,3]))\n dilation = morphology.dilation(eroded,np.ones([8,8]))\n\n labels = measure.label(dilation) # Different labels are displayed in different colors\n label_vals = np.unique(labels)\n regions = measure.regionprops(labels)\n good_labels = []\n for prop in regions:\n B = prop.bbox\n if B[2]-B[0]<row_size/10*9 and B[3]-B[1]<col_size/10*9 and B[0]>row_size/5 and B[2]<col_size/5*4:\n good_labels.append(prop.label)\n mask = np.ndarray([row_size,col_size],dtype=np.int8)\n mask[:] = 0\n\n # makes mask\n\n for N in good_labels:\n mask = mask + np.where(labels==N,1,0)\n mask = morphology.dilation(mask,np.ones([10,10])) # one last dilation\n final = mask * img\n \n # shows and saves output\n\n plt.imshow(final)\n im = Image.fromarray(final*128)\n im = im.convert(\"L\")\n im.save(S)\n \n return","title":""},{"docid":"8f505d555f82f16231df018f7a595583","score":"0.5363443","text":"def scan_single_win_size(image, clf, rescale, hyperparams, box_color=(0,0,1.0), heatmap=None):\n # get important hyperparams\n y_start = hyperparams['Y_START']\n y_stop = hyperparams['Y_STOP']\n color_space = hyperparams['COLOR_SPACE']\n spatial_feat = hyperparams['SPATIAL_FEAT']\n spatial_size = hyperparams['SPATIAL_SIZE']\n hist_feat = hyperparams['HIST_FEAT']\n hist_bin = hyperparams['HIST_BIN']\n hist_range = hyperparams['HIST_RANGE']\n hog_orient = hyperparams['HOG_ORIENT']\n hog_cell_per_block = hyperparams['HOG_CELL_PER_BLOCK']\n hog_pix_per_cell = hyperparams['HOG_PIX_PER_CELL']\n hog_feat = hyperparams['HOG_FEAT']\n hog_channel = hyperparams['HOG_CHANNEL']\n # if heatmap was not provided, initialize it\n if heatmap is None:\n heatmap = np.zeros(image.shape[0:2])\n # copy image for visualization purposes\n draw_image = np.zeros_like(image)\n # image to be analyzed is color-converted to target color space and resized to match\n # appropriately rescaled window size\n rescaled_image = convert_color(image[y_start:y_stop, :, :], color_space)\n if rescale != 1:\n imshape = image.shape\n rescaled_image = cv2.resize(rescaled_image, (np.int(imshape[1] / rescale), np.int(imshape[0] / rescale)))\n # Define number of blocks\n n_x_blocks = (rescaled_image.shape[1] // hog_pix_per_cell) - hog_cell_per_block + 1\n n_y_blocks = (rescaled_image.shape[0] // hog_pix_per_cell) - hog_cell_per_block + 1\n window = 64 # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell\n blocks_per_window = (window // hog_pix_per_cell) - hog_cell_per_block + 1\n cells_per_step = 2 # Instead of overlap, define how many cells to step\n n_x_steps = (n_x_blocks - blocks_per_window) // cells_per_step\n n_y_steps = (n_y_blocks - blocks_per_window) // cells_per_step\n # Prepare the hog features\n hogs = []\n if hog_feat:\n for channel in range(0, rescaled_image.shape[2]):\n hogs.append(get_hog_features(rescaled_image[:, :, channel],\n hog_orient, hog_pix_per_cell, hog_cell_per_block, feature_vec=False))\n # Go through blocks of image step by step\n for x_window in range(n_x_steps):\n for y_window in range(n_y_steps):\n y_pos = y_window * cells_per_step\n x_pos = x_window * cells_per_step\n x_left = x_pos * hog_pix_per_cell\n y_top = y_pos * hog_pix_per_cell\n sub_image = rescaled_image[y_top:y_top + window, x_left:x_left + window]\n # Extract HOG for this patch\n features = []\n if spatial_feat:\n features.append(get_spatial_features(sub_image, size=spatial_size))\n if hist_feat:\n features.append(get_hist_features(sub_image, nbins=hist_bin, bins_range=hist_range)[4])\n if hog_feat:\n if hog_channel == \"ALL\":\n for h in hogs:\n features.append(h[y_pos:y_pos+blocks_per_window, x_pos:x_pos+blocks_per_window].ravel())\n else:\n features.append(hogs[hog_channel][y_pos:y_pos+blocks_per_window, x_pos:x_pos+blocks_per_window].ravel())\n # Scale features and make a prediction\n test_features = np.hstack(features).reshape(1, -1)\n test_prediction = clf.predict(test_features)\n if test_prediction == 1:\n # If prediction is true, re-calculate sliding window position\n x_box_left = np.int(x_left * rescale)\n y_box_top = np.int(y_top * rescale)\n box_size = np.int(window * rescale)\n # if box color was set, draw box on image\n if box_color is not None:\n cv2.rectangle(draw_image, (x_box_left, y_box_top + y_start),\n (x_box_left + box_size, y_box_top + box_size + y_start), box_color, 6)\n # add heat to heatmap\n heatmap[y_box_top+y_start:y_box_top+box_size+y_start, x_box_left:x_box_left+box_size] += \\\n clf.decision_function(test_features)\n return draw_image, heatmap","title":""},{"docid":"fbf4a311e3a4f57f07111b2c41c5fc4d","score":"0.53324604","text":"def fake_gaussian(img, vertical_horizontal_sigma, iter=3):\n sigma_vertical, sigma_horizontal = vertical_horizontal_sigma\n h_blured = box_filter1d(img, sigma_horizontal, horizontal=True, iter=iter)\n blured = box_filter1d(h_blured, sigma_vertical, horizontal=False, iter=iter)\n return blured","title":""},{"docid":"cdd64389a4a90162027c9cd24685d3be","score":"0.5294273","text":"def find_gaussian(img,sigma=25):\n method = 'cv2.TM_CCOEFF_NORMED'\n size=3*sigma\n template = gaussian(size,sigma)\n template/=template.max()\n template*=255\n template = template.astype(np.uint8)\n \n threshold = 0.9\n w, h = template.shape[::-1]\n \n img2 = img.copy()\n meth = eval(method)\n\n # Apply template Matching\n res = cv2.matchTemplate(img2,template,meth)\n #Filters location map so that only one gaussian is found per contiguous location\n location_map = res >= threshold*np.max(res)\n location_map,nr = ndi.label(location_map)\n list_x = []\n list_y = []\n for label in range(1,nr+1):\n tmp=location_map==label\n if np.count_nonzero(tmp)>1:\n points = np.where(tmp)\n l = len(points[0])\n cx = (np.sum(points[0]) + l/2)/l\n cy = (np.sum(points[1]) + l/2 )/l\n list_x.append(cx)\n list_y.append(cy)\n loc= (np.asarray(list_x),np.asarray(list_y))\n stack_to_remove = np.zeros((size,size,len(loc[0])))\n i=0\n for pt in zip(*loc[::-1]):\n cv2.rectangle(img2, pt, (pt[0] + w, pt[1] + h), 255, 2)\n stack_to_remove[:,:,i] = img[pt[1]:pt[1]+w,pt[0]:pt[0]+h]\n i+=1\n return stack_to_remove,loc","title":""},{"docid":"992d4d156b6cffc376794bb4a677c00d","score":"0.5263325","text":"def gaussExpand(img: np.ndarray, gs_k: np.ndarray) -> np.ndarray:\r\n if img.ndim == 3:\r\n expanded = np.zeros((2 * img.shape[0], 2 * img.shape[1], img.shape[2]))\r\n else:\r\n expanded = np.zeros((2 * img.shape[0], 2 * img.shape[1]))\r\n expanded[::2, ::2] = img\r\n expanded = cv2.filter2D(expanded, -1, gs_k, borderType=cv2.BORDER_REPLICATE)\r\n return expanded","title":""},{"docid":"38627cee9501498f883c59775c3aafc2","score":"0.5245353","text":"def generate_heatmap_extract(self, bbox):\n return heatmaps[0].extract(bbox) # full extact","title":""},{"docid":"d69bca198eabf5d296386f3517e37829","score":"0.524353","text":"def superpixel_plot(im,seg,title = \"Superpixels\"):\n clust = np.unique(seg)\n mapper_dict = {i: im[seg == i].mean(axis = 0)/255. for i in clust}\n\n seg_img = np.zeros((seg.shape[0],seg.shape[1],3))\n for i in clust:\n seg_img[seg == i] = mapper_dict[i]\n \n plot_image(seg_img,title)\n \n return","title":""},{"docid":"be4b8c288187d8008e121f493629e9c9","score":"0.52330476","text":"def heatmap(shred, bin_type, save_output = True):\n # format dataframes \n df_subsetDEG = shred.shred_modules_df_2.copy() # load the gene module annotation dataframe\n\n if bin_type == \"bin\":\n df_heatmap = shred.heatmap_matrix.copy() # load heatmap matrix\n df_bin_meta = shred.bin_metadata.loc[df_heatmap.columns, :].copy() # order the bin metadata\n elif bin_type == \"superbin\":\n df_heatmap = shred.heatmap_matrix_super.copy()\n df_bin_meta = shred.superbin_metadata.loc[df_heatmap.columns, :].copy() # order the bin metadata\n else:\n raise Exception(\"bin_type should be either bin or superbin.\")\n\n n_genes = df_heatmap.shape[0]\n n_bins = df_heatmap.shape[1]\n n_modules = len(np.unique(df_subsetDEG[\"Status\"]))\n\n # initialize\n fig = plt.figure(figsize = (15, 15))\n gs = GridSpec(nrows = 3, ncols = 4, height_ratios=(0.10,0.05, 0.85), width_ratios = (0.70, 0.04, 0.07, 0.19))\n gs.update(wspace = 0.025, hspace = 0.025)\n\n #####----- bin metadata barplot ------#####\n ax0 = fig.add_subplot(gs[0,0])\n columns = []\n labels_for_legend = [] #initialize for legends\n colors_for_legend = []\n for column in df_bin_meta.columns:\n if column != \"n_cells\":\n columns.append(column)\n df_size = pd.DataFrame(df_bin_meta.groupby(columns).size())\n df_size.rename({0: \"n_cells\"}, axis = 1, inplace = True)\n labels = [i[-1] if type(i) == tuple else i for i in df_size.index.values]\n labels_for_legend += labels\n \n color_dict = dict(zip(labels, cycler_colors[:df_size.shape[0]])) # avoid different colors for duplicate keys\n count_aggregrated = 0\n for idx, label in enumerate(labels):\n count = df_size.iloc[idx, 0]\n ax0.barh(y = column, width = count, height = 1, left = count_aggregrated, color = color_dict[label])\n ax0.set_xticklabels(\"\")\n ax0.set_xticks([])\n count_aggregrated += count\n\n # legend initialization\n colors = [matplotlib.colors.rgb2hex(ax0.patches[i].get_facecolor()) for i in range(len(ax0.patches))]\n handles = [plt.Rectangle((0,0),1,1, color = i) for i in colors]\n labels_unique = []\n handles_unique = []\n\n # remove duplicates\n for k in range(len(labels_for_legend)):\n if not labels_for_legend[k] in labels_unique:\n labels_unique.append(labels_for_legend[k])\n handles_unique.append(handles[k])\n\n ax0_1eg = fig.add_subplot(gs[0,3])\n leg1 = ax0_1eg.legend(handles_unique, labels_unique, prop = {'size': 10}, \n loc = 'upper left', title = \"bin metadata\", ncol = 5)\n leg1._legend_box.aligh = \"left\"\n ax0_1eg.add_artist(leg1)\n ax0_1eg.axis('off')\n\n # other formats\n ax0.set_xlim(0, n_bins)\n ax0.spines['left'].set_visible(False)\n ax0.spines['right'].set_visible(False) \n ax0.spines['top'].set_visible(False) \n ax0.spines['bottom'].set_visible(False) \n\n #####----- n_cell histograms ------#####\n counts = df_bin_meta[\"n_cells\"]\n ax1 = fig.add_subplot(gs[1,0])\n ax1.bar(x = list(range(n_bins)), height = counts, width = 1)\n ax1.set_xticklabels(\"\")\n ax1.set_xticks([])\n ax1.set_xlim(0, n_bins)\n ax1.set_ylabel(\"n_cells\", rotation = 0, labelpad = 24)\n\n #####----- gene module barplots ------#####\n ax2 = fig.add_subplot(gs[2,2])\n columns = [\"Status\", \"reference_group\",\"shred_plan\"]\n labels_for_legend = []\n\n for column in columns:\n count_aggregrated = 0\n labels = np.unique(df_subsetDEG[column])\n labels_for_legend += list(labels)\n label_counts = dict(df_subsetDEG[column].value_counts())\n for label in labels:\n count = label_counts[label]\n ax2.bar(x = column, height = count, width = 1, bottom = count_aggregrated)\n ax2.set_yticklabels(\"\")\n ax2.set_yticks([])\n count_aggregrated += count\n ax2.set_xticklabels(columns, rotation = 35, ha = \"right\")\n ax2.set_ylim(0, n_genes)\n ax2.spines['left'].set_visible(False) \n ax2.spines['right'].set_visible(False) \n ax2.spines['top'].set_visible(False) \n ax2.spines['bottom'].set_visible(False) \n\n # set legend\n n_Status = len(np.unique(df_subsetDEG[\"Status\"]))\n n_reference_groups = len(np.unique(df_subsetDEG[\"reference_group\"]))\n\n colors = [matplotlib.colors.rgb2hex(ax2.patches[i].get_facecolor()) for i in range(len(ax2.patches))]\n handles = [plt.Rectangle((0,0),1,1, color = i) for i in colors]\n # legend for status\n ax2_leg1 = fig.add_subplot(gs[2,3])\n leg1 = ax2_leg1.legend(handles[:n_Status], labels_for_legend[:n_Status], \n prop = {'size': 10}, loc = 'upper left', title = \"Status\", ncol=4)\n leg1._legend_box.aligh = \"left\"\n ax2_leg1.add_artist(leg1)\n # legend for others (reference and shred_plan)\n ax2_leg2 = fig.add_subplot(gs[2,3])\n leg2 = ax2_leg2.legend(handles[n_Status:(n_Status + n_reference_groups)], labels_for_legend[n_Status:(n_Status + n_reference_groups)], \n prop = {'size': 10}, loc = 'center left',title = \"reference group\")\n leg3 = ax2_leg2.legend(handles[(n_Status + n_reference_groups):], labels_for_legend[(n_Status + n_reference_groups):], \n prop = {'size': 10}, loc = 'lower left',title = \"shred plan\")\n leg2._legend_box.aligh = \"left\"\n leg3._legend_box.aligh = \"left\"\n ax2_leg2.add_artist(leg2)\n ax2_leg2.add_artist(leg3)\n\n ax2_leg1.axis('off')\n ax2_leg2.axis('off')\n\n #####----- gene module score histogram ------##### \n ax3 = fig.add_subplot(gs[2,1])\n ax3.barh(y = list(range(n_genes)), width = list(df_subsetDEG[\"logfoldchanges\"]), height = 1)\n ax3.xaxis.tick_top()\n ax3.set_yticklabels(\"\")\n ax3.set_yticks([])\n ax3.set_ylim(0, n_genes)\n ax3.set_xlabel(\"logFC\", rotation = 35, ha = \"right\")\n ax3.plot([1, 1], [0, 5200], color = \"#990000\", linestyle = \"dashed\", lw = 0.8)\n\n #####----- main heatmap ------#####\n ax5 = fig.add_subplot(gs[2,0])\n sns.heatmap(df_heatmap, vmin = 0, vmax = 10, yticklabels=False, xticklabels=False, cmap = \"bwr\", ax = ax5, cbar = False)\n ax5.set_ylabel(\"\")\n\n output_name = (shred.output_folder + \"/figures/heatmap.png\") if bin_type == \"bin\" else (shred.output_folder + \"/figures/heatmap_superbin.png\")\n if save_output:\n fig.savefig(output_name, bbox_inches = \"tight\")","title":""},{"docid":"17a011f47e3f1b6067601de67bd1f611","score":"0.52180654","text":"def visual_heatmap(data):\n # normalize data for display\n data = (data - data.min()) / (data.max() - data.min())\n\n # force the number of filters to be square\n n = int(np.ceil(np.sqrt(data.shape[0])))\n padding = (((0, n ** 2 - data.shape[0]),\n (0, 1), (0, 1)) # add some space between filters\n + ((0, 0),) * (data.ndim - 3)) # don't pad the last dimension (if there is one)\n data = np.pad(data, padding, mode='constant', constant_values=1) # pad with ones (white)\n\n # tile the filters into an image\n data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n\n plt.imshow(data)\n plt.axis('off')","title":""},{"docid":"2ead1c6b846ef437cfa086c7bc0c5f6f","score":"0.52153224","text":"def make_montage_sagittal(overlay, underlay, png_name, cbar_name):\n import matplotlib\n import commands\n import os\n import numpy as np\n matplotlib.rcParams.update({'font.size': 5})\n ###\n try:\n from mpl_toolkits.axes_grid1 import ImageGrid\n except:\n from mpl_toolkits.axes_grid import ImageGrid\n import matplotlib.cm as cm\n import matplotlib.pyplot as plt\n import matplotlib.colors as col\n import nibabel as nb\n def determine_start_and_end(data, direction, percent):\n x, y, z = data.shape\n xx1 = 0\n xx2 = x - 1\n zz1 = 0\n zz2 = z - 1\n total_non_zero_voxels = len(np.nonzero(data.flatten())[0])\n thresh = percent * float(total_non_zero_voxels)\n start = None\n end = None\n if 'axial' in direction:\n while(zz2 > 0):\n d = len(np.nonzero(data[:, :, zz2].flatten())[0])\n if float(d) > thresh:\n break\n zz2 -= 1\n while(zz1 < zz2):\n d = len(np.nonzero(data[:, :, zz1].flatten())[0])\n if float(d) > thresh:\n break\n zz1 += 1\n start = zz1\n end = zz2\n else:\n while(xx2 > 0):\n d = len(np.nonzero(data[xx2, :, :].flatten())[0])\n if float(d) > thresh:\n break\n xx2 -= 1\n while(xx1 < xx2):\n d = len(np.nonzero(data[xx1, :, :].flatten())[0])\n if float(d) > thresh:\n break\n xx1 += 1\n start = xx1\n end = xx2\n return start, end\n def get_spacing(across, down, dimension):\n space = 10\n prod = (across*down*space)\n if prod > dimension:\n while(across*down*space) > dimension:\n space -= 1\n else:\n while(across*down*space) < dimension:\n space += 1\n return space\n\n Y = nb.load(underlay).get_data()\n X = nb.load(overlay).get_data()\n X = X.astype(np.float16)\n Y = Y.astype(np.float16)\n\n\n if 'skull_vis' in png_name:\n X[X < 20.0] = 0.0\n if 'skull_vis' in png_name or 't1_edge_on_mean_func_in_t1' in png_name or 'MNI_edge_on_mean_func_mni' in png_name:\n max_ = np.nanmax(np.abs(X.flatten()))\n X[X != 0.0] = max_\n print '^^', np.unique(X)\n\n x1, x2 = determine_start_and_end(Y, 'sagittal', 0.0001)\n spacing = get_spacing(6, 3, x2 - x1)\n x, y, z = Y.shape\n fig = plt.figure(1)\n max_ = np.max(np.abs(Y))\n\n if ('snr' in png_name) or ('reho' in png_name) or ('vmhc' in png_name) or ('sca_' in png_name) or ('alff' in png_name) or ('centrality' in png_name) or ('temporal_regression_sca' in png_name) or ('temporal_dual_regression' in png_name):\n grid = ImageGrid(fig, 111, nrows_ncols=(3, 6), share_all=True, aspect=True, cbar_mode=\"single\", cbar_pad=0.5, direction=\"row\")\n else:\n grid = ImageGrid(fig, 111, nrows_ncols=(3, 6), share_all=True, aspect=True, cbar_mode=\"None\", direction=\"row\")\n\n xx = x1\n for i in range(6*3):\n\n if xx >= x2:\n break\n\n im = grid[i].imshow(np.rot90(Y[xx, :, :]), cmap=cm.Greys_r)\n grid[i].get_xaxis().set_visible(False)\n grid[i].get_yaxis().set_visible(False)\n xx += spacing\n\n x, y, z = X.shape\n X[X == 0.0] = np.nan\n max_ = np.nanmax(np.abs(X.flatten()))\n print '~~', max_\n xx = x1\n for i in range(6*3):\n\n\n if xx >= x2:\n break\n im = None\n if cbar_name is 'red_to_blue':\n\n im = grid[i].imshow(np.rot90(X[xx, :, :]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=0, vmax=max_) ###\n elif cbar_name is 'green':\n im = grid[i].imshow(np.rot90(X[xx, :, :]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=0, vmax=max_)\n else:\n im = grid[i].imshow(np.rot90(X[xx, :, :]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=- max_, vmax=max_)\n xx += spacing\n cbar = grid.cbar_axes[0].colorbar(im)\n\n if 'snr' in png_name:\n cbar.ax.set_yticks(drange(0, max_))\n\n elif ('reho' in png_name) or ('vmhc' in png_name) or ('sca_' in png_name) or ('alff' in png_name) or ('centrality' in png_name) or ('temporal_regression_sca' in png_name) or ('temporal_dual_regression' in png_name):\n cbar.ax.set_yticks(drange(-max_, max_))\n\n\n plt.show()\n plt.axis(\"off\")\n png_name = os.path.join(os.getcwd(), png_name)\n plt.savefig(png_name, dpi=300, bbox_inches='tight')\n plt.close()\n matplotlib.rcdefaults()\n\n return png_name","title":""},{"docid":"930f701d70f7adf34d6309e02fab3cec","score":"0.5213234","text":"def heatmap(self, name=\"\", display=True, saveFile = False, saveFig = False, fileLocation=\"\", fullscreen=False, normalization='refbasal'):\n if fileLocation == '':\n fileLocation = self.fileLocation\n modules.heatmap(self.cellFullIntersection.copy(), self.cellLines, self.timePoints, name, display, saveFile, saveFig, fileLocation, fullscreen, normalization)","title":""},{"docid":"94f4a9b83728b6a47dde804c9c15a787","score":"0.5212856","text":"def gaussian2kp(self, heatmap):\n shape = heatmap.shape\n heatmap = heatmap.unsqueeze(-1)\n grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0)\n value = (heatmap * grid).sum(dim=(2, 3))\n kp = {'value': value}\n return kp","title":""},{"docid":"e2018914a6845c9cab1fe14380b8b11f","score":"0.52071184","text":"def features_sigma(img,\n sigma,\n intensity=True,\n edges=True,\n texture=True):\n\n features = []\n\n gx,gy = np.meshgrid(np.arange(img.shape[1]), np.arange(img.shape[0]))\n # print(gx.shape)\n #features.append(gx)\n gx = filters.gaussian(gx, sigma)\n gy = filters.gaussian(gy, sigma)\n\n features.append(np.sqrt(gx**2 + gy**2)) #use polar radius of pixel locations as cartesian coordinates\n\n del gx, gy\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Location features extracted using sigma= %f' % (sigma))\n\n img_blur = filters.gaussian(img, sigma)\n\n if intensity:\n features.append(img_blur)\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Intensity features extracted using sigma= %f' % (sigma))\n\n if edges:\n features.append(filters.sobel(img_blur))\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Edge features extracted using sigma= %f' % (sigma))\n\n if texture:\n H_elems = [\n np.gradient(np.gradient(img_blur)[ax0], axis=ax1)\n for ax0, ax1 in itertools.combinations_with_replacement(range(img.ndim), 2)\n ]\n\n eigvals = feature.hessian_matrix_eigvals(H_elems)\n del H_elems\n\n for eigval_mat in eigvals:\n features.append(eigval_mat)\n del eigval_mat\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Texture features extracted using sigma= %f' % (sigma))\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Image features extracted using sigma= %f' % (sigma))\n\n return features","title":""},{"docid":"48c5e0c01e8c4e85d2d5ed7829d2e78d","score":"0.5205915","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n \n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n\n cbar = ax.figure.colorbar(im, ax=ax, cax = cax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n #cbar = plt.colorbar(im, cax=cax)\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels, fontdict = font)\n ax.set_yticklabels(row_labels, fontdict = font)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"8e3f0359ee0bd6b73db3a9c5aba63bf0","score":"0.5198854","text":"def heat_map(path):\r\n x, y= np.loadtxt(fname=path, delimiter='\\t',dtype=int,\r\n usecols = (1,2), skiprows=100, unpack = True)\r\n\r\n fig, (ax,ax2) = plt.subplots(nrows=2, sharex=True, figsize=(20,10))\r\n\r\n extent = [x[0]-(x[1]-x[0])/2, x[-1]+(x[1]-x[0])/2,0,1]\r\n ax.imshow(y[np.newaxis,:], cmap=\"plasma\", aspect=\"auto\", extent=extent)\r\n ax2.plot(x,y)\r\n plt.tight_layout()\r\n return plt.show()","title":""},{"docid":"ef2678180e2d1a198275f2cc48006665","score":"0.5198667","text":"def get_attention_heatmap(self, attention):\n # Options: cv2.COLORMAP_PLASMA, cv2.COLORMAP_JET, etc.\n #attention = tf.reshape(attention, (1, np.prod(attention.shape)))\n #attention = tf.nn.softmax(attention)\n vis_attention = np.float32(attention).reshape((224, 224))\n vis_attention = vis_attention - np.min(vis_attention)\n vis_attention = 255 * vis_attention / np.max(vis_attention)\n vis_attention = cv2.applyColorMap(np.uint8(vis_attention), cv2.COLORMAP_JET)\n vis_attention = cv2.cvtColor(vis_attention, cv2.COLOR_RGB2BGR)\n return vis_attention","title":""},{"docid":"0e6f666fe52837aaaa765293ad3d4d69","score":"0.51950836","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", vmin = 0, vmax = 100, **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs, vmin = vmin, vmax = vmax)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, fraction=0.0272, pad=0.04, extend='both', ticks=[-100, -50, 0, 50, 100], **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"2a019203dd83eb47b3cfcc2ed1729bc7","score":"0.51921654","text":"def plt_gm_clusters(df_all, model):\n\n # color_iter = itertools.cycle([cmap(i) for i in range(cmap.N)])\n\n color_iter = itertools.cycle([cmap(i) for i in range(clus_params['n_components'])])\n\n df = df_all[featureSet_dic[clus_params['feat_list']]].copy()\n\n XX = df.values\n Y_ = model.predict(XX) # predict labels for each model\n\n plt.figure(figsize=(8, 6))\n splot = plt.subplot(1, 1, 1)\n\n for i, (mean, cov, color) in enumerate(zip(model.means_, model.covariances_, color_iter)):\n\n if \"MEAN\" in clus_params['feat_list']:\n v, w = linalg.eigh(cov)\n else:\n\n subset = [0, 5] # mean torque L & R\n v, w = linalg.eigh(cov[np.ix_(subset, subset)])\n mean = np.array([mean[0], mean[5]])\n\n if not np.any(Y_ == i):\n continue\n\n if \"MEAN\" in clus_params['feat_list']:\n plt.scatter(XX[Y_ == i, 0], XX[Y_ == i, 1], color=color, s=60)\n else:\n plt.scatter(XX[Y_ == i, 0], XX[Y_ == i, 5], color=color, s=60)\n\n # Plot an ellipse to show the Gaussian component\n angle = np.arctan2(w[0][1], w[0][0])\n angle = 180. * angle / np.pi # convert to degrees\n v = 2. * np.sqrt(2.) * np.sqrt(v)\n ell = mpl.patches.Ellipse(mean, v[0], v[1], 180. + angle, color=color)\n ell.set_clip_box(splot.bbox)\n ell.set_alpha(.5)\n splot.add_artist(ell)\n\n plt.xticks(())\n plt.yticks(())\n\n plt.title('Subject: {}, feature set: {}'.format(USER, clus_params['feat_list']))\n plt.subplots_adjust(hspace=.35, bottom=.02)\n plt.show()","title":""},{"docid":"409b7a9b0f1849d9e2f99fadb09ab8f8","score":"0.51895446","text":"def process_image(self, image, debug=False):\n # Get old heatmap from internal deque representation or initialize with zeros if unknown\n old_heatmap = np.zeros(image.shape[0:2]) if len(self.heatmap_deque) == 0 else sum(self.heatmap_deque)\n # Scale image from 0.0..1.0\n modified_image = np.copy(image)/255.\n # Find cars in image\n _, draw_image, labels_heatmap, new_heatmap, agg_heatmap = find_cars_image(modified_image, self.clf,\n self.hyperparams, self.box_color, old_heatmap=old_heatmap)\n # Overlay bounding boxes on top of image\n draw_image[(draw_image == 0).all(2)] = modified_image[(draw_image == 0).all(2)]\n # Add new heatmap to deque\n self.heatmap_deque.append(new_heatmap)\n # In debug mode ...\n if debug:\n # ... create a new canvas\n result_image = np.zeros_like(image).astype(float)\n h = result_image.shape[0]\n w = result_image.shape[1]\n # get the new heatmap and aggregated heatmap as \"fancy heatmap\" representation\n new_heatmap_rgb = fancy_heatmap(new_heatmap, self.hyperparams[\"HEAT_THRESHOLD\"])\n agg_heatmap_rgb = fancy_heatmap(agg_heatmap, self.hyperparams[\"HEAT_THRESHOLD\"])\n # labels_heatmap_rgb will just be the grayscale value scaled to 0..1 and replicated across all 3 channels\n labels_heatmap_rgb = cv2.merge([normalize(labels_heatmap, norm='max')]*3)\n # place all the different images into their respective quadrant of the output canvas\n result_image[0:h//2, 0:w//2] = cv2.resize(draw_image, (w//2, h//2), interpolation=cv2.INTER_AREA)\n result_image[h//2:h, 0:w//2] = cv2.resize(labels_heatmap_rgb, (w//2, h-h//2), interpolation=cv2.INTER_AREA)\n result_image[0:h//2, w//2:w] = cv2.resize(new_heatmap_rgb, (w-w//2, h//2), interpolation=cv2.INTER_AREA)\n result_image[h//2:h, w//2:w] = cv2.resize(agg_heatmap_rgb, (w-w//2, h-h//2), interpolation=cv2.INTER_AREA)\n # rescale result to 0..255\n result_image = (result_image*255).astype(int)\n else:\n # For non-debug mode, just take scaled-back the find_cars draw_image return value\n result_image = (draw_image*255).astype(int)\n return result_image","title":""},{"docid":"4019419ee65994b243cea1cd588722a0","score":"0.51833916","text":"def fit_gaussian(self, mask=None):\n data = self.data\n mask = numpy.logical_or(mask, numpy.ma.getmaskarray(data))\n fdata = data[~mask].data\n xdata = numpy.asarray([cm[~mask]\n for cm in self.bset.cmesh]).transpose()\n scale, mean, cov = fit_ndgaussian(xdata, fdata)\n return scale, mean, cov","title":""},{"docid":"119bafceb73995adbae350f5e0b81242","score":"0.51833475","text":"def heatmap(data, row_labels, col_labels, ax=None,\r\n cbar_kw={}, cbarlabel=\"\", title = \"Default\", x_title=\" \",y_title=\" \",saveFile = None, **kwargs):","title":""},{"docid":"19bbe1579ce43c093b6c347bf94e397b","score":"0.5178392","text":"def config_hog_search(self, img):\n img = img.astype(np.float32)/255 # scale image: (png: 0-1, jpg: 0-255)\n img_crop = img[self.ys[0]:self.ys[1], :, :] # crop img to relative dimensions (ROI)\n clr_trf = cvf.transform_colorspace(img_crop, self.color_space) # transform color space from RGB\n clr_trf = cvu.scale_image(clr_trf, self.scale) # scale rather than selecting diffferent window sizes\n ch1, ch2, ch3 = [clr_trf[:,:,ch] for ch in range(3)] # HOG operates on per channel basis\n\n # get hog features as multi-dimensional array per channel for the entire image\n params={'orient':self.orient, 'pix_per_cell':self.pix_per_cell, 'cells_per_block':self.cells_per_block}\n params.update({'vis':False, 'feature_vec':False})\n hog1,hog2,hog3 = [cvf.get_hog_features(ch, **params) for ch in ([ch1,ch2,ch3])]\n # block configurations\n self.nfeat_per_block = self.orient*self.cells_per_block**2\n self.nblocks_per_window = (self.window //self.pix_per_cell) -1\n # number_blocks: define number of hog cells across an image according to channel dimensions\n # number_steps: define number of steps to occur across hog array to extract features\n rows, cols = ch1.shape[:2]\n number_blocks = lambda max_dim, pix_per_cell: (max_dim // pix_per_cell) -1\n number_steps = lambda nblocks, nblocks_window, cells_step: (nblocks - nblocks_window)//cells_step\n self.nxblocks, self.nyblocks = number_blocks(cols, self.pix_per_cell), number_blocks(rows, self.pix_per_cell)\n nxsteps, nysteps = \\\n [number_steps(it, self.nblocks_per_window, self.cells_per_step) for it in (self.nxblocks, self.nyblocks) ]\n return clr_trf, (nxsteps, nysteps), (hog1,hog2,hog3)","title":""},{"docid":"eefd6d3aa8eab2766e417c05a2a0d0be","score":"0.51754767","text":"def plotFace(original,blurred):\n plt.subplot(121),plt.imshow(original,cmap=cm.Greys_r),plt.title('Original')\n plt.xticks([]), plt.yticks([])\n plt.subplot(122),plt.imshow(blurred,cmap=cm.Greys_r),plt.title('Gaussian Blurred')\n plt.xticks([]), plt.yticks([])\n return None","title":""},{"docid":"46d79fdb149cbf3d2b51ff544b6d221e","score":"0.5174099","text":"def heatmap(data, row_labels, col_labels, ax=None,\r\n cbar_kw={}, cbarlabel=\"\", **kwargs):\r\n\r\n if not ax:\r\n ax = plt.gca()\r\n\r\n # Plot the heatmap\r\n im = ax.imshow(data, **kwargs)\r\n\r\n # Create colorbar\r\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\r\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\r\n\r\n # We want to show all ticks...\r\n n_ticks = 20\r\n if data.shape[1]>n_ticks :\r\n ax.set_xticks(np.linspace(0 ,data.shape[1], num = n_ticks , dtype=int))\r\n ax.set_yticks(np.linspace(0 ,data.shape[0], num = n_ticks , dtype=int))\r\n ax.set_xticklabels(np.linspace(0 ,data.shape[1], num = n_ticks , dtype=int))\r\n ax.set_yticklabels(np.linspace(0 ,data.shape[0], num = n_ticks , dtype=int))\r\n else:\r\n ax.set_xticks(np.arange(data.shape[1]))\r\n ax.set_yticks(np.arange(data.shape[0]))\r\n # ... and label them with the respective list entries.\r\n ax.set_xticklabels(col_labels)\r\n ax.set_yticklabels(row_labels)\r\n\r\n # Let the horizontal axes labeling appear on top.\r\n ax.tick_params(top=True, bottom=False,\r\n labeltop=True, labelbottom=False)\r\n\r\n # Rotate the tick labels and set their alignment.\r\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",rotation_mode=\"anchor\")\r\n\r\n # Turn spines off and create white grid.\r\n for edge, spine in ax.spines.items():\r\n spine.set_visible(False)\r\n\r\n ax.set_xticks(np.arange(data.shape[1]+1)-0.5, minor=True)\r\n ax.set_yticks(np.arange(data.shape[0]+1)-0.5, minor=True)\r\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=0.0005)\r\n ax.tick_params(which=\"minor\", bottom=False, left=False)\r\n\r\n return im, cbar","title":""},{"docid":"22af15c0f2b496d3b83bbd04808ebb85","score":"0.51733655","text":"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\"\", **kwargs):\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap 绘制热图\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar 创建颜色条\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks... 显示所有的刻度\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries. 用相应的列表条目分别标记行和列\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top. 让水平轴标签显示在顶部\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment. 旋转刻度标签并设置其对齐方式。\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid. 关闭spines 创建白色的网格\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"e41845e59d9d7d3f5d576cb7bb0c25a4","score":"0.516938","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n #ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n #ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n #ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"c3f553196c470eb5662d433a5a5417f8","score":"0.516195","text":"def add_gaussian(self, A=1000, sigma=(200, 200), x0=None):\n if x0 is None:\n x0 = self._beam_position\n \n self.map += np.asarray(np.round(\n gaussian2d(self._X, self._Y, A, sigma, x0)), dtype=np.int32)","title":""},{"docid":"5b3b8bb7d503e1d5c88d8912e894f64b","score":"0.51597476","text":"def addGaussian(ax, ismulti):\n shape = (96, 288) #ax.shape[:2]\n intensity_noise = np.random.uniform(low=0, high=0.05)\n if ismulti:\n ax[:,:,0] = ax[:,:,0]*(1+ intensity_noise*np.random.normal(loc=0, scale=1, size=shape[0]*shape[1]).reshape(shape[0],shape[1]))\n else:\n ax[:,:,0] = ax[:,:,0] + intensity_noise*np.random.normal(loc=0, scale=1, size=shape[0]*shape[1]).reshape(shape[0],shape[1])\n return ax","title":""},{"docid":"b765ef00cef548c70358defa6894dc2b","score":"0.51581675","text":"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n # ax.spines[:].set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n # ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"eb66f75c64905a2f5920b5150f2b67cb","score":"0.5150999","text":"def gaussian_blur(self,img):\n return cv2.GaussianBlur(img, (self.kernel_size, self.kernel_size), 0)","title":""},{"docid":"d7bda04b7fadfe11cc84d3651d1b7e31","score":"0.51371485","text":"def addGaussian(self, xwidth=100., ywidth=100., xcen=None, ycen=None, value=1.0):\n if xcen == None:\n xcen = self.nx/2.0\n if ycen == None:\n ycen = self.ny/2.0\n self.fimage = None\n gaussian = numpy.exp(-(self.xx-xcen)**2/(2.0*xwidth**2) - (self.yy-ycen)**2/(2.0*ywidth**2))\n self.image += gaussian * value / gaussian.max()\n return","title":""},{"docid":"a163bf7c46434c94e422facb08beba95","score":"0.5136858","text":"def make_heatmap(df, dataset, remove_constant=False):\n sns.set(style=\"white\")\n temp_df = df.copy()\n\n # remove constant values\n if remove_constant:\n constants = [x for x in temp_df if np.std(temp_df[x]) == 0]\n temp_df.drop(columns=constants, inplace=True)\n\n # Compute the correlation matrix\n corr = temp_df.corr()\n\n # Set up the matplotlib figure\n f, ax = plt.subplots(figsize=(15, 10))\n\n # Generate a custom diverging colormap\n cmap = sns.diverging_palette(220, 10, as_cmap=True)\n\n # Draw the heatmap with the mask and correct aspect ratio\n sns.heatmap(corr, cmap=cmap,\n square=True, xticklabels=corr.columns.values, yticklabels=corr.columns.values,\n linewidths=0.5, cbar_kws={\"shrink\": .5}, ax=ax)\n\n if remove_constant:\n plt.savefig('plots/%s/heatmap_no_constants.png' % dataset, bbox_inches='tight')\n else:\n plt.savefig('plots/%s/heatmap.png' % dataset, bbox_inches='tight')","title":""},{"docid":"b9ee7d7b2278977103aab99bb57a20d2","score":"0.51285064","text":"def heatmap_plot(savefigure=False):\n treenumber,depth = [1, 5, 10, 50, 100, 500, 1000], list(range(1,11))\n iter_list = list(product(treenumber, depth))\n result_list = np.load('npy-data/result_big_run.npy')\n heat_arr = result_list.reshape((len(treenumber), len(depth)))\n\n fig, ax = plt.subplots(figsize=(8,8))\n im = ax.matshow(heat_arr, cmap=plt.cm.coolwarm, vmin=.8, vmax=1)\n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.2)\n\n ax.set_xticks(np.arange(heat_arr.shape[1]))\n ax.set_xticklabels(depth)\n ax.set_yticks(np.arange(heat_arr.shape[0]))\n ax.set_yticklabels(treenumber)\n ax.set_xlim(-.5,heat_arr.shape[1]-.5)\n ax.set_ylim(heat_arr.shape[0]-.5,-.5)\n ax.set_xlabel(\"Maximum depth\")\n ax.set_ylabel(\"Number of trees\")\n ax.xaxis.set_ticks_position('bottom')\n plt.colorbar(im, cax=cax)\n for i, z in enumerate(heat_arr):\n for j in range(len(z)):\n ax.text(j, i, '{:.3f}'.format(z[j]), ha='center', va='center')\n if savefigure:\n plt.savefig('img/xgb_heatmap_maxdepth_n_estimators.pdf', bbox_inches='tight')\n plt.show()","title":""},{"docid":"4ad02b1319cbf09edbe8daaf52b6708f","score":"0.5128202","text":"def plot_heatmap(data, title, xlabel, ylabel):\n hm = plt.pcolor(data, cmap=cm.gray_r, vmin=0, vmax=1)\n\n plt.tick_params(labelsize=20)\n plt.axes().set_aspect('equal')\n\n plt.title(title, fontsize=33)\n plt.xlabel(xlabel, fontsize=30)\n plt.ylabel(ylabel, fontsize=30)\n\n Plotter.show(title)","title":""},{"docid":"3be437c578562a58f4af95f167dd0925","score":"0.51241964","text":"def generate_heatmap(density_map,rate=0,color_type=2):\n\n if rate > 0:\n heatmap = np.abs(density_map) * rate\n else:\n max_density_value=np.max(np.abs(density_map))\n if max_density_value==0:\n max_density_value=1\n heatmap = np.abs(density_map) / max_density_value * 250\n heatmap = cv2.applyColorMap(np.uint8(heatmap), color_type)\n return heatmap","title":""},{"docid":"f6efeef36690371e4235327dbb178770","score":"0.51183945","text":"def bayes_matting(img, trimap, sigma_d=10, it=1):\n assert img.shape[:-1] == trimap.shape\n\n img = img / 255.0\n nrows, ncols = img.shape[:-1]\n\n # initial alpha guess\n alpha = np.zeros(trimap.shape)\n alpha[trimap == 255] = 1\n alpha[trimap == 128] = 0.5\n\n B = img[alpha == 0] # background pixel mask\n F = img[alpha == 1] # foreground pixel mask\n\n mean_B = np.mean(B, axis=0)\n cov_B = np.cov(B.T)\n mean_F = np.mean(F, axis=0)\n cov_F = np.cov(F.T)\n\n try:\n inv_cov_B = np.linalg.inv(cov_B)\n inv_cov_F = np.linalg.inv(cov_F)\n except LinAlgError:\n print(\"LinAlgError\")\n\n for i in range(it):\n print(\"Iteration {}\".format(i))\n for row in range(nrows):\n for col in range(ncols):\n if trimap[row, col] == 128:\n f, g = calculate_fg(img[row, col], alpha[row, col], mean_F, inv_cov_F, mean_B, inv_cov_B, sigma_d)\n alpha[row, col] = calculate_alpha(img[row, col], f, g)\n\n alpha = np.clip(alpha, 0, 1)\n\n return alpha","title":""},{"docid":"2d853e1e069bffdc62c025f6f0db1bfc","score":"0.5111385","text":"def plot_macros_gaussian(feature_config_filepath, feature_config_key, macros_filepath, save_dir, words, feature_labels, confusion_matrix_filepath, threshold, mode):\n gaussian_dir = os.path.join(save_dir, 'visualization', 'gaussian', str(mode))\n # if os.path.exists(gaussian_dir):\n # shutil.rmtree(gaussian_dir)\n # os.makedirs(gaussian_dir)\n\n all_features = load_json(feature_config_filepath)[str(feature_config_key)]\n macros_data = get_macros(all_features, macros_filepath)\n\n if not feature_labels:\n feature_labels = all_features\n\n if not words:\n words = macros_data.keys()\n\n if mode == 0:\n each_word_and_feature_graphs(macros_data, gaussian_dir, words, feature_labels)\n elif mode == 1:\n each_confused_word_and_feature_graphs(macros_data, gaussian_dir, words, feature_labels, confusion_matrix_filepath, threshold)","title":""},{"docid":"a6d0623d2fd14108b28b2f966753538c","score":"0.51045966","text":"def add_greyscale(im, s_angle, x_data, y_data):\n if rnd.random() < 0.5:\n grey = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n x_data.append(cv2.cvtColor(grey, cv2.COLOR_GRAY2BGR))\n y_data.append(s_angle)","title":""},{"docid":"0c2709c6931dd84bf3bae8fcaa01a81e","score":"0.51033026","text":"def gaussian(centre, k, intensity, xpos):\r\n\treturn intensity * np.exp(- np.power(k * (xpos - centre), 2))","title":""},{"docid":"24b36632f737b0977ff0c8b357697087","score":"0.5103105","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"24b36632f737b0977ff0c8b357697087","score":"0.5103105","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"24b36632f737b0977ff0c8b357697087","score":"0.5103105","text":"def heatmap(data, row_labels, col_labels, ax=None,\n cbar_kw={}, cbarlabel=\"\", **kwargs):\n\n if not ax:\n ax = plt.gca()\n\n # Plot the heatmap\n im = ax.imshow(data, **kwargs)\n\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n\n # We want to show all ticks...\n ax.set_xticks(np.arange(data.shape[1]))\n ax.set_yticks(np.arange(data.shape[0]))\n # ... and label them with the respective list entries.\n ax.set_xticklabels(col_labels)\n ax.set_yticklabels(row_labels)\n\n # Let the horizontal axes labeling appear on top.\n ax.tick_params(top=True, bottom=False,\n labeltop=True, labelbottom=False)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Turn spines off and create white grid.\n for edge, spine in ax.spines.items():\n spine.set_visible(False)\n\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n return im, cbar","title":""},{"docid":"51796244a3acacda9d45944ef778d8f9","score":"0.5100558","text":"def augment_image(im):\n # First crop out the face to save reduce computation load\n bb = im.landmarks['bb'].lms\n bb_vec = bb.as_vector()\n bb_ul = (np.array([bb_vec[0], bb_vec[1]]) - bb.centre()) * 2\n bb_lr = (np.array([bb_vec[4], bb_vec[5]]) - bb.centre()) * 2\n ul = bb_ul + bb.centre()\n lr = bb_lr + bb.centre()\n im = im.crop(ul, lr, constrain_to_boundary=True)\n if im.pixels.shape[0] == 1:\n pix = np.zeros((3, im.pixels.shape[1], im.pixels.shape[2]))\n pix[:,] = im.pixels\n im.pixels = pix\n\n beta = 0.3\n cx = np.random.uniform(-beta, beta)\n cy = np.random.uniform(-beta, beta)\n fx = 1.0\n fy = np.random.uniform(0.6, 1.4)\n max_rotation = 30\n theta = np.random.uniform(-max_rotation, max_rotation)\n\n rotation = menpo.transform.Rotation.init_from_2d_ccw_angle(theta)\n shear = menpo.transform.Affine(np.array([[1, cx, 0],[cy, 1, 0], [0,0,1]]))\n scale = menpo.transform.Affine(np.array([[fx, 0, 0],[0, fy, 0], [0,0,1]]))\n T = scale.compose_after(shear).compose_after(rotation)\n\n t_im = im.transform_about_centre(T)\n\n t_im = add_color_jetting(t_im)\n t_im = add_occlusion(t_im)\n\n\n new_bb = t_im.landmarks['PTS'].lms.bounding_box()\n\n #new_bb contains the gt bounding box\n augmented_bb = add_bb_noise(new_bb)\n augmented_bb = augmented_bb.reshape((4,2))\n augmented_bb = menpo.shape.PointCloud(augmented_bb)\n t_im.landmarks['bb'] = menpo.landmark.LandmarkGroup.init_with_all_label(augmented_bb)\n\n return t_im","title":""},{"docid":"244aa631c9ba261d95a9ad6f963fddd6","score":"0.50956684","text":"def get_mch_heatmap(ensemble, methylation_type, grouping, clustering, level, ptile_start, ptile_end, normalize_row, query):\n\ttsne_type = 'mCH_ndim2_perp20'\n\n\tif normalize_row:\n\t\tnormal_or_original = '(normalized by row)'\n\telse:\n\t\tnormal_or_original = ''\n\n\ttitle = level.title() + \" gene body \" + methylation_type + \" by cluster \" + normal_or_original + \": <br>\"\n\tgenes = query.split()\n\n\ts=''\n\tfor i in genes:\n\t\ts=s+','+i\n\n\tgene_labels = list()\n\tgene_info_df = pd.DataFrame()\n\tgene_infos = get_gene_by_id(genes)\n\tfor i, gene in enumerate(gene_infos):\n\t\tgene_name = gene['gene_name']\n\t\tgene_labels.append(gene_name)\n\t\tif i > 0 and i % 10 == 0:\n\t\t\ttitle += \"<br>\"\n\t\ttitle += gene_name + \"+\"\n\t\tgene_info_df[gene_name] = median_cluster_mch(get_gene_methylation(ensemble, methylation_type, gene['gene_id'], grouping, clustering, level, True), grouping, clustering)\n\t\tif gene_info_df[gene_name].empty:\n\t\t\traise FailToGraphException\n\n\ttitle = title[:-1] # Gets rid of last '+'\n\n\tgene_info_df.reset_index(inplace=True)\n\tif grouping == 'annotation':\n\t\tgene_info_df['annotation_cat'] = pd.Categorical(gene_info_df['annotation_'+clustering], cluster_annotation_order)\n\t\tgene_info_df.sort_values(by='annotation_cat', inplace=True)\n\t\tgene_info_df.drop('annotation_cat', axis=1, inplace=True)\n\t\tgene_info_df.set_index(grouping+'_'+clustering, inplace=True)\n\telif grouping == 'cluster':\n\t\tgene_info_df.sort_values(by='cluster_'+clustering, inplace=True)\n\t\tgene_info_df.set_index(grouping+'_'+clustering, inplace=True)\n\telif grouping == 'dataset' or grouping == 'target_region' or grouping == 'slice' or grouping == 'sex':\n\t\tgene_info_df.sort_values(by=grouping, inplace=True)\n\t\tgene_info_df.set_index(grouping, inplace=True)\n\telse:\n\t\tgrouping = 'cluster'\n\t\tgene_info_df.sort_values(by='cluster_'+clustering, inplace=True)\n\t\tgene_info_df.set_index(grouping+'_'+clustering, inplace=True)\n\n\t# For some reason, Plotly doesn't allow 'None' as a group on the x-axis for heatmaps.\n\tif gene_info_df.index.tolist() == ['None']:\n\t\tgene_info_df.index = ['N/A']\n\n\tclusters_labels = gene_info_df.index.tolist()\n\tif grouping == 'cluster':\n\t\tclusters_labels = ['Cluster '+str(i) for i in clusters_labels]\n\n\tnormal_or_original = 'Original'\n\tif normalize_row:\n\t\tfor gene in gene_info_df:\n\t\t\t# z-score\n\t\t\t# gene_info_df[gene] = (gene_info_df[gene] - gene_info_df[gene].mean()) / gene_info_df[gene].std()\n\t\t\t# min-max\n\t\t\tgene_range = gene_info_df[gene].max() - gene_info_df[gene].min()\n\t\t\tif (gene_range==0):\n\t\t\t\tgene_range = 1\n\t\t\tgene_info_df[gene] = (gene_info_df[gene] - gene_info_df[gene].min()) / gene_range\n\t\tnormal_or_original = 'Normalized'\n\n\tgene_info_dict = gene_info_df.to_dict(into=OrderedDict)\n\n\tx, y, text, hover, mch = list(), list(), list(), list(), list()\n\ti = 0\n\tname_prepend = \"\"\n\tif grouping == 'cluster':\n\t\tname_prepend = 'cluster_'\n\tfor key in list(gene_info_dict.keys()):\n\t\tj = 0\n\t\ty.append(key)\n\t\tmch.append(list(gene_info_dict[key].values()))\n\t\tfor cluster in list(gene_info_dict[key].keys()):\n\t\t\tx.append(name_prepend+str(cluster))\n\t\t\ttext.append(build_hover_text(OrderedDict([('Gene', key),\n\t\t\t\t\t\t\t\t\t\t\t\t\t (grouping.title(), x[j]),\n\t\t\t\t\t\t\t\t\t\t\t\t\t (methylation_type, mch[i][j])\n\t\t\t\t\t\t\t\t\t\t\t\t\t ])))\n\t\t\tj += 1\n\t\thover.append(text)\n\t\ttext = []\n\t\ti += 1\n\n\tflat_mch = list(chain.from_iterable(mch))\n\tmch_dataframe = pd.DataFrame(flat_mch).dropna()\n\n\t# Hierarchical clustering and dendrogram\n\tmch = np.array(mch)\n\tfigure = ff.create_dendrogram(mch, orientation=\"right\", labels=tuple([i for i in range(len(genes))])) # TODO: Figure out how to set the colorscale\n\tfor i in range(len(figure['data'])):\n\t\tfigure['data'][i]['xaxis'] = 'x2'\n\tdendro_leaves = figure['layout']['yaxis']['ticktext']\n\tdendro_leaves = list(map(int, dendro_leaves))\n\tmch = mch[dendro_leaves,:] # Reorder the genes according to the clustering\n\tgenes_labels = [gene_labels[i] for i in dendro_leaves]\n\thover_old = hover\n\t# hover = [hover_old[i] for i in dendro_leaves]\n\thover = [str(i) for i in dendro_leaves]\n\n\n\tdendro_top = ff.create_dendrogram(mch.transpose(), orientation=\"bottom\", labels=tuple([i for i in range(mch.shape[1])]))\n\tfor i in range(len(dendro_top['data'])):\n\t\tdendro_top['data'][i]['yaxis'] = 'y2'\n\tdendro_top_leaves = dendro_top['layout']['xaxis']['ticktext']\n\tdendro_top_leaves = list(map(int, dendro_top_leaves))\n\tmch = mch[:,dendro_top_leaves] # Reorder the genes according to the clustering\n\tclusters_labels = [clusters_labels[i] for i in dendro_top_leaves]\n\tmch = list(mch)\n\tfigure.add_traces(dendro_top['data'])\n\n\t# Set color scale limits\n\tstart = mch_dataframe.quantile(ptile_start).values[0].tolist()\n\tend = mch_dataframe.quantile(ptile_end).values[0].tolist()\n\tend = max(end,start+0.01)\n\n\tcolorbar_tickval = list(arange(start, end, (end - start) / 4))\n\tcolorbar_tickval[0] = start\n\tcolorbar_tickval.append(end)\n\tcolorbar_ticktext = [\n\t\tstr(round(x, num_sigfigs_ticklabels)) for x in arange(start, end, (end - start) / 4)\n\t]\n\tif normalize_row == True:\n\t\tcolorbar_ticktext[0] = str(round(start, num_sigfigs_ticklabels))\n\telse:\n\t\tif (round(start,num_sigfigs_ticklabels)) == 0:\n\t\t\tcolorbar_ticktext[0] = str(round(start,num_sigfigs_ticklabels))\n\t\telse:\n\t\t\tcolorbar_ticktext[0] = '<' + str(round(start, num_sigfigs_ticklabels))\n\tcolorbar_ticktext.append('>' + str(round(end, num_sigfigs_ticklabels)))\n\n\t# Due to a weird bug(?) in plotly, the number of elements in tickvals and ticktext\n\t# must be greater than or equal to number of genes in query. Else, javascript throws\n\t# Uncaught Typeerrors when trying to hover over genes. (Tomo 12/11/17)\n\twhile len(colorbar_tickval) < len(genes):\n\t\tcolorbar_tickval.insert(0,start)\n\t\tif normalize_row == True:\n\t\t\tcolorbar_ticktext.insert(0, str(round(start, num_sigfigs_ticklabels)))\n\t\telse:\n\t\t\tcolorbar_ticktext.insert(0, '<' + str(round(start, num_sigfigs_ticklabels)))\n\n\ttrace = Heatmap(\n\t\tx=dendro_top_leaves,\n\t\ty=dendro_leaves,\n\t\tz=mch,\n\t\txtype=\"array\", ytype=\"array\",\n\t\ttext=hover,\n\t\tcolorscale='Viridis',\n\t\tcolorbar={\n\t\t\t'x': 1.0,\n\t\t\t'len': 0.5,\n\t\t\t'title': level.capitalize() + ' ' + methylation_type,\n\t\t\t'titleside': 'right',\n\t\t\t'tickmode': 'array',\n\t\t\t'tickvals': colorbar_tickval,\n\t\t\t'ticktext': colorbar_ticktext,\n\t\t\t'thickness': 10,\n\t\t\t'tickfont': {'size': 10}\n\t\t\t},\n\t\thoverinfo='text',\n\t\tzmin=start,zmax=end,zauto=False, # Clip the extreme edges of the colorscale\n\t\t)\n\ttrace['y'] = figure['layout']['yaxis']['tickvals']\n\ttrace['x'] = dendro_top['layout']['xaxis']['tickvals']\n\tfigure.add_traces([trace])\n\n\tlayout = Layout(\n\t\theight=max(600*len(genes)/20,550), # EAM Adjust the height of the heatmap according to the number of genes displayed\n\t\twidth=1000,\n\t\tpaper_bgcolor='rgba(0,0,0,0)',\n\t\tplot_bgcolor='rgba(0,0,0,0)',\n\t\tshowlegend=False,\n\t\thovermode='closest',\n\t\ttitle=title,\n\t\t# titlefont={'color': 'rgba(1,2,2,1)',\n\t\t# 'size': 16},\n\t\tmargin={'l': 0,\n\t\t\t\t'r': 0,\n\t\t\t\t'b': 100,\n\t\t\t\t't': 150,},\n\t\txaxis={\n\t\t\t'side': 'bottom',\n\t\t\t'tickangle': -45,\n\t\t\t'title': 'Clusters',\n\t\t\t'tickfont': {'size': 12},\n\t\t\t'showticklabels': True,\n\t\t\t'tickmode': 'array',\n\t\t\t'tickvals':trace['x'],\n\t\t\t'ticktext':clusters_labels,\n\t\t\t},\n\t\tyaxis={\n\t\t\t# 'tickangle': 15,\n\t\t\t'tickfont': {'size': 12},\n\t\t\t'showticklabels': True,\n\t\t\t'ticks':\"outside\",\n\t\t\t'tickmode': 'array',\n\t\t\t'tickvals':trace['y'],\n\t\t\t'ticktext':genes_labels,\n\t\t\t},\n\t\t)\n\tlayout['yaxis'].update({'domain': [0, .85]})\n\tlayout['xaxis'].update({'domain': [0.2, 1]})\n\tlayout.update({'hovermode': 'closest'})\n\tlayout.update({'xaxis2': {\n\t\t\t'showticklabels': False\n\t\t\t}})\n\tlayout.update({'yaxis2': {\n\t\t\t'showticklabels': False\n\t\t\t}})\n\tlayout['xaxis2'].update({'domain': [0, 0.1]})\n\tlayout['yaxis2'].update({'domain': [0.86, 1]})\n\tfor xx in ['xaxis','yaxis','xaxis2','yaxis2']:\n\t\tlayout[xx].update({'mirror': False,\n\t\t\t\t\t\t 'showgrid': False,\n\t\t\t\t\t\t 'showline': False,\n\t\t\t\t\t\t 'zeroline': False})\n\n\t# Available colorscales:\n\t# https://community.plot.ly/t/what-colorscales-are-available-in-plotly-and-which-are-the-default/2079\n\tupdatemenus = list([\n\t\tdict(\n\t\t\tbuttons=list([\n\t\t\t\tdict(\n\t\t\t\t\targs=['colorscale', 'Viridis'],\n\t\t\t\t\tlabel='Viridis',\n\t\t\t\t\tmethod='restyle'\n\t\t\t\t),\n\t\t\t\tdict(\n\t\t\t\t\targs=['colorscale', 'Bluered'],\n\t\t\t\t\tlabel='Bluered',\n\t\t\t\t\tmethod='restyle'\n\t\t\t\t),\n\t\t\t\tdict(\n\t\t\t\t\targs=['colorscale', 'Blackbody'],\n\t\t\t\t\tlabel='Blackbody',\n\t\t\t\t\tmethod='restyle'\n\t\t\t\t),\n\t\t\t\tdict(\n\t\t\t\t\targs=['colorscale', 'Electric'],\n\t\t\t\t\tlabel='Electric',\n\t\t\t\t\tmethod='restyle'\n\t\t\t\t),\n\t\t\t\tdict(\n\t\t\t\t\targs=['colorscale', 'Earth'],\n\t\t\t\t\tlabel='Earth',\n\t\t\t\t\tmethod='restyle'\n\t\t\t\t),\n\t\t\t\tdict(\n\t\t\t\t\targs=['colorscale', 'Jet'],\n\t\t\t\t\tlabel='Jet',\n\t\t\t\t\tmethod='restyle'\n\t\t\t\t),\n\t\t\t\tdict(\n\t\t\t\t\targs=['colorscale', 'Rainbow'],\n\t\t\t\t\tlabel='Rainbow',\n\t\t\t\t\tmethod='restyle'\n\t\t\t\t),\n\t\t\t\tdict(\n\t\t\t\t\targs=['colorscale', 'Picnic'],\n\t\t\t\t\tlabel='Picnic',\n\t\t\t\t\tmethod='restyle'\n\t\t\t\t),\n\t\t\t\tdict(\n\t\t\t\t\targs=['colorscale', 'Portland'],\n\t\t\t\t\tlabel='Portland',\n\t\t\t\t\tmethod='restyle'\n\t\t\t\t),\n\t\t\t\tdict(\n\t\t\t\t\targs=['colorscale', 'YlGnBu'],\n\t\t\t\t\tlabel='YlGnBu',\n\t\t\t\t\tmethod='restyle'\n\t\t\t\t)\n\t\t\t]),\n\t\t\tdirection='down',\n\t\t\tshowactive=True,\n\t\t\tx=-0.1,\n\t\t\txanchor='left',\n\t\t\ty=1.43,\n\t\t\tyanchor='top'\n\t\t)\n\t])\n\n\tlayout['updatemenus'] = updatemenus\n\n\t# layout['annotations'].extend([Annotation(text=title,\n\t# x=0.5,\n\t# y=1.3,\n\t# xanchor=\"center\",\n\t# yanchor=\"top\",\n\t# showarrow=False,\n\t# xref=\"paper\",\n\t# yref=\"paper\",\n\t# font={'size': 16,\n\t# 'color': 'black',})])\n\n\tfigure['layout'] = layout\n\n\treturn plotly.offline.plot(figure,\n\t\toutput_type='div',\n\t\tshow_link=False,\n\t\tinclude_plotlyjs=False)","title":""},{"docid":"fe3635c787d4f7e71437af8f97ecfc01","score":"0.5090728","text":"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\"\", **kwargs):\r\n\r\n if not ax:\r\n ax = plt.gca()\r\n\r\n # Plot the heatmap\r\n im = ax.imshow(data, **kwargs)\r\n\r\n # Create colorbar\r\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\r\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\r\n\r\n # We want to show all ticks...\r\n ax.set_xticks(np.arange(data.shape[1]))\r\n ax.set_yticks(np.arange(data.shape[0]))\r\n # ... and label them with the respective list entries.\r\n ax.set_xticklabels(col_labels)\r\n ax.set_yticklabels(row_labels)\r\n\r\n # Let the horizontal axes labeling appear on top.\r\n ax.tick_params(top=True, bottom=False,\r\n labeltop=True, labelbottom=False)\r\n\r\n # Rotate the tick labels and set their alignment.\r\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\r\n rotation_mode=\"anchor\")\r\n\r\n # Turn spines off and create white grid.\r\n # ax.spines[:].set_visible(False)\r\n\r\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\r\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\r\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\r\n ax.tick_params(which=\"minor\", bottom=False, left=False)\r\n\r\n return im, cbar","title":""},{"docid":"0e745027b255d23ea5c5a9f0679296d1","score":"0.5090131","text":"def Generate_BG_Template(outputSize=300, angularSize = 10, fileOut = 'BGRateMap.pickle' ):\r\n template = np.zeros((outputSize,outputSize))\r\n ppd=float(outputSize)/float(angularSize) # pixels per deg\r\n \r\n events110 = ParseFermi.Import_File('photons.txt', energyRange = (120000,140000),lonRange=(-5,5),latRange = (-5,5))\r\n events130 = ParseFermi.Import_File('photons.txt', energyRange = (100000,120000),lonRange=(-5,5),latRange = (-5,5))\r\n events150 = ParseFermi.Import_File('photons.txt', energyRange = (140000,200000),lonRange=(-5,5),latRange = (-5,5))\r\n \r\n for i in range(10000,200001,20000):\r\n if i == 130000:\r\n continue\r\n events = ParseFermi.Import_File('photons.txt', energyRange = (i-10000,i+10000),lonRange=(-5,5),latRange = (-5,5))\r\n BG = np.zeros((outputSize,outputSize)) \r\n for j in events:\r\n xIDX = int(j[1]*ppd+float(outputSize/2))\r\n yIDX = int(j[2]*ppd+float(outputSize/2))\r\n BG[yIDX][xIDX] += 1.0\r\n \r\n psfDeg = .2+float(200)/float(i)\r\n psfOut = psfDeg*ppd\r\n #print i/1e3, psfDeg, psfOut\r\n \r\n template += scipy.ndimage.filters.gaussian_filter(BG, psfOut)\r\n \r\n template = template/np.max(template)\r\n \r\n # Write to file \r\n outFile = open(fileOut, \"wb\" )\r\n pickle.dump(template, outFile)\r\n print 'Rate Map saved to ', fileOut\r\n \r\n plt.imshow(scipy.fliplr(template), 'jet',extent=[5,-5,-5,5])\r\n\r\n plt.xlabel(r'$l [^\\circ]$')\r\n plt.ylabel(r'$b [^\\circ]$')\r\n plt.xlim(5,-5)\r\n plt.ylim(-5,5)\r\n plt.colorbar()\r\n\r\n x,y = Find_Centroid(template)\r\n x,y = (x/ppd -angularSize/2.0,) ,(y/ppd -angularSize/2.0,)\r\n print x,y\r\n plt.scatter(x,y, s=10, c='r', marker = '+')\r\n \r\n X,Y = FormatEvents(events110)\r\n plt.scatter(X, Y, label = '100-120 GeV', marker = 'o' , c = 'k')\r\n \r\n X,Y = FormatEvents(events130)\r\n plt.scatter(X, Y, label = '120-140 GeV', marker = 'o' , c = 'r')\r\n \r\n X,Y = FormatEvents(events150)\r\n plt.scatter(X, Y, label = '140-200 GeV', marker = 'o' , c = 'g' )\r\n \r\n from matplotlib.font_manager import FontProperties\r\n fontP = FontProperties()\r\n fontP.set_size('small')\r\n plt.legend(loc=1, ncol=1, fancybox=True, shadow=False,prop=fontP,borderaxespad=0.,labelspacing = .2)\r\n \r\n from matplotlib.backends.backend_pdf import PdfPages\r\n if fileOut != '':\r\n pp = PdfPages(fileOut + '_sideband.pdf')\r\n plt.savefig(pp, format='pdf')\r\n print \"Figures saved to \", str(fileOut)+ '_sideband.pdf\\n',\r\n pp.close()\r\n \r\n plt.show()\r\n return template","title":""},{"docid":"83025979f7cc41c2cbf7d35ea3be84a5","score":"0.50868076","text":"def plot_heatmap(model_dir, name, features, labels, num_classes):\n features_sort, _ = utils.sort_dataset(features, labels, \n classes=num_classes, stack=False)\n features_sort_ = np.vstack(features_sort)\n sim_mat = np.abs(features_sort_ @ features_sort_.T)\n\n # plt.rc('text', usetex=False)\n # plt.rcParams['font.family'] = 'serif'\n # plt.rcParams['font.serif'] = ['Times New Roman'] #+ plt.rcParams['font.serif']\n\n fig, ax = plt.subplots(figsize=(7, 5), sharey=True, sharex=True)\n im = ax.imshow(sim_mat, cmap='Blues')\n fig.colorbar(im, pad=0.02, drawedges=0, ticks=[0, 0.5, 1])\n ax.set_xticks(np.linspace(0, len(labels), num_classes+1))\n ax.set_yticks(np.linspace(0, len(labels), num_classes+1))\n [tick.label.set_fontsize(10) for tick in ax.xaxis.get_major_ticks()] \n [tick.label.set_fontsize(10) for tick in ax.yaxis.get_major_ticks()]\n fig.tight_layout()\n\n save_dir = os.path.join(model_dir, 'figures', 'heatmaps')\n os.makedirs(save_dir, exist_ok=True)\n file_name = os.path.join(save_dir, f\"{name}.png\")\n fig.savefig(file_name)\n print(\"Plot saved to: {}\".format(file_name))\n plt.close()","title":""},{"docid":"41720e814b91c4f9cba91f09874b4067","score":"0.5072641","text":"def heatmap(\n df: pd.DataFrame, figsize: tuple = (10, 10), scale: float = 1.4\n) -> sns.heatmap:\n\n # keep only quantitative features\n df = create_quanti_df(df)\n print(f\"Number of quantitaive columns: {df.shape[1]}\")\n # calcaulate features correlations\n corr = df.corr() * 100\n # create mask for the upper triangle\n mask = np.triu(np.ones_like(corr, dtype=bool))\n # create figure\n plt.figure(figsize=figsize)\n # create heatmap\n sns.set(font_scale=scale)\n sns.heatmap(corr, mask=mask, annot=True, cmap=\"coolwarm\", fmt=\".0f\")\n sns.reset_orig()\n plt.show()\n return","title":""},{"docid":"e2283271d70dfc9b95b9121d55407682","score":"0.50693595","text":"def watershed(mask, img, plotImage = False, kernelSize = None):\n imgCopy = img.copy()\n maskCopy = np.array(mask.copy(), dtype=np.uint8)\n \n if kernelSize is None:\n kernelSize = 2\n\n # Finding sure foreground area\n #dist_transform = cv2.distanceTransform(mask, cv2.DIST_L2, 5)\n #ret, sure_fg = cv2.threshold(dist_transform,0.3*dist_transform.max(),255,0) #change the second argument to change the sensitivity \n maskClosed = skimage.morphology.closing(np.array(maskCopy, dtype=np.uint8))\n maskClosed = skimage.morphology.closing(np.array(maskClosed, dtype=np.uint8))\n kernel = np.ones((kernelSize,kernelSize), np.uint8)\n # maskCopy = img_as_bool(maskCopy)\n sure_fg = cv2.erode(maskClosed, kernel, iterations = 2) ###\n sure_fg = skimage.morphology.closing(np.array(sure_fg, dtype=np.uint8))\n # kernel = np.ones((2,2), np.uint8)\n # sure_fg = binary_closing(sure_fg, kernel)\n \n # sure background area\n #kernel = np.ones((5, 5), np.uint8)\n #sure_bg = cv2.dilate(mask, kernel, iterations = 1)\n sure_fg_bool = 1 - img_as_bool(sure_fg)\n # sure_bg = np.uint8(1 - morphology.medial_axis(sure_fg_bool)) ### \n sure_bg = np.uint8(1 - morphology.skeletonize(sure_fg_bool))\n sure_bg[0, :] = 1\n sure_bg[-1, :] = 1\n sure_bg[:, 0] = 1\n sure_bg[:, -1] = 1\n \n # Finding unknown region\n sure_fg = np.uint8(sure_fg)\n unknown = cv2.subtract(sure_bg, sure_fg)\n \n if plotImage:\n plt.figure()\n plt.imshow(sure_fg)\n plt.title(\"Inner Marker\")\n plt.figure()\n plt.imshow(sure_bg)\n plt.title(\"Outer Marker\")\n plt.figure()\n plt.imshow(unknown)\n plt.title(\"Unknown\")\n \n # Marker labelling\n ret, markers = cv2.connectedComponents(sure_fg)\n\n # Add one to all labels so that sure background is not 0, but 1\n markers = markers+1\n\n # Now, mark the region of unknown with zero\n markers[unknown==1] = 0\n \n if plotImage:\n plt.figure()\n plt.imshow(markers, cmap='jet')\n plt.title(\"Markers\")\n \n # Do watershed\n markers = cv2.watershed(imgCopy, markers)\n \n imgCopy[markers == -1] = [0, 255 ,0]\n\n if plotImage:\n plt.figure()\n plt.imshow(markers,cmap='jet')\n plt.title(\"Mask\")\n plt.figure()\n plt.imshow(img)\n plt.title(\"Original Image\")\n plt.figure()\n plt.imshow(imgCopy)\n plt.title(\"Marked Image\")\n plt.show()\n\n return markers","title":""},{"docid":"db20ad45f923767ded8f27bed305444a","score":"0.50616443","text":"def bigaussian(mu, wid, x, m = 0.5):\n lx = x.shape[0]\n ix = np.where(x == mu)[0][0]\n \n y = np.ones(lx)\n y[0:ix] = gaussian(mu, wid * m, x[0:ix])\n y[ix+1:lx] = gaussian(mu, wid * (1 - m), x[ix+1:lx]) \n \n return y","title":""},{"docid":"e64dbc54278c459cf4a3b45c0fadc679","score":"0.5060408","text":"def _FWHMGauss(sigma, pixel=12):\n return sigma*2*np.sqrt(2*np.log(2))*pixel","title":""},{"docid":"41aa67feafb484e704424dc665dadb30","score":"0.506033","text":"def plot_heatmap(mi, output):\n\tfig = plt.figure()\n\tdata = np.array(mi)\n\tfig, ax = plt.subplots()\n\theatmap = ax.pcolor(data, cmap=plt.cm.jet)\n\n\tax.invert_yaxis()\n\tax.xaxis.tick_top()\n\n\tax.set_xlabel('Seq 2')\n\tax.set_ylabel('Seq 1')\n\tax.xaxis.set_label_position('top')\n\n\tax.set_xlim(0, len(mi[0]))\n\tax.set_ylim(len(mi), 0)\n\n\txmajor_ticks = np.arange(0, len(mi[1]), 20)\n\txminor_ticks = np.arange(0, len(mi[1]), 1)\n\n\tymajor_ticks = np.arange(0, len(mi), 20)\n\tyminor_ticks = np.arange(0, len(mi), 1)\n\n\tax.tick_params(axis = 'both', which = 'major', labelsize = 5)\n\tax.tick_params(axis = 'both', which = 'minor', labelsize = 0)\n\n\tax.set_xticks(xmajor_ticks)\n\tax.set_xticks(xminor_ticks, minor = True)\n\tax.set_yticks(ymajor_ticks)\n\tax.set_yticks(yminor_ticks, minor = True)\n\n\tax.tick_params(which = 'both', direction = 'out')\n\n\tplt.xticks(rotation=90)\n\n\tcb = plt.colorbar(heatmap)\n\tcb.set_label('MI value')\n\n\tfig.savefig(output, dpi = 700)","title":""},{"docid":"b791443ac60b063226878a563cca1c94","score":"0.5057443","text":"def output_heatmap(model, last_conv_layer, img):\n # predict the image class\n preds = model.predict(img)\n # find the class index\n index = np.argmax(preds[0])\n # This is the entry in the prediction vector\n target_output = model.output[:, index]\n # get the last conv layer\n last_conv_layer = model.get_layer(last_conv_layer)\n\n # compute the gradient of the output feature map with this target class\n grads = K.gradients(target_output, last_conv_layer.output)[0]\n\n # mean the gradient over a specific feature map channel\n pooled_grads = K.mean(grads, axis=(0, 1, 2))\n\n # this function returns the output of last_conv_layer and grads\n # given the input picture\n iterate = K.function([model.input], [pooled_grads, last_conv_layer.output[0]])\n pooled_grads_value, conv_layer_output_value = iterate([img])\n\n # We multiply each channel in the feature map array\n # by \"how important this channel is\" with regard to the target class\n\n for i in range(conv_layer_output_value.shape[-1]):\n conv_layer_output_value[:, :, i] *= pooled_grads_value[i]\n\n # The channel-wise mean of the resulting feature map\n # is our heatmap of class activation\n heatmap = np.mean(conv_layer_output_value, axis=-1)\n heatmap = np.maximum(heatmap, 0)\n heatmap /= np.max(heatmap)\n\n return heatmap","title":""},{"docid":"df66a5836337c3acf6fa822c08507fda","score":"0.5056677","text":"def gauss_kern(size, sigma=1.0):\n h1 = size[0]\n h2 = size[1]\n x, y = np.mgrid[0:h2, 0:h1]\n x = x-h2/2\n y = y-h1/2\n g = np.exp( -( x**2 + y**2 ) / (2*sigma**2) );\n return g / g.sum()","title":""},{"docid":"ecd79330882098da6d86d29cf12481a6","score":"0.50544626","text":"def estbg(im, mask=None, bins=None, plotalot=False, rout=(3,200), badval=nan):\n # 2009-09-02 17:13 IJC: Created!\n # 2009-09-04 15:07 IJC: Added RemoveOutliers option. Use only non-empty bins in fit.\n # 2009-09-08 15:32 IJC: Error returned is now divided by sqrt(N) for SDOM\n # 2009-11-03 00:16 IJC: Improved guess for gaussian dispersion\n # 2011-05-18 11:47 IJMC: Moved (e)gaussian imports to analysis.\n # 2012-01-01 21:04 IJMC: Added badval option\n # 2012-08-15 17:45 IJMC: Numpy's new histogram no longer accepts 'new' keyword\n # 2013-03-20 08:22 IJMC: Now works better even for small numbers\n # of pixels; thanks to A. Weigel @\n # ETH-Zurich for catching this!\n\n from numpy import histogram, mean, median, sqrt, linspace, isfinite, ones,std\n from pylab import find\n from scipy import optimize\n #from analysis import removeoutliers, egaussian, gaussian, stdr\n if plotalot:\n from pylab import figure, errorbar, plot, colorbar, title, hist, mean, std\n ##from analysis import imshow\n\n def gaussianChiSquared(guess, x, y, err):\n return (egaussian(guess, x, y, e=err)**2).sum()\n\n\n if mask==None:\n mask = ones(im.shape)\n dat = im.ravel()[find(mask<>0)]\n if plotalot:\n figure(); plot(im.ravel()); plot(dat)\n print mean(dat), std(dat), rout[0]*std(dat)\n print len(dat), (abs(dat-mean(dat))<(rout[0]*std(dat))).sum()\n figure(); plot(dat-mean(dat)); \n plot([0,len(dat)], [rout[0]*std(dat),rout[0]*std(dat)],'--k')\n plot([0,len(dat)], [-rout[0]*std(dat),-rout[0]*std(dat)],'--k')\n dat = removeoutliers(dat, rout[0], remove='both', center='mean', niter=rout[1], verbose=plotalot)\n ndat = len(dat)\n\n if ndat==0:\n print \"No data to work with!\"\n return (badval, badval)\n if bins==None:\n if plotalot: print \"no bins entered!\"\n datmean = dat.mean()\n datstd = stdr(dat, nsigma=3)\n nunique = len(np.unique(dat.ravel()))\n #pdb.set_trace()\n if nunique > len(dat)/20.:\n dobin = False\n else:\n dobin = True\n bins = linspace(dat.min(), dat.max(), nunique/2)\n\n if plotalot: \n print \"dat.mean, dat.std>>\" + str((dat.mean(), dat.std()))\n\n\n #if plotalot:\n # figure(); hout = hist(dat[datIndex],bins)\n #else:\n \n if dobin:\n binwidth = mean(bins[1::]-bins[:-1])\n bincenter = 0.5*(bins[1::]+bins[:-1])\n datIndex = (dat>=bins.min()) * (dat<=bins.max())\n hout = histogram(dat[datIndex], bins) #,new=True)\n gy = hout[0]\n erry = sqrt(gy)\n usableIndex = gy>0\n\n eff_binwidth = mean(bins[usableIndex][1::]-bins[usableIndex][:-1])\n guess = [gy.sum()*eff_binwidth, std(dat[datIndex]), median(dat[datIndex])]\n\n if 1.0*usableIndex.sum()/usableIndex.size < 0.5:\n out = guess\n else:\n out = optimize.fmin(gaussianChiSquared, guess, \\\n args=(bincenter[usableIndex],gy[usableIndex], erry[usableIndex]), \\\n disp=plotalot)\n\n if plotalot:\n from pylab import figure, errorbar, plot, colorbar, title\n from nsdata import imshow\n print 'guess>>',guess\n print 'fit>>',out\n figure()\n imshow(im); colorbar()\n figure()\n errorbar(bincenter[usableIndex], gy[usableIndex], erry[usableIndex], fmt='ob')\n plot(bincenter, gaussian(out, bincenter),'-r', linewidth=2)\n title('Mean: %f, Std. Dev.: %f' % (out[2], out[1]))\n\n ret = out[2], out[1]/sqrt(ndat)\n else:\n ret = datmean, datstd/sqrt(ndat)\n\n return ret","title":""},{"docid":"7823ce4f2d065ce46cfc80bd8f847a87","score":"0.5051405","text":"def calcHOGFeatures(\n self,\n img, # Image in some color space\n orientations, # Number of orientations\n pix_per_cell, # Pixels per cell\n cell_per_block, # Cells per block\n block_norm = 'L2-Hys', # Block normalization function\n transform_sqrt = False, # Do SQRT transformation first\n visualise = False # Visualise HOG features\n ):\n\n res = skimage.feature.hog(\n img,\n orientations = orientations,\n pixels_per_cell = (pix_per_cell, pix_per_cell),\n cells_per_block = (cell_per_block, cell_per_block),\n block_norm = block_norm,\n transform_sqrt = transform_sqrt,\n visualise = visualise,\n feature_vector = False)\n\n return res","title":""},{"docid":"c797377502881f4e490a44d6dad2a681","score":"0.5050367","text":"def generate_hm(height, width, kpts, maxlenght, weight):\n\n def makeGaussian(height, width, sigma=3, center=None):\n \"\"\" Make a square gaussian kernel.\n size is the length of a side of the square\n sigma is full-width-half-maximum, which\n can be thought of as an effective radius.\n \"\"\"\n x = np.arange(0, width, 1, float)\n y = np.arange(0, height, 1, float)[:, np.newaxis]\n if center is None:\n x0 = width // 2\n y0 = height // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)\n\n num = kpts.shape[0]\n\n hm = np.zeros((height, width, num), dtype=np.float32)\n for i in range(num):\n if not (np.array_equal(kpts[i], [-1, -1])):\n s = 7 # int(np.sqrt(maxlenght) * maxlenght * 10 / 4096) + 2\n hm[:, :, i] = makeGaussian(height, width, sigma=s, center=(kpts[i, 0], kpts[i, 1]))\n else:\n hm[:, :, i] = np.zeros((height, width))\n return hm","title":""},{"docid":"bc9d3eef3ef0f72d6796ff0cac954641","score":"0.50474685","text":"def gaussianblur_transform(im):\n im_gblur = cv2.GaussianBlur(im,(5,5),0)\n return im_gblur","title":""},{"docid":"4c65887f43b28224cdbbdb83c863f862","score":"0.5038349","text":"def create_heatmap(df):\n\n fig = go.Figure(data=go.Heatmap(\n z=df.values.tolist(),\n x=df.columns,\n #y=[classifier for classifier in df.index.values.tolist()],\n y = df.index.values.tolist(),\n hoverongaps = False,\n xgap = 3,\n ygap = 3,\n colorscale=[[0.0, 'rgb(165,0,38)'], [0.1111111111111111, 'rgb(215,48,39)'], [0.2222222222222222, 'rgb(244,109,67)'], [0.3333333333333333, 'rgb(253,174,97)'], [0.4444444444444444, 'rgb(254,224,144)'], [0.5555555555555556, 'rgb(224,243,248)'], [0.6666666666666666, 'rgb(171,217,233)'], [0.7777777777777778, 'rgb(116,173,209)'], [0.8888888888888888, 'rgb(69,117,180)'], [1.0, 'rgb(49,54,149)']]\n ),\n )\n return fig","title":""},{"docid":"af3584dc7d52701df0c66f12d26913bf","score":"0.5036482","text":"def plot_means_heatmap(self,\n scale_rows=True,\n cmap='viridis',\n figsize=(6, 6),\n annot=False,\n valfmt=\"{x:.1f}\",\n fontsize=12,\n fontweight=\"bold\",\n textcolors=[\"white\", \"black\"]):\n clmeans_df = self.cluster_means()\n clmeans_np = clmeans_df.to_numpy()\n if (scale_rows):\n cbarlabel = \"Normalized Values\"\n else:\n cbarlabel = \"Values\"\n\n fig, ax = plt.subplots(figsize=figsize)\n im, cbar = vh.heatmap(clmeans_np,\n row_labels=clmeans_df.index,\n col_labels=clmeans_df.columns,\n ax=ax,\n scale_rows=scale_rows,\n cmap=cmap,\n cbarlabel=cbarlabel)\n\n if annot:\n vh.annotate_heatmap(im,\n valfmt=valfmt,\n size=fontsize,\n fontweight=fontweight,\n textcolors=textcolors)","title":""},{"docid":"01bb583cd963dd04ca56d35a64ad4fee","score":"0.50287306","text":"def heatmap(filename, data):\n\n fig, ax = ppl.subplots(1)\n ppl.pcolormesh(fig, ax, data, vmin=-0.0016, vmax=0.0016)\n fig.savefig(filename + \".png\")","title":""},{"docid":"d6601c0138536945071353434f4bbb0a","score":"0.502068","text":"def plot_heatmap(mi):\n\tfig = plt.figure()\n\tdata = np.array(mi)\n\tfig, ax = plt.subplots()\n\theatmap = ax.pcolor(data, cmap=plt.cm.jet)\n\n\tax.tick_params(direction='out')\n\n\tmajorLocator = MultipleLocator(20)\n\tmajorFormatter = FormatStrFormatter('%d')\n\tminorLocator = MultipleLocator(1)\n\n\tax.xaxis.set_major_locator(majorLocator)\n\tax.xaxis.set_major_formatter(majorFormatter)\n\tax.xaxis.set_minor_locator(minorLocator)\n\n\tax.yaxis.set_major_locator(majorLocator)\n\tax.yaxis.set_major_formatter(majorFormatter)\n\tax.yaxis.set_minor_locator(minorLocator)\n\n\tax.invert_yaxis()\n\tax.xaxis.tick_top()\n\n\t###check which seq belongs to each axe\n\tax.set_xlabel('Seq 2')\n\tax.set_ylabel('Seq 1')\n\n\tax.set_xlim(0, len(mi[1]))\n\tax.set_ylim(len(mi), 0)\n\n\tplt.xticks(rotation=90)\n\n\tcb = plt.colorbar(heatmap)\n\tcb.set_label('MI value')\n\n\t#pdf = PdfPages('heatmap.pdf')\n\t#pdf.savefig(fig)\n\tfig.savefig('heatmap.png')\n\t#pdf.close()","title":""},{"docid":"e262d8a1c66a244b1a029655dfb09e97","score":"0.5017516","text":"def _cmd_heatmap(args):\n cnarrs = []\n for fname in args.filenames:\n cnarr = read_cna(fname)\n if args.adjust_xy:\n is_sample_female = verify_sample_sex(\n cnarr, args.sample_sex, args.male_reference, args.diploid_parx_genome\n )\n cnarr = cnarr.shift_xx(args.male_reference, is_sample_female, args.diploid_parx_genome)\n cnarrs.append(cnarr)\n heatmap.do_heatmap(\n cnarrs,\n args.chromosome,\n args.desaturate,\n args.by_bin,\n args.delim_sampl,\n args.vertical,\n args.title,\n )\n if args.output:\n oformat = os.path.splitext(args.output)[-1].replace(\".\", \"\")\n pyplot.savefig(args.output, format=oformat, bbox_inches=\"tight\")\n logging.info(\"Wrote %s\", args.output)\n else:\n pyplot.show()","title":""},{"docid":"3f8d99788d7f0420d7cd470178243e34","score":"0.5016424","text":"def plot_gaussian(mu,var,epsilon=0.089881552536):\n X1,X2 = np.meshgrid(np.linspace(0,35),np.linspace(0,35))\n Z = get_probability(np.array([X1.flatten(),X2.flatten()]).T, mu, var)\n Z = np.reshape(Z, X1.shape)\n # Z[Z<epsilon] = 0\n # Z[Z>epsilon] = 1\n c = contour(X1, X2, Z, levels=[epsilon], lw=3, label=\"Gaussian Boundary\")\n clabel(c, inline=1, fmt='Epsilon=%3.5e', colors='k',fontsize=8)","title":""},{"docid":"cad78da51902ad37aed14a3a38b610a5","score":"0.5016406","text":"def create_gaussian_filter(size, sigma):\n h = size[0] #height of the template\n w = size[1] #width of the template \n if h % 2 == 0: h += 1 #add 1 if dimensions are even\n if w % 2 == 0: w += 1\n x = math.floor(h/2)\n y = math.floor(w/2) \n sum = 0\n #create our template\n template = np.zeros((h,w))\n #fill the template in with the numbers from Gaussian distribution\n for i in range(h):\n for j in range(w):\n template[i,j] = math.exp(-((((j-x)**2)+((i-y)**2))/(2*(sigma**2))))\n sum = sum + template[i,j]\n #normalise the numbers\n gaussian_filter = template/sum\n return gaussian_filter","title":""},{"docid":"502afa56d7fc12c7b27a8eba017cdff5","score":"0.50153375","text":"def gaussian2kp(heatmap, kp_variance='matrix', clip_variance=None):\n shape = heatmap.shape\n # adding small eps to avoid 'nan' in variance\n heatmap = heatmap.unsqueeze(-1) + 1e-7\n grid = make_coordinate_grid(shape[3:], heatmap.type()).unsqueeze_(0).unsqueeze_(0).unsqueeze_(0)\n\n # print(grid.shape)\n # sys.exit()\n\n mean = (heatmap * grid).sum(dim=(3, 4))\n\n kp = {'mean': mean.permute(0, 2, 1, 3)}\n\n if kp_variance == 'matrix':\n mean_sub = grid - mean.unsqueeze(-2).unsqueeze(-2)\n var = torch.matmul(mean_sub.unsqueeze(-1), mean_sub.unsqueeze(-2))\n var = var * heatmap.unsqueeze(-1)\n var = var.sum(dim=(3, 4))\n var = var.permute(0, 2, 1, 3, 4)\n if clip_variance:\n min_norm = torch.tensor(clip_variance).type(var.type())\n sg = smallest_singular(var).unsqueeze(-1)\n var = torch.max(min_norm, sg) * var / sg\n kp['var'] = var\n\n elif kp_variance == 'single':\n mean_sub = grid - mean.unsqueeze(-2).unsqueeze(-2)\n var = mean_sub ** 2\n var = var * heatmap\n var = var.sum(dim=(3, 4))\n var = var.mean(dim=-1, keepdim=True)\n var = var.unsqueeze(-1)\n var = var.permute(0, 2, 1, 3, 4)\n kp['var'] = var\n\n return kp","title":""}],"string":"[\n {\n \"docid\": \"f243def6ba73436b53aeb3f0168fde06\",\n \"score\": \"0.63595766\",\n \"text\": \"def process_image(img):\\n avg_heat = np.zeros_like(image[:, :, 0]).astype(np.float)\\n out_img, out_boxes, heat_map = find_cars(img, scale)\\n\\n # -----\\n heat = np.zeros_like(image[:, :, 0]).astype(np.float)\\n # Add heat to each box in box list\\n heat = add_heat(heat, out_boxes)\\n\\n # Apply threshold to help remove false positives\\n heat = apply_threshold(heat, 1)\\n # heat = apply_threshold(heat, 2)\\n\\n avg_heat = cv2.addWeighted(avg_heat, 0.8, heat, 0.2, 0.)\\n\\n # Apply threshold to help remove near-zero noise\\n heatmap = apply_threshold(cv2.blur(avg_heat, (15, 15)), 0.5)\\n\\n # Visualize the heatmap when displaying\\n heatmap = np.clip(heatmap, 0, 255) # limit the values in the heatmap array\\n\\n # -----\\n # Find final boxes from heatmap using label function\\n labels = label(heat_map)\\n # Draw bounding boxes on a copy of the image\\n output_img = draw_labeled_bboxes(np.copy(img), labels)\\n\\n # -----\\n r = 377.0 / avg_heat.shape[1] # calculate height\\n dim = (377, int(avg_heat.shape[0] * r)) # width, height\\n resized = cv2.resize(avg_heat, dim, interpolation=cv2.INTER_AREA)\\n # add to output_img\\n output_img[0:0 + resized.shape[0], 0:0 + resized.shape[1]] = np.repeat(resized[:, :, np.newaxis], 3,\\n axis=2) * 255\\n output_img = cv2.putText(output_img, \\\"Heatmap\\\", (34, 34), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2,\\n cv2.LINE_AA)\\n\\n # -----\\n return output_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9140b69aa7a98a7f531328fe38959b0\",\n \"score\": \"0.63407224\",\n \"text\": \"def learnGauss(metricArray): \\n fit = gaussReg() \\n n= 100 #You should probably change this...\\n overlap = 0.3 \\n imageSize = [100,400]\\n densMap(fit, metricArray, n, overlap, imageSize )\\n overlayMap('SmallTile.jpg', 'ContourPlot.jpg') \\n # Final map is saved as OverlayMap.jpg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"952114e6abc2bbbcfbca555055f473e2\",\n \"score\": \"0.629014\",\n \"text\": \"def add_heat(img, bbox_list):\\n heatmap = np.zeros_like(img[:,:,0]).astype(np.float)\\n for box in bbox_list:\\n # Assuming each \\\"box\\\" takes the form ((x1, y1), (x2, y2))\\n heatmap[box[0][0]:box[0][1], box[1][0]:box[1][1]] += 1\\n #heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1\\n return heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c47ebbf628fd6d7788f1c05fbaef245c\",\n \"score\": \"0.6085818\",\n \"text\": \"def generate_heatmap(self):\\n data = []\\n for j in range(len(self.generations[0])):\\n row = []\\n for i in range(len(self.generations)):\\n row.append(self.generations[i][j].fitness)\\n data.append(row)\\n data = np.array(data)\\n\\n # Display log error in colorbar.\\n tick_range = range(\\n math.floor(math.log10(data.min().min())),\\n 1 + math.ceil(math.log10(data.max().max())))\\n cbar_ticks = [math.pow(10, i) for i in tick_range]\\n log_norm = LogNorm(vmin=data.min().min(), vmax=data.max().max())\\n\\n plt.figure(figsize=(10, 5))\\n ax = sns.heatmap(\\n data,\\n cmap='viridis',\\n xticklabels=2,\\n yticklabels=2,\\n norm=log_norm,\\n cbar_kws={'ticks': cbar_ticks, 'aspect': 15})\\n\\n hfont = {'fontname': 'Helvetica'}\\n plt.xlabel('Generation', **hfont)\\n plt.ylabel('Individual', **hfont)\\n plt.xticks(\\n [i for i in range(0, self.config.max_generations, 5)],\\n [i for i in range(0, self.config.max_generations, 5)])\\n plt.yticks(\\n [i for i in range(0, self.config.population_size, 5)],\\n [i for i in range(0, self.config.population_size, 5)])\\n\\n ax.invert_yaxis()\\n ax.collections[0].colorbar.set_label('Error')\\n ax.spines['right'].set_visible(False)\\n ax.spines['top'].set_visible(False)\\n plt.savefig('figures/Parameter Tuning Figure/heatmap.svg')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cee4d8d72785e8ba316194a9e7e0a123\",\n \"score\": \"0.59814197\",\n \"text\": \"def add_character_others(image, weight_map, weight_val, bbox):\\n\\n\\tif not Polygon(bbox.reshape([4, 2]).astype(np.int32)).is_valid:\\n\\t\\treturn image\\n\\n\\ttop_left = np.array([np.min(bbox[:, 0]), np.min(bbox[:, 1])]).astype(np.int32)\\n\\tif top_left[1] > image.shape[0] or top_left[0] > image.shape[1]:\\n\\t\\treturn image, weight_map\\n\\tbbox -= top_left[None, :]\\n\\ttransformed = four_point_transform(gaussian_heatmap.copy(), bbox.astype(np.float32))\\n\\n\\tstart_row = max(top_left[1], 0) - top_left[1]\\n\\tstart_col = max(top_left[0], 0) - top_left[0]\\n\\tend_row = min(top_left[1] + transformed.shape[0], image.shape[0])\\n\\tend_col = min(top_left[0] + transformed.shape[1], image.shape[1])\\n\\timage[max(top_left[1], 0):end_row, max(top_left[0], 0):end_col] += \\\\\\n\\t\\ttransformed[\\n\\t\\tstart_row:end_row - top_left[1],\\n\\t\\tstart_col:end_col - top_left[0]]\\n\\n\\tweight_map[max(top_left[1], 0):end_row, max(top_left[0], 0):end_col] += \\\\\\n\\t\\tnp.float32(transformed[\\n\\t\\t\\tstart_row:end_row - top_left[1],\\n\\t\\t\\tstart_col:end_col - top_left[0]] != 0)*weight_val\\n\\n\\treturn image, weight_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba45dae9b86cf41dc4e87f9217ec3381\",\n \"score\": \"0.59801453\",\n \"text\": \"def add_heat(heatmap, bboxes, amount):\\n for bbox in bboxes:\\n window = heatmap[bbox[0][1]:bbox[1][1], bbox[0][0]:bbox[1][0]]\\n window[window < 256 - amount] += amount\\n return heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef2603db3b8dbde282dd6bdbaa4326a\",\n \"score\": \"0.5906126\",\n \"text\": \"def generate_heatmap(height, width, landmarks, sigma=3):\\n num_landmarks = landmarks.shape[0]\\n hm = np.zeros((height, width, num_landmarks), dtype=np.float32)\\n for i in range(num_landmarks):\\n if not np.array_equal(landmarks[i], [-1, -1]):\\n\\n hm[:, :, i] = gaussian_k(landmarks[i][0] * height,\\n landmarks[i][1] * width,\\n sigma,\\n height, width)\\n else:\\n hm[:, :, i] = np.zeros((height, width))\\n\\n return hm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d1f30de35f51410811e5d59100b989b\",\n \"score\": \"0.5886273\",\n \"text\": \"def draw_heat_map(image, heat_map):\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6a30ab63d733f76be034cc57ce061c6\",\n \"score\": \"0.5851643\",\n \"text\": \"def add_heat(heatmap, bbox_list):\\n # Iterate through list of bboxes\\n for box in bbox_list:\\n # Add += 1 for all pixels inside each bbox\\n # Assuming each \\\"box\\\" takes the form ((x1, y1), (x2, y2))\\n heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1\\n\\n # Return updated heatmap\\n return heatmap # Iterate through list of bboxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c830000d22e81a20333e22f42d17c049\",\n \"score\": \"0.5835045\",\n \"text\": \"def getHeatMap(size_w, size_h, boxes):\\n kernel = np.array([[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]]).astype(np.uint8)\\n heat = torch.zeros(size=(size_h, size_w))\\n for box in boxes:\\n sx, sy, ex, ey = box\\n sx, sy, ex, ey = int(sx), int(sy), int(ex), int(ey)\\n heat[sy:ey, sx:ex] += 1\\n heat = cv2.dilate(np.float32(heat), kernel= kernel, iterations=1)\\n heat = cv2.GaussianBlur(np.array(heat), (5, 5), sigmaX=0)\\n return heat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82268b91df7f1d27dd09a6236db5f55b\",\n \"score\": \"0.58092785\",\n \"text\": \"def _gaussian_blur(heatmaps, kernel=11):\\n assert kernel % 2 == 1\\n border = (kernel - 1) // 2\\n batch_size = heatmaps.shape[0]\\n num_joints = heatmaps.shape[1]\\n height = heatmaps.shape[2]\\n width = heatmaps.shape[3]\\n for i in range(batch_size):\\n for j in range(num_joints):\\n origin_max = np.max(heatmaps[i, j])\\n dr = np.zeros((height + 2 * border, width + 2 * border), dtype=np.float32)\\n dr[border:-border, border:-border] = heatmaps[i, j].copy()\\n dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)\\n heatmaps[i, j] = dr[border:-border, border:-border].copy()\\n heatmaps[i, j] *= origin_max / np.max(heatmaps[i, j])\\n return heatmaps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb93b3dc853f0f7b657d17a9fbe4ec26\",\n \"score\": \"0.5804239\",\n \"text\": \"def add_heat(heatmap, bbox_list):\\n for box in bbox_list:\\n # Add += 1 for all pixels inside each bbox\\n # Assuming each \\\"box\\\" takes the form ((x1, y1), (x2, y2))\\n heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1\\n\\n # Return updated heatmap\\n return heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"045b11eb12811396ec5ebf3f66f8565e\",\n \"score\": \"0.5801142\",\n \"text\": \"def _gaussian_weighted_map_const_multi(\\n y_grid, x_grid, heatmap, points_y, points_x, boxes,\\n gaussian_denom_ratio):\\n num_instances, _ = _get_shape(boxes, 2)\\n height, width, num_keypoints = _get_shape(heatmap, 3)\\n\\n # [height, width, num_instances, num_keypoints].\\n # Note that we intentionally avoid using tf.newaxis as TfLite converter\\n # doesn't like it.\\n y_diff = (\\n tf.reshape(y_grid, [height, width, 1, 1]) -\\n tf.reshape(points_y, [1, 1, num_instances, num_keypoints]))\\n x_diff = (\\n tf.reshape(x_grid, [height, width, 1, 1]) -\\n tf.reshape(points_x, [1, 1, num_instances, num_keypoints]))\\n distance_square = y_diff * y_diff + x_diff * x_diff\\n\\n y_min, x_min, y_max, x_max = tf.split(boxes, 4, axis=1)\\n\\n # Make the mask with all 1.0 in the box regions.\\n # Shape: [height, width, num_instances]\\n in_boxes = tf.math.logical_and(\\n tf.math.logical_and(\\n tf.reshape(y_grid, [height, width, 1]) >= tf.reshape(\\n y_min, [1, 1, num_instances]),\\n tf.reshape(y_grid, [height, width, 1]) < tf.reshape(\\n y_max, [1, 1, num_instances])),\\n tf.math.logical_and(\\n tf.reshape(x_grid, [height, width, 1]) >= tf.reshape(\\n x_min, [1, 1, num_instances]),\\n tf.reshape(x_grid, [height, width, 1]) < tf.reshape(\\n x_max, [1, 1, num_instances])))\\n in_boxes = tf.cast(in_boxes, dtype=tf.float32)\\n\\n gaussian_denom = tf.cast(\\n tf.minimum(height, width), dtype=tf.float32) * gaussian_denom_ratio\\n # shape: [height, width, num_instances, num_keypoints]\\n gaussian_map = tf.exp((-1 * distance_square) / gaussian_denom)\\n return tf.expand_dims(heatmap, axis=2) * gaussian_map * tf.reshape(\\n in_boxes, [height, width, num_instances, 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527520b3d95728439eb344fea96f62e9\",\n \"score\": \"0.5798712\",\n \"text\": \"def generate_heatmap(self):\\n data = []\\n for j in range(len(self.generations[0])):\\n row = []\\n for i in range(len(self.generations)):\\n row.append(self.generations[i][j].fitness)\\n data.append(row)\\n data = np.array(data)\\n\\n plt.figure()\\n ax = sns.heatmap(\\n data,\\n cmap='RdBu',\\n xticklabels=2,\\n yticklabels=2)\\n\\n hfont = {'fontname': 'Helvetica'}\\n plt.xlabel('Generation', **hfont)\\n plt.ylabel('Individual', **hfont)\\n ax.invert_yaxis()\\n ax.axhline(linewidth=4, color='black')\\n ax.axvline(linewidth=4, color='black')\\n ax.collections[0].colorbar.set_label('Fitness')\\n plt.savefig('figures/Voltage Clamp Figure/Single VC Optimization/'\\n 'heatmap.svg')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30d3d2ed122b04a2d58839f205bd0a30\",\n \"score\": \"0.57727194\",\n \"text\": \"def generate_heatmap(masked_index_image,heatmap_filename):\\n plt.figure(figsize = (2*7.2,12.8))\\n cmap = plt.cm.RdYlGn_r\\n cmap.set_bad(color='black')\\n norm = cl.Normalize(vmin=0, vmax=masked_index_image.max())\\n imgplot = plt.imshow(masked_index_image,cmap=cmap,norm=norm)\\n plt.colorbar()\\n plt.savefig(heatmap_filename,dpi=100)\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b072ec06ef0b3aa50eb88e617fda502f\",\n \"score\": \"0.5737168\",\n \"text\": \"def fancy_heatmap(heatmap, threshold):\\n # get some dimensions of the image, size and strength to write the text in etc.\\n y = int(np.ceil(heatmap.shape[0]*.1))\\n x = int(np.ceil(heatmap.shape[1]*.05))\\n size = int(np.ceil(heatmap.shape[0]*.002))\\n strength = int(np.ceil(heatmap.shape[0]*.004))\\n # Maximum value of heatmap\\n m = max(heatmap.ravel())\\n # Normalized threshold (color values need to be in range 0..1)\\n norm_threshold = 0 if m == 0 else threshold/m\\n # Red channel will contain the heatmap, but normalized\\n r = normalize(heatmap, norm='max')\\n # Green and blue will also contain the heatmap, but values below (normalized) threshold set to zero\\n gb = np.copy(r)\\n gb[gb <= norm_threshold] = 0\\n # Resulting image with all three channels merged and added text\\n result = cv2.merge([r, gb, gb])\\n cv2.putText(result, \\\"Max: {:.2f}\\\".format(m), (x, y), cv2.FONT_HERSHEY_SIMPLEX, size, (1.,1.,1.), strength)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"489b5eba9008b80019c9e6e5d7b48dcf\",\n \"score\": \"0.5697161\",\n \"text\": \"def generate_2d_guassian(height, width, y0, x0, visibility=2, sigma=1, scale=12):\\n heatmap = tf.zeros((height, width))\\n\\n # this gaussian patch is 7x7, let's get four corners of it first\\n xmin = x0 - 3 * sigma\\n ymin = y0 - 3 * sigma\\n xmax = x0 + 3 * sigma\\n ymax = y0 + 3 * sigma\\n # if the patch is out of image boundary we simply return nothing according to the source code\\n # [1]\\\"In these cases the joint is either truncated or severely occluded, so for\\n # supervision a ground truth heatmap of all zeros is provided.\\\"\\n if xmin >= width or ymin >= height or xmax < 0 or ymax < 0 or visibility == 0:\\n return heatmap\\n\\n size = 6 * sigma + 1\\n x, y = tf.meshgrid(tf.range(0, 6 * sigma + 1, 1), tf.range(0, 6 * sigma + 1, 1), indexing='xy')\\n\\n # the center of the gaussian patch should be 1\\n center_x = size // 2\\n center_y = size // 2\\n\\n # generate this 7x7 gaussian patch\\n gaussian_patch = tf.cast(tf.math.exp(\\n -(tf.square(x - center_x) + tf.math.square(y - center_y)) / (tf.math.square(sigma) * 2)) * scale,\\n dtype=tf.float32)\\n\\n # part of the patch could be out of the boundary, so we need to determine the valid range\\n # if xmin = -2, it means the 2 left-most columns are invalid, which is max(0, -(-2)) = 2\\n patch_xmin = tf.math.maximum(0, -xmin)\\n patch_ymin = tf.math.maximum(0, -ymin)\\n # if xmin = 59, xmax = 66, but our output is 64x64, then we should discard 2 right-most columns\\n # which is min(64, 66) - 59 = 5, and column 6 and 7 are discarded\\n patch_xmax = tf.math.minimum(xmax, width) - xmin\\n patch_ymax = tf.math.minimum(ymax, height) - ymin\\n\\n # also, we need to determine where to put this patch in the whole heatmap\\n heatmap_xmin = tf.math.maximum(0, xmin)\\n heatmap_ymin = tf.math.maximum(0, ymin)\\n heatmap_xmax = tf.math.minimum(xmax, width)\\n heatmap_ymax = tf.math.minimum(ymax, height)\\n\\n # finally, insert this patch into the heatmap\\n indices = tf.TensorArray(tf.int32, 1, dynamic_size=True)\\n updates = tf.TensorArray(tf.float32, 1, dynamic_size=True)\\n\\n count = 0\\n\\n for j in tf.range(patch_ymin, patch_ymax):\\n for i in tf.range(patch_xmin, patch_xmax):\\n indices = indices.write(count, [heatmap_ymin + j, heatmap_xmin + i])\\n updates = updates.write(count, gaussian_patch[j][i])\\n count += 1\\n\\n heatmap = tf.tensor_scatter_nd_update(heatmap, indices.stack(), updates.stack())\\n\\n # unfortunately, the code below doesn't work because\\n # tensorflow.python.framework.ops.EagerTensor' object does not support item assignment\\n # heatmap[heatmap_ymin:heatmap_ymax, heatmap_xmin:heatmap_xmax] = gaussian_patch[patch_ymin:patch_ymax,patch_xmin:patch_xmax]\\n\\n return heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb96ceaa06b60c8818417f4ba7ea769\",\n \"score\": \"0.5645221\",\n \"text\": \"def find_cars(img, scale):\\n img_boxes = [] # Clears img_boxes so we don't keep unwanted heatmap history\\n count = 0\\n draw_img = np.copy(img)\\n\\n # Make a heatmap of zeros\\n heatmap = np.zeros_like(img[:, :, 0])\\n\\n # IMPORTANT : reading *.jpeg's (scaled 0-255, aka scaling needed), but\\n # # trained on *.png's (scaled 0-1, aka scaling not needed)\\n if img.dtype == 'uint8':\\n img = img.astype(np.float32) / 255 # aka scaling needed\\n\\n img_tosearch = img[ystart:ystop, :, :]\\n ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')\\n\\n if scale != 1: # resize whole image instead of separate windows\\n imshape = ctrans_tosearch.shape\\n ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1] / scale), np.int(imshape[0] / scale)))\\n\\n ch1 = ctrans_tosearch[:, :, 0]\\n ch2 = ctrans_tosearch[:, :, 1]\\n ch3 = ctrans_tosearch[:, :, 2]\\n\\n # Define blocks and steps as above\\n # These hold the number of HOG cells\\n nxblocks = (ch1.shape[1] // pix_per_cell) - 1 # Note : '//' causes integers to be result, instead of floats\\n nyblocks = (ch1.shape[0] // pix_per_cell) - 1\\n # How many features per block are we going to be extracting\\n nfeat_per_block = orient * cell_per_block ** 2\\n window = 64\\n nblocks_per_window = (window // pix_per_cell) - 1\\n # aka 75% overlap between cells\\n cells_per_step = 2 # Instead of overlap, define how many cells to step\\n nxsteps = (nxblocks - nblocks_per_window) // cells_per_step\\n nysteps = (nyblocks - nblocks_per_window) // cells_per_step\\n\\n # Compute individual channel HOG features for the entire image\\n hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n\\n for xb in range(nxsteps):\\n for yb in range(nysteps):\\n count += 1\\n ypos = yb * cells_per_step\\n xpos = xb * cells_per_step\\n # Extract HOG for this patch\\n hog_feat1 = hog1[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\\n hog_feat2 = hog2[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\\n hog_feat3 = hog3[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\\n hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\\n\\n xleft = xpos * pix_per_cell\\n ytop = ypos * pix_per_cell\\n\\n # Extract the image patch\\n subimg = cv2.resize(ctrans_tosearch[ytop:ytop + window, xleft:xleft + window], (64, 64))\\n\\n # Get colour features\\n spatial_features = bin_spatial(subimg, size=spatial_size)\\n hist_features = color_hist(subimg, nbins=hist_bins)\\n\\n # Scale features and make a prediction\\n test_features = X_scaler.transform(\\n np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))\\n test_prediction = svc.predict((test_features))\\n\\n if test_prediction == 1:\\n xbox_left = np.int(xleft * scale)\\n ytop_draw = np.int(ytop * scale)\\n win_draw = np.int(window * scale)\\n cv2.rectangle(draw_img, (xbox_left, ytop_draw + ystart),\\n (xbox_left + win_draw, ytop_draw + win_draw + ystart), (0, 0, 255))\\n img_boxes.append(\\n ((xbox_left, ytop_draw + ystart), (xbox_left + win_draw, ytop_draw + win_draw + ystart)))\\n heatmap[ytop_draw + ystart:ytop_draw + win_draw + ystart, xbox_left:xbox_left + win_draw] += 1\\n\\n return draw_img, img_boxes, heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b8291a766e94e155a923290efacbd87\",\n \"score\": \"0.5586054\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n plt.title('epsilon')\\n plt.ylabel('gamma')\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1] + 1) - .5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0] + 1) - .5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9256a6340d01503dedc6f182c2ee569c\",\n \"score\": \"0.5572046\",\n \"text\": \"def fit_gaussian2d(image):\\n\\n # Estimate center of target\\n y_mean, x_mean = np.array(image.shape) // 2 # Center guess\\n\\n # Create model to fit\\n model = models.Gaussian2D(amplitude=image.max(),\\n x_mean=x_mean,\\n y_mean=y_mean,\\n fixed={}\\n )\\n\\n # Fit model to grid\\n fitted_model, fit = fit_model(image, model)\\n\\n return fitted_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb7e3c192d3f6ad425c8c72436c5f265\",\n \"score\": \"0.5538218\",\n \"text\": \"def visualize_heatmap_activation(model, test_img):\\n preds = model.predict(test_img)\\n\\n # Setting up the Grad-CAM algorithm\\n img_output = model.output[:, np.argmax(preds[0])]\\n last_conv_layer = model.get_layer('conv2d_3')\\n grads = backend.gradients(img_output, last_conv_layer.output)[0]\\n pooled_grads = backend.mean(grads, axis=(0, 1, 2))\\n\\n iterate = backend.function([model.input], [pooled_grads, last_conv_layer.output[0]])\\n pooled_grads_value, conv_layer_output_value = iterate([test_img])\\n\\n for i in range(len(pooled_grads_value)):\\n conv_layer_output_value[:, :, i] *= pooled_grads_value[i]\\n\\n heatmap = np.mean(conv_layer_output_value, axis=-1)\\n\\n # Heatmap post-processing\\n heatmap = np.maximum(heatmap, 0)\\n heatmap /= np.max(heatmap)\\n\\n # Display the test image alongside the heatmap\\n plt.pcolormesh(test_img[0, :, :, 0])\\n plt.figure()\\n plt.pcolormesh(heatmap, cmap=\\\"magma\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00d7479a009b11cd91cc27c94b00998f\",\n \"score\": \"0.55210364\",\n \"text\": \"def _make_gaussian_maps(x, gaussians, size=None, scaling=6.):\\n if size is None:\\n size = x.shape[-2:]\\n bs = x.shape[0]\\n else:\\n size = [size] * 2\\n bs = 1\\n dtype = x.dtype\\n device = x.device\\n\\n gaussian_maps = []\\n map_template = torch.ones(*size, dtype=dtype, device=device)\\n meshgrids = torch.meshgrid(\\n [torch.linspace(0, 1, size[0], dtype=dtype, device=device),\\n torch.linspace(0, 1, size[1], dtype=dtype, device=device),])\\n\\n for gaussian_idx, yx_mu_logstd in enumerate(torch.unbind(gaussians)):\\n map = map_template.clone()\\n for mu_logstd, mgrid in zip(yx_mu_logstd, meshgrids):\\n mu = mu_logstd[0]\\n std = torch.exp(mu_logstd[1])\\n map *= torch.exp(-((mgrid - mu) / std) ** 2 / 2)\\n\\n map *= scaling\\n gaussian_maps.append(map)\\n\\n gaussian_maps = torch.stack(gaussian_maps)\\n gaussian_maps = gaussian_maps.unsqueeze(0).expand(bs, -1, -1, -1)\\n return gaussian_maps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2973662162cb8121f4e4e7cbdf27b55\",\n \"score\": \"0.55196005\",\n \"text\": \"def add_heat(heatmap, bbox_list):\\n for box in bbox_list:\\n\\n # Assuming each \\\"box\\\" takes the form ((x1, y1), (x2, y2))\\n heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1\\n\\n return heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bb65a49638361c188b3be3f571259d5\",\n \"score\": \"0.55166715\",\n \"text\": \"def create_heatmap(num_maps, height, width, all_joints, sigma, stride):\\n heatmap = np.zeros((height, width, num_maps), dtype=np.float64)\\n\\n for joints in all_joints:\\n for plane_idx, joint in enumerate(joints):\\n if joint:\\n _put_heatmap_on_plane(heatmap, plane_idx, joint, sigma, height, width, stride)\\n\\n # background\\n heatmap[:, :, -1] = np.clip(1.0 - np.amax(heatmap, axis=2), 0.0, 1.0)\\n\\n return heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c587c4fb31a735b55ddf5104fcf011\",\n \"score\": \"0.54902524\",\n \"text\": \"def add_1d_heatmaps(self,\\n maps: dict,\\n background: np.ndarray,\\n map2d_size: Union[tuple, list],\\n K: int,\\n interval: int = 10):\\n y_startpoint, x_startpoint = [int(1.1*map2d_size[1]),\\n int(0.1*map2d_size[0])],\\\\\\n [int(0.1*map2d_size[1]),\\n int(1.1*map2d_size[0])]\\n x_startpoint[1] += interval * 2\\n y_startpoint[0] += interval * 2\\n add = interval + 10\\n for i in range(K):\\n self.image_cover(background, maps['x'][i], x_startpoint[0],\\n x_startpoint[1])\\n cv.putText(background, str(i),\\n (x_startpoint[0] - 30, x_startpoint[1] + 10),\\n cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)\\n self.image_cover(background, maps['y'][i], y_startpoint[0],\\n y_startpoint[1])\\n cv.putText(background, str(i),\\n (y_startpoint[0], y_startpoint[1] - 5),\\n cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)\\n x_startpoint[1] += add\\n y_startpoint[0] += add\\n return background[:x_startpoint[1] + y_startpoint[1] +\\n 1, :y_startpoint[0] + x_startpoint[0] + 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d7dabf0a71c1ac94db05462ae1ef82c\",\n \"score\": \"0.54839283\",\n \"text\": \"def make_gaussian_label_image(mu_x, mu_y, num_x_px, num_y_px, var=0.05):\\n x = np.linspace(0, 1, num_x_px)\\n y = np.linspace(0, 1, num_y_px)\\n xs, ys = np.meshgrid(x, y)\\n locs = np.concatenate([xs[:, :, np.newaxis], ys[:, :, np.newaxis]], axis=2)\\n locs_reshaped = locs.reshape(np.prod(locs.shape[:-1]), locs.shape[-1])\\n\\n rv = multivariate_normal([mu_x, mu_y], [[var, 0], [0, var]])\\n return rv.pdf(locs_reshaped).reshape(locs.shape[:-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08ed450750248f0963fa8c9d05ea2310\",\n \"score\": \"0.5466378\",\n \"text\": \"def gaussian_proba_map(img):\\n method = 'cv2.TM_CCOEFF_NORMED'\\n sigmas = [41,31,21,11]\\n out = np.zeros(img.shape)\\n for sigma in sigmas:\\n size=3*sigma\\n template = gaussian(size,sigma)\\n template/=template.max()\\n template*=255\\n template = template.astype(np.uint8)\\n \\n img2 = img.copy()\\n meth = eval(method)\\n # Apply template Matching\\n res = cv2.matchTemplate(img2,template,meth)\\n res = np.pad(res,size/2,mode='constant')\\n to_replace = res>out\\n out[to_replace] = res[to_replace]\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f81771501de9795414ad3e296002f42\",\n \"score\": \"0.5454012\",\n \"text\": \"def create_heat_map(self, ax=None, block=True):\\n plt.figure()\\n ax = sns.heatmap(data=self.data, fmt=\\\"\\\", cmap='RdYlGn', linewidths=0.3, ax=ax)\\n ax.invert_yaxis()\\n ax.set(xlabel='Books index', ylabel='Books values over iterations', title='Heat map for the prediction result'\\n ' of each book over iterations')\\n\\n # plt.show(block=block)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65cd6f6bdcb772f058737d6f0b7668d7\",\n \"score\": \"0.54503685\",\n \"text\": \"def heatmap(args):\\n p = OptionParser(heatmap.__doc__)\\n p.add_option(\\\"--stacks\\\",\\n default=\\\"Exons,Introns,DNA_transposons,Retrotransposons\\\",\\n help=\\\"Features to plot in stackplot [default: %default]\\\")\\n p.add_option(\\\"--heatmaps\\\",\\n default=\\\"Copia,Gypsy,hAT,Helitron,Introns,Exons\\\",\\n help=\\\"Features to plot in heatmaps [default: %default]\\\")\\n p.add_option(\\\"--meres\\\", default=None,\\n help=\\\"Extra centromere / telomere features [default: %default]\\\")\\n add_window_options(p)\\n opts, args, iopts = p.set_image_options(args, figsize=\\\"8x5\\\")\\n\\n if len(args) != 2:\\n sys.exit(not p.print_help())\\n\\n fastafile, chr = args\\n window, shift, subtract = check_window_options(opts)\\n\\n stacks = opts.stacks.split(\\\",\\\")\\n heatmaps = opts.heatmaps.split(\\\",\\\")\\n stackbeds = get_beds(stacks)\\n heatmapbeds = get_beds(heatmaps)\\n stackbins = get_binfiles(stackbeds, fastafile, shift, subtract=subtract)\\n heatmapbins = get_binfiles(heatmapbeds, fastafile, shift, subtract=subtract)\\n\\n margin = .06\\n inner = .015\\n clen = Sizes(fastafile).mapping[chr]\\n\\n fig = plt.figure(1, (iopts.w, iopts.h))\\n root = fig.add_axes([0, 0, 1, 1])\\n\\n # Gauge\\n ratio = draw_gauge(root, margin, clen, rightmargin=4 * margin)\\n yinterval = .3\\n xx = margin\\n yy = 1 - margin\\n yy -= yinterval\\n xlen = clen / ratio\\n cc = chr\\n if \\\"_\\\" in chr:\\n ca, cb = chr.split(\\\"_\\\")\\n cc = ca[0].upper() + cb\\n\\n root.add_patch(Rectangle((xx, yy), xlen, yinterval - inner, color=gray))\\n ax = fig.add_axes([xx, yy, xlen, yinterval - inner])\\n\\n nbins = get_nbins(clen, shift)\\n\\n owindow = clen / 100\\n if owindow > window:\\n window = owindow / shift * shift\\n\\n stackplot(ax, stackbins, nbins, palette, chr, window, shift)\\n ax.text(.1, .9, cc, va=\\\"top\\\", zorder=100, transform=ax.transAxes,\\n bbox=dict(boxstyle=\\\"round\\\", fc=\\\"w\\\", alpha=.5))\\n\\n # Legends\\n xx += xlen + .01\\n yspace = (yinterval - inner) / (len(stackbins) + 1)\\n yy = 1 - margin - yinterval\\n for s, p in zip(stacks, palette):\\n s = s.replace(\\\"_\\\", \\\" \\\")\\n s = Registration.get(s, s)\\n\\n yy += yspace\\n root.add_patch(Rectangle((xx, yy), inner, inner, color=p, lw=0))\\n root.text(xx + 1.5 * inner, yy, s, size=10)\\n\\n yh = .05 # Heatmap height\\n # Heatmaps\\n xx = margin\\n yy = 1 - margin - yinterval - inner\\n for s, p in zip(heatmaps, heatmapbins):\\n s = s.replace(\\\"_\\\", \\\" \\\")\\n s = Registration.get(s, s)\\n\\n yy -= yh\\n m = stackarray(p, chr, window, shift)\\n\\n Y = np.array([m, m])\\n root.imshow(Y, extent=(xx, xx + xlen, yy, yy + yh - inner),\\n interpolation=\\\"nearest\\\", aspect=\\\"auto\\\")\\n root.text(xx + xlen + .01, yy, s, size=10)\\n\\n yy -= yh\\n\\n meres = opts.meres\\n if meres:\\n bed = Bed(meres)\\n for b in bed:\\n if b.seqid != chr:\\n continue\\n pos = (b.start + b.end) / 2\\n cpos = pos / ratio\\n xx = margin + cpos\\n accn = b.accn.capitalize()\\n root.add_patch(CirclePolygon((xx, yy), radius=.01, fc=\\\"m\\\", ec=\\\"m\\\"))\\n root.text(xx + .014, yy, accn, va=\\\"center\\\", color=\\\"m\\\")\\n\\n root.set_xlim(0, 1)\\n root.set_ylim(0, 1)\\n root.set_axis_off()\\n\\n image_name = chr + \\\".\\\" + iopts.format\\n savefig(image_name, dpi=iopts.dpi, iopts=iopts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b545016a27d205133e3e90546ecc147\",\n \"score\": \"0.5447263\",\n \"text\": \"def generateHeatMask( im ):\\n featref = computeFeatures(im)\\n h,w,nbp = im.shape\\n print(\\\"w:%d,h:%d\\\"%(w,h))\\n heatMap = np.zeros((h,w,1), dtype=np.int8)\\n black = [0,0,0]\\n white = [255,255,255]\\n rMaxDiff = 0\\n for j in range(h):\\n print(\\\"j:%d\\\" % j )\\n for i in range(w):\\n #~ print(\\\"i:%d\\\" % i )\\n arDiff = []\\n for color in [black,white]:\\n #~ print(\\\"color:%s\\\" % color)\\n imt = np.copy(im)\\n \\n # quite same time on my tablet !!! \\n # (would have think the [] would be far fastest!)\\n if 0:\\n cv2.circle(imt, (i,j), 1, color )\\n else:\\n imt[j,i]=color\\n if 0:\\n cv2.imshow(\\\"imt\\\",imt)\\n cv2.waitKey(1)\\n #~ feat = computeFeatures(imt)\\n #~ rDiff = diffFeatures(featref,feat)\\n rDiff = mseFloat(im,imt)\\n arDiff.append(rDiff)\\n #~ print(rDiff)\\n rDiff = max(arDiff)\\n if rDiff > rMaxDiff:\\n rMaxDiff = rDiff\\n heatMap[j,i] = rDiff*10\\n print(\\\"rMaxDiff: %5.3f\\\" % rMaxDiff )\\n #~ print(dir(cv2))\\n #~ heatMap = cv2.resize(heatMap,(w*2,h*2))\\n cv2.namedWindow(\\\"heat\\\",cv2.CV_WINDOW_AUTOSIZE|cv2.WINDOW_NORMAL)\\n cv2.imshow(\\\"heat\\\",heatMap)\\n cv2.resizeWindow(\\\"heat\\\",600,480)\\n cv2.waitKey(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1ea515f2a909fe0027da3d4fcabbf1f\",\n \"score\": \"0.542023\",\n \"text\": \"def heatmap(mat, x_label=None, y_label=None, axes=None,\\n title=None, save=False):\\n sns.heatmap(mat)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49df70c1282bafe379f4145ad7b9c40e\",\n \"score\": \"0.5408016\",\n \"text\": \"def find_cars(img,\\n clf,\\n scaler,\\n color_space,\\n spatial_size,\\n hist_bins,\\n scale,\\n cells_per_step,\\n x_start_stop,\\n y_start_stop,\\n orient,\\n pix_per_cell,\\n cell_per_block):\\n draw_img = np.copy(img)\\n\\n heatmap = np.zeros_like(img[:, :, 0])\\n\\n img = img.astype(np.float32)/255\\n\\n img_to_search = img[y_start_stop[0]:y_start_stop[1], x_start_stop[0]:x_start_stop[1], :]\\n\\n # color transformed image\\n ctrans_to_search = change_color_space(img_to_search, colorspace=color_space)\\n\\n if scale != 1:\\n imshape = ctrans_to_search.shape\\n ctrans_to_search = cv2.resize(ctrans_to_search, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))\\n\\n ch1 = ctrans_to_search[:, :, 0]\\n ch2 = ctrans_to_search[:, :, 1]\\n ch3 = ctrans_to_search[:, :, 2]\\n\\n nxblocks = (ch1.shape[1] // pix_per_cell) - 1 # number of hog cells\\n nyblocks = (ch1.shape[0] // pix_per_cell) - 1\\n nfeat_per_block = orient*cell_per_block**2\\n window = 64\\n nblocks_per_window = (window // pix_per_cell) - 1\\n\\n nxsteps = (nxblocks - nblocks_per_window) // cells_per_step\\n nysteps = (nyblocks - nblocks_per_window) // cells_per_step\\n\\n # compute individual channel HOG features for the intire image\\n hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n hog2 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n hog3 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n\\n for xb in range(nxsteps):\\n for yb in range(nysteps):\\n ypos = yb*cells_per_step\\n xpos = xb*cells_per_step\\n # extract hog features for this patch\\n hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()\\n hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()\\n hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()\\n hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\\n\\n xleft = xpos*pix_per_cell\\n ytop = ypos*pix_per_cell\\n\\n # extract the image path\\n subimg = cv2.resize(ctrans_to_search[ytop:ytop+window, xleft:xleft+window], (64,64))\\n\\n # get color features\\n spatial_features = get_bin_spatial(subimg, size=spatial_size)\\n hist_features = get_color_hist(subimg, nbins=hist_bins)\\n\\n # scale features and make prediction\\n test_features = scaler.transform(np.hstack((spatial_features, hist_features, hog_features)))\\n\\n test_prediction = clf.predict(test_features)\\n\\n if test_prediction == 1:\\n xbox_left = np.int(xleft*scale)\\n ytop_draw = np.int(ytop*scale)\\n win_draw = np.int(window*scale)\\n cv2.rectangle(draw_img, (xbox_left+x_start_stop[0], ytop_draw+y_start_stop[0]), (xbox_left+win_draw+x_start_stop[0], ytop_draw+win_draw+y_start_stop[0]), (0,0,255), 6)\\n heatmap[ytop_draw+y_start_stop[0]:ytop_draw+win_draw+y_start_stop[0], xbox_left+x_start_stop[0]:xbox_left+win_draw+x_start_stop[0]] += 1\\n\\n return draw_img, heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adcffd6c5cc1b2376463c6852f50f883\",\n \"score\": \"0.5385292\",\n \"text\": \"def gaussExpand(img: np.ndarray, gs_k: np.ndarray) -> np.ndarray:\\r\\n gs_k = (gs_k / gs_k.sum()) * 4\\r\\n if img.ndim == 3:\\r\\n h, w, d = img.shape[:3]\\r\\n newImg = np.zeros((2 * h, 2 * w, d))\\r\\n else:\\r\\n h, w = img.shape[:2]\\r\\n newImg = np.zeros((2 * h, 2 * w))\\r\\n newImg[::2, ::2] = img\\r\\n image = cv2.filter2D(newImg, -1, gs_k, cv2.BORDER_REPLICATE)\\r\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d9b9fba0544b38e74f62691507b0978\",\n \"score\": \"0.53726906\",\n \"text\": \"def add_heat(heatmap, bbox_list, heatmap_history):\\n # Iterate through list of bboxes\\n for bounding_box in bbox_list:\\n box = bounding_box.box\\n # Add += 1 for all pixels inside each bbox\\n # Assuming each \\\"box\\\" takes the form ((x1, y1), (x2, y2))\\n prob = np.full_like(heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]], bounding_box.probability, dtype=np.float)\\n heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] = np.maximum(heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]],\\n prob)\\n\\n total_heatmap = heatmap.copy()\\n for idx in range(len(heatmap_history)):\\n total_heatmap = np.add(total_heatmap, heatmap_history[idx])\\n # Return updated heatmap\\n return total_heatmap, heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7e5b56ad9af517c0b6548d5bec138b0\",\n \"score\": \"0.53710204\",\n \"text\": \"def make_lungmask(img, display=False):\\n row_size= img.shape[0]\\n col_size = img.shape[1]\\n \\n mean = np.mean(img)\\n std = np.std(img)\\n img = img-mean\\n img = img/std\\n\\n # uses hounsfield values near lungs to normalize images\\n\\n middle = img[int(col_size/5):int(col_size/5*4),int(row_size/5):int(row_size/5*4)] \\n mean = np.mean(middle) \\n max = np.max(img)\\n min = np.min(img)\\n img[img==max]=mean\\n img[img==min]=mean\\n \\n # uses kmeans to separate foreground (soft tissue / bone) and background (lung/air)\\n\\n kmeans = KMeans(n_clusters=2).fit(np.reshape(middle,[np.prod(middle.shape),1]))\\n centers = sorted(kmeans.cluster_centers_.flatten())\\n threshold = np.mean(centers)\\n thresh_img = np.where(img<threshold,1.0,0.0)\\n\\n # performs erosion and dilation\\n\\n eroded = morphology.erosion(thresh_img,np.ones([3,3]))\\n dilation = morphology.dilation(eroded,np.ones([8,8]))\\n\\n labels = measure.label(dilation) # Different labels are displayed in different colors\\n label_vals = np.unique(labels)\\n regions = measure.regionprops(labels)\\n good_labels = []\\n for prop in regions:\\n B = prop.bbox\\n if B[2]-B[0]<row_size/10*9 and B[3]-B[1]<col_size/10*9 and B[0]>row_size/5 and B[2]<col_size/5*4:\\n good_labels.append(prop.label)\\n mask = np.ndarray([row_size,col_size],dtype=np.int8)\\n mask[:] = 0\\n\\n # makes mask\\n\\n for N in good_labels:\\n mask = mask + np.where(labels==N,1,0)\\n mask = morphology.dilation(mask,np.ones([10,10])) # one last dilation\\n final = mask * img\\n \\n # shows and saves output\\n\\n plt.imshow(final)\\n im = Image.fromarray(final*128)\\n im = im.convert(\\\"L\\\")\\n im.save(S)\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f505d555f82f16231df018f7a595583\",\n \"score\": \"0.5363443\",\n \"text\": \"def scan_single_win_size(image, clf, rescale, hyperparams, box_color=(0,0,1.0), heatmap=None):\\n # get important hyperparams\\n y_start = hyperparams['Y_START']\\n y_stop = hyperparams['Y_STOP']\\n color_space = hyperparams['COLOR_SPACE']\\n spatial_feat = hyperparams['SPATIAL_FEAT']\\n spatial_size = hyperparams['SPATIAL_SIZE']\\n hist_feat = hyperparams['HIST_FEAT']\\n hist_bin = hyperparams['HIST_BIN']\\n hist_range = hyperparams['HIST_RANGE']\\n hog_orient = hyperparams['HOG_ORIENT']\\n hog_cell_per_block = hyperparams['HOG_CELL_PER_BLOCK']\\n hog_pix_per_cell = hyperparams['HOG_PIX_PER_CELL']\\n hog_feat = hyperparams['HOG_FEAT']\\n hog_channel = hyperparams['HOG_CHANNEL']\\n # if heatmap was not provided, initialize it\\n if heatmap is None:\\n heatmap = np.zeros(image.shape[0:2])\\n # copy image for visualization purposes\\n draw_image = np.zeros_like(image)\\n # image to be analyzed is color-converted to target color space and resized to match\\n # appropriately rescaled window size\\n rescaled_image = convert_color(image[y_start:y_stop, :, :], color_space)\\n if rescale != 1:\\n imshape = image.shape\\n rescaled_image = cv2.resize(rescaled_image, (np.int(imshape[1] / rescale), np.int(imshape[0] / rescale)))\\n # Define number of blocks\\n n_x_blocks = (rescaled_image.shape[1] // hog_pix_per_cell) - hog_cell_per_block + 1\\n n_y_blocks = (rescaled_image.shape[0] // hog_pix_per_cell) - hog_cell_per_block + 1\\n window = 64 # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell\\n blocks_per_window = (window // hog_pix_per_cell) - hog_cell_per_block + 1\\n cells_per_step = 2 # Instead of overlap, define how many cells to step\\n n_x_steps = (n_x_blocks - blocks_per_window) // cells_per_step\\n n_y_steps = (n_y_blocks - blocks_per_window) // cells_per_step\\n # Prepare the hog features\\n hogs = []\\n if hog_feat:\\n for channel in range(0, rescaled_image.shape[2]):\\n hogs.append(get_hog_features(rescaled_image[:, :, channel],\\n hog_orient, hog_pix_per_cell, hog_cell_per_block, feature_vec=False))\\n # Go through blocks of image step by step\\n for x_window in range(n_x_steps):\\n for y_window in range(n_y_steps):\\n y_pos = y_window * cells_per_step\\n x_pos = x_window * cells_per_step\\n x_left = x_pos * hog_pix_per_cell\\n y_top = y_pos * hog_pix_per_cell\\n sub_image = rescaled_image[y_top:y_top + window, x_left:x_left + window]\\n # Extract HOG for this patch\\n features = []\\n if spatial_feat:\\n features.append(get_spatial_features(sub_image, size=spatial_size))\\n if hist_feat:\\n features.append(get_hist_features(sub_image, nbins=hist_bin, bins_range=hist_range)[4])\\n if hog_feat:\\n if hog_channel == \\\"ALL\\\":\\n for h in hogs:\\n features.append(h[y_pos:y_pos+blocks_per_window, x_pos:x_pos+blocks_per_window].ravel())\\n else:\\n features.append(hogs[hog_channel][y_pos:y_pos+blocks_per_window, x_pos:x_pos+blocks_per_window].ravel())\\n # Scale features and make a prediction\\n test_features = np.hstack(features).reshape(1, -1)\\n test_prediction = clf.predict(test_features)\\n if test_prediction == 1:\\n # If prediction is true, re-calculate sliding window position\\n x_box_left = np.int(x_left * rescale)\\n y_box_top = np.int(y_top * rescale)\\n box_size = np.int(window * rescale)\\n # if box color was set, draw box on image\\n if box_color is not None:\\n cv2.rectangle(draw_image, (x_box_left, y_box_top + y_start),\\n (x_box_left + box_size, y_box_top + box_size + y_start), box_color, 6)\\n # add heat to heatmap\\n heatmap[y_box_top+y_start:y_box_top+box_size+y_start, x_box_left:x_box_left+box_size] += \\\\\\n clf.decision_function(test_features)\\n return draw_image, heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbf4a311e3a4f57f07111b2c41c5fc4d\",\n \"score\": \"0.53324604\",\n \"text\": \"def fake_gaussian(img, vertical_horizontal_sigma, iter=3):\\n sigma_vertical, sigma_horizontal = vertical_horizontal_sigma\\n h_blured = box_filter1d(img, sigma_horizontal, horizontal=True, iter=iter)\\n blured = box_filter1d(h_blured, sigma_vertical, horizontal=False, iter=iter)\\n return blured\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdd64389a4a90162027c9cd24685d3be\",\n \"score\": \"0.5294273\",\n \"text\": \"def find_gaussian(img,sigma=25):\\n method = 'cv2.TM_CCOEFF_NORMED'\\n size=3*sigma\\n template = gaussian(size,sigma)\\n template/=template.max()\\n template*=255\\n template = template.astype(np.uint8)\\n \\n threshold = 0.9\\n w, h = template.shape[::-1]\\n \\n img2 = img.copy()\\n meth = eval(method)\\n\\n # Apply template Matching\\n res = cv2.matchTemplate(img2,template,meth)\\n #Filters location map so that only one gaussian is found per contiguous location\\n location_map = res >= threshold*np.max(res)\\n location_map,nr = ndi.label(location_map)\\n list_x = []\\n list_y = []\\n for label in range(1,nr+1):\\n tmp=location_map==label\\n if np.count_nonzero(tmp)>1:\\n points = np.where(tmp)\\n l = len(points[0])\\n cx = (np.sum(points[0]) + l/2)/l\\n cy = (np.sum(points[1]) + l/2 )/l\\n list_x.append(cx)\\n list_y.append(cy)\\n loc= (np.asarray(list_x),np.asarray(list_y))\\n stack_to_remove = np.zeros((size,size,len(loc[0])))\\n i=0\\n for pt in zip(*loc[::-1]):\\n cv2.rectangle(img2, pt, (pt[0] + w, pt[1] + h), 255, 2)\\n stack_to_remove[:,:,i] = img[pt[1]:pt[1]+w,pt[0]:pt[0]+h]\\n i+=1\\n return stack_to_remove,loc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"992d4d156b6cffc376794bb4a677c00d\",\n \"score\": \"0.5263325\",\n \"text\": \"def gaussExpand(img: np.ndarray, gs_k: np.ndarray) -> np.ndarray:\\r\\n if img.ndim == 3:\\r\\n expanded = np.zeros((2 * img.shape[0], 2 * img.shape[1], img.shape[2]))\\r\\n else:\\r\\n expanded = np.zeros((2 * img.shape[0], 2 * img.shape[1]))\\r\\n expanded[::2, ::2] = img\\r\\n expanded = cv2.filter2D(expanded, -1, gs_k, borderType=cv2.BORDER_REPLICATE)\\r\\n return expanded\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38627cee9501498f883c59775c3aafc2\",\n \"score\": \"0.5245353\",\n \"text\": \"def generate_heatmap_extract(self, bbox):\\n return heatmaps[0].extract(bbox) # full extact\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d69bca198eabf5d296386f3517e37829\",\n \"score\": \"0.524353\",\n \"text\": \"def superpixel_plot(im,seg,title = \\\"Superpixels\\\"):\\n clust = np.unique(seg)\\n mapper_dict = {i: im[seg == i].mean(axis = 0)/255. for i in clust}\\n\\n seg_img = np.zeros((seg.shape[0],seg.shape[1],3))\\n for i in clust:\\n seg_img[seg == i] = mapper_dict[i]\\n \\n plot_image(seg_img,title)\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be4b8c288187d8008e121f493629e9c9\",\n \"score\": \"0.52330476\",\n \"text\": \"def heatmap(shred, bin_type, save_output = True):\\n # format dataframes \\n df_subsetDEG = shred.shred_modules_df_2.copy() # load the gene module annotation dataframe\\n\\n if bin_type == \\\"bin\\\":\\n df_heatmap = shred.heatmap_matrix.copy() # load heatmap matrix\\n df_bin_meta = shred.bin_metadata.loc[df_heatmap.columns, :].copy() # order the bin metadata\\n elif bin_type == \\\"superbin\\\":\\n df_heatmap = shred.heatmap_matrix_super.copy()\\n df_bin_meta = shred.superbin_metadata.loc[df_heatmap.columns, :].copy() # order the bin metadata\\n else:\\n raise Exception(\\\"bin_type should be either bin or superbin.\\\")\\n\\n n_genes = df_heatmap.shape[0]\\n n_bins = df_heatmap.shape[1]\\n n_modules = len(np.unique(df_subsetDEG[\\\"Status\\\"]))\\n\\n # initialize\\n fig = plt.figure(figsize = (15, 15))\\n gs = GridSpec(nrows = 3, ncols = 4, height_ratios=(0.10,0.05, 0.85), width_ratios = (0.70, 0.04, 0.07, 0.19))\\n gs.update(wspace = 0.025, hspace = 0.025)\\n\\n #####----- bin metadata barplot ------#####\\n ax0 = fig.add_subplot(gs[0,0])\\n columns = []\\n labels_for_legend = [] #initialize for legends\\n colors_for_legend = []\\n for column in df_bin_meta.columns:\\n if column != \\\"n_cells\\\":\\n columns.append(column)\\n df_size = pd.DataFrame(df_bin_meta.groupby(columns).size())\\n df_size.rename({0: \\\"n_cells\\\"}, axis = 1, inplace = True)\\n labels = [i[-1] if type(i) == tuple else i for i in df_size.index.values]\\n labels_for_legend += labels\\n \\n color_dict = dict(zip(labels, cycler_colors[:df_size.shape[0]])) # avoid different colors for duplicate keys\\n count_aggregrated = 0\\n for idx, label in enumerate(labels):\\n count = df_size.iloc[idx, 0]\\n ax0.barh(y = column, width = count, height = 1, left = count_aggregrated, color = color_dict[label])\\n ax0.set_xticklabels(\\\"\\\")\\n ax0.set_xticks([])\\n count_aggregrated += count\\n\\n # legend initialization\\n colors = [matplotlib.colors.rgb2hex(ax0.patches[i].get_facecolor()) for i in range(len(ax0.patches))]\\n handles = [plt.Rectangle((0,0),1,1, color = i) for i in colors]\\n labels_unique = []\\n handles_unique = []\\n\\n # remove duplicates\\n for k in range(len(labels_for_legend)):\\n if not labels_for_legend[k] in labels_unique:\\n labels_unique.append(labels_for_legend[k])\\n handles_unique.append(handles[k])\\n\\n ax0_1eg = fig.add_subplot(gs[0,3])\\n leg1 = ax0_1eg.legend(handles_unique, labels_unique, prop = {'size': 10}, \\n loc = 'upper left', title = \\\"bin metadata\\\", ncol = 5)\\n leg1._legend_box.aligh = \\\"left\\\"\\n ax0_1eg.add_artist(leg1)\\n ax0_1eg.axis('off')\\n\\n # other formats\\n ax0.set_xlim(0, n_bins)\\n ax0.spines['left'].set_visible(False)\\n ax0.spines['right'].set_visible(False) \\n ax0.spines['top'].set_visible(False) \\n ax0.spines['bottom'].set_visible(False) \\n\\n #####----- n_cell histograms ------#####\\n counts = df_bin_meta[\\\"n_cells\\\"]\\n ax1 = fig.add_subplot(gs[1,0])\\n ax1.bar(x = list(range(n_bins)), height = counts, width = 1)\\n ax1.set_xticklabels(\\\"\\\")\\n ax1.set_xticks([])\\n ax1.set_xlim(0, n_bins)\\n ax1.set_ylabel(\\\"n_cells\\\", rotation = 0, labelpad = 24)\\n\\n #####----- gene module barplots ------#####\\n ax2 = fig.add_subplot(gs[2,2])\\n columns = [\\\"Status\\\", \\\"reference_group\\\",\\\"shred_plan\\\"]\\n labels_for_legend = []\\n\\n for column in columns:\\n count_aggregrated = 0\\n labels = np.unique(df_subsetDEG[column])\\n labels_for_legend += list(labels)\\n label_counts = dict(df_subsetDEG[column].value_counts())\\n for label in labels:\\n count = label_counts[label]\\n ax2.bar(x = column, height = count, width = 1, bottom = count_aggregrated)\\n ax2.set_yticklabels(\\\"\\\")\\n ax2.set_yticks([])\\n count_aggregrated += count\\n ax2.set_xticklabels(columns, rotation = 35, ha = \\\"right\\\")\\n ax2.set_ylim(0, n_genes)\\n ax2.spines['left'].set_visible(False) \\n ax2.spines['right'].set_visible(False) \\n ax2.spines['top'].set_visible(False) \\n ax2.spines['bottom'].set_visible(False) \\n\\n # set legend\\n n_Status = len(np.unique(df_subsetDEG[\\\"Status\\\"]))\\n n_reference_groups = len(np.unique(df_subsetDEG[\\\"reference_group\\\"]))\\n\\n colors = [matplotlib.colors.rgb2hex(ax2.patches[i].get_facecolor()) for i in range(len(ax2.patches))]\\n handles = [plt.Rectangle((0,0),1,1, color = i) for i in colors]\\n # legend for status\\n ax2_leg1 = fig.add_subplot(gs[2,3])\\n leg1 = ax2_leg1.legend(handles[:n_Status], labels_for_legend[:n_Status], \\n prop = {'size': 10}, loc = 'upper left', title = \\\"Status\\\", ncol=4)\\n leg1._legend_box.aligh = \\\"left\\\"\\n ax2_leg1.add_artist(leg1)\\n # legend for others (reference and shred_plan)\\n ax2_leg2 = fig.add_subplot(gs[2,3])\\n leg2 = ax2_leg2.legend(handles[n_Status:(n_Status + n_reference_groups)], labels_for_legend[n_Status:(n_Status + n_reference_groups)], \\n prop = {'size': 10}, loc = 'center left',title = \\\"reference group\\\")\\n leg3 = ax2_leg2.legend(handles[(n_Status + n_reference_groups):], labels_for_legend[(n_Status + n_reference_groups):], \\n prop = {'size': 10}, loc = 'lower left',title = \\\"shred plan\\\")\\n leg2._legend_box.aligh = \\\"left\\\"\\n leg3._legend_box.aligh = \\\"left\\\"\\n ax2_leg2.add_artist(leg2)\\n ax2_leg2.add_artist(leg3)\\n\\n ax2_leg1.axis('off')\\n ax2_leg2.axis('off')\\n\\n #####----- gene module score histogram ------##### \\n ax3 = fig.add_subplot(gs[2,1])\\n ax3.barh(y = list(range(n_genes)), width = list(df_subsetDEG[\\\"logfoldchanges\\\"]), height = 1)\\n ax3.xaxis.tick_top()\\n ax3.set_yticklabels(\\\"\\\")\\n ax3.set_yticks([])\\n ax3.set_ylim(0, n_genes)\\n ax3.set_xlabel(\\\"logFC\\\", rotation = 35, ha = \\\"right\\\")\\n ax3.plot([1, 1], [0, 5200], color = \\\"#990000\\\", linestyle = \\\"dashed\\\", lw = 0.8)\\n\\n #####----- main heatmap ------#####\\n ax5 = fig.add_subplot(gs[2,0])\\n sns.heatmap(df_heatmap, vmin = 0, vmax = 10, yticklabels=False, xticklabels=False, cmap = \\\"bwr\\\", ax = ax5, cbar = False)\\n ax5.set_ylabel(\\\"\\\")\\n\\n output_name = (shred.output_folder + \\\"/figures/heatmap.png\\\") if bin_type == \\\"bin\\\" else (shred.output_folder + \\\"/figures/heatmap_superbin.png\\\")\\n if save_output:\\n fig.savefig(output_name, bbox_inches = \\\"tight\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17a011f47e3f1b6067601de67bd1f611\",\n \"score\": \"0.52180654\",\n \"text\": \"def visual_heatmap(data):\\n # normalize data for display\\n data = (data - data.min()) / (data.max() - data.min())\\n\\n # force the number of filters to be square\\n n = int(np.ceil(np.sqrt(data.shape[0])))\\n padding = (((0, n ** 2 - data.shape[0]),\\n (0, 1), (0, 1)) # add some space between filters\\n + ((0, 0),) * (data.ndim - 3)) # don't pad the last dimension (if there is one)\\n data = np.pad(data, padding, mode='constant', constant_values=1) # pad with ones (white)\\n\\n # tile the filters into an image\\n data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\\n data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\\n\\n plt.imshow(data)\\n plt.axis('off')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ead1c6b846ef437cfa086c7bc0c5f6f\",\n \"score\": \"0.52153224\",\n \"text\": \"def make_montage_sagittal(overlay, underlay, png_name, cbar_name):\\n import matplotlib\\n import commands\\n import os\\n import numpy as np\\n matplotlib.rcParams.update({'font.size': 5})\\n ###\\n try:\\n from mpl_toolkits.axes_grid1 import ImageGrid\\n except:\\n from mpl_toolkits.axes_grid import ImageGrid\\n import matplotlib.cm as cm\\n import matplotlib.pyplot as plt\\n import matplotlib.colors as col\\n import nibabel as nb\\n def determine_start_and_end(data, direction, percent):\\n x, y, z = data.shape\\n xx1 = 0\\n xx2 = x - 1\\n zz1 = 0\\n zz2 = z - 1\\n total_non_zero_voxels = len(np.nonzero(data.flatten())[0])\\n thresh = percent * float(total_non_zero_voxels)\\n start = None\\n end = None\\n if 'axial' in direction:\\n while(zz2 > 0):\\n d = len(np.nonzero(data[:, :, zz2].flatten())[0])\\n if float(d) > thresh:\\n break\\n zz2 -= 1\\n while(zz1 < zz2):\\n d = len(np.nonzero(data[:, :, zz1].flatten())[0])\\n if float(d) > thresh:\\n break\\n zz1 += 1\\n start = zz1\\n end = zz2\\n else:\\n while(xx2 > 0):\\n d = len(np.nonzero(data[xx2, :, :].flatten())[0])\\n if float(d) > thresh:\\n break\\n xx2 -= 1\\n while(xx1 < xx2):\\n d = len(np.nonzero(data[xx1, :, :].flatten())[0])\\n if float(d) > thresh:\\n break\\n xx1 += 1\\n start = xx1\\n end = xx2\\n return start, end\\n def get_spacing(across, down, dimension):\\n space = 10\\n prod = (across*down*space)\\n if prod > dimension:\\n while(across*down*space) > dimension:\\n space -= 1\\n else:\\n while(across*down*space) < dimension:\\n space += 1\\n return space\\n\\n Y = nb.load(underlay).get_data()\\n X = nb.load(overlay).get_data()\\n X = X.astype(np.float16)\\n Y = Y.astype(np.float16)\\n\\n\\n if 'skull_vis' in png_name:\\n X[X < 20.0] = 0.0\\n if 'skull_vis' in png_name or 't1_edge_on_mean_func_in_t1' in png_name or 'MNI_edge_on_mean_func_mni' in png_name:\\n max_ = np.nanmax(np.abs(X.flatten()))\\n X[X != 0.0] = max_\\n print '^^', np.unique(X)\\n\\n x1, x2 = determine_start_and_end(Y, 'sagittal', 0.0001)\\n spacing = get_spacing(6, 3, x2 - x1)\\n x, y, z = Y.shape\\n fig = plt.figure(1)\\n max_ = np.max(np.abs(Y))\\n\\n if ('snr' in png_name) or ('reho' in png_name) or ('vmhc' in png_name) or ('sca_' in png_name) or ('alff' in png_name) or ('centrality' in png_name) or ('temporal_regression_sca' in png_name) or ('temporal_dual_regression' in png_name):\\n grid = ImageGrid(fig, 111, nrows_ncols=(3, 6), share_all=True, aspect=True, cbar_mode=\\\"single\\\", cbar_pad=0.5, direction=\\\"row\\\")\\n else:\\n grid = ImageGrid(fig, 111, nrows_ncols=(3, 6), share_all=True, aspect=True, cbar_mode=\\\"None\\\", direction=\\\"row\\\")\\n\\n xx = x1\\n for i in range(6*3):\\n\\n if xx >= x2:\\n break\\n\\n im = grid[i].imshow(np.rot90(Y[xx, :, :]), cmap=cm.Greys_r)\\n grid[i].get_xaxis().set_visible(False)\\n grid[i].get_yaxis().set_visible(False)\\n xx += spacing\\n\\n x, y, z = X.shape\\n X[X == 0.0] = np.nan\\n max_ = np.nanmax(np.abs(X.flatten()))\\n print '~~', max_\\n xx = x1\\n for i in range(6*3):\\n\\n\\n if xx >= x2:\\n break\\n im = None\\n if cbar_name is 'red_to_blue':\\n\\n im = grid[i].imshow(np.rot90(X[xx, :, :]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=0, vmax=max_) ###\\n elif cbar_name is 'green':\\n im = grid[i].imshow(np.rot90(X[xx, :, :]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=0, vmax=max_)\\n else:\\n im = grid[i].imshow(np.rot90(X[xx, :, :]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=- max_, vmax=max_)\\n xx += spacing\\n cbar = grid.cbar_axes[0].colorbar(im)\\n\\n if 'snr' in png_name:\\n cbar.ax.set_yticks(drange(0, max_))\\n\\n elif ('reho' in png_name) or ('vmhc' in png_name) or ('sca_' in png_name) or ('alff' in png_name) or ('centrality' in png_name) or ('temporal_regression_sca' in png_name) or ('temporal_dual_regression' in png_name):\\n cbar.ax.set_yticks(drange(-max_, max_))\\n\\n\\n plt.show()\\n plt.axis(\\\"off\\\")\\n png_name = os.path.join(os.getcwd(), png_name)\\n plt.savefig(png_name, dpi=300, bbox_inches='tight')\\n plt.close()\\n matplotlib.rcdefaults()\\n\\n return png_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"930f701d70f7adf34d6309e02fab3cec\",\n \"score\": \"0.5213234\",\n \"text\": \"def heatmap(self, name=\\\"\\\", display=True, saveFile = False, saveFig = False, fileLocation=\\\"\\\", fullscreen=False, normalization='refbasal'):\\n if fileLocation == '':\\n fileLocation = self.fileLocation\\n modules.heatmap(self.cellFullIntersection.copy(), self.cellLines, self.timePoints, name, display, saveFile, saveFig, fileLocation, fullscreen, normalization)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94f4a9b83728b6a47dde804c9c15a787\",\n \"score\": \"0.5212856\",\n \"text\": \"def gaussian2kp(self, heatmap):\\n shape = heatmap.shape\\n heatmap = heatmap.unsqueeze(-1)\\n grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0)\\n value = (heatmap * grid).sum(dim=(2, 3))\\n kp = {'value': value}\\n return kp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2018914a6845c9cab1fe14380b8b11f\",\n \"score\": \"0.52071184\",\n \"text\": \"def features_sigma(img,\\n sigma,\\n intensity=True,\\n edges=True,\\n texture=True):\\n\\n features = []\\n\\n gx,gy = np.meshgrid(np.arange(img.shape[1]), np.arange(img.shape[0]))\\n # print(gx.shape)\\n #features.append(gx)\\n gx = filters.gaussian(gx, sigma)\\n gy = filters.gaussian(gy, sigma)\\n\\n features.append(np.sqrt(gx**2 + gy**2)) #use polar radius of pixel locations as cartesian coordinates\\n\\n del gx, gy\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Location features extracted using sigma= %f' % (sigma))\\n\\n img_blur = filters.gaussian(img, sigma)\\n\\n if intensity:\\n features.append(img_blur)\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Intensity features extracted using sigma= %f' % (sigma))\\n\\n if edges:\\n features.append(filters.sobel(img_blur))\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Edge features extracted using sigma= %f' % (sigma))\\n\\n if texture:\\n H_elems = [\\n np.gradient(np.gradient(img_blur)[ax0], axis=ax1)\\n for ax0, ax1 in itertools.combinations_with_replacement(range(img.ndim), 2)\\n ]\\n\\n eigvals = feature.hessian_matrix_eigvals(H_elems)\\n del H_elems\\n\\n for eigval_mat in eigvals:\\n features.append(eigval_mat)\\n del eigval_mat\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Texture features extracted using sigma= %f' % (sigma))\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Image features extracted using sigma= %f' % (sigma))\\n\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48c5e0c01e8c4e85d2d5ed7829d2e78d\",\n \"score\": \"0.5205915\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n \\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.05)\\n\\n cbar = ax.figure.colorbar(im, ax=ax, cax = cax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n #cbar = plt.colorbar(im, cax=cax)\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels, fontdict = font)\\n ax.set_yticklabels(row_labels, fontdict = font)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3f0359ee0bd6b73db3a9c5aba63bf0\",\n \"score\": \"0.5198854\",\n \"text\": \"def heat_map(path):\\r\\n x, y= np.loadtxt(fname=path, delimiter='\\\\t',dtype=int,\\r\\n usecols = (1,2), skiprows=100, unpack = True)\\r\\n\\r\\n fig, (ax,ax2) = plt.subplots(nrows=2, sharex=True, figsize=(20,10))\\r\\n\\r\\n extent = [x[0]-(x[1]-x[0])/2, x[-1]+(x[1]-x[0])/2,0,1]\\r\\n ax.imshow(y[np.newaxis,:], cmap=\\\"plasma\\\", aspect=\\\"auto\\\", extent=extent)\\r\\n ax2.plot(x,y)\\r\\n plt.tight_layout()\\r\\n return plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef2678180e2d1a198275f2cc48006665\",\n \"score\": \"0.5198667\",\n \"text\": \"def get_attention_heatmap(self, attention):\\n # Options: cv2.COLORMAP_PLASMA, cv2.COLORMAP_JET, etc.\\n #attention = tf.reshape(attention, (1, np.prod(attention.shape)))\\n #attention = tf.nn.softmax(attention)\\n vis_attention = np.float32(attention).reshape((224, 224))\\n vis_attention = vis_attention - np.min(vis_attention)\\n vis_attention = 255 * vis_attention / np.max(vis_attention)\\n vis_attention = cv2.applyColorMap(np.uint8(vis_attention), cv2.COLORMAP_JET)\\n vis_attention = cv2.cvtColor(vis_attention, cv2.COLOR_RGB2BGR)\\n return vis_attention\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e6f666fe52837aaaa765293ad3d4d69\",\n \"score\": \"0.51950836\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", vmin = 0, vmax = 100, **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs, vmin = vmin, vmax = vmax)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, fraction=0.0272, pad=0.04, extend='both', ticks=[-100, -50, 0, 50, 100], **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a019203dd83eb47b3cfcc2ed1729bc7\",\n \"score\": \"0.51921654\",\n \"text\": \"def plt_gm_clusters(df_all, model):\\n\\n # color_iter = itertools.cycle([cmap(i) for i in range(cmap.N)])\\n\\n color_iter = itertools.cycle([cmap(i) for i in range(clus_params['n_components'])])\\n\\n df = df_all[featureSet_dic[clus_params['feat_list']]].copy()\\n\\n XX = df.values\\n Y_ = model.predict(XX) # predict labels for each model\\n\\n plt.figure(figsize=(8, 6))\\n splot = plt.subplot(1, 1, 1)\\n\\n for i, (mean, cov, color) in enumerate(zip(model.means_, model.covariances_, color_iter)):\\n\\n if \\\"MEAN\\\" in clus_params['feat_list']:\\n v, w = linalg.eigh(cov)\\n else:\\n\\n subset = [0, 5] # mean torque L & R\\n v, w = linalg.eigh(cov[np.ix_(subset, subset)])\\n mean = np.array([mean[0], mean[5]])\\n\\n if not np.any(Y_ == i):\\n continue\\n\\n if \\\"MEAN\\\" in clus_params['feat_list']:\\n plt.scatter(XX[Y_ == i, 0], XX[Y_ == i, 1], color=color, s=60)\\n else:\\n plt.scatter(XX[Y_ == i, 0], XX[Y_ == i, 5], color=color, s=60)\\n\\n # Plot an ellipse to show the Gaussian component\\n angle = np.arctan2(w[0][1], w[0][0])\\n angle = 180. * angle / np.pi # convert to degrees\\n v = 2. * np.sqrt(2.) * np.sqrt(v)\\n ell = mpl.patches.Ellipse(mean, v[0], v[1], 180. + angle, color=color)\\n ell.set_clip_box(splot.bbox)\\n ell.set_alpha(.5)\\n splot.add_artist(ell)\\n\\n plt.xticks(())\\n plt.yticks(())\\n\\n plt.title('Subject: {}, feature set: {}'.format(USER, clus_params['feat_list']))\\n plt.subplots_adjust(hspace=.35, bottom=.02)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"409b7a9b0f1849d9e2f99fadb09ab8f8\",\n \"score\": \"0.51895446\",\n \"text\": \"def process_image(self, image, debug=False):\\n # Get old heatmap from internal deque representation or initialize with zeros if unknown\\n old_heatmap = np.zeros(image.shape[0:2]) if len(self.heatmap_deque) == 0 else sum(self.heatmap_deque)\\n # Scale image from 0.0..1.0\\n modified_image = np.copy(image)/255.\\n # Find cars in image\\n _, draw_image, labels_heatmap, new_heatmap, agg_heatmap = find_cars_image(modified_image, self.clf,\\n self.hyperparams, self.box_color, old_heatmap=old_heatmap)\\n # Overlay bounding boxes on top of image\\n draw_image[(draw_image == 0).all(2)] = modified_image[(draw_image == 0).all(2)]\\n # Add new heatmap to deque\\n self.heatmap_deque.append(new_heatmap)\\n # In debug mode ...\\n if debug:\\n # ... create a new canvas\\n result_image = np.zeros_like(image).astype(float)\\n h = result_image.shape[0]\\n w = result_image.shape[1]\\n # get the new heatmap and aggregated heatmap as \\\"fancy heatmap\\\" representation\\n new_heatmap_rgb = fancy_heatmap(new_heatmap, self.hyperparams[\\\"HEAT_THRESHOLD\\\"])\\n agg_heatmap_rgb = fancy_heatmap(agg_heatmap, self.hyperparams[\\\"HEAT_THRESHOLD\\\"])\\n # labels_heatmap_rgb will just be the grayscale value scaled to 0..1 and replicated across all 3 channels\\n labels_heatmap_rgb = cv2.merge([normalize(labels_heatmap, norm='max')]*3)\\n # place all the different images into their respective quadrant of the output canvas\\n result_image[0:h//2, 0:w//2] = cv2.resize(draw_image, (w//2, h//2), interpolation=cv2.INTER_AREA)\\n result_image[h//2:h, 0:w//2] = cv2.resize(labels_heatmap_rgb, (w//2, h-h//2), interpolation=cv2.INTER_AREA)\\n result_image[0:h//2, w//2:w] = cv2.resize(new_heatmap_rgb, (w-w//2, h//2), interpolation=cv2.INTER_AREA)\\n result_image[h//2:h, w//2:w] = cv2.resize(agg_heatmap_rgb, (w-w//2, h-h//2), interpolation=cv2.INTER_AREA)\\n # rescale result to 0..255\\n result_image = (result_image*255).astype(int)\\n else:\\n # For non-debug mode, just take scaled-back the find_cars draw_image return value\\n result_image = (draw_image*255).astype(int)\\n return result_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4019419ee65994b243cea1cd588722a0\",\n \"score\": \"0.51833916\",\n \"text\": \"def fit_gaussian(self, mask=None):\\n data = self.data\\n mask = numpy.logical_or(mask, numpy.ma.getmaskarray(data))\\n fdata = data[~mask].data\\n xdata = numpy.asarray([cm[~mask]\\n for cm in self.bset.cmesh]).transpose()\\n scale, mean, cov = fit_ndgaussian(xdata, fdata)\\n return scale, mean, cov\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119bafceb73995adbae350f5e0b81242\",\n \"score\": \"0.51833475\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\r\\n cbar_kw={}, cbarlabel=\\\"\\\", title = \\\"Default\\\", x_title=\\\" \\\",y_title=\\\" \\\",saveFile = None, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19bbe1579ce43c093b6c347bf94e397b\",\n \"score\": \"0.5178392\",\n \"text\": \"def config_hog_search(self, img):\\n img = img.astype(np.float32)/255 # scale image: (png: 0-1, jpg: 0-255)\\n img_crop = img[self.ys[0]:self.ys[1], :, :] # crop img to relative dimensions (ROI)\\n clr_trf = cvf.transform_colorspace(img_crop, self.color_space) # transform color space from RGB\\n clr_trf = cvu.scale_image(clr_trf, self.scale) # scale rather than selecting diffferent window sizes\\n ch1, ch2, ch3 = [clr_trf[:,:,ch] for ch in range(3)] # HOG operates on per channel basis\\n\\n # get hog features as multi-dimensional array per channel for the entire image\\n params={'orient':self.orient, 'pix_per_cell':self.pix_per_cell, 'cells_per_block':self.cells_per_block}\\n params.update({'vis':False, 'feature_vec':False})\\n hog1,hog2,hog3 = [cvf.get_hog_features(ch, **params) for ch in ([ch1,ch2,ch3])]\\n # block configurations\\n self.nfeat_per_block = self.orient*self.cells_per_block**2\\n self.nblocks_per_window = (self.window //self.pix_per_cell) -1\\n # number_blocks: define number of hog cells across an image according to channel dimensions\\n # number_steps: define number of steps to occur across hog array to extract features\\n rows, cols = ch1.shape[:2]\\n number_blocks = lambda max_dim, pix_per_cell: (max_dim // pix_per_cell) -1\\n number_steps = lambda nblocks, nblocks_window, cells_step: (nblocks - nblocks_window)//cells_step\\n self.nxblocks, self.nyblocks = number_blocks(cols, self.pix_per_cell), number_blocks(rows, self.pix_per_cell)\\n nxsteps, nysteps = \\\\\\n [number_steps(it, self.nblocks_per_window, self.cells_per_step) for it in (self.nxblocks, self.nyblocks) ]\\n return clr_trf, (nxsteps, nysteps), (hog1,hog2,hog3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eefd6d3aa8eab2766e417c05a2a0d0be\",\n \"score\": \"0.51754767\",\n \"text\": \"def plotFace(original,blurred):\\n plt.subplot(121),plt.imshow(original,cmap=cm.Greys_r),plt.title('Original')\\n plt.xticks([]), plt.yticks([])\\n plt.subplot(122),plt.imshow(blurred,cmap=cm.Greys_r),plt.title('Gaussian Blurred')\\n plt.xticks([]), plt.yticks([])\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46d79fdb149cbf3d2b51ff544b6d221e\",\n \"score\": \"0.5174099\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\r\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\r\\n\\r\\n if not ax:\\r\\n ax = plt.gca()\\r\\n\\r\\n # Plot the heatmap\\r\\n im = ax.imshow(data, **kwargs)\\r\\n\\r\\n # Create colorbar\\r\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\r\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\r\\n\\r\\n # We want to show all ticks...\\r\\n n_ticks = 20\\r\\n if data.shape[1]>n_ticks :\\r\\n ax.set_xticks(np.linspace(0 ,data.shape[1], num = n_ticks , dtype=int))\\r\\n ax.set_yticks(np.linspace(0 ,data.shape[0], num = n_ticks , dtype=int))\\r\\n ax.set_xticklabels(np.linspace(0 ,data.shape[1], num = n_ticks , dtype=int))\\r\\n ax.set_yticklabels(np.linspace(0 ,data.shape[0], num = n_ticks , dtype=int))\\r\\n else:\\r\\n ax.set_xticks(np.arange(data.shape[1]))\\r\\n ax.set_yticks(np.arange(data.shape[0]))\\r\\n # ... and label them with the respective list entries.\\r\\n ax.set_xticklabels(col_labels)\\r\\n ax.set_yticklabels(row_labels)\\r\\n\\r\\n # Let the horizontal axes labeling appear on top.\\r\\n ax.tick_params(top=True, bottom=False,\\r\\n labeltop=True, labelbottom=False)\\r\\n\\r\\n # Rotate the tick labels and set their alignment.\\r\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",rotation_mode=\\\"anchor\\\")\\r\\n\\r\\n # Turn spines off and create white grid.\\r\\n for edge, spine in ax.spines.items():\\r\\n spine.set_visible(False)\\r\\n\\r\\n ax.set_xticks(np.arange(data.shape[1]+1)-0.5, minor=True)\\r\\n ax.set_yticks(np.arange(data.shape[0]+1)-0.5, minor=True)\\r\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=0.0005)\\r\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\r\\n\\r\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22af15c0f2b496d3b83bbd04808ebb85\",\n \"score\": \"0.51733655\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap 绘制热图\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar 创建颜色条\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks... 显示所有的刻度\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries. 用相应的列表条目分别标记行和列\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top. 让水平轴标签显示在顶部\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment. 旋转刻度标签并设置其对齐方式。\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid. 关闭spines 创建白色的网格\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e41845e59d9d7d3f5d576cb7bb0c25a4\",\n \"score\": \"0.516938\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n #ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n #ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n #ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3f553196c470eb5662d433a5a5417f8\",\n \"score\": \"0.516195\",\n \"text\": \"def add_gaussian(self, A=1000, sigma=(200, 200), x0=None):\\n if x0 is None:\\n x0 = self._beam_position\\n \\n self.map += np.asarray(np.round(\\n gaussian2d(self._X, self._Y, A, sigma, x0)), dtype=np.int32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b3b8bb7d503e1d5c88d8912e894f64b\",\n \"score\": \"0.51597476\",\n \"text\": \"def addGaussian(ax, ismulti):\\n shape = (96, 288) #ax.shape[:2]\\n intensity_noise = np.random.uniform(low=0, high=0.05)\\n if ismulti:\\n ax[:,:,0] = ax[:,:,0]*(1+ intensity_noise*np.random.normal(loc=0, scale=1, size=shape[0]*shape[1]).reshape(shape[0],shape[1]))\\n else:\\n ax[:,:,0] = ax[:,:,0] + intensity_noise*np.random.normal(loc=0, scale=1, size=shape[0]*shape[1]).reshape(shape[0],shape[1])\\n return ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b765ef00cef548c70358defa6894dc2b\",\n \"score\": \"0.51581675\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n # ax.spines[:].set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n # ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb66f75c64905a2f5920b5150f2b67cb\",\n \"score\": \"0.5150999\",\n \"text\": \"def gaussian_blur(self,img):\\n return cv2.GaussianBlur(img, (self.kernel_size, self.kernel_size), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7bda04b7fadfe11cc84d3651d1b7e31\",\n \"score\": \"0.51371485\",\n \"text\": \"def addGaussian(self, xwidth=100., ywidth=100., xcen=None, ycen=None, value=1.0):\\n if xcen == None:\\n xcen = self.nx/2.0\\n if ycen == None:\\n ycen = self.ny/2.0\\n self.fimage = None\\n gaussian = numpy.exp(-(self.xx-xcen)**2/(2.0*xwidth**2) - (self.yy-ycen)**2/(2.0*ywidth**2))\\n self.image += gaussian * value / gaussian.max()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a163bf7c46434c94e422facb08beba95\",\n \"score\": \"0.5136858\",\n \"text\": \"def make_heatmap(df, dataset, remove_constant=False):\\n sns.set(style=\\\"white\\\")\\n temp_df = df.copy()\\n\\n # remove constant values\\n if remove_constant:\\n constants = [x for x in temp_df if np.std(temp_df[x]) == 0]\\n temp_df.drop(columns=constants, inplace=True)\\n\\n # Compute the correlation matrix\\n corr = temp_df.corr()\\n\\n # Set up the matplotlib figure\\n f, ax = plt.subplots(figsize=(15, 10))\\n\\n # Generate a custom diverging colormap\\n cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n\\n # Draw the heatmap with the mask and correct aspect ratio\\n sns.heatmap(corr, cmap=cmap,\\n square=True, xticklabels=corr.columns.values, yticklabels=corr.columns.values,\\n linewidths=0.5, cbar_kws={\\\"shrink\\\": .5}, ax=ax)\\n\\n if remove_constant:\\n plt.savefig('plots/%s/heatmap_no_constants.png' % dataset, bbox_inches='tight')\\n else:\\n plt.savefig('plots/%s/heatmap.png' % dataset, bbox_inches='tight')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ee7d7b2278977103aab99bb57a20d2\",\n \"score\": \"0.51285064\",\n \"text\": \"def heatmap_plot(savefigure=False):\\n treenumber,depth = [1, 5, 10, 50, 100, 500, 1000], list(range(1,11))\\n iter_list = list(product(treenumber, depth))\\n result_list = np.load('npy-data/result_big_run.npy')\\n heat_arr = result_list.reshape((len(treenumber), len(depth)))\\n\\n fig, ax = plt.subplots(figsize=(8,8))\\n im = ax.matshow(heat_arr, cmap=plt.cm.coolwarm, vmin=.8, vmax=1)\\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.2)\\n\\n ax.set_xticks(np.arange(heat_arr.shape[1]))\\n ax.set_xticklabels(depth)\\n ax.set_yticks(np.arange(heat_arr.shape[0]))\\n ax.set_yticklabels(treenumber)\\n ax.set_xlim(-.5,heat_arr.shape[1]-.5)\\n ax.set_ylim(heat_arr.shape[0]-.5,-.5)\\n ax.set_xlabel(\\\"Maximum depth\\\")\\n ax.set_ylabel(\\\"Number of trees\\\")\\n ax.xaxis.set_ticks_position('bottom')\\n plt.colorbar(im, cax=cax)\\n for i, z in enumerate(heat_arr):\\n for j in range(len(z)):\\n ax.text(j, i, '{:.3f}'.format(z[j]), ha='center', va='center')\\n if savefigure:\\n plt.savefig('img/xgb_heatmap_maxdepth_n_estimators.pdf', bbox_inches='tight')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ad02b1319cbf09edbe8daaf52b6708f\",\n \"score\": \"0.5128202\",\n \"text\": \"def plot_heatmap(data, title, xlabel, ylabel):\\n hm = plt.pcolor(data, cmap=cm.gray_r, vmin=0, vmax=1)\\n\\n plt.tick_params(labelsize=20)\\n plt.axes().set_aspect('equal')\\n\\n plt.title(title, fontsize=33)\\n plt.xlabel(xlabel, fontsize=30)\\n plt.ylabel(ylabel, fontsize=30)\\n\\n Plotter.show(title)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be437c578562a58f4af95f167dd0925\",\n \"score\": \"0.51241964\",\n \"text\": \"def generate_heatmap(density_map,rate=0,color_type=2):\\n\\n if rate > 0:\\n heatmap = np.abs(density_map) * rate\\n else:\\n max_density_value=np.max(np.abs(density_map))\\n if max_density_value==0:\\n max_density_value=1\\n heatmap = np.abs(density_map) / max_density_value * 250\\n heatmap = cv2.applyColorMap(np.uint8(heatmap), color_type)\\n return heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6efeef36690371e4235327dbb178770\",\n \"score\": \"0.51183945\",\n \"text\": \"def bayes_matting(img, trimap, sigma_d=10, it=1):\\n assert img.shape[:-1] == trimap.shape\\n\\n img = img / 255.0\\n nrows, ncols = img.shape[:-1]\\n\\n # initial alpha guess\\n alpha = np.zeros(trimap.shape)\\n alpha[trimap == 255] = 1\\n alpha[trimap == 128] = 0.5\\n\\n B = img[alpha == 0] # background pixel mask\\n F = img[alpha == 1] # foreground pixel mask\\n\\n mean_B = np.mean(B, axis=0)\\n cov_B = np.cov(B.T)\\n mean_F = np.mean(F, axis=0)\\n cov_F = np.cov(F.T)\\n\\n try:\\n inv_cov_B = np.linalg.inv(cov_B)\\n inv_cov_F = np.linalg.inv(cov_F)\\n except LinAlgError:\\n print(\\\"LinAlgError\\\")\\n\\n for i in range(it):\\n print(\\\"Iteration {}\\\".format(i))\\n for row in range(nrows):\\n for col in range(ncols):\\n if trimap[row, col] == 128:\\n f, g = calculate_fg(img[row, col], alpha[row, col], mean_F, inv_cov_F, mean_B, inv_cov_B, sigma_d)\\n alpha[row, col] = calculate_alpha(img[row, col], f, g)\\n\\n alpha = np.clip(alpha, 0, 1)\\n\\n return alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d853e1e069bffdc62c025f6f0db1bfc\",\n \"score\": \"0.5111385\",\n \"text\": \"def plot_macros_gaussian(feature_config_filepath, feature_config_key, macros_filepath, save_dir, words, feature_labels, confusion_matrix_filepath, threshold, mode):\\n gaussian_dir = os.path.join(save_dir, 'visualization', 'gaussian', str(mode))\\n # if os.path.exists(gaussian_dir):\\n # shutil.rmtree(gaussian_dir)\\n # os.makedirs(gaussian_dir)\\n\\n all_features = load_json(feature_config_filepath)[str(feature_config_key)]\\n macros_data = get_macros(all_features, macros_filepath)\\n\\n if not feature_labels:\\n feature_labels = all_features\\n\\n if not words:\\n words = macros_data.keys()\\n\\n if mode == 0:\\n each_word_and_feature_graphs(macros_data, gaussian_dir, words, feature_labels)\\n elif mode == 1:\\n each_confused_word_and_feature_graphs(macros_data, gaussian_dir, words, feature_labels, confusion_matrix_filepath, threshold)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6d0623d2fd14108b28b2f966753538c\",\n \"score\": \"0.51045966\",\n \"text\": \"def add_greyscale(im, s_angle, x_data, y_data):\\n if rnd.random() < 0.5:\\n grey = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\\n x_data.append(cv2.cvtColor(grey, cv2.COLOR_GRAY2BGR))\\n y_data.append(s_angle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c2709c6931dd84bf3bae8fcaa01a81e\",\n \"score\": \"0.51033026\",\n \"text\": \"def gaussian(centre, k, intensity, xpos):\\r\\n\\treturn intensity * np.exp(- np.power(k * (xpos - centre), 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24b36632f737b0977ff0c8b357697087\",\n \"score\": \"0.5103105\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24b36632f737b0977ff0c8b357697087\",\n \"score\": \"0.5103105\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24b36632f737b0977ff0c8b357697087\",\n \"score\": \"0.5103105\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None,\\n cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\n\\n if not ax:\\n ax = plt.gca()\\n\\n # Plot the heatmap\\n im = ax.imshow(data, **kwargs)\\n\\n # Create colorbar\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\n\\n # We want to show all ticks...\\n ax.set_xticks(np.arange(data.shape[1]))\\n ax.set_yticks(np.arange(data.shape[0]))\\n # ... and label them with the respective list entries.\\n ax.set_xticklabels(col_labels)\\n ax.set_yticklabels(row_labels)\\n\\n # Let the horizontal axes labeling appear on top.\\n ax.tick_params(top=True, bottom=False,\\n labeltop=True, labelbottom=False)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\")\\n\\n # Turn spines off and create white grid.\\n for edge, spine in ax.spines.items():\\n spine.set_visible(False)\\n\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\n\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51796244a3acacda9d45944ef778d8f9\",\n \"score\": \"0.5100558\",\n \"text\": \"def augment_image(im):\\n # First crop out the face to save reduce computation load\\n bb = im.landmarks['bb'].lms\\n bb_vec = bb.as_vector()\\n bb_ul = (np.array([bb_vec[0], bb_vec[1]]) - bb.centre()) * 2\\n bb_lr = (np.array([bb_vec[4], bb_vec[5]]) - bb.centre()) * 2\\n ul = bb_ul + bb.centre()\\n lr = bb_lr + bb.centre()\\n im = im.crop(ul, lr, constrain_to_boundary=True)\\n if im.pixels.shape[0] == 1:\\n pix = np.zeros((3, im.pixels.shape[1], im.pixels.shape[2]))\\n pix[:,] = im.pixels\\n im.pixels = pix\\n\\n beta = 0.3\\n cx = np.random.uniform(-beta, beta)\\n cy = np.random.uniform(-beta, beta)\\n fx = 1.0\\n fy = np.random.uniform(0.6, 1.4)\\n max_rotation = 30\\n theta = np.random.uniform(-max_rotation, max_rotation)\\n\\n rotation = menpo.transform.Rotation.init_from_2d_ccw_angle(theta)\\n shear = menpo.transform.Affine(np.array([[1, cx, 0],[cy, 1, 0], [0,0,1]]))\\n scale = menpo.transform.Affine(np.array([[fx, 0, 0],[0, fy, 0], [0,0,1]]))\\n T = scale.compose_after(shear).compose_after(rotation)\\n\\n t_im = im.transform_about_centre(T)\\n\\n t_im = add_color_jetting(t_im)\\n t_im = add_occlusion(t_im)\\n\\n\\n new_bb = t_im.landmarks['PTS'].lms.bounding_box()\\n\\n #new_bb contains the gt bounding box\\n augmented_bb = add_bb_noise(new_bb)\\n augmented_bb = augmented_bb.reshape((4,2))\\n augmented_bb = menpo.shape.PointCloud(augmented_bb)\\n t_im.landmarks['bb'] = menpo.landmark.LandmarkGroup.init_with_all_label(augmented_bb)\\n\\n return t_im\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244aa631c9ba261d95a9ad6f963fddd6\",\n \"score\": \"0.50956684\",\n \"text\": \"def get_mch_heatmap(ensemble, methylation_type, grouping, clustering, level, ptile_start, ptile_end, normalize_row, query):\\n\\ttsne_type = 'mCH_ndim2_perp20'\\n\\n\\tif normalize_row:\\n\\t\\tnormal_or_original = '(normalized by row)'\\n\\telse:\\n\\t\\tnormal_or_original = ''\\n\\n\\ttitle = level.title() + \\\" gene body \\\" + methylation_type + \\\" by cluster \\\" + normal_or_original + \\\": <br>\\\"\\n\\tgenes = query.split()\\n\\n\\ts=''\\n\\tfor i in genes:\\n\\t\\ts=s+','+i\\n\\n\\tgene_labels = list()\\n\\tgene_info_df = pd.DataFrame()\\n\\tgene_infos = get_gene_by_id(genes)\\n\\tfor i, gene in enumerate(gene_infos):\\n\\t\\tgene_name = gene['gene_name']\\n\\t\\tgene_labels.append(gene_name)\\n\\t\\tif i > 0 and i % 10 == 0:\\n\\t\\t\\ttitle += \\\"<br>\\\"\\n\\t\\ttitle += gene_name + \\\"+\\\"\\n\\t\\tgene_info_df[gene_name] = median_cluster_mch(get_gene_methylation(ensemble, methylation_type, gene['gene_id'], grouping, clustering, level, True), grouping, clustering)\\n\\t\\tif gene_info_df[gene_name].empty:\\n\\t\\t\\traise FailToGraphException\\n\\n\\ttitle = title[:-1] # Gets rid of last '+'\\n\\n\\tgene_info_df.reset_index(inplace=True)\\n\\tif grouping == 'annotation':\\n\\t\\tgene_info_df['annotation_cat'] = pd.Categorical(gene_info_df['annotation_'+clustering], cluster_annotation_order)\\n\\t\\tgene_info_df.sort_values(by='annotation_cat', inplace=True)\\n\\t\\tgene_info_df.drop('annotation_cat', axis=1, inplace=True)\\n\\t\\tgene_info_df.set_index(grouping+'_'+clustering, inplace=True)\\n\\telif grouping == 'cluster':\\n\\t\\tgene_info_df.sort_values(by='cluster_'+clustering, inplace=True)\\n\\t\\tgene_info_df.set_index(grouping+'_'+clustering, inplace=True)\\n\\telif grouping == 'dataset' or grouping == 'target_region' or grouping == 'slice' or grouping == 'sex':\\n\\t\\tgene_info_df.sort_values(by=grouping, inplace=True)\\n\\t\\tgene_info_df.set_index(grouping, inplace=True)\\n\\telse:\\n\\t\\tgrouping = 'cluster'\\n\\t\\tgene_info_df.sort_values(by='cluster_'+clustering, inplace=True)\\n\\t\\tgene_info_df.set_index(grouping+'_'+clustering, inplace=True)\\n\\n\\t# For some reason, Plotly doesn't allow 'None' as a group on the x-axis for heatmaps.\\n\\tif gene_info_df.index.tolist() == ['None']:\\n\\t\\tgene_info_df.index = ['N/A']\\n\\n\\tclusters_labels = gene_info_df.index.tolist()\\n\\tif grouping == 'cluster':\\n\\t\\tclusters_labels = ['Cluster '+str(i) for i in clusters_labels]\\n\\n\\tnormal_or_original = 'Original'\\n\\tif normalize_row:\\n\\t\\tfor gene in gene_info_df:\\n\\t\\t\\t# z-score\\n\\t\\t\\t# gene_info_df[gene] = (gene_info_df[gene] - gene_info_df[gene].mean()) / gene_info_df[gene].std()\\n\\t\\t\\t# min-max\\n\\t\\t\\tgene_range = gene_info_df[gene].max() - gene_info_df[gene].min()\\n\\t\\t\\tif (gene_range==0):\\n\\t\\t\\t\\tgene_range = 1\\n\\t\\t\\tgene_info_df[gene] = (gene_info_df[gene] - gene_info_df[gene].min()) / gene_range\\n\\t\\tnormal_or_original = 'Normalized'\\n\\n\\tgene_info_dict = gene_info_df.to_dict(into=OrderedDict)\\n\\n\\tx, y, text, hover, mch = list(), list(), list(), list(), list()\\n\\ti = 0\\n\\tname_prepend = \\\"\\\"\\n\\tif grouping == 'cluster':\\n\\t\\tname_prepend = 'cluster_'\\n\\tfor key in list(gene_info_dict.keys()):\\n\\t\\tj = 0\\n\\t\\ty.append(key)\\n\\t\\tmch.append(list(gene_info_dict[key].values()))\\n\\t\\tfor cluster in list(gene_info_dict[key].keys()):\\n\\t\\t\\tx.append(name_prepend+str(cluster))\\n\\t\\t\\ttext.append(build_hover_text(OrderedDict([('Gene', key),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (grouping.title(), x[j]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (methylation_type, mch[i][j])\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ])))\\n\\t\\t\\tj += 1\\n\\t\\thover.append(text)\\n\\t\\ttext = []\\n\\t\\ti += 1\\n\\n\\tflat_mch = list(chain.from_iterable(mch))\\n\\tmch_dataframe = pd.DataFrame(flat_mch).dropna()\\n\\n\\t# Hierarchical clustering and dendrogram\\n\\tmch = np.array(mch)\\n\\tfigure = ff.create_dendrogram(mch, orientation=\\\"right\\\", labels=tuple([i for i in range(len(genes))])) # TODO: Figure out how to set the colorscale\\n\\tfor i in range(len(figure['data'])):\\n\\t\\tfigure['data'][i]['xaxis'] = 'x2'\\n\\tdendro_leaves = figure['layout']['yaxis']['ticktext']\\n\\tdendro_leaves = list(map(int, dendro_leaves))\\n\\tmch = mch[dendro_leaves,:] # Reorder the genes according to the clustering\\n\\tgenes_labels = [gene_labels[i] for i in dendro_leaves]\\n\\thover_old = hover\\n\\t# hover = [hover_old[i] for i in dendro_leaves]\\n\\thover = [str(i) for i in dendro_leaves]\\n\\n\\n\\tdendro_top = ff.create_dendrogram(mch.transpose(), orientation=\\\"bottom\\\", labels=tuple([i for i in range(mch.shape[1])]))\\n\\tfor i in range(len(dendro_top['data'])):\\n\\t\\tdendro_top['data'][i]['yaxis'] = 'y2'\\n\\tdendro_top_leaves = dendro_top['layout']['xaxis']['ticktext']\\n\\tdendro_top_leaves = list(map(int, dendro_top_leaves))\\n\\tmch = mch[:,dendro_top_leaves] # Reorder the genes according to the clustering\\n\\tclusters_labels = [clusters_labels[i] for i in dendro_top_leaves]\\n\\tmch = list(mch)\\n\\tfigure.add_traces(dendro_top['data'])\\n\\n\\t# Set color scale limits\\n\\tstart = mch_dataframe.quantile(ptile_start).values[0].tolist()\\n\\tend = mch_dataframe.quantile(ptile_end).values[0].tolist()\\n\\tend = max(end,start+0.01)\\n\\n\\tcolorbar_tickval = list(arange(start, end, (end - start) / 4))\\n\\tcolorbar_tickval[0] = start\\n\\tcolorbar_tickval.append(end)\\n\\tcolorbar_ticktext = [\\n\\t\\tstr(round(x, num_sigfigs_ticklabels)) for x in arange(start, end, (end - start) / 4)\\n\\t]\\n\\tif normalize_row == True:\\n\\t\\tcolorbar_ticktext[0] = str(round(start, num_sigfigs_ticklabels))\\n\\telse:\\n\\t\\tif (round(start,num_sigfigs_ticklabels)) == 0:\\n\\t\\t\\tcolorbar_ticktext[0] = str(round(start,num_sigfigs_ticklabels))\\n\\t\\telse:\\n\\t\\t\\tcolorbar_ticktext[0] = '<' + str(round(start, num_sigfigs_ticklabels))\\n\\tcolorbar_ticktext.append('>' + str(round(end, num_sigfigs_ticklabels)))\\n\\n\\t# Due to a weird bug(?) in plotly, the number of elements in tickvals and ticktext\\n\\t# must be greater than or equal to number of genes in query. Else, javascript throws\\n\\t# Uncaught Typeerrors when trying to hover over genes. (Tomo 12/11/17)\\n\\twhile len(colorbar_tickval) < len(genes):\\n\\t\\tcolorbar_tickval.insert(0,start)\\n\\t\\tif normalize_row == True:\\n\\t\\t\\tcolorbar_ticktext.insert(0, str(round(start, num_sigfigs_ticklabels)))\\n\\t\\telse:\\n\\t\\t\\tcolorbar_ticktext.insert(0, '<' + str(round(start, num_sigfigs_ticklabels)))\\n\\n\\ttrace = Heatmap(\\n\\t\\tx=dendro_top_leaves,\\n\\t\\ty=dendro_leaves,\\n\\t\\tz=mch,\\n\\t\\txtype=\\\"array\\\", ytype=\\\"array\\\",\\n\\t\\ttext=hover,\\n\\t\\tcolorscale='Viridis',\\n\\t\\tcolorbar={\\n\\t\\t\\t'x': 1.0,\\n\\t\\t\\t'len': 0.5,\\n\\t\\t\\t'title': level.capitalize() + ' ' + methylation_type,\\n\\t\\t\\t'titleside': 'right',\\n\\t\\t\\t'tickmode': 'array',\\n\\t\\t\\t'tickvals': colorbar_tickval,\\n\\t\\t\\t'ticktext': colorbar_ticktext,\\n\\t\\t\\t'thickness': 10,\\n\\t\\t\\t'tickfont': {'size': 10}\\n\\t\\t\\t},\\n\\t\\thoverinfo='text',\\n\\t\\tzmin=start,zmax=end,zauto=False, # Clip the extreme edges of the colorscale\\n\\t\\t)\\n\\ttrace['y'] = figure['layout']['yaxis']['tickvals']\\n\\ttrace['x'] = dendro_top['layout']['xaxis']['tickvals']\\n\\tfigure.add_traces([trace])\\n\\n\\tlayout = Layout(\\n\\t\\theight=max(600*len(genes)/20,550), # EAM Adjust the height of the heatmap according to the number of genes displayed\\n\\t\\twidth=1000,\\n\\t\\tpaper_bgcolor='rgba(0,0,0,0)',\\n\\t\\tplot_bgcolor='rgba(0,0,0,0)',\\n\\t\\tshowlegend=False,\\n\\t\\thovermode='closest',\\n\\t\\ttitle=title,\\n\\t\\t# titlefont={'color': 'rgba(1,2,2,1)',\\n\\t\\t# 'size': 16},\\n\\t\\tmargin={'l': 0,\\n\\t\\t\\t\\t'r': 0,\\n\\t\\t\\t\\t'b': 100,\\n\\t\\t\\t\\t't': 150,},\\n\\t\\txaxis={\\n\\t\\t\\t'side': 'bottom',\\n\\t\\t\\t'tickangle': -45,\\n\\t\\t\\t'title': 'Clusters',\\n\\t\\t\\t'tickfont': {'size': 12},\\n\\t\\t\\t'showticklabels': True,\\n\\t\\t\\t'tickmode': 'array',\\n\\t\\t\\t'tickvals':trace['x'],\\n\\t\\t\\t'ticktext':clusters_labels,\\n\\t\\t\\t},\\n\\t\\tyaxis={\\n\\t\\t\\t# 'tickangle': 15,\\n\\t\\t\\t'tickfont': {'size': 12},\\n\\t\\t\\t'showticklabels': True,\\n\\t\\t\\t'ticks':\\\"outside\\\",\\n\\t\\t\\t'tickmode': 'array',\\n\\t\\t\\t'tickvals':trace['y'],\\n\\t\\t\\t'ticktext':genes_labels,\\n\\t\\t\\t},\\n\\t\\t)\\n\\tlayout['yaxis'].update({'domain': [0, .85]})\\n\\tlayout['xaxis'].update({'domain': [0.2, 1]})\\n\\tlayout.update({'hovermode': 'closest'})\\n\\tlayout.update({'xaxis2': {\\n\\t\\t\\t'showticklabels': False\\n\\t\\t\\t}})\\n\\tlayout.update({'yaxis2': {\\n\\t\\t\\t'showticklabels': False\\n\\t\\t\\t}})\\n\\tlayout['xaxis2'].update({'domain': [0, 0.1]})\\n\\tlayout['yaxis2'].update({'domain': [0.86, 1]})\\n\\tfor xx in ['xaxis','yaxis','xaxis2','yaxis2']:\\n\\t\\tlayout[xx].update({'mirror': False,\\n\\t\\t\\t\\t\\t\\t 'showgrid': False,\\n\\t\\t\\t\\t\\t\\t 'showline': False,\\n\\t\\t\\t\\t\\t\\t 'zeroline': False})\\n\\n\\t# Available colorscales:\\n\\t# https://community.plot.ly/t/what-colorscales-are-available-in-plotly-and-which-are-the-default/2079\\n\\tupdatemenus = list([\\n\\t\\tdict(\\n\\t\\t\\tbuttons=list([\\n\\t\\t\\t\\tdict(\\n\\t\\t\\t\\t\\targs=['colorscale', 'Viridis'],\\n\\t\\t\\t\\t\\tlabel='Viridis',\\n\\t\\t\\t\\t\\tmethod='restyle'\\n\\t\\t\\t\\t),\\n\\t\\t\\t\\tdict(\\n\\t\\t\\t\\t\\targs=['colorscale', 'Bluered'],\\n\\t\\t\\t\\t\\tlabel='Bluered',\\n\\t\\t\\t\\t\\tmethod='restyle'\\n\\t\\t\\t\\t),\\n\\t\\t\\t\\tdict(\\n\\t\\t\\t\\t\\targs=['colorscale', 'Blackbody'],\\n\\t\\t\\t\\t\\tlabel='Blackbody',\\n\\t\\t\\t\\t\\tmethod='restyle'\\n\\t\\t\\t\\t),\\n\\t\\t\\t\\tdict(\\n\\t\\t\\t\\t\\targs=['colorscale', 'Electric'],\\n\\t\\t\\t\\t\\tlabel='Electric',\\n\\t\\t\\t\\t\\tmethod='restyle'\\n\\t\\t\\t\\t),\\n\\t\\t\\t\\tdict(\\n\\t\\t\\t\\t\\targs=['colorscale', 'Earth'],\\n\\t\\t\\t\\t\\tlabel='Earth',\\n\\t\\t\\t\\t\\tmethod='restyle'\\n\\t\\t\\t\\t),\\n\\t\\t\\t\\tdict(\\n\\t\\t\\t\\t\\targs=['colorscale', 'Jet'],\\n\\t\\t\\t\\t\\tlabel='Jet',\\n\\t\\t\\t\\t\\tmethod='restyle'\\n\\t\\t\\t\\t),\\n\\t\\t\\t\\tdict(\\n\\t\\t\\t\\t\\targs=['colorscale', 'Rainbow'],\\n\\t\\t\\t\\t\\tlabel='Rainbow',\\n\\t\\t\\t\\t\\tmethod='restyle'\\n\\t\\t\\t\\t),\\n\\t\\t\\t\\tdict(\\n\\t\\t\\t\\t\\targs=['colorscale', 'Picnic'],\\n\\t\\t\\t\\t\\tlabel='Picnic',\\n\\t\\t\\t\\t\\tmethod='restyle'\\n\\t\\t\\t\\t),\\n\\t\\t\\t\\tdict(\\n\\t\\t\\t\\t\\targs=['colorscale', 'Portland'],\\n\\t\\t\\t\\t\\tlabel='Portland',\\n\\t\\t\\t\\t\\tmethod='restyle'\\n\\t\\t\\t\\t),\\n\\t\\t\\t\\tdict(\\n\\t\\t\\t\\t\\targs=['colorscale', 'YlGnBu'],\\n\\t\\t\\t\\t\\tlabel='YlGnBu',\\n\\t\\t\\t\\t\\tmethod='restyle'\\n\\t\\t\\t\\t)\\n\\t\\t\\t]),\\n\\t\\t\\tdirection='down',\\n\\t\\t\\tshowactive=True,\\n\\t\\t\\tx=-0.1,\\n\\t\\t\\txanchor='left',\\n\\t\\t\\ty=1.43,\\n\\t\\t\\tyanchor='top'\\n\\t\\t)\\n\\t])\\n\\n\\tlayout['updatemenus'] = updatemenus\\n\\n\\t# layout['annotations'].extend([Annotation(text=title,\\n\\t# x=0.5,\\n\\t# y=1.3,\\n\\t# xanchor=\\\"center\\\",\\n\\t# yanchor=\\\"top\\\",\\n\\t# showarrow=False,\\n\\t# xref=\\\"paper\\\",\\n\\t# yref=\\\"paper\\\",\\n\\t# font={'size': 16,\\n\\t# 'color': 'black',})])\\n\\n\\tfigure['layout'] = layout\\n\\n\\treturn plotly.offline.plot(figure,\\n\\t\\toutput_type='div',\\n\\t\\tshow_link=False,\\n\\t\\tinclude_plotlyjs=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe3635c787d4f7e71437af8f97ecfc01\",\n \"score\": \"0.5090728\",\n \"text\": \"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\\\"\\\", **kwargs):\\r\\n\\r\\n if not ax:\\r\\n ax = plt.gca()\\r\\n\\r\\n # Plot the heatmap\\r\\n im = ax.imshow(data, **kwargs)\\r\\n\\r\\n # Create colorbar\\r\\n cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\\r\\n cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\")\\r\\n\\r\\n # We want to show all ticks...\\r\\n ax.set_xticks(np.arange(data.shape[1]))\\r\\n ax.set_yticks(np.arange(data.shape[0]))\\r\\n # ... and label them with the respective list entries.\\r\\n ax.set_xticklabels(col_labels)\\r\\n ax.set_yticklabels(row_labels)\\r\\n\\r\\n # Let the horizontal axes labeling appear on top.\\r\\n ax.tick_params(top=True, bottom=False,\\r\\n labeltop=True, labelbottom=False)\\r\\n\\r\\n # Rotate the tick labels and set their alignment.\\r\\n plt.setp(ax.get_xticklabels(), rotation=-30, ha=\\\"right\\\",\\r\\n rotation_mode=\\\"anchor\\\")\\r\\n\\r\\n # Turn spines off and create white grid.\\r\\n # ax.spines[:].set_visible(False)\\r\\n\\r\\n ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\\r\\n ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\\r\\n ax.grid(which=\\\"minor\\\", color=\\\"w\\\", linestyle='-', linewidth=3)\\r\\n ax.tick_params(which=\\\"minor\\\", bottom=False, left=False)\\r\\n\\r\\n return im, cbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e745027b255d23ea5c5a9f0679296d1\",\n \"score\": \"0.5090131\",\n \"text\": \"def Generate_BG_Template(outputSize=300, angularSize = 10, fileOut = 'BGRateMap.pickle' ):\\r\\n template = np.zeros((outputSize,outputSize))\\r\\n ppd=float(outputSize)/float(angularSize) # pixels per deg\\r\\n \\r\\n events110 = ParseFermi.Import_File('photons.txt', energyRange = (120000,140000),lonRange=(-5,5),latRange = (-5,5))\\r\\n events130 = ParseFermi.Import_File('photons.txt', energyRange = (100000,120000),lonRange=(-5,5),latRange = (-5,5))\\r\\n events150 = ParseFermi.Import_File('photons.txt', energyRange = (140000,200000),lonRange=(-5,5),latRange = (-5,5))\\r\\n \\r\\n for i in range(10000,200001,20000):\\r\\n if i == 130000:\\r\\n continue\\r\\n events = ParseFermi.Import_File('photons.txt', energyRange = (i-10000,i+10000),lonRange=(-5,5),latRange = (-5,5))\\r\\n BG = np.zeros((outputSize,outputSize)) \\r\\n for j in events:\\r\\n xIDX = int(j[1]*ppd+float(outputSize/2))\\r\\n yIDX = int(j[2]*ppd+float(outputSize/2))\\r\\n BG[yIDX][xIDX] += 1.0\\r\\n \\r\\n psfDeg = .2+float(200)/float(i)\\r\\n psfOut = psfDeg*ppd\\r\\n #print i/1e3, psfDeg, psfOut\\r\\n \\r\\n template += scipy.ndimage.filters.gaussian_filter(BG, psfOut)\\r\\n \\r\\n template = template/np.max(template)\\r\\n \\r\\n # Write to file \\r\\n outFile = open(fileOut, \\\"wb\\\" )\\r\\n pickle.dump(template, outFile)\\r\\n print 'Rate Map saved to ', fileOut\\r\\n \\r\\n plt.imshow(scipy.fliplr(template), 'jet',extent=[5,-5,-5,5])\\r\\n\\r\\n plt.xlabel(r'$l [^\\\\circ]$')\\r\\n plt.ylabel(r'$b [^\\\\circ]$')\\r\\n plt.xlim(5,-5)\\r\\n plt.ylim(-5,5)\\r\\n plt.colorbar()\\r\\n\\r\\n x,y = Find_Centroid(template)\\r\\n x,y = (x/ppd -angularSize/2.0,) ,(y/ppd -angularSize/2.0,)\\r\\n print x,y\\r\\n plt.scatter(x,y, s=10, c='r', marker = '+')\\r\\n \\r\\n X,Y = FormatEvents(events110)\\r\\n plt.scatter(X, Y, label = '100-120 GeV', marker = 'o' , c = 'k')\\r\\n \\r\\n X,Y = FormatEvents(events130)\\r\\n plt.scatter(X, Y, label = '120-140 GeV', marker = 'o' , c = 'r')\\r\\n \\r\\n X,Y = FormatEvents(events150)\\r\\n plt.scatter(X, Y, label = '140-200 GeV', marker = 'o' , c = 'g' )\\r\\n \\r\\n from matplotlib.font_manager import FontProperties\\r\\n fontP = FontProperties()\\r\\n fontP.set_size('small')\\r\\n plt.legend(loc=1, ncol=1, fancybox=True, shadow=False,prop=fontP,borderaxespad=0.,labelspacing = .2)\\r\\n \\r\\n from matplotlib.backends.backend_pdf import PdfPages\\r\\n if fileOut != '':\\r\\n pp = PdfPages(fileOut + '_sideband.pdf')\\r\\n plt.savefig(pp, format='pdf')\\r\\n print \\\"Figures saved to \\\", str(fileOut)+ '_sideband.pdf\\\\n',\\r\\n pp.close()\\r\\n \\r\\n plt.show()\\r\\n return template\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83025979f7cc41c2cbf7d35ea3be84a5\",\n \"score\": \"0.50868076\",\n \"text\": \"def plot_heatmap(model_dir, name, features, labels, num_classes):\\n features_sort, _ = utils.sort_dataset(features, labels, \\n classes=num_classes, stack=False)\\n features_sort_ = np.vstack(features_sort)\\n sim_mat = np.abs(features_sort_ @ features_sort_.T)\\n\\n # plt.rc('text', usetex=False)\\n # plt.rcParams['font.family'] = 'serif'\\n # plt.rcParams['font.serif'] = ['Times New Roman'] #+ plt.rcParams['font.serif']\\n\\n fig, ax = plt.subplots(figsize=(7, 5), sharey=True, sharex=True)\\n im = ax.imshow(sim_mat, cmap='Blues')\\n fig.colorbar(im, pad=0.02, drawedges=0, ticks=[0, 0.5, 1])\\n ax.set_xticks(np.linspace(0, len(labels), num_classes+1))\\n ax.set_yticks(np.linspace(0, len(labels), num_classes+1))\\n [tick.label.set_fontsize(10) for tick in ax.xaxis.get_major_ticks()] \\n [tick.label.set_fontsize(10) for tick in ax.yaxis.get_major_ticks()]\\n fig.tight_layout()\\n\\n save_dir = os.path.join(model_dir, 'figures', 'heatmaps')\\n os.makedirs(save_dir, exist_ok=True)\\n file_name = os.path.join(save_dir, f\\\"{name}.png\\\")\\n fig.savefig(file_name)\\n print(\\\"Plot saved to: {}\\\".format(file_name))\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41720e814b91c4f9cba91f09874b4067\",\n \"score\": \"0.5072641\",\n \"text\": \"def heatmap(\\n df: pd.DataFrame, figsize: tuple = (10, 10), scale: float = 1.4\\n) -> sns.heatmap:\\n\\n # keep only quantitative features\\n df = create_quanti_df(df)\\n print(f\\\"Number of quantitaive columns: {df.shape[1]}\\\")\\n # calcaulate features correlations\\n corr = df.corr() * 100\\n # create mask for the upper triangle\\n mask = np.triu(np.ones_like(corr, dtype=bool))\\n # create figure\\n plt.figure(figsize=figsize)\\n # create heatmap\\n sns.set(font_scale=scale)\\n sns.heatmap(corr, mask=mask, annot=True, cmap=\\\"coolwarm\\\", fmt=\\\".0f\\\")\\n sns.reset_orig()\\n plt.show()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2283271d70dfc9b95b9121d55407682\",\n \"score\": \"0.50693595\",\n \"text\": \"def watershed(mask, img, plotImage = False, kernelSize = None):\\n imgCopy = img.copy()\\n maskCopy = np.array(mask.copy(), dtype=np.uint8)\\n \\n if kernelSize is None:\\n kernelSize = 2\\n\\n # Finding sure foreground area\\n #dist_transform = cv2.distanceTransform(mask, cv2.DIST_L2, 5)\\n #ret, sure_fg = cv2.threshold(dist_transform,0.3*dist_transform.max(),255,0) #change the second argument to change the sensitivity \\n maskClosed = skimage.morphology.closing(np.array(maskCopy, dtype=np.uint8))\\n maskClosed = skimage.morphology.closing(np.array(maskClosed, dtype=np.uint8))\\n kernel = np.ones((kernelSize,kernelSize), np.uint8)\\n # maskCopy = img_as_bool(maskCopy)\\n sure_fg = cv2.erode(maskClosed, kernel, iterations = 2) ###\\n sure_fg = skimage.morphology.closing(np.array(sure_fg, dtype=np.uint8))\\n # kernel = np.ones((2,2), np.uint8)\\n # sure_fg = binary_closing(sure_fg, kernel)\\n \\n # sure background area\\n #kernel = np.ones((5, 5), np.uint8)\\n #sure_bg = cv2.dilate(mask, kernel, iterations = 1)\\n sure_fg_bool = 1 - img_as_bool(sure_fg)\\n # sure_bg = np.uint8(1 - morphology.medial_axis(sure_fg_bool)) ### \\n sure_bg = np.uint8(1 - morphology.skeletonize(sure_fg_bool))\\n sure_bg[0, :] = 1\\n sure_bg[-1, :] = 1\\n sure_bg[:, 0] = 1\\n sure_bg[:, -1] = 1\\n \\n # Finding unknown region\\n sure_fg = np.uint8(sure_fg)\\n unknown = cv2.subtract(sure_bg, sure_fg)\\n \\n if plotImage:\\n plt.figure()\\n plt.imshow(sure_fg)\\n plt.title(\\\"Inner Marker\\\")\\n plt.figure()\\n plt.imshow(sure_bg)\\n plt.title(\\\"Outer Marker\\\")\\n plt.figure()\\n plt.imshow(unknown)\\n plt.title(\\\"Unknown\\\")\\n \\n # Marker labelling\\n ret, markers = cv2.connectedComponents(sure_fg)\\n\\n # Add one to all labels so that sure background is not 0, but 1\\n markers = markers+1\\n\\n # Now, mark the region of unknown with zero\\n markers[unknown==1] = 0\\n \\n if plotImage:\\n plt.figure()\\n plt.imshow(markers, cmap='jet')\\n plt.title(\\\"Markers\\\")\\n \\n # Do watershed\\n markers = cv2.watershed(imgCopy, markers)\\n \\n imgCopy[markers == -1] = [0, 255 ,0]\\n\\n if plotImage:\\n plt.figure()\\n plt.imshow(markers,cmap='jet')\\n plt.title(\\\"Mask\\\")\\n plt.figure()\\n plt.imshow(img)\\n plt.title(\\\"Original Image\\\")\\n plt.figure()\\n plt.imshow(imgCopy)\\n plt.title(\\\"Marked Image\\\")\\n plt.show()\\n\\n return markers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db20ad45f923767ded8f27bed305444a\",\n \"score\": \"0.50616443\",\n \"text\": \"def bigaussian(mu, wid, x, m = 0.5):\\n lx = x.shape[0]\\n ix = np.where(x == mu)[0][0]\\n \\n y = np.ones(lx)\\n y[0:ix] = gaussian(mu, wid * m, x[0:ix])\\n y[ix+1:lx] = gaussian(mu, wid * (1 - m), x[ix+1:lx]) \\n \\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e64dbc54278c459cf4a3b45c0fadc679\",\n \"score\": \"0.5060408\",\n \"text\": \"def _FWHMGauss(sigma, pixel=12):\\n return sigma*2*np.sqrt(2*np.log(2))*pixel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41aa67feafb484e704424dc665dadb30\",\n \"score\": \"0.506033\",\n \"text\": \"def plot_heatmap(mi, output):\\n\\tfig = plt.figure()\\n\\tdata = np.array(mi)\\n\\tfig, ax = plt.subplots()\\n\\theatmap = ax.pcolor(data, cmap=plt.cm.jet)\\n\\n\\tax.invert_yaxis()\\n\\tax.xaxis.tick_top()\\n\\n\\tax.set_xlabel('Seq 2')\\n\\tax.set_ylabel('Seq 1')\\n\\tax.xaxis.set_label_position('top')\\n\\n\\tax.set_xlim(0, len(mi[0]))\\n\\tax.set_ylim(len(mi), 0)\\n\\n\\txmajor_ticks = np.arange(0, len(mi[1]), 20)\\n\\txminor_ticks = np.arange(0, len(mi[1]), 1)\\n\\n\\tymajor_ticks = np.arange(0, len(mi), 20)\\n\\tyminor_ticks = np.arange(0, len(mi), 1)\\n\\n\\tax.tick_params(axis = 'both', which = 'major', labelsize = 5)\\n\\tax.tick_params(axis = 'both', which = 'minor', labelsize = 0)\\n\\n\\tax.set_xticks(xmajor_ticks)\\n\\tax.set_xticks(xminor_ticks, minor = True)\\n\\tax.set_yticks(ymajor_ticks)\\n\\tax.set_yticks(yminor_ticks, minor = True)\\n\\n\\tax.tick_params(which = 'both', direction = 'out')\\n\\n\\tplt.xticks(rotation=90)\\n\\n\\tcb = plt.colorbar(heatmap)\\n\\tcb.set_label('MI value')\\n\\n\\tfig.savefig(output, dpi = 700)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b791443ac60b063226878a563cca1c94\",\n \"score\": \"0.5057443\",\n \"text\": \"def output_heatmap(model, last_conv_layer, img):\\n # predict the image class\\n preds = model.predict(img)\\n # find the class index\\n index = np.argmax(preds[0])\\n # This is the entry in the prediction vector\\n target_output = model.output[:, index]\\n # get the last conv layer\\n last_conv_layer = model.get_layer(last_conv_layer)\\n\\n # compute the gradient of the output feature map with this target class\\n grads = K.gradients(target_output, last_conv_layer.output)[0]\\n\\n # mean the gradient over a specific feature map channel\\n pooled_grads = K.mean(grads, axis=(0, 1, 2))\\n\\n # this function returns the output of last_conv_layer and grads\\n # given the input picture\\n iterate = K.function([model.input], [pooled_grads, last_conv_layer.output[0]])\\n pooled_grads_value, conv_layer_output_value = iterate([img])\\n\\n # We multiply each channel in the feature map array\\n # by \\\"how important this channel is\\\" with regard to the target class\\n\\n for i in range(conv_layer_output_value.shape[-1]):\\n conv_layer_output_value[:, :, i] *= pooled_grads_value[i]\\n\\n # The channel-wise mean of the resulting feature map\\n # is our heatmap of class activation\\n heatmap = np.mean(conv_layer_output_value, axis=-1)\\n heatmap = np.maximum(heatmap, 0)\\n heatmap /= np.max(heatmap)\\n\\n return heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df66a5836337c3acf6fa822c08507fda\",\n \"score\": \"0.5056677\",\n \"text\": \"def gauss_kern(size, sigma=1.0):\\n h1 = size[0]\\n h2 = size[1]\\n x, y = np.mgrid[0:h2, 0:h1]\\n x = x-h2/2\\n y = y-h1/2\\n g = np.exp( -( x**2 + y**2 ) / (2*sigma**2) );\\n return g / g.sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd79330882098da6d86d29cf12481a6\",\n \"score\": \"0.50544626\",\n \"text\": \"def estbg(im, mask=None, bins=None, plotalot=False, rout=(3,200), badval=nan):\\n # 2009-09-02 17:13 IJC: Created!\\n # 2009-09-04 15:07 IJC: Added RemoveOutliers option. Use only non-empty bins in fit.\\n # 2009-09-08 15:32 IJC: Error returned is now divided by sqrt(N) for SDOM\\n # 2009-11-03 00:16 IJC: Improved guess for gaussian dispersion\\n # 2011-05-18 11:47 IJMC: Moved (e)gaussian imports to analysis.\\n # 2012-01-01 21:04 IJMC: Added badval option\\n # 2012-08-15 17:45 IJMC: Numpy's new histogram no longer accepts 'new' keyword\\n # 2013-03-20 08:22 IJMC: Now works better even for small numbers\\n # of pixels; thanks to A. Weigel @\\n # ETH-Zurich for catching this!\\n\\n from numpy import histogram, mean, median, sqrt, linspace, isfinite, ones,std\\n from pylab import find\\n from scipy import optimize\\n #from analysis import removeoutliers, egaussian, gaussian, stdr\\n if plotalot:\\n from pylab import figure, errorbar, plot, colorbar, title, hist, mean, std\\n ##from analysis import imshow\\n\\n def gaussianChiSquared(guess, x, y, err):\\n return (egaussian(guess, x, y, e=err)**2).sum()\\n\\n\\n if mask==None:\\n mask = ones(im.shape)\\n dat = im.ravel()[find(mask<>0)]\\n if plotalot:\\n figure(); plot(im.ravel()); plot(dat)\\n print mean(dat), std(dat), rout[0]*std(dat)\\n print len(dat), (abs(dat-mean(dat))<(rout[0]*std(dat))).sum()\\n figure(); plot(dat-mean(dat)); \\n plot([0,len(dat)], [rout[0]*std(dat),rout[0]*std(dat)],'--k')\\n plot([0,len(dat)], [-rout[0]*std(dat),-rout[0]*std(dat)],'--k')\\n dat = removeoutliers(dat, rout[0], remove='both', center='mean', niter=rout[1], verbose=plotalot)\\n ndat = len(dat)\\n\\n if ndat==0:\\n print \\\"No data to work with!\\\"\\n return (badval, badval)\\n if bins==None:\\n if plotalot: print \\\"no bins entered!\\\"\\n datmean = dat.mean()\\n datstd = stdr(dat, nsigma=3)\\n nunique = len(np.unique(dat.ravel()))\\n #pdb.set_trace()\\n if nunique > len(dat)/20.:\\n dobin = False\\n else:\\n dobin = True\\n bins = linspace(dat.min(), dat.max(), nunique/2)\\n\\n if plotalot: \\n print \\\"dat.mean, dat.std>>\\\" + str((dat.mean(), dat.std()))\\n\\n\\n #if plotalot:\\n # figure(); hout = hist(dat[datIndex],bins)\\n #else:\\n \\n if dobin:\\n binwidth = mean(bins[1::]-bins[:-1])\\n bincenter = 0.5*(bins[1::]+bins[:-1])\\n datIndex = (dat>=bins.min()) * (dat<=bins.max())\\n hout = histogram(dat[datIndex], bins) #,new=True)\\n gy = hout[0]\\n erry = sqrt(gy)\\n usableIndex = gy>0\\n\\n eff_binwidth = mean(bins[usableIndex][1::]-bins[usableIndex][:-1])\\n guess = [gy.sum()*eff_binwidth, std(dat[datIndex]), median(dat[datIndex])]\\n\\n if 1.0*usableIndex.sum()/usableIndex.size < 0.5:\\n out = guess\\n else:\\n out = optimize.fmin(gaussianChiSquared, guess, \\\\\\n args=(bincenter[usableIndex],gy[usableIndex], erry[usableIndex]), \\\\\\n disp=plotalot)\\n\\n if plotalot:\\n from pylab import figure, errorbar, plot, colorbar, title\\n from nsdata import imshow\\n print 'guess>>',guess\\n print 'fit>>',out\\n figure()\\n imshow(im); colorbar()\\n figure()\\n errorbar(bincenter[usableIndex], gy[usableIndex], erry[usableIndex], fmt='ob')\\n plot(bincenter, gaussian(out, bincenter),'-r', linewidth=2)\\n title('Mean: %f, Std. Dev.: %f' % (out[2], out[1]))\\n\\n ret = out[2], out[1]/sqrt(ndat)\\n else:\\n ret = datmean, datstd/sqrt(ndat)\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7823ce4f2d065ce46cfc80bd8f847a87\",\n \"score\": \"0.5051405\",\n \"text\": \"def calcHOGFeatures(\\n self,\\n img, # Image in some color space\\n orientations, # Number of orientations\\n pix_per_cell, # Pixels per cell\\n cell_per_block, # Cells per block\\n block_norm = 'L2-Hys', # Block normalization function\\n transform_sqrt = False, # Do SQRT transformation first\\n visualise = False # Visualise HOG features\\n ):\\n\\n res = skimage.feature.hog(\\n img,\\n orientations = orientations,\\n pixels_per_cell = (pix_per_cell, pix_per_cell),\\n cells_per_block = (cell_per_block, cell_per_block),\\n block_norm = block_norm,\\n transform_sqrt = transform_sqrt,\\n visualise = visualise,\\n feature_vector = False)\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c797377502881f4e490a44d6dad2a681\",\n \"score\": \"0.5050367\",\n \"text\": \"def generate_hm(height, width, kpts, maxlenght, weight):\\n\\n def makeGaussian(height, width, sigma=3, center=None):\\n \\\"\\\"\\\" Make a square gaussian kernel.\\n size is the length of a side of the square\\n sigma is full-width-half-maximum, which\\n can be thought of as an effective radius.\\n \\\"\\\"\\\"\\n x = np.arange(0, width, 1, float)\\n y = np.arange(0, height, 1, float)[:, np.newaxis]\\n if center is None:\\n x0 = width // 2\\n y0 = height // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)\\n\\n num = kpts.shape[0]\\n\\n hm = np.zeros((height, width, num), dtype=np.float32)\\n for i in range(num):\\n if not (np.array_equal(kpts[i], [-1, -1])):\\n s = 7 # int(np.sqrt(maxlenght) * maxlenght * 10 / 4096) + 2\\n hm[:, :, i] = makeGaussian(height, width, sigma=s, center=(kpts[i, 0], kpts[i, 1]))\\n else:\\n hm[:, :, i] = np.zeros((height, width))\\n return hm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9d3eef3ef0f72d6796ff0cac954641\",\n \"score\": \"0.50474685\",\n \"text\": \"def gaussianblur_transform(im):\\n im_gblur = cv2.GaussianBlur(im,(5,5),0)\\n return im_gblur\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c65887f43b28224cdbbdb83c863f862\",\n \"score\": \"0.5038349\",\n \"text\": \"def create_heatmap(df):\\n\\n fig = go.Figure(data=go.Heatmap(\\n z=df.values.tolist(),\\n x=df.columns,\\n #y=[classifier for classifier in df.index.values.tolist()],\\n y = df.index.values.tolist(),\\n hoverongaps = False,\\n xgap = 3,\\n ygap = 3,\\n colorscale=[[0.0, 'rgb(165,0,38)'], [0.1111111111111111, 'rgb(215,48,39)'], [0.2222222222222222, 'rgb(244,109,67)'], [0.3333333333333333, 'rgb(253,174,97)'], [0.4444444444444444, 'rgb(254,224,144)'], [0.5555555555555556, 'rgb(224,243,248)'], [0.6666666666666666, 'rgb(171,217,233)'], [0.7777777777777778, 'rgb(116,173,209)'], [0.8888888888888888, 'rgb(69,117,180)'], [1.0, 'rgb(49,54,149)']]\\n ),\\n )\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af3584dc7d52701df0c66f12d26913bf\",\n \"score\": \"0.5036482\",\n \"text\": \"def plot_means_heatmap(self,\\n scale_rows=True,\\n cmap='viridis',\\n figsize=(6, 6),\\n annot=False,\\n valfmt=\\\"{x:.1f}\\\",\\n fontsize=12,\\n fontweight=\\\"bold\\\",\\n textcolors=[\\\"white\\\", \\\"black\\\"]):\\n clmeans_df = self.cluster_means()\\n clmeans_np = clmeans_df.to_numpy()\\n if (scale_rows):\\n cbarlabel = \\\"Normalized Values\\\"\\n else:\\n cbarlabel = \\\"Values\\\"\\n\\n fig, ax = plt.subplots(figsize=figsize)\\n im, cbar = vh.heatmap(clmeans_np,\\n row_labels=clmeans_df.index,\\n col_labels=clmeans_df.columns,\\n ax=ax,\\n scale_rows=scale_rows,\\n cmap=cmap,\\n cbarlabel=cbarlabel)\\n\\n if annot:\\n vh.annotate_heatmap(im,\\n valfmt=valfmt,\\n size=fontsize,\\n fontweight=fontweight,\\n textcolors=textcolors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01bb583cd963dd04ca56d35a64ad4fee\",\n \"score\": \"0.50287306\",\n \"text\": \"def heatmap(filename, data):\\n\\n fig, ax = ppl.subplots(1)\\n ppl.pcolormesh(fig, ax, data, vmin=-0.0016, vmax=0.0016)\\n fig.savefig(filename + \\\".png\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6601c0138536945071353434f4bbb0a\",\n \"score\": \"0.502068\",\n \"text\": \"def plot_heatmap(mi):\\n\\tfig = plt.figure()\\n\\tdata = np.array(mi)\\n\\tfig, ax = plt.subplots()\\n\\theatmap = ax.pcolor(data, cmap=plt.cm.jet)\\n\\n\\tax.tick_params(direction='out')\\n\\n\\tmajorLocator = MultipleLocator(20)\\n\\tmajorFormatter = FormatStrFormatter('%d')\\n\\tminorLocator = MultipleLocator(1)\\n\\n\\tax.xaxis.set_major_locator(majorLocator)\\n\\tax.xaxis.set_major_formatter(majorFormatter)\\n\\tax.xaxis.set_minor_locator(minorLocator)\\n\\n\\tax.yaxis.set_major_locator(majorLocator)\\n\\tax.yaxis.set_major_formatter(majorFormatter)\\n\\tax.yaxis.set_minor_locator(minorLocator)\\n\\n\\tax.invert_yaxis()\\n\\tax.xaxis.tick_top()\\n\\n\\t###check which seq belongs to each axe\\n\\tax.set_xlabel('Seq 2')\\n\\tax.set_ylabel('Seq 1')\\n\\n\\tax.set_xlim(0, len(mi[1]))\\n\\tax.set_ylim(len(mi), 0)\\n\\n\\tplt.xticks(rotation=90)\\n\\n\\tcb = plt.colorbar(heatmap)\\n\\tcb.set_label('MI value')\\n\\n\\t#pdf = PdfPages('heatmap.pdf')\\n\\t#pdf.savefig(fig)\\n\\tfig.savefig('heatmap.png')\\n\\t#pdf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e262d8a1c66a244b1a029655dfb09e97\",\n \"score\": \"0.5017516\",\n \"text\": \"def _cmd_heatmap(args):\\n cnarrs = []\\n for fname in args.filenames:\\n cnarr = read_cna(fname)\\n if args.adjust_xy:\\n is_sample_female = verify_sample_sex(\\n cnarr, args.sample_sex, args.male_reference, args.diploid_parx_genome\\n )\\n cnarr = cnarr.shift_xx(args.male_reference, is_sample_female, args.diploid_parx_genome)\\n cnarrs.append(cnarr)\\n heatmap.do_heatmap(\\n cnarrs,\\n args.chromosome,\\n args.desaturate,\\n args.by_bin,\\n args.delim_sampl,\\n args.vertical,\\n args.title,\\n )\\n if args.output:\\n oformat = os.path.splitext(args.output)[-1].replace(\\\".\\\", \\\"\\\")\\n pyplot.savefig(args.output, format=oformat, bbox_inches=\\\"tight\\\")\\n logging.info(\\\"Wrote %s\\\", args.output)\\n else:\\n pyplot.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8d99788d7f0420d7cd470178243e34\",\n \"score\": \"0.5016424\",\n \"text\": \"def plot_gaussian(mu,var,epsilon=0.089881552536):\\n X1,X2 = np.meshgrid(np.linspace(0,35),np.linspace(0,35))\\n Z = get_probability(np.array([X1.flatten(),X2.flatten()]).T, mu, var)\\n Z = np.reshape(Z, X1.shape)\\n # Z[Z<epsilon] = 0\\n # Z[Z>epsilon] = 1\\n c = contour(X1, X2, Z, levels=[epsilon], lw=3, label=\\\"Gaussian Boundary\\\")\\n clabel(c, inline=1, fmt='Epsilon=%3.5e', colors='k',fontsize=8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad78da51902ad37aed14a3a38b610a5\",\n \"score\": \"0.5016406\",\n \"text\": \"def create_gaussian_filter(size, sigma):\\n h = size[0] #height of the template\\n w = size[1] #width of the template \\n if h % 2 == 0: h += 1 #add 1 if dimensions are even\\n if w % 2 == 0: w += 1\\n x = math.floor(h/2)\\n y = math.floor(w/2) \\n sum = 0\\n #create our template\\n template = np.zeros((h,w))\\n #fill the template in with the numbers from Gaussian distribution\\n for i in range(h):\\n for j in range(w):\\n template[i,j] = math.exp(-((((j-x)**2)+((i-y)**2))/(2*(sigma**2))))\\n sum = sum + template[i,j]\\n #normalise the numbers\\n gaussian_filter = template/sum\\n return gaussian_filter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"502afa56d7fc12c7b27a8eba017cdff5\",\n \"score\": \"0.50153375\",\n \"text\": \"def gaussian2kp(heatmap, kp_variance='matrix', clip_variance=None):\\n shape = heatmap.shape\\n # adding small eps to avoid 'nan' in variance\\n heatmap = heatmap.unsqueeze(-1) + 1e-7\\n grid = make_coordinate_grid(shape[3:], heatmap.type()).unsqueeze_(0).unsqueeze_(0).unsqueeze_(0)\\n\\n # print(grid.shape)\\n # sys.exit()\\n\\n mean = (heatmap * grid).sum(dim=(3, 4))\\n\\n kp = {'mean': mean.permute(0, 2, 1, 3)}\\n\\n if kp_variance == 'matrix':\\n mean_sub = grid - mean.unsqueeze(-2).unsqueeze(-2)\\n var = torch.matmul(mean_sub.unsqueeze(-1), mean_sub.unsqueeze(-2))\\n var = var * heatmap.unsqueeze(-1)\\n var = var.sum(dim=(3, 4))\\n var = var.permute(0, 2, 1, 3, 4)\\n if clip_variance:\\n min_norm = torch.tensor(clip_variance).type(var.type())\\n sg = smallest_singular(var).unsqueeze(-1)\\n var = torch.max(min_norm, sg) * var / sg\\n kp['var'] = var\\n\\n elif kp_variance == 'single':\\n mean_sub = grid - mean.unsqueeze(-2).unsqueeze(-2)\\n var = mean_sub ** 2\\n var = var * heatmap\\n var = var.sum(dim=(3, 4))\\n var = var.mean(dim=-1, keepdim=True)\\n var = var.unsqueeze(-1)\\n var = var.permute(0, 2, 1, 3, 4)\\n kp['var'] = var\\n\\n return kp\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993483,"cells":{"query_id":{"kind":"string","value":"adc3c746496e5167389997c4151ae2f6"},"query":{"kind":"string","value":"Convenience method for making a table"},"positive_passages":{"kind":"list like","value":[{"docid":"81e610a25bbd00c09f72427cdfeb2245","score":"0.698341","text":"def make_table(self):\n hash_key = DynamoKey('id')\n self.dynamo.create_table('foobar', hash_key=hash_key)","title":""}],"string":"[\n {\n \"docid\": \"81e610a25bbd00c09f72427cdfeb2245\",\n \"score\": \"0.698341\",\n \"text\": \"def make_table(self):\\n hash_key = DynamoKey('id')\\n self.dynamo.create_table('foobar', hash_key=hash_key)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"917e9f127e58b7bf071b3275c62bd88c","score":"0.8010181","text":"def makeTable(self):\n\tcols = [Column(name=k, **v) for k,v in self.col_dict.items()]\n\treturn Table(cols)","title":""},{"docid":"0f41e7f18a334779321cd1b0d029b69d","score":"0.7826398","text":"def _generate_table(self, *args, **kwargs):\n return self","title":""},{"docid":"0acd50063c84903bb301d82f7c141740","score":"0.78208625","text":"def newTable(cols=1, rows=1, **kwargs):\n return Table(rows=rows, cols=cols, **kwargs)","title":""},{"docid":"c84cb3845d89419d107199e87eb98930","score":"0.78134394","text":"def create_table(self, rows, headers):\n table = list()\n table += [' | '.join(headers), '--- | ' * len(headers)]\n for row in rows:\n table.append(' | '.join(row))\n return table","title":""},{"docid":"96e44f7d73855fd90dde0bcc18f030b0","score":"0.7687951","text":"def table(*args, **kwargs):\n return el('table', *args, **kwargs)","title":""},{"docid":"afbcb72cb8ba0f905bb3ddff63f94b94","score":"0.76656","text":"def table_construct(*argv):\r\n\treturn table","title":""},{"docid":"831db99e6d2ff3fa26c45cfa14a88cdc","score":"0.7569239","text":"def _MakeTable(self):\n\n table = zip([\"Number of valid pre-responses\"\n ,\"Number of valid post-responses\"\n ,\"Number of matched responses\"\n ,\"Reported number of students in class\"\n ,\"Fraction of class participating in pre and post\"\n ],self.tableVals)\n self.templateVars['table1'] = table","title":""},{"docid":"fd7dc88b33b8adb3b1f20814f94436f7","score":"0.74902296","text":"def create_table(self):\n raise NotImplementedError(\"Subclasses should overwrite this Method.\")","title":""},{"docid":"b7cc49fbbd934fee739162ec792bc9f5","score":"0.7422955","text":"def table(*args, **kwargs):\n\n return dh.thirdparty.tabulate.tabulate(*args, **kwargs)","title":""},{"docid":"83f96eab5a2f344bca97e28282e8b4e8","score":"0.7399735","text":"def make_table(self):\n hash_key = DynamoKey('id')\n range_key = DynamoKey('num', data_type=NUMBER)\n self.dynamo.create_table('foobar', hash_key=hash_key,\n range_key=range_key)","title":""},{"docid":"f723a20ea292936c36f9af3666d29175","score":"0.73897487","text":"def make_table(h5file, group, table_name, table_fields):\n\n tab = h5file.create_table(group, table_name, table_fields, \"{0} table\".format(table_name))\n\n return tab","title":""},{"docid":"b8869efb3a591c97778d8a25410bd8a7","score":"0.73361653","text":"def table(self):\n return Table(self)","title":""},{"docid":"44e31b7374c714e18d008ade4e9b85ea","score":"0.7324948","text":"def make_table(page, rows, header=None):\n page.table(border=1)\n page.tr()\n page.th(header)\n page.tr.close()\n for row in rows:\n page.tr()\n page.td(row)\n page.tr.close()\n page.table.close()","title":""},{"docid":"30722bf7b7c8b5643fa9f086c60807e7","score":"0.7320095","text":"def makeTable(items, cls=\"table\", rcls=\"row\", tattr=''):\n#-------------------------------------------------------------------------------\n ln = len(items)\n\n s = ['<table class=\"%s\" %s>' % (cls, tattr)]\n if ln < 6:\n for i in items:\n s.append(' <tr class=\"%s\"><td>%s</td></tr>' % (rcls, i))\n\n elif ln < 13:\n col = int(math.floor((ln + 2) / 3))\n for i in range(col):\n s.append(' <tr class=\"%s\">' % rcls)\n for idx in range(0, ln, col):\n try:\n s.append(' <td>%s</td>' % (items[idx+i]))\n except IndexError:\n s.append(' <td> </td>')\n s.append(' </tr>')\n\n else:\n col = int(math.floor((ln + 5) / 6))\n for i in range(col):\n s.append(' <tr class=\"%s\">' % rcls)\n for idx in range(0, ln, col):\n try:\n s.append(' <td>%s</td>' % items[idx+i])\n except IndexError:\n s.append(' <td> </td>')\n s.append(' </tr>')\n\n s.append('</table>')\n return('\\n'.join(s))","title":""},{"docid":"dfba30f4de6514c4ef34aa0bf570598f","score":"0.7260074","text":"def create(self,title=\"\",header=(),footer=(),id=\"table\"):\n self.rowlist=[]\n self.tableid=\"%s%d\" % (id,self.count)\n \n self.title=title\n self.header=header\n self.footer=footer","title":""},{"docid":"17b22c102d3caa861fb83128558bd76b","score":"0.72547674","text":"def build_table(self):\n pd.set_option('display.max_rows', 500)\n pd.set_option('display.max_columns', 500)\n pd.set_option('display.width', 1000)\n\n df = pd.DataFrame({\n 'Title': self.titles,\n 'Year': self.years,\n 'Genre': self.genres,\n 'Length': self.runtimes,\n 'Rating': self.ratings,\n 'Summary': self.summaries\n })\n\n print(tabulate(df.head(10), showindex=False, headers=df.columns, tablefmt='fancy_grid'))","title":""},{"docid":"ce2bee9a4d84447024b6984b8ea88b31","score":"0.7192743","text":"def _make_table(self, tablename: str, data: list) -> None:\n sqlcode = f\"CREATE TABLE {tablename} (\"\n for column in data:\n sqlcode += f\"{column[0]} {column[1]}, \"\n sqlcode = sqlcode[:-2] + \");\"\n self._exec_sql_code(sqlcode)","title":""},{"docid":"ac06ae4a08b07ab463a5e243ba5e88f6","score":"0.71660256","text":"def table(headers, rows, **atts):\n\n th = HtmlElement(N.th).wrap\n td = HtmlElement(N.td).wrap\n headerMaterial = HtmlElements.wrapTable(headers, th)\n rowMaterial = HtmlElements.wrapTable(rows, td)\n material = HtmlElement(N.body).wrap(headerMaterial + rowMaterial)\n return HtmlElement(N.table).wrap(material, **atts)","title":""},{"docid":"9a883fdce86a26ed74f6a836e205ebe3","score":"0.7149349","text":"def table_format(self):\n raise NotImplementedError","title":""},{"docid":"14c1c8729875064e23d97990d98c0c5f","score":"0.7028462","text":"def add_table(self, **kwargs):\n rows = kwargs.get('rows')\n cols = kwargs.get('cols')\n\n table = None\n\n if rows and cols:\n table = self.myDocument.add_table(rows, cols)\n\n # If there is a table.\n if table:\n if kwargs.get('style'):\n table.style = kwargs.get('style')\n\n if kwargs.get('alignment'):\n if kwargs.get('alignment') == self.TABLE_ALIGNMENT_LEFT:\n table.alignment = WD_TABLE_ALIGNMENT.LEFT\n elif kwargs.get('alignment') == self.TABLE_ALIGNMENT_CENTER:\n table.alignment = WD_TABLE_ALIGNMENT.CENTER\n elif kwargs.get('alignment') == self.TABLE_ALIGNMENT_RIGHT:\n table.alignment = WD_TABLE_ALIGNMENT.RIGHT\n\n if kwargs.get('allow_autofit'):\n table.allow_autofit = kwargs.get('allow_autofit')\n\n return table","title":""},{"docid":"e1e041e9f601218328674e06dc558450","score":"0.69758976","text":"def style_table(self):","title":""},{"docid":"0327bb443333ec1124072cd84b058972","score":"0.6963866","text":"def make_table(nrows, ncols):\n\n return dedent(b\"\"\"\\\n <table>\n {rows}\n </table>\n \"\"\").format(\n rows=\"\\n\".join(\n \"<tr>{cells}</tr>\".format(cells=\"\".join(\"<td>{i}</td>\".format(i=i)\n for i in xrange(ncols)))\n for _ in xrange(nrows)))","title":""},{"docid":"da712d714c33ea81917464a068ce1952","score":"0.69533145","text":"def get_table(self) -> Table:\n title = Text(f'{self.filename}', style=Style(italic=True, bold=True, link=f'file://{self.filename.resolve()}'))\n table = Table(title=title, box=box.HEAVY_HEAD)\n\n if len(self.d.items()) == 0:\n table.add_column('No Functions Found', justify=\"center\", style=\"red\")\n if self.filename.suffix in extensions:\n table.add_row('Preprocessor Macros / Variable Decl. Only')\n else:\n table.add_row('Not a C/C++ Function')\n return table\n\n table.add_column(\"Type\", justify=\"center\", style=\"cyan\", no_wrap=True)\n table.add_column(\"Name\", justify=\"center\", style=\"white\")\n table.add_column(\"Line #\", justify=\"center\", style=\"cyan\")\n table.add_column(\"Indent\", justify=\"center\", style=\"cyan\")\n table.add_column(\"Definition?\", justify=\"center\", style=\"cyan\", no_wrap=True)\n\n for kind, cursor in self.d.items():\n for i, (name, info) in enumerate(cursor.items()):\n func_name = info['displayname'] if info['use_displayname'] else info['spelling']\n\n line_num = f\"{info['start']}:{info['end']}\" if info['start'] != info['end'] else f\"{info['start']}\"\n end = i == (len(cursor) - 1)\n\n func_type = Text('n/a', 'bright_black')\n if kind in ['functions', 'methods', 'constructors']:\n func_type = Text('yes', 'green') if info['definition'] else Text('no', 'red')\n\n table.add_row(f\"{kind.capitalize()[:-1]}\",\n func_name,\n line_num,\n f\"{info['indent']}\",\n func_type,\n end_section=end)\n return table","title":""},{"docid":"c688e178ce17d63e06e6fb626a70e0f2","score":"0.69507337","text":"def create_table(ws, header_row, table_name, data_dict, days_num, color, wb):\n create_sub_titles(ws, header_row, table_name, days_num)\n create_date_column(wb, ws, color, header_row, days_num) \n create_teacher_header_row(ws, header_row, wb)\n format_table(ws, header_row, color, days_num)\n for col in range(2, ws.max_column+1):\n column_array = []\n for row in range(header_row+1, header_row+days_num+1):\n data = paste_data(ws, col, row, header_row, data_dict, table_name)\n if data != '':\n column_array.append(data)\n paste_average(column_array, header_row, days_num, col, ws)\n ws.column_dimensions['A'].width = int(20)","title":""},{"docid":"e360f9288fa300235102f255b67be1c6","score":"0.69448096","text":"def table(self):\n self.html.append(\"<tr>\")\n\n for titles in self.titles:\n self.html.append(\"<th>%s</th>\\n\"%titles)\n\n self.html.append(\"</tr>\")\n for itervalues in self.dataset.itervalues():\n\n self.html.append(\"<tr>\\n\")\n\n for values in itervalues.values():\n self.html.append('<td>%s</td>\\n'%values)\n\n self.html.append(\"</tr>\\n\")\n\n self.html.append('</table>\\n</body>\\n</html>')\n self.htmlstring = \"\".join(self.html)\n return True","title":""},{"docid":"b0ffdfea416ff440472b818eea72efa9","score":"0.6944158","text":"def _generateTable(self):\n\n #\n # Create a table.\n # If no colum headers are provided, fall back on database field\n # names, ASSUMING that the entries are ModelDataBase children.\n # Note! the cellspacing is for IE8.\n #\n sPageBody = '<table class=\"tmtable\" id=\"' + self._sId + '\" cellspacing=\"0\">\\n';\n\n if not self._asColumnHeaders:\n self._asColumnHeaders = self._aoEntries[0].getDataAttributes();\n\n sPageBody += self._generateTableHeaders();\n\n #\n # Format the body and close the table.\n #\n sPageBody += ' <tbody>\\n';\n for iEntry in range(min(len(self._aoEntries), self._cItemsPerPage)):\n sPageBody += self._formatListEntryHtml(iEntry);\n sPageBody += ' </tbody>\\n' \\\n '</table>\\n';\n return sPageBody;","title":""},{"docid":"0fe478b1eccae64d4969e4bf0b65ac3f","score":"0.6942006","text":"def create_table(source_data):\n decimal_formatter = NumberFormatter(format='0.0')\n columns = [\n TableColumn(field=\"ids\", title=\"Id\"),\n TableColumn(field=\"x\", title=\"X\", formatter=decimal_formatter),\n TableColumn(field=\"y\", title=\"Y\", formatter=decimal_formatter),\n TableColumn(field=\"names\", title=\"Name\"),\n TableColumn(field=\"phones\", title=\"Phone\"),\n TableColumn(field=\"color\", title=\"Color\"),\n ]\n data_table = DataTable(source=source_data, columns=columns, width=400, height=280)\n return data_table","title":""},{"docid":"56f3e5a558bdf8eb6381421c8389991b","score":"0.69332004","text":"def _table(self):\n return Table(self._table_loc)","title":""},{"docid":"d82146595b6489e114a7c34c0d13d365","score":"0.69175965","text":"def table(self, header, body):\n str_format = (\n '<table class=\"table\">\\n<thead>{thead}</thead>\\n'\n '<tbody>\\n{tbody}</tbody>\\n</table>\\n'\n )\n return str_format.format(thead=header, tbody=body)","title":""},{"docid":"3332c7ababe20b3e9cac6af1ff77294f","score":"0.68649846","text":"def create_table(self):\n if self.achievement_test:\n table_header = \\\n self.create_achievement_test_table_header()\n table_data, student_record_data, raw_data = \\\n self.create_achievement_test_table_data()\n else:\n table_header, grading_instances_list = self.create_table_header()\n table_data, student_record_data, raw_data = \\\n self.create_table_data()\n data_table = gviz_api.DataTable(table_header)\n data_table.LoadData(table_data)\n json_table = data_table.ToJSon()\n json_student_record_data = simplejson.dumps(student_record_data)\n json_raw_data = simplejson.dumps(raw_data)\n return json_table, json_student_record_data, json_raw_data","title":""},{"docid":"b7b56b73c66cb7b7b8319aae787b8563","score":"0.68459904","text":"def add_table(self, rows, cols, width):\n from .table import Table\n tbl = CT_Tbl.new_tbl(rows, cols, width)\n self._element._insert_tbl(tbl)\n return Table(tbl, self)","title":""},{"docid":"5228181efc827469d45bed35e7871744","score":"0.6845761","text":"def _generate_tabular(lookup_table, interpolation='linear', points_unit=u.pix, **kwargs):\n if not isinstance(lookup_table, u.Quantity):\n raise TypeError(\"lookup_table must be a Quantity.\") # pragma: no cover\n\n ndim = lookup_table.ndim\n TabularND = tabular_model(ndim, name=f\"Tabular{ndim}D\")\n\n # The integer location is at the centre of the pixel.\n points = [(np.arange(size) - 0) * points_unit for size in lookup_table.shape]\n if len(points) == 1:\n points = points[0]\n\n kwargs = {'bounds_error': False,\n 'fill_value': np.nan,\n 'method': interpolation,\n **kwargs}\n\n if len(lookup_table) == 1:\n t = Length1Tabular(points, lookup_table, **kwargs)\n else:\n t = TabularND(points, lookup_table, **kwargs)\n\n # TODO: Remove this when there is a new gWCS release\n # Work around https://github.com/spacetelescope/gwcs/pull/331\n t.bounding_box = None\n\n return t","title":""},{"docid":"bd8a49d63d61ecdd28e4ba1dd9b7c2da","score":"0.68444943","text":"def table(contents):\n return '<table align=\"center\" border=\"0\" cellpadding=\"10\">\\n'+contents+'\\n</table>\\n'","title":""},{"docid":"f3e78660c1f5266e6dc4714efdb36318","score":"0.6842358","text":"def add_table(self, df):\n table = self.doc.add_table(df.shape[0] + 1, df.shape[1] + 1)\n table.style = self.TABLE_STYLE\n # column title\n for i, col in enumerate(df.columns):\n table.cell(0, i + 1).text = str(col)\n # row title\n for i, row in enumerate(df.index):\n table.cell(i + 1, 0).text = str(row)\n # values\n for i, row in enumerate(df.index):\n for e, item in enumerate(df.loc[row]):\n table.cell(i + 1, e + 1).text = str(item)\n\n return table","title":""},{"docid":"ae0211fe9fd5ff1ca4c2ecbed840ca24","score":"0.68143386","text":"def create_table(data, align, fontcolor, backcolor, box, together=True):\n\n data = copy(data)\n\n # nrow, ncol\n nrow = len(data)\n ncol = len(data[0])\n\n if align == \"NA\":\n align = zeros((nrow, ncol)).tolist()\n for i in range(nrow):\n for j in range(ncol):\n align[i][j] = 'LEFT'\n\n if fontcolor == \"NA\": # fontcolor per cell\n fontcolor = array(['black'] * nrow * ncol, dtype=object).reshape(nrow, ncol)\n\n if backcolor == \"NA\": # background color per cell\n backcolor = array(['white'] * nrow * ncol, dtype=object).reshape(nrow, ncol)\n\n # decimal formatting if int or float\n for i in range(nrow):\n for j in range(ncol):\n if isinstance(data[i][j], float):\n data[i][j] = \"{:.2f}\".format(float(data[i][j])) # dec = 2\n\n # setting TableStyle per cell\n table_styles = []\n for i in range(nrow):\n for j in range(ncol):\n # pylint: disable=E1121 # too many positional arguments for constructor call\n # reportlab colors.toColor works as expected\n reportlab_color = colors.toColor(backcolor[i][j])\n table_styles.extend([\n ('BACKGROUND', (j, i), (j, i), reportlab_color),\n ('ALIGN', (j, i), (j, i), align[i][j]),\n ('TEXTCOLOR', (j, i), (j, i), getattr(colors, fontcolor[i][j])),\n ])\n # type instead of box != \"NA\": avoid FutureWarning\n if not isinstance(box, str):\n for i in range(box.shape[0]):\n table_styles += [('BOX', (box[i, 1], box[i, 0]), (box[i, 3], box[i, 2]), 1,\n colors.black)]\n\n # creating table\n table = Table(data)\n table.setStyle(TableStyle(table_styles))\n # don't split tables over pages\n if together:\n table = KeepTogether(table)\n\n return table","title":""},{"docid":"23a873c749326c3127ff7fe1985c986c","score":"0.6810006","text":"def _to_table(contents, margin=1):\n titles = _len_title(contents)\n line = _to_line(titles, margin)\n header = _to_header(titles, margin)\n body = _to_body(titles, contents, margin)\n\n table = ''\n table += line + '\\n'\n table += header + '\\n'\n table += line + '\\n'\n table += '\\n'.join(body) + '\\n'\n table += line + '\\n'\n\n return table","title":""},{"docid":"8e41f2291b7a29604b7160b144184fd9","score":"0.67932326","text":"def add_table(self, rows, cols, left, top, width, height):\n id = self.__next_shape_id\n name = 'Table %d' % (id-1)\n graphicFrame = CT_GraphicalObjectFrame.new_table(\n id, name, rows, cols, left, top, width, height)\n self.__spTree.append(graphicFrame)\n table = _Table(graphicFrame)\n self.__shapes.append(table)\n return table","title":""},{"docid":"4030d1468701a35e0ad2b16b1c89f386","score":"0.67865855","text":"def table(self, **kwargs):\n return self._child(self.tables, _Table, kwargs)","title":""},{"docid":"63c6dc3368c50fe3e412abb36054972b","score":"0.677862","text":"def crear_tablero():\r\n tablero = []\r\n for i in range(3):\r\n tablero.append(['-'] * 3)\r\n return tablero","title":""},{"docid":"b122bc198e9f6ef999bcdd7dd5260055","score":"0.6751137","text":"def table(self, datatype=None):\n if datatype and not isinstance(datatype, list):\n datatype = [datatype]\n from ..element import Table\n return Table(self, **(dict(datatype=datatype) if datatype else {}))","title":""},{"docid":"0d9c24a6b2152504ec9f02daef14cc78","score":"0.67369354","text":"def make_table(self, columns):\n string = ['loop_\\n']\n length = max([len(i) for i in columns])\n widths = [0] * len(columns)\n types = [True] * len(columns)\n\n for i in range(length - 1):\n i += 1\n for j, col in enumerate(columns):\n\n try:\n _ = value(col[i])\n _ = error(col[i])\n except:\n types[j] = False\n try:\n if not len(col[i]) > 1:\n types[j] = False\n except:\n types[j] = False\n try:\n if len(col[i]) > widths[j]:\n widths[j] = len(col[i])\n except:\n pass\n for i, width in enumerate(widths):\n if width > 15:\n widths[i] = 5\n for i in range(length):\n for j, col in enumerate(columns):\n try:\n if types[j] and not col[i].endswith(')'):\n col[i] += ' '\n if col[i].startswith('_'):\n string.append('{0:>}'.format(col[i]))\n else:\n string.append('{0:>{1}}'.format(col[i], widths[j]))\n except:\n string.append('{:10}'.format('.'))\n if i == 0:\n string.append('\\n')\n if not i == 0:\n string.append('\\n')\n string.append('\\n')\n string = ' '.join(string)\n return string","title":""},{"docid":"2bf59af2d9a952c1d49340f72da1c6a8","score":"0.6733246","text":"def table():\n\n class TestTable(Table):\n name = django_tables2.Column()\n\n return TestTable([{\"name\": \"rest_framework\", \"name\": \"django\"}])","title":""},{"docid":"324aaefb3839161b00bc153e23aca020","score":"0.67247295","text":"def create_table(self):\n indent = ' ' * (len(self.__tablename__) + 1)\n columns_str = f',\\n{indent}'.join(f'{name} {str(column_type)}' for name, column_type in self._columns.items())\n create_table_sql = f'''CREATE TABLE IF NOT EXISTS\n{self.__tablename__}({columns_str})'''\n\n cursor.execute(create_table_sql)","title":""},{"docid":"bab68b95b36892beacfe0cc008506a03","score":"0.6708975","text":"def HoldingsTable(rows):\n return Table(['ticker', 'fraction', 'description'],\n [str, float, str],\n rows)","title":""},{"docid":"585afe289a01a09591c66ba1d65e25f9","score":"0.66905046","text":"def do_create_table(tbname, dicta):\n logger.display('create-table {0} {1}'.format(tbname, dicta))\n dbase.create_table(tbname, dicta)\n return True","title":""},{"docid":"52e3b73d7c10bb0355b1d29af67ecb06","score":"0.66787946","text":"def wrapper(*args, **kwargs):\n # Get a list of tuples containing table contents\n data = table_function(*args, **kwargs)\n\n # Format the data rows\n rows = []\n for row in data:\n formatted_row = []\n for item in row:\n if isinstance(item, float):\n formatted_item = '{:.2f}'.format(item)\n else:\n formatted_item = str(item)\n formatted_row.append(formatted_item)\n rows.append(formatted_row)\n\n # Create the table\n ax = kwargs[AX_ARG]\n table = ax.table(cellText=rows, loc='center')\n ax.set_xticks([])\n ax.set_yticks([])\n\n # Remove border\n [line.set_linewidth(0) for line in ax.spines.values()]\n\n # Non-index (first col) items left justified\n cells = table.properties()['celld']\n for row_i in range(len(data)):\n for col_i in range(1, len(data[row_i])):\n cells[row_i, col_i]._loc = 'left'\n\n # Remove table borders\n for cell in cells.values():\n cell.set_linewidth(0)\n\n # Make cells larger\n for cell in cells.values():\n cell.set_height(0.1)","title":""},{"docid":"6b27e989a06f3b44f4ab97d7692658bd","score":"0.6663263","text":"def generate_model_table( self ):\n\t\tdf = self.__create_model_df().sort_values( by=[\"Corpus\",\"Name\"] )\n\t\talignments = { \"Topics\" : \"right\", \"Documents\" : \"right\", \"Terms\" : \"right\" }\n\t\treturn DataFrameTable( df, id=\"model-table\", alignments=alignments, striped=False, hover=False ).generate_layout()","title":""},{"docid":"23549be3a4f2d91e4041765173baf34e","score":"0.663182","text":"def add_table(self, padding=5, spacing=0, border=1, class_name=None):\n table = HtmlTable(padding, spacing, border, class_name)\n self.children.append(table)\n return table","title":""},{"docid":"917c82ac260d6da425f470f54671775f","score":"0.66314054","text":"def create_table(self):\n create_table = 'CREATE TABLE {} (id INTEGER PRIMARY KEY, '.format(self.table_name)\n\n for attr in self.__get_props():\n create_table += attr + ' ' + str(self.__class__.__dict__[attr]) + ', '\n\n create_table = create_table[:-2] + ')'\n\n \"\"\" Ignore if table already exist \"\"\"\n try:\n self.cursor.execute(create_table)\n except sqlite3.OperationalError:\n pass","title":""},{"docid":"ab608b565144ea4b0a4c20b67e9e539b","score":"0.66308945","text":"def table(headings, data, log):\n cols = range(len(headings))\n assert all(len(item) == len(cols) for item in data)\n max_widths = reduce(lambda prev, cur: [(len(cur[i]) + 2)\n if (len(cur[i]) + 2) > prev[i]\n else prev[i]\n for i in cols],\n data,\n [len(item) + 2 for item in headings])\n log(\"|%s|\" % \"|\".join(item.center(max_widths[i]) for i, item in enumerate(headings)))\n log(\"|%s|\" % \"|\".join(\"-\" * max_widths[i] for i in cols))\n for row in data:\n log(\"|%s|\" % \"|\".join(\" %s\" % row[i].ljust(max_widths[i] - 1) for i in cols))\n log(\"\")","title":""},{"docid":"8691dd0c0e935a039e48c361846b4bc2","score":"0.66297275","text":"def Table(self, data, width=None, height=600):\n from tigerml.core.plots import hvPlot\n\n width = width or (len(data.columns) + 1) * 100\n return hvPlot(data).table(width=width, height=height)","title":""},{"docid":"10ec616d4bbc3ac559e39bc9e20c1b6e","score":"0.66265315","text":"def _generate_html_table(tst_data):\n\n colors = {\n u\"header\": u\"#7eade7\",\n u\"empty\": u\"#ffffff\",\n u\"body\": (u\"#e9f1fb\", u\"#d4e4f7\")\n }\n\n tbl = ET.Element(u\"table\", attrib=dict(width=u\"100%\", border=u\"0\"))\n\n trow = ET.SubElement(tbl, u\"tr\", attrib=dict(bgcolor=colors[u\"header\"]))\n thead = ET.SubElement(\n trow, u\"th\", attrib=dict(align=u\"left\", colspan=u\"6\")\n )\n thead.text = tst_data[u\"name\"]\n\n trow = ET.SubElement(tbl, u\"tr\", attrib=dict(bgcolor=colors[u\"empty\"]))\n thead = ET.SubElement(\n trow, u\"th\", attrib=dict(align=u\"left\", colspan=u\"6\")\n )\n thead.text = u\"\\t\"\n\n if tst_data.get(u\"show-run\", u\"No Data\") == u\"No Data\":\n trow = ET.SubElement(\n tbl, u\"tr\", attrib=dict(bgcolor=colors[u\"header\"])\n )\n tcol = ET.SubElement(\n trow, u\"td\", attrib=dict(align=u\"left\", colspan=u\"6\")\n )\n tcol.text = u\"No Data\"\n\n trow = ET.SubElement(\n tbl, u\"tr\", attrib=dict(bgcolor=colors[u\"empty\"])\n )\n thead = ET.SubElement(\n trow, u\"th\", attrib=dict(align=u\"left\", colspan=u\"6\")\n )\n font = ET.SubElement(\n thead, u\"font\", attrib=dict(size=u\"12px\", color=u\"#ffffff\")\n )\n font.text = u\".\"\n return str(ET.tostring(tbl, encoding=u\"unicode\"))\n\n tbl_hdr = (\n u\"Name\",\n u\"Nr of Vectors\",\n u\"Nr of Packets\",\n u\"Suspends\",\n u\"Cycles per Packet\",\n u\"Average Vector Size\"\n )\n\n for dut_data in tst_data[u\"show-run\"].values():\n trow = ET.SubElement(\n tbl, u\"tr\", attrib=dict(bgcolor=colors[u\"header\"])\n )\n tcol = ET.SubElement(\n trow, u\"td\", attrib=dict(align=u\"left\", colspan=u\"6\")\n )\n if dut_data.get(u\"threads\", None) is None:\n tcol.text = u\"No Data\"\n continue\n\n bold = ET.SubElement(tcol, u\"b\")\n bold.text = (\n f\"Host IP: {dut_data.get(u'host', '')}, \"\n f\"Socket: {dut_data.get(u'socket', '')}\"\n )\n trow = ET.SubElement(\n tbl, u\"tr\", attrib=dict(bgcolor=colors[u\"empty\"])\n )\n thead = ET.SubElement(\n trow, u\"th\", attrib=dict(align=u\"left\", colspan=u\"6\")\n )\n thead.text = u\"\\t\"\n\n for thread_nr, thread in dut_data[u\"threads\"].items():\n trow = ET.SubElement(\n tbl, u\"tr\", attrib=dict(bgcolor=colors[u\"header\"])\n )\n tcol = ET.SubElement(\n trow, u\"td\", attrib=dict(align=u\"left\", colspan=u\"6\")\n )\n bold = ET.SubElement(tcol, u\"b\")\n bold.text = u\"main\" if thread_nr == 0 else f\"worker_{thread_nr}\"\n trow = ET.SubElement(\n tbl, u\"tr\", attrib=dict(bgcolor=colors[u\"header\"])\n )\n for idx, col in enumerate(tbl_hdr):\n tcol = ET.SubElement(\n trow, u\"td\",\n attrib=dict(align=u\"right\" if idx else u\"left\")\n )\n font = ET.SubElement(\n tcol, u\"font\", attrib=dict(size=u\"2\")\n )\n bold = ET.SubElement(font, u\"b\")\n bold.text = col\n for row_nr, row in enumerate(thread):\n trow = ET.SubElement(\n tbl, u\"tr\",\n attrib=dict(bgcolor=colors[u\"body\"][row_nr % 2])\n )\n for idx, col in enumerate(row):\n tcol = ET.SubElement(\n trow, u\"td\",\n attrib=dict(align=u\"right\" if idx else u\"left\")\n )\n font = ET.SubElement(\n tcol, u\"font\", attrib=dict(size=u\"2\")\n )\n if isinstance(col, float):\n font.text = f\"{col:.2f}\"\n else:\n font.text = str(col)\n trow = ET.SubElement(\n tbl, u\"tr\", attrib=dict(bgcolor=colors[u\"empty\"])\n )\n thead = ET.SubElement(\n trow, u\"th\", attrib=dict(align=u\"left\", colspan=u\"6\")\n )\n thead.text = u\"\\t\"\n\n trow = ET.SubElement(tbl, u\"tr\", attrib=dict(bgcolor=colors[u\"empty\"]))\n thead = ET.SubElement(\n trow, u\"th\", attrib=dict(align=u\"left\", colspan=u\"6\")\n )\n font = ET.SubElement(\n thead, u\"font\", attrib=dict(size=u\"12px\", color=u\"#ffffff\")\n )\n font.text = u\".\"\n\n return str(ET.tostring(tbl, encoding=u\"unicode\"))","title":""},{"docid":"daddce48dd14d49969f2d1ac607c1878","score":"0.6625982","text":"def _ascii_table(headers, table_rows, sort_index = 1, descending = True,\n ndecimals = 8, dates_columns = None):\n\n if not dates_columns:\n dates_columns = ()\n\n if len(set(len(row) for row in table_rows)) != 1:\n raise ValueError(\"number of columns must be the same for all rows\")\n\n if len(headers) != len(table_rows[0]):\n raise ValueError(\"elements in 'headers' must equal that of columns\")\n\n # Sort the table by the specified column\n if sort_index is not None:\n table_rows.sort(key = operator.itemgetter(sort_index),\n reverse = descending)\n\n # Give format to all the data in the table as a string, as (1) a date,\n # (2) a real number with the specified number of decimals or (3) by\n # simply casting it to a string. The formatted data is stored as a\n # two-level dictionary, where the first index maps to the row of the\n # table and the second to the column.\n\n table_data = collections.defaultdict(dict)\n table_data[0][0] = '' # top-left 'corner' of table left empty\n\n # Populate the table with the headers...\n for column_index in range(len(headers)):\n table_data[0][column_index + 1] = headers[column_index]\n\n # ... and with all the data\n for row_index, row in enumerate(table_rows):\n table_data[row_index + 1][0] = str(row_index)\n for column_index, value in enumerate(row):\n if value is None:\n table_cell = ''\n elif column_index in dates_columns:\n table_cell = str(datetime.timedelta(seconds = value))\n elif isinstance(value, float):\n table_cell = '%.*f' % (ndecimals, value)\n else:\n table_cell = str(value)\n table_data[row_index + 1][column_index + 1] = table_cell\n\n # Determine how many characters are needed for each column. Two\n # different measures are made: the width of the entire column (i.e, the\n # maximum length among the cells that share the same column) and the\n # width of only the data (the maximum length among the cells that share\n # the same column, excluding the header). These values will be used to\n # right-justify the values and at the same time center them.\n\n widths = {}\n data_widths = {}\n for column_index in table_data[0].iterkeys():\n column_values = (table_data[row][column_index]\n for row in table_data.iterkeys())\n column_lengths = [len(str(x))\n for x in column_values if x is not None]\n # The maximum width is increased by two, so that no value in the\n # table touches the vertical cell borders (the '|' characters)\n widths[column_index] = max(column_lengths) + 2\n data_widths[column_index] = max(column_lengths[1:]) # only the data\n\n\n # Finally, populate the ASCII table with the headers...\n header_values = table_data[0].itervalues()\n header_str = '|'.join([x.center(widths[index])\n for index, x in enumerate(header_values)])\n output = header_str + '\\n' + '-' * len(header_str) + '\\n'\n\n # ... and with the rest of the data\n for row_index in sorted(table_data.iterkeys()):\n if not row_index: # headers (index == 0) already formatted\n continue\n row_values = table_data[row_index].values()\n output += row_values[0].ljust(data_widths[0]).center(widths[0]) + '|'\n output += '|'.join(x.rjust(data_widths[index]).center(widths[index])\n for index, x in enumerate(row_values) if index)\n output += '\\n'\n\n return output","title":""},{"docid":"e0a3bdb7a4768af78569c8671a416f2e","score":"0.6613774","text":"def make_table(table_name):\n return Table(table_name, metadata, autoload=True);","title":""},{"docid":"fae56b05ebaeb302da4ea0e5cba07966","score":"0.6605663","text":"def tablename():","title":""},{"docid":"7cf0b4de5b4ac4bf4edf440e12eb0d88","score":"0.65996456","text":"def __str__(self):\n attribs_str = \"\"\n if self.border: self.attribs['border'] = self.border\n if self.style: self.attribs['style'] = self.style\n if self.width: self.attribs['width'] = self.width\n if self.cellspacing: self.attribs['cellspacing'] = self.cellspacing\n if self.cellpadding: self.attribs['cellpadding'] = self.cellpadding\n for attr in self.attribs:\n attribs_str += ' %s=\"%s\"' % (attr, self.attribs[attr])\n result = '<TABLE%s>\\n' % attribs_str\n # insert column tags and attributes if specified:\n if self.col_width:\n for width in self.col_width:\n result += ' <COL width=\"%s\">\\n' % width\n # First insert a header row if specified:\n if self.header_row:\n if not isinstance(self.header_row, TableRow):\n result += str(TableRow(self.header_row, header=True))\n else:\n result += str(self.header_row)\n # Then all data rows:\n for row in self.rows:\n if not isinstance(row, TableRow):\n row = TableRow(row)\n # apply column alignments and styles to each row if specified:\n # (Mozilla bug workaround)\n if self.col_align and not row.col_align:\n row.col_align = self.col_align\n if self.col_char and not row.col_char:\n row.col_char = self.col_char\n if self.col_charoff and not row.col_charoff:\n row.col_charoff = self.col_charoff\n if self.col_valign and not row.col_valign:\n row.col_valign = self.col_valign\n if self.col_styles and not row.col_styles:\n row.col_styles = self.col_styles\n result += str(row)\n result += '</TABLE>'\n return result","title":""},{"docid":"43efda04f5ceb1c7d09657fa99fd0823","score":"0.65917933","text":"def table_row(self):\n self.add('<tr>')\n yield\n self.add('</tr>')","title":""},{"docid":"f18ba1fe0f0a31b5bfa50b07998573cc","score":"0.6590479","text":"def add_table(self, headers, data, align=0):\n table = ReportTable(headers, data, align)\n self.SubContents.append(table)\n return table","title":""},{"docid":"11e5ba84814c831e706585c0d4e29337","score":"0.65886223","text":"def Table(self, name, create=True, columns=1, keys=1, timeout=None):\n pass","title":""},{"docid":"33f955d03c802ebbcd529411fd886403","score":"0.6581862","text":"def table(*args, **kwargs):\n t = gca().table(*args, **kwargs)\n draw_if_interactive()\n return t","title":""},{"docid":"2c5d5fecbdcf0004c3f7ac0b5b40ee71","score":"0.6577191","text":"def table(self, rows, window_id=None, title=None, header=None, style=None):\n\n if not window_id:\n window_id = title # Can provide either of these,\n if not title:\n title = window_id # or both.\n table = html_table_from_rows(rows, title=title, header=header, style_dict=style)\n self.text(table, title=title, window_id=window_id)","title":""},{"docid":"e43baa2a5374727a3bd48f898f576df4","score":"0.6569342","text":"def table(table_function):\n\n @plot\n @functools.wraps(table_function)\n def wrapper(*args, **kwargs):\n \"\"\"Format the provided data into a matplotlib table\"\"\"\n # Get a list of tuples containing table contents\n data = table_function(*args, **kwargs)\n\n # Format the data rows\n rows = []\n for row in data:\n formatted_row = []\n for item in row:\n if isinstance(item, float):\n formatted_item = '{:.2f}'.format(item)\n else:\n formatted_item = str(item)\n formatted_row.append(formatted_item)\n rows.append(formatted_row)\n\n # Create the table\n ax = kwargs[AX_ARG]\n table = ax.table(cellText=rows, loc='center')\n ax.set_xticks([])\n ax.set_yticks([])\n\n # Remove border\n [line.set_linewidth(0) for line in ax.spines.values()]\n\n # Non-index (first col) items left justified\n cells = table.properties()['celld']\n for row_i in range(len(data)):\n for col_i in range(1, len(data[row_i])):\n cells[row_i, col_i]._loc = 'left'\n\n # Remove table borders\n for cell in cells.values():\n cell.set_linewidth(0)\n\n # Make cells larger\n for cell in cells.values():\n cell.set_height(0.1)\n\n # Decorate wrapper with plotting\n return wrapper","title":""},{"docid":"678b30b43c5baa1ec84d69cc678d5ef1","score":"0.6566881","text":"def create_table(self):\n self.auto_column_number = 1\n table_path = os.path.join(self.opts[\"data_dir\"], self.table_name())\n self.file = open_fw(table_path, encoding=self.encoding)\n self.output_file = open_csvw(self.file)\n column_list = self.table.get_insert_columns(join=False, create=True)\n self.output_file.writerow([u'{}'.format(val) for val in column_list])\n self.table_names.append((self.file, table_path))\n\n # Register all tables created to enable\n # testing python files having custom download function\n Engine.register_tables(self)","title":""},{"docid":"b7fe6e577db376257a3865feb0095b17","score":"0.6560861","text":"def create(self, table):\n\n if type(table) != DictionaryType:\n raise TypeError, 'expected dictionary type argument'\n raise NotImplementedError, '... Sorry!'","title":""},{"docid":"ed1a18891970b6673bce65a16192b687","score":"0.65606594","text":"def print_table(table):\n pass","title":""},{"docid":"c8ad9305f416a736ee3c8ec184b54ebc","score":"0.65599793","text":"def generate_table(sm, mean=False):\n return \"\"\"\n <table id={id} class='datatable'>\n {header}\n {body}\n </table>\n \"\"\".format(\n id = hash(sm),\n header = _generate_header(sm, mean=mean),\n body = _generate_body(sm, mean=mean)\n )","title":""},{"docid":"4551a6aad33e5df4aa915c2bd57d41b8","score":"0.65565616","text":"def create_html_table(header, users, user, count=1, status=None, table=None):\n if count == 1:\n table = '<div><tr>'\n for head in header:\n table += '<th style=\"border: 1px solid black\">'+head+'</th>'\n table += '</tr></div>'\n patch_groups = users[user].keys()\n # patch_groups.sort()\n sorted(patch_groups)\n for patch_group in patch_groups:\n for minion in users[user][patch_group]:\n if status is None:\n table += '<tr><td style=\"border: 1px solid black;\">'+str(count)\\\n +'</td><td style=\"border: 1px solid black;\">'\\\n +patch_group+'</td><td style=\"border: 1px solid black;\">'+minion+'</td></tr>'\n else:\n table += '<tr><td style=\"border: 1px solid black;\">'+str(count)\\\n +'</td><td style=\"border: 1px solid black;\">'+minion+\\\n '</td><td style=\"border: 1px solid black;\">'+patch_group+\\\n '</td><td style=\"border: 1px solid black;\">'+user+\\\n '</td><td style=\"border: 1px solid black;\">'+status[user]+'</td></tr>'\n count += 1\n return count, table","title":""},{"docid":"a2bc74c3d10ad9619444393036f0c250","score":"0.65514266","text":"def test_table():\n return pd.DataFrame(\n {\n \"AgeGroup\": pd.Series([\"18-24\"]),\n \"sex\": pd.Series([\"F\"]),\n \"demographic\": pd.Series([0]),\n \"date\": pd.Series([\"01/04/2019\"]),\n \"numerator\": pd.Series([10]),\n \"population\": pd.Series([20]),\n \"age_rates\": pd.Series([0.0002]),\n }\n )","title":""},{"docid":"7a784f29e4d5c3b54af160c4cb205cf2","score":"0.6549758","text":"def create_q_table(n_states,actions):\n q_table = pd.DataFrame(np.zeros((n_states, len(actions))),columns = actions)\n print(q_table)\n return q_table","title":""},{"docid":"36927722038913d87f17a38d4d92efd4","score":"0.6522562","text":"def _genere_table(self, entier: bool) -> Table:\n if entier:\n resultat = Table(title=\"Solution du problème : calendrier\")\n resultat.add_column(\"Tâche\")\n resultat.add_column(\"Nature\")\n resultat.add_column(\"Début\")\n resultat.add_column(\"Fin\")\n for date in self._dates:\n resultat.add_row(\n date.tache.nom,\n date.tache.correspondance,\n self._choix_affichage_date(date.date_debut),\n self._choix_affichage_date(date.date_fin),\n )\n else:\n resultat = Table(title=\"Solution du problème : calendrier\")\n resultat.add_column(\"Tâche\")\n resultat.add_column(\"Début\")\n resultat.add_column(\"Fin\")\n for date in self._dates:\n resultat.add_row(\n date.tache.nom,\n self._choix_affichage_date(date.date_debut),\n self._choix_affichage_date(date.date_fin),\n )\n\n return resultat","title":""},{"docid":"e01f97c93f0d32ab39556eb3d94efa88","score":"0.65205693","text":"def repr_as_table(headers, *args):\n assert len(headers) == len(args)\n copies = [[headers[i]] + args[i] for i in range(len(args))]\n max_lengths = [max([len(x) for x in arg]) for arg in copies]\n row_sep = '+-'\n for i in range(len(max_lengths)):\n row_sep += '-' * max_lengths[i]\n row_sep += '-+-'\n row_sep = row_sep[:-1] # remove extra '-' added in the loop\n\n lines = [row_sep]\n for i in range(len(copies[0])):\n line = '| '\n for j in range(len(copies)):\n line += copies[j][i].ljust(max_lengths[j]) + ' | '\n line = line[:-1] # remove extra space added in the loop\n lines.append(line)\n lines.append(row_sep)\n return os.linesep.join(lines)","title":""},{"docid":"712b2f54db9d03a5dd20e904915c9b38","score":"0.6519341","text":"def get_shapes_table(self):\n title_list = ['idx', 'class', '__str__', 'Perimeter', 'P. Formula', 'Area', 'A. Formula']\n table = []\n str_table = ''\n print(self.shapes)\n for iterator, shape in enumerate(self.shapes):\n table.append([str(iterator + 1), type(shape).__name__, shape.__str__(), str(round(shape.get_perimeter(), 1)),\n shape.get_perimeter_formula(), str(shape.get_area()), shape.get_area_formula()]) # adding all args\n print(table)\n column_length = []\n for title_iterator in range(len(title_list)):\n column_len = len(title_list[title_iterator])\n for line in table:\n if len(line[title_iterator]) > column_len:\n column_len = len(line[title_iterator])\n column_length.append(column_len) # making column lenght proper to the text size\n\n how_wide = 0\n for name in title_list:\n x = (column_length[title_list.index(name)])\n how_wide += (len((\"|{: <\" + str(x + 2) + \"}|\").format(name)))\n str_table += ('-' * how_wide)\n str_table += '\\n' # adding frames of a table in a proper size\n\n for name in title_list:\n str_table += \"|\"\n x = (column_length[title_list.index(name)])\n str_table += ((\"{: <\" + str(x + 2) + \"}\").format(name))\n str_table += \"|\"\n str_table += '\\n'\n str_table += ('-' * how_wide)\n\n for row in table:\n str_table += '\\n'\n str_table += \"|\"\n\n for element in row:\n x = (column_length[row.index(element)])\n str_table += ((\"|{: <\" + str(x + 2) + \"}|\").format(element))\n return str_table # returning a table as a string","title":""},{"docid":"f7827ab23e40eab6f0eb0e98ab272aca","score":"0.65178704","text":"def table(self, cls):\n return cls(self)","title":""},{"docid":"d6f74fbcbbba75f52ad58cd0b253a6cc","score":"0.65116113","text":"def test_make_dash_table(self):\n output = ui.make_dash_table(pd.DataFrame(np.asarray([[1, 2],\n [3, 4],\n [5, 6]])))\n # check return types\n self.assertTrue(isinstance(output, list))\n # check length and shape of the return types\n self.assertTrue(len(output) == 3)","title":""},{"docid":"cea48f1742c30c043e86f3bb127ec2bb","score":"0.64926445","text":"def _start_table(format_, headings):\n\n return _START_TABLE(format_, headings)","title":""},{"docid":"88ec33c3a776c7ebea4cba8ba321b16c","score":"0.6482987","text":"def _create_table(table_dictionary: dict, c: int):\n df = pd.DataFrame.from_dict(table_dictionary)\n table = html.Div([dash_table.DataTable(\n id=f'table_{c}',\n columns=[{\"name\": i, \"id\": i} for i in df.columns],\n css=[{'selector': '.dash-filter input', 'rule': 'text-align: left'}, {'selector': '.row', 'rule': 'margin: 0'}],\n style_cell={\n 'whiteSpace': 'normal',\n 'height': 'auto',\n 'text-align': 'left'\n },\n data=df.to_dict('records'))])\n return table","title":""},{"docid":"707582cf686d40b255d5ddaedd0902ec","score":"0.6477894","text":"def to_table(self):\n t = Table()\n t[\"name\"] = [p.name for p in self._parameters]\n t[\"value\"] = [p.value for p in self._parameters]\n if self.covariance is None:\n t[\"error\"] = np.nan\n else:\n t[\"error\"] = [self.error(idx) for idx in range(len(self))]\n\n t[\"unit\"] = [p.unit.to_string(\"fits\") for p in self._parameters]\n t[\"min\"] = [p.min for p in self._parameters]\n t[\"max\"] = [p.max for p in self._parameters]\n t[\"frozen\"] = [p.frozen for p in self._parameters]\n\n for name in [\"value\", \"error\", \"min\", \"max\"]:\n t[name].format = \".3e\"\n\n return t","title":""},{"docid":"e794659a70a494a3a52017e685471c2f","score":"0.64699477","text":"def InsertGenericTable(self, data, columns=[]):\n if self.Story == None:\n return\n if len(data) == 0:\n return\n self.Story.append(Spacer(1, 0.1*inch))\n self.Story.append(Table(data, colWidths=columns, style=None, rowHeights=None))\n self.Story.append(Spacer(1, 0.15*inch))","title":""},{"docid":"c790e0a17a9c0e0fe5dff463728f84c9","score":"0.64671314","text":"def table(self, limit: int = 0) -> PrettyTable:\n tbl = PrettyTable()\n\n if self._rowcount == -1 or self._columns or self._rows:\n tbl.field_names = [\"#\"] + [col[0] for col in self._columns]\n rows_to_add = self._rows[:limit] if limit else self._rows\n tbl.add_rows(\n [[i + 1] + list(row) for i, row in enumerate(rows_to_add)]\n )\n else:\n tbl.field_names = [\"Rows affected\"]\n tbl.add_row([self._rowcount])\n return tbl","title":""},{"docid":"3a2ede266d86eb432c651b08c36f5998","score":"0.6449009","text":"def make_student_table(self):\n pt = PrettyTable(field_names=[\"CWID\", \"Name\", \"Major\",\n \"Completed Courses\",\n \"Required Remaining\",\n \"Elective Remaining\"])\n pt.align = 'l'\n for student in self.students.values():\n pt.add_row([student.cwid, student.name,\n student.major, sorted(student.courses.keys()),\n self.required_remaining(student),\n self.elective_remaining(student)])\n return pt","title":""},{"docid":"dfbad4a97deed1aaf308bcbf52412b49","score":"0.6446894","text":"def create_table(self, table: str, columns: dict) -> bool:\n pass","title":""},{"docid":"9926d6fd51b8f6d20686fd0ba1331e40","score":"0.64393383","text":"def table(func):\n @functools.wraps(func)\n def wrapper_decorator(*args, **kwargs):\n args[0].table = PrettyTable() #equivalent to self.table = prettyTable()\n value = func(*args, **kwargs)\n return value\n return wrapper_decorator","title":""},{"docid":"2cba6da4daaa40caa5a35269436bf46d","score":"0.64374495","text":"def create_table(self):\n \n # create iterator\n patterns = self.create_pattern_generator()\n \n # populate table\n table = {self._hfunc(pattern, *self._hfunc_args, **self._hfunc_kwargs) :\n self._func(pattern, self._series, *self._func_args, **self._func_kwargs)\n for pattern in patterns}\n \n self._table = table","title":""},{"docid":"f4c27d8db44664da2e1abea5cafc9d38","score":"0.64168286","text":"def create_astropy_table(py_obj, h_group, name, **kwargs):\n data = py_obj.as_array()\n d = h_group.create_dataset(name, data=data, dtype=data.dtype, **kwargs)\n\n colnames = [bytes(cn, 'ascii') for cn in py_obj.colnames]\n d.attrs['colnames'] = colnames\n for key, value in py_obj.meta.items():\n d.attrs[key] = value\n return(d)","title":""},{"docid":"b3e76fb3732649740ab51dc78bd895e0","score":"0.6411617","text":"def createTable(self, tableName, columnFamilies):\r\n pass","title":""},{"docid":"53ab02c93822d6514ecac5a4ccc9ed13","score":"0.6411531","text":"def rst_table(self, array):\n # Determine cell width for each column\n cell_dict = {}\n for i, row in enumerate(array):\n for j, val in enumerate(row):\n if j not in cell_dict:\n cell_dict[j] = []\n cell_dict[j].append(val)\n for item in cell_dict:\n cell_dict[item] = max([len(x) for x in cell_dict[item]])+1 # Contains adapted width for each column\n\n # Format top line\n num_cols = len(array[0])\n form = '+'\n for col in range(num_cols):\n form += (cell_dict[col]+1)*'-'\n form += '+'\n form += '\\n'\n\n # Format values\n for i, row in enumerate(array):\n form += '| '\n for j, val in enumerate(row):\n cell_width = cell_dict[j]\n form += str(val) + (cell_width - len(val)) * ' ' + '| '\n form.rstrip()\n form += '\\n'\n\n # Seperation lines\n form += '+'\n if i == 0:\n sign = '='\n else:\n sign = '-'\n for col in range(num_cols):\n form += (cell_dict[col]+1)*sign\n form += '+'\n form += '\\n'\n return form","title":""},{"docid":"55e20bfaa72086edf5efaedaed77c955","score":"0.640563","text":"def create_table(self):\n try:\n c = connection.cursor()\n query = f'CREATE TABLE {self.table_name} (\\\n Sid SERIAL PRIMARY KEY, \\\n Region varchar(255), \\\n Country varchar(255), \\\n \"Item Type\" varchar(255), \\\n \"Sales Channel\" varchar(255), \\\n \"Order Priority\" varchar(255), \\\n \"Order ID\" varchar(255), \\\n \"Units Sold\" FLOAT,\\\n \"Unit Price\" FLOAT,\\\n \"Unit Cost\" FLOAT,\\\n \"Total Revenue\" FLOAT,\\\n \"Total Cost\" FLOAT,\\\n \"Total Profit\" FLOAT\\\n );'\n c.execute(query)\n df = pd.read_csv(self.file_name, skiprows=self.lines_read)\n self.headers = df.columns.to_list()\n tmp = \"\"\n for i in self.headers:\n if len(tmp) != 0:\n tmp += \",\"\n if len(str(i).split(\" \")) == 1:\n tmp += str(i)\n else:\n tmp += '\"' + str(i) + '\"'\n self.headers = tmp\n finally:\n c.close()","title":""},{"docid":"0b424e0f8b7451615d1fd09813ffc5fd","score":"0.63992774","text":"def table(self, cellspacing=0):\n self.add('<table cellspacing={0}>'.format(cellspacing))\n yield\n self.add('</table>')","title":""},{"docid":"7a52a2e0b43ade2453026fc6a1e30a63","score":"0.6398043","text":"def create_table_data(self):\n #build tables for data\n row = [\"\" for i in range(len(self.grading_instances_keys) + 1)]\n table_data = [list(row) for i in range(len(self.students))]\n row = [0 for i in range(len(self.grading_instances_keys) + 1)]\n raw_data = [list(row) for i in range(len(self.students))]\n row = [\"\" for i in range(len(self.grading_instances_keys))]\n student_record_data = [list(row) for i in range(len(self.students))]\n for y, student in enumerate(self.students):\n table_data[y][0] = student.full_name_lastname_first()\n raw_data[y][0] = table_data[y][0]\n for x, gd_inst_key in enumerate(self.grading_instances_keys):\n student_record_key = \\\n self.student_record_keys_dict[student.key()][gd_inst_key]\n grade = None\n if student_record_key:\n student_record_data[y][x] = str(student_record_key)\n student_record = \\\n SchoolDB.models.StudentsClass.get(student_record_key)\n grade = student_record.get_grade(gd_inst_key)\n if grade:\n grade_str = \"value=%5.1f\" %grade\n else:\n grade_str = \"\"\n grade = 0\n table_data[y][x+1] = \\\n '<input type=\"text\" class=\"entry-field numeric-field\" '+\\\n 'id=\"%s-%s-%s\" %s></input>' %(self.table_name, x, \n y, grade_str)\n raw_data[y][x+1] = grade\n return table_data, student_record_data, raw_data","title":""},{"docid":"44af56647c377ed92aa9d47b4ecceaa1","score":"0.6395119","text":"def print_tag_table(title, entries):\n header = ('Date', 'Amount', 'Balance', 'Description')\n rows = [(entry.date,\n '{:,.2f}'.format(entry.amount),\n '{:,.2f}'.format(entry.balance),\n entry.description) for entry in entries]\n table_data = [header] + rows\n table = SingleTable(table_data, title=title)\n table.justify_columns[1] = 'right'\n table.justify_columns[2] = 'right'\n print(table.table)","title":""},{"docid":"5f731297e9b4237c6d399439a350858f","score":"0.6389033","text":"def createTableString(self):\n\n return \"CREATE TABLE \" + self.table + \"(\" + ', '.join([columnName + \" \" + self.column_definitions[columnName][1]\n for columnName in self.column_definitions]) + \")\"","title":""},{"docid":"68cd75937e79d96b72d7c502ecc79fee","score":"0.6388881","text":"def build_table():\n faves = load_faves()\n myrow = \"\"\n contents = \"\"\n columns = 0\n for fave in faves[::-1]:\n data = parse_url(fave)\n color = '#FFFFFF'\n myrow += column(link(data['adj'], data['noun'],\n img(data['adj'], data['noun'],\n data['imgurl']), fave), color)\n columns = columns+1\n if columns >= 3:\n columns = 0\n contents += row(myrow)\n myrow = \"\"\n\n return table(contents)","title":""},{"docid":"d938baacc656bf02081fcd0588ef19f8","score":"0.6384709","text":"def generate_table(dataframe, max_rows=1):\n return html.Table(\n # Header\n [html.Tr([html.Th(col) for col in dataframe.columns])] +\n\n # Body\n [html.Tr([\n html.Td(dataframe.iloc[i][col]) for col in dataframe.columns\n ]) for i in range(min(len(dataframe), max_rows))]\n )","title":""},{"docid":"51a5fae3b47fc7aed6e9cabd2c29087c","score":"0.63815194","text":"def set_table(self):\n\n # order and put in a list\n self.display_data = []\n for key, value in sorted(self.results.iteritems(), key=lambda (k, v): (v, k), reverse=self.direction):\n self.display_data.append((key, value))\n\n # populate and display\n for i, row in enumerate(self.display_data):\n for j, col in enumerate(row):\n item = QtWidgets.QTableWidgetItem(str(col))\n self.setItem(i, j, item)","title":""},{"docid":"1517dfe390d337c249c373593096a8ff","score":"0.6378619","text":"def make_comparison_table(truncate=False):\n make_table('comparison', truncate)","title":""},{"docid":"0147d515b41f141ba0974cc0261cdcd3","score":"0.6378364","text":"def build_exam_table(exams,title,nTables,nExams,singletable=False):\n\n if singletable:\n x_coord = 10\n y_coord = 2.5\n width = 4.7\n else:\n x_coord = 0.5+nTables*4\n y_coord = 11.5\n width = 3.7\n if 'SAT' in title or 'ACT' in title:\n width = 3.3\n\n theTEX = r\"\"\"\n\\begin{textblock}{%.2f}(%.2f,%.2f)\n\\begin{table}\n\\caption{%s}\n\\centering\n\\begin{tabularx}{\\textwidth}{ X c }\n\\toprule\n{\\bf Subject} & {\\bf Score} \\\\\n\\midrule\n\"\"\" % (width,x_coord,y_coord,title)\n\n for exam in exams:\n if singletable: theTEX += '%s & %i \\\\\\\\[5pt] \\n' % (exam.subject, exam.score)\n else: theTEX += '%s & %i \\\\\\\\ \\n' % (exam.subject, exam.score)\n if not singletable: theTEX = theTEX.replace(\"Algebra II/Trigonometry\",\"Algebra II/Trig\")\n nExams += 1\n\n theTEX += r\"\"\"\\bottomrule\n\\end{tabularx}\n\\end{table}\n\\end{textblock}\n\n\"\"\"\n\n return theTEX","title":""},{"docid":"00b2c52234c112fc9d7d98582b36f871","score":"0.63624936","text":"def create_table(self):\n try:\n c = self.conn.cursor()\n c.execute(sql.sql_create_data_table)\n except Error as e:\n print(f\"error creating table: {e}\")","title":""}],"string":"[\n {\n \"docid\": \"917e9f127e58b7bf071b3275c62bd88c\",\n \"score\": \"0.8010181\",\n \"text\": \"def makeTable(self):\\n\\tcols = [Column(name=k, **v) for k,v in self.col_dict.items()]\\n\\treturn Table(cols)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f41e7f18a334779321cd1b0d029b69d\",\n \"score\": \"0.7826398\",\n \"text\": \"def _generate_table(self, *args, **kwargs):\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0acd50063c84903bb301d82f7c141740\",\n \"score\": \"0.78208625\",\n \"text\": \"def newTable(cols=1, rows=1, **kwargs):\\n return Table(rows=rows, cols=cols, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c84cb3845d89419d107199e87eb98930\",\n \"score\": \"0.78134394\",\n \"text\": \"def create_table(self, rows, headers):\\n table = list()\\n table += [' | '.join(headers), '--- | ' * len(headers)]\\n for row in rows:\\n table.append(' | '.join(row))\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e44f7d73855fd90dde0bcc18f030b0\",\n \"score\": \"0.7687951\",\n \"text\": \"def table(*args, **kwargs):\\n return el('table', *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afbcb72cb8ba0f905bb3ddff63f94b94\",\n \"score\": \"0.76656\",\n \"text\": \"def table_construct(*argv):\\r\\n\\treturn table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"831db99e6d2ff3fa26c45cfa14a88cdc\",\n \"score\": \"0.7569239\",\n \"text\": \"def _MakeTable(self):\\n\\n table = zip([\\\"Number of valid pre-responses\\\"\\n ,\\\"Number of valid post-responses\\\"\\n ,\\\"Number of matched responses\\\"\\n ,\\\"Reported number of students in class\\\"\\n ,\\\"Fraction of class participating in pre and post\\\"\\n ],self.tableVals)\\n self.templateVars['table1'] = table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd7dc88b33b8adb3b1f20814f94436f7\",\n \"score\": \"0.74902296\",\n \"text\": \"def create_table(self):\\n raise NotImplementedError(\\\"Subclasses should overwrite this Method.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7cc49fbbd934fee739162ec792bc9f5\",\n \"score\": \"0.7422955\",\n \"text\": \"def table(*args, **kwargs):\\n\\n return dh.thirdparty.tabulate.tabulate(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83f96eab5a2f344bca97e28282e8b4e8\",\n \"score\": \"0.7399735\",\n \"text\": \"def make_table(self):\\n hash_key = DynamoKey('id')\\n range_key = DynamoKey('num', data_type=NUMBER)\\n self.dynamo.create_table('foobar', hash_key=hash_key,\\n range_key=range_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f723a20ea292936c36f9af3666d29175\",\n \"score\": \"0.73897487\",\n \"text\": \"def make_table(h5file, group, table_name, table_fields):\\n\\n tab = h5file.create_table(group, table_name, table_fields, \\\"{0} table\\\".format(table_name))\\n\\n return tab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8869efb3a591c97778d8a25410bd8a7\",\n \"score\": \"0.73361653\",\n \"text\": \"def table(self):\\n return Table(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44e31b7374c714e18d008ade4e9b85ea\",\n \"score\": \"0.7324948\",\n \"text\": \"def make_table(page, rows, header=None):\\n page.table(border=1)\\n page.tr()\\n page.th(header)\\n page.tr.close()\\n for row in rows:\\n page.tr()\\n page.td(row)\\n page.tr.close()\\n page.table.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30722bf7b7c8b5643fa9f086c60807e7\",\n \"score\": \"0.7320095\",\n \"text\": \"def makeTable(items, cls=\\\"table\\\", rcls=\\\"row\\\", tattr=''):\\n#-------------------------------------------------------------------------------\\n ln = len(items)\\n\\n s = ['<table class=\\\"%s\\\" %s>' % (cls, tattr)]\\n if ln < 6:\\n for i in items:\\n s.append(' <tr class=\\\"%s\\\"><td>%s</td></tr>' % (rcls, i))\\n\\n elif ln < 13:\\n col = int(math.floor((ln + 2) / 3))\\n for i in range(col):\\n s.append(' <tr class=\\\"%s\\\">' % rcls)\\n for idx in range(0, ln, col):\\n try:\\n s.append(' <td>%s</td>' % (items[idx+i]))\\n except IndexError:\\n s.append(' <td> </td>')\\n s.append(' </tr>')\\n\\n else:\\n col = int(math.floor((ln + 5) / 6))\\n for i in range(col):\\n s.append(' <tr class=\\\"%s\\\">' % rcls)\\n for idx in range(0, ln, col):\\n try:\\n s.append(' <td>%s</td>' % items[idx+i])\\n except IndexError:\\n s.append(' <td> </td>')\\n s.append(' </tr>')\\n\\n s.append('</table>')\\n return('\\\\n'.join(s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfba30f4de6514c4ef34aa0bf570598f\",\n \"score\": \"0.7260074\",\n \"text\": \"def create(self,title=\\\"\\\",header=(),footer=(),id=\\\"table\\\"):\\n self.rowlist=[]\\n self.tableid=\\\"%s%d\\\" % (id,self.count)\\n \\n self.title=title\\n self.header=header\\n self.footer=footer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17b22c102d3caa861fb83128558bd76b\",\n \"score\": \"0.72547674\",\n \"text\": \"def build_table(self):\\n pd.set_option('display.max_rows', 500)\\n pd.set_option('display.max_columns', 500)\\n pd.set_option('display.width', 1000)\\n\\n df = pd.DataFrame({\\n 'Title': self.titles,\\n 'Year': self.years,\\n 'Genre': self.genres,\\n 'Length': self.runtimes,\\n 'Rating': self.ratings,\\n 'Summary': self.summaries\\n })\\n\\n print(tabulate(df.head(10), showindex=False, headers=df.columns, tablefmt='fancy_grid'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce2bee9a4d84447024b6984b8ea88b31\",\n \"score\": \"0.7192743\",\n \"text\": \"def _make_table(self, tablename: str, data: list) -> None:\\n sqlcode = f\\\"CREATE TABLE {tablename} (\\\"\\n for column in data:\\n sqlcode += f\\\"{column[0]} {column[1]}, \\\"\\n sqlcode = sqlcode[:-2] + \\\");\\\"\\n self._exec_sql_code(sqlcode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac06ae4a08b07ab463a5e243ba5e88f6\",\n \"score\": \"0.71660256\",\n \"text\": \"def table(headers, rows, **atts):\\n\\n th = HtmlElement(N.th).wrap\\n td = HtmlElement(N.td).wrap\\n headerMaterial = HtmlElements.wrapTable(headers, th)\\n rowMaterial = HtmlElements.wrapTable(rows, td)\\n material = HtmlElement(N.body).wrap(headerMaterial + rowMaterial)\\n return HtmlElement(N.table).wrap(material, **atts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a883fdce86a26ed74f6a836e205ebe3\",\n \"score\": \"0.7149349\",\n \"text\": \"def table_format(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14c1c8729875064e23d97990d98c0c5f\",\n \"score\": \"0.7028462\",\n \"text\": \"def add_table(self, **kwargs):\\n rows = kwargs.get('rows')\\n cols = kwargs.get('cols')\\n\\n table = None\\n\\n if rows and cols:\\n table = self.myDocument.add_table(rows, cols)\\n\\n # If there is a table.\\n if table:\\n if kwargs.get('style'):\\n table.style = kwargs.get('style')\\n\\n if kwargs.get('alignment'):\\n if kwargs.get('alignment') == self.TABLE_ALIGNMENT_LEFT:\\n table.alignment = WD_TABLE_ALIGNMENT.LEFT\\n elif kwargs.get('alignment') == self.TABLE_ALIGNMENT_CENTER:\\n table.alignment = WD_TABLE_ALIGNMENT.CENTER\\n elif kwargs.get('alignment') == self.TABLE_ALIGNMENT_RIGHT:\\n table.alignment = WD_TABLE_ALIGNMENT.RIGHT\\n\\n if kwargs.get('allow_autofit'):\\n table.allow_autofit = kwargs.get('allow_autofit')\\n\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1e041e9f601218328674e06dc558450\",\n \"score\": \"0.69758976\",\n \"text\": \"def style_table(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0327bb443333ec1124072cd84b058972\",\n \"score\": \"0.6963866\",\n \"text\": \"def make_table(nrows, ncols):\\n\\n return dedent(b\\\"\\\"\\\"\\\\\\n <table>\\n {rows}\\n </table>\\n \\\"\\\"\\\").format(\\n rows=\\\"\\\\n\\\".join(\\n \\\"<tr>{cells}</tr>\\\".format(cells=\\\"\\\".join(\\\"<td>{i}</td>\\\".format(i=i)\\n for i in xrange(ncols)))\\n for _ in xrange(nrows)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da712d714c33ea81917464a068ce1952\",\n \"score\": \"0.69533145\",\n \"text\": \"def get_table(self) -> Table:\\n title = Text(f'{self.filename}', style=Style(italic=True, bold=True, link=f'file://{self.filename.resolve()}'))\\n table = Table(title=title, box=box.HEAVY_HEAD)\\n\\n if len(self.d.items()) == 0:\\n table.add_column('No Functions Found', justify=\\\"center\\\", style=\\\"red\\\")\\n if self.filename.suffix in extensions:\\n table.add_row('Preprocessor Macros / Variable Decl. Only')\\n else:\\n table.add_row('Not a C/C++ Function')\\n return table\\n\\n table.add_column(\\\"Type\\\", justify=\\\"center\\\", style=\\\"cyan\\\", no_wrap=True)\\n table.add_column(\\\"Name\\\", justify=\\\"center\\\", style=\\\"white\\\")\\n table.add_column(\\\"Line #\\\", justify=\\\"center\\\", style=\\\"cyan\\\")\\n table.add_column(\\\"Indent\\\", justify=\\\"center\\\", style=\\\"cyan\\\")\\n table.add_column(\\\"Definition?\\\", justify=\\\"center\\\", style=\\\"cyan\\\", no_wrap=True)\\n\\n for kind, cursor in self.d.items():\\n for i, (name, info) in enumerate(cursor.items()):\\n func_name = info['displayname'] if info['use_displayname'] else info['spelling']\\n\\n line_num = f\\\"{info['start']}:{info['end']}\\\" if info['start'] != info['end'] else f\\\"{info['start']}\\\"\\n end = i == (len(cursor) - 1)\\n\\n func_type = Text('n/a', 'bright_black')\\n if kind in ['functions', 'methods', 'constructors']:\\n func_type = Text('yes', 'green') if info['definition'] else Text('no', 'red')\\n\\n table.add_row(f\\\"{kind.capitalize()[:-1]}\\\",\\n func_name,\\n line_num,\\n f\\\"{info['indent']}\\\",\\n func_type,\\n end_section=end)\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c688e178ce17d63e06e6fb626a70e0f2\",\n \"score\": \"0.69507337\",\n \"text\": \"def create_table(ws, header_row, table_name, data_dict, days_num, color, wb):\\n create_sub_titles(ws, header_row, table_name, days_num)\\n create_date_column(wb, ws, color, header_row, days_num) \\n create_teacher_header_row(ws, header_row, wb)\\n format_table(ws, header_row, color, days_num)\\n for col in range(2, ws.max_column+1):\\n column_array = []\\n for row in range(header_row+1, header_row+days_num+1):\\n data = paste_data(ws, col, row, header_row, data_dict, table_name)\\n if data != '':\\n column_array.append(data)\\n paste_average(column_array, header_row, days_num, col, ws)\\n ws.column_dimensions['A'].width = int(20)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e360f9288fa300235102f255b67be1c6\",\n \"score\": \"0.69448096\",\n \"text\": \"def table(self):\\n self.html.append(\\\"<tr>\\\")\\n\\n for titles in self.titles:\\n self.html.append(\\\"<th>%s</th>\\\\n\\\"%titles)\\n\\n self.html.append(\\\"</tr>\\\")\\n for itervalues in self.dataset.itervalues():\\n\\n self.html.append(\\\"<tr>\\\\n\\\")\\n\\n for values in itervalues.values():\\n self.html.append('<td>%s</td>\\\\n'%values)\\n\\n self.html.append(\\\"</tr>\\\\n\\\")\\n\\n self.html.append('</table>\\\\n</body>\\\\n</html>')\\n self.htmlstring = \\\"\\\".join(self.html)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0ffdfea416ff440472b818eea72efa9\",\n \"score\": \"0.6944158\",\n \"text\": \"def _generateTable(self):\\n\\n #\\n # Create a table.\\n # If no colum headers are provided, fall back on database field\\n # names, ASSUMING that the entries are ModelDataBase children.\\n # Note! the cellspacing is for IE8.\\n #\\n sPageBody = '<table class=\\\"tmtable\\\" id=\\\"' + self._sId + '\\\" cellspacing=\\\"0\\\">\\\\n';\\n\\n if not self._asColumnHeaders:\\n self._asColumnHeaders = self._aoEntries[0].getDataAttributes();\\n\\n sPageBody += self._generateTableHeaders();\\n\\n #\\n # Format the body and close the table.\\n #\\n sPageBody += ' <tbody>\\\\n';\\n for iEntry in range(min(len(self._aoEntries), self._cItemsPerPage)):\\n sPageBody += self._formatListEntryHtml(iEntry);\\n sPageBody += ' </tbody>\\\\n' \\\\\\n '</table>\\\\n';\\n return sPageBody;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fe478b1eccae64d4969e4bf0b65ac3f\",\n \"score\": \"0.6942006\",\n \"text\": \"def create_table(source_data):\\n decimal_formatter = NumberFormatter(format='0.0')\\n columns = [\\n TableColumn(field=\\\"ids\\\", title=\\\"Id\\\"),\\n TableColumn(field=\\\"x\\\", title=\\\"X\\\", formatter=decimal_formatter),\\n TableColumn(field=\\\"y\\\", title=\\\"Y\\\", formatter=decimal_formatter),\\n TableColumn(field=\\\"names\\\", title=\\\"Name\\\"),\\n TableColumn(field=\\\"phones\\\", title=\\\"Phone\\\"),\\n TableColumn(field=\\\"color\\\", title=\\\"Color\\\"),\\n ]\\n data_table = DataTable(source=source_data, columns=columns, width=400, height=280)\\n return data_table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56f3e5a558bdf8eb6381421c8389991b\",\n \"score\": \"0.69332004\",\n \"text\": \"def _table(self):\\n return Table(self._table_loc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d82146595b6489e114a7c34c0d13d365\",\n \"score\": \"0.69175965\",\n \"text\": \"def table(self, header, body):\\n str_format = (\\n '<table class=\\\"table\\\">\\\\n<thead>{thead}</thead>\\\\n'\\n '<tbody>\\\\n{tbody}</tbody>\\\\n</table>\\\\n'\\n )\\n return str_format.format(thead=header, tbody=body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3332c7ababe20b3e9cac6af1ff77294f\",\n \"score\": \"0.68649846\",\n \"text\": \"def create_table(self):\\n if self.achievement_test:\\n table_header = \\\\\\n self.create_achievement_test_table_header()\\n table_data, student_record_data, raw_data = \\\\\\n self.create_achievement_test_table_data()\\n else:\\n table_header, grading_instances_list = self.create_table_header()\\n table_data, student_record_data, raw_data = \\\\\\n self.create_table_data()\\n data_table = gviz_api.DataTable(table_header)\\n data_table.LoadData(table_data)\\n json_table = data_table.ToJSon()\\n json_student_record_data = simplejson.dumps(student_record_data)\\n json_raw_data = simplejson.dumps(raw_data)\\n return json_table, json_student_record_data, json_raw_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7b56b73c66cb7b7b8319aae787b8563\",\n \"score\": \"0.68459904\",\n \"text\": \"def add_table(self, rows, cols, width):\\n from .table import Table\\n tbl = CT_Tbl.new_tbl(rows, cols, width)\\n self._element._insert_tbl(tbl)\\n return Table(tbl, self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5228181efc827469d45bed35e7871744\",\n \"score\": \"0.6845761\",\n \"text\": \"def _generate_tabular(lookup_table, interpolation='linear', points_unit=u.pix, **kwargs):\\n if not isinstance(lookup_table, u.Quantity):\\n raise TypeError(\\\"lookup_table must be a Quantity.\\\") # pragma: no cover\\n\\n ndim = lookup_table.ndim\\n TabularND = tabular_model(ndim, name=f\\\"Tabular{ndim}D\\\")\\n\\n # The integer location is at the centre of the pixel.\\n points = [(np.arange(size) - 0) * points_unit for size in lookup_table.shape]\\n if len(points) == 1:\\n points = points[0]\\n\\n kwargs = {'bounds_error': False,\\n 'fill_value': np.nan,\\n 'method': interpolation,\\n **kwargs}\\n\\n if len(lookup_table) == 1:\\n t = Length1Tabular(points, lookup_table, **kwargs)\\n else:\\n t = TabularND(points, lookup_table, **kwargs)\\n\\n # TODO: Remove this when there is a new gWCS release\\n # Work around https://github.com/spacetelescope/gwcs/pull/331\\n t.bounding_box = None\\n\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd8a49d63d61ecdd28e4ba1dd9b7c2da\",\n \"score\": \"0.68444943\",\n \"text\": \"def table(contents):\\n return '<table align=\\\"center\\\" border=\\\"0\\\" cellpadding=\\\"10\\\">\\\\n'+contents+'\\\\n</table>\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3e78660c1f5266e6dc4714efdb36318\",\n \"score\": \"0.6842358\",\n \"text\": \"def add_table(self, df):\\n table = self.doc.add_table(df.shape[0] + 1, df.shape[1] + 1)\\n table.style = self.TABLE_STYLE\\n # column title\\n for i, col in enumerate(df.columns):\\n table.cell(0, i + 1).text = str(col)\\n # row title\\n for i, row in enumerate(df.index):\\n table.cell(i + 1, 0).text = str(row)\\n # values\\n for i, row in enumerate(df.index):\\n for e, item in enumerate(df.loc[row]):\\n table.cell(i + 1, e + 1).text = str(item)\\n\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae0211fe9fd5ff1ca4c2ecbed840ca24\",\n \"score\": \"0.68143386\",\n \"text\": \"def create_table(data, align, fontcolor, backcolor, box, together=True):\\n\\n data = copy(data)\\n\\n # nrow, ncol\\n nrow = len(data)\\n ncol = len(data[0])\\n\\n if align == \\\"NA\\\":\\n align = zeros((nrow, ncol)).tolist()\\n for i in range(nrow):\\n for j in range(ncol):\\n align[i][j] = 'LEFT'\\n\\n if fontcolor == \\\"NA\\\": # fontcolor per cell\\n fontcolor = array(['black'] * nrow * ncol, dtype=object).reshape(nrow, ncol)\\n\\n if backcolor == \\\"NA\\\": # background color per cell\\n backcolor = array(['white'] * nrow * ncol, dtype=object).reshape(nrow, ncol)\\n\\n # decimal formatting if int or float\\n for i in range(nrow):\\n for j in range(ncol):\\n if isinstance(data[i][j], float):\\n data[i][j] = \\\"{:.2f}\\\".format(float(data[i][j])) # dec = 2\\n\\n # setting TableStyle per cell\\n table_styles = []\\n for i in range(nrow):\\n for j in range(ncol):\\n # pylint: disable=E1121 # too many positional arguments for constructor call\\n # reportlab colors.toColor works as expected\\n reportlab_color = colors.toColor(backcolor[i][j])\\n table_styles.extend([\\n ('BACKGROUND', (j, i), (j, i), reportlab_color),\\n ('ALIGN', (j, i), (j, i), align[i][j]),\\n ('TEXTCOLOR', (j, i), (j, i), getattr(colors, fontcolor[i][j])),\\n ])\\n # type instead of box != \\\"NA\\\": avoid FutureWarning\\n if not isinstance(box, str):\\n for i in range(box.shape[0]):\\n table_styles += [('BOX', (box[i, 1], box[i, 0]), (box[i, 3], box[i, 2]), 1,\\n colors.black)]\\n\\n # creating table\\n table = Table(data)\\n table.setStyle(TableStyle(table_styles))\\n # don't split tables over pages\\n if together:\\n table = KeepTogether(table)\\n\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a873c749326c3127ff7fe1985c986c\",\n \"score\": \"0.6810006\",\n \"text\": \"def _to_table(contents, margin=1):\\n titles = _len_title(contents)\\n line = _to_line(titles, margin)\\n header = _to_header(titles, margin)\\n body = _to_body(titles, contents, margin)\\n\\n table = ''\\n table += line + '\\\\n'\\n table += header + '\\\\n'\\n table += line + '\\\\n'\\n table += '\\\\n'.join(body) + '\\\\n'\\n table += line + '\\\\n'\\n\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e41f2291b7a29604b7160b144184fd9\",\n \"score\": \"0.67932326\",\n \"text\": \"def add_table(self, rows, cols, left, top, width, height):\\n id = self.__next_shape_id\\n name = 'Table %d' % (id-1)\\n graphicFrame = CT_GraphicalObjectFrame.new_table(\\n id, name, rows, cols, left, top, width, height)\\n self.__spTree.append(graphicFrame)\\n table = _Table(graphicFrame)\\n self.__shapes.append(table)\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4030d1468701a35e0ad2b16b1c89f386\",\n \"score\": \"0.67865855\",\n \"text\": \"def table(self, **kwargs):\\n return self._child(self.tables, _Table, kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63c6dc3368c50fe3e412abb36054972b\",\n \"score\": \"0.677862\",\n \"text\": \"def crear_tablero():\\r\\n tablero = []\\r\\n for i in range(3):\\r\\n tablero.append(['-'] * 3)\\r\\n return tablero\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b122bc198e9f6ef999bcdd7dd5260055\",\n \"score\": \"0.6751137\",\n \"text\": \"def table(self, datatype=None):\\n if datatype and not isinstance(datatype, list):\\n datatype = [datatype]\\n from ..element import Table\\n return Table(self, **(dict(datatype=datatype) if datatype else {}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d9c24a6b2152504ec9f02daef14cc78\",\n \"score\": \"0.67369354\",\n \"text\": \"def make_table(self, columns):\\n string = ['loop_\\\\n']\\n length = max([len(i) for i in columns])\\n widths = [0] * len(columns)\\n types = [True] * len(columns)\\n\\n for i in range(length - 1):\\n i += 1\\n for j, col in enumerate(columns):\\n\\n try:\\n _ = value(col[i])\\n _ = error(col[i])\\n except:\\n types[j] = False\\n try:\\n if not len(col[i]) > 1:\\n types[j] = False\\n except:\\n types[j] = False\\n try:\\n if len(col[i]) > widths[j]:\\n widths[j] = len(col[i])\\n except:\\n pass\\n for i, width in enumerate(widths):\\n if width > 15:\\n widths[i] = 5\\n for i in range(length):\\n for j, col in enumerate(columns):\\n try:\\n if types[j] and not col[i].endswith(')'):\\n col[i] += ' '\\n if col[i].startswith('_'):\\n string.append('{0:>}'.format(col[i]))\\n else:\\n string.append('{0:>{1}}'.format(col[i], widths[j]))\\n except:\\n string.append('{:10}'.format('.'))\\n if i == 0:\\n string.append('\\\\n')\\n if not i == 0:\\n string.append('\\\\n')\\n string.append('\\\\n')\\n string = ' '.join(string)\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bf59af2d9a952c1d49340f72da1c6a8\",\n \"score\": \"0.6733246\",\n \"text\": \"def table():\\n\\n class TestTable(Table):\\n name = django_tables2.Column()\\n\\n return TestTable([{\\\"name\\\": \\\"rest_framework\\\", \\\"name\\\": \\\"django\\\"}])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"324aaefb3839161b00bc153e23aca020\",\n \"score\": \"0.67247295\",\n \"text\": \"def create_table(self):\\n indent = ' ' * (len(self.__tablename__) + 1)\\n columns_str = f',\\\\n{indent}'.join(f'{name} {str(column_type)}' for name, column_type in self._columns.items())\\n create_table_sql = f'''CREATE TABLE IF NOT EXISTS\\n{self.__tablename__}({columns_str})'''\\n\\n cursor.execute(create_table_sql)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bab68b95b36892beacfe0cc008506a03\",\n \"score\": \"0.6708975\",\n \"text\": \"def HoldingsTable(rows):\\n return Table(['ticker', 'fraction', 'description'],\\n [str, float, str],\\n rows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"585afe289a01a09591c66ba1d65e25f9\",\n \"score\": \"0.66905046\",\n \"text\": \"def do_create_table(tbname, dicta):\\n logger.display('create-table {0} {1}'.format(tbname, dicta))\\n dbase.create_table(tbname, dicta)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52e3b73d7c10bb0355b1d29af67ecb06\",\n \"score\": \"0.66787946\",\n \"text\": \"def wrapper(*args, **kwargs):\\n # Get a list of tuples containing table contents\\n data = table_function(*args, **kwargs)\\n\\n # Format the data rows\\n rows = []\\n for row in data:\\n formatted_row = []\\n for item in row:\\n if isinstance(item, float):\\n formatted_item = '{:.2f}'.format(item)\\n else:\\n formatted_item = str(item)\\n formatted_row.append(formatted_item)\\n rows.append(formatted_row)\\n\\n # Create the table\\n ax = kwargs[AX_ARG]\\n table = ax.table(cellText=rows, loc='center')\\n ax.set_xticks([])\\n ax.set_yticks([])\\n\\n # Remove border\\n [line.set_linewidth(0) for line in ax.spines.values()]\\n\\n # Non-index (first col) items left justified\\n cells = table.properties()['celld']\\n for row_i in range(len(data)):\\n for col_i in range(1, len(data[row_i])):\\n cells[row_i, col_i]._loc = 'left'\\n\\n # Remove table borders\\n for cell in cells.values():\\n cell.set_linewidth(0)\\n\\n # Make cells larger\\n for cell in cells.values():\\n cell.set_height(0.1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b27e989a06f3b44f4ab97d7692658bd\",\n \"score\": \"0.6663263\",\n \"text\": \"def generate_model_table( self ):\\n\\t\\tdf = self.__create_model_df().sort_values( by=[\\\"Corpus\\\",\\\"Name\\\"] )\\n\\t\\talignments = { \\\"Topics\\\" : \\\"right\\\", \\\"Documents\\\" : \\\"right\\\", \\\"Terms\\\" : \\\"right\\\" }\\n\\t\\treturn DataFrameTable( df, id=\\\"model-table\\\", alignments=alignments, striped=False, hover=False ).generate_layout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23549be3a4f2d91e4041765173baf34e\",\n \"score\": \"0.663182\",\n \"text\": \"def add_table(self, padding=5, spacing=0, border=1, class_name=None):\\n table = HtmlTable(padding, spacing, border, class_name)\\n self.children.append(table)\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"917c82ac260d6da425f470f54671775f\",\n \"score\": \"0.66314054\",\n \"text\": \"def create_table(self):\\n create_table = 'CREATE TABLE {} (id INTEGER PRIMARY KEY, '.format(self.table_name)\\n\\n for attr in self.__get_props():\\n create_table += attr + ' ' + str(self.__class__.__dict__[attr]) + ', '\\n\\n create_table = create_table[:-2] + ')'\\n\\n \\\"\\\"\\\" Ignore if table already exist \\\"\\\"\\\"\\n try:\\n self.cursor.execute(create_table)\\n except sqlite3.OperationalError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab608b565144ea4b0a4c20b67e9e539b\",\n \"score\": \"0.66308945\",\n \"text\": \"def table(headings, data, log):\\n cols = range(len(headings))\\n assert all(len(item) == len(cols) for item in data)\\n max_widths = reduce(lambda prev, cur: [(len(cur[i]) + 2)\\n if (len(cur[i]) + 2) > prev[i]\\n else prev[i]\\n for i in cols],\\n data,\\n [len(item) + 2 for item in headings])\\n log(\\\"|%s|\\\" % \\\"|\\\".join(item.center(max_widths[i]) for i, item in enumerate(headings)))\\n log(\\\"|%s|\\\" % \\\"|\\\".join(\\\"-\\\" * max_widths[i] for i in cols))\\n for row in data:\\n log(\\\"|%s|\\\" % \\\"|\\\".join(\\\" %s\\\" % row[i].ljust(max_widths[i] - 1) for i in cols))\\n log(\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8691dd0c0e935a039e48c361846b4bc2\",\n \"score\": \"0.66297275\",\n \"text\": \"def Table(self, data, width=None, height=600):\\n from tigerml.core.plots import hvPlot\\n\\n width = width or (len(data.columns) + 1) * 100\\n return hvPlot(data).table(width=width, height=height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ec616d4bbc3ac559e39bc9e20c1b6e\",\n \"score\": \"0.66265315\",\n \"text\": \"def _generate_html_table(tst_data):\\n\\n colors = {\\n u\\\"header\\\": u\\\"#7eade7\\\",\\n u\\\"empty\\\": u\\\"#ffffff\\\",\\n u\\\"body\\\": (u\\\"#e9f1fb\\\", u\\\"#d4e4f7\\\")\\n }\\n\\n tbl = ET.Element(u\\\"table\\\", attrib=dict(width=u\\\"100%\\\", border=u\\\"0\\\"))\\n\\n trow = ET.SubElement(tbl, u\\\"tr\\\", attrib=dict(bgcolor=colors[u\\\"header\\\"]))\\n thead = ET.SubElement(\\n trow, u\\\"th\\\", attrib=dict(align=u\\\"left\\\", colspan=u\\\"6\\\")\\n )\\n thead.text = tst_data[u\\\"name\\\"]\\n\\n trow = ET.SubElement(tbl, u\\\"tr\\\", attrib=dict(bgcolor=colors[u\\\"empty\\\"]))\\n thead = ET.SubElement(\\n trow, u\\\"th\\\", attrib=dict(align=u\\\"left\\\", colspan=u\\\"6\\\")\\n )\\n thead.text = u\\\"\\\\t\\\"\\n\\n if tst_data.get(u\\\"show-run\\\", u\\\"No Data\\\") == u\\\"No Data\\\":\\n trow = ET.SubElement(\\n tbl, u\\\"tr\\\", attrib=dict(bgcolor=colors[u\\\"header\\\"])\\n )\\n tcol = ET.SubElement(\\n trow, u\\\"td\\\", attrib=dict(align=u\\\"left\\\", colspan=u\\\"6\\\")\\n )\\n tcol.text = u\\\"No Data\\\"\\n\\n trow = ET.SubElement(\\n tbl, u\\\"tr\\\", attrib=dict(bgcolor=colors[u\\\"empty\\\"])\\n )\\n thead = ET.SubElement(\\n trow, u\\\"th\\\", attrib=dict(align=u\\\"left\\\", colspan=u\\\"6\\\")\\n )\\n font = ET.SubElement(\\n thead, u\\\"font\\\", attrib=dict(size=u\\\"12px\\\", color=u\\\"#ffffff\\\")\\n )\\n font.text = u\\\".\\\"\\n return str(ET.tostring(tbl, encoding=u\\\"unicode\\\"))\\n\\n tbl_hdr = (\\n u\\\"Name\\\",\\n u\\\"Nr of Vectors\\\",\\n u\\\"Nr of Packets\\\",\\n u\\\"Suspends\\\",\\n u\\\"Cycles per Packet\\\",\\n u\\\"Average Vector Size\\\"\\n )\\n\\n for dut_data in tst_data[u\\\"show-run\\\"].values():\\n trow = ET.SubElement(\\n tbl, u\\\"tr\\\", attrib=dict(bgcolor=colors[u\\\"header\\\"])\\n )\\n tcol = ET.SubElement(\\n trow, u\\\"td\\\", attrib=dict(align=u\\\"left\\\", colspan=u\\\"6\\\")\\n )\\n if dut_data.get(u\\\"threads\\\", None) is None:\\n tcol.text = u\\\"No Data\\\"\\n continue\\n\\n bold = ET.SubElement(tcol, u\\\"b\\\")\\n bold.text = (\\n f\\\"Host IP: {dut_data.get(u'host', '')}, \\\"\\n f\\\"Socket: {dut_data.get(u'socket', '')}\\\"\\n )\\n trow = ET.SubElement(\\n tbl, u\\\"tr\\\", attrib=dict(bgcolor=colors[u\\\"empty\\\"])\\n )\\n thead = ET.SubElement(\\n trow, u\\\"th\\\", attrib=dict(align=u\\\"left\\\", colspan=u\\\"6\\\")\\n )\\n thead.text = u\\\"\\\\t\\\"\\n\\n for thread_nr, thread in dut_data[u\\\"threads\\\"].items():\\n trow = ET.SubElement(\\n tbl, u\\\"tr\\\", attrib=dict(bgcolor=colors[u\\\"header\\\"])\\n )\\n tcol = ET.SubElement(\\n trow, u\\\"td\\\", attrib=dict(align=u\\\"left\\\", colspan=u\\\"6\\\")\\n )\\n bold = ET.SubElement(tcol, u\\\"b\\\")\\n bold.text = u\\\"main\\\" if thread_nr == 0 else f\\\"worker_{thread_nr}\\\"\\n trow = ET.SubElement(\\n tbl, u\\\"tr\\\", attrib=dict(bgcolor=colors[u\\\"header\\\"])\\n )\\n for idx, col in enumerate(tbl_hdr):\\n tcol = ET.SubElement(\\n trow, u\\\"td\\\",\\n attrib=dict(align=u\\\"right\\\" if idx else u\\\"left\\\")\\n )\\n font = ET.SubElement(\\n tcol, u\\\"font\\\", attrib=dict(size=u\\\"2\\\")\\n )\\n bold = ET.SubElement(font, u\\\"b\\\")\\n bold.text = col\\n for row_nr, row in enumerate(thread):\\n trow = ET.SubElement(\\n tbl, u\\\"tr\\\",\\n attrib=dict(bgcolor=colors[u\\\"body\\\"][row_nr % 2])\\n )\\n for idx, col in enumerate(row):\\n tcol = ET.SubElement(\\n trow, u\\\"td\\\",\\n attrib=dict(align=u\\\"right\\\" if idx else u\\\"left\\\")\\n )\\n font = ET.SubElement(\\n tcol, u\\\"font\\\", attrib=dict(size=u\\\"2\\\")\\n )\\n if isinstance(col, float):\\n font.text = f\\\"{col:.2f}\\\"\\n else:\\n font.text = str(col)\\n trow = ET.SubElement(\\n tbl, u\\\"tr\\\", attrib=dict(bgcolor=colors[u\\\"empty\\\"])\\n )\\n thead = ET.SubElement(\\n trow, u\\\"th\\\", attrib=dict(align=u\\\"left\\\", colspan=u\\\"6\\\")\\n )\\n thead.text = u\\\"\\\\t\\\"\\n\\n trow = ET.SubElement(tbl, u\\\"tr\\\", attrib=dict(bgcolor=colors[u\\\"empty\\\"]))\\n thead = ET.SubElement(\\n trow, u\\\"th\\\", attrib=dict(align=u\\\"left\\\", colspan=u\\\"6\\\")\\n )\\n font = ET.SubElement(\\n thead, u\\\"font\\\", attrib=dict(size=u\\\"12px\\\", color=u\\\"#ffffff\\\")\\n )\\n font.text = u\\\".\\\"\\n\\n return str(ET.tostring(tbl, encoding=u\\\"unicode\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daddce48dd14d49969f2d1ac607c1878\",\n \"score\": \"0.6625982\",\n \"text\": \"def _ascii_table(headers, table_rows, sort_index = 1, descending = True,\\n ndecimals = 8, dates_columns = None):\\n\\n if not dates_columns:\\n dates_columns = ()\\n\\n if len(set(len(row) for row in table_rows)) != 1:\\n raise ValueError(\\\"number of columns must be the same for all rows\\\")\\n\\n if len(headers) != len(table_rows[0]):\\n raise ValueError(\\\"elements in 'headers' must equal that of columns\\\")\\n\\n # Sort the table by the specified column\\n if sort_index is not None:\\n table_rows.sort(key = operator.itemgetter(sort_index),\\n reverse = descending)\\n\\n # Give format to all the data in the table as a string, as (1) a date,\\n # (2) a real number with the specified number of decimals or (3) by\\n # simply casting it to a string. The formatted data is stored as a\\n # two-level dictionary, where the first index maps to the row of the\\n # table and the second to the column.\\n\\n table_data = collections.defaultdict(dict)\\n table_data[0][0] = '' # top-left 'corner' of table left empty\\n\\n # Populate the table with the headers...\\n for column_index in range(len(headers)):\\n table_data[0][column_index + 1] = headers[column_index]\\n\\n # ... and with all the data\\n for row_index, row in enumerate(table_rows):\\n table_data[row_index + 1][0] = str(row_index)\\n for column_index, value in enumerate(row):\\n if value is None:\\n table_cell = ''\\n elif column_index in dates_columns:\\n table_cell = str(datetime.timedelta(seconds = value))\\n elif isinstance(value, float):\\n table_cell = '%.*f' % (ndecimals, value)\\n else:\\n table_cell = str(value)\\n table_data[row_index + 1][column_index + 1] = table_cell\\n\\n # Determine how many characters are needed for each column. Two\\n # different measures are made: the width of the entire column (i.e, the\\n # maximum length among the cells that share the same column) and the\\n # width of only the data (the maximum length among the cells that share\\n # the same column, excluding the header). These values will be used to\\n # right-justify the values and at the same time center them.\\n\\n widths = {}\\n data_widths = {}\\n for column_index in table_data[0].iterkeys():\\n column_values = (table_data[row][column_index]\\n for row in table_data.iterkeys())\\n column_lengths = [len(str(x))\\n for x in column_values if x is not None]\\n # The maximum width is increased by two, so that no value in the\\n # table touches the vertical cell borders (the '|' characters)\\n widths[column_index] = max(column_lengths) + 2\\n data_widths[column_index] = max(column_lengths[1:]) # only the data\\n\\n\\n # Finally, populate the ASCII table with the headers...\\n header_values = table_data[0].itervalues()\\n header_str = '|'.join([x.center(widths[index])\\n for index, x in enumerate(header_values)])\\n output = header_str + '\\\\n' + '-' * len(header_str) + '\\\\n'\\n\\n # ... and with the rest of the data\\n for row_index in sorted(table_data.iterkeys()):\\n if not row_index: # headers (index == 0) already formatted\\n continue\\n row_values = table_data[row_index].values()\\n output += row_values[0].ljust(data_widths[0]).center(widths[0]) + '|'\\n output += '|'.join(x.rjust(data_widths[index]).center(widths[index])\\n for index, x in enumerate(row_values) if index)\\n output += '\\\\n'\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0a3bdb7a4768af78569c8671a416f2e\",\n \"score\": \"0.6613774\",\n \"text\": \"def make_table(table_name):\\n return Table(table_name, metadata, autoload=True);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fae56b05ebaeb302da4ea0e5cba07966\",\n \"score\": \"0.6605663\",\n \"text\": \"def tablename():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf0b4de5b4ac4bf4edf440e12eb0d88\",\n \"score\": \"0.65996456\",\n \"text\": \"def __str__(self):\\n attribs_str = \\\"\\\"\\n if self.border: self.attribs['border'] = self.border\\n if self.style: self.attribs['style'] = self.style\\n if self.width: self.attribs['width'] = self.width\\n if self.cellspacing: self.attribs['cellspacing'] = self.cellspacing\\n if self.cellpadding: self.attribs['cellpadding'] = self.cellpadding\\n for attr in self.attribs:\\n attribs_str += ' %s=\\\"%s\\\"' % (attr, self.attribs[attr])\\n result = '<TABLE%s>\\\\n' % attribs_str\\n # insert column tags and attributes if specified:\\n if self.col_width:\\n for width in self.col_width:\\n result += ' <COL width=\\\"%s\\\">\\\\n' % width\\n # First insert a header row if specified:\\n if self.header_row:\\n if not isinstance(self.header_row, TableRow):\\n result += str(TableRow(self.header_row, header=True))\\n else:\\n result += str(self.header_row)\\n # Then all data rows:\\n for row in self.rows:\\n if not isinstance(row, TableRow):\\n row = TableRow(row)\\n # apply column alignments and styles to each row if specified:\\n # (Mozilla bug workaround)\\n if self.col_align and not row.col_align:\\n row.col_align = self.col_align\\n if self.col_char and not row.col_char:\\n row.col_char = self.col_char\\n if self.col_charoff and not row.col_charoff:\\n row.col_charoff = self.col_charoff\\n if self.col_valign and not row.col_valign:\\n row.col_valign = self.col_valign\\n if self.col_styles and not row.col_styles:\\n row.col_styles = self.col_styles\\n result += str(row)\\n result += '</TABLE>'\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43efda04f5ceb1c7d09657fa99fd0823\",\n \"score\": \"0.65917933\",\n \"text\": \"def table_row(self):\\n self.add('<tr>')\\n yield\\n self.add('</tr>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f18ba1fe0f0a31b5bfa50b07998573cc\",\n \"score\": \"0.6590479\",\n \"text\": \"def add_table(self, headers, data, align=0):\\n table = ReportTable(headers, data, align)\\n self.SubContents.append(table)\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11e5ba84814c831e706585c0d4e29337\",\n \"score\": \"0.65886223\",\n \"text\": \"def Table(self, name, create=True, columns=1, keys=1, timeout=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f955d03c802ebbcd529411fd886403\",\n \"score\": \"0.6581862\",\n \"text\": \"def table(*args, **kwargs):\\n t = gca().table(*args, **kwargs)\\n draw_if_interactive()\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c5d5fecbdcf0004c3f7ac0b5b40ee71\",\n \"score\": \"0.6577191\",\n \"text\": \"def table(self, rows, window_id=None, title=None, header=None, style=None):\\n\\n if not window_id:\\n window_id = title # Can provide either of these,\\n if not title:\\n title = window_id # or both.\\n table = html_table_from_rows(rows, title=title, header=header, style_dict=style)\\n self.text(table, title=title, window_id=window_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43baa2a5374727a3bd48f898f576df4\",\n \"score\": \"0.6569342\",\n \"text\": \"def table(table_function):\\n\\n @plot\\n @functools.wraps(table_function)\\n def wrapper(*args, **kwargs):\\n \\\"\\\"\\\"Format the provided data into a matplotlib table\\\"\\\"\\\"\\n # Get a list of tuples containing table contents\\n data = table_function(*args, **kwargs)\\n\\n # Format the data rows\\n rows = []\\n for row in data:\\n formatted_row = []\\n for item in row:\\n if isinstance(item, float):\\n formatted_item = '{:.2f}'.format(item)\\n else:\\n formatted_item = str(item)\\n formatted_row.append(formatted_item)\\n rows.append(formatted_row)\\n\\n # Create the table\\n ax = kwargs[AX_ARG]\\n table = ax.table(cellText=rows, loc='center')\\n ax.set_xticks([])\\n ax.set_yticks([])\\n\\n # Remove border\\n [line.set_linewidth(0) for line in ax.spines.values()]\\n\\n # Non-index (first col) items left justified\\n cells = table.properties()['celld']\\n for row_i in range(len(data)):\\n for col_i in range(1, len(data[row_i])):\\n cells[row_i, col_i]._loc = 'left'\\n\\n # Remove table borders\\n for cell in cells.values():\\n cell.set_linewidth(0)\\n\\n # Make cells larger\\n for cell in cells.values():\\n cell.set_height(0.1)\\n\\n # Decorate wrapper with plotting\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"678b30b43c5baa1ec84d69cc678d5ef1\",\n \"score\": \"0.6566881\",\n \"text\": \"def create_table(self):\\n self.auto_column_number = 1\\n table_path = os.path.join(self.opts[\\\"data_dir\\\"], self.table_name())\\n self.file = open_fw(table_path, encoding=self.encoding)\\n self.output_file = open_csvw(self.file)\\n column_list = self.table.get_insert_columns(join=False, create=True)\\n self.output_file.writerow([u'{}'.format(val) for val in column_list])\\n self.table_names.append((self.file, table_path))\\n\\n # Register all tables created to enable\\n # testing python files having custom download function\\n Engine.register_tables(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7fe6e577db376257a3865feb0095b17\",\n \"score\": \"0.6560861\",\n \"text\": \"def create(self, table):\\n\\n if type(table) != DictionaryType:\\n raise TypeError, 'expected dictionary type argument'\\n raise NotImplementedError, '... Sorry!'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed1a18891970b6673bce65a16192b687\",\n \"score\": \"0.65606594\",\n \"text\": \"def print_table(table):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8ad9305f416a736ee3c8ec184b54ebc\",\n \"score\": \"0.65599793\",\n \"text\": \"def generate_table(sm, mean=False):\\n return \\\"\\\"\\\"\\n <table id={id} class='datatable'>\\n {header}\\n {body}\\n </table>\\n \\\"\\\"\\\".format(\\n id = hash(sm),\\n header = _generate_header(sm, mean=mean),\\n body = _generate_body(sm, mean=mean)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4551a6aad33e5df4aa915c2bd57d41b8\",\n \"score\": \"0.65565616\",\n \"text\": \"def create_html_table(header, users, user, count=1, status=None, table=None):\\n if count == 1:\\n table = '<div><tr>'\\n for head in header:\\n table += '<th style=\\\"border: 1px solid black\\\">'+head+'</th>'\\n table += '</tr></div>'\\n patch_groups = users[user].keys()\\n # patch_groups.sort()\\n sorted(patch_groups)\\n for patch_group in patch_groups:\\n for minion in users[user][patch_group]:\\n if status is None:\\n table += '<tr><td style=\\\"border: 1px solid black;\\\">'+str(count)\\\\\\n +'</td><td style=\\\"border: 1px solid black;\\\">'\\\\\\n +patch_group+'</td><td style=\\\"border: 1px solid black;\\\">'+minion+'</td></tr>'\\n else:\\n table += '<tr><td style=\\\"border: 1px solid black;\\\">'+str(count)\\\\\\n +'</td><td style=\\\"border: 1px solid black;\\\">'+minion+\\\\\\n '</td><td style=\\\"border: 1px solid black;\\\">'+patch_group+\\\\\\n '</td><td style=\\\"border: 1px solid black;\\\">'+user+\\\\\\n '</td><td style=\\\"border: 1px solid black;\\\">'+status[user]+'</td></tr>'\\n count += 1\\n return count, table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2bc74c3d10ad9619444393036f0c250\",\n \"score\": \"0.65514266\",\n \"text\": \"def test_table():\\n return pd.DataFrame(\\n {\\n \\\"AgeGroup\\\": pd.Series([\\\"18-24\\\"]),\\n \\\"sex\\\": pd.Series([\\\"F\\\"]),\\n \\\"demographic\\\": pd.Series([0]),\\n \\\"date\\\": pd.Series([\\\"01/04/2019\\\"]),\\n \\\"numerator\\\": pd.Series([10]),\\n \\\"population\\\": pd.Series([20]),\\n \\\"age_rates\\\": pd.Series([0.0002]),\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a784f29e4d5c3b54af160c4cb205cf2\",\n \"score\": \"0.6549758\",\n \"text\": \"def create_q_table(n_states,actions):\\n q_table = pd.DataFrame(np.zeros((n_states, len(actions))),columns = actions)\\n print(q_table)\\n return q_table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36927722038913d87f17a38d4d92efd4\",\n \"score\": \"0.6522562\",\n \"text\": \"def _genere_table(self, entier: bool) -> Table:\\n if entier:\\n resultat = Table(title=\\\"Solution du problème : calendrier\\\")\\n resultat.add_column(\\\"Tâche\\\")\\n resultat.add_column(\\\"Nature\\\")\\n resultat.add_column(\\\"Début\\\")\\n resultat.add_column(\\\"Fin\\\")\\n for date in self._dates:\\n resultat.add_row(\\n date.tache.nom,\\n date.tache.correspondance,\\n self._choix_affichage_date(date.date_debut),\\n self._choix_affichage_date(date.date_fin),\\n )\\n else:\\n resultat = Table(title=\\\"Solution du problème : calendrier\\\")\\n resultat.add_column(\\\"Tâche\\\")\\n resultat.add_column(\\\"Début\\\")\\n resultat.add_column(\\\"Fin\\\")\\n for date in self._dates:\\n resultat.add_row(\\n date.tache.nom,\\n self._choix_affichage_date(date.date_debut),\\n self._choix_affichage_date(date.date_fin),\\n )\\n\\n return resultat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e01f97c93f0d32ab39556eb3d94efa88\",\n \"score\": \"0.65205693\",\n \"text\": \"def repr_as_table(headers, *args):\\n assert len(headers) == len(args)\\n copies = [[headers[i]] + args[i] for i in range(len(args))]\\n max_lengths = [max([len(x) for x in arg]) for arg in copies]\\n row_sep = '+-'\\n for i in range(len(max_lengths)):\\n row_sep += '-' * max_lengths[i]\\n row_sep += '-+-'\\n row_sep = row_sep[:-1] # remove extra '-' added in the loop\\n\\n lines = [row_sep]\\n for i in range(len(copies[0])):\\n line = '| '\\n for j in range(len(copies)):\\n line += copies[j][i].ljust(max_lengths[j]) + ' | '\\n line = line[:-1] # remove extra space added in the loop\\n lines.append(line)\\n lines.append(row_sep)\\n return os.linesep.join(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"712b2f54db9d03a5dd20e904915c9b38\",\n \"score\": \"0.6519341\",\n \"text\": \"def get_shapes_table(self):\\n title_list = ['idx', 'class', '__str__', 'Perimeter', 'P. Formula', 'Area', 'A. Formula']\\n table = []\\n str_table = ''\\n print(self.shapes)\\n for iterator, shape in enumerate(self.shapes):\\n table.append([str(iterator + 1), type(shape).__name__, shape.__str__(), str(round(shape.get_perimeter(), 1)),\\n shape.get_perimeter_formula(), str(shape.get_area()), shape.get_area_formula()]) # adding all args\\n print(table)\\n column_length = []\\n for title_iterator in range(len(title_list)):\\n column_len = len(title_list[title_iterator])\\n for line in table:\\n if len(line[title_iterator]) > column_len:\\n column_len = len(line[title_iterator])\\n column_length.append(column_len) # making column lenght proper to the text size\\n\\n how_wide = 0\\n for name in title_list:\\n x = (column_length[title_list.index(name)])\\n how_wide += (len((\\\"|{: <\\\" + str(x + 2) + \\\"}|\\\").format(name)))\\n str_table += ('-' * how_wide)\\n str_table += '\\\\n' # adding frames of a table in a proper size\\n\\n for name in title_list:\\n str_table += \\\"|\\\"\\n x = (column_length[title_list.index(name)])\\n str_table += ((\\\"{: <\\\" + str(x + 2) + \\\"}\\\").format(name))\\n str_table += \\\"|\\\"\\n str_table += '\\\\n'\\n str_table += ('-' * how_wide)\\n\\n for row in table:\\n str_table += '\\\\n'\\n str_table += \\\"|\\\"\\n\\n for element in row:\\n x = (column_length[row.index(element)])\\n str_table += ((\\\"|{: <\\\" + str(x + 2) + \\\"}|\\\").format(element))\\n return str_table # returning a table as a string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7827ab23e40eab6f0eb0e98ab272aca\",\n \"score\": \"0.65178704\",\n \"text\": \"def table(self, cls):\\n return cls(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6f74fbcbbba75f52ad58cd0b253a6cc\",\n \"score\": \"0.65116113\",\n \"text\": \"def test_make_dash_table(self):\\n output = ui.make_dash_table(pd.DataFrame(np.asarray([[1, 2],\\n [3, 4],\\n [5, 6]])))\\n # check return types\\n self.assertTrue(isinstance(output, list))\\n # check length and shape of the return types\\n self.assertTrue(len(output) == 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea48f1742c30c043e86f3bb127ec2bb\",\n \"score\": \"0.64926445\",\n \"text\": \"def _start_table(format_, headings):\\n\\n return _START_TABLE(format_, headings)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88ec33c3a776c7ebea4cba8ba321b16c\",\n \"score\": \"0.6482987\",\n \"text\": \"def _create_table(table_dictionary: dict, c: int):\\n df = pd.DataFrame.from_dict(table_dictionary)\\n table = html.Div([dash_table.DataTable(\\n id=f'table_{c}',\\n columns=[{\\\"name\\\": i, \\\"id\\\": i} for i in df.columns],\\n css=[{'selector': '.dash-filter input', 'rule': 'text-align: left'}, {'selector': '.row', 'rule': 'margin: 0'}],\\n style_cell={\\n 'whiteSpace': 'normal',\\n 'height': 'auto',\\n 'text-align': 'left'\\n },\\n data=df.to_dict('records'))])\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"707582cf686d40b255d5ddaedd0902ec\",\n \"score\": \"0.6477894\",\n \"text\": \"def to_table(self):\\n t = Table()\\n t[\\\"name\\\"] = [p.name for p in self._parameters]\\n t[\\\"value\\\"] = [p.value for p in self._parameters]\\n if self.covariance is None:\\n t[\\\"error\\\"] = np.nan\\n else:\\n t[\\\"error\\\"] = [self.error(idx) for idx in range(len(self))]\\n\\n t[\\\"unit\\\"] = [p.unit.to_string(\\\"fits\\\") for p in self._parameters]\\n t[\\\"min\\\"] = [p.min for p in self._parameters]\\n t[\\\"max\\\"] = [p.max for p in self._parameters]\\n t[\\\"frozen\\\"] = [p.frozen for p in self._parameters]\\n\\n for name in [\\\"value\\\", \\\"error\\\", \\\"min\\\", \\\"max\\\"]:\\n t[name].format = \\\".3e\\\"\\n\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e794659a70a494a3a52017e685471c2f\",\n \"score\": \"0.64699477\",\n \"text\": \"def InsertGenericTable(self, data, columns=[]):\\n if self.Story == None:\\n return\\n if len(data) == 0:\\n return\\n self.Story.append(Spacer(1, 0.1*inch))\\n self.Story.append(Table(data, colWidths=columns, style=None, rowHeights=None))\\n self.Story.append(Spacer(1, 0.15*inch))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c790e0a17a9c0e0fe5dff463728f84c9\",\n \"score\": \"0.64671314\",\n \"text\": \"def table(self, limit: int = 0) -> PrettyTable:\\n tbl = PrettyTable()\\n\\n if self._rowcount == -1 or self._columns or self._rows:\\n tbl.field_names = [\\\"#\\\"] + [col[0] for col in self._columns]\\n rows_to_add = self._rows[:limit] if limit else self._rows\\n tbl.add_rows(\\n [[i + 1] + list(row) for i, row in enumerate(rows_to_add)]\\n )\\n else:\\n tbl.field_names = [\\\"Rows affected\\\"]\\n tbl.add_row([self._rowcount])\\n return tbl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2ede266d86eb432c651b08c36f5998\",\n \"score\": \"0.6449009\",\n \"text\": \"def make_student_table(self):\\n pt = PrettyTable(field_names=[\\\"CWID\\\", \\\"Name\\\", \\\"Major\\\",\\n \\\"Completed Courses\\\",\\n \\\"Required Remaining\\\",\\n \\\"Elective Remaining\\\"])\\n pt.align = 'l'\\n for student in self.students.values():\\n pt.add_row([student.cwid, student.name,\\n student.major, sorted(student.courses.keys()),\\n self.required_remaining(student),\\n self.elective_remaining(student)])\\n return pt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfbad4a97deed1aaf308bcbf52412b49\",\n \"score\": \"0.6446894\",\n \"text\": \"def create_table(self, table: str, columns: dict) -> bool:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9926d6fd51b8f6d20686fd0ba1331e40\",\n \"score\": \"0.64393383\",\n \"text\": \"def table(func):\\n @functools.wraps(func)\\n def wrapper_decorator(*args, **kwargs):\\n args[0].table = PrettyTable() #equivalent to self.table = prettyTable()\\n value = func(*args, **kwargs)\\n return value\\n return wrapper_decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cba6da4daaa40caa5a35269436bf46d\",\n \"score\": \"0.64374495\",\n \"text\": \"def create_table(self):\\n \\n # create iterator\\n patterns = self.create_pattern_generator()\\n \\n # populate table\\n table = {self._hfunc(pattern, *self._hfunc_args, **self._hfunc_kwargs) :\\n self._func(pattern, self._series, *self._func_args, **self._func_kwargs)\\n for pattern in patterns}\\n \\n self._table = table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c27d8db44664da2e1abea5cafc9d38\",\n \"score\": \"0.64168286\",\n \"text\": \"def create_astropy_table(py_obj, h_group, name, **kwargs):\\n data = py_obj.as_array()\\n d = h_group.create_dataset(name, data=data, dtype=data.dtype, **kwargs)\\n\\n colnames = [bytes(cn, 'ascii') for cn in py_obj.colnames]\\n d.attrs['colnames'] = colnames\\n for key, value in py_obj.meta.items():\\n d.attrs[key] = value\\n return(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3e76fb3732649740ab51dc78bd895e0\",\n \"score\": \"0.6411617\",\n \"text\": \"def createTable(self, tableName, columnFamilies):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ab02c93822d6514ecac5a4ccc9ed13\",\n \"score\": \"0.6411531\",\n \"text\": \"def rst_table(self, array):\\n # Determine cell width for each column\\n cell_dict = {}\\n for i, row in enumerate(array):\\n for j, val in enumerate(row):\\n if j not in cell_dict:\\n cell_dict[j] = []\\n cell_dict[j].append(val)\\n for item in cell_dict:\\n cell_dict[item] = max([len(x) for x in cell_dict[item]])+1 # Contains adapted width for each column\\n\\n # Format top line\\n num_cols = len(array[0])\\n form = '+'\\n for col in range(num_cols):\\n form += (cell_dict[col]+1)*'-'\\n form += '+'\\n form += '\\\\n'\\n\\n # Format values\\n for i, row in enumerate(array):\\n form += '| '\\n for j, val in enumerate(row):\\n cell_width = cell_dict[j]\\n form += str(val) + (cell_width - len(val)) * ' ' + '| '\\n form.rstrip()\\n form += '\\\\n'\\n\\n # Seperation lines\\n form += '+'\\n if i == 0:\\n sign = '='\\n else:\\n sign = '-'\\n for col in range(num_cols):\\n form += (cell_dict[col]+1)*sign\\n form += '+'\\n form += '\\\\n'\\n return form\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55e20bfaa72086edf5efaedaed77c955\",\n \"score\": \"0.640563\",\n \"text\": \"def create_table(self):\\n try:\\n c = connection.cursor()\\n query = f'CREATE TABLE {self.table_name} (\\\\\\n Sid SERIAL PRIMARY KEY, \\\\\\n Region varchar(255), \\\\\\n Country varchar(255), \\\\\\n \\\"Item Type\\\" varchar(255), \\\\\\n \\\"Sales Channel\\\" varchar(255), \\\\\\n \\\"Order Priority\\\" varchar(255), \\\\\\n \\\"Order ID\\\" varchar(255), \\\\\\n \\\"Units Sold\\\" FLOAT,\\\\\\n \\\"Unit Price\\\" FLOAT,\\\\\\n \\\"Unit Cost\\\" FLOAT,\\\\\\n \\\"Total Revenue\\\" FLOAT,\\\\\\n \\\"Total Cost\\\" FLOAT,\\\\\\n \\\"Total Profit\\\" FLOAT\\\\\\n );'\\n c.execute(query)\\n df = pd.read_csv(self.file_name, skiprows=self.lines_read)\\n self.headers = df.columns.to_list()\\n tmp = \\\"\\\"\\n for i in self.headers:\\n if len(tmp) != 0:\\n tmp += \\\",\\\"\\n if len(str(i).split(\\\" \\\")) == 1:\\n tmp += str(i)\\n else:\\n tmp += '\\\"' + str(i) + '\\\"'\\n self.headers = tmp\\n finally:\\n c.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b424e0f8b7451615d1fd09813ffc5fd\",\n \"score\": \"0.63992774\",\n \"text\": \"def table(self, cellspacing=0):\\n self.add('<table cellspacing={0}>'.format(cellspacing))\\n yield\\n self.add('</table>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a52a2e0b43ade2453026fc6a1e30a63\",\n \"score\": \"0.6398043\",\n \"text\": \"def create_table_data(self):\\n #build tables for data\\n row = [\\\"\\\" for i in range(len(self.grading_instances_keys) + 1)]\\n table_data = [list(row) for i in range(len(self.students))]\\n row = [0 for i in range(len(self.grading_instances_keys) + 1)]\\n raw_data = [list(row) for i in range(len(self.students))]\\n row = [\\\"\\\" for i in range(len(self.grading_instances_keys))]\\n student_record_data = [list(row) for i in range(len(self.students))]\\n for y, student in enumerate(self.students):\\n table_data[y][0] = student.full_name_lastname_first()\\n raw_data[y][0] = table_data[y][0]\\n for x, gd_inst_key in enumerate(self.grading_instances_keys):\\n student_record_key = \\\\\\n self.student_record_keys_dict[student.key()][gd_inst_key]\\n grade = None\\n if student_record_key:\\n student_record_data[y][x] = str(student_record_key)\\n student_record = \\\\\\n SchoolDB.models.StudentsClass.get(student_record_key)\\n grade = student_record.get_grade(gd_inst_key)\\n if grade:\\n grade_str = \\\"value=%5.1f\\\" %grade\\n else:\\n grade_str = \\\"\\\"\\n grade = 0\\n table_data[y][x+1] = \\\\\\n '<input type=\\\"text\\\" class=\\\"entry-field numeric-field\\\" '+\\\\\\n 'id=\\\"%s-%s-%s\\\" %s></input>' %(self.table_name, x, \\n y, grade_str)\\n raw_data[y][x+1] = grade\\n return table_data, student_record_data, raw_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44af56647c377ed92aa9d47b4ecceaa1\",\n \"score\": \"0.6395119\",\n \"text\": \"def print_tag_table(title, entries):\\n header = ('Date', 'Amount', 'Balance', 'Description')\\n rows = [(entry.date,\\n '{:,.2f}'.format(entry.amount),\\n '{:,.2f}'.format(entry.balance),\\n entry.description) for entry in entries]\\n table_data = [header] + rows\\n table = SingleTable(table_data, title=title)\\n table.justify_columns[1] = 'right'\\n table.justify_columns[2] = 'right'\\n print(table.table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f731297e9b4237c6d399439a350858f\",\n \"score\": \"0.6389033\",\n \"text\": \"def createTableString(self):\\n\\n return \\\"CREATE TABLE \\\" + self.table + \\\"(\\\" + ', '.join([columnName + \\\" \\\" + self.column_definitions[columnName][1]\\n for columnName in self.column_definitions]) + \\\")\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68cd75937e79d96b72d7c502ecc79fee\",\n \"score\": \"0.6388881\",\n \"text\": \"def build_table():\\n faves = load_faves()\\n myrow = \\\"\\\"\\n contents = \\\"\\\"\\n columns = 0\\n for fave in faves[::-1]:\\n data = parse_url(fave)\\n color = '#FFFFFF'\\n myrow += column(link(data['adj'], data['noun'],\\n img(data['adj'], data['noun'],\\n data['imgurl']), fave), color)\\n columns = columns+1\\n if columns >= 3:\\n columns = 0\\n contents += row(myrow)\\n myrow = \\\"\\\"\\n\\n return table(contents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d938baacc656bf02081fcd0588ef19f8\",\n \"score\": \"0.6384709\",\n \"text\": \"def generate_table(dataframe, max_rows=1):\\n return html.Table(\\n # Header\\n [html.Tr([html.Th(col) for col in dataframe.columns])] +\\n\\n # Body\\n [html.Tr([\\n html.Td(dataframe.iloc[i][col]) for col in dataframe.columns\\n ]) for i in range(min(len(dataframe), max_rows))]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51a5fae3b47fc7aed6e9cabd2c29087c\",\n \"score\": \"0.63815194\",\n \"text\": \"def set_table(self):\\n\\n # order and put in a list\\n self.display_data = []\\n for key, value in sorted(self.results.iteritems(), key=lambda (k, v): (v, k), reverse=self.direction):\\n self.display_data.append((key, value))\\n\\n # populate and display\\n for i, row in enumerate(self.display_data):\\n for j, col in enumerate(row):\\n item = QtWidgets.QTableWidgetItem(str(col))\\n self.setItem(i, j, item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1517dfe390d337c249c373593096a8ff\",\n \"score\": \"0.6378619\",\n \"text\": \"def make_comparison_table(truncate=False):\\n make_table('comparison', truncate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0147d515b41f141ba0974cc0261cdcd3\",\n \"score\": \"0.6378364\",\n \"text\": \"def build_exam_table(exams,title,nTables,nExams,singletable=False):\\n\\n if singletable:\\n x_coord = 10\\n y_coord = 2.5\\n width = 4.7\\n else:\\n x_coord = 0.5+nTables*4\\n y_coord = 11.5\\n width = 3.7\\n if 'SAT' in title or 'ACT' in title:\\n width = 3.3\\n\\n theTEX = r\\\"\\\"\\\"\\n\\\\begin{textblock}{%.2f}(%.2f,%.2f)\\n\\\\begin{table}\\n\\\\caption{%s}\\n\\\\centering\\n\\\\begin{tabularx}{\\\\textwidth}{ X c }\\n\\\\toprule\\n{\\\\bf Subject} & {\\\\bf Score} \\\\\\\\\\n\\\\midrule\\n\\\"\\\"\\\" % (width,x_coord,y_coord,title)\\n\\n for exam in exams:\\n if singletable: theTEX += '%s & %i \\\\\\\\\\\\\\\\[5pt] \\\\n' % (exam.subject, exam.score)\\n else: theTEX += '%s & %i \\\\\\\\\\\\\\\\ \\\\n' % (exam.subject, exam.score)\\n if not singletable: theTEX = theTEX.replace(\\\"Algebra II/Trigonometry\\\",\\\"Algebra II/Trig\\\")\\n nExams += 1\\n\\n theTEX += r\\\"\\\"\\\"\\\\bottomrule\\n\\\\end{tabularx}\\n\\\\end{table}\\n\\\\end{textblock}\\n\\n\\\"\\\"\\\"\\n\\n return theTEX\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00b2c52234c112fc9d7d98582b36f871\",\n \"score\": \"0.63624936\",\n \"text\": \"def create_table(self):\\n try:\\n c = self.conn.cursor()\\n c.execute(sql.sql_create_data_table)\\n except Error as e:\\n print(f\\\"error creating table: {e}\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993484,"cells":{"query_id":{"kind":"string","value":"ab89c7636396ac096d9f684b45b0325c"},"query":{"kind":"string","value":"Check atom types for matched atoms are the same in both molecules. If not the same report to user and ask if they wish to continue and ends if not."},"positive_passages":{"kind":"list like","value":[{"docid":"3b8703a5cc93018b33404a9fa433f656","score":"0.72817165","text":"def check_atom_type_match(matched_idx_map, initial_mol_info, final_mol_info):\n\n init_names = initial_mol_info.struct.name()\n final_names = final_mol_info.struct.name()\n\n type_match_error = False\n\n for init_idx, final_idx in matched_idx_map.iteritems():\n\n init_type = initial_mol_info.atom_types[init_idx]\n final_type = final_mol_info.atom_types[final_idx]\n\n if init_type != final_type:\n init_name = init_names[init_idx]\n final_name = final_names[final_idx]\n\n print(\n 'WARNING: Matched atoms of different types: {0:s} ({1:s}) - {2:s} ({3:s})'.format(\n init_name,\n init_type,\n final_name,\n final_type))\n type_match_error = True\n\n if type_match_error:\n\n choice = input(\n \"Given type match error do you want to proceed (y/[n])?\")\n if choice.lower() != 'y':\n sys.exit(1)\n\n return","title":""}],"string":"[\n {\n \"docid\": \"3b8703a5cc93018b33404a9fa433f656\",\n \"score\": \"0.72817165\",\n \"text\": \"def check_atom_type_match(matched_idx_map, initial_mol_info, final_mol_info):\\n\\n init_names = initial_mol_info.struct.name()\\n final_names = final_mol_info.struct.name()\\n\\n type_match_error = False\\n\\n for init_idx, final_idx in matched_idx_map.iteritems():\\n\\n init_type = initial_mol_info.atom_types[init_idx]\\n final_type = final_mol_info.atom_types[final_idx]\\n\\n if init_type != final_type:\\n init_name = init_names[init_idx]\\n final_name = final_names[final_idx]\\n\\n print(\\n 'WARNING: Matched atoms of different types: {0:s} ({1:s}) - {2:s} ({3:s})'.format(\\n init_name,\\n init_type,\\n final_name,\\n final_type))\\n type_match_error = True\\n\\n if type_match_error:\\n\\n choice = input(\\n \\\"Given type match error do you want to proceed (y/[n])?\\\")\\n if choice.lower() != 'y':\\n sys.exit(1)\\n\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"acc86706fa2928bf1f4e37aa6956f931","score":"0.6576755","text":"def match_molecules(molecule1, molecule2, logger, onlyH):\r\n\r\n if logger.use_groups: # If group charges are to be used\r\n\r\n chg1, chg2 = molecule1.chg_grp, molecule2.chg_grp\r\n\r\n else:\r\n\r\n chg1, chg2 = molecule1.chg, molecule2.chg\r\n\r\n # Decompose both molecules [decompose_molecule function]\r\n [decomposed_molecule1,\r\n atom_types1, occurrences1] = decompose_molecule(chg1, molecule1.cor, molecule1.idf)\r\n\r\n [decomposed_molecule2,\r\n atom_types2, occurrences2] = decompose_molecule(chg2, molecule2.cor, molecule2.idf)\r\n\r\n # Determine the number of atom types\r\n n_atom_types = len(atom_types1)\r\n\r\n # Set up empty variables (lists)\r\n identifiers_molecule1, identifiers_molecule2 = [], []\r\n \r\n if onlyH == False:\r\n # Iterate through all atom types\r\n for atom_type in range(n_atom_types):\r\n # Extract coordinates and identifiers from decomposed molecules\r\n coords1, id1 = decomposed_molecule1[atom_type]\r\n coords2, id2 = decomposed_molecule2[atom_type]\r\n\r\n if logger.match_solv == 'standard':\r\n\r\n # Check for number of occurences of the actual atom type\r\n if occurrences1[atom_type] == 1:\r\n\r\n # Directly match atoms\r\n identifiers_molecule1.append(id1)\r\n identifiers_molecule2.append(id2)\r\n\r\n else: # If more than one atom of the type exists\r\n\r\n n_atoms = len(coords1) # Determine numer of atoms\r\n\r\n # Calculate permutation matrix for this atom type\r\n permutation_matrix = calc_permutation_matrix(coords1, coords2, logger)\r\n\r\n # Iterate through all atoms of this type\r\n for atom in range(n_atoms):\r\n\r\n # Find indices of minimum value in matrix (first index cooresponds to coords1)\r\n indices = np.where(permutation_matrix == np.min(permutation_matrix))\r\n\r\n if len(indices[0]) != 1: # Check if there is more than one solution\r\n \r\n # If there is: use the first match\r\n indices = np.array((indices[0][0], indices[1][0]))\r\n\r\n # Append identifiers corresponding to indices\r\n identifiers_molecule1.append(id1[indices[0]])\r\n identifiers_molecule2.append(id2[indices[1]])\r\n\r\n # Deplete corresponding row of first index and column of second index from matrix\r\n permutation_matrix = np.delete(permutation_matrix, indices[0], 0)\r\n permutation_matrix = np.delete(permutation_matrix, indices[1], 1)\r\n\r\n # Deplete identifier arrays\r\n id1 = np.delete(id1, indices[0])\r\n id2 = np.delete(id2, indices[1])\r\n\r\n elif logger.match_solv == 'hungarian':\r\n\r\n # Calculate permutation matrix for this atom type\r\n permutation_matrix = calc_permutation_matrix(coords1, coords2, logger, do_permute=True)\r\n\r\n # Solve the problem with the Hungarian algorithm\r\n indices_mol1, indices_mol2 = hungarian_solver(permutation_matrix)\r\n\r\n # Collect indices of the assignment\r\n identifiers_molecule1.extend(id1[indices_mol1])\r\n identifiers_molecule2.extend(id2[indices_mol2])\r\n\r\n elif onlyH == True:\r\n for atom_type in range(n_atom_types):\r\n coords1, id1 = decomposed_molecule1[atom_type]\r\n coords2, id2 = decomposed_molecule2[atom_type]\r\n\r\n if logger.match_solv == 'standard':\r\n\r\n # Check for number of occurences of the actual atom type\r\n if occurrences1[atom_type] == 1:\r\n\r\n # Directly match atoms\r\n identifiers_molecule1.append(id1)\r\n identifiers_molecule2.append(id2)\r\n\r\n else: # If more than one atom of the type exists\r\n\r\n n_atoms = len(coords1) # Determine numer of atoms\r\n \r\n # Calculate permutation matrix for this atom type\r\n permutation_matrix = calc_permutation_matrix(coords1, coords2, logger)\r\n\r\n # Iterate through only H atoms\r\n for atom in range(n_atoms):\r\n\r\n # Find indices of minimum value in matrix (first index cooresponds to coords1)\r\n indices = np.where(permutation_matrix == np.min(permutation_matrix))\r\n\r\n if len(indices[0]) != 1: # Check if there is more than one solution\r\n \r\n # If there is: use the first match\r\n indices = np.array((indices[0][0], indices[1][0]))\r\n\r\n # Append identifiers corresponding to indices\r\n identifiers_molecule1.append(id1[indices[0]])\r\n identifiers_molecule2.append(id2[indices[1]])\r\n\r\n # Deplete corresponding row of first index and column of second index from matrix\r\n permutation_matrix = np.delete(permutation_matrix, indices[0], 0)\r\n permutation_matrix = np.delete(permutation_matrix, indices[1], 1)\r\n\r\n # Deplete identifier arrays\r\n id1 = np.delete(id1, indices[0])\r\n id2 = np.delete(id2, indices[1])\r\n\r\n elif logger.match_solv == 'hungarian':\r\n\r\n # Calculate permutation matrix for this atom type\r\n if atom_type == 0:\r\n permutation_matrix = calc_permutation_matrix(coords1, coords2, logger, do_permute=True)\r\n else:\r\n permutation_matrix = calc_permutation_matrix(coords1, coords2, logger, do_permute=False)\r\n # Solve the problem with the Hungarian algorithm\r\n indices_mol1, indices_mol2 = hungarian_solver(permutation_matrix)\r\n\r\n # Collect indices of the assignment\r\n identifiers_molecule1.extend(id1[indices_mol1])\r\n identifiers_molecule2.extend(id2[indices_mol2])\r\n\r\n # Transform identifiers to single arrays containing the new positions\r\n pos_mol1 = np.ravel(np.array(identifiers_molecule1, dtype=np.int) - 1)\r\n pos_mol2 = np.ravel(np.array(identifiers_molecule2, dtype=np.int) - 1)\r\n return(pos_mol1, pos_mol2)\r\n # Update charges (should be redundant but just to be sure...)\r\n molecule1.chg = np.take(molecule1.chg, pos_mol1)\r\n molecule2.chg = np.take(molecule2.chg, pos_mol2)\r\n\r\n # Update coordinates of the coordinate arrays whose orientations were not changed\r\n molecule1.cor = np.take(molecule1.cor, pos_mol1, axis=0)\r\n molecule2.cor = np.take(molecule2.cor, pos_mol2, axis=0)\r\n\r\n # Update standard deviations\r\n molecule1.cor_std = np.take(molecule1.cor_std, pos_mol1, axis=0)\r\n molecule2.cor_std = np.take(molecule2.cor_std, pos_mol2, axis=0)\r\n\r\n if logger.use_groups: # If group charges were used, update group properties as well\r\n\r\n molecule1.get_group_charges(), molecule2.get_group_charges()\r\n molecule1.get_group_symbols(), molecule2.get_group_symbols()\r\n\r\n logger.is_matched = True # Log successful matching\r\n molecule1.show_mol = True # Set variable so result is shown\r","title":""},{"docid":"fbcae2728054801de8b0bd21142c9927","score":"0.6209729","text":"def check_atom_balance(entry_1: Union[dict, str, Molecule],\n entry_2: Union[dict, str, Molecule],\n verbose: Optional[bool] = True,\n ) -> bool:\n if not entry_1 or not entry_2:\n raise SpeciesError(f'Cannot compare entries. Got:\\n{entry_1}\\nand\\n{entry_2}')\n element_dict_1, element_dict_2 = dict(), dict()\n result = True\n\n # Count the number of each element.\n for element_dict, entry in zip([element_dict_1, element_dict_2], [entry_1, entry_2]):\n if isinstance(entry, Molecule):\n for atom in entry.atoms:\n symbol = atom.element.symbol\n element_dict[symbol] = element_dict.get(symbol, 0) + 1\n else:\n xyz = check_xyz_dict(entry)\n for symbol in xyz['symbols']:\n element_dict[symbol] = element_dict.get(symbol, 0) + 1\n\n # Compare elements.\n if len(list(element_dict_1.keys())) != len(list(element_dict_1.keys())):\n result = False\n if result:\n for symbol in element_dict_1.keys():\n if symbol not in list(element_dict_2.keys()):\n result = False\n break\n num_1, num_2 = element_dict_1[symbol], element_dict_2[symbol]\n if num_1 != num_2:\n result = False\n break\n\n if not result:\n if verbose:\n logger.error(f'\\nEntries have different types or numbers of elements, got:\\n'\n f'{element_dict_1}\\nand\\n{element_dict_2}\\n')\n return False\n\n return result","title":""},{"docid":"4d36ca41b7dd9ff6599b2ce2e22d4054","score":"0.5954933","text":"def check_two_types_same(arg1_name, arg1_type, arg2_name, arg2_type):\n if arg1_type != arg2_type:\n raise TypeError(f'The type of `{arg1_name}` and `{arg2_name}` should be same.')","title":""},{"docid":"45aeb357e0f7082207e1f1ae935455de","score":"0.5926925","text":"def _match_impropers(atom_types, generator):\n wildcard = generator.ff._atomClasses[\"\"]\n (\n atom_1_type,\n atom_2_type,\n atom_3_type,\n atom_4_type,\n ) = Forcefield.substitute_wildcards(atom_types, wildcard)\n match = None\n for improper in generator.improper:\n types1 = improper.types1\n types2 = improper.types2\n types3 = improper.types3\n types4 = improper.types4\n has_wildcard = wildcard in (types1, types2, types3, types4)\n if match is not None and has_wildcard:\n # Prefer specific definitions over ones with wildcards\n continue\n if atom_1_type in types1:\n for t2, t3, t4 in itertools.permutations(\n ((atom_2_type, 1), (atom_3_type, 2), (atom_4_type, 3))\n ):\n if t2[0] in types2 and t3[0] in types3 and t4[0] in types4:\n match = improper\n break\n return match","title":""},{"docid":"a3c05303222fa3a611892fe5ad8911e0","score":"0.5916589","text":"def _is_equal_same_type(self, other):\n return True","title":""},{"docid":"a3c05303222fa3a611892fe5ad8911e0","score":"0.5916589","text":"def _is_equal_same_type(self, other):\n return True","title":""},{"docid":"fb724a49d3cfab587576c52561d12cc7","score":"0.57364625","text":"def same_types(self, index1, index2):\n try:\n same = self.table[index1].type == self.table[index2].type != SharedData.TYPES.NO_TYPE\n except Exception:\n self.error()\n return same","title":""},{"docid":"62739e3b3877792d55060e659080dffd","score":"0.5639938","text":"def test_consistency():\n ff = vermouth.forcefield.ForceField(name='test_ff')\n itp_director = vermouth.gmx.itp_read.ITPDirector(ff)\n for inter_type in itp_director.atom_idxs:\n assert tuple(['moleculetype', inter_type]) in itp_director.METH_DICT","title":""},{"docid":"f5b3cd71d1346c4c25544c430cce3776","score":"0.56363684","text":"def types_match_reference(self, current_types=None):\n if current_types is None:\n current_types = self.current_smirks\n\n if len(current_types) != len(self.ref_labels):\n return set(), False\n\n current_assignments = get_typed_molecules(current_types, self.molecules)\n\n type_matches, checks_pass = match_reference(current_assignments, self.cluster_dict)\n return type_matches, checks_pass","title":""},{"docid":"66846e392ac7454a1e83bc8a8816d484","score":"0.5611787","text":"def verify(self):\r\n left_atoms = self.left.get_atoms()\r\n right_atoms = self.right.get_atoms()\r\n for i, j in left_atoms.items():\r\n if i in right_atoms:\r\n if j != right_atoms[i]:\r\n return False\r\n else:\r\n return False\r\n return True","title":""},{"docid":"8907e0aac79b96b3735f6ece09ed4f90","score":"0.56113464","text":"def symmetry_and_matching(molecule1, molecule2, logger, settings):\r\n\r\n def set_n_dev(molecule2, logger, settings):\r\n \"\"\" Gets a number of deviations to be shown from user \"\"\"\r\n\r\n n_dev = int(eval(input(\"\\nInput the number of deviations to be highlighted: \")))\r\n\r\n if n_dev < 1 or n_dev > molecule2.n_atoms: # do nothing\r\n\r\n logger.pt_wrong_n_dev()\r\n\r\n else:\r\n\r\n settings.n_dev = n_dev # Update n_dev\r\n\r\n def get_rot_n(logger):\r\n \"\"\" Gets a proper rotation from user \"\"\"\r\n\r\n n_rot = int(eval(input(\"\\nInput n for n-fold clockwise rotation: \")))\r\n\r\n if n_rot == 0: # Set n_rot to 1 if it is 0 by accident\r\n\r\n n_rot = 1\r\n logger.pt_wrong_n()\r\n\r\n # Return value(s)\r\n return n_rot\r\n\r\n choices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 20, 21, 24, -1, -2, -3, -4, -5, -6] # List of accepted choices\r\n\r\n # Dictionary of symmetry number and transformation correlations \r\n sym_number = {1: 'inv', 2: 'xy', 3: 'xz', 4: 'yz', 5: 'x', 6: 'y', 7: 'z'}\r\n sym_transf = {'xy': ac.z_axis, 'xz': ac.y_axis, 'yz': ac.x_axis, 'x': ac.x_axis, 'y': ac.y_axis, 'z': ac.z_axis}\r\n\r\n question = \"\\n>> Enter your choice: \"\r\n\r\n # Move molecules in standard orientation and print into to user\r\n molecule1.standard_orientation(logger, calc_for='molecule')\r\n molecule2.standard_orientation(logger, calc_for='molecule')\r\n logger.pt_standard_orientation()\r\n\r\n # Save the current status\r\n molecule1.make_save_point(reset_type='save')\r\n molecule2.make_save_point(reset_type='save')\r\n logger.pt_saved_status()\r\n\r\n while True:\r\n\r\n logger.pt_match_menu(settings)\r\n \r\n if molecule1.show_mol:\r\n\r\n viewmol_vtk = ap.Molecular_Viewer_vtk(settings)\r\n viewmol_vtk.make_initial_plot(molecule1, molecule2, settings)\r\n logger.pt_plotting() # Inform user of the new plot\r\n logger.pt_plotting_screenshot()\r\n viewmol_vtk.show(molecule1, molecule2, settings)\r\n molecule1.show_mol = False\r\n \r\n symmetry_operation = logger.get_menu_choice(choices, question, return_type='int')\r\n\r\n if symmetry_operation == -6: # Set number of deviations and update information\r\n\r\n set_n_dev(molecule2, logger, settings)\r\n\r\n if logger.is_matched:\r\n\r\n ac.highlight_dev(molecule1, molecule2, logger, settings)\r\n\r\n elif symmetry_operation == -5: # Reset the changes made by the matching algorithm\r\n\r\n logger.pt_match_reset(reset_type='save')\r\n molecule1.reset_molecule(settings, reset_type='save')\r\n molecule2.reset_molecule(settings, reset_type='save')\r\n\r\n molecule1.disord_pos, molecule2.disord_pos = None, None\r\n molecule1.disord_rmsd, molecule2.disord_rmsd = None, None\r\n\r\n # Update properties and bonds\r\n molecule1.update_properties(settings, get_bonds=True)\r\n molecule2.update_properties(settings, get_bonds=True)\r\n\r\n elif symmetry_operation == -4: # Change VTK plot settings\r\n\r\n settings.change_vtk_plot_settings(molecule1, molecule2, logger)\r\n\r\n elif symmetry_operation == -3: # Swap atoms in molecule 1\r\n\r\n molecule1.swap_atoms(logger)\r\n\r\n # Update properties and bonds\r\n molecule1.update_properties(settings, get_bonds=True)\r\n molecule2.update_properties(settings, get_bonds=True)\r\n\r\n if logger.is_matched:\r\n\r\n ac.highlight_dev(molecule1, molecule2, logger, settings)\r\n\r\n elif symmetry_operation == -2: # Change matching defaults\r\n\r\n molecule2.change_algorithms(logger)\r\n\r\n elif symmetry_operation == -1: # Match molecules\r\n\r\n ac.match_molecules(molecule1, molecule2, logger)\r\n\r\n # Update properties and bonds\r\n molecule1.update_properties(settings, get_bonds=True)\r\n molecule2.update_properties(settings, get_bonds=True)\r\n\r\n # Highlight deviations\r\n ac.highlight_dev(molecule1, molecule2, logger, settings)\r\n logger.pt_highest_deviations(molecule1, molecule2, settings)\r\n\r\n elif symmetry_operation == 0:\r\n\r\n if logger.is_matched:\r\n\r\n molecule1.calc_total_symOP() # Calculate total symmetry operation\r\n logger.pt_exit_sym_menu()\r\n break\r\n\r\n else:\r\n\r\n logger.pt_no_match()\r\n exit_yn = logger.get_menu_choice(['y', 'Y', 'n', 'N'], question, return_type='str')\r\n\r\n if exit_yn == 'y' or exit_yn == 'Y':\r\n\r\n molecule1.calc_total_symOP() # Calculate total symmetry operation\r\n break\r\n\r\n elif symmetry_operation == 1:\r\n\r\n logger.pt_sym_inversion()\r\n molecule1.inversion() # inversion\r\n\r\n elif symmetry_operation in [2, 3, 4]: # Reflections\r\n\r\n logger.pt_sym_reflection(sym_number[symmetry_operation])\r\n molecule1.sigma_refl(sym_transf[sym_number[symmetry_operation]])\r\n\r\n elif symmetry_operation in [5, 6, 7]: # Rotations\r\n\r\n logger.pt_rot_info()\r\n n_rot = get_rot_n(logger)\r\n logger.pt_sym_rotation(n_rot, sym_number[symmetry_operation])\r\n molecule1.cn_rotation(sym_transf[sym_number[symmetry_operation]], n_rot)\r\n\r\n elif symmetry_operation == 8:\r\n\r\n molecule1.show_mol = True\r\n\r\n elif symmetry_operation == 10: # Save current changes\r\n\r\n molecule1.make_save_point(reset_type='save'), molecule2.make_save_point(reset_type='save')\r\n logger.pt_saved_status()\r\n\r\n elif symmetry_operation == 20: # Export structures\r\n\r\n export_structure(molecule1, molecule2, logger, settings)\r\n\r\n else:\r\n\r\n logger.pt_invalid_input()","title":""},{"docid":"58de7898ef3d975083590d5bc742f7fd","score":"0.5559224","text":"def compare(self, x):\r\n elements1 = [i for i, j in self.get_atoms().items()]\r\n elements2 = [i for i, j in x.get_atoms().items()]\r\n for i in elements1:\r\n if i not in elements2:\r\n return False\r\n return True","title":""},{"docid":"ecba22fac5d9ffca7404902dbde3e413","score":"0.5551404","text":"def compatible_content_types(type1: str, type2: str) -> bool:\n type1, subtype1 = JSONSchema.split_content_type(type1)\n type2, subtype2 = JSONSchema.split_content_type(type2)\n return type1 == type2 and (\n subtype1 == '*' or subtype2 == '*' or subtype1 == subtype2\n )","title":""},{"docid":"3476e1b39dc8ec61d76ab76ef5c2a582","score":"0.5520482","text":"def has_identical_inputs_and_outputs_as(self, other: \"Activity\"):\n return sorted(u.entity.path for u in self.usages) == sorted(u.entity.path for u in other.usages) and sorted(\n g.entity.path for g in self.generations\n ) == sorted(g.entity.path for g in other.generations)","title":""},{"docid":"bba1c66b21a8bb87a655d2ef7c57a670","score":"0.5498743","text":"def isidenticalto(self,another):\n if ((self.type == another.type) and (self.label == another.label)):\n return 1\n else:\n return 0","title":""},{"docid":"b925839488146c4d898794de68478461","score":"0.5496395","text":"def _typecheck_input(x1_type, x2_type, prim_name=None):\n msg_prefix = f\"For '{prim_name}', the\" if prim_name else \"The\"\n const_utils.check_type_valid(x1_type, [mstype.float32, mstype.float16], 'x1')\n const_utils.check_type_valid(x2_type, [mstype.float32, mstype.float16], 'x2')\n if x1_type != x2_type:\n raise TypeError(f\"{msg_prefix} inputs must be the same type, but got x1_type: {x1_type} \"\n f\"and x2_type: {x2_type}.\")","title":""},{"docid":"5a0405199f0363dfd86b498774d5a431","score":"0.546156","text":"def _check_for_identicals(molecule, logger, settings, logg_for='molecule1'):\r\n\r\n pos_mol = check_for_identical_pos(molecule.cor, settings.delta)\r\n exclude_identicals(molecule, logger, pos_mol, logg_for)","title":""},{"docid":"07ed215cc5aed5c6a4579daac7ea87e6","score":"0.545922","text":"def is_two_objects_has_same_type(first: Any, second: Any) -> bool:\n return type(first) == type(second)","title":""},{"docid":"e5e5ff19f0085e831ca0708b9f0d7c3b","score":"0.5450432","text":"def best_match_reference_types(self, atomtypes, molecules):\n if self.reference_typed_molecules is None:\n if self.verbose: print('No reference molecules specified, so skipping likelihood calculation.')\n return None\n\n # Create bipartite graph (U,V,E) matching current atom types U with reference atom types V via edges E with weights equal to number of atoms typed in common.\n if self.verbose: print('Creating graph matching current atom types with reference atom types...')\n initial_time = time.time()\n import networkx as nx\n graph = nx.Graph()\n # Get current atomtypes and reference atom types\n current_atomtypes = [ typename for (smarts, typename) in atomtypes ]\n reference_atomtypes = [ typename for typename in self.reference_atomtypes ]\n # check that current atom types are not in reference atom types\n if set(current_atomtypes) & set(reference_atomtypes):\n raise Exception(\"Current and reference atom types must be unique\")\n # Add current atom types\n for atomtype in current_atomtypes:\n graph.add_node(atomtype, bipartite=0)\n # Add reference atom types\n for atomtype in reference_atomtypes:\n graph.add_node(atomtype, bipartite=1)\n # Add edges.\n atoms_in_common = dict()\n for current_atomtype in current_atomtypes:\n for reference_atomtype in reference_atomtypes:\n atoms_in_common[(current_atomtype,reference_atomtype)] = 0\n for (current_typed_molecule, reference_typed_molecule) in zip(molecules, self.reference_typed_molecules):\n for (current_typed_atom, reference_typed_atom) in zip(current_typed_molecule.GetAtoms(), reference_typed_molecule.GetAtoms()):\n current_atomtype = current_typed_atom.GetStringData(self.typetag)\n reference_atomtype = reference_typed_atom.GetType()\n atoms_in_common[(current_atomtype,reference_atomtype)] += 1\n for current_atomtype in current_atomtypes:\n for reference_atomtype in reference_atomtypes:\n weight = atoms_in_common[(current_atomtype,reference_atomtype)]\n graph.add_edge(current_atomtype, reference_atomtype, weight=weight)\n elapsed_time = time.time() - initial_time\n if self.verbose: print('Graph creation took %.3f s' % elapsed_time)\n\n # Compute maximum match\n if self.verbose: print('Computing maximum weight match...')\n initial_time = time.time()\n mate = nx.algorithms.max_weight_matching(graph, maxcardinality=False)\n elapsed_time = time.time() - initial_time\n if self.verbose: print('Maximum weight match took %.3f s' % elapsed_time)\n\n # Compute match dictionary and total number of matches.\n atom_type_matches = list()\n total_atom_type_matches = 0\n for current_atomtype in current_atomtypes:\n if current_atomtype in mate:\n reference_atomtype = mate[current_atomtype]\n counts = graph[current_atomtype][reference_atomtype]['weight']\n total_atom_type_matches += counts\n atom_type_matches.append( (current_atomtype, reference_atomtype, counts) )\n else:\n atom_type_matches.append( (current_atomtype, None, None) )\n\n # Report on matches\n if self.verbose:\n print(\"PROPOSED:\")\n self.show_type_matches(atom_type_matches)\n\n return (atom_type_matches, total_atom_type_matches)","title":""},{"docid":"fa5857a237bee8d4da813f6d2a7c35ae","score":"0.54449016","text":"def make_consistent(molecule1, molecule2, logger, settings):\r\n\r\n # --------- Basic outline for consistency establishment ---------\r\n # Check for atoms on identical positions\r\n # Check if consistent\r\n # If not: Check for groups\r\n # If not: Remove hydrogen atoms on carbons\r\n # If not: Check for groups\r\n # If not: Remove group-14 H atoms\r\n # If not: Check for groups\r\n # If not: Remove all hydrogen atoms\r\n # If not: Check for groups\r\n # If not: ERROR\r\n # If consistent and hydrogens left: ask user to remove them\r\n # ---------------------------------------------------------------\r\n\r\n def _n_hydrogen_atoms(chg):\r\n \"\"\" Returns the number of hydrogen atoms in the molecule \"\"\"\r\n\r\n return len(np.where(chg == 1)[0])\r\n\r\n def _hydrogens_left(chg1, chg2):\r\n \"\"\" Returns if there are hydrogen atoms in the molecules \"\"\"\r\n\r\n return 1 in chg1 or 1 in chg2\r\n\r\n def _if_carbons(chg1, chg2):\r\n \"\"\" Returns if there are carbon atoms in the molecules \"\"\"\r\n\r\n return len(np.where(chg1 == 6)[0]) > 0 or len(np.where(chg2 == 6)[0]) > 0\r\n\r\n def _are_consistent(chg1, chg2):\r\n \"\"\" Checks if the structures are consistent based on their charge arrays \"\"\"\r\n\r\n return np.allclose(np.sort(chg1), np.sort(chg2)) if len(chg1) == len(chg2) else False\r\n\r\n def _check_for_identicals(molecule, logger, settings, logg_for='molecule1'):\r\n \"\"\" Checks for identical occupations in the molecule \"\"\"\r\n\r\n pos_mol = check_for_identical_pos(molecule.cor, settings.delta)\r\n exclude_identicals(molecule, logger, pos_mol, logg_for)\r\n\r\n def _try_groups(molecule1, molecule2):\r\n \"\"\" Checks if matching problem can be solved with groups \"\"\"\r\n\r\n molecule1.get_group_charges(), molecule2.get_group_charges() # Get group properties\r\n\r\n if _are_consistent(molecule1.chg_grp, molecule2.chg_grp):\r\n\r\n return True\r\n\r\n else:\r\n\r\n molecule1.chg_grp, molecule2.chg_grp = None, None\r\n\r\n return False\r\n\r\n def _use_groups(molecule1, molecule2, logger):\r\n \"\"\" Uses groups in the matching process \"\"\"\r\n\r\n molecule1.get_group_charges(), molecule2.get_group_charges() # Get group properties\r\n molecule1.get_group_symbols(), molecule2.get_group_symbols() # Get group properties\r\n logger.lg_group_algorithm()\r\n logger.lg_consistent() # Logg event\r\n\r\n def _can_remove_carbon_hydrogens(molecule1, molecule2, settings, full_group_14=False):\r\n \"\"\" Checks if hydrogen atoms bound to carbon can be removed within consistency restrictions \"\"\"\r\n\r\n return len(molecule1.fnd_mult_H_atoms_bte(settings, full_group_14)), len(\r\n molecule2.fnd_mult_H_atoms_bte(settings, full_group_14))\r\n\r\n def _remove_carbon_hydrogens(molecule, settings, full_group_14=False):\r\n \"\"\" Removes hydrogen atoms bound to carbon in the molecule and returns number of removed atoms \"\"\"\r\n\r\n old_n_atoms = molecule.n_atoms\r\n\r\n molecule.rem_H_atoms_btc(settings, full_group_14) # Remove hydrogen atoms\r\n\r\n return old_n_atoms - molecule.n_atoms\r\n\r\n def _remove_all_hydrogens(molecule):\r\n \"\"\" Removes all hydrogen atoms in the molecule and returns number of removed atoms \"\"\"\r\n\r\n old_n_atoms = molecule.n_atoms\r\n\r\n molecule.rem_H_atoms_all() # Remove hydrogen atoms\r\n\r\n return old_n_atoms - molecule.n_atoms\r\n\r\n # Check for multiple atoms on identical positions and remove selected atoms\r\n _check_for_identicals(molecule1, logger, settings, logg_for='molecule1')\r\n _check_for_identicals(molecule2, logger, settings, logg_for='molecule2')\r\n\r\n # Determine initial number of atoms and hydrogen atoms\r\n init_n_atoms1, init_n_atoms2 = molecule1.n_atoms, molecule2.n_atoms\r\n n_atoms1_init, n_atoms2_init = molecule1.n_atoms, molecule2.n_atoms\r\n\r\n init_n_hydro1, init_n_hydro2 = _n_hydrogen_atoms(molecule1.chg), _n_hydrogen_atoms(molecule2.chg)\r\n\r\n # Log number of atoms\r\n logger.cons_init_at_mol1, logger.cons_init_at_mol2 = n_atoms1_init, n_atoms2_init\r\n logger.cons_init_at_H_mol1, logger.cons_init_at_H_mol2 = init_n_hydro1, init_n_hydro2\r\n\r\n # Print information about number of atoms and hydrogen atoms in the molecules\r\n logger.pt_consistency_start(init_n_atoms1, init_n_atoms2)\r\n logger.pt_number_h_atoms(init_n_hydro1, init_n_hydro2)\r\n\r\n # Begin with algorithm\r\n if not _are_consistent(molecule1.chg, molecule2.chg):\r\n\r\n if _try_groups(molecule1, molecule2): # CONSISTENT (GROUP)\r\n\r\n _use_groups(molecule1, molecule2, logger)\r\n\r\n else:\r\n\r\n n_atoms1_no_Hc = _remove_carbon_hydrogens(molecule1, settings, full_group_14=False)\r\n n_atoms2_no_Hc = _remove_carbon_hydrogens(molecule2, settings, full_group_14=False)\r\n\r\n logger.lg_rem_H_btc(n_atoms1_no_Hc, n_atoms2_no_Hc) # Log event + print number of removed H atoms\r\n\r\n hydrogens_left = _hydrogens_left(molecule1.chg, molecule2.chg) # Check for hydrogen atoms\r\n\r\n if not _are_consistent(molecule1.chg, molecule2.chg):\r\n\r\n if _try_groups(molecule1, molecule2): # CONSISTENT (GROUP)\r\n\r\n _use_groups(molecule1, molecule2, logger)\r\n\r\n elif hydrogens_left:\r\n\r\n n_atoms1_no_Hg14 = _remove_carbon_hydrogens(molecule1, settings, full_group_14=True)\r\n n_atoms2_no_Hg14 = _remove_carbon_hydrogens(molecule2, settings, full_group_14=True)\r\n\r\n logger.lg_rem_H_btg14(n_atoms1_no_Hg14, n_atoms2_no_Hg14) # Log event + print removed H atoms\r\n\r\n hydrogens_left = _hydrogens_left(molecule1.chg, molecule2.chg) # Check for hydrogen atoms\r\n\r\n if not _are_consistent(molecule1.chg, molecule2.chg):\r\n\r\n if _try_groups(molecule1, molecule2): # CONSISTENT (GROUP)\r\n\r\n _use_groups(molecule1, molecule2, logger)\r\n\r\n elif hydrogens_left:\r\n\r\n n_atoms1_no_H = _remove_all_hydrogens(molecule1)\r\n n_atoms2_no_H = _remove_all_hydrogens(molecule2)\r\n\r\n logger.lg_rem_H_all(n_atoms1_no_H, n_atoms2_no_H) # Log event + print removed H atoms\r\n\r\n if not _are_consistent(molecule1.chg, molecule2.chg):\r\n\r\n if _try_groups(molecule1, molecule2): # CONSISTENT (GROUP)\r\n\r\n _use_groups(molecule1, molecule2, logger)\r\n\r\n else:\r\n\r\n logger.pt_consistency_error() # Unsolvable problem\r\n\r\n else: # CONSISTENT (REGULAR)\r\n\r\n logger.lg_consistent() # Log event\r\n\r\n else:\r\n\r\n logger.pt_consistency_error() # Unsolvable problem\r\n\r\n else: # CONSISTENT (REGULAR)\r\n\r\n logger.lg_consistent() # Log event\r\n\r\n else:\r\n\r\n logger.pt_consistency_error() # Unsolvable problem\r\n\r\n else: # CONSISTENT (REGULAR)\r\n\r\n logger.lg_consistent() # Log event\r\n\r\n else: # CONSISTENT (REGULAR)\r\n\r\n logger.lg_consistent() # Log event\r\n\r\n # At this point we have established consistency between the molecules\r\n if logger.consist:\r\n\r\n init_n_atoms1, init_n_atoms2 = molecule1.n_atoms, molecule2.n_atoms\r\n init_n_hydro1, init_n_hydro2 = _n_hydrogen_atoms(molecule1.chg), _n_hydrogen_atoms(molecule2.chg)\r\n n_hydro1_c, n_hydro2_c = _can_remove_carbon_hydrogens(molecule1, molecule2, settings, full_group_14=False)\r\n n_hydro1_full, n_hydro2_full = _can_remove_carbon_hydrogens(molecule1, molecule2, settings, full_group_14=True)\r\n hydrogens_left = _hydrogens_left(molecule1.chg, molecule2.chg)\r\n\r\n logger.can_rem_H_btc = n_hydro1_c == n_hydro2_c\r\n logger.can_rem_H_btg14 = n_hydro1_full == n_hydro2_full and n_hydro1_full != n_hydro1_c\r\n\r\n # Log info after molecules are consistent\r\n logger.cons_at_mol1, logger.cons_at_mol2 = init_n_atoms1, init_n_atoms2\r\n logger.cons_at_H_mol1, logger.cons_at_H_mol2 = init_n_hydro1, init_n_hydro2\r\n\r\n if not hydrogens_left: # No hydrogen atoms left ... nothing can be done\r\n\r\n pass\r\n\r\n # Exclude all hydrogen atoms if desired\r\n elif not logger.rem_H_all and _n_hydrogen_atoms(molecule1.chg) == _n_hydrogen_atoms(molecule2.chg):\r\n\r\n choices = logger.pt_possibilities(init_n_hydro1, n_hydro2_c, n_hydro2_full)\r\n\r\n while True:\r\n\r\n operation = logger.get_menu_choice(choices, question=\"\\n>> Enter your choice: \", return_type='int')\r\n\r\n if operation == 0: # Remove all hydrogen atoms\r\n\r\n n_atoms1_no_H = _remove_all_hydrogens(molecule1)\r\n n_atoms2_no_H = _remove_all_hydrogens(molecule2)\r\n logger.lg_rem_H_all(n_atoms1_no_H, n_atoms2_no_H) # Log event + print number of removed H atoms\r\n logger.user_choice_rem_all_H = True\r\n\r\n if logger.use_groups: # Get group properties\r\n\r\n molecule1.get_group_charges(), molecule2.get_group_charges()\r\n molecule1.get_group_symbols(), molecule2.get_group_symbols()\r\n\r\n break\r\n\r\n elif operation == 1: # Remove hydrogens bound to carbon\r\n\r\n n_atoms1_no_Hc = _remove_carbon_hydrogens(molecule1, settings, full_group_14=False)\r\n n_atoms2_no_Hc = _remove_carbon_hydrogens(molecule2, settings, full_group_14=False)\r\n logger.lg_rem_H_btc(n_atoms1_no_Hc, n_atoms2_no_Hc) # Log event + print number of removed H atoms\r\n logger.user_choice_rem_btc_H = True\r\n\r\n if logger.use_groups: # Get group properties\r\n\r\n molecule1.get_group_charges(), molecule2.get_group_charges()\r\n molecule1.get_group_symbols(), molecule2.get_group_symbols()\r\n\r\n break\r\n\r\n elif operation == 2: # Remove hydrogens bound to group-14 elements\r\n\r\n n_atoms1_no_Hg14 = _remove_carbon_hydrogens(molecule1, settings, full_group_14=True)\r\n n_atoms2_no_Hg14 = _remove_carbon_hydrogens(molecule2, settings, full_group_14=True)\r\n logger.lg_rem_H_btg14(n_atoms1_no_Hg14, n_atoms2_no_Hg14) # Log event + print # removed H atoms\r\n logger.user_choice_rem_btg14_H = True\r\n\r\n if logger.use_groups: # Get group properties\r\n\r\n molecule1.get_group_charges(), molecule2.get_group_charges()\r\n molecule1.get_group_symbols(), molecule2.get_group_symbols()\r\n\r\n break\r\n\r\n elif operation == 3: # Do nothing\r\n\r\n break\r\n\r\n else:\r\n\r\n logger.pt_invalid_input()","title":""},{"docid":"5b37417f17431acc3a72941930d19f1f","score":"0.5442995","text":"def checkAtomToResName(self):\n \n for i in range(0,len(self.atomToResNameList)-1):\n \n firstSet = self.atomToResNameList[i]\n \n if not firstSet or isinstance(firstSet,ResidueAtomToResName):\n continue\n\n firstSetNmrAtoms = firstSet.nmrAtoms\n firstSetNumNmrAtoms = len(firstSetNmrAtoms)\n \n for j in range(i+1,len(self.atomToResNameList)):\n \n secondSet = self.atomToResNameList[j]\n \n if not secondSet or isinstance(secondSet,ResidueAtomToResName):\n continue\n \n secondSetNmrAtoms = secondSet.nmrAtoms\n \n #\n # Don't bother if same length: has to be same AtomToResName instance if same\n #\n \n comparisonStatus = -1\n\n if len(secondSetNmrAtoms) > firstSetNumNmrAtoms:\n \n #\n # First set subset of second?\n #\n \n subSet = firstSet\n fullSet = secondSet\n \n comparisonStatus = self.compareNmrAtoms(firstSetNmrAtoms,secondSetNmrAtoms)\n \n elif len(secondSetNmrAtoms) < firstSetNumNmrAtoms:\n\n #\n # Second set subset of first?\n #\n \n subSet = secondSet\n fullSet = firstSet\n \n comparisonStatus = self.compareNmrAtoms(secondSetNmrAtoms,firstSetNmrAtoms)\n \n \n if comparisonStatus == 0:\n \n if self.verbose:\n print \" Error: Partial subset detected. Is illegal!\"\n \n elif comparisonStatus == 1:\n \n if self.verbose:\n print \" Warning: Full subset detected for %s. Added to full set %s.\" % (str(subSet.resNameList),\n str(fullSet.resNameList))\n\n #\n # This situation is principle possible for prochirals (some stereo assigned, some not)\n # and for chemAtomSets with isEquivalent set to None (e.g. HE1 reported for Phe,\n # but also HE*).\n #\n # Best way seems to just add the subset to the full one: safest handling. \n #\n \n for resName in subSet.resNameList:\n \n #\n # Have to set all chemAtomSets for this one (if stereo becomes non\n # stereo, if single becomes multi!)\n #\n \n chemAtomSets = [subSet.chemAtomDict[subSet.nmrAtoms[0]].chemAtomSet]\n \n if chemAtomSets[0].isProchiral:\n if chemAtomSets[0].chemAtomSet:\n chemAtomSetList = list(chemAtomSets[0].chemAtomSet.chemAtomSets)\n otherChemAtomSet = chemAtomSetList[not chemAtomSetList.index(chemAtomSets[0])]\n \n chemAtomSets = list(chemAtomSets[0].chemAtomSet.chemAtomSets)\n \n else:\n chemAtomSets = []\n \n #\n # Keep track of unknown equivalents\n #\n \n keywds = {}\n \n if subSet.singleAtoms.has_key(resName):\n keywds['singleNmrAtom'] = subSet.singleAtoms[resName]\n chemAtomSets = []\n elif len(subSet.nmrAtoms) == 1:\n keywds['singleNmrAtom'] = subSet.nmrAtoms[0]\n chemAtomSets = []\n\n fullSet.addResName(resName,chemAtomSets,subSet.nmrAtoms,**keywds)\n\n self.atomToResNameList[self.atomToResNameList.index(subSet)] = None\n \n if subSet == firstSet:\n \n #\n # Have to end this loop and continue with the main one\n #\n \n break\n \n #\n # Clean up\n #\n \n while(self.atomToResNameList.count(None) > 0):\n \n self.atomToResNameList.pop(self.atomToResNameList.index(None))\n\n #\n # If there are multiple resNames for the atomToResName, then\n # check whether some are sysName subsets of others... merge\n # if that is the case.\n #\n\n for atomToResName in self.atomToResNameList:\n \n namingSystem = atomToResName.nmrAtoms[0].chemAtom.chemComp.findFirstNamingSystem(name = self.namingSystemName)\n \n if len(atomToResName.resNameList) > 1:\n \n if atomToResName.hasEqChemAtomSets:\n \n for nmrAtoms in atomToResName.nmrAtomGroups:\n \n chemAtomSet = atomToResName.chemAtomDict[nmrAtoms[0]].chemAtomSet \n chemAtomSetSysName = findChemAtomOrSetSysName(namingSystem,chemAtomSet)\n \n if not chemAtomSetSysName:\n chemAtomSetSysName = chemAtomSet.name\n else:\n chemAtomSetSysName = chemAtomSetSysName.sysName\n \n atomSetResName = \"\"\n \n for resName in atomToResName.hasEqChemAtomSets:\n (chainCode,seqCode,spinSystemId,seqInsertCode,atomSetName) = getNameInfo(resName)\n\n if chemAtomSetSysName == atomSetName:\n \n atomSetResName = resName\n break\n \n if atomSetResName:\n \n for nmrAtom in nmrAtoms:\n \n chemAtom = atomToResName.chemAtomDict[nmrAtom]\n chemAtomSysName = findChemAtomOrSetSysName(namingSystem,chemAtom)\n \n if not chemAtomSysName:\n chemAtomSysName = chemAtom.name\n else:\n chemAtomSysName = chemAtomSysName.sysName\n \n for resName in atomToResName.hasEqChemAtomSets:\n\n (chainCode,seqCode,spinSystemId,seqInsertCode,atomName) = getNameInfo(resName)\n\n if chemAtomSysName == atomName:\n \n if self.verbose:\n print \" Found equivalent atom group name match for %s - collapsing info on %s\" % (resName,atomSetResName)\n \n #\n # Copy associated info\n #\n \n resonance = self.joinResonanceFromResNames([resName],atomToResName.isotopeCode)\n\n for refResonance in self.resUnassignedNames[atomSetResName]:\n copyResonanceInfo(resonance,refResonance,nmrShiftSelect = self.nmrShiftSelect, toResName = atomSetResName,verbose = self.verboseCopy)\n \n #\n # Delete resName and resonance\n #\n\n self.deleteResonance(resonance,[resName])\n\n index = atomToResName.hasEqChemAtomSets.index(resName)\n atomToResName.hasEqChemAtomSets.pop(index)\n\n index = atomToResName.resNameList.index(resName)\n atomToResName.resNameList.pop(index)\n \n \"\"\"\n set = 0\n for atomToResName in self.atomToResNameList:\n \n print set,len(atomToResName.nmrAtoms)\n \n for resName in atomToResName.resNameList:\n \n print \" \",resName,\n \n chemAtomSets = atomToResName.chemAtomSets[resName]\n \n if chemAtomSets:\n for chemAtomSet in chemAtomSets:\n \n print chemAtomSet.name,\n \n print\n \n \n set += 1\n \"\"\"","title":""},{"docid":"9992a928431c52d5923d90e754d71049","score":"0.5408257","text":"def same_modalities(m1, m2):\n if m1 == 'epistemic by default' or m2 == 'epistemic by default':\n return True\n return m1 == m2","title":""},{"docid":"8bfff81edb50da27468dfddd7bd6ef4b","score":"0.5383365","text":"def _check_atom_mask_is_ideal(self):\n # atom_mask = prot.atom_mask\n ideal_atom_mask = self.ideal_atom_mask()\n # TODO\n # utils.assert_equal_nonterminal_atom_types(atom_mask, ideal_atom_mask)","title":""},{"docid":"b042af6ca594eb880c2d3f5207344515","score":"0.5365642","text":"def is_same_unit(unit1, unit2):\n all_types = FindUnitTypes()\n try:\n type1 = all_types[unit1]\n type2 = all_types[unit2]\n except KeyError:\n return False\n if type1 != type2:\n return False\n else:\n Synonyms = Converters[Simplify(type1)].Synonyms\n return Synonyms[Simplify(unit1)] == Synonyms[Simplify(unit2)]","title":""},{"docid":"94344d37a60dca6350ab287cbb279bac","score":"0.53316873","text":"def __eq__(self, other):\n return (type(self) == type(other)\n and self.modulo == other.modulo\n and self.residue == other.residue)","title":""},{"docid":"ee1c223d92df5793af1effe0ab09358e","score":"0.53302544","text":"def _are_similar(left, right):\n return isinstance(left, type(right)) or isinstance(right, type(left))","title":""},{"docid":"929a7fecb3f942daf21e649227493af8","score":"0.5291899","text":"def check(self, *args):\n\n file_a = input_string = self.string1_entry.get('1.0', 'end-1c')\n file_b = input_string = self.string2_entry.get('1.0', 'end-1c')\n \n list_a = [line.strip() for line in file_a.splitlines()]\n list_b = [line.strip() for line in file_b.splitlines()] \n \n size_a = len(list_a)\n size_b = len(list_b)\n \n oh_oh = 'Empty:'\n problem = 0\n if size_a == 0:\n oh_oh += ' Text A'\n problem += 1\n \n if size_b == 0:\n oh_oh += ' Text B'\n problem += 1\n \n if problem == 1:\n self.insert_result(oh_oh, 'Problem', 0)\n return\n elif problem == 2: #Both empty\n self.insert_result(oh_oh, 'Identical', 1)\n return\n \n result = []\n \n identical = True \n red_map = []\n correct_counter = 0\n space = max([len(line) for line in list_a])\n common_template = '%d:\\t%' + str(space) + 's |=| %''s'\n difference_template = '%d:\\t%' + str(space) + 's |X| %''s'\n #common_template = '%s |= %d =| %s\\n'\n #difference_template = '%s |X %d X| %s\\n'\n for index in range(min(size_a, size_b)):\n line_a = list_a.pop(0)\n line_b = list_b.pop(0)\n if line_a == line_b:\n correct_counter += 1\n result.append(common_template % (index+1, line_a, line_a))\n #result += common_template % (line_a, index+1, line_a)\n red_map.append(False)\n else:\n identical = False\n new_part = difference_template % (index+1, line_a, line_b)\n result.append(new_part)\n red_map.append(True)\n #result += difference_template % (line_a, index+1, line_b)\n \n if len(list_a) > 0:\n identical = False\n for line in list_a:\n index += 1\n new_part = difference_template % (index+1, line, '')\n result.append(new_part)\n red_map.append(True)\n #result += difference_template % (line, index+1, '')\n elif len(list_b) > 0:\n identical = False\n for line in list_b:\n index += 1\n new_part = difference_template % (index+1, '', line)\n result.append(new_part)\n red_map.append(True)\n #result += difference_template % ('', index+1, line)\n \n correct_ratio = correct_counter / max(size_a, size_b)\n \n if identical:\n summary = 'Identical'\n else:\n summary = 'Different'\n \n self.insert_result(result, summary, correct_ratio, red_map)","title":""},{"docid":"8b57131a1e4e47187586e66004fa4f47","score":"0.5286781","text":"def _checkdataconsistency(self, useroutputdata, useroutputsandbox):\n useroutputdata = useroutputdata.split(\";\")\n for data in useroutputdata:\n for item in useroutputsandbox:\n if data == item:\n LOG.error(\"Output data and sandbox should not contain the same things.\")\n return S_ERROR(\"Output data and sandbox should not contain the same things.\")\n if data.find(\"*\") > -1:\n LOG.error(\"Remove wildcard characters from output data definition: must be exact files\")\n return S_ERROR(\"Wildcard character in OutputData definition\")\n return S_OK()","title":""},{"docid":"d74f2e90d4579e7f46d444bad68c9752","score":"0.52670825","text":"def check_xyz_isomorphism(self,\n mol: Optional[Molecule] = None,\n xyz: Optional[dict] = None,\n allow_nonisomorphic_2d: Optional[bool] = False,\n verbose: Optional[bool] = True,\n ) -> bool:\n mol = mol or self.mol\n xyz = xyz or self.final_xyz\n isomorphic = False\n\n if mol is not None:\n\n s_mol, b_mol = None, None\n\n # 1. Perceive\n try:\n s_mol, b_mol = molecules_from_xyz(xyz, multiplicity=self.multiplicity, charge=self.charge)\n except Exception as e:\n if verbose:\n logger.error(f'Could not perceive the Cartesian coordinates of species {self.label}. This '\n f'might result in inconsistent atom order between the Cartesian and the 2D graph '\n f'representations. Got:\\n{e}')\n\n # 2. A. Check isomorphism with bond orders using b_mol\n if b_mol is not None:\n isomorphic = check_isomorphism(mol, b_mol)\n if not isomorphic and verbose:\n logger.error(f'The Cartesian coordinates of species {self.label} are not isomorphic with the 2D '\n f'structure {mol.copy(deep=True).to_smiles()} when considering bond orders.')\n\n # 2. B. Check isomorphism without bond orders using s_mol (only for charged or high spin multiplicity)\n if not isomorphic and s_mol is not None:\n isomorphic = check_isomorphism(mol, s_mol, convert_to_single_bonds=True)\n if not isomorphic and verbose:\n logger.error(f'The Cartesian coordinates of species {self.label} with charge {self.charge} '\n f'and multiplicity {self.multiplicity} are not isomorphic with the 2D '\n f'structure {mol.copy(deep=True).to_smiles()} even without considering bond orders.')\n\n # 2. C. Check isomorphism without bond orders NOT using s_mol\n if not isomorphic:\n isomorphic = are_coords_compliant_with_graph(xyz=xyz, mol=mol)\n if not isomorphic and verbose:\n logger.error(f'The Cartesian coordinates of species {self.label} are not compliant with the 2D '\n f'structure {mol.copy(deep=True).to_smiles()} even without considering bond orders.')\n\n # 3. Report and resolve\n if not isomorphic:\n if allow_nonisomorphic_2d:\n if verbose:\n logger.warning('Allowing nonisomorphic 2D')\n isomorphic = True\n elif verbose:\n if self.conf_is_isomorphic:\n # conformer was isomorphic, we don't allow nonisomorphism, but the optimized structure isn't\n logger.warning('Not allowing nonisomorphic 2D (the conformer WAS isomorphic with the 2D graph)')\n else:\n logger.warning('Not allowing nonisomorphic 2D')\n else:\n if verbose:\n logger.error(f'Cannot check isomorphism for species {self.label} '\n f'without the 2D graph connectivity information.')\n if allow_nonisomorphic_2d:\n isomorphic = True\n if verbose:\n logger.warning('Allowing nonisomorphic 2D')\n return isomorphic","title":""},{"docid":"9c994035966fa07aeb146ae3682abd6f","score":"0.52578926","text":"def is_isomorphic(self, other, either_direction=True, check_identical=False, check_only_label=False,\n check_template_rxn_products=False, generate_initial_map=False, strict=True, save_order=False):\n if check_template_rxn_products:\n try:\n species1 = self.products if self.is_forward else self.reactants\n species2 = other.products if other.is_forward else other.reactants\n except AttributeError:\n raise TypeError('Only use check_template_rxn_products flag for TemplateReactions.')\n\n return same_species_lists(species1, species2,\n check_identical=check_identical,\n only_check_label=check_only_label,\n generate_initial_map=generate_initial_map,\n strict=strict,\n save_order=save_order)\n\n # Compare reactants to reactants\n forward_reactants_match = same_species_lists(self.reactants, other.reactants,\n check_identical=check_identical,\n only_check_label=check_only_label,\n generate_initial_map=generate_initial_map,\n strict=strict,\n save_order=save_order)\n\n # Compare products to products\n forward_products_match = same_species_lists(self.products, other.products,\n check_identical=check_identical,\n only_check_label=check_only_label,\n generate_initial_map=generate_initial_map,\n strict=strict,\n save_order=save_order)\n\n # Compare specific_collider to specific_collider\n collider_match = (self.specific_collider == other.specific_collider)\n\n # Return now, if we can\n if forward_reactants_match and forward_products_match and collider_match:\n return True\n if not either_direction:\n return False\n\n # Compare reactants to products\n reverse_reactants_match = same_species_lists(self.reactants, other.products,\n check_identical=check_identical,\n only_check_label=check_only_label,\n generate_initial_map=generate_initial_map,\n strict=strict,\n save_order=save_order)\n\n # Compare products to reactants\n reverse_products_match = same_species_lists(self.products, other.reactants,\n check_identical=check_identical,\n only_check_label=check_only_label,\n generate_initial_map=generate_initial_map,\n strict=strict,\n save_order=save_order)\n\n # should have already returned if it matches forwards, or we're not allowed to match backwards\n return reverse_reactants_match and reverse_products_match and collider_match","title":""},{"docid":"ff7a6cd3b468aa1be07dc9af5533949e","score":"0.52490276","text":"def test_equal_on_type_mismatch(self):\n a = payloads.LocateResponsePayload()\n b = \"invalid\"\n\n self.assertFalse(a == b)\n self.assertFalse(b == a)","title":""},{"docid":"b2f2c64dcce598db0cca1afa9d86dec5","score":"0.52272934","text":"def sample_atomtypes(self):\n # Copy current atomtypes for proposal.\n proposed_atomtypes = copy.deepcopy(self.atomtypes)\n proposed_molecules = copy.deepcopy(self.molecules)\n natomtypes = len(proposed_atomtypes)\n ndecorators = len(self.decorators)\n\n valid_proposal = True\n\n if random.random() < 0.5:\n # Pick an atom type to destroy.\n atomtype_index = random.randint(0, natomtypes-1)\n (atomtype, typename) = proposed_atomtypes[atomtype_index]\n if self.verbose: print(\"Attempting to destroy atom type %s : %s...\" % (atomtype, typename))\n # Delete the atomtype.\n proposed_atomtypes.remove([atomtype, typename])\n # Try to type all molecules.\n try:\n self.type_molecules(proposed_atomtypes, proposed_molecules)\n except AtomTyper.TypingException as e:\n #print e\n # Reject since typing failed.\n if self.verbose: print(\"Typing failed; rejecting.\")\n valid_proposal = False\n else:\n # Pick an atomtype to subtype.\n atomtype_index = random.randint(0, natomtypes-1)\n # Pick a decorator to add.\n decorator_index = random.randint(0, ndecorators-1)\n # Create new atomtype to insert by appending decorator with 'and' operator.\n (atomtype, atomtype_typename) = self.atomtypes[atomtype_index]\n (decorator, decorator_typename) = self.decorators[decorator_index]\n result = re.match('\\[(.+)\\]', atomtype)\n proposed_atomtype = '[' + result.groups(1)[0] + '&' + decorator + ']'\n proposed_typename = atomtype_typename + ' ' + decorator_typename\n if self.verbose: print(\"Attempting to create new subtype: '%s' (%s) + '%s' (%s) -> '%s' (%s)\" % (atomtype, atomtype_typename, decorator, decorator_typename, proposed_atomtype, proposed_typename))\n\n # Check that we haven't already determined this atom type isn't matched in the dataset.\n if proposed_atomtype in self.atomtypes_with_no_matches:\n if self.verbose: print(\"Atom type '%s' (%s) unused in dataset; rejecting.\" % (proposed_atomtype, proposed_typename))\n return False\n\n # Check if proposed atomtype is already in set.\n existing_atomtypes = set()\n for (a, b) in self.atomtypes:\n existing_atomtypes.add(a)\n if proposed_atomtype in existing_atomtypes:\n if self.verbose: print(\"Atom type already exists; rejecting to avoid duplication.\")\n valid_proposal = False\n\n # Check for valid proposal before proceeding.\n if not valid_proposal:\n return False\n\n # Insert atomtype immediately after.\n proposed_atomtypes.insert(atomtype_index+1, [proposed_atomtype, proposed_typename])\n # Try to type all molecules.\n try:\n # Type molecules.\n self.type_molecules(proposed_atomtypes, proposed_molecules)\n # Compute updated statistics.\n [proposed_atom_typecounts, proposed_molecule_typecounts] = self.compute_type_statistics(proposed_atomtypes, proposed_molecules)\n # Reject if new type is unused.\n if (proposed_atom_typecounts[proposed_typename] == 0):\n # Reject because new type is unused in dataset.\n if self.verbose: print(\"Atom type '%s' (%s) unused in dataset; rejecting.\" % (proposed_atomtype, proposed_typename))\n valid_proposal = False\n # Store this atomtype to speed up future rejections\n self.atomtypes_with_no_matches.add(proposed_atomtype)\n # Reject if parent type is now unused, UNLESS it is a base type\n if (proposed_atom_typecounts[atomtype_typename] == 0) and (atomtype not in self.basetypes_smarts):\n # Reject because new type is unused in dataset.\n if self.verbose: print(\"Parent type '%s' (%s) now unused in dataset; rejecting.\" % (atomtype, atomtype_typename))\n valid_proposal = False\n except AtomTyper.TypingException as e:\n print(\"Exception: %s\" % str(e))\n # Reject since typing failed.\n if self.verbose: print(\"Typing failed for one or more molecules using proposed atomtypes; rejecting.\")\n valid_proposal = False\n\n # Check for valid proposal\n if not valid_proposal:\n return False\n\n if self.verbose: print('Proposal is valid...')\n\n # Accept automatically if no reference molecules\n accept = False\n if self.reference_typed_molecules is None:\n accept = True\n else:\n # Compute effective temperature\n if self.temperature == 0.0:\n effective_temperature = 1\n else:\n effective_temperature = (self.total_atoms * self.temperature)\n\n # Compute likelihood for accept/reject\n (proposed_atom_type_matches, proposed_total_atom_type_matches) = self.best_match_reference_types(proposed_atomtypes, proposed_molecules)\n log_P_accept = (proposed_total_atom_type_matches - self.total_atom_type_matches) / effective_temperature\n print('Proposal score: %d >> %d : log_P_accept = %.5e' % (self.total_atom_type_matches, proposed_total_atom_type_matches, log_P_accept))\n if (log_P_accept > 0.0) or (numpy.random.uniform() < numpy.exp(log_P_accept)):\n accept = True\n\n # Accept or reject\n if accept:\n self.atomtypes = proposed_atomtypes\n self.molecules = proposed_molecules\n self.atom_type_matches = proposed_atom_type_matches\n self.total_atom_type_matches = proposed_total_atom_type_matches\n return True\n else:\n return False","title":""},{"docid":"38672d47d2a7e4cb12864a9efaf502b9","score":"0.52102697","text":"def _mdtype_eq(omdtype, odata, nmdtype, ndata):\n if ndata is None:\n return False\n if omdtype != nmdtype:\n return False\n if odata.checksum != ndata.checksum:\n return False\n # If we turn --unique-md-filenames on without chaning the data,\n # then we'll get different filenames, but the same checksum.\n # Atm. just say they are different, to make sure we delete the\n # old files.\n orname = os.path.basename(odata.location[1])\n nrname = os.path.basename(ndata.location[1])\n if orname != nrname:\n return False\n return True","title":""},{"docid":"20052922bb82d91e2b6b8be77e54ab3d","score":"0.5203175","text":"def test_equal_on_type_mismatch(self):\n a = payloads.LocateRequestPayload()\n b = \"invalid\"\n\n self.assertFalse(a == b)\n self.assertFalse(b == a)","title":""},{"docid":"1f587a8a16005457fa4de04ab4fe135a","score":"0.5202249","text":"def test_eq_different_lengths(self):\n\n for typecode in VALID_CONTENT:\n b = gapbuffer(typecode, VALID_CONTENT[typecode])\n self.assertNotEqual(VALID_CONTENT[typecode] * 2, b)\n self.assertFalse(VALID_CONTENT[typecode] * 2 == b)\n\n for typecode in VALID_CONTENT:\n b = gapbuffer(typecode, VALID_CONTENT[typecode][:2])\n self.assertNotEqual(VALID_CONTENT[typecode], b)\n self.assertFalse(VALID_CONTENT[typecode] == b)","title":""},{"docid":"8abd42224b785426c8f333ebf08061b9","score":"0.5185528","text":"def satisfies(self, other):\n for key in self.params.keys():\n if key != \"q\" and other.params.get(key, None) != self.params.get(key, None):\n return False\n\n if (\n self.sub_type != \"*\"\n and other.sub_type != \"*\"\n and other.sub_type != self.sub_type\n ):\n return False\n\n if (\n self.main_type != \"*\"\n and other.main_type != \"*\"\n and other.main_type != self.main_type\n ):\n return False\n\n return True","title":""},{"docid":"1f8814dde9ed5eb92dee5cc29422e70d","score":"0.5178265","text":"def is_isomorphic(self, other: Union['ARCSpecies', Species, Molecule]) -> Optional[bool]:\n if self.mol is None:\n return None\n if isinstance(other, ARCSpecies):\n if other.mol is None:\n return None\n other = other.mol\n if isinstance(other, Molecule):\n if self.mol_list is not None and len(self.mol_list):\n for mol_ in [self.mol] + self.mol_list:\n if mol_.copy(deep=True).is_isomorphic(other.copy(deep=True)):\n return True\n return False\n else:\n return self.mol.copy(deep=True).is_isomorphic(other.copy(deep=True))\n if isinstance(other, Species):\n for other_mol in other.molecule:\n if self.mol_list is not None and len(self.mol_list):\n for mol_ in [self.mol] + self.mol_list:\n if mol_.copy(deep=True).is_isomorphic(other_mol.copy(deep=True)):\n return True\n else:\n return self.mol.copy(deep=True).is_isomorphic(other_mol.copy(deep=True))\n return False\n raise SpeciesError(f'Can only compare isomorphism to other ARCSpecies, RMG Species, or RMG Molecule '\n f'object instances, got {other} which is of type {type(other)}.')","title":""},{"docid":"96ccf04be8bf3fdccb9b1d57ef109cb9","score":"0.51713175","text":"def __eq__(self, other):\n return self.type == other.type and self.morphio_root_node.id == other.morphio_root_node.id","title":""},{"docid":"0ecd28b303c8ac3143c82295486fc6bb","score":"0.51591736","text":"def check_mol_sites(ms1, ms2, atomic=False, factor=1.0, tm=Tol_matrix(prototype=\"molecular\")):\n if atomic is False:\n es0 = ms1.get_ellipsoids()\n PBC_vectors = np.dot(create_matrix(PBC=ms1.PBC), ms1.lattice)\n PBC_ops = [SymmOp.from_rotation_and_translation(Euclidean_lattice, v) for v in PBC_vectors]\n es1 = []\n for op in PBC_ops:\n es1.append(np.dot(es0, op))\n es1 = np.squeeze(es1)\n truth_values = np.vectorize(check_intersection)(es1, ms2.get_ellipsoid())\n if np.sum(truth_values) < len(truth_values):\n return False\n else:\n return True\n\n elif atomic is True:\n #Get coordinates for both mol_sites\n c1, s1 = ms1.get_coords_and_species()\n c2, s2 = ms2.get_coords_and_species()\n\n #Calculate which distance matrix is smaller/faster\n m_length1 = len(ms1.mol.species)\n m_length2 = len(ms2.mol.species)\n wp_length1 = len(c1)\n wp_length2 = len(c2)\n size1 = m_length1 * wp_length2\n size2 = m_length2 * wp_length1\n\n #Case 1\n if size1 <= size2:\n coords_mol = c1[:m_length1]\n #Calculate tol matrix for species pairs\n tols = np.zeros((m_length1,m_length2))\n for i1, specie1 in enumerate(s1[:m_length1]):\n for i2, specie2 in enumerate(s2[:m_length2]):\n tols[i1][i2] = tm.get_tol(specie1, specie2)\n tols = np.repeat(tols, ms2.multiplicity, axis=1)\n d = distance_matrix(coords_mol, c2, ms1.lattice, PBC=ms1.PBC)\n\n #Case 2\n elif size1 > size2:\n coords_mol = c2[:m_length2]\n #Calculate tol matrix for species pairs\n tols = np.zeros((m_length2,m_length1))\n for i1, specie1 in enumerate(s2[:m_length2]):\n for i2, specie2 in enumerate(s1[:m_length1]):\n tols[i1][i2] = tm.get_tol(specie1, specie2)\n tols = np.repeat(tols, ms1.multiplicity, axis=1)\n d = distance_matrix(coords_mol, c1, ms1.lattice, PBC=ms1.PBC)\n\n #Check if distances are smaller than tolerances\n if (d<tols).any():\n return False\n return True","title":""},{"docid":"1249f53e4660e993a1ad2cacda836ee2","score":"0.5157512","text":"def show_type_matches(self, atom_type_matches):\n print('Atom type matches:')\n total_atom_type_matches = 0\n for (current_atomtype, reference_atomtype, counts) in atom_type_matches:\n if reference_atomtype is not None:\n print('%-64s matches %8s : %8d atoms matched' % (current_atomtype, reference_atomtype, counts))\n total_atom_type_matches += counts\n else:\n print('%-64s no match' % (current_atomtype))\n\n fraction_matched_atoms = float(total_atom_type_matches) / float(self.total_atoms)\n print('%d / %d total atoms match (%.3f %%)' % (total_atom_type_matches, self.total_atoms, fraction_matched_atoms * 100))","title":""},{"docid":"644fd40551bc86d01fafa2760ab639f1","score":"0.5142524","text":"def testToFailCombineTwoRingsIntoSubMolecule(self):\n\n smiles1 = 'C1CCCCC1'\n smiles2 = 'C1CCCCC1'\n mol1 = Molecule().fromSMILES(smiles1)\n mol2 = Molecule().fromSMILES(smiles2)\n\n ring1 = [atom for atom in mol1.atoms if atom.isNonHydrogen()]\n ring2 = [atom for atom in mol2.atoms if atom.isNonHydrogen()]\n with self.assertRaises(AssertionError):\n combined = combineTwoRingsIntoSubMolecule(ring1, ring2)","title":""},{"docid":"9f1c51cc1863efd95827d47ce8ea82b5","score":"0.51425236","text":"def test_equal_on_type_mismatch(self):\n a = sign.SignResponsePayload()\n b = 'invalid'\n\n self.assertFalse(a == b)\n self.assertFalse(b == a)","title":""},{"docid":"1bdc30f2b1f3fadb31a08d2ffb67f426","score":"0.51297927","text":"def compare_mutated_fasta_files(original_file_path, mutated_file_path):\n with open(original_file_path) as handle1:\n with open(mutated_file_path) as handle2:\n iter1 = SeqIO.parse(handle1, 'fasta')\n iter2 = SeqIO.parse(handle2, 'fasta')\n fillvalue = object()\n paired_records = zip_longest(iter1, iter2, fillvalue=fillvalue)\n for r1, r2 in paired_records:\n if type(r1) != type(r2):\n return False\n if r1.id != r2.id:\n return False\n if r1.name != r2.name:\n return False\n if r1.seq != r2.seq:\n return False\n if not r2.description.startswith(r1.description):\n return False\n return True","title":""},{"docid":"4f67185f43408d137f2f520267d08817","score":"0.5126452","text":"def _checkSigEquivalence(sig1, sig2):\n global __errorfexp\n global __errormatch\n if not (sig1 and sig2): # ommit empty signatures\n return False\n\n # if options.str:\n return sig1 == sig2","title":""},{"docid":"109d4291e59f62b084241e0617405e3f","score":"0.5118689","text":"def _assert_all_entries_match(lexicon1, lexicon2):\n eq_(set(lexicon1._entries.keys()), set(lexicon2._entries.keys()))\n for token in lexicon1._entries:\n _assert_entries_match(lexicon1, lexicon2, token)","title":""},{"docid":"ae29f98d3e5499a7d9ad9c131898b979","score":"0.50985867","text":"def is_consistent(left_ins: \"TypeIns\", right_ins: \"TypeIns\"):\n # TODO: protocol\n if left_ins == any_ins or right_ins == any_ins:\n return True\n\n if left_ins == none_ins:\n return nullable(right_ins)\n elif right_ins == none_ins:\n return nullable(left_ins)\n\n is_left_classins = isinstance(left_ins, TypeClassIns)\n is_right_classins = isinstance(right_ins, TypeClassIns)\n\n if is_left_classins and is_right_classins:\n return cls_consistent(left_ins, right_ins)\n\n # check typetype\n left_is_type = isinstance(left_ins, TypeType)\n right_is_type = isinstance(right_ins, TypeType)\n # implicitly invariant\n if left_is_type ^ right_is_type:\n return False\n if left_is_type and right_is_type:\n return left_ins.temp == right_ins.temp or left_ins.temp == any_temp\n\n # one of left_ins and right_ins is special types\n if is_left_classins:\n # left instance's type is normal class-defined type\n if right_ins.temp == union_temp:\n for union_ins in right_ins.bindlist:\n if not is_consistent(left_ins, union_ins):\n return False\n return True\n elif right_ins.temp == optional_temp:\n return False # left can't be None\n elif right_ins.temp == literal_temp:\n assert isinstance(right_ins, TypeLiteralIns)\n return is_consistent(left_ins, right_ins.get_value_type())\n else:\n if left_ins.temp == union_temp:\n for union_ins in left_ins.bindlist:\n if is_consistent(union_ins, right_ins):\n return True\n return False\n elif left_ins.temp == optional_temp:\n if is_consistent(left_ins.get_safe_bind(0), right_ins):\n return True\n return right_ins == none_ins\n elif left_ins.temp == literal_temp:\n assert isinstance(left_ins, TypeLiteralIns)\n if right_ins.temp != literal_temp:\n return False\n assert isinstance(right_ins, TypeLiteralIns)\n return right_ins.temp == literal_temp and left_ins.value == right_ins.value\n\n return left_ins.equiv(right_ins)","title":""},{"docid":"130ebd5aa733796d43ba993ff510892d","score":"0.5090661","text":"def _try_groups(molecule1, molecule2):\r\n\r\n molecule1.get_group_charges(), molecule2.get_group_charges() # Get group properties\r\n\r\n if _are_consistent(molecule1.chg_grp, molecule2.chg_grp):\r\n\r\n return True\r\n\r\n else:\r\n\r\n molecule1.chg_grp, molecule2.chg_grp = None, None\r\n\r\n return False","title":""},{"docid":"bdb884b29b4927c7a37aaf0f2ca7dd3b","score":"0.5089228","text":"def compare_types_internal(a: Any, b: Any) -> bool:\n import typing\n\n if isinstance(a, str) and isinstance(b, str):\n # Two strings (e.g. if both are stringly-typed mypy annotations).\n # Just check if they are identical.\n return a == b\n elif isinstance(a, str) and isinstance(b, type):\n # A string and a type.\n # Likely the first is a mypy stringly-typed annotation and the\n # second one a real type.\n # Check that the name of the real type is the same as the\n # stringly-typed one.\n return a == b.__name__\n elif a == dict and b == typing.Dict:\n return True\n elif is_union(a) and is_union(b):\n if len(a.__args__) == len(b.__args__):\n for ai, bi in list(zip(a.__args__, b.__args__)):\n if not compare_types(ai, bi):\n return False\n return True\n else:\n return False\n else:\n return a == b","title":""},{"docid":"4ced22308df4fd5422f4b022a804d7f2","score":"0.50857085","text":"def check_all_isomorphism(p1, p2):\n \n #use p1 as the comparison\n fixedCoordinates = set(p1)\n \n #get all coordinates\n coordinateSet = p2\n \n #get all x coordinates in a list, all y in a list, all z in a list\n coordinatesSplit = list(zip(*coordinateSet))\n \n #offset to reflect each coordinate by \n reflectPoints = [max(coor) for coor in coordinatesSplit]\n \n coorReflect = list(itertools.product('01', repeat=3))\n \n permutations = list(itertools.permutations('012'))\n \n #check each permutation of the coordinates\n for permute in permutations:\n \n #permutation indices\n i,j,k = permute[0], permute[1], permute[2]\n i = int(i)\n j = int(j)\n k = int(k)\n \n #get reflection args\n a,b,c = reflectPoints[i], reflectPoints[j], reflectPoints[k]\n \n #check each possible reflection of each coordinate\n for reflect in coorReflect:\n \n rX = int(reflect[0])\n rY = int(reflect[1])\n rZ = int(reflect[2])\n \n newCoordSet = set()\n \n for coord in coordinateSet:\n \n #permute the coordinates\n x,y,z = coord[i], coord[j], coord[k]\n \n #reflect the coordinates\n if rX:\n x = a-x\n if rY:\n y = b-y\n if rZ:\n z = c-z\n \n #add new coordinate under the map\n newCoord = (x, y, z)\n newCoordSet.add(newCoord)\n \n #check if mapping equals the comparison set\n if newCoordSet == fixedCoordinates:\n return True\n \n #at this point, none match\n return False","title":""},{"docid":"43d1355577c68dadc40afb73678cccf7","score":"0.50853086","text":"def emulate_assertListEqual(self, seq1, seq2, msg=None, seq_type=None):\n if seq_type is not None:\n seq_type_name = seq_type.__name__\n if not isinstance(seq1, seq_type):\n raise self.failureException('First sequence is not a %s: %s'\n % (seq_type_name, safe_repr(seq1)))\n if not isinstance(seq2, seq_type):\n raise self.failureException('Second sequence is not a %s: %s'\n % (seq_type_name, safe_repr(seq2)))\n else:\n seq_type_name = \"sequence\"\n\n differing = None\n try:\n len1 = len(seq1)\n except (TypeError, NotImplementedError):\n differing = 'First %s has no length. Non-sequence?' % (\n seq_type_name)\n\n if differing is None:\n try:\n len2 = len(seq2)\n except (TypeError, NotImplementedError):\n differing = 'Second %s has no length. Non-sequence?' % (\n seq_type_name)\n\n if differing is None:\n if seq1 == seq2:\n return\n\n seq1_repr = safe_repr(seq1)\n seq2_repr = safe_repr(seq2)\n if len(seq1_repr) > 30:\n seq1_repr = seq1_repr[:30] + '...'\n if len(seq2_repr) > 30:\n seq2_repr = seq2_repr[:30] + '...'\n elements = (seq_type_name.capitalize(), seq1_repr, seq2_repr)\n differing = '%ss differ: %s != %s\\n' % elements\n\n for i in range(min(len1, len2)):\n try:\n item1 = seq1[i]\n except (TypeError, IndexError, NotImplementedError):\n differing += ('\\nUnable to index element %d of first %s\\n' %\n (i, seq_type_name))\n break\n\n try:\n item2 = seq2[i]\n except (TypeError, IndexError, NotImplementedError):\n differing += ('\\nUnable to index element %d of second %s\\n' %\n (i, seq_type_name))\n break\n\n if item1 != item2:\n differing += ('\\nFirst differing element %d:\\n%s\\n%s\\n' %\n (i, safe_repr(item1), safe_repr(item2)))\n break\n else:\n if (len1 == len2 and seq_type is None and\n type(seq1) != type(seq2)):\n # The sequences are the same, but have differing types.\n return\n\n if len1 > len2:\n differing += ('\\nFirst %s contains %d additional '\n 'elements.\\n' % (seq_type_name, len1 - len2))\n try:\n differing += ('First extra element %d:\\n%s\\n' %\n (len2, safe_repr(seq1[len2])))\n except (TypeError, IndexError, NotImplementedError):\n differing += ('Unable to index element %d '\n 'of first %s\\n' % (len2, seq_type_name))\n elif len1 < len2:\n differing += ('\\nSecond %s contains %d additional '\n 'elements.\\n' % (seq_type_name, len2 - len1))\n try:\n differing += ('First extra element %d:\\n%s\\n' %\n (len1, safe_repr(seq2[len1])))\n except (TypeError, IndexError, NotImplementedError):\n differing += ('Unable to index element %d '\n 'of second %s\\n' % (len1, seq_type_name))\n standardMsg = differing\n diffMsg = '\\n' + '\\n'.join(\n difflib.ndiff(pprint.pformat(seq1).splitlines(),\n pprint.pformat(seq2).splitlines()))\n standardMsg = self._truncateMessage(standardMsg, diffMsg)\n msg = self._formatMessage(msg, standardMsg)\n self.fail(msg)","title":""},{"docid":"5232659ec89c71b3749ed94dd7411f30","score":"0.5051848","text":"def check_and_update(self, atoms, *args, **kwargs):\n if self.table.check_and_update([frozenset(c) for c in combinations(atoms, self.max_width)], *args, **kwargs) == 1:\n return True\n return False","title":""},{"docid":"efa7f39e53b38bb7659ba625aa641a70","score":"0.5042289","text":"def compareAtomInfoFiles(fileName1, fileName2, distanceThreshold):\n\n ###########################\n # Helper utility functions usable only inside this function\n def atomID(s):\n # Return the ID of the atom, which includes its chain, residue name,\n # residue number, atom name, and alternate separated by spaces\n w = s.split()\n return w[0]+\" \"+w[1]+\" \"+w[2]+\" \"+w[3]+\" \"+w[4]\n\n def position(s):\n # Return a tuple that has the location of the atom.\n w = s.split()\n return ( float(w[5]), float(w[6]), float(w[7]) )\n\n def radius(s):\n # Return the radius for the atom.\n w = s.split()\n return ( w[8] )\n\n def flag(s,f):\n # Return a flag for the atom, indexed by 0-2.\n w = s.split()\n return ( w[9+f] )\n\n def distance(a, b):\n # Return the distance between 3-space tuples a and b\n return math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 + (a[2]-b[2])**2 )\n\n ###########################\n # Initialize our return value to empty.\n ret = \"\"\n\n # Read the the data from each file.\n # Sort the lines from the file so that we'll get chain, residue, atom name sorting.\n with open(fileName1) as f:\n m1 = f.readlines()\n m1.sort(key=lambda x:atomID(x))\n\n with open(fileName2) as f:\n m2 = f.readlines()\n m2.sort(key=lambda x:atomID(x))\n\n # Compare the two and report on any differences.\n # Both lists are sorted, so we'll know which has a missing element compared\n # to the other. We walk from the beginning to the end of each list.\n m1ID = 0\n m2ID = 0\n while m1ID < len(m1) and m2ID < len(m2):\n\n # Atom names are unique within residues, so we can use that to\n # match atoms in one file from those in the other and print out\n # missing atoms and differences in positions.\n if atomID(m1[m1ID]) < atomID(m2[m2ID]):\n ret += 'Only in first: '+atomID(m1[m1ID])+'\\n'\n m1ID += 1\n continue\n\n if atomID(m1[m1ID]) > atomID(m2[m2ID]):\n ret += 'Only in second: '+atomID(m2[m2ID])+'\\n'\n m2ID += 1\n continue\n\n # Check the radius to see if they differ\n oldRadius = radius(m1[m1ID])\n newRadius = radius(m2[m2ID])\n if oldRadius != newRadius:\n ret += atomID(m1[m1ID])+' radii differ: old is {}, new is {}'.format(oldRadius,newRadius)+'\\n'\n\n # Check the flag fields to see if any of them differ\n for f in range(3):\n oldFlag = flag(m1[m1ID], f)\n newFlag = flag(m2[m2ID], f)\n if oldFlag != newFlag:\n ret += atomID(m1[m1ID])+' flags differ: old is {}, new is {}'.format(oldFlag,newFlag)+'\\n'\n\n # Check the distance between the atoms.\n dist = distance(position(m1[m1ID]),position(m2[m2ID]))\n if dist > distanceThreshold:\n ret += atomID(m1[m1ID])+' Distance between runs: {:.2f}'.format(dist)+'\\n'\n\n # Go on to next line\n m1ID += 1\n m2ID += 1\n\n # Finish up either list that is not done\n while m1ID < len(m1):\n ret += 'Only in first: '+atomID(m1[m1ID])+'\\n'\n m1ID += 1\n while m2ID < len(m2):\n ret += 'Only in second: '+atomID(m2[m2ID])+'\\n'\n m2ID += 1\n\n return ret","title":""},{"docid":"efa7f39e53b38bb7659ba625aa641a70","score":"0.5042289","text":"def compareAtomInfoFiles(fileName1, fileName2, distanceThreshold):\n\n ###########################\n # Helper utility functions usable only inside this function\n def atomID(s):\n # Return the ID of the atom, which includes its chain, residue name,\n # residue number, atom name, and alternate separated by spaces\n w = s.split()\n return w[0]+\" \"+w[1]+\" \"+w[2]+\" \"+w[3]+\" \"+w[4]\n\n def position(s):\n # Return a tuple that has the location of the atom.\n w = s.split()\n return ( float(w[5]), float(w[6]), float(w[7]) )\n\n def radius(s):\n # Return the radius for the atom.\n w = s.split()\n return ( w[8] )\n\n def flag(s,f):\n # Return a flag for the atom, indexed by 0-2.\n w = s.split()\n return ( w[9+f] )\n\n def distance(a, b):\n # Return the distance between 3-space tuples a and b\n return math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 + (a[2]-b[2])**2 )\n\n ###########################\n # Initialize our return value to empty.\n ret = \"\"\n\n # Read the the data from each file.\n # Sort the lines from the file so that we'll get chain, residue, atom name sorting.\n with open(fileName1) as f:\n m1 = f.readlines()\n m1.sort(key=lambda x:atomID(x))\n\n with open(fileName2) as f:\n m2 = f.readlines()\n m2.sort(key=lambda x:atomID(x))\n\n # Compare the two and report on any differences.\n # Both lists are sorted, so we'll know which has a missing element compared\n # to the other. We walk from the beginning to the end of each list.\n m1ID = 0\n m2ID = 0\n while m1ID < len(m1) and m2ID < len(m2):\n\n # Atom names are unique within residues, so we can use that to\n # match atoms in one file from those in the other and print out\n # missing atoms and differences in positions.\n if atomID(m1[m1ID]) < atomID(m2[m2ID]):\n ret += 'Only in first: '+atomID(m1[m1ID])+'\\n'\n m1ID += 1\n continue\n\n if atomID(m1[m1ID]) > atomID(m2[m2ID]):\n ret += 'Only in second: '+atomID(m2[m2ID])+'\\n'\n m2ID += 1\n continue\n\n # Check the radius to see if they differ\n oldRadius = radius(m1[m1ID])\n newRadius = radius(m2[m2ID])\n if oldRadius != newRadius:\n ret += atomID(m1[m1ID])+' radii differ: old is {}, new is {}'.format(oldRadius,newRadius)+'\\n'\n\n # Check the flag fields to see if any of them differ\n for f in range(3):\n oldFlag = flag(m1[m1ID], f)\n newFlag = flag(m2[m2ID], f)\n if oldFlag != newFlag:\n ret += atomID(m1[m1ID])+' flags differ: old is {}, new is {}'.format(oldFlag,newFlag)+'\\n'\n\n # Check the distance between the atoms.\n dist = distance(position(m1[m1ID]),position(m2[m2ID]))\n if dist > distanceThreshold:\n ret += atomID(m1[m1ID])+' Distance between runs: {:.2f}'.format(dist)+'\\n'\n\n # Go on to next line\n m1ID += 1\n m2ID += 1\n\n # Finish up either list that is not done\n while m1ID < len(m1):\n ret += 'Only in first: '+atomID(m1[m1ID])+'\\n'\n m1ID += 1\n while m2ID < len(m2):\n ret += 'Only in second: '+atomID(m2[m2ID])+'\\n'\n m2ID += 1\n\n return ret","title":""},{"docid":"6d09f4f7c85d16b6199594541e324bf8","score":"0.50410783","text":"def check_iequv(bn1, bn2):\n ## skeletons and immoralities\n (ske1, immor1) = bn1.get_skeleton_immor()\n (ske2, immor2) = bn2.get_skeleton_immor()\n\n ## comparison. \n if len(ske1) != len(ske2) or len(immor1) != len(immor2):\n return False\n\n ## Note that the edges are undirected so we need to check both ordering\n for (n1, n2) in immor1:\n if (n1, n2) not in immor2 and (n2, n1) not in immor2:\n return False\n for (n1, n2) in ske1:\n if (n1, n2) not in ske2 and (n2, n1) not in ske2:\n return False\n return True","title":""},{"docid":"79b6b97f15fd964d906fe0d567536d17","score":"0.5034577","text":"def test_comparing_different_ids(self):\n # GIVEN\n input_dict = TestFTSInputValidation.get_default_inputs()\n input_dict2 = TestFTSInputValidation.get_default_inputs() # ensure not same references\n input_dict2[\"greenyellow_intervals\"][\"sg3\"] = input_dict2[\"greenyellow_intervals\"][\"sg2\"]\n del input_dict2[\"greenyellow_intervals\"][\"sg2\"]\n fts1 = FixedTimeSchedule(**input_dict)\n fts2 = FixedTimeSchedule(**input_dict2)\n\n # WHEN\n same = fts1 == fts2\n\n self.assertEqual(same, False)","title":""},{"docid":"396cb1d465a352cc63a62cb977054cf7","score":"0.50305265","text":"def _is_consistent(self) -> bool:\n try:\n assert (\n type(self.dialogue_reference) == tuple\n ), \"Invalid type for 'dialogue_reference'. Expected 'tuple'. Found '{}'.\".format(\n type(self.dialogue_reference)\n )\n assert (\n type(self.dialogue_reference[0]) == str\n ), \"Invalid type for 'dialogue_reference[0]'. Expected 'str'. Found '{}'.\".format(\n type(self.dialogue_reference[0])\n )\n assert (\n type(self.dialogue_reference[1]) == str\n ), \"Invalid type for 'dialogue_reference[1]'. Expected 'str'. Found '{}'.\".format(\n type(self.dialogue_reference[1])\n )\n assert (\n type(self.message_id) == int\n ), \"Invalid type for 'message_id'. Expected 'int'. Found '{}'.\".format(\n type(self.message_id)\n )\n assert (\n type(self.target) == int\n ), \"Invalid type for 'target'. Expected 'int'. Found '{}'.\".format(\n type(self.target)\n )\n\n # Light Protocol Rule 2\n # Check correct performative\n assert (\n type(self.performative) == TProtocolMessage.Performative\n ), \"Invalid 'performative'. Expected either of '{}'. Found '{}'.\".format(\n self.valid_performatives, self.performative\n )\n\n # Check correct contents\n actual_nb_of_contents = len(self.body) - DEFAULT_BODY_SIZE\n expected_nb_of_contents = 0\n if self.performative == TProtocolMessage.Performative.PERFORMATIVE_CT:\n expected_nb_of_contents = 1\n assert (\n type(self.content_ct) == CustomDataModel\n ), \"Invalid type for content 'content_ct'. Expected 'DataModel'. Found '{}'.\".format(\n type(self.content_ct)\n )\n elif self.performative == TProtocolMessage.Performative.PERFORMATIVE_PT:\n expected_nb_of_contents = 5\n assert (\n type(self.content_bytes) == bytes\n ), \"Invalid type for content 'content_bytes'. Expected 'bytes'. Found '{}'.\".format(\n type(self.content_bytes)\n )\n assert (\n type(self.content_int) == int\n ), \"Invalid type for content 'content_int'. Expected 'int'. Found '{}'.\".format(\n type(self.content_int)\n )\n assert (\n type(self.content_float) == float\n ), \"Invalid type for content 'content_float'. Expected 'float'. Found '{}'.\".format(\n type(self.content_float)\n )\n assert (\n type(self.content_bool) == bool\n ), \"Invalid type for content 'content_bool'. Expected 'bool'. Found '{}'.\".format(\n type(self.content_bool)\n )\n assert (\n type(self.content_str) == str\n ), \"Invalid type for content 'content_str'. Expected 'str'. Found '{}'.\".format(\n type(self.content_str)\n )\n elif self.performative == TProtocolMessage.Performative.PERFORMATIVE_PCT:\n expected_nb_of_contents = 10\n assert (\n type(self.content_set_bytes) == frozenset\n ), \"Invalid type for content 'content_set_bytes'. Expected 'frozenset'. Found '{}'.\".format(\n type(self.content_set_bytes)\n )\n assert all(\n type(element) == bytes for element in self.content_set_bytes\n ), \"Invalid type for frozenset elements in content 'content_set_bytes'. Expected 'bytes'.\"\n assert (\n type(self.content_set_int) == frozenset\n ), \"Invalid type for content 'content_set_int'. Expected 'frozenset'. Found '{}'.\".format(\n type(self.content_set_int)\n )\n assert all(\n type(element) == int for element in self.content_set_int\n ), \"Invalid type for frozenset elements in content 'content_set_int'. Expected 'int'.\"\n assert (\n type(self.content_set_float) == frozenset\n ), \"Invalid type for content 'content_set_float'. Expected 'frozenset'. Found '{}'.\".format(\n type(self.content_set_float)\n )\n assert all(\n type(element) == float for element in self.content_set_float\n ), \"Invalid type for frozenset elements in content 'content_set_float'. Expected 'float'.\"\n assert (\n type(self.content_set_bool) == frozenset\n ), \"Invalid type for content 'content_set_bool'. Expected 'frozenset'. Found '{}'.\".format(\n type(self.content_set_bool)\n )\n assert all(\n type(element) == bool for element in self.content_set_bool\n ), \"Invalid type for frozenset elements in content 'content_set_bool'. Expected 'bool'.\"\n assert (\n type(self.content_set_str) == frozenset\n ), \"Invalid type for content 'content_set_str'. Expected 'frozenset'. Found '{}'.\".format(\n type(self.content_set_str)\n )\n assert all(\n type(element) == str for element in self.content_set_str\n ), \"Invalid type for frozenset elements in content 'content_set_str'. Expected 'str'.\"\n assert (\n type(self.content_list_bytes) == tuple\n ), \"Invalid type for content 'content_list_bytes'. Expected 'tuple'. Found '{}'.\".format(\n type(self.content_list_bytes)\n )\n assert all(\n type(element) == bytes for element in self.content_list_bytes\n ), \"Invalid type for tuple elements in content 'content_list_bytes'. Expected 'bytes'.\"\n assert (\n type(self.content_list_int) == tuple\n ), \"Invalid type for content 'content_list_int'. Expected 'tuple'. Found '{}'.\".format(\n type(self.content_list_int)\n )\n assert all(\n type(element) == int for element in self.content_list_int\n ), \"Invalid type for tuple elements in content 'content_list_int'. Expected 'int'.\"\n assert (\n type(self.content_list_float) == tuple\n ), \"Invalid type for content 'content_list_float'. Expected 'tuple'. Found '{}'.\".format(\n type(self.content_list_float)\n )\n assert all(\n type(element) == float for element in self.content_list_float\n ), \"Invalid type for tuple elements in content 'content_list_float'. Expected 'float'.\"\n assert (\n type(self.content_list_bool) == tuple\n ), \"Invalid type for content 'content_list_bool'. Expected 'tuple'. Found '{}'.\".format(\n type(self.content_list_bool)\n )\n assert all(\n type(element) == bool for element in self.content_list_bool\n ), \"Invalid type for tuple elements in content 'content_list_bool'. Expected 'bool'.\"\n assert (\n type(self.content_list_str) == tuple\n ), \"Invalid type for content 'content_list_str'. Expected 'tuple'. Found '{}'.\".format(\n type(self.content_list_str)\n )\n assert all(\n type(element) == str for element in self.content_list_str\n ), \"Invalid type for tuple elements in content 'content_list_str'. Expected 'str'.\"\n elif self.performative == TProtocolMessage.Performative.PERFORMATIVE_PMT:\n expected_nb_of_contents = 2\n assert (\n type(self.content_dict_bool_bytes) == dict\n ), \"Invalid type for content 'content_dict_bool_bytes'. Expected 'dict'. Found '{}'.\".format(\n type(self.content_dict_bool_bytes)\n )\n for (\n key_of_content_dict_bool_bytes,\n value_of_content_dict_bool_bytes,\n ) in self.content_dict_bool_bytes.items():\n assert (\n type(key_of_content_dict_bool_bytes) == bool\n ), \"Invalid type for dictionary keys in content 'content_dict_bool_bytes'. Expected 'bool'. Found '{}'.\".format(\n type(key_of_content_dict_bool_bytes)\n )\n assert (\n type(value_of_content_dict_bool_bytes) == bytes\n ), \"Invalid type for dictionary values in content 'content_dict_bool_bytes'. Expected 'bytes'. Found '{}'.\".format(\n type(value_of_content_dict_bool_bytes)\n )\n assert (\n type(self.content_dict_str_float) == dict\n ), \"Invalid type for content 'content_dict_str_float'. Expected 'dict'. Found '{}'.\".format(\n type(self.content_dict_str_float)\n )\n for (\n key_of_content_dict_str_float,\n value_of_content_dict_str_float,\n ) in self.content_dict_str_float.items():\n assert (\n type(key_of_content_dict_str_float) == str\n ), \"Invalid type for dictionary keys in content 'content_dict_str_float'. Expected 'str'. Found '{}'.\".format(\n type(key_of_content_dict_str_float)\n )\n assert (\n type(value_of_content_dict_str_float) == float\n ), \"Invalid type for dictionary values in content 'content_dict_str_float'. Expected 'float'. Found '{}'.\".format(\n type(value_of_content_dict_str_float)\n )\n elif self.performative == TProtocolMessage.Performative.PERFORMATIVE_MT:\n expected_nb_of_contents = 2\n assert (\n type(self.content_union_1) == CustomDataModel\n or type(self.content_union_1) == bool\n or type(self.content_union_1) == bytes\n or type(self.content_union_1) == dict\n or type(self.content_union_1) == float\n or type(self.content_union_1) == frozenset\n or type(self.content_union_1) == int\n or type(self.content_union_1) == str\n or type(self.content_union_1) == tuple\n ), \"Invalid type for content 'content_union_1'. Expected either of '['DataModel', 'bool', 'bytes', 'dict', 'float', 'frozenset', 'int', 'str', 'tuple']'. Found '{}'.\".format(\n type(self.content_union_1)\n )\n if type(self.content_union_1) == frozenset:\n assert all(\n type(element) == int for element in self.content_union_1\n ), \"Invalid type for elements of content 'content_union_1'. Expected 'int'.\"\n if type(self.content_union_1) == tuple:\n assert all(\n type(element) == bool for element in self.content_union_1\n ), \"Invalid type for tuple elements in content 'content_union_1'. Expected 'bool'.\"\n if type(self.content_union_1) == dict:\n for (\n key_of_content_union_1,\n value_of_content_union_1,\n ) in self.content_union_1.items():\n assert (\n type(key_of_content_union_1) == str\n and type(value_of_content_union_1) == int\n ), \"Invalid type for dictionary key, value in content 'content_union_1'. Expected 'str', 'int'.\"\n assert (\n type(self.content_union_2) == dict\n or type(self.content_union_2) == frozenset\n or type(self.content_union_2) == tuple\n ), \"Invalid type for content 'content_union_2'. Expected either of '['dict', 'frozenset', 'tuple']'. Found '{}'.\".format(\n type(self.content_union_2)\n )\n if type(self.content_union_2) == frozenset:\n assert (\n all(type(element) == bytes for element in self.content_union_2)\n or all(type(element) == int for element in self.content_union_2)\n or all(type(element) == str for element in self.content_union_2)\n ), \"Invalid type for frozenset elements in content 'content_union_2'. Expected either 'bytes' or 'int' or 'str'.\"\n if type(self.content_union_2) == tuple:\n assert (\n all(type(element) == bool for element in self.content_union_2)\n or all(\n type(element) == bytes for element in self.content_union_2\n )\n or all(\n type(element) == float for element in self.content_union_2\n )\n ), \"Invalid type for tuple elements in content 'content_union_2'. Expected either 'bool' or 'bytes' or 'float'.\"\n if type(self.content_union_2) == dict:\n for (\n key_of_content_union_2,\n value_of_content_union_2,\n ) in self.content_union_2.items():\n assert (\n (\n type(key_of_content_union_2) == bool\n and type(value_of_content_union_2) == bytes\n )\n or (\n type(key_of_content_union_2) == int\n and type(value_of_content_union_2) == float\n )\n or (\n type(key_of_content_union_2) == str\n and type(value_of_content_union_2) == int\n )\n ), \"Invalid type for dictionary key, value in content 'content_union_2'. Expected 'bool','bytes' or 'int','float' or 'str','int'.\"\n elif self.performative == TProtocolMessage.Performative.PERFORMATIVE_O:\n expected_nb_of_contents = 0\n if self.is_set(\"content_o_ct\"):\n expected_nb_of_contents += 1\n content_o_ct = cast(CustomDataModel, self.content_o_ct)\n assert (\n type(content_o_ct) == CustomDataModel\n ), \"Invalid type for content 'content_o_ct'. Expected 'DataModel'. Found '{}'.\".format(\n type(content_o_ct)\n )\n if self.is_set(\"content_o_bool\"):\n expected_nb_of_contents += 1\n content_o_bool = cast(bool, self.content_o_bool)\n assert (\n type(content_o_bool) == bool\n ), \"Invalid type for content 'content_o_bool'. Expected 'bool'. Found '{}'.\".format(\n type(content_o_bool)\n )\n if self.is_set(\"content_o_set_float\"):\n expected_nb_of_contents += 1\n content_o_set_float = cast(\n FrozenSet[float], self.content_o_set_float\n )\n assert (\n type(content_o_set_float) == frozenset\n ), \"Invalid type for content 'content_o_set_float'. Expected 'frozenset'. Found '{}'.\".format(\n type(content_o_set_float)\n )\n assert all(\n type(element) == float for element in content_o_set_float\n ), \"Invalid type for frozenset elements in content 'content_o_set_float'. Expected 'float'.\"\n if self.is_set(\"content_o_list_bytes\"):\n expected_nb_of_contents += 1\n content_o_list_bytes = cast(\n Tuple[bytes, ...], self.content_o_list_bytes\n )\n assert (\n type(content_o_list_bytes) == tuple\n ), \"Invalid type for content 'content_o_list_bytes'. Expected 'tuple'. Found '{}'.\".format(\n type(content_o_list_bytes)\n )\n assert all(\n type(element) == bytes for element in content_o_list_bytes\n ), \"Invalid type for tuple elements in content 'content_o_list_bytes'. Expected 'bytes'.\"\n if self.is_set(\"content_o_dict_str_int\"):\n expected_nb_of_contents += 1\n content_o_dict_str_int = cast(\n Dict[str, int], self.content_o_dict_str_int\n )\n assert (\n type(content_o_dict_str_int) == dict\n ), \"Invalid type for content 'content_o_dict_str_int'. Expected 'dict'. Found '{}'.\".format(\n type(content_o_dict_str_int)\n )\n for (\n key_of_content_o_dict_str_int,\n value_of_content_o_dict_str_int,\n ) in content_o_dict_str_int.items():\n assert (\n type(key_of_content_o_dict_str_int) == str\n ), \"Invalid type for dictionary keys in content 'content_o_dict_str_int'. Expected 'str'. Found '{}'.\".format(\n type(key_of_content_o_dict_str_int)\n )\n assert (\n type(value_of_content_o_dict_str_int) == int\n ), \"Invalid type for dictionary values in content 'content_o_dict_str_int'. Expected 'int'. Found '{}'.\".format(\n type(value_of_content_o_dict_str_int)\n )\n if self.is_set(\"content_o_union\"):\n expected_nb_of_contents += 1\n content_o_union = cast(\n Union[\n str,\n Dict[str, int],\n FrozenSet[int],\n FrozenSet[bytes],\n Tuple[bool, ...],\n Dict[str, float],\n ],\n self.content_o_union,\n )\n assert (\n type(content_o_union) == dict\n or type(content_o_union) == frozenset\n or type(content_o_union) == str\n or type(content_o_union) == tuple\n ), \"Invalid type for content 'content_o_union'. Expected either of '['dict', 'frozenset', 'str', 'tuple']'. Found '{}'.\".format(\n type(content_o_union)\n )\n if type(content_o_union) == frozenset:\n assert all(\n type(element) == bytes for element in content_o_union\n ) or all(\n type(element) == int for element in content_o_union\n ), \"Invalid type for frozenset elements in content 'content_o_union'. Expected either 'bytes' or 'int'.\"\n if type(content_o_union) == tuple:\n assert all(\n type(element) == bool for element in content_o_union\n ), \"Invalid type for tuple elements in content 'content_o_union'. Expected 'bool'.\"\n if type(content_o_union) == dict:\n for (\n key_of_content_o_union,\n value_of_content_o_union,\n ) in content_o_union.items():\n assert (\n type(key_of_content_o_union) == str\n and type(value_of_content_o_union) == float\n ), \"Invalid type for dictionary key, value in content 'content_o_union'. Expected 'str', 'float'.\"\n elif (\n self.performative\n == TProtocolMessage.Performative.PERFORMATIVE_EMPTY_CONTENTS\n ):\n expected_nb_of_contents = 0\n\n # Check correct content count\n assert (\n expected_nb_of_contents == actual_nb_of_contents\n ), \"Incorrect number of contents. Expected {}. Found {}\".format(\n expected_nb_of_contents, actual_nb_of_contents\n )\n\n # Light Protocol Rule 3\n if self.message_id == 1:\n assert (\n self.target == 0\n ), \"Invalid 'target'. Expected 0 (because 'message_id' is 1). Found {}.\".format(\n self.target\n )\n else:\n assert (\n 0 < self.target < self.message_id\n ), \"Invalid 'target'. Expected an integer between 1 and {} inclusive. Found {}.\".format(\n self.message_id - 1, self.target,\n )\n except (AssertionError, ValueError, KeyError) as e:\n logger.error(str(e))\n return False\n\n return True","title":""},{"docid":"ee0d21c03e1d34aad468a31f0f0bbb2f","score":"0.50302917","text":"def _apply_typemap(self, structure, typemap):\n for atom in structure.atoms:\n atom.id = typemap[atom.idx][\"atomtype\"]\n\n if not all([a.id for a in structure.atoms]):\n raise ValueError(\"Not all atoms in topology have atom types\")","title":""},{"docid":"6f4c125d8b0b24e97cb5a3b47279a4ac","score":"0.50284487","text":"def compare_types(self, var1, var2):\n return var1 == var2","title":""},{"docid":"d9b436e4b0ad8bc7080e1aa05fd7a927","score":"0.50283706","text":"def equal_equal_input_chord_input_chord(cls, a, b):\n return False","title":""},{"docid":"033c175d3d8f6c5b4eb2f73e0df29a78","score":"0.5028319","text":"def structural_equivalence(type1, type2, exclude_special_properties=True):\n type1_members = dir(type1)\n type2_members = dir(type2)\n\n # We do not consider values if only one of the compared types has one\n value_in_type1 = 'value' in type1_members\n value_in_type2 = 'value' in type1_members\n\n if value_in_type1 and not value_in_type2:\n type1_members.remove('value')\n\n if value_in_type2 and not value_in_type1:\n type1_members.remove('value')\n\n same_structure = type1_members == type2_members\n if not same_structure:\n return False\n\n for member in type1_members:\n if exclude_special_properties:\n if member in Type.special_properties_for_equality:\n continue\n\n # try:\n member1 = getattr(type1, member)\n member2 = getattr(type2, member)\n\n # If both are wrapper types, we compare it\n if isinstance(member1, Type): # and isinstance(member2, Type):\n if not member1.get_python_type() == member2.get_python_type():\n return False\n else:\n # Else we compare its types\n if not type(member1) == type(member2):\n return False\n # except:\n # return False\n\n # for member in type1_members:\n # if exclude_special_properties:\n # if member in Type.special_properties_for_equality:\n # continue\n # member1 = getattr(type1, member)\n # member2 = getattr(type2, member)\n # try:\n # # If both are wrapper types, we compare it\n # if not member1.get_python_type() == member2.get_python_type():\n # return False\n # except:\n # # Else we compare its types\n # if not type(member1) == type(member2):\n # return False\n\n # try:\n # member1 = getattr(type1, member)\n # member2 = getattr(type2, member)\n #\n # #If both are wrapper types, we compare it\n # if isinstance(member1, Type) and isinstance(member2, Type):\n # if not member1.get_python_type() == member2.get_python_type():\n # return False\n # else:\n # #Else we compare its types\n # if not type(member1) == type(member2):\n # return False\n # except:\n # return False\n\n # for member in type2_members:\n # # if member in non_comparable_members:\n # # continue\n # try:\n # member1 = getattr(type1, member)\n # member2 = getattr(type2, member)\n #\n # #If both are types, we compare it\n # if isinstance(member1, Type) and isinstance(member2, Type):\n # if not member1.get_python_type() == member2.get_python_type():\n # return False\n # else:\n # #Else we compare its types\n # if not type(member1) == type(member2):\n # return False\n # # if not getattr(type1, member) == getattr(type2, member):\n # # return False\n # except:\n # return False\n\n return True","title":""},{"docid":"9e17ba776459c823e1a1c84a69be3548","score":"0.5020715","text":"def test_equal_on_type_mismatch(self):\n a = sign.SignRequestPayload()\n b = 'invalid'\n\n self.assertFalse(a == b)\n self.assertFalse(b == a)","title":""},{"docid":"dd0b09445a7b22daa7a7d266fa9aa6ba","score":"0.5018225","text":"def show_guessed_atoms(self):\n if guess_box.get_pre_guesses() is not None: # shows guesses before confirming\n for pre_guess in guess_box.get_pre_guesses():\n coord_h = get_screen_coord(\"y\", pre_guess[0])\n coord_w = get_screen_coord(\"x\", pre_guess[1])\n screen.blit(atom, (coord_w + 3, coord_h + 3))\n\n if game.get_guesses() is not None: # shows guesses after confirming\n for pos in game.get_atoms(): # shows all flowers\n coord_h = get_screen_coord(\"y\", pos[0])\n coord_w = get_screen_coord(\"x\", pos[1])\n if pos in guess_box.get_pre_guesses(): # shows correct guess\n screen.blit(gray_out_atom, (coord_w + 3, coord_h + 3))\n screen.blit(correct, (coord_w + 3, coord_h + 3))\n\n else:\n screen.blit(atom, (coord_w + 3, coord_h + 3)) # shows flower that wasn't guessed\n\n for guess in game.get_guesses(): # shows wrong guesses\n coord_h = get_screen_coord(\"y\", guess[0])\n coord_w = get_screen_coord(\"x\", guess[1])\n if guess not in game.get_atoms():\n screen.blit(gray_out_atom, (coord_w + 3, coord_h + 3))\n screen.blit(wrong, (coord_w + 3, coord_h + 3))","title":""},{"docid":"0ae45cb065eac34ea5051104b8d45881","score":"0.50176364","text":"def compare_residues(\n e0: Union[Structure, Model, Chain],\n e1: Union[Structure, Model, Chain],\n verbose: bool = False,\n quick: bool = False,\n rtol: float = None,\n atol: float = None,\n) -> Dict[str, Any]:\n cmpdict: Dict[str, Any] = {}\n cmpdict[\"chains\"] = [] # list of chain IDs (union over both structures)\n cmpdict[\"residues\"] = 0 # count of not HETATM residues in longest chain\n cmpdict[\"rCount\"] = 0 # Biopython Residues (includes HETATMs, waters)\n cmpdict[\"rMatchCount\"] = 0 # full ID match Biopython Residues e0, e1\n cmpdict[\"rpnMismatchCount\"] = 0 # res prev, next links not matched\n cmpdict[\"aCount\"] = 0 # Atoms including disordered in longest e0 or e1\n cmpdict[\"disAtmCount\"] = 0 # disordered atoms in longest e0 or e1\n cmpdict[\"aCoordMatchCount\"] = 0 # atoms with coordinates match e0, e1\n cmpdict[\"aFullIdMatchCount\"] = 0 # atoms with full ID match e0, e1\n cmpdict[\"id0\"] = e0.get_full_id()\n cmpdict[\"id1\"] = e1.get_full_id()\n cmpdict[\"pass\"] = None\n cmpdict[\"report\"] = None\n\n if quick:\n if isinstance(e0, Chain):\n if (\n e0.internal_coord.atomArray is not None\n and np.shape(e0.internal_coord.atomArray)\n == np.shape(e1.internal_coord.atomArray)\n and np.allclose(\n e0.internal_coord.atomArray,\n e1.internal_coord.atomArray,\n rtol=1e-03 if rtol is None else rtol,\n atol=1e-03 if atol is None else atol,\n )\n ):\n cmpdict[\"aCount\"] = np.size(e0.internal_coord.atomArray, 0)\n cmpdict[\"aCoordMatchCount\"] = np.size(e0.internal_coord.atomArray, 0)\n if cmpdict[\"aCoordMatchCount\"] > 0:\n cmpdict[\"pass\"] = True\n else:\n cmpdict[\"pass\"] = False\n else:\n cmpdict[\"aCount\"] = (\n 0\n if e0.internal_coord.atomArray is None\n else np.size(e0.internal_coord.atomArray, 0)\n )\n cmpdict[\"pass\"] = False\n else:\n cmpdict[\"pass\"] = True\n for c0, c1 in zip_longest(e0.get_chains(), e1.get_chains()):\n if c0.internal_coord.atomArray is not None:\n if np.allclose(\n c0.internal_coord.atomArray,\n c1.internal_coord.atomArray,\n rtol=1e-03 if rtol is None else rtol,\n atol=1e-03 if atol is None else atol,\n ):\n cmpdict[\"aCoordMatchCount\"] += np.size(\n c0.internal_coord.atomArray, 0\n )\n else:\n cmpdict[\"pass\"] = False\n cmpdict[\"aCount\"] += np.size(c0.internal_coord.atomArray, 0)\n if cmpdict[\"aCoordMatchCount\"] < cmpdict[\"aCount\"]:\n cmpdict[\"pass\"] = False\n\n else:\n for r0, r1 in zip_longest(e0.get_residues(), e1.get_residues()):\n if 2 == r0.is_disordered() == r1.is_disordered():\n for dr0, dr1 in zip_longest(\n r0.child_dict.values(), r1.child_dict.values()\n ):\n _cmp_res(dr0, dr1, verbose, cmpdict, rtol=rtol, atol=atol)\n else:\n _cmp_res(r0, r1, verbose, cmpdict, rtol=rtol, atol=atol)\n\n if (\n cmpdict[\"rMatchCount\"] == cmpdict[\"rCount\"]\n and cmpdict[\"aCoordMatchCount\"] == cmpdict[\"aCount\"]\n and cmpdict[\"aFullIdMatchCount\"] == cmpdict[\"aCount\"]\n and cmpdict[\"rpnMismatchCount\"] == 0\n ):\n cmpdict[\"pass\"] = True\n else:\n cmpdict[\"pass\"] = False\n\n rstr = (\n \"{}:{} {} -- {} of {} residue IDs match; {} residues {} atom coords, \"\n \"{} full IDs of {} atoms ({} disordered) match : {}\".format(\n cmpdict[\"id0\"],\n cmpdict[\"id1\"],\n cmpdict[\"chains\"],\n cmpdict[\"rMatchCount\"],\n cmpdict[\"rCount\"],\n cmpdict[\"residues\"],\n cmpdict[\"aCoordMatchCount\"],\n cmpdict[\"aFullIdMatchCount\"],\n cmpdict[\"aCount\"],\n cmpdict[\"disAtmCount\"],\n \"ERROR\" if not cmpdict[\"pass\"] else \"ALL OK\",\n )\n )\n if not cmpdict[\"pass\"]:\n if cmpdict[\"rMatchCount\"] != cmpdict[\"rCount\"]:\n rstr += \" -RESIDUE IDS-\"\n if cmpdict[\"aCoordMatchCount\"] != cmpdict[\"aFullIdMatchCount\"]:\n rstr += \" -COORDINATES-\"\n if cmpdict[\"aFullIdMatchCount\"] != cmpdict[\"aCount\"]:\n rstr += \" -ATOM IDS-\"\n cmpdict[\"report\"] = rstr\n\n return cmpdict","title":""},{"docid":"819b0f42188e80ca25782898aac48f3c","score":"0.5003037","text":"def compare_fasta_files(file_path1, file_path2):\n with open(file_path1) as handle1:\n with open(file_path2) as handle2:\n iter1 = SeqIO.parse(handle1, 'fasta')\n iter2 = SeqIO.parse(handle2, 'fasta')\n fillvalue = object()\n paired_records = zip_longest(iter1, iter2, fillvalue=fillvalue)\n return all(r1.__dict__ == r2.__dict__ for r1, r2 in paired_records)","title":""},{"docid":"ff6131b97d273111040abd4c1029fa24","score":"0.499193","text":"def pair_exists(self, u, type1, type2):\n if(not self.nlist_ready):\n self._build_nlist(u) \n\n sel2 = u.atoms.selectAtoms(type2) \n for a in u.atoms.selectAtoms(type1):\n i = a.number\n nlist_accum = np.sum(self.nlist_lengths[:i]) if i > 0 else 0\n for j in self.nlist[nlist_accum:(nlist_accum + self.nlist_lengths[i])]:\n if(u.atoms[int(j)] in sel2):\n return True\n\n return False","title":""},{"docid":"082dafa48d0170642f961b74e16158ec","score":"0.49894404","text":"def test_isotope_equality(sym1, A1, m1, sym2, A2, m2, equal):\n i1 = isotope.Isotope(sym1, A1, m1)\n i2 = isotope.Isotope(sym2, A2, m2)\n if equal:\n assert i1 == i2\n else:\n assert i1 != i2","title":""},{"docid":"2924dc5ba22af01445e883d06d879d83","score":"0.49814403","text":"def is_compatible_with(cls: \"Algorithm\", input_types):\n my_inputs = cls.input_types()\n\n for needed in my_inputs:\n for having in input_types:\n if issubclass(having, needed):\n break\n else:\n return False\n\n return True","title":""},{"docid":"37ea6e0890f521c9712d7cb7152e1d74","score":"0.49767327","text":"def check_coords(mol0,mol1):\n \n keep_coords = True\n \n tmp_mol = mol0 + mol1\n mol0_natoms = len(mol0)\n #print \"Check\", len(mol0), len(mol1), len(tmp_mol), mol0_natoms\n for a0 in xrange(mol0_natoms-1):\n for a1 in xrange(mol0_natoms,len(tmp_mol)):\n #print a0,a1\n if tmp_mol[a0].symbol != \"X\" and tmp_mol[a1].symbol != \"X\":\n this_dist = tmp_mol.get_distance(a0, a1, mic = True)\n cutoff_dist = covalent_radii[tmp_mol[a0].number] + covalent_radii[tmp_mol[a1].number] + 0.3\n #print tmp_mol[a0].symbol, tmp_mol[a1].symbol,this_dist, cutoff_dist\n if this_dist < cutoff_dist:\n keep_coords = False\n #print \"returning false, overlap\"\n return keep_coords\n\n #Second way to fail out if we have PBC (first way - i.e. overlap, is covered above by using mic=True)\n # The centroid of the addition must be within the box\n #The +/- 1/2 test used here assume we've already centred the cell\n com = mol1.get_center_of_mass()\n #print com\n #axes = [i for i in range(len(mol0.get_pbc())) if mol0.pbc[i]]\n for axis in xrange(3):\n if mol0.pbc[axis]:\n if com[axis] > mol0.cell[axis,axis] or com[axis] < -mol0.cell[axis,axis]: #orthorhombic cell \n keep_coords = False\n #print \"returning false, PBC\"\n return keep_coords\n\n #print \"keeping coords \", keep_coords \n return keep_coords","title":""},{"docid":"21e5ca3212c1a45bc3c84b7c1c935324","score":"0.49674994","text":"def test_comparison_on_type_mismatch(self):\n a = attributes.ApplicationSpecificInformation(\n application_namespace=\"test_namespace\",\n application_data=\"test_data\"\n )\n b = \"invalid\"\n\n self.assertFalse(a == b)\n self.assertFalse(b == a)","title":""},{"docid":"e86c12314a4ba92f532536f548fefa2c","score":"0.49625188","text":"def transfer_spectra(dmim_cursor1, dmim_cursor2, rep_cursor):\n # count the number of dmim_ids without annotations\n noms2 = 0\n # count null metfrag scores\n score_null_p, score_null_m = 0, 0\n # iterate through all plates\n for n in range(1, 8):\n for dmim_id, well, name, adduct in dmim_cursor1.execute(qry_dmim_plate_n.format(n=n)).fetchall():\n matched = False\n cmpd = '{}_{}'.format(name, adduct)\n qdata1 = (cmpd, well, adduct)\n for spectrum, metfrag_score in rep_cursor.execute(qry_rep_plate_n_ms2.format(n=n), qdata1).fetchall():\n qdata2 = (dmim_id, spectrum, metfrag_score)\n dmim_cursor2.execute(qry_dmim_plate_n_ms2.format(n=n), qdata2)\n matched = True\n if metfrag_score is None:\n if '_met' in name:\n score_null_p += 1\n else:\n score_null_m += 1\n if not matched:\n noms2 += 1\n #print('ERROR! annotation for: {} {} {}'.format(dmim_id, well, met_n))\n print(noms2, 'have no MS2 spectra')\n print(score_null_p + score_null_m, 'have NULL metfrag_score', '({} parents, {} metabolites)'.format(score_null_p, score_null_m))","title":""},{"docid":"1bfeaab68ad93e1ce16f3bca04712ed3","score":"0.49602342","text":"def _test_matching_types (character, cls):\n if not isinstance(character, cls):\n # QAZ: error message.\n raise MismatchedTypesError()","title":""},{"docid":"1e31fd057d05f8c35d6b916338bde906","score":"0.49529004","text":"def geometries_are_equal(atomlistA, atomlistB, eps=1.0e-3):\n equal = True\n for (ZatA,posA),(ZatB,posB) in zip(atomlistA, atomlistB):\n equal = equal and (ZatA == ZatB)\n equal = equal and la.norm(np.array(posA)-np.array(posB)) < eps\n return equal","title":""},{"docid":"0edbcd9e74de3868170ed5889e8d8ab9","score":"0.4951187","text":"def check_connectivity(mol_0, mol_1):\n\n bonds_0 = undirected_bond_atoms(mol_0)\n bonds_1 = undirected_bond_atoms(mol_1)\n same = (bonds_0 == bonds_1)\n\n return same","title":""},{"docid":"6bb51583807e69a38618acee5379f49b","score":"0.4948739","text":"def compare_result(m, emu, result):\r\n\r\n global emulatePossibles\r\n\r\n sets = 0\r\n reduces = 0\r\n recIdx = len(m.rec)\r\n results = len(result)\r\n m1 = result[0] # use the first result to compare with others\r\n i = 0\r\n for r1 in m1.rec[recIdx:]:\r\n i += 1 # step idx of m1\r\n info = \"The #{0} step of the {1} emulate as {2} is same with follows:\".format(i, emu[0][0], emu[0][1])\r\n same = 0\r\n resultIdx = 0\r\n for m2 in result[1:]:\r\n resultIdx += 1\r\n idx = 0 # step idx of others\r\n for r2 in m2.rec[recIdx:]:\r\n idx += 1\r\n if (r1[0], r1[1], r1[2], r1[3]) == (r2[0], r2[1], r2[2], r2[3]):\r\n if ACTION_GET_INFO:\r\n info = info + \"\\nThe #{0} step of the {1} emulate as {2}\".format(idx, emu[resultIdx][0],\r\n emu[resultIdx][1])\r\n same += 1\r\n break\r\n if same == results - 1:\r\n if r1.t == \"s\":\r\n sets += 1\r\n m.setit(r1[0], r1[1], r1[2], d=\"compareResult for {0} emulate!\".format(results), info=info)\r\n else:\r\n reduces += 1\r\n m.reduce(r1[0], r1[1], r1[2], d=\"compareResult for {0} emulate!\".format(results), info=info)\r\n return sets, reduces","title":""},{"docid":"58d10d64a2345c5d408b35bae4201a30","score":"0.4941716","text":"def convert_molecules(args):\n if args.format:\n format1 = args.format\n format2 = args.format\n elif args.format2:\n format2 = args.format2\n else:\n format1 = DEFAULT_INPUT\n format2 = DEFAULT_INPUT\n mol1 = pybel.readfile(format1, args.file1).next()\n mol2 = pybel.readfile(format2, args.file2).next()\n return mol1, mol2","title":""},{"docid":"4be4d6970bbb7d6d01792404da6136f6","score":"0.49317375","text":"def is_isomorphic(matrix1, matrix2):\n matrix1, label1 = np.array(matrix1[0]), matrix1[1]\n matrix2, label2 = np.array(matrix2[0]), matrix2[1]\n assert np.shape(matrix1) == np.shape(matrix2)\n assert len(label1) == len(label2)\n\n vertices = np.shape(matrix1)[0]\n # Note: input and output in our constrained matrices always map to themselves\n # but this script does not enforce that.\n for perm in itertools.permutations(range(0, vertices)):\n pmatrix1, plabel1 = permute_module(matrix1, label1, perm)\n if np.array_equal(pmatrix1, matrix2) and plabel1 == label2:\n return True\n\n return False","title":""},{"docid":"9ee0476e525e844082175c6428db6a78","score":"0.49227783","text":"def sig_cmp(sig1, sig2):\n types1 = sig1.required\n types2 = sig2.required\n if len(types1) != len(types2):\n return False\n dup_pos = []\n dup_kw = {}\n for t1, t2 in zip(types1, types2):\n match = type_cmp(t1, t2)\n if match:\n dup_pos.append(match)\n else:\n break\n else:\n return tuple(dup_pos)\n kw_range = slice(len(dup_pos), len(types1))\n kwds1 = sig1.parameters[kw_range]\n kwds2 = sig2.parameters[kw_range]\n if set(kwds1) != set(kwds2):\n return False\n kwtypes1 = dict(zip(sig1.parameters, types1))\n kwtypes2 = dict(zip(sig2.parameters, types2))\n for kw in kwds1:\n match = type_cmp(kwtypes1[kw], kwtypes2[kw])\n if match:\n dup_kw[kw] = match\n else:\n break\n else:\n return tuple(dup_pos), dup_kw\n return False","title":""},{"docid":"02f94332d577ade2110f5b6022f70600","score":"0.4921804","text":"def is_similar_to(self, other, tolerance = 0.05):\r\n if self is other:\r\n return True\r\n if not isinstance(other, Structure):\r\n return False\r\n if self.number_of_atoms != other.number_of_atoms:\r\n return False\r\n if not self.lattice.is_similar_to(other.lattice, tolerance):\r\n return False\r\n if not np.all(np.array(self.atomic_symbols) == np.array(other.atomic_symbols)):\r\n return False\r\n for self_pos, other_pos in zip(self.frac_positions, other.frac_positions):\r\n diff = abs(self_pos - other_pos)\r\n exceed_indexes = np.where(diff > 0.5)\r\n diff[exceed_indexes] = 1 - diff[exceed_indexes]\r\n if np.any(np.linalg.norm(diff) > tolerance): \r\n return False\r\n break\r\n return True","title":""},{"docid":"b6cb00e4fd0075a1621d7ea0d9416d99","score":"0.49213678","text":"def automata_are_equivalent(automaton_a: Automaton, automaton_b: Automaton) -> bool:\n # Check if s0 and s0' are compatible, if they are not, don't do anything else\n if not states_are_compatible(automaton_a.initial_state, automaton_b.initial_state) :\n return False\n\n # Create a table for the transitions of each symbol\n\n p = automaton_a.initial_state\n q = automaton_b.initial_state\n\n # So that we don't process a pair more than once, initialize to the initial states\n processed_pairs = {\n \"{},{}\".format(p.state_id, q.state_id): True\n }\n\n # A queue of pair of states to follow\n states_to_follow_queue = deque([(p, q)])\n\n # Repeat until we find an incompatible state or we loop through all states\n while states_to_follow_queue:\n p, q = states_to_follow_queue.popleft()\n\n if not states_are_compatible(p, q):\n return False\n\n # Check where to transition, and check if both states have transitions for the symbol, if not, return False\n for symbol in p.transitions.keys():\n\n if symbol not in q.transitions:\n return False\n\n new_p = p.transitions.get(symbol)[0] # Transitions are lists, for DFA, they are of length == 1\n new_q = q.transitions.get(symbol)[0]\n\n # Check if we have not already visited this pair, if we have, skip it\n str_pair = \"{},{}\".format(new_p.state_id, new_q.state_id)\n\n if str_pair not in processed_pairs:\n # Add it to the states to visit queue\n processed_pairs[str_pair] = True\n states_to_follow_queue.append((new_p, new_q))\n\n # If, after iterating over all states, there were no incompatible pairs, the automata are equivalent\n return True","title":""},{"docid":"6804bd18398e8d38defba7e8759d39e4","score":"0.49159825","text":"def test_furtherReductionsMatchFromBothModels(self):\n print('Checking that all possible 2D models created from the original and reduced models match')\n \n for combo in permutations(self.subset,2):\n # Solve both for the current 2D problem\n a=fea(self.model,combo)\n b=fea(self.reduced_optlang,combo)\n \n # They should have the same facets\n disjoint_facets=a.get_facets()^b.get_facets()\n self.assertEqual(len(disjoint_facets),0)\n \n # They should have the same vertices\n disjoint_vertices=a.get_vertices()^b.get_vertices()\n self.assertEqual(len(disjoint_vertices),0)","title":""},{"docid":"e279501eb867f57d4d691bfcd5067392","score":"0.49140543","text":"def _assert_entries_match(lexicon1, lexicon2, token):\n eq_(set((tok.categ(), tok.semantics()) for tok in lexicon1._entries[token]),\n set((tok.categ(), tok.semantics()) for tok in lexicon2._entries[token]))","title":""},{"docid":"53caedba3d02d0e2d7569dd0861d4b03","score":"0.4912611","text":"def check(bs1, bs2):\n bs1_gen = inorder_traverse(bs1.root)\n bs2_gen = inorder_traverse(bs2.root)\n while True:\n try:\n n1 = bs1_gen.next()\n except StopIteration:\n try:\n n2 = bs2_gen.next()\n return False\n except StopIteration:\n print 'Exhausted all nodes -- bsts are the same'\n return True\n\n try:\n n2 = bs2_gen.next()\n except StopIteration:\n return False\n\n if n1 != n2:\n return False","title":""},{"docid":"ee3692fca0dfed7fab54574195e49446","score":"0.49119988","text":"def resolve_conflicts(self, atoms):\n neg = set()\n result = set()\n # split atoms into NEG and RESULT\n for atom in atoms:\n if atom.table.endswith('+'):\n result.add(atom)\n elif atom.table.endswith('-'):\n neg.add(atom)\n else:\n result.add(atom)\n # add elems from NEG only if their inverted version not in RESULT\n for atom in neg:\n if atom.invert_update() not in result: # slow: copying ATOM here\n result.add(atom)\n return result","title":""},{"docid":"185c4c3590afb0070326275f36dcdf83","score":"0.4910697","text":"def check_duplicate(entry_a, entry_b):\n try:\n entry_a = entry_a[\"metadata\"][\"music\"]\n except KeyError:\n entry_a = entry_a[\"metadata\"][\"custom_files\"]\n try:\n entry_b = entry_b[\"metadata\"][\"music\"]\n except KeyError:\n entry_b = entry_b[\"metadata\"][\"custom_files\"]\n for music_a in entry_a:\n for music_b in entry_b:\n if music_a[\"acrid\"] == music_b[\"acrid\"]:\n return True\n return False","title":""},{"docid":"723c904fecb09fc2ad21693d1154730c","score":"0.4908484","text":"def check_molecule_integrity(self, other_elements: List[str], other_coordinates: List[np.ndarray]):\n for k, (elem, coord, other_elem, other_coord) in enumerate(\n zip(self.atoms.get_chemical_symbols(), self.atoms.get_positions(),\n other_elements, other_coordinates)):\n if elem != other_elem:\n raise Exception(\n f'Element {elem} with index {k} in molecule differs from element {other_elem} in orb file!')\n\n if not np.allclose(coord, other_coord):\n raise Warning(\n f'Molecular coordinates of element {k} {coord} differ from coordinates in orb file {other_coord}')","title":""},{"docid":"7c426646ed08880eb6f874dbf4dc452d","score":"0.4899526","text":"def compare_input_metadata(\n test_case: TestCase,\n expected: Dict[str, GraphQLSchemaFieldType],\n received: Dict[str, GraphQLSchemaFieldType],\n) -> None:\n # First, assert that the sets of keys in both dicts are equal.\n test_case.assertEqual(set(six.iterkeys(expected)), set(six.iterkeys(received)))\n\n # Then, compare the values for each key in both dicts.\n for key in six.iterkeys(expected):\n expected_value = expected[key]\n received_value = received[key]\n\n test_case.assertTrue(\n is_same_type(expected_value, received_value),\n msg=\"{} != {}\".format(str(expected_value), str(received_value)),\n )","title":""},{"docid":"93090fef13f54dd5b9b7d4b6682802e6","score":"0.4894456","text":"def test_eq(self):\n\n for typecode in VALID_CONTENT:\n b = gapbuffer(typecode, VALID_CONTENT[typecode])\n self.assertEqual(VALID_CONTENT[typecode], b)","title":""},{"docid":"a8b023c80eb68fbb17cf25b763879983","score":"0.4891309","text":"def check_iso(graph1, graph2):\n return nx.is_isomorphic(graph1, graph2,\n node_match=iso.categorical_node_match(\n 'atomic_number', 0),\n edge_match=iso.categorical_edge_match('type',\n 'Single'))","title":""},{"docid":"20cca9e829ab68518f0bef1cdb6a047b","score":"0.48875916","text":"def __eq__(self, other):\n return (self.type == other.type and\n self.antecedent == other.antecedent and\n self.consequent == other.consequent)","title":""},{"docid":"614f278bd479ee9f602b77513c7f5fb4","score":"0.48837185","text":"def __call__(self, a, b):\n if not self._doenum: return not any(a.genes != b.genes)\n if hasattr(self.converter, \"__call__\"):\n if not hasattr(a, \"integer_hashcode\"): \n a.integer_hashcode = self.structure_hashcode(self.converter(a.genes))\n if not hasattr(b, \"integer_hashcode\"): \n b.integer_hashcode = self.structure_hashcode(self.converter(b.genes))\n return a.integer_hashcode == b.integer_hashcode\n\n else:\n return self.structure_hashcode(a) == self.structure_hashcode(b)","title":""},{"docid":"97e657251f65c6a58b00177467cb1655","score":"0.4877786","text":"def are_compatible(units1, units2):\n # Empty units always compatible\n if is_null_unit(units1) or is_null_unit(units2):\n return True\n if (not is_unit(units1)) or (not is_unit(units2)):\n return False\n x = add_units(1, units1)\n try:\n convert_to(x, units2)\n except ValueError:\n return False\n return True","title":""},{"docid":"c3a12d2b9a566877d1c41545174e40fa","score":"0.48730108","text":"def compare_ligands(initial_dir, initial_pdb, final_dir, final_pdb,\n output_dir, manual, tolerance=0.1, atom_tolerance=0.1,\n amino=False):\n\n # Read structure and topology information from PDBs\n # Identify matching ac, prep and frcmod files in input directories\n # RDKit requires atom name changes, update input files & move to output_dir\n initial_mol_info, final_mol_info = prepare_mols(initial_dir, final_dir,\n initial_pdb, final_pdb,\n output_dir, amino=amino)\n\n # Map charges from resp ac file to atom names and indexes\n initial_charge_map = create_charge_idx_map(initial_mol_info.ac_filename,\n initial_mol_info.struct)\n\n final_charge_map = create_charge_idx_map(final_mol_info.ac_filename,\n final_mol_info.struct)\n\n # Get dictionary containing AtomInfo for each atom\n # (uses RDKit index as key)\n initial_atom_info = get_atom_info_rdkit(initial_mol_info.mol,\n charge_map=initial_charge_map)\n\n final_atom_info = get_atom_info_rdkit(final_mol_info.mol,\n charge_map=final_charge_map)\n\n # Find largest matching region between PDBs (Miximal Common Substructure)\n\n rdkit_mols = [initial_mol_info.mol, final_mol_info.mol]\n mcs_result = rdFMCS.FindMCS(rdkit_mols, completeRingsOnly=True)\n\n if mcs_result:\n\n shared = Chem.MolFromSmarts(mcs_result.smartsString)\n\n initial_match_idxs = initial_mol_info.mol.GetSubstructMatch(shared)\n final_match_idxs = final_mol_info.mol.GetSubstructMatch(shared)\n\n # Map to get equivalent atom index in final molecule to the in the\n # initial model (matched region)\n matched_idx_map = dict(zip(initial_match_idxs, final_match_idxs))\n\n initial_match_idxs = remove_ring_error(initial_mol_info.mol,\n initial_match_idxs,\n atom_info=initial_atom_info)\n\n # Update final matched idx list and initial to final map to account\n # for removed ring atoms\n\n final_match_idxs = [matched_idx_map[x] for x in initial_match_idxs]\n\n matched_idx_map = dict(zip(initial_match_idxs, final_match_idxs))\n\n # This technique only works is a substantial portion of the two molecules\n # is the same i.e. if a side chain is exchanged. If you are changing the whole\n # molecule, than this method is not applicable, so the system exits.\n\n if float(len(initial_match_idxs))/float(len(initial_mol_info.mol.GetAtoms())) < 0.3:\n print(\"No sufficiently large matching region (excluding incomplete rings) could be identified!\\nTerminating.\")\n sys.exit(0)\n\n check_atom_type_match(\n matched_idx_map,\n initial_mol_info,\n final_mol_info)\n\n # Get all potential sub-matches with complete rings\n # (includes complete match)\n submatches = get_submatches(initial_mol_info.mol, initial_atom_info,\n initial_match_idxs)\n\n (q_diffs,\n q_max_atom_diffs) = get_charge_diffs_submatches(submatches,\n matched_idx_map,\n initial_charge_map,\n final_charge_map)\n\n # Manual selection of submatch by user or automatic choice\n if manual:\n\n selected_idx = get_user_submatch(submatches, initial_atom_info,\n q_diffs, q_max_atom_diffs)\n\n else:\n\n all_options = range(len(submatches))\n filtered_q_total = [\n x for x in all_options if q_diffs[x] < tolerance]\n filtered_q_atom = [\n x for x in filtered_q_total if q_max_atom_diffs[x] < atom_tolerance]\n\n all_filtered_matches = []\n\n if len(filtered_q_atom) > 0:\n\n for submatch_idx in filtered_q_atom:\n\n submatch = submatches[submatch_idx]\n\n no_heavy = 0\n\n for idx in submatch:\n\n if initial_atom_info[idx].name.strip(\n '0123456789') != 'H':\n no_heavy += 1\n\n if no_heavy > 1:\n all_filtered_matches.append(submatch_idx)\n\n if len(all_filtered_matches) > 0:\n\n selected_idx = all_filtered_matches[0]\n\n else:\n\n print(\n 'Warning: Only acceptable matches with at most 1 heavy atom found')\n selected_idx = filtered_q_atom[0]\n\n else:\n\n print(\"No options fit standard filter - select manually\\n\")\n\n selected_idx = get_user_submatch(submatches, initial_atom_info,\n q_diffs, q_max_atom_diffs)\n\n else:\n\n print(\"No matching region could be identified!\")\n sys.exit(1)\n\n selected_submatch = submatches[selected_idx]\n\n # Output all submatches and information informing choice\n output_submatches_file(submatches, selected_idx,\n q_diffs, q_max_atom_diffs,\n initial_atom_info, final_atom_info,\n matched_idx_map, output_dir)\n\n return (\n initial_mol_info,\n initial_atom_info,\n final_mol_info,\n final_atom_info,\n matched_idx_map,\n selected_submatch)","title":""},{"docid":"3ed7f47739a65f616d3d459e6b69e158","score":"0.4871031","text":"def type_of_interaction(base_residue, aa_residue, aa_coordinates, standard_aa_center, base_atoms):\n squared_xy_dist_list = []\n\n \"\"\"Defines different sets of amino acids\"\"\"\n planar_aa = set ([\"ARG\", \"ASN\", \"ASP\", \"GLU\", \"GLN\", \"HIS\", \"PHE\", \"TRP\", \"TYR\"])\n stacked_aliphatic = set([\"ALA\", \"CYS\", \"ILE\", \"LEU\", \"MET\", \"PRO\", \"SER\", \"THR\", \"VAL\"])\n # Note: LYS and GLY are not in the previous lists\n\n edge_to_edge_aa = set ([\"ARG\", \"ASN\", \"ASP\", \"CYS\", \"GLN\", \"GLU\", \"HIS\", \"LYS\", \"PHE\", \"SER\", \"THR\", \"TYR\", \"TRP\"])\n shb_aa = set ([\"ARG\", \"ASN\", \"ASP\", \"GLU\", \"GLN\", \"HIS\", \"LYS\", \"SER\", \"THR\", \"TYR\"])\n\n # calculate distances from aa atoms to base center\n for aa_atom in aa_residue.atoms(name=aa_fg[aa_residue.sequence]):\n key = aa_atom.name\n aa_x = aa_coordinates[key][0]\n aa_y = aa_coordinates[key][1]\n\n squared_xy_dist = (aa_x**2) + (aa_y**2)\n squared_xy_dist_list.append(squared_xy_dist)\n\n #print base_residue.unit_id(), aa_residue.unit_id(), min(squared_xy_dist_list), mean_z\n\n # for a stacking interaction, the x,y coordinate of at least one atom of the amino acid group needs to be\n # within sqrt(5) = 2.236 of the base center 0,0\n min_dist = np.sqrt(min(squared_xy_dist_list))\n\n if min_dist <= 2.236:\n #print base_residue.unit_id(), aa_residue.unit_id(), min(squared_xy_dist_list), mean_z\n if aa_residue.sequence in planar_aa:\n return stacking_planar_annotation(base_residue, aa_residue, min_dist)\n\n elif aa_residue.sequence in stacked_aliphatic:\n return stacking_non_planar_annotation(aa_residue, aa_coordinates, min_dist)\n\n else:\n return (\"other-stack\",{\"dist-xy-from-center\":min_dist})\n\n # check for interactions in the plane of the base\n mean_z = standard_aa_center[2]\n (num_hydrogen_bonds,hydrogen_bond_list) = count_hydrogen_bonds(base_residue, aa_residue, base_atoms)\n\n if -1.8 <= mean_z < 1.8:\n if aa_residue.sequence in edge_to_edge_aa:\n angle = calculate_angle_between_planar_residues(base_residue, aa_residue)\n if angle:\n if 0 <= angle <= 45 and num_hydrogen_bonds >= 2:\n return (\"pseudopair\",{\"hydrogen-bonds\":hydrogen_bond_list,\"angle-between-planes\":angle})\n elif 45 <= angle:\n return (\"perpendicular-edge\",{\"hydrogen-bonds\":hydrogen_bond_list,\"angle-between-planes\":angle})\n\n if aa_residue.sequence in shb_aa:\n# base_seq = base_residue.sequence\n# base_atoms = NAbaseheavyatoms[base_seq]\n if num_hydrogen_bonds >= 1:\n return (\"SHB\",{\"hydrogen-bonds\":hydrogen_bond_list})\n\n return (\"other-edge\",{\"hydrogen-bonds\":hydrogen_bond_list})\n\n return (\"other\",{\"dist-xy-from-center\":min_dist,\"hydrogen-bonds\":hydrogen_bond_list})","title":""},{"docid":"a6b32ea4b53225d90dbf9eb211a412d5","score":"0.48704225","text":"def compare_data_objects(self, other):\n return self.type == other.type","title":""},{"docid":"c43e97a723912bbe7151e8e36f4c6384","score":"0.48682833","text":"def test_different_code(self):\n hole1 = Hole(1, 100, [\"c\"], \"EXP\", 2)\n hole2 = Hole(1, 100, [\"d\"], \"ARI\", 2)\n self.assertFalse(hole1.is_equal(hole2))\n self.assertFalse(hole2.is_equal(hole1))","title":""},{"docid":"4fc63f6db35c070869ff27a1621c15c1","score":"0.4862124","text":"def equal_equal_primary_asset_type(cls, a, b):\n return False","title":""},{"docid":"6caf2273095160af7d8383a87d3a0bb2","score":"0.4862096","text":"def is_two_objects_is_the_same_objects(first: Any, second: Any) -> bool:\n return first is second","title":""},{"docid":"8797e1a798d142154cb1f6f2aa5067e9","score":"0.48611817","text":"def assess_rxn_match(rxn_name1, params1, rxn_param_dct2, print_output=False):\n # Get all possible orderings of the reactants and products for mech1\n [rcts1, prds1] = rxn_name1\n rcts1_perm = list(itertools.permutations(rcts1, len(rcts1)))\n prds1_perm = list(itertools.permutations(prds1, len(prds1)))\n em_param1 = params1[6] #'+M', '(+M)', or None\n\n rxn_name2 = None\n flip_rxn = None\n p_dep_same = None\n for rxn_name, params in rxn_param_dct2.items():\n [rcts2, prds2] = rxn_name\n em_param2 = params[6]\n if rcts2 in rcts1_perm and prds2 in prds1_perm:\n rxn_name2 = rxn_name\n flip_rxn = False\n\n if em_param2 == em_param1:\n p_dep_same = True\n else:\n p_dep_same = False\n break\n\n if rcts2 in prds1_perm and prds2 in rcts1_perm:\n rxn_name2 = rxn_name\n flip_rxn = True\n\n if em_param2 == em_param1:\n p_dep_same = True\n else:\n p_dep_same = False\n break\n\n if print_output:\n if rxn_name2:\n print('\\n- M2 RCTS', '+'.join(mech2_key[0]))\n print('- M2 PRDS', '+'.join(mech2_key[1]))\n else:\n print('\\n- NO M2 MATCH')\n\n return rxn_name2, flip_rxn, p_dep_same","title":""},{"docid":"22de7712f16e904091b2598a58376c32","score":"0.48608738","text":"def __eq__(self, other):\n try:\n return (self.overlap == other.overlap and\n self.tolerance == other.tolerance and\n self.mistakes == other.mistakes and\n type(self) is type(other))\n except AttributeError:\n return False","title":""},{"docid":"fa3c0cad5fa77223ea1c34e250f3f1f3","score":"0.4860405","text":"def is_ambiguous(self, molid):\n return self.label(molid) == 'a'","title":""}],"string":"[\n {\n \"docid\": \"acc86706fa2928bf1f4e37aa6956f931\",\n \"score\": \"0.6576755\",\n \"text\": \"def match_molecules(molecule1, molecule2, logger, onlyH):\\r\\n\\r\\n if logger.use_groups: # If group charges are to be used\\r\\n\\r\\n chg1, chg2 = molecule1.chg_grp, molecule2.chg_grp\\r\\n\\r\\n else:\\r\\n\\r\\n chg1, chg2 = molecule1.chg, molecule2.chg\\r\\n\\r\\n # Decompose both molecules [decompose_molecule function]\\r\\n [decomposed_molecule1,\\r\\n atom_types1, occurrences1] = decompose_molecule(chg1, molecule1.cor, molecule1.idf)\\r\\n\\r\\n [decomposed_molecule2,\\r\\n atom_types2, occurrences2] = decompose_molecule(chg2, molecule2.cor, molecule2.idf)\\r\\n\\r\\n # Determine the number of atom types\\r\\n n_atom_types = len(atom_types1)\\r\\n\\r\\n # Set up empty variables (lists)\\r\\n identifiers_molecule1, identifiers_molecule2 = [], []\\r\\n \\r\\n if onlyH == False:\\r\\n # Iterate through all atom types\\r\\n for atom_type in range(n_atom_types):\\r\\n # Extract coordinates and identifiers from decomposed molecules\\r\\n coords1, id1 = decomposed_molecule1[atom_type]\\r\\n coords2, id2 = decomposed_molecule2[atom_type]\\r\\n\\r\\n if logger.match_solv == 'standard':\\r\\n\\r\\n # Check for number of occurences of the actual atom type\\r\\n if occurrences1[atom_type] == 1:\\r\\n\\r\\n # Directly match atoms\\r\\n identifiers_molecule1.append(id1)\\r\\n identifiers_molecule2.append(id2)\\r\\n\\r\\n else: # If more than one atom of the type exists\\r\\n\\r\\n n_atoms = len(coords1) # Determine numer of atoms\\r\\n\\r\\n # Calculate permutation matrix for this atom type\\r\\n permutation_matrix = calc_permutation_matrix(coords1, coords2, logger)\\r\\n\\r\\n # Iterate through all atoms of this type\\r\\n for atom in range(n_atoms):\\r\\n\\r\\n # Find indices of minimum value in matrix (first index cooresponds to coords1)\\r\\n indices = np.where(permutation_matrix == np.min(permutation_matrix))\\r\\n\\r\\n if len(indices[0]) != 1: # Check if there is more than one solution\\r\\n \\r\\n # If there is: use the first match\\r\\n indices = np.array((indices[0][0], indices[1][0]))\\r\\n\\r\\n # Append identifiers corresponding to indices\\r\\n identifiers_molecule1.append(id1[indices[0]])\\r\\n identifiers_molecule2.append(id2[indices[1]])\\r\\n\\r\\n # Deplete corresponding row of first index and column of second index from matrix\\r\\n permutation_matrix = np.delete(permutation_matrix, indices[0], 0)\\r\\n permutation_matrix = np.delete(permutation_matrix, indices[1], 1)\\r\\n\\r\\n # Deplete identifier arrays\\r\\n id1 = np.delete(id1, indices[0])\\r\\n id2 = np.delete(id2, indices[1])\\r\\n\\r\\n elif logger.match_solv == 'hungarian':\\r\\n\\r\\n # Calculate permutation matrix for this atom type\\r\\n permutation_matrix = calc_permutation_matrix(coords1, coords2, logger, do_permute=True)\\r\\n\\r\\n # Solve the problem with the Hungarian algorithm\\r\\n indices_mol1, indices_mol2 = hungarian_solver(permutation_matrix)\\r\\n\\r\\n # Collect indices of the assignment\\r\\n identifiers_molecule1.extend(id1[indices_mol1])\\r\\n identifiers_molecule2.extend(id2[indices_mol2])\\r\\n\\r\\n elif onlyH == True:\\r\\n for atom_type in range(n_atom_types):\\r\\n coords1, id1 = decomposed_molecule1[atom_type]\\r\\n coords2, id2 = decomposed_molecule2[atom_type]\\r\\n\\r\\n if logger.match_solv == 'standard':\\r\\n\\r\\n # Check for number of occurences of the actual atom type\\r\\n if occurrences1[atom_type] == 1:\\r\\n\\r\\n # Directly match atoms\\r\\n identifiers_molecule1.append(id1)\\r\\n identifiers_molecule2.append(id2)\\r\\n\\r\\n else: # If more than one atom of the type exists\\r\\n\\r\\n n_atoms = len(coords1) # Determine numer of atoms\\r\\n \\r\\n # Calculate permutation matrix for this atom type\\r\\n permutation_matrix = calc_permutation_matrix(coords1, coords2, logger)\\r\\n\\r\\n # Iterate through only H atoms\\r\\n for atom in range(n_atoms):\\r\\n\\r\\n # Find indices of minimum value in matrix (first index cooresponds to coords1)\\r\\n indices = np.where(permutation_matrix == np.min(permutation_matrix))\\r\\n\\r\\n if len(indices[0]) != 1: # Check if there is more than one solution\\r\\n \\r\\n # If there is: use the first match\\r\\n indices = np.array((indices[0][0], indices[1][0]))\\r\\n\\r\\n # Append identifiers corresponding to indices\\r\\n identifiers_molecule1.append(id1[indices[0]])\\r\\n identifiers_molecule2.append(id2[indices[1]])\\r\\n\\r\\n # Deplete corresponding row of first index and column of second index from matrix\\r\\n permutation_matrix = np.delete(permutation_matrix, indices[0], 0)\\r\\n permutation_matrix = np.delete(permutation_matrix, indices[1], 1)\\r\\n\\r\\n # Deplete identifier arrays\\r\\n id1 = np.delete(id1, indices[0])\\r\\n id2 = np.delete(id2, indices[1])\\r\\n\\r\\n elif logger.match_solv == 'hungarian':\\r\\n\\r\\n # Calculate permutation matrix for this atom type\\r\\n if atom_type == 0:\\r\\n permutation_matrix = calc_permutation_matrix(coords1, coords2, logger, do_permute=True)\\r\\n else:\\r\\n permutation_matrix = calc_permutation_matrix(coords1, coords2, logger, do_permute=False)\\r\\n # Solve the problem with the Hungarian algorithm\\r\\n indices_mol1, indices_mol2 = hungarian_solver(permutation_matrix)\\r\\n\\r\\n # Collect indices of the assignment\\r\\n identifiers_molecule1.extend(id1[indices_mol1])\\r\\n identifiers_molecule2.extend(id2[indices_mol2])\\r\\n\\r\\n # Transform identifiers to single arrays containing the new positions\\r\\n pos_mol1 = np.ravel(np.array(identifiers_molecule1, dtype=np.int) - 1)\\r\\n pos_mol2 = np.ravel(np.array(identifiers_molecule2, dtype=np.int) - 1)\\r\\n return(pos_mol1, pos_mol2)\\r\\n # Update charges (should be redundant but just to be sure...)\\r\\n molecule1.chg = np.take(molecule1.chg, pos_mol1)\\r\\n molecule2.chg = np.take(molecule2.chg, pos_mol2)\\r\\n\\r\\n # Update coordinates of the coordinate arrays whose orientations were not changed\\r\\n molecule1.cor = np.take(molecule1.cor, pos_mol1, axis=0)\\r\\n molecule2.cor = np.take(molecule2.cor, pos_mol2, axis=0)\\r\\n\\r\\n # Update standard deviations\\r\\n molecule1.cor_std = np.take(molecule1.cor_std, pos_mol1, axis=0)\\r\\n molecule2.cor_std = np.take(molecule2.cor_std, pos_mol2, axis=0)\\r\\n\\r\\n if logger.use_groups: # If group charges were used, update group properties as well\\r\\n\\r\\n molecule1.get_group_charges(), molecule2.get_group_charges()\\r\\n molecule1.get_group_symbols(), molecule2.get_group_symbols()\\r\\n\\r\\n logger.is_matched = True # Log successful matching\\r\\n molecule1.show_mol = True # Set variable so result is shown\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbcae2728054801de8b0bd21142c9927\",\n \"score\": \"0.6209729\",\n \"text\": \"def check_atom_balance(entry_1: Union[dict, str, Molecule],\\n entry_2: Union[dict, str, Molecule],\\n verbose: Optional[bool] = True,\\n ) -> bool:\\n if not entry_1 or not entry_2:\\n raise SpeciesError(f'Cannot compare entries. Got:\\\\n{entry_1}\\\\nand\\\\n{entry_2}')\\n element_dict_1, element_dict_2 = dict(), dict()\\n result = True\\n\\n # Count the number of each element.\\n for element_dict, entry in zip([element_dict_1, element_dict_2], [entry_1, entry_2]):\\n if isinstance(entry, Molecule):\\n for atom in entry.atoms:\\n symbol = atom.element.symbol\\n element_dict[symbol] = element_dict.get(symbol, 0) + 1\\n else:\\n xyz = check_xyz_dict(entry)\\n for symbol in xyz['symbols']:\\n element_dict[symbol] = element_dict.get(symbol, 0) + 1\\n\\n # Compare elements.\\n if len(list(element_dict_1.keys())) != len(list(element_dict_1.keys())):\\n result = False\\n if result:\\n for symbol in element_dict_1.keys():\\n if symbol not in list(element_dict_2.keys()):\\n result = False\\n break\\n num_1, num_2 = element_dict_1[symbol], element_dict_2[symbol]\\n if num_1 != num_2:\\n result = False\\n break\\n\\n if not result:\\n if verbose:\\n logger.error(f'\\\\nEntries have different types or numbers of elements, got:\\\\n'\\n f'{element_dict_1}\\\\nand\\\\n{element_dict_2}\\\\n')\\n return False\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d36ca41b7dd9ff6599b2ce2e22d4054\",\n \"score\": \"0.5954933\",\n \"text\": \"def check_two_types_same(arg1_name, arg1_type, arg2_name, arg2_type):\\n if arg1_type != arg2_type:\\n raise TypeError(f'The type of `{arg1_name}` and `{arg2_name}` should be same.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45aeb357e0f7082207e1f1ae935455de\",\n \"score\": \"0.5926925\",\n \"text\": \"def _match_impropers(atom_types, generator):\\n wildcard = generator.ff._atomClasses[\\\"\\\"]\\n (\\n atom_1_type,\\n atom_2_type,\\n atom_3_type,\\n atom_4_type,\\n ) = Forcefield.substitute_wildcards(atom_types, wildcard)\\n match = None\\n for improper in generator.improper:\\n types1 = improper.types1\\n types2 = improper.types2\\n types3 = improper.types3\\n types4 = improper.types4\\n has_wildcard = wildcard in (types1, types2, types3, types4)\\n if match is not None and has_wildcard:\\n # Prefer specific definitions over ones with wildcards\\n continue\\n if atom_1_type in types1:\\n for t2, t3, t4 in itertools.permutations(\\n ((atom_2_type, 1), (atom_3_type, 2), (atom_4_type, 3))\\n ):\\n if t2[0] in types2 and t3[0] in types3 and t4[0] in types4:\\n match = improper\\n break\\n return match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c05303222fa3a611892fe5ad8911e0\",\n \"score\": \"0.5916589\",\n \"text\": \"def _is_equal_same_type(self, other):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c05303222fa3a611892fe5ad8911e0\",\n \"score\": \"0.5916589\",\n \"text\": \"def _is_equal_same_type(self, other):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb724a49d3cfab587576c52561d12cc7\",\n \"score\": \"0.57364625\",\n \"text\": \"def same_types(self, index1, index2):\\n try:\\n same = self.table[index1].type == self.table[index2].type != SharedData.TYPES.NO_TYPE\\n except Exception:\\n self.error()\\n return same\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62739e3b3877792d55060e659080dffd\",\n \"score\": \"0.5639938\",\n \"text\": \"def test_consistency():\\n ff = vermouth.forcefield.ForceField(name='test_ff')\\n itp_director = vermouth.gmx.itp_read.ITPDirector(ff)\\n for inter_type in itp_director.atom_idxs:\\n assert tuple(['moleculetype', inter_type]) in itp_director.METH_DICT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5b3cd71d1346c4c25544c430cce3776\",\n \"score\": \"0.56363684\",\n \"text\": \"def types_match_reference(self, current_types=None):\\n if current_types is None:\\n current_types = self.current_smirks\\n\\n if len(current_types) != len(self.ref_labels):\\n return set(), False\\n\\n current_assignments = get_typed_molecules(current_types, self.molecules)\\n\\n type_matches, checks_pass = match_reference(current_assignments, self.cluster_dict)\\n return type_matches, checks_pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66846e392ac7454a1e83bc8a8816d484\",\n \"score\": \"0.5611787\",\n \"text\": \"def verify(self):\\r\\n left_atoms = self.left.get_atoms()\\r\\n right_atoms = self.right.get_atoms()\\r\\n for i, j in left_atoms.items():\\r\\n if i in right_atoms:\\r\\n if j != right_atoms[i]:\\r\\n return False\\r\\n else:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8907e0aac79b96b3735f6ece09ed4f90\",\n \"score\": \"0.56113464\",\n \"text\": \"def symmetry_and_matching(molecule1, molecule2, logger, settings):\\r\\n\\r\\n def set_n_dev(molecule2, logger, settings):\\r\\n \\\"\\\"\\\" Gets a number of deviations to be shown from user \\\"\\\"\\\"\\r\\n\\r\\n n_dev = int(eval(input(\\\"\\\\nInput the number of deviations to be highlighted: \\\")))\\r\\n\\r\\n if n_dev < 1 or n_dev > molecule2.n_atoms: # do nothing\\r\\n\\r\\n logger.pt_wrong_n_dev()\\r\\n\\r\\n else:\\r\\n\\r\\n settings.n_dev = n_dev # Update n_dev\\r\\n\\r\\n def get_rot_n(logger):\\r\\n \\\"\\\"\\\" Gets a proper rotation from user \\\"\\\"\\\"\\r\\n\\r\\n n_rot = int(eval(input(\\\"\\\\nInput n for n-fold clockwise rotation: \\\")))\\r\\n\\r\\n if n_rot == 0: # Set n_rot to 1 if it is 0 by accident\\r\\n\\r\\n n_rot = 1\\r\\n logger.pt_wrong_n()\\r\\n\\r\\n # Return value(s)\\r\\n return n_rot\\r\\n\\r\\n choices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 20, 21, 24, -1, -2, -3, -4, -5, -6] # List of accepted choices\\r\\n\\r\\n # Dictionary of symmetry number and transformation correlations \\r\\n sym_number = {1: 'inv', 2: 'xy', 3: 'xz', 4: 'yz', 5: 'x', 6: 'y', 7: 'z'}\\r\\n sym_transf = {'xy': ac.z_axis, 'xz': ac.y_axis, 'yz': ac.x_axis, 'x': ac.x_axis, 'y': ac.y_axis, 'z': ac.z_axis}\\r\\n\\r\\n question = \\\"\\\\n>> Enter your choice: \\\"\\r\\n\\r\\n # Move molecules in standard orientation and print into to user\\r\\n molecule1.standard_orientation(logger, calc_for='molecule')\\r\\n molecule2.standard_orientation(logger, calc_for='molecule')\\r\\n logger.pt_standard_orientation()\\r\\n\\r\\n # Save the current status\\r\\n molecule1.make_save_point(reset_type='save')\\r\\n molecule2.make_save_point(reset_type='save')\\r\\n logger.pt_saved_status()\\r\\n\\r\\n while True:\\r\\n\\r\\n logger.pt_match_menu(settings)\\r\\n \\r\\n if molecule1.show_mol:\\r\\n\\r\\n viewmol_vtk = ap.Molecular_Viewer_vtk(settings)\\r\\n viewmol_vtk.make_initial_plot(molecule1, molecule2, settings)\\r\\n logger.pt_plotting() # Inform user of the new plot\\r\\n logger.pt_plotting_screenshot()\\r\\n viewmol_vtk.show(molecule1, molecule2, settings)\\r\\n molecule1.show_mol = False\\r\\n \\r\\n symmetry_operation = logger.get_menu_choice(choices, question, return_type='int')\\r\\n\\r\\n if symmetry_operation == -6: # Set number of deviations and update information\\r\\n\\r\\n set_n_dev(molecule2, logger, settings)\\r\\n\\r\\n if logger.is_matched:\\r\\n\\r\\n ac.highlight_dev(molecule1, molecule2, logger, settings)\\r\\n\\r\\n elif symmetry_operation == -5: # Reset the changes made by the matching algorithm\\r\\n\\r\\n logger.pt_match_reset(reset_type='save')\\r\\n molecule1.reset_molecule(settings, reset_type='save')\\r\\n molecule2.reset_molecule(settings, reset_type='save')\\r\\n\\r\\n molecule1.disord_pos, molecule2.disord_pos = None, None\\r\\n molecule1.disord_rmsd, molecule2.disord_rmsd = None, None\\r\\n\\r\\n # Update properties and bonds\\r\\n molecule1.update_properties(settings, get_bonds=True)\\r\\n molecule2.update_properties(settings, get_bonds=True)\\r\\n\\r\\n elif symmetry_operation == -4: # Change VTK plot settings\\r\\n\\r\\n settings.change_vtk_plot_settings(molecule1, molecule2, logger)\\r\\n\\r\\n elif symmetry_operation == -3: # Swap atoms in molecule 1\\r\\n\\r\\n molecule1.swap_atoms(logger)\\r\\n\\r\\n # Update properties and bonds\\r\\n molecule1.update_properties(settings, get_bonds=True)\\r\\n molecule2.update_properties(settings, get_bonds=True)\\r\\n\\r\\n if logger.is_matched:\\r\\n\\r\\n ac.highlight_dev(molecule1, molecule2, logger, settings)\\r\\n\\r\\n elif symmetry_operation == -2: # Change matching defaults\\r\\n\\r\\n molecule2.change_algorithms(logger)\\r\\n\\r\\n elif symmetry_operation == -1: # Match molecules\\r\\n\\r\\n ac.match_molecules(molecule1, molecule2, logger)\\r\\n\\r\\n # Update properties and bonds\\r\\n molecule1.update_properties(settings, get_bonds=True)\\r\\n molecule2.update_properties(settings, get_bonds=True)\\r\\n\\r\\n # Highlight deviations\\r\\n ac.highlight_dev(molecule1, molecule2, logger, settings)\\r\\n logger.pt_highest_deviations(molecule1, molecule2, settings)\\r\\n\\r\\n elif symmetry_operation == 0:\\r\\n\\r\\n if logger.is_matched:\\r\\n\\r\\n molecule1.calc_total_symOP() # Calculate total symmetry operation\\r\\n logger.pt_exit_sym_menu()\\r\\n break\\r\\n\\r\\n else:\\r\\n\\r\\n logger.pt_no_match()\\r\\n exit_yn = logger.get_menu_choice(['y', 'Y', 'n', 'N'], question, return_type='str')\\r\\n\\r\\n if exit_yn == 'y' or exit_yn == 'Y':\\r\\n\\r\\n molecule1.calc_total_symOP() # Calculate total symmetry operation\\r\\n break\\r\\n\\r\\n elif symmetry_operation == 1:\\r\\n\\r\\n logger.pt_sym_inversion()\\r\\n molecule1.inversion() # inversion\\r\\n\\r\\n elif symmetry_operation in [2, 3, 4]: # Reflections\\r\\n\\r\\n logger.pt_sym_reflection(sym_number[symmetry_operation])\\r\\n molecule1.sigma_refl(sym_transf[sym_number[symmetry_operation]])\\r\\n\\r\\n elif symmetry_operation in [5, 6, 7]: # Rotations\\r\\n\\r\\n logger.pt_rot_info()\\r\\n n_rot = get_rot_n(logger)\\r\\n logger.pt_sym_rotation(n_rot, sym_number[symmetry_operation])\\r\\n molecule1.cn_rotation(sym_transf[sym_number[symmetry_operation]], n_rot)\\r\\n\\r\\n elif symmetry_operation == 8:\\r\\n\\r\\n molecule1.show_mol = True\\r\\n\\r\\n elif symmetry_operation == 10: # Save current changes\\r\\n\\r\\n molecule1.make_save_point(reset_type='save'), molecule2.make_save_point(reset_type='save')\\r\\n logger.pt_saved_status()\\r\\n\\r\\n elif symmetry_operation == 20: # Export structures\\r\\n\\r\\n export_structure(molecule1, molecule2, logger, settings)\\r\\n\\r\\n else:\\r\\n\\r\\n logger.pt_invalid_input()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58de7898ef3d975083590d5bc742f7fd\",\n \"score\": \"0.5559224\",\n \"text\": \"def compare(self, x):\\r\\n elements1 = [i for i, j in self.get_atoms().items()]\\r\\n elements2 = [i for i, j in x.get_atoms().items()]\\r\\n for i in elements1:\\r\\n if i not in elements2:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecba22fac5d9ffca7404902dbde3e413\",\n \"score\": \"0.5551404\",\n \"text\": \"def compatible_content_types(type1: str, type2: str) -> bool:\\n type1, subtype1 = JSONSchema.split_content_type(type1)\\n type2, subtype2 = JSONSchema.split_content_type(type2)\\n return type1 == type2 and (\\n subtype1 == '*' or subtype2 == '*' or subtype1 == subtype2\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3476e1b39dc8ec61d76ab76ef5c2a582\",\n \"score\": \"0.5520482\",\n \"text\": \"def has_identical_inputs_and_outputs_as(self, other: \\\"Activity\\\"):\\n return sorted(u.entity.path for u in self.usages) == sorted(u.entity.path for u in other.usages) and sorted(\\n g.entity.path for g in self.generations\\n ) == sorted(g.entity.path for g in other.generations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba1c66b21a8bb87a655d2ef7c57a670\",\n \"score\": \"0.5498743\",\n \"text\": \"def isidenticalto(self,another):\\n if ((self.type == another.type) and (self.label == another.label)):\\n return 1\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b925839488146c4d898794de68478461\",\n \"score\": \"0.5496395\",\n \"text\": \"def _typecheck_input(x1_type, x2_type, prim_name=None):\\n msg_prefix = f\\\"For '{prim_name}', the\\\" if prim_name else \\\"The\\\"\\n const_utils.check_type_valid(x1_type, [mstype.float32, mstype.float16], 'x1')\\n const_utils.check_type_valid(x2_type, [mstype.float32, mstype.float16], 'x2')\\n if x1_type != x2_type:\\n raise TypeError(f\\\"{msg_prefix} inputs must be the same type, but got x1_type: {x1_type} \\\"\\n f\\\"and x2_type: {x2_type}.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a0405199f0363dfd86b498774d5a431\",\n \"score\": \"0.546156\",\n \"text\": \"def _check_for_identicals(molecule, logger, settings, logg_for='molecule1'):\\r\\n\\r\\n pos_mol = check_for_identical_pos(molecule.cor, settings.delta)\\r\\n exclude_identicals(molecule, logger, pos_mol, logg_for)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ed215cc5aed5c6a4579daac7ea87e6\",\n \"score\": \"0.545922\",\n \"text\": \"def is_two_objects_has_same_type(first: Any, second: Any) -> bool:\\n return type(first) == type(second)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e5ff19f0085e831ca0708b9f0d7c3b\",\n \"score\": \"0.5450432\",\n \"text\": \"def best_match_reference_types(self, atomtypes, molecules):\\n if self.reference_typed_molecules is None:\\n if self.verbose: print('No reference molecules specified, so skipping likelihood calculation.')\\n return None\\n\\n # Create bipartite graph (U,V,E) matching current atom types U with reference atom types V via edges E with weights equal to number of atoms typed in common.\\n if self.verbose: print('Creating graph matching current atom types with reference atom types...')\\n initial_time = time.time()\\n import networkx as nx\\n graph = nx.Graph()\\n # Get current atomtypes and reference atom types\\n current_atomtypes = [ typename for (smarts, typename) in atomtypes ]\\n reference_atomtypes = [ typename for typename in self.reference_atomtypes ]\\n # check that current atom types are not in reference atom types\\n if set(current_atomtypes) & set(reference_atomtypes):\\n raise Exception(\\\"Current and reference atom types must be unique\\\")\\n # Add current atom types\\n for atomtype in current_atomtypes:\\n graph.add_node(atomtype, bipartite=0)\\n # Add reference atom types\\n for atomtype in reference_atomtypes:\\n graph.add_node(atomtype, bipartite=1)\\n # Add edges.\\n atoms_in_common = dict()\\n for current_atomtype in current_atomtypes:\\n for reference_atomtype in reference_atomtypes:\\n atoms_in_common[(current_atomtype,reference_atomtype)] = 0\\n for (current_typed_molecule, reference_typed_molecule) in zip(molecules, self.reference_typed_molecules):\\n for (current_typed_atom, reference_typed_atom) in zip(current_typed_molecule.GetAtoms(), reference_typed_molecule.GetAtoms()):\\n current_atomtype = current_typed_atom.GetStringData(self.typetag)\\n reference_atomtype = reference_typed_atom.GetType()\\n atoms_in_common[(current_atomtype,reference_atomtype)] += 1\\n for current_atomtype in current_atomtypes:\\n for reference_atomtype in reference_atomtypes:\\n weight = atoms_in_common[(current_atomtype,reference_atomtype)]\\n graph.add_edge(current_atomtype, reference_atomtype, weight=weight)\\n elapsed_time = time.time() - initial_time\\n if self.verbose: print('Graph creation took %.3f s' % elapsed_time)\\n\\n # Compute maximum match\\n if self.verbose: print('Computing maximum weight match...')\\n initial_time = time.time()\\n mate = nx.algorithms.max_weight_matching(graph, maxcardinality=False)\\n elapsed_time = time.time() - initial_time\\n if self.verbose: print('Maximum weight match took %.3f s' % elapsed_time)\\n\\n # Compute match dictionary and total number of matches.\\n atom_type_matches = list()\\n total_atom_type_matches = 0\\n for current_atomtype in current_atomtypes:\\n if current_atomtype in mate:\\n reference_atomtype = mate[current_atomtype]\\n counts = graph[current_atomtype][reference_atomtype]['weight']\\n total_atom_type_matches += counts\\n atom_type_matches.append( (current_atomtype, reference_atomtype, counts) )\\n else:\\n atom_type_matches.append( (current_atomtype, None, None) )\\n\\n # Report on matches\\n if self.verbose:\\n print(\\\"PROPOSED:\\\")\\n self.show_type_matches(atom_type_matches)\\n\\n return (atom_type_matches, total_atom_type_matches)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5857a237bee8d4da813f6d2a7c35ae\",\n \"score\": \"0.54449016\",\n \"text\": \"def make_consistent(molecule1, molecule2, logger, settings):\\r\\n\\r\\n # --------- Basic outline for consistency establishment ---------\\r\\n # Check for atoms on identical positions\\r\\n # Check if consistent\\r\\n # If not: Check for groups\\r\\n # If not: Remove hydrogen atoms on carbons\\r\\n # If not: Check for groups\\r\\n # If not: Remove group-14 H atoms\\r\\n # If not: Check for groups\\r\\n # If not: Remove all hydrogen atoms\\r\\n # If not: Check for groups\\r\\n # If not: ERROR\\r\\n # If consistent and hydrogens left: ask user to remove them\\r\\n # ---------------------------------------------------------------\\r\\n\\r\\n def _n_hydrogen_atoms(chg):\\r\\n \\\"\\\"\\\" Returns the number of hydrogen atoms in the molecule \\\"\\\"\\\"\\r\\n\\r\\n return len(np.where(chg == 1)[0])\\r\\n\\r\\n def _hydrogens_left(chg1, chg2):\\r\\n \\\"\\\"\\\" Returns if there are hydrogen atoms in the molecules \\\"\\\"\\\"\\r\\n\\r\\n return 1 in chg1 or 1 in chg2\\r\\n\\r\\n def _if_carbons(chg1, chg2):\\r\\n \\\"\\\"\\\" Returns if there are carbon atoms in the molecules \\\"\\\"\\\"\\r\\n\\r\\n return len(np.where(chg1 == 6)[0]) > 0 or len(np.where(chg2 == 6)[0]) > 0\\r\\n\\r\\n def _are_consistent(chg1, chg2):\\r\\n \\\"\\\"\\\" Checks if the structures are consistent based on their charge arrays \\\"\\\"\\\"\\r\\n\\r\\n return np.allclose(np.sort(chg1), np.sort(chg2)) if len(chg1) == len(chg2) else False\\r\\n\\r\\n def _check_for_identicals(molecule, logger, settings, logg_for='molecule1'):\\r\\n \\\"\\\"\\\" Checks for identical occupations in the molecule \\\"\\\"\\\"\\r\\n\\r\\n pos_mol = check_for_identical_pos(molecule.cor, settings.delta)\\r\\n exclude_identicals(molecule, logger, pos_mol, logg_for)\\r\\n\\r\\n def _try_groups(molecule1, molecule2):\\r\\n \\\"\\\"\\\" Checks if matching problem can be solved with groups \\\"\\\"\\\"\\r\\n\\r\\n molecule1.get_group_charges(), molecule2.get_group_charges() # Get group properties\\r\\n\\r\\n if _are_consistent(molecule1.chg_grp, molecule2.chg_grp):\\r\\n\\r\\n return True\\r\\n\\r\\n else:\\r\\n\\r\\n molecule1.chg_grp, molecule2.chg_grp = None, None\\r\\n\\r\\n return False\\r\\n\\r\\n def _use_groups(molecule1, molecule2, logger):\\r\\n \\\"\\\"\\\" Uses groups in the matching process \\\"\\\"\\\"\\r\\n\\r\\n molecule1.get_group_charges(), molecule2.get_group_charges() # Get group properties\\r\\n molecule1.get_group_symbols(), molecule2.get_group_symbols() # Get group properties\\r\\n logger.lg_group_algorithm()\\r\\n logger.lg_consistent() # Logg event\\r\\n\\r\\n def _can_remove_carbon_hydrogens(molecule1, molecule2, settings, full_group_14=False):\\r\\n \\\"\\\"\\\" Checks if hydrogen atoms bound to carbon can be removed within consistency restrictions \\\"\\\"\\\"\\r\\n\\r\\n return len(molecule1.fnd_mult_H_atoms_bte(settings, full_group_14)), len(\\r\\n molecule2.fnd_mult_H_atoms_bte(settings, full_group_14))\\r\\n\\r\\n def _remove_carbon_hydrogens(molecule, settings, full_group_14=False):\\r\\n \\\"\\\"\\\" Removes hydrogen atoms bound to carbon in the molecule and returns number of removed atoms \\\"\\\"\\\"\\r\\n\\r\\n old_n_atoms = molecule.n_atoms\\r\\n\\r\\n molecule.rem_H_atoms_btc(settings, full_group_14) # Remove hydrogen atoms\\r\\n\\r\\n return old_n_atoms - molecule.n_atoms\\r\\n\\r\\n def _remove_all_hydrogens(molecule):\\r\\n \\\"\\\"\\\" Removes all hydrogen atoms in the molecule and returns number of removed atoms \\\"\\\"\\\"\\r\\n\\r\\n old_n_atoms = molecule.n_atoms\\r\\n\\r\\n molecule.rem_H_atoms_all() # Remove hydrogen atoms\\r\\n\\r\\n return old_n_atoms - molecule.n_atoms\\r\\n\\r\\n # Check for multiple atoms on identical positions and remove selected atoms\\r\\n _check_for_identicals(molecule1, logger, settings, logg_for='molecule1')\\r\\n _check_for_identicals(molecule2, logger, settings, logg_for='molecule2')\\r\\n\\r\\n # Determine initial number of atoms and hydrogen atoms\\r\\n init_n_atoms1, init_n_atoms2 = molecule1.n_atoms, molecule2.n_atoms\\r\\n n_atoms1_init, n_atoms2_init = molecule1.n_atoms, molecule2.n_atoms\\r\\n\\r\\n init_n_hydro1, init_n_hydro2 = _n_hydrogen_atoms(molecule1.chg), _n_hydrogen_atoms(molecule2.chg)\\r\\n\\r\\n # Log number of atoms\\r\\n logger.cons_init_at_mol1, logger.cons_init_at_mol2 = n_atoms1_init, n_atoms2_init\\r\\n logger.cons_init_at_H_mol1, logger.cons_init_at_H_mol2 = init_n_hydro1, init_n_hydro2\\r\\n\\r\\n # Print information about number of atoms and hydrogen atoms in the molecules\\r\\n logger.pt_consistency_start(init_n_atoms1, init_n_atoms2)\\r\\n logger.pt_number_h_atoms(init_n_hydro1, init_n_hydro2)\\r\\n\\r\\n # Begin with algorithm\\r\\n if not _are_consistent(molecule1.chg, molecule2.chg):\\r\\n\\r\\n if _try_groups(molecule1, molecule2): # CONSISTENT (GROUP)\\r\\n\\r\\n _use_groups(molecule1, molecule2, logger)\\r\\n\\r\\n else:\\r\\n\\r\\n n_atoms1_no_Hc = _remove_carbon_hydrogens(molecule1, settings, full_group_14=False)\\r\\n n_atoms2_no_Hc = _remove_carbon_hydrogens(molecule2, settings, full_group_14=False)\\r\\n\\r\\n logger.lg_rem_H_btc(n_atoms1_no_Hc, n_atoms2_no_Hc) # Log event + print number of removed H atoms\\r\\n\\r\\n hydrogens_left = _hydrogens_left(molecule1.chg, molecule2.chg) # Check for hydrogen atoms\\r\\n\\r\\n if not _are_consistent(molecule1.chg, molecule2.chg):\\r\\n\\r\\n if _try_groups(molecule1, molecule2): # CONSISTENT (GROUP)\\r\\n\\r\\n _use_groups(molecule1, molecule2, logger)\\r\\n\\r\\n elif hydrogens_left:\\r\\n\\r\\n n_atoms1_no_Hg14 = _remove_carbon_hydrogens(molecule1, settings, full_group_14=True)\\r\\n n_atoms2_no_Hg14 = _remove_carbon_hydrogens(molecule2, settings, full_group_14=True)\\r\\n\\r\\n logger.lg_rem_H_btg14(n_atoms1_no_Hg14, n_atoms2_no_Hg14) # Log event + print removed H atoms\\r\\n\\r\\n hydrogens_left = _hydrogens_left(molecule1.chg, molecule2.chg) # Check for hydrogen atoms\\r\\n\\r\\n if not _are_consistent(molecule1.chg, molecule2.chg):\\r\\n\\r\\n if _try_groups(molecule1, molecule2): # CONSISTENT (GROUP)\\r\\n\\r\\n _use_groups(molecule1, molecule2, logger)\\r\\n\\r\\n elif hydrogens_left:\\r\\n\\r\\n n_atoms1_no_H = _remove_all_hydrogens(molecule1)\\r\\n n_atoms2_no_H = _remove_all_hydrogens(molecule2)\\r\\n\\r\\n logger.lg_rem_H_all(n_atoms1_no_H, n_atoms2_no_H) # Log event + print removed H atoms\\r\\n\\r\\n if not _are_consistent(molecule1.chg, molecule2.chg):\\r\\n\\r\\n if _try_groups(molecule1, molecule2): # CONSISTENT (GROUP)\\r\\n\\r\\n _use_groups(molecule1, molecule2, logger)\\r\\n\\r\\n else:\\r\\n\\r\\n logger.pt_consistency_error() # Unsolvable problem\\r\\n\\r\\n else: # CONSISTENT (REGULAR)\\r\\n\\r\\n logger.lg_consistent() # Log event\\r\\n\\r\\n else:\\r\\n\\r\\n logger.pt_consistency_error() # Unsolvable problem\\r\\n\\r\\n else: # CONSISTENT (REGULAR)\\r\\n\\r\\n logger.lg_consistent() # Log event\\r\\n\\r\\n else:\\r\\n\\r\\n logger.pt_consistency_error() # Unsolvable problem\\r\\n\\r\\n else: # CONSISTENT (REGULAR)\\r\\n\\r\\n logger.lg_consistent() # Log event\\r\\n\\r\\n else: # CONSISTENT (REGULAR)\\r\\n\\r\\n logger.lg_consistent() # Log event\\r\\n\\r\\n # At this point we have established consistency between the molecules\\r\\n if logger.consist:\\r\\n\\r\\n init_n_atoms1, init_n_atoms2 = molecule1.n_atoms, molecule2.n_atoms\\r\\n init_n_hydro1, init_n_hydro2 = _n_hydrogen_atoms(molecule1.chg), _n_hydrogen_atoms(molecule2.chg)\\r\\n n_hydro1_c, n_hydro2_c = _can_remove_carbon_hydrogens(molecule1, molecule2, settings, full_group_14=False)\\r\\n n_hydro1_full, n_hydro2_full = _can_remove_carbon_hydrogens(molecule1, molecule2, settings, full_group_14=True)\\r\\n hydrogens_left = _hydrogens_left(molecule1.chg, molecule2.chg)\\r\\n\\r\\n logger.can_rem_H_btc = n_hydro1_c == n_hydro2_c\\r\\n logger.can_rem_H_btg14 = n_hydro1_full == n_hydro2_full and n_hydro1_full != n_hydro1_c\\r\\n\\r\\n # Log info after molecules are consistent\\r\\n logger.cons_at_mol1, logger.cons_at_mol2 = init_n_atoms1, init_n_atoms2\\r\\n logger.cons_at_H_mol1, logger.cons_at_H_mol2 = init_n_hydro1, init_n_hydro2\\r\\n\\r\\n if not hydrogens_left: # No hydrogen atoms left ... nothing can be done\\r\\n\\r\\n pass\\r\\n\\r\\n # Exclude all hydrogen atoms if desired\\r\\n elif not logger.rem_H_all and _n_hydrogen_atoms(molecule1.chg) == _n_hydrogen_atoms(molecule2.chg):\\r\\n\\r\\n choices = logger.pt_possibilities(init_n_hydro1, n_hydro2_c, n_hydro2_full)\\r\\n\\r\\n while True:\\r\\n\\r\\n operation = logger.get_menu_choice(choices, question=\\\"\\\\n>> Enter your choice: \\\", return_type='int')\\r\\n\\r\\n if operation == 0: # Remove all hydrogen atoms\\r\\n\\r\\n n_atoms1_no_H = _remove_all_hydrogens(molecule1)\\r\\n n_atoms2_no_H = _remove_all_hydrogens(molecule2)\\r\\n logger.lg_rem_H_all(n_atoms1_no_H, n_atoms2_no_H) # Log event + print number of removed H atoms\\r\\n logger.user_choice_rem_all_H = True\\r\\n\\r\\n if logger.use_groups: # Get group properties\\r\\n\\r\\n molecule1.get_group_charges(), molecule2.get_group_charges()\\r\\n molecule1.get_group_symbols(), molecule2.get_group_symbols()\\r\\n\\r\\n break\\r\\n\\r\\n elif operation == 1: # Remove hydrogens bound to carbon\\r\\n\\r\\n n_atoms1_no_Hc = _remove_carbon_hydrogens(molecule1, settings, full_group_14=False)\\r\\n n_atoms2_no_Hc = _remove_carbon_hydrogens(molecule2, settings, full_group_14=False)\\r\\n logger.lg_rem_H_btc(n_atoms1_no_Hc, n_atoms2_no_Hc) # Log event + print number of removed H atoms\\r\\n logger.user_choice_rem_btc_H = True\\r\\n\\r\\n if logger.use_groups: # Get group properties\\r\\n\\r\\n molecule1.get_group_charges(), molecule2.get_group_charges()\\r\\n molecule1.get_group_symbols(), molecule2.get_group_symbols()\\r\\n\\r\\n break\\r\\n\\r\\n elif operation == 2: # Remove hydrogens bound to group-14 elements\\r\\n\\r\\n n_atoms1_no_Hg14 = _remove_carbon_hydrogens(molecule1, settings, full_group_14=True)\\r\\n n_atoms2_no_Hg14 = _remove_carbon_hydrogens(molecule2, settings, full_group_14=True)\\r\\n logger.lg_rem_H_btg14(n_atoms1_no_Hg14, n_atoms2_no_Hg14) # Log event + print # removed H atoms\\r\\n logger.user_choice_rem_btg14_H = True\\r\\n\\r\\n if logger.use_groups: # Get group properties\\r\\n\\r\\n molecule1.get_group_charges(), molecule2.get_group_charges()\\r\\n molecule1.get_group_symbols(), molecule2.get_group_symbols()\\r\\n\\r\\n break\\r\\n\\r\\n elif operation == 3: # Do nothing\\r\\n\\r\\n break\\r\\n\\r\\n else:\\r\\n\\r\\n logger.pt_invalid_input()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b37417f17431acc3a72941930d19f1f\",\n \"score\": \"0.5442995\",\n \"text\": \"def checkAtomToResName(self):\\n \\n for i in range(0,len(self.atomToResNameList)-1):\\n \\n firstSet = self.atomToResNameList[i]\\n \\n if not firstSet or isinstance(firstSet,ResidueAtomToResName):\\n continue\\n\\n firstSetNmrAtoms = firstSet.nmrAtoms\\n firstSetNumNmrAtoms = len(firstSetNmrAtoms)\\n \\n for j in range(i+1,len(self.atomToResNameList)):\\n \\n secondSet = self.atomToResNameList[j]\\n \\n if not secondSet or isinstance(secondSet,ResidueAtomToResName):\\n continue\\n \\n secondSetNmrAtoms = secondSet.nmrAtoms\\n \\n #\\n # Don't bother if same length: has to be same AtomToResName instance if same\\n #\\n \\n comparisonStatus = -1\\n\\n if len(secondSetNmrAtoms) > firstSetNumNmrAtoms:\\n \\n #\\n # First set subset of second?\\n #\\n \\n subSet = firstSet\\n fullSet = secondSet\\n \\n comparisonStatus = self.compareNmrAtoms(firstSetNmrAtoms,secondSetNmrAtoms)\\n \\n elif len(secondSetNmrAtoms) < firstSetNumNmrAtoms:\\n\\n #\\n # Second set subset of first?\\n #\\n \\n subSet = secondSet\\n fullSet = firstSet\\n \\n comparisonStatus = self.compareNmrAtoms(secondSetNmrAtoms,firstSetNmrAtoms)\\n \\n \\n if comparisonStatus == 0:\\n \\n if self.verbose:\\n print \\\" Error: Partial subset detected. Is illegal!\\\"\\n \\n elif comparisonStatus == 1:\\n \\n if self.verbose:\\n print \\\" Warning: Full subset detected for %s. Added to full set %s.\\\" % (str(subSet.resNameList),\\n str(fullSet.resNameList))\\n\\n #\\n # This situation is principle possible for prochirals (some stereo assigned, some not)\\n # and for chemAtomSets with isEquivalent set to None (e.g. HE1 reported for Phe,\\n # but also HE*).\\n #\\n # Best way seems to just add the subset to the full one: safest handling. \\n #\\n \\n for resName in subSet.resNameList:\\n \\n #\\n # Have to set all chemAtomSets for this one (if stereo becomes non\\n # stereo, if single becomes multi!)\\n #\\n \\n chemAtomSets = [subSet.chemAtomDict[subSet.nmrAtoms[0]].chemAtomSet]\\n \\n if chemAtomSets[0].isProchiral:\\n if chemAtomSets[0].chemAtomSet:\\n chemAtomSetList = list(chemAtomSets[0].chemAtomSet.chemAtomSets)\\n otherChemAtomSet = chemAtomSetList[not chemAtomSetList.index(chemAtomSets[0])]\\n \\n chemAtomSets = list(chemAtomSets[0].chemAtomSet.chemAtomSets)\\n \\n else:\\n chemAtomSets = []\\n \\n #\\n # Keep track of unknown equivalents\\n #\\n \\n keywds = {}\\n \\n if subSet.singleAtoms.has_key(resName):\\n keywds['singleNmrAtom'] = subSet.singleAtoms[resName]\\n chemAtomSets = []\\n elif len(subSet.nmrAtoms) == 1:\\n keywds['singleNmrAtom'] = subSet.nmrAtoms[0]\\n chemAtomSets = []\\n\\n fullSet.addResName(resName,chemAtomSets,subSet.nmrAtoms,**keywds)\\n\\n self.atomToResNameList[self.atomToResNameList.index(subSet)] = None\\n \\n if subSet == firstSet:\\n \\n #\\n # Have to end this loop and continue with the main one\\n #\\n \\n break\\n \\n #\\n # Clean up\\n #\\n \\n while(self.atomToResNameList.count(None) > 0):\\n \\n self.atomToResNameList.pop(self.atomToResNameList.index(None))\\n\\n #\\n # If there are multiple resNames for the atomToResName, then\\n # check whether some are sysName subsets of others... merge\\n # if that is the case.\\n #\\n\\n for atomToResName in self.atomToResNameList:\\n \\n namingSystem = atomToResName.nmrAtoms[0].chemAtom.chemComp.findFirstNamingSystem(name = self.namingSystemName)\\n \\n if len(atomToResName.resNameList) > 1:\\n \\n if atomToResName.hasEqChemAtomSets:\\n \\n for nmrAtoms in atomToResName.nmrAtomGroups:\\n \\n chemAtomSet = atomToResName.chemAtomDict[nmrAtoms[0]].chemAtomSet \\n chemAtomSetSysName = findChemAtomOrSetSysName(namingSystem,chemAtomSet)\\n \\n if not chemAtomSetSysName:\\n chemAtomSetSysName = chemAtomSet.name\\n else:\\n chemAtomSetSysName = chemAtomSetSysName.sysName\\n \\n atomSetResName = \\\"\\\"\\n \\n for resName in atomToResName.hasEqChemAtomSets:\\n (chainCode,seqCode,spinSystemId,seqInsertCode,atomSetName) = getNameInfo(resName)\\n\\n if chemAtomSetSysName == atomSetName:\\n \\n atomSetResName = resName\\n break\\n \\n if atomSetResName:\\n \\n for nmrAtom in nmrAtoms:\\n \\n chemAtom = atomToResName.chemAtomDict[nmrAtom]\\n chemAtomSysName = findChemAtomOrSetSysName(namingSystem,chemAtom)\\n \\n if not chemAtomSysName:\\n chemAtomSysName = chemAtom.name\\n else:\\n chemAtomSysName = chemAtomSysName.sysName\\n \\n for resName in atomToResName.hasEqChemAtomSets:\\n\\n (chainCode,seqCode,spinSystemId,seqInsertCode,atomName) = getNameInfo(resName)\\n\\n if chemAtomSysName == atomName:\\n \\n if self.verbose:\\n print \\\" Found equivalent atom group name match for %s - collapsing info on %s\\\" % (resName,atomSetResName)\\n \\n #\\n # Copy associated info\\n #\\n \\n resonance = self.joinResonanceFromResNames([resName],atomToResName.isotopeCode)\\n\\n for refResonance in self.resUnassignedNames[atomSetResName]:\\n copyResonanceInfo(resonance,refResonance,nmrShiftSelect = self.nmrShiftSelect, toResName = atomSetResName,verbose = self.verboseCopy)\\n \\n #\\n # Delete resName and resonance\\n #\\n\\n self.deleteResonance(resonance,[resName])\\n\\n index = atomToResName.hasEqChemAtomSets.index(resName)\\n atomToResName.hasEqChemAtomSets.pop(index)\\n\\n index = atomToResName.resNameList.index(resName)\\n atomToResName.resNameList.pop(index)\\n \\n \\\"\\\"\\\"\\n set = 0\\n for atomToResName in self.atomToResNameList:\\n \\n print set,len(atomToResName.nmrAtoms)\\n \\n for resName in atomToResName.resNameList:\\n \\n print \\\" \\\",resName,\\n \\n chemAtomSets = atomToResName.chemAtomSets[resName]\\n \\n if chemAtomSets:\\n for chemAtomSet in chemAtomSets:\\n \\n print chemAtomSet.name,\\n \\n print\\n \\n \\n set += 1\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9992a928431c52d5923d90e754d71049\",\n \"score\": \"0.5408257\",\n \"text\": \"def same_modalities(m1, m2):\\n if m1 == 'epistemic by default' or m2 == 'epistemic by default':\\n return True\\n return m1 == m2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bfff81edb50da27468dfddd7bd6ef4b\",\n \"score\": \"0.5383365\",\n \"text\": \"def _check_atom_mask_is_ideal(self):\\n # atom_mask = prot.atom_mask\\n ideal_atom_mask = self.ideal_atom_mask()\\n # TODO\\n # utils.assert_equal_nonterminal_atom_types(atom_mask, ideal_atom_mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b042af6ca594eb880c2d3f5207344515\",\n \"score\": \"0.5365642\",\n \"text\": \"def is_same_unit(unit1, unit2):\\n all_types = FindUnitTypes()\\n try:\\n type1 = all_types[unit1]\\n type2 = all_types[unit2]\\n except KeyError:\\n return False\\n if type1 != type2:\\n return False\\n else:\\n Synonyms = Converters[Simplify(type1)].Synonyms\\n return Synonyms[Simplify(unit1)] == Synonyms[Simplify(unit2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94344d37a60dca6350ab287cbb279bac\",\n \"score\": \"0.53316873\",\n \"text\": \"def __eq__(self, other):\\n return (type(self) == type(other)\\n and self.modulo == other.modulo\\n and self.residue == other.residue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee1c223d92df5793af1effe0ab09358e\",\n \"score\": \"0.53302544\",\n \"text\": \"def _are_similar(left, right):\\n return isinstance(left, type(right)) or isinstance(right, type(left))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"929a7fecb3f942daf21e649227493af8\",\n \"score\": \"0.5291899\",\n \"text\": \"def check(self, *args):\\n\\n file_a = input_string = self.string1_entry.get('1.0', 'end-1c')\\n file_b = input_string = self.string2_entry.get('1.0', 'end-1c')\\n \\n list_a = [line.strip() for line in file_a.splitlines()]\\n list_b = [line.strip() for line in file_b.splitlines()] \\n \\n size_a = len(list_a)\\n size_b = len(list_b)\\n \\n oh_oh = 'Empty:'\\n problem = 0\\n if size_a == 0:\\n oh_oh += ' Text A'\\n problem += 1\\n \\n if size_b == 0:\\n oh_oh += ' Text B'\\n problem += 1\\n \\n if problem == 1:\\n self.insert_result(oh_oh, 'Problem', 0)\\n return\\n elif problem == 2: #Both empty\\n self.insert_result(oh_oh, 'Identical', 1)\\n return\\n \\n result = []\\n \\n identical = True \\n red_map = []\\n correct_counter = 0\\n space = max([len(line) for line in list_a])\\n common_template = '%d:\\\\t%' + str(space) + 's |=| %''s'\\n difference_template = '%d:\\\\t%' + str(space) + 's |X| %''s'\\n #common_template = '%s |= %d =| %s\\\\n'\\n #difference_template = '%s |X %d X| %s\\\\n'\\n for index in range(min(size_a, size_b)):\\n line_a = list_a.pop(0)\\n line_b = list_b.pop(0)\\n if line_a == line_b:\\n correct_counter += 1\\n result.append(common_template % (index+1, line_a, line_a))\\n #result += common_template % (line_a, index+1, line_a)\\n red_map.append(False)\\n else:\\n identical = False\\n new_part = difference_template % (index+1, line_a, line_b)\\n result.append(new_part)\\n red_map.append(True)\\n #result += difference_template % (line_a, index+1, line_b)\\n \\n if len(list_a) > 0:\\n identical = False\\n for line in list_a:\\n index += 1\\n new_part = difference_template % (index+1, line, '')\\n result.append(new_part)\\n red_map.append(True)\\n #result += difference_template % (line, index+1, '')\\n elif len(list_b) > 0:\\n identical = False\\n for line in list_b:\\n index += 1\\n new_part = difference_template % (index+1, '', line)\\n result.append(new_part)\\n red_map.append(True)\\n #result += difference_template % ('', index+1, line)\\n \\n correct_ratio = correct_counter / max(size_a, size_b)\\n \\n if identical:\\n summary = 'Identical'\\n else:\\n summary = 'Different'\\n \\n self.insert_result(result, summary, correct_ratio, red_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b57131a1e4e47187586e66004fa4f47\",\n \"score\": \"0.5286781\",\n \"text\": \"def _checkdataconsistency(self, useroutputdata, useroutputsandbox):\\n useroutputdata = useroutputdata.split(\\\";\\\")\\n for data in useroutputdata:\\n for item in useroutputsandbox:\\n if data == item:\\n LOG.error(\\\"Output data and sandbox should not contain the same things.\\\")\\n return S_ERROR(\\\"Output data and sandbox should not contain the same things.\\\")\\n if data.find(\\\"*\\\") > -1:\\n LOG.error(\\\"Remove wildcard characters from output data definition: must be exact files\\\")\\n return S_ERROR(\\\"Wildcard character in OutputData definition\\\")\\n return S_OK()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74f2e90d4579e7f46d444bad68c9752\",\n \"score\": \"0.52670825\",\n \"text\": \"def check_xyz_isomorphism(self,\\n mol: Optional[Molecule] = None,\\n xyz: Optional[dict] = None,\\n allow_nonisomorphic_2d: Optional[bool] = False,\\n verbose: Optional[bool] = True,\\n ) -> bool:\\n mol = mol or self.mol\\n xyz = xyz or self.final_xyz\\n isomorphic = False\\n\\n if mol is not None:\\n\\n s_mol, b_mol = None, None\\n\\n # 1. Perceive\\n try:\\n s_mol, b_mol = molecules_from_xyz(xyz, multiplicity=self.multiplicity, charge=self.charge)\\n except Exception as e:\\n if verbose:\\n logger.error(f'Could not perceive the Cartesian coordinates of species {self.label}. This '\\n f'might result in inconsistent atom order between the Cartesian and the 2D graph '\\n f'representations. Got:\\\\n{e}')\\n\\n # 2. A. Check isomorphism with bond orders using b_mol\\n if b_mol is not None:\\n isomorphic = check_isomorphism(mol, b_mol)\\n if not isomorphic and verbose:\\n logger.error(f'The Cartesian coordinates of species {self.label} are not isomorphic with the 2D '\\n f'structure {mol.copy(deep=True).to_smiles()} when considering bond orders.')\\n\\n # 2. B. Check isomorphism without bond orders using s_mol (only for charged or high spin multiplicity)\\n if not isomorphic and s_mol is not None:\\n isomorphic = check_isomorphism(mol, s_mol, convert_to_single_bonds=True)\\n if not isomorphic and verbose:\\n logger.error(f'The Cartesian coordinates of species {self.label} with charge {self.charge} '\\n f'and multiplicity {self.multiplicity} are not isomorphic with the 2D '\\n f'structure {mol.copy(deep=True).to_smiles()} even without considering bond orders.')\\n\\n # 2. C. Check isomorphism without bond orders NOT using s_mol\\n if not isomorphic:\\n isomorphic = are_coords_compliant_with_graph(xyz=xyz, mol=mol)\\n if not isomorphic and verbose:\\n logger.error(f'The Cartesian coordinates of species {self.label} are not compliant with the 2D '\\n f'structure {mol.copy(deep=True).to_smiles()} even without considering bond orders.')\\n\\n # 3. Report and resolve\\n if not isomorphic:\\n if allow_nonisomorphic_2d:\\n if verbose:\\n logger.warning('Allowing nonisomorphic 2D')\\n isomorphic = True\\n elif verbose:\\n if self.conf_is_isomorphic:\\n # conformer was isomorphic, we don't allow nonisomorphism, but the optimized structure isn't\\n logger.warning('Not allowing nonisomorphic 2D (the conformer WAS isomorphic with the 2D graph)')\\n else:\\n logger.warning('Not allowing nonisomorphic 2D')\\n else:\\n if verbose:\\n logger.error(f'Cannot check isomorphism for species {self.label} '\\n f'without the 2D graph connectivity information.')\\n if allow_nonisomorphic_2d:\\n isomorphic = True\\n if verbose:\\n logger.warning('Allowing nonisomorphic 2D')\\n return isomorphic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c994035966fa07aeb146ae3682abd6f\",\n \"score\": \"0.52578926\",\n \"text\": \"def is_isomorphic(self, other, either_direction=True, check_identical=False, check_only_label=False,\\n check_template_rxn_products=False, generate_initial_map=False, strict=True, save_order=False):\\n if check_template_rxn_products:\\n try:\\n species1 = self.products if self.is_forward else self.reactants\\n species2 = other.products if other.is_forward else other.reactants\\n except AttributeError:\\n raise TypeError('Only use check_template_rxn_products flag for TemplateReactions.')\\n\\n return same_species_lists(species1, species2,\\n check_identical=check_identical,\\n only_check_label=check_only_label,\\n generate_initial_map=generate_initial_map,\\n strict=strict,\\n save_order=save_order)\\n\\n # Compare reactants to reactants\\n forward_reactants_match = same_species_lists(self.reactants, other.reactants,\\n check_identical=check_identical,\\n only_check_label=check_only_label,\\n generate_initial_map=generate_initial_map,\\n strict=strict,\\n save_order=save_order)\\n\\n # Compare products to products\\n forward_products_match = same_species_lists(self.products, other.products,\\n check_identical=check_identical,\\n only_check_label=check_only_label,\\n generate_initial_map=generate_initial_map,\\n strict=strict,\\n save_order=save_order)\\n\\n # Compare specific_collider to specific_collider\\n collider_match = (self.specific_collider == other.specific_collider)\\n\\n # Return now, if we can\\n if forward_reactants_match and forward_products_match and collider_match:\\n return True\\n if not either_direction:\\n return False\\n\\n # Compare reactants to products\\n reverse_reactants_match = same_species_lists(self.reactants, other.products,\\n check_identical=check_identical,\\n only_check_label=check_only_label,\\n generate_initial_map=generate_initial_map,\\n strict=strict,\\n save_order=save_order)\\n\\n # Compare products to reactants\\n reverse_products_match = same_species_lists(self.products, other.reactants,\\n check_identical=check_identical,\\n only_check_label=check_only_label,\\n generate_initial_map=generate_initial_map,\\n strict=strict,\\n save_order=save_order)\\n\\n # should have already returned if it matches forwards, or we're not allowed to match backwards\\n return reverse_reactants_match and reverse_products_match and collider_match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff7a6cd3b468aa1be07dc9af5533949e\",\n \"score\": \"0.52490276\",\n \"text\": \"def test_equal_on_type_mismatch(self):\\n a = payloads.LocateResponsePayload()\\n b = \\\"invalid\\\"\\n\\n self.assertFalse(a == b)\\n self.assertFalse(b == a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2f2c64dcce598db0cca1afa9d86dec5\",\n \"score\": \"0.52272934\",\n \"text\": \"def sample_atomtypes(self):\\n # Copy current atomtypes for proposal.\\n proposed_atomtypes = copy.deepcopy(self.atomtypes)\\n proposed_molecules = copy.deepcopy(self.molecules)\\n natomtypes = len(proposed_atomtypes)\\n ndecorators = len(self.decorators)\\n\\n valid_proposal = True\\n\\n if random.random() < 0.5:\\n # Pick an atom type to destroy.\\n atomtype_index = random.randint(0, natomtypes-1)\\n (atomtype, typename) = proposed_atomtypes[atomtype_index]\\n if self.verbose: print(\\\"Attempting to destroy atom type %s : %s...\\\" % (atomtype, typename))\\n # Delete the atomtype.\\n proposed_atomtypes.remove([atomtype, typename])\\n # Try to type all molecules.\\n try:\\n self.type_molecules(proposed_atomtypes, proposed_molecules)\\n except AtomTyper.TypingException as e:\\n #print e\\n # Reject since typing failed.\\n if self.verbose: print(\\\"Typing failed; rejecting.\\\")\\n valid_proposal = False\\n else:\\n # Pick an atomtype to subtype.\\n atomtype_index = random.randint(0, natomtypes-1)\\n # Pick a decorator to add.\\n decorator_index = random.randint(0, ndecorators-1)\\n # Create new atomtype to insert by appending decorator with 'and' operator.\\n (atomtype, atomtype_typename) = self.atomtypes[atomtype_index]\\n (decorator, decorator_typename) = self.decorators[decorator_index]\\n result = re.match('\\\\[(.+)\\\\]', atomtype)\\n proposed_atomtype = '[' + result.groups(1)[0] + '&' + decorator + ']'\\n proposed_typename = atomtype_typename + ' ' + decorator_typename\\n if self.verbose: print(\\\"Attempting to create new subtype: '%s' (%s) + '%s' (%s) -> '%s' (%s)\\\" % (atomtype, atomtype_typename, decorator, decorator_typename, proposed_atomtype, proposed_typename))\\n\\n # Check that we haven't already determined this atom type isn't matched in the dataset.\\n if proposed_atomtype in self.atomtypes_with_no_matches:\\n if self.verbose: print(\\\"Atom type '%s' (%s) unused in dataset; rejecting.\\\" % (proposed_atomtype, proposed_typename))\\n return False\\n\\n # Check if proposed atomtype is already in set.\\n existing_atomtypes = set()\\n for (a, b) in self.atomtypes:\\n existing_atomtypes.add(a)\\n if proposed_atomtype in existing_atomtypes:\\n if self.verbose: print(\\\"Atom type already exists; rejecting to avoid duplication.\\\")\\n valid_proposal = False\\n\\n # Check for valid proposal before proceeding.\\n if not valid_proposal:\\n return False\\n\\n # Insert atomtype immediately after.\\n proposed_atomtypes.insert(atomtype_index+1, [proposed_atomtype, proposed_typename])\\n # Try to type all molecules.\\n try:\\n # Type molecules.\\n self.type_molecules(proposed_atomtypes, proposed_molecules)\\n # Compute updated statistics.\\n [proposed_atom_typecounts, proposed_molecule_typecounts] = self.compute_type_statistics(proposed_atomtypes, proposed_molecules)\\n # Reject if new type is unused.\\n if (proposed_atom_typecounts[proposed_typename] == 0):\\n # Reject because new type is unused in dataset.\\n if self.verbose: print(\\\"Atom type '%s' (%s) unused in dataset; rejecting.\\\" % (proposed_atomtype, proposed_typename))\\n valid_proposal = False\\n # Store this atomtype to speed up future rejections\\n self.atomtypes_with_no_matches.add(proposed_atomtype)\\n # Reject if parent type is now unused, UNLESS it is a base type\\n if (proposed_atom_typecounts[atomtype_typename] == 0) and (atomtype not in self.basetypes_smarts):\\n # Reject because new type is unused in dataset.\\n if self.verbose: print(\\\"Parent type '%s' (%s) now unused in dataset; rejecting.\\\" % (atomtype, atomtype_typename))\\n valid_proposal = False\\n except AtomTyper.TypingException as e:\\n print(\\\"Exception: %s\\\" % str(e))\\n # Reject since typing failed.\\n if self.verbose: print(\\\"Typing failed for one or more molecules using proposed atomtypes; rejecting.\\\")\\n valid_proposal = False\\n\\n # Check for valid proposal\\n if not valid_proposal:\\n return False\\n\\n if self.verbose: print('Proposal is valid...')\\n\\n # Accept automatically if no reference molecules\\n accept = False\\n if self.reference_typed_molecules is None:\\n accept = True\\n else:\\n # Compute effective temperature\\n if self.temperature == 0.0:\\n effective_temperature = 1\\n else:\\n effective_temperature = (self.total_atoms * self.temperature)\\n\\n # Compute likelihood for accept/reject\\n (proposed_atom_type_matches, proposed_total_atom_type_matches) = self.best_match_reference_types(proposed_atomtypes, proposed_molecules)\\n log_P_accept = (proposed_total_atom_type_matches - self.total_atom_type_matches) / effective_temperature\\n print('Proposal score: %d >> %d : log_P_accept = %.5e' % (self.total_atom_type_matches, proposed_total_atom_type_matches, log_P_accept))\\n if (log_P_accept > 0.0) or (numpy.random.uniform() < numpy.exp(log_P_accept)):\\n accept = True\\n\\n # Accept or reject\\n if accept:\\n self.atomtypes = proposed_atomtypes\\n self.molecules = proposed_molecules\\n self.atom_type_matches = proposed_atom_type_matches\\n self.total_atom_type_matches = proposed_total_atom_type_matches\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38672d47d2a7e4cb12864a9efaf502b9\",\n \"score\": \"0.52102697\",\n \"text\": \"def _mdtype_eq(omdtype, odata, nmdtype, ndata):\\n if ndata is None:\\n return False\\n if omdtype != nmdtype:\\n return False\\n if odata.checksum != ndata.checksum:\\n return False\\n # If we turn --unique-md-filenames on without chaning the data,\\n # then we'll get different filenames, but the same checksum.\\n # Atm. just say they are different, to make sure we delete the\\n # old files.\\n orname = os.path.basename(odata.location[1])\\n nrname = os.path.basename(ndata.location[1])\\n if orname != nrname:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20052922bb82d91e2b6b8be77e54ab3d\",\n \"score\": \"0.5203175\",\n \"text\": \"def test_equal_on_type_mismatch(self):\\n a = payloads.LocateRequestPayload()\\n b = \\\"invalid\\\"\\n\\n self.assertFalse(a == b)\\n self.assertFalse(b == a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f587a8a16005457fa4de04ab4fe135a\",\n \"score\": \"0.5202249\",\n \"text\": \"def test_eq_different_lengths(self):\\n\\n for typecode in VALID_CONTENT:\\n b = gapbuffer(typecode, VALID_CONTENT[typecode])\\n self.assertNotEqual(VALID_CONTENT[typecode] * 2, b)\\n self.assertFalse(VALID_CONTENT[typecode] * 2 == b)\\n\\n for typecode in VALID_CONTENT:\\n b = gapbuffer(typecode, VALID_CONTENT[typecode][:2])\\n self.assertNotEqual(VALID_CONTENT[typecode], b)\\n self.assertFalse(VALID_CONTENT[typecode] == b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8abd42224b785426c8f333ebf08061b9\",\n \"score\": \"0.5185528\",\n \"text\": \"def satisfies(self, other):\\n for key in self.params.keys():\\n if key != \\\"q\\\" and other.params.get(key, None) != self.params.get(key, None):\\n return False\\n\\n if (\\n self.sub_type != \\\"*\\\"\\n and other.sub_type != \\\"*\\\"\\n and other.sub_type != self.sub_type\\n ):\\n return False\\n\\n if (\\n self.main_type != \\\"*\\\"\\n and other.main_type != \\\"*\\\"\\n and other.main_type != self.main_type\\n ):\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f8814dde9ed5eb92dee5cc29422e70d\",\n \"score\": \"0.5178265\",\n \"text\": \"def is_isomorphic(self, other: Union['ARCSpecies', Species, Molecule]) -> Optional[bool]:\\n if self.mol is None:\\n return None\\n if isinstance(other, ARCSpecies):\\n if other.mol is None:\\n return None\\n other = other.mol\\n if isinstance(other, Molecule):\\n if self.mol_list is not None and len(self.mol_list):\\n for mol_ in [self.mol] + self.mol_list:\\n if mol_.copy(deep=True).is_isomorphic(other.copy(deep=True)):\\n return True\\n return False\\n else:\\n return self.mol.copy(deep=True).is_isomorphic(other.copy(deep=True))\\n if isinstance(other, Species):\\n for other_mol in other.molecule:\\n if self.mol_list is not None and len(self.mol_list):\\n for mol_ in [self.mol] + self.mol_list:\\n if mol_.copy(deep=True).is_isomorphic(other_mol.copy(deep=True)):\\n return True\\n else:\\n return self.mol.copy(deep=True).is_isomorphic(other_mol.copy(deep=True))\\n return False\\n raise SpeciesError(f'Can only compare isomorphism to other ARCSpecies, RMG Species, or RMG Molecule '\\n f'object instances, got {other} which is of type {type(other)}.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96ccf04be8bf3fdccb9b1d57ef109cb9\",\n \"score\": \"0.51713175\",\n \"text\": \"def __eq__(self, other):\\n return self.type == other.type and self.morphio_root_node.id == other.morphio_root_node.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ecd28b303c8ac3143c82295486fc6bb\",\n \"score\": \"0.51591736\",\n \"text\": \"def check_mol_sites(ms1, ms2, atomic=False, factor=1.0, tm=Tol_matrix(prototype=\\\"molecular\\\")):\\n if atomic is False:\\n es0 = ms1.get_ellipsoids()\\n PBC_vectors = np.dot(create_matrix(PBC=ms1.PBC), ms1.lattice)\\n PBC_ops = [SymmOp.from_rotation_and_translation(Euclidean_lattice, v) for v in PBC_vectors]\\n es1 = []\\n for op in PBC_ops:\\n es1.append(np.dot(es0, op))\\n es1 = np.squeeze(es1)\\n truth_values = np.vectorize(check_intersection)(es1, ms2.get_ellipsoid())\\n if np.sum(truth_values) < len(truth_values):\\n return False\\n else:\\n return True\\n\\n elif atomic is True:\\n #Get coordinates for both mol_sites\\n c1, s1 = ms1.get_coords_and_species()\\n c2, s2 = ms2.get_coords_and_species()\\n\\n #Calculate which distance matrix is smaller/faster\\n m_length1 = len(ms1.mol.species)\\n m_length2 = len(ms2.mol.species)\\n wp_length1 = len(c1)\\n wp_length2 = len(c2)\\n size1 = m_length1 * wp_length2\\n size2 = m_length2 * wp_length1\\n\\n #Case 1\\n if size1 <= size2:\\n coords_mol = c1[:m_length1]\\n #Calculate tol matrix for species pairs\\n tols = np.zeros((m_length1,m_length2))\\n for i1, specie1 in enumerate(s1[:m_length1]):\\n for i2, specie2 in enumerate(s2[:m_length2]):\\n tols[i1][i2] = tm.get_tol(specie1, specie2)\\n tols = np.repeat(tols, ms2.multiplicity, axis=1)\\n d = distance_matrix(coords_mol, c2, ms1.lattice, PBC=ms1.PBC)\\n\\n #Case 2\\n elif size1 > size2:\\n coords_mol = c2[:m_length2]\\n #Calculate tol matrix for species pairs\\n tols = np.zeros((m_length2,m_length1))\\n for i1, specie1 in enumerate(s2[:m_length2]):\\n for i2, specie2 in enumerate(s1[:m_length1]):\\n tols[i1][i2] = tm.get_tol(specie1, specie2)\\n tols = np.repeat(tols, ms1.multiplicity, axis=1)\\n d = distance_matrix(coords_mol, c1, ms1.lattice, PBC=ms1.PBC)\\n\\n #Check if distances are smaller than tolerances\\n if (d<tols).any():\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1249f53e4660e993a1ad2cacda836ee2\",\n \"score\": \"0.5157512\",\n \"text\": \"def show_type_matches(self, atom_type_matches):\\n print('Atom type matches:')\\n total_atom_type_matches = 0\\n for (current_atomtype, reference_atomtype, counts) in atom_type_matches:\\n if reference_atomtype is not None:\\n print('%-64s matches %8s : %8d atoms matched' % (current_atomtype, reference_atomtype, counts))\\n total_atom_type_matches += counts\\n else:\\n print('%-64s no match' % (current_atomtype))\\n\\n fraction_matched_atoms = float(total_atom_type_matches) / float(self.total_atoms)\\n print('%d / %d total atoms match (%.3f %%)' % (total_atom_type_matches, self.total_atoms, fraction_matched_atoms * 100))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"644fd40551bc86d01fafa2760ab639f1\",\n \"score\": \"0.5142524\",\n \"text\": \"def testToFailCombineTwoRingsIntoSubMolecule(self):\\n\\n smiles1 = 'C1CCCCC1'\\n smiles2 = 'C1CCCCC1'\\n mol1 = Molecule().fromSMILES(smiles1)\\n mol2 = Molecule().fromSMILES(smiles2)\\n\\n ring1 = [atom for atom in mol1.atoms if atom.isNonHydrogen()]\\n ring2 = [atom for atom in mol2.atoms if atom.isNonHydrogen()]\\n with self.assertRaises(AssertionError):\\n combined = combineTwoRingsIntoSubMolecule(ring1, ring2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f1c51cc1863efd95827d47ce8ea82b5\",\n \"score\": \"0.51425236\",\n \"text\": \"def test_equal_on_type_mismatch(self):\\n a = sign.SignResponsePayload()\\n b = 'invalid'\\n\\n self.assertFalse(a == b)\\n self.assertFalse(b == a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bdc30f2b1f3fadb31a08d2ffb67f426\",\n \"score\": \"0.51297927\",\n \"text\": \"def compare_mutated_fasta_files(original_file_path, mutated_file_path):\\n with open(original_file_path) as handle1:\\n with open(mutated_file_path) as handle2:\\n iter1 = SeqIO.parse(handle1, 'fasta')\\n iter2 = SeqIO.parse(handle2, 'fasta')\\n fillvalue = object()\\n paired_records = zip_longest(iter1, iter2, fillvalue=fillvalue)\\n for r1, r2 in paired_records:\\n if type(r1) != type(r2):\\n return False\\n if r1.id != r2.id:\\n return False\\n if r1.name != r2.name:\\n return False\\n if r1.seq != r2.seq:\\n return False\\n if not r2.description.startswith(r1.description):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f67185f43408d137f2f520267d08817\",\n \"score\": \"0.5126452\",\n \"text\": \"def _checkSigEquivalence(sig1, sig2):\\n global __errorfexp\\n global __errormatch\\n if not (sig1 and sig2): # ommit empty signatures\\n return False\\n\\n # if options.str:\\n return sig1 == sig2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"109d4291e59f62b084241e0617405e3f\",\n \"score\": \"0.5118689\",\n \"text\": \"def _assert_all_entries_match(lexicon1, lexicon2):\\n eq_(set(lexicon1._entries.keys()), set(lexicon2._entries.keys()))\\n for token in lexicon1._entries:\\n _assert_entries_match(lexicon1, lexicon2, token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae29f98d3e5499a7d9ad9c131898b979\",\n \"score\": \"0.50985867\",\n \"text\": \"def is_consistent(left_ins: \\\"TypeIns\\\", right_ins: \\\"TypeIns\\\"):\\n # TODO: protocol\\n if left_ins == any_ins or right_ins == any_ins:\\n return True\\n\\n if left_ins == none_ins:\\n return nullable(right_ins)\\n elif right_ins == none_ins:\\n return nullable(left_ins)\\n\\n is_left_classins = isinstance(left_ins, TypeClassIns)\\n is_right_classins = isinstance(right_ins, TypeClassIns)\\n\\n if is_left_classins and is_right_classins:\\n return cls_consistent(left_ins, right_ins)\\n\\n # check typetype\\n left_is_type = isinstance(left_ins, TypeType)\\n right_is_type = isinstance(right_ins, TypeType)\\n # implicitly invariant\\n if left_is_type ^ right_is_type:\\n return False\\n if left_is_type and right_is_type:\\n return left_ins.temp == right_ins.temp or left_ins.temp == any_temp\\n\\n # one of left_ins and right_ins is special types\\n if is_left_classins:\\n # left instance's type is normal class-defined type\\n if right_ins.temp == union_temp:\\n for union_ins in right_ins.bindlist:\\n if not is_consistent(left_ins, union_ins):\\n return False\\n return True\\n elif right_ins.temp == optional_temp:\\n return False # left can't be None\\n elif right_ins.temp == literal_temp:\\n assert isinstance(right_ins, TypeLiteralIns)\\n return is_consistent(left_ins, right_ins.get_value_type())\\n else:\\n if left_ins.temp == union_temp:\\n for union_ins in left_ins.bindlist:\\n if is_consistent(union_ins, right_ins):\\n return True\\n return False\\n elif left_ins.temp == optional_temp:\\n if is_consistent(left_ins.get_safe_bind(0), right_ins):\\n return True\\n return right_ins == none_ins\\n elif left_ins.temp == literal_temp:\\n assert isinstance(left_ins, TypeLiteralIns)\\n if right_ins.temp != literal_temp:\\n return False\\n assert isinstance(right_ins, TypeLiteralIns)\\n return right_ins.temp == literal_temp and left_ins.value == right_ins.value\\n\\n return left_ins.equiv(right_ins)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"130ebd5aa733796d43ba993ff510892d\",\n \"score\": \"0.5090661\",\n \"text\": \"def _try_groups(molecule1, molecule2):\\r\\n\\r\\n molecule1.get_group_charges(), molecule2.get_group_charges() # Get group properties\\r\\n\\r\\n if _are_consistent(molecule1.chg_grp, molecule2.chg_grp):\\r\\n\\r\\n return True\\r\\n\\r\\n else:\\r\\n\\r\\n molecule1.chg_grp, molecule2.chg_grp = None, None\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdb884b29b4927c7a37aaf0f2ca7dd3b\",\n \"score\": \"0.5089228\",\n \"text\": \"def compare_types_internal(a: Any, b: Any) -> bool:\\n import typing\\n\\n if isinstance(a, str) and isinstance(b, str):\\n # Two strings (e.g. if both are stringly-typed mypy annotations).\\n # Just check if they are identical.\\n return a == b\\n elif isinstance(a, str) and isinstance(b, type):\\n # A string and a type.\\n # Likely the first is a mypy stringly-typed annotation and the\\n # second one a real type.\\n # Check that the name of the real type is the same as the\\n # stringly-typed one.\\n return a == b.__name__\\n elif a == dict and b == typing.Dict:\\n return True\\n elif is_union(a) and is_union(b):\\n if len(a.__args__) == len(b.__args__):\\n for ai, bi in list(zip(a.__args__, b.__args__)):\\n if not compare_types(ai, bi):\\n return False\\n return True\\n else:\\n return False\\n else:\\n return a == b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ced22308df4fd5422f4b022a804d7f2\",\n \"score\": \"0.50857085\",\n \"text\": \"def check_all_isomorphism(p1, p2):\\n \\n #use p1 as the comparison\\n fixedCoordinates = set(p1)\\n \\n #get all coordinates\\n coordinateSet = p2\\n \\n #get all x coordinates in a list, all y in a list, all z in a list\\n coordinatesSplit = list(zip(*coordinateSet))\\n \\n #offset to reflect each coordinate by \\n reflectPoints = [max(coor) for coor in coordinatesSplit]\\n \\n coorReflect = list(itertools.product('01', repeat=3))\\n \\n permutations = list(itertools.permutations('012'))\\n \\n #check each permutation of the coordinates\\n for permute in permutations:\\n \\n #permutation indices\\n i,j,k = permute[0], permute[1], permute[2]\\n i = int(i)\\n j = int(j)\\n k = int(k)\\n \\n #get reflection args\\n a,b,c = reflectPoints[i], reflectPoints[j], reflectPoints[k]\\n \\n #check each possible reflection of each coordinate\\n for reflect in coorReflect:\\n \\n rX = int(reflect[0])\\n rY = int(reflect[1])\\n rZ = int(reflect[2])\\n \\n newCoordSet = set()\\n \\n for coord in coordinateSet:\\n \\n #permute the coordinates\\n x,y,z = coord[i], coord[j], coord[k]\\n \\n #reflect the coordinates\\n if rX:\\n x = a-x\\n if rY:\\n y = b-y\\n if rZ:\\n z = c-z\\n \\n #add new coordinate under the map\\n newCoord = (x, y, z)\\n newCoordSet.add(newCoord)\\n \\n #check if mapping equals the comparison set\\n if newCoordSet == fixedCoordinates:\\n return True\\n \\n #at this point, none match\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d1355577c68dadc40afb73678cccf7\",\n \"score\": \"0.50853086\",\n \"text\": \"def emulate_assertListEqual(self, seq1, seq2, msg=None, seq_type=None):\\n if seq_type is not None:\\n seq_type_name = seq_type.__name__\\n if not isinstance(seq1, seq_type):\\n raise self.failureException('First sequence is not a %s: %s'\\n % (seq_type_name, safe_repr(seq1)))\\n if not isinstance(seq2, seq_type):\\n raise self.failureException('Second sequence is not a %s: %s'\\n % (seq_type_name, safe_repr(seq2)))\\n else:\\n seq_type_name = \\\"sequence\\\"\\n\\n differing = None\\n try:\\n len1 = len(seq1)\\n except (TypeError, NotImplementedError):\\n differing = 'First %s has no length. Non-sequence?' % (\\n seq_type_name)\\n\\n if differing is None:\\n try:\\n len2 = len(seq2)\\n except (TypeError, NotImplementedError):\\n differing = 'Second %s has no length. Non-sequence?' % (\\n seq_type_name)\\n\\n if differing is None:\\n if seq1 == seq2:\\n return\\n\\n seq1_repr = safe_repr(seq1)\\n seq2_repr = safe_repr(seq2)\\n if len(seq1_repr) > 30:\\n seq1_repr = seq1_repr[:30] + '...'\\n if len(seq2_repr) > 30:\\n seq2_repr = seq2_repr[:30] + '...'\\n elements = (seq_type_name.capitalize(), seq1_repr, seq2_repr)\\n differing = '%ss differ: %s != %s\\\\n' % elements\\n\\n for i in range(min(len1, len2)):\\n try:\\n item1 = seq1[i]\\n except (TypeError, IndexError, NotImplementedError):\\n differing += ('\\\\nUnable to index element %d of first %s\\\\n' %\\n (i, seq_type_name))\\n break\\n\\n try:\\n item2 = seq2[i]\\n except (TypeError, IndexError, NotImplementedError):\\n differing += ('\\\\nUnable to index element %d of second %s\\\\n' %\\n (i, seq_type_name))\\n break\\n\\n if item1 != item2:\\n differing += ('\\\\nFirst differing element %d:\\\\n%s\\\\n%s\\\\n' %\\n (i, safe_repr(item1), safe_repr(item2)))\\n break\\n else:\\n if (len1 == len2 and seq_type is None and\\n type(seq1) != type(seq2)):\\n # The sequences are the same, but have differing types.\\n return\\n\\n if len1 > len2:\\n differing += ('\\\\nFirst %s contains %d additional '\\n 'elements.\\\\n' % (seq_type_name, len1 - len2))\\n try:\\n differing += ('First extra element %d:\\\\n%s\\\\n' %\\n (len2, safe_repr(seq1[len2])))\\n except (TypeError, IndexError, NotImplementedError):\\n differing += ('Unable to index element %d '\\n 'of first %s\\\\n' % (len2, seq_type_name))\\n elif len1 < len2:\\n differing += ('\\\\nSecond %s contains %d additional '\\n 'elements.\\\\n' % (seq_type_name, len2 - len1))\\n try:\\n differing += ('First extra element %d:\\\\n%s\\\\n' %\\n (len1, safe_repr(seq2[len1])))\\n except (TypeError, IndexError, NotImplementedError):\\n differing += ('Unable to index element %d '\\n 'of second %s\\\\n' % (len1, seq_type_name))\\n standardMsg = differing\\n diffMsg = '\\\\n' + '\\\\n'.join(\\n difflib.ndiff(pprint.pformat(seq1).splitlines(),\\n pprint.pformat(seq2).splitlines()))\\n standardMsg = self._truncateMessage(standardMsg, diffMsg)\\n msg = self._formatMessage(msg, standardMsg)\\n self.fail(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5232659ec89c71b3749ed94dd7411f30\",\n \"score\": \"0.5051848\",\n \"text\": \"def check_and_update(self, atoms, *args, **kwargs):\\n if self.table.check_and_update([frozenset(c) for c in combinations(atoms, self.max_width)], *args, **kwargs) == 1:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa7f39e53b38bb7659ba625aa641a70\",\n \"score\": \"0.5042289\",\n \"text\": \"def compareAtomInfoFiles(fileName1, fileName2, distanceThreshold):\\n\\n ###########################\\n # Helper utility functions usable only inside this function\\n def atomID(s):\\n # Return the ID of the atom, which includes its chain, residue name,\\n # residue number, atom name, and alternate separated by spaces\\n w = s.split()\\n return w[0]+\\\" \\\"+w[1]+\\\" \\\"+w[2]+\\\" \\\"+w[3]+\\\" \\\"+w[4]\\n\\n def position(s):\\n # Return a tuple that has the location of the atom.\\n w = s.split()\\n return ( float(w[5]), float(w[6]), float(w[7]) )\\n\\n def radius(s):\\n # Return the radius for the atom.\\n w = s.split()\\n return ( w[8] )\\n\\n def flag(s,f):\\n # Return a flag for the atom, indexed by 0-2.\\n w = s.split()\\n return ( w[9+f] )\\n\\n def distance(a, b):\\n # Return the distance between 3-space tuples a and b\\n return math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 + (a[2]-b[2])**2 )\\n\\n ###########################\\n # Initialize our return value to empty.\\n ret = \\\"\\\"\\n\\n # Read the the data from each file.\\n # Sort the lines from the file so that we'll get chain, residue, atom name sorting.\\n with open(fileName1) as f:\\n m1 = f.readlines()\\n m1.sort(key=lambda x:atomID(x))\\n\\n with open(fileName2) as f:\\n m2 = f.readlines()\\n m2.sort(key=lambda x:atomID(x))\\n\\n # Compare the two and report on any differences.\\n # Both lists are sorted, so we'll know which has a missing element compared\\n # to the other. We walk from the beginning to the end of each list.\\n m1ID = 0\\n m2ID = 0\\n while m1ID < len(m1) and m2ID < len(m2):\\n\\n # Atom names are unique within residues, so we can use that to\\n # match atoms in one file from those in the other and print out\\n # missing atoms and differences in positions.\\n if atomID(m1[m1ID]) < atomID(m2[m2ID]):\\n ret += 'Only in first: '+atomID(m1[m1ID])+'\\\\n'\\n m1ID += 1\\n continue\\n\\n if atomID(m1[m1ID]) > atomID(m2[m2ID]):\\n ret += 'Only in second: '+atomID(m2[m2ID])+'\\\\n'\\n m2ID += 1\\n continue\\n\\n # Check the radius to see if they differ\\n oldRadius = radius(m1[m1ID])\\n newRadius = radius(m2[m2ID])\\n if oldRadius != newRadius:\\n ret += atomID(m1[m1ID])+' radii differ: old is {}, new is {}'.format(oldRadius,newRadius)+'\\\\n'\\n\\n # Check the flag fields to see if any of them differ\\n for f in range(3):\\n oldFlag = flag(m1[m1ID], f)\\n newFlag = flag(m2[m2ID], f)\\n if oldFlag != newFlag:\\n ret += atomID(m1[m1ID])+' flags differ: old is {}, new is {}'.format(oldFlag,newFlag)+'\\\\n'\\n\\n # Check the distance between the atoms.\\n dist = distance(position(m1[m1ID]),position(m2[m2ID]))\\n if dist > distanceThreshold:\\n ret += atomID(m1[m1ID])+' Distance between runs: {:.2f}'.format(dist)+'\\\\n'\\n\\n # Go on to next line\\n m1ID += 1\\n m2ID += 1\\n\\n # Finish up either list that is not done\\n while m1ID < len(m1):\\n ret += 'Only in first: '+atomID(m1[m1ID])+'\\\\n'\\n m1ID += 1\\n while m2ID < len(m2):\\n ret += 'Only in second: '+atomID(m2[m2ID])+'\\\\n'\\n m2ID += 1\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa7f39e53b38bb7659ba625aa641a70\",\n \"score\": \"0.5042289\",\n \"text\": \"def compareAtomInfoFiles(fileName1, fileName2, distanceThreshold):\\n\\n ###########################\\n # Helper utility functions usable only inside this function\\n def atomID(s):\\n # Return the ID of the atom, which includes its chain, residue name,\\n # residue number, atom name, and alternate separated by spaces\\n w = s.split()\\n return w[0]+\\\" \\\"+w[1]+\\\" \\\"+w[2]+\\\" \\\"+w[3]+\\\" \\\"+w[4]\\n\\n def position(s):\\n # Return a tuple that has the location of the atom.\\n w = s.split()\\n return ( float(w[5]), float(w[6]), float(w[7]) )\\n\\n def radius(s):\\n # Return the radius for the atom.\\n w = s.split()\\n return ( w[8] )\\n\\n def flag(s,f):\\n # Return a flag for the atom, indexed by 0-2.\\n w = s.split()\\n return ( w[9+f] )\\n\\n def distance(a, b):\\n # Return the distance between 3-space tuples a and b\\n return math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 + (a[2]-b[2])**2 )\\n\\n ###########################\\n # Initialize our return value to empty.\\n ret = \\\"\\\"\\n\\n # Read the the data from each file.\\n # Sort the lines from the file so that we'll get chain, residue, atom name sorting.\\n with open(fileName1) as f:\\n m1 = f.readlines()\\n m1.sort(key=lambda x:atomID(x))\\n\\n with open(fileName2) as f:\\n m2 = f.readlines()\\n m2.sort(key=lambda x:atomID(x))\\n\\n # Compare the two and report on any differences.\\n # Both lists are sorted, so we'll know which has a missing element compared\\n # to the other. We walk from the beginning to the end of each list.\\n m1ID = 0\\n m2ID = 0\\n while m1ID < len(m1) and m2ID < len(m2):\\n\\n # Atom names are unique within residues, so we can use that to\\n # match atoms in one file from those in the other and print out\\n # missing atoms and differences in positions.\\n if atomID(m1[m1ID]) < atomID(m2[m2ID]):\\n ret += 'Only in first: '+atomID(m1[m1ID])+'\\\\n'\\n m1ID += 1\\n continue\\n\\n if atomID(m1[m1ID]) > atomID(m2[m2ID]):\\n ret += 'Only in second: '+atomID(m2[m2ID])+'\\\\n'\\n m2ID += 1\\n continue\\n\\n # Check the radius to see if they differ\\n oldRadius = radius(m1[m1ID])\\n newRadius = radius(m2[m2ID])\\n if oldRadius != newRadius:\\n ret += atomID(m1[m1ID])+' radii differ: old is {}, new is {}'.format(oldRadius,newRadius)+'\\\\n'\\n\\n # Check the flag fields to see if any of them differ\\n for f in range(3):\\n oldFlag = flag(m1[m1ID], f)\\n newFlag = flag(m2[m2ID], f)\\n if oldFlag != newFlag:\\n ret += atomID(m1[m1ID])+' flags differ: old is {}, new is {}'.format(oldFlag,newFlag)+'\\\\n'\\n\\n # Check the distance between the atoms.\\n dist = distance(position(m1[m1ID]),position(m2[m2ID]))\\n if dist > distanceThreshold:\\n ret += atomID(m1[m1ID])+' Distance between runs: {:.2f}'.format(dist)+'\\\\n'\\n\\n # Go on to next line\\n m1ID += 1\\n m2ID += 1\\n\\n # Finish up either list that is not done\\n while m1ID < len(m1):\\n ret += 'Only in first: '+atomID(m1[m1ID])+'\\\\n'\\n m1ID += 1\\n while m2ID < len(m2):\\n ret += 'Only in second: '+atomID(m2[m2ID])+'\\\\n'\\n m2ID += 1\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d09f4f7c85d16b6199594541e324bf8\",\n \"score\": \"0.50410783\",\n \"text\": \"def check_iequv(bn1, bn2):\\n ## skeletons and immoralities\\n (ske1, immor1) = bn1.get_skeleton_immor()\\n (ske2, immor2) = bn2.get_skeleton_immor()\\n\\n ## comparison. \\n if len(ske1) != len(ske2) or len(immor1) != len(immor2):\\n return False\\n\\n ## Note that the edges are undirected so we need to check both ordering\\n for (n1, n2) in immor1:\\n if (n1, n2) not in immor2 and (n2, n1) not in immor2:\\n return False\\n for (n1, n2) in ske1:\\n if (n1, n2) not in ske2 and (n2, n1) not in ske2:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79b6b97f15fd964d906fe0d567536d17\",\n \"score\": \"0.5034577\",\n \"text\": \"def test_comparing_different_ids(self):\\n # GIVEN\\n input_dict = TestFTSInputValidation.get_default_inputs()\\n input_dict2 = TestFTSInputValidation.get_default_inputs() # ensure not same references\\n input_dict2[\\\"greenyellow_intervals\\\"][\\\"sg3\\\"] = input_dict2[\\\"greenyellow_intervals\\\"][\\\"sg2\\\"]\\n del input_dict2[\\\"greenyellow_intervals\\\"][\\\"sg2\\\"]\\n fts1 = FixedTimeSchedule(**input_dict)\\n fts2 = FixedTimeSchedule(**input_dict2)\\n\\n # WHEN\\n same = fts1 == fts2\\n\\n self.assertEqual(same, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"396cb1d465a352cc63a62cb977054cf7\",\n \"score\": \"0.50305265\",\n \"text\": \"def _is_consistent(self) -> bool:\\n try:\\n assert (\\n type(self.dialogue_reference) == tuple\\n ), \\\"Invalid type for 'dialogue_reference'. Expected 'tuple'. Found '{}'.\\\".format(\\n type(self.dialogue_reference)\\n )\\n assert (\\n type(self.dialogue_reference[0]) == str\\n ), \\\"Invalid type for 'dialogue_reference[0]'. Expected 'str'. Found '{}'.\\\".format(\\n type(self.dialogue_reference[0])\\n )\\n assert (\\n type(self.dialogue_reference[1]) == str\\n ), \\\"Invalid type for 'dialogue_reference[1]'. Expected 'str'. Found '{}'.\\\".format(\\n type(self.dialogue_reference[1])\\n )\\n assert (\\n type(self.message_id) == int\\n ), \\\"Invalid type for 'message_id'. Expected 'int'. Found '{}'.\\\".format(\\n type(self.message_id)\\n )\\n assert (\\n type(self.target) == int\\n ), \\\"Invalid type for 'target'. Expected 'int'. Found '{}'.\\\".format(\\n type(self.target)\\n )\\n\\n # Light Protocol Rule 2\\n # Check correct performative\\n assert (\\n type(self.performative) == TProtocolMessage.Performative\\n ), \\\"Invalid 'performative'. Expected either of '{}'. Found '{}'.\\\".format(\\n self.valid_performatives, self.performative\\n )\\n\\n # Check correct contents\\n actual_nb_of_contents = len(self.body) - DEFAULT_BODY_SIZE\\n expected_nb_of_contents = 0\\n if self.performative == TProtocolMessage.Performative.PERFORMATIVE_CT:\\n expected_nb_of_contents = 1\\n assert (\\n type(self.content_ct) == CustomDataModel\\n ), \\\"Invalid type for content 'content_ct'. Expected 'DataModel'. Found '{}'.\\\".format(\\n type(self.content_ct)\\n )\\n elif self.performative == TProtocolMessage.Performative.PERFORMATIVE_PT:\\n expected_nb_of_contents = 5\\n assert (\\n type(self.content_bytes) == bytes\\n ), \\\"Invalid type for content 'content_bytes'. Expected 'bytes'. Found '{}'.\\\".format(\\n type(self.content_bytes)\\n )\\n assert (\\n type(self.content_int) == int\\n ), \\\"Invalid type for content 'content_int'. Expected 'int'. Found '{}'.\\\".format(\\n type(self.content_int)\\n )\\n assert (\\n type(self.content_float) == float\\n ), \\\"Invalid type for content 'content_float'. Expected 'float'. Found '{}'.\\\".format(\\n type(self.content_float)\\n )\\n assert (\\n type(self.content_bool) == bool\\n ), \\\"Invalid type for content 'content_bool'. Expected 'bool'. Found '{}'.\\\".format(\\n type(self.content_bool)\\n )\\n assert (\\n type(self.content_str) == str\\n ), \\\"Invalid type for content 'content_str'. Expected 'str'. Found '{}'.\\\".format(\\n type(self.content_str)\\n )\\n elif self.performative == TProtocolMessage.Performative.PERFORMATIVE_PCT:\\n expected_nb_of_contents = 10\\n assert (\\n type(self.content_set_bytes) == frozenset\\n ), \\\"Invalid type for content 'content_set_bytes'. Expected 'frozenset'. Found '{}'.\\\".format(\\n type(self.content_set_bytes)\\n )\\n assert all(\\n type(element) == bytes for element in self.content_set_bytes\\n ), \\\"Invalid type for frozenset elements in content 'content_set_bytes'. Expected 'bytes'.\\\"\\n assert (\\n type(self.content_set_int) == frozenset\\n ), \\\"Invalid type for content 'content_set_int'. Expected 'frozenset'. Found '{}'.\\\".format(\\n type(self.content_set_int)\\n )\\n assert all(\\n type(element) == int for element in self.content_set_int\\n ), \\\"Invalid type for frozenset elements in content 'content_set_int'. Expected 'int'.\\\"\\n assert (\\n type(self.content_set_float) == frozenset\\n ), \\\"Invalid type for content 'content_set_float'. Expected 'frozenset'. Found '{}'.\\\".format(\\n type(self.content_set_float)\\n )\\n assert all(\\n type(element) == float for element in self.content_set_float\\n ), \\\"Invalid type for frozenset elements in content 'content_set_float'. Expected 'float'.\\\"\\n assert (\\n type(self.content_set_bool) == frozenset\\n ), \\\"Invalid type for content 'content_set_bool'. Expected 'frozenset'. Found '{}'.\\\".format(\\n type(self.content_set_bool)\\n )\\n assert all(\\n type(element) == bool for element in self.content_set_bool\\n ), \\\"Invalid type for frozenset elements in content 'content_set_bool'. Expected 'bool'.\\\"\\n assert (\\n type(self.content_set_str) == frozenset\\n ), \\\"Invalid type for content 'content_set_str'. Expected 'frozenset'. Found '{}'.\\\".format(\\n type(self.content_set_str)\\n )\\n assert all(\\n type(element) == str for element in self.content_set_str\\n ), \\\"Invalid type for frozenset elements in content 'content_set_str'. Expected 'str'.\\\"\\n assert (\\n type(self.content_list_bytes) == tuple\\n ), \\\"Invalid type for content 'content_list_bytes'. Expected 'tuple'. Found '{}'.\\\".format(\\n type(self.content_list_bytes)\\n )\\n assert all(\\n type(element) == bytes for element in self.content_list_bytes\\n ), \\\"Invalid type for tuple elements in content 'content_list_bytes'. Expected 'bytes'.\\\"\\n assert (\\n type(self.content_list_int) == tuple\\n ), \\\"Invalid type for content 'content_list_int'. Expected 'tuple'. Found '{}'.\\\".format(\\n type(self.content_list_int)\\n )\\n assert all(\\n type(element) == int for element in self.content_list_int\\n ), \\\"Invalid type for tuple elements in content 'content_list_int'. Expected 'int'.\\\"\\n assert (\\n type(self.content_list_float) == tuple\\n ), \\\"Invalid type for content 'content_list_float'. Expected 'tuple'. Found '{}'.\\\".format(\\n type(self.content_list_float)\\n )\\n assert all(\\n type(element) == float for element in self.content_list_float\\n ), \\\"Invalid type for tuple elements in content 'content_list_float'. Expected 'float'.\\\"\\n assert (\\n type(self.content_list_bool) == tuple\\n ), \\\"Invalid type for content 'content_list_bool'. Expected 'tuple'. Found '{}'.\\\".format(\\n type(self.content_list_bool)\\n )\\n assert all(\\n type(element) == bool for element in self.content_list_bool\\n ), \\\"Invalid type for tuple elements in content 'content_list_bool'. Expected 'bool'.\\\"\\n assert (\\n type(self.content_list_str) == tuple\\n ), \\\"Invalid type for content 'content_list_str'. Expected 'tuple'. Found '{}'.\\\".format(\\n type(self.content_list_str)\\n )\\n assert all(\\n type(element) == str for element in self.content_list_str\\n ), \\\"Invalid type for tuple elements in content 'content_list_str'. Expected 'str'.\\\"\\n elif self.performative == TProtocolMessage.Performative.PERFORMATIVE_PMT:\\n expected_nb_of_contents = 2\\n assert (\\n type(self.content_dict_bool_bytes) == dict\\n ), \\\"Invalid type for content 'content_dict_bool_bytes'. Expected 'dict'. Found '{}'.\\\".format(\\n type(self.content_dict_bool_bytes)\\n )\\n for (\\n key_of_content_dict_bool_bytes,\\n value_of_content_dict_bool_bytes,\\n ) in self.content_dict_bool_bytes.items():\\n assert (\\n type(key_of_content_dict_bool_bytes) == bool\\n ), \\\"Invalid type for dictionary keys in content 'content_dict_bool_bytes'. Expected 'bool'. Found '{}'.\\\".format(\\n type(key_of_content_dict_bool_bytes)\\n )\\n assert (\\n type(value_of_content_dict_bool_bytes) == bytes\\n ), \\\"Invalid type for dictionary values in content 'content_dict_bool_bytes'. Expected 'bytes'. Found '{}'.\\\".format(\\n type(value_of_content_dict_bool_bytes)\\n )\\n assert (\\n type(self.content_dict_str_float) == dict\\n ), \\\"Invalid type for content 'content_dict_str_float'. Expected 'dict'. Found '{}'.\\\".format(\\n type(self.content_dict_str_float)\\n )\\n for (\\n key_of_content_dict_str_float,\\n value_of_content_dict_str_float,\\n ) in self.content_dict_str_float.items():\\n assert (\\n type(key_of_content_dict_str_float) == str\\n ), \\\"Invalid type for dictionary keys in content 'content_dict_str_float'. Expected 'str'. Found '{}'.\\\".format(\\n type(key_of_content_dict_str_float)\\n )\\n assert (\\n type(value_of_content_dict_str_float) == float\\n ), \\\"Invalid type for dictionary values in content 'content_dict_str_float'. Expected 'float'. Found '{}'.\\\".format(\\n type(value_of_content_dict_str_float)\\n )\\n elif self.performative == TProtocolMessage.Performative.PERFORMATIVE_MT:\\n expected_nb_of_contents = 2\\n assert (\\n type(self.content_union_1) == CustomDataModel\\n or type(self.content_union_1) == bool\\n or type(self.content_union_1) == bytes\\n or type(self.content_union_1) == dict\\n or type(self.content_union_1) == float\\n or type(self.content_union_1) == frozenset\\n or type(self.content_union_1) == int\\n or type(self.content_union_1) == str\\n or type(self.content_union_1) == tuple\\n ), \\\"Invalid type for content 'content_union_1'. Expected either of '['DataModel', 'bool', 'bytes', 'dict', 'float', 'frozenset', 'int', 'str', 'tuple']'. Found '{}'.\\\".format(\\n type(self.content_union_1)\\n )\\n if type(self.content_union_1) == frozenset:\\n assert all(\\n type(element) == int for element in self.content_union_1\\n ), \\\"Invalid type for elements of content 'content_union_1'. Expected 'int'.\\\"\\n if type(self.content_union_1) == tuple:\\n assert all(\\n type(element) == bool for element in self.content_union_1\\n ), \\\"Invalid type for tuple elements in content 'content_union_1'. Expected 'bool'.\\\"\\n if type(self.content_union_1) == dict:\\n for (\\n key_of_content_union_1,\\n value_of_content_union_1,\\n ) in self.content_union_1.items():\\n assert (\\n type(key_of_content_union_1) == str\\n and type(value_of_content_union_1) == int\\n ), \\\"Invalid type for dictionary key, value in content 'content_union_1'. Expected 'str', 'int'.\\\"\\n assert (\\n type(self.content_union_2) == dict\\n or type(self.content_union_2) == frozenset\\n or type(self.content_union_2) == tuple\\n ), \\\"Invalid type for content 'content_union_2'. Expected either of '['dict', 'frozenset', 'tuple']'. Found '{}'.\\\".format(\\n type(self.content_union_2)\\n )\\n if type(self.content_union_2) == frozenset:\\n assert (\\n all(type(element) == bytes for element in self.content_union_2)\\n or all(type(element) == int for element in self.content_union_2)\\n or all(type(element) == str for element in self.content_union_2)\\n ), \\\"Invalid type for frozenset elements in content 'content_union_2'. Expected either 'bytes' or 'int' or 'str'.\\\"\\n if type(self.content_union_2) == tuple:\\n assert (\\n all(type(element) == bool for element in self.content_union_2)\\n or all(\\n type(element) == bytes for element in self.content_union_2\\n )\\n or all(\\n type(element) == float for element in self.content_union_2\\n )\\n ), \\\"Invalid type for tuple elements in content 'content_union_2'. Expected either 'bool' or 'bytes' or 'float'.\\\"\\n if type(self.content_union_2) == dict:\\n for (\\n key_of_content_union_2,\\n value_of_content_union_2,\\n ) in self.content_union_2.items():\\n assert (\\n (\\n type(key_of_content_union_2) == bool\\n and type(value_of_content_union_2) == bytes\\n )\\n or (\\n type(key_of_content_union_2) == int\\n and type(value_of_content_union_2) == float\\n )\\n or (\\n type(key_of_content_union_2) == str\\n and type(value_of_content_union_2) == int\\n )\\n ), \\\"Invalid type for dictionary key, value in content 'content_union_2'. Expected 'bool','bytes' or 'int','float' or 'str','int'.\\\"\\n elif self.performative == TProtocolMessage.Performative.PERFORMATIVE_O:\\n expected_nb_of_contents = 0\\n if self.is_set(\\\"content_o_ct\\\"):\\n expected_nb_of_contents += 1\\n content_o_ct = cast(CustomDataModel, self.content_o_ct)\\n assert (\\n type(content_o_ct) == CustomDataModel\\n ), \\\"Invalid type for content 'content_o_ct'. Expected 'DataModel'. Found '{}'.\\\".format(\\n type(content_o_ct)\\n )\\n if self.is_set(\\\"content_o_bool\\\"):\\n expected_nb_of_contents += 1\\n content_o_bool = cast(bool, self.content_o_bool)\\n assert (\\n type(content_o_bool) == bool\\n ), \\\"Invalid type for content 'content_o_bool'. Expected 'bool'. Found '{}'.\\\".format(\\n type(content_o_bool)\\n )\\n if self.is_set(\\\"content_o_set_float\\\"):\\n expected_nb_of_contents += 1\\n content_o_set_float = cast(\\n FrozenSet[float], self.content_o_set_float\\n )\\n assert (\\n type(content_o_set_float) == frozenset\\n ), \\\"Invalid type for content 'content_o_set_float'. Expected 'frozenset'. Found '{}'.\\\".format(\\n type(content_o_set_float)\\n )\\n assert all(\\n type(element) == float for element in content_o_set_float\\n ), \\\"Invalid type for frozenset elements in content 'content_o_set_float'. Expected 'float'.\\\"\\n if self.is_set(\\\"content_o_list_bytes\\\"):\\n expected_nb_of_contents += 1\\n content_o_list_bytes = cast(\\n Tuple[bytes, ...], self.content_o_list_bytes\\n )\\n assert (\\n type(content_o_list_bytes) == tuple\\n ), \\\"Invalid type for content 'content_o_list_bytes'. Expected 'tuple'. Found '{}'.\\\".format(\\n type(content_o_list_bytes)\\n )\\n assert all(\\n type(element) == bytes for element in content_o_list_bytes\\n ), \\\"Invalid type for tuple elements in content 'content_o_list_bytes'. Expected 'bytes'.\\\"\\n if self.is_set(\\\"content_o_dict_str_int\\\"):\\n expected_nb_of_contents += 1\\n content_o_dict_str_int = cast(\\n Dict[str, int], self.content_o_dict_str_int\\n )\\n assert (\\n type(content_o_dict_str_int) == dict\\n ), \\\"Invalid type for content 'content_o_dict_str_int'. Expected 'dict'. Found '{}'.\\\".format(\\n type(content_o_dict_str_int)\\n )\\n for (\\n key_of_content_o_dict_str_int,\\n value_of_content_o_dict_str_int,\\n ) in content_o_dict_str_int.items():\\n assert (\\n type(key_of_content_o_dict_str_int) == str\\n ), \\\"Invalid type for dictionary keys in content 'content_o_dict_str_int'. Expected 'str'. Found '{}'.\\\".format(\\n type(key_of_content_o_dict_str_int)\\n )\\n assert (\\n type(value_of_content_o_dict_str_int) == int\\n ), \\\"Invalid type for dictionary values in content 'content_o_dict_str_int'. Expected 'int'. Found '{}'.\\\".format(\\n type(value_of_content_o_dict_str_int)\\n )\\n if self.is_set(\\\"content_o_union\\\"):\\n expected_nb_of_contents += 1\\n content_o_union = cast(\\n Union[\\n str,\\n Dict[str, int],\\n FrozenSet[int],\\n FrozenSet[bytes],\\n Tuple[bool, ...],\\n Dict[str, float],\\n ],\\n self.content_o_union,\\n )\\n assert (\\n type(content_o_union) == dict\\n or type(content_o_union) == frozenset\\n or type(content_o_union) == str\\n or type(content_o_union) == tuple\\n ), \\\"Invalid type for content 'content_o_union'. Expected either of '['dict', 'frozenset', 'str', 'tuple']'. Found '{}'.\\\".format(\\n type(content_o_union)\\n )\\n if type(content_o_union) == frozenset:\\n assert all(\\n type(element) == bytes for element in content_o_union\\n ) or all(\\n type(element) == int for element in content_o_union\\n ), \\\"Invalid type for frozenset elements in content 'content_o_union'. Expected either 'bytes' or 'int'.\\\"\\n if type(content_o_union) == tuple:\\n assert all(\\n type(element) == bool for element in content_o_union\\n ), \\\"Invalid type for tuple elements in content 'content_o_union'. Expected 'bool'.\\\"\\n if type(content_o_union) == dict:\\n for (\\n key_of_content_o_union,\\n value_of_content_o_union,\\n ) in content_o_union.items():\\n assert (\\n type(key_of_content_o_union) == str\\n and type(value_of_content_o_union) == float\\n ), \\\"Invalid type for dictionary key, value in content 'content_o_union'. Expected 'str', 'float'.\\\"\\n elif (\\n self.performative\\n == TProtocolMessage.Performative.PERFORMATIVE_EMPTY_CONTENTS\\n ):\\n expected_nb_of_contents = 0\\n\\n # Check correct content count\\n assert (\\n expected_nb_of_contents == actual_nb_of_contents\\n ), \\\"Incorrect number of contents. Expected {}. Found {}\\\".format(\\n expected_nb_of_contents, actual_nb_of_contents\\n )\\n\\n # Light Protocol Rule 3\\n if self.message_id == 1:\\n assert (\\n self.target == 0\\n ), \\\"Invalid 'target'. Expected 0 (because 'message_id' is 1). Found {}.\\\".format(\\n self.target\\n )\\n else:\\n assert (\\n 0 < self.target < self.message_id\\n ), \\\"Invalid 'target'. Expected an integer between 1 and {} inclusive. Found {}.\\\".format(\\n self.message_id - 1, self.target,\\n )\\n except (AssertionError, ValueError, KeyError) as e:\\n logger.error(str(e))\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee0d21c03e1d34aad468a31f0f0bbb2f\",\n \"score\": \"0.50302917\",\n \"text\": \"def _apply_typemap(self, structure, typemap):\\n for atom in structure.atoms:\\n atom.id = typemap[atom.idx][\\\"atomtype\\\"]\\n\\n if not all([a.id for a in structure.atoms]):\\n raise ValueError(\\\"Not all atoms in topology have atom types\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f4c125d8b0b24e97cb5a3b47279a4ac\",\n \"score\": \"0.50284487\",\n \"text\": \"def compare_types(self, var1, var2):\\n return var1 == var2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9b436e4b0ad8bc7080e1aa05fd7a927\",\n \"score\": \"0.50283706\",\n \"text\": \"def equal_equal_input_chord_input_chord(cls, a, b):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"033c175d3d8f6c5b4eb2f73e0df29a78\",\n \"score\": \"0.5028319\",\n \"text\": \"def structural_equivalence(type1, type2, exclude_special_properties=True):\\n type1_members = dir(type1)\\n type2_members = dir(type2)\\n\\n # We do not consider values if only one of the compared types has one\\n value_in_type1 = 'value' in type1_members\\n value_in_type2 = 'value' in type1_members\\n\\n if value_in_type1 and not value_in_type2:\\n type1_members.remove('value')\\n\\n if value_in_type2 and not value_in_type1:\\n type1_members.remove('value')\\n\\n same_structure = type1_members == type2_members\\n if not same_structure:\\n return False\\n\\n for member in type1_members:\\n if exclude_special_properties:\\n if member in Type.special_properties_for_equality:\\n continue\\n\\n # try:\\n member1 = getattr(type1, member)\\n member2 = getattr(type2, member)\\n\\n # If both are wrapper types, we compare it\\n if isinstance(member1, Type): # and isinstance(member2, Type):\\n if not member1.get_python_type() == member2.get_python_type():\\n return False\\n else:\\n # Else we compare its types\\n if not type(member1) == type(member2):\\n return False\\n # except:\\n # return False\\n\\n # for member in type1_members:\\n # if exclude_special_properties:\\n # if member in Type.special_properties_for_equality:\\n # continue\\n # member1 = getattr(type1, member)\\n # member2 = getattr(type2, member)\\n # try:\\n # # If both are wrapper types, we compare it\\n # if not member1.get_python_type() == member2.get_python_type():\\n # return False\\n # except:\\n # # Else we compare its types\\n # if not type(member1) == type(member2):\\n # return False\\n\\n # try:\\n # member1 = getattr(type1, member)\\n # member2 = getattr(type2, member)\\n #\\n # #If both are wrapper types, we compare it\\n # if isinstance(member1, Type) and isinstance(member2, Type):\\n # if not member1.get_python_type() == member2.get_python_type():\\n # return False\\n # else:\\n # #Else we compare its types\\n # if not type(member1) == type(member2):\\n # return False\\n # except:\\n # return False\\n\\n # for member in type2_members:\\n # # if member in non_comparable_members:\\n # # continue\\n # try:\\n # member1 = getattr(type1, member)\\n # member2 = getattr(type2, member)\\n #\\n # #If both are types, we compare it\\n # if isinstance(member1, Type) and isinstance(member2, Type):\\n # if not member1.get_python_type() == member2.get_python_type():\\n # return False\\n # else:\\n # #Else we compare its types\\n # if not type(member1) == type(member2):\\n # return False\\n # # if not getattr(type1, member) == getattr(type2, member):\\n # # return False\\n # except:\\n # return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e17ba776459c823e1a1c84a69be3548\",\n \"score\": \"0.5020715\",\n \"text\": \"def test_equal_on_type_mismatch(self):\\n a = sign.SignRequestPayload()\\n b = 'invalid'\\n\\n self.assertFalse(a == b)\\n self.assertFalse(b == a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0b09445a7b22daa7a7d266fa9aa6ba\",\n \"score\": \"0.5018225\",\n \"text\": \"def show_guessed_atoms(self):\\n if guess_box.get_pre_guesses() is not None: # shows guesses before confirming\\n for pre_guess in guess_box.get_pre_guesses():\\n coord_h = get_screen_coord(\\\"y\\\", pre_guess[0])\\n coord_w = get_screen_coord(\\\"x\\\", pre_guess[1])\\n screen.blit(atom, (coord_w + 3, coord_h + 3))\\n\\n if game.get_guesses() is not None: # shows guesses after confirming\\n for pos in game.get_atoms(): # shows all flowers\\n coord_h = get_screen_coord(\\\"y\\\", pos[0])\\n coord_w = get_screen_coord(\\\"x\\\", pos[1])\\n if pos in guess_box.get_pre_guesses(): # shows correct guess\\n screen.blit(gray_out_atom, (coord_w + 3, coord_h + 3))\\n screen.blit(correct, (coord_w + 3, coord_h + 3))\\n\\n else:\\n screen.blit(atom, (coord_w + 3, coord_h + 3)) # shows flower that wasn't guessed\\n\\n for guess in game.get_guesses(): # shows wrong guesses\\n coord_h = get_screen_coord(\\\"y\\\", guess[0])\\n coord_w = get_screen_coord(\\\"x\\\", guess[1])\\n if guess not in game.get_atoms():\\n screen.blit(gray_out_atom, (coord_w + 3, coord_h + 3))\\n screen.blit(wrong, (coord_w + 3, coord_h + 3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ae45cb065eac34ea5051104b8d45881\",\n \"score\": \"0.50176364\",\n \"text\": \"def compare_residues(\\n e0: Union[Structure, Model, Chain],\\n e1: Union[Structure, Model, Chain],\\n verbose: bool = False,\\n quick: bool = False,\\n rtol: float = None,\\n atol: float = None,\\n) -> Dict[str, Any]:\\n cmpdict: Dict[str, Any] = {}\\n cmpdict[\\\"chains\\\"] = [] # list of chain IDs (union over both structures)\\n cmpdict[\\\"residues\\\"] = 0 # count of not HETATM residues in longest chain\\n cmpdict[\\\"rCount\\\"] = 0 # Biopython Residues (includes HETATMs, waters)\\n cmpdict[\\\"rMatchCount\\\"] = 0 # full ID match Biopython Residues e0, e1\\n cmpdict[\\\"rpnMismatchCount\\\"] = 0 # res prev, next links not matched\\n cmpdict[\\\"aCount\\\"] = 0 # Atoms including disordered in longest e0 or e1\\n cmpdict[\\\"disAtmCount\\\"] = 0 # disordered atoms in longest e0 or e1\\n cmpdict[\\\"aCoordMatchCount\\\"] = 0 # atoms with coordinates match e0, e1\\n cmpdict[\\\"aFullIdMatchCount\\\"] = 0 # atoms with full ID match e0, e1\\n cmpdict[\\\"id0\\\"] = e0.get_full_id()\\n cmpdict[\\\"id1\\\"] = e1.get_full_id()\\n cmpdict[\\\"pass\\\"] = None\\n cmpdict[\\\"report\\\"] = None\\n\\n if quick:\\n if isinstance(e0, Chain):\\n if (\\n e0.internal_coord.atomArray is not None\\n and np.shape(e0.internal_coord.atomArray)\\n == np.shape(e1.internal_coord.atomArray)\\n and np.allclose(\\n e0.internal_coord.atomArray,\\n e1.internal_coord.atomArray,\\n rtol=1e-03 if rtol is None else rtol,\\n atol=1e-03 if atol is None else atol,\\n )\\n ):\\n cmpdict[\\\"aCount\\\"] = np.size(e0.internal_coord.atomArray, 0)\\n cmpdict[\\\"aCoordMatchCount\\\"] = np.size(e0.internal_coord.atomArray, 0)\\n if cmpdict[\\\"aCoordMatchCount\\\"] > 0:\\n cmpdict[\\\"pass\\\"] = True\\n else:\\n cmpdict[\\\"pass\\\"] = False\\n else:\\n cmpdict[\\\"aCount\\\"] = (\\n 0\\n if e0.internal_coord.atomArray is None\\n else np.size(e0.internal_coord.atomArray, 0)\\n )\\n cmpdict[\\\"pass\\\"] = False\\n else:\\n cmpdict[\\\"pass\\\"] = True\\n for c0, c1 in zip_longest(e0.get_chains(), e1.get_chains()):\\n if c0.internal_coord.atomArray is not None:\\n if np.allclose(\\n c0.internal_coord.atomArray,\\n c1.internal_coord.atomArray,\\n rtol=1e-03 if rtol is None else rtol,\\n atol=1e-03 if atol is None else atol,\\n ):\\n cmpdict[\\\"aCoordMatchCount\\\"] += np.size(\\n c0.internal_coord.atomArray, 0\\n )\\n else:\\n cmpdict[\\\"pass\\\"] = False\\n cmpdict[\\\"aCount\\\"] += np.size(c0.internal_coord.atomArray, 0)\\n if cmpdict[\\\"aCoordMatchCount\\\"] < cmpdict[\\\"aCount\\\"]:\\n cmpdict[\\\"pass\\\"] = False\\n\\n else:\\n for r0, r1 in zip_longest(e0.get_residues(), e1.get_residues()):\\n if 2 == r0.is_disordered() == r1.is_disordered():\\n for dr0, dr1 in zip_longest(\\n r0.child_dict.values(), r1.child_dict.values()\\n ):\\n _cmp_res(dr0, dr1, verbose, cmpdict, rtol=rtol, atol=atol)\\n else:\\n _cmp_res(r0, r1, verbose, cmpdict, rtol=rtol, atol=atol)\\n\\n if (\\n cmpdict[\\\"rMatchCount\\\"] == cmpdict[\\\"rCount\\\"]\\n and cmpdict[\\\"aCoordMatchCount\\\"] == cmpdict[\\\"aCount\\\"]\\n and cmpdict[\\\"aFullIdMatchCount\\\"] == cmpdict[\\\"aCount\\\"]\\n and cmpdict[\\\"rpnMismatchCount\\\"] == 0\\n ):\\n cmpdict[\\\"pass\\\"] = True\\n else:\\n cmpdict[\\\"pass\\\"] = False\\n\\n rstr = (\\n \\\"{}:{} {} -- {} of {} residue IDs match; {} residues {} atom coords, \\\"\\n \\\"{} full IDs of {} atoms ({} disordered) match : {}\\\".format(\\n cmpdict[\\\"id0\\\"],\\n cmpdict[\\\"id1\\\"],\\n cmpdict[\\\"chains\\\"],\\n cmpdict[\\\"rMatchCount\\\"],\\n cmpdict[\\\"rCount\\\"],\\n cmpdict[\\\"residues\\\"],\\n cmpdict[\\\"aCoordMatchCount\\\"],\\n cmpdict[\\\"aFullIdMatchCount\\\"],\\n cmpdict[\\\"aCount\\\"],\\n cmpdict[\\\"disAtmCount\\\"],\\n \\\"ERROR\\\" if not cmpdict[\\\"pass\\\"] else \\\"ALL OK\\\",\\n )\\n )\\n if not cmpdict[\\\"pass\\\"]:\\n if cmpdict[\\\"rMatchCount\\\"] != cmpdict[\\\"rCount\\\"]:\\n rstr += \\\" -RESIDUE IDS-\\\"\\n if cmpdict[\\\"aCoordMatchCount\\\"] != cmpdict[\\\"aFullIdMatchCount\\\"]:\\n rstr += \\\" -COORDINATES-\\\"\\n if cmpdict[\\\"aFullIdMatchCount\\\"] != cmpdict[\\\"aCount\\\"]:\\n rstr += \\\" -ATOM IDS-\\\"\\n cmpdict[\\\"report\\\"] = rstr\\n\\n return cmpdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"819b0f42188e80ca25782898aac48f3c\",\n \"score\": \"0.5003037\",\n \"text\": \"def compare_fasta_files(file_path1, file_path2):\\n with open(file_path1) as handle1:\\n with open(file_path2) as handle2:\\n iter1 = SeqIO.parse(handle1, 'fasta')\\n iter2 = SeqIO.parse(handle2, 'fasta')\\n fillvalue = object()\\n paired_records = zip_longest(iter1, iter2, fillvalue=fillvalue)\\n return all(r1.__dict__ == r2.__dict__ for r1, r2 in paired_records)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6131b97d273111040abd4c1029fa24\",\n \"score\": \"0.499193\",\n \"text\": \"def pair_exists(self, u, type1, type2):\\n if(not self.nlist_ready):\\n self._build_nlist(u) \\n\\n sel2 = u.atoms.selectAtoms(type2) \\n for a in u.atoms.selectAtoms(type1):\\n i = a.number\\n nlist_accum = np.sum(self.nlist_lengths[:i]) if i > 0 else 0\\n for j in self.nlist[nlist_accum:(nlist_accum + self.nlist_lengths[i])]:\\n if(u.atoms[int(j)] in sel2):\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"082dafa48d0170642f961b74e16158ec\",\n \"score\": \"0.49894404\",\n \"text\": \"def test_isotope_equality(sym1, A1, m1, sym2, A2, m2, equal):\\n i1 = isotope.Isotope(sym1, A1, m1)\\n i2 = isotope.Isotope(sym2, A2, m2)\\n if equal:\\n assert i1 == i2\\n else:\\n assert i1 != i2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2924dc5ba22af01445e883d06d879d83\",\n \"score\": \"0.49814403\",\n \"text\": \"def is_compatible_with(cls: \\\"Algorithm\\\", input_types):\\n my_inputs = cls.input_types()\\n\\n for needed in my_inputs:\\n for having in input_types:\\n if issubclass(having, needed):\\n break\\n else:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37ea6e0890f521c9712d7cb7152e1d74\",\n \"score\": \"0.49767327\",\n \"text\": \"def check_coords(mol0,mol1):\\n \\n keep_coords = True\\n \\n tmp_mol = mol0 + mol1\\n mol0_natoms = len(mol0)\\n #print \\\"Check\\\", len(mol0), len(mol1), len(tmp_mol), mol0_natoms\\n for a0 in xrange(mol0_natoms-1):\\n for a1 in xrange(mol0_natoms,len(tmp_mol)):\\n #print a0,a1\\n if tmp_mol[a0].symbol != \\\"X\\\" and tmp_mol[a1].symbol != \\\"X\\\":\\n this_dist = tmp_mol.get_distance(a0, a1, mic = True)\\n cutoff_dist = covalent_radii[tmp_mol[a0].number] + covalent_radii[tmp_mol[a1].number] + 0.3\\n #print tmp_mol[a0].symbol, tmp_mol[a1].symbol,this_dist, cutoff_dist\\n if this_dist < cutoff_dist:\\n keep_coords = False\\n #print \\\"returning false, overlap\\\"\\n return keep_coords\\n\\n #Second way to fail out if we have PBC (first way - i.e. overlap, is covered above by using mic=True)\\n # The centroid of the addition must be within the box\\n #The +/- 1/2 test used here assume we've already centred the cell\\n com = mol1.get_center_of_mass()\\n #print com\\n #axes = [i for i in range(len(mol0.get_pbc())) if mol0.pbc[i]]\\n for axis in xrange(3):\\n if mol0.pbc[axis]:\\n if com[axis] > mol0.cell[axis,axis] or com[axis] < -mol0.cell[axis,axis]: #orthorhombic cell \\n keep_coords = False\\n #print \\\"returning false, PBC\\\"\\n return keep_coords\\n\\n #print \\\"keeping coords \\\", keep_coords \\n return keep_coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21e5ca3212c1a45bc3c84b7c1c935324\",\n \"score\": \"0.49674994\",\n \"text\": \"def test_comparison_on_type_mismatch(self):\\n a = attributes.ApplicationSpecificInformation(\\n application_namespace=\\\"test_namespace\\\",\\n application_data=\\\"test_data\\\"\\n )\\n b = \\\"invalid\\\"\\n\\n self.assertFalse(a == b)\\n self.assertFalse(b == a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e86c12314a4ba92f532536f548fefa2c\",\n \"score\": \"0.49625188\",\n \"text\": \"def transfer_spectra(dmim_cursor1, dmim_cursor2, rep_cursor):\\n # count the number of dmim_ids without annotations\\n noms2 = 0\\n # count null metfrag scores\\n score_null_p, score_null_m = 0, 0\\n # iterate through all plates\\n for n in range(1, 8):\\n for dmim_id, well, name, adduct in dmim_cursor1.execute(qry_dmim_plate_n.format(n=n)).fetchall():\\n matched = False\\n cmpd = '{}_{}'.format(name, adduct)\\n qdata1 = (cmpd, well, adduct)\\n for spectrum, metfrag_score in rep_cursor.execute(qry_rep_plate_n_ms2.format(n=n), qdata1).fetchall():\\n qdata2 = (dmim_id, spectrum, metfrag_score)\\n dmim_cursor2.execute(qry_dmim_plate_n_ms2.format(n=n), qdata2)\\n matched = True\\n if metfrag_score is None:\\n if '_met' in name:\\n score_null_p += 1\\n else:\\n score_null_m += 1\\n if not matched:\\n noms2 += 1\\n #print('ERROR! annotation for: {} {} {}'.format(dmim_id, well, met_n))\\n print(noms2, 'have no MS2 spectra')\\n print(score_null_p + score_null_m, 'have NULL metfrag_score', '({} parents, {} metabolites)'.format(score_null_p, score_null_m))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bfeaab68ad93e1ce16f3bca04712ed3\",\n \"score\": \"0.49602342\",\n \"text\": \"def _test_matching_types (character, cls):\\n if not isinstance(character, cls):\\n # QAZ: error message.\\n raise MismatchedTypesError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e31fd057d05f8c35d6b916338bde906\",\n \"score\": \"0.49529004\",\n \"text\": \"def geometries_are_equal(atomlistA, atomlistB, eps=1.0e-3):\\n equal = True\\n for (ZatA,posA),(ZatB,posB) in zip(atomlistA, atomlistB):\\n equal = equal and (ZatA == ZatB)\\n equal = equal and la.norm(np.array(posA)-np.array(posB)) < eps\\n return equal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0edbcd9e74de3868170ed5889e8d8ab9\",\n \"score\": \"0.4951187\",\n \"text\": \"def check_connectivity(mol_0, mol_1):\\n\\n bonds_0 = undirected_bond_atoms(mol_0)\\n bonds_1 = undirected_bond_atoms(mol_1)\\n same = (bonds_0 == bonds_1)\\n\\n return same\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bb51583807e69a38618acee5379f49b\",\n \"score\": \"0.4948739\",\n \"text\": \"def compare_result(m, emu, result):\\r\\n\\r\\n global emulatePossibles\\r\\n\\r\\n sets = 0\\r\\n reduces = 0\\r\\n recIdx = len(m.rec)\\r\\n results = len(result)\\r\\n m1 = result[0] # use the first result to compare with others\\r\\n i = 0\\r\\n for r1 in m1.rec[recIdx:]:\\r\\n i += 1 # step idx of m1\\r\\n info = \\\"The #{0} step of the {1} emulate as {2} is same with follows:\\\".format(i, emu[0][0], emu[0][1])\\r\\n same = 0\\r\\n resultIdx = 0\\r\\n for m2 in result[1:]:\\r\\n resultIdx += 1\\r\\n idx = 0 # step idx of others\\r\\n for r2 in m2.rec[recIdx:]:\\r\\n idx += 1\\r\\n if (r1[0], r1[1], r1[2], r1[3]) == (r2[0], r2[1], r2[2], r2[3]):\\r\\n if ACTION_GET_INFO:\\r\\n info = info + \\\"\\\\nThe #{0} step of the {1} emulate as {2}\\\".format(idx, emu[resultIdx][0],\\r\\n emu[resultIdx][1])\\r\\n same += 1\\r\\n break\\r\\n if same == results - 1:\\r\\n if r1.t == \\\"s\\\":\\r\\n sets += 1\\r\\n m.setit(r1[0], r1[1], r1[2], d=\\\"compareResult for {0} emulate!\\\".format(results), info=info)\\r\\n else:\\r\\n reduces += 1\\r\\n m.reduce(r1[0], r1[1], r1[2], d=\\\"compareResult for {0} emulate!\\\".format(results), info=info)\\r\\n return sets, reduces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d10d64a2345c5d408b35bae4201a30\",\n \"score\": \"0.4941716\",\n \"text\": \"def convert_molecules(args):\\n if args.format:\\n format1 = args.format\\n format2 = args.format\\n elif args.format2:\\n format2 = args.format2\\n else:\\n format1 = DEFAULT_INPUT\\n format2 = DEFAULT_INPUT\\n mol1 = pybel.readfile(format1, args.file1).next()\\n mol2 = pybel.readfile(format2, args.file2).next()\\n return mol1, mol2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4be4d6970bbb7d6d01792404da6136f6\",\n \"score\": \"0.49317375\",\n \"text\": \"def is_isomorphic(matrix1, matrix2):\\n matrix1, label1 = np.array(matrix1[0]), matrix1[1]\\n matrix2, label2 = np.array(matrix2[0]), matrix2[1]\\n assert np.shape(matrix1) == np.shape(matrix2)\\n assert len(label1) == len(label2)\\n\\n vertices = np.shape(matrix1)[0]\\n # Note: input and output in our constrained matrices always map to themselves\\n # but this script does not enforce that.\\n for perm in itertools.permutations(range(0, vertices)):\\n pmatrix1, plabel1 = permute_module(matrix1, label1, perm)\\n if np.array_equal(pmatrix1, matrix2) and plabel1 == label2:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee0476e525e844082175c6428db6a78\",\n \"score\": \"0.49227783\",\n \"text\": \"def sig_cmp(sig1, sig2):\\n types1 = sig1.required\\n types2 = sig2.required\\n if len(types1) != len(types2):\\n return False\\n dup_pos = []\\n dup_kw = {}\\n for t1, t2 in zip(types1, types2):\\n match = type_cmp(t1, t2)\\n if match:\\n dup_pos.append(match)\\n else:\\n break\\n else:\\n return tuple(dup_pos)\\n kw_range = slice(len(dup_pos), len(types1))\\n kwds1 = sig1.parameters[kw_range]\\n kwds2 = sig2.parameters[kw_range]\\n if set(kwds1) != set(kwds2):\\n return False\\n kwtypes1 = dict(zip(sig1.parameters, types1))\\n kwtypes2 = dict(zip(sig2.parameters, types2))\\n for kw in kwds1:\\n match = type_cmp(kwtypes1[kw], kwtypes2[kw])\\n if match:\\n dup_kw[kw] = match\\n else:\\n break\\n else:\\n return tuple(dup_pos), dup_kw\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f94332d577ade2110f5b6022f70600\",\n \"score\": \"0.4921804\",\n \"text\": \"def is_similar_to(self, other, tolerance = 0.05):\\r\\n if self is other:\\r\\n return True\\r\\n if not isinstance(other, Structure):\\r\\n return False\\r\\n if self.number_of_atoms != other.number_of_atoms:\\r\\n return False\\r\\n if not self.lattice.is_similar_to(other.lattice, tolerance):\\r\\n return False\\r\\n if not np.all(np.array(self.atomic_symbols) == np.array(other.atomic_symbols)):\\r\\n return False\\r\\n for self_pos, other_pos in zip(self.frac_positions, other.frac_positions):\\r\\n diff = abs(self_pos - other_pos)\\r\\n exceed_indexes = np.where(diff > 0.5)\\r\\n diff[exceed_indexes] = 1 - diff[exceed_indexes]\\r\\n if np.any(np.linalg.norm(diff) > tolerance): \\r\\n return False\\r\\n break\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6cb00e4fd0075a1621d7ea0d9416d99\",\n \"score\": \"0.49213678\",\n \"text\": \"def automata_are_equivalent(automaton_a: Automaton, automaton_b: Automaton) -> bool:\\n # Check if s0 and s0' are compatible, if they are not, don't do anything else\\n if not states_are_compatible(automaton_a.initial_state, automaton_b.initial_state) :\\n return False\\n\\n # Create a table for the transitions of each symbol\\n\\n p = automaton_a.initial_state\\n q = automaton_b.initial_state\\n\\n # So that we don't process a pair more than once, initialize to the initial states\\n processed_pairs = {\\n \\\"{},{}\\\".format(p.state_id, q.state_id): True\\n }\\n\\n # A queue of pair of states to follow\\n states_to_follow_queue = deque([(p, q)])\\n\\n # Repeat until we find an incompatible state or we loop through all states\\n while states_to_follow_queue:\\n p, q = states_to_follow_queue.popleft()\\n\\n if not states_are_compatible(p, q):\\n return False\\n\\n # Check where to transition, and check if both states have transitions for the symbol, if not, return False\\n for symbol in p.transitions.keys():\\n\\n if symbol not in q.transitions:\\n return False\\n\\n new_p = p.transitions.get(symbol)[0] # Transitions are lists, for DFA, they are of length == 1\\n new_q = q.transitions.get(symbol)[0]\\n\\n # Check if we have not already visited this pair, if we have, skip it\\n str_pair = \\\"{},{}\\\".format(new_p.state_id, new_q.state_id)\\n\\n if str_pair not in processed_pairs:\\n # Add it to the states to visit queue\\n processed_pairs[str_pair] = True\\n states_to_follow_queue.append((new_p, new_q))\\n\\n # If, after iterating over all states, there were no incompatible pairs, the automata are equivalent\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6804bd18398e8d38defba7e8759d39e4\",\n \"score\": \"0.49159825\",\n \"text\": \"def test_furtherReductionsMatchFromBothModels(self):\\n print('Checking that all possible 2D models created from the original and reduced models match')\\n \\n for combo in permutations(self.subset,2):\\n # Solve both for the current 2D problem\\n a=fea(self.model,combo)\\n b=fea(self.reduced_optlang,combo)\\n \\n # They should have the same facets\\n disjoint_facets=a.get_facets()^b.get_facets()\\n self.assertEqual(len(disjoint_facets),0)\\n \\n # They should have the same vertices\\n disjoint_vertices=a.get_vertices()^b.get_vertices()\\n self.assertEqual(len(disjoint_vertices),0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e279501eb867f57d4d691bfcd5067392\",\n \"score\": \"0.49140543\",\n \"text\": \"def _assert_entries_match(lexicon1, lexicon2, token):\\n eq_(set((tok.categ(), tok.semantics()) for tok in lexicon1._entries[token]),\\n set((tok.categ(), tok.semantics()) for tok in lexicon2._entries[token]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53caedba3d02d0e2d7569dd0861d4b03\",\n \"score\": \"0.4912611\",\n \"text\": \"def check(bs1, bs2):\\n bs1_gen = inorder_traverse(bs1.root)\\n bs2_gen = inorder_traverse(bs2.root)\\n while True:\\n try:\\n n1 = bs1_gen.next()\\n except StopIteration:\\n try:\\n n2 = bs2_gen.next()\\n return False\\n except StopIteration:\\n print 'Exhausted all nodes -- bsts are the same'\\n return True\\n\\n try:\\n n2 = bs2_gen.next()\\n except StopIteration:\\n return False\\n\\n if n1 != n2:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee3692fca0dfed7fab54574195e49446\",\n \"score\": \"0.49119988\",\n \"text\": \"def resolve_conflicts(self, atoms):\\n neg = set()\\n result = set()\\n # split atoms into NEG and RESULT\\n for atom in atoms:\\n if atom.table.endswith('+'):\\n result.add(atom)\\n elif atom.table.endswith('-'):\\n neg.add(atom)\\n else:\\n result.add(atom)\\n # add elems from NEG only if their inverted version not in RESULT\\n for atom in neg:\\n if atom.invert_update() not in result: # slow: copying ATOM here\\n result.add(atom)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"185c4c3590afb0070326275f36dcdf83\",\n \"score\": \"0.4910697\",\n \"text\": \"def check_duplicate(entry_a, entry_b):\\n try:\\n entry_a = entry_a[\\\"metadata\\\"][\\\"music\\\"]\\n except KeyError:\\n entry_a = entry_a[\\\"metadata\\\"][\\\"custom_files\\\"]\\n try:\\n entry_b = entry_b[\\\"metadata\\\"][\\\"music\\\"]\\n except KeyError:\\n entry_b = entry_b[\\\"metadata\\\"][\\\"custom_files\\\"]\\n for music_a in entry_a:\\n for music_b in entry_b:\\n if music_a[\\\"acrid\\\"] == music_b[\\\"acrid\\\"]:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"723c904fecb09fc2ad21693d1154730c\",\n \"score\": \"0.4908484\",\n \"text\": \"def check_molecule_integrity(self, other_elements: List[str], other_coordinates: List[np.ndarray]):\\n for k, (elem, coord, other_elem, other_coord) in enumerate(\\n zip(self.atoms.get_chemical_symbols(), self.atoms.get_positions(),\\n other_elements, other_coordinates)):\\n if elem != other_elem:\\n raise Exception(\\n f'Element {elem} with index {k} in molecule differs from element {other_elem} in orb file!')\\n\\n if not np.allclose(coord, other_coord):\\n raise Warning(\\n f'Molecular coordinates of element {k} {coord} differ from coordinates in orb file {other_coord}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c426646ed08880eb6f874dbf4dc452d\",\n \"score\": \"0.4899526\",\n \"text\": \"def compare_input_metadata(\\n test_case: TestCase,\\n expected: Dict[str, GraphQLSchemaFieldType],\\n received: Dict[str, GraphQLSchemaFieldType],\\n) -> None:\\n # First, assert that the sets of keys in both dicts are equal.\\n test_case.assertEqual(set(six.iterkeys(expected)), set(six.iterkeys(received)))\\n\\n # Then, compare the values for each key in both dicts.\\n for key in six.iterkeys(expected):\\n expected_value = expected[key]\\n received_value = received[key]\\n\\n test_case.assertTrue(\\n is_same_type(expected_value, received_value),\\n msg=\\\"{} != {}\\\".format(str(expected_value), str(received_value)),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93090fef13f54dd5b9b7d4b6682802e6\",\n \"score\": \"0.4894456\",\n \"text\": \"def test_eq(self):\\n\\n for typecode in VALID_CONTENT:\\n b = gapbuffer(typecode, VALID_CONTENT[typecode])\\n self.assertEqual(VALID_CONTENT[typecode], b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8b023c80eb68fbb17cf25b763879983\",\n \"score\": \"0.4891309\",\n \"text\": \"def check_iso(graph1, graph2):\\n return nx.is_isomorphic(graph1, graph2,\\n node_match=iso.categorical_node_match(\\n 'atomic_number', 0),\\n edge_match=iso.categorical_edge_match('type',\\n 'Single'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20cca9e829ab68518f0bef1cdb6a047b\",\n \"score\": \"0.48875916\",\n \"text\": \"def __eq__(self, other):\\n return (self.type == other.type and\\n self.antecedent == other.antecedent and\\n self.consequent == other.consequent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614f278bd479ee9f602b77513c7f5fb4\",\n \"score\": \"0.48837185\",\n \"text\": \"def __call__(self, a, b):\\n if not self._doenum: return not any(a.genes != b.genes)\\n if hasattr(self.converter, \\\"__call__\\\"):\\n if not hasattr(a, \\\"integer_hashcode\\\"): \\n a.integer_hashcode = self.structure_hashcode(self.converter(a.genes))\\n if not hasattr(b, \\\"integer_hashcode\\\"): \\n b.integer_hashcode = self.structure_hashcode(self.converter(b.genes))\\n return a.integer_hashcode == b.integer_hashcode\\n\\n else:\\n return self.structure_hashcode(a) == self.structure_hashcode(b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97e657251f65c6a58b00177467cb1655\",\n \"score\": \"0.4877786\",\n \"text\": \"def are_compatible(units1, units2):\\n # Empty units always compatible\\n if is_null_unit(units1) or is_null_unit(units2):\\n return True\\n if (not is_unit(units1)) or (not is_unit(units2)):\\n return False\\n x = add_units(1, units1)\\n try:\\n convert_to(x, units2)\\n except ValueError:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3a12d2b9a566877d1c41545174e40fa\",\n \"score\": \"0.48730108\",\n \"text\": \"def compare_ligands(initial_dir, initial_pdb, final_dir, final_pdb,\\n output_dir, manual, tolerance=0.1, atom_tolerance=0.1,\\n amino=False):\\n\\n # Read structure and topology information from PDBs\\n # Identify matching ac, prep and frcmod files in input directories\\n # RDKit requires atom name changes, update input files & move to output_dir\\n initial_mol_info, final_mol_info = prepare_mols(initial_dir, final_dir,\\n initial_pdb, final_pdb,\\n output_dir, amino=amino)\\n\\n # Map charges from resp ac file to atom names and indexes\\n initial_charge_map = create_charge_idx_map(initial_mol_info.ac_filename,\\n initial_mol_info.struct)\\n\\n final_charge_map = create_charge_idx_map(final_mol_info.ac_filename,\\n final_mol_info.struct)\\n\\n # Get dictionary containing AtomInfo for each atom\\n # (uses RDKit index as key)\\n initial_atom_info = get_atom_info_rdkit(initial_mol_info.mol,\\n charge_map=initial_charge_map)\\n\\n final_atom_info = get_atom_info_rdkit(final_mol_info.mol,\\n charge_map=final_charge_map)\\n\\n # Find largest matching region between PDBs (Miximal Common Substructure)\\n\\n rdkit_mols = [initial_mol_info.mol, final_mol_info.mol]\\n mcs_result = rdFMCS.FindMCS(rdkit_mols, completeRingsOnly=True)\\n\\n if mcs_result:\\n\\n shared = Chem.MolFromSmarts(mcs_result.smartsString)\\n\\n initial_match_idxs = initial_mol_info.mol.GetSubstructMatch(shared)\\n final_match_idxs = final_mol_info.mol.GetSubstructMatch(shared)\\n\\n # Map to get equivalent atom index in final molecule to the in the\\n # initial model (matched region)\\n matched_idx_map = dict(zip(initial_match_idxs, final_match_idxs))\\n\\n initial_match_idxs = remove_ring_error(initial_mol_info.mol,\\n initial_match_idxs,\\n atom_info=initial_atom_info)\\n\\n # Update final matched idx list and initial to final map to account\\n # for removed ring atoms\\n\\n final_match_idxs = [matched_idx_map[x] for x in initial_match_idxs]\\n\\n matched_idx_map = dict(zip(initial_match_idxs, final_match_idxs))\\n\\n # This technique only works is a substantial portion of the two molecules\\n # is the same i.e. if a side chain is exchanged. If you are changing the whole\\n # molecule, than this method is not applicable, so the system exits.\\n\\n if float(len(initial_match_idxs))/float(len(initial_mol_info.mol.GetAtoms())) < 0.3:\\n print(\\\"No sufficiently large matching region (excluding incomplete rings) could be identified!\\\\nTerminating.\\\")\\n sys.exit(0)\\n\\n check_atom_type_match(\\n matched_idx_map,\\n initial_mol_info,\\n final_mol_info)\\n\\n # Get all potential sub-matches with complete rings\\n # (includes complete match)\\n submatches = get_submatches(initial_mol_info.mol, initial_atom_info,\\n initial_match_idxs)\\n\\n (q_diffs,\\n q_max_atom_diffs) = get_charge_diffs_submatches(submatches,\\n matched_idx_map,\\n initial_charge_map,\\n final_charge_map)\\n\\n # Manual selection of submatch by user or automatic choice\\n if manual:\\n\\n selected_idx = get_user_submatch(submatches, initial_atom_info,\\n q_diffs, q_max_atom_diffs)\\n\\n else:\\n\\n all_options = range(len(submatches))\\n filtered_q_total = [\\n x for x in all_options if q_diffs[x] < tolerance]\\n filtered_q_atom = [\\n x for x in filtered_q_total if q_max_atom_diffs[x] < atom_tolerance]\\n\\n all_filtered_matches = []\\n\\n if len(filtered_q_atom) > 0:\\n\\n for submatch_idx in filtered_q_atom:\\n\\n submatch = submatches[submatch_idx]\\n\\n no_heavy = 0\\n\\n for idx in submatch:\\n\\n if initial_atom_info[idx].name.strip(\\n '0123456789') != 'H':\\n no_heavy += 1\\n\\n if no_heavy > 1:\\n all_filtered_matches.append(submatch_idx)\\n\\n if len(all_filtered_matches) > 0:\\n\\n selected_idx = all_filtered_matches[0]\\n\\n else:\\n\\n print(\\n 'Warning: Only acceptable matches with at most 1 heavy atom found')\\n selected_idx = filtered_q_atom[0]\\n\\n else:\\n\\n print(\\\"No options fit standard filter - select manually\\\\n\\\")\\n\\n selected_idx = get_user_submatch(submatches, initial_atom_info,\\n q_diffs, q_max_atom_diffs)\\n\\n else:\\n\\n print(\\\"No matching region could be identified!\\\")\\n sys.exit(1)\\n\\n selected_submatch = submatches[selected_idx]\\n\\n # Output all submatches and information informing choice\\n output_submatches_file(submatches, selected_idx,\\n q_diffs, q_max_atom_diffs,\\n initial_atom_info, final_atom_info,\\n matched_idx_map, output_dir)\\n\\n return (\\n initial_mol_info,\\n initial_atom_info,\\n final_mol_info,\\n final_atom_info,\\n matched_idx_map,\\n selected_submatch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ed7f47739a65f616d3d459e6b69e158\",\n \"score\": \"0.4871031\",\n \"text\": \"def type_of_interaction(base_residue, aa_residue, aa_coordinates, standard_aa_center, base_atoms):\\n squared_xy_dist_list = []\\n\\n \\\"\\\"\\\"Defines different sets of amino acids\\\"\\\"\\\"\\n planar_aa = set ([\\\"ARG\\\", \\\"ASN\\\", \\\"ASP\\\", \\\"GLU\\\", \\\"GLN\\\", \\\"HIS\\\", \\\"PHE\\\", \\\"TRP\\\", \\\"TYR\\\"])\\n stacked_aliphatic = set([\\\"ALA\\\", \\\"CYS\\\", \\\"ILE\\\", \\\"LEU\\\", \\\"MET\\\", \\\"PRO\\\", \\\"SER\\\", \\\"THR\\\", \\\"VAL\\\"])\\n # Note: LYS and GLY are not in the previous lists\\n\\n edge_to_edge_aa = set ([\\\"ARG\\\", \\\"ASN\\\", \\\"ASP\\\", \\\"CYS\\\", \\\"GLN\\\", \\\"GLU\\\", \\\"HIS\\\", \\\"LYS\\\", \\\"PHE\\\", \\\"SER\\\", \\\"THR\\\", \\\"TYR\\\", \\\"TRP\\\"])\\n shb_aa = set ([\\\"ARG\\\", \\\"ASN\\\", \\\"ASP\\\", \\\"GLU\\\", \\\"GLN\\\", \\\"HIS\\\", \\\"LYS\\\", \\\"SER\\\", \\\"THR\\\", \\\"TYR\\\"])\\n\\n # calculate distances from aa atoms to base center\\n for aa_atom in aa_residue.atoms(name=aa_fg[aa_residue.sequence]):\\n key = aa_atom.name\\n aa_x = aa_coordinates[key][0]\\n aa_y = aa_coordinates[key][1]\\n\\n squared_xy_dist = (aa_x**2) + (aa_y**2)\\n squared_xy_dist_list.append(squared_xy_dist)\\n\\n #print base_residue.unit_id(), aa_residue.unit_id(), min(squared_xy_dist_list), mean_z\\n\\n # for a stacking interaction, the x,y coordinate of at least one atom of the amino acid group needs to be\\n # within sqrt(5) = 2.236 of the base center 0,0\\n min_dist = np.sqrt(min(squared_xy_dist_list))\\n\\n if min_dist <= 2.236:\\n #print base_residue.unit_id(), aa_residue.unit_id(), min(squared_xy_dist_list), mean_z\\n if aa_residue.sequence in planar_aa:\\n return stacking_planar_annotation(base_residue, aa_residue, min_dist)\\n\\n elif aa_residue.sequence in stacked_aliphatic:\\n return stacking_non_planar_annotation(aa_residue, aa_coordinates, min_dist)\\n\\n else:\\n return (\\\"other-stack\\\",{\\\"dist-xy-from-center\\\":min_dist})\\n\\n # check for interactions in the plane of the base\\n mean_z = standard_aa_center[2]\\n (num_hydrogen_bonds,hydrogen_bond_list) = count_hydrogen_bonds(base_residue, aa_residue, base_atoms)\\n\\n if -1.8 <= mean_z < 1.8:\\n if aa_residue.sequence in edge_to_edge_aa:\\n angle = calculate_angle_between_planar_residues(base_residue, aa_residue)\\n if angle:\\n if 0 <= angle <= 45 and num_hydrogen_bonds >= 2:\\n return (\\\"pseudopair\\\",{\\\"hydrogen-bonds\\\":hydrogen_bond_list,\\\"angle-between-planes\\\":angle})\\n elif 45 <= angle:\\n return (\\\"perpendicular-edge\\\",{\\\"hydrogen-bonds\\\":hydrogen_bond_list,\\\"angle-between-planes\\\":angle})\\n\\n if aa_residue.sequence in shb_aa:\\n# base_seq = base_residue.sequence\\n# base_atoms = NAbaseheavyatoms[base_seq]\\n if num_hydrogen_bonds >= 1:\\n return (\\\"SHB\\\",{\\\"hydrogen-bonds\\\":hydrogen_bond_list})\\n\\n return (\\\"other-edge\\\",{\\\"hydrogen-bonds\\\":hydrogen_bond_list})\\n\\n return (\\\"other\\\",{\\\"dist-xy-from-center\\\":min_dist,\\\"hydrogen-bonds\\\":hydrogen_bond_list})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b32ea4b53225d90dbf9eb211a412d5\",\n \"score\": \"0.48704225\",\n \"text\": \"def compare_data_objects(self, other):\\n return self.type == other.type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c43e97a723912bbe7151e8e36f4c6384\",\n \"score\": \"0.48682833\",\n \"text\": \"def test_different_code(self):\\n hole1 = Hole(1, 100, [\\\"c\\\"], \\\"EXP\\\", 2)\\n hole2 = Hole(1, 100, [\\\"d\\\"], \\\"ARI\\\", 2)\\n self.assertFalse(hole1.is_equal(hole2))\\n self.assertFalse(hole2.is_equal(hole1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc63f6db35c070869ff27a1621c15c1\",\n \"score\": \"0.4862124\",\n \"text\": \"def equal_equal_primary_asset_type(cls, a, b):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6caf2273095160af7d8383a87d3a0bb2\",\n \"score\": \"0.4862096\",\n \"text\": \"def is_two_objects_is_the_same_objects(first: Any, second: Any) -> bool:\\n return first is second\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8797e1a798d142154cb1f6f2aa5067e9\",\n \"score\": \"0.48611817\",\n \"text\": \"def assess_rxn_match(rxn_name1, params1, rxn_param_dct2, print_output=False):\\n # Get all possible orderings of the reactants and products for mech1\\n [rcts1, prds1] = rxn_name1\\n rcts1_perm = list(itertools.permutations(rcts1, len(rcts1)))\\n prds1_perm = list(itertools.permutations(prds1, len(prds1)))\\n em_param1 = params1[6] #'+M', '(+M)', or None\\n\\n rxn_name2 = None\\n flip_rxn = None\\n p_dep_same = None\\n for rxn_name, params in rxn_param_dct2.items():\\n [rcts2, prds2] = rxn_name\\n em_param2 = params[6]\\n if rcts2 in rcts1_perm and prds2 in prds1_perm:\\n rxn_name2 = rxn_name\\n flip_rxn = False\\n\\n if em_param2 == em_param1:\\n p_dep_same = True\\n else:\\n p_dep_same = False\\n break\\n\\n if rcts2 in prds1_perm and prds2 in rcts1_perm:\\n rxn_name2 = rxn_name\\n flip_rxn = True\\n\\n if em_param2 == em_param1:\\n p_dep_same = True\\n else:\\n p_dep_same = False\\n break\\n\\n if print_output:\\n if rxn_name2:\\n print('\\\\n- M2 RCTS', '+'.join(mech2_key[0]))\\n print('- M2 PRDS', '+'.join(mech2_key[1]))\\n else:\\n print('\\\\n- NO M2 MATCH')\\n\\n return rxn_name2, flip_rxn, p_dep_same\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22de7712f16e904091b2598a58376c32\",\n \"score\": \"0.48608738\",\n \"text\": \"def __eq__(self, other):\\n try:\\n return (self.overlap == other.overlap and\\n self.tolerance == other.tolerance and\\n self.mistakes == other.mistakes and\\n type(self) is type(other))\\n except AttributeError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3c0cad5fa77223ea1c34e250f3f1f3\",\n \"score\": \"0.4860405\",\n \"text\": \"def is_ambiguous(self, molid):\\n return self.label(molid) == 'a'\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993485,"cells":{"query_id":{"kind":"string","value":"2e0aef6d6ccd975462cef39574e85761"},"query":{"kind":"string","value":"Helper function to compute our content and style feature representations. This function will simply load and preprocess both the content and style images from their path. Then it will feed them through the network to obtain the outputs of the intermediate layers."},"positive_passages":{"kind":"list like","value":[{"docid":"d4208e32afe57cbe7499df2e38a92d0c","score":"0.69074523","text":"def feature_representations(\n self, content_img: np.array, style_img: np.array\n ) -> Tuple[tf.Tensor, tf.Tensor]:\n\n content_img = self._process_img(content_img)\n style_img = self._process_img(style_img)\n\n style_outputs = self._model(style_img)\n content_outputs = self._model(content_img)\n\n self._num_style_layers = len(self._style_layers)\n\n # Get the style and content feature representations from our model\n style_features = [\n style_layer[0] for style_layer in style_outputs[: self._num_style_layers]\n ]\n content_features = [\n content_layer[0]\n for content_layer in content_outputs[self._num_style_layers :]\n ]\n return content_features, style_features","title":""}],"string":"[\n {\n \"docid\": \"d4208e32afe57cbe7499df2e38a92d0c\",\n \"score\": \"0.69074523\",\n \"text\": \"def feature_representations(\\n self, content_img: np.array, style_img: np.array\\n ) -> Tuple[tf.Tensor, tf.Tensor]:\\n\\n content_img = self._process_img(content_img)\\n style_img = self._process_img(style_img)\\n\\n style_outputs = self._model(style_img)\\n content_outputs = self._model(content_img)\\n\\n self._num_style_layers = len(self._style_layers)\\n\\n # Get the style and content feature representations from our model\\n style_features = [\\n style_layer[0] for style_layer in style_outputs[: self._num_style_layers]\\n ]\\n content_features = [\\n content_layer[0]\\n for content_layer in content_outputs[self._num_style_layers :]\\n ]\\n return content_features, style_features\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f3f5bf2fac38a5d95359f7beb6529362","score":"0.64880633","text":"def stylize(network, initial, content, styles, iterations,\n content_weight, style_weight, style_blend_weights, tv_weight,\n learning_rate, print_iterations=None, checkpoint_iterations=None):\n shape = (1,) + content.shape\n style_shapes = [(1,) + style.shape for style in styles]\n content_features = {}\n style_fetures = [{} for _ in styles]\n\n # compute content features in feedforward mode\n g = tf.Graph()\n with g.as_default(), g.device('cpu:0'), tf.Session as sess:\n image = tf.placeholder('float', shape=shape)\n nets, mean_pixel = vgg.net(network, image)\n content_pre = np.array([vgg.preprocess(content.mean_pixel)])\n content_features[CONTENT_LAYER] = nets[CONTENT_LAYER].eval(feed_dict={image: content_pre})\n\n # compute style features in feedforward mode\n for i in range(len(styles)):\n g = tf.Graph()\n with g.as_default(), g.device('cpu:0'), tf.Session as sess:\n image = tf.placeholder('float', shape=style_shapes[i])\n nets, _ = vgg.net(network, image)\n stype_pre = np.array([vgg.preprocess(styles[i], mean_pixel)])\n for layer in STYLE_LAYERS:\n features = nets[layer].eval(feed_dict={image: stype_pre})\n features = np.reshape(features, (-1, features.shape[3]))\n gram = np.matmul(features.T, features) / features.size\n style_fetures[i][layer] = gram\n\n # make stylized image using backpropogation\n with tf.Graph().as_default():\n if initial is None:\n noise = np.random.normal(size=shape, scale=(np.std(content) * 0.1))\n initial = tf.random_normal(shape) * 0.256\n else:\n initial = np.array([vgg.preprocess(initial, mean_pixel)])\n initial = initial.astype('float32')\n image = tf.Variable(initial)\n net, _ = vgg.net(network.image)\n\n # content loss\n content_loss = content_weight * (2 * tf.nn.l2_loss(\n net[CONTENT_LAYER] - content_features[CONTENT_LAYER]) / content_features[CONTENT_LAYER].size)\n\n # style_loss\n style_loss = 0\n for i in range(len(styles)):\n style_losses = []\n for style_layer in STYLE_LAYERS:\n layer = net[style_layer]\n _, height, weight, number = map(lambda j: j.value, layer.get_shape())\n size = height * weight * number\n feats = tf.reshape(layer, (-1, number))\n gram = tf.matmul(tf.transpose(feats), feats) / size\n style_gram = style_fetures[i][style_layer]\n style_losses.append(2 * tf.nn.l2_loss(gram - style_gram) / style_gram.size)\n style_loss += style_weight * style_blend_weights[i] * reduce(tf.add, style_losses)\n\n # total varation denoising\n tv_y_size = _tensor_size(image[:, 1:, :, :])\n tv_x_size = _tensor_size(image[:, 1:, :, :])\n tv_loss = tv_weight * 2 * (\n tf.nn.l2_loss(image[:, 1:, :, :] - image[:, :shape[1] - 1, :, :]) / tv_y_size +\n tf.nn.l2_loss(image[:, 1:, :, :] - image[:, :shape[2] - 1, :, :]) / tv_x_size\n )\n\n # overall loss\n loss = content_loss + style_loss + tv_loss\n\n # optimizer setup\n train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n\n def print_process(j, last=False):\n stderr.write('Iteration %d%d\\n' % (j + 1, iterations))\n if last or (print_iterations and j % print_iterations == 0):\n stderr.write(' content loss: %g\\n' % content_loss.eval())\n stderr.write(' style loss: %g\\n' % style_loss.eval())\n stderr.write(' tv loss: %g\\n' % tv_loss.eval())\n stderr.write(' total loss: %g\\n' % loss.eval())\n\n # optimization\n best_loss = float('inf')\n best = None\n with tf.Session as sess:\n sess.run(tf.initialize_all_variables())\n for i in range(iterations):\n last_step = (i == iterations - 1)\n print_process(i, last=last_step)\n train_step.run()\n\n if (checkpoint_iterations and i % checkpoint_iterations == 0) or last_step:\n this_loss = loss.eval()\n if this_loss < best_loss:\n best_loss = this_loss\n best = image.eval()\n yield (\n (None if last_step else i),\n vgg.unprocess(best.reshape(shape[1:]), mean_pixel)\n )","title":""},{"docid":"71ccf4746816190e8ee34006acab173e","score":"0.64056176","text":"def load_images(content_path, style_path, max_dim):\n content_image = img_utils.load_img(content_path, max_dim)\n logging.info(\"Shape content image: %s\", content_image.get_shape())\n style_image = img_utils.load_img(style_path, max_dim)\n logging.info(\"Shape style image: %s\", style_image.get_shape())\n return content_image, style_image","title":""},{"docid":"42a9cb5e4602c3055c44521373ddb9e6","score":"0.62286264","text":"def optimize(args):\n if args.cuda:\n ctx = mx.gpu(0)\n else:\n ctx = mx.cpu(0)\n # load the content and style target\n content_image = utils.tensor_load_rgbimage(args.content_image,ctx, size=args.content_size, keep_asp=True)\n content_image = utils.subtract_imagenet_mean_preprocess_batch(content_image)\n style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size)\n style_image = utils.subtract_imagenet_mean_preprocess_batch(style_image)\n # load the pre-trained vgg-16 and extract features\n vgg = net.Vgg16()\n utils.init_vgg_params(vgg, 'models', ctx=ctx)\n # content feature\n f_xc_c = vgg(content_image)[1]\n # style feature\n features_style = vgg(style_image)\n gram_style = [net.gram_matrix(y) for y in features_style]\n # output\n output = Parameter('output', shape=content_image.shape)\n output.initialize(ctx=ctx)\n output.set_data(content_image)\n # optimizer\n trainer = gluon.Trainer([output], 'adam',\n {'learning_rate': args.lr})\n mse_loss = gluon.loss.L2Loss()\n\n # optimizing the images\n for e in range(args.iters):\n utils.imagenet_clamp_batch(output.data(), 0, 255)\n # fix BN for pre-trained vgg\n with autograd.record():\n features_y = vgg(output.data())\n content_loss = 2 * args.content_weight * mse_loss(features_y[1], f_xc_c)\n style_loss = 0.\n for m in range(len(features_y)):\n gram_y = net.gram_matrix(features_y[m])\n gram_s = gram_style[m]\n style_loss = style_loss + 2 * args.style_weight * mse_loss(gram_y, gram_s)\n total_loss = content_loss + style_loss\n total_loss.backward()\n\n trainer.step(1)\n if (e + 1) % args.log_interval == 0:\n print('loss:{:.2f}'.format(total_loss.asnumpy()[0]))\n\n # save the image\n output = utils.add_imagenet_mean_batch(output.data())\n utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda)","title":""},{"docid":"92be832df7bbe912d7aad3ffea91acae","score":"0.6170639","text":"def run(content_image_dir, style_image_dir, host_name, overwrite):\n rest_api_url = \"http://{}:5000/stylize/\".format(host_name)\n\n content_filenames_iterator = next(os.walk(content_image_dir))[2]\n content_filenames = []\n for filename in content_filenames_iterator:\n if \"__label__\" in filename:\n continue\n if (\n filename.lower().endswith(\".jpg\")\n or filename.lower().endswith(\".png\")\n or filename.lower().endswith(\".jpeg\")\n ):\n content_filenames.append(filename)\n\n style_filenames_iterator = next(os.walk(style_image_dir))[2]\n style_filenames = []\n for filename in style_filenames_iterator:\n if \"__label__\" in filename:\n continue\n if (\n filename.lower().endswith(\".jpg\")\n or filename.lower().endswith(\".png\")\n or filename.lower().endswith(\".jpeg\")\n ):\n style_filenames.append(filename)\n\n for content_filename in tqdm(content_filenames):\n for style_filename in style_filenames:\n if content_filename == style_filename:\n continue\n\n start_time = time.time()\n\n content_label_filename = get_label_filename(content_filename)\n style_label_filename = get_label_filename(style_filename)\n output_image_file_path = os.path.join(\n STYLIZED_IMAGE_DIR, content_filename + \"__\" + style_filename + \".png\"\n )\n if os.path.isfile(output_image_file_path) and not overwrite:\n print(\n \"Skipping, because {} already exists\".format(output_image_file_path)\n )\n continue\n\n content_image = Image.open(\n os.path.join(content_image_dir, content_filename)\n )\n style_image = Image.open(os.path.join(style_image_dir, style_filename))\n\n content_segmentation_image = None\n style_segmentation_image = None\n try:\n content_segmentation_image = Image.open(\n os.path.join(content_image_dir, content_label_filename)\n )\n style_segmentation_image = Image.open(\n os.path.join(style_image_dir, style_label_filename)\n )\n except FileNotFoundError as e:\n print(e)\n pass\n\n data = {\n \"content_image_base64\": pil_image_to_base64(content_image),\n \"content_segmentation_base64\": pil_image_to_base64(\n content_segmentation_image\n ),\n \"style_image_base64\": pil_image_to_base64(style_image),\n \"style_segmentation_base64\": pil_image_to_base64(\n style_segmentation_image\n ),\n }\n\n r = requests.post(rest_api_url, json=data, timeout=60)\n json_data = r.json()\n output_image = base64_png_image_to_pillow_image(\n json_data[\"output_image_base64\"]\n )\n output_image.save(output_image_file_path, format=\"PNG\")\n print(\"Stylized one image ({} s)\".format(time.time() - start_time))","title":""},{"docid":"652a5e98824a2f2734238f2bd1e1e66d","score":"0.6169471","text":"def main():\n content_image, style_image = load_images(known_args.content_path, known_args.style_path, known_args.max_dim)\n extractor = create_model()\n content_targets = extractor(content_image)['content'] # dict with vgg content and style values for content image\n style_targets = extractor(style_image)['style'] # dict with vgg content and style values for style image\n if known_args.initialize_white_noise:\n generated_image = tf.Variable(\n tf.random.uniform(content_image.get_shape(), minval=0.0, maxval=1.0, dtype=content_image.dtype, seed=42)\n )\n else:\n generated_image = tf.Variable(content_image) # initialize generated image from content image\n img_utils.store_tensor_image(generated_image, known_args.output_path)\n\n opt = tf.optimizers.Adam(learning_rate=known_args.learning_rate, beta_1=0.99, epsilon=1e-1)\n\n start = time.time()\n\n step = 0\n total_loss = None\n for ep in range(known_args.epochs):\n for st in range(known_args.steps_per_epoch):\n step += 1\n total_loss = train_step(generated_image, content_targets, style_targets, extractor, opt)\n output_path = ('_ep'+str(ep).zfill(3)+'.').join(known_args.output_path.rsplit('.', 1))\n img_utils.store_tensor_image(generated_image, output_path)\n logging.info(\"Train step: %s. Loss %s\", step, total_loss.numpy())\n\n end = time.time()\n logging.info(\"Total time: {:.1f}\".format(end - start))","title":""},{"docid":"2efef2a4f6b09157c4848136a0cbc14e","score":"0.615392","text":"def generate_stylized_image(args):\n device = torch.device(\"cuda\" if args.cuda else \"cpu\")\n\n content_image = utils.load_image(args.content_image, scale=content_scale)\n content_transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Lambda(lambda x: x.mul(255))\n ])\n content_image = content_transform(content_image)\n content_image = content_image.unsqueeze(0).to(device)\n\n with torch.no_grad():\n style_model = TransformerNet(style_number=args.style_num)\n state_dict = torch.load(args.model)\n style_model.load_state_dict(state_dict, strict=False)\n style_model.to(device)\n output = style_model(content_image, style_id = [args.style_id]).cpu()\n\n utils.save_image('output/'+args.output_image+'_style'+str(args.style_id)+'.jpg', output[0])","title":""},{"docid":"e1bda492b46455b7e3041685a1cf99dc","score":"0.5997467","text":"def build_network(self, content_img, style_img, content_weight, style_weight, save_trigger=False): # TODO: change this to be stateless\n n_conv = 0\n n_relu = 0\n n_pool = 0\n for key, layer in self.submodule._modules.items():\n #iterate through dicitonary of pretrained modules1\n conv_name = 'conv_' + str(n_conv)\n relu_name = 'relu_' + str(n_relu)\n pool_name = 'pool_' + str(n_pool)\n if isinstance(layer, torch.nn.modules.conv.Conv2d):\n self.ArtNet.add_module(conv_name, layer)\n if conv_name in self._content_layers:\n target = self.ArtNet.forward(content_img.cuda()).clone()\n if save_trigger:\n self.content_targets += [target.data.cpu()]\n content_loss = ContentLoss(target, content_weight).cuda()\n self.ArtNet.add_module('content_loss_' + str(n_conv), content_loss)\n if self._unbuilt:\n self.content_losses.append(content_loss)\n\n if conv_name in self._style_layers:\n target_feature = self.ArtNet.forward(style_img.cuda()).clone()\n if save_trigger:\n self.style_targets += [target_feature.data.cpu()]\n target_feature_gram = self.gram.forward(target_feature)\n style_loss = StyleLoss(target_feature_gram, style_weight).cuda()\n self.ArtNet.add_module('style_loss_' + str(n_conv), style_loss)\n if self._unbuilt:\n self.style_losses.append(style_loss)\n n_conv += 1\n\n if isinstance(layer, torch.nn.modules.activation.ReLU):\n self.ArtNet.add_module(relu_name, layer)\n if relu_name in self._content_layers:\n target = self.ArtNet.forward(content_img.cuda()).clone()\n if save_trigger:\n self.content_targets += [target.data.cpu()]\n content_loss = ContentLoss(target, content_weight).cuda()\n self.ArtNet.add_module(\"content_loss_\" + str(n_relu), content_loss)\n if self._unbuilt:\n self.content_losses.append(content_loss)\n\n if relu_name in self._style_layers:\n target_feature = self.ArtNet.forward(style_img.cuda()).clone()\n if save_trigger:\n self.style_targets += [target_feature.data.cpu()]\n target_feature_gram = self.gram.forward(target_feature)\n style_loss = StyleLoss(target_feature_gram, style_weight).cuda()\n self.ArtNet.add_module(\"style_loss_\" + str(n_relu), style_loss)\n if self._unbuilt:\n self.style_losses.append(style_loss)\n n_relu += 1\n\n if isinstance(layer, torch.nn.modules.pooling.MaxPool2d):\n self.ArtNet.add_module(pool_name, layer)\n n_pool += 1\n self._unbuilt = False\n return","title":""},{"docid":"776411496d6c83e6c7a19c6339472b19","score":"0.59838206","text":"def load_img_util (num_sample,path_write,dim_img,preprocess_img,channel,mode_pre_img, pattern_img='train'):\n from keras.preprocessing import image\n from keras.applications.resnet50 import preprocess_input as pre_resnet50\n from keras.applications.vgg16 import preprocess_input as pre_vgg16\n from keras.applications.vgg19 import preprocess_input as pre_vgg19\n from keras.applications.inception_v3 import preprocess_input as pre_incep\n \n temp = []\n for i in range(0,num_sample): #load samples for learning\n image_path = os.path.join(path_write, str(pattern_img) +str(\"_\") + str(i) +\".png\") \n if dim_img==-1: #if use real img\n if channel == 4:\n #img = imread(image_path)\n #img = img.astype('float32')\n print ('waiting for fixing issues from from scipy.misc import imread')\n exit()\n elif channel == 1:\n img = image.load_img(image_path,grayscale=True)\n else: \n img = image.load_img(image_path)\n else: #if select dimension\n \n if channel == 1:\n img = image.load_img(image_path,grayscale=True, target_size=(dim_img, dim_img))\n \n else:\n img = image.load_img(image_path,target_size=(dim_img, dim_img))\n\n x = image.img_to_array(img) \n # x = preprocess_input(x)\n if preprocess_img=='vgg16':\n x = pre_vgg16(x, mode= mode_pre_img)\n elif preprocess_img=='resnet50':\n x = pre_resnet50(x, mode= mode_pre_img)\n elif preprocess_img=='vgg19':\n x = pre_vgg19(x, mode= mode_pre_img)\n elif preprocess_img=='incep':\n x = pre_incep(x)\n \n temp.append(x) \n\n return np.stack(temp)","title":""},{"docid":"e1587db2fd467a08b485e5e3da6acfd4","score":"0.59218526","text":"def pre_process(width, height, path):\n #containers for pre-processed image data and class labels\n data = []\n labels = []\n\n #images directory contains 3 sub-directories: 'poison_ivy', 'poison_oak', 'poison_sumac'\n #randomly get image paths and shuffle them\n # current path 'C:\\\\Users\\\\jltsa\\\\Desktop\\\\Project_2\\\\images'\n image_paths = sorted(list(paths.list_images(path)))\n random.seed(42)\n random.shuffle(image_paths)\n\n #preprocess images to width x height pixels as required for LeNet\n for image_path in image_paths:\n image = cv2.imread(image_path)\n if image is not None:\n image = cv2.resize(image, (width, height))\n image = img_to_array(image)\n data.append(image)\n \n #Extract class labels\n label = image_path.split(os.path.sep)[-2]\n if label == 'poison_ivy':\n label = 0\n elif label == 'poison_oak':\n label = 1\n else:\n label = 2\n labels.append(label)\n \n #Scal pixel intensities from 0 to 1\n data = np.array(data, dtype='float') / 255.0\n labels = np.array(labels)\n \n return data, labels","title":""},{"docid":"9d29e0105fd37dcec564bc9e9b9ad51e","score":"0.5902463","text":"def neural_transform(content_image, style_image, output_image, content_size=256,\n style_scale=1.0, cuda=False, model='backend/models/21styles.model'):\n\n style_size = int(content_size * style_scale) # Round down to the nearest integer\n\n content_image = neural_style.utils.tensor_load_rgbimage(content_image, size=content_size, keep_asp=True)\n content_image = content_image.unsqueeze(0)\n style = neural_style.utils.tensor_load_rgbimage(style_image, size=style_size)\n style = style.unsqueeze(0)\n style = neural_style.utils.preprocess_batch(style)\n\n style_model = Net(ngf=128)\n style_model.load_state_dict(torch.load(model), False)\n\n if cuda:\n style_model.cuda()\n content_image = content_image.cuda()\n style = style.cuda()\n\n style_v = Variable(style)\n\n content_image = Variable(neural_style.utils.preprocess_batch(content_image))\n style_model.setTarget(style_v)\n\n output = style_model(content_image)\n neural_style.utils.tensor_save_bgrimage(output.data[0], output_image, cuda)\n return output_image","title":""},{"docid":"00a0ff81ac76ee7bdd888134c6c2564e","score":"0.58949983","text":"def extract_features(model, img_paths, batch_size=1):\n import os\n import numpy as np\n from keras.applications.resnet50 import preprocess_input\n from keras.preprocessing import image\n \n n = len(img_paths)\n img_array = np.zeros((n, 224, 224, 3))\n \n for i, path in enumerate(img_paths):\n img = image.load_img(path, target_size=(224, 224))\n img = image.img_to_array(img)\n img = np.expand_dims(img, axis=0)\n x = preprocess_input(img)\n img_array[i] = x\n \n X = model.predict(img_array, batch_size=batch_size, verbose=0)\n X = X.reshape((n, 2048))\n\n for index in range(X.shape[0]):\n img_path = img_paths[index]\n img_vector = X[index]\n return X","title":""},{"docid":"0446498a7a5d73e61435108b1ac6c4d8","score":"0.58798593","text":"def _build_model(self, train_mode=None):\n\n #rgb_scaled = rgb * 255.0\n\n bgr = self._images\n bgr = self._pre_process_images(bgr)\n\n # Convert RGB to BGR\n blue, green, red = tf.split(axis=3, num_or_size_splits=3, value=bgr)\n assert red.get_shape().as_list()[1:] == [224, 224, 1]\n assert green.get_shape().as_list()[1:] == [224, 224, 1]\n assert blue.get_shape().as_list()[1:] == [224, 224, 1]\n bgr = tf.concat(axis=3, values=[\n blue - VGG_MEAN[0],\n green - VGG_MEAN[1],\n red - VGG_MEAN[2],\n ])\n assert bgr.get_shape().as_list()[1:] == [224, 224, 3]\n\n\n with tf.variable_scope('extract_feature_map'):\n self.conv1_1 = self.conv_layer(bgr, 3, 64, \"conv1_1\")\n self.conv1_2 = self.conv_layer(self.conv1_1, 64, 64, \"conv1_2\")\n self.pool1 = self.max_pool(self.conv1_2, 'pool1')\n\n self.conv2_1 = self.conv_layer(self.pool1, 64, 128, \"conv2_1\")\n self.conv2_2 = self.conv_layer(self.conv2_1, 128, 128, \"conv2_2\")\n self.pool2 = self.max_pool(self.conv2_2, 'pool2')\n\n self.conv3_1 = self.conv_layer(self.pool2, 128, 256, \"conv3_1\")\n self.conv3_2 = self.conv_layer(self.conv3_1, 256, 256, \"conv3_2\")\n self.conv3_3 = self.conv_layer(self.conv3_2, 256, 256, \"conv3_3\")\n self.conv3_4 = self.conv_layer(self.conv3_3, 256, 256, \"conv3_4\")\n self.pool3 = self.max_pool(self.conv3_4, 'pool3')\n\n self.conv4_1 = self.conv_layer(self.pool3, 256, 512, \"conv4_1\")\n self.conv4_2 = self.conv_layer(self.conv4_1, 512, 512, \"conv4_2\")\n self.conv4_3 = self.conv_layer(self.conv4_2, 512, 512, \"conv4_3\")\n self.conv4_4 = self.conv_layer(self.conv4_3, 512, 512, \"conv4_4\")\n self.pool4 = self.max_pool(self.conv4_4, 'pool4')\n\n self.conv5_1 = self.conv_layer(self.pool4, 512, 512, \"conv5_1\")\n self.conv5_2 = self.conv_layer(self.conv5_1, 512, 512, \"conv5_2\")\n self.conv5_3 = self.conv_layer(self.conv5_2, 512, 512, \"conv5_3\")\n self.conv5_4 = self.conv_layer(self.conv5_3, 512, 512, \"conv5_4\")\n self.pool5 = self.max_pool(self.conv5_4, 'pool5')\n\n self.fc6 = self.fc_layer(self.pool5, 25088, 4096, \"fc6\") # 25088 = ((224 // (2 ** 5)) ** 2) * 512\n self.relu6 = tf.nn.relu(self.fc6)\n if train_mode is not None:\n self.relu6 = tf.cond(train_mode, lambda: tf.nn.dropout(self.relu6, self.dropout), lambda: self.relu6)\n elif self.trainable:\n self.relu6 = tf.nn.dropout(self.relu6, self.dropout)\n\n self.fc7 = self.fc_layer(self.relu6, 4096, 4096, \"fc7\")\n self.relu7 = tf.nn.relu(self.fc7)\n if train_mode is not None:\n self.relu7 = tf.cond(train_mode, lambda: tf.nn.dropout(self.relu7, self.dropout), lambda: self.relu7)\n elif self.trainable:\n self.relu7 = tf.nn.dropout(self.relu7, self.dropout)\n self.fc8 = self.fc_layer(self.relu7, 4096, 1000, \"fc8\")\n\n with tf.variable_scope('logits'):\n self.logits = self._fully_connected(self.fc8, self.hps.num_classes, \"logits\")\n self.predictions = tf.nn.softmax(self.logits, name=\"prob\")\n\n with tf.variable_scope('costs'):\n xent = tf.nn.softmax_cross_entropy_with_logits(\n logits=self.logits, labels=self.labels)\n\n self.costs = tf.reduce_mean(xent, name='xent')\n self.costs += self._decay()\n\n tf.summary.scalar('cost', self.costs)\n\n\n self.data_dict = None","title":""},{"docid":"9b9e65c595cc1ac781688c320b183a04","score":"0.5866423","text":"def style_transfer(content_image, style_image,\n content_layer_ids, style_layer_ids,\n weight_content=1.5, weight_style=10.0,\n weight_denoise=0.3,\n num_iterations=120, step_size=10.0):\n\n # Create an instance of the VGG16-model. This is done\n # in each call of this function, because we will add\n # operations to the graph so it can grow very large\n # and run out of RAM if we keep using the same instance.\n model = vgg16.VGG16()\n\n # Create a TensorFlow-session.\n session = tf.InteractiveSession(graph=model.graph)\n\n # Print the names of the content-layers.\n print(\"Content layers:\")\n print(model.get_layer_names(content_layer_ids))\n print()\n\n # Print the names of the style-layers.\n print(\"Style layers:\")\n print(model.get_layer_names(style_layer_ids))\n print()\n\n # Create the loss-function for the content-layers and -image.\n loss_content = create_content_loss(session=session,\n model=model,\n content_image=content_image,\n layer_ids=content_layer_ids)\n\n # Create the loss-function for the style-layers and -image.\n loss_style = create_style_loss(session=session,\n model=model,\n style_image=style_image,\n layer_ids=style_layer_ids) \n\n # Create the loss-function for the denoising of the mixed-image.\n loss_denoise = create_denoise_loss(model)\n\n # Create TensorFlow variables for adjusting the values of\n # the loss-functions. This is explained below.\n adj_content = tf.Variable(1e-10, name='adj_content')\n adj_style = tf.Variable(1e-10, name='adj_style')\n adj_denoise = tf.Variable(1e-10, name='adj_denoise')\n\n # Initialize the adjustment values for the loss-functions.\n session.run([adj_content.initializer,\n adj_style.initializer,\n adj_denoise.initializer])\n\n # Create TensorFlow operations for updating the adjustment values.\n # These are basically just the reciprocal values of the\n # loss-functions, with a small value 1e-10 added to avoid the\n # possibility of division by zero.\n update_adj_content = adj_content.assign(1.0 / (loss_content + 1e-10))\n update_adj_style = adj_style.assign(1.0 / (loss_style + 1e-10))\n update_adj_denoise = adj_denoise.assign(1.0 / (loss_denoise + 1e-10))\n\n # This is the weighted loss-function that we will minimize\n # below in order to generate the mixed-image.\n # Because we multiply the loss-values with their reciprocal\n # adjustment values, we can use relative weights for the\n # loss-functions that are easier to select, as they are\n # independent of the exact choice of style- and content-layers.\n loss_combined = weight_content * adj_content * loss_content + weight_style * adj_style * loss_style + weight_denoise * adj_denoise * loss_denoise\n\n # Use TensorFlow to get the mathematical function for the\n # gradient of the combined loss-function with regard to\n # the input image.\n gradient = tf.gradients(loss_combined, model.input)\n\n # List of tensors that we will run in each optimization iteration.\n run_list = [gradient, update_adj_content, update_adj_style, update_adj_denoise]\n\n # The mixed-image is initialized with random noise.\n # It is the same size as the content-image.\n mixed_image = np.random.rand(*content_image.shape) + 128\n\n for i in range(num_iterations):\n # Create a feed-dict with the mixed-image.\n feed_dict = model.create_feed_dict(image=mixed_image)\n\n # Use TensorFlow to calculate the value of the\n # gradient, as well as updating the adjustment values.\n grad, adj_content_val, adj_style_val, adj_denoise_val = session.run(run_list, feed_dict=feed_dict)\n\n # Reduce the dimensionality of the gradient.\n grad = np.squeeze(grad)\n\n # Scale the step-size according to the gradient-values.\n step_size_scaled = step_size / (np.std(grad) + 1e-8)\n\n # Update the image by following the gradient.\n mixed_image -= grad * step_size_scaled\n\n # Ensure the image has valid pixel-values between 0 and 255.\n mixed_image = np.clip(mixed_image, 0.0, 255.0)\n\n # Print a little progress-indicator.\n print(\". \", end=\"\")\n\n # Display status once every 10 iterations, and the last.\n if (i % 10 == 0) or (i == num_iterations - 1):\n print()\n print(\"Iteration:\", i)\n\n # Print adjustment weights for loss-functions.\n msg = \"Weight Adj. for Content: {0:.2e}, Style: {1:.2e}, Denoise: {2:.2e}\"\n print(msg.format(adj_content_val, adj_style_val, adj_denoise_val))\n\n # Plot the content-, style- and mixed-images.\n plot_images(content_image=content_image,\n style_image=style_image,\n mixed_image=mixed_image)\n \n print()\n print(\"Final image:\")\n plot_image_big(mixed_image)\n\n # Close the TensorFlow session to release its resources.\n session.close()\n \n # Return the mixed-image.\n return mixed_image","title":""},{"docid":"09845a23b3adb96dc8240e6ddf2ffcaa","score":"0.5849688","text":"def segmentation_example_use(path_to_model, path_to_data):\n model = Unet(CATEGORIES)\n model.load_state_dict(load(path_to_model, map_location=DEVICE))\n model.to(DEVICE)\n model.eval()\n\n image_names = sorted(listdir(path_to_data))\n\n for image_name in image_names:\n print(f\"Image: {image_name}\")\n img = load_img(path_to_data, image_name)\n img_tensor = TRANSFORMATIONS_TORCH(img)\n img_tensor.to(DEVICE)\n\n mask = model(img_tensor.unsqueeze(0))\n\n plt.imshow(\n mask.squeeze().detach().cpu().numpy().argmax(0) / CATEGORIES, cmap=cm.gray\n )\n plt.show()","title":""},{"docid":"7e9c1de4eda6305386f5f038fd34ba7a","score":"0.58450574","text":"def extract_feature(net, image_list, layer_name, image_as_grey = False):\n \n blobs = OrderedDict( [(k, v.data) for k, v in net.blobs.items()])\n\n #blobs = OrderedDict( [(k, v.data) for k, v in net.blobs.items()])\n shp = blobs[layer_name].shape\n #print blobs['data'].shape\n\n batch_size = blobs['data'].shape[0]\n #print blobs[layer_name].shape\n #print 'debug-------\\nexit'\n #exit()\n\n #params = OrderedDict( [(k, (v[0].data,v[1].data)) for k, v in net.params.items()])\n if len(shp) is 2:\n features_shape = (len(image_list), shp[1])\n elif len(shp) is 3:\n features_shape = (len(image_list), shp[1], shp[2])\n elif len(shp) is 4:\n features_shape = (len(image_list), shp[1], shp[2], shp[3])\n\n features = np.empty(features_shape, dtype='float32', order='C')\n img_batch = []\n for cnt, path in zip(range(features_shape[0]), image_list):\n img = caffe.io.load_image(path, color = not image_as_grey)\n if image_as_grey and img.shape[2] != 1:\n img = skimage.color.rgb2gray(img)\n img = img[:, :, np.newaxis]\n #if cnt == 0:\n # print 'image shape: ', img.shape\n #print img[0:10,0:10,:]\n #exit()\n img_batch.append(img)\n #print 'image shape: ', img.shape\n #print path, type(img), img.mean()\n if (len(img_batch) == batch_size) or cnt==features_shape[0]-1:\n scores = net.predict(img_batch, oversample=False)\n '''\n print 'blobs[%s].shape' % (layer_name,)\n tmp = blobs[layer_name]\n print tmp.shape, type(tmp)\n tmp2 = tmp.copy()\n print tmp2.shape, type(tmp2)\n print blobs[layer_name].copy().shape\n print cnt, len(img_batch)\n print batch_size\n #exit()\n\n #print img_batch[0:10]\n #print blobs[layer_name][:,:,0,0]\n #exit()\n '''\n\n # must call blobs_data(v) again, because it invokes (mutable_)cpu_data() which\n # syncs the memory between GPU and CPU\n blobs = OrderedDict( [(k, v.data) for k, v in net.blobs.items()])\n\n #print '%d images processed' % (cnt+1,)\n\n #print blobs[layer_name][0,:,:,:]\n # items of blobs are references, must make copy!\n features[cnt-len(img_batch)+1:cnt+1] = blobs[layer_name][0:len(img_batch)].copy()\n img_batch = []\n\n #features.append(blobs[layer_name][0,:,:,:].copy())\n features = np.asarray(features, dtype='float32')\n return features","title":""},{"docid":"3685de0675229d3c3e8e6af5625f98ba","score":"0.579015","text":"def extract(self, key_image_path):\n name = os.path.splitext(os.path.split(path)[-1])[0]\n frames_index = list(map(lambda x : x.split('.')[0],os.listdir(key_image_path)))\n frames_index.sort()\n frames_paths = list(map(lambda x: '{}/{}.jpg'.format(key_image_path,x),frames_index))\n images_input = [preprocess_input(image.img_to_array(image.load_img(im, target_size=(224, 224))), mode='torch')\n for im in frames_paths]\n feature = self.DenseNet201_model.predict(images_input)\n return name,feature","title":""},{"docid":"0799f85381759b87c464ef5fdf3a9b67","score":"0.57721","text":"def load_examples( lab_colorization=True, which_direction=\"AtoB\", flip=True, scale_size=286, batch_size=1):\n ##should change input_dir \n input_dir = './facade/test'\n \n #input images should either be .jpg or .png or .jpeg\n input_paths = glob.glob(os.path.join(input_dir, '*.[jJ][pP][gG]'))\n decode = tf.image.decode_jpeg\n if len(input_paths) == 0:\n input_paths = glob.glob(os.path.join(input_dir, '*.[jJ][pP][eE][gG]'))\n decode = tf.image.decode_jpeg\n if len(input_paths) == 0:\n input_paths = glob.glob(os.path.join(input_dir, '*.[pP][nN][gG]'))\n decode = tf.image.decode_png\n\n #Sort images by name\n def get_name(path):\n name, _ = os.path.splitext(os.path.basename(path))\n return name\n # if the image names are numbers, sort by the value \n if all(get_name(path).isdigit() for path in input_paths):\n input_paths = sorted(input_paths, key=lambda path: int(get_name(path)))\n #built in function, return a new sorted list\n else:\n input_paths = sorted(input_paths)\n print('1- input_paths shape is ', len(input_paths))\n #read, decode and post process images\n with tf.name_scope(\"load_images\"):\n # Create a queue node (or operation) in the currently active graph\n path_queue = tf.train.string_input_producer(input_paths)\n\t\t\n #Read the files\n reader = tf.WholeFileReader()\n paths, contents = reader.read(path_queue)\n \n #Decode the images\n raw_input = decode(contents)\n raw_input = tf.image.convert_image_dtype(raw_input, dtype=tf.float32)\n \n print('Before assertion the last dimension is ',list(raw_input.get_shape())[2])\n \n\t\t#make the last dimension 3 so that we can unstack the colors\n assertion = tf.assert_equal(tf.shape(raw_input)[2], 3, message=\"image does not have 3 channels\")\n with tf.control_dependencies([assertion]):\n raw_input = tf.identity(raw_input)\n \n raw_input.set_shape([None, None, 3])\n\n print('last dimension is ',list(raw_input.get_shape())[2])\n \n if lab_colorization:\n # load color and brightness from image, no B image exists here\n\t\t\t#convert RGB information to LAB \n lab = rgb_to_lab(raw_input)\n L_chan, a_chan, b_chan = preprocess_lab(lab)\n\t\t\tprint('last dimension of L_chan is ',list(L_chan.get_shape()))\n\t\t\t#expand_dim won't add dimension to a tensor. \n a_images = tf.expand_dims(L_chan, axis=2)\n b_images = tf.stack([a_chan, b_chan], axis=2)\n else:\n # break apart image pair and move to range [-1, 1]\n width = tf.shape(raw_input)[1] # [height, width, channels]\n\t\t\t#left half and right side\n a_images = preprocess(raw_input[:,:width//2,:])\n b_images = preprocess(raw_input[:,width//2:,:])\n \n if which_direction == \"AtoB\":\n inputs, targets = [a_images, b_images]\n elif which_direction == \"BtoA\":\n inputs, targets = [b_images, a_images]\n else:\n raise Exception(\"invalid direction\")\n\n\n # !!IMPORTANT: seed for image operations to do the SAME operations to both input and output images\n seed = random.randint(0, 2**31 - 1)\n\n def transform(image):\n r = image\n if flip:\n r = tf.image.random_flip_left_right(r, seed=seed)\n \n # area produces a nice downscaling, but does nearest neighbor for upscaling\n # assume we're going to be doing downscaling here\n r = tf.image.resize_images(r, [scale_size, scale_size], method=tf.image.ResizeMethod.AREA)\n\n offset = tf.cast(tf.floor(tf.random_uniform([2], 0, scale_size - CROP_SIZE + 1, seed=seed)), dtype=tf.int32)\n if scale_size > CROP_SIZE:\n r = tf.image.crop_to_bounding_box(r, offset[0], offset[1], CROP_SIZE, CROP_SIZE)\n elif scale_size < CROP_SIZE:\n raise Exception(\"scale size cannot be less than crop size\")\n return r\n\t\n #distort the input images\n with tf.name_scope(\"input_images\"):\n input_images = transform(inputs)\n #distort the target images\n with tf.name_scope(\"target_images\"):\n target_images = transform(targets)\n \n \n paths_batch, inputs_batch, targets_batch = tf.train.batch([paths, input_images, target_images], batch_size = batch_size)\n steps_per_epoch = int(math.ceil(len(input_paths) /batch_size))\n \n print('2- count is ',len(input_paths))\n print('3- steps_per_epoch is ',steps_per_epoch)\n\n return paths_batch, inputs_batch, targets_batch, len(input_paths), steps_per_epoch","title":""},{"docid":"8ae10d1078792cfdf0f1659e859bd009","score":"0.5767853","text":"def extract_from_file(self, im_path: FilePath) -> Tuple[Tensor, ...]:\n\n def _load_img_and_batch(p):\n image_loader = IMAGE_LOADERS[self._extractor_name]\n img = image_loader(im_path)\n batched_img = tf.expand_dims(img, 0)\n return batched_img\n\n if self._dataset:\n features_cmprsd = np.load(im_path)\n features = tuple(\n [features_cmprsd[k] for k in _extractors_features[self._extractor_name]]\n )\n else:\n batched_features = self.extraction_model(_load_img_and_batch(im_path))\n features = tf.squeeze(batched_features, axis=0)\n\n return features","title":""},{"docid":"5acec1bda753e360793cbe2ae4b6fcfc","score":"0.57550895","text":"def process_images_and_labels(self):\n print(\"loading data onto reel\")\n self._load_reel()\n print(\"...done\")\n\n print(\"splitting to train and test sets\")\n self._split_to_train_and_test()\n print(\"...done\")","title":""},{"docid":"c754db75de30f780861f5f074ce48d18","score":"0.572533","text":"def load_wider():\n\n def get_dirmapper(dirpath):\n \"\"\"return dir mapper for wider face\n \"\"\"\n mapper = {}\n for d in os.listdir(dirpath):\n dir_id = d.split('--')[0]\n mapper[dir_id] = os.path.join(dirpath, d)\n return mapper\n\n train_mapper = get_dirmapper(os.path.join(cfg.WIDER_DIR, 'WIDER_train', 'images'))\n val_mapper = get_dirmapper(os.path.join(cfg.WIDER_DIR, 'WIDER_val', 'images'))\n\n def gen(text, mapper):\n fin = open(text, 'r')\n\n result = []\n while True:\n line = fin.readline()\n if not line: break # eof\n name = line.strip()\n dir_id = name.split('_')[0]\n img_path = os.path.join(mapper[dir_id], name + '.jpg')\n face_n = int(fin.readline().strip())\n\n bboxes = []\n for i in range(face_n):\n line = fin.readline().strip()\n components = line.split(' ')\n x, y, w, h = [float(_) for _ in components]\n\n size = min(w, h)\n # only large enough\n if size > 12:\n bbox = [x, y, w, h]\n bboxes.append(bbox)\n\n # # for debug\n # img = cv2.imread(img_path)\n # for bbox in bboxes:\n # x, y, w, h = bbox\n # cv2.rectangle(img, (int(x), int(y)), (int(x+w), int(y+h)), (0,0,255), 1)\n # cv2.imshow('img', img)\n # cv2.waitKey(0)\n\n if len(bboxes) > 0:\n bboxes = np.asarray(bboxes, dtype=np.float32)\n bboxes[:, 2] += bboxes[:, 0]\n bboxes[:, 3] += bboxes[:, 1]\n result.append([img_path, bboxes])\n fin.close()\n return result\n\n txt_dir = os.path.join(cfg.WIDER_DIR, 'wider_face_split')\n train_data = gen(os.path.join(txt_dir, 'wider_face_train.txt'), train_mapper)\n val_data = gen(os.path.join(txt_dir, 'wider_face_val.txt'), val_mapper)\n return (train_data, val_data)","title":""},{"docid":"82fb2e4ee6daf54281bbb4499092678d","score":"0.5716167","text":"def process_path(file_path: str) -> Tensor:\n ext = file_path.split(\".\")[-1]\n img = tf.io.read_file(file_path)\n if ext == \"png\":\n img = decode_img(img)\n else:\n img = decode_img(img, png=False)\n return img","title":""},{"docid":"3f71d7385f099796ac9c45ee6b28475a","score":"0.570502","text":"def load_and_interpret(model_path, input_paths, baseline=-1, dictionary_path=None,\n strip_alpha=False, smooth_factor=7, save=False, save_dir=None):\n model_dir = os.path.dirname(model_path)\n if save_dir is None:\n save_dir = model_dir\n network = keras.models.load_model(model_path)\n input_type = network.input.dtype\n input_shape = network.input.shape\n n_channels = 0 if len(input_shape) == 3 else input_shape[3]\n\n dic = load_dict(dictionary_path)\n if len(input_paths) == 1 and os.path.isdir(input_paths[0]):\n loader = PathLoader(input_paths[0])\n input_paths = [path[0] for path in loader.path_pairs]\n inputs = [load_image(input_paths[i], strip_alpha=strip_alpha, channels=n_channels) for i in range(len(input_paths))]\n max_shapes = np.maximum.reduce([inp.shape for inp in inputs], axis=0)\n tf_image = tf.stack([tf.image.resize_with_crop_or_pad(tf.convert_to_tensor(im, dtype=input_type), max_shapes[0],\n max_shapes[1]) for im in inputs], axis=0)\n if is_number(baseline):\n baseline_gen = tf.constant_initializer(float(baseline))\n baseline_image = baseline_gen(shape=tf_image.shape, dtype=input_type)\n else:\n baseline_image = load_image(baseline)\n baseline_image = tf.convert_to_tensor(baseline_image, dtype=input_type)\n\n interpret_model(network, tf_image, baseline_input=baseline_image, decode_dictionary=dic, smooth=smooth_factor,\n save=save, save_path=save_dir)","title":""},{"docid":"f7d760a41edf5b19a21b94ede40c15fc","score":"0.56957924","text":"def read_and_preprocess(example_data):\n # See create_dataset.py: cy - boxdef.N:cy + boxdef.N\n height = 2 * params['predsize']\n width = 2 * params['predsize']\n parsed = tf.parse_single_example(\n example_data, {\n 'ref': tf.VarLenFeature(tf.float32),\n 'ltg': tf.VarLenFeature(tf.float32),\n 'has_ltg': tf.FixedLenFeature([], tf.int64, 1),\n })\n parsed['ref'] = _sparse_to_dense(parsed['ref'], height * width)\n parsed['ltg'] = _sparse_to_dense(parsed['ltg'], height * width)\n label = tf.cast(tf.reshape(parsed['has_ltg'], shape=[]), dtype=tf.int32)\n print('shape of label {}'.format(label.shape))\n\n img = reshape_into_image(parsed, params)\n return img, label","title":""},{"docid":"d61f25f3962525bc175054bd8af59c33","score":"0.5683075","text":"def read_in_images(self,files_input, files_labeled):\n \n post_process = False\n \n if post_process == False:\n img_input = np.zeros((len(files_input),self.img_size[0]**2))\n if self.classifier == \"membrane_synapse\":\n img_labels = np.zeros((len(files_labeled),2*self.img_size[0]**2))\n else:\n img_labels = np.zeros((len(files_labeled),self.img_size[0]**2))\n\n for n in xrange(len(files_input)):\n File = files_input[n]\n img_temp = Image.open(File) \n img_temp = np.array(img_temp).ravel() \n img_input[n] = img_temp \n\n File = files_labeled[n]\n img_temp = Image.open(File) \n img_temp = np.array(img_temp)\n img_temp = img_temp.ravel()\n\n img_temp.flags.writeable = True\n\n if self.classifier == 'membrane':\n img_temp[img_temp>0] = 1\n #img_temp = self.find_edges(img_temp)\n elif self.classifier == 'synapse':\n img_temp = self.find_synapse(img_temp,File)\n elif self.classifier == 'membrane_synapse':\n img_temp = self.find_mem_syn(img_temp,File)\n\n img_labels[n] = img_temp\n\n if self.classifier == \"membrane\" or self.classifier == \"synapse\":\n output_dim = 1\n elif self.classifier == \"membrane_synapse\":\n output_dim = 2\n else:\n print \"Error: invalid classifier\"\n exit()\n\n img_input = img_input.reshape(img_input.shape[0], self.img_size[0],self.img_size[1])\n img_labels = img_labels.reshape(img_labels.shape[0], output_dim, self.img_size[0],self.img_size[1])\n \n else:\n img_input = np.zeros((len(files_input),self.img_size[0]**2))\n if self.classifier == \"membrane_synapse\":\n img_labels = np.zeros((len(files_labeled),2*self.img_size[0]**2))\n else:\n img_labels = np.zeros((len(files_labeled),self.img_size[0]**2))\n \n for n in xrange(len(files_input)):\n File = files_input[n]\n img_temp = Image.open(File) \n \n if n == 0:\n img_size_input = np.array(img_temp).shape[0]\n img_size_input = (img_size_input,img_size_input)\n gap = (self.img_size[0]-img_size_input[0])/2\n \n img_temp = img_temp.resize((self.img_size[0], self.img_size[1]), PIL.Image.ANTIALIAS) \n img_temp = np.array(img_temp).ravel() \n \n img_input[n] = img_temp \n\n File = files_labeled[n]\n img_temp = Image.open(File)\n \n if gap>0:\n img_temp = np.array(img_temp)[gap:-gap,gap:-gap]\n img_temp = Image.fromarray(np.uint8(img_temp))\n img_temp = img_temp.resize((self.img_size[0], self.img_size[1]), PIL.Image.ANTIALIAS)\n \n img_temp = np.array(img_temp).reshape(img_temp.size)\n img_temp.flags.writeable = True\n\n if self.classifier == 'membrane':\n img_temp = self.find_edges(img_temp)\n elif self.classifier == 'synapse':\n img_temp = self.find_synapse(img_temp,File)\n elif self.classifier == 'membrane_synapse':\n img_temp = self.find_mem_syn(img_temp,File)\n\n img_labels[n] = img_temp.ravel()\n \n\n if self.classifier == \"membrane\" or self.classifier == \"synapse\":\n output_dim = 1\n elif self.classifier == \"membrane_synapse\":\n output_dim = 2\n else:\n print \"Error: invalid classifier\"\n exit()\n \n img_input = img_input.reshape(img_input.shape[0], self.img_size[0],self.img_size[1])\n img_labels = img_labels.reshape(img_labels.shape[0], output_dim, self.img_size[0],self.img_size[1])\n\n return img_input, img_labels","title":""},{"docid":"14bd3e8dd1e13bb1f9a37cab2a1273e9","score":"0.5673028","text":"def get_trainingImages(path2, zeroScale, oneScale, twoScale):\n trainingImages=[]\n for i in path2:\n im = np.load(i)\n im[0,:,:] = im[0,:,:]/zeroScale\n im[1,:,:] = im[1,:,:]/oneScale\n im[2,:,:] = im[2,:,:]/twoScale\n trainingImages.append(im)\n new_images = np.stack(trainingImages, axis = 3)\n collman = np.transpose(new_images,(0,3,1,2))\n \n return trainingImages, collman","title":""},{"docid":"38a6147c79e658244adf957ad1fd4801","score":"0.5672668","text":"def preprocess(filename, patch_size, augmentation, model_name):\n # return image_resized, label\n label = get_label(filename)\n #label = tf.expand_dims(label, -1) # make each scalar label a vector of length 1 to match model\n image = get_image(filename, patch_size) # float32 in [0, 1)\n if augmentation:\n image = augment(image)\n image = tf.clip_by_value(image, 0, 1)\n image = normalize(image, model_name)\n return image, label, filename","title":""},{"docid":"7716e39fb8bebdf786284946fac742ba","score":"0.56632483","text":"def process_img_from_path(img_path):\n return preprocess_img(plt.imread(img_path))","title":""},{"docid":"4d9da9e3e880d3d42d186a52ce5d37ad","score":"0.5657443","text":"def process_image(image_path: str) -> Dict[str, object]:\n image = cv2.imread(image_path) # cv2 opens in BGR\n return localizer_tensorflow_serving_inference(image, SERVING_URL)","title":""},{"docid":"c4e3f92beb4c7ca65d79a2c2c9e7a3f1","score":"0.56532025","text":"def feat_extract(pretrained=False, **kwargs):\n model_urls = {\n 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',\n 'resnet52': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',\n 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',\n }\n logger = kwargs['opts'].logger\n # resnet\"x\", x = 1 + sum(layers)x3\n if kwargs['structure'] == 'resnet40':\n model = ResNet(Bottleneck, [3, 4, 6], kwargs['in_c'])\n elif kwargs['structure'] == 'resnet19':\n model = ResNet(Bottleneck, [2, 2, 2], kwargs['in_c'])\n elif kwargs['structure'] == 'resnet12':\n dropblock_size = 5 if 'imagenet' in kwargs['opts'].dataset.name else 2\n model = resnet12(avg_pool=False, drop_rate=0.1, dropblock_size=dropblock_size)\n elif kwargs['structure'] == 'resnet52':\n model = ResNet(Bottleneck, [4, 8, 5], kwargs['in_c'])\n elif kwargs['structure'] == 'resnet34':\n model = ResNet(Bottleneck, [3, 4, 4], kwargs['in_c'])\n elif kwargs['structure'] == 'shallow':\n model = CNNEncoder(kwargs['in_c'])\n else:\n raise NameError('structure not known {} ...'.format(kwargs['structure']))\n if pretrained:\n logger('Using pre-trained model from pytorch official webiste, {:s}'.format(kwargs['structure']))\n model.load_state_dict(model_zoo.load_url(model_urls[kwargs['structure']]), strict=False)\n return model","title":""},{"docid":"50c491e9ffce712c15be44bac96a0d81","score":"0.56462336","text":"def preprocess(self):\n for part in ['part_A', 'part_B']:\n for dataset in ['test_data', 'train_data']:\n dataset_directory = os.path.join(self.database_directory, part, dataset)\n ground_truth_directory = os.path.join(dataset_directory, 'ground-truth')\n original_images_directory = os.path.join(dataset_directory, 'images')\n for mat_filename in os.listdir(ground_truth_directory):\n file_name = mat_filename[3:-3]\n mat_path = os.path.join(ground_truth_directory, mat_filename)\n original_image_path = os.path.join(original_images_directory, file_name + 'jpg')\n image = imageio.imread(original_image_path)\n mat = scipy.io.loadmat(mat_path)\n head_positions = mat['image_info'][0, 0][0][0][0]\n head_positions = self.get_y_x_head_positions(head_positions)\n self.generate_labels_for_example(dataset_directory, file_name, image, head_positions)","title":""},{"docid":"d6940b4f31862597b7814b798f8ba2ed","score":"0.56444764","text":"def GetLoader(s_image_dir,c_image_dir, \n style_selected_dir, content_selected_dir,\n crop_size=178, batch_size=16, num_workers=8, \n colorJitterEnable=True, colorConfig={\"brightness\":0.05,\"contrast\":0.05,\"saturation\":0.05,\"hue\":0.05}):\n \n s_transforms = []\n c_transforms = []\n \n s_transforms.append(T.Resize(768))\n # s_transforms.append(T.Resize(900))\n c_transforms.append(T.Resize(768))\n\n s_transforms.append(T.RandomCrop(crop_size,pad_if_needed=True,padding_mode='reflect'))\n c_transforms.append(T.RandomCrop(crop_size))\n\n s_transforms.append(T.RandomHorizontalFlip())\n c_transforms.append(T.RandomHorizontalFlip())\n \n s_transforms.append(T.RandomVerticalFlip())\n c_transforms.append(T.RandomVerticalFlip())\n\n if colorJitterEnable:\n if colorConfig is not None:\n print(\"Enable color jitter!\")\n colorBrightness = colorConfig[\"brightness\"]\n colorContrast = colorConfig[\"contrast\"]\n colorSaturation = colorConfig[\"saturation\"]\n colorHue = (-colorConfig[\"hue\"],colorConfig[\"hue\"])\n s_transforms.append(T.ColorJitter(brightness=colorBrightness,\\\n contrast=colorContrast,saturation=colorSaturation, hue=colorHue))\n c_transforms.append(T.ColorJitter(brightness=colorBrightness,\\\n contrast=colorContrast,saturation=colorSaturation, hue=colorHue))\n s_transforms.append(T.ToTensor())\n c_transforms.append(T.ToTensor())\n\n s_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))\n c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))\n \n s_transforms = T.Compose(s_transforms)\n c_transforms = T.Compose(c_transforms)\n\n content_dataset = TotalDataset(c_image_dir,s_image_dir, content_selected_dir, style_selected_dir\n , c_transforms,s_transforms)\n content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size,\n drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True)\n prefetcher = DataPrefetcher(content_data_loader)\n return prefetcher","title":""},{"docid":"9ff9e95b3dbe60827c406210404fa7ab","score":"0.56330514","text":"def image_generation(model,\n content_loss,\n style_loss,\n content_image,\n style_image,\n steps = 300):\n generation_image = content_image.clone().detach().requires_grad_(True)\n optimizer = optim.LBFGS([generation_image])\n style_layer = ['conv1','conv2','conv3','conv4','conv5']\n content_layer = ['conv4']\n i = 0\n name = \"\"\n style_losses = []\n content_losses = []\n print(content_image.requires_grad)\n print(style_image.requires_grad)\n for step in tqdm(range(steps)):\n def closure(generation_image,content_image,style_image):\n for i,layer in enumerate(model.children()):\n if isinstance(layer,nn.Conv2d):\n name = \"conv%d\"%(i+1)\n elif isinstance(layer,nn.ReLU):\n name = \"relu\"\n elif isinstance(layer,nn.MaxPool2d):\n name = \"maxpool\"\n if i == 0:\n c = layer(content_image).detach()\n s = layer(style_image).detach()\n g = layer(generation_image).detach()\n else:\n c = layer(c).detach()\n s = layer(s).detach()\n g = layer(g).detach()\n if name in style_layer:\n style_losses.append(style_loss(generation_image,style_image))\n elif name in content_layer:\n content_losses.append(content_loss(generation_image,content_image))\n loss = sum(style_losses) + sum(content_losses)\n print(loss) \n loss.backward()\n return loss\n optimizer.step(closure(generation_image,content_image,style_image))\n return generation_image","title":""},{"docid":"8c7368a8d86d31f3aea4dd8da9453cd9","score":"0.5624543","text":"def extract_features(images,\n is_training=True,\n network_backbone='mobilenet_v2', \n activation_fn=tf.nn.relu6,\n normalizer_fn=tf.layers.BatchNormalization(),\n output_stride=8):\n with tf.variable_scope('Encoder', reuse=tf.AUTO_REUSE):\n # Extract features\n feature_map, low_level_features = feature_extractor(\n images=images,\n model_variant=network_backbone,\n is_training=is_training)\n \n # Perform ASSP op\n assp_features = atrous_spatial_pyramid_pooling(\n network_backbone,\n feature_map,\n depth=256,\n activation_fn=activation_fn,\n normalizer_fn=normalizer_fn,\n output_stride=8)\n\n # Merge all\n with tf.variable_scope('1x1_Conv'):\n encoded_features = tf.layers.Conv2D(256, (1, 1))(assp_features)\n encoded_features = normalizer_fn(encoded_features)\n encoded_features = activation_fn(encoded_features)\n\n return encoded_features, low_level_features","title":""},{"docid":"f0adc5c81a4bbed006ceb80b2a73aebd","score":"0.5611783","text":"def ComputeFeatures(self):\n if self.train_images == None or self.test_images == None:\n sys.exit(\"Please specify the corpus.\")\n train_sizes = map(len, self.train_images)\n train_size = sum(train_sizes)\n test_sizes = map(len, self.test_images)\n train_images = util.UngroupLists(self.train_images)\n test_images = util.UngroupLists(self.test_images)\n images = train_images + test_images\n # Compute features for all images.\n input_states = map(self.model.MakeStateFromFilename, images)\n start_time = time.time()\n features = self.ComputeFeaturesFromInputStates(input_states)\n self.compute_feature_time = time.time() - start_time\n # Split results by training/testing set\n train_features, test_features = util.SplitList(features, [train_size])\n # Split training set by class\n train_features = util.SplitList(train_features, train_sizes)\n # Split testing set by class\n test_features = util.SplitList(test_features, test_sizes)\n # Store features as list of 2D arrays\n self.train_features = [ np.array(f, util.ACTIVATION_DTYPE)\n for f in train_features ]\n self.test_features = [ np.array(f, util.ACTIVATION_DTYPE)\n for f in test_features ]","title":""},{"docid":"b9297f5dc129401c11d60c81e77fda72","score":"0.5602348","text":"def __load_and_unify_gen(self, image_paths: np.ndarray) -> (np.ndarray, str):\n for img_path in image_paths:\n img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE).astype(np.uint8)\n filename = IOHelper.get_file_clear_name(img_path)\n if not isinstance(img, np.ndarray):\n raise FileNotFoundError('Unable to load image: {}'.format(img_path))\n\n desired_rows, desired_cols, desired_channels = self.__desired_shape\n\n # Decrease image shape if needed\n rows_to_decrease = min(desired_rows, img.shape[0])\n cols_to_decrease = min(desired_cols, img.shape[1])\n changed_img = cv2.resize(img, (cols_to_decrease, rows_to_decrease)) # new width and height\n\n # Increase image shape if needed\n rows_to_increase = max(desired_rows, img.shape[0])\n cols_to_increase = max(desired_cols, img.shape[1])\n rows_diff = abs(img.shape[0] - rows_to_increase)\n cols_diff = abs(img.shape[1] - cols_to_increase)\n\n if rows_diff % 2 == 0:\n top_rows_pad = int(rows_diff / 2)\n bottom_rows_pad = int(rows_diff / 2)\n else:\n top_rows_pad = int(math.floor(rows_diff / 2))\n bottom_rows_pad = int(math.floor(rows_diff / 2)) + 1\n if cols_diff % 2 == 0:\n left_cols_pad = int(cols_diff / 2)\n right_cols_pad = int(cols_diff / 2)\n else:\n left_cols_pad = int(math.floor(cols_diff / 2))\n right_cols_pad = int(math.floor(cols_diff / 2)) + 1\n\n if desired_channels == 1:\n pad_color = 0\n else:\n pad_color = np.zeros((3,), dtype=np.uint8)\n changed_img = cv2.copyMakeBorder(\n changed_img,\n top_rows_pad,\n bottom_rows_pad,\n left_cols_pad,\n right_cols_pad,\n cv2.BORDER_CONSTANT,\n pad_color\n )\n\n yield changed_img, filename","title":""},{"docid":"47dfe568ce49659cbafb1501bdb400c8","score":"0.55990183","text":"def get_image_features(images_paths):\n \n # Extract images features\n images = [load_image(x)[0] for x in images_paths]\n \n # x => (batch_size, 299, 299, 3)\n x = tf.convert_to_tensor(images)\n \n # x => (batch_size, 10, 10, 2048)\n x = extractor(x)\n \n # x => (batch_size, img_feature_size, 2048)\n x = tf.reshape(x, (x.shape[0], -1, x.shape[3]))\n \n # features => (batch_size, img_feature_size, image_context_size)\n features = encoder(x)\n \n return features","title":""},{"docid":"13a3d5e182f8e99ab4cd9fae083ca0eb","score":"0.55980575","text":"def gen_batch_function( image_shape):\n def get_batches_fn(batch_size):\n \"\"\"\n Create batches of training data\n :param batch_size: Batch Size\n :return: Batches of training data\n \"\"\"\n \n print(\"Working\")\n image_paths=[]\n label_paths=[]\n counter=0\n for dirname, _, filenames in os.walk('/content/cityscape_train/'):\n counter+=1\n \n for filename in filenames:\n #img_path = os.path.join(dirname, filename)\n #image_paths.append(img_path)\n \n for dirname1, _, filenames1 in os.walk('/content/get_index/train'):\n for filename1 in filenames1:\n if(filename1==filename):\n \n gd_path = os.path.join(dirname1, filename1)\n label_paths.append(gd_path)\n img_path = os.path.join(dirname, filename)\n image_paths.append(img_path)\n # Loop through batches and grab images, yielding each batch\n for batch_i in range(0, len(image_paths), batch_size):\n images = []\n gt_images = []\n for idx , image_file in enumerate(image_paths[batch_i:batch_i+batch_size]):\n gt_image_file = label_paths[batch_i+idx]\n \n # Re-size to image_shape\n image = cv2.imread(image_file)\n image = cv2.resize(image, (224,224), interpolation = cv2.INTER_AREA)\n gt_image = cv2.imread(gt_image_file,cv2.IMREAD_GRAYSCALE)\n gt_image = cv2.resize(gt_image, (224,224), interpolation = cv2.INTER_AREA)\n # Create \"one-hot-like\" labels by class\n color_list=[]\n for color_id in range(19):\n gt_bg = np.array(gt_image == color_id).astype(np.uint8)\n gt_bg = gt_bg.reshape(*gt_bg.shape, 1)\n color_list.append(gt_bg)\n gt_image = np.concatenate(color_list, axis=2)\n images.append(image)\n gt_images.append(gt_image)\n\n yield np.array(images), np.array(gt_images)\n return get_batches_fn","title":""},{"docid":"1521feedd1762c7dca688b9a96a2762c","score":"0.5571109","text":"def import_data():\n train_set = input_data.Data((FLAGS.image_size, FLAGS.image_size), (1,1))\n eval_set = input_data.Data((FLAGS.image_size, FLAGS.image_size), (1,1))\n\n train_set.add_examples(\"../data/cascade/positives/positives2.png\", 7500, 100, 1)\n train_set.add_examples(\"../data/detect/train/20038_negatives.png\", 10000, 100, 0)\n \n eval_set.add_examples(\"../data/detect/eval/1510_positives.png\", 1510, 100, 1)\n #eval_set.add_examples(\"../data/detect/eval/4054_negatives.png\", 4054, 100, 0)\n eval_set.add_examples(\"../data/test/3709_negatives.png\", 3710, 100, 0)\n \n train_set.finalize()\n eval_set.finalize()\n \n return train_set, eval_set","title":""},{"docid":"880b2536283ff43bc1311b9ec84ec900","score":"0.55643016","text":"def extract_features(self, preprocessed_inputs):\n image_shape = preprocessed_inputs.get_shape()\n image_shape.assert_has_rank(4)\n image_height = image_shape[1].value\n image_width = image_shape[2].value\n\n if image_height is None or image_width is None:\n shape_assert = tf.Assert(\n tf.logical_and(tf.equal(tf.shape(preprocessed_inputs)[1], 256),\n tf.equal(tf.shape(preprocessed_inputs)[2], 256)),\n ['image size must be 256 in both height and width.'])\n with tf.control_dependencies([shape_assert]):\n preprocessed_inputs = tf.identity(preprocessed_inputs)\n elif image_height != 256 or image_width != 256:\n raise ValueError('image size must be = 256 in both height and width;'\n ' image dim = %d,%d' % (image_height, image_width))\n\n feature_map_layout = {\n 'from_layer': [\n 'Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '', ''\n ],\n 'layer_depth': [-1, -1, 512, 256, 256],\n 'conv_kernel_size': [-1, -1, 3, 3, 2],\n 'use_explicit_padding': self._use_explicit_padding,\n 'use_depthwise': self._use_depthwise,\n }\n\n with tf.variable_scope('MobilenetV1',\n reuse=self._reuse_weights) as scope:\n with slim.arg_scope(\n mobilenet_v1.mobilenet_v1_arg_scope(is_training=None)):\n with (slim.arg_scope(self._conv_hyperparams_fn())\n if self._override_base_feature_extractor_hyperparams\n else context_manager.IdentityContextManager()):\n _, image_features = mobilenet_v1.mobilenet_v1_base(\n ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),\n final_endpoint='Conv2d_13_pointwise',\n min_depth=self._min_depth,\n depth_multiplier=self._depth_multiplier,\n use_explicit_padding=self._use_explicit_padding,\n scope=scope)\n with slim.arg_scope(self._conv_hyperparams_fn()):\n feature_maps = feature_map_generators.multi_resolution_feature_maps(\n feature_map_layout=feature_map_layout,\n depth_multiplier=self._depth_multiplier,\n min_depth=self._min_depth,\n insert_1x1_conv=True,\n image_features=image_features)\n\n return feature_maps.values()","title":""},{"docid":"7cd612ee8cfde9fcbc86d5980d22a945","score":"0.5563253","text":"def style_transfer(content_image, style_image,\n content_layer_ids, style_layer_ids,\n alpha=1.5, beta=10.0,\n weight_denoise=0.3,\n num_iterations=120, step_size=10.0):\n initializeVGG16()\n model = vgg16.VGG16()\n\n # Create a TensorFlow-session.\n session = tf.InteractiveSession(graph=model.graph)\n\n # Print the names of the content-layers.\n print()\n print(\"Content layers:\")\n print(model.get_layer_names(content_layer_ids))\n print()\n\n # Print the names of the style-layers.\n print(\"Style layers:\")\n print(model.get_layer_names(style_layer_ids))\n print()\n\n # Create the loss-function for the content-layers and -image.\n loss_content = create_content_loss(session=session,\n model=model,\n content_image=content_image,\n layer_ids=content_layer_ids)\n\n # Create the loss-function for the style-layers and -image.\n loss_style = create_style_loss(session=session,\n model=model,\n style_image=style_image,\n layer_ids=style_layer_ids) \n\n # Create the loss-function for the denoising of the mixed-image.\n loss_denoise = create_denoise_loss(model)\n\n # Create TensorFlow variables for adjusting the values of\n # the loss-functions. This is explained below.\n\n ## Think of these as bias\n adj_content = tf.Variable(1e-10, name='adj_content')\n adj_style = tf.Variable(1e-10, name='adj_style')\n adj_denoise = tf.Variable(1e-10, name='adj_denoise')\n\n # Initialize the adjustment values for the loss-functions.\n session.run([adj_content.initializer,\n adj_style.initializer,\n adj_denoise.initializer])\n\n # Create TensorFlow operations for updating the adjustment values.\n # These are basically just the reciprocal values of the\n # loss-functions, with a small value 1e-10 added to avoid the\n # possibility of division by zero.\n update_adj_content = adj_content.assign(1.0 / (loss_content + 1e-10))\n update_adj_style = adj_style.assign(1.0 / (loss_style + 1e-10))\n update_adj_denoise = adj_denoise.assign(1.0 / (loss_denoise + 1e-10))\n\n loss_combined = alpha * adj_content * loss_content + \\\n beta * adj_style * loss_style + \\\n weight_denoise * adj_denoise * loss_denoise\n\n # Compute gradient\n gradient = tf.gradients(loss_combined, model.input)\n\n # List of tensors that we will run in each optimization iteration.\n run_list = [gradient, update_adj_content, update_adj_style, update_adj_denoise]\n\n # The mixed-image is initialized with random noise.\n # It is the same size as the content-image.\n mixed_image = np.random.rand(*content_image.shape) + 128\n\n for i in range(num_iterations):\n # Create a feed-dict with the mixed-image.\n feed_dict = model.create_feed_dict(image=mixed_image)\n\n # Use TensorFlow to calculate the value of the\n # gradient, as well as updating the adjustment values.\n grad, adj_content_val, adj_style_val, adj_denoise_val = session.run(run_list, feed_dict=feed_dict)\n\n # Reduce the dimensionality of the gradient.\n grad = np.squeeze(grad)\n\n # Scale the step-size according to the gradient-values.\n step_size_scaled = step_size / (np.std(grad) + 1e-8)\n\n # Update the image by following the gradient.\n ## Gradient descent\n mixed_image -= grad * step_size_scaled\n\n # Ensure the image has valid pixel-values between 0 and 255.\n mixed_image = np.clip(mixed_image, 0.0, 255.0)\n\n # Display status once every 10 iterations, and the last.\n if (i % 10 == 0) or (i == num_iterations - 1):\n print()\n print(\"Epoch:\", i)\n\n # Print adjustment weights for loss-functions.\n msg = \"Weight Adj. for Content: {0:.2e}, Style: {1:.2e}, Denoise: {2:.2e}\"\n print(msg.format(adj_content_val, adj_style_val, adj_denoise_val))\n \n #imageUtils.save_image(mixed_image, \"mixed\" + str(i) + \".png\")\n\n # Close the TensorFlow session to release its resources.\n session.close()\n \n # Return the mixed-image.\n return mixed_image","title":""},{"docid":"a086dd8bc3b8d8e088a227fff47a5406","score":"0.5557795","text":"def apply_template(template, content_values, resize_pixel_layers=True):\n psd_file_path = Path(template[TEMPLATE_KEY_PSD_FILE])\n color_mode = ColorMode(template[TEMPLATE_KEY_PSD_COLOR_MODE])\n psd = get_psd_or_raise(psd_file_path)\n\n layers = get_all_psd_layers(psd)\n # fonts dict to avoid create many instances of same font\n fonts = {}\n excluded_layers = list(template[TEMPLATE_KEY_EXCLUDED_LAYERS])\n input_fields = template[TEMPLATE_KEY_INPUT_FIELDS]\n layer_images = []\n for field in input_fields:\n input_value = None\n try:\n input_value = __get_field_value(field, content_values)\n except KeyError:\n raise FieldNotFilledError(field['value'])\n\n layer = __get_layer_by_id(\n layers, field[TEMPLATE_KEY_INPUT_FIELD_LAYER_ID]\n )\n if PSDLayer(layer.kind) is PSDLayer.TYPE:\n text = __apply_text_layer_filter(layer, input_value)\n text_layer_properties = get_text_layer_properties(layer)\n font, font_size, fill_color = text_layer_properties\n layer_image = __get_image_type_field(\n color_mode, fonts, field, layer, text\n )\n excluded_layers.append(layer.layer_id)\n layer_images.append((field, layer, layer_image))\n else:\n layer_image = None\n if is_url(input_value):\n request_url = urlopen(input_value)\n image_stream = BytesIO(request_url.read())\n layer_image = Image.open(image_stream)\n elif re.search(BASE64_IMAGE_PATTERN, input_value):\n image_data = re.sub('^data:image/.+;base64,', '', input_value)\n layer_image = Image.open(BytesIO(base64.b64decode(image_data)))\n else:\n layer_image = Image.open(input_value)\n excluded_layers.append(layer.layer_id)\n layer_images.append((field, layer, layer_image))\n\n final_image = render_psd(\n psd_file_path, excluded_layers, original_size=True\n )\n for field, layer, layer_image in layer_images:\n position = field[TEMPLATE_KEY_INPUT_FIELD_POSITION]\n size = field[TEMPLATE_KEY_INPUT_FIELD_SIZE]\n if PSDLayer(layer.kind) is PSDLayer.TYPE:\n # value in this context is text\n position = __get_text_poisition_by_aligment(layer, layer_image)\n else:\n if resize_pixel_layers:\n layer_image = layer_image.resize(size)\n else:\n box = (0, 0, size[0], size[1])\n layer_image = layer_image.resize(size, box=box)\n insert_pil_image(final_image, layer_image, position)\n\n return final_image","title":""},{"docid":"21032e23f1e00f80e84e05c77dcd3cf6","score":"0.55558175","text":"def _load_renderings(self, args):\n # Load images.\n imgdir_suffix = \"\"\n if args.factor > 0:\n imgdir_suffix = \"_{}\".format(args.factor)\n factor = args.factor\n else:\n factor = 1\n imgdir = path.join(args.data_dir, \"images\" + imgdir_suffix)\n if not utils.file_exists(imgdir):\n raise ValueError(\"Image folder {} doesn't exist.\".format(imgdir))\n\n # Sorting is important. Otherwise examples cannot be identified by indices.\n imgfiles = [\n path.join(imgdir, f)\n for f in sorted(utils.listdir(imgdir))\n if f.endswith(\"JPG\") or f.endswith(\"jpg\") or f.endswith(\"png\")\n ]\n if args.first_n_images:\n imgfiles = imgfiles[:args.first_n_images]\n images = []\n for imgfile in imgfiles:\n with utils.open_file(imgfile, \"rb\") as imgin:\n image = np.array(Image.open(imgin), dtype=np.float32) / 255.\n images.append(image)\n # For shapenet, len(images)=24, images[0].shape=(224, 224, 3)\n images = np.stack(images, axis=-1) # (224, 224, 3, 24)\n logging.info(\"Loaded images: %s\", str(images.shape))\n\n # Load poses and bds.\n with utils.open_file(path.join(args.data_dir, \"poses_bounds.npy\"),\n \"rb\") as fp:\n poses_arr = np.load(fp)\n return self._process_renderings(args, images, poses_arr, factor)","title":""},{"docid":"71b44d13fc4e214addfd8e0c70c599a3","score":"0.55557287","text":"def image_to_tf_data(img_path,\n mask_path,\n xml_path,\n label_map_dict,\n filename):\n with tf.gfile.GFile(img_path, 'rb') as fid:\n encoded_jpg = fid.read()\n encoded_jpg_io = io.BytesIO(encoded_jpg)\n image = PIL.Image.open(encoded_jpg_io)\n width = np.asarray(image).shape[1]\n height = np.asarray(image).shape[0]\n if image.format != 'JPEG':\n raise ValueError('Image format not JPEG')\n key = hashlib.sha256(encoded_jpg).hexdigest()\n\n with tf.gfile.GFile(mask_path, 'rb') as fid:\n encoded_mask_png = fid.read()\n encoded_png_io = io.BytesIO(encoded_mask_png)\n mask = PIL.Image.open(encoded_png_io)\n mask_np = np.asarray(mask.convert('L'))\n if mask.format != 'PNG':\n raise ValueError('Mask format not PNG')\n\n data = []\n classes = []\n classes_text = []\n xmins = []\n ymins = []\n xmaxs = []\n ymaxs = []\n encoded_mask_png_list = []\n\n if(True == FLAGS.bboxes_provided):\n if not os.path.exists(xml_path):\n logging.warning('Could not find %s, ignoring example.', xml_path)\n return\n with tf.gfile.GFile(xml_path, 'r') as fid:\n xml_str = fid.read()\n xml = etree.fromstring(xml_str)\n data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation']\n if 'object' in data:\n for obj in data['object']:\n class_name = obj['name']\n pixel_val = int(label_map_dict[class_name][1])\n xmin = float(obj['bndbox']['xmin'])\n xmax = float(obj['bndbox']['xmax'])\n ymin = float(obj['bndbox']['ymin'])\n ymax = float(obj['bndbox']['ymax'])\n print(filename, 'bounding box for',\n class_name, xmin, xmax, ymin, ymax)\n xmins.append(xmin / width)\n ymins.append(ymin / height)\n xmaxs.append(xmax / width)\n ymaxs.append(ymax / height)\n\n classes_text.append(class_name.encode('utf8'))\n classes.append(label_map_dict[class_name][0])\n\n mask_np_black = mask_np*0\n mask_np_black[int(ymin):int(ymax), int(xmin):int(\n xmax)] = mask_np[int(ymin):int(ymax), int(xmin):int(xmax)]\n mask_remapped = (mask_np_black == pixel_val).astype(np.uint8)\n img = PIL.Image.fromarray(mask_remapped)\n output = io.BytesIO()\n img.save(output, format='PNG')\n encoded_mask_png_list.append(output.getvalue())\n else:\n for key in label_map_dict.keys():\n class_name = key\n pixel_val = int(label_map_dict[class_name][1])\n nonbackground_indices_x = np.any(mask_np == pixel_val, axis=0)\n nonbackground_indices_y = np.any(mask_np == pixel_val, axis=1)\n nonzero_x_indices = np.where(nonbackground_indices_x)\n nonzero_y_indices = np.where(nonbackground_indices_y)\n\n if np.asarray(nonzero_x_indices).shape[1] > 0 and np.asarray(nonzero_y_indices).shape[1] > 0:\n xmin = float(np.min(nonzero_x_indices))\n xmax = float(np.max(nonzero_x_indices))\n ymin = float(np.min(nonzero_y_indices))\n ymax = float(np.max(nonzero_y_indices))\n print(filename, 'bounding box for',\n class_name, xmin, xmax, ymin, ymax)\n\n xmins.append(xmin / width)\n ymins.append(ymin / height)\n xmaxs.append(xmax / width)\n ymaxs.append(ymax / height)\n\n classes_text.append(class_name.encode('utf8'))\n classes.append(label_map_dict[class_name][0])\n\n mask_remapped = (mask_np == pixel_val).astype(np.uint8)\n img = PIL.Image.fromarray(mask_remapped)\n output = io.BytesIO()\n img.save(output, format='PNG')\n encoded_mask_png_list.append(output.getvalue())\n\n feature_dict = {\n 'image/height': dataset_util.int64_feature(height),\n 'image/width': dataset_util.int64_feature(width),\n 'image/filename': dataset_util.bytes_feature(filename.encode('utf8')),\n 'image/source_id': dataset_util.bytes_feature(filename.encode('utf8')),\n 'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),\n 'image/encoded': dataset_util.bytes_feature(encoded_jpg),\n 'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),\n 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),\n 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),\n 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),\n 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),\n 'image/object/class/text': dataset_util.bytes_list_feature(classes_text),\n 'image/object/class/label': dataset_util.int64_list_feature(classes),\n 'image/object/mask': dataset_util.bytes_list_feature(encoded_mask_png_list)}\n tf_data = tf.train.Example(\n features=tf.train.Features(feature=feature_dict))\n return tf_data","title":""},{"docid":"f5a10c9311463ca95cba591461b0db28","score":"0.55555487","text":"def extract_features(self, preprocessed_inputs):\n if self._depth_multiplier != 1.0:\n raise ValueError('Depth multiplier not supported.')\n\n preprocessed_inputs = shape_utils.check_min_image_dim(\n 129, preprocessed_inputs)\n\n feature_map_layout = {\n 'from_layer': ['block2', 'block4', '', '', '', ''],\n 'layer_depth': [-1, -1, 512, 256, 256, 128],\n 'use_explicit_padding': self._use_explicit_padding,\n 'use_depthwise': self._use_depthwise,\n }\n\n with tf.variable_scope(\n self._resnet_scope_name, reuse=self._reuse_weights) as scope:\n with slim.arg_scope(resnet_v1.resnet_arg_scope()):\n with (slim.arg_scope(self._conv_hyperparams_fn())\n if self._override_base_feature_extractor_hyperparams else\n context_manager.IdentityContextManager()):\n _, image_features = self._resnet_base_fn(\n inputs=ops.pad_to_multiple(preprocessed_inputs,\n self._pad_to_multiple),\n num_classes=None,\n is_training=None,\n global_pool=False,\n output_stride=None,\n store_non_strided_activations=True,\n scope=scope)\n image_features = self._filter_features(image_features)\n\n with slim.arg_scope(self._conv_hyperparams_fn()):\n feature_maps = feature_map_generators.multi_resolution_feature_maps(\n feature_map_layout=feature_map_layout,\n depth_multiplier=self._depth_multiplier,\n min_depth=self._min_depth,\n insert_1x1_conv=True,\n image_features=image_features)\n\n # print(\"^^^^^\")\n # print(feature_maps)\n # print(type(feature_maps))\n # print(\"^^^^^\")\n \n return feature_maps.values()","title":""},{"docid":"dce789c0a12084303431ea578755cc2f","score":"0.5548635","text":"def transfer_imgs(self, style_idx, char_id, save=False, save_name=\"\"):\n\n # Generate source image\n src_img = self.generate_source_img(char_id, font='SimSun')\n # Get font style embedding\n font_cat_embedding = self.cat_embedding_dict[style_idx]\n\n # Transform the source image and embedding to construct input for model\n inputs_data = self.data_process(src_img, font_cat_embedding, style_idx)\n\n self.model.set_input_inference(inputs_data)\n self.model.test()\n # output_img = self.model.fake_B\n if save:\n self.save_img(self.model.fake_B,'infered_{}_fake_B.png'.format(save_name))\n self.save_img(self.src_img,'infered_{}_real_A.png'.format(save_name))\n # return output_img","title":""},{"docid":"cdc8794d4787dcc989739b9b8ef051a0","score":"0.55485266","text":"def main(pb_file, img_file):\n with tf.gfile.GFile(pb_file, \"rb\") as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n\n with tf.Graph().as_default() as graph:\n tf.import_graph_def(graph_def, name=prefix)\n\n for op in graph.get_operations():\n print(op.name)\n\n x = graph.get_tensor_by_name('%s/input_1:0' % prefix)\n y = graph.get_tensor_by_name('%s/output_0:0' % prefix)\n\n images = np.load(img_file).astype(float)\n img_h = images.shape[1]\n img_w = images.shape[2]\n\n with tf.Session(graph=graph) as sess:\n for img in images:\n batched = img.reshape(-1, img_h, img_w, 3)\n normalized = standardize(batched)\n\n pred = sess.run(y, feed_dict={\n x: normalized\n })\n plt.imshow(pred.reshape(img_h, img_w))\n plt.show()","title":""},{"docid":"b0cae14afc4f95be0722adead7938b93","score":"0.55448353","text":"def _get_datasets(\n dataset_path: str, batch_size: int, is_evaluation: bool = False,\n):\n # Build the dataset going through the classes directories and collecting the images:\n images = []\n labels = []\n for label, directory in enumerate([\"with_mask\", \"without_mask\"]):\n images_directory = os.path.join(dataset_path, directory)\n images_files = [\n os.path.join(images_directory, file)\n for file in os.listdir(images_directory)\n if os.path.isfile(os.path.join(images_directory, file))\n ]\n for image_file in images_files:\n image = keras.preprocessing.image.load_img(\n image_file, target_size=(224, 224)\n )\n image = keras.preprocessing.image.img_to_array(image)\n image = keras.applications.mobilenet_v2.preprocess_input(image)\n images.append(image)\n labels.append(label)\n\n # Convert the images and labels to NumPy arrays\n images = np.array(images, dtype=\"float32\")\n labels = np.array(labels)\n\n # Perform one-hot encoding on the labels:\n labels = LabelBinarizer().fit_transform(labels)\n labels = keras.utils.to_categorical(labels)\n\n # Check if its an evaluation, if so, use the entire data:\n if is_evaluation:\n return images, labels\n\n # Split the dataset into training and validation sets:\n x_train, x_test, y_train, y_test = train_test_split(\n images, labels, test_size=0.2, stratify=labels, random_state=42,\n )\n\n # Construct the training image generator for data augmentation:\n image_data_generator = keras.preprocessing.image.ImageDataGenerator(\n rotation_range=20,\n zoom_range=0.15,\n width_shift_range=0.2,\n height_shift_range=0.2,\n shear_range=0.15,\n horizontal_flip=True,\n fill_mode=\"nearest\",\n )\n\n return (\n image_data_generator.flow(x_train, y_train, batch_size=batch_size),\n (x_test, y_test),\n )","title":""},{"docid":"75819fd7b8c98ef255cab1a49d911165","score":"0.5540418","text":"def loadRawData(self, path, ft_dim, num_samples):\n # Compile a list of JPG images in the source folder. Then verify that\n # a) each is a valid JPG file and b) all images have the same size.\n fnames = self.findTrainingFiles(path, num_samples)\n\n # Load and verify that the pickled meta data for each JPG file\n # specifies the same set of class labels.\n int2name = self.getLabelData(fnames)\n num_cls = len(int2name)\n\n # Compute the height and width that input images must have to be\n # compatible with the selected output feature size.\n im_dim = orpac_net.waveletToImageDim(Shape(None, *ft_dim.hw()))\n\n # Fill in channel information: Images must always be RGB and the\n # feature output channels are available via a utility method.\n im_dim.chan = 3\n ft_dim.chan = orpac_net.Orpac.numOutputChannels(num_cls)\n\n # Compile all the features that have not been compiled already.\n self.compileMissingFeatures(fnames, ft_dim)\n\n # Load the compiled training data alongside each image.\n train = self.loadTrainingData(fnames, im_dim, ft_dim, num_cls)\n return im_dim, ft_dim, int2name, train","title":""},{"docid":"e5a10679159a05417e9e902404728a6e","score":"0.5539904","text":"def generate_intermediate_format_from_files(filepath, platform, import_anims=True):\n imported_namedata = NameInterface.from_file(filepath + '.name')\n imported_skeldata = SkelInterface.from_file(filepath + '.skel')\n imported_geomdata = GeomInterface.from_file(filepath + '.geom', platform)\n\n directory = os.path.split(filepath)\n filename = directory[-1]\n directory = os.path.join(*directory[:-1])\n\n # Always import the base anim, because it plays a special role in skeleton construction\n imported_animdata = {filename: AnimInterface.from_file(filepath + '.anim')}\n if import_anims:\n for afile in os.listdir(directory):\n afilepath = os.path.join(directory, afile)\n if afile[-4:] == 'anim' and afile[:len(filename)] == filename and afile[:-4] != filename:\n afile_name, afile_ext = os.path.splitext(afile)\n print(afile)\n imported_animdata[afile_name] = AnimInterface.from_file(afilepath)\n\n images_directory = os.path.join(*os.path.split(filepath)[:-1], 'images')\n model_data = IntermediateFormat()\n\n model_data.material_name_hashes = {dscs_name_hash(name): name for name in imported_namedata.material_names}\n model_data.bone_name_hashes = {dscs_name_hash(name): name for name in imported_namedata.bone_names}\n add_meshes(model_data, imported_geomdata)\n add_textures(model_data, imported_geomdata, images_directory)\n add_materials(model_data, imported_namedata, imported_geomdata)\n add_skeleton(model_data, imported_namedata, imported_skeldata, imported_geomdata)\n add_anims(model_data, imported_animdata)\n add_lights(model_data, imported_geomdata.light_sources)\n model_data.cameras = imported_geomdata.cameras\n\n return model_data","title":""},{"docid":"0f092fa9ded5dd5ed276709da21c3335","score":"0.5537652","text":"def predict():\n\n import numpy as np\n import torch\n from tqdm import tqdm\n import os\n from matplotlib import pyplot as plt\n from ..utils import Config\n from ..layer import HomogeneousShapeLayer\n from ..networks import SingleShapeNetwork\n from shapedata.single_shape import SingleShapeDataProcessing, \\\n SingleShapeSingleImage2D\n from shapedata.io import pts_exporter\n import argparse\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-v\", \"--visualize\", action=\"store_true\",\n help=\"If Flag is specified, results will be plotted\")\n parser.add_argument(\"-d\", \"--in_path\", type=str, help=\"Input Data Dir\")\n parser.add_argument(\"-s\", \"--out_path\", default=\"./outputs\", type=str,\n help=\"Output Data Dir\")\n parser.add_argument(\"-w\", \"--weight_file\", type=str, help=\"Model Weights\")\n parser.add_argument(\"-c\", \"--config_file\", type=str, help=\"Configuration\")\n\n args = parser.parse_args()\n config = Config()\n config_dict = config(os.path.abspath(args.config_file))\n\n try:\n net = torch.jit.load(os.path.abspath(args.weight_file))\n net.eval()\n net.cpu()\n\n except RuntimeError:\n net_layer = HomogeneousShapeLayer\n\n if config_dict[\"training\"].pop(\"mixed_prec\", False):\n try:\n from apex import amp\n amp.init()\n except:\n pass\n\n shapes = np.load(os.path.abspath(config_dict[\"layer\"].pop(\"pca_path\"))\n )[\"shapes\"][:config_dict[\"layer\"].pop(\"num_shape_params\") + 1]\n\n net = SingleShapeNetwork(\n net_layer, {\"shapes\": shapes,\n **config_dict[\"layer\"]},\n img_size=config_dict[\"data\"][\"img_size\"],\n **config_dict[\"network\"])\n\n state = torch.load(os.path.abspath(args.weight_file))\n try:\n net.load_state_dict(state[\"state_dict\"][\"model\"])\n except KeyError:\n try:\n net.load_state_dict(state[\"model\"])\n except KeyError:\n net.load_state_dict(state)\n\n net = net.to(\"cpu\")\n net = net.eval()\n\n data = SingleShapeDataProcessing._get_files(\n os.path.abspath(args.in_path), extensions=[\".png\", \".jpg\"])\n\n def process_sample(sample, img_size, net, device, crop=0.1):\n lmk_bounds = sample.get_landmark_bounds(sample.lmk)\n min_y, min_x, max_y, max_x = lmk_bounds\n range_x = max_x - min_x\n range_y = max_y - min_y\n\n center_x = min_x + range_x / 2\n center_y = min_y + range_y / 2\n\n max_range = np.floor(max(range_x, range_y) * (1 + crop))\n\n tmp = sample.crop(center_y - max_range / 2,\n center_x - max_range / 2,\n center_y + max_range / 2,\n center_x + max_range / 2)\n\n max_range += 1\n\n crop_range_x = tmp.img.shape[1]\n crop_range_y = tmp.img.shape[0]\n colour_channels = tmp.img.shape[2]\n\n # zero padding\n if max_range - crop_range_x != 0:\n img_temp = np.zeros((int(round(crop_range_y)),\n int(round(max_range - crop_range_x)),\n colour_channels))\n\n tmp.img = np.concatenate((tmp.img, img_temp), axis=1)\n\n # zero padding\n if max_range - crop_range_y != 0:\n img_temp = np.zeros((int(round(max_range - crop_range_y)),\n int(round(max_range)),\n colour_channels))\n\n tmp.img = np.concatenate((tmp.img, img_temp), axis=0)\n\n # convert to torch tensor\n img_tensor = torch.from_numpy(\n tmp.to_grayscale().resize((img_size, img_size)).img.transpose(2, 0,\n 1)\n ).to(torch.float).unsqueeze(0).to(device)\n\n # obtain prediction\n pred = net(img_tensor)['pred'][0].cpu().numpy()\n\n # remap to original image\n pred = pred * np.array([max_range / img_size, max_range / img_size])\n img_add_bound_x = max(center_x - max_range / 2, 0)\n img_add_bound_y = max(center_y - max_range / 2, 0)\n pred = pred + np.asarray([img_add_bound_y,\n img_add_bound_x])\n return pred\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n with torch.no_grad():\n\n if torch.cuda.is_available():\n net = net.cuda()\n\n if args.visualize:\n pred_path = os.path.join(os.path.abspath(args.out_path), \"pred\")\n vis_path = os.path.join(os.path.abspath(args.out_path),\n \"visualization\")\n os.makedirs(vis_path, exist_ok=True)\n else:\n pred_path = os.path.abspath(args.out_path)\n\n os.makedirs(pred_path, exist_ok=True)\n\n for idx, file in enumerate(tqdm(data)):\n\n _data = SingleShapeSingleImage2D.from_files(file)\n\n pred = process_sample(_data, img_size=config_dict[\"data\"][\"img_size\"], net=net,\n device=device)\n\n fname = os.path.split(_data.img_file)[-1].rsplit(\".\", 1)[0]\n\n if args.visualize:\n view_kwargs = {}\n if _data.is_gray:\n view_kwargs[\"cmap\"] = \"gray\"\n fig = _data.view(True, **view_kwargs)\n plt.gca().scatter(pred[:, 1], pred[:, 0], s=5, c=\"C1\")\n plt.gca().legend([\"GT\", \"Pred\"])\n plt.gcf().savefig(os.path.join(vis_path, fname + \".png\"))\n plt.close()\n\n _data.save(pred_path, fname, \"PTS\")\n pts_exporter(pred, os.path.join(pred_path, fname + \"_pred.pts\"))","title":""},{"docid":"656cbef93594bbe96065f9f6ffee840d","score":"0.5534027","text":"def convert_DSB_to_semantic_masks(dataset_path, out_path):\n\n #Write output directories\n out_images_path = os.path.join(out_path, \"images\")\n out_masks_path = os.path.join(out_path, \"masks\")\n\n if not os.path.exists(out_images_path):\n os.makedirs(out_images_path)\n if not os.path.exists(out_masks_path):\n os.makedirs(out_masks_path)\n\n num_images = len(os.listdir(dataset_path)) #Used for logging\n\n for i, subdir in enumerate(os.listdir(dataset_path)):\n\n if (i % 50 == 0):\n print(f\"Processing image {i} out of {num_images}\")\n\n if not subdir.startswith('.') and os.path.isdir(os.path.join(dataset_path, subdir)): #Ignore hidden files, e.g. DSStore\n\n images_subdir = os.path.join(dataset_path, subdir, \"images\")\n masks_subdir = os.path.join(dataset_path, subdir, \"masks\")\n\n image_size = [0, 0]\n\n for file in os.listdir(images_subdir):\n #Read and save the image immediately\n im = Image.open(os.path.join(images_subdir, file))\n im.save(os.path.join(out_images_path, subdir+\".png\"))\n image_size = [im.size[1], im.size[0]] #PIL reads in column order, so inverting size\n\n semantic_mask = np.zeros(image_size, dtype=\"uint8\")\n\n for file in os.listdir(masks_subdir):\n mask = Image.open(os.path.join(masks_subdir, file))\n mask_np = np.asarray(mask)\n semantic_mask[mask_np == 255] = 255\n\n Image.fromarray(semantic_mask).save(os.path.join(out_masks_path, subdir+\".png\"))","title":""},{"docid":"acb1944d128b9da04499dd246d93f03b","score":"0.5529732","text":"def run_on_images(self, img_paths, batch_size: int = 16):\n assert (\n self._dataset is not None\n ), \"No dataset is give, cannot run extractor on images\"\n\n imageloader = IMAGE_LOADERS[self._extractor_name]\n ds = dataloader_from_img_paths(img_paths, imageloader, batch_size)\n\n self._features_dir = _captionds_features_dir[self._dataset.name]\n self._features_dir.mkdir(parents=True, exist_ok=True)\n\n def _obtain_feats_pth(p):\n filepath = p.numpy().decode(\"utf-8\")\n filename = Path(filepath).name\n features_filepath = self._features_dir / f\"{filename}.npz\"\n\n self.im_to_features[filepath] = str(features_filepath.absolute())\n\n return features_filepath\n\n for im, im_pth in tqdm(ds):\n feat_pths = [_obtain_feats_pth(p) for p in im_pth]\n\n if all([fp.exists() for fp in feat_pths]):\n continue\n\n features = self.extraction_model(im)\n features = [f for f in zip(*features)]\n\n for feats, fp in zip(features, feat_pths):\n to_save = {\n fn: f.numpy()\n for f, fn in zip(feats, _extractors_features[self._extractor_name])\n }\n np.savez_compressed(fp, **to_save)","title":""},{"docid":"9d65efa2d7f2f2ef552ded627cff8c6a","score":"0.552233","text":"def convert_images(input_path, output_path, image_paths, labels, crop_boxes):","title":""},{"docid":"ab6d99edaafe57ab6a0b00e931f50b12","score":"0.55158067","text":"def get_image_folders():\n\n enhancers = {\n 0: lambda image, f: ImageEnhance.Color(image).enhance(f),\n 1: lambda image, f: ImageEnhance.Contrast(image).enhance(f),\n 2: lambda image, f: ImageEnhance.Brightness(image).enhance(f),\n 3: lambda image, f: ImageEnhance.Sharpness(image).enhance(f)\n }\n\n # intensities of enhancers\n factors = {\n 0: lambda: np.clip(np.random.normal(1.0, 0.3), 0.4, 1.6),\n 1: lambda: np.clip(np.random.normal(1.0, 0.15), 0.7, 1.3),\n 2: lambda: np.clip(np.random.normal(1.0, 0.15), 0.7, 1.3),\n 3: lambda: np.clip(np.random.normal(1.0, 0.3), 0.4, 1.6),\n }\n\n # randomly change color of an image\n def enhance(image):\n order = [0, 1, 2, 3]\n np.random.shuffle(order)\n # random enhancers in random order\n for i in order:\n f = factors[i]()\n image = enhancers[i](image, f)\n return image\n\n def rotate(image):\n degree = np.clip(np.random.normal(0.0, 15.0), -40.0, 40.0)\n return image.rotate(degree, Image.BICUBIC)\n\n # training data augmentation on the fly\n train_transform = transforms.Compose([\n transforms.Lambda(rotate),\n transforms.RandomCrop(56),\n transforms.RandomHorizontalFlip(),\n transforms.Lambda(enhance),\n transforms.ToTensor(),\n transforms.Normalize(\n mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225]\n ),\n ])\n\n # for validation data\n val_transform = transforms.Compose([\n transforms.CenterCrop(56),\n transforms.ToTensor(),\n transforms.Normalize(\n mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225]\n ),\n ])\n\n # mean and std are taken from here:\n # http://pytorch.org/docs/master/torchvision/models.html\n\n train_folder = ImageFolder(TRAIN_DIR, train_transform)\n val_folder = ImageFolder(VAL_DIR, val_transform)\n return train_folder, val_folder","title":""},{"docid":"fa8be5e5892bff91e9446892b5f63b62","score":"0.5491997","text":"def main(args):\n vgg = caffe.Net(\n VGG_PROTOTXT, VGG_MODEL, caffe.TEST,\n )\n\n style = Art(vgg, args)\n\n # Collect art from the wikiart folder\n if args.artist:\n try:\n args.style_images = glob.glob(\n './wikiart/{}/*'.format(args.artist)\n )\n except Exception as e:\n print \"Couldn't get images for artist {}, check dir!\".format(\n args.artist\n )\n raise e\n\n num_simages = len(args.style_images)\n\n if num_simages > 2:\n sample = args.style_images[:2]\n else:\n sample = args.style_images\n\n # Make the directory for this run\n raw_dirname = './img/{}__{}-w{}-{}'.format(\n args.content_image.split('.')[0],\n '_'.join(os.path.basename(s).split('.')[0] for s in sample),\n str(args.width),\n str(args.numiter)\n )\n\n # Get a unique dirname if the directory already exists\n dirname = raw_dirname\n unique = 1\n while os.path.exists(dirname):\n dirname = raw_dirname + '-' + str(unique)\n unique += 1\n\n debug_print(\"Starting {}\".format(dirname))\n\n os.mkdir(dirname)\n\n # TODO this is bad, but whatever. save this dirname as an attr\n style.dirname = dirname\n\n # NOTE: For now, we assume style weights are equal\n style_image_weights = [1. / num_simages] * num_simages\n\n # Get style and content targets\n debug_print(\"Setting up content targets\")\n style.set_content_target(args.content_image)\n debug_print(\"Setting up style targets\")\n style.set_style_targets(args.style_images, style_image_weights)\n debug_print(\"Done initialization\")\n\n # Get the candidate image\n debug_print(\"Running gradient descent...\")\n new_img = style.go(maxiter=args.numiter)\n\n debug_print(\"Done gradient descent, saving image...\")\n\n imsave(\n '{}/final.jpg'.format(dirname),\n skimage.img_as_ubyte(new_img)\n )\n\n debug_print(\"Done: Saved {}\".format(dirname))","title":""},{"docid":"9743f67db7b1f2214477740b1f161245","score":"0.5490244","text":"def get_preprocess(self, *args, **kwargs) -> Dict:\n input_shape = get_input_shape(self.deploy_cfg)\n model_cfg = process_model_config(self.model_cfg, [''], input_shape)\n pipeline = model_cfg.test_pipeline\n data_preprocessor = self.model_cfg.model.data_preprocessor\n\n lift = dict(type='Lift', transforms=[])\n lift['transforms'].append(dict(type='LoadImageFromFile'))\n transforms2index = {}\n for i, trans in enumerate(pipeline):\n transforms2index[trans['type']] = i\n lift_key = [\n 'Resize', 'Normalize', 'TenCrop', 'ThreeCrop', 'CenterCrop'\n ]\n for key in lift_key:\n if key == 'Normalize':\n assert key not in transforms2index\n mean = data_preprocessor.get('mean', [0, 0, 0])\n std = data_preprocessor.get('std', [1, 1, 1])\n trans = dict(type='Normalize', mean=mean, std=std, to_rgb=True)\n lift['transforms'].append(trans)\n if key in transforms2index:\n index = transforms2index[key]\n if key == 'Resize' and 'scale' in pipeline[index]:\n value = pipeline[index].pop('scale')\n if len(value) == 2 and value[0] == -1:\n value = value[::-1]\n pipeline[index]['size'] = value\n lift['transforms'].append(pipeline[index])\n\n meta_keys = [\n 'valid_ratio', 'flip', 'img_norm_cfg', 'filename', 'ori_shape',\n 'pad_shape', 'img_shape', 'flip_direction', 'scale_factor',\n 'ori_filename'\n ]\n other = []\n must_key = ['FormatShape', 'PackActionInputs']\n for key in must_key:\n assert key in transforms2index\n index = transforms2index[key]\n if key == 'PackActionInputs':\n if 'meta_keys' in pipeline[index]:\n meta_keys += pipeline[index]['meta_keys']\n pipeline[index]['meta_keys'] = list(set(meta_keys))\n pipeline[index]['keys'] = ['img']\n pipeline[index]['type'] = 'Collect'\n other.append(pipeline[index])\n\n reorder = [lift, *other]\n return reorder","title":""},{"docid":"3082f5c2c0f84ca5271b810143288c55","score":"0.5476209","text":"def data_prep(path, img_rows, img_cols, color):\n \n images = []\n labels = []\n for image_class in os.listdir(path):\n print('image_class =', image_class)\n path_to_class_directory = os.path.join(path, image_class)\n for img_name in os.listdir(path_to_class_directory):\n true_path = os.path.join(path_to_class_directory, img_name)\n if color:\n images.append(cv2.imread(true_path, 1)/255.0)\n else:\n images.append(cv2.imread(true_path, 0)/255.0) # greyscale\n labels.append(os.listdir(path).index(image_class))\n data = list(zip(images, labels))\n np.random.shuffle(data)\n images, labels = zip(*data)\n images = [cv2.resize(img, (img_rows, img_cols), cv2.INTER_AREA) for img in images] # resize images to all be the same\n if color:\n images = np.array(images).reshape(len(images), img_rows, img_cols, 3)\n else:\n images = np.array(images).reshape(len(images), img_rows, img_cols, 1)\n labels = keras.utils.to_categorical(labels, num_classes=len(os.listdir(path)))\n return images, labels","title":""},{"docid":"4010ccd369871617bebea9f159372b52","score":"0.5472248","text":"def Keras_multiple_features_extraction(folder,base_model, preprocessing, layer, crop_size, save_activations,out_prefix, GUI=None):\t\n # Get a proper name for the .txt list of images\n s=os.sep\n gender=folder.split(s)[-1]\n \n # Create a list of images\n mf.list_generator(folder, gender, 'png')\n \n # Create folders in which store features and activations files\n mf.folders_creator('Results',['Features','Activations'])\n \n # Read .txt list of images and load it as list\n f=open(gender+'.txt','r')\n lines = f.readlines()\n lines = [line.rstrip() for line in lines]\n f.close()\n \n \n # Take 1st word of each line as an image name, creating a names list\n img_list = [line.split(' ')[0] for line in lines]\n # Take 2nd word of each line as a class, creating a labels list\n lab = [line.split(' ')[1] for line in lines]\n \n if GUI!=None:\n # If GUI mode is ON, initialize a Progress bar\n currentImage=Label(GUI, text=' '*50)\n currentImage.grid(row = 0, column =1)\n progress = Progressbar(GUI, orient=\"horizontal\",length=200, mode=\"determinate\")\n progress.grid(row=1, column=1)\n value = 0\n progress[\"value\"] = 0\n progress[\"maximum\"] = len(lines)\n step=1\n \n\n \n # Initialize some usefull empty lists \n feats=[]\n img_ids=[]\n labels=[]\n \n # Initialize to 0 some counters and flag\n i=0\n count=0\n file_created=0\n \n \n # Process all images in the list\n for im in img_list:\n \n if i==0:\n # Clear terminal\n try:\n os.system('clear')\n except:\n os.system('cls')\n print('Features Extraction from ' + folder)\n # Initialize progress bar\n bar=progressbar.ProgressBar(max_value=len(lines))\n \n # Update progress bar\n bar.update(i)\n \n \n # Extract features from image\n feat=Keras_extract_feature(base_model, preprocessing, im, crop_size, layer)\n \n \n if save_activations==True:\n if i==0:\n # Create folders in which store features and activations files\n mf.folders_creator('Results'+s+'Activations'+s+gender)\n # Visualize activations\n M=mf.visualize_activations(np.swapaxes(np.squeeze(feat),2,0))\n \n # Reshape features matrix to an array\n feat=feat.reshape((1,feat.size))\n feat=np.squeeze(feat)\n \n # Append features array to features list\n feats.append(feat)\n # Append label to labels list\n labels.append(int(lab[i]))\n \n if GUI!=None:\n # If GUI mode is ON, update Progress bar value\n a=value/len(lines)*100\n currentImage[\"text\"]='Processing...'+\"{0:.1f}\".format(round(a,1)) +' % '\n currentImage.update()\n value += step\n progress[\"value\"] = int(value)\n progress.update()\n \n # Abbreviete current image path and append it to image ids list\n tmp=im.split(s)\n im=s.join([tmp[-2],tmp[-1]])\n img_ids.append(np.string_(im))\n \n \n if save_activations==True:\n # Write activations\n n='Results'+s+'Activations'+s+gender+s+out_prefix+'_'+im.split(s)[1]\n mf.save_as_image(M,n)\n \n if file_created == 0:\n # Create an Hierarchical Data Format (.h5) file in which\n # store Features, Labels and Names of the processed images\n feats_shape = [len(img_list),feat.shape[0]];\n name='Results'+s+'Features'+s+out_prefix+'_'+gender+'.h5'\n f = h5py.File(name,\"w\")\n feats_dset = f.create_dataset(\"feats\",feats_shape,compression=\"gzip\")\n img_id_dset = f.create_dataset(\"img_id\",(len(img_list),),dtype=\"S40\")\n labels_dset = f.create_dataset(\"labels\",(len(img_list),),compression=\"gzip\")\n file_created = 1\n \n N=100\t\t\t\n if (i+1) % N == 0:\n # Every N processed images save data to the created .h5 file\n # and clean the list variables to empty the RAM\n \n # Convert lists to numpy arrays\n feats=np.asarray(feats)\n labels=np.asarray(labels)\n \n # Write data \n feats_dset[i-N+1:i+1] = feats \n img_id_dset[i-N+1:i+1] = img_ids\n labels_dset[i-N+1:i+1] = labels\n \n # Clean list variables\n feats = []\n labels = []\n img_ids=[]\n \n count=-1\n \n i+=1\n count+=1\n \n # Convert lists to numpy arrays\n feats=np.asarray(feats)\n labels=np.asarray(labels)\n # Write remaining data\n feats_dset[i-count:i+1] = feats\n img_id_dset[i-count:i+1] = img_ids\n labels_dset[i-count:i+1] = labels\n # Clean list variables\n feats = []\n labels = []\n img_ids=[]\n \n # Close file\n f.close()\n\n if GUI!=None:\n # If GUI mode is ON, update Progress bar value\n currentImage[\"text\"]=' Completed '\n currentImage.update()\n value += step\n progress[\"value\"] = int(value)\n progress.update()","title":""},{"docid":"1b69cc4dba3d6f49211243aa314ffb44","score":"0.54700017","text":"def process_path(file_path):\n\n label_path = tf.strings.join(\n [f\"./{data_dir}/split-label/\", tf.strings.split(file_path, os.sep)[-1]]\n )\n image = tf.io.read_file(file_path)\n image = tf.image.decode_png(image, channels=3)\n label = tf.io.read_file(label_path)\n label = tf.image.decode_png(label, channels=3)\n\n return image, label","title":""},{"docid":"2944cb1bd36c2180f11127995b4f515a","score":"0.5464237","text":"def get_style_model_and_losses(\n cnn,\n normalization_mean,\n normalization_std,\n style_img,\n content_img,\n content_layers=content_layers_default,\n style_layers=style_layers_default,\n):\n cnn = copy.deepcopy(cnn)\n\n normalization = Normalization(normalization_mean, normalization_std).to(device)\n\n content_losses = []\n style_losses = []\n\n model = nn.Sequential(normalization)\n\n i = 0\n for layer in cnn.children():\n if isinstance(layer, nn.Conv2d):\n i += 1\n name = \"conv_{}\".format(i)\n elif isinstance(layer, nn.ReLU):\n name = \"relu_{}\".format(i)\n layer = nn.ReLU(inplace=False)\n elif isinstance(layer, nn.MaxPool2d):\n name = \"pool_{}\".format(i)\n elif isinstance(layer, nn.BatchNorm2d):\n name = \"bn_{}\".format(i)\n else:\n raise RuntimeError(\"Unrecognized layer: {}\".format(layer.__class__.__name__))\n\n model.add_module(name, layer)\n\n if name in content_layers:\n target = model(content_img).detach()\n content_loss = ContentLoss(target)\n model.add_module(\"content_loss_{}\".format(i), content_loss)\n content_losses.append(content_loss)\n\n if name in style_layers:\n target_feature = model(style_img).detach()\n style_loss = StyleLoss(target_feature)\n model.add_module(\"style_loss_{}\".format(i), style_loss)\n style_losses.append(style_loss)\n\n for i in range(len(model) - 1, -1, -1):\n if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):\n break\n\n model = model[: (i + 1)]\n\n return model, style_losses, content_losses","title":""},{"docid":"3a2f2aea9432b78f0720e2bb6c943529","score":"0.5463096","text":"def preprocess_data():\n input_args = get_input_args()\n data_dir = input_args.dir\n\n train_dir = data_dir + 'train'\n valid_dir = data_dir + 'valid'\n test_dir = data_dir + 'test'\n\n train_transforms = transforms.Compose([transforms.RandomRotation(30),\n transforms.RandomResizedCrop(224),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])\n valid_transforms = transforms.Compose([transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])\n test_transforms = transforms.Compose([transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])\n \n\n \n train_data = datasets.ImageFolder(train_dir, transform=train_transforms)\n valid_data = datasets.ImageFolder(valid_dir, transform=valid_transforms)\n test_data = datasets.ImageFolder(test_dir, transform=test_transforms)\n \n\n \n trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\n validloader = torch.utils.data.DataLoader(valid_data, batch_size=32)\n testloader = torch.utils.data.DataLoader(test_data, batch_size=32)\n \n\n return train_data, valid_data, test_data, trainloader, validloader, testloader","title":""},{"docid":"4e9c3d9afb1fd6af18a4e48d81c622ac","score":"0.5438395","text":"def image_preprocessing(self):\n\t\tdef pil_loader(path):\n\t\t\t\"\"\"Load images from /eval/ subfolder, convert to greyscale and resized it as squared\"\"\"\n\t\t\twith open(path, 'rb') as f:\n\t\t\t\twith Image.open(f) as img:\n\t\t\t\t\tsqrWidth = np.ceil(np.sqrt(img.size[0]*img.size[1])).astype(int)\n\t\t\t\t\treturn img.convert('L').resize((sqrWidth, sqrWidth))\n\n\t\tkwargs = {'num_workers': 1, 'pin_memory': True} if self._cuda else {}\n\t\tself._eval_loader = torch.utils.data.DataLoader(ImageFolder(root=self._evalf,\n\t\t\ttransform=transforms.Compose([\n\t\t\t\t\t\t transforms.Scale(28),\n\t\t\t\t\t\t transforms.CenterCrop(28),\n\t\t\t\t\t\t transforms.ToTensor(),\n\t\t\t\t\t\t transforms.Normalize((0.1307,), (0.3081,))\n\t\t\t\t\t ]), loader=pil_loader), batch_size=1, **kwargs)","title":""},{"docid":"ee9da6b52dc9c791d5295c70d9f22578","score":"0.54379606","text":"def splitTransform(self):\n\t\t#path_merge = \"transform\"\n\t\t#path_train = \"transform/data/\"\n\t\t#path_label = \"transform/label/\"\n\t\tpath_merge = \"deform/deform_norm2\"\n\t\tpath_train_image = \"deform/train/image/\"\n\t\tpath_train_label = \"deform/train/label/\"\n\n\t\t#\n\t\ttrain_imgs = glob.glob(path_merge+\"/*.\"+self.img_type)\n\t\t#\n\t\tfor imgname in train_imgs:\n\n\t\t\timg = cv2.imread(imgname)\n\t\t\timg_train = img[:,:,2]#cv2 read image rgb->bgr\n\t\t\timg_label = img[:,:,0]\n\t\t\t#\n\t\t\tmidname = imgname[imgname.rindex(\"/\")+1:imgname.rindex(\".\"+self.img_type)]\n\t\t\tcv2.imwrite(path_train_image+midname+\".\"+self.img_type,img_train)\n\t\t\tcv2.imwrite(path_train_label+midname+\".\"+self.img_type,img_label)","title":""},{"docid":"c75371d4dc61002f1573538e44751f0d","score":"0.5435713","text":"def get_features(image, model, layers=None):\r\n \r\n ## Need the layers for the content and style representations of an image\r\n if layers is None:\r\n layers = {'0': 'conv1_1', \r\n \"5\": \"conv2_1\", \r\n \"10\": \"conv3_1\", \r\n \"19\": \"conv4_1\", \r\n '21': 'conv4_2',\r\n \"28\": \"conv5_1\"}\r\n \r\n features = {}\r\n x = image\r\n # model._modules is a dictionary holding each module in the model\r\n for name, layer in model._modules.items():\r\n x = layer(x)\r\n if name in layers:\r\n features[layers[name]] = x\r\n \r\n return features","title":""},{"docid":"1b8591f20fd64e9c42449825b4c4af8a","score":"0.54354364","text":"def process(self, input_image_path):\n image_raw, image_resized = self._load_and_resize(input_image_path)\n image_preprocessed = self._shuffle_and_normalize(image_resized)\n return image_raw, image_preprocessed","title":""},{"docid":"7d2cd3e5afe107f23f918ee0603cc72e","score":"0.54243505","text":"def load_fashion_mnist():\n dirname = 'fashion-mnist'\n files = [\n 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',\n 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'\n ]\n\n paths = [os.path.join(FLAGS.datasets_dir, dirname, fname) for fname in files]\n\n with tf.gfile.GFile(paths[0], 'rb') as f:\n lbpath = gzip.GzipFile(fileobj=f)\n y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)\n\n with tf.gfile.GFile(paths[1], 'rb') as f:\n imgpath = gzip.GzipFile(fileobj=f)\n x_train = np.frombuffer(\n imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)\n\n with tf.gfile.GFile(paths[2], 'rb') as f:\n lbpath = gzip.GzipFile(fileobj=f)\n y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)\n\n with tf.gfile.GFile(paths[3], 'rb') as f:\n imgpath = gzip.GzipFile(fileobj=f)\n x_test = np.frombuffer(\n imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)\n\n return (x_train, y_train), (x_test, y_test)","title":""},{"docid":"9cb5a1d7302691ea8047e50417032813","score":"0.54200256","text":"def process_img(content, new_size = [128, 128]):\n img = tf.io.read_file(content)\n img = tf.io.decode_png(img)\n\n img = tf.image.convert_image_dtype(img, tf.float32)\n # img = tf.image.resize(img, new_size)\n img = tf.scalar_mul(tf.constant(1/255), img, name = \"img\")\n # Text read\n\n return img, new_size","title":""},{"docid":"baa6827a14499a829fa46d41ca04beee","score":"0.54034704","text":"def compute_features(vgg_weights, pooling_type, input_img, layers):\n input = tf.placeholder(tf.float32, shape=input_img.shape)\n net = Model.build_image_net(input, vgg_weights, pooling_type)\n features = {}\n with tf.Session() as sess:\n for layer in layers:\n features[layer] = sess.run(net[layer], feed_dict={input: input_img})\n return features","title":""},{"docid":"fc269245a86c1c5625f7c2fede6bffa7","score":"0.5393708","text":"def get_hpatches_data(files, norm, **config):\n n = tf.constant(255.)\n\n def _get_shape(image):\n return tf.shape(image)[:2]\n\n def _read_image(path):\n return cv.imread(path.decode('utf-8'))\n\n def _preprocess(image, norm):\n tf.Tensor.set_shape(image, [None, None, 3])\n image = tf.image.rgb_to_grayscale(image)\n if config['preprocessing']['resize']:\n image = ratio_preserving_resize(image, **config['preprocessing'])\n if norm:\n image = tf.cast(tf.cast(image, dtype=tf.float32) / n, dtype=tf.float32)\n else:\n image = tf.cast(image, dtype=tf.float32)\n return image\n\n def _adapt_homography_to_preprocessing(zip_data):\n H = tf.cast(zip_data['homography'], dtype=tf.float32)\n source_size = tf.cast(zip_data['shape'], dtype=tf.float32)\n source_warped_size = tf.cast(zip_data['warped_shape'], dtype=tf.float32)\n target_size = tf.cast(tf.convert_to_tensor(config['preprocessing']['resize']), dtype=tf.float32)\n\n # Compute the scaling ratio due to te resizing for both images\n s = tf.reduce_max(tf.cast(tf.divide(target_size, source_size), dtype=tf.float32))\n up_scale = tf.diag(tf.stack([1. / s, 1. / s, tf.constant(1.)]))\n warped_s = tf.reduce_max(tf.divide(target_size, source_warped_size))\n down_scale = tf.diag(tf.stack([warped_s, warped_s, tf.constant(1.)]))\n\n # Compute the translation due to the crop for both images\n pad_y = tf.cast((source_size[0]*s - target_size[0]) / tf.constant(2.), dtype=tf.int32)\n pad_x = tf.cast((source_size[1]*s - target_size[1]) / tf.constant(2.), dtype=tf.int32)\n translation = tf.stack([tf.constant(1), tf.constant(0), pad_x,\n tf.constant(0), tf.constant(1), pad_y,\n tf.constant(0), tf.constant(0), tf.constant(1)])\n translation = tf.cast(tf.reshape(translation, [3, 3]), dtype=tf.float32)\n pad_y = tf.cast((source_warped_size[0]*warped_s - target_size[0]) / tf.constant(2.), dtype=tf.int32)\n pad_x = tf.cast((source_warped_size[1]*warped_s - target_size[1]) / tf.constant(2.), dtype=tf.int32)\n warped_translation = tf.stack([tf.constant(1), tf.constant(0), -pad_x,\n tf.constant(0), tf.constant(1), -pad_y,\n tf.constant(0), tf.constant(0), tf.constant(1)])\n warped_translation = tf.cast(tf.reshape(warped_translation, [3, 3]), dtype=tf.float32)\n\n H = tf.matmul(warped_translation, tf.matmul(down_scale, tf.matmul(H, tf.matmul(up_scale, translation))))\n return H\n\n def add_mask(homography):\n H = tf.cast(homography, dtype=tf.float32)\n H_new = H * tf.constant([[1., 1., 1.], [1., 1., 1.], [0.9, 0.9, 0.9]])\n H_flat = invert_homography(mat2flat(H_new[tf.newaxis, ...]))\n size = tf.constant([216, 288])\n mask = H_transform(tf.ones(size), H_flat, interpolation='NEAREST')\n kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (config['valid_border_margin']*2,)*2)\n kernel[0, 1], kernel[3, 0], kernel[3, 3] = 1, 0, 0\n mask = tf.nn.erosion2d(mask[tf.newaxis, ..., tf.newaxis], tf.cast(tf.constant(kernel)[..., tf.newaxis],\n dtype=tf.float32),\n [1, 1, 1, 1], [1, 1, 1, 1], 'SAME')[0, ..., 0] + 1.\n return {'H': H, 'mask': mask}\n\n images = tf.data.Dataset.from_tensor_slices(files['image_paths'])\n images = images.map(lambda path: tf.py_func(_read_image, [path], tf.uint8))\n warped_images = tf.data.Dataset.from_tensor_slices(files['warped_image_paths'])\n warped_images = warped_images.map(lambda path: tf.py_func(_read_image, [path], tf.uint8))\n homographies = tf.data.Dataset.from_tensor_slices(np.array(files['homography']))\n if config['preprocessing']['resize']:\n shapes = images.map(_get_shape)\n warped_shapes = warped_images.map(_get_shape)\n homographies = tf.data.Dataset.zip({'homography': homographies, 'shape': shapes, 'warped_shape': warped_shapes})\n homographies = homographies.map(_adapt_homography_to_preprocessing)\n if config['events']:\n homographies = homographies.map(add_mask)\n\n images = images.map(lambda d: _preprocess(d, norm=norm))\n warped_images = warped_images.map(lambda d: _preprocess(d, norm=norm))\n\n data = tf.data.Dataset.zip({'image': images, 'warped_image': warped_images, 'homography': homographies})\n\n return data","title":""},{"docid":"866c85bdff14291cef49d8ddb1e69325","score":"0.5392781","text":"def extract_features(self, preprocessed_inputs):\n preprocessed_inputs = shape_utils.check_min_image_dim(\n 33, preprocessed_inputs)\n padded_inputs = ops.pad_to_multiple(\n preprocessed_inputs, self._pad_to_multiple)\n\n feature_map_layout = {\n 'from_layer': ['C4', 'C5', '', '', '', ''],\n # Do not specify the layer depths (number of filters) for C4 and C5, as\n # their values are determined based on the backbone.\n 'layer_depth': [-1, -1, 512, 256, 256, 128],\n 'use_depthwise': self._use_depthwise,\n 'use_explicit_padding': self._use_explicit_padding,\n }\n\n with tf.variable_scope(self._scope_name, reuse=self._reuse_weights):\n with slim.arg_scope([slim.batch_norm],\n is_training=self._is_training,\n epsilon=0.01, decay=0.99, center=True, scale=True):\n endpoints = self._backbone_fn(\n padded_inputs,\n multiplier=self._depth_multiplier)\n\n image_features = {'C4': endpoints['C4'], 'C5': endpoints['C5']}\n with slim.arg_scope(self._conv_hyperparams_fn()):\n feature_maps = feature_map_generators.multi_resolution_feature_maps(\n feature_map_layout=feature_map_layout,\n depth_multiplier=self._depth_multiplier,\n min_depth=self._min_depth,\n insert_1x1_conv=True,\n image_features=image_features)\n\n return list(feature_maps.values())","title":""},{"docid":"1343c7c985d4328dad9ebeaacdecf90f","score":"0.53893375","text":"def bi_pre_process(width, height, path, label_name):\n #containers for pre-processed image data and class labels\n data = []\n labels = []\n\n #images directory contains 3 sub-directories: 'poison_ivy', 'poison_oak', 'poison_sumac'\n #randomly get image paths and shuffle them\n # current path 'C:\\\\Users\\\\jltsa\\\\Desktop\\\\Project_2\\\\images'\n image_paths = sorted(list(paths.list_images(path)))\n random.seed(42)\n random.shuffle(image_paths)\n\n #preprocess images to width x height pixels as required for LeNet\n for image_path in image_paths:\n image = cv2.imread(image_path)\n if image is not None:\n image = cv2.resize(image, (width, height))\n image = img_to_array(image)\n data.append(image)\n \n #Extract class labels\n label = image_path.split(os.path.sep)[-2]\n if label == label_name:\n label = 0\n else:\n label = 1\n labels.append(label)\n \n #Scal pixel intensities from 0 to 1\n data = np.array(data, dtype='float') / 255.0\n labels = np.array(labels)\n \n return data, labels","title":""},{"docid":"53a9ed512ebf016f2d8761fef81ef545","score":"0.53843814","text":"def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),\n hist_bins=32, orient=9, \n pix_per_cell=8, cell_per_block=2, hog_channel=0,\n spatial_feat=True, hist_feat=True, hog_feat=True):\n\n \n features = []\n for file in imgs:\n file_features = []\n image = cv2.imread(file)\n if color_space != 'RGB':\n if color_space == 'HSV':\n feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n elif color_space == 'LUV':\n feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2LUV)\n elif color_space == 'HLS':\n feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)\n elif color_space == 'YUV':\n feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)\n elif color_space == 'YCrCb':\n feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)\n else: feature_image = np.copy(image) \n\n #Compute spatial features\n if spatial_feat == True:\n spatial_features = bin_spatial(feature_image, size=spatial_size)\n file_features.append(spatial_features)\n #Compute color histogram features\n if hist_feat == True:\n hist_features = color_hist(feature_image, nbins=hist_bins)\n file_features.append(hist_features)\n #Compute hog features\n if hog_feat == True:\n if hog_channel == 'ALL':\n hog_features = []\n for channel in range(feature_image.shape[2]):\n hog_features.append(get_hog_features(feature_image[:,:,channel], \n orient, pix_per_cell, cell_per_block, \n vis=False, feature_vec=True))\n hog_features = np.ravel(hog_features) \n else:\n hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, \n pix_per_cell, cell_per_block, vis=False, feature_vec=True)\n # Append the new feature vector to the features list\n file_features.append(hog_features)\n features.append(np.concatenate(file_features))\n return features","title":""},{"docid":"c44e340ecf1e9de4ff5e6a31f3202888","score":"0.5380845","text":"def process2(path, img_size=(160, 160), batch_size=2000):\n\n datagen = tf.keras.preprocessing.image.ImageDataGenerator()\n\n\n generator = datagen.flow_from_directory(\n path,\n target_size=img_size,\n batch_size=batch_size,\n class_mode='binary',\n shuffle=False\n )\n\n\n return generator","title":""},{"docid":"83b345ff186508b86e96fea9e1a1bf70","score":"0.53800577","text":"def train_or_load_character_recognition_model(train_image_paths, serialization_folder):\n # TODO - Istrenirati model ako vec nije istreniran, ili ga samo ucitati iz foldera za serijalizaciju\n # Big letters\n colored_image_1 = load_image(train_image_paths[0])\n binary_image_1 = image_bin(image_gray(colored_image_1))\n kernel_11 = np.ones((2, 2)) # bigger\n dilated_image_11 = cv.dilate(invert(binary_image_1), kernel_11, iterations=2)\n kernel_2 = np.ones((3, 3)) # smaller\n eroded_image_1 = cv.erode(dilated_image_11, kernel_2, iterations=4)\n selected_regions_1, letters_1, _ = select_roi(colored_image_1.copy(), eroded_image_1)\n inputs_1 = prepare_for_ann(letters_1)\n # print('Number of detected regions:', len(letters_1))\n # cv.imwrite('big.png', selected_regions_1)\n\n # Small letters\n colored_image_2 = load_image(train_image_paths[1])\n binary_image_2 = image_bin(image_gray(colored_image_2))\n kernel_21 = np.ones((2, 2)) # bigger\n dilated_image_21 = cv.dilate(invert(binary_image_2), kernel_21, iterations=1)\n kernel_22 = np.ones((3, 3)) # smaller\n eroded_image_2 = cv.erode(dilated_image_21, kernel_22, iterations=5)\n selected_regions_2, letters_2, _ = select_roi(colored_image_2.copy(), eroded_image_2)\n inputs_2 = prepare_for_ann(letters_2)\n # print('Number of detected regions:', len(letters_2))\n # cv.imwrite('lil.png', selected_regions_2)\n\n alphabet = ['A', 'B', 'C', 'Č', 'Ć', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R',\n 'S', 'Š', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'Ž', 'a', 'b', 'c', 'č', 'ć', 'd', 'e', 'f', 'g', 'h',\n 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 'š', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'ž']\n outputs = convert_output(alphabet)\n\n # Trained model - ann\n ann = load_trained_ann(serialization_folder)\n if ann is None:\n print(\"Training of the model started.\")\n ann = create_ann()\n ann = train_ann(ann, inputs_1 + inputs_2, outputs)\n print(\"Training of the model finished.\")\n serialize_ann(ann, serialization_folder)\n\n return ann","title":""},{"docid":"c1045e429c79dde142436d8bcf5de095","score":"0.53792596","text":"def demoSingleImage(imagePath, classifierModelTrained):\n\tnet = objectClassifierArgs(numClasses=10, train=False)\n\tnet.load_state_dict(torch.load(classifierModelTrained))\n\tnet = net.to(device)\n\tnet.eval()\n\tclasses = {0:'T-shirt/top', 1:'Trouser', 2:'Pullover', 3:'Dress', 4:'Coat', 5:'Sandal', 6:'Shirt', 7:'Sneaker', 8:'Bag', 9:'Ankle boot'}\n\n\timg = cv2.imread(imagePath)\n\timg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\timg = Image.fromarray(img)\n\timg = PIL.ImageOps.invert(img)\n\tif not args.useModel == 'vgg16_bn':\n\t\tpreprocess = transforms.Compose([transforms.Resize((224,224), interpolation=2),transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])\n\t\timg_tensor = preprocess(img)\n\t\timg_variable = Variable(img_tensor.unsqueeze(0)).to(device)\n\t\timg_variable = torch.squeeze(torch.stack((img_variable,img_variable,img_variable),2),0)\n\telse:\n\t\tif useModel == 'CustomNet2':\n\t\t\tpreprocess = transforms.Compose([transforms.Resize((28,28), interpolation=2),transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])\n\t\t\timg_tensor = preprocess(img_pil)\n\t\t\timg_variable = Variable(img_tensor.unsqueeze(0)).to(device)\n\t\telif useModel == 'CustomVGG16_BN':\n\t\t\tweight_softmax = np.squeeze(params[-8].data.cpu().numpy())\n\t\t\tpreprocess = transforms.Compose([transforms.Resize((224,224), interpolation=2),transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])\n\t\t\timg_tensor = preprocess(img_pil)\n\t\t\timg_variable = Variable(img_tensor.unsqueeze(0)).to(device)\n\t\t\timg_variable = torch.squeeze(torch.stack((img_variable,img_variable,img_variable),2),0)\n\t\t\n\n\t\n\tlogit = net(img_variable)\n\tprobs, preds = torch.max(logit, 1)\n\tprint('output for the top1 prediction: %s' % classes[preds.item()])","title":""},{"docid":"81052c365c22b415a178e0d953602e1c","score":"0.536082","text":"def process_img(self,img_path):\n original = load_img(filename, target_size = self.target_size)\n numpy_image = self.preprocessing( img_to_array(original))\n image_batch = np.expand_dims(numpy_image, axis =0)\n return image_batch","title":""},{"docid":"b940ce5885c98241c5ac5d4e700eabcc","score":"0.535503","text":"def extract_deep_feats(img_fname):\n if not os.path.exists('./features'):\n os.makedirs('./features')\n\n img = imread(img_fname, mode='RGB')\n img = (img - img.mean()) / img.std()\n img = imresize(img, [224, 224])\n\n graph = tf.Graph()\n with graph.as_default():\n input_maps = tf.placeholder(tf.float32, [None, 224, 224, 3])\n output, average_image, class_names = vgg_face(config['vgg_face_model_mat_file'], input_maps)\n\n img = (img - img.mean()) / img.std()\n img = imresize(img, [224, 224])\n\n # run the graph\n with tf.Session(graph=graph) as sess:\n [out] = sess.run([output], feed_dict={input_maps: [img]})\n\n feat = {}\n\n print(out.keys())\n\n for k, v in out.items():\n if k in ['conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1',\n 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2',\n 'conv5_3', 'relu5_3', 'pool5', 'fc6', 'relu6', 'fc7', 'relu7', 'fc8']:\n feat[k] = v.tolist()\n\n print(out)\n with open('./features/{0}.pkl'.format(img_fname.split('/')[-1].split('.')[0]), mode='wb') as f:\n pickle.dump(feat, f)\n\n sess.close()","title":""},{"docid":"69725dbe4df2d22a17d14075ae40d568","score":"0.53544015","text":"def demo(path_to_data):\n color_mapping = {'difficult': [185, 51, 173], # purple\n 'gametocyte': [255, 99, 25], # orange\n 'leukocyte': [0, 0, 255], # blue\n 'red blood cell': [255, 0, 0], # red\n 'schizont': [252, 204, 10], # yellow\n 'ring': [153, 102, 51], # brown\n 'trophozoite': [0, 147, 60] # green\n }\n\n with open(path_to_data + \"/training.json\") as json_file:\n files = json.load(json_file)\n fig, axs = plt.subplots(ncols=2, nrows=2)\n for file in files:\n # read the image\n file_path = file['image']['pathname']\n fig.suptitle(file_path + \"\\nclick on plot to see next image\")\n print(f\"processing file: {file_path}\")\n start_time = timeit.default_timer()\n image = io.imread(path_to_data + file_path)\n\n # no background removal (top right)\n processed1 = apply_pre_processing(image, background_rem=False, morph_filter=False)\n axs[0][1].imshow(processed1)\n axs[0][1].set_title(\"no background rem.\")\n # background removal, w/o morphological filter on background mask (bottom left)\n processed2 = apply_pre_processing(image, background_rem=True, morph_filter=False)\n axs[1][0].imshow(processed2)\n axs[1][0].set_title(\"background rem. (no morph)\")\n # background removal with morph. filters on background mask (bottom right)\n processed3 = apply_pre_processing(image, background_rem=True, morph_filter=True)\n axs[1][1].imshow(processed3)\n axs[1][1].set_title(\"background rem. (with morph)\")\n\n # draw bounding boxes\n for obj in file['objects']:\n bounding_box = obj['bounding_box']\n minimum = bounding_box['minimum']\n start = (minimum['r'], minimum['c'])\n maximum = bounding_box['maximum']\n stop = (maximum['r'] - 2, maximum['c'] - 2)\n rr, cc = draw.rectangle_perimeter(start=start, end=stop)\n image[rr, cc] = color_mapping[obj['category']]\n\n # image before pre-processing with bounding boxes (top left)\n axs[0][0].imshow(image)\n axs[0][0].set_title(\"input with ground truth\")\n fig.show()\n print(f\"elapsed time: {timeit.default_timer() - start_time} seconds\")\n plt.ginput() # wait for click before doing more processing","title":""},{"docid":"5e608d7cd3ecfcc3cf0c3bd441322d87","score":"0.5349802","text":"def get_preprocess(self, *args, **kwargs) -> Dict:\n input_shape = get_input_shape(self.deploy_cfg)\n model_cfg = process_model_config(self.model_cfg, [''], input_shape)\n pipeline = model_cfg.test_dataloader.dataset.pipeline\n meta_keys = [\n 'filename', 'ori_filename', 'ori_shape', 'img_shape', 'pad_shape',\n 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg',\n 'valid_ratio'\n ]\n transforms = [\n item for item in pipeline if 'Random' not in item['type']\n and 'Annotation' not in item['type']\n ]\n for i, transform in enumerate(transforms):\n if transform['type'] == 'PackTextDetInputs':\n meta_keys += transform[\n 'meta_keys'] if 'meta_keys' in transform else []\n transform['meta_keys'] = list(set(meta_keys))\n transform['keys'] = ['img']\n transforms[i]['type'] = 'Collect'\n if transform['type'] == 'Resize':\n transforms[i]['size'] = transforms[i].pop('scale')\n\n if 'data_preprocessor' in model_cfg.model:\n data_preprocessor = model_cfg.model.data_preprocessor\n elif 'MMDetWrapper' == self.model_cfg.model.type:\n data_preprocessor = model_cfg.model.cfg.data_preprocessor\n else:\n raise ValueError(f'Unsupported model config {model_cfg.model} ')\n transforms.insert(-1, dict(type='DefaultFormatBundle'))\n transforms.insert(\n -2,\n dict(\n type='Pad',\n size_divisor=data_preprocessor.get('pad_size_divisor', 1)))\n transforms.insert(\n -3,\n dict(\n type='Normalize',\n to_rgb=data_preprocessor.get('bgr_to_rgb', False),\n mean=data_preprocessor.get('mean', [0, 0, 0]),\n std=data_preprocessor.get('std', [1, 1, 1])))\n return transforms","title":""},{"docid":"b3e4848b6e31eb4efa7d86ec08d12d87","score":"0.53478116","text":"def create_model_input(folder_path):\n image_paths = []\n images = []\n file_names = os.listdir(folder_path)\n for image_name in file_names:\n \n # Reading image the same way it is done while training\n img = np.asarray(Image.open(os.path.join(folder_path, image_name)).convert('RGB'))[:, :, ::-1]\n \n images.append(img)\n image_paths.append(image_name)\n return images, image_paths","title":""},{"docid":"90490a6992b0bfd4d646667bbefc3f47","score":"0.5344868","text":"def _preprocess_image_for_classification(image_url, model_descriptor):\n image_file = _download_image(image_url)\n\n input_shape = model_descriptor[\"arguments\"][\"input_shape\"]\n transpose = False\n if 'transpose' in model_descriptor[\"arguments\"]:\n transpose = True\n if len(input_shape) == 4 and not transpose:\n dimensions = input_shape[1:3]\n elif len(input_shape) == 4 and transpose:\n dimensions = input_shape[2:4]\n elif len(input_shape) == 3 and not transpose:\n dimensions = input_shape[0:2]\n elif len(input_shape) == 3 and transpose:\n dimensions = input_shape[1:3]\n\n orig_image = tf.keras.preprocessing.image.load_img(\n image_file, target_size=dimensions\n )\n numpy_image = tf.keras.preprocessing.image.img_to_array(orig_image)\n if transpose:\n # if transpose is set to true then expected input is CHW, instead of HWC\n numpy_image = numpy_image.transpose([2, 0, 1])\n\n if len(input_shape) == 4:\n numpy_image = np.expand_dims(numpy_image, axis=0)\n processed_image = tf.keras.applications.imagenet_utils.preprocess_input(\n numpy_image, mode=\"caffe\"\n )\n\n return processed_image","title":""},{"docid":"1d5f395e968627627de3d5a613d57c2b","score":"0.5340327","text":"def build_vocabulary(image_paths, vocab_size):\n\n # TODO: Implement this function!\n # vocab_size = 10000\n num_imgs = len(image_paths)\n images = []\n for i in range(num_imgs):\n image = imread(image_paths[i], as_gray=True)\n images.append(image)\n\n features = []\n descriptors = [\n \"hog\",\n \"gaussian_pyramid\",\n # \"gist\",\n # \"daisy\",\n ]\n ################### baseline implementation ################\n if \"hog\" in descriptors:\n output = Parallel(n_jobs=-1)(delayed(hog_feature)(i) for i in images)\n output = np.concatenate(np.array(output), axis=0)\n features.append(output)\n\n ################## pyramid gaussian #####################\n if \"gaussian_pyramid\" in descriptors:\n output = Parallel(n_jobs=-1)(\n delayed(gaussian_pyramid_feature)(i) for i in images\n )\n output = np.concatenate(np.array(output), axis=0)\n features.append(output)\n\n #################### gist descriptor #####################\n if \"gist\" in descriptors:\n output = Parallel(n_jobs=-1)(delayed(gist_feature)(i) for i in images)\n output = np.concatenate(np.array(output), axis=0)\n features.append(output)\n\n if \"daisy\" in descriptors:\n output = Parallel(n_jobs=-1)(delayed(daisy_feature)(i) for i in images)\n output = np.concatenate(np.array(output), axis=0)\n features.append(output)\n\n if \"sift\" in descriptors:\n output = Parallel(n_jobs=-1)(delayed(daisy_feature)(i) for i in images)\n output = np.concatenate(np.array(output), axis=0)\n features.append(output)\n\n features = np.concatenate(np.array(features), axis=0)\n kmeans = cluster.MiniBatchKMeans(\n n_clusters=vocab_size,\n init_size=3 * max(vocab_size, 100),\n max_iter=100,\n )\n kmeans.fit(features)\n vocab = kmeans.cluster_centers_\n return vocab","title":""},{"docid":"854dbc7131ca1b2b6dcac6df334f2a1e","score":"0.5330453","text":"def run_content_traversal(\n model: pl.LightningModule,\n content_code: torch.Tensor,\n style_code: torch.Tensor,\n traversal_start: Union[float, Iterable[float]],\n traversal_end: Union[float, Iterable[float]],\n n_traversals: int,\n show: bool = True,\n title: str = '',\n to_save: bool = True,\n out_path: Optional[Path] = None,\n verbose: bool=False,\n) -> torch.Tensor:\n is_training = model.training\n model.eval()\n content_dim = content_code.shape[-1]\n try:\n traversal_start[0]\n except TypeError:\n traversal_start = torch.zeros(content_dim).fill_(traversal_start)\n try:\n traversal_end[0]\n except TypeError:\n traversal_end = torch.zeros(content_dim).fill_(traversal_end)\n with torch.no_grad():\n # Traverse for each dim\n grids = [] # k,v = dim_i, batch of recons while traversing in dim_i direction (n_traversals, *dim_x)\n for dim_i in range(content_dim):\n min_dim_i = traversal_start[dim_i]\n max_dim_i = traversal_end[dim_i]\n if verbose:\n print(min_dim_i, max_dim_i)\n c_traversals = get_traversals(content_code, dim_i, min_dim_i, max_dim_i, n_traversals)\n\n # Pass to the decoder\n dict_z = {\n \"c\": c_traversals,\n \"s\": style_code.repeat((n_traversals, 1))\n }\n z = model.combine_content_style(dict_z)\n recons = model.decode(z)\n\n grid = torchvision.utils.make_grid(recons, nrow=1) # Caveat: nrow is num of colms!\n grids.append(grid)\n grids = torch.cat(grids, dim=2)\n\n if show:\n show_timg(grids, title=title)\n if to_save:\n out_path = out_path or Path(f'./content_traversal_{now2str()}.png')\n torchvision.utils.save_image(grids, out_path)\n model.train(is_training)","title":""},{"docid":"804f60f42fa171704e5361a4f868da27","score":"0.5329856","text":"def data_preprocess(path='/home/xinsheng/yizhehang/Research/Chinese_Font_Transfer/Font2img/img_lib',\n source_font='heiti',\n target_font='lixuke'):\n # load the source fonts and target fonts\n source_path = path+'/'+source_font\n source_fonts = list()\n target_path = path + '/' + target_font\n target_fonts = list()\n files = os.listdir(source_path)\n mid1 = np.zeros((64, 64, 1))\n mid2 = np.zeros((64, 64, 1)) \n\n \n for file in files:\n #load a source font\n img1 = Image.open(source_path+'/'+file)\n np1 = np.array(img1)\n mid1 = np1[:, :, 0]\n mid1 = mid1[:,:,np.newaxis]\n source_fonts.append(mid1)\n\n #load a target font\n img2 = Image.open(target_path + '/'+file)\n np2 = np.array(img2)\n mid2 = np2[:, :, 0]\n mid2 = mid2[:,:,np.newaxis]\n target_fonts.append(mid2)\n\n source_fonts = np.array(source_fonts)\n source_fonts = source_fonts.astype(np.float32)\n source_fonts /= 255\n\n target_fonts = np.array(target_fonts)\n target_fonts = target_fonts.astype(np.float32)\n target_fonts /= 255\n\n\n return source_fonts, target_fonts","title":""},{"docid":"3be195a010036dcb16cce54ab469974a","score":"0.53280276","text":"def process_patches(images, net, transformer):\n # caffe.io.load_image converts to [0,1], so our transformer sets it back to [0,255]\n # but the skimage lib already works with [0, 255] so we convert it to float with img_as_float\n data = np.zeros(net.blobs['data'].data.shape)\n for i in range(len(images)):\n data[i] = transformer.preprocess('data', img_as_float(images[i]))\n net.forward(data=data)\n output = net.blobs['conv1_1_D'].data[:len(images)]\n output = np.swapaxes(np.swapaxes(output, 1, 3), 1, 2)\n return output","title":""},{"docid":"48e5a536e719f437385d50a37c711cad","score":"0.53275716","text":"def process(path, img_size=(160, 160), batch_size=2000):\n\n datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n preprocessing_function=normalize, )\n\n generator = datagen.flow_from_directory(\n path,\n target_size=img_size,\n batch_size=batch_size,\n class_mode='binary')\n (x, y) = next(generator)\n # normalizing Y vector\n y = y * 0.8 + 0.1\n return x, y","title":""},{"docid":"1e2e090df992da2865c28d364fd01be7","score":"0.5326675","text":"def extract_bottleneck_features(preprocessing_output_path, bottleneck_features_output_path, resized_folder_path):\n \n #Import preprocessing data\n data = pd.read_csv(os.path.join(preprocessing_output_path,'boxes_preprocessing_single.csv'), sep=';')\n data['annotation_literal'] = data['annotation'].apply(literal_eval)\n data['box_standard'] = data['box_standard'].apply(literal_eval)\n \n #Select sequences with only one label\n data = data.loc[data['annotation_literal'].str.len() == 1] \n \n #Group all bird species into one class\n data['ann_animal'] = \"\"\n data['ann_animal'] = data['annotation_literal'].apply(group_birds)\n \n #Encode labels\n le = LabelEncoder()\n le.fit(data.ann_animal)\n data['label'] = le.transform(data.ann_animal)\n \n #Save data corresponding to the bottleneck features\n data.to_csv(os.path.join(bottleneck_features_output_path,'bottleneck_data.csv'), index=False, sep=';')\n \n #Image size\n dim_x = 270\n dim_y = 480\n dim_z = 3\n \n #Parameters\n batch_size = 128\n steps_per_epoch = np.ceil(len(data) / batch_size)\n \n #Build the ResNet50 network\n model = ResNet50(include_top=False, weights='imagenet')\n \n #Create generator\n generator = DataGenerator(data, dim_x = dim_x, dim_y = dim_y, dim_z = dim_z, \n batch_size=batch_size, augmentation=False, shuffle=False, mode='train').generate()\n \n #Extract and save bottleneck features\n bottleneck_features = model.predict_generator(generator, steps_per_epoch)\n np.save(os.path.join(bottleneck_features_output_path, 'bottleneck_features.npy'), bottleneck_features)","title":""},{"docid":"09d0d3d2546945563678a57cd0f6d206","score":"0.5325292","text":"def compute_features(img, filename=None, net=None):\n if filename is not None:\n if img is None:\n img = misc.imread(os.path.join(IMG_DIR, filename), mode='L')\n name = filename.split('.')[0]\n seg = get_seg(name+'.bmp')\n else:\n # forward pass to get the segmentation\n in_ = np.array(img, dtype=np.float32)\n in_ = in_[:, :, ::-1]\n in_ -= MEAN\n in_ = in_.transpose((2, 0, 1))\n\n net.blobs['data'].reshape(1, *in_.shape)\n net.blobs['data'].data[...] = in_\n\n net.forward()\n seg = net.blobs['score_sem'].data[0].argmax(axis=0)\n\n m, n = img.shape[:2]\n # print('segmentation done')\n neighb = compute_neighb(img)\n # print('neighbourhood created')\n daisy = compute_daisy(img)\n # print('daisy done')\n feats = np.concatenate((neighb, daisy, seg), axis=2)\n return feats.reshape((m*n, feats.shape[2]))","title":""},{"docid":"fe762a01e854e54679c4cba505f8262f","score":"0.5322813","text":"def data_prepare(print_image_shape=False, print_input_shape=False):\n def reshape_mask(origin, cate, num_class):\n return cate.reshape((origin.shape[0], origin.shape[1], origin.shape[2], num_class))\n\n train_imgs, train_det_masks, train_cls_masks = load_dataset(dataset=DATA_DIR, type='train', reshape_size=(256, 256))\n valid_imgs, valid_det_masks, valid_cls_masks = load_dataset(dataset=DATA_DIR, type='validation',\n reshape_size=(256, 256))\n test_imgs, test_det_masks, test_cls_masks = load_dataset(dataset=DATA_DIR, type='test', reshape_size=(256, 256))\n\n if print_image_shape:\n print('Image shape print below: ')\n print('train_imgs: {}, train_cls_masks: {}'.format(train_imgs.shape, train_cls_masks.shape))\n print('valid_imgs: {}, valid_cls_masks: {}'.format(valid_imgs.shape, valid_cls_masks.shape))\n print('test_imgs: {}, test_cls_masks: {}'.format(test_imgs.shape, test_cls_masks.shape))\n print()\n #train_cls_masks.reshape((train_cls_masks.shape[0], train_cls_masks.shape[1], train_cls_masks.shape[2], 1))\n #valid_cls_masks.reshape((valid_cls_masks.shape[0], train_cls_masks.shape[1], train_cls_masks.shape[2], 1))\n np.set_printoptions(np.nan)\n print(train_cls_masks.shape)\n print('unique: ', np.unique(train_cls_masks))\n train_cls = np_utils.to_categorical(train_cls_masks, 5)\n #train_cls = reshape_mask(train_cls_masks, train_cls, 5)\n\n valid_cls = np_utils.to_categorical(valid_cls_masks, 5)\n #valid_cls = reshape_mask(valid_cls_masks, valid_cls, 5)\n\n test_cls = np_utils.to_categorical(test_cls_masks, 5)\n #test_cls = reshape_mask(test_cls_masks, test_cls, 5)\n\n if print_input_shape:\n print('input shape print below: ')\n print('train_imgs: {}, train_cls: {}'.format(train_imgs.shape, train_cls.shape))\n print('valid_imgs: {}, valid_cls: {}'.format(valid_imgs.shape, valid_cls.shape))\n print('test_imgs: {}, test_cls: {}'.format(test_imgs.shape, test_cls.shape))\n print()\n return [train_imgs, train_cls, valid_imgs, valid_cls, test_imgs, test_cls]","title":""},{"docid":"74f22c13d05c69fee9065a545668f30e","score":"0.5321833","text":"def extract_hx(path_train,\n path_output = None,\n epochs = 50,\n predict_name = 'ukb_image_examples-587x587.tfrecord',\n train_name = 'eval.tfrecord'):\n\n # Start Code\n config = ml_collections.ConfigDict()\n config.dataset = ml_collections.ConfigDict({\n 'path': path_train,\n 'predict': predict_name,\n 'train': train_name,\n 'num_train_examples': 79355,\n 'batch_size': 16,\n 'image_size': (587, 587),\n 'random_horizontal_flip': True,\n 'random_vertical_flip': True,\n 'random_brightness_max_delta': 0.1147528,\n 'random_saturation_lower': 0.5597273,\n 'random_saturation_upper': 1.2748845,\n 'random_hue_max_delta': 0.0251488,\n 'random_contrast_lower': 0.9996807,\n 'random_contrast_upper': 1.7704824,\n 'use_cache': False,\n })\n\n config.train = ml_collections.ConfigDict({\n 'use_mixed_precision': True,\n 'use_distributed_training': False,\n 'max_num_steps': 250000,\n 'log_step_freq': 15,\n 'fit_verbose': 1,\n 'initial_epoch': 0,\n })\n\n config.outcomes = [\n ml_collections.ConfigDict({\n 'name': 'vertical_cup_to_disc',\n 'type': 'regression',\n 'num_classes': 1,\n 'loss': 'mse',\n 'loss_weight': 1.0,\n }),\n ml_collections.ConfigDict({\n 'name': 'vertical_cd_visibility',\n 'type': 'classification',\n 'num_classes': 3,\n 'loss': 'ce',\n 'loss_weight': 1.0,\n }),\n ml_collections.ConfigDict({\n 'name': 'glaucoma_suspect_risk',\n 'type': 'classification',\n 'num_classes': 4,\n 'loss': 'ce',\n 'loss_weight': 1.0,\n }),\n ml_collections.ConfigDict({\n 'name': 'glaucoma_gradability',\n 'type': 'classification',\n 'num_classes': 3,\n 'loss': 'ce',\n 'loss_weight': 1.0,\n }),\n ]\n\n config.model = ml_collections.ConfigDict({\n 'backbone': 'inceptionv3',\n 'backbone_drop_rate': 0.2,\n 'input_shape': (587, 587, 3),\n 'weights': 'imagenet',\n 'weight_decay': 0.00004,\n })\n\n # optimizer\n config.opt = ml_collections.ConfigDict({\n 'optimizer': 'adam',\n 'learning_rate': 0.001,\n 'beta_1': 0.9,\n 'beta_2': 0.999,\n 'epsilon': 0.1,\n 'amsgrad': False,\n 'use_model_averaging': True,\n 'update_model_averaging_weights': False,\n })\n config.outcomes_sim = [\n ml_collections.ConfigDict({\n 'name': 'sim_0_mu0',\n 'type': 'regression',\n 'num_classes': 1,\n 'loss': 'mse',\n 'loss_weight': 1.0,\n }),\n ml_collections.ConfigDict({\n 'name': 'sim_0_mu1',\n 'type': 'regression',\n 'num_classes': 1,\n 'loss': 'mse',\n 'loss_weight': 1.0,\n })\n ]\n\n train_ds, eval_ds = ukb_utils.build_datasets(config.dataset, config.outcomes,\n cache=False,)\n\n verbose = config.train.get('fit_verbose', 0) if 'train' in config else 0\n\n ukb_model_utils = None # replace with import from source in documentation.\n model = ukb_model_utils.compile_model(config)\n\n model.fit(\n train_ds,\n epochs=epochs,\n initial_epoch=config.train.get('initial_epoch', 0),\n steps_per_epoch=config.train.log_step_freq,\n verbose=verbose)\n\n extract = tf.keras.Model(model.inputs, model.layers[-5].output)\n\n path_out = os.path.join(path_output, 'hx.csv')\n _extraction(eval_ds, path_out, extract)","title":""},{"docid":"6194fd418f6918679ac390fa7ec561ba","score":"0.5321559","text":"def importCelebImages():\n\n if DEBUG:\n images = os.listdir(path)[1:15]\n else:\n images = os.listdir(path)[1:]\n images_WithFeatures = []\n for index, imageName in tqdm(zip(range(len(images)), images), desc=\"Processing celebrity images\"):\n im = cv2.resize(cv2.imread(path + \"/\" + imageName, 0), (128, 128))\n features = [computeLBPDescriptor(i) for i in breakImageApart(im)]\n images_WithFeatures.append((index, features))\n return images_WithFeatures","title":""},{"docid":"8f138a647f3b3b00b479cc1a11f4e0e2","score":"0.5315962","text":"def gen_batch_function(image_dir, image_paths, image_shape, output_shape):\n def get_batches_fn(batch_size, should_schuffle=True):\n \"\"\"\n Create batches of training data\n :param batch_size: Batch Size\n :return: Batches of training data\n \"\"\"\n\n background_color = np.array([255, 255, 255])\n image_path = 'origin.jpg'\n masks_path = ['Box11.jpg', 'Box16.jpg']\n if should_schuffle:\n random.shuffle(image_paths)\n for batch_i in range(0, len(image_paths), batch_size):\n images = []\n gt_images = []\n for image_file in image_paths[batch_i:batch_i+batch_size]:\n try:\n img = imutils.rotate_bound(scipy.misc.imread(os.path.join(image_dir, image_file, image_path), mode='RGB'), 90)\n img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)\n if random.random() < 0.5:\n image = cv2.resize(img, (image_shape[1], image_shape[0]), interpolation=cv2.INTER_NEAREST) / 255\n else:\n image = cv2.resize(img, (image_shape[1], image_shape[0]), interpolation=cv2.INTER_CUBIC) / 255\n labels = None\n\n for mask_path in masks_path:\n gt_image = None\n try:\n gt_image = scipy.misc.imread(os.path.join(image_dir, image_file, mask_path))\n gt_image = cv2.resize(gt_image, (image_shape[1], image_shape[0]),\n interpolation=cv2.INTER_CUBIC)\n except Exception as e:\n pass\n if gt_image is None:\n gt_bg = np.full(output_shape, False, dtype=bool)\n else:\n gt_bg = np.all(gt_image == background_color, axis=2)\n\n gt_bg = gt_bg.reshape(*gt_bg.shape, 1)\n if labels is None:\n labels = gt_bg\n else:\n labels = np.concatenate((labels, gt_bg), axis=2)\n outside_area = np.invert(np.any(labels, axis=2))\n outside_area = outside_area.reshape(*outside_area.shape, 1)\n labels = np.concatenate((outside_area, labels), axis=2)\n gt_images.append(labels)\n images.append(image)\n except Exception as e:\n pass\n\n yield np.array(images), np.array(gt_images)\n return get_batches_fn","title":""},{"docid":"5ecc47b32f6a20102c53ca3a2401681d","score":"0.5307604","text":"def get_features(filepath):\n \n \n # get features for classifiers","title":""},{"docid":"332aacde10b463d69c359fb2938d7cc8","score":"0.5301027","text":"def preprocess_caltech256(path='256_ObjectCategories'):\n folders = os.listdir(path)\n data = {'id': [], 'class': []}\n for folder in folders:\n class_name = re.findall(r'(?:\\d{3}\\.)(.+)', folder)[0].replace('-101', '')\n class_path = os.path.join(path, folder)\n for file in os.listdir(class_path):\n if not any([ext in file for ext in ['jpg', 'png', 'jpeg']]):\n continue\n data['class'].append(class_name)\n data['id'].append(os.path.abspath(os.path.join(class_path, file)))\n\n df = pd.DataFrame(data)\n train_df, test_df = train_test_split(df, test_size=0.2, random_state=123)\n return train_df, test_df","title":""},{"docid":"d89e262bf9941d5b34bcb1c5a7678d5a","score":"0.5294225","text":"def run_style_transfer(cnn, normalization_mean, normalization_std,\n content_img, style_img, input_img, style_mask, content_mask, laplacian_m,\n num_steps=3000,\n style_weight=1000000, content_weight=100, tv_weight=0.0001, rl_weight=1):\n print(\"Buliding the style transfer model..\")\n model, style_losses, content_losses, tv_losses = get_style_model_and_losses(cnn,\n normalization_mean, normalization_std, style_img, content_img, style_mask, content_mask, laplacian_m)\n optimizer = get_input_optimizer(input_img)\n\n print(\"Optimizing...\")\n print('*'*20)\n print(\"Style_weith: {} Content_weighti: {} \\\n TV_loss_weight: {} Realistic_loss_weight: {}\".format \\\n (style_weight, content_weight, tv_weight, rl_weight))\n print('*'*20)\n run = [0]\n \n best_loss = 1e10 \n best_input = input_img.data \n\n while run[0] <= num_steps:\n\n def closure(): \n nonlocal best_loss\n nonlocal input_img\n nonlocal best_input\n\n input_img.data.clamp_(0, 1)\n optimizer.zero_grad()\n\n model(input_img)\n\n style_score = 0\n content_score = 0\n tv_score = 0\n \n \n for sl in style_losses:\n style_score += sl.loss\n\n for cl in content_losses:\n content_score += cl.loss\n\n for tl in tv_losses:\n tv_score += tl.loss\n \n style_score *= style_weight\n content_score *= content_weight\n tv_score *= tv_weight \n\n # Two stage optimaztion pipline \n if run[0] > num_steps // 2:\n # Realistic loss relate sparse matrix computing, \n # which do not support autogard in pytorch, so we compute it separately.\n rl_score, part_grid = realistic_loss_grad(input_img, laplacian_m)\n rl_score *= rl_weight\n\n loss = style_score + content_score + tv_score + rl_score\n\n # Store the best result for outputing\n if loss < best_loss:\n # print(best_loss)\n best_loss = loss\n best_input = input_img.data.clone()\n else:\n loss = style_score + content_score + tv_score\n\n rl_score = torch.zeros(1) # Just to print\n\n if loss < best_loss and run[0] > 0:\n # print(best_loss)\n best_loss = loss\n best_input = input_img.data\n\n if run[0] == num_steps // 2:\n # Store the best temp result to initialize second stage input\n input_img.data = best_input\n best_loss = 1e10\n\n loss.backward()\n \n # Gradient cliping deal with gradient exploding\n clip_grad_norm(model.parameters(), 15.0)\n \n run[0] += 1\n if run[0] % 50 == 0:\n print(\"run {}/{}:\".format(run, num_steps))\n \n print('Style Loss: {:4f} Content Loss: {:4f} TV Loss: {:4f} real loss: {:4f}'.format(\n style_score.item(), content_score.item(), tv_score.item(), rl_score.item()))\n\n print('Total Loss: ', loss.item())\n \n saved_img = input_img.clone() \n saved_img.data.clamp_(0, 1)\n utils.save_pic(saved_img, run[0])\n return loss\n\n optimizer.step(closure)\n \n # a last corrention...\n input_img.data = best_input\n input_img.data.clamp_(0, 1)\n \n return input_img","title":""},{"docid":"b158faec9ce23fb88e6e243c3af44081","score":"0.52795094","text":"def get_model(modelfile: str) -> Dict[str, Any]:\n tarfolder = check_and_create_model(modelfile)\n if not tarfolder:\n return\n\n model_yml = yaml.safe_load(open(os.path.join(tarfolder, \"model.yml\")))\n if model_yml[\"type\"] == \"mlp\":\n layers = []\n for layer in model_yml[\"layers\"]:\n layertmp = {}\n\n f = h5py.File(os.path.join(tarfolder, layer[\"b\"][\"filename\"]), \"r\")\n layertmp[\"b\"] = f[layer[\"b\"][\"filename\"]][()]\n\n f = h5py.File(os.path.join(tarfolder, layer[\"W\"][\"filename\"]), \"r\")\n layertmp[\"W\"] = f[layer[\"W\"][\"filename\"]][()]\n\n layertmp[\"activation\"] = get_af(layer[\"activation\"])\n\n layers.append(layertmp)\n model_yml[\"layers\"] = layers\n inputs = []\n\n # if sys.version_info.major < 3:\n # mode = 'rb'\n # arguments = {}\n # else:\n mode = \"rt\"\n arguments = {\"newline\": \"\", \"encoding\": \"utf8\"}\n\n input_semantics_file = os.path.join(tarfolder, \"input_semantics.csv\")\n with open(input_semantics_file, mode, **arguments) as csvfile:\n spamreader = csv.reader(csvfile, delimiter=\"\\n\", quotechar='\"')\n for row in spamreader:\n inputs.append(row[0])\n outputs = get_outputs(os.path.join(tarfolder, \"output_semantics.csv\"))\n model_yml[\"inputs\"] = inputs\n model_yml[\"outputs\"] = outputs\n\n # Cleanup\n shutil.rmtree(tarfolder)\n return model_yml","title":""},{"docid":"dba0d89abac5b42d39e65ce63af83ff6","score":"0.52737784","text":"def preparing_data(im_path, img_type):\n images = sorted(glob.glob(im_path + '/*' + img_type))\n images_name = []\n input_images = []\n for im_name in images:\n input_images.append(cv2.imread(im_name)[:, :, ::-1])\n images_name.append(im_name.split('/')[-1].split('.')[0])\n input_images = np.asarray(input_images)\n input_images = adjust_dynamic_range(input_images.astype(np.float32), [0, 255], [-1., 1.])\n input_images = input_images.transpose(0, 3, 1, 2)\n return input_images, images_name","title":""},{"docid":"35b71214402b6693973dda236717a45e","score":"0.52730495","text":"def loader_cityscapes(txt_path, cityscapes_path, batch_size):\r\n\r\n\t\tlines = open(txt_path, 'r').readlines()\r\n\t\ttotal_files = len(lines)\r\n\r\n\t\timages = []\r\n\t\tgts = []\r\n\t\tfor line in lines:\r\n\t\t\tline = line.strip().split(\" \")\r\n\t\t\timages.append(line[0])\r\n\t\t\tgts.append(line[1])\r\n\r\n\t\tif str(batch_size).lower() == 'all':\r\n\t\t\tbatch_size = total_files\r\n\t\t\twhile (1):\r\n\t\t\t\tbatch_idxs = np.random.randint(0, total_files, batch_size)\r\n\r\n\t\t\t\tlabels = []\r\n\t\t\t\tfor jj in batch_idxs:\r\n\t\t\t\t\timg = Image.open(cityscapes_path + gts[jj])\r\n\t\t\t\t\timg = np.array(img)\r\n\t\t\t\t\t#img5 = scale_downsample(img, 0.5, 0.5)\r\n\r\n\t\t\t\t\tlabels.append(img)\r\n\r\n\t\t\t\tlabels = torch.tensor(labels)\r\n\r\n\t\t\t\tyield labels\r\n\r\n\t\tidx = 0\r\n\t\twhile (1):\r\n\t\t\tbatch_idxs = np.random.randint(0, total_files, batch_size)\r\n\r\n\t\t\tinputs = []\r\n\t\t\tlabels = []\r\n\t\t\tfor jj in batch_idxs:\r\n\r\n\t\t\t\timg = plt.imread(cityscapes_path + images[jj])\r\n\t\t\t\t#img5 = scale_downsample(img, 0.5, 0.5)\r\n\t\t\t\tinputs.append(img)\r\n\r\n\t\t\t\timg = Image.open(cityscapes_path + gts[jj])\r\n\t\t\t\timg = np.array(img)\r\n\t\t\t\t#img5 = scale_downsample(img, 0.5, 0.5)\r\n\t\t\t\tlabels.append(img)\r\n\r\n\t\t\tinputs = np.stack(inputs, axis=2)\r\n\t\t\tinputs = torch.tensor(inputs).transpose(0, 2).transpose(1, 3)\r\n\r\n\t\t\tlabels = torch.tensor(labels)\r\n\r\n\t\t\tyield inputs, labels","title":""},{"docid":"88a1390802bd73ea86a7109108abb2a1","score":"0.5271522","text":"def prepare2train(train_bees, val_bees, test_bees, field_name):\n # Bees already splitted to train, validation and test\n # Load and transform images to have equal width/height/channels. \n # read_img function is defined in the beginning to use in both health and subspecies. \n # Use np.stack to get NumPy array for CNN input\n\n # Train data\n train_X = np.stack(train_bees['file'].apply(read_img))\n #train_y = to_categorical(train_bees[field_name].values)\n train_y = pd.get_dummies(train_bees[field_name], drop_first=False)\n\n # Validation during training data to calc val_loss metric\n val_X = np.stack(val_bees['file'].apply(read_img))\n #val_y = to_categorical(val_bees[field_name].values)\n val_y = pd.get_dummies(val_bees[field_name], drop_first=False)\n\n # Test data\n test_X = np.stack(test_bees['file'].apply(read_img))\n #test_y = to_categorical(test_bees[field_name].values)\n test_y = pd.get_dummies(test_bees[field_name], drop_first=False)\n\n # Data augmentation - a little bit rotate, zoom and shift input images.\n generator = ImageDataGenerator(\n featurewise_center=False, # set input mean to 0 over the dataset\n samplewise_center=False, # set each sample mean to 0\n featurewise_std_normalization=False, # divide inputs by std of the dataset\n samplewise_std_normalization=False, # divide each input by its std\n zca_whitening=False, # apply ZCA whitening\n rotation_range=180, # randomly rotate images in the range (degrees, 0 to 180)\n zoom_range = 0.1, # Randomly zoom image \n width_shift_range=0.2, # randomly shift images horizontally (fraction of total width)\n height_shift_range=0.2, # randomly shift images vertically (fraction of total height)\n horizontal_flip=True, # randomly flip images\n vertical_flip=True)\n generator.fit(train_X)\n return (generator, train_X, val_X, test_X, train_y, val_y, test_y)","title":""},{"docid":"fbb8fc5996c1c61f4e1fdc5c57170c3a","score":"0.5268959","text":"def transformation():\n try:\n data = request.get_json()\n img = data['img']\n img = np.array(img)\n except Exception:\n return jsonify({'message': 'Content is not string type'}), 400\n \n category1, category2, category3, category4 = CF.main_classify(img, main_model)\n\n if category1[0] == '의자':\n standard = CF.sub_classify(img, CF.chair_subclass, sub_model_chair)\n elif category1[0] == '침대':\n standard = CF.sub_classify(img, CF.bed_subclass, sub_model_bed)\n else :\n standard = ''\n\n result = {\n \"category\" : [category1, category2, category3, category4],\n \"standard\" : standard\n }\n\n result = jsonify(result)\n return result","title":""}],"string":"[\n {\n \"docid\": \"f3f5bf2fac38a5d95359f7beb6529362\",\n \"score\": \"0.64880633\",\n \"text\": \"def stylize(network, initial, content, styles, iterations,\\n content_weight, style_weight, style_blend_weights, tv_weight,\\n learning_rate, print_iterations=None, checkpoint_iterations=None):\\n shape = (1,) + content.shape\\n style_shapes = [(1,) + style.shape for style in styles]\\n content_features = {}\\n style_fetures = [{} for _ in styles]\\n\\n # compute content features in feedforward mode\\n g = tf.Graph()\\n with g.as_default(), g.device('cpu:0'), tf.Session as sess:\\n image = tf.placeholder('float', shape=shape)\\n nets, mean_pixel = vgg.net(network, image)\\n content_pre = np.array([vgg.preprocess(content.mean_pixel)])\\n content_features[CONTENT_LAYER] = nets[CONTENT_LAYER].eval(feed_dict={image: content_pre})\\n\\n # compute style features in feedforward mode\\n for i in range(len(styles)):\\n g = tf.Graph()\\n with g.as_default(), g.device('cpu:0'), tf.Session as sess:\\n image = tf.placeholder('float', shape=style_shapes[i])\\n nets, _ = vgg.net(network, image)\\n stype_pre = np.array([vgg.preprocess(styles[i], mean_pixel)])\\n for layer in STYLE_LAYERS:\\n features = nets[layer].eval(feed_dict={image: stype_pre})\\n features = np.reshape(features, (-1, features.shape[3]))\\n gram = np.matmul(features.T, features) / features.size\\n style_fetures[i][layer] = gram\\n\\n # make stylized image using backpropogation\\n with tf.Graph().as_default():\\n if initial is None:\\n noise = np.random.normal(size=shape, scale=(np.std(content) * 0.1))\\n initial = tf.random_normal(shape) * 0.256\\n else:\\n initial = np.array([vgg.preprocess(initial, mean_pixel)])\\n initial = initial.astype('float32')\\n image = tf.Variable(initial)\\n net, _ = vgg.net(network.image)\\n\\n # content loss\\n content_loss = content_weight * (2 * tf.nn.l2_loss(\\n net[CONTENT_LAYER] - content_features[CONTENT_LAYER]) / content_features[CONTENT_LAYER].size)\\n\\n # style_loss\\n style_loss = 0\\n for i in range(len(styles)):\\n style_losses = []\\n for style_layer in STYLE_LAYERS:\\n layer = net[style_layer]\\n _, height, weight, number = map(lambda j: j.value, layer.get_shape())\\n size = height * weight * number\\n feats = tf.reshape(layer, (-1, number))\\n gram = tf.matmul(tf.transpose(feats), feats) / size\\n style_gram = style_fetures[i][style_layer]\\n style_losses.append(2 * tf.nn.l2_loss(gram - style_gram) / style_gram.size)\\n style_loss += style_weight * style_blend_weights[i] * reduce(tf.add, style_losses)\\n\\n # total varation denoising\\n tv_y_size = _tensor_size(image[:, 1:, :, :])\\n tv_x_size = _tensor_size(image[:, 1:, :, :])\\n tv_loss = tv_weight * 2 * (\\n tf.nn.l2_loss(image[:, 1:, :, :] - image[:, :shape[1] - 1, :, :]) / tv_y_size +\\n tf.nn.l2_loss(image[:, 1:, :, :] - image[:, :shape[2] - 1, :, :]) / tv_x_size\\n )\\n\\n # overall loss\\n loss = content_loss + style_loss + tv_loss\\n\\n # optimizer setup\\n train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)\\n\\n def print_process(j, last=False):\\n stderr.write('Iteration %d%d\\\\n' % (j + 1, iterations))\\n if last or (print_iterations and j % print_iterations == 0):\\n stderr.write(' content loss: %g\\\\n' % content_loss.eval())\\n stderr.write(' style loss: %g\\\\n' % style_loss.eval())\\n stderr.write(' tv loss: %g\\\\n' % tv_loss.eval())\\n stderr.write(' total loss: %g\\\\n' % loss.eval())\\n\\n # optimization\\n best_loss = float('inf')\\n best = None\\n with tf.Session as sess:\\n sess.run(tf.initialize_all_variables())\\n for i in range(iterations):\\n last_step = (i == iterations - 1)\\n print_process(i, last=last_step)\\n train_step.run()\\n\\n if (checkpoint_iterations and i % checkpoint_iterations == 0) or last_step:\\n this_loss = loss.eval()\\n if this_loss < best_loss:\\n best_loss = this_loss\\n best = image.eval()\\n yield (\\n (None if last_step else i),\\n vgg.unprocess(best.reshape(shape[1:]), mean_pixel)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ccf4746816190e8ee34006acab173e\",\n \"score\": \"0.64056176\",\n \"text\": \"def load_images(content_path, style_path, max_dim):\\n content_image = img_utils.load_img(content_path, max_dim)\\n logging.info(\\\"Shape content image: %s\\\", content_image.get_shape())\\n style_image = img_utils.load_img(style_path, max_dim)\\n logging.info(\\\"Shape style image: %s\\\", style_image.get_shape())\\n return content_image, style_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42a9cb5e4602c3055c44521373ddb9e6\",\n \"score\": \"0.62286264\",\n \"text\": \"def optimize(args):\\n if args.cuda:\\n ctx = mx.gpu(0)\\n else:\\n ctx = mx.cpu(0)\\n # load the content and style target\\n content_image = utils.tensor_load_rgbimage(args.content_image,ctx, size=args.content_size, keep_asp=True)\\n content_image = utils.subtract_imagenet_mean_preprocess_batch(content_image)\\n style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size)\\n style_image = utils.subtract_imagenet_mean_preprocess_batch(style_image)\\n # load the pre-trained vgg-16 and extract features\\n vgg = net.Vgg16()\\n utils.init_vgg_params(vgg, 'models', ctx=ctx)\\n # content feature\\n f_xc_c = vgg(content_image)[1]\\n # style feature\\n features_style = vgg(style_image)\\n gram_style = [net.gram_matrix(y) for y in features_style]\\n # output\\n output = Parameter('output', shape=content_image.shape)\\n output.initialize(ctx=ctx)\\n output.set_data(content_image)\\n # optimizer\\n trainer = gluon.Trainer([output], 'adam',\\n {'learning_rate': args.lr})\\n mse_loss = gluon.loss.L2Loss()\\n\\n # optimizing the images\\n for e in range(args.iters):\\n utils.imagenet_clamp_batch(output.data(), 0, 255)\\n # fix BN for pre-trained vgg\\n with autograd.record():\\n features_y = vgg(output.data())\\n content_loss = 2 * args.content_weight * mse_loss(features_y[1], f_xc_c)\\n style_loss = 0.\\n for m in range(len(features_y)):\\n gram_y = net.gram_matrix(features_y[m])\\n gram_s = gram_style[m]\\n style_loss = style_loss + 2 * args.style_weight * mse_loss(gram_y, gram_s)\\n total_loss = content_loss + style_loss\\n total_loss.backward()\\n\\n trainer.step(1)\\n if (e + 1) % args.log_interval == 0:\\n print('loss:{:.2f}'.format(total_loss.asnumpy()[0]))\\n\\n # save the image\\n output = utils.add_imagenet_mean_batch(output.data())\\n utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92be832df7bbe912d7aad3ffea91acae\",\n \"score\": \"0.6170639\",\n \"text\": \"def run(content_image_dir, style_image_dir, host_name, overwrite):\\n rest_api_url = \\\"http://{}:5000/stylize/\\\".format(host_name)\\n\\n content_filenames_iterator = next(os.walk(content_image_dir))[2]\\n content_filenames = []\\n for filename in content_filenames_iterator:\\n if \\\"__label__\\\" in filename:\\n continue\\n if (\\n filename.lower().endswith(\\\".jpg\\\")\\n or filename.lower().endswith(\\\".png\\\")\\n or filename.lower().endswith(\\\".jpeg\\\")\\n ):\\n content_filenames.append(filename)\\n\\n style_filenames_iterator = next(os.walk(style_image_dir))[2]\\n style_filenames = []\\n for filename in style_filenames_iterator:\\n if \\\"__label__\\\" in filename:\\n continue\\n if (\\n filename.lower().endswith(\\\".jpg\\\")\\n or filename.lower().endswith(\\\".png\\\")\\n or filename.lower().endswith(\\\".jpeg\\\")\\n ):\\n style_filenames.append(filename)\\n\\n for content_filename in tqdm(content_filenames):\\n for style_filename in style_filenames:\\n if content_filename == style_filename:\\n continue\\n\\n start_time = time.time()\\n\\n content_label_filename = get_label_filename(content_filename)\\n style_label_filename = get_label_filename(style_filename)\\n output_image_file_path = os.path.join(\\n STYLIZED_IMAGE_DIR, content_filename + \\\"__\\\" + style_filename + \\\".png\\\"\\n )\\n if os.path.isfile(output_image_file_path) and not overwrite:\\n print(\\n \\\"Skipping, because {} already exists\\\".format(output_image_file_path)\\n )\\n continue\\n\\n content_image = Image.open(\\n os.path.join(content_image_dir, content_filename)\\n )\\n style_image = Image.open(os.path.join(style_image_dir, style_filename))\\n\\n content_segmentation_image = None\\n style_segmentation_image = None\\n try:\\n content_segmentation_image = Image.open(\\n os.path.join(content_image_dir, content_label_filename)\\n )\\n style_segmentation_image = Image.open(\\n os.path.join(style_image_dir, style_label_filename)\\n )\\n except FileNotFoundError as e:\\n print(e)\\n pass\\n\\n data = {\\n \\\"content_image_base64\\\": pil_image_to_base64(content_image),\\n \\\"content_segmentation_base64\\\": pil_image_to_base64(\\n content_segmentation_image\\n ),\\n \\\"style_image_base64\\\": pil_image_to_base64(style_image),\\n \\\"style_segmentation_base64\\\": pil_image_to_base64(\\n style_segmentation_image\\n ),\\n }\\n\\n r = requests.post(rest_api_url, json=data, timeout=60)\\n json_data = r.json()\\n output_image = base64_png_image_to_pillow_image(\\n json_data[\\\"output_image_base64\\\"]\\n )\\n output_image.save(output_image_file_path, format=\\\"PNG\\\")\\n print(\\\"Stylized one image ({} s)\\\".format(time.time() - start_time))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"652a5e98824a2f2734238f2bd1e1e66d\",\n \"score\": \"0.6169471\",\n \"text\": \"def main():\\n content_image, style_image = load_images(known_args.content_path, known_args.style_path, known_args.max_dim)\\n extractor = create_model()\\n content_targets = extractor(content_image)['content'] # dict with vgg content and style values for content image\\n style_targets = extractor(style_image)['style'] # dict with vgg content and style values for style image\\n if known_args.initialize_white_noise:\\n generated_image = tf.Variable(\\n tf.random.uniform(content_image.get_shape(), minval=0.0, maxval=1.0, dtype=content_image.dtype, seed=42)\\n )\\n else:\\n generated_image = tf.Variable(content_image) # initialize generated image from content image\\n img_utils.store_tensor_image(generated_image, known_args.output_path)\\n\\n opt = tf.optimizers.Adam(learning_rate=known_args.learning_rate, beta_1=0.99, epsilon=1e-1)\\n\\n start = time.time()\\n\\n step = 0\\n total_loss = None\\n for ep in range(known_args.epochs):\\n for st in range(known_args.steps_per_epoch):\\n step += 1\\n total_loss = train_step(generated_image, content_targets, style_targets, extractor, opt)\\n output_path = ('_ep'+str(ep).zfill(3)+'.').join(known_args.output_path.rsplit('.', 1))\\n img_utils.store_tensor_image(generated_image, output_path)\\n logging.info(\\\"Train step: %s. Loss %s\\\", step, total_loss.numpy())\\n\\n end = time.time()\\n logging.info(\\\"Total time: {:.1f}\\\".format(end - start))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2efef2a4f6b09157c4848136a0cbc14e\",\n \"score\": \"0.615392\",\n \"text\": \"def generate_stylized_image(args):\\n device = torch.device(\\\"cuda\\\" if args.cuda else \\\"cpu\\\")\\n\\n content_image = utils.load_image(args.content_image, scale=content_scale)\\n content_transform = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Lambda(lambda x: x.mul(255))\\n ])\\n content_image = content_transform(content_image)\\n content_image = content_image.unsqueeze(0).to(device)\\n\\n with torch.no_grad():\\n style_model = TransformerNet(style_number=args.style_num)\\n state_dict = torch.load(args.model)\\n style_model.load_state_dict(state_dict, strict=False)\\n style_model.to(device)\\n output = style_model(content_image, style_id = [args.style_id]).cpu()\\n\\n utils.save_image('output/'+args.output_image+'_style'+str(args.style_id)+'.jpg', output[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1bda492b46455b7e3041685a1cf99dc\",\n \"score\": \"0.5997467\",\n \"text\": \"def build_network(self, content_img, style_img, content_weight, style_weight, save_trigger=False): # TODO: change this to be stateless\\n n_conv = 0\\n n_relu = 0\\n n_pool = 0\\n for key, layer in self.submodule._modules.items():\\n #iterate through dicitonary of pretrained modules1\\n conv_name = 'conv_' + str(n_conv)\\n relu_name = 'relu_' + str(n_relu)\\n pool_name = 'pool_' + str(n_pool)\\n if isinstance(layer, torch.nn.modules.conv.Conv2d):\\n self.ArtNet.add_module(conv_name, layer)\\n if conv_name in self._content_layers:\\n target = self.ArtNet.forward(content_img.cuda()).clone()\\n if save_trigger:\\n self.content_targets += [target.data.cpu()]\\n content_loss = ContentLoss(target, content_weight).cuda()\\n self.ArtNet.add_module('content_loss_' + str(n_conv), content_loss)\\n if self._unbuilt:\\n self.content_losses.append(content_loss)\\n\\n if conv_name in self._style_layers:\\n target_feature = self.ArtNet.forward(style_img.cuda()).clone()\\n if save_trigger:\\n self.style_targets += [target_feature.data.cpu()]\\n target_feature_gram = self.gram.forward(target_feature)\\n style_loss = StyleLoss(target_feature_gram, style_weight).cuda()\\n self.ArtNet.add_module('style_loss_' + str(n_conv), style_loss)\\n if self._unbuilt:\\n self.style_losses.append(style_loss)\\n n_conv += 1\\n\\n if isinstance(layer, torch.nn.modules.activation.ReLU):\\n self.ArtNet.add_module(relu_name, layer)\\n if relu_name in self._content_layers:\\n target = self.ArtNet.forward(content_img.cuda()).clone()\\n if save_trigger:\\n self.content_targets += [target.data.cpu()]\\n content_loss = ContentLoss(target, content_weight).cuda()\\n self.ArtNet.add_module(\\\"content_loss_\\\" + str(n_relu), content_loss)\\n if self._unbuilt:\\n self.content_losses.append(content_loss)\\n\\n if relu_name in self._style_layers:\\n target_feature = self.ArtNet.forward(style_img.cuda()).clone()\\n if save_trigger:\\n self.style_targets += [target_feature.data.cpu()]\\n target_feature_gram = self.gram.forward(target_feature)\\n style_loss = StyleLoss(target_feature_gram, style_weight).cuda()\\n self.ArtNet.add_module(\\\"style_loss_\\\" + str(n_relu), style_loss)\\n if self._unbuilt:\\n self.style_losses.append(style_loss)\\n n_relu += 1\\n\\n if isinstance(layer, torch.nn.modules.pooling.MaxPool2d):\\n self.ArtNet.add_module(pool_name, layer)\\n n_pool += 1\\n self._unbuilt = False\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776411496d6c83e6c7a19c6339472b19\",\n \"score\": \"0.59838206\",\n \"text\": \"def load_img_util (num_sample,path_write,dim_img,preprocess_img,channel,mode_pre_img, pattern_img='train'):\\n from keras.preprocessing import image\\n from keras.applications.resnet50 import preprocess_input as pre_resnet50\\n from keras.applications.vgg16 import preprocess_input as pre_vgg16\\n from keras.applications.vgg19 import preprocess_input as pre_vgg19\\n from keras.applications.inception_v3 import preprocess_input as pre_incep\\n \\n temp = []\\n for i in range(0,num_sample): #load samples for learning\\n image_path = os.path.join(path_write, str(pattern_img) +str(\\\"_\\\") + str(i) +\\\".png\\\") \\n if dim_img==-1: #if use real img\\n if channel == 4:\\n #img = imread(image_path)\\n #img = img.astype('float32')\\n print ('waiting for fixing issues from from scipy.misc import imread')\\n exit()\\n elif channel == 1:\\n img = image.load_img(image_path,grayscale=True)\\n else: \\n img = image.load_img(image_path)\\n else: #if select dimension\\n \\n if channel == 1:\\n img = image.load_img(image_path,grayscale=True, target_size=(dim_img, dim_img))\\n \\n else:\\n img = image.load_img(image_path,target_size=(dim_img, dim_img))\\n\\n x = image.img_to_array(img) \\n # x = preprocess_input(x)\\n if preprocess_img=='vgg16':\\n x = pre_vgg16(x, mode= mode_pre_img)\\n elif preprocess_img=='resnet50':\\n x = pre_resnet50(x, mode= mode_pre_img)\\n elif preprocess_img=='vgg19':\\n x = pre_vgg19(x, mode= mode_pre_img)\\n elif preprocess_img=='incep':\\n x = pre_incep(x)\\n \\n temp.append(x) \\n\\n return np.stack(temp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1587db2fd467a08b485e5e3da6acfd4\",\n \"score\": \"0.59218526\",\n \"text\": \"def pre_process(width, height, path):\\n #containers for pre-processed image data and class labels\\n data = []\\n labels = []\\n\\n #images directory contains 3 sub-directories: 'poison_ivy', 'poison_oak', 'poison_sumac'\\n #randomly get image paths and shuffle them\\n # current path 'C:\\\\\\\\Users\\\\\\\\jltsa\\\\\\\\Desktop\\\\\\\\Project_2\\\\\\\\images'\\n image_paths = sorted(list(paths.list_images(path)))\\n random.seed(42)\\n random.shuffle(image_paths)\\n\\n #preprocess images to width x height pixels as required for LeNet\\n for image_path in image_paths:\\n image = cv2.imread(image_path)\\n if image is not None:\\n image = cv2.resize(image, (width, height))\\n image = img_to_array(image)\\n data.append(image)\\n \\n #Extract class labels\\n label = image_path.split(os.path.sep)[-2]\\n if label == 'poison_ivy':\\n label = 0\\n elif label == 'poison_oak':\\n label = 1\\n else:\\n label = 2\\n labels.append(label)\\n \\n #Scal pixel intensities from 0 to 1\\n data = np.array(data, dtype='float') / 255.0\\n labels = np.array(labels)\\n \\n return data, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d29e0105fd37dcec564bc9e9b9ad51e\",\n \"score\": \"0.5902463\",\n \"text\": \"def neural_transform(content_image, style_image, output_image, content_size=256,\\n style_scale=1.0, cuda=False, model='backend/models/21styles.model'):\\n\\n style_size = int(content_size * style_scale) # Round down to the nearest integer\\n\\n content_image = neural_style.utils.tensor_load_rgbimage(content_image, size=content_size, keep_asp=True)\\n content_image = content_image.unsqueeze(0)\\n style = neural_style.utils.tensor_load_rgbimage(style_image, size=style_size)\\n style = style.unsqueeze(0)\\n style = neural_style.utils.preprocess_batch(style)\\n\\n style_model = Net(ngf=128)\\n style_model.load_state_dict(torch.load(model), False)\\n\\n if cuda:\\n style_model.cuda()\\n content_image = content_image.cuda()\\n style = style.cuda()\\n\\n style_v = Variable(style)\\n\\n content_image = Variable(neural_style.utils.preprocess_batch(content_image))\\n style_model.setTarget(style_v)\\n\\n output = style_model(content_image)\\n neural_style.utils.tensor_save_bgrimage(output.data[0], output_image, cuda)\\n return output_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a0ff81ac76ee7bdd888134c6c2564e\",\n \"score\": \"0.58949983\",\n \"text\": \"def extract_features(model, img_paths, batch_size=1):\\n import os\\n import numpy as np\\n from keras.applications.resnet50 import preprocess_input\\n from keras.preprocessing import image\\n \\n n = len(img_paths)\\n img_array = np.zeros((n, 224, 224, 3))\\n \\n for i, path in enumerate(img_paths):\\n img = image.load_img(path, target_size=(224, 224))\\n img = image.img_to_array(img)\\n img = np.expand_dims(img, axis=0)\\n x = preprocess_input(img)\\n img_array[i] = x\\n \\n X = model.predict(img_array, batch_size=batch_size, verbose=0)\\n X = X.reshape((n, 2048))\\n\\n for index in range(X.shape[0]):\\n img_path = img_paths[index]\\n img_vector = X[index]\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0446498a7a5d73e61435108b1ac6c4d8\",\n \"score\": \"0.58798593\",\n \"text\": \"def _build_model(self, train_mode=None):\\n\\n #rgb_scaled = rgb * 255.0\\n\\n bgr = self._images\\n bgr = self._pre_process_images(bgr)\\n\\n # Convert RGB to BGR\\n blue, green, red = tf.split(axis=3, num_or_size_splits=3, value=bgr)\\n assert red.get_shape().as_list()[1:] == [224, 224, 1]\\n assert green.get_shape().as_list()[1:] == [224, 224, 1]\\n assert blue.get_shape().as_list()[1:] == [224, 224, 1]\\n bgr = tf.concat(axis=3, values=[\\n blue - VGG_MEAN[0],\\n green - VGG_MEAN[1],\\n red - VGG_MEAN[2],\\n ])\\n assert bgr.get_shape().as_list()[1:] == [224, 224, 3]\\n\\n\\n with tf.variable_scope('extract_feature_map'):\\n self.conv1_1 = self.conv_layer(bgr, 3, 64, \\\"conv1_1\\\")\\n self.conv1_2 = self.conv_layer(self.conv1_1, 64, 64, \\\"conv1_2\\\")\\n self.pool1 = self.max_pool(self.conv1_2, 'pool1')\\n\\n self.conv2_1 = self.conv_layer(self.pool1, 64, 128, \\\"conv2_1\\\")\\n self.conv2_2 = self.conv_layer(self.conv2_1, 128, 128, \\\"conv2_2\\\")\\n self.pool2 = self.max_pool(self.conv2_2, 'pool2')\\n\\n self.conv3_1 = self.conv_layer(self.pool2, 128, 256, \\\"conv3_1\\\")\\n self.conv3_2 = self.conv_layer(self.conv3_1, 256, 256, \\\"conv3_2\\\")\\n self.conv3_3 = self.conv_layer(self.conv3_2, 256, 256, \\\"conv3_3\\\")\\n self.conv3_4 = self.conv_layer(self.conv3_3, 256, 256, \\\"conv3_4\\\")\\n self.pool3 = self.max_pool(self.conv3_4, 'pool3')\\n\\n self.conv4_1 = self.conv_layer(self.pool3, 256, 512, \\\"conv4_1\\\")\\n self.conv4_2 = self.conv_layer(self.conv4_1, 512, 512, \\\"conv4_2\\\")\\n self.conv4_3 = self.conv_layer(self.conv4_2, 512, 512, \\\"conv4_3\\\")\\n self.conv4_4 = self.conv_layer(self.conv4_3, 512, 512, \\\"conv4_4\\\")\\n self.pool4 = self.max_pool(self.conv4_4, 'pool4')\\n\\n self.conv5_1 = self.conv_layer(self.pool4, 512, 512, \\\"conv5_1\\\")\\n self.conv5_2 = self.conv_layer(self.conv5_1, 512, 512, \\\"conv5_2\\\")\\n self.conv5_3 = self.conv_layer(self.conv5_2, 512, 512, \\\"conv5_3\\\")\\n self.conv5_4 = self.conv_layer(self.conv5_3, 512, 512, \\\"conv5_4\\\")\\n self.pool5 = self.max_pool(self.conv5_4, 'pool5')\\n\\n self.fc6 = self.fc_layer(self.pool5, 25088, 4096, \\\"fc6\\\") # 25088 = ((224 // (2 ** 5)) ** 2) * 512\\n self.relu6 = tf.nn.relu(self.fc6)\\n if train_mode is not None:\\n self.relu6 = tf.cond(train_mode, lambda: tf.nn.dropout(self.relu6, self.dropout), lambda: self.relu6)\\n elif self.trainable:\\n self.relu6 = tf.nn.dropout(self.relu6, self.dropout)\\n\\n self.fc7 = self.fc_layer(self.relu6, 4096, 4096, \\\"fc7\\\")\\n self.relu7 = tf.nn.relu(self.fc7)\\n if train_mode is not None:\\n self.relu7 = tf.cond(train_mode, lambda: tf.nn.dropout(self.relu7, self.dropout), lambda: self.relu7)\\n elif self.trainable:\\n self.relu7 = tf.nn.dropout(self.relu7, self.dropout)\\n self.fc8 = self.fc_layer(self.relu7, 4096, 1000, \\\"fc8\\\")\\n\\n with tf.variable_scope('logits'):\\n self.logits = self._fully_connected(self.fc8, self.hps.num_classes, \\\"logits\\\")\\n self.predictions = tf.nn.softmax(self.logits, name=\\\"prob\\\")\\n\\n with tf.variable_scope('costs'):\\n xent = tf.nn.softmax_cross_entropy_with_logits(\\n logits=self.logits, labels=self.labels)\\n\\n self.costs = tf.reduce_mean(xent, name='xent')\\n self.costs += self._decay()\\n\\n tf.summary.scalar('cost', self.costs)\\n\\n\\n self.data_dict = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b9e65c595cc1ac781688c320b183a04\",\n \"score\": \"0.5866423\",\n \"text\": \"def style_transfer(content_image, style_image,\\n content_layer_ids, style_layer_ids,\\n weight_content=1.5, weight_style=10.0,\\n weight_denoise=0.3,\\n num_iterations=120, step_size=10.0):\\n\\n # Create an instance of the VGG16-model. This is done\\n # in each call of this function, because we will add\\n # operations to the graph so it can grow very large\\n # and run out of RAM if we keep using the same instance.\\n model = vgg16.VGG16()\\n\\n # Create a TensorFlow-session.\\n session = tf.InteractiveSession(graph=model.graph)\\n\\n # Print the names of the content-layers.\\n print(\\\"Content layers:\\\")\\n print(model.get_layer_names(content_layer_ids))\\n print()\\n\\n # Print the names of the style-layers.\\n print(\\\"Style layers:\\\")\\n print(model.get_layer_names(style_layer_ids))\\n print()\\n\\n # Create the loss-function for the content-layers and -image.\\n loss_content = create_content_loss(session=session,\\n model=model,\\n content_image=content_image,\\n layer_ids=content_layer_ids)\\n\\n # Create the loss-function for the style-layers and -image.\\n loss_style = create_style_loss(session=session,\\n model=model,\\n style_image=style_image,\\n layer_ids=style_layer_ids) \\n\\n # Create the loss-function for the denoising of the mixed-image.\\n loss_denoise = create_denoise_loss(model)\\n\\n # Create TensorFlow variables for adjusting the values of\\n # the loss-functions. This is explained below.\\n adj_content = tf.Variable(1e-10, name='adj_content')\\n adj_style = tf.Variable(1e-10, name='adj_style')\\n adj_denoise = tf.Variable(1e-10, name='adj_denoise')\\n\\n # Initialize the adjustment values for the loss-functions.\\n session.run([adj_content.initializer,\\n adj_style.initializer,\\n adj_denoise.initializer])\\n\\n # Create TensorFlow operations for updating the adjustment values.\\n # These are basically just the reciprocal values of the\\n # loss-functions, with a small value 1e-10 added to avoid the\\n # possibility of division by zero.\\n update_adj_content = adj_content.assign(1.0 / (loss_content + 1e-10))\\n update_adj_style = adj_style.assign(1.0 / (loss_style + 1e-10))\\n update_adj_denoise = adj_denoise.assign(1.0 / (loss_denoise + 1e-10))\\n\\n # This is the weighted loss-function that we will minimize\\n # below in order to generate the mixed-image.\\n # Because we multiply the loss-values with their reciprocal\\n # adjustment values, we can use relative weights for the\\n # loss-functions that are easier to select, as they are\\n # independent of the exact choice of style- and content-layers.\\n loss_combined = weight_content * adj_content * loss_content + weight_style * adj_style * loss_style + weight_denoise * adj_denoise * loss_denoise\\n\\n # Use TensorFlow to get the mathematical function for the\\n # gradient of the combined loss-function with regard to\\n # the input image.\\n gradient = tf.gradients(loss_combined, model.input)\\n\\n # List of tensors that we will run in each optimization iteration.\\n run_list = [gradient, update_adj_content, update_adj_style, update_adj_denoise]\\n\\n # The mixed-image is initialized with random noise.\\n # It is the same size as the content-image.\\n mixed_image = np.random.rand(*content_image.shape) + 128\\n\\n for i in range(num_iterations):\\n # Create a feed-dict with the mixed-image.\\n feed_dict = model.create_feed_dict(image=mixed_image)\\n\\n # Use TensorFlow to calculate the value of the\\n # gradient, as well as updating the adjustment values.\\n grad, adj_content_val, adj_style_val, adj_denoise_val = session.run(run_list, feed_dict=feed_dict)\\n\\n # Reduce the dimensionality of the gradient.\\n grad = np.squeeze(grad)\\n\\n # Scale the step-size according to the gradient-values.\\n step_size_scaled = step_size / (np.std(grad) + 1e-8)\\n\\n # Update the image by following the gradient.\\n mixed_image -= grad * step_size_scaled\\n\\n # Ensure the image has valid pixel-values between 0 and 255.\\n mixed_image = np.clip(mixed_image, 0.0, 255.0)\\n\\n # Print a little progress-indicator.\\n print(\\\". \\\", end=\\\"\\\")\\n\\n # Display status once every 10 iterations, and the last.\\n if (i % 10 == 0) or (i == num_iterations - 1):\\n print()\\n print(\\\"Iteration:\\\", i)\\n\\n # Print adjustment weights for loss-functions.\\n msg = \\\"Weight Adj. for Content: {0:.2e}, Style: {1:.2e}, Denoise: {2:.2e}\\\"\\n print(msg.format(adj_content_val, adj_style_val, adj_denoise_val))\\n\\n # Plot the content-, style- and mixed-images.\\n plot_images(content_image=content_image,\\n style_image=style_image,\\n mixed_image=mixed_image)\\n \\n print()\\n print(\\\"Final image:\\\")\\n plot_image_big(mixed_image)\\n\\n # Close the TensorFlow session to release its resources.\\n session.close()\\n \\n # Return the mixed-image.\\n return mixed_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09845a23b3adb96dc8240e6ddf2ffcaa\",\n \"score\": \"0.5849688\",\n \"text\": \"def segmentation_example_use(path_to_model, path_to_data):\\n model = Unet(CATEGORIES)\\n model.load_state_dict(load(path_to_model, map_location=DEVICE))\\n model.to(DEVICE)\\n model.eval()\\n\\n image_names = sorted(listdir(path_to_data))\\n\\n for image_name in image_names:\\n print(f\\\"Image: {image_name}\\\")\\n img = load_img(path_to_data, image_name)\\n img_tensor = TRANSFORMATIONS_TORCH(img)\\n img_tensor.to(DEVICE)\\n\\n mask = model(img_tensor.unsqueeze(0))\\n\\n plt.imshow(\\n mask.squeeze().detach().cpu().numpy().argmax(0) / CATEGORIES, cmap=cm.gray\\n )\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e9c1de4eda6305386f5f038fd34ba7a\",\n \"score\": \"0.58450574\",\n \"text\": \"def extract_feature(net, image_list, layer_name, image_as_grey = False):\\n \\n blobs = OrderedDict( [(k, v.data) for k, v in net.blobs.items()])\\n\\n #blobs = OrderedDict( [(k, v.data) for k, v in net.blobs.items()])\\n shp = blobs[layer_name].shape\\n #print blobs['data'].shape\\n\\n batch_size = blobs['data'].shape[0]\\n #print blobs[layer_name].shape\\n #print 'debug-------\\\\nexit'\\n #exit()\\n\\n #params = OrderedDict( [(k, (v[0].data,v[1].data)) for k, v in net.params.items()])\\n if len(shp) is 2:\\n features_shape = (len(image_list), shp[1])\\n elif len(shp) is 3:\\n features_shape = (len(image_list), shp[1], shp[2])\\n elif len(shp) is 4:\\n features_shape = (len(image_list), shp[1], shp[2], shp[3])\\n\\n features = np.empty(features_shape, dtype='float32', order='C')\\n img_batch = []\\n for cnt, path in zip(range(features_shape[0]), image_list):\\n img = caffe.io.load_image(path, color = not image_as_grey)\\n if image_as_grey and img.shape[2] != 1:\\n img = skimage.color.rgb2gray(img)\\n img = img[:, :, np.newaxis]\\n #if cnt == 0:\\n # print 'image shape: ', img.shape\\n #print img[0:10,0:10,:]\\n #exit()\\n img_batch.append(img)\\n #print 'image shape: ', img.shape\\n #print path, type(img), img.mean()\\n if (len(img_batch) == batch_size) or cnt==features_shape[0]-1:\\n scores = net.predict(img_batch, oversample=False)\\n '''\\n print 'blobs[%s].shape' % (layer_name,)\\n tmp = blobs[layer_name]\\n print tmp.shape, type(tmp)\\n tmp2 = tmp.copy()\\n print tmp2.shape, type(tmp2)\\n print blobs[layer_name].copy().shape\\n print cnt, len(img_batch)\\n print batch_size\\n #exit()\\n\\n #print img_batch[0:10]\\n #print blobs[layer_name][:,:,0,0]\\n #exit()\\n '''\\n\\n # must call blobs_data(v) again, because it invokes (mutable_)cpu_data() which\\n # syncs the memory between GPU and CPU\\n blobs = OrderedDict( [(k, v.data) for k, v in net.blobs.items()])\\n\\n #print '%d images processed' % (cnt+1,)\\n\\n #print blobs[layer_name][0,:,:,:]\\n # items of blobs are references, must make copy!\\n features[cnt-len(img_batch)+1:cnt+1] = blobs[layer_name][0:len(img_batch)].copy()\\n img_batch = []\\n\\n #features.append(blobs[layer_name][0,:,:,:].copy())\\n features = np.asarray(features, dtype='float32')\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3685de0675229d3c3e8e6af5625f98ba\",\n \"score\": \"0.579015\",\n \"text\": \"def extract(self, key_image_path):\\n name = os.path.splitext(os.path.split(path)[-1])[0]\\n frames_index = list(map(lambda x : x.split('.')[0],os.listdir(key_image_path)))\\n frames_index.sort()\\n frames_paths = list(map(lambda x: '{}/{}.jpg'.format(key_image_path,x),frames_index))\\n images_input = [preprocess_input(image.img_to_array(image.load_img(im, target_size=(224, 224))), mode='torch')\\n for im in frames_paths]\\n feature = self.DenseNet201_model.predict(images_input)\\n return name,feature\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0799f85381759b87c464ef5fdf3a9b67\",\n \"score\": \"0.57721\",\n \"text\": \"def load_examples( lab_colorization=True, which_direction=\\\"AtoB\\\", flip=True, scale_size=286, batch_size=1):\\n ##should change input_dir \\n input_dir = './facade/test'\\n \\n #input images should either be .jpg or .png or .jpeg\\n input_paths = glob.glob(os.path.join(input_dir, '*.[jJ][pP][gG]'))\\n decode = tf.image.decode_jpeg\\n if len(input_paths) == 0:\\n input_paths = glob.glob(os.path.join(input_dir, '*.[jJ][pP][eE][gG]'))\\n decode = tf.image.decode_jpeg\\n if len(input_paths) == 0:\\n input_paths = glob.glob(os.path.join(input_dir, '*.[pP][nN][gG]'))\\n decode = tf.image.decode_png\\n\\n #Sort images by name\\n def get_name(path):\\n name, _ = os.path.splitext(os.path.basename(path))\\n return name\\n # if the image names are numbers, sort by the value \\n if all(get_name(path).isdigit() for path in input_paths):\\n input_paths = sorted(input_paths, key=lambda path: int(get_name(path)))\\n #built in function, return a new sorted list\\n else:\\n input_paths = sorted(input_paths)\\n print('1- input_paths shape is ', len(input_paths))\\n #read, decode and post process images\\n with tf.name_scope(\\\"load_images\\\"):\\n # Create a queue node (or operation) in the currently active graph\\n path_queue = tf.train.string_input_producer(input_paths)\\n\\t\\t\\n #Read the files\\n reader = tf.WholeFileReader()\\n paths, contents = reader.read(path_queue)\\n \\n #Decode the images\\n raw_input = decode(contents)\\n raw_input = tf.image.convert_image_dtype(raw_input, dtype=tf.float32)\\n \\n print('Before assertion the last dimension is ',list(raw_input.get_shape())[2])\\n \\n\\t\\t#make the last dimension 3 so that we can unstack the colors\\n assertion = tf.assert_equal(tf.shape(raw_input)[2], 3, message=\\\"image does not have 3 channels\\\")\\n with tf.control_dependencies([assertion]):\\n raw_input = tf.identity(raw_input)\\n \\n raw_input.set_shape([None, None, 3])\\n\\n print('last dimension is ',list(raw_input.get_shape())[2])\\n \\n if lab_colorization:\\n # load color and brightness from image, no B image exists here\\n\\t\\t\\t#convert RGB information to LAB \\n lab = rgb_to_lab(raw_input)\\n L_chan, a_chan, b_chan = preprocess_lab(lab)\\n\\t\\t\\tprint('last dimension of L_chan is ',list(L_chan.get_shape()))\\n\\t\\t\\t#expand_dim won't add dimension to a tensor. \\n a_images = tf.expand_dims(L_chan, axis=2)\\n b_images = tf.stack([a_chan, b_chan], axis=2)\\n else:\\n # break apart image pair and move to range [-1, 1]\\n width = tf.shape(raw_input)[1] # [height, width, channels]\\n\\t\\t\\t#left half and right side\\n a_images = preprocess(raw_input[:,:width//2,:])\\n b_images = preprocess(raw_input[:,width//2:,:])\\n \\n if which_direction == \\\"AtoB\\\":\\n inputs, targets = [a_images, b_images]\\n elif which_direction == \\\"BtoA\\\":\\n inputs, targets = [b_images, a_images]\\n else:\\n raise Exception(\\\"invalid direction\\\")\\n\\n\\n # !!IMPORTANT: seed for image operations to do the SAME operations to both input and output images\\n seed = random.randint(0, 2**31 - 1)\\n\\n def transform(image):\\n r = image\\n if flip:\\n r = tf.image.random_flip_left_right(r, seed=seed)\\n \\n # area produces a nice downscaling, but does nearest neighbor for upscaling\\n # assume we're going to be doing downscaling here\\n r = tf.image.resize_images(r, [scale_size, scale_size], method=tf.image.ResizeMethod.AREA)\\n\\n offset = tf.cast(tf.floor(tf.random_uniform([2], 0, scale_size - CROP_SIZE + 1, seed=seed)), dtype=tf.int32)\\n if scale_size > CROP_SIZE:\\n r = tf.image.crop_to_bounding_box(r, offset[0], offset[1], CROP_SIZE, CROP_SIZE)\\n elif scale_size < CROP_SIZE:\\n raise Exception(\\\"scale size cannot be less than crop size\\\")\\n return r\\n\\t\\n #distort the input images\\n with tf.name_scope(\\\"input_images\\\"):\\n input_images = transform(inputs)\\n #distort the target images\\n with tf.name_scope(\\\"target_images\\\"):\\n target_images = transform(targets)\\n \\n \\n paths_batch, inputs_batch, targets_batch = tf.train.batch([paths, input_images, target_images], batch_size = batch_size)\\n steps_per_epoch = int(math.ceil(len(input_paths) /batch_size))\\n \\n print('2- count is ',len(input_paths))\\n print('3- steps_per_epoch is ',steps_per_epoch)\\n\\n return paths_batch, inputs_batch, targets_batch, len(input_paths), steps_per_epoch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae10d1078792cfdf0f1659e859bd009\",\n \"score\": \"0.5767853\",\n \"text\": \"def extract_from_file(self, im_path: FilePath) -> Tuple[Tensor, ...]:\\n\\n def _load_img_and_batch(p):\\n image_loader = IMAGE_LOADERS[self._extractor_name]\\n img = image_loader(im_path)\\n batched_img = tf.expand_dims(img, 0)\\n return batched_img\\n\\n if self._dataset:\\n features_cmprsd = np.load(im_path)\\n features = tuple(\\n [features_cmprsd[k] for k in _extractors_features[self._extractor_name]]\\n )\\n else:\\n batched_features = self.extraction_model(_load_img_and_batch(im_path))\\n features = tf.squeeze(batched_features, axis=0)\\n\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5acec1bda753e360793cbe2ae4b6fcfc\",\n \"score\": \"0.57550895\",\n \"text\": \"def process_images_and_labels(self):\\n print(\\\"loading data onto reel\\\")\\n self._load_reel()\\n print(\\\"...done\\\")\\n\\n print(\\\"splitting to train and test sets\\\")\\n self._split_to_train_and_test()\\n print(\\\"...done\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c754db75de30f780861f5f074ce48d18\",\n \"score\": \"0.572533\",\n \"text\": \"def load_wider():\\n\\n def get_dirmapper(dirpath):\\n \\\"\\\"\\\"return dir mapper for wider face\\n \\\"\\\"\\\"\\n mapper = {}\\n for d in os.listdir(dirpath):\\n dir_id = d.split('--')[0]\\n mapper[dir_id] = os.path.join(dirpath, d)\\n return mapper\\n\\n train_mapper = get_dirmapper(os.path.join(cfg.WIDER_DIR, 'WIDER_train', 'images'))\\n val_mapper = get_dirmapper(os.path.join(cfg.WIDER_DIR, 'WIDER_val', 'images'))\\n\\n def gen(text, mapper):\\n fin = open(text, 'r')\\n\\n result = []\\n while True:\\n line = fin.readline()\\n if not line: break # eof\\n name = line.strip()\\n dir_id = name.split('_')[0]\\n img_path = os.path.join(mapper[dir_id], name + '.jpg')\\n face_n = int(fin.readline().strip())\\n\\n bboxes = []\\n for i in range(face_n):\\n line = fin.readline().strip()\\n components = line.split(' ')\\n x, y, w, h = [float(_) for _ in components]\\n\\n size = min(w, h)\\n # only large enough\\n if size > 12:\\n bbox = [x, y, w, h]\\n bboxes.append(bbox)\\n\\n # # for debug\\n # img = cv2.imread(img_path)\\n # for bbox in bboxes:\\n # x, y, w, h = bbox\\n # cv2.rectangle(img, (int(x), int(y)), (int(x+w), int(y+h)), (0,0,255), 1)\\n # cv2.imshow('img', img)\\n # cv2.waitKey(0)\\n\\n if len(bboxes) > 0:\\n bboxes = np.asarray(bboxes, dtype=np.float32)\\n bboxes[:, 2] += bboxes[:, 0]\\n bboxes[:, 3] += bboxes[:, 1]\\n result.append([img_path, bboxes])\\n fin.close()\\n return result\\n\\n txt_dir = os.path.join(cfg.WIDER_DIR, 'wider_face_split')\\n train_data = gen(os.path.join(txt_dir, 'wider_face_train.txt'), train_mapper)\\n val_data = gen(os.path.join(txt_dir, 'wider_face_val.txt'), val_mapper)\\n return (train_data, val_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82fb2e4ee6daf54281bbb4499092678d\",\n \"score\": \"0.5716167\",\n \"text\": \"def process_path(file_path: str) -> Tensor:\\n ext = file_path.split(\\\".\\\")[-1]\\n img = tf.io.read_file(file_path)\\n if ext == \\\"png\\\":\\n img = decode_img(img)\\n else:\\n img = decode_img(img, png=False)\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f71d7385f099796ac9c45ee6b28475a\",\n \"score\": \"0.570502\",\n \"text\": \"def load_and_interpret(model_path, input_paths, baseline=-1, dictionary_path=None,\\n strip_alpha=False, smooth_factor=7, save=False, save_dir=None):\\n model_dir = os.path.dirname(model_path)\\n if save_dir is None:\\n save_dir = model_dir\\n network = keras.models.load_model(model_path)\\n input_type = network.input.dtype\\n input_shape = network.input.shape\\n n_channels = 0 if len(input_shape) == 3 else input_shape[3]\\n\\n dic = load_dict(dictionary_path)\\n if len(input_paths) == 1 and os.path.isdir(input_paths[0]):\\n loader = PathLoader(input_paths[0])\\n input_paths = [path[0] for path in loader.path_pairs]\\n inputs = [load_image(input_paths[i], strip_alpha=strip_alpha, channels=n_channels) for i in range(len(input_paths))]\\n max_shapes = np.maximum.reduce([inp.shape for inp in inputs], axis=0)\\n tf_image = tf.stack([tf.image.resize_with_crop_or_pad(tf.convert_to_tensor(im, dtype=input_type), max_shapes[0],\\n max_shapes[1]) for im in inputs], axis=0)\\n if is_number(baseline):\\n baseline_gen = tf.constant_initializer(float(baseline))\\n baseline_image = baseline_gen(shape=tf_image.shape, dtype=input_type)\\n else:\\n baseline_image = load_image(baseline)\\n baseline_image = tf.convert_to_tensor(baseline_image, dtype=input_type)\\n\\n interpret_model(network, tf_image, baseline_input=baseline_image, decode_dictionary=dic, smooth=smooth_factor,\\n save=save, save_path=save_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7d760a41edf5b19a21b94ede40c15fc\",\n \"score\": \"0.56957924\",\n \"text\": \"def read_and_preprocess(example_data):\\n # See create_dataset.py: cy - boxdef.N:cy + boxdef.N\\n height = 2 * params['predsize']\\n width = 2 * params['predsize']\\n parsed = tf.parse_single_example(\\n example_data, {\\n 'ref': tf.VarLenFeature(tf.float32),\\n 'ltg': tf.VarLenFeature(tf.float32),\\n 'has_ltg': tf.FixedLenFeature([], tf.int64, 1),\\n })\\n parsed['ref'] = _sparse_to_dense(parsed['ref'], height * width)\\n parsed['ltg'] = _sparse_to_dense(parsed['ltg'], height * width)\\n label = tf.cast(tf.reshape(parsed['has_ltg'], shape=[]), dtype=tf.int32)\\n print('shape of label {}'.format(label.shape))\\n\\n img = reshape_into_image(parsed, params)\\n return img, label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d61f25f3962525bc175054bd8af59c33\",\n \"score\": \"0.5683075\",\n \"text\": \"def read_in_images(self,files_input, files_labeled):\\n \\n post_process = False\\n \\n if post_process == False:\\n img_input = np.zeros((len(files_input),self.img_size[0]**2))\\n if self.classifier == \\\"membrane_synapse\\\":\\n img_labels = np.zeros((len(files_labeled),2*self.img_size[0]**2))\\n else:\\n img_labels = np.zeros((len(files_labeled),self.img_size[0]**2))\\n\\n for n in xrange(len(files_input)):\\n File = files_input[n]\\n img_temp = Image.open(File) \\n img_temp = np.array(img_temp).ravel() \\n img_input[n] = img_temp \\n\\n File = files_labeled[n]\\n img_temp = Image.open(File) \\n img_temp = np.array(img_temp)\\n img_temp = img_temp.ravel()\\n\\n img_temp.flags.writeable = True\\n\\n if self.classifier == 'membrane':\\n img_temp[img_temp>0] = 1\\n #img_temp = self.find_edges(img_temp)\\n elif self.classifier == 'synapse':\\n img_temp = self.find_synapse(img_temp,File)\\n elif self.classifier == 'membrane_synapse':\\n img_temp = self.find_mem_syn(img_temp,File)\\n\\n img_labels[n] = img_temp\\n\\n if self.classifier == \\\"membrane\\\" or self.classifier == \\\"synapse\\\":\\n output_dim = 1\\n elif self.classifier == \\\"membrane_synapse\\\":\\n output_dim = 2\\n else:\\n print \\\"Error: invalid classifier\\\"\\n exit()\\n\\n img_input = img_input.reshape(img_input.shape[0], self.img_size[0],self.img_size[1])\\n img_labels = img_labels.reshape(img_labels.shape[0], output_dim, self.img_size[0],self.img_size[1])\\n \\n else:\\n img_input = np.zeros((len(files_input),self.img_size[0]**2))\\n if self.classifier == \\\"membrane_synapse\\\":\\n img_labels = np.zeros((len(files_labeled),2*self.img_size[0]**2))\\n else:\\n img_labels = np.zeros((len(files_labeled),self.img_size[0]**2))\\n \\n for n in xrange(len(files_input)):\\n File = files_input[n]\\n img_temp = Image.open(File) \\n \\n if n == 0:\\n img_size_input = np.array(img_temp).shape[0]\\n img_size_input = (img_size_input,img_size_input)\\n gap = (self.img_size[0]-img_size_input[0])/2\\n \\n img_temp = img_temp.resize((self.img_size[0], self.img_size[1]), PIL.Image.ANTIALIAS) \\n img_temp = np.array(img_temp).ravel() \\n \\n img_input[n] = img_temp \\n\\n File = files_labeled[n]\\n img_temp = Image.open(File)\\n \\n if gap>0:\\n img_temp = np.array(img_temp)[gap:-gap,gap:-gap]\\n img_temp = Image.fromarray(np.uint8(img_temp))\\n img_temp = img_temp.resize((self.img_size[0], self.img_size[1]), PIL.Image.ANTIALIAS)\\n \\n img_temp = np.array(img_temp).reshape(img_temp.size)\\n img_temp.flags.writeable = True\\n\\n if self.classifier == 'membrane':\\n img_temp = self.find_edges(img_temp)\\n elif self.classifier == 'synapse':\\n img_temp = self.find_synapse(img_temp,File)\\n elif self.classifier == 'membrane_synapse':\\n img_temp = self.find_mem_syn(img_temp,File)\\n\\n img_labels[n] = img_temp.ravel()\\n \\n\\n if self.classifier == \\\"membrane\\\" or self.classifier == \\\"synapse\\\":\\n output_dim = 1\\n elif self.classifier == \\\"membrane_synapse\\\":\\n output_dim = 2\\n else:\\n print \\\"Error: invalid classifier\\\"\\n exit()\\n \\n img_input = img_input.reshape(img_input.shape[0], self.img_size[0],self.img_size[1])\\n img_labels = img_labels.reshape(img_labels.shape[0], output_dim, self.img_size[0],self.img_size[1])\\n\\n return img_input, img_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14bd3e8dd1e13bb1f9a37cab2a1273e9\",\n \"score\": \"0.5673028\",\n \"text\": \"def get_trainingImages(path2, zeroScale, oneScale, twoScale):\\n trainingImages=[]\\n for i in path2:\\n im = np.load(i)\\n im[0,:,:] = im[0,:,:]/zeroScale\\n im[1,:,:] = im[1,:,:]/oneScale\\n im[2,:,:] = im[2,:,:]/twoScale\\n trainingImages.append(im)\\n new_images = np.stack(trainingImages, axis = 3)\\n collman = np.transpose(new_images,(0,3,1,2))\\n \\n return trainingImages, collman\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38a6147c79e658244adf957ad1fd4801\",\n \"score\": \"0.5672668\",\n \"text\": \"def preprocess(filename, patch_size, augmentation, model_name):\\n # return image_resized, label\\n label = get_label(filename)\\n #label = tf.expand_dims(label, -1) # make each scalar label a vector of length 1 to match model\\n image = get_image(filename, patch_size) # float32 in [0, 1)\\n if augmentation:\\n image = augment(image)\\n image = tf.clip_by_value(image, 0, 1)\\n image = normalize(image, model_name)\\n return image, label, filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7716e39fb8bebdf786284946fac742ba\",\n \"score\": \"0.56632483\",\n \"text\": \"def process_img_from_path(img_path):\\n return preprocess_img(plt.imread(img_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d9da9e3e880d3d42d186a52ce5d37ad\",\n \"score\": \"0.5657443\",\n \"text\": \"def process_image(image_path: str) -> Dict[str, object]:\\n image = cv2.imread(image_path) # cv2 opens in BGR\\n return localizer_tensorflow_serving_inference(image, SERVING_URL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e3f92beb4c7ca65d79a2c2c9e7a3f1\",\n \"score\": \"0.56532025\",\n \"text\": \"def feat_extract(pretrained=False, **kwargs):\\n model_urls = {\\n 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\\n 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',\\n 'resnet52': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\\n 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',\\n 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',\\n }\\n logger = kwargs['opts'].logger\\n # resnet\\\"x\\\", x = 1 + sum(layers)x3\\n if kwargs['structure'] == 'resnet40':\\n model = ResNet(Bottleneck, [3, 4, 6], kwargs['in_c'])\\n elif kwargs['structure'] == 'resnet19':\\n model = ResNet(Bottleneck, [2, 2, 2], kwargs['in_c'])\\n elif kwargs['structure'] == 'resnet12':\\n dropblock_size = 5 if 'imagenet' in kwargs['opts'].dataset.name else 2\\n model = resnet12(avg_pool=False, drop_rate=0.1, dropblock_size=dropblock_size)\\n elif kwargs['structure'] == 'resnet52':\\n model = ResNet(Bottleneck, [4, 8, 5], kwargs['in_c'])\\n elif kwargs['structure'] == 'resnet34':\\n model = ResNet(Bottleneck, [3, 4, 4], kwargs['in_c'])\\n elif kwargs['structure'] == 'shallow':\\n model = CNNEncoder(kwargs['in_c'])\\n else:\\n raise NameError('structure not known {} ...'.format(kwargs['structure']))\\n if pretrained:\\n logger('Using pre-trained model from pytorch official webiste, {:s}'.format(kwargs['structure']))\\n model.load_state_dict(model_zoo.load_url(model_urls[kwargs['structure']]), strict=False)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c491e9ffce712c15be44bac96a0d81\",\n \"score\": \"0.56462336\",\n \"text\": \"def preprocess(self):\\n for part in ['part_A', 'part_B']:\\n for dataset in ['test_data', 'train_data']:\\n dataset_directory = os.path.join(self.database_directory, part, dataset)\\n ground_truth_directory = os.path.join(dataset_directory, 'ground-truth')\\n original_images_directory = os.path.join(dataset_directory, 'images')\\n for mat_filename in os.listdir(ground_truth_directory):\\n file_name = mat_filename[3:-3]\\n mat_path = os.path.join(ground_truth_directory, mat_filename)\\n original_image_path = os.path.join(original_images_directory, file_name + 'jpg')\\n image = imageio.imread(original_image_path)\\n mat = scipy.io.loadmat(mat_path)\\n head_positions = mat['image_info'][0, 0][0][0][0]\\n head_positions = self.get_y_x_head_positions(head_positions)\\n self.generate_labels_for_example(dataset_directory, file_name, image, head_positions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6940b4f31862597b7814b798f8ba2ed\",\n \"score\": \"0.56444764\",\n \"text\": \"def GetLoader(s_image_dir,c_image_dir, \\n style_selected_dir, content_selected_dir,\\n crop_size=178, batch_size=16, num_workers=8, \\n colorJitterEnable=True, colorConfig={\\\"brightness\\\":0.05,\\\"contrast\\\":0.05,\\\"saturation\\\":0.05,\\\"hue\\\":0.05}):\\n \\n s_transforms = []\\n c_transforms = []\\n \\n s_transforms.append(T.Resize(768))\\n # s_transforms.append(T.Resize(900))\\n c_transforms.append(T.Resize(768))\\n\\n s_transforms.append(T.RandomCrop(crop_size,pad_if_needed=True,padding_mode='reflect'))\\n c_transforms.append(T.RandomCrop(crop_size))\\n\\n s_transforms.append(T.RandomHorizontalFlip())\\n c_transforms.append(T.RandomHorizontalFlip())\\n \\n s_transforms.append(T.RandomVerticalFlip())\\n c_transforms.append(T.RandomVerticalFlip())\\n\\n if colorJitterEnable:\\n if colorConfig is not None:\\n print(\\\"Enable color jitter!\\\")\\n colorBrightness = colorConfig[\\\"brightness\\\"]\\n colorContrast = colorConfig[\\\"contrast\\\"]\\n colorSaturation = colorConfig[\\\"saturation\\\"]\\n colorHue = (-colorConfig[\\\"hue\\\"],colorConfig[\\\"hue\\\"])\\n s_transforms.append(T.ColorJitter(brightness=colorBrightness,\\\\\\n contrast=colorContrast,saturation=colorSaturation, hue=colorHue))\\n c_transforms.append(T.ColorJitter(brightness=colorBrightness,\\\\\\n contrast=colorContrast,saturation=colorSaturation, hue=colorHue))\\n s_transforms.append(T.ToTensor())\\n c_transforms.append(T.ToTensor())\\n\\n s_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))\\n c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))\\n \\n s_transforms = T.Compose(s_transforms)\\n c_transforms = T.Compose(c_transforms)\\n\\n content_dataset = TotalDataset(c_image_dir,s_image_dir, content_selected_dir, style_selected_dir\\n , c_transforms,s_transforms)\\n content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size,\\n drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True)\\n prefetcher = DataPrefetcher(content_data_loader)\\n return prefetcher\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ff9e95b3dbe60827c406210404fa7ab\",\n \"score\": \"0.56330514\",\n \"text\": \"def image_generation(model,\\n content_loss,\\n style_loss,\\n content_image,\\n style_image,\\n steps = 300):\\n generation_image = content_image.clone().detach().requires_grad_(True)\\n optimizer = optim.LBFGS([generation_image])\\n style_layer = ['conv1','conv2','conv3','conv4','conv5']\\n content_layer = ['conv4']\\n i = 0\\n name = \\\"\\\"\\n style_losses = []\\n content_losses = []\\n print(content_image.requires_grad)\\n print(style_image.requires_grad)\\n for step in tqdm(range(steps)):\\n def closure(generation_image,content_image,style_image):\\n for i,layer in enumerate(model.children()):\\n if isinstance(layer,nn.Conv2d):\\n name = \\\"conv%d\\\"%(i+1)\\n elif isinstance(layer,nn.ReLU):\\n name = \\\"relu\\\"\\n elif isinstance(layer,nn.MaxPool2d):\\n name = \\\"maxpool\\\"\\n if i == 0:\\n c = layer(content_image).detach()\\n s = layer(style_image).detach()\\n g = layer(generation_image).detach()\\n else:\\n c = layer(c).detach()\\n s = layer(s).detach()\\n g = layer(g).detach()\\n if name in style_layer:\\n style_losses.append(style_loss(generation_image,style_image))\\n elif name in content_layer:\\n content_losses.append(content_loss(generation_image,content_image))\\n loss = sum(style_losses) + sum(content_losses)\\n print(loss) \\n loss.backward()\\n return loss\\n optimizer.step(closure(generation_image,content_image,style_image))\\n return generation_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c7368a8d86d31f3aea4dd8da9453cd9\",\n \"score\": \"0.5624543\",\n \"text\": \"def extract_features(images,\\n is_training=True,\\n network_backbone='mobilenet_v2', \\n activation_fn=tf.nn.relu6,\\n normalizer_fn=tf.layers.BatchNormalization(),\\n output_stride=8):\\n with tf.variable_scope('Encoder', reuse=tf.AUTO_REUSE):\\n # Extract features\\n feature_map, low_level_features = feature_extractor(\\n images=images,\\n model_variant=network_backbone,\\n is_training=is_training)\\n \\n # Perform ASSP op\\n assp_features = atrous_spatial_pyramid_pooling(\\n network_backbone,\\n feature_map,\\n depth=256,\\n activation_fn=activation_fn,\\n normalizer_fn=normalizer_fn,\\n output_stride=8)\\n\\n # Merge all\\n with tf.variable_scope('1x1_Conv'):\\n encoded_features = tf.layers.Conv2D(256, (1, 1))(assp_features)\\n encoded_features = normalizer_fn(encoded_features)\\n encoded_features = activation_fn(encoded_features)\\n\\n return encoded_features, low_level_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0adc5c81a4bbed006ceb80b2a73aebd\",\n \"score\": \"0.5611783\",\n \"text\": \"def ComputeFeatures(self):\\n if self.train_images == None or self.test_images == None:\\n sys.exit(\\\"Please specify the corpus.\\\")\\n train_sizes = map(len, self.train_images)\\n train_size = sum(train_sizes)\\n test_sizes = map(len, self.test_images)\\n train_images = util.UngroupLists(self.train_images)\\n test_images = util.UngroupLists(self.test_images)\\n images = train_images + test_images\\n # Compute features for all images.\\n input_states = map(self.model.MakeStateFromFilename, images)\\n start_time = time.time()\\n features = self.ComputeFeaturesFromInputStates(input_states)\\n self.compute_feature_time = time.time() - start_time\\n # Split results by training/testing set\\n train_features, test_features = util.SplitList(features, [train_size])\\n # Split training set by class\\n train_features = util.SplitList(train_features, train_sizes)\\n # Split testing set by class\\n test_features = util.SplitList(test_features, test_sizes)\\n # Store features as list of 2D arrays\\n self.train_features = [ np.array(f, util.ACTIVATION_DTYPE)\\n for f in train_features ]\\n self.test_features = [ np.array(f, util.ACTIVATION_DTYPE)\\n for f in test_features ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9297f5dc129401c11d60c81e77fda72\",\n \"score\": \"0.5602348\",\n \"text\": \"def __load_and_unify_gen(self, image_paths: np.ndarray) -> (np.ndarray, str):\\n for img_path in image_paths:\\n img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE).astype(np.uint8)\\n filename = IOHelper.get_file_clear_name(img_path)\\n if not isinstance(img, np.ndarray):\\n raise FileNotFoundError('Unable to load image: {}'.format(img_path))\\n\\n desired_rows, desired_cols, desired_channels = self.__desired_shape\\n\\n # Decrease image shape if needed\\n rows_to_decrease = min(desired_rows, img.shape[0])\\n cols_to_decrease = min(desired_cols, img.shape[1])\\n changed_img = cv2.resize(img, (cols_to_decrease, rows_to_decrease)) # new width and height\\n\\n # Increase image shape if needed\\n rows_to_increase = max(desired_rows, img.shape[0])\\n cols_to_increase = max(desired_cols, img.shape[1])\\n rows_diff = abs(img.shape[0] - rows_to_increase)\\n cols_diff = abs(img.shape[1] - cols_to_increase)\\n\\n if rows_diff % 2 == 0:\\n top_rows_pad = int(rows_diff / 2)\\n bottom_rows_pad = int(rows_diff / 2)\\n else:\\n top_rows_pad = int(math.floor(rows_diff / 2))\\n bottom_rows_pad = int(math.floor(rows_diff / 2)) + 1\\n if cols_diff % 2 == 0:\\n left_cols_pad = int(cols_diff / 2)\\n right_cols_pad = int(cols_diff / 2)\\n else:\\n left_cols_pad = int(math.floor(cols_diff / 2))\\n right_cols_pad = int(math.floor(cols_diff / 2)) + 1\\n\\n if desired_channels == 1:\\n pad_color = 0\\n else:\\n pad_color = np.zeros((3,), dtype=np.uint8)\\n changed_img = cv2.copyMakeBorder(\\n changed_img,\\n top_rows_pad,\\n bottom_rows_pad,\\n left_cols_pad,\\n right_cols_pad,\\n cv2.BORDER_CONSTANT,\\n pad_color\\n )\\n\\n yield changed_img, filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47dfe568ce49659cbafb1501bdb400c8\",\n \"score\": \"0.55990183\",\n \"text\": \"def get_image_features(images_paths):\\n \\n # Extract images features\\n images = [load_image(x)[0] for x in images_paths]\\n \\n # x => (batch_size, 299, 299, 3)\\n x = tf.convert_to_tensor(images)\\n \\n # x => (batch_size, 10, 10, 2048)\\n x = extractor(x)\\n \\n # x => (batch_size, img_feature_size, 2048)\\n x = tf.reshape(x, (x.shape[0], -1, x.shape[3]))\\n \\n # features => (batch_size, img_feature_size, image_context_size)\\n features = encoder(x)\\n \\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a3d5e182f8e99ab4cd9fae083ca0eb\",\n \"score\": \"0.55980575\",\n \"text\": \"def gen_batch_function( image_shape):\\n def get_batches_fn(batch_size):\\n \\\"\\\"\\\"\\n Create batches of training data\\n :param batch_size: Batch Size\\n :return: Batches of training data\\n \\\"\\\"\\\"\\n \\n print(\\\"Working\\\")\\n image_paths=[]\\n label_paths=[]\\n counter=0\\n for dirname, _, filenames in os.walk('/content/cityscape_train/'):\\n counter+=1\\n \\n for filename in filenames:\\n #img_path = os.path.join(dirname, filename)\\n #image_paths.append(img_path)\\n \\n for dirname1, _, filenames1 in os.walk('/content/get_index/train'):\\n for filename1 in filenames1:\\n if(filename1==filename):\\n \\n gd_path = os.path.join(dirname1, filename1)\\n label_paths.append(gd_path)\\n img_path = os.path.join(dirname, filename)\\n image_paths.append(img_path)\\n # Loop through batches and grab images, yielding each batch\\n for batch_i in range(0, len(image_paths), batch_size):\\n images = []\\n gt_images = []\\n for idx , image_file in enumerate(image_paths[batch_i:batch_i+batch_size]):\\n gt_image_file = label_paths[batch_i+idx]\\n \\n # Re-size to image_shape\\n image = cv2.imread(image_file)\\n image = cv2.resize(image, (224,224), interpolation = cv2.INTER_AREA)\\n gt_image = cv2.imread(gt_image_file,cv2.IMREAD_GRAYSCALE)\\n gt_image = cv2.resize(gt_image, (224,224), interpolation = cv2.INTER_AREA)\\n # Create \\\"one-hot-like\\\" labels by class\\n color_list=[]\\n for color_id in range(19):\\n gt_bg = np.array(gt_image == color_id).astype(np.uint8)\\n gt_bg = gt_bg.reshape(*gt_bg.shape, 1)\\n color_list.append(gt_bg)\\n gt_image = np.concatenate(color_list, axis=2)\\n images.append(image)\\n gt_images.append(gt_image)\\n\\n yield np.array(images), np.array(gt_images)\\n return get_batches_fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1521feedd1762c7dca688b9a96a2762c\",\n \"score\": \"0.5571109\",\n \"text\": \"def import_data():\\n train_set = input_data.Data((FLAGS.image_size, FLAGS.image_size), (1,1))\\n eval_set = input_data.Data((FLAGS.image_size, FLAGS.image_size), (1,1))\\n\\n train_set.add_examples(\\\"../data/cascade/positives/positives2.png\\\", 7500, 100, 1)\\n train_set.add_examples(\\\"../data/detect/train/20038_negatives.png\\\", 10000, 100, 0)\\n \\n eval_set.add_examples(\\\"../data/detect/eval/1510_positives.png\\\", 1510, 100, 1)\\n #eval_set.add_examples(\\\"../data/detect/eval/4054_negatives.png\\\", 4054, 100, 0)\\n eval_set.add_examples(\\\"../data/test/3709_negatives.png\\\", 3710, 100, 0)\\n \\n train_set.finalize()\\n eval_set.finalize()\\n \\n return train_set, eval_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"880b2536283ff43bc1311b9ec84ec900\",\n \"score\": \"0.55643016\",\n \"text\": \"def extract_features(self, preprocessed_inputs):\\n image_shape = preprocessed_inputs.get_shape()\\n image_shape.assert_has_rank(4)\\n image_height = image_shape[1].value\\n image_width = image_shape[2].value\\n\\n if image_height is None or image_width is None:\\n shape_assert = tf.Assert(\\n tf.logical_and(tf.equal(tf.shape(preprocessed_inputs)[1], 256),\\n tf.equal(tf.shape(preprocessed_inputs)[2], 256)),\\n ['image size must be 256 in both height and width.'])\\n with tf.control_dependencies([shape_assert]):\\n preprocessed_inputs = tf.identity(preprocessed_inputs)\\n elif image_height != 256 or image_width != 256:\\n raise ValueError('image size must be = 256 in both height and width;'\\n ' image dim = %d,%d' % (image_height, image_width))\\n\\n feature_map_layout = {\\n 'from_layer': [\\n 'Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '', ''\\n ],\\n 'layer_depth': [-1, -1, 512, 256, 256],\\n 'conv_kernel_size': [-1, -1, 3, 3, 2],\\n 'use_explicit_padding': self._use_explicit_padding,\\n 'use_depthwise': self._use_depthwise,\\n }\\n\\n with tf.variable_scope('MobilenetV1',\\n reuse=self._reuse_weights) as scope:\\n with slim.arg_scope(\\n mobilenet_v1.mobilenet_v1_arg_scope(is_training=None)):\\n with (slim.arg_scope(self._conv_hyperparams_fn())\\n if self._override_base_feature_extractor_hyperparams\\n else context_manager.IdentityContextManager()):\\n _, image_features = mobilenet_v1.mobilenet_v1_base(\\n ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),\\n final_endpoint='Conv2d_13_pointwise',\\n min_depth=self._min_depth,\\n depth_multiplier=self._depth_multiplier,\\n use_explicit_padding=self._use_explicit_padding,\\n scope=scope)\\n with slim.arg_scope(self._conv_hyperparams_fn()):\\n feature_maps = feature_map_generators.multi_resolution_feature_maps(\\n feature_map_layout=feature_map_layout,\\n depth_multiplier=self._depth_multiplier,\\n min_depth=self._min_depth,\\n insert_1x1_conv=True,\\n image_features=image_features)\\n\\n return feature_maps.values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cd612ee8cfde9fcbc86d5980d22a945\",\n \"score\": \"0.5563253\",\n \"text\": \"def style_transfer(content_image, style_image,\\n content_layer_ids, style_layer_ids,\\n alpha=1.5, beta=10.0,\\n weight_denoise=0.3,\\n num_iterations=120, step_size=10.0):\\n initializeVGG16()\\n model = vgg16.VGG16()\\n\\n # Create a TensorFlow-session.\\n session = tf.InteractiveSession(graph=model.graph)\\n\\n # Print the names of the content-layers.\\n print()\\n print(\\\"Content layers:\\\")\\n print(model.get_layer_names(content_layer_ids))\\n print()\\n\\n # Print the names of the style-layers.\\n print(\\\"Style layers:\\\")\\n print(model.get_layer_names(style_layer_ids))\\n print()\\n\\n # Create the loss-function for the content-layers and -image.\\n loss_content = create_content_loss(session=session,\\n model=model,\\n content_image=content_image,\\n layer_ids=content_layer_ids)\\n\\n # Create the loss-function for the style-layers and -image.\\n loss_style = create_style_loss(session=session,\\n model=model,\\n style_image=style_image,\\n layer_ids=style_layer_ids) \\n\\n # Create the loss-function for the denoising of the mixed-image.\\n loss_denoise = create_denoise_loss(model)\\n\\n # Create TensorFlow variables for adjusting the values of\\n # the loss-functions. This is explained below.\\n\\n ## Think of these as bias\\n adj_content = tf.Variable(1e-10, name='adj_content')\\n adj_style = tf.Variable(1e-10, name='adj_style')\\n adj_denoise = tf.Variable(1e-10, name='adj_denoise')\\n\\n # Initialize the adjustment values for the loss-functions.\\n session.run([adj_content.initializer,\\n adj_style.initializer,\\n adj_denoise.initializer])\\n\\n # Create TensorFlow operations for updating the adjustment values.\\n # These are basically just the reciprocal values of the\\n # loss-functions, with a small value 1e-10 added to avoid the\\n # possibility of division by zero.\\n update_adj_content = adj_content.assign(1.0 / (loss_content + 1e-10))\\n update_adj_style = adj_style.assign(1.0 / (loss_style + 1e-10))\\n update_adj_denoise = adj_denoise.assign(1.0 / (loss_denoise + 1e-10))\\n\\n loss_combined = alpha * adj_content * loss_content + \\\\\\n beta * adj_style * loss_style + \\\\\\n weight_denoise * adj_denoise * loss_denoise\\n\\n # Compute gradient\\n gradient = tf.gradients(loss_combined, model.input)\\n\\n # List of tensors that we will run in each optimization iteration.\\n run_list = [gradient, update_adj_content, update_adj_style, update_adj_denoise]\\n\\n # The mixed-image is initialized with random noise.\\n # It is the same size as the content-image.\\n mixed_image = np.random.rand(*content_image.shape) + 128\\n\\n for i in range(num_iterations):\\n # Create a feed-dict with the mixed-image.\\n feed_dict = model.create_feed_dict(image=mixed_image)\\n\\n # Use TensorFlow to calculate the value of the\\n # gradient, as well as updating the adjustment values.\\n grad, adj_content_val, adj_style_val, adj_denoise_val = session.run(run_list, feed_dict=feed_dict)\\n\\n # Reduce the dimensionality of the gradient.\\n grad = np.squeeze(grad)\\n\\n # Scale the step-size according to the gradient-values.\\n step_size_scaled = step_size / (np.std(grad) + 1e-8)\\n\\n # Update the image by following the gradient.\\n ## Gradient descent\\n mixed_image -= grad * step_size_scaled\\n\\n # Ensure the image has valid pixel-values between 0 and 255.\\n mixed_image = np.clip(mixed_image, 0.0, 255.0)\\n\\n # Display status once every 10 iterations, and the last.\\n if (i % 10 == 0) or (i == num_iterations - 1):\\n print()\\n print(\\\"Epoch:\\\", i)\\n\\n # Print adjustment weights for loss-functions.\\n msg = \\\"Weight Adj. for Content: {0:.2e}, Style: {1:.2e}, Denoise: {2:.2e}\\\"\\n print(msg.format(adj_content_val, adj_style_val, adj_denoise_val))\\n \\n #imageUtils.save_image(mixed_image, \\\"mixed\\\" + str(i) + \\\".png\\\")\\n\\n # Close the TensorFlow session to release its resources.\\n session.close()\\n \\n # Return the mixed-image.\\n return mixed_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a086dd8bc3b8d8e088a227fff47a5406\",\n \"score\": \"0.5557795\",\n \"text\": \"def apply_template(template, content_values, resize_pixel_layers=True):\\n psd_file_path = Path(template[TEMPLATE_KEY_PSD_FILE])\\n color_mode = ColorMode(template[TEMPLATE_KEY_PSD_COLOR_MODE])\\n psd = get_psd_or_raise(psd_file_path)\\n\\n layers = get_all_psd_layers(psd)\\n # fonts dict to avoid create many instances of same font\\n fonts = {}\\n excluded_layers = list(template[TEMPLATE_KEY_EXCLUDED_LAYERS])\\n input_fields = template[TEMPLATE_KEY_INPUT_FIELDS]\\n layer_images = []\\n for field in input_fields:\\n input_value = None\\n try:\\n input_value = __get_field_value(field, content_values)\\n except KeyError:\\n raise FieldNotFilledError(field['value'])\\n\\n layer = __get_layer_by_id(\\n layers, field[TEMPLATE_KEY_INPUT_FIELD_LAYER_ID]\\n )\\n if PSDLayer(layer.kind) is PSDLayer.TYPE:\\n text = __apply_text_layer_filter(layer, input_value)\\n text_layer_properties = get_text_layer_properties(layer)\\n font, font_size, fill_color = text_layer_properties\\n layer_image = __get_image_type_field(\\n color_mode, fonts, field, layer, text\\n )\\n excluded_layers.append(layer.layer_id)\\n layer_images.append((field, layer, layer_image))\\n else:\\n layer_image = None\\n if is_url(input_value):\\n request_url = urlopen(input_value)\\n image_stream = BytesIO(request_url.read())\\n layer_image = Image.open(image_stream)\\n elif re.search(BASE64_IMAGE_PATTERN, input_value):\\n image_data = re.sub('^data:image/.+;base64,', '', input_value)\\n layer_image = Image.open(BytesIO(base64.b64decode(image_data)))\\n else:\\n layer_image = Image.open(input_value)\\n excluded_layers.append(layer.layer_id)\\n layer_images.append((field, layer, layer_image))\\n\\n final_image = render_psd(\\n psd_file_path, excluded_layers, original_size=True\\n )\\n for field, layer, layer_image in layer_images:\\n position = field[TEMPLATE_KEY_INPUT_FIELD_POSITION]\\n size = field[TEMPLATE_KEY_INPUT_FIELD_SIZE]\\n if PSDLayer(layer.kind) is PSDLayer.TYPE:\\n # value in this context is text\\n position = __get_text_poisition_by_aligment(layer, layer_image)\\n else:\\n if resize_pixel_layers:\\n layer_image = layer_image.resize(size)\\n else:\\n box = (0, 0, size[0], size[1])\\n layer_image = layer_image.resize(size, box=box)\\n insert_pil_image(final_image, layer_image, position)\\n\\n return final_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21032e23f1e00f80e84e05c77dcd3cf6\",\n \"score\": \"0.55558175\",\n \"text\": \"def _load_renderings(self, args):\\n # Load images.\\n imgdir_suffix = \\\"\\\"\\n if args.factor > 0:\\n imgdir_suffix = \\\"_{}\\\".format(args.factor)\\n factor = args.factor\\n else:\\n factor = 1\\n imgdir = path.join(args.data_dir, \\\"images\\\" + imgdir_suffix)\\n if not utils.file_exists(imgdir):\\n raise ValueError(\\\"Image folder {} doesn't exist.\\\".format(imgdir))\\n\\n # Sorting is important. Otherwise examples cannot be identified by indices.\\n imgfiles = [\\n path.join(imgdir, f)\\n for f in sorted(utils.listdir(imgdir))\\n if f.endswith(\\\"JPG\\\") or f.endswith(\\\"jpg\\\") or f.endswith(\\\"png\\\")\\n ]\\n if args.first_n_images:\\n imgfiles = imgfiles[:args.first_n_images]\\n images = []\\n for imgfile in imgfiles:\\n with utils.open_file(imgfile, \\\"rb\\\") as imgin:\\n image = np.array(Image.open(imgin), dtype=np.float32) / 255.\\n images.append(image)\\n # For shapenet, len(images)=24, images[0].shape=(224, 224, 3)\\n images = np.stack(images, axis=-1) # (224, 224, 3, 24)\\n logging.info(\\\"Loaded images: %s\\\", str(images.shape))\\n\\n # Load poses and bds.\\n with utils.open_file(path.join(args.data_dir, \\\"poses_bounds.npy\\\"),\\n \\\"rb\\\") as fp:\\n poses_arr = np.load(fp)\\n return self._process_renderings(args, images, poses_arr, factor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71b44d13fc4e214addfd8e0c70c599a3\",\n \"score\": \"0.55557287\",\n \"text\": \"def image_to_tf_data(img_path,\\n mask_path,\\n xml_path,\\n label_map_dict,\\n filename):\\n with tf.gfile.GFile(img_path, 'rb') as fid:\\n encoded_jpg = fid.read()\\n encoded_jpg_io = io.BytesIO(encoded_jpg)\\n image = PIL.Image.open(encoded_jpg_io)\\n width = np.asarray(image).shape[1]\\n height = np.asarray(image).shape[0]\\n if image.format != 'JPEG':\\n raise ValueError('Image format not JPEG')\\n key = hashlib.sha256(encoded_jpg).hexdigest()\\n\\n with tf.gfile.GFile(mask_path, 'rb') as fid:\\n encoded_mask_png = fid.read()\\n encoded_png_io = io.BytesIO(encoded_mask_png)\\n mask = PIL.Image.open(encoded_png_io)\\n mask_np = np.asarray(mask.convert('L'))\\n if mask.format != 'PNG':\\n raise ValueError('Mask format not PNG')\\n\\n data = []\\n classes = []\\n classes_text = []\\n xmins = []\\n ymins = []\\n xmaxs = []\\n ymaxs = []\\n encoded_mask_png_list = []\\n\\n if(True == FLAGS.bboxes_provided):\\n if not os.path.exists(xml_path):\\n logging.warning('Could not find %s, ignoring example.', xml_path)\\n return\\n with tf.gfile.GFile(xml_path, 'r') as fid:\\n xml_str = fid.read()\\n xml = etree.fromstring(xml_str)\\n data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation']\\n if 'object' in data:\\n for obj in data['object']:\\n class_name = obj['name']\\n pixel_val = int(label_map_dict[class_name][1])\\n xmin = float(obj['bndbox']['xmin'])\\n xmax = float(obj['bndbox']['xmax'])\\n ymin = float(obj['bndbox']['ymin'])\\n ymax = float(obj['bndbox']['ymax'])\\n print(filename, 'bounding box for',\\n class_name, xmin, xmax, ymin, ymax)\\n xmins.append(xmin / width)\\n ymins.append(ymin / height)\\n xmaxs.append(xmax / width)\\n ymaxs.append(ymax / height)\\n\\n classes_text.append(class_name.encode('utf8'))\\n classes.append(label_map_dict[class_name][0])\\n\\n mask_np_black = mask_np*0\\n mask_np_black[int(ymin):int(ymax), int(xmin):int(\\n xmax)] = mask_np[int(ymin):int(ymax), int(xmin):int(xmax)]\\n mask_remapped = (mask_np_black == pixel_val).astype(np.uint8)\\n img = PIL.Image.fromarray(mask_remapped)\\n output = io.BytesIO()\\n img.save(output, format='PNG')\\n encoded_mask_png_list.append(output.getvalue())\\n else:\\n for key in label_map_dict.keys():\\n class_name = key\\n pixel_val = int(label_map_dict[class_name][1])\\n nonbackground_indices_x = np.any(mask_np == pixel_val, axis=0)\\n nonbackground_indices_y = np.any(mask_np == pixel_val, axis=1)\\n nonzero_x_indices = np.where(nonbackground_indices_x)\\n nonzero_y_indices = np.where(nonbackground_indices_y)\\n\\n if np.asarray(nonzero_x_indices).shape[1] > 0 and np.asarray(nonzero_y_indices).shape[1] > 0:\\n xmin = float(np.min(nonzero_x_indices))\\n xmax = float(np.max(nonzero_x_indices))\\n ymin = float(np.min(nonzero_y_indices))\\n ymax = float(np.max(nonzero_y_indices))\\n print(filename, 'bounding box for',\\n class_name, xmin, xmax, ymin, ymax)\\n\\n xmins.append(xmin / width)\\n ymins.append(ymin / height)\\n xmaxs.append(xmax / width)\\n ymaxs.append(ymax / height)\\n\\n classes_text.append(class_name.encode('utf8'))\\n classes.append(label_map_dict[class_name][0])\\n\\n mask_remapped = (mask_np == pixel_val).astype(np.uint8)\\n img = PIL.Image.fromarray(mask_remapped)\\n output = io.BytesIO()\\n img.save(output, format='PNG')\\n encoded_mask_png_list.append(output.getvalue())\\n\\n feature_dict = {\\n 'image/height': dataset_util.int64_feature(height),\\n 'image/width': dataset_util.int64_feature(width),\\n 'image/filename': dataset_util.bytes_feature(filename.encode('utf8')),\\n 'image/source_id': dataset_util.bytes_feature(filename.encode('utf8')),\\n 'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),\\n 'image/encoded': dataset_util.bytes_feature(encoded_jpg),\\n 'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),\\n 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),\\n 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),\\n 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),\\n 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),\\n 'image/object/class/text': dataset_util.bytes_list_feature(classes_text),\\n 'image/object/class/label': dataset_util.int64_list_feature(classes),\\n 'image/object/mask': dataset_util.bytes_list_feature(encoded_mask_png_list)}\\n tf_data = tf.train.Example(\\n features=tf.train.Features(feature=feature_dict))\\n return tf_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a10c9311463ca95cba591461b0db28\",\n \"score\": \"0.55555487\",\n \"text\": \"def extract_features(self, preprocessed_inputs):\\n if self._depth_multiplier != 1.0:\\n raise ValueError('Depth multiplier not supported.')\\n\\n preprocessed_inputs = shape_utils.check_min_image_dim(\\n 129, preprocessed_inputs)\\n\\n feature_map_layout = {\\n 'from_layer': ['block2', 'block4', '', '', '', ''],\\n 'layer_depth': [-1, -1, 512, 256, 256, 128],\\n 'use_explicit_padding': self._use_explicit_padding,\\n 'use_depthwise': self._use_depthwise,\\n }\\n\\n with tf.variable_scope(\\n self._resnet_scope_name, reuse=self._reuse_weights) as scope:\\n with slim.arg_scope(resnet_v1.resnet_arg_scope()):\\n with (slim.arg_scope(self._conv_hyperparams_fn())\\n if self._override_base_feature_extractor_hyperparams else\\n context_manager.IdentityContextManager()):\\n _, image_features = self._resnet_base_fn(\\n inputs=ops.pad_to_multiple(preprocessed_inputs,\\n self._pad_to_multiple),\\n num_classes=None,\\n is_training=None,\\n global_pool=False,\\n output_stride=None,\\n store_non_strided_activations=True,\\n scope=scope)\\n image_features = self._filter_features(image_features)\\n\\n with slim.arg_scope(self._conv_hyperparams_fn()):\\n feature_maps = feature_map_generators.multi_resolution_feature_maps(\\n feature_map_layout=feature_map_layout,\\n depth_multiplier=self._depth_multiplier,\\n min_depth=self._min_depth,\\n insert_1x1_conv=True,\\n image_features=image_features)\\n\\n # print(\\\"^^^^^\\\")\\n # print(feature_maps)\\n # print(type(feature_maps))\\n # print(\\\"^^^^^\\\")\\n \\n return feature_maps.values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dce789c0a12084303431ea578755cc2f\",\n \"score\": \"0.5548635\",\n \"text\": \"def transfer_imgs(self, style_idx, char_id, save=False, save_name=\\\"\\\"):\\n\\n # Generate source image\\n src_img = self.generate_source_img(char_id, font='SimSun')\\n # Get font style embedding\\n font_cat_embedding = self.cat_embedding_dict[style_idx]\\n\\n # Transform the source image and embedding to construct input for model\\n inputs_data = self.data_process(src_img, font_cat_embedding, style_idx)\\n\\n self.model.set_input_inference(inputs_data)\\n self.model.test()\\n # output_img = self.model.fake_B\\n if save:\\n self.save_img(self.model.fake_B,'infered_{}_fake_B.png'.format(save_name))\\n self.save_img(self.src_img,'infered_{}_real_A.png'.format(save_name))\\n # return output_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdc8794d4787dcc989739b9b8ef051a0\",\n \"score\": \"0.55485266\",\n \"text\": \"def main(pb_file, img_file):\\n with tf.gfile.GFile(pb_file, \\\"rb\\\") as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n\\n with tf.Graph().as_default() as graph:\\n tf.import_graph_def(graph_def, name=prefix)\\n\\n for op in graph.get_operations():\\n print(op.name)\\n\\n x = graph.get_tensor_by_name('%s/input_1:0' % prefix)\\n y = graph.get_tensor_by_name('%s/output_0:0' % prefix)\\n\\n images = np.load(img_file).astype(float)\\n img_h = images.shape[1]\\n img_w = images.shape[2]\\n\\n with tf.Session(graph=graph) as sess:\\n for img in images:\\n batched = img.reshape(-1, img_h, img_w, 3)\\n normalized = standardize(batched)\\n\\n pred = sess.run(y, feed_dict={\\n x: normalized\\n })\\n plt.imshow(pred.reshape(img_h, img_w))\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0cae14afc4f95be0722adead7938b93\",\n \"score\": \"0.55448353\",\n \"text\": \"def _get_datasets(\\n dataset_path: str, batch_size: int, is_evaluation: bool = False,\\n):\\n # Build the dataset going through the classes directories and collecting the images:\\n images = []\\n labels = []\\n for label, directory in enumerate([\\\"with_mask\\\", \\\"without_mask\\\"]):\\n images_directory = os.path.join(dataset_path, directory)\\n images_files = [\\n os.path.join(images_directory, file)\\n for file in os.listdir(images_directory)\\n if os.path.isfile(os.path.join(images_directory, file))\\n ]\\n for image_file in images_files:\\n image = keras.preprocessing.image.load_img(\\n image_file, target_size=(224, 224)\\n )\\n image = keras.preprocessing.image.img_to_array(image)\\n image = keras.applications.mobilenet_v2.preprocess_input(image)\\n images.append(image)\\n labels.append(label)\\n\\n # Convert the images and labels to NumPy arrays\\n images = np.array(images, dtype=\\\"float32\\\")\\n labels = np.array(labels)\\n\\n # Perform one-hot encoding on the labels:\\n labels = LabelBinarizer().fit_transform(labels)\\n labels = keras.utils.to_categorical(labels)\\n\\n # Check if its an evaluation, if so, use the entire data:\\n if is_evaluation:\\n return images, labels\\n\\n # Split the dataset into training and validation sets:\\n x_train, x_test, y_train, y_test = train_test_split(\\n images, labels, test_size=0.2, stratify=labels, random_state=42,\\n )\\n\\n # Construct the training image generator for data augmentation:\\n image_data_generator = keras.preprocessing.image.ImageDataGenerator(\\n rotation_range=20,\\n zoom_range=0.15,\\n width_shift_range=0.2,\\n height_shift_range=0.2,\\n shear_range=0.15,\\n horizontal_flip=True,\\n fill_mode=\\\"nearest\\\",\\n )\\n\\n return (\\n image_data_generator.flow(x_train, y_train, batch_size=batch_size),\\n (x_test, y_test),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75819fd7b8c98ef255cab1a49d911165\",\n \"score\": \"0.5540418\",\n \"text\": \"def loadRawData(self, path, ft_dim, num_samples):\\n # Compile a list of JPG images in the source folder. Then verify that\\n # a) each is a valid JPG file and b) all images have the same size.\\n fnames = self.findTrainingFiles(path, num_samples)\\n\\n # Load and verify that the pickled meta data for each JPG file\\n # specifies the same set of class labels.\\n int2name = self.getLabelData(fnames)\\n num_cls = len(int2name)\\n\\n # Compute the height and width that input images must have to be\\n # compatible with the selected output feature size.\\n im_dim = orpac_net.waveletToImageDim(Shape(None, *ft_dim.hw()))\\n\\n # Fill in channel information: Images must always be RGB and the\\n # feature output channels are available via a utility method.\\n im_dim.chan = 3\\n ft_dim.chan = orpac_net.Orpac.numOutputChannels(num_cls)\\n\\n # Compile all the features that have not been compiled already.\\n self.compileMissingFeatures(fnames, ft_dim)\\n\\n # Load the compiled training data alongside each image.\\n train = self.loadTrainingData(fnames, im_dim, ft_dim, num_cls)\\n return im_dim, ft_dim, int2name, train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5a10679159a05417e9e902404728a6e\",\n \"score\": \"0.5539904\",\n \"text\": \"def generate_intermediate_format_from_files(filepath, platform, import_anims=True):\\n imported_namedata = NameInterface.from_file(filepath + '.name')\\n imported_skeldata = SkelInterface.from_file(filepath + '.skel')\\n imported_geomdata = GeomInterface.from_file(filepath + '.geom', platform)\\n\\n directory = os.path.split(filepath)\\n filename = directory[-1]\\n directory = os.path.join(*directory[:-1])\\n\\n # Always import the base anim, because it plays a special role in skeleton construction\\n imported_animdata = {filename: AnimInterface.from_file(filepath + '.anim')}\\n if import_anims:\\n for afile in os.listdir(directory):\\n afilepath = os.path.join(directory, afile)\\n if afile[-4:] == 'anim' and afile[:len(filename)] == filename and afile[:-4] != filename:\\n afile_name, afile_ext = os.path.splitext(afile)\\n print(afile)\\n imported_animdata[afile_name] = AnimInterface.from_file(afilepath)\\n\\n images_directory = os.path.join(*os.path.split(filepath)[:-1], 'images')\\n model_data = IntermediateFormat()\\n\\n model_data.material_name_hashes = {dscs_name_hash(name): name for name in imported_namedata.material_names}\\n model_data.bone_name_hashes = {dscs_name_hash(name): name for name in imported_namedata.bone_names}\\n add_meshes(model_data, imported_geomdata)\\n add_textures(model_data, imported_geomdata, images_directory)\\n add_materials(model_data, imported_namedata, imported_geomdata)\\n add_skeleton(model_data, imported_namedata, imported_skeldata, imported_geomdata)\\n add_anims(model_data, imported_animdata)\\n add_lights(model_data, imported_geomdata.light_sources)\\n model_data.cameras = imported_geomdata.cameras\\n\\n return model_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f092fa9ded5dd5ed276709da21c3335\",\n \"score\": \"0.5537652\",\n \"text\": \"def predict():\\n\\n import numpy as np\\n import torch\\n from tqdm import tqdm\\n import os\\n from matplotlib import pyplot as plt\\n from ..utils import Config\\n from ..layer import HomogeneousShapeLayer\\n from ..networks import SingleShapeNetwork\\n from shapedata.single_shape import SingleShapeDataProcessing, \\\\\\n SingleShapeSingleImage2D\\n from shapedata.io import pts_exporter\\n import argparse\\n\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-v\\\", \\\"--visualize\\\", action=\\\"store_true\\\",\\n help=\\\"If Flag is specified, results will be plotted\\\")\\n parser.add_argument(\\\"-d\\\", \\\"--in_path\\\", type=str, help=\\\"Input Data Dir\\\")\\n parser.add_argument(\\\"-s\\\", \\\"--out_path\\\", default=\\\"./outputs\\\", type=str,\\n help=\\\"Output Data Dir\\\")\\n parser.add_argument(\\\"-w\\\", \\\"--weight_file\\\", type=str, help=\\\"Model Weights\\\")\\n parser.add_argument(\\\"-c\\\", \\\"--config_file\\\", type=str, help=\\\"Configuration\\\")\\n\\n args = parser.parse_args()\\n config = Config()\\n config_dict = config(os.path.abspath(args.config_file))\\n\\n try:\\n net = torch.jit.load(os.path.abspath(args.weight_file))\\n net.eval()\\n net.cpu()\\n\\n except RuntimeError:\\n net_layer = HomogeneousShapeLayer\\n\\n if config_dict[\\\"training\\\"].pop(\\\"mixed_prec\\\", False):\\n try:\\n from apex import amp\\n amp.init()\\n except:\\n pass\\n\\n shapes = np.load(os.path.abspath(config_dict[\\\"layer\\\"].pop(\\\"pca_path\\\"))\\n )[\\\"shapes\\\"][:config_dict[\\\"layer\\\"].pop(\\\"num_shape_params\\\") + 1]\\n\\n net = SingleShapeNetwork(\\n net_layer, {\\\"shapes\\\": shapes,\\n **config_dict[\\\"layer\\\"]},\\n img_size=config_dict[\\\"data\\\"][\\\"img_size\\\"],\\n **config_dict[\\\"network\\\"])\\n\\n state = torch.load(os.path.abspath(args.weight_file))\\n try:\\n net.load_state_dict(state[\\\"state_dict\\\"][\\\"model\\\"])\\n except KeyError:\\n try:\\n net.load_state_dict(state[\\\"model\\\"])\\n except KeyError:\\n net.load_state_dict(state)\\n\\n net = net.to(\\\"cpu\\\")\\n net = net.eval()\\n\\n data = SingleShapeDataProcessing._get_files(\\n os.path.abspath(args.in_path), extensions=[\\\".png\\\", \\\".jpg\\\"])\\n\\n def process_sample(sample, img_size, net, device, crop=0.1):\\n lmk_bounds = sample.get_landmark_bounds(sample.lmk)\\n min_y, min_x, max_y, max_x = lmk_bounds\\n range_x = max_x - min_x\\n range_y = max_y - min_y\\n\\n center_x = min_x + range_x / 2\\n center_y = min_y + range_y / 2\\n\\n max_range = np.floor(max(range_x, range_y) * (1 + crop))\\n\\n tmp = sample.crop(center_y - max_range / 2,\\n center_x - max_range / 2,\\n center_y + max_range / 2,\\n center_x + max_range / 2)\\n\\n max_range += 1\\n\\n crop_range_x = tmp.img.shape[1]\\n crop_range_y = tmp.img.shape[0]\\n colour_channels = tmp.img.shape[2]\\n\\n # zero padding\\n if max_range - crop_range_x != 0:\\n img_temp = np.zeros((int(round(crop_range_y)),\\n int(round(max_range - crop_range_x)),\\n colour_channels))\\n\\n tmp.img = np.concatenate((tmp.img, img_temp), axis=1)\\n\\n # zero padding\\n if max_range - crop_range_y != 0:\\n img_temp = np.zeros((int(round(max_range - crop_range_y)),\\n int(round(max_range)),\\n colour_channels))\\n\\n tmp.img = np.concatenate((tmp.img, img_temp), axis=0)\\n\\n # convert to torch tensor\\n img_tensor = torch.from_numpy(\\n tmp.to_grayscale().resize((img_size, img_size)).img.transpose(2, 0,\\n 1)\\n ).to(torch.float).unsqueeze(0).to(device)\\n\\n # obtain prediction\\n pred = net(img_tensor)['pred'][0].cpu().numpy()\\n\\n # remap to original image\\n pred = pred * np.array([max_range / img_size, max_range / img_size])\\n img_add_bound_x = max(center_x - max_range / 2, 0)\\n img_add_bound_y = max(center_y - max_range / 2, 0)\\n pred = pred + np.asarray([img_add_bound_y,\\n img_add_bound_x])\\n return pred\\n\\n device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\\n with torch.no_grad():\\n\\n if torch.cuda.is_available():\\n net = net.cuda()\\n\\n if args.visualize:\\n pred_path = os.path.join(os.path.abspath(args.out_path), \\\"pred\\\")\\n vis_path = os.path.join(os.path.abspath(args.out_path),\\n \\\"visualization\\\")\\n os.makedirs(vis_path, exist_ok=True)\\n else:\\n pred_path = os.path.abspath(args.out_path)\\n\\n os.makedirs(pred_path, exist_ok=True)\\n\\n for idx, file in enumerate(tqdm(data)):\\n\\n _data = SingleShapeSingleImage2D.from_files(file)\\n\\n pred = process_sample(_data, img_size=config_dict[\\\"data\\\"][\\\"img_size\\\"], net=net,\\n device=device)\\n\\n fname = os.path.split(_data.img_file)[-1].rsplit(\\\".\\\", 1)[0]\\n\\n if args.visualize:\\n view_kwargs = {}\\n if _data.is_gray:\\n view_kwargs[\\\"cmap\\\"] = \\\"gray\\\"\\n fig = _data.view(True, **view_kwargs)\\n plt.gca().scatter(pred[:, 1], pred[:, 0], s=5, c=\\\"C1\\\")\\n plt.gca().legend([\\\"GT\\\", \\\"Pred\\\"])\\n plt.gcf().savefig(os.path.join(vis_path, fname + \\\".png\\\"))\\n plt.close()\\n\\n _data.save(pred_path, fname, \\\"PTS\\\")\\n pts_exporter(pred, os.path.join(pred_path, fname + \\\"_pred.pts\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"656cbef93594bbe96065f9f6ffee840d\",\n \"score\": \"0.5534027\",\n \"text\": \"def convert_DSB_to_semantic_masks(dataset_path, out_path):\\n\\n #Write output directories\\n out_images_path = os.path.join(out_path, \\\"images\\\")\\n out_masks_path = os.path.join(out_path, \\\"masks\\\")\\n\\n if not os.path.exists(out_images_path):\\n os.makedirs(out_images_path)\\n if not os.path.exists(out_masks_path):\\n os.makedirs(out_masks_path)\\n\\n num_images = len(os.listdir(dataset_path)) #Used for logging\\n\\n for i, subdir in enumerate(os.listdir(dataset_path)):\\n\\n if (i % 50 == 0):\\n print(f\\\"Processing image {i} out of {num_images}\\\")\\n\\n if not subdir.startswith('.') and os.path.isdir(os.path.join(dataset_path, subdir)): #Ignore hidden files, e.g. DSStore\\n\\n images_subdir = os.path.join(dataset_path, subdir, \\\"images\\\")\\n masks_subdir = os.path.join(dataset_path, subdir, \\\"masks\\\")\\n\\n image_size = [0, 0]\\n\\n for file in os.listdir(images_subdir):\\n #Read and save the image immediately\\n im = Image.open(os.path.join(images_subdir, file))\\n im.save(os.path.join(out_images_path, subdir+\\\".png\\\"))\\n image_size = [im.size[1], im.size[0]] #PIL reads in column order, so inverting size\\n\\n semantic_mask = np.zeros(image_size, dtype=\\\"uint8\\\")\\n\\n for file in os.listdir(masks_subdir):\\n mask = Image.open(os.path.join(masks_subdir, file))\\n mask_np = np.asarray(mask)\\n semantic_mask[mask_np == 255] = 255\\n\\n Image.fromarray(semantic_mask).save(os.path.join(out_masks_path, subdir+\\\".png\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb1944d128b9da04499dd246d93f03b\",\n \"score\": \"0.5529732\",\n \"text\": \"def run_on_images(self, img_paths, batch_size: int = 16):\\n assert (\\n self._dataset is not None\\n ), \\\"No dataset is give, cannot run extractor on images\\\"\\n\\n imageloader = IMAGE_LOADERS[self._extractor_name]\\n ds = dataloader_from_img_paths(img_paths, imageloader, batch_size)\\n\\n self._features_dir = _captionds_features_dir[self._dataset.name]\\n self._features_dir.mkdir(parents=True, exist_ok=True)\\n\\n def _obtain_feats_pth(p):\\n filepath = p.numpy().decode(\\\"utf-8\\\")\\n filename = Path(filepath).name\\n features_filepath = self._features_dir / f\\\"{filename}.npz\\\"\\n\\n self.im_to_features[filepath] = str(features_filepath.absolute())\\n\\n return features_filepath\\n\\n for im, im_pth in tqdm(ds):\\n feat_pths = [_obtain_feats_pth(p) for p in im_pth]\\n\\n if all([fp.exists() for fp in feat_pths]):\\n continue\\n\\n features = self.extraction_model(im)\\n features = [f for f in zip(*features)]\\n\\n for feats, fp in zip(features, feat_pths):\\n to_save = {\\n fn: f.numpy()\\n for f, fn in zip(feats, _extractors_features[self._extractor_name])\\n }\\n np.savez_compressed(fp, **to_save)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d65efa2d7f2f2ef552ded627cff8c6a\",\n \"score\": \"0.552233\",\n \"text\": \"def convert_images(input_path, output_path, image_paths, labels, crop_boxes):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab6d99edaafe57ab6a0b00e931f50b12\",\n \"score\": \"0.55158067\",\n \"text\": \"def get_image_folders():\\n\\n enhancers = {\\n 0: lambda image, f: ImageEnhance.Color(image).enhance(f),\\n 1: lambda image, f: ImageEnhance.Contrast(image).enhance(f),\\n 2: lambda image, f: ImageEnhance.Brightness(image).enhance(f),\\n 3: lambda image, f: ImageEnhance.Sharpness(image).enhance(f)\\n }\\n\\n # intensities of enhancers\\n factors = {\\n 0: lambda: np.clip(np.random.normal(1.0, 0.3), 0.4, 1.6),\\n 1: lambda: np.clip(np.random.normal(1.0, 0.15), 0.7, 1.3),\\n 2: lambda: np.clip(np.random.normal(1.0, 0.15), 0.7, 1.3),\\n 3: lambda: np.clip(np.random.normal(1.0, 0.3), 0.4, 1.6),\\n }\\n\\n # randomly change color of an image\\n def enhance(image):\\n order = [0, 1, 2, 3]\\n np.random.shuffle(order)\\n # random enhancers in random order\\n for i in order:\\n f = factors[i]()\\n image = enhancers[i](image, f)\\n return image\\n\\n def rotate(image):\\n degree = np.clip(np.random.normal(0.0, 15.0), -40.0, 40.0)\\n return image.rotate(degree, Image.BICUBIC)\\n\\n # training data augmentation on the fly\\n train_transform = transforms.Compose([\\n transforms.Lambda(rotate),\\n transforms.RandomCrop(56),\\n transforms.RandomHorizontalFlip(),\\n transforms.Lambda(enhance),\\n transforms.ToTensor(),\\n transforms.Normalize(\\n mean=[0.485, 0.456, 0.406],\\n std=[0.229, 0.224, 0.225]\\n ),\\n ])\\n\\n # for validation data\\n val_transform = transforms.Compose([\\n transforms.CenterCrop(56),\\n transforms.ToTensor(),\\n transforms.Normalize(\\n mean=[0.485, 0.456, 0.406],\\n std=[0.229, 0.224, 0.225]\\n ),\\n ])\\n\\n # mean and std are taken from here:\\n # http://pytorch.org/docs/master/torchvision/models.html\\n\\n train_folder = ImageFolder(TRAIN_DIR, train_transform)\\n val_folder = ImageFolder(VAL_DIR, val_transform)\\n return train_folder, val_folder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa8be5e5892bff91e9446892b5f63b62\",\n \"score\": \"0.5491997\",\n \"text\": \"def main(args):\\n vgg = caffe.Net(\\n VGG_PROTOTXT, VGG_MODEL, caffe.TEST,\\n )\\n\\n style = Art(vgg, args)\\n\\n # Collect art from the wikiart folder\\n if args.artist:\\n try:\\n args.style_images = glob.glob(\\n './wikiart/{}/*'.format(args.artist)\\n )\\n except Exception as e:\\n print \\\"Couldn't get images for artist {}, check dir!\\\".format(\\n args.artist\\n )\\n raise e\\n\\n num_simages = len(args.style_images)\\n\\n if num_simages > 2:\\n sample = args.style_images[:2]\\n else:\\n sample = args.style_images\\n\\n # Make the directory for this run\\n raw_dirname = './img/{}__{}-w{}-{}'.format(\\n args.content_image.split('.')[0],\\n '_'.join(os.path.basename(s).split('.')[0] for s in sample),\\n str(args.width),\\n str(args.numiter)\\n )\\n\\n # Get a unique dirname if the directory already exists\\n dirname = raw_dirname\\n unique = 1\\n while os.path.exists(dirname):\\n dirname = raw_dirname + '-' + str(unique)\\n unique += 1\\n\\n debug_print(\\\"Starting {}\\\".format(dirname))\\n\\n os.mkdir(dirname)\\n\\n # TODO this is bad, but whatever. save this dirname as an attr\\n style.dirname = dirname\\n\\n # NOTE: For now, we assume style weights are equal\\n style_image_weights = [1. / num_simages] * num_simages\\n\\n # Get style and content targets\\n debug_print(\\\"Setting up content targets\\\")\\n style.set_content_target(args.content_image)\\n debug_print(\\\"Setting up style targets\\\")\\n style.set_style_targets(args.style_images, style_image_weights)\\n debug_print(\\\"Done initialization\\\")\\n\\n # Get the candidate image\\n debug_print(\\\"Running gradient descent...\\\")\\n new_img = style.go(maxiter=args.numiter)\\n\\n debug_print(\\\"Done gradient descent, saving image...\\\")\\n\\n imsave(\\n '{}/final.jpg'.format(dirname),\\n skimage.img_as_ubyte(new_img)\\n )\\n\\n debug_print(\\\"Done: Saved {}\\\".format(dirname))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9743f67db7b1f2214477740b1f161245\",\n \"score\": \"0.5490244\",\n \"text\": \"def get_preprocess(self, *args, **kwargs) -> Dict:\\n input_shape = get_input_shape(self.deploy_cfg)\\n model_cfg = process_model_config(self.model_cfg, [''], input_shape)\\n pipeline = model_cfg.test_pipeline\\n data_preprocessor = self.model_cfg.model.data_preprocessor\\n\\n lift = dict(type='Lift', transforms=[])\\n lift['transforms'].append(dict(type='LoadImageFromFile'))\\n transforms2index = {}\\n for i, trans in enumerate(pipeline):\\n transforms2index[trans['type']] = i\\n lift_key = [\\n 'Resize', 'Normalize', 'TenCrop', 'ThreeCrop', 'CenterCrop'\\n ]\\n for key in lift_key:\\n if key == 'Normalize':\\n assert key not in transforms2index\\n mean = data_preprocessor.get('mean', [0, 0, 0])\\n std = data_preprocessor.get('std', [1, 1, 1])\\n trans = dict(type='Normalize', mean=mean, std=std, to_rgb=True)\\n lift['transforms'].append(trans)\\n if key in transforms2index:\\n index = transforms2index[key]\\n if key == 'Resize' and 'scale' in pipeline[index]:\\n value = pipeline[index].pop('scale')\\n if len(value) == 2 and value[0] == -1:\\n value = value[::-1]\\n pipeline[index]['size'] = value\\n lift['transforms'].append(pipeline[index])\\n\\n meta_keys = [\\n 'valid_ratio', 'flip', 'img_norm_cfg', 'filename', 'ori_shape',\\n 'pad_shape', 'img_shape', 'flip_direction', 'scale_factor',\\n 'ori_filename'\\n ]\\n other = []\\n must_key = ['FormatShape', 'PackActionInputs']\\n for key in must_key:\\n assert key in transforms2index\\n index = transforms2index[key]\\n if key == 'PackActionInputs':\\n if 'meta_keys' in pipeline[index]:\\n meta_keys += pipeline[index]['meta_keys']\\n pipeline[index]['meta_keys'] = list(set(meta_keys))\\n pipeline[index]['keys'] = ['img']\\n pipeline[index]['type'] = 'Collect'\\n other.append(pipeline[index])\\n\\n reorder = [lift, *other]\\n return reorder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3082f5c2c0f84ca5271b810143288c55\",\n \"score\": \"0.5476209\",\n \"text\": \"def data_prep(path, img_rows, img_cols, color):\\n \\n images = []\\n labels = []\\n for image_class in os.listdir(path):\\n print('image_class =', image_class)\\n path_to_class_directory = os.path.join(path, image_class)\\n for img_name in os.listdir(path_to_class_directory):\\n true_path = os.path.join(path_to_class_directory, img_name)\\n if color:\\n images.append(cv2.imread(true_path, 1)/255.0)\\n else:\\n images.append(cv2.imread(true_path, 0)/255.0) # greyscale\\n labels.append(os.listdir(path).index(image_class))\\n data = list(zip(images, labels))\\n np.random.shuffle(data)\\n images, labels = zip(*data)\\n images = [cv2.resize(img, (img_rows, img_cols), cv2.INTER_AREA) for img in images] # resize images to all be the same\\n if color:\\n images = np.array(images).reshape(len(images), img_rows, img_cols, 3)\\n else:\\n images = np.array(images).reshape(len(images), img_rows, img_cols, 1)\\n labels = keras.utils.to_categorical(labels, num_classes=len(os.listdir(path)))\\n return images, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4010ccd369871617bebea9f159372b52\",\n \"score\": \"0.5472248\",\n \"text\": \"def Keras_multiple_features_extraction(folder,base_model, preprocessing, layer, crop_size, save_activations,out_prefix, GUI=None):\\t\\n # Get a proper name for the .txt list of images\\n s=os.sep\\n gender=folder.split(s)[-1]\\n \\n # Create a list of images\\n mf.list_generator(folder, gender, 'png')\\n \\n # Create folders in which store features and activations files\\n mf.folders_creator('Results',['Features','Activations'])\\n \\n # Read .txt list of images and load it as list\\n f=open(gender+'.txt','r')\\n lines = f.readlines()\\n lines = [line.rstrip() for line in lines]\\n f.close()\\n \\n \\n # Take 1st word of each line as an image name, creating a names list\\n img_list = [line.split(' ')[0] for line in lines]\\n # Take 2nd word of each line as a class, creating a labels list\\n lab = [line.split(' ')[1] for line in lines]\\n \\n if GUI!=None:\\n # If GUI mode is ON, initialize a Progress bar\\n currentImage=Label(GUI, text=' '*50)\\n currentImage.grid(row = 0, column =1)\\n progress = Progressbar(GUI, orient=\\\"horizontal\\\",length=200, mode=\\\"determinate\\\")\\n progress.grid(row=1, column=1)\\n value = 0\\n progress[\\\"value\\\"] = 0\\n progress[\\\"maximum\\\"] = len(lines)\\n step=1\\n \\n\\n \\n # Initialize some usefull empty lists \\n feats=[]\\n img_ids=[]\\n labels=[]\\n \\n # Initialize to 0 some counters and flag\\n i=0\\n count=0\\n file_created=0\\n \\n \\n # Process all images in the list\\n for im in img_list:\\n \\n if i==0:\\n # Clear terminal\\n try:\\n os.system('clear')\\n except:\\n os.system('cls')\\n print('Features Extraction from ' + folder)\\n # Initialize progress bar\\n bar=progressbar.ProgressBar(max_value=len(lines))\\n \\n # Update progress bar\\n bar.update(i)\\n \\n \\n # Extract features from image\\n feat=Keras_extract_feature(base_model, preprocessing, im, crop_size, layer)\\n \\n \\n if save_activations==True:\\n if i==0:\\n # Create folders in which store features and activations files\\n mf.folders_creator('Results'+s+'Activations'+s+gender)\\n # Visualize activations\\n M=mf.visualize_activations(np.swapaxes(np.squeeze(feat),2,0))\\n \\n # Reshape features matrix to an array\\n feat=feat.reshape((1,feat.size))\\n feat=np.squeeze(feat)\\n \\n # Append features array to features list\\n feats.append(feat)\\n # Append label to labels list\\n labels.append(int(lab[i]))\\n \\n if GUI!=None:\\n # If GUI mode is ON, update Progress bar value\\n a=value/len(lines)*100\\n currentImage[\\\"text\\\"]='Processing...'+\\\"{0:.1f}\\\".format(round(a,1)) +' % '\\n currentImage.update()\\n value += step\\n progress[\\\"value\\\"] = int(value)\\n progress.update()\\n \\n # Abbreviete current image path and append it to image ids list\\n tmp=im.split(s)\\n im=s.join([tmp[-2],tmp[-1]])\\n img_ids.append(np.string_(im))\\n \\n \\n if save_activations==True:\\n # Write activations\\n n='Results'+s+'Activations'+s+gender+s+out_prefix+'_'+im.split(s)[1]\\n mf.save_as_image(M,n)\\n \\n if file_created == 0:\\n # Create an Hierarchical Data Format (.h5) file in which\\n # store Features, Labels and Names of the processed images\\n feats_shape = [len(img_list),feat.shape[0]];\\n name='Results'+s+'Features'+s+out_prefix+'_'+gender+'.h5'\\n f = h5py.File(name,\\\"w\\\")\\n feats_dset = f.create_dataset(\\\"feats\\\",feats_shape,compression=\\\"gzip\\\")\\n img_id_dset = f.create_dataset(\\\"img_id\\\",(len(img_list),),dtype=\\\"S40\\\")\\n labels_dset = f.create_dataset(\\\"labels\\\",(len(img_list),),compression=\\\"gzip\\\")\\n file_created = 1\\n \\n N=100\\t\\t\\t\\n if (i+1) % N == 0:\\n # Every N processed images save data to the created .h5 file\\n # and clean the list variables to empty the RAM\\n \\n # Convert lists to numpy arrays\\n feats=np.asarray(feats)\\n labels=np.asarray(labels)\\n \\n # Write data \\n feats_dset[i-N+1:i+1] = feats \\n img_id_dset[i-N+1:i+1] = img_ids\\n labels_dset[i-N+1:i+1] = labels\\n \\n # Clean list variables\\n feats = []\\n labels = []\\n img_ids=[]\\n \\n count=-1\\n \\n i+=1\\n count+=1\\n \\n # Convert lists to numpy arrays\\n feats=np.asarray(feats)\\n labels=np.asarray(labels)\\n # Write remaining data\\n feats_dset[i-count:i+1] = feats\\n img_id_dset[i-count:i+1] = img_ids\\n labels_dset[i-count:i+1] = labels\\n # Clean list variables\\n feats = []\\n labels = []\\n img_ids=[]\\n \\n # Close file\\n f.close()\\n\\n if GUI!=None:\\n # If GUI mode is ON, update Progress bar value\\n currentImage[\\\"text\\\"]=' Completed '\\n currentImage.update()\\n value += step\\n progress[\\\"value\\\"] = int(value)\\n progress.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b69cc4dba3d6f49211243aa314ffb44\",\n \"score\": \"0.54700017\",\n \"text\": \"def process_path(file_path):\\n\\n label_path = tf.strings.join(\\n [f\\\"./{data_dir}/split-label/\\\", tf.strings.split(file_path, os.sep)[-1]]\\n )\\n image = tf.io.read_file(file_path)\\n image = tf.image.decode_png(image, channels=3)\\n label = tf.io.read_file(label_path)\\n label = tf.image.decode_png(label, channels=3)\\n\\n return image, label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2944cb1bd36c2180f11127995b4f515a\",\n \"score\": \"0.5464237\",\n \"text\": \"def get_style_model_and_losses(\\n cnn,\\n normalization_mean,\\n normalization_std,\\n style_img,\\n content_img,\\n content_layers=content_layers_default,\\n style_layers=style_layers_default,\\n):\\n cnn = copy.deepcopy(cnn)\\n\\n normalization = Normalization(normalization_mean, normalization_std).to(device)\\n\\n content_losses = []\\n style_losses = []\\n\\n model = nn.Sequential(normalization)\\n\\n i = 0\\n for layer in cnn.children():\\n if isinstance(layer, nn.Conv2d):\\n i += 1\\n name = \\\"conv_{}\\\".format(i)\\n elif isinstance(layer, nn.ReLU):\\n name = \\\"relu_{}\\\".format(i)\\n layer = nn.ReLU(inplace=False)\\n elif isinstance(layer, nn.MaxPool2d):\\n name = \\\"pool_{}\\\".format(i)\\n elif isinstance(layer, nn.BatchNorm2d):\\n name = \\\"bn_{}\\\".format(i)\\n else:\\n raise RuntimeError(\\\"Unrecognized layer: {}\\\".format(layer.__class__.__name__))\\n\\n model.add_module(name, layer)\\n\\n if name in content_layers:\\n target = model(content_img).detach()\\n content_loss = ContentLoss(target)\\n model.add_module(\\\"content_loss_{}\\\".format(i), content_loss)\\n content_losses.append(content_loss)\\n\\n if name in style_layers:\\n target_feature = model(style_img).detach()\\n style_loss = StyleLoss(target_feature)\\n model.add_module(\\\"style_loss_{}\\\".format(i), style_loss)\\n style_losses.append(style_loss)\\n\\n for i in range(len(model) - 1, -1, -1):\\n if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):\\n break\\n\\n model = model[: (i + 1)]\\n\\n return model, style_losses, content_losses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2f2aea9432b78f0720e2bb6c943529\",\n \"score\": \"0.5463096\",\n \"text\": \"def preprocess_data():\\n input_args = get_input_args()\\n data_dir = input_args.dir\\n\\n train_dir = data_dir + 'train'\\n valid_dir = data_dir + 'valid'\\n test_dir = data_dir + 'test'\\n\\n train_transforms = transforms.Compose([transforms.RandomRotation(30),\\n transforms.RandomResizedCrop(224),\\n transforms.RandomHorizontalFlip(),\\n transforms.ToTensor(),\\n transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])\\n valid_transforms = transforms.Compose([transforms.Resize(256),\\n transforms.CenterCrop(224),\\n transforms.ToTensor(),\\n transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])\\n test_transforms = transforms.Compose([transforms.Resize(256),\\n transforms.CenterCrop(224),\\n transforms.ToTensor(),\\n transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])\\n \\n\\n \\n train_data = datasets.ImageFolder(train_dir, transform=train_transforms)\\n valid_data = datasets.ImageFolder(valid_dir, transform=valid_transforms)\\n test_data = datasets.ImageFolder(test_dir, transform=test_transforms)\\n \\n\\n \\n trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\\n validloader = torch.utils.data.DataLoader(valid_data, batch_size=32)\\n testloader = torch.utils.data.DataLoader(test_data, batch_size=32)\\n \\n\\n return train_data, valid_data, test_data, trainloader, validloader, testloader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e9c3d9afb1fd6af18a4e48d81c622ac\",\n \"score\": \"0.5438395\",\n \"text\": \"def image_preprocessing(self):\\n\\t\\tdef pil_loader(path):\\n\\t\\t\\t\\\"\\\"\\\"Load images from /eval/ subfolder, convert to greyscale and resized it as squared\\\"\\\"\\\"\\n\\t\\t\\twith open(path, 'rb') as f:\\n\\t\\t\\t\\twith Image.open(f) as img:\\n\\t\\t\\t\\t\\tsqrWidth = np.ceil(np.sqrt(img.size[0]*img.size[1])).astype(int)\\n\\t\\t\\t\\t\\treturn img.convert('L').resize((sqrWidth, sqrWidth))\\n\\n\\t\\tkwargs = {'num_workers': 1, 'pin_memory': True} if self._cuda else {}\\n\\t\\tself._eval_loader = torch.utils.data.DataLoader(ImageFolder(root=self._evalf,\\n\\t\\t\\ttransform=transforms.Compose([\\n\\t\\t\\t\\t\\t\\t transforms.Scale(28),\\n\\t\\t\\t\\t\\t\\t transforms.CenterCrop(28),\\n\\t\\t\\t\\t\\t\\t transforms.ToTensor(),\\n\\t\\t\\t\\t\\t\\t transforms.Normalize((0.1307,), (0.3081,))\\n\\t\\t\\t\\t\\t ]), loader=pil_loader), batch_size=1, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee9da6b52dc9c791d5295c70d9f22578\",\n \"score\": \"0.54379606\",\n \"text\": \"def splitTransform(self):\\n\\t\\t#path_merge = \\\"transform\\\"\\n\\t\\t#path_train = \\\"transform/data/\\\"\\n\\t\\t#path_label = \\\"transform/label/\\\"\\n\\t\\tpath_merge = \\\"deform/deform_norm2\\\"\\n\\t\\tpath_train_image = \\\"deform/train/image/\\\"\\n\\t\\tpath_train_label = \\\"deform/train/label/\\\"\\n\\n\\t\\t#\\n\\t\\ttrain_imgs = glob.glob(path_merge+\\\"/*.\\\"+self.img_type)\\n\\t\\t#\\n\\t\\tfor imgname in train_imgs:\\n\\n\\t\\t\\timg = cv2.imread(imgname)\\n\\t\\t\\timg_train = img[:,:,2]#cv2 read image rgb->bgr\\n\\t\\t\\timg_label = img[:,:,0]\\n\\t\\t\\t#\\n\\t\\t\\tmidname = imgname[imgname.rindex(\\\"/\\\")+1:imgname.rindex(\\\".\\\"+self.img_type)]\\n\\t\\t\\tcv2.imwrite(path_train_image+midname+\\\".\\\"+self.img_type,img_train)\\n\\t\\t\\tcv2.imwrite(path_train_label+midname+\\\".\\\"+self.img_type,img_label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c75371d4dc61002f1573538e44751f0d\",\n \"score\": \"0.5435713\",\n \"text\": \"def get_features(image, model, layers=None):\\r\\n \\r\\n ## Need the layers for the content and style representations of an image\\r\\n if layers is None:\\r\\n layers = {'0': 'conv1_1', \\r\\n \\\"5\\\": \\\"conv2_1\\\", \\r\\n \\\"10\\\": \\\"conv3_1\\\", \\r\\n \\\"19\\\": \\\"conv4_1\\\", \\r\\n '21': 'conv4_2',\\r\\n \\\"28\\\": \\\"conv5_1\\\"}\\r\\n \\r\\n features = {}\\r\\n x = image\\r\\n # model._modules is a dictionary holding each module in the model\\r\\n for name, layer in model._modules.items():\\r\\n x = layer(x)\\r\\n if name in layers:\\r\\n features[layers[name]] = x\\r\\n \\r\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b8591f20fd64e9c42449825b4c4af8a\",\n \"score\": \"0.54354364\",\n \"text\": \"def process(self, input_image_path):\\n image_raw, image_resized = self._load_and_resize(input_image_path)\\n image_preprocessed = self._shuffle_and_normalize(image_resized)\\n return image_raw, image_preprocessed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d2cd3e5afe107f23f918ee0603cc72e\",\n \"score\": \"0.54243505\",\n \"text\": \"def load_fashion_mnist():\\n dirname = 'fashion-mnist'\\n files = [\\n 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',\\n 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'\\n ]\\n\\n paths = [os.path.join(FLAGS.datasets_dir, dirname, fname) for fname in files]\\n\\n with tf.gfile.GFile(paths[0], 'rb') as f:\\n lbpath = gzip.GzipFile(fileobj=f)\\n y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)\\n\\n with tf.gfile.GFile(paths[1], 'rb') as f:\\n imgpath = gzip.GzipFile(fileobj=f)\\n x_train = np.frombuffer(\\n imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)\\n\\n with tf.gfile.GFile(paths[2], 'rb') as f:\\n lbpath = gzip.GzipFile(fileobj=f)\\n y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)\\n\\n with tf.gfile.GFile(paths[3], 'rb') as f:\\n imgpath = gzip.GzipFile(fileobj=f)\\n x_test = np.frombuffer(\\n imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)\\n\\n return (x_train, y_train), (x_test, y_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cb5a1d7302691ea8047e50417032813\",\n \"score\": \"0.54200256\",\n \"text\": \"def process_img(content, new_size = [128, 128]):\\n img = tf.io.read_file(content)\\n img = tf.io.decode_png(img)\\n\\n img = tf.image.convert_image_dtype(img, tf.float32)\\n # img = tf.image.resize(img, new_size)\\n img = tf.scalar_mul(tf.constant(1/255), img, name = \\\"img\\\")\\n # Text read\\n\\n return img, new_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baa6827a14499a829fa46d41ca04beee\",\n \"score\": \"0.54034704\",\n \"text\": \"def compute_features(vgg_weights, pooling_type, input_img, layers):\\n input = tf.placeholder(tf.float32, shape=input_img.shape)\\n net = Model.build_image_net(input, vgg_weights, pooling_type)\\n features = {}\\n with tf.Session() as sess:\\n for layer in layers:\\n features[layer] = sess.run(net[layer], feed_dict={input: input_img})\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc269245a86c1c5625f7c2fede6bffa7\",\n \"score\": \"0.5393708\",\n \"text\": \"def get_hpatches_data(files, norm, **config):\\n n = tf.constant(255.)\\n\\n def _get_shape(image):\\n return tf.shape(image)[:2]\\n\\n def _read_image(path):\\n return cv.imread(path.decode('utf-8'))\\n\\n def _preprocess(image, norm):\\n tf.Tensor.set_shape(image, [None, None, 3])\\n image = tf.image.rgb_to_grayscale(image)\\n if config['preprocessing']['resize']:\\n image = ratio_preserving_resize(image, **config['preprocessing'])\\n if norm:\\n image = tf.cast(tf.cast(image, dtype=tf.float32) / n, dtype=tf.float32)\\n else:\\n image = tf.cast(image, dtype=tf.float32)\\n return image\\n\\n def _adapt_homography_to_preprocessing(zip_data):\\n H = tf.cast(zip_data['homography'], dtype=tf.float32)\\n source_size = tf.cast(zip_data['shape'], dtype=tf.float32)\\n source_warped_size = tf.cast(zip_data['warped_shape'], dtype=tf.float32)\\n target_size = tf.cast(tf.convert_to_tensor(config['preprocessing']['resize']), dtype=tf.float32)\\n\\n # Compute the scaling ratio due to te resizing for both images\\n s = tf.reduce_max(tf.cast(tf.divide(target_size, source_size), dtype=tf.float32))\\n up_scale = tf.diag(tf.stack([1. / s, 1. / s, tf.constant(1.)]))\\n warped_s = tf.reduce_max(tf.divide(target_size, source_warped_size))\\n down_scale = tf.diag(tf.stack([warped_s, warped_s, tf.constant(1.)]))\\n\\n # Compute the translation due to the crop for both images\\n pad_y = tf.cast((source_size[0]*s - target_size[0]) / tf.constant(2.), dtype=tf.int32)\\n pad_x = tf.cast((source_size[1]*s - target_size[1]) / tf.constant(2.), dtype=tf.int32)\\n translation = tf.stack([tf.constant(1), tf.constant(0), pad_x,\\n tf.constant(0), tf.constant(1), pad_y,\\n tf.constant(0), tf.constant(0), tf.constant(1)])\\n translation = tf.cast(tf.reshape(translation, [3, 3]), dtype=tf.float32)\\n pad_y = tf.cast((source_warped_size[0]*warped_s - target_size[0]) / tf.constant(2.), dtype=tf.int32)\\n pad_x = tf.cast((source_warped_size[1]*warped_s - target_size[1]) / tf.constant(2.), dtype=tf.int32)\\n warped_translation = tf.stack([tf.constant(1), tf.constant(0), -pad_x,\\n tf.constant(0), tf.constant(1), -pad_y,\\n tf.constant(0), tf.constant(0), tf.constant(1)])\\n warped_translation = tf.cast(tf.reshape(warped_translation, [3, 3]), dtype=tf.float32)\\n\\n H = tf.matmul(warped_translation, tf.matmul(down_scale, tf.matmul(H, tf.matmul(up_scale, translation))))\\n return H\\n\\n def add_mask(homography):\\n H = tf.cast(homography, dtype=tf.float32)\\n H_new = H * tf.constant([[1., 1., 1.], [1., 1., 1.], [0.9, 0.9, 0.9]])\\n H_flat = invert_homography(mat2flat(H_new[tf.newaxis, ...]))\\n size = tf.constant([216, 288])\\n mask = H_transform(tf.ones(size), H_flat, interpolation='NEAREST')\\n kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (config['valid_border_margin']*2,)*2)\\n kernel[0, 1], kernel[3, 0], kernel[3, 3] = 1, 0, 0\\n mask = tf.nn.erosion2d(mask[tf.newaxis, ..., tf.newaxis], tf.cast(tf.constant(kernel)[..., tf.newaxis],\\n dtype=tf.float32),\\n [1, 1, 1, 1], [1, 1, 1, 1], 'SAME')[0, ..., 0] + 1.\\n return {'H': H, 'mask': mask}\\n\\n images = tf.data.Dataset.from_tensor_slices(files['image_paths'])\\n images = images.map(lambda path: tf.py_func(_read_image, [path], tf.uint8))\\n warped_images = tf.data.Dataset.from_tensor_slices(files['warped_image_paths'])\\n warped_images = warped_images.map(lambda path: tf.py_func(_read_image, [path], tf.uint8))\\n homographies = tf.data.Dataset.from_tensor_slices(np.array(files['homography']))\\n if config['preprocessing']['resize']:\\n shapes = images.map(_get_shape)\\n warped_shapes = warped_images.map(_get_shape)\\n homographies = tf.data.Dataset.zip({'homography': homographies, 'shape': shapes, 'warped_shape': warped_shapes})\\n homographies = homographies.map(_adapt_homography_to_preprocessing)\\n if config['events']:\\n homographies = homographies.map(add_mask)\\n\\n images = images.map(lambda d: _preprocess(d, norm=norm))\\n warped_images = warped_images.map(lambda d: _preprocess(d, norm=norm))\\n\\n data = tf.data.Dataset.zip({'image': images, 'warped_image': warped_images, 'homography': homographies})\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"866c85bdff14291cef49d8ddb1e69325\",\n \"score\": \"0.5392781\",\n \"text\": \"def extract_features(self, preprocessed_inputs):\\n preprocessed_inputs = shape_utils.check_min_image_dim(\\n 33, preprocessed_inputs)\\n padded_inputs = ops.pad_to_multiple(\\n preprocessed_inputs, self._pad_to_multiple)\\n\\n feature_map_layout = {\\n 'from_layer': ['C4', 'C5', '', '', '', ''],\\n # Do not specify the layer depths (number of filters) for C4 and C5, as\\n # their values are determined based on the backbone.\\n 'layer_depth': [-1, -1, 512, 256, 256, 128],\\n 'use_depthwise': self._use_depthwise,\\n 'use_explicit_padding': self._use_explicit_padding,\\n }\\n\\n with tf.variable_scope(self._scope_name, reuse=self._reuse_weights):\\n with slim.arg_scope([slim.batch_norm],\\n is_training=self._is_training,\\n epsilon=0.01, decay=0.99, center=True, scale=True):\\n endpoints = self._backbone_fn(\\n padded_inputs,\\n multiplier=self._depth_multiplier)\\n\\n image_features = {'C4': endpoints['C4'], 'C5': endpoints['C5']}\\n with slim.arg_scope(self._conv_hyperparams_fn()):\\n feature_maps = feature_map_generators.multi_resolution_feature_maps(\\n feature_map_layout=feature_map_layout,\\n depth_multiplier=self._depth_multiplier,\\n min_depth=self._min_depth,\\n insert_1x1_conv=True,\\n image_features=image_features)\\n\\n return list(feature_maps.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1343c7c985d4328dad9ebeaacdecf90f\",\n \"score\": \"0.53893375\",\n \"text\": \"def bi_pre_process(width, height, path, label_name):\\n #containers for pre-processed image data and class labels\\n data = []\\n labels = []\\n\\n #images directory contains 3 sub-directories: 'poison_ivy', 'poison_oak', 'poison_sumac'\\n #randomly get image paths and shuffle them\\n # current path 'C:\\\\\\\\Users\\\\\\\\jltsa\\\\\\\\Desktop\\\\\\\\Project_2\\\\\\\\images'\\n image_paths = sorted(list(paths.list_images(path)))\\n random.seed(42)\\n random.shuffle(image_paths)\\n\\n #preprocess images to width x height pixels as required for LeNet\\n for image_path in image_paths:\\n image = cv2.imread(image_path)\\n if image is not None:\\n image = cv2.resize(image, (width, height))\\n image = img_to_array(image)\\n data.append(image)\\n \\n #Extract class labels\\n label = image_path.split(os.path.sep)[-2]\\n if label == label_name:\\n label = 0\\n else:\\n label = 1\\n labels.append(label)\\n \\n #Scal pixel intensities from 0 to 1\\n data = np.array(data, dtype='float') / 255.0\\n labels = np.array(labels)\\n \\n return data, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a9ed512ebf016f2d8761fef81ef545\",\n \"score\": \"0.53843814\",\n \"text\": \"def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),\\n hist_bins=32, orient=9, \\n pix_per_cell=8, cell_per_block=2, hog_channel=0,\\n spatial_feat=True, hist_feat=True, hog_feat=True):\\n\\n \\n features = []\\n for file in imgs:\\n file_features = []\\n image = cv2.imread(file)\\n if color_space != 'RGB':\\n if color_space == 'HSV':\\n feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\\n elif color_space == 'LUV':\\n feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2LUV)\\n elif color_space == 'HLS':\\n feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)\\n elif color_space == 'YUV':\\n feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)\\n elif color_space == 'YCrCb':\\n feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)\\n else: feature_image = np.copy(image) \\n\\n #Compute spatial features\\n if spatial_feat == True:\\n spatial_features = bin_spatial(feature_image, size=spatial_size)\\n file_features.append(spatial_features)\\n #Compute color histogram features\\n if hist_feat == True:\\n hist_features = color_hist(feature_image, nbins=hist_bins)\\n file_features.append(hist_features)\\n #Compute hog features\\n if hog_feat == True:\\n if hog_channel == 'ALL':\\n hog_features = []\\n for channel in range(feature_image.shape[2]):\\n hog_features.append(get_hog_features(feature_image[:,:,channel], \\n orient, pix_per_cell, cell_per_block, \\n vis=False, feature_vec=True))\\n hog_features = np.ravel(hog_features) \\n else:\\n hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, \\n pix_per_cell, cell_per_block, vis=False, feature_vec=True)\\n # Append the new feature vector to the features list\\n file_features.append(hog_features)\\n features.append(np.concatenate(file_features))\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c44e340ecf1e9de4ff5e6a31f3202888\",\n \"score\": \"0.5380845\",\n \"text\": \"def process2(path, img_size=(160, 160), batch_size=2000):\\n\\n datagen = tf.keras.preprocessing.image.ImageDataGenerator()\\n\\n\\n generator = datagen.flow_from_directory(\\n path,\\n target_size=img_size,\\n batch_size=batch_size,\\n class_mode='binary',\\n shuffle=False\\n )\\n\\n\\n return generator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b345ff186508b86e96fea9e1a1bf70\",\n \"score\": \"0.53800577\",\n \"text\": \"def train_or_load_character_recognition_model(train_image_paths, serialization_folder):\\n # TODO - Istrenirati model ako vec nije istreniran, ili ga samo ucitati iz foldera za serijalizaciju\\n # Big letters\\n colored_image_1 = load_image(train_image_paths[0])\\n binary_image_1 = image_bin(image_gray(colored_image_1))\\n kernel_11 = np.ones((2, 2)) # bigger\\n dilated_image_11 = cv.dilate(invert(binary_image_1), kernel_11, iterations=2)\\n kernel_2 = np.ones((3, 3)) # smaller\\n eroded_image_1 = cv.erode(dilated_image_11, kernel_2, iterations=4)\\n selected_regions_1, letters_1, _ = select_roi(colored_image_1.copy(), eroded_image_1)\\n inputs_1 = prepare_for_ann(letters_1)\\n # print('Number of detected regions:', len(letters_1))\\n # cv.imwrite('big.png', selected_regions_1)\\n\\n # Small letters\\n colored_image_2 = load_image(train_image_paths[1])\\n binary_image_2 = image_bin(image_gray(colored_image_2))\\n kernel_21 = np.ones((2, 2)) # bigger\\n dilated_image_21 = cv.dilate(invert(binary_image_2), kernel_21, iterations=1)\\n kernel_22 = np.ones((3, 3)) # smaller\\n eroded_image_2 = cv.erode(dilated_image_21, kernel_22, iterations=5)\\n selected_regions_2, letters_2, _ = select_roi(colored_image_2.copy(), eroded_image_2)\\n inputs_2 = prepare_for_ann(letters_2)\\n # print('Number of detected regions:', len(letters_2))\\n # cv.imwrite('lil.png', selected_regions_2)\\n\\n alphabet = ['A', 'B', 'C', 'Č', 'Ć', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R',\\n 'S', 'Š', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'Ž', 'a', 'b', 'c', 'č', 'ć', 'd', 'e', 'f', 'g', 'h',\\n 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 'š', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'ž']\\n outputs = convert_output(alphabet)\\n\\n # Trained model - ann\\n ann = load_trained_ann(serialization_folder)\\n if ann is None:\\n print(\\\"Training of the model started.\\\")\\n ann = create_ann()\\n ann = train_ann(ann, inputs_1 + inputs_2, outputs)\\n print(\\\"Training of the model finished.\\\")\\n serialize_ann(ann, serialization_folder)\\n\\n return ann\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1045e429c79dde142436d8bcf5de095\",\n \"score\": \"0.53792596\",\n \"text\": \"def demoSingleImage(imagePath, classifierModelTrained):\\n\\tnet = objectClassifierArgs(numClasses=10, train=False)\\n\\tnet.load_state_dict(torch.load(classifierModelTrained))\\n\\tnet = net.to(device)\\n\\tnet.eval()\\n\\tclasses = {0:'T-shirt/top', 1:'Trouser', 2:'Pullover', 3:'Dress', 4:'Coat', 5:'Sandal', 6:'Shirt', 7:'Sneaker', 8:'Bag', 9:'Ankle boot'}\\n\\n\\timg = cv2.imread(imagePath)\\n\\timg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n\\timg = Image.fromarray(img)\\n\\timg = PIL.ImageOps.invert(img)\\n\\tif not args.useModel == 'vgg16_bn':\\n\\t\\tpreprocess = transforms.Compose([transforms.Resize((224,224), interpolation=2),transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])\\n\\t\\timg_tensor = preprocess(img)\\n\\t\\timg_variable = Variable(img_tensor.unsqueeze(0)).to(device)\\n\\t\\timg_variable = torch.squeeze(torch.stack((img_variable,img_variable,img_variable),2),0)\\n\\telse:\\n\\t\\tif useModel == 'CustomNet2':\\n\\t\\t\\tpreprocess = transforms.Compose([transforms.Resize((28,28), interpolation=2),transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])\\n\\t\\t\\timg_tensor = preprocess(img_pil)\\n\\t\\t\\timg_variable = Variable(img_tensor.unsqueeze(0)).to(device)\\n\\t\\telif useModel == 'CustomVGG16_BN':\\n\\t\\t\\tweight_softmax = np.squeeze(params[-8].data.cpu().numpy())\\n\\t\\t\\tpreprocess = transforms.Compose([transforms.Resize((224,224), interpolation=2),transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])\\n\\t\\t\\timg_tensor = preprocess(img_pil)\\n\\t\\t\\timg_variable = Variable(img_tensor.unsqueeze(0)).to(device)\\n\\t\\t\\timg_variable = torch.squeeze(torch.stack((img_variable,img_variable,img_variable),2),0)\\n\\t\\t\\n\\n\\t\\n\\tlogit = net(img_variable)\\n\\tprobs, preds = torch.max(logit, 1)\\n\\tprint('output for the top1 prediction: %s' % classes[preds.item()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81052c365c22b415a178e0d953602e1c\",\n \"score\": \"0.536082\",\n \"text\": \"def process_img(self,img_path):\\n original = load_img(filename, target_size = self.target_size)\\n numpy_image = self.preprocessing( img_to_array(original))\\n image_batch = np.expand_dims(numpy_image, axis =0)\\n return image_batch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b940ce5885c98241c5ac5d4e700eabcc\",\n \"score\": \"0.535503\",\n \"text\": \"def extract_deep_feats(img_fname):\\n if not os.path.exists('./features'):\\n os.makedirs('./features')\\n\\n img = imread(img_fname, mode='RGB')\\n img = (img - img.mean()) / img.std()\\n img = imresize(img, [224, 224])\\n\\n graph = tf.Graph()\\n with graph.as_default():\\n input_maps = tf.placeholder(tf.float32, [None, 224, 224, 3])\\n output, average_image, class_names = vgg_face(config['vgg_face_model_mat_file'], input_maps)\\n\\n img = (img - img.mean()) / img.std()\\n img = imresize(img, [224, 224])\\n\\n # run the graph\\n with tf.Session(graph=graph) as sess:\\n [out] = sess.run([output], feed_dict={input_maps: [img]})\\n\\n feat = {}\\n\\n print(out.keys())\\n\\n for k, v in out.items():\\n if k in ['conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1',\\n 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2',\\n 'conv5_3', 'relu5_3', 'pool5', 'fc6', 'relu6', 'fc7', 'relu7', 'fc8']:\\n feat[k] = v.tolist()\\n\\n print(out)\\n with open('./features/{0}.pkl'.format(img_fname.split('/')[-1].split('.')[0]), mode='wb') as f:\\n pickle.dump(feat, f)\\n\\n sess.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69725dbe4df2d22a17d14075ae40d568\",\n \"score\": \"0.53544015\",\n \"text\": \"def demo(path_to_data):\\n color_mapping = {'difficult': [185, 51, 173], # purple\\n 'gametocyte': [255, 99, 25], # orange\\n 'leukocyte': [0, 0, 255], # blue\\n 'red blood cell': [255, 0, 0], # red\\n 'schizont': [252, 204, 10], # yellow\\n 'ring': [153, 102, 51], # brown\\n 'trophozoite': [0, 147, 60] # green\\n }\\n\\n with open(path_to_data + \\\"/training.json\\\") as json_file:\\n files = json.load(json_file)\\n fig, axs = plt.subplots(ncols=2, nrows=2)\\n for file in files:\\n # read the image\\n file_path = file['image']['pathname']\\n fig.suptitle(file_path + \\\"\\\\nclick on plot to see next image\\\")\\n print(f\\\"processing file: {file_path}\\\")\\n start_time = timeit.default_timer()\\n image = io.imread(path_to_data + file_path)\\n\\n # no background removal (top right)\\n processed1 = apply_pre_processing(image, background_rem=False, morph_filter=False)\\n axs[0][1].imshow(processed1)\\n axs[0][1].set_title(\\\"no background rem.\\\")\\n # background removal, w/o morphological filter on background mask (bottom left)\\n processed2 = apply_pre_processing(image, background_rem=True, morph_filter=False)\\n axs[1][0].imshow(processed2)\\n axs[1][0].set_title(\\\"background rem. (no morph)\\\")\\n # background removal with morph. filters on background mask (bottom right)\\n processed3 = apply_pre_processing(image, background_rem=True, morph_filter=True)\\n axs[1][1].imshow(processed3)\\n axs[1][1].set_title(\\\"background rem. (with morph)\\\")\\n\\n # draw bounding boxes\\n for obj in file['objects']:\\n bounding_box = obj['bounding_box']\\n minimum = bounding_box['minimum']\\n start = (minimum['r'], minimum['c'])\\n maximum = bounding_box['maximum']\\n stop = (maximum['r'] - 2, maximum['c'] - 2)\\n rr, cc = draw.rectangle_perimeter(start=start, end=stop)\\n image[rr, cc] = color_mapping[obj['category']]\\n\\n # image before pre-processing with bounding boxes (top left)\\n axs[0][0].imshow(image)\\n axs[0][0].set_title(\\\"input with ground truth\\\")\\n fig.show()\\n print(f\\\"elapsed time: {timeit.default_timer() - start_time} seconds\\\")\\n plt.ginput() # wait for click before doing more processing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e608d7cd3ecfcc3cf0c3bd441322d87\",\n \"score\": \"0.5349802\",\n \"text\": \"def get_preprocess(self, *args, **kwargs) -> Dict:\\n input_shape = get_input_shape(self.deploy_cfg)\\n model_cfg = process_model_config(self.model_cfg, [''], input_shape)\\n pipeline = model_cfg.test_dataloader.dataset.pipeline\\n meta_keys = [\\n 'filename', 'ori_filename', 'ori_shape', 'img_shape', 'pad_shape',\\n 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg',\\n 'valid_ratio'\\n ]\\n transforms = [\\n item for item in pipeline if 'Random' not in item['type']\\n and 'Annotation' not in item['type']\\n ]\\n for i, transform in enumerate(transforms):\\n if transform['type'] == 'PackTextDetInputs':\\n meta_keys += transform[\\n 'meta_keys'] if 'meta_keys' in transform else []\\n transform['meta_keys'] = list(set(meta_keys))\\n transform['keys'] = ['img']\\n transforms[i]['type'] = 'Collect'\\n if transform['type'] == 'Resize':\\n transforms[i]['size'] = transforms[i].pop('scale')\\n\\n if 'data_preprocessor' in model_cfg.model:\\n data_preprocessor = model_cfg.model.data_preprocessor\\n elif 'MMDetWrapper' == self.model_cfg.model.type:\\n data_preprocessor = model_cfg.model.cfg.data_preprocessor\\n else:\\n raise ValueError(f'Unsupported model config {model_cfg.model} ')\\n transforms.insert(-1, dict(type='DefaultFormatBundle'))\\n transforms.insert(\\n -2,\\n dict(\\n type='Pad',\\n size_divisor=data_preprocessor.get('pad_size_divisor', 1)))\\n transforms.insert(\\n -3,\\n dict(\\n type='Normalize',\\n to_rgb=data_preprocessor.get('bgr_to_rgb', False),\\n mean=data_preprocessor.get('mean', [0, 0, 0]),\\n std=data_preprocessor.get('std', [1, 1, 1])))\\n return transforms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3e4848b6e31eb4efa7d86ec08d12d87\",\n \"score\": \"0.53478116\",\n \"text\": \"def create_model_input(folder_path):\\n image_paths = []\\n images = []\\n file_names = os.listdir(folder_path)\\n for image_name in file_names:\\n \\n # Reading image the same way it is done while training\\n img = np.asarray(Image.open(os.path.join(folder_path, image_name)).convert('RGB'))[:, :, ::-1]\\n \\n images.append(img)\\n image_paths.append(image_name)\\n return images, image_paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90490a6992b0bfd4d646667bbefc3f47\",\n \"score\": \"0.5344868\",\n \"text\": \"def _preprocess_image_for_classification(image_url, model_descriptor):\\n image_file = _download_image(image_url)\\n\\n input_shape = model_descriptor[\\\"arguments\\\"][\\\"input_shape\\\"]\\n transpose = False\\n if 'transpose' in model_descriptor[\\\"arguments\\\"]:\\n transpose = True\\n if len(input_shape) == 4 and not transpose:\\n dimensions = input_shape[1:3]\\n elif len(input_shape) == 4 and transpose:\\n dimensions = input_shape[2:4]\\n elif len(input_shape) == 3 and not transpose:\\n dimensions = input_shape[0:2]\\n elif len(input_shape) == 3 and transpose:\\n dimensions = input_shape[1:3]\\n\\n orig_image = tf.keras.preprocessing.image.load_img(\\n image_file, target_size=dimensions\\n )\\n numpy_image = tf.keras.preprocessing.image.img_to_array(orig_image)\\n if transpose:\\n # if transpose is set to true then expected input is CHW, instead of HWC\\n numpy_image = numpy_image.transpose([2, 0, 1])\\n\\n if len(input_shape) == 4:\\n numpy_image = np.expand_dims(numpy_image, axis=0)\\n processed_image = tf.keras.applications.imagenet_utils.preprocess_input(\\n numpy_image, mode=\\\"caffe\\\"\\n )\\n\\n return processed_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d5f395e968627627de3d5a613d57c2b\",\n \"score\": \"0.5340327\",\n \"text\": \"def build_vocabulary(image_paths, vocab_size):\\n\\n # TODO: Implement this function!\\n # vocab_size = 10000\\n num_imgs = len(image_paths)\\n images = []\\n for i in range(num_imgs):\\n image = imread(image_paths[i], as_gray=True)\\n images.append(image)\\n\\n features = []\\n descriptors = [\\n \\\"hog\\\",\\n \\\"gaussian_pyramid\\\",\\n # \\\"gist\\\",\\n # \\\"daisy\\\",\\n ]\\n ################### baseline implementation ################\\n if \\\"hog\\\" in descriptors:\\n output = Parallel(n_jobs=-1)(delayed(hog_feature)(i) for i in images)\\n output = np.concatenate(np.array(output), axis=0)\\n features.append(output)\\n\\n ################## pyramid gaussian #####################\\n if \\\"gaussian_pyramid\\\" in descriptors:\\n output = Parallel(n_jobs=-1)(\\n delayed(gaussian_pyramid_feature)(i) for i in images\\n )\\n output = np.concatenate(np.array(output), axis=0)\\n features.append(output)\\n\\n #################### gist descriptor #####################\\n if \\\"gist\\\" in descriptors:\\n output = Parallel(n_jobs=-1)(delayed(gist_feature)(i) for i in images)\\n output = np.concatenate(np.array(output), axis=0)\\n features.append(output)\\n\\n if \\\"daisy\\\" in descriptors:\\n output = Parallel(n_jobs=-1)(delayed(daisy_feature)(i) for i in images)\\n output = np.concatenate(np.array(output), axis=0)\\n features.append(output)\\n\\n if \\\"sift\\\" in descriptors:\\n output = Parallel(n_jobs=-1)(delayed(daisy_feature)(i) for i in images)\\n output = np.concatenate(np.array(output), axis=0)\\n features.append(output)\\n\\n features = np.concatenate(np.array(features), axis=0)\\n kmeans = cluster.MiniBatchKMeans(\\n n_clusters=vocab_size,\\n init_size=3 * max(vocab_size, 100),\\n max_iter=100,\\n )\\n kmeans.fit(features)\\n vocab = kmeans.cluster_centers_\\n return vocab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854dbc7131ca1b2b6dcac6df334f2a1e\",\n \"score\": \"0.5330453\",\n \"text\": \"def run_content_traversal(\\n model: pl.LightningModule,\\n content_code: torch.Tensor,\\n style_code: torch.Tensor,\\n traversal_start: Union[float, Iterable[float]],\\n traversal_end: Union[float, Iterable[float]],\\n n_traversals: int,\\n show: bool = True,\\n title: str = '',\\n to_save: bool = True,\\n out_path: Optional[Path] = None,\\n verbose: bool=False,\\n) -> torch.Tensor:\\n is_training = model.training\\n model.eval()\\n content_dim = content_code.shape[-1]\\n try:\\n traversal_start[0]\\n except TypeError:\\n traversal_start = torch.zeros(content_dim).fill_(traversal_start)\\n try:\\n traversal_end[0]\\n except TypeError:\\n traversal_end = torch.zeros(content_dim).fill_(traversal_end)\\n with torch.no_grad():\\n # Traverse for each dim\\n grids = [] # k,v = dim_i, batch of recons while traversing in dim_i direction (n_traversals, *dim_x)\\n for dim_i in range(content_dim):\\n min_dim_i = traversal_start[dim_i]\\n max_dim_i = traversal_end[dim_i]\\n if verbose:\\n print(min_dim_i, max_dim_i)\\n c_traversals = get_traversals(content_code, dim_i, min_dim_i, max_dim_i, n_traversals)\\n\\n # Pass to the decoder\\n dict_z = {\\n \\\"c\\\": c_traversals,\\n \\\"s\\\": style_code.repeat((n_traversals, 1))\\n }\\n z = model.combine_content_style(dict_z)\\n recons = model.decode(z)\\n\\n grid = torchvision.utils.make_grid(recons, nrow=1) # Caveat: nrow is num of colms!\\n grids.append(grid)\\n grids = torch.cat(grids, dim=2)\\n\\n if show:\\n show_timg(grids, title=title)\\n if to_save:\\n out_path = out_path or Path(f'./content_traversal_{now2str()}.png')\\n torchvision.utils.save_image(grids, out_path)\\n model.train(is_training)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"804f60f42fa171704e5361a4f868da27\",\n \"score\": \"0.5329856\",\n \"text\": \"def data_preprocess(path='/home/xinsheng/yizhehang/Research/Chinese_Font_Transfer/Font2img/img_lib',\\n source_font='heiti',\\n target_font='lixuke'):\\n # load the source fonts and target fonts\\n source_path = path+'/'+source_font\\n source_fonts = list()\\n target_path = path + '/' + target_font\\n target_fonts = list()\\n files = os.listdir(source_path)\\n mid1 = np.zeros((64, 64, 1))\\n mid2 = np.zeros((64, 64, 1)) \\n\\n \\n for file in files:\\n #load a source font\\n img1 = Image.open(source_path+'/'+file)\\n np1 = np.array(img1)\\n mid1 = np1[:, :, 0]\\n mid1 = mid1[:,:,np.newaxis]\\n source_fonts.append(mid1)\\n\\n #load a target font\\n img2 = Image.open(target_path + '/'+file)\\n np2 = np.array(img2)\\n mid2 = np2[:, :, 0]\\n mid2 = mid2[:,:,np.newaxis]\\n target_fonts.append(mid2)\\n\\n source_fonts = np.array(source_fonts)\\n source_fonts = source_fonts.astype(np.float32)\\n source_fonts /= 255\\n\\n target_fonts = np.array(target_fonts)\\n target_fonts = target_fonts.astype(np.float32)\\n target_fonts /= 255\\n\\n\\n return source_fonts, target_fonts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be195a010036dcb16cce54ab469974a\",\n \"score\": \"0.53280276\",\n \"text\": \"def process_patches(images, net, transformer):\\n # caffe.io.load_image converts to [0,1], so our transformer sets it back to [0,255]\\n # but the skimage lib already works with [0, 255] so we convert it to float with img_as_float\\n data = np.zeros(net.blobs['data'].data.shape)\\n for i in range(len(images)):\\n data[i] = transformer.preprocess('data', img_as_float(images[i]))\\n net.forward(data=data)\\n output = net.blobs['conv1_1_D'].data[:len(images)]\\n output = np.swapaxes(np.swapaxes(output, 1, 3), 1, 2)\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48e5a536e719f437385d50a37c711cad\",\n \"score\": \"0.53275716\",\n \"text\": \"def process(path, img_size=(160, 160), batch_size=2000):\\n\\n datagen = tf.keras.preprocessing.image.ImageDataGenerator(\\n preprocessing_function=normalize, )\\n\\n generator = datagen.flow_from_directory(\\n path,\\n target_size=img_size,\\n batch_size=batch_size,\\n class_mode='binary')\\n (x, y) = next(generator)\\n # normalizing Y vector\\n y = y * 0.8 + 0.1\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e2e090df992da2865c28d364fd01be7\",\n \"score\": \"0.5326675\",\n \"text\": \"def extract_bottleneck_features(preprocessing_output_path, bottleneck_features_output_path, resized_folder_path):\\n \\n #Import preprocessing data\\n data = pd.read_csv(os.path.join(preprocessing_output_path,'boxes_preprocessing_single.csv'), sep=';')\\n data['annotation_literal'] = data['annotation'].apply(literal_eval)\\n data['box_standard'] = data['box_standard'].apply(literal_eval)\\n \\n #Select sequences with only one label\\n data = data.loc[data['annotation_literal'].str.len() == 1] \\n \\n #Group all bird species into one class\\n data['ann_animal'] = \\\"\\\"\\n data['ann_animal'] = data['annotation_literal'].apply(group_birds)\\n \\n #Encode labels\\n le = LabelEncoder()\\n le.fit(data.ann_animal)\\n data['label'] = le.transform(data.ann_animal)\\n \\n #Save data corresponding to the bottleneck features\\n data.to_csv(os.path.join(bottleneck_features_output_path,'bottleneck_data.csv'), index=False, sep=';')\\n \\n #Image size\\n dim_x = 270\\n dim_y = 480\\n dim_z = 3\\n \\n #Parameters\\n batch_size = 128\\n steps_per_epoch = np.ceil(len(data) / batch_size)\\n \\n #Build the ResNet50 network\\n model = ResNet50(include_top=False, weights='imagenet')\\n \\n #Create generator\\n generator = DataGenerator(data, dim_x = dim_x, dim_y = dim_y, dim_z = dim_z, \\n batch_size=batch_size, augmentation=False, shuffle=False, mode='train').generate()\\n \\n #Extract and save bottleneck features\\n bottleneck_features = model.predict_generator(generator, steps_per_epoch)\\n np.save(os.path.join(bottleneck_features_output_path, 'bottleneck_features.npy'), bottleneck_features)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09d0d3d2546945563678a57cd0f6d206\",\n \"score\": \"0.5325292\",\n \"text\": \"def compute_features(img, filename=None, net=None):\\n if filename is not None:\\n if img is None:\\n img = misc.imread(os.path.join(IMG_DIR, filename), mode='L')\\n name = filename.split('.')[0]\\n seg = get_seg(name+'.bmp')\\n else:\\n # forward pass to get the segmentation\\n in_ = np.array(img, dtype=np.float32)\\n in_ = in_[:, :, ::-1]\\n in_ -= MEAN\\n in_ = in_.transpose((2, 0, 1))\\n\\n net.blobs['data'].reshape(1, *in_.shape)\\n net.blobs['data'].data[...] = in_\\n\\n net.forward()\\n seg = net.blobs['score_sem'].data[0].argmax(axis=0)\\n\\n m, n = img.shape[:2]\\n # print('segmentation done')\\n neighb = compute_neighb(img)\\n # print('neighbourhood created')\\n daisy = compute_daisy(img)\\n # print('daisy done')\\n feats = np.concatenate((neighb, daisy, seg), axis=2)\\n return feats.reshape((m*n, feats.shape[2]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe762a01e854e54679c4cba505f8262f\",\n \"score\": \"0.5322813\",\n \"text\": \"def data_prepare(print_image_shape=False, print_input_shape=False):\\n def reshape_mask(origin, cate, num_class):\\n return cate.reshape((origin.shape[0], origin.shape[1], origin.shape[2], num_class))\\n\\n train_imgs, train_det_masks, train_cls_masks = load_dataset(dataset=DATA_DIR, type='train', reshape_size=(256, 256))\\n valid_imgs, valid_det_masks, valid_cls_masks = load_dataset(dataset=DATA_DIR, type='validation',\\n reshape_size=(256, 256))\\n test_imgs, test_det_masks, test_cls_masks = load_dataset(dataset=DATA_DIR, type='test', reshape_size=(256, 256))\\n\\n if print_image_shape:\\n print('Image shape print below: ')\\n print('train_imgs: {}, train_cls_masks: {}'.format(train_imgs.shape, train_cls_masks.shape))\\n print('valid_imgs: {}, valid_cls_masks: {}'.format(valid_imgs.shape, valid_cls_masks.shape))\\n print('test_imgs: {}, test_cls_masks: {}'.format(test_imgs.shape, test_cls_masks.shape))\\n print()\\n #train_cls_masks.reshape((train_cls_masks.shape[0], train_cls_masks.shape[1], train_cls_masks.shape[2], 1))\\n #valid_cls_masks.reshape((valid_cls_masks.shape[0], train_cls_masks.shape[1], train_cls_masks.shape[2], 1))\\n np.set_printoptions(np.nan)\\n print(train_cls_masks.shape)\\n print('unique: ', np.unique(train_cls_masks))\\n train_cls = np_utils.to_categorical(train_cls_masks, 5)\\n #train_cls = reshape_mask(train_cls_masks, train_cls, 5)\\n\\n valid_cls = np_utils.to_categorical(valid_cls_masks, 5)\\n #valid_cls = reshape_mask(valid_cls_masks, valid_cls, 5)\\n\\n test_cls = np_utils.to_categorical(test_cls_masks, 5)\\n #test_cls = reshape_mask(test_cls_masks, test_cls, 5)\\n\\n if print_input_shape:\\n print('input shape print below: ')\\n print('train_imgs: {}, train_cls: {}'.format(train_imgs.shape, train_cls.shape))\\n print('valid_imgs: {}, valid_cls: {}'.format(valid_imgs.shape, valid_cls.shape))\\n print('test_imgs: {}, test_cls: {}'.format(test_imgs.shape, test_cls.shape))\\n print()\\n return [train_imgs, train_cls, valid_imgs, valid_cls, test_imgs, test_cls]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74f22c13d05c69fee9065a545668f30e\",\n \"score\": \"0.5321833\",\n \"text\": \"def extract_hx(path_train,\\n path_output = None,\\n epochs = 50,\\n predict_name = 'ukb_image_examples-587x587.tfrecord',\\n train_name = 'eval.tfrecord'):\\n\\n # Start Code\\n config = ml_collections.ConfigDict()\\n config.dataset = ml_collections.ConfigDict({\\n 'path': path_train,\\n 'predict': predict_name,\\n 'train': train_name,\\n 'num_train_examples': 79355,\\n 'batch_size': 16,\\n 'image_size': (587, 587),\\n 'random_horizontal_flip': True,\\n 'random_vertical_flip': True,\\n 'random_brightness_max_delta': 0.1147528,\\n 'random_saturation_lower': 0.5597273,\\n 'random_saturation_upper': 1.2748845,\\n 'random_hue_max_delta': 0.0251488,\\n 'random_contrast_lower': 0.9996807,\\n 'random_contrast_upper': 1.7704824,\\n 'use_cache': False,\\n })\\n\\n config.train = ml_collections.ConfigDict({\\n 'use_mixed_precision': True,\\n 'use_distributed_training': False,\\n 'max_num_steps': 250000,\\n 'log_step_freq': 15,\\n 'fit_verbose': 1,\\n 'initial_epoch': 0,\\n })\\n\\n config.outcomes = [\\n ml_collections.ConfigDict({\\n 'name': 'vertical_cup_to_disc',\\n 'type': 'regression',\\n 'num_classes': 1,\\n 'loss': 'mse',\\n 'loss_weight': 1.0,\\n }),\\n ml_collections.ConfigDict({\\n 'name': 'vertical_cd_visibility',\\n 'type': 'classification',\\n 'num_classes': 3,\\n 'loss': 'ce',\\n 'loss_weight': 1.0,\\n }),\\n ml_collections.ConfigDict({\\n 'name': 'glaucoma_suspect_risk',\\n 'type': 'classification',\\n 'num_classes': 4,\\n 'loss': 'ce',\\n 'loss_weight': 1.0,\\n }),\\n ml_collections.ConfigDict({\\n 'name': 'glaucoma_gradability',\\n 'type': 'classification',\\n 'num_classes': 3,\\n 'loss': 'ce',\\n 'loss_weight': 1.0,\\n }),\\n ]\\n\\n config.model = ml_collections.ConfigDict({\\n 'backbone': 'inceptionv3',\\n 'backbone_drop_rate': 0.2,\\n 'input_shape': (587, 587, 3),\\n 'weights': 'imagenet',\\n 'weight_decay': 0.00004,\\n })\\n\\n # optimizer\\n config.opt = ml_collections.ConfigDict({\\n 'optimizer': 'adam',\\n 'learning_rate': 0.001,\\n 'beta_1': 0.9,\\n 'beta_2': 0.999,\\n 'epsilon': 0.1,\\n 'amsgrad': False,\\n 'use_model_averaging': True,\\n 'update_model_averaging_weights': False,\\n })\\n config.outcomes_sim = [\\n ml_collections.ConfigDict({\\n 'name': 'sim_0_mu0',\\n 'type': 'regression',\\n 'num_classes': 1,\\n 'loss': 'mse',\\n 'loss_weight': 1.0,\\n }),\\n ml_collections.ConfigDict({\\n 'name': 'sim_0_mu1',\\n 'type': 'regression',\\n 'num_classes': 1,\\n 'loss': 'mse',\\n 'loss_weight': 1.0,\\n })\\n ]\\n\\n train_ds, eval_ds = ukb_utils.build_datasets(config.dataset, config.outcomes,\\n cache=False,)\\n\\n verbose = config.train.get('fit_verbose', 0) if 'train' in config else 0\\n\\n ukb_model_utils = None # replace with import from source in documentation.\\n model = ukb_model_utils.compile_model(config)\\n\\n model.fit(\\n train_ds,\\n epochs=epochs,\\n initial_epoch=config.train.get('initial_epoch', 0),\\n steps_per_epoch=config.train.log_step_freq,\\n verbose=verbose)\\n\\n extract = tf.keras.Model(model.inputs, model.layers[-5].output)\\n\\n path_out = os.path.join(path_output, 'hx.csv')\\n _extraction(eval_ds, path_out, extract)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6194fd418f6918679ac390fa7ec561ba\",\n \"score\": \"0.5321559\",\n \"text\": \"def importCelebImages():\\n\\n if DEBUG:\\n images = os.listdir(path)[1:15]\\n else:\\n images = os.listdir(path)[1:]\\n images_WithFeatures = []\\n for index, imageName in tqdm(zip(range(len(images)), images), desc=\\\"Processing celebrity images\\\"):\\n im = cv2.resize(cv2.imread(path + \\\"/\\\" + imageName, 0), (128, 128))\\n features = [computeLBPDescriptor(i) for i in breakImageApart(im)]\\n images_WithFeatures.append((index, features))\\n return images_WithFeatures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f138a647f3b3b00b479cc1a11f4e0e2\",\n \"score\": \"0.5315962\",\n \"text\": \"def gen_batch_function(image_dir, image_paths, image_shape, output_shape):\\n def get_batches_fn(batch_size, should_schuffle=True):\\n \\\"\\\"\\\"\\n Create batches of training data\\n :param batch_size: Batch Size\\n :return: Batches of training data\\n \\\"\\\"\\\"\\n\\n background_color = np.array([255, 255, 255])\\n image_path = 'origin.jpg'\\n masks_path = ['Box11.jpg', 'Box16.jpg']\\n if should_schuffle:\\n random.shuffle(image_paths)\\n for batch_i in range(0, len(image_paths), batch_size):\\n images = []\\n gt_images = []\\n for image_file in image_paths[batch_i:batch_i+batch_size]:\\n try:\\n img = imutils.rotate_bound(scipy.misc.imread(os.path.join(image_dir, image_file, image_path), mode='RGB'), 90)\\n img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)\\n if random.random() < 0.5:\\n image = cv2.resize(img, (image_shape[1], image_shape[0]), interpolation=cv2.INTER_NEAREST) / 255\\n else:\\n image = cv2.resize(img, (image_shape[1], image_shape[0]), interpolation=cv2.INTER_CUBIC) / 255\\n labels = None\\n\\n for mask_path in masks_path:\\n gt_image = None\\n try:\\n gt_image = scipy.misc.imread(os.path.join(image_dir, image_file, mask_path))\\n gt_image = cv2.resize(gt_image, (image_shape[1], image_shape[0]),\\n interpolation=cv2.INTER_CUBIC)\\n except Exception as e:\\n pass\\n if gt_image is None:\\n gt_bg = np.full(output_shape, False, dtype=bool)\\n else:\\n gt_bg = np.all(gt_image == background_color, axis=2)\\n\\n gt_bg = gt_bg.reshape(*gt_bg.shape, 1)\\n if labels is None:\\n labels = gt_bg\\n else:\\n labels = np.concatenate((labels, gt_bg), axis=2)\\n outside_area = np.invert(np.any(labels, axis=2))\\n outside_area = outside_area.reshape(*outside_area.shape, 1)\\n labels = np.concatenate((outside_area, labels), axis=2)\\n gt_images.append(labels)\\n images.append(image)\\n except Exception as e:\\n pass\\n\\n yield np.array(images), np.array(gt_images)\\n return get_batches_fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ecc47b32f6a20102c53ca3a2401681d\",\n \"score\": \"0.5307604\",\n \"text\": \"def get_features(filepath):\\n \\n \\n # get features for classifiers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332aacde10b463d69c359fb2938d7cc8\",\n \"score\": \"0.5301027\",\n \"text\": \"def preprocess_caltech256(path='256_ObjectCategories'):\\n folders = os.listdir(path)\\n data = {'id': [], 'class': []}\\n for folder in folders:\\n class_name = re.findall(r'(?:\\\\d{3}\\\\.)(.+)', folder)[0].replace('-101', '')\\n class_path = os.path.join(path, folder)\\n for file in os.listdir(class_path):\\n if not any([ext in file for ext in ['jpg', 'png', 'jpeg']]):\\n continue\\n data['class'].append(class_name)\\n data['id'].append(os.path.abspath(os.path.join(class_path, file)))\\n\\n df = pd.DataFrame(data)\\n train_df, test_df = train_test_split(df, test_size=0.2, random_state=123)\\n return train_df, test_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d89e262bf9941d5b34bcb1c5a7678d5a\",\n \"score\": \"0.5294225\",\n \"text\": \"def run_style_transfer(cnn, normalization_mean, normalization_std,\\n content_img, style_img, input_img, style_mask, content_mask, laplacian_m,\\n num_steps=3000,\\n style_weight=1000000, content_weight=100, tv_weight=0.0001, rl_weight=1):\\n print(\\\"Buliding the style transfer model..\\\")\\n model, style_losses, content_losses, tv_losses = get_style_model_and_losses(cnn,\\n normalization_mean, normalization_std, style_img, content_img, style_mask, content_mask, laplacian_m)\\n optimizer = get_input_optimizer(input_img)\\n\\n print(\\\"Optimizing...\\\")\\n print('*'*20)\\n print(\\\"Style_weith: {} Content_weighti: {} \\\\\\n TV_loss_weight: {} Realistic_loss_weight: {}\\\".format \\\\\\n (style_weight, content_weight, tv_weight, rl_weight))\\n print('*'*20)\\n run = [0]\\n \\n best_loss = 1e10 \\n best_input = input_img.data \\n\\n while run[0] <= num_steps:\\n\\n def closure(): \\n nonlocal best_loss\\n nonlocal input_img\\n nonlocal best_input\\n\\n input_img.data.clamp_(0, 1)\\n optimizer.zero_grad()\\n\\n model(input_img)\\n\\n style_score = 0\\n content_score = 0\\n tv_score = 0\\n \\n \\n for sl in style_losses:\\n style_score += sl.loss\\n\\n for cl in content_losses:\\n content_score += cl.loss\\n\\n for tl in tv_losses:\\n tv_score += tl.loss\\n \\n style_score *= style_weight\\n content_score *= content_weight\\n tv_score *= tv_weight \\n\\n # Two stage optimaztion pipline \\n if run[0] > num_steps // 2:\\n # Realistic loss relate sparse matrix computing, \\n # which do not support autogard in pytorch, so we compute it separately.\\n rl_score, part_grid = realistic_loss_grad(input_img, laplacian_m)\\n rl_score *= rl_weight\\n\\n loss = style_score + content_score + tv_score + rl_score\\n\\n # Store the best result for outputing\\n if loss < best_loss:\\n # print(best_loss)\\n best_loss = loss\\n best_input = input_img.data.clone()\\n else:\\n loss = style_score + content_score + tv_score\\n\\n rl_score = torch.zeros(1) # Just to print\\n\\n if loss < best_loss and run[0] > 0:\\n # print(best_loss)\\n best_loss = loss\\n best_input = input_img.data\\n\\n if run[0] == num_steps // 2:\\n # Store the best temp result to initialize second stage input\\n input_img.data = best_input\\n best_loss = 1e10\\n\\n loss.backward()\\n \\n # Gradient cliping deal with gradient exploding\\n clip_grad_norm(model.parameters(), 15.0)\\n \\n run[0] += 1\\n if run[0] % 50 == 0:\\n print(\\\"run {}/{}:\\\".format(run, num_steps))\\n \\n print('Style Loss: {:4f} Content Loss: {:4f} TV Loss: {:4f} real loss: {:4f}'.format(\\n style_score.item(), content_score.item(), tv_score.item(), rl_score.item()))\\n\\n print('Total Loss: ', loss.item())\\n \\n saved_img = input_img.clone() \\n saved_img.data.clamp_(0, 1)\\n utils.save_pic(saved_img, run[0])\\n return loss\\n\\n optimizer.step(closure)\\n \\n # a last corrention...\\n input_img.data = best_input\\n input_img.data.clamp_(0, 1)\\n \\n return input_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b158faec9ce23fb88e6e243c3af44081\",\n \"score\": \"0.52795094\",\n \"text\": \"def get_model(modelfile: str) -> Dict[str, Any]:\\n tarfolder = check_and_create_model(modelfile)\\n if not tarfolder:\\n return\\n\\n model_yml = yaml.safe_load(open(os.path.join(tarfolder, \\\"model.yml\\\")))\\n if model_yml[\\\"type\\\"] == \\\"mlp\\\":\\n layers = []\\n for layer in model_yml[\\\"layers\\\"]:\\n layertmp = {}\\n\\n f = h5py.File(os.path.join(tarfolder, layer[\\\"b\\\"][\\\"filename\\\"]), \\\"r\\\")\\n layertmp[\\\"b\\\"] = f[layer[\\\"b\\\"][\\\"filename\\\"]][()]\\n\\n f = h5py.File(os.path.join(tarfolder, layer[\\\"W\\\"][\\\"filename\\\"]), \\\"r\\\")\\n layertmp[\\\"W\\\"] = f[layer[\\\"W\\\"][\\\"filename\\\"]][()]\\n\\n layertmp[\\\"activation\\\"] = get_af(layer[\\\"activation\\\"])\\n\\n layers.append(layertmp)\\n model_yml[\\\"layers\\\"] = layers\\n inputs = []\\n\\n # if sys.version_info.major < 3:\\n # mode = 'rb'\\n # arguments = {}\\n # else:\\n mode = \\\"rt\\\"\\n arguments = {\\\"newline\\\": \\\"\\\", \\\"encoding\\\": \\\"utf8\\\"}\\n\\n input_semantics_file = os.path.join(tarfolder, \\\"input_semantics.csv\\\")\\n with open(input_semantics_file, mode, **arguments) as csvfile:\\n spamreader = csv.reader(csvfile, delimiter=\\\"\\\\n\\\", quotechar='\\\"')\\n for row in spamreader:\\n inputs.append(row[0])\\n outputs = get_outputs(os.path.join(tarfolder, \\\"output_semantics.csv\\\"))\\n model_yml[\\\"inputs\\\"] = inputs\\n model_yml[\\\"outputs\\\"] = outputs\\n\\n # Cleanup\\n shutil.rmtree(tarfolder)\\n return model_yml\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dba0d89abac5b42d39e65ce63af83ff6\",\n \"score\": \"0.52737784\",\n \"text\": \"def preparing_data(im_path, img_type):\\n images = sorted(glob.glob(im_path + '/*' + img_type))\\n images_name = []\\n input_images = []\\n for im_name in images:\\n input_images.append(cv2.imread(im_name)[:, :, ::-1])\\n images_name.append(im_name.split('/')[-1].split('.')[0])\\n input_images = np.asarray(input_images)\\n input_images = adjust_dynamic_range(input_images.astype(np.float32), [0, 255], [-1., 1.])\\n input_images = input_images.transpose(0, 3, 1, 2)\\n return input_images, images_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b71214402b6693973dda236717a45e\",\n \"score\": \"0.52730495\",\n \"text\": \"def loader_cityscapes(txt_path, cityscapes_path, batch_size):\\r\\n\\r\\n\\t\\tlines = open(txt_path, 'r').readlines()\\r\\n\\t\\ttotal_files = len(lines)\\r\\n\\r\\n\\t\\timages = []\\r\\n\\t\\tgts = []\\r\\n\\t\\tfor line in lines:\\r\\n\\t\\t\\tline = line.strip().split(\\\" \\\")\\r\\n\\t\\t\\timages.append(line[0])\\r\\n\\t\\t\\tgts.append(line[1])\\r\\n\\r\\n\\t\\tif str(batch_size).lower() == 'all':\\r\\n\\t\\t\\tbatch_size = total_files\\r\\n\\t\\t\\twhile (1):\\r\\n\\t\\t\\t\\tbatch_idxs = np.random.randint(0, total_files, batch_size)\\r\\n\\r\\n\\t\\t\\t\\tlabels = []\\r\\n\\t\\t\\t\\tfor jj in batch_idxs:\\r\\n\\t\\t\\t\\t\\timg = Image.open(cityscapes_path + gts[jj])\\r\\n\\t\\t\\t\\t\\timg = np.array(img)\\r\\n\\t\\t\\t\\t\\t#img5 = scale_downsample(img, 0.5, 0.5)\\r\\n\\r\\n\\t\\t\\t\\t\\tlabels.append(img)\\r\\n\\r\\n\\t\\t\\t\\tlabels = torch.tensor(labels)\\r\\n\\r\\n\\t\\t\\t\\tyield labels\\r\\n\\r\\n\\t\\tidx = 0\\r\\n\\t\\twhile (1):\\r\\n\\t\\t\\tbatch_idxs = np.random.randint(0, total_files, batch_size)\\r\\n\\r\\n\\t\\t\\tinputs = []\\r\\n\\t\\t\\tlabels = []\\r\\n\\t\\t\\tfor jj in batch_idxs:\\r\\n\\r\\n\\t\\t\\t\\timg = plt.imread(cityscapes_path + images[jj])\\r\\n\\t\\t\\t\\t#img5 = scale_downsample(img, 0.5, 0.5)\\r\\n\\t\\t\\t\\tinputs.append(img)\\r\\n\\r\\n\\t\\t\\t\\timg = Image.open(cityscapes_path + gts[jj])\\r\\n\\t\\t\\t\\timg = np.array(img)\\r\\n\\t\\t\\t\\t#img5 = scale_downsample(img, 0.5, 0.5)\\r\\n\\t\\t\\t\\tlabels.append(img)\\r\\n\\r\\n\\t\\t\\tinputs = np.stack(inputs, axis=2)\\r\\n\\t\\t\\tinputs = torch.tensor(inputs).transpose(0, 2).transpose(1, 3)\\r\\n\\r\\n\\t\\t\\tlabels = torch.tensor(labels)\\r\\n\\r\\n\\t\\t\\tyield inputs, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88a1390802bd73ea86a7109108abb2a1\",\n \"score\": \"0.5271522\",\n \"text\": \"def prepare2train(train_bees, val_bees, test_bees, field_name):\\n # Bees already splitted to train, validation and test\\n # Load and transform images to have equal width/height/channels. \\n # read_img function is defined in the beginning to use in both health and subspecies. \\n # Use np.stack to get NumPy array for CNN input\\n\\n # Train data\\n train_X = np.stack(train_bees['file'].apply(read_img))\\n #train_y = to_categorical(train_bees[field_name].values)\\n train_y = pd.get_dummies(train_bees[field_name], drop_first=False)\\n\\n # Validation during training data to calc val_loss metric\\n val_X = np.stack(val_bees['file'].apply(read_img))\\n #val_y = to_categorical(val_bees[field_name].values)\\n val_y = pd.get_dummies(val_bees[field_name], drop_first=False)\\n\\n # Test data\\n test_X = np.stack(test_bees['file'].apply(read_img))\\n #test_y = to_categorical(test_bees[field_name].values)\\n test_y = pd.get_dummies(test_bees[field_name], drop_first=False)\\n\\n # Data augmentation - a little bit rotate, zoom and shift input images.\\n generator = ImageDataGenerator(\\n featurewise_center=False, # set input mean to 0 over the dataset\\n samplewise_center=False, # set each sample mean to 0\\n featurewise_std_normalization=False, # divide inputs by std of the dataset\\n samplewise_std_normalization=False, # divide each input by its std\\n zca_whitening=False, # apply ZCA whitening\\n rotation_range=180, # randomly rotate images in the range (degrees, 0 to 180)\\n zoom_range = 0.1, # Randomly zoom image \\n width_shift_range=0.2, # randomly shift images horizontally (fraction of total width)\\n height_shift_range=0.2, # randomly shift images vertically (fraction of total height)\\n horizontal_flip=True, # randomly flip images\\n vertical_flip=True)\\n generator.fit(train_X)\\n return (generator, train_X, val_X, test_X, train_y, val_y, test_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbb8fc5996c1c61f4e1fdc5c57170c3a\",\n \"score\": \"0.5268959\",\n \"text\": \"def transformation():\\n try:\\n data = request.get_json()\\n img = data['img']\\n img = np.array(img)\\n except Exception:\\n return jsonify({'message': 'Content is not string type'}), 400\\n \\n category1, category2, category3, category4 = CF.main_classify(img, main_model)\\n\\n if category1[0] == '의자':\\n standard = CF.sub_classify(img, CF.chair_subclass, sub_model_chair)\\n elif category1[0] == '침대':\\n standard = CF.sub_classify(img, CF.bed_subclass, sub_model_bed)\\n else :\\n standard = ''\\n\\n result = {\\n \\\"category\\\" : [category1, category2, category3, category4],\\n \\\"standard\\\" : standard\\n }\\n\\n result = jsonify(result)\\n return result\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993486,"cells":{"query_id":{"kind":"string","value":"b7db9c4c6c9fb6dd1d332e25a176ebb7"},"query":{"kind":"string","value":"Lookup quote starting position using google diff_match_patch (levenshtein) algorithm"},"positive_passages":{"kind":"list like","value":[{"docid":"d44958809665a563a50dc4b7074aa5b5","score":"0.7858059","text":"def quote_start_position(self):\n\n quote_locate = diff_match_patch()\n quote_locate.Diff_Timeout = 5.0 \t# Tweak this?\n quote_locate.Match_Threshold = 0.5 # Tweak this?\n\n # Guess a big distance so that starting guess location is unimportant\n quote_locate.Match_Distance = (self.text_length() * 2) # Tweak this?\n quote_start_position = quote_locate.match_bitap(\n self.text,\n self.quote,\n self.estimated_starting_location()\n )\n if (quote_start_position >= 0):\n return quote_start_position\n else:\n return -1","title":""}],"string":"[\n {\n \"docid\": \"d44958809665a563a50dc4b7074aa5b5\",\n \"score\": \"0.7858059\",\n \"text\": \"def quote_start_position(self):\\n\\n quote_locate = diff_match_patch()\\n quote_locate.Diff_Timeout = 5.0 \\t# Tweak this?\\n quote_locate.Match_Threshold = 0.5 # Tweak this?\\n\\n # Guess a big distance so that starting guess location is unimportant\\n quote_locate.Match_Distance = (self.text_length() * 2) # Tweak this?\\n quote_start_position = quote_locate.match_bitap(\\n self.text,\\n self.quote,\\n self.estimated_starting_location()\\n )\\n if (quote_start_position >= 0):\\n return quote_start_position\\n else:\\n return -1\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6199483675f4357c9c9a305fcdc5b449","score":"0.65813494","text":"def findClosestMatchingQuote(self, quote, threshold=.9):\n quotelen = len(quote.split(\" \"))\n chaptersubstrings = []\n lst_of_substrings = []\n for chapter in self.chapter_lists:\n for i in range(len(chapter)-quotelen):\n #Make a substring for every substring of size quotelen\n #Turn the sub array ito a string\n lst_of_substrings.append(\" \".join(chapter[i:i+quotelen]))\n chaptersubstrings.append(lst_of_substrings)\n lst_of_substrings= []\n\n def helper(threshold):\n if threshold < 0:\n return \"\",-1\n print(\"Threshold is\", threshold)\n best = []\n for i in range(len(self.chapter_lists)):\n good = get_close_matches(quote,chaptersubstrings[i], n =3, cutoff=threshold)\n if good:\n #add the best match to a list\n best.append(good[0])\n #Compare the best of all the chapters together and pick the best one\n final_match = get_close_matches(quote, best, n = 1, cutoff= threshold)\n if not final_match:\n return helper(threshold-.1)\n final_match = final_match[0]\n final_idx = best.index(final_match)+1\n return final_match, final_idx\n return helper(threshold)","title":""},{"docid":"ffeef885adc0134fc3a08e810c26603f","score":"0.6223548","text":"def suggest_closest_word(self, word: str) -> str:\n start = 100000000000\n flag = ''\n for item in self.entries:\n temp = self.__levenshtein_dist__(word, item, False)\n if start > temp >= 1:\n flag = item\n return item","title":""},{"docid":"c1645cb303389da4e2ced438ca9a2fe1","score":"0.5943613","text":"def _levenshtein(s1, s2):\n if len(s1) < len(s2):\n return _levenshtein(s2, s1)\n if len(s2) == 0:\n return len(s1)\n previous_row = range(len(s2) + 1)\n for i, c1 in enumerate(s1):\n current_row = [i + 1]\n for j, c2 in enumerate(s2):\n insertions = previous_row[\n j + 1] + 1\n deletions = current_row[j] + 1\n substitutions = previous_row[j] + (c1 != c2)\n current_row.append(min(insertions, deletions, substitutions))\n previous_row = current_row\n return previous_row[-1]","title":""},{"docid":"6a74843b1dd7882dd31d8c0bf881a5bc","score":"0.5943308","text":"def phones_for_closest_match(word):\n by_distance = []\n for possibility in pronouncing.pronunciations:\n\n # levenstein\n distance = editdistance.eval(possibility, word)\n\n # give a bonus for same first letter / last letter\n if possibility.startswith(word[0]):\n distance -= 1\n if possibility.endswith(word[-1]):\n distance -= 1 \n\n # break ties with difference in length\n character_difference = abs(len(possibility) - len(word))\n by_distance.append((distance, character_difference, possibility))\n\n # find the lowest (final tie breaker is alphabetical, oh well)\n d_edit, d_length, suggestion = min(by_distance)\n\n # return the suggestion and the phones for the suggestion\n return suggestion, pronouncing.phones_for_word(suggestion)","title":""},{"docid":"a7bfd6fdab33ec67e70caffcbe9e23f7","score":"0.5924451","text":"def same_as_previous_search(self, regexp, direction, where, match,\n pos = None):\n\n\n #\n # We consider this to be the same occurence as last search iif:\n #\n # a. Start/end position of both occurences are the same\n # b. Both occurences were found using same *regexp*\n # c. Both occurences were found using same *where*\n # d. Cursor hasn't moved since last search.\n #\n # Condition d. is because if the user moves the cursor to an other\n # location and redoes the same search again, he/she probably means\n # to do a new search starting from this new cursor location.\n #\n # Note that two occurences may be deemed identical even if they were\n # found using different *direction*. This is because if the user does\n # a search forward and then reverses the direction, we don't want\n # to go back to the occurence found in the forward direction (instead\n # we want to go directly to the one preceding).\n #\n # On the other hand, two occurences will be deemed identical only if\n # they were found using same *where* (condition c.). This is so you can\n # search for an occurence and then move the cursor to the beginning/end\n # of that occurence using a command like: \"before that\" or \"after that\"\n #\n answer = 0\n if self.last_search != None:\n last_regexp, last_direction, last_where, \\\n last_match, last_alt_pos = self.last_search\n trace('SourceBuff.same_as_previous_search', \n 'last reg = %s, where = %d, range = %s, pos = %s' % \\\n (last_regexp, last_where, last_match, last_alt_pos))\n trace('SourceBuff.same_as_previous_search', \n 'reg = %s, where = %d, range = %s, pos = %s' % \\\n (regexp, where, match, self.cur_pos()))\n if match == last_match and regexp == last_regexp and \\\n where == last_where:\n pos = last_alt_pos\n if pos is None:\n pos = self.pos_extremity(last_match, last_where)\n if self.cur_pos() == pos:\n answer = 1\n\n return answer","title":""},{"docid":"92b2fadb319942792acd7359bde8623e","score":"0.58724356","text":"def levenshtein_distance(s1, s2):\n if len(s1) < len(s2):\n return levenshtein_distance(s2, s1)\n # len(s1) >= len(s2)\n if len(s2) == 0:\n return len(s1)\n previous_row = range(len(s2) + 1)\n for i, c1 in enumerate(s1):\n current_row = [i + 1]\n for j, c2 in enumerate(s2):\n insertions = previous_row[j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer\n deletions = current_row[j] + 1 # than s2\n substitutions = previous_row[j] + (c1 != c2)\n current_row.append(min(insertions, deletions, substitutions))\n previous_row = current_row\n return previous_row[-1]","title":""},{"docid":"3fc3f0cd2d7d567e895e07c5207a910f","score":"0.58191997","text":"def levenshtein_distance_original_brute_force(str1, str2):\n edits = [[x for x in range(len(str1) + 1)] for _ in range(len(str2) + 1)]\n\n for i in range(1, len(str2) + 1):\n edits[i][0] = edits[i - 1][0] + 1\n\n for i in range(1, len(str2) + 1):\n for j in range(1, len(str1) + 1):\n if str2[i - 1] == str1[j - 1]:\n edits[i][j] = edits[i - 1][j - 1]\n else:\n edits[i][j] = 1 + min(edits[i - 1][j - 1], edits[i - 1][j], edits[i][j - 1])\n\n return edits[-1][-1]","title":""},{"docid":"3aa996af826842007c63ef4493cd7b7c","score":"0.579019","text":"def wer(original, result):\r\n # The WER ist calculated on word (and NOT on character) level.\r\n # Therefore we split the strings into words first:\r\n ref_id = original.split()[0]\r\n ref = original.split()[1:]\r\n hyp_id = result.split()[0]\r\n hyp = result.split()[1:]\r\n assert(ref_id == hyp_id)\r\n \r\n return levenshtein(ref, hyp) / float(len(ref))","title":""},{"docid":"33dd23ffaed8a730c13556ffa86c380f","score":"0.57856673","text":"def matchOffset(leftPaddedReference, leftPaddedQuery):\n offset = 0\n for queryChar, referenceChar in zip(leftPaddedQuery, leftPaddedReference):\n if queryChar not in \" -\":\n break\n offset += referenceChar not in \" -\"\n\n return offset","title":""},{"docid":"1f1f402c611b998d63c992a2f8775b05","score":"0.5749957","text":"def levenshtein_distance(s1, s2):\n if len(s1) < len(s2):\n return levenshtein_distance(s2, s1)\n\n # len(s1) >= len(s2)\n if len(s2) == 0:\n return len(s1)\n\n previous_row = range(len(s2) + 1)\n for i, c1 in enumerate(s1):\n current_row = [i + 1]\n for j, c2 in enumerate(s2):\n # j+1 instead of j since previous_row and current_row are one\n # character longer than s2\n insertions = previous_row[j + 1] + 1\n deletions = current_row[j] + 1\n substitutions = previous_row[j] + (c1 != c2)\n current_row.append(min(insertions, deletions, substitutions))\n previous_row = current_row\n\n return previous_row[-1]","title":""},{"docid":"6545b58ca3e8dac05815feaafc07f251","score":"0.57094544","text":"def levenshtein(left, right):\n return 1 if left == right else -1","title":""},{"docid":"42041ff9b4c18c7e4f2b6f6b5b00acdf","score":"0.5699307","text":"def test_search_for_same_word(example_iter):\n assert find_shortest_distance_iterator(\n example_iter, \"development\", \"development\") == 1","title":""},{"docid":"592d4307b15b8ff6eac754d8f1dfd9c3","score":"0.5692577","text":"def substitute(self, match):\n start, end, word = self.completer.locate(self.orig_cursor_offset,\n self.orig_line)\n return (start + len(match),\n self.orig_line[:start] + match + self.orig_line[end:])","title":""},{"docid":"9286962c0f2e2fd5e336e0457d63c421","score":"0.56407744","text":"def find_closest(searchstr,barcodes,trantab):\n if searchstr in barcodes:\n return(searchstr)\n else:\n #######this matches and replaces the item in the row by the multiseq barcode within hamthresh\n dists = fast_hamm(searchstr,barcodes,trantab)\n ###return the barcode with the lowest hamdist if hamdist == 1\n i = dists.index(min(dists))\n if dists[i] <= 1:\n return(barcodes[i])\n else:\n return(searchstr)","title":""},{"docid":"4be69d06f175e9e21aa6d5f2b2efbd57","score":"0.5640555","text":"def get_distance(word, trans, word_to_translation):\n references = word_to_translation[word]\n assert len(references) != 0, 'wrong/unknown word'\n return min(editdistance.eval(trans, ref) for ref in references)","title":""},{"docid":"682cddb0c71e3d17dafaf730f425135d","score":"0.563643","text":"def calculate_score(s1, s2, l1, l2, startpoint):\n matched = \"\" # to hold string displaying alignements\n score = 0\n for i in range(l2):\n if (i + startpoint) < l1: # the startpoint must be less than the length of the longer sequence\n if s1[startpoint + i] == s2[i]: # if the bases match\n matched = matched + \"*\" # add \"*\" to the new string to indicate match\n score = score + 1\n else:\n matched = matched + \"-\" # add \"-\" to the new string to indicate no match\n\n # some formatted output\n print(\".\" * startpoint + matched) \n print(\".\" * startpoint + s2)\n print(s1)\n print(score) \n print(\" \") # returns blank line\n\n return score, matched","title":""},{"docid":"5333da3b8c810d39da073ea916435156","score":"0.5616125","text":"def levenshtein(seq1, seq2):\n oneago = None\n thisrow = list(range(1, len(seq2) + 1)) + [0]\n for x in range(len(seq1)):\n twoago, oneago, thisrow = oneago, thisrow, [0] * len(seq2) + [x + 1]\n for y in range(len(seq2)):\n delcost = oneago[y] + 1\n addcost = thisrow[y - 1] + 1\n subcost = oneago[y - 1] + (seq1[x] != seq2[y])\n thisrow[y] = min(delcost, addcost, subcost)\n return thisrow[len(seq2) - 1]","title":""},{"docid":"36a11f0460820e05e7efd534a86b8a5d","score":"0.55983454","text":"def levenshteinDistance(s1, s2):\n if len(s1) > len(s2):\n s1, s2 = s2, s1\n distances = range(len(s1) + 1)\n for index2, char2 in enumerate(s2):\n newDistances = [index2+1]\n for index1, char1 in enumerate(s1):\n if char1.lower() == char2.lower():\n newDistances.append(distances[index1])\n else:\n newDistances.append(\n 1 + min((distances[index1],\n distances[index1+1],\n newDistances[-1])))\n distances = newDistances\n return distances[-1]","title":""},{"docid":"3d44d1d7cf768b229e57a29f28448eee","score":"0.5581027","text":"def levenshtein_distance_original_optimal(str1, str2):\n small = str1 if len(str1) < len(str2) else str2\n big = str1 if len(str1) >= len(str2) else str2\n even_edits = [x for x in range(len(small) + 1)]\n odd_edits = [None for _ in range(len(small) + 1)]\n for i in range(1, len(big) + 1):\n if i % 2 == 1:\n current_edits = odd_edits\n previous_edits = even_edits\n else:\n current_edits = even_edits\n previous_edits = odd_edits\n current_edits[0] = i\n for j in range(1, len(small) + 1):\n if big[i - 1] == small[j - 1]:\n current_edits[j] = previous_edits[j - 1]\n else:\n current_edits[j] = 1 + min(previous_edits[j - 1], previous_edits[j], current_edits[j - 1])\n return even_edits[-1] if len(big) % 2 == 0 else odd_edits[-1]","title":""},{"docid":"a543deab3f4940710caefa08a183aad4","score":"0.5580113","text":"def getTxtRelPositions(transcript_id,txt_annot,readPosition):#modified so that it returns strand +/- instead of S/AS\n #Great, now figure out position relative to CDSStart\n #To do this, I will calculate the distance from the txtStart to readPosition, and txtStart to CDSStart. I'll subtract the two.\n cdsStart=min([entry[0] for entry in txt_annot['CDS']])\n exons=txt_annot['exon']\n exonStarts=[exon[0] for exon in exons]\n exonEnds=[exon[1] for exon in exons]\n exonStarts.sort(),exonEnds.sort()\n #the zip function works differently in python2 and python3.\n #So changed the next line of code to work via list comprehension\n #https://stackoverflow.com/questions/31683959/the-zip-function-in-python-3\n #exons=zip(exonStarts,exonEnds)\n exons=[(exonStarts[ii],exonEnds[ii]) for ii in range(len(exonStarts))]\n #Edit: Apparently the exons are not necessarily orderd in the gtf file.\n \n txtStart_cdsStart_dist=assignReadsToGenes4.getDist(exons,cdsStart)\n txtStart_readPosition_dist=assignReadsToGenes4.getDist(exons,readPosition)\n readPosition_rel_to_CDSstart=txtStart_readPosition_dist-txtStart_cdsStart_dist\n \n #now do the same thing with the cdsEnd\n cdsEnd=max([entry[1] for entry in txt_annot['CDS']])\n txtStart_cdsEnd_dist=assignReadsToGenes4.getDist(exons,cdsEnd)\n #already determined txtStart_readPosition_dist\n readPosition_rel_to_CDSend=txtStart_readPosition_dist-txtStart_cdsEnd_dist\n \n #stranded issues. Although ensembl defines start_codon and stop_codon as those exact locations on the - and + strand, here I find the start/stop by taking min/max of CDS exon boundaries, so I need to flip it\n if txt_annot['strand']=='+':\n return ':'.join([transcript_id,str(readPosition_rel_to_CDSstart),str(readPosition_rel_to_CDSend)])\n else:\n return ':'.join([transcript_id,str(-readPosition_rel_to_CDSend),str(-readPosition_rel_to_CDSstart)])","title":""},{"docid":"75b72dc44e242d9a260830e7242de37d","score":"0.5579813","text":"def get_closest_exon(chromosome,pos1,pos2,db=\"ensembl.sql\"):\n \n gene_start = pos1 if pos1 < pos2 else pos2\n gene_end = pos2 if pos2 > pos1 else pos1\n\n con = sqlite3.connect(db)\n cur = con.cursor()\n cur.execute(\"SELECT * FROM exons WHERE chromosome = '{0}' AND \\\n ({1} BETWEEN exon_start-100000 AND exon_end+100000 OR \\\n {2} BETWEEN exon_start-100000 AND exon_end+100000)\".format(\n chromosome,gene_start,gene_end\n ))\n ret = cur.fetchall()\n \n min_dist = 100000\n min_gene = \"\"\n \n \n for i in range(0,len(ret)):\n exon_start = int(ret[i][4])\n exon_end = int(ret[i][5])\n gene = ret[i][2]\n if((exon_start <= gene_start <= exon_end) or (exon_start <= gene_end <= exon_end)):\n return 0, gene # not SUPER accurate, we avoid alternative transcripts\n else:\n dist = min(abs(gene_end-exon_start),abs(gene_start-exon_end))\n if(dist < min_dist):\n min_dist = dist\n min_gene = gene\n return min_dist, min_gene","title":""},{"docid":"a5ace4fbfb9ca1e32961e934dc6b4eac","score":"0.55763656","text":"def levenshtein_distance(s1,s2):\n if len(s1) == 0:\n return len(s2)\n if len(s2) == 0:\n return len(s1)\n return min([int(s1[0] != s2[0]) + \\\n levenshtein_distance(s1[1:],s2[1:]),\\\n 1+levenshtein_distance(s1[1:],s2),\\\n 1+levenshtein_distance(s1,s2[1:])])","title":""},{"docid":"2563056bffc9dab3c8a28c6061f829c4","score":"0.55576456","text":"def fast_levenshtein_distance(self, source, target):\n return int(editdistance.eval(source, target))","title":""},{"docid":"e3875814c0ae04667f33602b77d2fbb6","score":"0.55130947","text":"def damerauLevenshteinHomerDistance(seq1, seq2):\n\n oneago = None\n thisrow = list(range(1, len(seq2) + 1)) + [0]\n for x in range(len(seq1)):\n # Python lists wrap around for negative indices, so put the\n # leftmost column at the *end* of the list. This matches with\n # the zero-indexed strings and saves extra calculation.\n twoago, oneago, thisrow = oneago, thisrow, [0] * len(seq2) + [x + 1]\n for y in range(len(seq2)):\n delcost = oneago[y] + 1\n addcost = thisrow[y - 1] + 1\n subcost = oneago[y - 1] + (seq1[x] != seq2[y])\n thisrow[y] = min(delcost, addcost, subcost)\n # This block deals with transpositions\n if (x > 0 and y > 0 and seq1[x] == seq2[y-1]\n and seq1[x-1] == seq2[y] and seq1[x] != seq2[y]):\n thisrow[y] = min(thisrow[y], twoago[y-2] + 1)\n return thisrow[len(seq2) - 1]","title":""},{"docid":"7c6391fd4e0565319580e32fe9e60574","score":"0.55097616","text":"def levenshteinDistance(string1, string2):\n # smaller string is string1. Switch if not the case\n if len(string1) > len(string2):\n string1, string2 = string2, string1\n editDistances = range(len(string1) + 1)\n for idx2, char2 in enumerate(string2):\n newDistances = [idx2 + 1]\n for idx1, char1 in enumerate(string1):\n if char1 == char2:\n newDistances.append(editDistances[idx1])\n else:\n newDistances.append(1 + min((editDistances[idx1], editDistances[idx1 + 1], newDistances[-1])))\n editDistances = newDistances\n return editDistances[-1]","title":""},{"docid":"f423b7819392e5ea38715deda190ddc8","score":"0.549971","text":"def get_closest_match(self, cells, matching_threshold):\n return_value = []\n distances = [Levenshtein.distance(self.start_md, u''.join(cell['source'])) for cell in cells]\n if min(distances) > matching_threshold:\n return return_value\n\n best_match = argmin(distances)\n if len(self.stop_md) == 0:\n end_offset = len(cells) - best_match\n else:\n distances = [Levenshtein.distance(self.stop_md, u''.join(cell['source'])) for cell in cells[best_match:]]\n if min(distances) > matching_threshold:\n return return_value\n end_offset = argmin(distances)\n if len(self.question_heading) != 0:\n return_value.append(NotebookExtractor.markdown_heading_cell(self.question_heading,'##'))\n return_value.append(cells[best_match])\n return_value.extend(cells[best_match+1:best_match+end_offset])\n return return_value","title":""},{"docid":"c1c9dfed66ad52ee23cb379210b62d4f","score":"0.5495784","text":"def levenshtein_distance(ref, hyp):\n m = len(ref)\n n = len(hyp)\n\n # special case\n if ref == hyp:\n return 0\n if m == 0:\n return n\n if n == 0:\n return m\n\n if m < n:\n ref, hyp = hyp, ref\n m, n = n, m\n\n # use O(min(m, n)) space\n distance = np.zeros((2, n + 1), dtype=np.int32)\n\n # initialize distance matrix\n for j in range(0,n + 1):\n distance[0][j] = j\n\n # calculate levenshtein distance\n for i in range(1, m + 1):\n prev_row_idx = (i - 1) % 2\n cur_row_idx = i % 2\n distance[cur_row_idx][0] = i\n for j in range(1, n + 1):\n if ref[i - 1] == hyp[j - 1]:\n distance[cur_row_idx][j] = distance[prev_row_idx][j - 1]\n else:\n s_num = distance[prev_row_idx][j - 1] + 1\n i_num = distance[cur_row_idx][j - 1] + 1\n d_num = distance[prev_row_idx][j] + 1\n distance[cur_row_idx][j] = min(s_num, i_num, d_num)\n\n return distance[m % 2][n]","title":""},{"docid":"7703312383c1cfa8e6a7be695d5eae58","score":"0.5491491","text":"def align_needle(target,template):\n #dans le cas où cout du gap < substitution\n #align = pairwise2.align.globalxx(target, template)\n #dans le cas où cout du gap > substitution - BLOSUM 62\n matrix = matlist.blosum62\n align = pairwise2.align.globaldx(target, template, matrix) \n #on cherche l'alignement avec le plus haut score et le plus petit nombre \n #de gap\n res = align[0]\n for i in align:\n if i[2] >= res[2] and i[4] <= res[4]:\n res = i\n print(\"Sequence target : \" + res[0])\n print(\"Sequence template: \" + res[1])\n print(\"Score : \" + str(res[2]))\n return res","title":""},{"docid":"b4730dc38d74c3e2440de3880c00612d","score":"0.5486138","text":"def calc_str_distance(command, candidate):\n return editdistance.eval(command, candidate)","title":""},{"docid":"2f30c1d3bb52bac4223f18840ae73e08","score":"0.5478767","text":"def levenshteinDistance1(str1, str2):\n\n sol = np.zeros((len(str1) + 1, len(str2) + 1))\n for i in range(sol.shape[0]):\n # sol[i,j] # row i # columon j\n sol[i, 0] = i\n for j in range(sol.shape[1]):\n # sol[i,j] # row i # columon j\n sol[0, j] = j\n str1 = \" \" + str1\n str2 = \" \" + str2\n for j in range(1, sol.shape[1]):\n for i in range(1, sol.shape[0]):\n if str1[i] == str2[j]:\n cost = 0\n else:\n cost = 1\n sol[i, j] = min(sol[i-1, j] + 1, sol[i, j-1] + 1, sol[i-1, j-1] + cost)\n return int(sol[sol.shape[0]-1, sol.shape[1]-1])","title":""},{"docid":"e90550f9031c020a69b95b2c9f6c79df","score":"0.5478676","text":"def get_distance(seq1, seq2, S, gap_pen, score):\n # დისტანცია წარმოვიდიგნოთ როგორც\n # დისტანცია პირველიდან საერთო მშობლამდე + დისტანცია მეორედან საერთო მშობლამდე\n # ოკამის სამართებლის პრინციპით ვთქვათ, რომ თანაბრად დაშორდნენ მშობელს სიგრძეებით\n # ანუ საერთო მშობლის სიგრძე იქნებოდა სიგრძეების ჯამის ნახევარი\n # ყველაზე კარგ შესაძლო ვარიანტში ორივე ემთხვევა საერთო მშობელს(ანუ ერთმანეთსაც)\n # ყველაზე ცუდ ვარიანტში უსასრულო აცდენა აქვთ ანუ ქულა მინუს უსასრულობაა.\n # ერთმანეთთან დამთხვევის ქულა შევადაროთ საერთო მშობელთან მაქსიმალურ დამთხვევის ქულას\n # და ასე განვსაზღვროთ დისტანცია.\n\n # (-∞, max_score] ინტერვალი უნდა გადავაქციოთ [0, +∞) ინტერვალად\n # ამისათვის გავამრავლოთ მინუსზე და დავუმატოთ max_score\n\n # მემგონი ნამეტან floatებს ვწერ მარა python2.7 არ ვიცი როგორ მუშაობს და არ გააფუჭებს არაფერს\n max_score = float(float(len(seq1) + len(seq2))/2.0) * 3.0\n distance = float(float(score) * -1.0 + max_score)\n\n return distance","title":""},{"docid":"8a933b4a23b756628dc7c2e10b4eab3c","score":"0.5462667","text":"def marker_match(hit) -> Optional[str]:\n\n if hit['correct'].item():\n return re.sub(pattern='\\D*(\\d+)',\n repl='\\\\1',\n string=str(hit['qseqid'].item()))\n return None","title":""},{"docid":"1ca59eb08db7e5b7ef07cdae72e4e715","score":"0.5455519","text":"def _subfn(self, match):\n self.matched += 1\n old = match.group(0)\n new = None\n offset = match.start(0) # Offset to all other patterns\n for key, value in match.groupdict().items():\n table = self.lookalikes.get(key[0].upper())\n if table is None:\n continue\n start = match.start(key)\n if start == -1:\n continue\n start -= offset\n for pos, ch in enumerate(value.lower(), start=start):\n if ch in table:\n if new is None:\n new = list(old)\n new[pos] = table[ch].upper()\n if new:\n new = \"\".join(new)\n self.corrections[old] = new\n self.fixed += 1\n return new\n return old","title":""},{"docid":"c12080aa70d1f74c2a63897b3519824d","score":"0.54554814","text":"def levenshtein_func(seq1, seq2):\n\n size_x = len(seq1) + 1\n size_y = len(seq2) + 1\n matrix = np.zeros ((size_x, size_y))\n for x in range(size_x):\n matrix [x, 0] = x\n for y in range(size_y):\n matrix [0, y] = y\n\n for x in range(1, size_x):\n for y in range(1, size_y):\n # Costs are used in a way that substitution and delete are costly, but addition is fairly cheap\n if seq1[x-1] == seq2[y-1]:\n matrix [x,y] = min(\n matrix[x-1, y] + 3,\n matrix[x-1, y-1],\n matrix[x, y-1] + 3\n )\n else:\n matrix [x,y] = min(\n matrix[x-1,y] + 2,\n matrix[x-1,y-1] + 6,\n matrix[x,y-1] + 6\n )\n\n return matrix[size_x - 1, size_y - 1]","title":""},{"docid":"efadad5b2b71245cf70b264a7debf90e","score":"0.545042","text":"def best_matching_start_symbol(self, transcription):\n\n best_match = None\n\n for symbol in self.symbols.keys():\n if transcription.startswith(symbol) and \\\n (best_match is None or len(symbol) > len(best_match)):\n best_match = symbol\n\n return best_match","title":""},{"docid":"8836659f760f3b3a1c9438e06a831198","score":"0.5436356","text":"def relative_edit_distance(s1: str, s2: str) -> float:\n\n if not s1 and not s2:\n return 0.0\n return edit_distance(s1, s2) / max(len(s1), len(s2))","title":""},{"docid":"e35fa2a5d87af4082dc22736fc426225","score":"0.5433679","text":"def _minimumEditDistance1(tokens_s1, tokens_s2):\n if len(tokens_s1) > len(tokens_s2):\n tokens_s1, tokens_s2 = tokens_s2, tokens_s1\n\n distances = range(len(tokens_s1) + 1)\n for index2, char2 in enumerate(tokens_s2):\n newDistances = [index2 + 1]\n for index1, char1 in enumerate(tokens_s1):\n if char1 == char2:\n newDistances.append(distances[index1])\n else:\n min_dist = min((distances[index1],\n distances[index1 + 1],\n newDistances[-1]))\n newDistances.append(1 + min_dist)\n distances = newDistances\n return distances[-1]","title":""},{"docid":"7457147cd5fd3f3250fb1c19e6cbafa7","score":"0.5432468","text":"def test_reverse_order(example_sentence):\n assert find_shortest_distance(example_sentence, \"development\", \"motivation\") == 2","title":""},{"docid":"8ae358469b0f6159f57221b5142fdf5c","score":"0.5432414","text":"def _levenshtein_distance(ref, hyp):\n m = len(ref)\n n = len(hyp)\n\n # special case\n if ref == hyp:\n return 0\n if m == 0:\n return n\n if n == 0:\n return m\n\n if m < n:\n ref, hyp = hyp, ref\n m, n = n, m\n\n # use O(min(m, n)) space\n distance = np.zeros((2, n + 1), dtype=np.int32)\n\n # initialize distance matrix\n for j in range(0,n + 1):\n distance[0][j] = j\n\n # calculate levenshtein distance\n for i in range(1, m + 1):\n prev_row_idx = (i - 1) % 2\n cur_row_idx = i % 2\n distance[cur_row_idx][0] = i\n for j in range(1, n + 1):\n if ref[i - 1] == hyp[j - 1]:\n distance[cur_row_idx][j] = distance[prev_row_idx][j - 1]\n else:\n s_num = distance[prev_row_idx][j - 1] + 1\n i_num = distance[cur_row_idx][j - 1] + 1\n d_num = distance[prev_row_idx][j] + 1\n distance[cur_row_idx][j] = min(s_num, i_num, d_num)\n\n return distance[m % 2][n]","title":""},{"docid":"d2eb245af2123fcb7d0dfcd9dbf22545","score":"0.54211354","text":"def needle_in_haystack(needle, haystack):\n needle = re.sub(r'\\W', '', needle).lower()\n diff = diff_match_patch()\n diff.Match_Distance = 5000 # default is 1000\n diff.Match_Threshold = .5 # default is .5\n lines = haystack.splitlines()\n for line in lines:\n line2 = re.sub(r'\\W', '', line).lower()\n value = diff.match_main(line2, needle, 0)\n if value is not -1:\n return line\n return 'no match in %d lines' % (len(lines), )","title":""},{"docid":"777e31c41ceb911d1acceff4e4e95c29","score":"0.5421053","text":"def levenshtein_distance_brute_force(str1, str2):\n # We want str1 -> str2\n\n # Initialize results matrix\n # This step takes O(n * m) time and O(n * m) space\n results = initialize_results_brute_force(str1, str2)\n\n # Get min number of edits (Levenshtein distance)\n # This step takes O(n * m) time and O(1) space\n answer = get_min_edits_brute_force(str1, str2, results)\n\n return answer","title":""},{"docid":"19fbdddbce88c82ac8b7af5f9244903a","score":"0.5419143","text":"def FindPosition(self, dc, context, index, forceLineStart):","title":""},{"docid":"19fbdddbce88c82ac8b7af5f9244903a","score":"0.5419143","text":"def FindPosition(self, dc, context, index, forceLineStart):","title":""},{"docid":"19fbdddbce88c82ac8b7af5f9244903a","score":"0.5419143","text":"def FindPosition(self, dc, context, index, forceLineStart):","title":""},{"docid":"19fbdddbce88c82ac8b7af5f9244903a","score":"0.5419143","text":"def FindPosition(self, dc, context, index, forceLineStart):","title":""},{"docid":"17a5743b2f48694b97424cffaefbb015","score":"0.5397152","text":"def levenshtein_distance(phrase_1, phrase_2, grain=\"char\", ignore_non_alnumspc=True, ignore_space=True, ignore_numeric=True, ignore_case=True):\n\t# Preprocess text phrase into list of edit units\n\tl_1 = phrase_preprocessing(phrase_1, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\n\tl_2 = phrase_preprocessing(phrase_2, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\n\tlen_1, len_2 = len(l_1), len(l_2)\n\n\t# Early exit if one of the lists is empty\n\tif len_1 == 0 or len_2 == 0: return max(len_1,len_2)\n\n\t# Dynamic programming solver\n\tmanipulation = [[0 for _ in range(len_2+1)] for _ in range(len_1+1)]\n\tfor row in range(len_1+1): manipulation[row][0] = row\n\tfor col in range(len_2+1): manipulation[0][col] = col\n\n\t# Allowed edit: insert, delete, substitute\n\tfor i in range(1,len_1+1):\n\t\tfor j in range(1, len_2+1):\n\t\t\tcost = 1 if l_1[i-1] != l_2[j-1] else 0\n\t\t\tmanipulation[i][j] = min(manipulation[i-1][j-1]+cost, manipulation[i-1][j]+1, manipulation[i][j-1]+1)\n\n\treturn manipulation[-1][-1]","title":""},{"docid":"72af68ee53fa7d64d98607d6ba399164","score":"0.53968966","text":"def find_closest_match(arr, target):\n min_diff = -1","title":""},{"docid":"a50b2f0e7f95c2271810c257a21e76a9","score":"0.53853464","text":"def test_jellyfish_levenshtein_distance(self):\n raise SkipTest('This one raises a MemoryError')\n\n # Import it here to avoid issues with missing dependencies in CI\n import jellyfish\n\n def jelly_fuzzy(str_a, str_b, ratio):\n str_a = str_a.replace('\\0', '')\n str_b = str_b.replace('\\0', '')\n minl = min(len(str_a), len(str_b))\n return (jellyfish.levenshtein_distance(str_a, str_b) / minl) > ratio\n\n self.generic_fuzzy_string_diff_runner_against_404(jelly_fuzzy, IS_EQUAL_RATIO)","title":""},{"docid":"bb18578fceb2f16a68f69590f20c5e6d","score":"0.5384339","text":"def findSyn(position_in_prot,vcfposition,altnucleotide,start):\n## count_func = 0\n## snpChromosome = {}\n## snpPositions = []\n## for element, value in proteinlistAlt.items():\n## \n## if proteinDict[count_func] != value:\n#### print proteinDict[count_func] , value\n## \n## snpPositions.append(count_func)\n#### print 'found change in %s' %(count_func)\n## count_func +=1\n\n \n # Python seems to always round to the lower integer. POSSIBLE BUG- need to improve the rounding algorythm\n \n position = (int(vcfposition) - int(start)) / 3\n## print 'looking for change in %s' %(position), \n if position == snpPositions_in_prot:\n synity = 'NonSyn'\n if position not in snpPositions:\n synity = 'Syn' \n \n return synity","title":""},{"docid":"915dc298543feaf1d2672bf430d326f6","score":"0.5379248","text":"def _improve_entity_span(doc_tokens, input_start, input_end, tokenizer,\n orig_entity_text):\n tok_entity_text = \" \".join(tokenizer.basic_tokenizer.tokenize(orig_entity_text))\n\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start:(new_end + 1)])\n if text_span == tok_entity_text:\n return (new_start, new_end)\n\n return (input_start, input_end)","title":""},{"docid":"bec34277e5c483bf4998f6748f611200","score":"0.53682613","text":"def proximity(a, b):\n return difflib.SequenceMatcher(None, a, b).ratio()","title":""},{"docid":"c5e73302c5fcd9c32e2e0730b4abe7c9","score":"0.53682566","text":"def _get_offsets_with_fuzzy_matching(haystack, needle, offset_init=0):\n search_idx = offset_init\n start_idx = None\n end_idx = None\n tokens = needle.split()\n\n # If this isn't a multi-term annotation, no need for fancy matchings.\n if len(tokens) == 1:\n start_idx = haystack.find(tokens[0], search_idx)\n end_idx = start_idx + len(tokens[0])\n return start_idx, end_idx\n\n # Otherwise we iterate the tokens 2-by-2 until we get a full match.\n token_idx = 1\n cur_idx, cur_token = -1, \"\"\n while token_idx < len(tokens):\n prv_token = tokens[token_idx - 1]\n prv_idx = haystack.find(prv_token, search_idx)\n prv_offset = prv_idx + len(prv_token)\n cur_token = tokens[token_idx]\n cur_idx = haystack.find(cur_token, prv_offset)\n # In every iteration we check if the next word starts from where\n # the previous ends.\n if cur_idx in (prv_offset, prv_offset + 1):\n if start_idx is None:\n start_idx = prv_idx\n token_idx += 1\n # If it doesn't, then it must be an accidental match, reset the index.\n else:\n start_idx = None\n token_idx = 1\n search_idx = prv_idx + len(prv_token)\n end_idx = cur_idx + len(cur_token)\n\n return start_idx, end_idx","title":""},{"docid":"fe2e2dadac1783f6902254fcd0aa2bcc","score":"0.53658676","text":"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):\n tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\n\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start : (new_end + 1)])\n if text_span == tok_answer_text:\n return (new_start, new_end)\n\n return (input_start, input_end)","title":""},{"docid":"cf7fea2d0a303ce4d286c3a298ce2a48","score":"0.5349472","text":"def _findPrev(s, char, pos):\n\tmatch = ord(char)\n\tpos = pos - 1\n\twhile pos >= 0:\n\t\tif s.GetCharAt(pos) == match:\n\t\t\treturn pos\n\t\tpos = pos - 1\n\t\n\treturn -1","title":""},{"docid":"a11818ea12c680b1c3172f31ce1badc8","score":"0.534201","text":"def compute_text_offset(score):\n if len(score) == 1:\n return 135\n elif len(score) == 2:\n return 120\n elif len(score) == 3:\n return 105\n else:\n return 90","title":""},{"docid":"90d12d4d44acc13a3d58c93f94adfb88","score":"0.5340846","text":"def compute_diff(original_part_text, edit_text):\n # Separate into words and linebreaks.\n part_words = original_part_text.split()\n edit_words = edit_text.split()\n edit_word_lists = [line.split() for line in edit_text.splitlines()]\n edit_linebreak_indices = []\n for i, line in enumerate(edit_word_lists):\n if i == 0:\n edit_linebreak_indices.append(len(line))\n else:\n edit_linebreak_indices.append(\n len(line) + edit_linebreak_indices[-1])\n\n # Now compute word-oriented opcodes.\n opcodes_with_replace = SequenceMatcher(\n a=part_words, b=edit_words, autojunk=False).get_opcodes()\n opcodes = []\n for operation in opcodes_with_replace:\n if operation[0] == \"replace\":\n opcodes.append((\"delete\", operation[1], operation[2],\n operation[3], operation[3]))\n opcodes.append((\"insert\", operation[1], operation[1],\n operation[3], operation[4]))\n else:\n opcodes.append(operation)\n\n # Convert the opcodes into a single-string diff\n diff = \"\"\n for operation in opcodes:\n if operation[0] == \"delete\":\n diff += \"- \" + \" \".join(\n part_words[operation[1] : operation[2]]) + \" \\n\"\n elif operation[0] == \"insert\":\n diff += \"+ \" + \" \".join(\n edit_words[operation[3] : operation[4]]) + \" \\n\"\n elif operation[0] == \"equal\":\n diff += \" \" + \" \".join(\n edit_words[operation[3] : operation[4]]) + \" \\n\"\n\n diff = diff[:-2]\n diff_lines = diff.splitlines()\n if (len(diff_lines) >= 2 and \n ((diff_lines[-2].startswith(\"+\") and diff_lines[-1].startswith(\"-\")) or \n (diff_lines[-2].startswith(\"-\") and diff_lines[-1].startswith(\"+\")) or\n (diff_lines[-2].startswith(\" \") and diff_lines[-1].startswith(\"-\")))):\n length2 = len(diff_lines[-1])\n length1 = len(diff) - length2\n diff = diff[:length1-2] + diff[length1-1:]\n\n return diff","title":""},{"docid":"b6fbf581b152e22d0b6dca0cf845015d","score":"0.5331644","text":"def linker_pos(seq, linker, n_mismatch):\n gap_char = \"-\"\n pos = str(seq).find(linker)\n if pos > 0:\n seq_region = seq[pos:pos+len(linker)]\n linker_region = linker\n else:\n aligns = pairwise2.align.localms(str(seq), str(linker),\n 5.0, -4.0, -9.0, -0.5, one_alignment_only=True,\n gap_char=gap_char)\n if len(aligns) > 0:\n seq_a, linker_a, _, start, end = aligns[0]\n linker_region = linker_a[start:end]\n seq_region = seq_a[start:end]\n pos = seq.find(seq_region.replace(gap_char, \"\"))\n else:\n seq_region, linker_region = (\"\", \"\")\n matches = sum((1 if s == linker_region[i] else 0) for i, s in\n enumerate(seq_region))\n gaps = seq_region.count(\"-\")\n cur_mismatch = len(linker) - matches + gaps\n if cur_mismatch <= n_mismatch:\n assert pos >= 0\n return pos, pos + len(seq_region.replace(gap_char, \"\"))\n else:\n return (None, None)","title":""},{"docid":"b7567832096fa9516b21d92e7361ff78","score":"0.5328881","text":"def levenshtein(left, right):\n return _with_binary_expr(exprs.Levenshtein, left=left, right=right)","title":""},{"docid":"3f97f69d04783cf4a2f390029e8b0f91","score":"0.5326987","text":"def test_closest_dirty(self):\n\n grid = ['b-ooo',\n '-dooo',\n 'ooooo',\n 'ooooo',\n 'ooooo']\n result = botClean.find_closest([0, 0], grid, 5, 5, 'd')\n self.assertEqual(result, [1, 1])\n\n grid = ['d-ooo',\n '-dooo',\n 'ooooo',\n 'ooooo',\n 'ooooo']\n result = botClean.find_closest([0, 0], grid, 5, 5, 'd')\n self.assertEqual(result, [0, 0])\n\n grid = ['ooooo',\n 'ooooo',\n 'ooooo',\n 'ooo--',\n 'ooo-b']\n result = botClean.find_closest([4, 4], grid, 5, 5, 'd')\n self.assertEqual(result, None)","title":""},{"docid":"2aee8aa742cb5fb39b9c9904045d9e28","score":"0.53252864","text":"def _pos_for_example(self):\n srchStr = 'def %s('%self.codeL[self.pcount].func_name\n i = 0\n while i< len(self.lines):\n if self.lines[i].find( srchStr ) >= 0:\n line = i\n break\n i += 1\n return self.textCtrl.XYToPosition(0,line)","title":""},{"docid":"f1830d66e1a036011f0f9856f824bdcc","score":"0.5310087","text":"def comp_position(text, line, begidx, endidx):\n return len(shell_split(line[:begidx])) - 1","title":""},{"docid":"c8875fb5a5eb699b46d22387fc5698dd","score":"0.5308194","text":"def span_from_match(\n match: re.Match,\n span_from_group: bool,\n) -> Tuple[int, int]:\n if not span_from_group:\n start_char, end_char = match.start(), match.end()\n else:\n start_char, end_char = next(filter(lambda x: x[0] >= 0, spans_generator(match)))\n return start_char, end_char","title":""},{"docid":"7bfd0ecc4f8e0d3c09bb66b6d38189f9","score":"0.5303389","text":"def levenshtein(s1, s2, allow_substring=False):\n len1, len2 = len(s1), len(s2)\n lev = []\n for i in range(len1 + 1):\n lev.append([0] * (len2 + 1))\n for i in range(len1 + 1):\n lev[i][0] = i\n for j in range(len2 + 1):\n lev[0][j] = 0 if allow_substring else j\n for i in range(len1):\n for j in range(len2):\n lev[i + 1][j + 1] = min(lev[i][j + 1] + 1, lev[i + 1][j] + 1, lev[i][j] + (s1[i] != s2[j]))\n return min(lev[len1]) if allow_substring else lev[len1][len2]","title":""},{"docid":"6aeb19c77192e72232a59b28897dfe70","score":"0.5303254","text":"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):\n\n tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\n\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start:(new_end + 1)])\n if text_span == tok_answer_text:\n return new_start, new_end\n\n return input_start, input_end","title":""},{"docid":"83fe1f9ff60e60d1ab0a5a69f5a2379b","score":"0.52982104","text":"def getChapterQuoteAppears(self,quote):\n chapter_strings = []\n for i in range(len(self.chapter_lists)):\n chapter_strings.append(\" \".join(self.chapter_lists[i]))\n\n j = 1\n for chapter in chapter_strings:\n if chapter.find(quote) != -1:\n return j\n j+=1\n return -1","title":""},{"docid":"a5a21f9bc794c639f7ca89958d2c6de3","score":"0.52962655","text":"def edit_distance_from_aln_strings(str1, str2):\n assert len(str1) == len(str2)\n edit_distance = 0\n in_gap = False\n\n for i, char1 in enumerate(str1):\n if char1 == \"-\" or str2[i] == \"-\":\n if not in_gap:\n in_gap = True\n edit_distance += 1\n else:\n in_gap = False\n\n if char1 != str2[i]:\n edit_distance += 1\n\n return edit_distance","title":""},{"docid":"cf1c8b70539568afc73e9e2076d05d6b","score":"0.5294241","text":"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):\n tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start : (new_end + 1)])\n if text_span == tok_answer_text:\n return new_start, new_end\n return input_start, input_end","title":""},{"docid":"2fbe384de22d0c87cbc0468461d7080d","score":"0.5284241","text":"def _levenshtein(s1, s2):\n\n if (s1, s2) in StringLevenshtein._map:\n return StringLevenshtein._map[(s1, s2)]\n\n m = len(s1)\n n = len(s2)\n\n if m == 0:\n result = n\n\n elif n == 0:\n result = m\n\n elif s1[0] == s2[0]:\n result = StringLevenshtein._levenshtein(s1[1:], s2[1:])\n\n else:\n result = 1 + min(\n StringLevenshtein._levenshtein(s1[1:], s2),\n StringLevenshtein._levenshtein(s1, s2[1:]),\n StringLevenshtein._levenshtein(s1[1:], s2[1:])\n )\n\n StringLevenshtein._map[(s1, s2)] = result\n return result","title":""},{"docid":"7cd297411a4922973bad3e58c84ac21c","score":"0.52734447","text":"def getwpidx(self,txt,lat=999999.,lon=999999):\n name = txt.upper()\n try:\n i = self.wpid.index(name)\n except:\n return -1\n \n # if no pos is specified, get first occurence\n if not lat<99999.:\n return i\n\n # If pos is specified check for more and return closest\n else: \n idx = []\n idx.append(i)\n found = True\n while i<len(self.wpid)-1 and found:\n try:\n i = self.wpid.index(name,i+1)\n idx.append(i)\n except:\n found = False\n if len(idx)==1:\n return idx[0]\n else:\n imin = idx[0]\n dmin = geo.kwikdist(lat,lon,self.wplat[imin],self.wplon[imin]) \n for i in idx[1:]:\n d = geo.kwikdist(lat,lon,self.wplat[i],self.wplon[i])\n if d<dmin:\n imin = i\n dmin = d\n return imin","title":""},{"docid":"e585cdb9d82c39a9e009c3266b4b616d","score":"0.52700233","text":"def iterative_levenshtein(s, t):\n rows = len(s) + 1\n cols = len(t) + 1\n dist = [[0 for x in range(cols)] for x in range(rows)]\n # source prefixes can be transformed into empty strings\n # by deletions:\n for i in range(1, rows):\n dist[i][0] = i\n # target prefixes can be created from an empty source string\n # by inserting the characters\n for i in range(1, cols):\n dist[0][i] = i\n\n for col in range(1, cols):\n for row in range(1, rows):\n if s[row - 1] == t[col - 1]:\n cost = 0\n else:\n cost = 1\n dist[row][col] = min(dist[row - 1][col] + 1, # deletion\n dist[row][col - 1] + 1, # insertion\n dist[row - 1][col - 1] + cost) # substitution\n\n return dist[row][col]","title":""},{"docid":"e4bdd9fcd3c6fb9f706a778c2d415503","score":"0.52690554","text":"def substitute(cls, cursor_offset, line, match):\n start, end, word = cls.locate(cursor_offset, line)\n result = start + len(match), line[:start] + match + line[end:]\n return result","title":""},{"docid":"c6ad8c8a5ce20931c198533b3695269f","score":"0.52635247","text":"def find_closest_line(search_key, all_lines):\n for line_key in all_lines:\n # search for match in line number and direction\n if line_key[0:2] == search_key[0:2]:\n return line_key\n # no match was found\n return None","title":""},{"docid":"841a9190ca23f02915518bbd44ccc07a","score":"0.5259066","text":"def match_fixed_start_offset(self, from_, to, match):\n pass","title":""},{"docid":"f537cf504fb21fb908843d14ac13f464","score":"0.52490187","text":"def edit_distance_dp(str1, str2):\n rows = len(str1) + 1\n cols = len(str2) + 1\n dp_table = [[0 for j in range(cols)] for i in range(rows)]\n\n for row in range(rows):\n for col in range(cols):\n if row == 0:\n dp_table[row][col] = col\n elif col == 0:\n dp_table[row][col] = row\n # If last characters match\n elif str1[row - 1] == str2[col - 1]:\n dp_table[row][col] = dp_table[row - 1][col - 1]\n else: \n # If last characters are not same, consider all three\n # operations on last character of first string, \n # compute minimum cost for all three operations and take\n # minimum of three values.\n dp_table[row][col] = 1 + min(\n dp_table[row][col - 1], # Insert\n dp_table[row - 1][col], # Remove\n dp_table[row - 1][col - 1] # Replace\n ) \n\n return dp_table[rows-1][cols-1]","title":""},{"docid":"45c2d91bc13f2b44d9e9554b7285f6f4","score":"0.5248359","text":"def direct_quotations(doc):\n if isinstance(doc, textacy.Doc):\n if doc.lang != 'en':\n raise NotImplementedError('sorry, English-language texts only :(')\n doc = doc.spacy_doc\n quote_end_punct = {',', '.', '?', '!'}\n quote_indexes = set(itertoolz.concat(\n (m.start(), m.end() - 1) for m in re.finditer(r\"(\\\".*?\\\")|(''.*?'')|(``.*?'')\", doc.string)))\n quote_positions = list(itertoolz.partition(\n 2, sorted(tok.i for tok in doc if tok.idx in quote_indexes)))\n sents = list(doc.sents)\n sent_positions = [(sent.start, sent.end) for sent in sents]\n\n for q0, q1 in quote_positions:\n quote = doc[q0: q1 + 1]\n\n # we're only looking for direct quotes, not indirect or mixed\n if not any(char in quote_end_punct for char in quote.text[-4:]):\n continue\n\n # get adjacent sentences\n candidate_sent_indexes = []\n for i, (s0, s1) in enumerate(sent_positions):\n\n if s0 <= q1 + 1 and s1 > q1:\n candidate_sent_indexes.append(i)\n elif s0 < q0 and s1 >= q0 - 1:\n candidate_sent_indexes.append(i)\n\n for si in candidate_sent_indexes:\n sent = sents[si]\n\n # get any reporting verbs\n rvs = [tok for tok in sent\n if spacy_utils.preserve_case(tok) is False and\n tok.lemma_ in REPORTING_VERBS and\n tok.pos_ == 'VERB' and\n not any(oq0 <= tok.i <= oq1 for oq0, oq1 in quote_positions)]\n\n # get target offset against which to measure distances of NEs\n if rvs:\n if len(rvs) == 1:\n rv = rvs[0]\n else:\n min_rv_dist = 1000\n for rv_candidate in rvs:\n rv_dist = min(abs(rv_candidate.i - qp) for qp in (q0, q1))\n if rv_dist < min_rv_dist:\n rv = rv_candidate\n min_rv_dist = rv_dist\n else:\n break\n else:\n # TODO: do we have no other recourse?!\n continue\n\n try:\n # rv_subj = _find_subjects(rv)[0]\n rv_subj = get_subjects_of_verb(rv)[0]\n except IndexError:\n continue\n # if rv_subj.text in {'he', 'she'}:\n # for ne in named_entities(doc, good_ne_types={'PERSON'}):\n # if ne.start < rv_subj.i:\n # speaker = ne\n # else:\n # break\n # else:\n span = get_span_for_compound_noun(rv_subj)\n speaker = doc[span[0]: span[1] + 1]\n\n yield (speaker, rv, quote)\n break","title":""},{"docid":"feeecb351a50991b59bff3064b18d4ed","score":"0.5242717","text":"def RNA_position(bedline, GI):\n \n mRNA_pos = 0\n pre_pos = 0\n exon_frac = None\n intron_frac = None\n nearest_type=None\n chr, start, stop, name, score, strand, thickstart, thickstop = str(bedline).strip().split(\"\\t\")\n thickstart, thickstop = map(int, (thickstart, thickstop))\n position = int((thickstart + thickstop)/2)\n try:\n gene, n, reads = name.split(\"_\")\n except:\n \n #takes first gene if there are multiple overlapping \n gene, n, reads = name.split(\";\")[0].split(\"_\")\n\n for exN in range(GI[gene]['numEx']):\n exstart, exstop = map(int, GI[gene]['exons'][exN].split(\"-\"))\n exlen = exstop-exstart+1\n if position >= exstart and position <= exstop:\n if strand == \"+\":\n mRNA_pos += position - exstart\n exon_frac = np.round(((position-exstart)/float(exlen)), 3)\n pre_pos += position - exstart \n else:\n mRNA_pos += exstop - position\n exon_frac = np.round(((exstop - position)/float(exlen)), 3)\n pre_pos += exstop - position\n\n mRNA_frac = np.round((mRNA_pos/float(GI[gene]['mRNA_length'])), 3)\n premRNA_frac = np.round((pre_pos/float(GI[gene]['premRNA_length'])), 3)\n\n if mRNA_frac < 0 or mRNA_frac > 1:\n print \"mRNA_frac is bad: %f, gene %s\" %(mRNA_frac, gene)\n\n if premRNA_frac < 0 or premRNA_frac > 1:\n print \"premRNA_frac is bad: %f, gene %s\" %(premRNA_frac, gene)\n\n nearest_type = GI[gene]['types'][exN]\n return mRNA_frac, premRNA_frac, exon_frac, None, nearest_type\n else:\n mRNA_pos += exlen\n pre_pos += exlen\n if exN < GI[gene]['numEx']: #there are only exN - 1 introns\n intstart, intstop = map(int, GI[gene]['introns'][exN].split(\"-\"))\n intlen = intstop-intstart+1\n if position >= intstart and position <= intstop:\n mRNA_pos = None \n if strand == \"+\":\n pre_pos += position - intstart\n intron_frac = np.round(((position-intstart)/float(intlen)), 3)\n else:\n pre_pos += intstop - position\n intron_frac = np.round(((intstop - position)/float(intlen)), 3)\n premRNA_frac = np.round((pre_pos/float(GI[gene]['premRNA_length'])), 3)\n if premRNA_frac > 0.5:\n nearest_type=GI[gene]['types'][exN+1]\n else:\n nearest_type=GI[gene]['types'][exN]\n\n\n if premRNA_frac < 0 or premRNA_frac > 1:\n print \"premRNA_frac is bad: %f, gene %s\" %(premRNA_frac, gene)\n\n\n \n return None, premRNA_frac, None, intron_frac, nearest_type\n else:\n pre_pos += intlen\n return None, None, None, None, None #shouldn't ever get here. this would mean that the clusters doesn't fall within the gene","title":""},{"docid":"8d2b7aaf59280ed6996e4e881907e3c5","score":"0.52367175","text":"def find_closest_string(self, query, dictionary, thresh=0.90):\n dist = {i:Levenshtein.jaro_winkler(query, i) for i in dictionary}\n dist = sorted(dist.items(), key=operator.itemgetter(1), reverse=True)\n if dist[0][1] >= thresh:\n return dist[0][0]\n else:\n return None","title":""},{"docid":"ed45716ee48f4e420e8aca21ca3846c1","score":"0.5235785","text":"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\n orig_answer_text):\n\n # The SQuAD annotations are character based. We first project them to\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\n # often find a \"better match\". For example:\n #\n # Question: What year was John Smith born?\n # Context: The leader was John Smith (1895-1943).\n # Answer: 1895\n #\n # The original whitespace-tokenized answer will be \"(1895-1943).\". However\n # after tokenization, our tokens will be \"( 1895 - 1943 ) .\". So we can match\n # the exact answer, 1895.\n #\n # However, this is not always possible. Consider the following:\n #\n # Question: What country is the top exporter of electornics?\n # Context: The Japanese electronics industry is the lagest in the world.\n # Answer: Japan\n #\n # In this case, the annotator chose \"Japan\" as a character sub-span of\n # the word \"Japanese\". Since our WordPiece tokenizer does not split\n # \"Japanese\", we just use \"Japanese\" as the annotation. This is fairly rare\n # in SQuAD, but does happen.\n tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\n\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start:(new_end + 1)])\n if text_span == tok_answer_text:\n return (new_start, new_end)\n\n return (input_start, input_end)","title":""},{"docid":"ed45716ee48f4e420e8aca21ca3846c1","score":"0.5235785","text":"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\n orig_answer_text):\n\n # The SQuAD annotations are character based. We first project them to\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\n # often find a \"better match\". For example:\n #\n # Question: What year was John Smith born?\n # Context: The leader was John Smith (1895-1943).\n # Answer: 1895\n #\n # The original whitespace-tokenized answer will be \"(1895-1943).\". However\n # after tokenization, our tokens will be \"( 1895 - 1943 ) .\". So we can match\n # the exact answer, 1895.\n #\n # However, this is not always possible. Consider the following:\n #\n # Question: What country is the top exporter of electornics?\n # Context: The Japanese electronics industry is the lagest in the world.\n # Answer: Japan\n #\n # In this case, the annotator chose \"Japan\" as a character sub-span of\n # the word \"Japanese\". Since our WordPiece tokenizer does not split\n # \"Japanese\", we just use \"Japanese\" as the annotation. This is fairly rare\n # in SQuAD, but does happen.\n tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\n\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start:(new_end + 1)])\n if text_span == tok_answer_text:\n return (new_start, new_end)\n\n return (input_start, input_end)","title":""},{"docid":"c766f0a113880a274bf644c2fef4ae8b","score":"0.523083","text":"def find3(strng, ch, start=0): \n ix = start\n while ix < len(strng):\n if strng[ix] == ch:\n return ix\n ix += 1\n return -1","title":""},{"docid":"0ae8d686062ccf9e30520e23ffcf311a","score":"0.5230132","text":"def fuzzy_string_match(str_ref, str_hyp):\n\n # The higher the score, the higher the similarity between the two strings.\n return fuzz.token_sort_ratio(str_ref, str_hyp) / 100.0","title":""},{"docid":"f6eddd55f1a2eadf626d0ae9f5a37791","score":"0.5205989","text":"def substring_edit_distance(s: str, t: str) -> int:\n\n if not s:\n return len(t)\n if not t:\n return 0\n\n M = [[0 for _ in range(len(t) + 1)] for _ in range(len(s) + 1)]\n # M[i][j] is the minimum number of t-edits required to make t[:j] a suffix of s[:i].\n for i in range(len(s) + 1):\n M[i][0] = 0\n for j in range(len(t) + 1):\n M[0][j] = j\n\n for i in range(1, len(s) + 1):\n for j in range(1, len(t) + 1):\n cost = 0 if s[i - 1] == t[j - 1] else 1\n M[i][j] = min(\n [\n 1 + M[i - 1][j],\n 1 + M[i][j - 1],\n cost + M[i - 1][j - 1],\n ])\n\n return min(M[i][len(t)] for i in range(len(s) + 1))","title":""},{"docid":"e2d79a2425e8def5a99b81f4ab618b9c","score":"0.52014494","text":"def find_diff_start(old_src, new_src):\n old_lines = old_src.split(\"\\n\")\n new_lines = new_src.split(\"\\n\")\n\n for line in range(min(len(old_lines), len(new_lines))):\n old = old_lines[line]\n new = new_lines[line]\n if old == new:\n continue\n for col in range(min(len(old), len(new))):\n if old[col] != new[col]:\n return line, col\n # Difference at the end of the line...\n return line, min(len(old), len(new))\n # Difference at the end of the file...\n return min(len(old_lines), len(new_lines)), 0","title":""},{"docid":"e2d79a2425e8def5a99b81f4ab618b9c","score":"0.52014494","text":"def find_diff_start(old_src, new_src):\n old_lines = old_src.split(\"\\n\")\n new_lines = new_src.split(\"\\n\")\n\n for line in range(min(len(old_lines), len(new_lines))):\n old = old_lines[line]\n new = new_lines[line]\n if old == new:\n continue\n for col in range(min(len(old), len(new))):\n if old[col] != new[col]:\n return line, col\n # Difference at the end of the line...\n return line, min(len(old), len(new))\n # Difference at the end of the file...\n return min(len(old_lines), len(new_lines)), 0","title":""},{"docid":"dbdb80fc03a7b67d0b80a2c1f58121d6","score":"0.52014196","text":"def exact_location(self, text):\n try:\n first = []\n last = []\n location = self.get_location(text)\n text = self._clean_text(text)\n text = text.lower()\n first_thousand = text[:300]\n last_thousand = text[len(text)-300:]\n for item in location:\n if item in first_thousand:\n first.append(item)\n if item in last_thousand:\n last.append(item)\n if not last and first:\n location = first\n if not first and last:\n location = last\n if last and first:\n location = list(set(first) & set(last))\n return location\n except Exception as error:\n return error","title":""},{"docid":"0fd530e4856d855768a41f340abc875d","score":"0.52014095","text":"def lcs_distance(phrase_1, phrase_2, grain=\"char\", ignore_non_alnumspc=True, ignore_space=True, ignore_numeric=True, ignore_case=True):\n\t# Preprocess text phrase into list of edit units\n\tl_1 = phrase_preprocessing(phrase_1, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\n\tl_2 = phrase_preprocessing(phrase_2, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\n\tlen_1, len_2 = len(l_1), len(l_2)\n\n\t# Early exit if one of the lists is empty\n\tif len_1 == 0 or len_2 == 0: return max(len_1,len_2)\n\n\t# Dynamic programming solver\n\tmanipulation = [[0 for _ in range(len_2+1)] for _ in range(len_1+1)]\n\tfor row in range(len_1+1): manipulation[row][0] = 0\n\tfor col in range(len_2+1): manipulation[0][col] = 0\n\n\t# Allowed edit: insert, delete\n\tfor i in range(1,len_1+1):\n\t\tfor j in range(1, len_2+1):\n\t\t\tmanipulation[i][j] = manipulation[i-1][j-1] + 1 if l_1[i-1] == l_2[j-1] else max(manipulation[i][j-1], manipulation[i-1][j])\n\n\tdistance = len_1 + len_2 - 2 * manipulation[-1][-1]\n\n\treturn distance","title":""},{"docid":"06507dae3b59a04141c13ab3a82cfc10","score":"0.5199065","text":"def best_matching_start_ignore_symbol(self, transcription):\n best_match = None\n\n for sym in self.ignore_symbols:\n if transcription.startswith(sym) and \\\n (best_match is None or len(sym) < len(best_match)):\n best_match = sym\n\n return best_match","title":""},{"docid":"5a7a37f5d6dbe7f054e99f5d1c4d2d06","score":"0.51974034","text":"def test_replace_at_start(self):\n res = align_utils.str_mod(self.string, 0, self.insert1, replace=True)\n self.assertEqual(res, \"ghijklmn\")","title":""},{"docid":"8d9c26d9e2f2b3877832872a5750d9ce","score":"0.5192631","text":"def correct_positions(dependency_parse):\n\n for ii in dependency_parse.to_conll(10).split('\\n'):\n if not ii:\n continue\n\n fields = ii.split('\\t')\n child_pos = int(fields[0])\n head_pos = int(fields[6])\n child_word = fields[1]\n if not all(x in punctuation for x in child_word):\n yield (head_pos, child_pos)","title":""},{"docid":"3f2d79293f73aa6f56397d9deae5eab1","score":"0.51835716","text":"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\n orig_answer_text):\n\n # The SQuAD annotations are character based. We first project them to\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\n # often find a \"better match\". For example:\n #\n # Question: What year was John Smith born?\n # Context: The leader was John Smith (1895-1943).\n # Answer: 1895\n #\n # The original whitespace-tokenized answer will be \"(1895-1943).\". However\n # after tokenization, our tokens will be \"( 1895 - 1943 ) .\". So we can match\n # the exact answer, 1895.\n #\n # However, this is not always possible. Consider the following:\n #\n # Question: What country is the top exporter of electornics?\n # Context: The Japanese electronics industry is the lagest in the world.\n # Answer: Japan\n #\n # In this case, the annotator chose \"Japan\" as a character sub-span of\n # the word \"Japanese\". Since our WordPiece tokenizer does not split\n # \"Japanese\", we just use \"Japanese\" as the annotation. This is fairly rare\n # in SQuAD, but does happen.\n tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\n\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start:(new_end + 1)])\n if text_span == tok_answer_text:\n return new_start, new_end\n\n return input_start, input_end","title":""},{"docid":"13c1ff9c048392baec5d5119e7fdf7f7","score":"0.5182412","text":"def test_move_exact_matches_to_top(self):\n matches = self.autocomp.suggest('Ma')\n setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', True)\n matches2 = self.autocomp.suggest('Ma')\n self.assertNotEqual(matches['stock'][0]['search_name'], matches2['stock'][0]['search_name'])\n setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', False)","title":""},{"docid":"b824a1ddce9e66ca24a1dd0f435c4fb2","score":"0.5169095","text":"def test_pride_sort():\n\n out = getoutput(f'python {prg} ../pride_and_prejudice.txt')\n basis = \"mr - 786\"\n to_find = \"elizabeth - 635\"\n\n assert to_find in out[out.index(basis) + 1:]","title":""},{"docid":"43b731e00ac37376c1154a18ed9c37c6","score":"0.5165766","text":"def dameraulevenshtein(seq1, seq2):\n # codesnippet:D0DE4716-B6E6-4161-9219-2903BF8F547F\n # Conceptually, this is based on a len(seq1) + 1 * len(seq2) + 1 matrix.\n # However, only the current and two previous rows are needed at once,\n # so we only store those.\n oneago = None\n thisrow = list(range(1, len(seq2) + 1)) + [0]\n for x in range(len(seq1)):\n # Python lists wrap around for negative indices, so put the\n # leftmost column at the *end* of the list. This matches with\n # the zero-indexed strings and saves extra calculation.\n twoago, oneago, thisrow = oneago, thisrow, [0] * len(seq2) + [x + 1]\n for y in range(len(seq2)):\n delcost = oneago[y] + 1\n addcost = thisrow[y - 1] + 1\n subcost = oneago[y - 1] + (seq1[x] != seq2[y])\n thisrow[y] = min(delcost, addcost, subcost)\n # This block deals with transpositions\n if (x > 0 and y > 0 and seq1[x] == seq2[y - 1]\n and seq1[x - 1] == seq2[y] and seq1[x] != seq2[y]):\n thisrow[y] = min(thisrow[y], twoago[y - 2] + 1)\n return thisrow[len(seq2) - 1]","title":""},{"docid":"870cae6c224c89080e993f63798dbc0c","score":"0.5160745","text":"def levenshteinDistance(str1, len_str1, str2, len_str2):\n\n # base cases \n if len_str1 == 0:\n return len_str2\n \n if len_str2 == 0:\n return len_str1\n \n if str1[len_str1] == str2[len_str2]:\n cost = 0\n else:\n cost = 1\n\n return min(levenshteinDistance(str1[:-1], len_str1 - 1, str2, len_str2) + 1,\n levenshteinDistance(str1, len_str1 , str2[:-1], len_str2 - 1) + 1,\n levenshteinDistance(str1[:-1], len_str1 - 1, str2[:-1], len_str2 - 1) + cost)","title":""},{"docid":"3c8725f955305b5413fc3423467c284e","score":"0.51538527","text":"def get_min_edits_brute_force(str1, str2, results):\n for row_idx in range(len(str2)):\n for col_idx in range(len(str1)):\n if str1[col_idx] == str2[row_idx]:\n results[row_idx + 1][col_idx + 1] = results[row_idx][col_idx]\n else:\n results[row_idx + 1][col_idx + 1] = 1 + min(results[row_idx][col_idx],\n results[row_idx][col_idx + 1],\n results[row_idx + 1][col_idx])\n\n return results[-1][-1]","title":""},{"docid":"8e10927fd41bea59cb4fe8204fdc29d6","score":"0.51506245","text":"def test_closest_unknown(self):\n\n grid = ['b-ooo',\n '-dooo',\n 'ooooo',\n 'ooooo',\n 'ooooo']\n result = botClean.find_closest([0, 0], grid, 5, 5, 'o')\n self.assertEqual(result, [2, 0])\n\n grid = ['ooooo',\n 'ooooo',\n 'ooooo',\n 'ooo--',\n 'ooo-b']\n result = botClean.find_closest([4, 4], grid, 5, 5, 'o')\n self.assertEqual(result, [2, 4])","title":""},{"docid":"f51c0a4a394819efb513a73b14c28967","score":"0.5145103","text":"def damerau_levenshtein_distance(phrase_1, phrase_2, grain=\"char\", ignore_non_alnumspc=True, ignore_space=True, ignore_numeric=True, ignore_case=True):\n\t# Preprocess text phrase into list of edit units\n\tl_1 = phrase_preprocessing(phrase_1, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\n\tl_2 = phrase_preprocessing(phrase_2, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\n\tlen_1, len_2 = len(l_1), len(l_2)\n\n\t# Early exit if one of the lists is empty\n\tif len_1 == 0 or len_2 == 0: return max(len_1,len_2)\n\n\t# Dynamic programming solver\n\tmanipulation = [[0 for _ in range(len_2+1)] for _ in range(len_1+1)]\n\tfor row in range(len_1+1): manipulation[row][0] = row\n\tfor col in range(len_2+1): manipulation[0][col] = col\n\n\t# Allowed edit: insert, delete, substitute, transpose of adjacent characters\n\tfor i in range(1,len_1+1):\n\t\tfor j in range(1, len_2+1):\n\t\t\tcost_1 = 1 if l_1[i-1] != l_2[j-1] else 0\n\t\t\tif i >= 2 and j >= 2:\n\t\t\t\tcost_2 = 1 if l_1[i-2] == l_2[j-1] and l_1[i-1] == l_2[j-2] else 9527\n\t\t\t\tmanipulation[i][j] = min(manipulation[i-1][j-1]+cost_1, manipulation[i-2][j-2]+cost_2, manipulation[i-1][j]+1, manipulation[i][j-1]+1)\n\t\t\telse:\n\t\t\t\tmanipulation[i][j] = min(manipulation[i-1][j-1]+cost_1, manipulation[i-1][j]+1, manipulation[i][j-1]+1)\n\n\treturn manipulation[-1][-1]","title":""},{"docid":"b83d257c91f1af5d64b056bf54509b96","score":"0.5135298","text":"def _edit_dist_step(lev, i, j, s1, s2, threshold):\n c1 = s1[i - 1]\n c2 = s2[j - 1]\n\n # skipping a character in s1\n a = lev[i - 1][j] + 1\n # skipping a character in s2\n b = lev[i][j - 1] + 1\n # substitution\n c = lev[i - 1][j - 1] + (c1 != c2)\n\n # pick the cheapest\n lev[i][j] = min(a, b, c)\n if lev[i][j] <= threshold:\n return True\n return False","title":""},{"docid":"a2d54e79d7a1a1b1ea9bd49ca2f8ed1d","score":"0.51319325","text":"def find_offset(pattern, template): \n\n real_corr = crr_2d( pattern, template)\n\n\n best_match , match_value = find_best_match( real_corr )\n #print( best_match )\n\n return (best_match[0] - 2 * pattern.shape[0], best_match[1] - 2 * pattern.shape[1]), match_value","title":""},{"docid":"affa637574cc4881dba9f0c3b7cb247e","score":"0.5131755","text":"def iterative_levenshtein(s, t):\n rows = len(s) + 1\n cols = len(t) + 1\n dist = [[0 for x in range(cols)] for x in range(rows)]\n # source prefixes can be transformed into empty strings\n # by deletions:\n for i in range(1, rows):\n dist[i][0] = i\n # target prefixes can be created from an empty source string\n # by inserting the characters\n for i in range(1, cols):\n dist[0][i] = i\n\n for col in range(1, cols):\n for row in range(1, rows):\n if s[row - 1] == t[col - 1]:\n cost = 0\n else:\n cost = 1\n dist[row][col] = min(dist[row - 1][col] + 1, # deletion\n dist[row][col - 1] + 1, # insertion\n dist[row - 1][col - 1] + cost) # substitution\n return dist","title":""}],"string":"[\n {\n \"docid\": \"6199483675f4357c9c9a305fcdc5b449\",\n \"score\": \"0.65813494\",\n \"text\": \"def findClosestMatchingQuote(self, quote, threshold=.9):\\n quotelen = len(quote.split(\\\" \\\"))\\n chaptersubstrings = []\\n lst_of_substrings = []\\n for chapter in self.chapter_lists:\\n for i in range(len(chapter)-quotelen):\\n #Make a substring for every substring of size quotelen\\n #Turn the sub array ito a string\\n lst_of_substrings.append(\\\" \\\".join(chapter[i:i+quotelen]))\\n chaptersubstrings.append(lst_of_substrings)\\n lst_of_substrings= []\\n\\n def helper(threshold):\\n if threshold < 0:\\n return \\\"\\\",-1\\n print(\\\"Threshold is\\\", threshold)\\n best = []\\n for i in range(len(self.chapter_lists)):\\n good = get_close_matches(quote,chaptersubstrings[i], n =3, cutoff=threshold)\\n if good:\\n #add the best match to a list\\n best.append(good[0])\\n #Compare the best of all the chapters together and pick the best one\\n final_match = get_close_matches(quote, best, n = 1, cutoff= threshold)\\n if not final_match:\\n return helper(threshold-.1)\\n final_match = final_match[0]\\n final_idx = best.index(final_match)+1\\n return final_match, final_idx\\n return helper(threshold)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffeef885adc0134fc3a08e810c26603f\",\n \"score\": \"0.6223548\",\n \"text\": \"def suggest_closest_word(self, word: str) -> str:\\n start = 100000000000\\n flag = ''\\n for item in self.entries:\\n temp = self.__levenshtein_dist__(word, item, False)\\n if start > temp >= 1:\\n flag = item\\n return item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1645cb303389da4e2ced438ca9a2fe1\",\n \"score\": \"0.5943613\",\n \"text\": \"def _levenshtein(s1, s2):\\n if len(s1) < len(s2):\\n return _levenshtein(s2, s1)\\n if len(s2) == 0:\\n return len(s1)\\n previous_row = range(len(s2) + 1)\\n for i, c1 in enumerate(s1):\\n current_row = [i + 1]\\n for j, c2 in enumerate(s2):\\n insertions = previous_row[\\n j + 1] + 1\\n deletions = current_row[j] + 1\\n substitutions = previous_row[j] + (c1 != c2)\\n current_row.append(min(insertions, deletions, substitutions))\\n previous_row = current_row\\n return previous_row[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a74843b1dd7882dd31d8c0bf881a5bc\",\n \"score\": \"0.5943308\",\n \"text\": \"def phones_for_closest_match(word):\\n by_distance = []\\n for possibility in pronouncing.pronunciations:\\n\\n # levenstein\\n distance = editdistance.eval(possibility, word)\\n\\n # give a bonus for same first letter / last letter\\n if possibility.startswith(word[0]):\\n distance -= 1\\n if possibility.endswith(word[-1]):\\n distance -= 1 \\n\\n # break ties with difference in length\\n character_difference = abs(len(possibility) - len(word))\\n by_distance.append((distance, character_difference, possibility))\\n\\n # find the lowest (final tie breaker is alphabetical, oh well)\\n d_edit, d_length, suggestion = min(by_distance)\\n\\n # return the suggestion and the phones for the suggestion\\n return suggestion, pronouncing.phones_for_word(suggestion)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7bfd6fdab33ec67e70caffcbe9e23f7\",\n \"score\": \"0.5924451\",\n \"text\": \"def same_as_previous_search(self, regexp, direction, where, match,\\n pos = None):\\n\\n\\n #\\n # We consider this to be the same occurence as last search iif:\\n #\\n # a. Start/end position of both occurences are the same\\n # b. Both occurences were found using same *regexp*\\n # c. Both occurences were found using same *where*\\n # d. Cursor hasn't moved since last search.\\n #\\n # Condition d. is because if the user moves the cursor to an other\\n # location and redoes the same search again, he/she probably means\\n # to do a new search starting from this new cursor location.\\n #\\n # Note that two occurences may be deemed identical even if they were\\n # found using different *direction*. This is because if the user does\\n # a search forward and then reverses the direction, we don't want\\n # to go back to the occurence found in the forward direction (instead\\n # we want to go directly to the one preceding).\\n #\\n # On the other hand, two occurences will be deemed identical only if\\n # they were found using same *where* (condition c.). This is so you can\\n # search for an occurence and then move the cursor to the beginning/end\\n # of that occurence using a command like: \\\"before that\\\" or \\\"after that\\\"\\n #\\n answer = 0\\n if self.last_search != None:\\n last_regexp, last_direction, last_where, \\\\\\n last_match, last_alt_pos = self.last_search\\n trace('SourceBuff.same_as_previous_search', \\n 'last reg = %s, where = %d, range = %s, pos = %s' % \\\\\\n (last_regexp, last_where, last_match, last_alt_pos))\\n trace('SourceBuff.same_as_previous_search', \\n 'reg = %s, where = %d, range = %s, pos = %s' % \\\\\\n (regexp, where, match, self.cur_pos()))\\n if match == last_match and regexp == last_regexp and \\\\\\n where == last_where:\\n pos = last_alt_pos\\n if pos is None:\\n pos = self.pos_extremity(last_match, last_where)\\n if self.cur_pos() == pos:\\n answer = 1\\n\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b2fadb319942792acd7359bde8623e\",\n \"score\": \"0.58724356\",\n \"text\": \"def levenshtein_distance(s1, s2):\\n if len(s1) < len(s2):\\n return levenshtein_distance(s2, s1)\\n # len(s1) >= len(s2)\\n if len(s2) == 0:\\n return len(s1)\\n previous_row = range(len(s2) + 1)\\n for i, c1 in enumerate(s1):\\n current_row = [i + 1]\\n for j, c2 in enumerate(s2):\\n insertions = previous_row[j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer\\n deletions = current_row[j] + 1 # than s2\\n substitutions = previous_row[j] + (c1 != c2)\\n current_row.append(min(insertions, deletions, substitutions))\\n previous_row = current_row\\n return previous_row[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc3f0cd2d7d567e895e07c5207a910f\",\n \"score\": \"0.58191997\",\n \"text\": \"def levenshtein_distance_original_brute_force(str1, str2):\\n edits = [[x for x in range(len(str1) + 1)] for _ in range(len(str2) + 1)]\\n\\n for i in range(1, len(str2) + 1):\\n edits[i][0] = edits[i - 1][0] + 1\\n\\n for i in range(1, len(str2) + 1):\\n for j in range(1, len(str1) + 1):\\n if str2[i - 1] == str1[j - 1]:\\n edits[i][j] = edits[i - 1][j - 1]\\n else:\\n edits[i][j] = 1 + min(edits[i - 1][j - 1], edits[i - 1][j], edits[i][j - 1])\\n\\n return edits[-1][-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa996af826842007c63ef4493cd7b7c\",\n \"score\": \"0.579019\",\n \"text\": \"def wer(original, result):\\r\\n # The WER ist calculated on word (and NOT on character) level.\\r\\n # Therefore we split the strings into words first:\\r\\n ref_id = original.split()[0]\\r\\n ref = original.split()[1:]\\r\\n hyp_id = result.split()[0]\\r\\n hyp = result.split()[1:]\\r\\n assert(ref_id == hyp_id)\\r\\n \\r\\n return levenshtein(ref, hyp) / float(len(ref))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33dd23ffaed8a730c13556ffa86c380f\",\n \"score\": \"0.57856673\",\n \"text\": \"def matchOffset(leftPaddedReference, leftPaddedQuery):\\n offset = 0\\n for queryChar, referenceChar in zip(leftPaddedQuery, leftPaddedReference):\\n if queryChar not in \\\" -\\\":\\n break\\n offset += referenceChar not in \\\" -\\\"\\n\\n return offset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1f402c611b998d63c992a2f8775b05\",\n \"score\": \"0.5749957\",\n \"text\": \"def levenshtein_distance(s1, s2):\\n if len(s1) < len(s2):\\n return levenshtein_distance(s2, s1)\\n\\n # len(s1) >= len(s2)\\n if len(s2) == 0:\\n return len(s1)\\n\\n previous_row = range(len(s2) + 1)\\n for i, c1 in enumerate(s1):\\n current_row = [i + 1]\\n for j, c2 in enumerate(s2):\\n # j+1 instead of j since previous_row and current_row are one\\n # character longer than s2\\n insertions = previous_row[j + 1] + 1\\n deletions = current_row[j] + 1\\n substitutions = previous_row[j] + (c1 != c2)\\n current_row.append(min(insertions, deletions, substitutions))\\n previous_row = current_row\\n\\n return previous_row[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6545b58ca3e8dac05815feaafc07f251\",\n \"score\": \"0.57094544\",\n \"text\": \"def levenshtein(left, right):\\n return 1 if left == right else -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42041ff9b4c18c7e4f2b6f6b5b00acdf\",\n \"score\": \"0.5699307\",\n \"text\": \"def test_search_for_same_word(example_iter):\\n assert find_shortest_distance_iterator(\\n example_iter, \\\"development\\\", \\\"development\\\") == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"592d4307b15b8ff6eac754d8f1dfd9c3\",\n \"score\": \"0.5692577\",\n \"text\": \"def substitute(self, match):\\n start, end, word = self.completer.locate(self.orig_cursor_offset,\\n self.orig_line)\\n return (start + len(match),\\n self.orig_line[:start] + match + self.orig_line[end:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9286962c0f2e2fd5e336e0457d63c421\",\n \"score\": \"0.56407744\",\n \"text\": \"def find_closest(searchstr,barcodes,trantab):\\n if searchstr in barcodes:\\n return(searchstr)\\n else:\\n #######this matches and replaces the item in the row by the multiseq barcode within hamthresh\\n dists = fast_hamm(searchstr,barcodes,trantab)\\n ###return the barcode with the lowest hamdist if hamdist == 1\\n i = dists.index(min(dists))\\n if dists[i] <= 1:\\n return(barcodes[i])\\n else:\\n return(searchstr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4be69d06f175e9e21aa6d5f2b2efbd57\",\n \"score\": \"0.5640555\",\n \"text\": \"def get_distance(word, trans, word_to_translation):\\n references = word_to_translation[word]\\n assert len(references) != 0, 'wrong/unknown word'\\n return min(editdistance.eval(trans, ref) for ref in references)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"682cddb0c71e3d17dafaf730f425135d\",\n \"score\": \"0.563643\",\n \"text\": \"def calculate_score(s1, s2, l1, l2, startpoint):\\n matched = \\\"\\\" # to hold string displaying alignements\\n score = 0\\n for i in range(l2):\\n if (i + startpoint) < l1: # the startpoint must be less than the length of the longer sequence\\n if s1[startpoint + i] == s2[i]: # if the bases match\\n matched = matched + \\\"*\\\" # add \\\"*\\\" to the new string to indicate match\\n score = score + 1\\n else:\\n matched = matched + \\\"-\\\" # add \\\"-\\\" to the new string to indicate no match\\n\\n # some formatted output\\n print(\\\".\\\" * startpoint + matched) \\n print(\\\".\\\" * startpoint + s2)\\n print(s1)\\n print(score) \\n print(\\\" \\\") # returns blank line\\n\\n return score, matched\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5333da3b8c810d39da073ea916435156\",\n \"score\": \"0.5616125\",\n \"text\": \"def levenshtein(seq1, seq2):\\n oneago = None\\n thisrow = list(range(1, len(seq2) + 1)) + [0]\\n for x in range(len(seq1)):\\n twoago, oneago, thisrow = oneago, thisrow, [0] * len(seq2) + [x + 1]\\n for y in range(len(seq2)):\\n delcost = oneago[y] + 1\\n addcost = thisrow[y - 1] + 1\\n subcost = oneago[y - 1] + (seq1[x] != seq2[y])\\n thisrow[y] = min(delcost, addcost, subcost)\\n return thisrow[len(seq2) - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36a11f0460820e05e7efd534a86b8a5d\",\n \"score\": \"0.55983454\",\n \"text\": \"def levenshteinDistance(s1, s2):\\n if len(s1) > len(s2):\\n s1, s2 = s2, s1\\n distances = range(len(s1) + 1)\\n for index2, char2 in enumerate(s2):\\n newDistances = [index2+1]\\n for index1, char1 in enumerate(s1):\\n if char1.lower() == char2.lower():\\n newDistances.append(distances[index1])\\n else:\\n newDistances.append(\\n 1 + min((distances[index1],\\n distances[index1+1],\\n newDistances[-1])))\\n distances = newDistances\\n return distances[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d44d1d7cf768b229e57a29f28448eee\",\n \"score\": \"0.5581027\",\n \"text\": \"def levenshtein_distance_original_optimal(str1, str2):\\n small = str1 if len(str1) < len(str2) else str2\\n big = str1 if len(str1) >= len(str2) else str2\\n even_edits = [x for x in range(len(small) + 1)]\\n odd_edits = [None for _ in range(len(small) + 1)]\\n for i in range(1, len(big) + 1):\\n if i % 2 == 1:\\n current_edits = odd_edits\\n previous_edits = even_edits\\n else:\\n current_edits = even_edits\\n previous_edits = odd_edits\\n current_edits[0] = i\\n for j in range(1, len(small) + 1):\\n if big[i - 1] == small[j - 1]:\\n current_edits[j] = previous_edits[j - 1]\\n else:\\n current_edits[j] = 1 + min(previous_edits[j - 1], previous_edits[j], current_edits[j - 1])\\n return even_edits[-1] if len(big) % 2 == 0 else odd_edits[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a543deab3f4940710caefa08a183aad4\",\n \"score\": \"0.5580113\",\n \"text\": \"def getTxtRelPositions(transcript_id,txt_annot,readPosition):#modified so that it returns strand +/- instead of S/AS\\n #Great, now figure out position relative to CDSStart\\n #To do this, I will calculate the distance from the txtStart to readPosition, and txtStart to CDSStart. I'll subtract the two.\\n cdsStart=min([entry[0] for entry in txt_annot['CDS']])\\n exons=txt_annot['exon']\\n exonStarts=[exon[0] for exon in exons]\\n exonEnds=[exon[1] for exon in exons]\\n exonStarts.sort(),exonEnds.sort()\\n #the zip function works differently in python2 and python3.\\n #So changed the next line of code to work via list comprehension\\n #https://stackoverflow.com/questions/31683959/the-zip-function-in-python-3\\n #exons=zip(exonStarts,exonEnds)\\n exons=[(exonStarts[ii],exonEnds[ii]) for ii in range(len(exonStarts))]\\n #Edit: Apparently the exons are not necessarily orderd in the gtf file.\\n \\n txtStart_cdsStart_dist=assignReadsToGenes4.getDist(exons,cdsStart)\\n txtStart_readPosition_dist=assignReadsToGenes4.getDist(exons,readPosition)\\n readPosition_rel_to_CDSstart=txtStart_readPosition_dist-txtStart_cdsStart_dist\\n \\n #now do the same thing with the cdsEnd\\n cdsEnd=max([entry[1] for entry in txt_annot['CDS']])\\n txtStart_cdsEnd_dist=assignReadsToGenes4.getDist(exons,cdsEnd)\\n #already determined txtStart_readPosition_dist\\n readPosition_rel_to_CDSend=txtStart_readPosition_dist-txtStart_cdsEnd_dist\\n \\n #stranded issues. Although ensembl defines start_codon and stop_codon as those exact locations on the - and + strand, here I find the start/stop by taking min/max of CDS exon boundaries, so I need to flip it\\n if txt_annot['strand']=='+':\\n return ':'.join([transcript_id,str(readPosition_rel_to_CDSstart),str(readPosition_rel_to_CDSend)])\\n else:\\n return ':'.join([transcript_id,str(-readPosition_rel_to_CDSend),str(-readPosition_rel_to_CDSstart)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b72dc44e242d9a260830e7242de37d\",\n \"score\": \"0.5579813\",\n \"text\": \"def get_closest_exon(chromosome,pos1,pos2,db=\\\"ensembl.sql\\\"):\\n \\n gene_start = pos1 if pos1 < pos2 else pos2\\n gene_end = pos2 if pos2 > pos1 else pos1\\n\\n con = sqlite3.connect(db)\\n cur = con.cursor()\\n cur.execute(\\\"SELECT * FROM exons WHERE chromosome = '{0}' AND \\\\\\n ({1} BETWEEN exon_start-100000 AND exon_end+100000 OR \\\\\\n {2} BETWEEN exon_start-100000 AND exon_end+100000)\\\".format(\\n chromosome,gene_start,gene_end\\n ))\\n ret = cur.fetchall()\\n \\n min_dist = 100000\\n min_gene = \\\"\\\"\\n \\n \\n for i in range(0,len(ret)):\\n exon_start = int(ret[i][4])\\n exon_end = int(ret[i][5])\\n gene = ret[i][2]\\n if((exon_start <= gene_start <= exon_end) or (exon_start <= gene_end <= exon_end)):\\n return 0, gene # not SUPER accurate, we avoid alternative transcripts\\n else:\\n dist = min(abs(gene_end-exon_start),abs(gene_start-exon_end))\\n if(dist < min_dist):\\n min_dist = dist\\n min_gene = gene\\n return min_dist, min_gene\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5ace4fbfb9ca1e32961e934dc6b4eac\",\n \"score\": \"0.55763656\",\n \"text\": \"def levenshtein_distance(s1,s2):\\n if len(s1) == 0:\\n return len(s2)\\n if len(s2) == 0:\\n return len(s1)\\n return min([int(s1[0] != s2[0]) + \\\\\\n levenshtein_distance(s1[1:],s2[1:]),\\\\\\n 1+levenshtein_distance(s1[1:],s2),\\\\\\n 1+levenshtein_distance(s1,s2[1:])])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2563056bffc9dab3c8a28c6061f829c4\",\n \"score\": \"0.55576456\",\n \"text\": \"def fast_levenshtein_distance(self, source, target):\\n return int(editdistance.eval(source, target))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3875814c0ae04667f33602b77d2fbb6\",\n \"score\": \"0.55130947\",\n \"text\": \"def damerauLevenshteinHomerDistance(seq1, seq2):\\n\\n oneago = None\\n thisrow = list(range(1, len(seq2) + 1)) + [0]\\n for x in range(len(seq1)):\\n # Python lists wrap around for negative indices, so put the\\n # leftmost column at the *end* of the list. This matches with\\n # the zero-indexed strings and saves extra calculation.\\n twoago, oneago, thisrow = oneago, thisrow, [0] * len(seq2) + [x + 1]\\n for y in range(len(seq2)):\\n delcost = oneago[y] + 1\\n addcost = thisrow[y - 1] + 1\\n subcost = oneago[y - 1] + (seq1[x] != seq2[y])\\n thisrow[y] = min(delcost, addcost, subcost)\\n # This block deals with transpositions\\n if (x > 0 and y > 0 and seq1[x] == seq2[y-1]\\n and seq1[x-1] == seq2[y] and seq1[x] != seq2[y]):\\n thisrow[y] = min(thisrow[y], twoago[y-2] + 1)\\n return thisrow[len(seq2) - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c6391fd4e0565319580e32fe9e60574\",\n \"score\": \"0.55097616\",\n \"text\": \"def levenshteinDistance(string1, string2):\\n # smaller string is string1. Switch if not the case\\n if len(string1) > len(string2):\\n string1, string2 = string2, string1\\n editDistances = range(len(string1) + 1)\\n for idx2, char2 in enumerate(string2):\\n newDistances = [idx2 + 1]\\n for idx1, char1 in enumerate(string1):\\n if char1 == char2:\\n newDistances.append(editDistances[idx1])\\n else:\\n newDistances.append(1 + min((editDistances[idx1], editDistances[idx1 + 1], newDistances[-1])))\\n editDistances = newDistances\\n return editDistances[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f423b7819392e5ea38715deda190ddc8\",\n \"score\": \"0.549971\",\n \"text\": \"def get_closest_match(self, cells, matching_threshold):\\n return_value = []\\n distances = [Levenshtein.distance(self.start_md, u''.join(cell['source'])) for cell in cells]\\n if min(distances) > matching_threshold:\\n return return_value\\n\\n best_match = argmin(distances)\\n if len(self.stop_md) == 0:\\n end_offset = len(cells) - best_match\\n else:\\n distances = [Levenshtein.distance(self.stop_md, u''.join(cell['source'])) for cell in cells[best_match:]]\\n if min(distances) > matching_threshold:\\n return return_value\\n end_offset = argmin(distances)\\n if len(self.question_heading) != 0:\\n return_value.append(NotebookExtractor.markdown_heading_cell(self.question_heading,'##'))\\n return_value.append(cells[best_match])\\n return_value.extend(cells[best_match+1:best_match+end_offset])\\n return return_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c9dfed66ad52ee23cb379210b62d4f\",\n \"score\": \"0.5495784\",\n \"text\": \"def levenshtein_distance(ref, hyp):\\n m = len(ref)\\n n = len(hyp)\\n\\n # special case\\n if ref == hyp:\\n return 0\\n if m == 0:\\n return n\\n if n == 0:\\n return m\\n\\n if m < n:\\n ref, hyp = hyp, ref\\n m, n = n, m\\n\\n # use O(min(m, n)) space\\n distance = np.zeros((2, n + 1), dtype=np.int32)\\n\\n # initialize distance matrix\\n for j in range(0,n + 1):\\n distance[0][j] = j\\n\\n # calculate levenshtein distance\\n for i in range(1, m + 1):\\n prev_row_idx = (i - 1) % 2\\n cur_row_idx = i % 2\\n distance[cur_row_idx][0] = i\\n for j in range(1, n + 1):\\n if ref[i - 1] == hyp[j - 1]:\\n distance[cur_row_idx][j] = distance[prev_row_idx][j - 1]\\n else:\\n s_num = distance[prev_row_idx][j - 1] + 1\\n i_num = distance[cur_row_idx][j - 1] + 1\\n d_num = distance[prev_row_idx][j] + 1\\n distance[cur_row_idx][j] = min(s_num, i_num, d_num)\\n\\n return distance[m % 2][n]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7703312383c1cfa8e6a7be695d5eae58\",\n \"score\": \"0.5491491\",\n \"text\": \"def align_needle(target,template):\\n #dans le cas où cout du gap < substitution\\n #align = pairwise2.align.globalxx(target, template)\\n #dans le cas où cout du gap > substitution - BLOSUM 62\\n matrix = matlist.blosum62\\n align = pairwise2.align.globaldx(target, template, matrix) \\n #on cherche l'alignement avec le plus haut score et le plus petit nombre \\n #de gap\\n res = align[0]\\n for i in align:\\n if i[2] >= res[2] and i[4] <= res[4]:\\n res = i\\n print(\\\"Sequence target : \\\" + res[0])\\n print(\\\"Sequence template: \\\" + res[1])\\n print(\\\"Score : \\\" + str(res[2]))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4730dc38d74c3e2440de3880c00612d\",\n \"score\": \"0.5486138\",\n \"text\": \"def calc_str_distance(command, candidate):\\n return editdistance.eval(command, candidate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f30c1d3bb52bac4223f18840ae73e08\",\n \"score\": \"0.5478767\",\n \"text\": \"def levenshteinDistance1(str1, str2):\\n\\n sol = np.zeros((len(str1) + 1, len(str2) + 1))\\n for i in range(sol.shape[0]):\\n # sol[i,j] # row i # columon j\\n sol[i, 0] = i\\n for j in range(sol.shape[1]):\\n # sol[i,j] # row i # columon j\\n sol[0, j] = j\\n str1 = \\\" \\\" + str1\\n str2 = \\\" \\\" + str2\\n for j in range(1, sol.shape[1]):\\n for i in range(1, sol.shape[0]):\\n if str1[i] == str2[j]:\\n cost = 0\\n else:\\n cost = 1\\n sol[i, j] = min(sol[i-1, j] + 1, sol[i, j-1] + 1, sol[i-1, j-1] + cost)\\n return int(sol[sol.shape[0]-1, sol.shape[1]-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e90550f9031c020a69b95b2c9f6c79df\",\n \"score\": \"0.5478676\",\n \"text\": \"def get_distance(seq1, seq2, S, gap_pen, score):\\n # დისტანცია წარმოვიდიგნოთ როგორც\\n # დისტანცია პირველიდან საერთო მშობლამდე + დისტანცია მეორედან საერთო მშობლამდე\\n # ოკამის სამართებლის პრინციპით ვთქვათ, რომ თანაბრად დაშორდნენ მშობელს სიგრძეებით\\n # ანუ საერთო მშობლის სიგრძე იქნებოდა სიგრძეების ჯამის ნახევარი\\n # ყველაზე კარგ შესაძლო ვარიანტში ორივე ემთხვევა საერთო მშობელს(ანუ ერთმანეთსაც)\\n # ყველაზე ცუდ ვარიანტში უსასრულო აცდენა აქვთ ანუ ქულა მინუს უსასრულობაა.\\n # ერთმანეთთან დამთხვევის ქულა შევადაროთ საერთო მშობელთან მაქსიმალურ დამთხვევის ქულას\\n # და ასე განვსაზღვროთ დისტანცია.\\n\\n # (-∞, max_score] ინტერვალი უნდა გადავაქციოთ [0, +∞) ინტერვალად\\n # ამისათვის გავამრავლოთ მინუსზე და დავუმატოთ max_score\\n\\n # მემგონი ნამეტან floatებს ვწერ მარა python2.7 არ ვიცი როგორ მუშაობს და არ გააფუჭებს არაფერს\\n max_score = float(float(len(seq1) + len(seq2))/2.0) * 3.0\\n distance = float(float(score) * -1.0 + max_score)\\n\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a933b4a23b756628dc7c2e10b4eab3c\",\n \"score\": \"0.5462667\",\n \"text\": \"def marker_match(hit) -> Optional[str]:\\n\\n if hit['correct'].item():\\n return re.sub(pattern='\\\\D*(\\\\d+)',\\n repl='\\\\\\\\1',\\n string=str(hit['qseqid'].item()))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ca59eb08db7e5b7ef07cdae72e4e715\",\n \"score\": \"0.5455519\",\n \"text\": \"def _subfn(self, match):\\n self.matched += 1\\n old = match.group(0)\\n new = None\\n offset = match.start(0) # Offset to all other patterns\\n for key, value in match.groupdict().items():\\n table = self.lookalikes.get(key[0].upper())\\n if table is None:\\n continue\\n start = match.start(key)\\n if start == -1:\\n continue\\n start -= offset\\n for pos, ch in enumerate(value.lower(), start=start):\\n if ch in table:\\n if new is None:\\n new = list(old)\\n new[pos] = table[ch].upper()\\n if new:\\n new = \\\"\\\".join(new)\\n self.corrections[old] = new\\n self.fixed += 1\\n return new\\n return old\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c12080aa70d1f74c2a63897b3519824d\",\n \"score\": \"0.54554814\",\n \"text\": \"def levenshtein_func(seq1, seq2):\\n\\n size_x = len(seq1) + 1\\n size_y = len(seq2) + 1\\n matrix = np.zeros ((size_x, size_y))\\n for x in range(size_x):\\n matrix [x, 0] = x\\n for y in range(size_y):\\n matrix [0, y] = y\\n\\n for x in range(1, size_x):\\n for y in range(1, size_y):\\n # Costs are used in a way that substitution and delete are costly, but addition is fairly cheap\\n if seq1[x-1] == seq2[y-1]:\\n matrix [x,y] = min(\\n matrix[x-1, y] + 3,\\n matrix[x-1, y-1],\\n matrix[x, y-1] + 3\\n )\\n else:\\n matrix [x,y] = min(\\n matrix[x-1,y] + 2,\\n matrix[x-1,y-1] + 6,\\n matrix[x,y-1] + 6\\n )\\n\\n return matrix[size_x - 1, size_y - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efadad5b2b71245cf70b264a7debf90e\",\n \"score\": \"0.545042\",\n \"text\": \"def best_matching_start_symbol(self, transcription):\\n\\n best_match = None\\n\\n for symbol in self.symbols.keys():\\n if transcription.startswith(symbol) and \\\\\\n (best_match is None or len(symbol) > len(best_match)):\\n best_match = symbol\\n\\n return best_match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8836659f760f3b3a1c9438e06a831198\",\n \"score\": \"0.5436356\",\n \"text\": \"def relative_edit_distance(s1: str, s2: str) -> float:\\n\\n if not s1 and not s2:\\n return 0.0\\n return edit_distance(s1, s2) / max(len(s1), len(s2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e35fa2a5d87af4082dc22736fc426225\",\n \"score\": \"0.5433679\",\n \"text\": \"def _minimumEditDistance1(tokens_s1, tokens_s2):\\n if len(tokens_s1) > len(tokens_s2):\\n tokens_s1, tokens_s2 = tokens_s2, tokens_s1\\n\\n distances = range(len(tokens_s1) + 1)\\n for index2, char2 in enumerate(tokens_s2):\\n newDistances = [index2 + 1]\\n for index1, char1 in enumerate(tokens_s1):\\n if char1 == char2:\\n newDistances.append(distances[index1])\\n else:\\n min_dist = min((distances[index1],\\n distances[index1 + 1],\\n newDistances[-1]))\\n newDistances.append(1 + min_dist)\\n distances = newDistances\\n return distances[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7457147cd5fd3f3250fb1c19e6cbafa7\",\n \"score\": \"0.5432468\",\n \"text\": \"def test_reverse_order(example_sentence):\\n assert find_shortest_distance(example_sentence, \\\"development\\\", \\\"motivation\\\") == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae358469b0f6159f57221b5142fdf5c\",\n \"score\": \"0.5432414\",\n \"text\": \"def _levenshtein_distance(ref, hyp):\\n m = len(ref)\\n n = len(hyp)\\n\\n # special case\\n if ref == hyp:\\n return 0\\n if m == 0:\\n return n\\n if n == 0:\\n return m\\n\\n if m < n:\\n ref, hyp = hyp, ref\\n m, n = n, m\\n\\n # use O(min(m, n)) space\\n distance = np.zeros((2, n + 1), dtype=np.int32)\\n\\n # initialize distance matrix\\n for j in range(0,n + 1):\\n distance[0][j] = j\\n\\n # calculate levenshtein distance\\n for i in range(1, m + 1):\\n prev_row_idx = (i - 1) % 2\\n cur_row_idx = i % 2\\n distance[cur_row_idx][0] = i\\n for j in range(1, n + 1):\\n if ref[i - 1] == hyp[j - 1]:\\n distance[cur_row_idx][j] = distance[prev_row_idx][j - 1]\\n else:\\n s_num = distance[prev_row_idx][j - 1] + 1\\n i_num = distance[cur_row_idx][j - 1] + 1\\n d_num = distance[prev_row_idx][j] + 1\\n distance[cur_row_idx][j] = min(s_num, i_num, d_num)\\n\\n return distance[m % 2][n]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2eb245af2123fcb7d0dfcd9dbf22545\",\n \"score\": \"0.54211354\",\n \"text\": \"def needle_in_haystack(needle, haystack):\\n needle = re.sub(r'\\\\W', '', needle).lower()\\n diff = diff_match_patch()\\n diff.Match_Distance = 5000 # default is 1000\\n diff.Match_Threshold = .5 # default is .5\\n lines = haystack.splitlines()\\n for line in lines:\\n line2 = re.sub(r'\\\\W', '', line).lower()\\n value = diff.match_main(line2, needle, 0)\\n if value is not -1:\\n return line\\n return 'no match in %d lines' % (len(lines), )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"777e31c41ceb911d1acceff4e4e95c29\",\n \"score\": \"0.5421053\",\n \"text\": \"def levenshtein_distance_brute_force(str1, str2):\\n # We want str1 -> str2\\n\\n # Initialize results matrix\\n # This step takes O(n * m) time and O(n * m) space\\n results = initialize_results_brute_force(str1, str2)\\n\\n # Get min number of edits (Levenshtein distance)\\n # This step takes O(n * m) time and O(1) space\\n answer = get_min_edits_brute_force(str1, str2, results)\\n\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fbdddbce88c82ac8b7af5f9244903a\",\n \"score\": \"0.5419143\",\n \"text\": \"def FindPosition(self, dc, context, index, forceLineStart):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fbdddbce88c82ac8b7af5f9244903a\",\n \"score\": \"0.5419143\",\n \"text\": \"def FindPosition(self, dc, context, index, forceLineStart):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fbdddbce88c82ac8b7af5f9244903a\",\n \"score\": \"0.5419143\",\n \"text\": \"def FindPosition(self, dc, context, index, forceLineStart):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fbdddbce88c82ac8b7af5f9244903a\",\n \"score\": \"0.5419143\",\n \"text\": \"def FindPosition(self, dc, context, index, forceLineStart):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17a5743b2f48694b97424cffaefbb015\",\n \"score\": \"0.5397152\",\n \"text\": \"def levenshtein_distance(phrase_1, phrase_2, grain=\\\"char\\\", ignore_non_alnumspc=True, ignore_space=True, ignore_numeric=True, ignore_case=True):\\n\\t# Preprocess text phrase into list of edit units\\n\\tl_1 = phrase_preprocessing(phrase_1, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\\n\\tl_2 = phrase_preprocessing(phrase_2, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\\n\\tlen_1, len_2 = len(l_1), len(l_2)\\n\\n\\t# Early exit if one of the lists is empty\\n\\tif len_1 == 0 or len_2 == 0: return max(len_1,len_2)\\n\\n\\t# Dynamic programming solver\\n\\tmanipulation = [[0 for _ in range(len_2+1)] for _ in range(len_1+1)]\\n\\tfor row in range(len_1+1): manipulation[row][0] = row\\n\\tfor col in range(len_2+1): manipulation[0][col] = col\\n\\n\\t# Allowed edit: insert, delete, substitute\\n\\tfor i in range(1,len_1+1):\\n\\t\\tfor j in range(1, len_2+1):\\n\\t\\t\\tcost = 1 if l_1[i-1] != l_2[j-1] else 0\\n\\t\\t\\tmanipulation[i][j] = min(manipulation[i-1][j-1]+cost, manipulation[i-1][j]+1, manipulation[i][j-1]+1)\\n\\n\\treturn manipulation[-1][-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72af68ee53fa7d64d98607d6ba399164\",\n \"score\": \"0.53968966\",\n \"text\": \"def find_closest_match(arr, target):\\n min_diff = -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a50b2f0e7f95c2271810c257a21e76a9\",\n \"score\": \"0.53853464\",\n \"text\": \"def test_jellyfish_levenshtein_distance(self):\\n raise SkipTest('This one raises a MemoryError')\\n\\n # Import it here to avoid issues with missing dependencies in CI\\n import jellyfish\\n\\n def jelly_fuzzy(str_a, str_b, ratio):\\n str_a = str_a.replace('\\\\0', '')\\n str_b = str_b.replace('\\\\0', '')\\n minl = min(len(str_a), len(str_b))\\n return (jellyfish.levenshtein_distance(str_a, str_b) / minl) > ratio\\n\\n self.generic_fuzzy_string_diff_runner_against_404(jelly_fuzzy, IS_EQUAL_RATIO)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb18578fceb2f16a68f69590f20c5e6d\",\n \"score\": \"0.5384339\",\n \"text\": \"def findSyn(position_in_prot,vcfposition,altnucleotide,start):\\n## count_func = 0\\n## snpChromosome = {}\\n## snpPositions = []\\n## for element, value in proteinlistAlt.items():\\n## \\n## if proteinDict[count_func] != value:\\n#### print proteinDict[count_func] , value\\n## \\n## snpPositions.append(count_func)\\n#### print 'found change in %s' %(count_func)\\n## count_func +=1\\n\\n \\n # Python seems to always round to the lower integer. POSSIBLE BUG- need to improve the rounding algorythm\\n \\n position = (int(vcfposition) - int(start)) / 3\\n## print 'looking for change in %s' %(position), \\n if position == snpPositions_in_prot:\\n synity = 'NonSyn'\\n if position not in snpPositions:\\n synity = 'Syn' \\n \\n return synity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"915dc298543feaf1d2672bf430d326f6\",\n \"score\": \"0.5379248\",\n \"text\": \"def _improve_entity_span(doc_tokens, input_start, input_end, tokenizer,\\n orig_entity_text):\\n tok_entity_text = \\\" \\\".join(tokenizer.basic_tokenizer.tokenize(orig_entity_text))\\n\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start:(new_end + 1)])\\n if text_span == tok_entity_text:\\n return (new_start, new_end)\\n\\n return (input_start, input_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bec34277e5c483bf4998f6748f611200\",\n \"score\": \"0.53682613\",\n \"text\": \"def proximity(a, b):\\n return difflib.SequenceMatcher(None, a, b).ratio()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5e73302c5fcd9c32e2e0730b4abe7c9\",\n \"score\": \"0.53682566\",\n \"text\": \"def _get_offsets_with_fuzzy_matching(haystack, needle, offset_init=0):\\n search_idx = offset_init\\n start_idx = None\\n end_idx = None\\n tokens = needle.split()\\n\\n # If this isn't a multi-term annotation, no need for fancy matchings.\\n if len(tokens) == 1:\\n start_idx = haystack.find(tokens[0], search_idx)\\n end_idx = start_idx + len(tokens[0])\\n return start_idx, end_idx\\n\\n # Otherwise we iterate the tokens 2-by-2 until we get a full match.\\n token_idx = 1\\n cur_idx, cur_token = -1, \\\"\\\"\\n while token_idx < len(tokens):\\n prv_token = tokens[token_idx - 1]\\n prv_idx = haystack.find(prv_token, search_idx)\\n prv_offset = prv_idx + len(prv_token)\\n cur_token = tokens[token_idx]\\n cur_idx = haystack.find(cur_token, prv_offset)\\n # In every iteration we check if the next word starts from where\\n # the previous ends.\\n if cur_idx in (prv_offset, prv_offset + 1):\\n if start_idx is None:\\n start_idx = prv_idx\\n token_idx += 1\\n # If it doesn't, then it must be an accidental match, reset the index.\\n else:\\n start_idx = None\\n token_idx = 1\\n search_idx = prv_idx + len(prv_token)\\n end_idx = cur_idx + len(cur_token)\\n\\n return start_idx, end_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe2e2dadac1783f6902254fcd0aa2bcc\",\n \"score\": \"0.53658676\",\n \"text\": \"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):\\n tok_answer_text = \\\" \\\".join(tokenizer.tokenize(orig_answer_text))\\n\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start : (new_end + 1)])\\n if text_span == tok_answer_text:\\n return (new_start, new_end)\\n\\n return (input_start, input_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf7fea2d0a303ce4d286c3a298ce2a48\",\n \"score\": \"0.5349472\",\n \"text\": \"def _findPrev(s, char, pos):\\n\\tmatch = ord(char)\\n\\tpos = pos - 1\\n\\twhile pos >= 0:\\n\\t\\tif s.GetCharAt(pos) == match:\\n\\t\\t\\treturn pos\\n\\t\\tpos = pos - 1\\n\\t\\n\\treturn -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a11818ea12c680b1c3172f31ce1badc8\",\n \"score\": \"0.534201\",\n \"text\": \"def compute_text_offset(score):\\n if len(score) == 1:\\n return 135\\n elif len(score) == 2:\\n return 120\\n elif len(score) == 3:\\n return 105\\n else:\\n return 90\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90d12d4d44acc13a3d58c93f94adfb88\",\n \"score\": \"0.5340846\",\n \"text\": \"def compute_diff(original_part_text, edit_text):\\n # Separate into words and linebreaks.\\n part_words = original_part_text.split()\\n edit_words = edit_text.split()\\n edit_word_lists = [line.split() for line in edit_text.splitlines()]\\n edit_linebreak_indices = []\\n for i, line in enumerate(edit_word_lists):\\n if i == 0:\\n edit_linebreak_indices.append(len(line))\\n else:\\n edit_linebreak_indices.append(\\n len(line) + edit_linebreak_indices[-1])\\n\\n # Now compute word-oriented opcodes.\\n opcodes_with_replace = SequenceMatcher(\\n a=part_words, b=edit_words, autojunk=False).get_opcodes()\\n opcodes = []\\n for operation in opcodes_with_replace:\\n if operation[0] == \\\"replace\\\":\\n opcodes.append((\\\"delete\\\", operation[1], operation[2],\\n operation[3], operation[3]))\\n opcodes.append((\\\"insert\\\", operation[1], operation[1],\\n operation[3], operation[4]))\\n else:\\n opcodes.append(operation)\\n\\n # Convert the opcodes into a single-string diff\\n diff = \\\"\\\"\\n for operation in opcodes:\\n if operation[0] == \\\"delete\\\":\\n diff += \\\"- \\\" + \\\" \\\".join(\\n part_words[operation[1] : operation[2]]) + \\\" \\\\n\\\"\\n elif operation[0] == \\\"insert\\\":\\n diff += \\\"+ \\\" + \\\" \\\".join(\\n edit_words[operation[3] : operation[4]]) + \\\" \\\\n\\\"\\n elif operation[0] == \\\"equal\\\":\\n diff += \\\" \\\" + \\\" \\\".join(\\n edit_words[operation[3] : operation[4]]) + \\\" \\\\n\\\"\\n\\n diff = diff[:-2]\\n diff_lines = diff.splitlines()\\n if (len(diff_lines) >= 2 and \\n ((diff_lines[-2].startswith(\\\"+\\\") and diff_lines[-1].startswith(\\\"-\\\")) or \\n (diff_lines[-2].startswith(\\\"-\\\") and diff_lines[-1].startswith(\\\"+\\\")) or\\n (diff_lines[-2].startswith(\\\" \\\") and diff_lines[-1].startswith(\\\"-\\\")))):\\n length2 = len(diff_lines[-1])\\n length1 = len(diff) - length2\\n diff = diff[:length1-2] + diff[length1-1:]\\n\\n return diff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6fbf581b152e22d0b6dca0cf845015d\",\n \"score\": \"0.5331644\",\n \"text\": \"def linker_pos(seq, linker, n_mismatch):\\n gap_char = \\\"-\\\"\\n pos = str(seq).find(linker)\\n if pos > 0:\\n seq_region = seq[pos:pos+len(linker)]\\n linker_region = linker\\n else:\\n aligns = pairwise2.align.localms(str(seq), str(linker),\\n 5.0, -4.0, -9.0, -0.5, one_alignment_only=True,\\n gap_char=gap_char)\\n if len(aligns) > 0:\\n seq_a, linker_a, _, start, end = aligns[0]\\n linker_region = linker_a[start:end]\\n seq_region = seq_a[start:end]\\n pos = seq.find(seq_region.replace(gap_char, \\\"\\\"))\\n else:\\n seq_region, linker_region = (\\\"\\\", \\\"\\\")\\n matches = sum((1 if s == linker_region[i] else 0) for i, s in\\n enumerate(seq_region))\\n gaps = seq_region.count(\\\"-\\\")\\n cur_mismatch = len(linker) - matches + gaps\\n if cur_mismatch <= n_mismatch:\\n assert pos >= 0\\n return pos, pos + len(seq_region.replace(gap_char, \\\"\\\"))\\n else:\\n return (None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7567832096fa9516b21d92e7361ff78\",\n \"score\": \"0.5328881\",\n \"text\": \"def levenshtein(left, right):\\n return _with_binary_expr(exprs.Levenshtein, left=left, right=right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f97f69d04783cf4a2f390029e8b0f91\",\n \"score\": \"0.5326987\",\n \"text\": \"def test_closest_dirty(self):\\n\\n grid = ['b-ooo',\\n '-dooo',\\n 'ooooo',\\n 'ooooo',\\n 'ooooo']\\n result = botClean.find_closest([0, 0], grid, 5, 5, 'd')\\n self.assertEqual(result, [1, 1])\\n\\n grid = ['d-ooo',\\n '-dooo',\\n 'ooooo',\\n 'ooooo',\\n 'ooooo']\\n result = botClean.find_closest([0, 0], grid, 5, 5, 'd')\\n self.assertEqual(result, [0, 0])\\n\\n grid = ['ooooo',\\n 'ooooo',\\n 'ooooo',\\n 'ooo--',\\n 'ooo-b']\\n result = botClean.find_closest([4, 4], grid, 5, 5, 'd')\\n self.assertEqual(result, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aee8aa742cb5fb39b9c9904045d9e28\",\n \"score\": \"0.53252864\",\n \"text\": \"def _pos_for_example(self):\\n srchStr = 'def %s('%self.codeL[self.pcount].func_name\\n i = 0\\n while i< len(self.lines):\\n if self.lines[i].find( srchStr ) >= 0:\\n line = i\\n break\\n i += 1\\n return self.textCtrl.XYToPosition(0,line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1830d66e1a036011f0f9856f824bdcc\",\n \"score\": \"0.5310087\",\n \"text\": \"def comp_position(text, line, begidx, endidx):\\n return len(shell_split(line[:begidx])) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8875fb5a5eb699b46d22387fc5698dd\",\n \"score\": \"0.5308194\",\n \"text\": \"def span_from_match(\\n match: re.Match,\\n span_from_group: bool,\\n) -> Tuple[int, int]:\\n if not span_from_group:\\n start_char, end_char = match.start(), match.end()\\n else:\\n start_char, end_char = next(filter(lambda x: x[0] >= 0, spans_generator(match)))\\n return start_char, end_char\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bfd0ecc4f8e0d3c09bb66b6d38189f9\",\n \"score\": \"0.5303389\",\n \"text\": \"def levenshtein(s1, s2, allow_substring=False):\\n len1, len2 = len(s1), len(s2)\\n lev = []\\n for i in range(len1 + 1):\\n lev.append([0] * (len2 + 1))\\n for i in range(len1 + 1):\\n lev[i][0] = i\\n for j in range(len2 + 1):\\n lev[0][j] = 0 if allow_substring else j\\n for i in range(len1):\\n for j in range(len2):\\n lev[i + 1][j + 1] = min(lev[i][j + 1] + 1, lev[i + 1][j] + 1, lev[i][j] + (s1[i] != s2[j]))\\n return min(lev[len1]) if allow_substring else lev[len1][len2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aeb19c77192e72232a59b28897dfe70\",\n \"score\": \"0.5303254\",\n \"text\": \"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):\\n\\n tok_answer_text = \\\" \\\".join(tokenizer.tokenize(orig_answer_text))\\n\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start:(new_end + 1)])\\n if text_span == tok_answer_text:\\n return new_start, new_end\\n\\n return input_start, input_end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83fe1f9ff60e60d1ab0a5a69f5a2379b\",\n \"score\": \"0.52982104\",\n \"text\": \"def getChapterQuoteAppears(self,quote):\\n chapter_strings = []\\n for i in range(len(self.chapter_lists)):\\n chapter_strings.append(\\\" \\\".join(self.chapter_lists[i]))\\n\\n j = 1\\n for chapter in chapter_strings:\\n if chapter.find(quote) != -1:\\n return j\\n j+=1\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a21f9bc794c639f7ca89958d2c6de3\",\n \"score\": \"0.52962655\",\n \"text\": \"def edit_distance_from_aln_strings(str1, str2):\\n assert len(str1) == len(str2)\\n edit_distance = 0\\n in_gap = False\\n\\n for i, char1 in enumerate(str1):\\n if char1 == \\\"-\\\" or str2[i] == \\\"-\\\":\\n if not in_gap:\\n in_gap = True\\n edit_distance += 1\\n else:\\n in_gap = False\\n\\n if char1 != str2[i]:\\n edit_distance += 1\\n\\n return edit_distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf1c8b70539568afc73e9e2076d05d6b\",\n \"score\": \"0.5294241\",\n \"text\": \"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):\\n tok_answer_text = \\\" \\\".join(tokenizer.tokenize(orig_answer_text))\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start : (new_end + 1)])\\n if text_span == tok_answer_text:\\n return new_start, new_end\\n return input_start, input_end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fbe384de22d0c87cbc0468461d7080d\",\n \"score\": \"0.5284241\",\n \"text\": \"def _levenshtein(s1, s2):\\n\\n if (s1, s2) in StringLevenshtein._map:\\n return StringLevenshtein._map[(s1, s2)]\\n\\n m = len(s1)\\n n = len(s2)\\n\\n if m == 0:\\n result = n\\n\\n elif n == 0:\\n result = m\\n\\n elif s1[0] == s2[0]:\\n result = StringLevenshtein._levenshtein(s1[1:], s2[1:])\\n\\n else:\\n result = 1 + min(\\n StringLevenshtein._levenshtein(s1[1:], s2),\\n StringLevenshtein._levenshtein(s1, s2[1:]),\\n StringLevenshtein._levenshtein(s1[1:], s2[1:])\\n )\\n\\n StringLevenshtein._map[(s1, s2)] = result\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cd297411a4922973bad3e58c84ac21c\",\n \"score\": \"0.52734447\",\n \"text\": \"def getwpidx(self,txt,lat=999999.,lon=999999):\\n name = txt.upper()\\n try:\\n i = self.wpid.index(name)\\n except:\\n return -1\\n \\n # if no pos is specified, get first occurence\\n if not lat<99999.:\\n return i\\n\\n # If pos is specified check for more and return closest\\n else: \\n idx = []\\n idx.append(i)\\n found = True\\n while i<len(self.wpid)-1 and found:\\n try:\\n i = self.wpid.index(name,i+1)\\n idx.append(i)\\n except:\\n found = False\\n if len(idx)==1:\\n return idx[0]\\n else:\\n imin = idx[0]\\n dmin = geo.kwikdist(lat,lon,self.wplat[imin],self.wplon[imin]) \\n for i in idx[1:]:\\n d = geo.kwikdist(lat,lon,self.wplat[i],self.wplon[i])\\n if d<dmin:\\n imin = i\\n dmin = d\\n return imin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e585cdb9d82c39a9e009c3266b4b616d\",\n \"score\": \"0.52700233\",\n \"text\": \"def iterative_levenshtein(s, t):\\n rows = len(s) + 1\\n cols = len(t) + 1\\n dist = [[0 for x in range(cols)] for x in range(rows)]\\n # source prefixes can be transformed into empty strings\\n # by deletions:\\n for i in range(1, rows):\\n dist[i][0] = i\\n # target prefixes can be created from an empty source string\\n # by inserting the characters\\n for i in range(1, cols):\\n dist[0][i] = i\\n\\n for col in range(1, cols):\\n for row in range(1, rows):\\n if s[row - 1] == t[col - 1]:\\n cost = 0\\n else:\\n cost = 1\\n dist[row][col] = min(dist[row - 1][col] + 1, # deletion\\n dist[row][col - 1] + 1, # insertion\\n dist[row - 1][col - 1] + cost) # substitution\\n\\n return dist[row][col]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bdd9fcd3c6fb9f706a778c2d415503\",\n \"score\": \"0.52690554\",\n \"text\": \"def substitute(cls, cursor_offset, line, match):\\n start, end, word = cls.locate(cursor_offset, line)\\n result = start + len(match), line[:start] + match + line[end:]\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6ad8c8a5ce20931c198533b3695269f\",\n \"score\": \"0.52635247\",\n \"text\": \"def find_closest_line(search_key, all_lines):\\n for line_key in all_lines:\\n # search for match in line number and direction\\n if line_key[0:2] == search_key[0:2]:\\n return line_key\\n # no match was found\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"841a9190ca23f02915518bbd44ccc07a\",\n \"score\": \"0.5259066\",\n \"text\": \"def match_fixed_start_offset(self, from_, to, match):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f537cf504fb21fb908843d14ac13f464\",\n \"score\": \"0.52490187\",\n \"text\": \"def edit_distance_dp(str1, str2):\\n rows = len(str1) + 1\\n cols = len(str2) + 1\\n dp_table = [[0 for j in range(cols)] for i in range(rows)]\\n\\n for row in range(rows):\\n for col in range(cols):\\n if row == 0:\\n dp_table[row][col] = col\\n elif col == 0:\\n dp_table[row][col] = row\\n # If last characters match\\n elif str1[row - 1] == str2[col - 1]:\\n dp_table[row][col] = dp_table[row - 1][col - 1]\\n else: \\n # If last characters are not same, consider all three\\n # operations on last character of first string, \\n # compute minimum cost for all three operations and take\\n # minimum of three values.\\n dp_table[row][col] = 1 + min(\\n dp_table[row][col - 1], # Insert\\n dp_table[row - 1][col], # Remove\\n dp_table[row - 1][col - 1] # Replace\\n ) \\n\\n return dp_table[rows-1][cols-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45c2d91bc13f2b44d9e9554b7285f6f4\",\n \"score\": \"0.5248359\",\n \"text\": \"def direct_quotations(doc):\\n if isinstance(doc, textacy.Doc):\\n if doc.lang != 'en':\\n raise NotImplementedError('sorry, English-language texts only :(')\\n doc = doc.spacy_doc\\n quote_end_punct = {',', '.', '?', '!'}\\n quote_indexes = set(itertoolz.concat(\\n (m.start(), m.end() - 1) for m in re.finditer(r\\\"(\\\\\\\".*?\\\\\\\")|(''.*?'')|(``.*?'')\\\", doc.string)))\\n quote_positions = list(itertoolz.partition(\\n 2, sorted(tok.i for tok in doc if tok.idx in quote_indexes)))\\n sents = list(doc.sents)\\n sent_positions = [(sent.start, sent.end) for sent in sents]\\n\\n for q0, q1 in quote_positions:\\n quote = doc[q0: q1 + 1]\\n\\n # we're only looking for direct quotes, not indirect or mixed\\n if not any(char in quote_end_punct for char in quote.text[-4:]):\\n continue\\n\\n # get adjacent sentences\\n candidate_sent_indexes = []\\n for i, (s0, s1) in enumerate(sent_positions):\\n\\n if s0 <= q1 + 1 and s1 > q1:\\n candidate_sent_indexes.append(i)\\n elif s0 < q0 and s1 >= q0 - 1:\\n candidate_sent_indexes.append(i)\\n\\n for si in candidate_sent_indexes:\\n sent = sents[si]\\n\\n # get any reporting verbs\\n rvs = [tok for tok in sent\\n if spacy_utils.preserve_case(tok) is False and\\n tok.lemma_ in REPORTING_VERBS and\\n tok.pos_ == 'VERB' and\\n not any(oq0 <= tok.i <= oq1 for oq0, oq1 in quote_positions)]\\n\\n # get target offset against which to measure distances of NEs\\n if rvs:\\n if len(rvs) == 1:\\n rv = rvs[0]\\n else:\\n min_rv_dist = 1000\\n for rv_candidate in rvs:\\n rv_dist = min(abs(rv_candidate.i - qp) for qp in (q0, q1))\\n if rv_dist < min_rv_dist:\\n rv = rv_candidate\\n min_rv_dist = rv_dist\\n else:\\n break\\n else:\\n # TODO: do we have no other recourse?!\\n continue\\n\\n try:\\n # rv_subj = _find_subjects(rv)[0]\\n rv_subj = get_subjects_of_verb(rv)[0]\\n except IndexError:\\n continue\\n # if rv_subj.text in {'he', 'she'}:\\n # for ne in named_entities(doc, good_ne_types={'PERSON'}):\\n # if ne.start < rv_subj.i:\\n # speaker = ne\\n # else:\\n # break\\n # else:\\n span = get_span_for_compound_noun(rv_subj)\\n speaker = doc[span[0]: span[1] + 1]\\n\\n yield (speaker, rv, quote)\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feeecb351a50991b59bff3064b18d4ed\",\n \"score\": \"0.5242717\",\n \"text\": \"def RNA_position(bedline, GI):\\n \\n mRNA_pos = 0\\n pre_pos = 0\\n exon_frac = None\\n intron_frac = None\\n nearest_type=None\\n chr, start, stop, name, score, strand, thickstart, thickstop = str(bedline).strip().split(\\\"\\\\t\\\")\\n thickstart, thickstop = map(int, (thickstart, thickstop))\\n position = int((thickstart + thickstop)/2)\\n try:\\n gene, n, reads = name.split(\\\"_\\\")\\n except:\\n \\n #takes first gene if there are multiple overlapping \\n gene, n, reads = name.split(\\\";\\\")[0].split(\\\"_\\\")\\n\\n for exN in range(GI[gene]['numEx']):\\n exstart, exstop = map(int, GI[gene]['exons'][exN].split(\\\"-\\\"))\\n exlen = exstop-exstart+1\\n if position >= exstart and position <= exstop:\\n if strand == \\\"+\\\":\\n mRNA_pos += position - exstart\\n exon_frac = np.round(((position-exstart)/float(exlen)), 3)\\n pre_pos += position - exstart \\n else:\\n mRNA_pos += exstop - position\\n exon_frac = np.round(((exstop - position)/float(exlen)), 3)\\n pre_pos += exstop - position\\n\\n mRNA_frac = np.round((mRNA_pos/float(GI[gene]['mRNA_length'])), 3)\\n premRNA_frac = np.round((pre_pos/float(GI[gene]['premRNA_length'])), 3)\\n\\n if mRNA_frac < 0 or mRNA_frac > 1:\\n print \\\"mRNA_frac is bad: %f, gene %s\\\" %(mRNA_frac, gene)\\n\\n if premRNA_frac < 0 or premRNA_frac > 1:\\n print \\\"premRNA_frac is bad: %f, gene %s\\\" %(premRNA_frac, gene)\\n\\n nearest_type = GI[gene]['types'][exN]\\n return mRNA_frac, premRNA_frac, exon_frac, None, nearest_type\\n else:\\n mRNA_pos += exlen\\n pre_pos += exlen\\n if exN < GI[gene]['numEx']: #there are only exN - 1 introns\\n intstart, intstop = map(int, GI[gene]['introns'][exN].split(\\\"-\\\"))\\n intlen = intstop-intstart+1\\n if position >= intstart and position <= intstop:\\n mRNA_pos = None \\n if strand == \\\"+\\\":\\n pre_pos += position - intstart\\n intron_frac = np.round(((position-intstart)/float(intlen)), 3)\\n else:\\n pre_pos += intstop - position\\n intron_frac = np.round(((intstop - position)/float(intlen)), 3)\\n premRNA_frac = np.round((pre_pos/float(GI[gene]['premRNA_length'])), 3)\\n if premRNA_frac > 0.5:\\n nearest_type=GI[gene]['types'][exN+1]\\n else:\\n nearest_type=GI[gene]['types'][exN]\\n\\n\\n if premRNA_frac < 0 or premRNA_frac > 1:\\n print \\\"premRNA_frac is bad: %f, gene %s\\\" %(premRNA_frac, gene)\\n\\n\\n \\n return None, premRNA_frac, None, intron_frac, nearest_type\\n else:\\n pre_pos += intlen\\n return None, None, None, None, None #shouldn't ever get here. this would mean that the clusters doesn't fall within the gene\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d2b7aaf59280ed6996e4e881907e3c5\",\n \"score\": \"0.52367175\",\n \"text\": \"def find_closest_string(self, query, dictionary, thresh=0.90):\\n dist = {i:Levenshtein.jaro_winkler(query, i) for i in dictionary}\\n dist = sorted(dist.items(), key=operator.itemgetter(1), reverse=True)\\n if dist[0][1] >= thresh:\\n return dist[0][0]\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed45716ee48f4e420e8aca21ca3846c1\",\n \"score\": \"0.5235785\",\n \"text\": \"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\\n orig_answer_text):\\n\\n # The SQuAD annotations are character based. We first project them to\\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\\n # often find a \\\"better match\\\". For example:\\n #\\n # Question: What year was John Smith born?\\n # Context: The leader was John Smith (1895-1943).\\n # Answer: 1895\\n #\\n # The original whitespace-tokenized answer will be \\\"(1895-1943).\\\". However\\n # after tokenization, our tokens will be \\\"( 1895 - 1943 ) .\\\". So we can match\\n # the exact answer, 1895.\\n #\\n # However, this is not always possible. Consider the following:\\n #\\n # Question: What country is the top exporter of electornics?\\n # Context: The Japanese electronics industry is the lagest in the world.\\n # Answer: Japan\\n #\\n # In this case, the annotator chose \\\"Japan\\\" as a character sub-span of\\n # the word \\\"Japanese\\\". Since our WordPiece tokenizer does not split\\n # \\\"Japanese\\\", we just use \\\"Japanese\\\" as the annotation. This is fairly rare\\n # in SQuAD, but does happen.\\n tok_answer_text = \\\" \\\".join(tokenizer.tokenize(orig_answer_text))\\n\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start:(new_end + 1)])\\n if text_span == tok_answer_text:\\n return (new_start, new_end)\\n\\n return (input_start, input_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed45716ee48f4e420e8aca21ca3846c1\",\n \"score\": \"0.5235785\",\n \"text\": \"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\\n orig_answer_text):\\n\\n # The SQuAD annotations are character based. We first project them to\\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\\n # often find a \\\"better match\\\". For example:\\n #\\n # Question: What year was John Smith born?\\n # Context: The leader was John Smith (1895-1943).\\n # Answer: 1895\\n #\\n # The original whitespace-tokenized answer will be \\\"(1895-1943).\\\". However\\n # after tokenization, our tokens will be \\\"( 1895 - 1943 ) .\\\". So we can match\\n # the exact answer, 1895.\\n #\\n # However, this is not always possible. Consider the following:\\n #\\n # Question: What country is the top exporter of electornics?\\n # Context: The Japanese electronics industry is the lagest in the world.\\n # Answer: Japan\\n #\\n # In this case, the annotator chose \\\"Japan\\\" as a character sub-span of\\n # the word \\\"Japanese\\\". Since our WordPiece tokenizer does not split\\n # \\\"Japanese\\\", we just use \\\"Japanese\\\" as the annotation. This is fairly rare\\n # in SQuAD, but does happen.\\n tok_answer_text = \\\" \\\".join(tokenizer.tokenize(orig_answer_text))\\n\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start:(new_end + 1)])\\n if text_span == tok_answer_text:\\n return (new_start, new_end)\\n\\n return (input_start, input_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c766f0a113880a274bf644c2fef4ae8b\",\n \"score\": \"0.523083\",\n \"text\": \"def find3(strng, ch, start=0): \\n ix = start\\n while ix < len(strng):\\n if strng[ix] == ch:\\n return ix\\n ix += 1\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ae8d686062ccf9e30520e23ffcf311a\",\n \"score\": \"0.5230132\",\n \"text\": \"def fuzzy_string_match(str_ref, str_hyp):\\n\\n # The higher the score, the higher the similarity between the two strings.\\n return fuzz.token_sort_ratio(str_ref, str_hyp) / 100.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6eddd55f1a2eadf626d0ae9f5a37791\",\n \"score\": \"0.5205989\",\n \"text\": \"def substring_edit_distance(s: str, t: str) -> int:\\n\\n if not s:\\n return len(t)\\n if not t:\\n return 0\\n\\n M = [[0 for _ in range(len(t) + 1)] for _ in range(len(s) + 1)]\\n # M[i][j] is the minimum number of t-edits required to make t[:j] a suffix of s[:i].\\n for i in range(len(s) + 1):\\n M[i][0] = 0\\n for j in range(len(t) + 1):\\n M[0][j] = j\\n\\n for i in range(1, len(s) + 1):\\n for j in range(1, len(t) + 1):\\n cost = 0 if s[i - 1] == t[j - 1] else 1\\n M[i][j] = min(\\n [\\n 1 + M[i - 1][j],\\n 1 + M[i][j - 1],\\n cost + M[i - 1][j - 1],\\n ])\\n\\n return min(M[i][len(t)] for i in range(len(s) + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2d79a2425e8def5a99b81f4ab618b9c\",\n \"score\": \"0.52014494\",\n \"text\": \"def find_diff_start(old_src, new_src):\\n old_lines = old_src.split(\\\"\\\\n\\\")\\n new_lines = new_src.split(\\\"\\\\n\\\")\\n\\n for line in range(min(len(old_lines), len(new_lines))):\\n old = old_lines[line]\\n new = new_lines[line]\\n if old == new:\\n continue\\n for col in range(min(len(old), len(new))):\\n if old[col] != new[col]:\\n return line, col\\n # Difference at the end of the line...\\n return line, min(len(old), len(new))\\n # Difference at the end of the file...\\n return min(len(old_lines), len(new_lines)), 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2d79a2425e8def5a99b81f4ab618b9c\",\n \"score\": \"0.52014494\",\n \"text\": \"def find_diff_start(old_src, new_src):\\n old_lines = old_src.split(\\\"\\\\n\\\")\\n new_lines = new_src.split(\\\"\\\\n\\\")\\n\\n for line in range(min(len(old_lines), len(new_lines))):\\n old = old_lines[line]\\n new = new_lines[line]\\n if old == new:\\n continue\\n for col in range(min(len(old), len(new))):\\n if old[col] != new[col]:\\n return line, col\\n # Difference at the end of the line...\\n return line, min(len(old), len(new))\\n # Difference at the end of the file...\\n return min(len(old_lines), len(new_lines)), 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbdb80fc03a7b67d0b80a2c1f58121d6\",\n \"score\": \"0.52014196\",\n \"text\": \"def exact_location(self, text):\\n try:\\n first = []\\n last = []\\n location = self.get_location(text)\\n text = self._clean_text(text)\\n text = text.lower()\\n first_thousand = text[:300]\\n last_thousand = text[len(text)-300:]\\n for item in location:\\n if item in first_thousand:\\n first.append(item)\\n if item in last_thousand:\\n last.append(item)\\n if not last and first:\\n location = first\\n if not first and last:\\n location = last\\n if last and first:\\n location = list(set(first) & set(last))\\n return location\\n except Exception as error:\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd530e4856d855768a41f340abc875d\",\n \"score\": \"0.52014095\",\n \"text\": \"def lcs_distance(phrase_1, phrase_2, grain=\\\"char\\\", ignore_non_alnumspc=True, ignore_space=True, ignore_numeric=True, ignore_case=True):\\n\\t# Preprocess text phrase into list of edit units\\n\\tl_1 = phrase_preprocessing(phrase_1, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\\n\\tl_2 = phrase_preprocessing(phrase_2, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\\n\\tlen_1, len_2 = len(l_1), len(l_2)\\n\\n\\t# Early exit if one of the lists is empty\\n\\tif len_1 == 0 or len_2 == 0: return max(len_1,len_2)\\n\\n\\t# Dynamic programming solver\\n\\tmanipulation = [[0 for _ in range(len_2+1)] for _ in range(len_1+1)]\\n\\tfor row in range(len_1+1): manipulation[row][0] = 0\\n\\tfor col in range(len_2+1): manipulation[0][col] = 0\\n\\n\\t# Allowed edit: insert, delete\\n\\tfor i in range(1,len_1+1):\\n\\t\\tfor j in range(1, len_2+1):\\n\\t\\t\\tmanipulation[i][j] = manipulation[i-1][j-1] + 1 if l_1[i-1] == l_2[j-1] else max(manipulation[i][j-1], manipulation[i-1][j])\\n\\n\\tdistance = len_1 + len_2 - 2 * manipulation[-1][-1]\\n\\n\\treturn distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06507dae3b59a04141c13ab3a82cfc10\",\n \"score\": \"0.5199065\",\n \"text\": \"def best_matching_start_ignore_symbol(self, transcription):\\n best_match = None\\n\\n for sym in self.ignore_symbols:\\n if transcription.startswith(sym) and \\\\\\n (best_match is None or len(sym) < len(best_match)):\\n best_match = sym\\n\\n return best_match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a7a37f5d6dbe7f054e99f5d1c4d2d06\",\n \"score\": \"0.51974034\",\n \"text\": \"def test_replace_at_start(self):\\n res = align_utils.str_mod(self.string, 0, self.insert1, replace=True)\\n self.assertEqual(res, \\\"ghijklmn\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d9c26d9e2f2b3877832872a5750d9ce\",\n \"score\": \"0.5192631\",\n \"text\": \"def correct_positions(dependency_parse):\\n\\n for ii in dependency_parse.to_conll(10).split('\\\\n'):\\n if not ii:\\n continue\\n\\n fields = ii.split('\\\\t')\\n child_pos = int(fields[0])\\n head_pos = int(fields[6])\\n child_word = fields[1]\\n if not all(x in punctuation for x in child_word):\\n yield (head_pos, child_pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2d79293f73aa6f56397d9deae5eab1\",\n \"score\": \"0.51835716\",\n \"text\": \"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\\n orig_answer_text):\\n\\n # The SQuAD annotations are character based. We first project them to\\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\\n # often find a \\\"better match\\\". For example:\\n #\\n # Question: What year was John Smith born?\\n # Context: The leader was John Smith (1895-1943).\\n # Answer: 1895\\n #\\n # The original whitespace-tokenized answer will be \\\"(1895-1943).\\\". However\\n # after tokenization, our tokens will be \\\"( 1895 - 1943 ) .\\\". So we can match\\n # the exact answer, 1895.\\n #\\n # However, this is not always possible. Consider the following:\\n #\\n # Question: What country is the top exporter of electornics?\\n # Context: The Japanese electronics industry is the lagest in the world.\\n # Answer: Japan\\n #\\n # In this case, the annotator chose \\\"Japan\\\" as a character sub-span of\\n # the word \\\"Japanese\\\". Since our WordPiece tokenizer does not split\\n # \\\"Japanese\\\", we just use \\\"Japanese\\\" as the annotation. This is fairly rare\\n # in SQuAD, but does happen.\\n tok_answer_text = \\\" \\\".join(tokenizer.tokenize(orig_answer_text))\\n\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start:(new_end + 1)])\\n if text_span == tok_answer_text:\\n return new_start, new_end\\n\\n return input_start, input_end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13c1ff9c048392baec5d5119e7fdf7f7\",\n \"score\": \"0.5182412\",\n \"text\": \"def test_move_exact_matches_to_top(self):\\n matches = self.autocomp.suggest('Ma')\\n setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', True)\\n matches2 = self.autocomp.suggest('Ma')\\n self.assertNotEqual(matches['stock'][0]['search_name'], matches2['stock'][0]['search_name'])\\n setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b824a1ddce9e66ca24a1dd0f435c4fb2\",\n \"score\": \"0.5169095\",\n \"text\": \"def test_pride_sort():\\n\\n out = getoutput(f'python {prg} ../pride_and_prejudice.txt')\\n basis = \\\"mr - 786\\\"\\n to_find = \\\"elizabeth - 635\\\"\\n\\n assert to_find in out[out.index(basis) + 1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b731e00ac37376c1154a18ed9c37c6\",\n \"score\": \"0.5165766\",\n \"text\": \"def dameraulevenshtein(seq1, seq2):\\n # codesnippet:D0DE4716-B6E6-4161-9219-2903BF8F547F\\n # Conceptually, this is based on a len(seq1) + 1 * len(seq2) + 1 matrix.\\n # However, only the current and two previous rows are needed at once,\\n # so we only store those.\\n oneago = None\\n thisrow = list(range(1, len(seq2) + 1)) + [0]\\n for x in range(len(seq1)):\\n # Python lists wrap around for negative indices, so put the\\n # leftmost column at the *end* of the list. This matches with\\n # the zero-indexed strings and saves extra calculation.\\n twoago, oneago, thisrow = oneago, thisrow, [0] * len(seq2) + [x + 1]\\n for y in range(len(seq2)):\\n delcost = oneago[y] + 1\\n addcost = thisrow[y - 1] + 1\\n subcost = oneago[y - 1] + (seq1[x] != seq2[y])\\n thisrow[y] = min(delcost, addcost, subcost)\\n # This block deals with transpositions\\n if (x > 0 and y > 0 and seq1[x] == seq2[y - 1]\\n and seq1[x - 1] == seq2[y] and seq1[x] != seq2[y]):\\n thisrow[y] = min(thisrow[y], twoago[y - 2] + 1)\\n return thisrow[len(seq2) - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"870cae6c224c89080e993f63798dbc0c\",\n \"score\": \"0.5160745\",\n \"text\": \"def levenshteinDistance(str1, len_str1, str2, len_str2):\\n\\n # base cases \\n if len_str1 == 0:\\n return len_str2\\n \\n if len_str2 == 0:\\n return len_str1\\n \\n if str1[len_str1] == str2[len_str2]:\\n cost = 0\\n else:\\n cost = 1\\n\\n return min(levenshteinDistance(str1[:-1], len_str1 - 1, str2, len_str2) + 1,\\n levenshteinDistance(str1, len_str1 , str2[:-1], len_str2 - 1) + 1,\\n levenshteinDistance(str1[:-1], len_str1 - 1, str2[:-1], len_str2 - 1) + cost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c8725f955305b5413fc3423467c284e\",\n \"score\": \"0.51538527\",\n \"text\": \"def get_min_edits_brute_force(str1, str2, results):\\n for row_idx in range(len(str2)):\\n for col_idx in range(len(str1)):\\n if str1[col_idx] == str2[row_idx]:\\n results[row_idx + 1][col_idx + 1] = results[row_idx][col_idx]\\n else:\\n results[row_idx + 1][col_idx + 1] = 1 + min(results[row_idx][col_idx],\\n results[row_idx][col_idx + 1],\\n results[row_idx + 1][col_idx])\\n\\n return results[-1][-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e10927fd41bea59cb4fe8204fdc29d6\",\n \"score\": \"0.51506245\",\n \"text\": \"def test_closest_unknown(self):\\n\\n grid = ['b-ooo',\\n '-dooo',\\n 'ooooo',\\n 'ooooo',\\n 'ooooo']\\n result = botClean.find_closest([0, 0], grid, 5, 5, 'o')\\n self.assertEqual(result, [2, 0])\\n\\n grid = ['ooooo',\\n 'ooooo',\\n 'ooooo',\\n 'ooo--',\\n 'ooo-b']\\n result = botClean.find_closest([4, 4], grid, 5, 5, 'o')\\n self.assertEqual(result, [2, 4])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f51c0a4a394819efb513a73b14c28967\",\n \"score\": \"0.5145103\",\n \"text\": \"def damerau_levenshtein_distance(phrase_1, phrase_2, grain=\\\"char\\\", ignore_non_alnumspc=True, ignore_space=True, ignore_numeric=True, ignore_case=True):\\n\\t# Preprocess text phrase into list of edit units\\n\\tl_1 = phrase_preprocessing(phrase_1, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\\n\\tl_2 = phrase_preprocessing(phrase_2, grain=grain, ignore_non_alnumspc=ignore_non_alnumspc, ignore_numeric=ignore_numeric, ignore_case=ignore_case, ignore_space=ignore_space)\\n\\tlen_1, len_2 = len(l_1), len(l_2)\\n\\n\\t# Early exit if one of the lists is empty\\n\\tif len_1 == 0 or len_2 == 0: return max(len_1,len_2)\\n\\n\\t# Dynamic programming solver\\n\\tmanipulation = [[0 for _ in range(len_2+1)] for _ in range(len_1+1)]\\n\\tfor row in range(len_1+1): manipulation[row][0] = row\\n\\tfor col in range(len_2+1): manipulation[0][col] = col\\n\\n\\t# Allowed edit: insert, delete, substitute, transpose of adjacent characters\\n\\tfor i in range(1,len_1+1):\\n\\t\\tfor j in range(1, len_2+1):\\n\\t\\t\\tcost_1 = 1 if l_1[i-1] != l_2[j-1] else 0\\n\\t\\t\\tif i >= 2 and j >= 2:\\n\\t\\t\\t\\tcost_2 = 1 if l_1[i-2] == l_2[j-1] and l_1[i-1] == l_2[j-2] else 9527\\n\\t\\t\\t\\tmanipulation[i][j] = min(manipulation[i-1][j-1]+cost_1, manipulation[i-2][j-2]+cost_2, manipulation[i-1][j]+1, manipulation[i][j-1]+1)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tmanipulation[i][j] = min(manipulation[i-1][j-1]+cost_1, manipulation[i-1][j]+1, manipulation[i][j-1]+1)\\n\\n\\treturn manipulation[-1][-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b83d257c91f1af5d64b056bf54509b96\",\n \"score\": \"0.5135298\",\n \"text\": \"def _edit_dist_step(lev, i, j, s1, s2, threshold):\\n c1 = s1[i - 1]\\n c2 = s2[j - 1]\\n\\n # skipping a character in s1\\n a = lev[i - 1][j] + 1\\n # skipping a character in s2\\n b = lev[i][j - 1] + 1\\n # substitution\\n c = lev[i - 1][j - 1] + (c1 != c2)\\n\\n # pick the cheapest\\n lev[i][j] = min(a, b, c)\\n if lev[i][j] <= threshold:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d54e79d7a1a1b1ea9bd49ca2f8ed1d\",\n \"score\": \"0.51319325\",\n \"text\": \"def find_offset(pattern, template): \\n\\n real_corr = crr_2d( pattern, template)\\n\\n\\n best_match , match_value = find_best_match( real_corr )\\n #print( best_match )\\n\\n return (best_match[0] - 2 * pattern.shape[0], best_match[1] - 2 * pattern.shape[1]), match_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"affa637574cc4881dba9f0c3b7cb247e\",\n \"score\": \"0.5131755\",\n \"text\": \"def iterative_levenshtein(s, t):\\n rows = len(s) + 1\\n cols = len(t) + 1\\n dist = [[0 for x in range(cols)] for x in range(rows)]\\n # source prefixes can be transformed into empty strings\\n # by deletions:\\n for i in range(1, rows):\\n dist[i][0] = i\\n # target prefixes can be created from an empty source string\\n # by inserting the characters\\n for i in range(1, cols):\\n dist[0][i] = i\\n\\n for col in range(1, cols):\\n for row in range(1, rows):\\n if s[row - 1] == t[col - 1]:\\n cost = 0\\n else:\\n cost = 1\\n dist[row][col] = min(dist[row - 1][col] + 1, # deletion\\n dist[row][col - 1] + 1, # insertion\\n dist[row - 1][col - 1] + cost) # substitution\\n return dist\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993487,"cells":{"query_id":{"kind":"string","value":"2fd056d69913ba40d9b0321433b46e94"},"query":{"kind":"string","value":"Check package identifier is valid before querying results. Check package identifier is valid before querying results. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response."},"positive_passages":{"kind":"list like","value":[{"docid":"384efe157aa2adbca036f6da249728a2","score":"0.0","text":"def vulnerabilities_list1_with_http_info(self, owner, repo, package, **kwargs):\n\n all_params = ['owner', 'repo', 'package', 'page', 'page_size']\n all_params.append('callback')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method vulnerabilities_list1\" % key\n )\n params[key] = val\n del params['kwargs']\n # verify the required parameter 'owner' is set\n if ('owner' not in params) or (params['owner'] is None):\n raise ValueError(\"Missing the required parameter `owner` when calling `vulnerabilities_list1`\")\n # verify the required parameter 'repo' is set\n if ('repo' not in params) or (params['repo'] is None):\n raise ValueError(\"Missing the required parameter `repo` when calling `vulnerabilities_list1`\")\n # verify the required parameter 'package' is set\n if ('package' not in params) or (params['package'] is None):\n raise ValueError(\"Missing the required parameter `package` when calling `vulnerabilities_list1`\")\n\n\n collection_formats = {}\n\n path_params = {}\n if 'owner' in params:\n path_params['owner'] = params['owner']\n if 'repo' in params:\n path_params['repo'] = params['repo']\n if 'package' in params:\n path_params['package'] = params['package']\n\n query_params = []\n if 'page' in params:\n query_params.append(('page', params['page']))\n if 'page_size' in params:\n query_params.append(('page_size', params['page_size']))\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n # Authentication setting\n auth_settings = ['apikey']\n\n return self.api_client.call_api('/vulnerabilities/{owner}/{repo}/{package}/', 'GET',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='list[VulnerabilityScanResultsList]',\n auth_settings=auth_settings,\n callback=params.get('callback'),\n _return_http_data_only=params.get('_return_http_data_only'),\n _preload_content=params.get('_preload_content', True),\n _request_timeout=params.get('_request_timeout'),\n collection_formats=collection_formats)","title":""}],"string":"[\n {\n \"docid\": \"384efe157aa2adbca036f6da249728a2\",\n \"score\": \"0.0\",\n \"text\": \"def vulnerabilities_list1_with_http_info(self, owner, repo, package, **kwargs):\\n\\n all_params = ['owner', 'repo', 'package', 'page', 'page_size']\\n all_params.append('callback')\\n all_params.append('_return_http_data_only')\\n all_params.append('_preload_content')\\n all_params.append('_request_timeout')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method vulnerabilities_list1\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n # verify the required parameter 'owner' is set\\n if ('owner' not in params) or (params['owner'] is None):\\n raise ValueError(\\\"Missing the required parameter `owner` when calling `vulnerabilities_list1`\\\")\\n # verify the required parameter 'repo' is set\\n if ('repo' not in params) or (params['repo'] is None):\\n raise ValueError(\\\"Missing the required parameter `repo` when calling `vulnerabilities_list1`\\\")\\n # verify the required parameter 'package' is set\\n if ('package' not in params) or (params['package'] is None):\\n raise ValueError(\\\"Missing the required parameter `package` when calling `vulnerabilities_list1`\\\")\\n\\n\\n collection_formats = {}\\n\\n path_params = {}\\n if 'owner' in params:\\n path_params['owner'] = params['owner']\\n if 'repo' in params:\\n path_params['repo'] = params['repo']\\n if 'package' in params:\\n path_params['package'] = params['package']\\n\\n query_params = []\\n if 'page' in params:\\n query_params.append(('page', params['page']))\\n if 'page_size' in params:\\n query_params.append(('page_size', params['page_size']))\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n # Authentication setting\\n auth_settings = ['apikey']\\n\\n return self.api_client.call_api('/vulnerabilities/{owner}/{repo}/{package}/', 'GET',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='list[VulnerabilityScanResultsList]',\\n auth_settings=auth_settings,\\n callback=params.get('callback'),\\n _return_http_data_only=params.get('_return_http_data_only'),\\n _preload_content=params.get('_preload_content', True),\\n _request_timeout=params.get('_request_timeout'),\\n collection_formats=collection_formats)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"aec3eda9c5ef29ed467555c6c8f7127c","score":"0.6192965","text":"def is_package_id_valid(package_id: str) -> None:\n pattern = re.compile(r\"^pkg_[1-9a-zA-Z]+$\")\n\n if not package_id.startswith(\"pkg_\"):\n raise ValidationError(\n message=f\"[{package_id[0:4]}] is not a valid package ID prefix.\",\n source=ErrorSource.SHIPENGINE.value,\n error_type=ErrorType.VALIDATION.value,\n error_code=ErrorCode.INVALID_IDENTIFIER.value,\n )\n\n if not pattern.match(package_id):\n raise ValidationError(\n message=f\"[{package_id}] is not a valid package ID.\",\n source=ErrorSource.SHIPENGINE.value,\n error_type=ErrorType.VALIDATION.value,\n error_code=ErrorCode.INVALID_IDENTIFIER.value,\n )","title":""},{"docid":"82a3eec23f06e957bf35abfa85ec3a21","score":"0.6061845","text":"def check_pkg(pkg_name, jamf_url, enc_creds):\n headers = {\n \"authorization\": \"Basic {}\".format(enc_creds),\n \"accept\": \"application/json\",\n }\n url = \"{}/JSSResource/packages/name/{}\".format(jamf_url, pkg_name)\n r = requests.get(url, headers=headers)\n if r.status_code == 200:\n obj = json.loads(r.text)\n try:\n obj_id = str(obj[\"package\"][\"id\"])\n except KeyError:\n obj_id = \"-1\"\n else:\n obj_id = \"-1\"\n return obj_id","title":""},{"docid":"3306df13cd18c1744d9f04263432bc89","score":"0.60180765","text":"def valid_package(package_name):\n response = requests.head('https://pypi.python.org/pypi/%s' % package_name)\n if response.status_code != 404:\n response.raise_for_status()\n return response.status_code != 404","title":""},{"docid":"96574262832c9df4fec9c8fe9746cb1c","score":"0.59993696","text":"def _check_package(self, package_name: str) -> bool:\n\n command = f\"dpkg-query -W -f='${{Status}}' {package_name} | grep -c 'ok installed'\"\n stdout, stderr = self.ssh_client.execute_command(command)\n\n self.logger.debug(f\"stdout to check package {package_name}: {stdout}\")\n self.logger.debug(f\"stderr to check package {package_name}: {stderr}\")\n\n if stdout[0].rstrip() == '1':\n return True\n else:\n return False","title":""},{"docid":"dfe5dba6406de9de4799116b3fd9780f","score":"0.59151304","text":"def verify_package_and_version(self, package: tuple) -> bool:\n if self.check_element_presence(LocatorType.XPath, \"//div[@class='PackageQueue__validCount flex \"\n \"justify--right align-items--center']\",\n GigantumConstants.ELEMENT_PRESENCE_TIMEOUT.value):\n div_package_details = self.ui_element.find_elements_by_xpath(\"//div[@class='PackageQueue__row \"\n \"flex align-items--center justify--right']\")\n if div_package_details is not None:\n for package_detail in div_package_details:\n if self.__compare_package(package, package_detail):\n return True\n return False","title":""},{"docid":"0fdf0d5789c8a1d2fe07dd9f4df1d7f5","score":"0.585399","text":"def is_package_installed(package_name, validation_command='--version'):\n try:\n subprocess.check_output(\"{PKG} {CMD}\".format(PKG=package_name, CMD=validation_command), shell=True)\n return True\n except:\n return False","title":""},{"docid":"6e60a11e15ebed78be4a5472079c8415","score":"0.5731821","text":"def exist(self, package):\n if not self.exist_command_d:\n raise NotImplementedError(\"There is no 'exist' command defined.\")\n cmd = list(self.exist_command_d)\n cmd.append(package)\n self.logger.info(\n \"Check if host %s have %s package\", self.host, package\n )\n return self._run_command_on_host(cmd)","title":""},{"docid":"7004e03ddd146cb05355abc6e14059c6","score":"0.5671941","text":"def search_package(self, package_attrs):\n\n try:\n resp = requests.get('{}/api/action/package_show'.format(self.ckan_url),\n params={ 'id': package_attrs['name'] })\n resp.raise_for_status()\n except requests.exceptions.HTTPError:\n print('Error retrieving package `{}`: {}'.format(package_attrs['name'], resp.text))\n return None\n else:\n return resp.json()","title":""},{"docid":"0fcb1ddd0adacb5126022e3f35205f7e","score":"0.56563824","text":"def lookup_package_id(package_name):\n try:\n search_query = \"SELECT id FROM packages WHERE title = ?\"\n \n package_id = query_db(\n query=search_query,\n args = [ package_name ],\n one=True\n )\n\n return package_id['id'] if package_id is not None else None\n except Exception as err:\n current_app.logger.error(\"Unhandled Error in lookup_package_id {pn} : {e}\".format(\n pn = package_name,\n e=err\n ))\n\n raise UnhandledError()","title":""},{"docid":"8fe8b0153e88a03662e601c78a620fdf","score":"0.5619238","text":"def _checkPackage(self, p):\n if not ic.check[p]:\n raise(PE.PyARequiredImport(\"The package '\" + str(p) + \"' is not currently installed.\",\n solution=\"Please install \" + str(p)))","title":""},{"docid":"20be2c716c1841b7d86023b615e1411d","score":"0.5610147","text":"def test_check_package_zip(self):\n self.assertFalse(package.check_package_exists(TEST_ZIP_REQ, None))","title":""},{"docid":"bdf7e3ead5bccd9b13cf44fd2f8d9145","score":"0.55990976","text":"async def query(self, package):\n index_path = self._index_path(package)\n return await self._exists(index_path)","title":""},{"docid":"82c374e5c43d942423e496c55be600be","score":"0.5547239","text":"def check_package_installed(cls, package_name):\n return find_executable(package_name)","title":""},{"docid":"ffb0010608d9f4f727e3d62ca19d6671","score":"0.54923445","text":"def check_package_name(self):\n package_name = self.meta.get('package', {}).get('name', '')\n\n if package_name == '':\n return Error(self.recipe_dir, 'C2101', 'Missing package name in meta.yaml')\n\n if not self.name_pat.match(package_name) or package_name.endswith(('.', '-', '_')):\n return Error(self.recipe_dir, 'C2102', u'Found invalid package name \"{}\" in meta.yaml' .format(package_name))\n\n seq = get_bad_seq(package_name)\n if seq:\n return Error(self.recipe_dir, 'C2103', u'Found invalid sequence \"{}\" in package name' .format(seq))","title":""},{"docid":"af124317ff26ea4bef9b052fa465437a","score":"0.5490487","text":"def check(self):\n if self.empty():\n return True\n else:\n return utils.return_code_zero(self.check_cmd + self.specific_packages)","title":""},{"docid":"352d0907bedd7d656aa164e830b27115","score":"0.544021","text":"def _validate_package_name(package_name: str):\n if re.fullmatch(PublicId.PACKAGE_NAME_REGEX, package_name) is None:\n raise click.BadParameter(\"{} is not a valid package name.\".format(package_name))","title":""},{"docid":"a4c49882b5f865e4363f7009a3f311a8","score":"0.54385006","text":"def _CheckIsInstalled(self, package_name):\n command = 'rpm -qi {0:s} >/dev/null 2>&1'.format(package_name)\n exit_code = subprocess.call(command, shell=True)\n return exit_code == 0","title":""},{"docid":"9ac03ce2b0d923234990d2c859087cfa","score":"0.54373354","text":"def exists(self, package: str) -> bool:\n\n name, version = self.parse_package_id(package)\n if self.pkgcache.exists(name, version):\n return True\n\n if self.stocks.exists_source_version(name, version):\n return True\n\n for subpool in self.subpools:\n if subpool.exists(package):\n return True\n\n return False","title":""},{"docid":"ef5e30f76713c4f2adcf6bf6f0c343d6","score":"0.53682554","text":"def check_package_name(self):\n package_name = self.info.get('name')\n if package_name is None:\n return Error(self.path, 'C1101', 'Missing package name in info/index.json')\n\n if not self.name_pat.match(package_name) or package_name.endswith(('.', '-', '_')):\n return Error(self.path, 'C1103', 'Found invalid package name in info/index.json')\n\n if package_name != self.name:\n return Error(self.path, 'C1102', u'Found package name in info/index.json \"{}\" does not match filename \"{}\"' .format(package_name, self.name))","title":""},{"docid":"fcbbc6e57799a3c2bbeb888e834112ad","score":"0.53363067","text":"def _package_exists(self, filename):\n return os.path.exists(filename)","title":""},{"docid":"373472b317545bd61f60b0c219ba36c3","score":"0.5270876","text":"def input_package_name(self, package_name: str) -> bool:\n if self.package_title_input is not None:\n self.package_title_input.send_keys(package_name)\n return True\n return False","title":""},{"docid":"1f0edc2784f55b2aac574b89372215ba","score":"0.52645904","text":"def has_dependency(self, package_id: PackageID) -> bool:\n pass","title":""},{"docid":"4fd427a57aad2568fd6536997869f190","score":"0.525871","text":"def has(self, package_name):\n state = self.__multithreaded_cmd(self.__has, package_name=package_name)\n _id, has = 0, 1\n result = [device[has] for device in state]\n if all(result):\n print(\"All the {} devices have {} installed.\".format(\n len(result), package_name))\n return\n print(\"{}/{} devices does not have {} installed:\".format(\n result.count(False),\n len(result),\n package_name))\n for device in state:\n if not device[has]:\n print(\" {}\".format(device[_id]))","title":""},{"docid":"139f6f7d93e822f52c0040f2e6abad9c","score":"0.5253097","text":"def check_package_version(self):\n package_version = self.meta.get('package', {}).get('version', '')\n\n if package_version == '':\n return Error(self.recipe_dir, 'C2104', 'Missing package version in meta.yaml')\n\n if isinstance(package_version, str):\n if not self.version_pat.match(package_version) or package_version.startswith(('_', '.')) or package_version.endswith(('_', '.')):\n return Error(self.recipe_dir, 'C2105', u'Found invalid package version \"{}\" in meta.yaml' .format(package_version))\n\n seq = get_bad_seq(package_version)\n if seq:\n return Error(self.recipe_dir, 'C2106', u'Found invalid sequence \"{}\" in package version' .format(seq))","title":""},{"docid":"5f9c8b5b9df7ee76c169d61bc23701e3","score":"0.52514017","text":"def _package_available(package_name: str) -> bool:\n try:\n return find_spec(package_name) is not None\n except ModuleNotFoundError:\n return False","title":""},{"docid":"68d985d22add2be0e1f29f408bc21b4c","score":"0.5251276","text":"def mark_found(package):\n\n with connection() as cursor:\n cursor.execute(\"\"\"\n UPDATE\n package_stats\n SET\n is_missing = FALSE,\n missing_error = '',\n removed = FALSE\n WHERE\n package = %s\n \"\"\", [package])","title":""},{"docid":"fbe0b1d5934954da45dec979aff215ee","score":"0.52396005","text":"def package_status(pkg_id):\n\n url = env.pkg_status_api + '/' + pkg_id\n\n resp = requests.get(url, timeout=env.timeout, headers=env.header)\n\n pyld = json.loads(resp.text)\n LOG.debug(pyld)\n\n env.set_return_header(resp.headers)\n\n if resp.status_code != 200:\n return False, str(pyld)\n\n return True, pyld","title":""},{"docid":"1c71e6d04e00593beeac4224b3bcf5d7","score":"0.5230559","text":"def test_check_package_global(self):\n installed_package = list(pkg_resources.working_set)[0].project_name\n\n self.assertTrue(package.check_package_exists(installed_package, None))","title":""},{"docid":"c6d0fa8da0bc74a89612a7f0b6dc5af5","score":"0.52287936","text":"def isAvailable(self, packageName):\n\t\tpass","title":""},{"docid":"3b5742bf4301cf56c7cef9d37ff6569c","score":"0.5206684","text":"def check_item_in_database(package_name='', package_version='', d='', i=1,\n outputfile=sys.stdout, output_type='text',\n minimum_date='2002-01-01'):\n package = ''\n if package_version is None:\n package_version = ''\n if package_name is None:\n package_name == ''\n if package_name == '' and package_version == '':\n print (\"check_item_in_database: both package_name and package_version\\\n are null\", file=outputfile)\n return False\n elif package_name == '' and package_version != '':\n package = ':' + package_version\n elif package_version == '' and package_name != '':\n package = package_name + ':'\n else:\n if package_name != '' and package_version != '':\n package = \":\" + package_name + ':' + package_version\n else:\n return False\n try:\n p = CheckNVDByPackage(package=package, database=d, items=i,\n minimum_date=minimum_date)\n # report is a list of ordered tuples containing CVE findings\n report = p.get_data()\n # setup an object to contain the data\n j_data = collect_json\n j_data = j_data()\n if output_type == 'json' or output_type == 'html':\n j = {}\n # pull the items out of the report, and stuff them\n # in a collection of json\n for item in report:\n j['search_item_string'] = package\n j['requested_package_name'] = package_name\n j['requested_package_version'] = package_version\n j['requested_output_type'] = output_type\n j['cve'] = item[0]\n j['cvss_base_score'] = item[1]\n cpe = item[2].split(',')\n cpe.sort\n cpe = ','.join(cpe)\n j['cpe'] = cpe\n j['date'] = item[3]\n j['summary'] = item[4]\n j_data.append(j)\n # I have no idea why j needed to be 'nulled' out,\n # but this fixed a problem with not getting all items\n j = {}\n if output_type == 'json':\n \"\"\" This code has reached spaghetti code proportions\n and we need to get output managed elsewhere.\n refactor.\n \"\"\"\n j_data = json.dumps(j_data.get_json_data())\n print (j_data, file=outputfile)\n return True\n if output_type == 'html':\n \"\"\" This code has reached spaghetti code proportions\n and we need to get output managed elsewhere.\n refactor.\n \"\"\"\n html = ''\n html = lib.html_out.cve_table_content(package, j_data.get_json_data())\n if html is not None:\n # whew, there be dragons\n if __DEBUG__:\n frameinfo = getframeinfo(currentframe())\n print (\"** Debug breadcrumbs, HMTL out: %s, line: %s\" %\n (frameinfo.filename, frameinfo.lineno))\n html = lib.html_out.html_wrap_content(html)\n print (html, file=outputfile)\n return True\n else:\n return\n detailed_report = {}\n if output_type == 'text':\n \"\"\" This code has reached spaghetti code proportions\n and we need to get output managed elsewhere.\n refactor.\n \"\"\"\n if len(p) > 0:\n detailed_report[package] = report\n detailed_vuln_report(detailed_report, outputfile)\n except Exception, e:\n print (\"falco: check_item_in_database: %s\" % (e,), file=outputfile)\n if re.search(r'no such table', str(e)):\n print (\"\\tFalco requires copying the cpe into\", file=outputfile)\n print (\"\\ta cpe fts3 virtual table, after the\", file=outputfile)\n print (\"\\cvedb.\",\n file=outputfile)\n print (\"\\tis setup. Run $ falco -c\", file=outputfile)\n print (\"\\tand then try again.\", file=outputfile)\n exit(1)\n if len(p) > 0:\n return True\n else:\n return False","title":""},{"docid":"28bd3eb107836e8ac3506151e5920843","score":"0.5199931","text":"def _check_import_package(package_helper, command):\n LOGGER.debug(f\"Trying to import a package with [{command}] ...\")\n rc = package_helper.running_container.exec_run(command)\n return rc.exit_code","title":""},{"docid":"4b24a88ff2ea39b8520533f1e8db3ae4","score":"0.51809335","text":"def require_valid_package_version_code(\n cls, package_version_code: int\n ) -> None:\n if package_version_code is None:\n raise utils.ValidationError('No package version code supplied.')\n if not isinstance(package_version_code, int):\n raise utils.ValidationError(\n 'Package version code must be an int, received: %r.' % (\n package_version_code))\n if package_version_code < feconf.MINIMUM_ANDROID_PACKAGE_VERSION_CODE:\n raise utils.ValidationError(\n 'The package version code is not a valid int. The minimum '\n 'supported version is %d, received: %d.' % (\n feconf.MINIMUM_ANDROID_PACKAGE_VERSION_CODE,\n package_version_code))","title":""},{"docid":"12074b033935c13301b155c6b06db0d7","score":"0.5153052","text":"def find_mps_invalid_return_packages(package, center):\n # Cyclic import\n from package.utils import package_find\n\n invalid_count = 0\n invalid_wbns = list()\n\n master_wbn = package.get('mwn')\n\n query_dict = {'mwn': master_wbn, 'cs.st': {'$ne': u'LT'}}\n\n fields = {'wbn':1, 'cs':1, 'dd': 1}\n\n child_cursor = package_find(filter_criteria=query_dict, fields=fields,\n return_dict=True, use_replicaSet=True)\n\n for child in child_cursor:\n cs = child.get('cs', {})\n dd = child.get('dd', {})\n if cs.get('ss') in dispatch_settings.RDTO_SS_LIST and \\\n cs.get('st') in dispatch_settings.RDTO_ST_LIST:\n if cs.get('sl') == center:\n if dd.get('rto') or dd.get('ist') or dd.get('dto') \\\n or dd.get('srt'):\n invalid_count = invalid_count + 1\n invalid_wbns.append(child.get('wbn', ''))\n else:\n invalid_count = invalid_count + 1\n invalid_wbns.append(child.get('wbn', ''))\n else:\n invalid_count = invalid_count + 1\n invalid_wbns.append(child.get('wbn', ''))\n\n return invalid_count, invalid_wbns","title":""},{"docid":"70377f57300f7d2db1a49770103f4d92","score":"0.51495457","text":"def validate(self, item):\n attempt, pkg_analyzer, journal_and_issue_data = item[:3]\n j_publisher_name = journal_and_issue_data.get('journal', {}).get('publisher_name', None)\n if j_publisher_name:\n data = pkg_analyzer.xml\n xml_publisher_name = data.findtext('.//journal-meta/publisher/publisher-name')\n\n if xml_publisher_name:\n if self._normalize_data(xml_publisher_name) == self._normalize_data(j_publisher_name):\n r = [models.Status.ok, 'Valid publisher name: ' + xml_publisher_name]\n else:\n r = [models.Status.error, 'Mismatched data: %s. Expected: %s' % (xml_publisher_name, j_publisher_name)]\n else:\n r = [models.Status.error, 'Missing data: publisher name']\n else:\n r = [models.Status.error, 'Missing data: publisher name, in scieloapi']\n return r","title":""},{"docid":"d7bee47d6f7f04a30611fe5cc9c2331b","score":"0.5144293","text":"def has_package(self, name):\n if not name:\n raise ValueError('invalid package name')\n name = name.upper()\n for p in self.packagelist:\n for pn in p.name:\n if pn.upper() == name:\n return True\n return False","title":""},{"docid":"d9237fc0999953a86932571a3f13a3c1","score":"0.51237273","text":"def check_package_version(self):\n package_version = str(self.info.get('version'))\n if package_version == 'None':\n return Error(self.path, 'C1104', 'Missing package version in info/index.json')\n if package_version.startswith(('_', '.')) or package_version.endswith(('_', '.')):\n return Error(self.path, 'C1107', \"Package version in info/index.json cannot start or end with '_' or '.'\")\n if not self.version_pat.match(package_version) or get_bad_seq(package_version):\n return Error(self.path, 'C1105', 'Found invalid version number in info/index.json')\n if package_version != self.version:\n return Error(self.path, 'C1106', u'Found package version in info/index.json \"{}\" does not match filename version \"{}\"' .format(package_version, self.version))","title":""},{"docid":"310d6d7f91ba7c47ce0b0e7a6f243484","score":"0.50995976","text":"def _package_exists(name: str) -> bool:\n try:\n get_distribution(name)\n except DistributionNotFound:\n return False\n else:\n return True","title":""},{"docid":"fa3916c9eaa0eb418e72e0fbd3c9dcf4","score":"0.50798416","text":"def test_cannot_create_package_with_id(self):\n # Check id does not exist\n id = 1001\n with self.assertRaises(ValidationError) as e:\n self.Package.get_or_create(id)\n self.assertEqual(e.exception.args[0], f\"Package not found for identifier {id}\")\n # Try to create package\n with self.assertRaises(ValidationError) as e:\n self.Package.get_or_create(id, create=True)\n self.assertEqual(\n e.exception.args[0],\n f\"Cannot create a new Package for stock.quant.package with identifier of type {type(id)}\",\n )","title":""},{"docid":"f3f6bfd9bce98f290d69bb3551639b78","score":"0.5075812","text":"def package_check(package_name):\n\n def decorator(func):\n \"\"\"Pass the function through to the wrapper.\"\"\"\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n \"\"\"Wrap functions that use the decorator function.\"\"\"\n\n message_warn = ' '.join(['{:} must be installed'.format(package_name),\n 'to use {:}.'.format(func.__name__),\n 'See instructions at',\n 'https://github.com/pysat/pysatMissions'])\n try:\n func(*args, **kwargs)\n except NameError as nerr:\n # Triggered if call is made to package that is not installed\n if package_name in str(nerr):\n warnings.warn(message_warn, stacklevel=2)\n else:\n # Error is unrelated to optional package, raise original.\n raise nerr\n\n return wrapper\n\n return decorator","title":""},{"docid":"2747a11c787857b94f1770f500647a0f","score":"0.5055945","text":"def check(regid, check_valid=False):\n row = anlicenses.select().where(anlicenses.c.regid==regid).execute().fetchone()\n return row['pdserial'] if row is not None and (\n not check_valid or row['valid'] != 'N') else False","title":""},{"docid":"1fc7b40e6a1daffe05d11b612f774bff","score":"0.50413394","text":"def has_package(self, package):\n return package in [pkg[0] for pkg in self.packages if pkg]","title":""},{"docid":"7af6eae3b88f338de1e768770797de47","score":"0.5028316","text":"def test_wrong_id_should_fail(self):\n with pytest.raises(ValueError):\n PackageId('id')","title":""},{"docid":"da9bc99f3ec59e0bb941d1fa995c1e94","score":"0.5023925","text":"def is_installed(self, package):\n ret = False\n out = self.shell(['pm', 'list', 'packages'], silent=True)\n if out is not None:\n for line in out.splitlines():\n if line.find(package) >= 0:\n ret = True\n break\n return ret","title":""},{"docid":"c49614b84dfc94e86045536b30e412e3","score":"0.5022208","text":"def process_package(pack, notifier):\n logger.debug('Started processing %s.' % pack)\n\n pack_reporter = package.CheckinReporter(pack.primary_path)\n pack_reporter.tell('Started the identification process.')\n\n try:\n attempt = checkin.get_attempt(pack)\n\n except excepts.MissingXML as e:\n pack.mark_as_failed(silence=True)\n pack_reporter.tell('The package must have only one XML file.')\n\n except excepts.InvalidXML as e:\n pack.mark_as_failed(silence=True)\n for msg in e.message:\n pack_reporter.tell(msg)\n\n except excepts.DuplicatedPackage as e:\n pack.mark_as_failed(silence=True)\n pack_reporter.tell('The package was already deposited.')\n\n except Exception as e:\n pack.mark_as_failed(silence=True)\n pack_reporter.tell('The package could not be processed due to an unexpected error. Our engineers have been notified.')\n\n else:\n # Create a notification to keep track of the checkin process\n session = models.Session()\n checkin_notifier = notifier(attempt, session)\n checkin_notifier.start()\n\n if attempt.is_valid:\n notification_msg = 'Attempt ready to be validated'\n notification_status = models.Status.ok\n else:\n notification_msg = 'Attempt cannot be validated'\n notification_status = models.Status.error\n\n pack_reporter.tell('The package is ready to be validated.')\n checkin_notifier.tell('XML validates against the SPS Schema',\n notification_status, 'Checkin')\n checkin_notifier.tell(notification_msg, notification_status, 'Checkin')\n checkin_notifier.end()\n\n attempt.proceed_to_validation = True\n transaction.commit()","title":""},{"docid":"c023cbb9f2e6a27ab142caff5815b2d8","score":"0.5015223","text":"def test_packagevalidate(self):\n with pytest.raises(AttributeError):\n cobra.services.UploadPackage(self.fakePackage, validate=True)","title":""},{"docid":"ca2ab6a895f6188b00d7922bb6cf9716","score":"0.50132483","text":"def check_and_install_package(package):\n\tis_installed = int(commands.getoutput('dpkg -l '+package+' 2>&1 | grep -c \"^ii\"'))\n\tif is_installed:\n\t\tprint \"Package \"+package+\" is installed.\"\n\telse:\n\t\tprint \"Package \"+package+\" is required but not installed.\"\n\t\tinstall = \"\"\n\t\twhile install not in (\"y\", \"n\"):\n\t\t\tinstall = raw_input('Install it now ? (y/n)')\n\t\t\tif install == \"n\":\n\t\t\t\tprint \"Exiting on missing package.\"\n\t\t\t\tsys.exit(1);\n\t\tos.system('sudo apt-get install -y '+package)","title":""},{"docid":"baf759a3b4af6aeba40d8e41557dcafd","score":"0.5008721","text":"def find_pkg(self, pkg, check_tag=True, cbk_fct=None):\n \n # if '-' in name, a tag was given.\n if '-' in pkg:\n tag = pkg.split('/')[-1]\n pkg = pkg.split('-',1)[0]\n elif check_tag:\n raise ValueError('no tag was given for [%s]' % (pkg,))\n else:\n tag = None\n\n orig_pkg = pkg\n # check if we need to find the full pkg path\n if '/' in pkg:\n pkg = pkg.split('/')[-1]\n\n args = {\n 'glite': (\n \"select packages.path,packages.packageName,packages.archive \"\n \"where repositories.repositoryName='AtlasOfflineRepository' \"\n \"and packages.packageName='%s' and packages.archive=0\" % pkg\n ),\n }\n\n result = self.exec_cmd(cmd='SearchQuery', args=args)\n if not result:\n raise RuntimeError(\n 'could not resolve [%s] to full package path' %\n (pkg,)\n )\n res_dict = result.to_dict()\n if not 'Element_Info' in res_dict:\n raise RuntimeError(\n 'could not resolve [%s] to full package path' %\n (pkg,)\n )\n \n pkg_list = []\n for v in res_dict['Element_Info'].values():\n pkg_list.append(v) # += [v['path'] + v['packageName']]\n\n idx = 0\n if len(pkg_list) == 0:\n raise RuntimeError('package [%s] does not exist' % pkg)\n\n elif len(pkg_list)>1:\n ambiguous = True\n if '/' in orig_pkg:\n pkg_candidates = []\n for i,v in enumerate(pkg_list):\n if orig_pkg in v['path']+v['packageName']:\n pkg_candidates.append(i)\n if len(pkg_candidates) == 1:\n idx = pkg_candidates[0]\n pkg = pkg_list[idx]\n ambiguous = False\n pass\n\n if ambiguous:\n self.msg.info('multiple packages found for [%s]:', pkg)\n for i,v in enumerate(pkg_list):\n self.msg.info(' %i) %s', i, v['path']+v['packageName'])\n if cbk_fct:\n try:\n n = cbk_fct()\n except StopIteration:\n raise RuntimeError(\n 'multiple packages found for [%s]' % pkg\n )\n idx = n\n pkg = pkg_list[n]\n else:\n raise RuntimeError('multiple packages found for [%s]' % pkg)\n\n else:\n idx = 0\n pkg = pkg_list[0]\n\n # normalize...\n if pkg['path'][0] != '/':\n pkg['path'] = '/%s' % pkg['path']\n\n pkg.update(dict(packagePath=pkg['path'],\n id=str(pkg['AMIELEMENTID'])))\n if tag:\n pkg['packageTag'] = tag\n del pkg['path']\n del pkg['AMIELEMENTID']\n \n # remove all unicode strings...\n _pkg = dict(pkg)\n pkg = {}\n \n for k,v in _pkg.iteritems():\n if isinstance(v, basestring):\n v = str(v)\n pkg[str(k)] = v\n \n \n ## if tag is None:\n ## tag = tag_list[idx]\n \n ## print \"-\"*80\n ## print res_dict\n ## print \"-\"*80\n return pkg","title":""},{"docid":"39a97dbf034266dd31fcf10cc3121308","score":"0.4993555","text":"def query(cls, release_type, package_name, args):\n # only use the first value\n if len(args) > 0:\n return (args[0] in cls.valid_values(release_type, package_name), args[0])\n else:\n return (False, None)","title":""},{"docid":"71dae375d43b962dacf4b47a7f2f8af1","score":"0.49933568","text":"def test_create_then_get_new_package_success_by_id(self):\n test_package = self.Package.get_or_create(\"Test Pack 2\", create=True)\n get_package = self.Package.get_or_create(test_package.id)\n self.assertEqual(test_package.name, get_package.name)","title":""},{"docid":"336420321b1c8fb4dafab23e2fc345ba","score":"0.49919316","text":"def _r_pkg_ver_chk(self):\n\n global g_r_pkg_ver_chk_script\n self._log(\"\")\n self._log(\"Conducting R package/version check...\")\n out_file_name = os.path.join(self.output_dir, \"package_version_check_out.txt\")\n out = open(out_file_name, \"w\")\n\n cmd = [\"R\", \"--vanilla\", \"-f\", g_r_pkg_ver_chk_script, \"--args\", \"check\", ]\n\n child = subprocess.Popen(args=cmd, stdout=out)\n rv = child.wait()\n if self.terminated:\n return\n if rv == 1:\n self._log(\"\")\n self._log(\"ERROR: \" + g_r_pkg_ver_chk_script + \" failed.\")\n self._log(\" See \" + out_file_name)\n sys.exit(1)\n out.close()","title":""},{"docid":"2c105386876033a6f925626102efff04","score":"0.4973631","text":"def check_apt_package(pkgname, friendly_name):\n\n cmd = [\"apt\", \"-qq\", \"list\", pkgname]\n proc = subprocess.run( # nosec - B603 (subprocess call)\n cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, check=False)\n if proc.returncode == 0 and '[installed]' in proc.stdout:\n return True\n else:\n raise AppException(\n Codes.MISSING_PREREQUISITE,\n f\"{friendly_name} has to be installed on this machine for the provisioning process to succeed.\\n\"\n f\" {_TS_REF}\")","title":""},{"docid":"643039f996e031e218577b5f998bebab","score":"0.49622446","text":"def check(self):\n result = Result()\n try:\n distro_package_vers = get_distro_package_versions()\n if not distro_package_vers:\n result.add_error('ERROR: distro packages info is not found.')\n except (AttributeError, RuntimeError, URLError):\n result.add_error('ERROR: Unable to fetch package information from rosdistro.')\n local_package_vers = get_local_package_versions()\n if not local_package_vers:\n result.add_error('ERROR: local package info is not found.')\n if result.error != 0:\n return result\n\n warning_msgs = compare_versions(local_package_vers, distro_package_vers)\n for msg in warning_msgs:\n result.add_warning(msg)\n return result","title":""},{"docid":"c9215815d643534ea4e22483094695cc","score":"0.49581543","text":"async def query(self, package):\n await asyncio.sleep(0)\n return package in self.forward","title":""},{"docid":"c572836aea1cfcca73e1499e99c88e72","score":"0.4935848","text":"def spdx_package(self) -> 'outputs.PackageInfoOccurrenceResponse':\n return pulumi.get(self, \"spdx_package\")","title":""},{"docid":"28accff2b662b7fc97f56d5508ec1922","score":"0.49281135","text":"def test_valid_package_name(self):\n package_name = 'my-awesome-package'\n self.assertTrue(self.deb.is_valid_package_name(package_name))","title":""},{"docid":"132bfc4729edea3df60c05e48664b20b","score":"0.48970926","text":"def verify_request(self):\r\n \r\n self.log.debug(\"Verifying packaging request\")\r\n \r\n def verify_dir_and_owner(path, uid):\r\n try:\r\n info = os.lstat(path)\r\n except OSError as e:\r\n raise PackagerError(\"Can't stat %s: %s\" % (path, e))\r\n if info.st_uid != uid:\r\n raise PackagerError(\"%s isn't owned by %d\" % (path, uid))\r\n if stat.S_ISLNK(info.st_mode):\r\n raise PackagerError(\"%s is a soft link\" % path)\r\n if not stat.S_ISDIR(info.st_mode):\r\n raise PackagerError(\"%s is not a directory\" % path)\r\n \r\n # Check owner and type of directories.\r\n verify_dir_and_owner(self.request.pkgroot, self.uid)\r\n self.log.debug(\"pkgroot ok\")\r\n verify_dir_and_owner(self.request.pkgdir, self.uid)\r\n self.log.debug(\"pkgdir ok\")\r\n \r\n # Check name.\r\n if len(self.request.pkgname) > 80:\r\n raise PackagerError(\"Package name too long\")\r\n if not self.re_pkgname.search(self.request.pkgname):\r\n raise PackagerError(\"Invalid package name\")\r\n if self.request.pkgname.lower().endswith(\".pkg\"):\r\n raise PackagerError(\"Package name mustn't include '.pkg'\")\r\n self.log.debug(\"pkgname ok\")\r\n \r\n # Check ID.\r\n if len(self.request.id) > 80:\r\n raise PackagerError(\"Package id too long\")\r\n components = self.request.id.split(\".\")\r\n if len(components) < 2:\r\n raise PackagerError(\"Invalid package id\")\r\n for comp in components:\r\n if not self.re_id.search(comp):\r\n raise PackagerError(\"Invalid package id\")\r\n self.log.debug(\"id ok\")\r\n \r\n # Check version.\r\n if len(self.request.version) > 40:\r\n raise PackagerError(\"Version too long\")\r\n components = self.request.version.split(\".\")\r\n if len(components) < 1:\r\n raise PackagerError(\"Invalid version\")\r\n for comp in components:\r\n if not self.re_version.search(comp):\r\n raise PackagerError(\"Invalid version\")\r\n self.log.debug(\"version ok\")\r\n \r\n # Make sure infofile and resources exist and can be read.\r\n if self.request.infofile:\r\n try:\r\n with open(self.request.infofile, \"rb\") as f:\r\n pass\r\n except (IOError, OSError) as e:\r\n raise PackagerError(\"Can't open infofile: %s\" % e)\r\n self.log.debug(\"infofile ok\")\r\n\r\n # Make sure scripts is a directory and its contents\r\n # are executable.\r\n if self.request.scripts:\r\n if self.request.pkgtype == \"bundle\":\r\n raise PackagerError(\r\n \"Installer scripts are not supported with \"\r\n \"bundle package types.\")\r\n if not os.path.isdir(self.request.scripts):\r\n raise PackagerError(\r\n \"Can't find scripts directory: %s\"\r\n % self.request.scripts)\r\n for script in [\"preinstall\", \"postinstall\"]:\r\n script_path = os.path.join(self.request.scripts, script)\r\n if os.path.exists(script_path) \\\r\n and not os.access(script_path, os.X_OK):\r\n raise PackagerError(\r\n \"%s script found in %s but it is not executable!\"\r\n % (script, self.request.scripts))\r\n self.log.debug(\"scripts ok\")\r\n \r\n # FIXME: resources temporarily unsupported.\r\n #if self.request.resources:\r\n # try:\r\n # os.listdir(self.request.resources)\r\n # except OSError as e:\r\n # raise PackagerError(\"Can't list Resources: %s\" % e)\r\n # self.log.debug(\"resources ok\")\r\n \r\n # Leave chown verification until after the pkgroot has been copied.\r\n \r\n self.log.info(\"Packaging request verified\")","title":""},{"docid":"9c638c939e345b582eb23ceba1971977","score":"0.4895481","text":"def check_package_installed(command_runner, conda, name, version):\n output, stderr = run_conda_package_command(\n command_runner, name, version, [conda, 'install', '--json', '--dry-run', get_install_target(name, version)])\n\n if 'message' in output and output['message'] == 'All requested packages already installed.':\n return True\n elif 'actions' in output and len(output['actions']) > 0:\n return False\n else:\n raise CondaUnexpectedOutputError(output, stderr)","title":""},{"docid":"5b7b4c2f406d6febffdd66adab6f7ec1","score":"0.48941055","text":"def find_missing_count(package, center, return_flag=False):\n # Cyclic import\n from package.utils import package_find\n\n missing_count = 0\n missing_wbns = list()\n found_wbns = list()\n\n master_wbn = package.get('mwn')\n if return_flag:\n query_dict = {'mwn': master_wbn,\n 'cs.st': {'$ne': u'LT', '$eq': u'RT'}}\n else:\n query_dict = {'mwn': master_wbn,\n 'cs.st': {'$ne': u'LT'}}\n\n fields = {'wbn':1, 'cs':1}\n\n child_cursor = package_find(filter_criteria=query_dict, fields=fields,\n return_dict=True, use_replicaSet=True)\n\n for child in child_cursor:\n if child.get('cs', {}).get('sl') != center:\n missing_count = missing_count + 1\n missing_wbns.append(child.get('wbn', ''))\n else:\n found_wbns.append(child.get('wbn', ''))\n if missing_count:\n # Apply NSL on the shipments which have reached DC or RCN\n mps_hold_shipments.delay(found_wbns)\n\n return missing_count, missing_wbns","title":""},{"docid":"e96a6cc98596a9e2b56c5b4d925e5862","score":"0.48735833","text":"def is_valid_pkg_name(pkg_name):\n if keyword.iskeyword(pkg_name):\n return False\n return pkg_name.isidentifier()","title":""},{"docid":"6efcdf5d5eb9170c42cb7cfdd5293ab1","score":"0.48706594","text":"async def check_pack_name(request):\n log_request(request)\n conn = await create_connection()\n response = await packs_query.packs_search_duplicate(\n conn, escape_user_input(request.args.get(\"name\"))\n )\n conn.close()\n\n return json({\"exists\": bool(response)})","title":""},{"docid":"c7b3797d99c99379c822faf0cf126374","score":"0.48701146","text":"async def _is_depended_on(self, package):\n file_path = self._reverse_index_path(package)\n return await self._exists(file_path)","title":""},{"docid":"48877d96b702233b28c2a19a6fbc97bf","score":"0.4867458","text":"def is_package_installed(package_name: str) -> bool:\n return run_and_check_result('dpkg-query -l ' + package_name)","title":""},{"docid":"8e46a1e2d09fc4f63c055556bc1d2c9b","score":"0.48665035","text":"def test_check_package_local(self):\n lib_dir = get_python_lib()\n installed_package = list(pkg_resources.working_set)[0].project_name\n\n self.assertTrue(\n package.check_package_exists(installed_package, lib_dir)\n )","title":""},{"docid":"d6774868c7edb6f8bf5c41b8dc444e61","score":"0.48619893","text":"def query(self, package):\n pass","title":""},{"docid":"ced22b933c67d17a5b7009422d5a1162","score":"0.48606348","text":"def check(self):\n result = Result()\n distro_package_vers = get_distro_package_versions()\n if not distro_package_vers:\n doctor_error('distro packages info is not found.')\n result.add_error()\n local_package_vers = get_local_package_versions()\n if not local_package_vers:\n doctor_error('local package info is not found.')\n result.add_error()\n if result.error != 0:\n return result\n\n compare_versions(result, local_package_vers, distro_package_vers)\n return result","title":""},{"docid":"48f2def467ff66e612a2db51b819b456","score":"0.4857025","text":"def check_application_launch_errors(self, prefix, package_name, id_):\n logger.debug(\n 'prefix: {}, package_name: {}'.format(prefix, package_name)\n )\n root_prefix = self.api.ROOT_PREFIX\n out_path, err_path, id_ = get_package_logs(\n package_name,\n prefix=prefix,\n root_prefix=root_prefix,\n id_=id_,\n )\n err_log = ''\n if os.path.isfile(err_path):\n with open(err_path, 'r') as f:\n err_log = f.read()\n\n # Only show the message box error dialog if the words were found in\n # the error log\n e_low = err_log.lower()\n display_msg = e_low and 'error' in e_low or 'exception' in e_low\n if display_msg:\n self.tracker.track_page(\n '/home/errors',\n pagetitle='Launch errors dialog.',\n )\n\n self._dialog_message_box = MessageBoxError(\n text='Application <b>{}</b> launch may have '\n 'produced errors.'.format(package_name),\n title='Application launch error',\n error=err_log,\n report=False,\n learn_more=None\n )\n self._dialog_message_box.exec_()\n self._dialog_message_box = None\n self.tracker.track_page('/home')","title":""},{"docid":"049fc5d0c6c78655c40fc5d71609921e","score":"0.48485154","text":"def package_is_installed(spec: TranslateSpec, package: str) -> bool:\n p = run(spec.g, ['rpm', '-q', package], raiseOnError=False)\n logging.debug('rpm -q: {}'.format(p))\n return p.code == 0","title":""},{"docid":"0bce6ab116182acfdd3a1bd5556f21e7","score":"0.48424947","text":"def test_catkin_discovery_plugin_scan_invalid_packagexml():\n cdp = CatkinDiscoveryPlugin()\n package = Package('invalid_package_nopackagexml',\n os.path.join(os.path.dirname(__file__),\n 'invalid_package_nopackagexml'))\n cdp.scan(package, 'level')\n assert not package['catkin']","title":""},{"docid":"006d6ec66e629854ab0fcdab75076164","score":"0.48415828","text":"def check(pkgs):\n packages = defaultdict(int)\n required_pkgs = \"required_pkgs.txt\"\n url = \"https://debian.osuosl.org/debian/dists/buster/main/binary-amd64/Packages.gz\"\n\n url_data = urlparse(url)\n if not url_data.scheme.startswith(\"file\"):\n if not os.path.exists(pkgs):\n six.print_(\"Downloading package list...\")\n six.moves.urllib.request.urlretrieve(url, pkgs)\n else:\n print(f\"Invalid URL: {url}\")\n sys.exit(1)\n\n with gzip.open(pkgs, \"rt\") as pkg_file:\n for line in pkg_file:\n new_line = line.rstrip(os.linesep)\n if new_line.startswith(\"Package\"):\n pkg = new_line.split(\": \")[1]\n packages[pkg] += 1\n\n with open(required_pkgs, \"rt\") as required:\n for line in required:\n new_line = line.rstrip(os.linesep)\n if new_line not in packages:\n six.print_(new_line)","title":""},{"docid":"080b5c3100e1d2c14df83acac14906eb","score":"0.48368376","text":"def package_check(pkg_name, version=None, app=None, checker=LooseVersion,\r\n exc_failed_import=ImportError,\r\n exc_failed_check=RuntimeError):\r\n\r\n if app:\r\n msg = '%s requires %s' % (app, pkg_name)\r\n else:\r\n msg = 'Nipype requires %s' % pkg_name\r\n if version:\r\n msg += ' with version >= %s' % (version,)\r\n try:\r\n mod = __import__(pkg_name)\r\n except ImportError:\r\n raise exc_failed_import(msg)\r\n if not version:\r\n return\r\n try:\r\n have_version = mod.__version__\r\n except AttributeError:\r\n raise exc_failed_check('Cannot find version for %s' % pkg_name)\r\n if checker(have_version) < checker(version):\r\n raise exc_failed_check(msg)","title":""},{"docid":"f6e7f4b9c66f54f71e8a16c5841ce447","score":"0.48211604","text":"def test_packagedonotvalidate(self):\n packageUpload = cobra.services.UploadPackage(self.fakePackage)\n assert packageUpload.devicePackagePath == self.fakePackage","title":""},{"docid":"78d59d4162c7bb7d6db259994fa93fd4","score":"0.48195696","text":"def VerifyPackage(self, entry, modlist, checksums=True):\n if 'version' not in entry.attrib:\n self.logger.info(\"Cannot verify unversioned package %s\" %\n (entry.attrib['name']))\n return False\n\n pkgname = entry.get('name')\n if pkgname not in self.pkg_cache:\n self.logger.info(\"Package %s not installed\" % (entry.get('name')))\n entry.set('current_exists', 'false')\n return False\n\n installed_version = self.pkg_cache[pkgname]\n candidate_versions = self._get_candidate_versions(pkgname)\n if candidate_versions is not None:\n candidate_version = candidate_versions[0]\n else:\n self.logger.error(\"Package %s is installed but no candidate\"\n \"version was found.\" % (entry.get('name')))\n return False\n\n if entry.get('version').startswith('auto'):\n desired_version = candidate_version\n entry.set('version', \"auto: %s\" % desired_version)\n elif entry.get('version').startswith('any'):\n desired_version = installed_version\n entry.set('version', \"any: %s\" % desired_version)\n else:\n desired_version = entry.get('version')\n\n if desired_version != installed_version:\n entry.set('current_version', installed_version)\n entry.set('qtext', \"Modify Package %s (%s -> %s)? (y/N) \" %\n (entry.get('name'), entry.get('current_version'),\n desired_version))\n return False\n else:\n # version matches\n if (not Bcfg2.Options.setup.quick and\n entry.get('verify', 'true') == 'true' and\n checksums):\n pkgsums = self.VerifyChecksums(entry, modlist)\n return pkgsums\n return True","title":""},{"docid":"8704b299972e2df5836d49ebbbe814b0","score":"0.48192906","text":"def check_group_id(self, group_id):","title":""},{"docid":"91a324a381085b1f77a8adc8301bdff1","score":"0.48163658","text":"def is_valid_package(package_name):\n try:\n apackage = import_module('%s.tasks'%package_name)\n except ImportError:\n return False\n tasks = Collection.from_module(apackage).task_names\n if tasks == {}:\n return False\n return True","title":""},{"docid":"c2e3ae343818be892467a7f7a287e8d8","score":"0.48013705","text":"def return_package(self) -> Optional['outputs.PackageInformationResponse']:\n return pulumi.get(self, \"return_package\")","title":""},{"docid":"71e68c5bf5262ca0cda57e7b26f29913","score":"0.47998446","text":"def CheckAndroidPackage(URL, packageAndActivity, flags): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"d2956e3bb7cdaa6f33d6afcf144224a9","score":"0.47888482","text":"def test_invalid_package_name(self):\n package_name = 'fhjdskfh-900u78*'\n self.assertFalse(self.deb.is_valid_package_name(package_name))","title":""},{"docid":"71e676bd9a0f59dfefda5cf115755fa6","score":"0.47844812","text":"def get_package(package_uuid):\n\n # get package info\n resp = requests.get(env.pkg_api + '/' + package_uuid,\n timeout=env.timeout,\n headers=env.header)\n\n env.set_return_header(resp.headers)\n\n if resp.status_code != 200:\n LOG.debug(\"Request for package returned with \" +\n (str(resp.status_code)))\n return False, json.loads(resp.text)\n\n return True, json.loads(resp.text)","title":""},{"docid":"66ae20ef11335c4f62fabfe0a3660382","score":"0.47786415","text":"def checkHasKey(self, _id):\n for t in self.package:\n if t.type == 1 and t.id == _id:\n return True\n return False","title":""},{"docid":"b20f65bf95dbf72258c0203f1941a59a","score":"0.47582924","text":"def package_ensure( package ):\n\tif run(\"dpkg-query -W -f='${Status}' %s ; true\" % package).find(\"installed\") == -1:\n\t\tpackage_install(package)","title":""},{"docid":"4f69a40b710f6527975a9d013f2258a6","score":"0.47562665","text":"def is_package_present(package_name):\n try:\n import_module(package_name)\n return True\n except ImportError:\n return False","title":""},{"docid":"ede859faeedd5223092db8ba1e916cfd","score":"0.47281814","text":"def testPackage(self):\n self.assertRaises(ValueError, Package, self.udd.psql)\n rs = SourcePackage(self.udd.psql, package=\"glibc\", release='sid')\n self.assertTrue(rs)\n self.assertTrue(rs.Found())\n rne = SourcePackage(self.udd.psql, package=\"nosuchpackage\")\n self.assertFalse(rne.Found())","title":""},{"docid":"158ebdecc1970c990e6215607baba2a8","score":"0.4727367","text":"def _check_pip_package(self, dependency):\n pip_dependency_name, pip_dependency_version = dependency.split('==')\n pip_api_url = \"https://pypi.python.org/pypi/{}/json\".format(pip_dependency_name)\n try:\n response = requests.get(pip_api_url, timeout=10)\n except requests.exceptions.Timeout:\n self.warned.append(('general-7', f'PyPi API timed out: {pip_api_url}'))\n except requests.exceptions.ConnectionError:\n self.warned.append(('general-7', f'PyPi API Connection error: {pip_api_url}'))\n else:\n if response.status_code == 200:\n pip_dep_json = response.json()\n latest_dependency_version = pip_dep_json['info']['version']\n if parse_version(pip_dependency_version) < parse_version(latest_dependency_version):\n self.warned.append(('general-7', f'Version {pip_dependency_version} of {pip_dependency_name}'\n f' is not the latest available: {latest_dependency_version}'))\n else:\n self.failed.append(('general-7', f'Could not find pip dependency using the PyPi API: {dependency}'))","title":""},{"docid":"a6d4e54c1931c2ff48578bad67f1d22a","score":"0.47202945","text":"def _validate_output(self, r):\n has_failed = False\n for line in r.split('\\n'):\n m = re.search(\"(.+): does not update installed package.\", line)\n if m:\n has_failed = False\n\n m = re.search(\"Package (matching ){0,1}(.+) already installed\",\n line)\n if has_failed:\n r.failed = True\n else:\n r.failed = False\n return r","title":""},{"docid":"e0d72ca92b63db9586d7572176c765cc","score":"0.4718275","text":"def validate_mps(mwn, did, center, return_flag=False):\n # Cyclic import\n from package.utils import package_find\n from package.models import find_by_wbns\n\n missing_list = list()\n deliver_together = False\n return_together = False\n\n response = dict()\n\n # Only string is required, object not required\n did = unicode(did)\n\n pkg = find_by_wbns([mwn], ['cl'])\n\n client_cache = get_client_cache(pkg.get(mwn, {}).get('cl'))\n\n if client_cache.get('delivery_behaviour', {})\\\n .get(u'Complete', False):\n deliver_together = True\n\n if client_cache.get('return_behaviour', {})\\\n .get(u'Complete', False):\n return_together = True\n\n if return_flag:\n behaviour = return_together\n else:\n behaviour = deliver_together\n\n query_dict = {'mwn': mwn, 'cs.sl': {'$eq': center}, 'dd.id': {'$ne': did}}\n\n fields = {'wbn'}\n\n packages = package_find(filter_criteria=query_dict, fields=fields,\n return_dict=True, use_replicaSet=True)\n\n for p in packages:\n missing_list.append(p.get('wbn'))\n if missing_list:\n response.update({mwn: missing_list})\n return response, behaviour","title":""},{"docid":"44ffdbdc6962f63438d0c9ac603088fd","score":"0.4717647","text":"def _check_import(self, import_id):\n\n res = self._auth_send('api/v1/imports/{}'.format(import_id),\n 'GET')\n return res","title":""},{"docid":"44ffdbdc6962f63438d0c9ac603088fd","score":"0.4717647","text":"def _check_import(self, import_id):\n\n res = self._auth_send('api/v1/imports/{}'.format(import_id),\n 'GET')\n return res","title":""},{"docid":"b1306930a04e42ec61054a5b3c047f5b","score":"0.4709468","text":"def _check_id_presence(self, root, namespaces, version): # noqa\n to_check = itertools.chain(\n common.STIX_CORE_COMPONENTS,\n common.CYBOX_CORE_COMPONENTS\n )\n\n results = BestPracticeWarningCollection('Missing IDs')\n xpath = \" | \".join(\"//%s\" % x for x in to_check)\n nodes = root.xpath(xpath, namespaces=namespaces)\n\n for node in nodes:\n if any(x in node.attrib for x in ('id', 'idref')):\n continue\n\n warning = BestPracticeWarning(node=node)\n results.append(warning)\n\n return results","title":""},{"docid":"13100775bae02abb2ed44244e5f01444","score":"0.46999785","text":"def mark_missing_by_name(package, error, needs_review):\n\n with connection() as cursor:\n cursor.execute(\"\"\"\n UPDATE\n package_stats\n SET\n is_missing = TRUE,\n missing_error = %s,\n needs_review = %s\n WHERE\n package = %s\n \"\"\", [error, needs_review, package])","title":""},{"docid":"60cc0638820821db3cc08e0cf0d1202e","score":"0.46966282","text":"def __compare_package(self, package: tuple, package_detail: WebElement):\n agent = self.driver.capabilities['browserName']\n package_detail_split = package_detail.text.split('\\n')\n if len(package_detail_split) > 0:\n if agent.lower() in ['chrome', 'firefox']:\n package_name, package_version = package_detail_split[1].strip(), package_detail_split[2].strip()\n # Compare package-details on UI with the package-details from argument\n if package_name == package.name and package_version == package.version:\n return True\n elif agent.lower() == 'safari':\n # Safari returns a single string with no delimiters so check in a less strict way\n # by just seeing if the name and version are in the string\n if package.name in package_detail_split[0] and package.version in package_detail_split[0]:\n return True\n else:\n raise ValueError(f\"Unsupported browser type while verifying package version: {agent}\")\n return False","title":""},{"docid":"e6c92b15974008905cb9e70d53e11085","score":"0.4692243","text":"def appCheck( self ):\n # FIXME: Rename this to appIDCheck? or add a check for isntalled apps\n for i in range( 15 ):\n # TODO modify retry or add a new version that accepts looking for\n # a value in a return list instead of needing to match the entire\n # return value to retry\n appResults = main.Cluster.command( \"appToIDCheck\" )\n appCheck = all( i == main.TRUE for i in appResults )\n if appCheck:\n break\n else:\n time.sleep( 5 )\n\n if not appCheck:\n ctrl = main.Cluster.active( 0 )\n main.log.debug( \"%s apps: %s\" % ( ctrl.name, ctrl.pprint( ctrl.apps() ) ) )\n main.log.debug( \"%s appIDs: %s\" % ( ctrl.name, ctrl.pprint( ctrl.appIDs() ) ) )\n return appCheck","title":""},{"docid":"cee49479532c5bb15ab73378d22f8857","score":"0.46868974","text":"def input_package_version(self, version: str) -> bool:\n package_version_radio_btn = self.get_locator(LocatorType.XPath, \"//div[@class='AddPackageForm__version \"\n \"flex align-items--center']/label[2]\"\n \"/span[contains(text(),'Specify')]\")\n package_version_input = self.get_locator(LocatorType.XPath, \"//div[@class='AddPackageForm__version \"\n \"flex align-items--center']/label[2]/input[2]\")\n if package_version_radio_btn is not None:\n package_version_radio_btn.click()\n if package_version_input is not None:\n package_version_input.send_keys(version)\n return True\n return False","title":""},{"docid":"7a94e141ef55ad87f6b1203d9822005c","score":"0.4676334","text":"def query(package, db):\n try:\n n, v, m = preprocess_package(package)\n for item in db[n]:\n for spec in item['specs']:\n if control_condition(v, spec):\n yield item\n return None\n except KeyError:\n return None","title":""},{"docid":"cc5d5dbe56388c79be06a3aecc258dd5","score":"0.46745682","text":"def check(self):\n # error_list = []\n # self._check_id()","title":""},{"docid":"fd9b264c8ac4db6c33e261edaa9ede78","score":"0.4667281","text":"def check_package(package, allowed_operands, regex=\"(^[\\w*\\-*]+)*\\s*([<>=]+)*\\s*(\\d+.*)*\\s*\"):\n p_name, operand, version_specifier = operand_parse(package, regex=regex)\n msg = \"\"\n if p_name is None:\n msg += f\"ERROR: Requirement definition '{package}' is not valid.\"\n else:\n if operand not in allowed_operands:\n msg += f\"ERROR: Operand '{operand}' is not allowed in requirement specifiers.\"\n p_name = None\n if version_specifier is None and operand is None:\n available_versions = get_available_package_versions(p_name)\n v = available_versions[0]\n if v == 'none':\n msg += f\"ERROR: Package '{p_name}' has not any available version.\"\n p_name = None\n else:\n release_versions = [item for item in available_versions if not re.search('[a-zA-Z]', item)]\n # print(release_versions)\n if not release_versions:\n version_specifier = available_versions[-1]\n msg += f\"WARNING : Version information not given for '{p_name}' choosing available latest \" \\\n f\"version : '{version_specifier}' \"\n else:\n version_specifier = release_versions[-1]\n msg += f\"WARNING : Version information not given for '{p_name}' choosing available latest \" \\\n f\"release version : '{version_specifier}' \"\n print_warning(msg)\n\n return p_name, version_specifier, msg","title":""},{"docid":"b9131b28b37332b49890909188a27dde","score":"0.4662561","text":"def is_installed(self, package_name):\n apt_cache = apt.cache.Cache()\n apt_cache.open()\n if (package_name.strip() in apt_cache and\n apt_cache[package_name.strip()].is_installed):\n # apt_cache.close()\n return True\n # apt_cache.close()\n return False","title":""},{"docid":"023434b17252b5aa046fe7dc504ec883","score":"0.46622023","text":"def search(self, package_filter, data_input) -> 'package':\r\n if package_filter is None and data_input is None:\r\n return self\r\n else:\r\n for package in self.table:\r\n if (package is not self.EMPTY_SINCE_START and\r\n getattr(package, package_filter) == data_input):\r\n print(package)\r\n return '---------'","title":""},{"docid":"206a5a74acf84780e118609d1825312a","score":"0.4655302","text":"def have_any_lambda_package_version(self, package_name):\n return lambda_packages.get(package_name, {}).get(self.runtime) is not None","title":""},{"docid":"e96ff078b1f89ced8ddf5b084020c4d7","score":"0.46549374","text":"def verify_item_in_stock(id):\n # get the book with id\n book = Book.query.get(id)\n # check if there is a book with that id if not send a message with 404 says there is no book with this id\n if book:\n # if book found check if its exist at the stock if send responce with 200 status code and its id and the quantity\n # if nits out of stock send message says This book is currently unavailable\n if book.quantity > 0:\n return verify_schema.jsonify(book), 200\n else:\n return {\"message\": \"This book is currently unavailable\"}, 410\n else:\n return {\"message\": \"There is no book with this ID\"}, 404","title":""},{"docid":"fd9c71db070c2eee916c7f263cb1cf98","score":"0.4643235","text":"def test_catkin_discovery_plugin_scan_valid():\n cdp = CatkinDiscoveryPlugin()\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\n 'valid_package'))\n cdp.scan(package, 'level')\n assert package['catkin']","title":""},{"docid":"e95157478172522f1485739f6cb6e38f","score":"0.46369296","text":"def _queryPackagesReply(self, msg, query):\n pnames = []\n self._lock.acquire()\n try:\n for name in self._packages.iterkeys():\n qmfData = QmfData.create({SchemaClassId.KEY_PACKAGE:name},\n _object_id=\"_package\")\n if query.evaluate(qmfData):\n pnames.append(name)\n\n self._send_query_response(ContentType.schema_package,\n msg.correlation_id,\n msg.reply_to,\n pnames)\n finally:\n self._lock.release()","title":""}],"string":"[\n {\n \"docid\": \"aec3eda9c5ef29ed467555c6c8f7127c\",\n \"score\": \"0.6192965\",\n \"text\": \"def is_package_id_valid(package_id: str) -> None:\\n pattern = re.compile(r\\\"^pkg_[1-9a-zA-Z]+$\\\")\\n\\n if not package_id.startswith(\\\"pkg_\\\"):\\n raise ValidationError(\\n message=f\\\"[{package_id[0:4]}] is not a valid package ID prefix.\\\",\\n source=ErrorSource.SHIPENGINE.value,\\n error_type=ErrorType.VALIDATION.value,\\n error_code=ErrorCode.INVALID_IDENTIFIER.value,\\n )\\n\\n if not pattern.match(package_id):\\n raise ValidationError(\\n message=f\\\"[{package_id}] is not a valid package ID.\\\",\\n source=ErrorSource.SHIPENGINE.value,\\n error_type=ErrorType.VALIDATION.value,\\n error_code=ErrorCode.INVALID_IDENTIFIER.value,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a3eec23f06e957bf35abfa85ec3a21\",\n \"score\": \"0.6061845\",\n \"text\": \"def check_pkg(pkg_name, jamf_url, enc_creds):\\n headers = {\\n \\\"authorization\\\": \\\"Basic {}\\\".format(enc_creds),\\n \\\"accept\\\": \\\"application/json\\\",\\n }\\n url = \\\"{}/JSSResource/packages/name/{}\\\".format(jamf_url, pkg_name)\\n r = requests.get(url, headers=headers)\\n if r.status_code == 200:\\n obj = json.loads(r.text)\\n try:\\n obj_id = str(obj[\\\"package\\\"][\\\"id\\\"])\\n except KeyError:\\n obj_id = \\\"-1\\\"\\n else:\\n obj_id = \\\"-1\\\"\\n return obj_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3306df13cd18c1744d9f04263432bc89\",\n \"score\": \"0.60180765\",\n \"text\": \"def valid_package(package_name):\\n response = requests.head('https://pypi.python.org/pypi/%s' % package_name)\\n if response.status_code != 404:\\n response.raise_for_status()\\n return response.status_code != 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96574262832c9df4fec9c8fe9746cb1c\",\n \"score\": \"0.59993696\",\n \"text\": \"def _check_package(self, package_name: str) -> bool:\\n\\n command = f\\\"dpkg-query -W -f='${{Status}}' {package_name} | grep -c 'ok installed'\\\"\\n stdout, stderr = self.ssh_client.execute_command(command)\\n\\n self.logger.debug(f\\\"stdout to check package {package_name}: {stdout}\\\")\\n self.logger.debug(f\\\"stderr to check package {package_name}: {stderr}\\\")\\n\\n if stdout[0].rstrip() == '1':\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfe5dba6406de9de4799116b3fd9780f\",\n \"score\": \"0.59151304\",\n \"text\": \"def verify_package_and_version(self, package: tuple) -> bool:\\n if self.check_element_presence(LocatorType.XPath, \\\"//div[@class='PackageQueue__validCount flex \\\"\\n \\\"justify--right align-items--center']\\\",\\n GigantumConstants.ELEMENT_PRESENCE_TIMEOUT.value):\\n div_package_details = self.ui_element.find_elements_by_xpath(\\\"//div[@class='PackageQueue__row \\\"\\n \\\"flex align-items--center justify--right']\\\")\\n if div_package_details is not None:\\n for package_detail in div_package_details:\\n if self.__compare_package(package, package_detail):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fdf0d5789c8a1d2fe07dd9f4df1d7f5\",\n \"score\": \"0.585399\",\n \"text\": \"def is_package_installed(package_name, validation_command='--version'):\\n try:\\n subprocess.check_output(\\\"{PKG} {CMD}\\\".format(PKG=package_name, CMD=validation_command), shell=True)\\n return True\\n except:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e60a11e15ebed78be4a5472079c8415\",\n \"score\": \"0.5731821\",\n \"text\": \"def exist(self, package):\\n if not self.exist_command_d:\\n raise NotImplementedError(\\\"There is no 'exist' command defined.\\\")\\n cmd = list(self.exist_command_d)\\n cmd.append(package)\\n self.logger.info(\\n \\\"Check if host %s have %s package\\\", self.host, package\\n )\\n return self._run_command_on_host(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7004e03ddd146cb05355abc6e14059c6\",\n \"score\": \"0.5671941\",\n \"text\": \"def search_package(self, package_attrs):\\n\\n try:\\n resp = requests.get('{}/api/action/package_show'.format(self.ckan_url),\\n params={ 'id': package_attrs['name'] })\\n resp.raise_for_status()\\n except requests.exceptions.HTTPError:\\n print('Error retrieving package `{}`: {}'.format(package_attrs['name'], resp.text))\\n return None\\n else:\\n return resp.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fcb1ddd0adacb5126022e3f35205f7e\",\n \"score\": \"0.56563824\",\n \"text\": \"def lookup_package_id(package_name):\\n try:\\n search_query = \\\"SELECT id FROM packages WHERE title = ?\\\"\\n \\n package_id = query_db(\\n query=search_query,\\n args = [ package_name ],\\n one=True\\n )\\n\\n return package_id['id'] if package_id is not None else None\\n except Exception as err:\\n current_app.logger.error(\\\"Unhandled Error in lookup_package_id {pn} : {e}\\\".format(\\n pn = package_name,\\n e=err\\n ))\\n\\n raise UnhandledError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe8b0153e88a03662e601c78a620fdf\",\n \"score\": \"0.5619238\",\n \"text\": \"def _checkPackage(self, p):\\n if not ic.check[p]:\\n raise(PE.PyARequiredImport(\\\"The package '\\\" + str(p) + \\\"' is not currently installed.\\\",\\n solution=\\\"Please install \\\" + str(p)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20be2c716c1841b7d86023b615e1411d\",\n \"score\": \"0.5610147\",\n \"text\": \"def test_check_package_zip(self):\\n self.assertFalse(package.check_package_exists(TEST_ZIP_REQ, None))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdf7e3ead5bccd9b13cf44fd2f8d9145\",\n \"score\": \"0.55990976\",\n \"text\": \"async def query(self, package):\\n index_path = self._index_path(package)\\n return await self._exists(index_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82c374e5c43d942423e496c55be600be\",\n \"score\": \"0.5547239\",\n \"text\": \"def check_package_installed(cls, package_name):\\n return find_executable(package_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb0010608d9f4f727e3d62ca19d6671\",\n \"score\": \"0.54923445\",\n \"text\": \"def check_package_name(self):\\n package_name = self.meta.get('package', {}).get('name', '')\\n\\n if package_name == '':\\n return Error(self.recipe_dir, 'C2101', 'Missing package name in meta.yaml')\\n\\n if not self.name_pat.match(package_name) or package_name.endswith(('.', '-', '_')):\\n return Error(self.recipe_dir, 'C2102', u'Found invalid package name \\\"{}\\\" in meta.yaml' .format(package_name))\\n\\n seq = get_bad_seq(package_name)\\n if seq:\\n return Error(self.recipe_dir, 'C2103', u'Found invalid sequence \\\"{}\\\" in package name' .format(seq))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af124317ff26ea4bef9b052fa465437a\",\n \"score\": \"0.5490487\",\n \"text\": \"def check(self):\\n if self.empty():\\n return True\\n else:\\n return utils.return_code_zero(self.check_cmd + self.specific_packages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"352d0907bedd7d656aa164e830b27115\",\n \"score\": \"0.544021\",\n \"text\": \"def _validate_package_name(package_name: str):\\n if re.fullmatch(PublicId.PACKAGE_NAME_REGEX, package_name) is None:\\n raise click.BadParameter(\\\"{} is not a valid package name.\\\".format(package_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4c49882b5f865e4363f7009a3f311a8\",\n \"score\": \"0.54385006\",\n \"text\": \"def _CheckIsInstalled(self, package_name):\\n command = 'rpm -qi {0:s} >/dev/null 2>&1'.format(package_name)\\n exit_code = subprocess.call(command, shell=True)\\n return exit_code == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ac03ce2b0d923234990d2c859087cfa\",\n \"score\": \"0.54373354\",\n \"text\": \"def exists(self, package: str) -> bool:\\n\\n name, version = self.parse_package_id(package)\\n if self.pkgcache.exists(name, version):\\n return True\\n\\n if self.stocks.exists_source_version(name, version):\\n return True\\n\\n for subpool in self.subpools:\\n if subpool.exists(package):\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef5e30f76713c4f2adcf6bf6f0c343d6\",\n \"score\": \"0.53682554\",\n \"text\": \"def check_package_name(self):\\n package_name = self.info.get('name')\\n if package_name is None:\\n return Error(self.path, 'C1101', 'Missing package name in info/index.json')\\n\\n if not self.name_pat.match(package_name) or package_name.endswith(('.', '-', '_')):\\n return Error(self.path, 'C1103', 'Found invalid package name in info/index.json')\\n\\n if package_name != self.name:\\n return Error(self.path, 'C1102', u'Found package name in info/index.json \\\"{}\\\" does not match filename \\\"{}\\\"' .format(package_name, self.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcbbc6e57799a3c2bbeb888e834112ad\",\n \"score\": \"0.53363067\",\n \"text\": \"def _package_exists(self, filename):\\n return os.path.exists(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373472b317545bd61f60b0c219ba36c3\",\n \"score\": \"0.5270876\",\n \"text\": \"def input_package_name(self, package_name: str) -> bool:\\n if self.package_title_input is not None:\\n self.package_title_input.send_keys(package_name)\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f0edc2784f55b2aac574b89372215ba\",\n \"score\": \"0.52645904\",\n \"text\": \"def has_dependency(self, package_id: PackageID) -> bool:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fd427a57aad2568fd6536997869f190\",\n \"score\": \"0.525871\",\n \"text\": \"def has(self, package_name):\\n state = self.__multithreaded_cmd(self.__has, package_name=package_name)\\n _id, has = 0, 1\\n result = [device[has] for device in state]\\n if all(result):\\n print(\\\"All the {} devices have {} installed.\\\".format(\\n len(result), package_name))\\n return\\n print(\\\"{}/{} devices does not have {} installed:\\\".format(\\n result.count(False),\\n len(result),\\n package_name))\\n for device in state:\\n if not device[has]:\\n print(\\\" {}\\\".format(device[_id]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"139f6f7d93e822f52c0040f2e6abad9c\",\n \"score\": \"0.5253097\",\n \"text\": \"def check_package_version(self):\\n package_version = self.meta.get('package', {}).get('version', '')\\n\\n if package_version == '':\\n return Error(self.recipe_dir, 'C2104', 'Missing package version in meta.yaml')\\n\\n if isinstance(package_version, str):\\n if not self.version_pat.match(package_version) or package_version.startswith(('_', '.')) or package_version.endswith(('_', '.')):\\n return Error(self.recipe_dir, 'C2105', u'Found invalid package version \\\"{}\\\" in meta.yaml' .format(package_version))\\n\\n seq = get_bad_seq(package_version)\\n if seq:\\n return Error(self.recipe_dir, 'C2106', u'Found invalid sequence \\\"{}\\\" in package version' .format(seq))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f9c8b5b9df7ee76c169d61bc23701e3\",\n \"score\": \"0.52514017\",\n \"text\": \"def _package_available(package_name: str) -> bool:\\n try:\\n return find_spec(package_name) is not None\\n except ModuleNotFoundError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d985d22add2be0e1f29f408bc21b4c\",\n \"score\": \"0.5251276\",\n \"text\": \"def mark_found(package):\\n\\n with connection() as cursor:\\n cursor.execute(\\\"\\\"\\\"\\n UPDATE\\n package_stats\\n SET\\n is_missing = FALSE,\\n missing_error = '',\\n removed = FALSE\\n WHERE\\n package = %s\\n \\\"\\\"\\\", [package])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbe0b1d5934954da45dec979aff215ee\",\n \"score\": \"0.52396005\",\n \"text\": \"def package_status(pkg_id):\\n\\n url = env.pkg_status_api + '/' + pkg_id\\n\\n resp = requests.get(url, timeout=env.timeout, headers=env.header)\\n\\n pyld = json.loads(resp.text)\\n LOG.debug(pyld)\\n\\n env.set_return_header(resp.headers)\\n\\n if resp.status_code != 200:\\n return False, str(pyld)\\n\\n return True, pyld\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c71e6d04e00593beeac4224b3bcf5d7\",\n \"score\": \"0.5230559\",\n \"text\": \"def test_check_package_global(self):\\n installed_package = list(pkg_resources.working_set)[0].project_name\\n\\n self.assertTrue(package.check_package_exists(installed_package, None))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6d0fa8da0bc74a89612a7f0b6dc5af5\",\n \"score\": \"0.52287936\",\n \"text\": \"def isAvailable(self, packageName):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b5742bf4301cf56c7cef9d37ff6569c\",\n \"score\": \"0.5206684\",\n \"text\": \"def check_item_in_database(package_name='', package_version='', d='', i=1,\\n outputfile=sys.stdout, output_type='text',\\n minimum_date='2002-01-01'):\\n package = ''\\n if package_version is None:\\n package_version = ''\\n if package_name is None:\\n package_name == ''\\n if package_name == '' and package_version == '':\\n print (\\\"check_item_in_database: both package_name and package_version\\\\\\n are null\\\", file=outputfile)\\n return False\\n elif package_name == '' and package_version != '':\\n package = ':' + package_version\\n elif package_version == '' and package_name != '':\\n package = package_name + ':'\\n else:\\n if package_name != '' and package_version != '':\\n package = \\\":\\\" + package_name + ':' + package_version\\n else:\\n return False\\n try:\\n p = CheckNVDByPackage(package=package, database=d, items=i,\\n minimum_date=minimum_date)\\n # report is a list of ordered tuples containing CVE findings\\n report = p.get_data()\\n # setup an object to contain the data\\n j_data = collect_json\\n j_data = j_data()\\n if output_type == 'json' or output_type == 'html':\\n j = {}\\n # pull the items out of the report, and stuff them\\n # in a collection of json\\n for item in report:\\n j['search_item_string'] = package\\n j['requested_package_name'] = package_name\\n j['requested_package_version'] = package_version\\n j['requested_output_type'] = output_type\\n j['cve'] = item[0]\\n j['cvss_base_score'] = item[1]\\n cpe = item[2].split(',')\\n cpe.sort\\n cpe = ','.join(cpe)\\n j['cpe'] = cpe\\n j['date'] = item[3]\\n j['summary'] = item[4]\\n j_data.append(j)\\n # I have no idea why j needed to be 'nulled' out,\\n # but this fixed a problem with not getting all items\\n j = {}\\n if output_type == 'json':\\n \\\"\\\"\\\" This code has reached spaghetti code proportions\\n and we need to get output managed elsewhere.\\n refactor.\\n \\\"\\\"\\\"\\n j_data = json.dumps(j_data.get_json_data())\\n print (j_data, file=outputfile)\\n return True\\n if output_type == 'html':\\n \\\"\\\"\\\" This code has reached spaghetti code proportions\\n and we need to get output managed elsewhere.\\n refactor.\\n \\\"\\\"\\\"\\n html = ''\\n html = lib.html_out.cve_table_content(package, j_data.get_json_data())\\n if html is not None:\\n # whew, there be dragons\\n if __DEBUG__:\\n frameinfo = getframeinfo(currentframe())\\n print (\\\"** Debug breadcrumbs, HMTL out: %s, line: %s\\\" %\\n (frameinfo.filename, frameinfo.lineno))\\n html = lib.html_out.html_wrap_content(html)\\n print (html, file=outputfile)\\n return True\\n else:\\n return\\n detailed_report = {}\\n if output_type == 'text':\\n \\\"\\\"\\\" This code has reached spaghetti code proportions\\n and we need to get output managed elsewhere.\\n refactor.\\n \\\"\\\"\\\"\\n if len(p) > 0:\\n detailed_report[package] = report\\n detailed_vuln_report(detailed_report, outputfile)\\n except Exception, e:\\n print (\\\"falco: check_item_in_database: %s\\\" % (e,), file=outputfile)\\n if re.search(r'no such table', str(e)):\\n print (\\\"\\\\tFalco requires copying the cpe into\\\", file=outputfile)\\n print (\\\"\\\\ta cpe fts3 virtual table, after the\\\", file=outputfile)\\n print (\\\"\\\\cvedb.\\\",\\n file=outputfile)\\n print (\\\"\\\\tis setup. Run $ falco -c\\\", file=outputfile)\\n print (\\\"\\\\tand then try again.\\\", file=outputfile)\\n exit(1)\\n if len(p) > 0:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28bd3eb107836e8ac3506151e5920843\",\n \"score\": \"0.5199931\",\n \"text\": \"def _check_import_package(package_helper, command):\\n LOGGER.debug(f\\\"Trying to import a package with [{command}] ...\\\")\\n rc = package_helper.running_container.exec_run(command)\\n return rc.exit_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b24a88ff2ea39b8520533f1e8db3ae4\",\n \"score\": \"0.51809335\",\n \"text\": \"def require_valid_package_version_code(\\n cls, package_version_code: int\\n ) -> None:\\n if package_version_code is None:\\n raise utils.ValidationError('No package version code supplied.')\\n if not isinstance(package_version_code, int):\\n raise utils.ValidationError(\\n 'Package version code must be an int, received: %r.' % (\\n package_version_code))\\n if package_version_code < feconf.MINIMUM_ANDROID_PACKAGE_VERSION_CODE:\\n raise utils.ValidationError(\\n 'The package version code is not a valid int. The minimum '\\n 'supported version is %d, received: %d.' % (\\n feconf.MINIMUM_ANDROID_PACKAGE_VERSION_CODE,\\n package_version_code))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12074b033935c13301b155c6b06db0d7\",\n \"score\": \"0.5153052\",\n \"text\": \"def find_mps_invalid_return_packages(package, center):\\n # Cyclic import\\n from package.utils import package_find\\n\\n invalid_count = 0\\n invalid_wbns = list()\\n\\n master_wbn = package.get('mwn')\\n\\n query_dict = {'mwn': master_wbn, 'cs.st': {'$ne': u'LT'}}\\n\\n fields = {'wbn':1, 'cs':1, 'dd': 1}\\n\\n child_cursor = package_find(filter_criteria=query_dict, fields=fields,\\n return_dict=True, use_replicaSet=True)\\n\\n for child in child_cursor:\\n cs = child.get('cs', {})\\n dd = child.get('dd', {})\\n if cs.get('ss') in dispatch_settings.RDTO_SS_LIST and \\\\\\n cs.get('st') in dispatch_settings.RDTO_ST_LIST:\\n if cs.get('sl') == center:\\n if dd.get('rto') or dd.get('ist') or dd.get('dto') \\\\\\n or dd.get('srt'):\\n invalid_count = invalid_count + 1\\n invalid_wbns.append(child.get('wbn', ''))\\n else:\\n invalid_count = invalid_count + 1\\n invalid_wbns.append(child.get('wbn', ''))\\n else:\\n invalid_count = invalid_count + 1\\n invalid_wbns.append(child.get('wbn', ''))\\n\\n return invalid_count, invalid_wbns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70377f57300f7d2db1a49770103f4d92\",\n \"score\": \"0.51495457\",\n \"text\": \"def validate(self, item):\\n attempt, pkg_analyzer, journal_and_issue_data = item[:3]\\n j_publisher_name = journal_and_issue_data.get('journal', {}).get('publisher_name', None)\\n if j_publisher_name:\\n data = pkg_analyzer.xml\\n xml_publisher_name = data.findtext('.//journal-meta/publisher/publisher-name')\\n\\n if xml_publisher_name:\\n if self._normalize_data(xml_publisher_name) == self._normalize_data(j_publisher_name):\\n r = [models.Status.ok, 'Valid publisher name: ' + xml_publisher_name]\\n else:\\n r = [models.Status.error, 'Mismatched data: %s. Expected: %s' % (xml_publisher_name, j_publisher_name)]\\n else:\\n r = [models.Status.error, 'Missing data: publisher name']\\n else:\\n r = [models.Status.error, 'Missing data: publisher name, in scieloapi']\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7bee47d6f7f04a30611fe5cc9c2331b\",\n \"score\": \"0.5144293\",\n \"text\": \"def has_package(self, name):\\n if not name:\\n raise ValueError('invalid package name')\\n name = name.upper()\\n for p in self.packagelist:\\n for pn in p.name:\\n if pn.upper() == name:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9237fc0999953a86932571a3f13a3c1\",\n \"score\": \"0.51237273\",\n \"text\": \"def check_package_version(self):\\n package_version = str(self.info.get('version'))\\n if package_version == 'None':\\n return Error(self.path, 'C1104', 'Missing package version in info/index.json')\\n if package_version.startswith(('_', '.')) or package_version.endswith(('_', '.')):\\n return Error(self.path, 'C1107', \\\"Package version in info/index.json cannot start or end with '_' or '.'\\\")\\n if not self.version_pat.match(package_version) or get_bad_seq(package_version):\\n return Error(self.path, 'C1105', 'Found invalid version number in info/index.json')\\n if package_version != self.version:\\n return Error(self.path, 'C1106', u'Found package version in info/index.json \\\"{}\\\" does not match filename version \\\"{}\\\"' .format(package_version, self.version))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"310d6d7f91ba7c47ce0b0e7a6f243484\",\n \"score\": \"0.50995976\",\n \"text\": \"def _package_exists(name: str) -> bool:\\n try:\\n get_distribution(name)\\n except DistributionNotFound:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3916c9eaa0eb418e72e0fbd3c9dcf4\",\n \"score\": \"0.50798416\",\n \"text\": \"def test_cannot_create_package_with_id(self):\\n # Check id does not exist\\n id = 1001\\n with self.assertRaises(ValidationError) as e:\\n self.Package.get_or_create(id)\\n self.assertEqual(e.exception.args[0], f\\\"Package not found for identifier {id}\\\")\\n # Try to create package\\n with self.assertRaises(ValidationError) as e:\\n self.Package.get_or_create(id, create=True)\\n self.assertEqual(\\n e.exception.args[0],\\n f\\\"Cannot create a new Package for stock.quant.package with identifier of type {type(id)}\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3f6bfd9bce98f290d69bb3551639b78\",\n \"score\": \"0.5075812\",\n \"text\": \"def package_check(package_name):\\n\\n def decorator(func):\\n \\\"\\\"\\\"Pass the function through to the wrapper.\\\"\\\"\\\"\\n\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n \\\"\\\"\\\"Wrap functions that use the decorator function.\\\"\\\"\\\"\\n\\n message_warn = ' '.join(['{:} must be installed'.format(package_name),\\n 'to use {:}.'.format(func.__name__),\\n 'See instructions at',\\n 'https://github.com/pysat/pysatMissions'])\\n try:\\n func(*args, **kwargs)\\n except NameError as nerr:\\n # Triggered if call is made to package that is not installed\\n if package_name in str(nerr):\\n warnings.warn(message_warn, stacklevel=2)\\n else:\\n # Error is unrelated to optional package, raise original.\\n raise nerr\\n\\n return wrapper\\n\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2747a11c787857b94f1770f500647a0f\",\n \"score\": \"0.5055945\",\n \"text\": \"def check(regid, check_valid=False):\\n row = anlicenses.select().where(anlicenses.c.regid==regid).execute().fetchone()\\n return row['pdserial'] if row is not None and (\\n not check_valid or row['valid'] != 'N') else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fc7b40e6a1daffe05d11b612f774bff\",\n \"score\": \"0.50413394\",\n \"text\": \"def has_package(self, package):\\n return package in [pkg[0] for pkg in self.packages if pkg]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7af6eae3b88f338de1e768770797de47\",\n \"score\": \"0.5028316\",\n \"text\": \"def test_wrong_id_should_fail(self):\\n with pytest.raises(ValueError):\\n PackageId('id')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da9bc99f3ec59e0bb941d1fa995c1e94\",\n \"score\": \"0.5023925\",\n \"text\": \"def is_installed(self, package):\\n ret = False\\n out = self.shell(['pm', 'list', 'packages'], silent=True)\\n if out is not None:\\n for line in out.splitlines():\\n if line.find(package) >= 0:\\n ret = True\\n break\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c49614b84dfc94e86045536b30e412e3\",\n \"score\": \"0.5022208\",\n \"text\": \"def process_package(pack, notifier):\\n logger.debug('Started processing %s.' % pack)\\n\\n pack_reporter = package.CheckinReporter(pack.primary_path)\\n pack_reporter.tell('Started the identification process.')\\n\\n try:\\n attempt = checkin.get_attempt(pack)\\n\\n except excepts.MissingXML as e:\\n pack.mark_as_failed(silence=True)\\n pack_reporter.tell('The package must have only one XML file.')\\n\\n except excepts.InvalidXML as e:\\n pack.mark_as_failed(silence=True)\\n for msg in e.message:\\n pack_reporter.tell(msg)\\n\\n except excepts.DuplicatedPackage as e:\\n pack.mark_as_failed(silence=True)\\n pack_reporter.tell('The package was already deposited.')\\n\\n except Exception as e:\\n pack.mark_as_failed(silence=True)\\n pack_reporter.tell('The package could not be processed due to an unexpected error. Our engineers have been notified.')\\n\\n else:\\n # Create a notification to keep track of the checkin process\\n session = models.Session()\\n checkin_notifier = notifier(attempt, session)\\n checkin_notifier.start()\\n\\n if attempt.is_valid:\\n notification_msg = 'Attempt ready to be validated'\\n notification_status = models.Status.ok\\n else:\\n notification_msg = 'Attempt cannot be validated'\\n notification_status = models.Status.error\\n\\n pack_reporter.tell('The package is ready to be validated.')\\n checkin_notifier.tell('XML validates against the SPS Schema',\\n notification_status, 'Checkin')\\n checkin_notifier.tell(notification_msg, notification_status, 'Checkin')\\n checkin_notifier.end()\\n\\n attempt.proceed_to_validation = True\\n transaction.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c023cbb9f2e6a27ab142caff5815b2d8\",\n \"score\": \"0.5015223\",\n \"text\": \"def test_packagevalidate(self):\\n with pytest.raises(AttributeError):\\n cobra.services.UploadPackage(self.fakePackage, validate=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca2ab6a895f6188b00d7922bb6cf9716\",\n \"score\": \"0.50132483\",\n \"text\": \"def check_and_install_package(package):\\n\\tis_installed = int(commands.getoutput('dpkg -l '+package+' 2>&1 | grep -c \\\"^ii\\\"'))\\n\\tif is_installed:\\n\\t\\tprint \\\"Package \\\"+package+\\\" is installed.\\\"\\n\\telse:\\n\\t\\tprint \\\"Package \\\"+package+\\\" is required but not installed.\\\"\\n\\t\\tinstall = \\\"\\\"\\n\\t\\twhile install not in (\\\"y\\\", \\\"n\\\"):\\n\\t\\t\\tinstall = raw_input('Install it now ? (y/n)')\\n\\t\\t\\tif install == \\\"n\\\":\\n\\t\\t\\t\\tprint \\\"Exiting on missing package.\\\"\\n\\t\\t\\t\\tsys.exit(1);\\n\\t\\tos.system('sudo apt-get install -y '+package)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baf759a3b4af6aeba40d8e41557dcafd\",\n \"score\": \"0.5008721\",\n \"text\": \"def find_pkg(self, pkg, check_tag=True, cbk_fct=None):\\n \\n # if '-' in name, a tag was given.\\n if '-' in pkg:\\n tag = pkg.split('/')[-1]\\n pkg = pkg.split('-',1)[0]\\n elif check_tag:\\n raise ValueError('no tag was given for [%s]' % (pkg,))\\n else:\\n tag = None\\n\\n orig_pkg = pkg\\n # check if we need to find the full pkg path\\n if '/' in pkg:\\n pkg = pkg.split('/')[-1]\\n\\n args = {\\n 'glite': (\\n \\\"select packages.path,packages.packageName,packages.archive \\\"\\n \\\"where repositories.repositoryName='AtlasOfflineRepository' \\\"\\n \\\"and packages.packageName='%s' and packages.archive=0\\\" % pkg\\n ),\\n }\\n\\n result = self.exec_cmd(cmd='SearchQuery', args=args)\\n if not result:\\n raise RuntimeError(\\n 'could not resolve [%s] to full package path' %\\n (pkg,)\\n )\\n res_dict = result.to_dict()\\n if not 'Element_Info' in res_dict:\\n raise RuntimeError(\\n 'could not resolve [%s] to full package path' %\\n (pkg,)\\n )\\n \\n pkg_list = []\\n for v in res_dict['Element_Info'].values():\\n pkg_list.append(v) # += [v['path'] + v['packageName']]\\n\\n idx = 0\\n if len(pkg_list) == 0:\\n raise RuntimeError('package [%s] does not exist' % pkg)\\n\\n elif len(pkg_list)>1:\\n ambiguous = True\\n if '/' in orig_pkg:\\n pkg_candidates = []\\n for i,v in enumerate(pkg_list):\\n if orig_pkg in v['path']+v['packageName']:\\n pkg_candidates.append(i)\\n if len(pkg_candidates) == 1:\\n idx = pkg_candidates[0]\\n pkg = pkg_list[idx]\\n ambiguous = False\\n pass\\n\\n if ambiguous:\\n self.msg.info('multiple packages found for [%s]:', pkg)\\n for i,v in enumerate(pkg_list):\\n self.msg.info(' %i) %s', i, v['path']+v['packageName'])\\n if cbk_fct:\\n try:\\n n = cbk_fct()\\n except StopIteration:\\n raise RuntimeError(\\n 'multiple packages found for [%s]' % pkg\\n )\\n idx = n\\n pkg = pkg_list[n]\\n else:\\n raise RuntimeError('multiple packages found for [%s]' % pkg)\\n\\n else:\\n idx = 0\\n pkg = pkg_list[0]\\n\\n # normalize...\\n if pkg['path'][0] != '/':\\n pkg['path'] = '/%s' % pkg['path']\\n\\n pkg.update(dict(packagePath=pkg['path'],\\n id=str(pkg['AMIELEMENTID'])))\\n if tag:\\n pkg['packageTag'] = tag\\n del pkg['path']\\n del pkg['AMIELEMENTID']\\n \\n # remove all unicode strings...\\n _pkg = dict(pkg)\\n pkg = {}\\n \\n for k,v in _pkg.iteritems():\\n if isinstance(v, basestring):\\n v = str(v)\\n pkg[str(k)] = v\\n \\n \\n ## if tag is None:\\n ## tag = tag_list[idx]\\n \\n ## print \\\"-\\\"*80\\n ## print res_dict\\n ## print \\\"-\\\"*80\\n return pkg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39a97dbf034266dd31fcf10cc3121308\",\n \"score\": \"0.4993555\",\n \"text\": \"def query(cls, release_type, package_name, args):\\n # only use the first value\\n if len(args) > 0:\\n return (args[0] in cls.valid_values(release_type, package_name), args[0])\\n else:\\n return (False, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71dae375d43b962dacf4b47a7f2f8af1\",\n \"score\": \"0.49933568\",\n \"text\": \"def test_create_then_get_new_package_success_by_id(self):\\n test_package = self.Package.get_or_create(\\\"Test Pack 2\\\", create=True)\\n get_package = self.Package.get_or_create(test_package.id)\\n self.assertEqual(test_package.name, get_package.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"336420321b1c8fb4dafab23e2fc345ba\",\n \"score\": \"0.49919316\",\n \"text\": \"def _r_pkg_ver_chk(self):\\n\\n global g_r_pkg_ver_chk_script\\n self._log(\\\"\\\")\\n self._log(\\\"Conducting R package/version check...\\\")\\n out_file_name = os.path.join(self.output_dir, \\\"package_version_check_out.txt\\\")\\n out = open(out_file_name, \\\"w\\\")\\n\\n cmd = [\\\"R\\\", \\\"--vanilla\\\", \\\"-f\\\", g_r_pkg_ver_chk_script, \\\"--args\\\", \\\"check\\\", ]\\n\\n child = subprocess.Popen(args=cmd, stdout=out)\\n rv = child.wait()\\n if self.terminated:\\n return\\n if rv == 1:\\n self._log(\\\"\\\")\\n self._log(\\\"ERROR: \\\" + g_r_pkg_ver_chk_script + \\\" failed.\\\")\\n self._log(\\\" See \\\" + out_file_name)\\n sys.exit(1)\\n out.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c105386876033a6f925626102efff04\",\n \"score\": \"0.4973631\",\n \"text\": \"def check_apt_package(pkgname, friendly_name):\\n\\n cmd = [\\\"apt\\\", \\\"-qq\\\", \\\"list\\\", pkgname]\\n proc = subprocess.run( # nosec - B603 (subprocess call)\\n cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, check=False)\\n if proc.returncode == 0 and '[installed]' in proc.stdout:\\n return True\\n else:\\n raise AppException(\\n Codes.MISSING_PREREQUISITE,\\n f\\\"{friendly_name} has to be installed on this machine for the provisioning process to succeed.\\\\n\\\"\\n f\\\" {_TS_REF}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"643039f996e031e218577b5f998bebab\",\n \"score\": \"0.49622446\",\n \"text\": \"def check(self):\\n result = Result()\\n try:\\n distro_package_vers = get_distro_package_versions()\\n if not distro_package_vers:\\n result.add_error('ERROR: distro packages info is not found.')\\n except (AttributeError, RuntimeError, URLError):\\n result.add_error('ERROR: Unable to fetch package information from rosdistro.')\\n local_package_vers = get_local_package_versions()\\n if not local_package_vers:\\n result.add_error('ERROR: local package info is not found.')\\n if result.error != 0:\\n return result\\n\\n warning_msgs = compare_versions(local_package_vers, distro_package_vers)\\n for msg in warning_msgs:\\n result.add_warning(msg)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9215815d643534ea4e22483094695cc\",\n \"score\": \"0.49581543\",\n \"text\": \"async def query(self, package):\\n await asyncio.sleep(0)\\n return package in self.forward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c572836aea1cfcca73e1499e99c88e72\",\n \"score\": \"0.4935848\",\n \"text\": \"def spdx_package(self) -> 'outputs.PackageInfoOccurrenceResponse':\\n return pulumi.get(self, \\\"spdx_package\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28accff2b662b7fc97f56d5508ec1922\",\n \"score\": \"0.49281135\",\n \"text\": \"def test_valid_package_name(self):\\n package_name = 'my-awesome-package'\\n self.assertTrue(self.deb.is_valid_package_name(package_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132bfc4729edea3df60c05e48664b20b\",\n \"score\": \"0.48970926\",\n \"text\": \"def verify_request(self):\\r\\n \\r\\n self.log.debug(\\\"Verifying packaging request\\\")\\r\\n \\r\\n def verify_dir_and_owner(path, uid):\\r\\n try:\\r\\n info = os.lstat(path)\\r\\n except OSError as e:\\r\\n raise PackagerError(\\\"Can't stat %s: %s\\\" % (path, e))\\r\\n if info.st_uid != uid:\\r\\n raise PackagerError(\\\"%s isn't owned by %d\\\" % (path, uid))\\r\\n if stat.S_ISLNK(info.st_mode):\\r\\n raise PackagerError(\\\"%s is a soft link\\\" % path)\\r\\n if not stat.S_ISDIR(info.st_mode):\\r\\n raise PackagerError(\\\"%s is not a directory\\\" % path)\\r\\n \\r\\n # Check owner and type of directories.\\r\\n verify_dir_and_owner(self.request.pkgroot, self.uid)\\r\\n self.log.debug(\\\"pkgroot ok\\\")\\r\\n verify_dir_and_owner(self.request.pkgdir, self.uid)\\r\\n self.log.debug(\\\"pkgdir ok\\\")\\r\\n \\r\\n # Check name.\\r\\n if len(self.request.pkgname) > 80:\\r\\n raise PackagerError(\\\"Package name too long\\\")\\r\\n if not self.re_pkgname.search(self.request.pkgname):\\r\\n raise PackagerError(\\\"Invalid package name\\\")\\r\\n if self.request.pkgname.lower().endswith(\\\".pkg\\\"):\\r\\n raise PackagerError(\\\"Package name mustn't include '.pkg'\\\")\\r\\n self.log.debug(\\\"pkgname ok\\\")\\r\\n \\r\\n # Check ID.\\r\\n if len(self.request.id) > 80:\\r\\n raise PackagerError(\\\"Package id too long\\\")\\r\\n components = self.request.id.split(\\\".\\\")\\r\\n if len(components) < 2:\\r\\n raise PackagerError(\\\"Invalid package id\\\")\\r\\n for comp in components:\\r\\n if not self.re_id.search(comp):\\r\\n raise PackagerError(\\\"Invalid package id\\\")\\r\\n self.log.debug(\\\"id ok\\\")\\r\\n \\r\\n # Check version.\\r\\n if len(self.request.version) > 40:\\r\\n raise PackagerError(\\\"Version too long\\\")\\r\\n components = self.request.version.split(\\\".\\\")\\r\\n if len(components) < 1:\\r\\n raise PackagerError(\\\"Invalid version\\\")\\r\\n for comp in components:\\r\\n if not self.re_version.search(comp):\\r\\n raise PackagerError(\\\"Invalid version\\\")\\r\\n self.log.debug(\\\"version ok\\\")\\r\\n \\r\\n # Make sure infofile and resources exist and can be read.\\r\\n if self.request.infofile:\\r\\n try:\\r\\n with open(self.request.infofile, \\\"rb\\\") as f:\\r\\n pass\\r\\n except (IOError, OSError) as e:\\r\\n raise PackagerError(\\\"Can't open infofile: %s\\\" % e)\\r\\n self.log.debug(\\\"infofile ok\\\")\\r\\n\\r\\n # Make sure scripts is a directory and its contents\\r\\n # are executable.\\r\\n if self.request.scripts:\\r\\n if self.request.pkgtype == \\\"bundle\\\":\\r\\n raise PackagerError(\\r\\n \\\"Installer scripts are not supported with \\\"\\r\\n \\\"bundle package types.\\\")\\r\\n if not os.path.isdir(self.request.scripts):\\r\\n raise PackagerError(\\r\\n \\\"Can't find scripts directory: %s\\\"\\r\\n % self.request.scripts)\\r\\n for script in [\\\"preinstall\\\", \\\"postinstall\\\"]:\\r\\n script_path = os.path.join(self.request.scripts, script)\\r\\n if os.path.exists(script_path) \\\\\\r\\n and not os.access(script_path, os.X_OK):\\r\\n raise PackagerError(\\r\\n \\\"%s script found in %s but it is not executable!\\\"\\r\\n % (script, self.request.scripts))\\r\\n self.log.debug(\\\"scripts ok\\\")\\r\\n \\r\\n # FIXME: resources temporarily unsupported.\\r\\n #if self.request.resources:\\r\\n # try:\\r\\n # os.listdir(self.request.resources)\\r\\n # except OSError as e:\\r\\n # raise PackagerError(\\\"Can't list Resources: %s\\\" % e)\\r\\n # self.log.debug(\\\"resources ok\\\")\\r\\n \\r\\n # Leave chown verification until after the pkgroot has been copied.\\r\\n \\r\\n self.log.info(\\\"Packaging request verified\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c638c939e345b582eb23ceba1971977\",\n \"score\": \"0.4895481\",\n \"text\": \"def check_package_installed(command_runner, conda, name, version):\\n output, stderr = run_conda_package_command(\\n command_runner, name, version, [conda, 'install', '--json', '--dry-run', get_install_target(name, version)])\\n\\n if 'message' in output and output['message'] == 'All requested packages already installed.':\\n return True\\n elif 'actions' in output and len(output['actions']) > 0:\\n return False\\n else:\\n raise CondaUnexpectedOutputError(output, stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b7b4c2f406d6febffdd66adab6f7ec1\",\n \"score\": \"0.48941055\",\n \"text\": \"def find_missing_count(package, center, return_flag=False):\\n # Cyclic import\\n from package.utils import package_find\\n\\n missing_count = 0\\n missing_wbns = list()\\n found_wbns = list()\\n\\n master_wbn = package.get('mwn')\\n if return_flag:\\n query_dict = {'mwn': master_wbn,\\n 'cs.st': {'$ne': u'LT', '$eq': u'RT'}}\\n else:\\n query_dict = {'mwn': master_wbn,\\n 'cs.st': {'$ne': u'LT'}}\\n\\n fields = {'wbn':1, 'cs':1}\\n\\n child_cursor = package_find(filter_criteria=query_dict, fields=fields,\\n return_dict=True, use_replicaSet=True)\\n\\n for child in child_cursor:\\n if child.get('cs', {}).get('sl') != center:\\n missing_count = missing_count + 1\\n missing_wbns.append(child.get('wbn', ''))\\n else:\\n found_wbns.append(child.get('wbn', ''))\\n if missing_count:\\n # Apply NSL on the shipments which have reached DC or RCN\\n mps_hold_shipments.delay(found_wbns)\\n\\n return missing_count, missing_wbns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e96a6cc98596a9e2b56c5b4d925e5862\",\n \"score\": \"0.48735833\",\n \"text\": \"def is_valid_pkg_name(pkg_name):\\n if keyword.iskeyword(pkg_name):\\n return False\\n return pkg_name.isidentifier()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6efcdf5d5eb9170c42cb7cfdd5293ab1\",\n \"score\": \"0.48706594\",\n \"text\": \"async def check_pack_name(request):\\n log_request(request)\\n conn = await create_connection()\\n response = await packs_query.packs_search_duplicate(\\n conn, escape_user_input(request.args.get(\\\"name\\\"))\\n )\\n conn.close()\\n\\n return json({\\\"exists\\\": bool(response)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7b3797d99c99379c822faf0cf126374\",\n \"score\": \"0.48701146\",\n \"text\": \"async def _is_depended_on(self, package):\\n file_path = self._reverse_index_path(package)\\n return await self._exists(file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48877d96b702233b28c2a19a6fbc97bf\",\n \"score\": \"0.4867458\",\n \"text\": \"def is_package_installed(package_name: str) -> bool:\\n return run_and_check_result('dpkg-query -l ' + package_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e46a1e2d09fc4f63c055556bc1d2c9b\",\n \"score\": \"0.48665035\",\n \"text\": \"def test_check_package_local(self):\\n lib_dir = get_python_lib()\\n installed_package = list(pkg_resources.working_set)[0].project_name\\n\\n self.assertTrue(\\n package.check_package_exists(installed_package, lib_dir)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6774868c7edb6f8bf5c41b8dc444e61\",\n \"score\": \"0.48619893\",\n \"text\": \"def query(self, package):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ced22b933c67d17a5b7009422d5a1162\",\n \"score\": \"0.48606348\",\n \"text\": \"def check(self):\\n result = Result()\\n distro_package_vers = get_distro_package_versions()\\n if not distro_package_vers:\\n doctor_error('distro packages info is not found.')\\n result.add_error()\\n local_package_vers = get_local_package_versions()\\n if not local_package_vers:\\n doctor_error('local package info is not found.')\\n result.add_error()\\n if result.error != 0:\\n return result\\n\\n compare_versions(result, local_package_vers, distro_package_vers)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48f2def467ff66e612a2db51b819b456\",\n \"score\": \"0.4857025\",\n \"text\": \"def check_application_launch_errors(self, prefix, package_name, id_):\\n logger.debug(\\n 'prefix: {}, package_name: {}'.format(prefix, package_name)\\n )\\n root_prefix = self.api.ROOT_PREFIX\\n out_path, err_path, id_ = get_package_logs(\\n package_name,\\n prefix=prefix,\\n root_prefix=root_prefix,\\n id_=id_,\\n )\\n err_log = ''\\n if os.path.isfile(err_path):\\n with open(err_path, 'r') as f:\\n err_log = f.read()\\n\\n # Only show the message box error dialog if the words were found in\\n # the error log\\n e_low = err_log.lower()\\n display_msg = e_low and 'error' in e_low or 'exception' in e_low\\n if display_msg:\\n self.tracker.track_page(\\n '/home/errors',\\n pagetitle='Launch errors dialog.',\\n )\\n\\n self._dialog_message_box = MessageBoxError(\\n text='Application <b>{}</b> launch may have '\\n 'produced errors.'.format(package_name),\\n title='Application launch error',\\n error=err_log,\\n report=False,\\n learn_more=None\\n )\\n self._dialog_message_box.exec_()\\n self._dialog_message_box = None\\n self.tracker.track_page('/home')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"049fc5d0c6c78655c40fc5d71609921e\",\n \"score\": \"0.48485154\",\n \"text\": \"def package_is_installed(spec: TranslateSpec, package: str) -> bool:\\n p = run(spec.g, ['rpm', '-q', package], raiseOnError=False)\\n logging.debug('rpm -q: {}'.format(p))\\n return p.code == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bce6ab116182acfdd3a1bd5556f21e7\",\n \"score\": \"0.48424947\",\n \"text\": \"def test_catkin_discovery_plugin_scan_invalid_packagexml():\\n cdp = CatkinDiscoveryPlugin()\\n package = Package('invalid_package_nopackagexml',\\n os.path.join(os.path.dirname(__file__),\\n 'invalid_package_nopackagexml'))\\n cdp.scan(package, 'level')\\n assert not package['catkin']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"006d6ec66e629854ab0fcdab75076164\",\n \"score\": \"0.48415828\",\n \"text\": \"def check(pkgs):\\n packages = defaultdict(int)\\n required_pkgs = \\\"required_pkgs.txt\\\"\\n url = \\\"https://debian.osuosl.org/debian/dists/buster/main/binary-amd64/Packages.gz\\\"\\n\\n url_data = urlparse(url)\\n if not url_data.scheme.startswith(\\\"file\\\"):\\n if not os.path.exists(pkgs):\\n six.print_(\\\"Downloading package list...\\\")\\n six.moves.urllib.request.urlretrieve(url, pkgs)\\n else:\\n print(f\\\"Invalid URL: {url}\\\")\\n sys.exit(1)\\n\\n with gzip.open(pkgs, \\\"rt\\\") as pkg_file:\\n for line in pkg_file:\\n new_line = line.rstrip(os.linesep)\\n if new_line.startswith(\\\"Package\\\"):\\n pkg = new_line.split(\\\": \\\")[1]\\n packages[pkg] += 1\\n\\n with open(required_pkgs, \\\"rt\\\") as required:\\n for line in required:\\n new_line = line.rstrip(os.linesep)\\n if new_line not in packages:\\n six.print_(new_line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080b5c3100e1d2c14df83acac14906eb\",\n \"score\": \"0.48368376\",\n \"text\": \"def package_check(pkg_name, version=None, app=None, checker=LooseVersion,\\r\\n exc_failed_import=ImportError,\\r\\n exc_failed_check=RuntimeError):\\r\\n\\r\\n if app:\\r\\n msg = '%s requires %s' % (app, pkg_name)\\r\\n else:\\r\\n msg = 'Nipype requires %s' % pkg_name\\r\\n if version:\\r\\n msg += ' with version >= %s' % (version,)\\r\\n try:\\r\\n mod = __import__(pkg_name)\\r\\n except ImportError:\\r\\n raise exc_failed_import(msg)\\r\\n if not version:\\r\\n return\\r\\n try:\\r\\n have_version = mod.__version__\\r\\n except AttributeError:\\r\\n raise exc_failed_check('Cannot find version for %s' % pkg_name)\\r\\n if checker(have_version) < checker(version):\\r\\n raise exc_failed_check(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6e7f4b9c66f54f71e8a16c5841ce447\",\n \"score\": \"0.48211604\",\n \"text\": \"def test_packagedonotvalidate(self):\\n packageUpload = cobra.services.UploadPackage(self.fakePackage)\\n assert packageUpload.devicePackagePath == self.fakePackage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d59d4162c7bb7d6db259994fa93fd4\",\n \"score\": \"0.48195696\",\n \"text\": \"def VerifyPackage(self, entry, modlist, checksums=True):\\n if 'version' not in entry.attrib:\\n self.logger.info(\\\"Cannot verify unversioned package %s\\\" %\\n (entry.attrib['name']))\\n return False\\n\\n pkgname = entry.get('name')\\n if pkgname not in self.pkg_cache:\\n self.logger.info(\\\"Package %s not installed\\\" % (entry.get('name')))\\n entry.set('current_exists', 'false')\\n return False\\n\\n installed_version = self.pkg_cache[pkgname]\\n candidate_versions = self._get_candidate_versions(pkgname)\\n if candidate_versions is not None:\\n candidate_version = candidate_versions[0]\\n else:\\n self.logger.error(\\\"Package %s is installed but no candidate\\\"\\n \\\"version was found.\\\" % (entry.get('name')))\\n return False\\n\\n if entry.get('version').startswith('auto'):\\n desired_version = candidate_version\\n entry.set('version', \\\"auto: %s\\\" % desired_version)\\n elif entry.get('version').startswith('any'):\\n desired_version = installed_version\\n entry.set('version', \\\"any: %s\\\" % desired_version)\\n else:\\n desired_version = entry.get('version')\\n\\n if desired_version != installed_version:\\n entry.set('current_version', installed_version)\\n entry.set('qtext', \\\"Modify Package %s (%s -> %s)? (y/N) \\\" %\\n (entry.get('name'), entry.get('current_version'),\\n desired_version))\\n return False\\n else:\\n # version matches\\n if (not Bcfg2.Options.setup.quick and\\n entry.get('verify', 'true') == 'true' and\\n checksums):\\n pkgsums = self.VerifyChecksums(entry, modlist)\\n return pkgsums\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8704b299972e2df5836d49ebbbe814b0\",\n \"score\": \"0.48192906\",\n \"text\": \"def check_group_id(self, group_id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a324a381085b1f77a8adc8301bdff1\",\n \"score\": \"0.48163658\",\n \"text\": \"def is_valid_package(package_name):\\n try:\\n apackage = import_module('%s.tasks'%package_name)\\n except ImportError:\\n return False\\n tasks = Collection.from_module(apackage).task_names\\n if tasks == {}:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2e3ae343818be892467a7f7a287e8d8\",\n \"score\": \"0.48013705\",\n \"text\": \"def return_package(self) -> Optional['outputs.PackageInformationResponse']:\\n return pulumi.get(self, \\\"return_package\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71e68c5bf5262ca0cda57e7b26f29913\",\n \"score\": \"0.47998446\",\n \"text\": \"def CheckAndroidPackage(URL, packageAndActivity, flags): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2956e3bb7cdaa6f33d6afcf144224a9\",\n \"score\": \"0.47888482\",\n \"text\": \"def test_invalid_package_name(self):\\n package_name = 'fhjdskfh-900u78*'\\n self.assertFalse(self.deb.is_valid_package_name(package_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71e676bd9a0f59dfefda5cf115755fa6\",\n \"score\": \"0.47844812\",\n \"text\": \"def get_package(package_uuid):\\n\\n # get package info\\n resp = requests.get(env.pkg_api + '/' + package_uuid,\\n timeout=env.timeout,\\n headers=env.header)\\n\\n env.set_return_header(resp.headers)\\n\\n if resp.status_code != 200:\\n LOG.debug(\\\"Request for package returned with \\\" +\\n (str(resp.status_code)))\\n return False, json.loads(resp.text)\\n\\n return True, json.loads(resp.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66ae20ef11335c4f62fabfe0a3660382\",\n \"score\": \"0.47786415\",\n \"text\": \"def checkHasKey(self, _id):\\n for t in self.package:\\n if t.type == 1 and t.id == _id:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b20f65bf95dbf72258c0203f1941a59a\",\n \"score\": \"0.47582924\",\n \"text\": \"def package_ensure( package ):\\n\\tif run(\\\"dpkg-query -W -f='${Status}' %s ; true\\\" % package).find(\\\"installed\\\") == -1:\\n\\t\\tpackage_install(package)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f69a40b710f6527975a9d013f2258a6\",\n \"score\": \"0.47562665\",\n \"text\": \"def is_package_present(package_name):\\n try:\\n import_module(package_name)\\n return True\\n except ImportError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede859faeedd5223092db8ba1e916cfd\",\n \"score\": \"0.47281814\",\n \"text\": \"def testPackage(self):\\n self.assertRaises(ValueError, Package, self.udd.psql)\\n rs = SourcePackage(self.udd.psql, package=\\\"glibc\\\", release='sid')\\n self.assertTrue(rs)\\n self.assertTrue(rs.Found())\\n rne = SourcePackage(self.udd.psql, package=\\\"nosuchpackage\\\")\\n self.assertFalse(rne.Found())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"158ebdecc1970c990e6215607baba2a8\",\n \"score\": \"0.4727367\",\n \"text\": \"def _check_pip_package(self, dependency):\\n pip_dependency_name, pip_dependency_version = dependency.split('==')\\n pip_api_url = \\\"https://pypi.python.org/pypi/{}/json\\\".format(pip_dependency_name)\\n try:\\n response = requests.get(pip_api_url, timeout=10)\\n except requests.exceptions.Timeout:\\n self.warned.append(('general-7', f'PyPi API timed out: {pip_api_url}'))\\n except requests.exceptions.ConnectionError:\\n self.warned.append(('general-7', f'PyPi API Connection error: {pip_api_url}'))\\n else:\\n if response.status_code == 200:\\n pip_dep_json = response.json()\\n latest_dependency_version = pip_dep_json['info']['version']\\n if parse_version(pip_dependency_version) < parse_version(latest_dependency_version):\\n self.warned.append(('general-7', f'Version {pip_dependency_version} of {pip_dependency_name}'\\n f' is not the latest available: {latest_dependency_version}'))\\n else:\\n self.failed.append(('general-7', f'Could not find pip dependency using the PyPi API: {dependency}'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6d4e54c1931c2ff48578bad67f1d22a\",\n \"score\": \"0.47202945\",\n \"text\": \"def _validate_output(self, r):\\n has_failed = False\\n for line in r.split('\\\\n'):\\n m = re.search(\\\"(.+): does not update installed package.\\\", line)\\n if m:\\n has_failed = False\\n\\n m = re.search(\\\"Package (matching ){0,1}(.+) already installed\\\",\\n line)\\n if has_failed:\\n r.failed = True\\n else:\\n r.failed = False\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d72ca92b63db9586d7572176c765cc\",\n \"score\": \"0.4718275\",\n \"text\": \"def validate_mps(mwn, did, center, return_flag=False):\\n # Cyclic import\\n from package.utils import package_find\\n from package.models import find_by_wbns\\n\\n missing_list = list()\\n deliver_together = False\\n return_together = False\\n\\n response = dict()\\n\\n # Only string is required, object not required\\n did = unicode(did)\\n\\n pkg = find_by_wbns([mwn], ['cl'])\\n\\n client_cache = get_client_cache(pkg.get(mwn, {}).get('cl'))\\n\\n if client_cache.get('delivery_behaviour', {})\\\\\\n .get(u'Complete', False):\\n deliver_together = True\\n\\n if client_cache.get('return_behaviour', {})\\\\\\n .get(u'Complete', False):\\n return_together = True\\n\\n if return_flag:\\n behaviour = return_together\\n else:\\n behaviour = deliver_together\\n\\n query_dict = {'mwn': mwn, 'cs.sl': {'$eq': center}, 'dd.id': {'$ne': did}}\\n\\n fields = {'wbn'}\\n\\n packages = package_find(filter_criteria=query_dict, fields=fields,\\n return_dict=True, use_replicaSet=True)\\n\\n for p in packages:\\n missing_list.append(p.get('wbn'))\\n if missing_list:\\n response.update({mwn: missing_list})\\n return response, behaviour\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44ffdbdc6962f63438d0c9ac603088fd\",\n \"score\": \"0.4717647\",\n \"text\": \"def _check_import(self, import_id):\\n\\n res = self._auth_send('api/v1/imports/{}'.format(import_id),\\n 'GET')\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44ffdbdc6962f63438d0c9ac603088fd\",\n \"score\": \"0.4717647\",\n \"text\": \"def _check_import(self, import_id):\\n\\n res = self._auth_send('api/v1/imports/{}'.format(import_id),\\n 'GET')\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1306930a04e42ec61054a5b3c047f5b\",\n \"score\": \"0.4709468\",\n \"text\": \"def _check_id_presence(self, root, namespaces, version): # noqa\\n to_check = itertools.chain(\\n common.STIX_CORE_COMPONENTS,\\n common.CYBOX_CORE_COMPONENTS\\n )\\n\\n results = BestPracticeWarningCollection('Missing IDs')\\n xpath = \\\" | \\\".join(\\\"//%s\\\" % x for x in to_check)\\n nodes = root.xpath(xpath, namespaces=namespaces)\\n\\n for node in nodes:\\n if any(x in node.attrib for x in ('id', 'idref')):\\n continue\\n\\n warning = BestPracticeWarning(node=node)\\n results.append(warning)\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13100775bae02abb2ed44244e5f01444\",\n \"score\": \"0.46999785\",\n \"text\": \"def mark_missing_by_name(package, error, needs_review):\\n\\n with connection() as cursor:\\n cursor.execute(\\\"\\\"\\\"\\n UPDATE\\n package_stats\\n SET\\n is_missing = TRUE,\\n missing_error = %s,\\n needs_review = %s\\n WHERE\\n package = %s\\n \\\"\\\"\\\", [error, needs_review, package])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60cc0638820821db3cc08e0cf0d1202e\",\n \"score\": \"0.46966282\",\n \"text\": \"def __compare_package(self, package: tuple, package_detail: WebElement):\\n agent = self.driver.capabilities['browserName']\\n package_detail_split = package_detail.text.split('\\\\n')\\n if len(package_detail_split) > 0:\\n if agent.lower() in ['chrome', 'firefox']:\\n package_name, package_version = package_detail_split[1].strip(), package_detail_split[2].strip()\\n # Compare package-details on UI with the package-details from argument\\n if package_name == package.name and package_version == package.version:\\n return True\\n elif agent.lower() == 'safari':\\n # Safari returns a single string with no delimiters so check in a less strict way\\n # by just seeing if the name and version are in the string\\n if package.name in package_detail_split[0] and package.version in package_detail_split[0]:\\n return True\\n else:\\n raise ValueError(f\\\"Unsupported browser type while verifying package version: {agent}\\\")\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6c92b15974008905cb9e70d53e11085\",\n \"score\": \"0.4692243\",\n \"text\": \"def appCheck( self ):\\n # FIXME: Rename this to appIDCheck? or add a check for isntalled apps\\n for i in range( 15 ):\\n # TODO modify retry or add a new version that accepts looking for\\n # a value in a return list instead of needing to match the entire\\n # return value to retry\\n appResults = main.Cluster.command( \\\"appToIDCheck\\\" )\\n appCheck = all( i == main.TRUE for i in appResults )\\n if appCheck:\\n break\\n else:\\n time.sleep( 5 )\\n\\n if not appCheck:\\n ctrl = main.Cluster.active( 0 )\\n main.log.debug( \\\"%s apps: %s\\\" % ( ctrl.name, ctrl.pprint( ctrl.apps() ) ) )\\n main.log.debug( \\\"%s appIDs: %s\\\" % ( ctrl.name, ctrl.pprint( ctrl.appIDs() ) ) )\\n return appCheck\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cee49479532c5bb15ab73378d22f8857\",\n \"score\": \"0.46868974\",\n \"text\": \"def input_package_version(self, version: str) -> bool:\\n package_version_radio_btn = self.get_locator(LocatorType.XPath, \\\"//div[@class='AddPackageForm__version \\\"\\n \\\"flex align-items--center']/label[2]\\\"\\n \\\"/span[contains(text(),'Specify')]\\\")\\n package_version_input = self.get_locator(LocatorType.XPath, \\\"//div[@class='AddPackageForm__version \\\"\\n \\\"flex align-items--center']/label[2]/input[2]\\\")\\n if package_version_radio_btn is not None:\\n package_version_radio_btn.click()\\n if package_version_input is not None:\\n package_version_input.send_keys(version)\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a94e141ef55ad87f6b1203d9822005c\",\n \"score\": \"0.4676334\",\n \"text\": \"def query(package, db):\\n try:\\n n, v, m = preprocess_package(package)\\n for item in db[n]:\\n for spec in item['specs']:\\n if control_condition(v, spec):\\n yield item\\n return None\\n except KeyError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc5d5dbe56388c79be06a3aecc258dd5\",\n \"score\": \"0.46745682\",\n \"text\": \"def check(self):\\n # error_list = []\\n # self._check_id()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd9b264c8ac4db6c33e261edaa9ede78\",\n \"score\": \"0.4667281\",\n \"text\": \"def check_package(package, allowed_operands, regex=\\\"(^[\\\\w*\\\\-*]+)*\\\\s*([<>=]+)*\\\\s*(\\\\d+.*)*\\\\s*\\\"):\\n p_name, operand, version_specifier = operand_parse(package, regex=regex)\\n msg = \\\"\\\"\\n if p_name is None:\\n msg += f\\\"ERROR: Requirement definition '{package}' is not valid.\\\"\\n else:\\n if operand not in allowed_operands:\\n msg += f\\\"ERROR: Operand '{operand}' is not allowed in requirement specifiers.\\\"\\n p_name = None\\n if version_specifier is None and operand is None:\\n available_versions = get_available_package_versions(p_name)\\n v = available_versions[0]\\n if v == 'none':\\n msg += f\\\"ERROR: Package '{p_name}' has not any available version.\\\"\\n p_name = None\\n else:\\n release_versions = [item for item in available_versions if not re.search('[a-zA-Z]', item)]\\n # print(release_versions)\\n if not release_versions:\\n version_specifier = available_versions[-1]\\n msg += f\\\"WARNING : Version information not given for '{p_name}' choosing available latest \\\" \\\\\\n f\\\"version : '{version_specifier}' \\\"\\n else:\\n version_specifier = release_versions[-1]\\n msg += f\\\"WARNING : Version information not given for '{p_name}' choosing available latest \\\" \\\\\\n f\\\"release version : '{version_specifier}' \\\"\\n print_warning(msg)\\n\\n return p_name, version_specifier, msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9131b28b37332b49890909188a27dde\",\n \"score\": \"0.4662561\",\n \"text\": \"def is_installed(self, package_name):\\n apt_cache = apt.cache.Cache()\\n apt_cache.open()\\n if (package_name.strip() in apt_cache and\\n apt_cache[package_name.strip()].is_installed):\\n # apt_cache.close()\\n return True\\n # apt_cache.close()\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"023434b17252b5aa046fe7dc504ec883\",\n \"score\": \"0.46622023\",\n \"text\": \"def search(self, package_filter, data_input) -> 'package':\\r\\n if package_filter is None and data_input is None:\\r\\n return self\\r\\n else:\\r\\n for package in self.table:\\r\\n if (package is not self.EMPTY_SINCE_START and\\r\\n getattr(package, package_filter) == data_input):\\r\\n print(package)\\r\\n return '---------'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"206a5a74acf84780e118609d1825312a\",\n \"score\": \"0.4655302\",\n \"text\": \"def have_any_lambda_package_version(self, package_name):\\n return lambda_packages.get(package_name, {}).get(self.runtime) is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e96ff078b1f89ced8ddf5b084020c4d7\",\n \"score\": \"0.46549374\",\n \"text\": \"def verify_item_in_stock(id):\\n # get the book with id\\n book = Book.query.get(id)\\n # check if there is a book with that id if not send a message with 404 says there is no book with this id\\n if book:\\n # if book found check if its exist at the stock if send responce with 200 status code and its id and the quantity\\n # if nits out of stock send message says This book is currently unavailable\\n if book.quantity > 0:\\n return verify_schema.jsonify(book), 200\\n else:\\n return {\\\"message\\\": \\\"This book is currently unavailable\\\"}, 410\\n else:\\n return {\\\"message\\\": \\\"There is no book with this ID\\\"}, 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd9c71db070c2eee916c7f263cb1cf98\",\n \"score\": \"0.4643235\",\n \"text\": \"def test_catkin_discovery_plugin_scan_valid():\\n cdp = CatkinDiscoveryPlugin()\\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\\n 'valid_package'))\\n cdp.scan(package, 'level')\\n assert package['catkin']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e95157478172522f1485739f6cb6e38f\",\n \"score\": \"0.46369296\",\n \"text\": \"def _queryPackagesReply(self, msg, query):\\n pnames = []\\n self._lock.acquire()\\n try:\\n for name in self._packages.iterkeys():\\n qmfData = QmfData.create({SchemaClassId.KEY_PACKAGE:name},\\n _object_id=\\\"_package\\\")\\n if query.evaluate(qmfData):\\n pnames.append(name)\\n\\n self._send_query_response(ContentType.schema_package,\\n msg.correlation_id,\\n msg.reply_to,\\n pnames)\\n finally:\\n self._lock.release()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993488,"cells":{"query_id":{"kind":"string","value":"ca766eae32d3138b488dc464fd2fc166"},"query":{"kind":"string","value":"Structured SVM loss function, naive implementation (with loops). Inputs have dimension D, there are C classes, and we operate on minibatches of N examples."},"positive_passages":{"kind":"list like","value":[{"docid":"f73b08783021763c99cd131751eff8fa","score":"0.63592327","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n \n # Iterate through each point, summing as we go, and then divide by n afterwards\n # Issues with numerical precision using this approach (?)\n for i in xrange(num_train):\n \n scores = X[i].dot(W) # C-dimensional vector\n correct_class_score = scores[y[i]]\n \n # Now compute margins across all classes for this point X[i]\n num_positive_margin = 0\n for j in xrange(num_classes): \n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n num_positive_margin += 1\n dW[:, j] += X[i]\n dW[:, y[i]] -= num_positive_margin * X[i]\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n dW /= num_train\n\n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n dW += 2 * reg * W\n \n # With reference to:\n # http://cs231n.github.io/optimization-1/#computing-the-gradient-analytically-with-calculus\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n\n\n return loss, dW","title":""}],"string":"[\n {\n \"docid\": \"f73b08783021763c99cd131751eff8fa\",\n \"score\": \"0.63592327\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n \\n # Iterate through each point, summing as we go, and then divide by n afterwards\\n # Issues with numerical precision using this approach (?)\\n for i in xrange(num_train):\\n \\n scores = X[i].dot(W) # C-dimensional vector\\n correct_class_score = scores[y[i]]\\n \\n # Now compute margins across all classes for this point X[i]\\n num_positive_margin = 0\\n for j in xrange(num_classes): \\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n num_positive_margin += 1\\n dW[:, j] += X[i]\\n dW[:, y[i]] -= num_positive_margin * X[i]\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n dW /= num_train\\n\\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n dW += 2 * reg * W\\n \\n # With reference to:\\n # http://cs231n.github.io/optimization-1/#computing-the-gradient-analytically-with-calculus\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n\\n\\n return loss, dW\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"67adf20f38500772f8f93f199913c491","score":"0.6719385","text":"def svm_loss_vectorized(W, X, y, reg):\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n\n #############################################################################\n # Dummy data #\n #############################################################################\n\n # d = 3\n # N = 6\n # c = 4\n #\n # X = np.array( range(N * d) )\n # X += 1\n # X = X.reshape(N, d)\n #\n # W = np.array( range(20, 20 + 2 * d * c, 2), dtype=float )\n # W /= max(W)\n # np.random.shuffle(W)\n # W = W.reshape(d, c)\n #\n # y = np.random.randint(0, high=c, size=N)\n\n #############################################################################\n # IMPL #\n #############################################################################\n\n num_train = X.shape[0]\n d = X.shape[1]\n c = W.shape[1]\n\n scores = X.dot(W)\n correct_class_scores = scores[range(y.shape[0]), y]\n all_losses = np.zeros(shape=(num_train, c))\n all_losses = 1 + scores - correct_class_scores[:, np.newaxis]\n all_losses = (all_losses > 0) * all_losses\n all_losses[range(y.shape[0]), y] = 0\n\n losses = np.sum(all_losses, axis=1)\n\n total_loss = sum(losses)\n total_loss /= num_train\n\n total_loss += 0.5 * reg * np.sum(np.square(W))\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n\n dW = np.zeros(shape=(d, c))\n\n # Partially vectorized code\n #\n # for i in xrange(num_train):\n # count = np.sum(all_losses[i]>0)\n # dW[:, all_losses[i]>0] += X[i][:, np.newaxis]\n # dW[:, y[i]] -= count * X[i]\n #\n # dW /= num_train\n # dW += reg * np.sum(W)\n\n # Completely vectorized code\n #\n # count = np.sum(all_losses > 0, axis=1)\n # print(\"Shapes: \", X.shape, count.shape, (X * count[:, np.newaxis]).shape)\n #\n # dW /= num_train\n # dW += reg * np.sum(W)\n\n # Fully vectorized version. Roughly 10x faster.\n # I gave up!\n # copied from https://github.com/bruceoutdoors/\n\n X_mask = np.zeros(all_losses.shape)\n X_mask[all_losses > 0] = 1\n incorrect_counts = np.sum(X_mask, axis=1)\n X_mask[np.arange(num_train), y] = -incorrect_counts\n dW = X.T.dot(X_mask)\n\n dW /= num_train # average out weights\n dW += reg * W # regularize the weights\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return total_loss, dW","title":""},{"docid":"0e27c180386d3c0d401285fd86d59f16","score":"0.67064327","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n\n \n num_classes = W.shape[1]\n num_train = X.shape[0]\n \n scores = X.dot(W) # [N x C] # [500 x 10]\n\n # Produce a column-wise one-hot boolean (binary) integer matrix from y\n true_class_mask = np.zeros(scores.shape)\n true_class_mask[np.arange(num_train), y] = 1\n \n # To get the vector of true class scores, we simply multiply the scores with\n # the mask in order to get another [N x C] matrix, and sum along columns\n # to get an N-dimensional vector\n true_class_scores = np.sum(scores * true_class_mask, axis=1)\n \n # Now we can broadcast these true class scores to compute the matrix of margins,\n # which is also [N x C]. We hardcode delta to 1\n # Broadcasting requires the same trailing dimensions,\n # so we transpose and transpose back\n margins = (scores.T - true_class_scores.T).T + 1\n \n # Now we need to zero the margins corresponding to the true class label\n # we can do this by an elementwise multiply with the (boolean) negative \n # of the true class matrix\n margin_mask = -true_class_mask + 1\n margins = margins * margin_mask\n \n # Now we zero the margins which are less than 0 to represent the max(0, .) operation\n margin_mask2 = (margins > 0).astype(int)\n margins = margins * margin_mask2\n\n # And finally the loss is simply the sum of all remaining margins, divided by N\n loss = np.sum(margins) / num_train\n \n # Account for regularization\n loss += reg * np.sum(W * W)\n \n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n \n \n # Compute wrt w for j != y[i]\n dW1 = X.T.dot(margin_mask2) # View as matrix product == sum of vector outer products\n\n # Compute wrt j == y[i]\n margins_boolean = (margins > 0).astype(int)\n num_positive_margins = np.sum(margins_boolean, axis=1).reshape(num_train, 1)\n dW2 = X.T.dot(-true_class_mask * num_positive_margins)\n dW = (dW1.T + dW2.T).T / num_train\n \n # Account for regularization\n dW += 2 * reg * W\n \n \n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"fdfe0131dc6700b0fb09daff2d536e8b","score":"0.6633743","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n numExamples = X.shape[0] \n scoresMat = np.dot(X,W)\n\n LossMat = scoresMat - (scoresMat[range(numExamples),y])[:, None] + 1\n LossMat[LossMat < 0] = 0 \n LossMat[np.arange(numExamples),y] = 0\n #loss mat is a matrix with num_examples x num_classes that has the ammount of loss acumulated from each example and each class classifier\n\n LossPerItem = LossMat.sum(axis = 1) #the total loss accumulated from each example\n\n TotalLoss = LossPerItem.sum()\n TotalLoss /= numExamples\n loss = TotalLoss\n\n loss += reg * np.sum(W * W)\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n binaryLossMat = np.zeros(LossMat.shape)\n binaryLossMat = LossMat\n binaryLossMat[binaryLossMat > 0] = 1 \n #binary loss mat is a num_examples x num_classes matrix with 1 in i,j place if the i_th example created loss with the j_th class classifier\n #note that by definition of loss function if example[i] is part of class j then it contributes 0 loss and is 0 in bin loss mat\n\n totalBinLossPerExample = binaryLossMat.sum(axis = 1) #basically the count of how many classifiers accumulated loss on an example\n\n binaryLossMat[np.arange(numExamples), y] = -totalBinLossPerExample \n #now binaryLossMat is matrix like before but if example[i] has corect class j, then the ixj spot has -totalBinLossPerExample[i] \n\n dW = X.T.dot(binaryLossMat)\n #this is the trick part X is num_examples x num_dim_per_example matrix, transposed is num_dim_per_example x num_examples matrix\n #num_dim_per_example x num_examples matrix (dot) num_examples x num_classes = num_dim_per_example x num_classes matrix\n #pretend there is one class and so one classifier. Then multiplying the first colum of binaryLossMat by first row of X.T is\n #calculating the first element of the graident by adding up how many times the first element of each example that generates a loss for that class\n # (or subtracts if that example was part of that class)\n #moving to the next gradient element below, it how many times the second element of each example should be included in that class gradient\n\n dW /= numExamples\n dW += reg * 2 * W\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","title":""},{"docid":"5f20894a35bc82779696469aefec5fc8","score":"0.66028476","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n \"\"\"\n delta = 1\n scores = W.dot(X)\n margins = np.maximum(0, scores - scores[y] + delta)\n margins[y] = 0\n loss = np.sum(margin)\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n\n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W)\n \"\"\"\n \"\"\"\n ##########erste loesung\n ### loss\n # wxy chooses scores of right labels\n # its shape is (#samples,)\n wx = W.dot(X)\n delta = 1\n wxy = [ wx[y[i], i] for i in xrange(wx.shape[1]) ]\n \n # judge expression\n # remember to exclude on y[i]'s\n judge = wx - wxy + delta\n # make judge 0 on y[i]\n for i in xrange(wx.shape[1]):\n judge[y[i], i] = 0\n \n # mass is a matrix holding all useful temp results\n # shape of judge is (#class, #train)\n mass = np.maximum(0, judge)\n \n loss = np.sum(mass) / X.shape[1]\n loss += 0.5 * reg * np.sum(W * W)\n \"\"\"\n \"\"\"\n scores = W.dot(X)\n loss = np.zeros(scores.shape)\n init = np.zeros(scores.shape)\n loss = scores - np.choose(y,scores)\n loss[np.where(loss!=0)] += 1 \n loss[np.where(loss<=0)] = 0\n init[np.where(loss>0)] = 1\n loss1 = loss\n loss = np.sum(loss)\n loss /= num_train\n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W) \n \"\"\"\n ### zweite und am besten loesung erste part\n WX = W.dot(X)\n num_classes = W.shape[0]\n num_train = X.shape[1]\n margin = WX - WX[y, np.arange(num_train)] + 1\n margin[y, np.arange(num_train)] = 0\n margin[margin < 0] = 0\n loss = sum(sum(margin))\n loss /= num_train\n loss += 0.5 * reg * np.sum(W * W)\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n \"\"\"\n scores = W.dot(X)\n margins = np.maximum(0, scores - scores[y] + delta)\n ddW = np.sum(X,axis=1)\n margins[y] = 0\n ddW[y] -= ddW\n dW = np.sum(ddW)\n loss = np.sum(margin)\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n dW /= num_train\n \n # add regularization term\n loss += 0.5 * reg * np.sum(W * W)\n dW += reg * W\n \"\"\"\n \"\"\"\n ### erste loesung\n # continue on last snippet\n # weight to be producted by X\n # its shape is (#classes, #samples)\n weight = np.array((judge > 0).astype(int))\n \n # weights on y[i] needs special care\n weight_yi = -np.sum(weight, axis=0)\n for i in xrange(wx.shape[1]):\n weight[y[i], i] = weight_yi[i]\n \n # half vectorized\n for i in xrange(X.shape[1]):\n ddW = X[:, i] * weight[:, i].reshape(-1, 1)\n dW += ddW\n \n dW /= X.shape[1]\n dW += reg * W\n \"\"\"\n \"\"\"\n k1 = np.sum(init,axis=0)\n k2 = -k1*X\n dW = np.tile(np.sum(X,axis=1),(num_classes,1))\n #np.add.at(dW,y,-kT-XT)\n #np.add.at(dW,y,k2.transpose())\n #y2 = np.repeat(np.arange(num_classes),num_train).reshape(num_classes,num_train)\n #np.add.at(dW,np.int_(y2),X.transpose())\n #np.add.at(dW,y,-X.transpose())\n #dW /= num_train\n \"\"\"\n ### zweite und am besten loesung zweite part\n grad = np.zeros([W.shape[0], X.shape[1]])\n grad_wj = margin > 0\n grad_y = -1*sum(grad_wj)\n grad = grad + grad_wj\n grad[y, np.arange(num_train)] = grad_y\n dW = grad.dot(X.T)\n dW /= num_train\n dW += reg * W\n \n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"03dfae6cc66a0e97cca45dc64ff3bed8","score":"0.65555876","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n delta = 1.0\n num_classes = W.shape[1] # C\n num_train = X.shape[0] # N\n y = y.astype(int) # Par défault y est un tableau de float qui génère une erreur pour la transformation en 1-hot-E\n\n\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n # compute the loss\n scores = np.matmul(X,W) # Matrice N,C\n y_one_hot = np.zeros((y.size, num_classes))\n y_one_hot[np.arange(y.size),y] = 1 # Matrice N,C\n \n # numpy.multiply : Multiply arguments element-wise.\n correct_class_score = np.sum(np.multiply(scores, y_one_hot), axis=1) # Vecteur de taille N\n # On le transforme en matrice pour enlever le score de la bonne classe à tous les éléments d'une ligne de scores\n margins = scores - np.array([correct_class_score,]*num_classes).T + delta\n margins = np.maximum(0, margins)\n \n # on y-th position scores[y] - scores[y] canceled and gave delta. We want\n # to ignore the y-th position and only consider margin on max wrong class\n # multiplication terme à terme par (1-y_one_hot) qui va renvoyer 0 juste pour l'élément s_yi\n margins = np.multiply(margins, (1-y_one_hot))\n \n loss = np.sum(margins)/num_train\n \n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n \n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n # cf notes\n\n s_grad = 1*(margins>0) + np.multiply(y_one_hot, - np.array([np.sum(margins>0, axis=1),]*num_classes).T )\n dW = np.matmul(X.T, s_grad)\n dW /= num_train\n \n # Add regularization to the gradient.\n dW += 2* reg * W\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","title":""},{"docid":"ed7ad812f2598efb3dc9fb65c85410c8","score":"0.65491706","text":"def svm_loss_naive(W, X, y, reg):\n \n\n \n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[0]\n num_train = X.shape[1]\n loss = 0.0\n for i in xrange(num_train):\n train=X[i,:]\n # train=train.reshape(1,-1) # 1 x D\n print train.shape\n print W.shape\n \n scores = np.dot(train,W) # 1 x C\n\n print scores.shape\n\n correct_class_score = scores[y[i]]\n for j in xrange(num_classes):\n if j == y[i]:\n temp_score=scores\n temp_score=np.delete(temp_score,y[i])\n predicted_class_weight=W[y[i],:]\n predicted_class_weight=predicted_class_weight.reshape(-1,1)\n temp_score=temp_score-(np.dot(predicted_class_weight.T,train))+1\n num_violators=np.sum(temp_score>0)\n \n dW[j,:]=dW[j,:]-num_violators*train.T\n \n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n # dW[j,:]=dW[j,:]+train.T\n loss += margin\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n\n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W)\n \n dW/=num_train\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n\n\n return loss, dW","title":""},{"docid":"5e79549f92231ed904bb62343afa08d2","score":"0.6521443","text":"def svm_loss_naive(W, X, y, reg):\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n\n \"\"\"\n 2 May notes:\n the gradient flow to W for\n 1) the correct class is -(margin_count * x_i)\n 2) all other classes is x_i if margin>0\n let's try this idea out] and then do a power nap before Copenhagen\n\n we also want the gradient to be divided by N and then added to the gradient\n of regularisation\n\n The gradient of the SVM loss, according to derivation by calculus, can be\n acquired effectively by:\n\n For each image (data point):\n 1. count the number of classes that failed to meet the margin requirement\n 2. the contribution to the loss from this image = x[i] * num_counted for all i ~= yi\n negative that for i = yi\n\n \"\"\"\n\n num_classes = W.shape[1]\n num_train = X.shape[0]\n\n loss = 0.0\n dW = np.zeros(W.shape) # initialise dW\n\n for i in xrange(num_train):\n # calculate scores\n scores = X[i].dot(W)\n scores_correct = scores[y[i]]\n\n margin_count = 0 # reset margin count for each image\n dW_temp = np.zeros(W.shape) # reset dW_temp matrix for each image\n\n for j in xrange(num_classes):\n if j == y[i]: # skip correct class\n continue\n margin = scores[j] - scores_correct + 1\n if margin > 0: # max(0, margin)\n loss += margin\n margin_count += 1\n dW_temp[:, j] = X[i].T\n\n dW_temp[:, y[i]] = - margin_count * X[i].T\n dW += dW_temp # add to total gradient\n\n # final loss and dW for return\n loss = loss/num_train + np.sum((np.power(W, 2))) # average, add regularisation\n dW = dW/num_train + 2.0*reg*W\n\n return loss, dW","title":""},{"docid":"93920d298cb99650bd7c6473dd96a42e","score":"0.6498648","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n #############################################################################\n # TEST: run the following test code to check\n # a = np.random.randint(10, high=20, size=20) # generate a of size 20, where each element if in [10, 20)\n # a = np.reshape(a, (5, 4))\n # y_index = np.random.randint(4, size=5) # generate y_index of size 5, where each element is up to 4\n # y = np.choose(y_index, a.T) # choose ys from each row of a at y_index\n # margin = np.subtract(a, y[:, np.newaxis]) + 1 # subtract each row with corresponding elements in y\n #############################################################################\n scores = X.dot(W) # 500, 10\n num_train = scores.shape[0] # 500\n correct_class_score = np.choose(y, scores.T) # 500,\n margin = np.subtract(scores, correct_class_score[:, np.newaxis]) + 1\n margin[range(num_train), list(y)] = 0\n loss = np.sum(margin, where=[margin>0]) / num_train + reg * np.sum(W*W)\n pass\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n # reference: https://github.com/hartikainen/stanford-cs231n/blob/master/assignment1/cs231n/classifiers/linear_svm.py\n grad_mask = (margin > 0).astype(int)\n grad_mask[np.arange(y.shape[0]), y] = - np.sum(grad_mask, axis=1)\n dW = np.dot(X.T, grad_mask)\n\n dW /= float(num_train)\n dW += reg * W\n pass\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","title":""},{"docid":"0ca160c0c85df5022dc9400fdc96e668","score":"0.6451991","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n count_missed_margin = 0 # Count the number of classes that don't meet the margin\n # criteria. Used for Gradient.\n \n for i in range(num_train): # For each training example\n scores = X[i].dot(W) # returns 1xC vector, ~prob that X[i] is of class c\n correct_class_score = scores[y[i]] # returns the ~prob for the correct class\n for j in range(num_classes): # For each class\n if j == y[i]: # skip the correct class \n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n # Takes difference of the score for every other class and the correct score\n # Adds in a delta. The SVM \"wants\" the correct score to be greater than\n # all other scores by at least 'delta'. i.e. we want negative\n # margin. (see line below)\n if margin > 0:\n loss += margin # For all \"bad\" margins (not diff not > delta value)\n # add them up, that's our loss, which we want to minimize\n count_missed_margin += 1\n # Gradient for all columns of the wrong class (if margin in not within\n # limit) is X[i]\n dW[:,j] += X[i]\n #dW[:,y[i]] -= X[i] <- Alternative to doing the single assignment below\n\n # Gradient for the column with the correct class is simply X[i]\n # scaled by the number of missed classes.\n dW[:,y[i]] -= count_missed_margin*X[i]\n count_missed_margin = 0\n\n \n \n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n \n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n \n \n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n dW = np.divide(dW,num_train)\n dW = np.add(dW,reg*W)\n \n return loss, dW","title":""},{"docid":"9aa41ccc99e7fd902d38fb9cddd46173","score":"0.64205873","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n num_train = X.shape[0]\n delta = 1.0\n scores = X.dot(W)\n\n #scores now has (N,C) where each row is a row of scores for that specific training image\n # compute the margins for all classes in one vector operation\n correct_class_scores=scores[np.arange(num_train),y]\n margins = np.maximum(0, scores - correct_class_scores[:,np.newaxis] + delta)\n\n # on y-th position scores[y] - scores[y] canceled and gave delta. We want\n # to ignore the y-th position and only consider margin on max wrong class\n\n margins[np.arange(num_train),y] = 0\n\n #reuse the margin information to calculate the gradient. Margin has the dimension (N,C)\n #for each row need to count the number of positive numbers other than the correct class \n #and the corresponding weight's class column will receive a negative gradient update and count \n #the number of times incorrect rows needs a positive gradient update form a matrix (N,C)\n counter=np.zeros(margins.shape) \n mask=(margins>0)\n counter[mask]=1\n correst_class_mutiplier=-np.sum(mask,axis=1)\n counter[np.arange(num_train),y]=correst_class_mutiplier\n\n #now use the counter to add corresponding Xs to dW\n dW=X.T.dot(counter)\n\n loss = np.sum(margins)\n\n loss /= num_train\n\n #average dW as well\n dW /= num_train\n\n # Add regularization to the loss.\n\n loss += reg * np.sum(W * W)\n\n # Also add regularization to the gradient\n\n dW += 2*reg*W\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n \n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","title":""},{"docid":"74e642de8f6a5f1378402dc270667005","score":"0.6418873","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n num_train = X.shape[1]\n dW = np.zeros(W.shape) # initialize the gradient as zero\n scores = W.dot(X) # C * N vector \n true_scores = scores[y, np.arange(num_train)] # true_scores[i] contains\n mat = scores - true_scores + 1\n mat[y, np.arange(num_train)] = 0\n mat = np.maximum(np.zeros(scores.shape), mat)\n loss = np.sum(mat)/num_train\n loss += 0.5 * reg * np.sum(W*W)\n\n \n bin = mat\n bin[mat > 0] = 1 # bin is of size C * N\n col_sum = np.sum(bin, axis = 0) # mat is of size 1 * N the number of times > 0 for a data point for all classes\n bin[y, np.arange(num_train)] = -col_sum[np.arange(num_train)]\n dW = np.dot(bin, X.T) # dW is a C * D\n dW /= num_train\n \n dW += reg* W\n\n\n\n return loss, dW","title":""},{"docid":"44e18d8d09e99236de088f4682b57f0c","score":"0.6412422","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n num_train = X.shape[0]\n scores = X.dot(W) # NxC (Training Examples x Classes)\n correct_class_scores = scores[np.arange(0,num_train),y].reshape(-1,1)\n\n margin = scores - correct_class_scores + 1\n margin[np.arange(0,num_train), y] = 0 # Set correct score to 0\n margin[margin<0] = 0 # set margins that \"pass\" to zero, so they don't \n # affect the loss.\n loss = np.sum(margin)\n # Take all margins that are greater than 0, sum them up\n loss /= num_train # Dvide by number of training examples\n loss += reg*np.sum(W*W) # add regularization\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n \n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n #Gradient\n # dW has C columns, one for each class\n # The gradient is as such:\n # For each Xi, we add it to the column if:\n # it doesn't pass the margin test for that row\n # AND\n # that row isn't the correct row for that sample\n # Above we've already created a matrix \"margin\" where\n # the dimensions are samples-by-classes, that indicates the margin\n # of each sample for each class, but is set to 0 where the margin\n # passed (i.e. was negative) or where that class was the correct class\n # so we only need to create a mask matrix where every instance of 'margin'\n # that is > 0 = 1 (or True, which is equivalent in Python). Then we can \n # dot this mask with X, which will essentially do the above.\n miss_margin = margin > 0 # already accounts for j != yi\n dWwj = X.T.dot(miss_margin)\n # We also count how many classes for which that sample didn't \n # pass the margin test, call this Z\n # for the column of dW that corresponds to the correct class for Xi\n # we add -Z*Xi\n cm = np.zeros(miss_margin.shape)\n cm[np.arange(0,num_train),y] = -1*np.sum(miss_margin, axis=1) \n dWyi = X.T.dot(cm) \n # multiply all samples by this number\n # now we need to subtract this from every column of dW that is the correct class for that sample\n dW = dWyi + dWwj\n dW /= num_train\n dW += reg*W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"358490be280c88429f92e59e516f81ff","score":"0.6410807","text":"def svm_loss_vectorized(W, X, y, reg):\n \n \n \n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n num_train=X.shape[0]\n num_classes=W.shape[1]\n \n \n \n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n scores=np.dot(X,W) # C x N\n \n # print scores.shape \n # print y.shape\n # print y\n \n #indexes=np.array([[i,elem] for i,elem in zip(range(num_train,y))])\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n \n correct_scores=scores[np.arange(num_train),y]\n \n #for j in range(num_train):\n # correct_scores[j]=scores[y[j],j]\n \n # correct_scores=correct_scores.reshape(1,-1)\n \n # margin=(scores-(correct_scores))+1\n margin=((scores.T-correct_scores).T)+1\n \n margin[np.arange(num_train),y]=0\n \n #for j in range(num_train):\n # margin[y[j],j]=0\n #\n #print margin[margin>1]\n \n loss=np.sum(margin[margin>0])\n loss=loss/num_train\n \n regularization_term=reg*np.sum(np.power(W,2))\n loss=loss+regularization_term\n\n\n #caculating the number of classes that have violated the margin across the entire training\n #data\n thres=np.maximum(np.zeros((num_train,num_classes)),margin)\n \n binary=thres\n binary[thres>0]=1\n \n col_sum=np.sum(binary,axis=1)\n\n # print 'col_sum',col_sum.shape\n\n # print 'binary',binary.shape\n \n binary[np.arange(num_train),y]=-col_sum[range(num_train)]\n \n # print 'binary',binary.shape\n\n dW=np.dot(X.T,binary)\n dW=dW/num_train\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"f384e1e51e695d0241459b748a7795a4","score":"0.638657","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n \n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n Q = np.zeros((num_train, num_classes))\n loss = 0.0\n for i in xrange(num_train):\n dDot = np.zeros(num_classes) \n scores = X[i].dot(W) #1x3073 dot 3073x10 = 1x10\n xi = X[i, :]\n correct_class_score = scores[y[i]]\n for j in xrange(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n Q[i,j] = 1\n Q[i,y[i]] -= 1\n dW[:,j] += xi\n dW[:,y[i]] -= xi\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n dW /= num_train\n\n #add regularization derivative\n dW += reg * 2 * W\n \n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n\n # print(\"X shape : \", X.shape, \", W shape :\", W.shape, \", dDot shape\", dDot.shape, \"dW shape\", dW.shape)\n #print(\"dDot = \", dDot[:10])\n #print(Q[:10,:])\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n #dW = X.T.dot(dDot) \n #print(\"dW = \", dW[:10]) \n return loss, dW","title":""},{"docid":"4067d44751ef2021219b157173ef5681","score":"0.6326962","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n y_hat = np.dot(X, W) # score for each class for each data\n y_index = [np.arange(len(y)), np.array([y[i] for i in range(len(y))])] # index of true label for each training data\n y_true_label_score = y_hat[y_index[0], y_index[1]] # one dimension array of score for true class\n y_true_label_score_array = np.array([np.repeat(y_true_label_score[i], y_hat.shape[1]) for i in range(len(y_true_label_score))]) # repeat score of true class for each class\n ones_array = np.ones(y_true_label_score_array.shape)\n # add_ones_back_true_label_array\n S_ij_matrix = (y_hat - y_true_label_score_array + ones_array)\n y_loss_array = S_ij_matrix.clip(0.0) # hinge loss, for each class for each data, score_j - score_true_label + 1\n y_loss_sum = np.sum(y_loss_array) - y_hat.shape[0] # total loss, aggregate for data and all classes\n penalty = reg * np.sum(np.square(W)) # regularization term\n loss = (y_loss_sum + penalty) / X.shape[0]\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n # derivative formula\n # j is not the true class, j' is the true class\n # delta_L_i / delta_W_tj = X_it if S_ij - S_ij' + 1 > 0 else 0\n # j is the true class:\n # delta L_i / delta_W_tj' = sum_j(indicator(S_ij - Sij' + 1) * X_it\n\n # derivative for j, not the true class\n S_ij_matrix_bool = np.ones(S_ij_matrix.shape) #\n S_ij_matrix_bool[S_ij_matrix <= 0] = 0 # classes the loss is 0, which gradient is 0\n S_ij_matrix_bool[y_index[0], y_index[1]] = 0 # set the true class gradient 0\n S_ij_derivative = np.dot(np.transpose(X), S_ij_matrix_bool) / X.shape[0] # delta_L / delta_W_tj = mean_i( delta_L_i / delta_W_tj)\n # derivative for j', the true class\n tmp_index_array = np.zeros(S_ij_matrix_bool.shape)\n # tmp_index_array[y_index[0], y_index[1]] = np.sign(S_ij_matrix).clip(0.1).sum(1) # number of class that contribute to the gradient for i-th data\n tmp_index_array[y_index[0], y_index[1]] = S_ij_matrix_bool.sum(1)\n S_true_class_derivative = - np.dot(np.transpose(X), tmp_index_array) / X.shape[0]\n dW = S_ij_derivative + S_true_class_derivative + 2 * reg * W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n return loss, dW","title":""},{"docid":"c68daa19d3a1d97363ee98e6345fc9eb","score":"0.6322888","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n num_train = X.shape[0]\n # Compute scores with a simple matrix multiplication\n scores = X.dot(W)\n # Extract correct scores using numpy indexing capabilities\n # First vector imdicates the index for the first dimension (a range)\n # second vector is the correct class index y\n correct_scores = scores[np.arange(0,num_train),y]\n # Calculate margin for each individual score. Note that we need to transpose\n # the scores matrix to benefit from broadcast\n margin = scores.T - correct_scores + 1\n # Force margin to zero for correct classes (using y as indices)\n margin[y,np.arange(0,num_train)] = 0\n # We accumulate loss if margin is strictly positive\n loss = np.sum(margin[margin > 0])\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n\n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W)\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n # We will reuse the loss matrix we calculated before\n # First, convert our margin matrix to a binary one\n dL = (margin > 0).astype(int)\n # We still use a transposed matrix (which is counter intuitive):\n # In each datapoint column, the row that corresponds to the 'correct' class\n # weights negatively as much as the sum of the 'wrong' classes weight\n dL[y,np.arange(0,num_train)] = -1 * np.sum(dL, axis=0)\n # The gradient is the result of the multiplication of the new matrix with the\n # data matrix.\n # Note that the result must be transposed back to match the gradient shape\n dW = (dL.dot(X)).T\n # Normalize the matrix\n dW /= num_train\n # Add regularization\n dW += reg*W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"130b2e5d92e1adab00efcd40a1a9b1d5","score":"0.6312452","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in xrange(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n for j in xrange(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n # https://zhuanlan.zhihu.com/p/21478575\n # Induction: Denote k as 0,1,...D-1\n # Denote loss[i, j] as the classify score on class_j at sample i\n # if margin <= 0, loss[i, j] is constant 0, derivative of loss at this pos with respect to any W is 0\n # Here we computed loss[i, j] = sum(X[i, k] * W[k, j]) - sum(X[i, k] * W[k, y[i]]) + 1\n # if loss[i, j] > 0 then the partial derivative of loss[i, j] with respect to W[k, j] is X[i, k]\n # else the partial derivative of loss[i, j] with respect to W[k, j] is 0\n # the partial derivative of loss[i, j] with respect to W[k,\n # y[i]] is -X[i, k]\n dW[:, j] += X[i]\n dW[:, y[i]] -= X[i]\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n\n # Add regularization to the loss.\n loss += .5 * reg * np.sum(W * W)\n dW /= num_train\n dW += reg * W\n ##########################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n ##########################################################################\n\n return loss, dW","title":""},{"docid":"aa9b41f53818df0a1770fe871c7aea17","score":"0.62921715","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n num_train = X.shape[0]\n scores = np.dot(X,W)\n correct_class_score = np.choose(y, scores.T) # np.choose uses y to select elements from scores.T\n # print(correct_class_score)\n # Need to remove correct class scores as we dont need to calculate loss/margin for those\n mask = np.ones(scores.shape,dtype=bool)\n mask[range(scores.shape[0]),y] = False\n scores_ = scores[mask].reshape(scores.shape[0],scores.shape[1]-1)\n\n # Calculate all our margins all at once\n margin = scores_ - correct_class_score[..., np.newaxis] + 1\n # print(correct_class_score[...,np.newaxis].shape)\n # We only add margin if it is greater than 0\n margin[margin<0] = 0\n\n # Average our data loss over the size of the batch and add reg. term to the loss\n loss = np.sum(margin)/num_train\n loss += reg*np.sum(W*W)\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n original_margin = scores - correct_class_score[...,np.newaxis] + 1\n # print(original_margin.shape)\n # Mask to identify where the margin is greater than 0\n pos_margin_mask = (original_margin>0).astype(float)\n # print(pos_margin_mask.shape)\n # Count how many times>0 for each image but dont count the correct class so -1\n sum_margin = pos_margin_mask.sum(1) -1\n\n # Make the correct class margin be negative total of how many > 0\n pos_margin_mask[range(pos_margin_mask.shape[0]),y] = -sum_margin\n\n dW = np.dot(X.T, pos_margin_mask)\n\n dW = dW/num_train + 2*reg*W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"9ed2eeed3a5954545b98ae7ce88f882a","score":"0.62709504","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n delta = 1\n num_train = X.shape[0]\n\n scores = X.dot(W)\n correct_scores = np.reshape(scores[np.arange(num_train), y], (num_train, 1))\n\n margins = np.maximum(0, scores - correct_scores + delta)\n margins[np.arange(num_train), y] = 0\n\n loss += np.sum(margins) / num_train\n loss += reg * np.sum(W * W)\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n indicator_margins = np.where(margins > 0, 1, 0)\n\n # Adding dW_y_i to dW. \n #\n # We scale X according to the number of nonzero elements in each row \n # of indicator_margins, and then index into y_ith columns of dW using\n # the y_ith row of X. \n #\n # NOTE: Not sure how to do without for loop.\n\n scaled_X = X * np.reshape(np.sum(indicator_margins, axis=1), (num_train, 1))\n for i in range(num_train):\n dW[:, y[i]] -= scaled_X[i]\n \n # Something like \"dW[:, y] -= scaled_X\" doesn't seem to work above. dW is sized \n # up to (3073, 500), which is not what we want. We need to be able to increment \n # the same column multiple times. \n \n # Adding dW_j to dW. \n #\n # np.nonzero gives indices of nonzero elements. \n \n nonzero_indices = np.where(indicator_margins)\n X_i = nonzero_indices[0]\n W_j = nonzero_indices[1]\n\n for index in range(len(X_i)):\n dW[:, W_j[index]] += X[X_i[index]]\n\n dW /= num_train\n dW += 2 * reg * W\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","title":""},{"docid":"68f90d0f677254fb095f0004316c0cce","score":"0.6251825","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n y = y.astype(int) # Par défault y est un tableau de float qui génère une erreur pour la transformation en 1-hot-E\n\n loss = 0.0\n for i in range(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n count = 0\n for j in range(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n count += 1\n # Calcul du gradient en un point\n # On peut l'intégrer dans la boucle précédente, mais les maths en deviennent moins lisibles\n for j in range(num_classes):\n if j == y[i]:\n dW[:,j] += - count * X[i]\n else:\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n dW[:,j] += X[i]\n # dW[:,j] += 1. * X[i]\n # else: # Ne sert à rien sauf à expliquer le calcul\n # dW[:,j] += 0. * X[i]\n\n \n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n dW /= num_train\n\n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n dW += 2* reg * W\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n pass\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n \n return loss, dW","title":""},{"docid":"04dab0f68689f3ff8189724fd4d2e455","score":"0.6238996","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n delta = 1 # margin separation set to 1\n\n # get some dimensions\n num_train = X.shape[0]\n num_classes = W.shape[1]\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n\n scores = X.dot(W)\n correct_scores = scores[np.arange(num_train), y]\n correct_scores = np.reshape(correct_scores, (correct_scores.shape[0], -1))\n\n margin = scores - correct_scores + delta\n margin = np.maximum(0, margin)\n\n reg_term = reg * np.sum(np.power(W, 2))\n loss_term = np.sum(margin) - num_train * delta # remove the margins for j=yi\n loss = loss_term/num_train + reg_term\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n\n coeff_mtrx = np.zeros((num_train, num_classes))\n margin[np.arange(num_train), y] = 0 # remove the margin for j = yi (delta)\n coeff_mtrx[margin > 0] = 1\n margin_count_vec = np.sum(margin > 0, axis=1)\n coeff_mtrx[np.arange(num_train), y] = -margin_count_vec\n\n dW = np.dot(X.T, coeff_mtrx)/num_train + 2.0*reg*W\n\n\n ######### implement with a loop over training images ######\n # # ver1: check coeff matrix idea\n # dW = np.zeros(W.shape) # initialise\n # for i in xrange(num_train):\n # dW += np.outer(X[i].T, coeff_mtrx[i])\n # dW = dW/num_train + 2*reg*W\n\n\n # # ver2: by-pass the coeff matrix idea, produce coefficients in each loop\n # dW = np.zeros(W.shape) # initialise\n # for i in xrange(num_train):\n # coeff_vec = np.zeros((num_classes,))\n # coeff_vec[margin[i] > 0] = 1\n # coeff_vec[y[i]] = -np.sum(margin[i] > 0)\n # dW += np.outer(X[i].T, coeff_vec)\n #\n # dW = dW/num_train + 2*reg*W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"47969aa89dd88f1594e03e0416795fe5","score":"0.62326443","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n n_sample = X.shape[0]\n hinge_loss = 0.0\n for i in xrange(n_sample):\n scores = np.dot(X[i],W)\n correct_class_score = scores[y[i]]\n margin = np.maximum(0,scores - correct_class_score + 1)\n margin[y[i]] = 0\n hinge_loss += np.sum(margin) / n_sample\n\n margin = scores - correct_class_score + 1\n margin = (margin > 0) * 1\n margin[y[i]] = 0\n margin[y[i]] = -np.sum(margin)\n dW_single_sample = np.outer(X[i],margin) / n_sample\n dW += dW_single_sample + reg * W / n_sample\n '''\n for j in xrange(n_class):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n data_loss += margin / n_sample\n '''\n reg_loss = 0.5 * reg * np.sum(W ** 2)\n # Add regularization to the loss.\n loss = hinge_loss + reg_loss\n\n return loss, dW","title":""},{"docid":"55c8b6e9588a8fd9f944bf07bb345f69","score":"0.62129825","text":"def svm_loss_naive(W, X, y, reg):\n\n #Initialize gradient and loss to zero\n dW = np.zeros(W.shape) \n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n\n # SVM loss is defined as: \n # Li = sum j != yi max(0, w^T_j*xi - w^T_yi*xi + 1)\n\n for i in xrange(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n num_greater_margin = 0\n\n for j in xrange(num_classes):\n #Skip correct class\n if j == y[i]:\n continue\n #For every other class, calculate the sum and gradient\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n num_greater_margin += 1\n loss += margin\n dW[:, j] = dW[:, j] + X[i, :]\n\n #Correct class gradient\n dW[:, y[i]] = dW[:, y[i]] - X[i, :]*num_greater_margin \n \n #Average loss.\n loss /= num_train\n\n #Add regularization to the loss.\n loss += reg * np.sum(W * W)\n\n dW = dW / num_train + 2*reg *W\n\n return loss, dW","title":""},{"docid":"6de82613fefde2b75308874e1d38decd","score":"0.62007946","text":"def _train(x,y):\n\t# D = Number of attributes\n\tD = x.shape[1] + 1\n\t# K = Number of classes\n\tK = y.shape[1]\n\t\n\t# Build the sums of xi*xi' and xi*yi'\n\tsum1 = np.zeros((D,D)) # init placeholder\n\tsum2 = np.zeros((D,K))\n\ti = 0\n\tfor x_i in x:\t\t\t\t\t\t# loop over all vectors\n\t\tx_i = np.append(1, x_i) \t\t# augment vector with a 1 \n\t\ty_i = y[i]\t\t\t\t\t\t\n\t\tsum1 += np.outer(x_i, x_i)\t\t# find xi*xi'\n\t\tsum2 += np.outer(x_i, y_i)\t\t# find xi*yi'\n\t\ti += 1\n\t\n\t# Check that condition number is finite\n\t# and therefore sum1 is nonsingular (invertable)\n\twhile matrix_rank(sum1) != D:\n\t\t# Naive choice of sigma.\n\t\t# Could cause inaccuracies when sum1 has small values\n\t\t# However, in most cases the matrix WILL be invertable\n\t\tsum1 = sum1 + 0.001 * np.eye(D) \n\t\n\t# Return weight vector\n\t# Weight vector multiplies sums and inverse of sum1\n\treturn np.dot(inv(sum1),sum2)","title":""},{"docid":"1fb133d87240c23351ca679e37f6fc87","score":"0.61952215","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n \"\"\"\n num_classes = W.shape[0]\n num_train = X.shape[1]\n loss = 0.0\n delta = 1.0\n\n for i in xrange(num_train):\n scores = W.dot(X[:, i])\n correct_class_score = scores[y[i]]\n for j in xrange(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + delta # note delta = 1\n if margin > 0:\n loss += margin\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n\n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W)\n \"\"\"\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n #\n \"\"\"\n #############meine loesung\n for i in xrange(num_train):\n\tddW = np.zeros(W.shape)\n\tscores = W.dot(X[:, i])\n\tcorrect_class_score = scores[y[i]]\n\tfor j in xrange(num_classes):\n\t\tif j == y[i]:\n\t\t\tcontinue\n\t\tmargin = scores[j] - correct_class_score + delta\n\t\tif margin > 0:\n \t\t loss += margin\n\t\t#loss += max(0, margin)\n\t\t ddW[j] += X[:,i] # the error btw analytical and numerical in this approach is too much because ddWyi += ddW[j] ddW[y[i]] = -ddWyi weren't done.\n \tdW += ddW\n loss /= num_train\n dW /= num_train\n \n\n loss += 0.5 * reg * np.sum(W * W)\n dW += reg*W\n #\n \"\"\"\n \"\"\"\n ############## erste loesung\n\n for i in xrange(num_train):\n \tddW = np.zeros(W.shape)\n \tddWyi = np.zeros(W[0].shape) ###?????????????????\n \tscores = W.dot(X[:, i])\n \tcorrect_class_score = scores[y[i]]\n \tfor j in xrange(num_classes):\n \t\tif j == y[i]:\n \t\tcontinue\n \t\tmargin = scores[j] - correct_class_score + delta\n \t\tif margin > 0:\n \t\t loss += margin\n \t\t ddW[j] = X[:, i] ## be careful, it's a reference\n \t\t ddWyi += ddW[j] #important to get correct results\n \tddW[y[i]] = -ddWyi #important to get correct results\n \tdW += ddW\n \n # divided by num_train\n loss /= num_train\n dW /= num_train\n \n # add regularization term\n loss += 0.5 * reg * np.sum(W * W)\n dW += reg * W\n \"\"\"\n \"\"\"\n def svm_loss_naive(W, X, y, reg):\n\n Structured SVM loss function, naive implementation (with loops)\n Inputs:\n - W: C x D array of weights\n - X: D x N array of data. Data are D-dimensional columns\n - y: 1-dimensional array of length N with labels 0...K-1, for K classes\n - reg: (float) regularization strength\n Returns:\n a tuple of:\n - loss as single float\n - gradient with respect to weights W; an array of same shape as W\n \n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[0]\n num_train = X.shape[1]\n loss = np.zeros(1)\n for i in xrange(num_train):\n scores = W.dot(X[:, i])\n correct_class_score = scores[y[i]]\n init = 0\n for j in xrange(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n\tinit +=1\n dW[j,:] += X[:,i]\n dW[y[i],:] += -init*X[:,i]\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train \n dW /= num_train\n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W)\n dW += reg*W\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n \"\"\"\n ### zweite und am besten loesong\n\n # compute the loss and the gradient\n num_classes = W.shape[0]\n num_train = X.shape[1]\n\n grad_helper = np.zeros([num_classes, num_train])\n loss = 0.0\n for i in xrange(num_train):\n scores = W.dot(X[:, i])\n correct_class_score = scores[y[i]]\n for j in xrange(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n grad_helper[j,i] += 1\n grad_helper[y[i],i] -= 1\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n\n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W)\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n \n dW = grad_helper.dot(X.T)\n dW /= num_train\n dW += reg * W\n return loss, dW","title":""},{"docid":"7fb9bc0ee0668eb0e9a692ce3d1f58f6","score":"0.6186058","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n # print(\"ini Shape:\", dW.shape, W.shape)\n\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in xrange(num_train):\n # for each training example\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n count = 0\n for j in xrange(num_classes):\n # for each class\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n count += 1\n loss += margin\n dW[:, j] += X[i]\n # print(\"ffi Shape:\", dW.shape)\n dW[:, y[i]] -= count * X[i]\n # print(\"f Shape:\", dW.shape)\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n dW /= num_train\n # print(\"o Shape:\", dW.shape)\n\n # Add regularization to the loss.\n # print(\"Loss before reg: \", loss)\n loss += 0.5 * reg * np.sum(W * W)\n # print(\"Loss after reg: \", loss)\n dW += reg * W\n # print(\"o2 Shape:\", dW.shape)\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"0965c85b990b083ff105f5f6d16c6c07","score":"0.6181599","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in range(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n for j in range(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n dW[:, j] += X[i, :]\n dW[:, y[i]] += -X[i, :]\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n dW /= num_train\n\n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n dW += reg * W\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n #############################################################################\n # NUMERICAL way of gradient esitmation, which is super slow and not accurate\n # h = 0.0001 \n # for ii in range(dW.shape[0]):\n # for jj in range(dW.shape[1]):\n #\n # W_after = W\n # W_after[ii, jj] += h\n # loss_after = 0.0\n # for i in range(num_train):\n # scores = X[i].dot(W_after)\n # correct_class_score = scores[y[i]]\n # for j in range(num_classes):\n # if j == y[i]:\n # continue\n # margin = scores[j] - correct_class_score + 1 # note delta = 1\n # if margin > 0:\n # loss_after += margin\n #\n # # Right now the loss is a sum over all training examples, but we want it\n # # to be an average instead so we divide by num_train.\n # loss_after /= num_train\n #\n # # Add regularization to the loss.\n # loss_after += reg * np.sum(W * W)\n #\n # # update dW\n # dW[ii, jj] = (loss_after - loss) / h\n #############################################################################\n # ANALYTICAL way of gradient calculation, which is fast and accurate\n # Reference: https://math.stackexchange.com/questions/2572318/derivation-of-gradient-of-svm-loss\n # Reference: http://cs231n.github.io/linear-classify/#svm\n # Reference: https://blog.csdn.net/lanchunhui/article/details/70991228\n ### Loss function for i-th sample (TeX):\n # \\begin{split}\n # L_i =&\\sum_{j\\neq y_i}\\max(0, s_j-s_{y_i}+1)\\\\\n # =&\\sum_{j\\neq y_i} \\left[ \\max(0, w_j^Tx_i - w_{y_i}^Tx_i + 1) \\right]\n # \\end{split}\n ### Gradient Derivation (TeX):\n # \\begin{split}\n # &\\nabla_{w_{y_i}} L_i = - \\left( \\sum_{j\\neq y_i} \\mathbb{1}(w_j^Tx_i - w_{y_i}^Tx_i + \\Delta > 0) \\right) x_i\\\\\n # &\\nabla_{w_j} L_i = \\mathbb{1}(w_j^Tx_i - w_{y_i}^Tx_i + \\Delta > 0) x_i\\quad j\\neq y_i\n # \\end{split}\n ### implementation is embeded in the code above\n # TEST: run the following test to check\n # import numpy as np\n # a = np.zeros((3, 2))\n # b = np.random.rand(6)\n # b = np.reshape(b, (2, 3))\n # a[:,0] += b[0,:]\n #############################################################################\n \n pass\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n \n return loss, dW","title":""},{"docid":"1a59c3e229f7523304177e3c002f0fb6","score":"0.61783314","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in loss. #\n #############################################################################\n n = X.shape[0]\n wx = X.dot(W) #calculate wx\n wyix = wx[np.arange(n),y,None] #extract all the predictions for the correct class\n P = wx - wyix + 1 #calculate loss\n P[np.arange(n),y] = 0 #set correct class prediction to zero\n P = np.maximum(0,P) # min threshold to zero\n loss = np.sum(P)/n\n reg_loss = reg * np.sum(W*W)\n tot_loss = loss +reg_loss\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dW. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n P[P>0] = 1 \n P[np.arange(n),y] = -np.sum(P, axis=1)\n #print(P[:10,:])\n dW = (X.T.dot(P))/n + reg * 2 * W\n \n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"a40166bc89cf0ab5b6c839ad640006fd","score":"0.61744106","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in range(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n for j in range(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n # This is the wrong class for this data, but it still receives a\n # good score relative to the correct class\n # This contributes to the loss\n loss += margin\n # The gradient for this class is increased by the value of the\n # data in all dimensions for this datapoint\n dW[:,j] += X[i]\n # This also contributes to decrease the gradient of the correct class\n # in reverse proportions\n dW[:,y[i]] -= X[i]\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n\n # Average gradient as well\n dW /= num_train\n\n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W)\n\n # Also add regularisation to the gradient\n # regularization matrix is 0.5 * reg * W*W, so dReg = 0.5 * reg * 2 * W\n dW += reg*W\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n\n\n return loss, dW","title":""},{"docid":"e71b6936010e992af3bff990da27310b","score":"0.6157359","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in range(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n count=0\n\n for j in range(num_classes):\n if j==y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n dW[:,j]+=X[i]\n count+=1\n\n #the gradient has to be calculated in two parts where one part is the row corresponding to the correct class\n dW[:,y[i]]+=-count*X[i]\n \n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n\n #average dW as well\n dW /= num_train\n\n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n\n # Also add regularization to the gradient\n dW += 2*reg*W\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n pass\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n \n return loss, dW","title":""},{"docid":"d8fd1ab1778ffaffb90e8ee2ede17194","score":"0.61330837","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n dWInstance = np.zeros(W.shape)\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in range(num_train):\n dWInstance = np.zeros(W.shape)\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n for j in range(num_classes):\n \n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n \n if margin > 0:\n loss += margin\n dWInstance[:,j] = X[i] #grad\n\n RowSum = dWInstance.sum(axis=1) #grad\n dWInstance[:,y[i]] = -RowSum #grad\n dW += dWInstance #grad\n\n \n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n dW /= num_train\n\n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n #reg gradient coming later\n dW += reg * 2 * W\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n \n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n \n return loss, dW","title":""},{"docid":"21da71440abd493d8c2650f80aee1ffd","score":"0.6111685","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in xrange(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n num_classes_greater_margin = 0\n for j in xrange(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n num_classes_greater_margin += 1\n # Gradient for non-correct class weight\n dW[:,j] += X[i,:]\n loss += margin\n # Gradient for correct class weight\n dW[:,y[i]] += -X[i,:]*num_classes_greater_margin \n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n\n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n # Average our gradient across the batch and add gradient of regularization term\n dW = dW/num_train + 2*reg*W\n\n return loss, dW","title":""},{"docid":"0bfd659025bba8840cdf60a2fd452c69","score":"0.6101851","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in range(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n\n for j in range(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n dW[:,j] += X[i]\n dW[:,y[i]] -= X[i]\n \n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n \n loss /= num_train\n dW /= num_train\n\n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n dW += 2 * reg * W \n\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather than first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n \n return loss, dW","title":""},{"docid":"c3e97745039ddd9383df2f09f5a70316","score":"0.60587275","text":"def classify(DD, CD):\n\n # do K-fold cross validation\n all_data = np.concatenate((DD,CD))\n all_class = np.vstack((np.ones((DD.shape[0], 1)), np.zeros((CD.shape[0], 1))))\n N = all_data.shape[0]\n K = 5\n accuracies = np.zeros((K, 1))\n sensitivities = np.zeros((K, 1))\n specificities = np.zeros((K, 1))\n randIndices = np.random.permutation(range(0, N))\n\n for fold in range(0, K) :\n i_test = randIndices[fold * (N // K) : (fold + 1) * (N // K)]\n i_train = [val for val in randIndices if val not in i_test]\n c_train = all_class[i_train]\n c_test = all_class[i_test] \n\n # train the model using features all_data[i_train,:] and classes c_train \n # TODO\n my_model = svm.SVC(kernel=\"linear\")\n c_train = np.ravel( c_train )\n my_model.fit( all_data[i_train, :] , c_train )\n \n my_predictions = my_model.predict(all_data[i_test, :])\n\n # # compute the % errors using all_data[i_test,:], c_test, and the model you trained\n # # TODO\n TP = len( [i for i in range(0,c_test.size) if my_predictions[i]==1 and c_test[i]==1] )\n TN = len( [i for i in range(0,c_test.size) if my_predictions[i]==0 and c_test[i]==0] )\n FP = len( [i for i in range(0,c_test.size) if my_predictions[i]==1 and c_test[i]==0] )\n FN = len( [i for i in range(0,c_test.size) if my_predictions[i]==0 and c_test[i]==1] )\n\n accuracies[fold] = (TP + TN) / (TP + TN + FP + FN)\n sensitivities[fold] = TP / (TP + FN)\n specificities[fold] = TN / (TN + FP)\n\n\n # report result mean and variance, over all folds, of each of accuracy, specificity, and sensitivity\n # TODO\n print('mean accurary', np.mean(accuracies))\n print('mean sensitivity', np.mean(sensitivities))\n print('mean specificity', np.mean(specificities))\n print('variance accurary', np.var(accuracies))\n print('variance sensitivity', np.var(sensitivities))\n print('variance specificity', np.var(specificities))","title":""},{"docid":"c8c33baea9e2e07946e8d248f4db22b6","score":"0.60409695","text":"def softmax_classifier(W, input, label, lamda):\n\n ############################################################################\n # TODO: Put your code here\n# print(\"INPUT:\",input.shape)\n# print(\"W:\",W.shape) \n# print(\"label:\",label.shape)\n \n # Pre-processing\n N, D = input.shape\n C = W.shape[1]\n \n # Scores\n s = np.dot(input,W) # (NxC)\n prediction = s.argmax(axis=1) # (N,)\n \n # Loss\n e_s = np.exp(s) # (NxC)\n e_y = np.sum( (e_s * label), axis=1 ) # (N,)\n e_sum = np.sum(e_s, axis=1) # (N,)\n \n loss = np.sum( -np.log( e_y / e_sum ) )\n \n # Gradient\n ds = e_s / e_sum[:,None] # (NxC)\n ds -= label # (NxC)\n gradient = np.dot(input.T, ds)\n \n # Average loss\n loss /= N\n gradient /= N\n\n # Regularization\n loss += lamda * np.sum(W*W)\n gradient += lamda * 2 * W\n\n ############################################################################\n\n return loss, gradient, prediction","title":""},{"docid":"250f03465ccedb8e0aa9bc2f53294de5","score":"0.6033295","text":"def svm_loss_vectorized(theta, X, y, reg):\n J = 0.0\n dtheta = np.zeros(theta.shape) # initialize the gradient as zero\n delta = 1.0\n m = X.shape[0]\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the structured SVM loss, storing the #\n # result in variable J. #\n #############################################################################\n \n scores = X.dot(theta)\n #print scores[np.arange(m),y].shape\n margin = np.maximum(0, (scores.T - scores[np.arange(m),y]).T + delta)\n margin[np.arange(m),y]=0\n J = 1./m * np.sum(margin) + 0.5*reg*np.sum(theta.T.dot(theta))\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n #This mask flags the examples whose margin is greater than 0\n X_mask = np.zeros(margin.shape)\n X_mask[margin>0] = 1\n \n #count number of examples with margin>0\n c = np.sum(X_mask,axis=1)\n X_mask[np.arange(m),y] = -c\n \n dtheta = X.T.dot(X_mask)\n dtheta /= m\n dtheta += reg * theta\n\n\n #############################################################################\n # TODO: #\n # Implement a vectorized version of the gradient for the structured SVM #\n # loss, storing the result in dtheta. #\n # #\n # Hint: Instead of computing the gradient from scratch, it may be easier #\n # to reuse some of the intermediate values that you used to compute the #\n # loss. #\n #############################################################################\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return J, dtheta","title":""},{"docid":"82f7967608dd8317e017e43eda23c1d2","score":"0.5975871","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in xrange(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n for j in xrange(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n\n if margin > 0:\n loss += margin\n # calcualte tmp_dW if margin is larger than 0\n tmp_dW = np.zeros(W.shape)\n tmp_dW[:, j] = X[i, :]\n tmp_dW[:, y[i]] = - X[i, ]\n dW = dW + tmp_dW\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n dW /= num_train\n\n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n dW = dW + 2 * reg * W\n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dW. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n return loss, dW","title":""},{"docid":"2c20daa97013ebabc246b7d4f5bac4d3","score":"0.5968399","text":"def svm_loss_vectorized(W, X, y, reg):\n\n n_sample, n_feature = X.shape\n assert n_feature == W.shape[0]\n\n scores = np.dot(X,W)\n correct_class_scores = scores[np.arange(n_sample),y].reshape(n_sample,1)\n scores = np.maximum(0, scores - correct_class_scores + 1.0)\n\n scores[np.arange(n_sample),y] = 0\n num_pos = np.sum(scores > 0, axis = 1)\n\n hinge_loss = np.sum(scores) / n_sample\n reg_loss = 0.5 * reg * np.sum(W ** 2)\n loss = hinge_loss + reg_loss\n\n dscores = np.zeros(scores.shape)\n dscores[scores > 0] = 1\n dscores[np.arange(n_sample),y] -= num_pos\n dW = np.dot(X.T,dscores) / n_sample + reg * W\n\n return loss, dW","title":""},{"docid":"dd02a666c89c9f7f5c4a5bbfdf193205","score":"0.59637064","text":"def svm_loss_vectorized(W, X, y, reg):\n num_train = X.shape[0]\n\n # Here's a bit faster, yet still naive approach that we're going to vectorize:\n #\n # for i in xrange(num_train):\n # scores = X[i].dot(W)\n # correct_class_score = scores[y[i]]\n # loss_values = np.maximum(scores - correct_class_score + 1, 0)\n # loss_values[y[i]] = 0\n # loss += np.sum(loss_values)\n\n # Compute the scores in one multiply.\n scores = X.dot(W)\n\n # Get the correct values in one slicing operation -> [np.arange(num_train), y].\n # Note that `correct_scores` is an array of shape `(num_train)`.\n # See advanced indexing: http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html\n # The other option is sadly slow:\n # correct_scores = np.diag(scores[y,:])\n correct_scores = scores[np.arange(num_train), y]\n\n # The broadcasting rules aren't trivial.\n # Can't compute `scores - correct_scores`, but can if transpose `scores` first.\n # See http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html\n loss_values = (scores.T - correct_scores + 1).T\n loss_values[np.arange(num_train), y] = 0\n hinge_values = np.maximum(loss_values, 0)\n\n # Now it's pretty easy.\n loss = np.sum(hinge_values) / num_train\n loss += 0.5 * reg * np.sum(W * W)\n\n # Compute the gradient.\n # Idea: perform the two operations simultaneously\n # (1) for all j: dW[j,:] = sum_{i, j produces positive margin with i} X[:,i].T\n # (2) for all i: dW[y[i],:] = sum_{j != y_i, j produces positive margin with i} -X[:,i].T\n\n # First transform `hinge_values` is a matrix of zeros and ones.\n # The values of `X` should be added to `dW` where the coefficient is one (1).\n grad_coeff = hinge_values\n grad_coeff[hinge_values > 0] = 1\n\n # Also the values of `X` should be subtracted from `dW` on the correct classes -> [np.arange(num_train), y] (2).\n # The number of times it's subtracted corresponds to the number of positive margins, i.e. the sum per each row.\n sums_per_row = np.sum(grad_coeff, axis=1)\n grad_coeff[np.arange(num_train), y] = -sums_per_row[np.arange(num_train)]\n\n # Finally add up all values of `X` with computed coefficients.\n dW = np.dot(X.T, grad_coeff)\n\n # Boilerplate stuff.\n dW /= num_train\n dW += reg * W\n\n return loss, dW","title":""},{"docid":"7ca75532a856848b5f660fdb4fc60287","score":"0.5937453","text":"def svm_loss(x, y):\n m = x.shape[0]\n correct_class_output = x[np.arange(m), y]\n margins = np.maximum(0, x - correct_class_output[:, np.newaxis] + 1.0)\n margins[np.arange(m), y] = 0\n loss = np.sum(margins) / m\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(m), y] -= num_pos\n dx /= m\n return loss, dx","title":""},{"docid":"2abe517eeba099e79c6d652eefde7cc7","score":"0.58827865","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n dx /= N\n return loss, dx","title":""},{"docid":"2abe517eeba099e79c6d652eefde7cc7","score":"0.58827865","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n dx /= N\n return loss, dx","title":""},{"docid":"2abe517eeba099e79c6d652eefde7cc7","score":"0.58827865","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n dx /= N\n return loss, dx","title":""},{"docid":"590ce80f6d8d006500c2fa24b56deb8d","score":"0.5872257","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n\n dx /= N\n return loss, dx","title":""},{"docid":"20f303b247a3061b9e2686ec9b85c333","score":"0.5866176","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_classes = W.shape[0]\n num_train = X.shape[1]\n loss = 0.0\n for i in xrange(num_train):\n scores = W.dot(X[:, i])\n correct_class_score = scores[y[i]]\n for j in xrange(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n dW[j, :] += X[:, i].T \n dW[y[i], :] -= X[:, i].T\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n \n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W)\n dW /= num_train\n \n dW += reg* W\n\n\n\n return loss, dW","title":""},{"docid":"9bc9b062dfac061e4e075ebf69aca767","score":"0.5829779","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n dx /= N\n return loss, dx","title":""},{"docid":"9bc9b062dfac061e4e075ebf69aca767","score":"0.5829779","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n dx /= N\n return loss, dx","title":""},{"docid":"9bc9b062dfac061e4e075ebf69aca767","score":"0.5829779","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n dx /= N\n return loss, dx","title":""},{"docid":"9bc9b062dfac061e4e075ebf69aca767","score":"0.5829779","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n dx /= N\n return loss, dx","title":""},{"docid":"9bc9b062dfac061e4e075ebf69aca767","score":"0.5829779","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n dx /= N\n return loss, dx","title":""},{"docid":"50da406f1ac82f85ce9555c4c5d25ec6","score":"0.5818512","text":"def svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n num_classes = W.shape[1]\n num_train = X.shape[0]\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n class_scores = np.dot(X,W)\n \n correct_class_scores = np.choose(y, class_scores.T)\n\n #Loss\n mask = np.ones(class_scores.shape, dtype=bool)\n mask[range(class_scores.shape[0]), y] = False\n wrong_class_scores = class_scores[mask].reshape(class_scores.shape[0],class_scores.shape[1]-1)\n margins = wrong_class_scores - correct_class_scores[...,np.newaxis] + 1\n margins[margins < 0] = 0\n loss = np.sum(margins) / num_train + reg * np.sum(W * W)\n\n #Gradient\n \n margins = class_scores - correct_class_scores[...,np.newaxis] + 1\n mask = (margins > 0 ).astype(float)\n #Correct class always has margin 1, so it is counted in mask\n num_scores_greater_zero = mask.sum(1) - 1\n mask[range(num_train),y] = -num_scores_greater_zero\n dW = np.dot(X.T,mask)\n\n dW = dW / num_train + 2 * reg * W\n\n\n\n return loss, dW","title":""},{"docid":"37b636fe872a25da76353ae72e3855d3","score":"0.581444","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(\n 0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n dx /= N\n return loss, dx","title":""},{"docid":"0d81075c27be84d6bf3e3ef430f5b6c1","score":"0.58142465","text":"def binary_svm_loss(theta, X, y, C):\n\n m, d = X.shape\n grad = np.zeros(theta.shape)\n J = 0\n\n ############################################################################\n # TODO #\n # Implement the binary SVM hinge loss function here #\n # 4 - 5 lines of vectorized code expected #\n ############################################################################\n correctness = y * X.dot(theta)\n J = 1./(2*m) * sum(theta**2) + 1.*C/m * sum(1-correctness[correctness<1])\n grad = 1./m * theta + 1.*C/m * -y[correctness<1].dot(X[correctness<1])\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return J, grad","title":""},{"docid":"62e1d55d6013849dac8d5675923c852c","score":"0.58121675","text":"def svm_loss(x, y):\r\n N = x.shape[0]\r\n correct_class_scores = x[np.arange(N), y]\r\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\r\n margins[np.arange(N), y] = 0\r\n loss = np.sum(margins) / N\r\n num_pos = np.sum(margins > 0, axis=1)\r\n \r\n dx = np.zeros_like(x)\r\n dx[margins > 0] = 1\r\n dx[np.arange(N), y] -= num_pos\r\n dx /= N\r\n \r\n return loss, dx","title":""},{"docid":"86a1a78ec2bdf183c6993a2bafdc6972","score":"0.57685","text":"def svm_loss_naive_original(W, X, y, reg):\n\n dW = np.zeros(W.shape) # initialize the gradient as zero\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in xrange(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n for j in xrange(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n \n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n loss += reg * np.sum(W * W) # Add regularization to the loss.\n return loss, dW","title":""},{"docid":"001cb9af979e1ad26e15fe43f21b3dd3","score":"0.57509357","text":"def train(self, input_vec, true_label, iteration_cnt, gamma_0, d):\n input_vec =np.array(input_vec, ndmin = 2).T # column array vector\n interm_val_vec_1 = act_func(np.dot(self.wt_mat_layer_0, input_vec)) # col vector\n interm_val_vec_2 = act_func(np.dot(self.wt_mat_layer_1, np.concatenate((interm_val_vec_1, [[1]]), axis = 0)))\n output = np.dot(self.wt_mat_layer_2, np.concatenate((interm_val_vec_2, [[1]]), axis = 0))\n # calculate the partial derivative matrix\n # gradient W_2\n output_error = output - true_label\n grad_w_2 = output_error*(np.concatenate((interm_val_vec_2, [[1]]), axis = 0)).T\n # gradient W_1\n hidden_error_vec_2 = output_error*(self.wt_mat_layer_2[0,:][:-1]) # row vector\n temp = hidden_error_vec_2*(interm_val_vec_2.T) *(1- (interm_val_vec_2.T) ) # row vector\n tt = np.concatenate((interm_val_vec_1, [[1]]), axis = 0)\n grad_w_1 = np.dot( tt, temp).T #matrix\n # gradient W_0\n alpha_vec = self.wt_mat_layer_2[0,:][:-1]\n beta_vec = phi(interm_val_vec_2.T) # row vec\n ab = alpha_vec*beta_vec # row \n tpp = np.zeros((self.num_of_hidden_1_nodes -1,1 ))\n for i in range(self.num_of_hidden_1_nodes -1):\n tpp[i,0] = output_error*np.inner(ab, self.wt_mat_layer_1[:,i].T)*phi(interm_val_vec_1.T)[0,i]\n grad_w_0 = np.dot(tpp, input_vec.T)\n # update W_2,W_1, W_0 weights\n self.wt_mat_layer_2 = self.wt_mat_layer_2 - gamma(iteration_cnt, gamma_0, d)*grad_w_2\n self.wt_mat_layer_1 = self.wt_mat_layer_1 - gamma(iteration_cnt, gamma_0, d)*grad_w_1 \n self.wt_mat_layer_0 = self.wt_mat_layer_0 - gamma(iteration_cnt, gamma_0, d)*grad_w_0 \n iteration_cnt = iteration_cnt + 1\n return iteration_cnt","title":""},{"docid":"6075e3fb716491215fd63e16f1c8a0cd","score":"0.57156193","text":"def svm_loss(x, y):\n N = x.shape[0]\n correct_class_scores = x[np.arange(N), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0) # np.newaxis 插入新维度，方便广播\n margins[np.arange(N), y] = 0\n loss = np.sum(margins) / N\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(N), y] -= num_pos\n dx /= N\n\n return loss, dx","title":""},{"docid":"e2b1b5e3918b9075df5275227562ee31","score":"0.5711591","text":"def svm_loss(x, y):\n m = x.shape[0]\n correct_class_scores = x[np.arange(m), y]\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\n margins[np.arange(m), y] = 0\n loss = np.sum(margins) / m\n num_pos = np.sum(margins > 0, axis=1)\n dx = np.zeros_like(x)\n dx[margins > 0] = 1\n dx[np.arange(m), y] -= num_pos\n dx /= m\n return loss, dx","title":""},{"docid":"bb3c3011dc71408d87b89d40ed2c66e0","score":"0.570973","text":"def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n\n # compute the loss and the gradient\n num_train = X.shape[0]\n num_classes = W.shape[1]\n loss = 0.0\n for i in xrange(num_train):\n scores = X[i].dot(W)\n correct_class_score = scores[y[i]]\n for j in xrange(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1 # note delta = 1\n if margin > 0:\n loss += margin\n dW[:,j ] += X[i,:].T # the grad from class j\n dW[:,y[i]] -= X[i,:].T # the grad from class y[i]\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n dW /= num_train\n\n # Add regularization.\n loss += 0.5 * reg * np.sum(W * W)\n dW += reg * W\n\n return loss, dW","title":""},{"docid":"0f1689166a60cc4b38361eac318bc8a2","score":"0.5695019","text":"def _trainSVMBestC(k_m, labels, model_dir, inds, tags, pb):\n for ind in inds:\n label = labels[:,ind]\n #print \"train %dth fingerprint now (select C)...\" % (ind+1)\n C = numpy.array([2**-5,2**-4,2**-3,2**-2,2**-1,2**0,2**1,2**2,2**3,\n 2**4,2**5,2**6,2**7,2**8,2**9,2**10])\n n = len(k_m)\n accs = []\n #res = result(ind)\n for c in C:\n pred = numpy.zeros(n) # store the predict output\n for i in range(1,6):\n test = tags==i\n train = ~(tags==i)\n test = numpy.array(range(len(k_m)))[test].tolist()\n train = numpy.array(range(len(k_m)))[train].tolist()\n\n train_km = k_m[numpy.ix_(train,train)]\n test_km = k_m[numpy.ix_(test,train)]\n train_label = label[train]\n test_label = label[test]\n n_train = len(train_km)\n n_test = len(test_km)\n\n # formating for libsvm\n train_km = numpy.append(numpy.array(range(1,n_train+1)).reshape(n_train,1), train_km,1).tolist()\n test_km = numpy.append(numpy.array(range(1,n_test+1)).reshape(n_test,1), test_km,1).tolist()\n\n prob = svm_problem(train_label, train_km, isKernel=True)\n param = svm_parameter('-t 4 -c %f -b 0 -q' % c)\n m = svm_train(prob,param)\n p_label, p_acc, p_val=svm_predict(test_label,test_km, m,'-b 0 -q')\n pred[numpy.ix_(test)] = p_label\n acc = sum(pred==label) / float(n)\n accs.append(acc)\n accs = numpy.array(accs)\n best_c = C[accs==max(accs)][0]\n\n # find the best c, using the best c to train\n kernel = numpy.append(numpy.array(\n range(1,n+1)).reshape(n,1),k_m,1).tolist()\n\n if pb:\n prob = svm_problem(label, kernel, isKernel=True)\n param = svm_parameter('-t 4 -c %f -b 1 -q' % best_c)\n m = svm_train(prob, param)\n svm_save_model('%s/%d.model' % (model_dir,ind), m)\n else:\n prob = svm_problem(label, kernel, isKernel=True)\n param = svm_parameter('-t 4 -c %f -b 0 -q' % best_c)\n m = svm_train(prob, param)\n svm_save_model('%s/%d.model' % (model_dir,ind), m)\n\n #res.ind = ind\n #res.acc = max(accs)\n #res.model = m\n #Queue.put(res)","title":""},{"docid":"fbf5a3f2500fce00e4f49b1e25dfabc5","score":"0.5672929","text":"def svm_loss_naive(theta, X, y, reg):\n\n delta = 1.0\n dtheta = np.zeros(theta.shape) # initialize the gradient as zero\n\n # compute the loss function\n\n K = theta.shape[1]\n m = X.shape[0]\n J = 0.0\n for i in xrange(m):\n scores = X[i,:].dot(theta)\n correct_class_score = scores[y[i]]\n for j in xrange(K):\n if j == y[i]:\n continue\n margin = max(0,scores[j] - correct_class_score + delta)\n J += margin\n p = scores[j] - correct_class_score + delta\n if p > 0:\n dtheta[:,j] += X[i]\n dtheta[:,y[i]] -= X[i]\n\n # Right now the loss is a sum over all training examples, but we want it\n # To be an average instead so we divide by num_train.\n J /= m\n dtheta /= m\n\n # Add regularization to the loss.\n J += 0.5 * reg * np.sum(theta * theta)\n dtheta += reg * theta\n \n #############################################################################\n # TODO: #\n # Compute the gradient of the loss function and store it dtheta. #\n # Rather that first computing the loss and then computing the derivative, #\n # it may be simpler to compute the derivative at the same time that the #\n # loss is being computed. As a result you may need to modify some of the #\n # code above to compute the gradient. #\n #############################################################################\n \n return J, dtheta","title":""},{"docid":"a4fe5e6d979c9d83caa6664147fa441e","score":"0.56671536","text":"def lin_svm_optimize(x_train, y_train, C=1):\n\n lin_svm = svm.SVC(kernel='linear', C=C, class_weight='balanced', random_state=1)\n lin_scores = cross_val_score(lin_svm, x_train, y_train, cv=5)\n lin_avg = sum(lin_scores) / len(lin_scores)\n return lin_avg","title":""},{"docid":"5bfe263ec4f465303e1cab997504ee02","score":"0.56584895","text":"def softmax_loss_vectorized(W, X, y, reg):\n# print y.shape\n# print X.shape\n# Label_data = np.dstack((y,X))\n# print Label_data\n# Label_data.sort()\n# y = Label_data[:,0]\n# x = Label_data[:,1:]\n\n #Sorting the input based on the class\n num_classes = W.shape[1]\n num_train = X.shape[0]\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n X1 = X.dot(W)\n scores = pow(math.e,X1)\n #normarlizing the score\n scores = (scores.T/np.sum(scores, axis = 1)).T\n \n Y = np.zeros([num_train, num_classes])\n for i,row in enumerate(Y):\n row[y[i]] = 1\n \n loss_ma = np.ma.log(scores*Y)\n loss = np.sum( - loss_ma.filled(0))/num_train + 0.5 * reg * np.sum(W * W) \n \n dX1 = scores - Y\n dW = ((dX1.T).dot(X)).T/num_train + reg*W\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n pass\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"12244802ac27a1455b16b24a13c33f03","score":"0.5654248","text":"def predict(self, X, loss_func=hamming_losses) :\r\n\r\n n,d = X.shape\r\n num_classes, num_classifiers = self.R.shape\r\n\r\n # setup predictions\r\n y = np.zeros(n)\r\n\r\n ### ========== TODO : START ========== ###\r\n # part d: predict multiclass class\r\n #\r\n # HERE IS ONE WAY (THERE MAY BE OTHER APPROACHES)\r\n #\r\n # for each example\r\n # predict distances to hyperplanes using SVC.decision_function(...)\r\n # find class with minimum loss (be sure to look up in self.classes)\r\n #\r\n # if you have a choice between multiple occurrences of the minimum values,\r\n # use the index corresponding to the first occurrence\r\n for i in range(n):\r\n dists = np.empty(num_classifiers)\r\n for j in range(num_classifiers):\r\n X_i_reshaped = X[i,:].reshape(1,-1)\r\n dist = self.svms[j].decision_function(X_i_reshaped)\r\n\r\n dists[j] = dist\r\n\r\n\r\n losses = loss_func(self.R, dists)\r\n best_class_index = np.argmin(losses)\r\n y[i] = self.classes[best_class_index]\r\n\r\n ### ========== TODO : END ========== ###\r\n\r\n return y","title":""},{"docid":"a72e93f22fa54c167f86f8c1cf24ad44","score":"0.5653842","text":"def train_svms_with(sample_points: torch.tensor, sample_labels: torch.tensor,\r\n *hidden_states: typing.Tuple[torch.tensor]) -> SVMTrajectory:\r\n\r\n num_points = sample_points.shape[0]\r\n train_points = int(num_points * (13.0/15.0))\r\n output_dim = sample_labels.max().item()\r\n if not isinstance(output_dim, int):\r\n raise ValueError(f'expected output_dim is int, got {output_dim}')\r\n\r\n overall = []\r\n\r\n if output_dim > 2:\r\n masks = [sample_labels == lbl for lbl in range(output_dim)]\r\n by_label_vs_all = []\r\n\r\n for state in hidden_states:\r\n if not torch.is_tensor(state):\r\n raise ValueError(f'expected hidden state is tensor, got {state} (type={type(state)})')\r\n if len(state.shape) != 2:\r\n raise ValueError(f'expected state has shape [batch_size, layer_size] but has shape {state.shape}')\r\n if state.shape[0] != num_points:\r\n raise ValueError(f'expected state has shape [batch_size={num_points}, layer_size] but has shape {state.shape}')\r\n if state.dtype == torch.float:\r\n state = state.double()\r\n elif state.dtype != torch.double:\r\n raise ValueError(f'expected state has dtype float-like, but has {state.dtype}')\r\n\r\n classifier = svm.LinearSVC(max_iter=5000)\r\n classifier.fit(state[:train_points], sample_labels[:train_points])\r\n mean_acc = classifier.score(state[train_points:], sample_labels[train_points:])\r\n\r\n if not isinstance(mean_acc, float):\r\n raise ValueError(f'expected mean_acc is float, got {mean_acc} (type={type(mean_acc)})')\r\n\r\n overall.append(mean_acc)\r\n\r\n if output_dim > 2:\r\n layer_accs = []\r\n for lbl in range(output_dim):\r\n classifier = svm.LinearSVC(max_iter=5000)\r\n classifier.fit(state[:train_points], masks[lbl][:train_points])\r\n mean_acc = classifier.score(state[train_points:], masks[lbl][train_points:])\r\n layer_accs.append(mean_acc)\r\n by_label_vs_all.append(layer_accs)\r\n\r\n if output_dim <= 2:\r\n return SVMTrajectory(overall=torch.tensor(overall, dtype=torch.double), by_label_vs_all=None)\r\n else:\r\n return SVMTrajectory(overall=torch.tensor(overall, dtype=torch.double),\r\n by_label_vs_all=torch.tensor(by_label_vs_all, dtype=torch.double))","title":""},{"docid":"aeb2bca0adc60305c9c69e952eda1fd0","score":"0.5641954","text":"def multi_contrastive_loss(y, d):\n global margin\n Q = 4\n print \"margin =\", margin\n\n label = K.transpose(y)\n p = K.transpose(d)\n return K.mean(K.cast(K.equal(label, 0), 'float') * p +\n K.cast(K.equal(label, 1), 'float') * ((K.maximum(margin - p, 0)))+\n K.cast(K.equal(label, 2), 'float') * K.maximum(margin - p, 0))\n # return K.mean((1 - label) * Q * K.square(d) + label * K.exp(-2.77 / Q * d))","title":""},{"docid":"90c557b0852621071686b68398e8582f","score":"0.5636727","text":"def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n # In this naive implementation we have a for loop over the N samples\n for i, x in enumerate(X):\n \n #############################################################################\n # TODO: Compute the cross-entropy loss using explicit loops and store the #\n # sum of losses in \"loss\". #\n # If you are not careful in implementing softmax, it is easy to run into #\n # numeric instability, because exp(a) is huge if a is large. #\n #############################################################################\n # TODO: should use explicit loops here\n z = x.dot(W) #calculate z based on the formula z = xW\n p = np.exp(z) / np.sum(np.exp(z)) #calculating the normalized probabilities\n pc = p[y[i]] #probability of the true/correct label\n loss += - np.log(pc) #finding, adding the cross-entropy loss for true label\n \n #############################################################################\n # TODO: Compute the gradient using explicit loops and store the sum over #\n # samples in dW. #\n #############################################################################\n \n # source https://cs231n.github.io/neural-networks-case-study/\n for j in range(W.shape[1]): # looping over 10 classes\n if j == y[i]: # checking whether the class is same or not\n dW[:, j] += (np.exp(z[y[i]]) / np.sum(np.exp(z)) - 1) * x\n # in case of the correct class, substract -1 for negative influence on\n # the loss\n else:\n dW[:, j] += np.exp(z[j]) / np.sum(np.exp(z)) * x\n # in other cases, perform basic calculus based on the gradient formula\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n # now we turn the sum into an average by dividing with N\n loss /= X.shape[0]\n dW /= X.shape[0]\n\n # Add regularization to the loss and gradients.\n loss += reg * np.sum(W * W)\n dW += reg * 2 * W\n\n return loss, dW","title":""},{"docid":"a120bba147dd6f3c9395f0a5b86e8202","score":"0.56269467","text":"def naiveSoftmaxLossAndGradient(centerWordVec, outsideWordIdx, outsideVectors, dataset):\n\n ### YOUR CODE HERE\n ### This is the basic version\n\n # 在HW中, outsideVectors 是 U, 每一列就是一个 word 的vector\n # 在代码中, 没必要 centerWordVec = centerWordVec.reshape(-1,1) # 初始化为列形式 = hw公式表示的初始形式\n # 直接放在dot的右边, 计算时, 就以列向量的形式进行broadcasting\n # 在代码中, outsideVectors 每一 row就是一个 word 的vector\n # softmax 的input应该是一个行向量. 如果是列向量, 就无法计算了.\n y_pred = softmax(np.dot(outsideVectors, centerWordVec)) # input 为一个1-array, numpy默认为行向量\n # print(\"#\"*30, \"\\n y_pred shape:\", y_pred.shape,\"\\n\",y_pred)\n\n ### Based on the provement (c i), the simplified CE is Negative Summation of (y_true*log(y_predict)),\n ### since y is one-hot vector, only the kth y is 1 and the other are 0.\n ### the loss should be -1*log(softmax(input));\n ### Therefore, we can just treat the outsideWordIdx of y_pred as y_hat\n loss = -np.log(y_pred[outsideWordIdx]) # 如果这里只有一个outsideword的话, 那么这样没问题. 如果不止一个, 则外面需要求和\n\n ### According to provement (c i), the gradient of loss related to center word is (y_pred - y_true)*U.T (如果都是vector,那么dot不影响)\n y_pred[outsideWordIdx] -= 1 # For grad_Vc of the outside words, the first part is y_pred - 1; 因为只有他们存在梯度,所有只有改变他们的必要\n gradCenterVec = np.dot(y_pred, outsideVectors) # 1x5 * 5x3 = 1x3 ~ gradCenterVec\n\n ### According to (c ii), the gradient of loss related to the one outside word is (y_pred - y_true)*v_c.T\n # gradOutsideVecs = np.dot(y_pred.reshape(y_pred.shape[0],1), centerWordVec.reshape(1, centerWordVec.shape[0]) )\n gradOutsideVecs = np.outer(y_pred, centerWordVec) # 5x1 * 1x3 = 5x3 ~ gradOutsideVecs\n # print(\"#\"*30,\"\\n\", gradOutsideVecs)\n ##########################################################\n\n ### END YOUR CODE\n\n return loss, gradCenterVec, gradOutsideVecs","title":""},{"docid":"d1e4d35ef33ce5b78c213cf48928ccba","score":"0.5607843","text":"def mc_lda_model(D, S, V, K, w_obs):\n\n alpha = tf.zeros(K) + 0.01\n\n beta, phi = [None] * S, [None] * S\n for s in range(S):\n beta[s] = tf.zeros(V[s]) + 0.01\n phi[s] = Dirichlet(concentration=beta[s],\n sample_shape=K)\n\n theta = [None] * D\n w = [[None] * S for d in range(D)]\n z = [[None] * S for d in range(D)]\n\n for d in range(D):\n theta[d] = Dirichlet(concentration=alpha)\n\n for s in range(S):\n w[d][s] = ParamMixture(mixing_weights=theta[d],\n component_params={'probs': phi[s]},\n component_dist=Categorical,\n sample_shape=len(w_obs[d][s]),\n validate_args=True)\n\n z[d][s] = w[d][s].cat\n\n return alpha, beta, theta, phi, z, w","title":""},{"docid":"30c055b587b6d4a0ce1a39a19b4490cc","score":"0.5603729","text":"def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n \n N=X.shape[0]\n C=W.shape[1] # num of classes\n D=W.shape[0] # D=3\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n\n for i in range(N):\n score=X[i,:] @ W\n score-=score.max() # shift to <0\n \n partition=np.sum(np.exp(score))\n\n margin=-score[y[i]]+np.log(partition)\n loss+=margin\n\n for j in range(C):\n for k in range(D):\n if j==y[i]:\n dW[k,j]-=X[i,k]\n dW[k,j]+=X[i,k]*np.exp(score[j])/partition\n \n loss/=N\n dW/=N\n\n loss+=reg*np.sum(W*W)\n dW+=2*reg*W\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"f8c597f569d3179544e25029a97afb59","score":"0.5599227","text":"def svm_loss(x, y):\n N = x.shape[0]\n x = x.reshape(-1)\n y = 2 * y - 1\n loss = np.zeros((len(x), 1))\n loss = np.maximum(0, 1 - y * x)\n loss = np.mean(loss)\n dx = np.zeros((len(x), 1))\n for i in range(len(x)):\n if x[i] * y[i] < 1: \n dx[i] = -y[i]\n dx = dx/np.shape(x)[0]\n dx = dx.reshape(-1)\n return loss, dx","title":""},{"docid":"466b9710fbfcf469cd66e2dde61e40c3","score":"0.5578418","text":"def nonlinearSVM(x, y, c, kernel='rbf', gamma='auto'):\n model = sklearn.svm.SVC(C=c, kernel=kernel, gamma=gamma, decision_function_shape='ovr')\n model.fit(x, y)\n return model","title":""},{"docid":"9af09f8007b5452f7c8771fa60ef4aa4","score":"0.5543761","text":"def svm_loss(x, y):\n\tN = x.shape[0]\n\tloss = 0\n\tdx = np.zeros(x.shape)\n\tfor i in range(0, N):\n\t\tif(y[i] == 0):\n\t\t\ty[i] = -1\n\t\ttemp = 1-x[i]*y[i]\n\t\tif(temp > 0):\n\t\t\tloss += temp\n\t\t\tdx[i] = -y[i]\n\t\t\t\n\tloss /= x.shape[0]\n\tdx /= x.shape[0]\n\treturn loss, dx","title":""},{"docid":"e9c6c6863117c60414e81c12625d1bd4","score":"0.55404276","text":"def train_sgd(train_features,\n train_labels,\n penalty_weights,\n skip_grid_search,\n evaluation,\n algorithm,\n num_jobs,\n penalty,\n alpha,\n l1_ratio):\n if algorithm == 'sgd-logistic':\n loss = 'log'\n elif algorithm == 'sgd-svm':\n loss = 'hinge'\n else:\n sys.exit('bad algorithm provided to sgd model.')\n \n # requested grid search. find best parameters, to achieve highest average score\n if not skip_grid_search:\n clf = grid_search.grid_search(evaluation,\n train_features,\n train_labels,\n penalty_weights,\n algorithm,\n num_jobs)\n \n params = clf.best_params_\n penalty = params['penalty']\n alpha = params['alpha']\n if 'l1_ratio' in params:\n l1_ratio = params['l1_ratio']\n # Now perform the training on full train data.\n n_iter = numpy.ceil(5*(10**6) / train_features.shape[0])\n model = SGDClassifier(loss=loss,\n penalty=penalty,\n alpha=alpha,\n l1_ratio=l1_ratio,\n class_weight=penalty_weights,\n n_iter=n_iter,\n n_jobs=num_jobs)\n model = model.fit(train_features, train_labels)\n \n return model","title":""},{"docid":"34f941f832a91d2d6a0012a755291f70","score":"0.5536596","text":"def compute_sigma_mles(train_data, train_labels):\r\n _, D = train_data.shape\r\n K = len(np.unique(train_labels))\r\n\r\n covariances = np.zeros((K, D, D))\r\n # Compute covariances\r\n\r\n for i in range(K):\r\n indices = train_data[train_labels == i]\r\n indices -= np.mean(indices, axis=0)\r\n covariances[i] = np.dot(np.transpose(indices), indices) / float(indices.shape[0]) + 0.01 * np.eye(D)\r\n\r\n return covariances","title":""},{"docid":"27748084996daa6bfb1fc3c2a03b3291","score":"0.5532704","text":"def loss_kd_class_weight(y_student, y_true, y_teacher, params):\n T = params.temperature\n class_num = torch.tensor([5000, 2997, 1796, 1077, 645, 387, 232, 139, 83, 50])\n reversed_class_num = 1.0 / class_num\n class_weight = reversed_class_num / reversed_class_num.sum()\n\n # take max confidence of student model as coefficient of CE loss_fn\n # max_prob = torch.max(F.softmax(y_student, dim=1).detach(), dim=1).values\n # take max confidence of teacher model as coefficient of CE loss_fn\n\n y_student_log_sm = F.log_softmax(y_student, dim=1) # N*C\n onehot_target = utils.scalar2onehot(y_true, params.num_classes).cuda() # N*C\n\n weighted_y_student_log_sm = y_student_log_sm * class_weight.cuda() # N*C\n loss_CE = - torch.sum(onehot_target * weighted_y_student_log_sm, dim=1) # N*1\n\n y_student_log_T_softmax = F.log_softmax(y_student / T, dim=1) # N*C\n y_teacher_T_softmax = F.softmax(y_teacher / T, dim=1) # N*C\n\n # max_prob = torch.max(F.softmax(y_teacher / T, dim=1).detach(), dim=1).values\n # max_prob = torch.max(y_teacher_T_softmax.detach(), dim=1).values\n # print('Max confidence of teacher model:', torch.mean(max_prob))\n # params.maxT = torch.mean(max_prob)\n\n kl_div_each = F.kl_div(y_student_log_T_softmax, y_teacher_T_softmax, reduction='none') * (T * T) # N*C\n\n loss_KLD = torch.mean(kl_div_each, dim=1) # N*1\n\n # print('\\nLoss_kd:\\nCE:{}\\nKLD:{}\\n'.format(loss_CE.detach()[0], loss_KLD.detach()[0]))\n _loss = torch.mean((1. - params.alpha) * loss_CE + params.alpha * loss_KLD)\n # loss = torch.mean(max_prob * loss_CE + (torch.ones_like(max_prob) - max_prob) * loss_KLD)\n\n return _loss","title":""},{"docid":"fb2e6f8177b0b3fdc9a5274644c3b357","score":"0.552854","text":"def loss(self, X, y=None, reg=0.0, verbose=False):\n # Unpack variables from the params dictionary\n W1, b1 = self.params['W1'], self.params['b1']\n W2, b2 = self.params['W2'], self.params['b2']\n N, D = X.shape\n num_train = N\n num_classes = D\n\n # Initialize intermediate values and local gradients for the stages\n # of the computational graph implementation of SVM\n \n dW1 = np.zeros(W1.shape)\n db1 = np.zeros(b1.shape)\n dW2 = np.zeros(W2.shape)\n db2 = np.zeros(b2.shape)\n \n scores = np.zeros((num_train,num_classes)) # intermediate value scores\n dscores = np.zeros((num_train,num_classes)) # and its gradient\n\n norm_scores = np.zeros(scores.shape) # intermediate value margins\n dnorm_scores = np.zeros(scores.shape) # and its gradient\n dnorm_scores_1 = np.zeros(scores.shape) # and its gradient\n\n correct_scores = np.zeros(num_train) # intermediate value margins\n dcorrect_scores = np.zeros(num_train) # and its gradient\n \n exp_scores = np.zeros(scores.shape) # intermediate value margins\n dexp_scores = np.zeros(scores.shape) # and its gradient\n lg_exp_scores = np.zeros(scores.shape) # its local gradient\n\n sums = np.zeros(num_train) # intermediate value margins\n dsums = np.zeros(num_train) # and its gradient\n\n log_sums = np.zeros(num_train) # intermediate value margins\n dlog_sums = np.zeros(num_train) # its gradient\n lg_log_sums = np.zeros(num_train) # its local gradient\n \n losses = np.zeros(num_train) # loss and\n dlosses = np.zeros(num_train) # its gradient\n data_loss = 0\n reg_loss = 0\n \n # Compute the forward pass\n scores = None\n #############################################################################\n # TODO: Perform the forward pass, computing the class scores for the input. #\n # Store the result in the scores variable, which should be an array of #\n # shape (N, C). #\n #############################################################################\n\n \"\"\"\n We dealing with a 2-layer NN followed by a softmax classifier with the following \n architecture:\n\n input - fully connected layer - ReLU - fully connected layer - softmax\n \n The loss and gradient computation will be performed in 2 passes:\n - In the forward pass, the loss calculation is decomposed into 10 stages\n At the end of the forward pass, loss is calculated\n - In the backward pass, we apply chain rule to calculate the gradients\n from Stage 10 back to Stage 1\n At the end of the backward pass, the gradients dW1 and dW2 are calculated\n \n The input to the 2-layer NN is X, and the output is scores.\n The scores is then fed into the softmax classifier, which is defined as:\n \n L(i) = -Score_Correct_Class + log(summation(exp(Scores_of_all_Classes)))\n\n \"\"\"\n # 2-layer Neural Network Section:\n # Input - X\n # Output - scores\n \n # Stage 1 - X.dot(W1)+b1 --> o1 #[1] \n o1= X.dot(W1) + b1\n \n # Stage 2 - ReLU(o1) --> h1 #[2]\n h1 = np.maximum(o1, np.zeros(o1.shape))\n lg_h1 = np.zeros(o1.shape)\n lg_h1[np.nonzero(h1)] = 1 # local gradient of ReLU is 1\n # for any element which is >0\n\n # Stage 3 - h1.dot(W2)+b2 --> scores #[3] \n scores = h1.dot(W2) + b2\n\n if verbose:\n print \"\\nNeural Network Layer 1:\"\n print X.shape\n print W1.shape\n print o1\n print h1\n print lg_h1\n print \"\\nNeural Network Layer 2:\"\n print h1.shape\n print W2.shape\n print scores\n \n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n \n # If the targets are not given then jump out, we're done\n if y is None:\n return scores\n\n # Compute the loss\n loss = None\n #############################################################################\n # TODO: Finish the forward pass, and compute the loss. This should include #\n # both the data loss and L2 regularization for W1 and W2. Store the result #\n # in the variable loss, which should be a scalar. Use the Softmax #\n # classifier loss. So that your results match ours, multiply the #\n # regularization loss by 0.5 #\n #############################################################################\n\n # Softmax Classifier Section:\n # Input - scores\n # Output - data_loss\n \n # Stage 4 - scores - scale_const --> norm_scores #[4]\n # The scaling constant is an arbitrary constant. In this case, we pick the\n # maximum value in scores, so that elements of norm_scores are all 0 or \n # less. This is to prevent overflow error in subsequent calculation.\n # The local gradient in this gate is thus d(x-c)/dx = 1\n scale_const = np.max(scores)\n norm_scores = scores - scale_const\n\n # Stage 5 - score of correct class --> correct_scores #[5] \n correct_scores = norm_scores[np.arange(num_train), y]\n\n # Stage 6 - exp(norm_scores) --> exp_scores #[6] \n exp_scores = np.exp(norm_scores)\n lg_exp_scores = np.exp(norm_scores) # local gradient of exp(x) = exp(x)\n\n # Stage 7 - sum(exp_scores) --> sums #[7]\n sums = np.sum(exp_scores, axis=1)\n \n # Stage 8 - log(sums) --> log_sums #[8]\n log_sums = np.log(sums)\n lg_log_sums = 1.0/sums # local gradient of log(x) = 1/x\n \n # Stage 9 - log_sums - correct_scores --> losses #[9]\n losses = log_sums - correct_scores\n\n # Stage 10 - sum(losses)/num_train --> loss #[10]\n data_loss = np.sum(losses)/num_train\n\n # Reg Forward Pass \n reg_loss = 0.5 * reg * (np.sum(W1*W1)+np.sum(W2*W2))\n \n loss = data_loss + reg_loss\n \n if verbose:\n print \"Training data - Forward Pass\" \n print \"\\n Scores:\"\n print scores\n print \"\\n Scaling Constant:\"\n print scale_const\n print \"\\n Normalized Scores:\"\n print norm_scores\n print \"\\n True Labels (y):\"\n print y\n print \"\\n Scores of correct classes:\" \n print correct_scores\n print \"\\n Exp(Normalized Scores):\" \n print exp_scores\n print \"\\n Sum(Exp(Normalized Scores)):\" \n print sums\n print \"\\n Log(Sum):\" \n print log_sums\n print \"\\n Losses:\" \n print losses\n print \"\\n Data Loss:\"\n print data_loss\n print \"\\n Reg Loss:\"\n print reg_loss\n print \"\\n Loss:\"\n print loss\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n # Backward pass: compute gradients\n grads = {}\n #############################################################################\n # TODO: Compute the backward pass, computing the derivatives of the weights #\n # and biases. Store the results in the grads dictionary. For example, #\n # grads['W1'] should store the gradient on W1, and be a matrix of same size #\n #############################################################################\n \n # Softmax Segment \n # Backward Pass - 8 Stages in Reverse \n\n # Stage 10 - Backprop sum(losses)/num_train --> loss #[10]\n dlosses = np.zeros((num_train)) + 1.0/num_train\n \n # Stage 9 - Backprop log_sums - correct_scores --> losses #[9]\n dlog_sums = 1 * dlosses\n dcorrect_scores = -1 * dlosses\n \n # Stage 8 - Backprop log(sums) --> log_sums #[8]\n dsums = lg_log_sums * dlog_sums\n \n # Stage 7 - Backprop sum(exp_scores) --> sums #[7]\n dexp_scores = np.zeros(scores.shape) + dsums.reshape(-1,1) # Broadcast operation\n\n # Stage 6 - Backprop exp(scores) --> exp_scores #[6] \n dnorm_scores = lg_exp_scores * dexp_scores\n dnorm_scores_1 = np.copy(dnorm_scores) # Store d_norm_scores before adding\n # back dcorrect_scores\n \n # Stage 5 - Backprop score of correct class --> correct_scores #[5] \n dnorm_scores[np.arange(num_train), y] += dcorrect_scores\n\n # Stage 4 - Backprop scores - max(scores) --> norm_scores #[4] \n dscores = dnorm_scores\n\n \n # 2-layer Neural Network Section:\n # Backward Pass - 3 Stages in Reverse\n\n # Stage 3 - Backprop h1.dot(W2)+b2 --> scores #[3] \n dW2 += h1.T.dot(dscores)\n dh1 = dscores.dot(W2.T)\n db2 = np.sum(dscores, axis=0)\n\n # Stage 2 - Backprop ReLU(o1) --> h1 #[2]\n do1 = lg_h1 * dh1\n \n # Stage 1 - Backprop X.dot(W1)+b1 --> o1 #[1] \n dW1 += X.T.dot(do1)\n db1 = np.sum(do1, axis=0)\n\n # Reg Backward Pass\n dW1 += 2 * 0.5 * reg * W1\n dW2 += 2 * 0.5 * reg * W2\n \n grads['W1'] = dW1\n grads['W2'] = dW2\n grads['b1'] = db1\n grads['b2'] = db2\n \n if verbose:\n print \"\\n Training data - Backward Pass\" \n print \"\\n dloss:\"\n print dlosses\n print \"\\n dcorrect_scores:\"\n print dcorrect_scores\n print \"\\n dlog_sums:\" \n print dlog_sums \n print \"\\n lg_log_sums:\" \n print lg_log_sums\n print \"\\n sdums:\" \n print dsums \n print \"\\n dexp_scores:\" \n print dexp_scores \n print \"\\n lg_exp_scores:\" \n print lg_exp_scores\n print \"\\n dnorm_scores (before addback):\" \n print dnorm_scores_1\n print \"\\n dnorm_scores (after addback):\" \n print dnorm_scores \n print \"\\n dscores:\"\n print dscores\n print dscores.shape\n \n print \"\\n dW2:\"\n print dW2\n print dW2.shape\n print \"\\n dh2:\"\n print dh1\n print dh1.shape\n print \"\\n db2:\"\n print db2\n print db2.shape \n print \"\\n do1:\"\n print do1\n print \"\\n dW1:\"\n print dW1\n print \"\\n db1:\"\n print db1\n print db1.shape\n \n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, grads","title":""},{"docid":"2ea57dd8cef526d12c561c415810679c","score":"0.551168","text":"def svm_loss_vectorized(W, X, y, reg):\n num_train = X.shape[0]\n score = np.dot(X, W)\n delta = 1\n correct_class_score = score[np.arange(num_train), y]\n margin = np.maximum(.0, score - correct_class_score[:, np.newaxis] + 1)\n margin[np.arange(num_train), y] = .0\n loss = np.sum(margin) / num_train + .5 * reg * np.sum(W * W)\n\n # https://zhuanlan.zhihu.com/p/21478575\n margin[margin > 0] = 1.0\n incorrect_cnt = np.sum(margin, axis=1)\n margin[np.arange(num_train), y] = -incorrect_cnt\n dW = np.dot(X.transpose(), margin) / num_train + reg * W\n return loss, dW","title":""},{"docid":"8d09b956ba2bd5dce0c8b5f18f9f55c9","score":"0.5502266","text":"def compute_cost(Y, AL, parameters, lambd, *args):\n def cross_entropy_loss(Y, AL):\n \"\"\"\n Computes binary classification loss.\n \"\"\"\n return -np.multiply(Y, np.log(AL)) + np.multiply((1 - Y), np.log(1 - AL))\n\n def softmax_loss(Y):\n \"\"\"\n Computes multi-class classification loss L on all classes.\n \"\"\"\n Z = args[0]\n b = np.max(Z, axis=0)\n stable_loss = b + np.log(np.sum(np.exp(Z - b), axis=0))\n # return -np.atleast_2d(np.sum(np.log(AL) * (Y), axis=0))\n return np.atleast_2d(stable_loss)\n\n m = AL.shape[1]\n C = Y.shape[0] # number of classes\n\n if C == 1:\n # binary classification loss\n loss = cross_entropy_loss(Y, AL)\n else:\n # multi-class classification loss\n loss = softmax_loss(Y)\n\n J = np.mean(loss)\n\n # Adding regularization term if required\n if lambd != 0:\n # computing L2-regularization term\n L = int(len(parameters) / 2)\n reg_term = 0\n for l in range(1, L + 1):\n reg_term += np.nansum(np.square(parameters['W' + str(l)]))\n\n reg_term *= lambd / (2 * m)\n\n J += reg_term\n\n J = np.squeeze(J)\n\n assert (J.shape == ())\n return (J)","title":""},{"docid":"37d8d8011d039ba64d9f751080c5fdf0","score":"0.54967153","text":"def SGD(train, test, means, gamma=0.0005, num_features=10, lambda_user=0.1, lambda_item=0.1, max_it=10):\n it = 0\n # set seed\n np.random.seed(988)\n\n train_lil = sp.lil_matrix(train)\n train_dense = sp.lil_matrix.todense(train_lil)\n\n # init matrix\n user_features, item_features = init_SVD(train_dense, num_features) #user Z N na K item W d na K\n\n # find the non-zero ratings indices \n nz_row, nz_col = train.nonzero()\n nz_train = list(zip(nz_row, nz_col))\n nz_row, nz_col = test.nonzero()\n nz_test = list(zip(nz_row, nz_col))\n\n nonzero_rows_train = np.where(train_dense!=0)[0]\n nonzero_cols_train = np.where(train_dense!=0)[1]\n nonzero_rows_test = np.where(test.todense()!=0)[0]\n nonzero_cols_test = np.where(test.todense()!=0)[1]\n\n while (it<max_it): \n \n it += 1\n np.random.shuffle(nz_train)\n # decrease step size\n gamma /= 1.2\n nb = int(len(nz_train)/2)\n \n predict = np.dot(user_features, item_features)\n for d, n in nz_train[:nb]: \n # Calculate error on the prediction\n predict_error=train[d,n]-predict[d,n]\n # Update user and item features\n item_features[:,n]+=gamma*(predict_error*user_features[d,:].T-lambda_item*item_features[:,n])\n user_features[d,:]+=gamma*(predict_error*item_features[:,n].T-lambda_user*user_features[d,:])\n\n # Calculate again the predictino\n predict = np.dot(user_features, item_features)\n\n # Get the RMSE errors for this prediction and go to the next iteration\n rmse_train = compute_rmse(train, predict, nonzero_rows_train, nonzero_cols_train)\n denormalized = denormalize(predict, means)\n rmse_test = compute_rmse(test, denormalized , nonzero_rows_test, nonzero_cols_test)\n predictions.append(predict)\n print(\"It\", it, \"training error:\", rmse_train, \"testing error:\", rmse_test)\n\n rmse_test = compute_rmse(test, denormalize(predict, means), nonzero_rows_test, nonzero_cols_test)\n print(\"Testing error:\", rmse_test)\n\n return predictions","title":""},{"docid":"99038f891d24d4c4bc2ff4c326091cc3","score":"0.5490841","text":"def lista_network(input_dim, D, n_layers=10, activation=\"st\", lmbd=.1,\n weights=None):\n\n K, p = D.shape\n assert input_dim == p\n\n f_cost = LossL2(np.zeros(shape=(1, p)), D)\n Wz = np.eye(K, dtype=D.dtype) - D.dot(D.T) / f_cost.L\n Wx = D.T / f_cost.L\n if weights is None:\n weights = [(Wx, Wz)] * n_layers\n elif len(weights) < n_layers:\n weights = list(weights) + [(Wx, Wz)] * (n_layers - len(weights))\n\n # Define some layers to build the neural network\n x = Input(shape=(input_dim, ))\n cost_layer = get_cost_lasso_layer(x, D, lmbd)\n activation = get_soft_thresholding_layer(lmbd / f_cost.L)\n\n wx0, _ = weights[0]\n z = Dense(K, use_bias=False, kernel_initializer=lambda s: wx0)(x)\n z = activation(z)\n for k in range(n_layers - 1):\n wxk, wzk = weights[k + 1]\n y1 = Dense(K, use_bias=False, kernel_initializer=lambda s: wzk)(z)\n y2 = Dense(K, use_bias=False, kernel_initializer=lambda s: wxk)(x)\n z = activation(Add()([y1, y2]))\n\n model = Model(inputs=x, outputs=z)\n\n def loss(y, yp):\n return cost_layer(z)\n\n opt = keras.optimizers.rmsprop(lr=0.0001 / n_layers, decay=1e-6)\n model.compile(loss=loss, optimizer=opt)\n\n def export_weights():\n w = [(model.weights[0].eval(get_session()), None)]\n for wz, wx in zip(model.weights[1::2], model.weights[2::2]):\n w += [(wx.eval(get_session()), wz.eval(get_session()))]\n return w\n\n model.export_weights = export_weights\n\n return model","title":""},{"docid":"f0cd4134c40d11b5668366253494b34f","score":"0.54867834","text":"def linearSVM(x, y, c):\n model = LinearSVC(C=c, multi_class='ovr', max_iter=10000)\n model.fit(x, y)\n return model","title":""},{"docid":"03217e62420be2fe74f963618ee34054","score":"0.54723626","text":"def softmax_loss_vectorized(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n N=X.shape[0]\n C=W.shape[1] # num of classes\n D=W.shape[0] # D=3\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n score=X @ W\n score-=score.max(axis=1).reshape(-1,1)\n partition=np.sum(np.exp(score),axis=1).reshape(-1,1)\n \n loss=np.mean(-(score[np.arange(N),y])+np.log(partition))\n \n dW+=X.T @ (np.exp(score)/partition) \n map_mtx=np.zeros((N,C))\n map_mtx[np.arange(N),y]=1\n dW-=X.T @ map_mtx\n\n dW/=N\n\n loss+=reg*np.sum(W*W)\n dW+=2*reg*W\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"b1722dfc6a16eb443bde058da7ffd2d8","score":"0.54702085","text":"def update_plabels(V, Y, labeled_idx, unlabeled_idx,\n nclasses, k=50, max_iter=30, alpha=0.99):\n\n # normalize_L2 eats only doubles aka float32\n V = V.reshape(len(Y), -1).astype('float32')\n alpha = 0.99\n k = 50\n labels = Y\n # maxiter for Conjugate Gradient Solver\n max_iter = 20\n\n # kNN search for the graph\n d = V.shape[1]\n res = faiss.StandardGpuResources()\n flat_config = faiss.GpuIndexFlatConfig()\n flat_config.device = int(torch.cuda.device_count()) - 1\n # building the index takes some time\n index = faiss.GpuIndexFlatIP(res, d, flat_config)\n\n normalize_L2(V)\n index.add(V)\n N = V.shape[0]\n\n # D = similarity matrix\n # I = index matrix storing the indices of the k nn's per sample\n D, I = index.search(V, k + 1)\n\n # Create the graph\n # gamma parameter for manifold learning penalization as in eq 9\n gamma = 3\n D = D[:, 1:] ** gamma\n I = I[:, 1:]\n row_idx = np.arange(N)\n row_idx_rep = np.tile(row_idx, (k, 1)).T\n # create sparse matrix with 50 nns -> 50*V.shape[0] elements != 0\n W = scipy.sparse.csr_matrix((D.flatten('F'),\n (row_idx_rep.flatten('F'), I.flatten('F'))),\n shape=(N, N))\n W = W + W.T\n\n # Normalize the graph\n W = W - scipy.sparse.diags(W.diagonal())\n S = W.sum(axis=1)\n S[S == 0] = 1\n D = np.array(1. / np.sqrt(S))\n D = scipy.sparse.diags(D.reshape(-1))\n Wn = D * W * D\n\n # Initiliaze the y vector for each class (eq 5 from the paper,\n # normalized with the class size) and apply label propagation\n Z = np.zeros((N, nclasses))\n A = scipy.sparse.eye(Wn.shape[0]) - alpha * Wn\n for i in range(nclasses):\n # a = np.where(labels[labeled_idx] == 0)[0]\n # cur_idx = [labeled_idx[b] for b in a]\n # CHECK WHAT HAPPENS HERE\n cur_idx = labeled_idx[np.where(labels[labeled_idx] == i)]\n y = np.zeros((N,))\n y[cur_idx] = 1.0 / cur_idx.shape[0]\n # conjugate gradient from linalg\n f, _ = scipy.sparse.linalg.cg(A, y, tol=1e-6, maxiter=max_iter,\n atol=0)\n Z[:, i] = f\n\n # Handle numerical errors\n Z[Z < 0] = 0\n\n # Compute the weight for each instance based on the entropy\n # (eq 11 from the paper)\n probs_l1 = F.normalize(torch.tensor(Z), 1).numpy()\n probs_l1[probs_l1 < 0] = 0\n entropy = scipy.stats.entropy(probs_l1.T)\n weights = 1 - entropy / np.log(nclasses)\n weights = weights / np.max(weights)\n p_labels = np.argmax(probs_l1, 1)\n\n # Compute the accuracy of pseudolabels for statistical purposes\n correct_idx = (p_labels[labels != -1] == labels[labels != -1])\n print('pseudo label accuracy: {:0.5f}, mean entropy: {:0.5f}'.format(\n correct_idx.mean(), entropy.mean()))\n\n # fix the pseudo labels for the labeled data to its original label\n p_labels[labeled_idx] = labels[labeled_idx]\n weights[labeled_idx] = 1.0\n p_weights = weights.tolist()\n\n class_weights = []\n # Compute the class weights\n for i in range(nclasses):\n cur_idx = np.where(np.asarray(p_labels) == i)[0]\n class_weights.append(\n (float(labels.shape[0]) / nclasses) / cur_idx.size)\n\n return (p_labels, p_weights, class_weights)","title":""},{"docid":"a8aecedad7ce8d008c82b6f48a3ca861","score":"0.5466806","text":"def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n # loss_i = -(f_yi + stability_constant) + log(sum_j(exp(f_j + stability_constant))) \n \n num_samples = X.shape[0]\n num_classes = W.shape[1]\n \n for i in range(num_samples):\n # 1 x D * D x K -> 1 x K\n # logits\n Xi = X[i]\n \n # Scores 1 x K\n scores = Xi.dot(W)\n \n # numerical stability\n stability_constant = -np.max(scores)\n \n # exponentiate the scores for the sum\n scores_exp = np.exp(scores + stability_constant)\n scores_exp_sum = scores_exp.sum()\n \n # take the log of the sum\n scores_sum = np.log(scores_exp_sum)\n \n loss += -scores[y[i]] - stability_constant + scores_sum\n \n ##### Gradient #####\n \n \n for j in range(num_classes):\n # calculate the fraction for the gradient\n scores_exp_frac = scores_exp[j] / scores_exp_sum\n\n if j != y[i]:\n dW[:, j] += Xi * scores_exp_frac\n else:\n dW[:, j] += Xi * (scores_exp_frac - 1)\n \n \n loss /= num_samples\n loss += reg * np.sum(W * W)\n \n dW /= num_samples\n dW += 2 * reg * W\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","title":""},{"docid":"c7758a2f487966e594981f7d19c8f671","score":"0.5464327","text":"def kernel_regression_with_sgd(X,Y,kernel,mu,lmbda,num_iter):\n\n # Initialise alpha and errors\n N = np.shape(X)[0]\n D = np.shape(X)[1]\n alpha = np.zeros((N,1))\n errors = np.zeros((num_iter,1))\n\n K = np.zeros((N,N))\n for i in range(N):\n for j in range(N):\n K[i,j] = kernel(X[i],X[j])\n\n for step in range(num_iter):\n i = np.random.randint(N)\n x = X[i:i+1]\n y = Y[i]\n errors[step] = loss(alpha,K,Y)\n alpha = kernel_sgd_update_alpha(alpha,mu,y,i,K,lmbda)\n\n return K,alpha,errors","title":""},{"docid":"ff95354b91b2e4e3578cf9822ec08727","score":"0.5454151","text":"def sgdClassifier(trainingData,trainingLabels):\n from sklearn.linear_model import SGDClassifier\n clf = SGDClassifier(loss=\"hinge\", penalty=\"l2\")\n clf.fit(trainingData, trainingLabels)\n \n print \"Stochastic Gradient Descent Classifier generated with a training set size of\",len(trainingLabels)\n return clf","title":""},{"docid":"a80db1b4eed380bfb78cc1ba8704d1a0","score":"0.54514855","text":"def train_softmax(learning_rate=0.2, n_epochs=20000, n_in=200, n_hid=100,\r\n datasets=None,\r\n batch_size=None, start_num=4):\r\n if batch_size == None:\r\n batch_size = int(datasets[0].shape[0])\r\n\r\n train_set_x = to_shared_data(datasets[0])\r\n train_set_y = to_shared_data(datasets[1], dtype='int32')\r\n # compute number of minibatches for training, validation and testing\r\n n_train_batches = int(train_set_x.get_value(borrow=True).shape[0] / batch_size)\r\n # n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\r\n\r\n ######################\r\n # BUILD ACTUAL MODEL #\r\n ######################\r\n\r\n\r\n # allocate symbolic variables for the data\r\n index = T.lscalar() # index to a [mini]batch\r\n\r\n # generate symbolic variables for input (x and y represent a\r\n # minibatch)\r\n x = T.matrix('x') # data, presented as rasterized images\r\n y = T.ivector('y') # labels, presented as 1D vector of [int] labels\r\n classifiers = []\r\n outputs_ = []\r\n updates_ = []\r\n for i in range(0, start_num):\r\n classifier = SM(input=x, n_in=n_in, n_out=n_hid, n_out2=6)\r\n\r\n # the cost we minimize during training is the negative log likelihood of\r\n # the model in symbolic format\r\n cost = classifier.monitor_cost(y)\r\n\r\n\r\n # compute the gradient of cost with respect to theta = (W,b)\r\n g_W = T.grad(cost=cost, wrt=classifier.W)\r\n g_b = T.grad(cost=cost, wrt=classifier.b)\r\n g_W2 = T.grad(cost=cost, wrt=classifier.W2)\r\n g_b2 = T.grad(cost=cost, wrt=classifier.b2)\r\n g_rbW = T.grad(cost=cost, wrt=classifier.ae.rbm.W)\r\n g_rbh = T.grad(cost=cost, wrt=classifier.ae.rbm.hbias)\r\n g_aeW = T.grad(cost=cost, wrt=classifier.ae.W)\r\n g_aeb = T.grad(cost=cost, wrt=classifier.ae.b)\r\n\r\n\r\n\r\n # start-snippet-3\r\n # specify how to update the parameters of the model as a list of\r\n # (variable, update expression) pairs.\r\n updates = [(classifier.W, classifier.W - learning_rate * g_W),\r\n (classifier.b, classifier.b - learning_rate * g_b),\r\n (classifier.ae.rbm.W, classifier.ae.rbm.W - learning_rate * g_rbW),\r\n (classifier.ae.rbm.hbias, classifier.ae.rbm.hbias - learning_rate * g_rbh),\r\n (classifier.ae.W, classifier.ae.W - learning_rate * g_aeW),\r\n (classifier.ae.b, classifier.ae.b - learning_rate * g_aeb),\r\n (classifier.W2, classifier.W2 - learning_rate * g_W2),\r\n (classifier.b2, classifier.b2 - learning_rate * g_b2)\r\n ]\r\n # updates_ and outputs_ are used for multistart\r\n updates_.extend(updates)\r\n outputs_.append(classifier.errors(y))\r\n classifiers.append((classifier))\r\n\r\n train_model = theano.function(\r\n inputs=[index],\r\n outputs=outputs_,\r\n updates=updates_,\r\n givens={\r\n x: train_set_x[index * batch_size: (index + 1) * batch_size],\r\n y: train_set_y[index * batch_size: (index + 1) * batch_size]\r\n }\r\n )\r\n ###############\r\n # TRAIN MODEL #\r\n ###############\r\n\r\n\r\n\r\n start_time = timeit.default_timer()\r\n epoch = 0\r\n\r\n while (epoch < n_epochs):\r\n epoch = epoch + 1\r\n minibatch_avg_cost = []\r\n for minibatch_index in range(n_train_batches):\r\n minibatch_avg_cost.append(train_model(minibatch_index))\r\n print numpy.mean(minibatch_avg_cost, axis=0) * 100,'epoch ',epoch\r\n\r\n print 'best is ', numpy.argmin(numpy.mean(minibatch_avg_cost, axis=0))\r\n best = classifiers[numpy.argmin(minibatch_avg_cost)]\r\n\r\n\r\n # save the best model\r\n with open('best_model', 'wb') as f:\r\n pickle.dump(best, f)\r\n end_time = timeit.default_timer()","title":""},{"docid":"8423b62e0e0012f01f7cbdb4e3dbed81","score":"0.54391444","text":"def perceptron(x, d, mean, centers, c = 1, iterations = 100):\n inp = rbfActivation(x, mean)\n w = np.random.rand(x.shape[1], centers)\n t = Threshold()\n for i in range(iterations):\n for ind, point in enumerate(inp):\n net = point.dot(w)\n o = t.binary(net)\n out = -1*np.ones(centers)\n out[d[ind]] = 1\n w = w + c*(out - o)*(point.reshape(point.shape[0], 1))\n return w","title":""},{"docid":"c930e5c77cea78944bf5d0bd3abbc8e4","score":"0.54283535","text":"def cross_entropoy_loss_naive(W, X, y, reg):\n\n #return cross_entropoy_loss_vectorized(W, X, y, reg)\n # pylint: disable=too-many-locals\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n ############################################################################\n # TODO: Compute the cross-entropy loss and its gradient using explicit #\n # loops. Store the loss in loss and the gradient in dW. If you are not #\n # careful here, it is easy to run into numeric instability. Don't forget #\n # the regularization! #\n ############################################################################\n\n N = X.shape[0]\n D = X.shape[1]\n C = W.shape[1]\n\n reg_kind = \"L1\"\n\n outputs = np.zeros((C,))\n\n for num_sample, sample in enumerate(X):\n\n for num_class in range(C):\n incoming = 0.\n\n for var in range(D):\n incoming += sample[var]*W[var, num_class]\n\n outputs[num_class] = np.exp(incoming)\n\n outputs = outputs/sum(outputs)\n\n # print(outputs)\n\n loss += - np.log(outputs[y[num_sample]])\n\n loss += 0.5*reg*np.sum(W**2)\n\n for num_class in range(C):\n dW[:,num_class] += np.reshape(outputs[num_class] * sample, dW[:, num_class].shape) + reg * W[:, num_class]\n\n if num_class == y[num_sample]:\n dW[:,num_class] -= sample.reshape(dW[:, num_class].shape)\n\n dW = dW/N\n loss = loss/N\n\n\n\n ############################################################################\n # END OF YOUR CODE #\n ############################################################################\n\n return loss, dW","title":""},{"docid":"9b4cd88a782f8e0f8b0628e9ea648211","score":"0.5426194","text":"def loss_kd(y_student, y_true, y_teacher, params):\n\n T = params.temperature\n\n # take max confidence of student model as coefficient of CE loss_fn\n # max_prob = torch.max(F.softmax(y_student, dim=1).detach(), dim=1).values\n # take max confidence of teacher model as coefficient of CE loss_fn\n\n loss_CE = F.cross_entropy(y_student, y_true, reduction='none') # N*1\n\n y_student_log_T_softmax = F.log_softmax(y_student / T, dim=1) # N*C\n y_teacher_T_softmax = F.softmax(y_teacher / T, dim=1) # N*C\n\n # max_prob = torch.max(F.softmax(y_teacher / T, dim=1).detach(), dim=1).values\n # max_prob = torch.max(y_teacher_T_softmax.detach(), dim=1).values\n # print('Max confidence of teacher model:', torch.mean(max_prob))\n # params.maxT = torch.mean(max_prob)\n\n kl_div_each = F.kl_div(y_student_log_T_softmax, y_teacher_T_softmax, reduction='none') * (T * T) # N*C\n\n loss_KLD = torch.mean(kl_div_each, dim=1) # N*1\n\n # print('\\nLoss_kd:\\nCE:{}\\nKLD:{}\\n'.format(loss_CE.detach()[0], loss_KLD.detach()[0]))\n _loss = torch.mean((1. - params.alpha) * loss_CE + params.alpha * loss_KLD)\n # loss = torch.mean(max_prob * loss_CE + (torch.ones_like(max_prob) - max_prob) * loss_KLD)\n\n return _loss","title":""},{"docid":"81713d7fdc8e922aad6ff3ae95d9f669","score":"0.54250103","text":"def msnerv_obj_fct(x, sigma_ij, N, n_components, p_h, t_h, n_perp, w_cf):\r\n X = np.reshape(a=x, newshape=(N, n_components))\r\n # Computing the pairwise squared euclidean distances in the LDS\r\n dsm_lds = pairwise_dist(X=X, metric='sqeuclidean', tomatrix=True)\r\n # LDS multi-scale similarities. np.zeros(shape=(N,N,N)) is too big to be stored.\r\n s_ij = np.zeros(shape=(N,N))\r\n arr_ones = np.ones(shape=N)\r\n # For each perplexity\r\n for h in range(n_perp):\r\n # Computing the corresponding LDS similarities and updating s_ij\r\n s_ij += sne_lds_similarities_fast(dsm_lds=dsm_lds, arr_den_s_i=t_h[h]*arr_ones, compute_log=False)[0]\r\n # Scaling s_ij\r\n s_ij /= n_perp\r\n # Computing the cost function value\r\n return (1-w_cf)*scipy.special.rel_entr(sigma_ij, np.maximum(np.finfo(dtype=np.float64).eps, s_ij)).sum() + w_cf*scipy.special.rel_entr(s_ij, np.maximum(np.finfo(dtype=np.float64).eps, sigma_ij)).sum()","title":""},{"docid":"04d3374d9ddf30125eac4687ff79af02","score":"0.54176205","text":"def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n D, C = W.shape\n N = y.shape[0]\n \n scores = np.zeros((N, C))\n for sample_idx in range(N):\n for class_idx in range(C):\n for coord_idx in range(D):\n scores[sample_idx, class_idx] += X[sample_idx, coord_idx] * W[coord_idx, class_idx]\n \n probs = np.zeros_like(scores)\n for sample_idx in range(N):\n softmax_den = 0\n for class_idx in range(C):\n exponent = np.exp( scores[sample_idx, class_idx] )\n probs[sample_idx, class_idx] = exponent\n softmax_den += exponent\n for class_idx in range(C):\n probs[sample_idx, class_idx] /= softmax_den\n \n # Compute loss\n log_probs = 0\n for sample_idx in range(N):\n true_class_idx = y[sample_idx]\n log_probs += np.log( probs[sample_idx, true_class_idx] )\n \n data_loss = -(1 / N) * log_probs\n \n reg_loss = 0\n if reg != 0:\n for coord_idx in range(D):\n for class_idx in range(C):\n reg_loss += W[coord_idx, class_idx] ** 2\n \n reg_loss = 0.5 * reg * reg_loss\n \n loss = data_loss + reg_loss\n\n #Compute grad\n dscores = probs\n \n for sample_idx in range(N):\n true_class_idx = y[sample_idx]\n dscores[sample_idx, true_class_idx] -= 1\n \n for sample_idx in range(N):\n for class_idx in range(C):\n dscores[sample_idx, class_idx] /= N\n \n for coord_idx in range(D):\n for class_idx in range(C):\n for sample_idx in range(N): \n dW[coord_idx, class_idx] += X.T[coord_idx, sample_idx] * dscores[sample_idx, class_idx]\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","title":""},{"docid":"1551e8d7cc0e649ab30e417fc92409ce","score":"0.5417067","text":"def fcn2min(params, ACD,L, d):\n a0 = params['a0']\n a1 = params['a1']\n a2 = params['a2']\n loss = (a0+ACD*a1+L*a2 -d )**2\n return loss","title":""},{"docid":"a63543e0ae94bb33fdb374e9e9809a16","score":"0.5413414","text":"def train_with_hard_negatives(d, dtest,cbwords, cbsamps, codebook, cls, pos_table, neg_table,feature_type,\\\n iterations, kernel='chi2', L=2, \\\n testsize = 'max',randomize=False):\n # Featurize and pyramidize the input\n e = Extractor()\n M = codebook.shape[0]\n pyr_size = M*1./3.*(4**(L+1)-1)\n\n print 'get pos train pyrs'\n pos_pyrs = get_pyr(d,e,pos_table,pyr_size,L,codebook,feature_type,cls)\n print 'get neg train pyrs'\n neg_pyrs = get_pyr(d,e,neg_table,pyr_size,L,codebook,feature_type,cls)\n \n print 'built all positive pyramids'\n \n classification = np.asarray([1]*pos_table.arr.shape[0] + [-1]*neg_table.arr.shape[0])\n \n filename = config.data_dir + 'features/' + feature_type + '/svms/' + kernel + \\\n '/'+ cls\n ut.makedirs(config.data_dir + 'features/' + feature_type + '/svms/' + kernel)\n \n \n # with that we now determined our whole dataset D \n #D = np.concatenate((pos_pyrs, neg_pyrs, pos_test_pyr, neg_test_pyr))\n #Y = np.concatenate((classification, test_classification))\n #idx_train = np.arange(pos_pyrs.shape[0] + neg_pyrs.shape[0])\n #idx_test = np.arange(pos_test_pyr.shape[0] + neg_test_pyr.shape[0]) + idx_train.size\n train_pyrs = np.concatenate((pos_pyrs,neg_pyrs)) \n for i in range(iterations):\n # train new model - according to hard mining algorithm by Felszenswalb et al.\n # \"Object Detection with Discriminatively Trained Part Based Modles\"\n \n [test_pyrs, test_classification] = get_test_windows(testsize,dtest,e,\\\n pyr_size,L,codebook,feature_type,cls,\\\n pos_table.cols, randomize=randomize)\n print 'node',comm_rank,'training in round', i, 'with', np.sum(classification==1),'pos and',\\\n np.sum(classification == -1),'negs'\n print 'testing', test_pyrs.shape[0], 'new samples'\n print time.strftime('%m-%d %H:%M')\n \n # get new test samples\n \n \n # ----------1-------------\n model = train_svm(train_pyrs, classification, kernel)\n \n testY = svm_predict(np.concatenate((train_pyrs,test_pyrs)), model)\n result = testY\n print result\n \n res_train = testY[:train_pyrs.shape[0]]\n res_test = testY[train_pyrs.shape[0]:]\n \n # ----------3------------- \n # remove easy samples from train-set\n idx_tr_list = []\n for s_ind in range(res_train.shape[0]):\n if res_train[s_ind]*classification[s_ind] <= 1:\n idx_tr_list.append(s_ind)\n indices = np.matrix(idx_tr_list).reshape(1,len(idx_tr_list))\n indices = np.array(indices.astype(Int))[0] \n train_pyrs = train_pyrs[indices]\n classification = classification[indices]\n \n # ----------4-------------\n idx_hn_list = []\n new_hards = False\n for s_ind in range(res_test.shape[0]):\n if res_test[s_ind]*test_classification[s_ind] < 1:\n new_hards = True\n idx_hn_list.append(s_ind) \n\n nu_train_idc = np.matrix(idx_hn_list).reshape(1,len(idx_hn_list))\n nu_train_idc = np.array(nu_train_idc.astype(Int))[0] \n train_pyrs = np.vstack((train_pyrs, test_pyrs[nu_train_idc]))\n classification = np.concatenate((classification, test_classification[nu_train_idc]))\n \n test_result = result[-test_pyrs.shape[0]:]\n fp = np.sum(np.multiply(test_result < 0, np.transpose(np.matrix(test_classification == 1))))\n tp = np.sum(np.multiply(test_result > 0, np.transpose(np.matrix(test_classification == 1))))\n fn = np.sum(np.multiply(test_result > 0, np.transpose(np.matrix(test_classification == -1))))\n \n # save these to the training file\n prec = tp/float(tp+fp)\n rec = tp/float(tp+fn)\n print 'tp, fp:',tp,fp\n print 'prec, rec:', prec,rec\n with open(filename + '_train', \"a\") as myfile:\n myfile.write(str(prec) + ' ' + str(rec)+'\\n') \n \n # ----------2-------------\n if not new_hards:\n # no new_hards from test set,we want to quit.\n break\n # save the trained svm. \n #save_svm(model, filename)","title":""},{"docid":"6f2059396a2b316414474f0a2a44260c","score":"0.5402359","text":"def __call__(self, ctx, dloss):\n ls_dlogits = []\n s = len(ctx)\n for i in range(s):\n if ctx[i][1]<0:\n# dlogits_i = -ctx[i][0]+1/(1+np.e**(-ctx[i][1]))\n dlogits_i =np.e**(ctx[i][1])/(1+np.e**(ctx[i][1])) -ctx[i][0]\n ls_dlogits.append(dlogits_i)\n else:\n dlogits_i = 1 -ctx[i][0]-np.e**(-ctx[i][1])/(1+np.e**(-ctx[i][1]))\n ls_dlogits.append(dlogits_i)\n# try:\n# dlogits_i = 1 -ctx[i][0]-1/(1+np.e**(ctx[i][1]))\n# ls_dlogits.append(dlogits_i)\n# except:\n# # dlogits_i = 1 -ctx[i][0]-1/(1+np.e**(ctx[i][1]))\n# dlogits_i =np.e**(ctx[i][1])/(1+np.e**(ctx[i][1])) -ctx[i][0]\n# ls_dlogits.append(dlogits_i)\n dlogits = np.array(ls_dlogits)/s\n return dlogits","title":""},{"docid":"be4cf1448d0cce8c95d2a6ac8aef108b","score":"0.5402058","text":"def train_sgd(self, max_iter=100, learn_rate=0.01, lamb=0.1, delta=0.01):\r\n print '-' * 60\r\n print \"START TRAIN SGD:\"\r\n # y =[]\r\n m = len(self.sample_list)\r\n n = self.feat_dimension + 1\r\n k = len(self.label_set)\r\n\r\n J = 0.\r\n J_pre = 0.\r\n # grad = np.zeros(self.Theta.shape)\r\n rd = 0\r\n while rd < max_iter * m:\r\n if rd % m == 0 and rd != 0:\r\n loop = rd / m\r\n error = 0\r\n J = 0.\r\n prb = []\r\n for i in xrange(m):\r\n prb.append(self.predict(self.sample_list[i]))\r\n # the label of sample[i] not the index\r\n label = self.label_list[i]\r\n pred = prb[i].index(max(prb[i]))\r\n if label != self.label_set[pred]:\r\n error += 1\r\n J += math.log(prb[i][self.label_set.index(label)])\r\n\r\n J = -J / m + lamb * sum(sum(self.Theta * self.Theta)) / 2\r\n acc = 1 - error / float(m)\r\n print \"Iter %4d Cost:%4.4f Acc:%4.4f\" % (loop, J, acc)\r\n if loop != 1 and (J_pre - J) <= delta and J < J_pre:\r\n print \"\\n\\nReach the minimal cost value threshold!\"\r\n break\r\n J_pre = J\r\n\r\n i = random.randint(0, m - 1)\r\n label = self.label_list[i] # the label of sample[i] not the index\r\n pred_prb = self.predict(self.sample_list[i])\r\n pred = pred_prb.index(max(pred_prb))\r\n x = self.__getSampleVec(self.sample_list[i])\r\n y = self.label_set.index(label) # the index of sample[i]'s label\r\n for j in xrange(k):\r\n # grad[j] += ((1 if y==j else 0) - pred_prb[j]) * x[0]\r\n self.Theta[j] += learn_rate * \\\r\n (((1 if y == j else 0) -\r\n pred_prb[j]) * x[0] - lamb * self.Theta[j])\r\n\r\n # grad = -grad+lamb*self.Theta\r\n # self.Theta -= learn_rate * grad\r\n rd += 1\r\n\r\n if rd == max_iter * m:\r\n print \"Train loop has reached the maximum of iteration.\"\r\n\r\n print \"Training process finished.\"","title":""},{"docid":"76c4afda5213bd97e6bc88841a754011","score":"0.54009956","text":"def solve_lasso_sparse(lamb, X_m, y, N, d,init_w_vec):\n w_tilde = copy(init_w_vec)\n w_prev = zeros(d+1)\n\n \"\"\" Start with round 0 \"\"\"\n round_robin = 0\n\n \"\"\" Choose w_0 \"\"\"\n w_0 = init_w_vec[0]\n\n print(\"Lambda provided for lasso: \", lamb)\n\n # Precompute Xij^2\n # Flatten X\n X_arr = X_m.copy()\n X_arr.data **= 2\n\n # This variable will be updated whenever we change w\n Xw = X_m.dot(transpose(w_tilde[1:]))\n while round_robin < 3000 :\n # Make a copy of the old vector ..\n w_prev = copy(w_tilde)\n\n for feature_j in range(0,d):\n\n # convert feature j column to an array\n arr = array(X_m[:,feature_j].todense()).flatten()\n c_j = 2 * arr.dot(y - Xw + arr * w_tilde[feature_j+1] - w_0)\n w_tilde_j_old = w_tilde[feature_j+1]\n if(c_j < (-1 * lamb)):\n w_tilde[feature_j+1] = (c_j + lamb)/(2 *sum(X_m[:,feature_j].data))\n elif(c_j > lamb):\n w_tilde[feature_j+1] = (c_j - lamb)/(2 *sum(X_m[:,feature_j].data))\n else:\n w_tilde[feature_j+1] = 0\n delta_w = w_tilde[feature_j+1] - w_tilde_j_old\n Xw += delta_w * arr\n\n # Calculate the new w_0\n w_0 = sum(y - X_m.dot(w_tilde[1:]))/N\n # Update the new w_0\n w_tilde[0] = w_0\n # Create the diff vector which we'll use to compute c_next\n diff_w = w_tilde - w_prev\n # Compute whether we've reached a good enough precision to warrant any other iteration.\n new_error = max(abs(diff_w))\n #print \"Expected Error :\",new_error\n round_robin += 1\n if new_error < .001:\n return w_tilde, w_prev, new_error,round_robin\n print(\"Not done even after 3000 iterations - Exiting !\")\n return w_tilde,new_error,round_robin","title":""},{"docid":"e578f1b1e61b7bd215cfc6e2b50c4a45","score":"0.5398153","text":"def classification_error(self, X_d, Y_d):\n # first, convert labels to account for semi-supervised labeling\n Y_mask = 1.0 * (Y_d != 0)\n Y_d = Y_d - 1\n # compute the main PeaNet's output for X_d\n Y_p = self.get_pn_output(X_d)\n # get the implied class labels\n Y_c = np.argmax(Y_p, axis=1).reshape((Y_d.shape[0],1))\n # compute the classification error for points with valid labels\n err_rate = np.sum(((Y_d != Y_c) * Y_mask)) / np.sum(Y_mask)\n return err_rate","title":""},{"docid":"c98c9d13a0579f65830057c9cac106b3","score":"0.53904605","text":"def perceptron(vector_x, vector_y):\n\n X_train, X_test, y_train, y_test = train_test_split(vector_x, vector_y)\n scaler = StandardScaler() ##standardizes the data for outliers\n\n scaler.fit(X_train) ## each column will have a mean of 0 and standard deviation = 1\n X_train = scaler.transform(X_train) ##transforms the tests data so each feature is on the same scale\n X_test = scaler.transform(X_test)\n\n mlp = MLPClassifier(hidden_layer_sizes=(31,31,31), max_iter=500)\n mlp.fit(X_train, y_train)\n predictions = mlp.predict(X_test)\n\n print(confusion_matrix(y_test,predictions))\n print(classification_report(y_test, predictions))","title":""}],"string":"[\n {\n \"docid\": \"67adf20f38500772f8f93f199913c491\",\n \"score\": \"0.6719385\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n\\n #############################################################################\\n # Dummy data #\\n #############################################################################\\n\\n # d = 3\\n # N = 6\\n # c = 4\\n #\\n # X = np.array( range(N * d) )\\n # X += 1\\n # X = X.reshape(N, d)\\n #\\n # W = np.array( range(20, 20 + 2 * d * c, 2), dtype=float )\\n # W /= max(W)\\n # np.random.shuffle(W)\\n # W = W.reshape(d, c)\\n #\\n # y = np.random.randint(0, high=c, size=N)\\n\\n #############################################################################\\n # IMPL #\\n #############################################################################\\n\\n num_train = X.shape[0]\\n d = X.shape[1]\\n c = W.shape[1]\\n\\n scores = X.dot(W)\\n correct_class_scores = scores[range(y.shape[0]), y]\\n all_losses = np.zeros(shape=(num_train, c))\\n all_losses = 1 + scores - correct_class_scores[:, np.newaxis]\\n all_losses = (all_losses > 0) * all_losses\\n all_losses[range(y.shape[0]), y] = 0\\n\\n losses = np.sum(all_losses, axis=1)\\n\\n total_loss = sum(losses)\\n total_loss /= num_train\\n\\n total_loss += 0.5 * reg * np.sum(np.square(W))\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n\\n dW = np.zeros(shape=(d, c))\\n\\n # Partially vectorized code\\n #\\n # for i in xrange(num_train):\\n # count = np.sum(all_losses[i]>0)\\n # dW[:, all_losses[i]>0] += X[i][:, np.newaxis]\\n # dW[:, y[i]] -= count * X[i]\\n #\\n # dW /= num_train\\n # dW += reg * np.sum(W)\\n\\n # Completely vectorized code\\n #\\n # count = np.sum(all_losses > 0, axis=1)\\n # print(\\\"Shapes: \\\", X.shape, count.shape, (X * count[:, np.newaxis]).shape)\\n #\\n # dW /= num_train\\n # dW += reg * np.sum(W)\\n\\n # Fully vectorized version. Roughly 10x faster.\\n # I gave up!\\n # copied from https://github.com/bruceoutdoors/\\n\\n X_mask = np.zeros(all_losses.shape)\\n X_mask[all_losses > 0] = 1\\n incorrect_counts = np.sum(X_mask, axis=1)\\n X_mask[np.arange(num_train), y] = -incorrect_counts\\n dW = X.T.dot(X_mask)\\n\\n dW /= num_train # average out weights\\n dW += reg * W # regularize the weights\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return total_loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e27c180386d3c0d401285fd86d59f16\",\n \"score\": \"0.67064327\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n\\n \\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n \\n scores = X.dot(W) # [N x C] # [500 x 10]\\n\\n # Produce a column-wise one-hot boolean (binary) integer matrix from y\\n true_class_mask = np.zeros(scores.shape)\\n true_class_mask[np.arange(num_train), y] = 1\\n \\n # To get the vector of true class scores, we simply multiply the scores with\\n # the mask in order to get another [N x C] matrix, and sum along columns\\n # to get an N-dimensional vector\\n true_class_scores = np.sum(scores * true_class_mask, axis=1)\\n \\n # Now we can broadcast these true class scores to compute the matrix of margins,\\n # which is also [N x C]. We hardcode delta to 1\\n # Broadcasting requires the same trailing dimensions,\\n # so we transpose and transpose back\\n margins = (scores.T - true_class_scores.T).T + 1\\n \\n # Now we need to zero the margins corresponding to the true class label\\n # we can do this by an elementwise multiply with the (boolean) negative \\n # of the true class matrix\\n margin_mask = -true_class_mask + 1\\n margins = margins * margin_mask\\n \\n # Now we zero the margins which are less than 0 to represent the max(0, .) operation\\n margin_mask2 = (margins > 0).astype(int)\\n margins = margins * margin_mask2\\n\\n # And finally the loss is simply the sum of all remaining margins, divided by N\\n loss = np.sum(margins) / num_train\\n \\n # Account for regularization\\n loss += reg * np.sum(W * W)\\n \\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n \\n \\n # Compute wrt w for j != y[i]\\n dW1 = X.T.dot(margin_mask2) # View as matrix product == sum of vector outer products\\n\\n # Compute wrt j == y[i]\\n margins_boolean = (margins > 0).astype(int)\\n num_positive_margins = np.sum(margins_boolean, axis=1).reshape(num_train, 1)\\n dW2 = X.T.dot(-true_class_mask * num_positive_margins)\\n dW = (dW1.T + dW2.T).T / num_train\\n \\n # Account for regularization\\n dW += 2 * reg * W\\n \\n \\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdfe0131dc6700b0fb09daff2d536e8b\",\n \"score\": \"0.6633743\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n numExamples = X.shape[0] \\n scoresMat = np.dot(X,W)\\n\\n LossMat = scoresMat - (scoresMat[range(numExamples),y])[:, None] + 1\\n LossMat[LossMat < 0] = 0 \\n LossMat[np.arange(numExamples),y] = 0\\n #loss mat is a matrix with num_examples x num_classes that has the ammount of loss acumulated from each example and each class classifier\\n\\n LossPerItem = LossMat.sum(axis = 1) #the total loss accumulated from each example\\n\\n TotalLoss = LossPerItem.sum()\\n TotalLoss /= numExamples\\n loss = TotalLoss\\n\\n loss += reg * np.sum(W * W)\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n binaryLossMat = np.zeros(LossMat.shape)\\n binaryLossMat = LossMat\\n binaryLossMat[binaryLossMat > 0] = 1 \\n #binary loss mat is a num_examples x num_classes matrix with 1 in i,j place if the i_th example created loss with the j_th class classifier\\n #note that by definition of loss function if example[i] is part of class j then it contributes 0 loss and is 0 in bin loss mat\\n\\n totalBinLossPerExample = binaryLossMat.sum(axis = 1) #basically the count of how many classifiers accumulated loss on an example\\n\\n binaryLossMat[np.arange(numExamples), y] = -totalBinLossPerExample \\n #now binaryLossMat is matrix like before but if example[i] has corect class j, then the ixj spot has -totalBinLossPerExample[i] \\n\\n dW = X.T.dot(binaryLossMat)\\n #this is the trick part X is num_examples x num_dim_per_example matrix, transposed is num_dim_per_example x num_examples matrix\\n #num_dim_per_example x num_examples matrix (dot) num_examples x num_classes = num_dim_per_example x num_classes matrix\\n #pretend there is one class and so one classifier. Then multiplying the first colum of binaryLossMat by first row of X.T is\\n #calculating the first element of the graident by adding up how many times the first element of each example that generates a loss for that class\\n # (or subtracts if that example was part of that class)\\n #moving to the next gradient element below, it how many times the second element of each example should be included in that class gradient\\n\\n dW /= numExamples\\n dW += reg * 2 * W\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f20894a35bc82779696469aefec5fc8\",\n \"score\": \"0.66028476\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n \\\"\\\"\\\"\\n delta = 1\\n scores = W.dot(X)\\n margins = np.maximum(0, scores - scores[y] + delta)\\n margins[y] = 0\\n loss = np.sum(margin)\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n\\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W)\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n ##########erste loesung\\n ### loss\\n # wxy chooses scores of right labels\\n # its shape is (#samples,)\\n wx = W.dot(X)\\n delta = 1\\n wxy = [ wx[y[i], i] for i in xrange(wx.shape[1]) ]\\n \\n # judge expression\\n # remember to exclude on y[i]'s\\n judge = wx - wxy + delta\\n # make judge 0 on y[i]\\n for i in xrange(wx.shape[1]):\\n judge[y[i], i] = 0\\n \\n # mass is a matrix holding all useful temp results\\n # shape of judge is (#class, #train)\\n mass = np.maximum(0, judge)\\n \\n loss = np.sum(mass) / X.shape[1]\\n loss += 0.5 * reg * np.sum(W * W)\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n scores = W.dot(X)\\n loss = np.zeros(scores.shape)\\n init = np.zeros(scores.shape)\\n loss = scores - np.choose(y,scores)\\n loss[np.where(loss!=0)] += 1 \\n loss[np.where(loss<=0)] = 0\\n init[np.where(loss>0)] = 1\\n loss1 = loss\\n loss = np.sum(loss)\\n loss /= num_train\\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W) \\n \\\"\\\"\\\"\\n ### zweite und am besten loesung erste part\\n WX = W.dot(X)\\n num_classes = W.shape[0]\\n num_train = X.shape[1]\\n margin = WX - WX[y, np.arange(num_train)] + 1\\n margin[y, np.arange(num_train)] = 0\\n margin[margin < 0] = 0\\n loss = sum(sum(margin))\\n loss /= num_train\\n loss += 0.5 * reg * np.sum(W * W)\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n \\\"\\\"\\\"\\n scores = W.dot(X)\\n margins = np.maximum(0, scores - scores[y] + delta)\\n ddW = np.sum(X,axis=1)\\n margins[y] = 0\\n ddW[y] -= ddW\\n dW = np.sum(ddW)\\n loss = np.sum(margin)\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n dW /= num_train\\n \\n # add regularization term\\n loss += 0.5 * reg * np.sum(W * W)\\n dW += reg * W\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n ### erste loesung\\n # continue on last snippet\\n # weight to be producted by X\\n # its shape is (#classes, #samples)\\n weight = np.array((judge > 0).astype(int))\\n \\n # weights on y[i] needs special care\\n weight_yi = -np.sum(weight, axis=0)\\n for i in xrange(wx.shape[1]):\\n weight[y[i], i] = weight_yi[i]\\n \\n # half vectorized\\n for i in xrange(X.shape[1]):\\n ddW = X[:, i] * weight[:, i].reshape(-1, 1)\\n dW += ddW\\n \\n dW /= X.shape[1]\\n dW += reg * W\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n k1 = np.sum(init,axis=0)\\n k2 = -k1*X\\n dW = np.tile(np.sum(X,axis=1),(num_classes,1))\\n #np.add.at(dW,y,-kT-XT)\\n #np.add.at(dW,y,k2.transpose())\\n #y2 = np.repeat(np.arange(num_classes),num_train).reshape(num_classes,num_train)\\n #np.add.at(dW,np.int_(y2),X.transpose())\\n #np.add.at(dW,y,-X.transpose())\\n #dW /= num_train\\n \\\"\\\"\\\"\\n ### zweite und am besten loesung zweite part\\n grad = np.zeros([W.shape[0], X.shape[1]])\\n grad_wj = margin > 0\\n grad_y = -1*sum(grad_wj)\\n grad = grad + grad_wj\\n grad[y, np.arange(num_train)] = grad_y\\n dW = grad.dot(X.T)\\n dW /= num_train\\n dW += reg * W\\n \\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03dfae6cc66a0e97cca45dc64ff3bed8\",\n \"score\": \"0.65555876\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n delta = 1.0\\n num_classes = W.shape[1] # C\\n num_train = X.shape[0] # N\\n y = y.astype(int) # Par défault y est un tableau de float qui génère une erreur pour la transformation en 1-hot-E\\n\\n\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n # compute the loss\\n scores = np.matmul(X,W) # Matrice N,C\\n y_one_hot = np.zeros((y.size, num_classes))\\n y_one_hot[np.arange(y.size),y] = 1 # Matrice N,C\\n \\n # numpy.multiply : Multiply arguments element-wise.\\n correct_class_score = np.sum(np.multiply(scores, y_one_hot), axis=1) # Vecteur de taille N\\n # On le transforme en matrice pour enlever le score de la bonne classe à tous les éléments d'une ligne de scores\\n margins = scores - np.array([correct_class_score,]*num_classes).T + delta\\n margins = np.maximum(0, margins)\\n \\n # on y-th position scores[y] - scores[y] canceled and gave delta. We want\\n # to ignore the y-th position and only consider margin on max wrong class\\n # multiplication terme à terme par (1-y_one_hot) qui va renvoyer 0 juste pour l'élément s_yi\\n margins = np.multiply(margins, (1-y_one_hot))\\n \\n loss = np.sum(margins)/num_train\\n \\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n \\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n # cf notes\\n\\n s_grad = 1*(margins>0) + np.multiply(y_one_hot, - np.array([np.sum(margins>0, axis=1),]*num_classes).T )\\n dW = np.matmul(X.T, s_grad)\\n dW /= num_train\\n \\n # Add regularization to the gradient.\\n dW += 2* reg * W\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed7ad812f2598efb3dc9fb65c85410c8\",\n \"score\": \"0.65491706\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n \\n\\n \\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[0]\\n num_train = X.shape[1]\\n loss = 0.0\\n for i in xrange(num_train):\\n train=X[i,:]\\n # train=train.reshape(1,-1) # 1 x D\\n print train.shape\\n print W.shape\\n \\n scores = np.dot(train,W) # 1 x C\\n\\n print scores.shape\\n\\n correct_class_score = scores[y[i]]\\n for j in xrange(num_classes):\\n if j == y[i]:\\n temp_score=scores\\n temp_score=np.delete(temp_score,y[i])\\n predicted_class_weight=W[y[i],:]\\n predicted_class_weight=predicted_class_weight.reshape(-1,1)\\n temp_score=temp_score-(np.dot(predicted_class_weight.T,train))+1\\n num_violators=np.sum(temp_score>0)\\n \\n dW[j,:]=dW[j,:]-num_violators*train.T\\n \\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n # dW[j,:]=dW[j,:]+train.T\\n loss += margin\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n\\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W)\\n \\n dW/=num_train\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e79549f92231ed904bb62343afa08d2\",\n \"score\": \"0.6521443\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n\\n \\\"\\\"\\\"\\n 2 May notes:\\n the gradient flow to W for\\n 1) the correct class is -(margin_count * x_i)\\n 2) all other classes is x_i if margin>0\\n let's try this idea out] and then do a power nap before Copenhagen\\n\\n we also want the gradient to be divided by N and then added to the gradient\\n of regularisation\\n\\n The gradient of the SVM loss, according to derivation by calculus, can be\\n acquired effectively by:\\n\\n For each image (data point):\\n 1. count the number of classes that failed to meet the margin requirement\\n 2. the contribution to the loss from this image = x[i] * num_counted for all i ~= yi\\n negative that for i = yi\\n\\n \\\"\\\"\\\"\\n\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialise dW\\n\\n for i in xrange(num_train):\\n # calculate scores\\n scores = X[i].dot(W)\\n scores_correct = scores[y[i]]\\n\\n margin_count = 0 # reset margin count for each image\\n dW_temp = np.zeros(W.shape) # reset dW_temp matrix for each image\\n\\n for j in xrange(num_classes):\\n if j == y[i]: # skip correct class\\n continue\\n margin = scores[j] - scores_correct + 1\\n if margin > 0: # max(0, margin)\\n loss += margin\\n margin_count += 1\\n dW_temp[:, j] = X[i].T\\n\\n dW_temp[:, y[i]] = - margin_count * X[i].T\\n dW += dW_temp # add to total gradient\\n\\n # final loss and dW for return\\n loss = loss/num_train + np.sum((np.power(W, 2))) # average, add regularisation\\n dW = dW/num_train + 2.0*reg*W\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93920d298cb99650bd7c6473dd96a42e\",\n \"score\": \"0.6498648\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n #############################################################################\\n # TEST: run the following test code to check\\n # a = np.random.randint(10, high=20, size=20) # generate a of size 20, where each element if in [10, 20)\\n # a = np.reshape(a, (5, 4))\\n # y_index = np.random.randint(4, size=5) # generate y_index of size 5, where each element is up to 4\\n # y = np.choose(y_index, a.T) # choose ys from each row of a at y_index\\n # margin = np.subtract(a, y[:, np.newaxis]) + 1 # subtract each row with corresponding elements in y\\n #############################################################################\\n scores = X.dot(W) # 500, 10\\n num_train = scores.shape[0] # 500\\n correct_class_score = np.choose(y, scores.T) # 500,\\n margin = np.subtract(scores, correct_class_score[:, np.newaxis]) + 1\\n margin[range(num_train), list(y)] = 0\\n loss = np.sum(margin, where=[margin>0]) / num_train + reg * np.sum(W*W)\\n pass\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n # reference: https://github.com/hartikainen/stanford-cs231n/blob/master/assignment1/cs231n/classifiers/linear_svm.py\\n grad_mask = (margin > 0).astype(int)\\n grad_mask[np.arange(y.shape[0]), y] = - np.sum(grad_mask, axis=1)\\n dW = np.dot(X.T, grad_mask)\\n\\n dW /= float(num_train)\\n dW += reg * W\\n pass\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca160c0c85df5022dc9400fdc96e668\",\n \"score\": \"0.6451991\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n count_missed_margin = 0 # Count the number of classes that don't meet the margin\\n # criteria. Used for Gradient.\\n \\n for i in range(num_train): # For each training example\\n scores = X[i].dot(W) # returns 1xC vector, ~prob that X[i] is of class c\\n correct_class_score = scores[y[i]] # returns the ~prob for the correct class\\n for j in range(num_classes): # For each class\\n if j == y[i]: # skip the correct class \\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n # Takes difference of the score for every other class and the correct score\\n # Adds in a delta. The SVM \\\"wants\\\" the correct score to be greater than\\n # all other scores by at least 'delta'. i.e. we want negative\\n # margin. (see line below)\\n if margin > 0:\\n loss += margin # For all \\\"bad\\\" margins (not diff not > delta value)\\n # add them up, that's our loss, which we want to minimize\\n count_missed_margin += 1\\n # Gradient for all columns of the wrong class (if margin in not within\\n # limit) is X[i]\\n dW[:,j] += X[i]\\n #dW[:,y[i]] -= X[i] <- Alternative to doing the single assignment below\\n\\n # Gradient for the column with the correct class is simply X[i]\\n # scaled by the number of missed classes.\\n dW[:,y[i]] -= count_missed_margin*X[i]\\n count_missed_margin = 0\\n\\n \\n \\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n \\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n \\n \\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n dW = np.divide(dW,num_train)\\n dW = np.add(dW,reg*W)\\n \\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aa41ccc99e7fd902d38fb9cddd46173\",\n \"score\": \"0.64205873\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n num_train = X.shape[0]\\n delta = 1.0\\n scores = X.dot(W)\\n\\n #scores now has (N,C) where each row is a row of scores for that specific training image\\n # compute the margins for all classes in one vector operation\\n correct_class_scores=scores[np.arange(num_train),y]\\n margins = np.maximum(0, scores - correct_class_scores[:,np.newaxis] + delta)\\n\\n # on y-th position scores[y] - scores[y] canceled and gave delta. We want\\n # to ignore the y-th position and only consider margin on max wrong class\\n\\n margins[np.arange(num_train),y] = 0\\n\\n #reuse the margin information to calculate the gradient. Margin has the dimension (N,C)\\n #for each row need to count the number of positive numbers other than the correct class \\n #and the corresponding weight's class column will receive a negative gradient update and count \\n #the number of times incorrect rows needs a positive gradient update form a matrix (N,C)\\n counter=np.zeros(margins.shape) \\n mask=(margins>0)\\n counter[mask]=1\\n correst_class_mutiplier=-np.sum(mask,axis=1)\\n counter[np.arange(num_train),y]=correst_class_mutiplier\\n\\n #now use the counter to add corresponding Xs to dW\\n dW=X.T.dot(counter)\\n\\n loss = np.sum(margins)\\n\\n loss /= num_train\\n\\n #average dW as well\\n dW /= num_train\\n\\n # Add regularization to the loss.\\n\\n loss += reg * np.sum(W * W)\\n\\n # Also add regularization to the gradient\\n\\n dW += 2*reg*W\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n \\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74e642de8f6a5f1378402dc270667005\",\n \"score\": \"0.6418873\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n num_train = X.shape[1]\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n scores = W.dot(X) # C * N vector \\n true_scores = scores[y, np.arange(num_train)] # true_scores[i] contains\\n mat = scores - true_scores + 1\\n mat[y, np.arange(num_train)] = 0\\n mat = np.maximum(np.zeros(scores.shape), mat)\\n loss = np.sum(mat)/num_train\\n loss += 0.5 * reg * np.sum(W*W)\\n\\n \\n bin = mat\\n bin[mat > 0] = 1 # bin is of size C * N\\n col_sum = np.sum(bin, axis = 0) # mat is of size 1 * N the number of times > 0 for a data point for all classes\\n bin[y, np.arange(num_train)] = -col_sum[np.arange(num_train)]\\n dW = np.dot(bin, X.T) # dW is a C * D\\n dW /= num_train\\n \\n dW += reg* W\\n\\n\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44e18d8d09e99236de088f4682b57f0c\",\n \"score\": \"0.6412422\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n num_train = X.shape[0]\\n scores = X.dot(W) # NxC (Training Examples x Classes)\\n correct_class_scores = scores[np.arange(0,num_train),y].reshape(-1,1)\\n\\n margin = scores - correct_class_scores + 1\\n margin[np.arange(0,num_train), y] = 0 # Set correct score to 0\\n margin[margin<0] = 0 # set margins that \\\"pass\\\" to zero, so they don't \\n # affect the loss.\\n loss = np.sum(margin)\\n # Take all margins that are greater than 0, sum them up\\n loss /= num_train # Dvide by number of training examples\\n loss += reg*np.sum(W*W) # add regularization\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n \\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n #Gradient\\n # dW has C columns, one for each class\\n # The gradient is as such:\\n # For each Xi, we add it to the column if:\\n # it doesn't pass the margin test for that row\\n # AND\\n # that row isn't the correct row for that sample\\n # Above we've already created a matrix \\\"margin\\\" where\\n # the dimensions are samples-by-classes, that indicates the margin\\n # of each sample for each class, but is set to 0 where the margin\\n # passed (i.e. was negative) or where that class was the correct class\\n # so we only need to create a mask matrix where every instance of 'margin'\\n # that is > 0 = 1 (or True, which is equivalent in Python). Then we can \\n # dot this mask with X, which will essentially do the above.\\n miss_margin = margin > 0 # already accounts for j != yi\\n dWwj = X.T.dot(miss_margin)\\n # We also count how many classes for which that sample didn't \\n # pass the margin test, call this Z\\n # for the column of dW that corresponds to the correct class for Xi\\n # we add -Z*Xi\\n cm = np.zeros(miss_margin.shape)\\n cm[np.arange(0,num_train),y] = -1*np.sum(miss_margin, axis=1) \\n dWyi = X.T.dot(cm) \\n # multiply all samples by this number\\n # now we need to subtract this from every column of dW that is the correct class for that sample\\n dW = dWyi + dWwj\\n dW /= num_train\\n dW += reg*W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"358490be280c88429f92e59e516f81ff\",\n \"score\": \"0.6410807\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n \\n \\n \\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n num_train=X.shape[0]\\n num_classes=W.shape[1]\\n \\n \\n \\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n scores=np.dot(X,W) # C x N\\n \\n # print scores.shape \\n # print y.shape\\n # print y\\n \\n #indexes=np.array([[i,elem] for i,elem in zip(range(num_train,y))])\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n \\n correct_scores=scores[np.arange(num_train),y]\\n \\n #for j in range(num_train):\\n # correct_scores[j]=scores[y[j],j]\\n \\n # correct_scores=correct_scores.reshape(1,-1)\\n \\n # margin=(scores-(correct_scores))+1\\n margin=((scores.T-correct_scores).T)+1\\n \\n margin[np.arange(num_train),y]=0\\n \\n #for j in range(num_train):\\n # margin[y[j],j]=0\\n #\\n #print margin[margin>1]\\n \\n loss=np.sum(margin[margin>0])\\n loss=loss/num_train\\n \\n regularization_term=reg*np.sum(np.power(W,2))\\n loss=loss+regularization_term\\n\\n\\n #caculating the number of classes that have violated the margin across the entire training\\n #data\\n thres=np.maximum(np.zeros((num_train,num_classes)),margin)\\n \\n binary=thres\\n binary[thres>0]=1\\n \\n col_sum=np.sum(binary,axis=1)\\n\\n # print 'col_sum',col_sum.shape\\n\\n # print 'binary',binary.shape\\n \\n binary[np.arange(num_train),y]=-col_sum[range(num_train)]\\n \\n # print 'binary',binary.shape\\n\\n dW=np.dot(X.T,binary)\\n dW=dW/num_train\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f384e1e51e695d0241459b748a7795a4\",\n \"score\": \"0.638657\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n \\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n Q = np.zeros((num_train, num_classes))\\n loss = 0.0\\n for i in xrange(num_train):\\n dDot = np.zeros(num_classes) \\n scores = X[i].dot(W) #1x3073 dot 3073x10 = 1x10\\n xi = X[i, :]\\n correct_class_score = scores[y[i]]\\n for j in xrange(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n Q[i,j] = 1\\n Q[i,y[i]] -= 1\\n dW[:,j] += xi\\n dW[:,y[i]] -= xi\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n dW /= num_train\\n\\n #add regularization derivative\\n dW += reg * 2 * W\\n \\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n\\n # print(\\\"X shape : \\\", X.shape, \\\", W shape :\\\", W.shape, \\\", dDot shape\\\", dDot.shape, \\\"dW shape\\\", dW.shape)\\n #print(\\\"dDot = \\\", dDot[:10])\\n #print(Q[:10,:])\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n #dW = X.T.dot(dDot) \\n #print(\\\"dW = \\\", dW[:10]) \\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4067d44751ef2021219b157173ef5681\",\n \"score\": \"0.6326962\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n y_hat = np.dot(X, W) # score for each class for each data\\n y_index = [np.arange(len(y)), np.array([y[i] for i in range(len(y))])] # index of true label for each training data\\n y_true_label_score = y_hat[y_index[0], y_index[1]] # one dimension array of score for true class\\n y_true_label_score_array = np.array([np.repeat(y_true_label_score[i], y_hat.shape[1]) for i in range(len(y_true_label_score))]) # repeat score of true class for each class\\n ones_array = np.ones(y_true_label_score_array.shape)\\n # add_ones_back_true_label_array\\n S_ij_matrix = (y_hat - y_true_label_score_array + ones_array)\\n y_loss_array = S_ij_matrix.clip(0.0) # hinge loss, for each class for each data, score_j - score_true_label + 1\\n y_loss_sum = np.sum(y_loss_array) - y_hat.shape[0] # total loss, aggregate for data and all classes\\n penalty = reg * np.sum(np.square(W)) # regularization term\\n loss = (y_loss_sum + penalty) / X.shape[0]\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n # derivative formula\\n # j is not the true class, j' is the true class\\n # delta_L_i / delta_W_tj = X_it if S_ij - S_ij' + 1 > 0 else 0\\n # j is the true class:\\n # delta L_i / delta_W_tj' = sum_j(indicator(S_ij - Sij' + 1) * X_it\\n\\n # derivative for j, not the true class\\n S_ij_matrix_bool = np.ones(S_ij_matrix.shape) #\\n S_ij_matrix_bool[S_ij_matrix <= 0] = 0 # classes the loss is 0, which gradient is 0\\n S_ij_matrix_bool[y_index[0], y_index[1]] = 0 # set the true class gradient 0\\n S_ij_derivative = np.dot(np.transpose(X), S_ij_matrix_bool) / X.shape[0] # delta_L / delta_W_tj = mean_i( delta_L_i / delta_W_tj)\\n # derivative for j', the true class\\n tmp_index_array = np.zeros(S_ij_matrix_bool.shape)\\n # tmp_index_array[y_index[0], y_index[1]] = np.sign(S_ij_matrix).clip(0.1).sum(1) # number of class that contribute to the gradient for i-th data\\n tmp_index_array[y_index[0], y_index[1]] = S_ij_matrix_bool.sum(1)\\n S_true_class_derivative = - np.dot(np.transpose(X), tmp_index_array) / X.shape[0]\\n dW = S_ij_derivative + S_true_class_derivative + 2 * reg * W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c68daa19d3a1d97363ee98e6345fc9eb\",\n \"score\": \"0.6322888\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n num_train = X.shape[0]\\n # Compute scores with a simple matrix multiplication\\n scores = X.dot(W)\\n # Extract correct scores using numpy indexing capabilities\\n # First vector imdicates the index for the first dimension (a range)\\n # second vector is the correct class index y\\n correct_scores = scores[np.arange(0,num_train),y]\\n # Calculate margin for each individual score. Note that we need to transpose\\n # the scores matrix to benefit from broadcast\\n margin = scores.T - correct_scores + 1\\n # Force margin to zero for correct classes (using y as indices)\\n margin[y,np.arange(0,num_train)] = 0\\n # We accumulate loss if margin is strictly positive\\n loss = np.sum(margin[margin > 0])\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n\\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W)\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n # We will reuse the loss matrix we calculated before\\n # First, convert our margin matrix to a binary one\\n dL = (margin > 0).astype(int)\\n # We still use a transposed matrix (which is counter intuitive):\\n # In each datapoint column, the row that corresponds to the 'correct' class\\n # weights negatively as much as the sum of the 'wrong' classes weight\\n dL[y,np.arange(0,num_train)] = -1 * np.sum(dL, axis=0)\\n # The gradient is the result of the multiplication of the new matrix with the\\n # data matrix.\\n # Note that the result must be transposed back to match the gradient shape\\n dW = (dL.dot(X)).T\\n # Normalize the matrix\\n dW /= num_train\\n # Add regularization\\n dW += reg*W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"130b2e5d92e1adab00efcd40a1a9b1d5\",\n \"score\": \"0.6312452\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n for i in xrange(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n for j in xrange(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n # https://zhuanlan.zhihu.com/p/21478575\\n # Induction: Denote k as 0,1,...D-1\\n # Denote loss[i, j] as the classify score on class_j at sample i\\n # if margin <= 0, loss[i, j] is constant 0, derivative of loss at this pos with respect to any W is 0\\n # Here we computed loss[i, j] = sum(X[i, k] * W[k, j]) - sum(X[i, k] * W[k, y[i]]) + 1\\n # if loss[i, j] > 0 then the partial derivative of loss[i, j] with respect to W[k, j] is X[i, k]\\n # else the partial derivative of loss[i, j] with respect to W[k, j] is 0\\n # the partial derivative of loss[i, j] with respect to W[k,\\n # y[i]] is -X[i, k]\\n dW[:, j] += X[i]\\n dW[:, y[i]] -= X[i]\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n\\n # Add regularization to the loss.\\n loss += .5 * reg * np.sum(W * W)\\n dW /= num_train\\n dW += reg * W\\n ##########################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n ##########################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa9b41f53818df0a1770fe871c7aea17\",\n \"score\": \"0.62921715\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n num_train = X.shape[0]\\n scores = np.dot(X,W)\\n correct_class_score = np.choose(y, scores.T) # np.choose uses y to select elements from scores.T\\n # print(correct_class_score)\\n # Need to remove correct class scores as we dont need to calculate loss/margin for those\\n mask = np.ones(scores.shape,dtype=bool)\\n mask[range(scores.shape[0]),y] = False\\n scores_ = scores[mask].reshape(scores.shape[0],scores.shape[1]-1)\\n\\n # Calculate all our margins all at once\\n margin = scores_ - correct_class_score[..., np.newaxis] + 1\\n # print(correct_class_score[...,np.newaxis].shape)\\n # We only add margin if it is greater than 0\\n margin[margin<0] = 0\\n\\n # Average our data loss over the size of the batch and add reg. term to the loss\\n loss = np.sum(margin)/num_train\\n loss += reg*np.sum(W*W)\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n original_margin = scores - correct_class_score[...,np.newaxis] + 1\\n # print(original_margin.shape)\\n # Mask to identify where the margin is greater than 0\\n pos_margin_mask = (original_margin>0).astype(float)\\n # print(pos_margin_mask.shape)\\n # Count how many times>0 for each image but dont count the correct class so -1\\n sum_margin = pos_margin_mask.sum(1) -1\\n\\n # Make the correct class margin be negative total of how many > 0\\n pos_margin_mask[range(pos_margin_mask.shape[0]),y] = -sum_margin\\n\\n dW = np.dot(X.T, pos_margin_mask)\\n\\n dW = dW/num_train + 2*reg*W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed2eeed3a5954545b98ae7ce88f882a\",\n \"score\": \"0.62709504\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n delta = 1\\n num_train = X.shape[0]\\n\\n scores = X.dot(W)\\n correct_scores = np.reshape(scores[np.arange(num_train), y], (num_train, 1))\\n\\n margins = np.maximum(0, scores - correct_scores + delta)\\n margins[np.arange(num_train), y] = 0\\n\\n loss += np.sum(margins) / num_train\\n loss += reg * np.sum(W * W)\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n indicator_margins = np.where(margins > 0, 1, 0)\\n\\n # Adding dW_y_i to dW. \\n #\\n # We scale X according to the number of nonzero elements in each row \\n # of indicator_margins, and then index into y_ith columns of dW using\\n # the y_ith row of X. \\n #\\n # NOTE: Not sure how to do without for loop.\\n\\n scaled_X = X * np.reshape(np.sum(indicator_margins, axis=1), (num_train, 1))\\n for i in range(num_train):\\n dW[:, y[i]] -= scaled_X[i]\\n \\n # Something like \\\"dW[:, y] -= scaled_X\\\" doesn't seem to work above. dW is sized \\n # up to (3073, 500), which is not what we want. We need to be able to increment \\n # the same column multiple times. \\n \\n # Adding dW_j to dW. \\n #\\n # np.nonzero gives indices of nonzero elements. \\n \\n nonzero_indices = np.where(indicator_margins)\\n X_i = nonzero_indices[0]\\n W_j = nonzero_indices[1]\\n\\n for index in range(len(X_i)):\\n dW[:, W_j[index]] += X[X_i[index]]\\n\\n dW /= num_train\\n dW += 2 * reg * W\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68f90d0f677254fb095f0004316c0cce\",\n \"score\": \"0.6251825\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n y = y.astype(int) # Par défault y est un tableau de float qui génère une erreur pour la transformation en 1-hot-E\\n\\n loss = 0.0\\n for i in range(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n count = 0\\n for j in range(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n count += 1\\n # Calcul du gradient en un point\\n # On peut l'intégrer dans la boucle précédente, mais les maths en deviennent moins lisibles\\n for j in range(num_classes):\\n if j == y[i]:\\n dW[:,j] += - count * X[i]\\n else:\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n dW[:,j] += X[i]\\n # dW[:,j] += 1. * X[i]\\n # else: # Ne sert à rien sauf à expliquer le calcul\\n # dW[:,j] += 0. * X[i]\\n\\n \\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n dW /= num_train\\n\\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n dW += 2* reg * W\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n pass\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n \\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04dab0f68689f3ff8189724fd4d2e455\",\n \"score\": \"0.6238996\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n delta = 1 # margin separation set to 1\\n\\n # get some dimensions\\n num_train = X.shape[0]\\n num_classes = W.shape[1]\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n\\n scores = X.dot(W)\\n correct_scores = scores[np.arange(num_train), y]\\n correct_scores = np.reshape(correct_scores, (correct_scores.shape[0], -1))\\n\\n margin = scores - correct_scores + delta\\n margin = np.maximum(0, margin)\\n\\n reg_term = reg * np.sum(np.power(W, 2))\\n loss_term = np.sum(margin) - num_train * delta # remove the margins for j=yi\\n loss = loss_term/num_train + reg_term\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n\\n coeff_mtrx = np.zeros((num_train, num_classes))\\n margin[np.arange(num_train), y] = 0 # remove the margin for j = yi (delta)\\n coeff_mtrx[margin > 0] = 1\\n margin_count_vec = np.sum(margin > 0, axis=1)\\n coeff_mtrx[np.arange(num_train), y] = -margin_count_vec\\n\\n dW = np.dot(X.T, coeff_mtrx)/num_train + 2.0*reg*W\\n\\n\\n ######### implement with a loop over training images ######\\n # # ver1: check coeff matrix idea\\n # dW = np.zeros(W.shape) # initialise\\n # for i in xrange(num_train):\\n # dW += np.outer(X[i].T, coeff_mtrx[i])\\n # dW = dW/num_train + 2*reg*W\\n\\n\\n # # ver2: by-pass the coeff matrix idea, produce coefficients in each loop\\n # dW = np.zeros(W.shape) # initialise\\n # for i in xrange(num_train):\\n # coeff_vec = np.zeros((num_classes,))\\n # coeff_vec[margin[i] > 0] = 1\\n # coeff_vec[y[i]] = -np.sum(margin[i] > 0)\\n # dW += np.outer(X[i].T, coeff_vec)\\n #\\n # dW = dW/num_train + 2*reg*W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47969aa89dd88f1594e03e0416795fe5\",\n \"score\": \"0.62326443\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n n_sample = X.shape[0]\\n hinge_loss = 0.0\\n for i in xrange(n_sample):\\n scores = np.dot(X[i],W)\\n correct_class_score = scores[y[i]]\\n margin = np.maximum(0,scores - correct_class_score + 1)\\n margin[y[i]] = 0\\n hinge_loss += np.sum(margin) / n_sample\\n\\n margin = scores - correct_class_score + 1\\n margin = (margin > 0) * 1\\n margin[y[i]] = 0\\n margin[y[i]] = -np.sum(margin)\\n dW_single_sample = np.outer(X[i],margin) / n_sample\\n dW += dW_single_sample + reg * W / n_sample\\n '''\\n for j in xrange(n_class):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n data_loss += margin / n_sample\\n '''\\n reg_loss = 0.5 * reg * np.sum(W ** 2)\\n # Add regularization to the loss.\\n loss = hinge_loss + reg_loss\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c8b6e9588a8fd9f944bf07bb345f69\",\n \"score\": \"0.62129825\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n\\n #Initialize gradient and loss to zero\\n dW = np.zeros(W.shape) \\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n\\n # SVM loss is defined as: \\n # Li = sum j != yi max(0, w^T_j*xi - w^T_yi*xi + 1)\\n\\n for i in xrange(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n num_greater_margin = 0\\n\\n for j in xrange(num_classes):\\n #Skip correct class\\n if j == y[i]:\\n continue\\n #For every other class, calculate the sum and gradient\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n num_greater_margin += 1\\n loss += margin\\n dW[:, j] = dW[:, j] + X[i, :]\\n\\n #Correct class gradient\\n dW[:, y[i]] = dW[:, y[i]] - X[i, :]*num_greater_margin \\n \\n #Average loss.\\n loss /= num_train\\n\\n #Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n\\n dW = dW / num_train + 2*reg *W\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6de82613fefde2b75308874e1d38decd\",\n \"score\": \"0.62007946\",\n \"text\": \"def _train(x,y):\\n\\t# D = Number of attributes\\n\\tD = x.shape[1] + 1\\n\\t# K = Number of classes\\n\\tK = y.shape[1]\\n\\t\\n\\t# Build the sums of xi*xi' and xi*yi'\\n\\tsum1 = np.zeros((D,D)) # init placeholder\\n\\tsum2 = np.zeros((D,K))\\n\\ti = 0\\n\\tfor x_i in x:\\t\\t\\t\\t\\t\\t# loop over all vectors\\n\\t\\tx_i = np.append(1, x_i) \\t\\t# augment vector with a 1 \\n\\t\\ty_i = y[i]\\t\\t\\t\\t\\t\\t\\n\\t\\tsum1 += np.outer(x_i, x_i)\\t\\t# find xi*xi'\\n\\t\\tsum2 += np.outer(x_i, y_i)\\t\\t# find xi*yi'\\n\\t\\ti += 1\\n\\t\\n\\t# Check that condition number is finite\\n\\t# and therefore sum1 is nonsingular (invertable)\\n\\twhile matrix_rank(sum1) != D:\\n\\t\\t# Naive choice of sigma.\\n\\t\\t# Could cause inaccuracies when sum1 has small values\\n\\t\\t# However, in most cases the matrix WILL be invertable\\n\\t\\tsum1 = sum1 + 0.001 * np.eye(D) \\n\\t\\n\\t# Return weight vector\\n\\t# Weight vector multiplies sums and inverse of sum1\\n\\treturn np.dot(inv(sum1),sum2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb133d87240c23351ca679e37f6fc87\",\n \"score\": \"0.61952215\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n \\\"\\\"\\\"\\n num_classes = W.shape[0]\\n num_train = X.shape[1]\\n loss = 0.0\\n delta = 1.0\\n\\n for i in xrange(num_train):\\n scores = W.dot(X[:, i])\\n correct_class_score = scores[y[i]]\\n for j in xrange(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + delta # note delta = 1\\n if margin > 0:\\n loss += margin\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n\\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W)\\n \\\"\\\"\\\"\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n #\\n \\\"\\\"\\\"\\n #############meine loesung\\n for i in xrange(num_train):\\n\\tddW = np.zeros(W.shape)\\n\\tscores = W.dot(X[:, i])\\n\\tcorrect_class_score = scores[y[i]]\\n\\tfor j in xrange(num_classes):\\n\\t\\tif j == y[i]:\\n\\t\\t\\tcontinue\\n\\t\\tmargin = scores[j] - correct_class_score + delta\\n\\t\\tif margin > 0:\\n \\t\\t loss += margin\\n\\t\\t#loss += max(0, margin)\\n\\t\\t ddW[j] += X[:,i] # the error btw analytical and numerical in this approach is too much because ddWyi += ddW[j] ddW[y[i]] = -ddWyi weren't done.\\n \\tdW += ddW\\n loss /= num_train\\n dW /= num_train\\n \\n\\n loss += 0.5 * reg * np.sum(W * W)\\n dW += reg*W\\n #\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n ############## erste loesung\\n\\n for i in xrange(num_train):\\n \\tddW = np.zeros(W.shape)\\n \\tddWyi = np.zeros(W[0].shape) ###?????????????????\\n \\tscores = W.dot(X[:, i])\\n \\tcorrect_class_score = scores[y[i]]\\n \\tfor j in xrange(num_classes):\\n \\t\\tif j == y[i]:\\n \\t\\tcontinue\\n \\t\\tmargin = scores[j] - correct_class_score + delta\\n \\t\\tif margin > 0:\\n \\t\\t loss += margin\\n \\t\\t ddW[j] = X[:, i] ## be careful, it's a reference\\n \\t\\t ddWyi += ddW[j] #important to get correct results\\n \\tddW[y[i]] = -ddWyi #important to get correct results\\n \\tdW += ddW\\n \\n # divided by num_train\\n loss /= num_train\\n dW /= num_train\\n \\n # add regularization term\\n loss += 0.5 * reg * np.sum(W * W)\\n dW += reg * W\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n def svm_loss_naive(W, X, y, reg):\\n\\n Structured SVM loss function, naive implementation (with loops)\\n Inputs:\\n - W: C x D array of weights\\n - X: D x N array of data. Data are D-dimensional columns\\n - y: 1-dimensional array of length N with labels 0...K-1, for K classes\\n - reg: (float) regularization strength\\n Returns:\\n a tuple of:\\n - loss as single float\\n - gradient with respect to weights W; an array of same shape as W\\n \\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[0]\\n num_train = X.shape[1]\\n loss = np.zeros(1)\\n for i in xrange(num_train):\\n scores = W.dot(X[:, i])\\n correct_class_score = scores[y[i]]\\n init = 0\\n for j in xrange(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n\\tinit +=1\\n dW[j,:] += X[:,i]\\n dW[y[i],:] += -init*X[:,i]\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train \\n dW /= num_train\\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W)\\n dW += reg*W\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n \\\"\\\"\\\"\\n ### zweite und am besten loesong\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[0]\\n num_train = X.shape[1]\\n\\n grad_helper = np.zeros([num_classes, num_train])\\n loss = 0.0\\n for i in xrange(num_train):\\n scores = W.dot(X[:, i])\\n correct_class_score = scores[y[i]]\\n for j in xrange(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n grad_helper[j,i] += 1\\n grad_helper[y[i],i] -= 1\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n\\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W)\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n \\n dW = grad_helper.dot(X.T)\\n dW /= num_train\\n dW += reg * W\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb9bc0ee0668eb0e9a692ce3d1f58f6\",\n \"score\": \"0.6186058\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n # print(\\\"ini Shape:\\\", dW.shape, W.shape)\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n for i in xrange(num_train):\\n # for each training example\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n count = 0\\n for j in xrange(num_classes):\\n # for each class\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n count += 1\\n loss += margin\\n dW[:, j] += X[i]\\n # print(\\\"ffi Shape:\\\", dW.shape)\\n dW[:, y[i]] -= count * X[i]\\n # print(\\\"f Shape:\\\", dW.shape)\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n dW /= num_train\\n # print(\\\"o Shape:\\\", dW.shape)\\n\\n # Add regularization to the loss.\\n # print(\\\"Loss before reg: \\\", loss)\\n loss += 0.5 * reg * np.sum(W * W)\\n # print(\\\"Loss after reg: \\\", loss)\\n dW += reg * W\\n # print(\\\"o2 Shape:\\\", dW.shape)\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0965c85b990b083ff105f5f6d16c6c07\",\n \"score\": \"0.6181599\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n for i in range(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n for j in range(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n dW[:, j] += X[i, :]\\n dW[:, y[i]] += -X[i, :]\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n dW /= num_train\\n\\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n dW += reg * W\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n #############################################################################\\n # NUMERICAL way of gradient esitmation, which is super slow and not accurate\\n # h = 0.0001 \\n # for ii in range(dW.shape[0]):\\n # for jj in range(dW.shape[1]):\\n #\\n # W_after = W\\n # W_after[ii, jj] += h\\n # loss_after = 0.0\\n # for i in range(num_train):\\n # scores = X[i].dot(W_after)\\n # correct_class_score = scores[y[i]]\\n # for j in range(num_classes):\\n # if j == y[i]:\\n # continue\\n # margin = scores[j] - correct_class_score + 1 # note delta = 1\\n # if margin > 0:\\n # loss_after += margin\\n #\\n # # Right now the loss is a sum over all training examples, but we want it\\n # # to be an average instead so we divide by num_train.\\n # loss_after /= num_train\\n #\\n # # Add regularization to the loss.\\n # loss_after += reg * np.sum(W * W)\\n #\\n # # update dW\\n # dW[ii, jj] = (loss_after - loss) / h\\n #############################################################################\\n # ANALYTICAL way of gradient calculation, which is fast and accurate\\n # Reference: https://math.stackexchange.com/questions/2572318/derivation-of-gradient-of-svm-loss\\n # Reference: http://cs231n.github.io/linear-classify/#svm\\n # Reference: https://blog.csdn.net/lanchunhui/article/details/70991228\\n ### Loss function for i-th sample (TeX):\\n # \\\\begin{split}\\n # L_i =&\\\\sum_{j\\\\neq y_i}\\\\max(0, s_j-s_{y_i}+1)\\\\\\\\\\n # =&\\\\sum_{j\\\\neq y_i} \\\\left[ \\\\max(0, w_j^Tx_i - w_{y_i}^Tx_i + 1) \\\\right]\\n # \\\\end{split}\\n ### Gradient Derivation (TeX):\\n # \\\\begin{split}\\n # &\\\\nabla_{w_{y_i}} L_i = - \\\\left( \\\\sum_{j\\\\neq y_i} \\\\mathbb{1}(w_j^Tx_i - w_{y_i}^Tx_i + \\\\Delta > 0) \\\\right) x_i\\\\\\\\\\n # &\\\\nabla_{w_j} L_i = \\\\mathbb{1}(w_j^Tx_i - w_{y_i}^Tx_i + \\\\Delta > 0) x_i\\\\quad j\\\\neq y_i\\n # \\\\end{split}\\n ### implementation is embeded in the code above\\n # TEST: run the following test to check\\n # import numpy as np\\n # a = np.zeros((3, 2))\\n # b = np.random.rand(6)\\n # b = np.reshape(b, (2, 3))\\n # a[:,0] += b[0,:]\\n #############################################################################\\n \\n pass\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n \\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a59c3e229f7523304177e3c002f0fb6\",\n \"score\": \"0.61783314\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in loss. #\\n #############################################################################\\n n = X.shape[0]\\n wx = X.dot(W) #calculate wx\\n wyix = wx[np.arange(n),y,None] #extract all the predictions for the correct class\\n P = wx - wyix + 1 #calculate loss\\n P[np.arange(n),y] = 0 #set correct class prediction to zero\\n P = np.maximum(0,P) # min threshold to zero\\n loss = np.sum(P)/n\\n reg_loss = reg * np.sum(W*W)\\n tot_loss = loss +reg_loss\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dW. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n P[P>0] = 1 \\n P[np.arange(n),y] = -np.sum(P, axis=1)\\n #print(P[:10,:])\\n dW = (X.T.dot(P))/n + reg * 2 * W\\n \\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a40166bc89cf0ab5b6c839ad640006fd\",\n \"score\": \"0.61744106\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n for i in range(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n for j in range(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n # This is the wrong class for this data, but it still receives a\\n # good score relative to the correct class\\n # This contributes to the loss\\n loss += margin\\n # The gradient for this class is increased by the value of the\\n # data in all dimensions for this datapoint\\n dW[:,j] += X[i]\\n # This also contributes to decrease the gradient of the correct class\\n # in reverse proportions\\n dW[:,y[i]] -= X[i]\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n\\n # Average gradient as well\\n dW /= num_train\\n\\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W)\\n\\n # Also add regularisation to the gradient\\n # regularization matrix is 0.5 * reg * W*W, so dReg = 0.5 * reg * 2 * W\\n dW += reg*W\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e71b6936010e992af3bff990da27310b\",\n \"score\": \"0.6157359\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n for i in range(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n count=0\\n\\n for j in range(num_classes):\\n if j==y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n dW[:,j]+=X[i]\\n count+=1\\n\\n #the gradient has to be calculated in two parts where one part is the row corresponding to the correct class\\n dW[:,y[i]]+=-count*X[i]\\n \\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n\\n #average dW as well\\n dW /= num_train\\n\\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n\\n # Also add regularization to the gradient\\n dW += 2*reg*W\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n pass\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n \\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8fd1ab1778ffaffb90e8ee2ede17194\",\n \"score\": \"0.61330837\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n dWInstance = np.zeros(W.shape)\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n for i in range(num_train):\\n dWInstance = np.zeros(W.shape)\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n for j in range(num_classes):\\n \\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n \\n if margin > 0:\\n loss += margin\\n dWInstance[:,j] = X[i] #grad\\n\\n RowSum = dWInstance.sum(axis=1) #grad\\n dWInstance[:,y[i]] = -RowSum #grad\\n dW += dWInstance #grad\\n\\n \\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n dW /= num_train\\n\\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n #reg gradient coming later\\n dW += reg * 2 * W\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n \\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n \\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21da71440abd493d8c2650f80aee1ffd\",\n \"score\": \"0.6111685\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n for i in xrange(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n num_classes_greater_margin = 0\\n for j in xrange(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n num_classes_greater_margin += 1\\n # Gradient for non-correct class weight\\n dW[:,j] += X[i,:]\\n loss += margin\\n # Gradient for correct class weight\\n dW[:,y[i]] += -X[i,:]*num_classes_greater_margin \\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n\\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n # Average our gradient across the batch and add gradient of regularization term\\n dW = dW/num_train + 2*reg*W\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bfd659025bba8840cdf60a2fd452c69\",\n \"score\": \"0.6101851\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n for i in range(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n\\n for j in range(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n dW[:,j] += X[i]\\n dW[:,y[i]] -= X[i]\\n \\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n \\n loss /= num_train\\n dW /= num_train\\n\\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n dW += 2 * reg * W \\n\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather than first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n \\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3e97745039ddd9383df2f09f5a70316\",\n \"score\": \"0.60587275\",\n \"text\": \"def classify(DD, CD):\\n\\n # do K-fold cross validation\\n all_data = np.concatenate((DD,CD))\\n all_class = np.vstack((np.ones((DD.shape[0], 1)), np.zeros((CD.shape[0], 1))))\\n N = all_data.shape[0]\\n K = 5\\n accuracies = np.zeros((K, 1))\\n sensitivities = np.zeros((K, 1))\\n specificities = np.zeros((K, 1))\\n randIndices = np.random.permutation(range(0, N))\\n\\n for fold in range(0, K) :\\n i_test = randIndices[fold * (N // K) : (fold + 1) * (N // K)]\\n i_train = [val for val in randIndices if val not in i_test]\\n c_train = all_class[i_train]\\n c_test = all_class[i_test] \\n\\n # train the model using features all_data[i_train,:] and classes c_train \\n # TODO\\n my_model = svm.SVC(kernel=\\\"linear\\\")\\n c_train = np.ravel( c_train )\\n my_model.fit( all_data[i_train, :] , c_train )\\n \\n my_predictions = my_model.predict(all_data[i_test, :])\\n\\n # # compute the % errors using all_data[i_test,:], c_test, and the model you trained\\n # # TODO\\n TP = len( [i for i in range(0,c_test.size) if my_predictions[i]==1 and c_test[i]==1] )\\n TN = len( [i for i in range(0,c_test.size) if my_predictions[i]==0 and c_test[i]==0] )\\n FP = len( [i for i in range(0,c_test.size) if my_predictions[i]==1 and c_test[i]==0] )\\n FN = len( [i for i in range(0,c_test.size) if my_predictions[i]==0 and c_test[i]==1] )\\n\\n accuracies[fold] = (TP + TN) / (TP + TN + FP + FN)\\n sensitivities[fold] = TP / (TP + FN)\\n specificities[fold] = TN / (TN + FP)\\n\\n\\n # report result mean and variance, over all folds, of each of accuracy, specificity, and sensitivity\\n # TODO\\n print('mean accurary', np.mean(accuracies))\\n print('mean sensitivity', np.mean(sensitivities))\\n print('mean specificity', np.mean(specificities))\\n print('variance accurary', np.var(accuracies))\\n print('variance sensitivity', np.var(sensitivities))\\n print('variance specificity', np.var(specificities))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c33baea9e2e07946e8d248f4db22b6\",\n \"score\": \"0.60409695\",\n \"text\": \"def softmax_classifier(W, input, label, lamda):\\n\\n ############################################################################\\n # TODO: Put your code here\\n# print(\\\"INPUT:\\\",input.shape)\\n# print(\\\"W:\\\",W.shape) \\n# print(\\\"label:\\\",label.shape)\\n \\n # Pre-processing\\n N, D = input.shape\\n C = W.shape[1]\\n \\n # Scores\\n s = np.dot(input,W) # (NxC)\\n prediction = s.argmax(axis=1) # (N,)\\n \\n # Loss\\n e_s = np.exp(s) # (NxC)\\n e_y = np.sum( (e_s * label), axis=1 ) # (N,)\\n e_sum = np.sum(e_s, axis=1) # (N,)\\n \\n loss = np.sum( -np.log( e_y / e_sum ) )\\n \\n # Gradient\\n ds = e_s / e_sum[:,None] # (NxC)\\n ds -= label # (NxC)\\n gradient = np.dot(input.T, ds)\\n \\n # Average loss\\n loss /= N\\n gradient /= N\\n\\n # Regularization\\n loss += lamda * np.sum(W*W)\\n gradient += lamda * 2 * W\\n\\n ############################################################################\\n\\n return loss, gradient, prediction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"250f03465ccedb8e0aa9bc2f53294de5\",\n \"score\": \"0.6033295\",\n \"text\": \"def svm_loss_vectorized(theta, X, y, reg):\\n J = 0.0\\n dtheta = np.zeros(theta.shape) # initialize the gradient as zero\\n delta = 1.0\\n m = X.shape[0]\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the structured SVM loss, storing the #\\n # result in variable J. #\\n #############################################################################\\n \\n scores = X.dot(theta)\\n #print scores[np.arange(m),y].shape\\n margin = np.maximum(0, (scores.T - scores[np.arange(m),y]).T + delta)\\n margin[np.arange(m),y]=0\\n J = 1./m * np.sum(margin) + 0.5*reg*np.sum(theta.T.dot(theta))\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n #This mask flags the examples whose margin is greater than 0\\n X_mask = np.zeros(margin.shape)\\n X_mask[margin>0] = 1\\n \\n #count number of examples with margin>0\\n c = np.sum(X_mask,axis=1)\\n X_mask[np.arange(m),y] = -c\\n \\n dtheta = X.T.dot(X_mask)\\n dtheta /= m\\n dtheta += reg * theta\\n\\n\\n #############################################################################\\n # TODO: #\\n # Implement a vectorized version of the gradient for the structured SVM #\\n # loss, storing the result in dtheta. #\\n # #\\n # Hint: Instead of computing the gradient from scratch, it may be easier #\\n # to reuse some of the intermediate values that you used to compute the #\\n # loss. #\\n #############################################################################\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return J, dtheta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f7967608dd8317e017e43eda23c1d2\",\n \"score\": \"0.5975871\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n for i in xrange(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n for j in xrange(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n\\n if margin > 0:\\n loss += margin\\n # calcualte tmp_dW if margin is larger than 0\\n tmp_dW = np.zeros(W.shape)\\n tmp_dW[:, j] = X[i, :]\\n tmp_dW[:, y[i]] = - X[i, ]\\n dW = dW + tmp_dW\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n dW /= num_train\\n\\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n dW = dW + 2 * reg * W\\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dW. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c20daa97013ebabc246b7d4f5bac4d3\",\n \"score\": \"0.5968399\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n\\n n_sample, n_feature = X.shape\\n assert n_feature == W.shape[0]\\n\\n scores = np.dot(X,W)\\n correct_class_scores = scores[np.arange(n_sample),y].reshape(n_sample,1)\\n scores = np.maximum(0, scores - correct_class_scores + 1.0)\\n\\n scores[np.arange(n_sample),y] = 0\\n num_pos = np.sum(scores > 0, axis = 1)\\n\\n hinge_loss = np.sum(scores) / n_sample\\n reg_loss = 0.5 * reg * np.sum(W ** 2)\\n loss = hinge_loss + reg_loss\\n\\n dscores = np.zeros(scores.shape)\\n dscores[scores > 0] = 1\\n dscores[np.arange(n_sample),y] -= num_pos\\n dW = np.dot(X.T,dscores) / n_sample + reg * W\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd02a666c89c9f7f5c4a5bbfdf193205\",\n \"score\": \"0.59637064\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n num_train = X.shape[0]\\n\\n # Here's a bit faster, yet still naive approach that we're going to vectorize:\\n #\\n # for i in xrange(num_train):\\n # scores = X[i].dot(W)\\n # correct_class_score = scores[y[i]]\\n # loss_values = np.maximum(scores - correct_class_score + 1, 0)\\n # loss_values[y[i]] = 0\\n # loss += np.sum(loss_values)\\n\\n # Compute the scores in one multiply.\\n scores = X.dot(W)\\n\\n # Get the correct values in one slicing operation -> [np.arange(num_train), y].\\n # Note that `correct_scores` is an array of shape `(num_train)`.\\n # See advanced indexing: http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html\\n # The other option is sadly slow:\\n # correct_scores = np.diag(scores[y,:])\\n correct_scores = scores[np.arange(num_train), y]\\n\\n # The broadcasting rules aren't trivial.\\n # Can't compute `scores - correct_scores`, but can if transpose `scores` first.\\n # See http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html\\n loss_values = (scores.T - correct_scores + 1).T\\n loss_values[np.arange(num_train), y] = 0\\n hinge_values = np.maximum(loss_values, 0)\\n\\n # Now it's pretty easy.\\n loss = np.sum(hinge_values) / num_train\\n loss += 0.5 * reg * np.sum(W * W)\\n\\n # Compute the gradient.\\n # Idea: perform the two operations simultaneously\\n # (1) for all j: dW[j,:] = sum_{i, j produces positive margin with i} X[:,i].T\\n # (2) for all i: dW[y[i],:] = sum_{j != y_i, j produces positive margin with i} -X[:,i].T\\n\\n # First transform `hinge_values` is a matrix of zeros and ones.\\n # The values of `X` should be added to `dW` where the coefficient is one (1).\\n grad_coeff = hinge_values\\n grad_coeff[hinge_values > 0] = 1\\n\\n # Also the values of `X` should be subtracted from `dW` on the correct classes -> [np.arange(num_train), y] (2).\\n # The number of times it's subtracted corresponds to the number of positive margins, i.e. the sum per each row.\\n sums_per_row = np.sum(grad_coeff, axis=1)\\n grad_coeff[np.arange(num_train), y] = -sums_per_row[np.arange(num_train)]\\n\\n # Finally add up all values of `X` with computed coefficients.\\n dW = np.dot(X.T, grad_coeff)\\n\\n # Boilerplate stuff.\\n dW /= num_train\\n dW += reg * W\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ca75532a856848b5f660fdb4fc60287\",\n \"score\": \"0.5937453\",\n \"text\": \"def svm_loss(x, y):\\n m = x.shape[0]\\n correct_class_output = x[np.arange(m), y]\\n margins = np.maximum(0, x - correct_class_output[:, np.newaxis] + 1.0)\\n margins[np.arange(m), y] = 0\\n loss = np.sum(margins) / m\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(m), y] -= num_pos\\n dx /= m\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2abe517eeba099e79c6d652eefde7cc7\",\n \"score\": \"0.58827865\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n dx /= N\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2abe517eeba099e79c6d652eefde7cc7\",\n \"score\": \"0.58827865\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n dx /= N\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2abe517eeba099e79c6d652eefde7cc7\",\n \"score\": \"0.58827865\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n dx /= N\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"590ce80f6d8d006500c2fa24b56deb8d\",\n \"score\": \"0.5872257\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n\\n dx /= N\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f303b247a3061b9e2686ec9b85c333\",\n \"score\": \"0.5866176\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_classes = W.shape[0]\\n num_train = X.shape[1]\\n loss = 0.0\\n for i in xrange(num_train):\\n scores = W.dot(X[:, i])\\n correct_class_score = scores[y[i]]\\n for j in xrange(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n dW[j, :] += X[:, i].T \\n dW[y[i], :] -= X[:, i].T\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n \\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W)\\n dW /= num_train\\n \\n dW += reg* W\\n\\n\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc9b062dfac061e4e075ebf69aca767\",\n \"score\": \"0.5829779\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n dx /= N\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc9b062dfac061e4e075ebf69aca767\",\n \"score\": \"0.5829779\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n dx /= N\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc9b062dfac061e4e075ebf69aca767\",\n \"score\": \"0.5829779\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n dx /= N\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc9b062dfac061e4e075ebf69aca767\",\n \"score\": \"0.5829779\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n dx /= N\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc9b062dfac061e4e075ebf69aca767\",\n \"score\": \"0.5829779\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n dx /= N\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50da406f1ac82f85ce9555c4c5d25ec6\",\n \"score\": \"0.5818512\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n loss = 0.0\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n class_scores = np.dot(X,W)\\n \\n correct_class_scores = np.choose(y, class_scores.T)\\n\\n #Loss\\n mask = np.ones(class_scores.shape, dtype=bool)\\n mask[range(class_scores.shape[0]), y] = False\\n wrong_class_scores = class_scores[mask].reshape(class_scores.shape[0],class_scores.shape[1]-1)\\n margins = wrong_class_scores - correct_class_scores[...,np.newaxis] + 1\\n margins[margins < 0] = 0\\n loss = np.sum(margins) / num_train + reg * np.sum(W * W)\\n\\n #Gradient\\n \\n margins = class_scores - correct_class_scores[...,np.newaxis] + 1\\n mask = (margins > 0 ).astype(float)\\n #Correct class always has margin 1, so it is counted in mask\\n num_scores_greater_zero = mask.sum(1) - 1\\n mask[range(num_train),y] = -num_scores_greater_zero\\n dW = np.dot(X.T,mask)\\n\\n dW = dW / num_train + 2 * reg * W\\n\\n\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b636fe872a25da76353ae72e3855d3\",\n \"score\": \"0.581444\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(\\n 0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n dx /= N\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d81075c27be84d6bf3e3ef430f5b6c1\",\n \"score\": \"0.58142465\",\n \"text\": \"def binary_svm_loss(theta, X, y, C):\\n\\n m, d = X.shape\\n grad = np.zeros(theta.shape)\\n J = 0\\n\\n ############################################################################\\n # TODO #\\n # Implement the binary SVM hinge loss function here #\\n # 4 - 5 lines of vectorized code expected #\\n ############################################################################\\n correctness = y * X.dot(theta)\\n J = 1./(2*m) * sum(theta**2) + 1.*C/m * sum(1-correctness[correctness<1])\\n grad = 1./m * theta + 1.*C/m * -y[correctness<1].dot(X[correctness<1])\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return J, grad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62e1d55d6013849dac8d5675923c852c\",\n \"score\": \"0.58121675\",\n \"text\": \"def svm_loss(x, y):\\r\\n N = x.shape[0]\\r\\n correct_class_scores = x[np.arange(N), y]\\r\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\r\\n margins[np.arange(N), y] = 0\\r\\n loss = np.sum(margins) / N\\r\\n num_pos = np.sum(margins > 0, axis=1)\\r\\n \\r\\n dx = np.zeros_like(x)\\r\\n dx[margins > 0] = 1\\r\\n dx[np.arange(N), y] -= num_pos\\r\\n dx /= N\\r\\n \\r\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86a1a78ec2bdf183c6993a2bafdc6972\",\n \"score\": \"0.57685\",\n \"text\": \"def svm_loss_naive_original(W, X, y, reg):\\n\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n for i in xrange(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n for j in xrange(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n \\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n loss += reg * np.sum(W * W) # Add regularization to the loss.\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"001cb9af979e1ad26e15fe43f21b3dd3\",\n \"score\": \"0.57509357\",\n \"text\": \"def train(self, input_vec, true_label, iteration_cnt, gamma_0, d):\\n input_vec =np.array(input_vec, ndmin = 2).T # column array vector\\n interm_val_vec_1 = act_func(np.dot(self.wt_mat_layer_0, input_vec)) # col vector\\n interm_val_vec_2 = act_func(np.dot(self.wt_mat_layer_1, np.concatenate((interm_val_vec_1, [[1]]), axis = 0)))\\n output = np.dot(self.wt_mat_layer_2, np.concatenate((interm_val_vec_2, [[1]]), axis = 0))\\n # calculate the partial derivative matrix\\n # gradient W_2\\n output_error = output - true_label\\n grad_w_2 = output_error*(np.concatenate((interm_val_vec_2, [[1]]), axis = 0)).T\\n # gradient W_1\\n hidden_error_vec_2 = output_error*(self.wt_mat_layer_2[0,:][:-1]) # row vector\\n temp = hidden_error_vec_2*(interm_val_vec_2.T) *(1- (interm_val_vec_2.T) ) # row vector\\n tt = np.concatenate((interm_val_vec_1, [[1]]), axis = 0)\\n grad_w_1 = np.dot( tt, temp).T #matrix\\n # gradient W_0\\n alpha_vec = self.wt_mat_layer_2[0,:][:-1]\\n beta_vec = phi(interm_val_vec_2.T) # row vec\\n ab = alpha_vec*beta_vec # row \\n tpp = np.zeros((self.num_of_hidden_1_nodes -1,1 ))\\n for i in range(self.num_of_hidden_1_nodes -1):\\n tpp[i,0] = output_error*np.inner(ab, self.wt_mat_layer_1[:,i].T)*phi(interm_val_vec_1.T)[0,i]\\n grad_w_0 = np.dot(tpp, input_vec.T)\\n # update W_2,W_1, W_0 weights\\n self.wt_mat_layer_2 = self.wt_mat_layer_2 - gamma(iteration_cnt, gamma_0, d)*grad_w_2\\n self.wt_mat_layer_1 = self.wt_mat_layer_1 - gamma(iteration_cnt, gamma_0, d)*grad_w_1 \\n self.wt_mat_layer_0 = self.wt_mat_layer_0 - gamma(iteration_cnt, gamma_0, d)*grad_w_0 \\n iteration_cnt = iteration_cnt + 1\\n return iteration_cnt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6075e3fb716491215fd63e16f1c8a0cd\",\n \"score\": \"0.57156193\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n correct_class_scores = x[np.arange(N), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0) # np.newaxis 插入新维度，方便广播\\n margins[np.arange(N), y] = 0\\n loss = np.sum(margins) / N\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(N), y] -= num_pos\\n dx /= N\\n\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2b1b5e3918b9075df5275227562ee31\",\n \"score\": \"0.5711591\",\n \"text\": \"def svm_loss(x, y):\\n m = x.shape[0]\\n correct_class_scores = x[np.arange(m), y]\\n margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)\\n margins[np.arange(m), y] = 0\\n loss = np.sum(margins) / m\\n num_pos = np.sum(margins > 0, axis=1)\\n dx = np.zeros_like(x)\\n dx[margins > 0] = 1\\n dx[np.arange(m), y] -= num_pos\\n dx /= m\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb3c3011dc71408d87b89d40ed2c66e0\",\n \"score\": \"0.570973\",\n \"text\": \"def svm_loss_naive(W, X, y, reg):\\n dW = np.zeros(W.shape) # initialize the gradient as zero\\n\\n # compute the loss and the gradient\\n num_train = X.shape[0]\\n num_classes = W.shape[1]\\n loss = 0.0\\n for i in xrange(num_train):\\n scores = X[i].dot(W)\\n correct_class_score = scores[y[i]]\\n for j in xrange(num_classes):\\n if j == y[i]:\\n continue\\n margin = scores[j] - correct_class_score + 1 # note delta = 1\\n if margin > 0:\\n loss += margin\\n dW[:,j ] += X[i,:].T # the grad from class j\\n dW[:,y[i]] -= X[i,:].T # the grad from class y[i]\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n dW /= num_train\\n\\n # Add regularization.\\n loss += 0.5 * reg * np.sum(W * W)\\n dW += reg * W\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f1689166a60cc4b38361eac318bc8a2\",\n \"score\": \"0.5695019\",\n \"text\": \"def _trainSVMBestC(k_m, labels, model_dir, inds, tags, pb):\\n for ind in inds:\\n label = labels[:,ind]\\n #print \\\"train %dth fingerprint now (select C)...\\\" % (ind+1)\\n C = numpy.array([2**-5,2**-4,2**-3,2**-2,2**-1,2**0,2**1,2**2,2**3,\\n 2**4,2**5,2**6,2**7,2**8,2**9,2**10])\\n n = len(k_m)\\n accs = []\\n #res = result(ind)\\n for c in C:\\n pred = numpy.zeros(n) # store the predict output\\n for i in range(1,6):\\n test = tags==i\\n train = ~(tags==i)\\n test = numpy.array(range(len(k_m)))[test].tolist()\\n train = numpy.array(range(len(k_m)))[train].tolist()\\n\\n train_km = k_m[numpy.ix_(train,train)]\\n test_km = k_m[numpy.ix_(test,train)]\\n train_label = label[train]\\n test_label = label[test]\\n n_train = len(train_km)\\n n_test = len(test_km)\\n\\n # formating for libsvm\\n train_km = numpy.append(numpy.array(range(1,n_train+1)).reshape(n_train,1), train_km,1).tolist()\\n test_km = numpy.append(numpy.array(range(1,n_test+1)).reshape(n_test,1), test_km,1).tolist()\\n\\n prob = svm_problem(train_label, train_km, isKernel=True)\\n param = svm_parameter('-t 4 -c %f -b 0 -q' % c)\\n m = svm_train(prob,param)\\n p_label, p_acc, p_val=svm_predict(test_label,test_km, m,'-b 0 -q')\\n pred[numpy.ix_(test)] = p_label\\n acc = sum(pred==label) / float(n)\\n accs.append(acc)\\n accs = numpy.array(accs)\\n best_c = C[accs==max(accs)][0]\\n\\n # find the best c, using the best c to train\\n kernel = numpy.append(numpy.array(\\n range(1,n+1)).reshape(n,1),k_m,1).tolist()\\n\\n if pb:\\n prob = svm_problem(label, kernel, isKernel=True)\\n param = svm_parameter('-t 4 -c %f -b 1 -q' % best_c)\\n m = svm_train(prob, param)\\n svm_save_model('%s/%d.model' % (model_dir,ind), m)\\n else:\\n prob = svm_problem(label, kernel, isKernel=True)\\n param = svm_parameter('-t 4 -c %f -b 0 -q' % best_c)\\n m = svm_train(prob, param)\\n svm_save_model('%s/%d.model' % (model_dir,ind), m)\\n\\n #res.ind = ind\\n #res.acc = max(accs)\\n #res.model = m\\n #Queue.put(res)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbf5a3f2500fce00e4f49b1e25dfabc5\",\n \"score\": \"0.5672929\",\n \"text\": \"def svm_loss_naive(theta, X, y, reg):\\n\\n delta = 1.0\\n dtheta = np.zeros(theta.shape) # initialize the gradient as zero\\n\\n # compute the loss function\\n\\n K = theta.shape[1]\\n m = X.shape[0]\\n J = 0.0\\n for i in xrange(m):\\n scores = X[i,:].dot(theta)\\n correct_class_score = scores[y[i]]\\n for j in xrange(K):\\n if j == y[i]:\\n continue\\n margin = max(0,scores[j] - correct_class_score + delta)\\n J += margin\\n p = scores[j] - correct_class_score + delta\\n if p > 0:\\n dtheta[:,j] += X[i]\\n dtheta[:,y[i]] -= X[i]\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # To be an average instead so we divide by num_train.\\n J /= m\\n dtheta /= m\\n\\n # Add regularization to the loss.\\n J += 0.5 * reg * np.sum(theta * theta)\\n dtheta += reg * theta\\n \\n #############################################################################\\n # TODO: #\\n # Compute the gradient of the loss function and store it dtheta. #\\n # Rather that first computing the loss and then computing the derivative, #\\n # it may be simpler to compute the derivative at the same time that the #\\n # loss is being computed. As a result you may need to modify some of the #\\n # code above to compute the gradient. #\\n #############################################################################\\n \\n return J, dtheta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4fe5e6d979c9d83caa6664147fa441e\",\n \"score\": \"0.56671536\",\n \"text\": \"def lin_svm_optimize(x_train, y_train, C=1):\\n\\n lin_svm = svm.SVC(kernel='linear', C=C, class_weight='balanced', random_state=1)\\n lin_scores = cross_val_score(lin_svm, x_train, y_train, cv=5)\\n lin_avg = sum(lin_scores) / len(lin_scores)\\n return lin_avg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bfe263ec4f465303e1cab997504ee02\",\n \"score\": \"0.56584895\",\n \"text\": \"def softmax_loss_vectorized(W, X, y, reg):\\n# print y.shape\\n# print X.shape\\n# Label_data = np.dstack((y,X))\\n# print Label_data\\n# Label_data.sort()\\n# y = Label_data[:,0]\\n# x = Label_data[:,1:]\\n\\n #Sorting the input based on the class\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n X1 = X.dot(W)\\n scores = pow(math.e,X1)\\n #normarlizing the score\\n scores = (scores.T/np.sum(scores, axis = 1)).T\\n \\n Y = np.zeros([num_train, num_classes])\\n for i,row in enumerate(Y):\\n row[y[i]] = 1\\n \\n loss_ma = np.ma.log(scores*Y)\\n loss = np.sum( - loss_ma.filled(0))/num_train + 0.5 * reg * np.sum(W * W) \\n \\n dX1 = scores - Y\\n dW = ((dX1.T).dot(X)).T/num_train + reg*W\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n pass\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12244802ac27a1455b16b24a13c33f03\",\n \"score\": \"0.5654248\",\n \"text\": \"def predict(self, X, loss_func=hamming_losses) :\\r\\n\\r\\n n,d = X.shape\\r\\n num_classes, num_classifiers = self.R.shape\\r\\n\\r\\n # setup predictions\\r\\n y = np.zeros(n)\\r\\n\\r\\n ### ========== TODO : START ========== ###\\r\\n # part d: predict multiclass class\\r\\n #\\r\\n # HERE IS ONE WAY (THERE MAY BE OTHER APPROACHES)\\r\\n #\\r\\n # for each example\\r\\n # predict distances to hyperplanes using SVC.decision_function(...)\\r\\n # find class with minimum loss (be sure to look up in self.classes)\\r\\n #\\r\\n # if you have a choice between multiple occurrences of the minimum values,\\r\\n # use the index corresponding to the first occurrence\\r\\n for i in range(n):\\r\\n dists = np.empty(num_classifiers)\\r\\n for j in range(num_classifiers):\\r\\n X_i_reshaped = X[i,:].reshape(1,-1)\\r\\n dist = self.svms[j].decision_function(X_i_reshaped)\\r\\n\\r\\n dists[j] = dist\\r\\n\\r\\n\\r\\n losses = loss_func(self.R, dists)\\r\\n best_class_index = np.argmin(losses)\\r\\n y[i] = self.classes[best_class_index]\\r\\n\\r\\n ### ========== TODO : END ========== ###\\r\\n\\r\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a72e93f22fa54c167f86f8c1cf24ad44\",\n \"score\": \"0.5653842\",\n \"text\": \"def train_svms_with(sample_points: torch.tensor, sample_labels: torch.tensor,\\r\\n *hidden_states: typing.Tuple[torch.tensor]) -> SVMTrajectory:\\r\\n\\r\\n num_points = sample_points.shape[0]\\r\\n train_points = int(num_points * (13.0/15.0))\\r\\n output_dim = sample_labels.max().item()\\r\\n if not isinstance(output_dim, int):\\r\\n raise ValueError(f'expected output_dim is int, got {output_dim}')\\r\\n\\r\\n overall = []\\r\\n\\r\\n if output_dim > 2:\\r\\n masks = [sample_labels == lbl for lbl in range(output_dim)]\\r\\n by_label_vs_all = []\\r\\n\\r\\n for state in hidden_states:\\r\\n if not torch.is_tensor(state):\\r\\n raise ValueError(f'expected hidden state is tensor, got {state} (type={type(state)})')\\r\\n if len(state.shape) != 2:\\r\\n raise ValueError(f'expected state has shape [batch_size, layer_size] but has shape {state.shape}')\\r\\n if state.shape[0] != num_points:\\r\\n raise ValueError(f'expected state has shape [batch_size={num_points}, layer_size] but has shape {state.shape}')\\r\\n if state.dtype == torch.float:\\r\\n state = state.double()\\r\\n elif state.dtype != torch.double:\\r\\n raise ValueError(f'expected state has dtype float-like, but has {state.dtype}')\\r\\n\\r\\n classifier = svm.LinearSVC(max_iter=5000)\\r\\n classifier.fit(state[:train_points], sample_labels[:train_points])\\r\\n mean_acc = classifier.score(state[train_points:], sample_labels[train_points:])\\r\\n\\r\\n if not isinstance(mean_acc, float):\\r\\n raise ValueError(f'expected mean_acc is float, got {mean_acc} (type={type(mean_acc)})')\\r\\n\\r\\n overall.append(mean_acc)\\r\\n\\r\\n if output_dim > 2:\\r\\n layer_accs = []\\r\\n for lbl in range(output_dim):\\r\\n classifier = svm.LinearSVC(max_iter=5000)\\r\\n classifier.fit(state[:train_points], masks[lbl][:train_points])\\r\\n mean_acc = classifier.score(state[train_points:], masks[lbl][train_points:])\\r\\n layer_accs.append(mean_acc)\\r\\n by_label_vs_all.append(layer_accs)\\r\\n\\r\\n if output_dim <= 2:\\r\\n return SVMTrajectory(overall=torch.tensor(overall, dtype=torch.double), by_label_vs_all=None)\\r\\n else:\\r\\n return SVMTrajectory(overall=torch.tensor(overall, dtype=torch.double),\\r\\n by_label_vs_all=torch.tensor(by_label_vs_all, dtype=torch.double))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeb2bca0adc60305c9c69e952eda1fd0\",\n \"score\": \"0.5641954\",\n \"text\": \"def multi_contrastive_loss(y, d):\\n global margin\\n Q = 4\\n print \\\"margin =\\\", margin\\n\\n label = K.transpose(y)\\n p = K.transpose(d)\\n return K.mean(K.cast(K.equal(label, 0), 'float') * p +\\n K.cast(K.equal(label, 1), 'float') * ((K.maximum(margin - p, 0)))+\\n K.cast(K.equal(label, 2), 'float') * K.maximum(margin - p, 0))\\n # return K.mean((1 - label) * Q * K.square(d) + label * K.exp(-2.77 / Q * d))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90c557b0852621071686b68398e8582f\",\n \"score\": \"0.5636727\",\n \"text\": \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n # In this naive implementation we have a for loop over the N samples\\n for i, x in enumerate(X):\\n \\n #############################################################################\\n # TODO: Compute the cross-entropy loss using explicit loops and store the #\\n # sum of losses in \\\"loss\\\". #\\n # If you are not careful in implementing softmax, it is easy to run into #\\n # numeric instability, because exp(a) is huge if a is large. #\\n #############################################################################\\n # TODO: should use explicit loops here\\n z = x.dot(W) #calculate z based on the formula z = xW\\n p = np.exp(z) / np.sum(np.exp(z)) #calculating the normalized probabilities\\n pc = p[y[i]] #probability of the true/correct label\\n loss += - np.log(pc) #finding, adding the cross-entropy loss for true label\\n \\n #############################################################################\\n # TODO: Compute the gradient using explicit loops and store the sum over #\\n # samples in dW. #\\n #############################################################################\\n \\n # source https://cs231n.github.io/neural-networks-case-study/\\n for j in range(W.shape[1]): # looping over 10 classes\\n if j == y[i]: # checking whether the class is same or not\\n dW[:, j] += (np.exp(z[y[i]]) / np.sum(np.exp(z)) - 1) * x\\n # in case of the correct class, substract -1 for negative influence on\\n # the loss\\n else:\\n dW[:, j] += np.exp(z[j]) / np.sum(np.exp(z)) * x\\n # in other cases, perform basic calculus based on the gradient formula\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n # now we turn the sum into an average by dividing with N\\n loss /= X.shape[0]\\n dW /= X.shape[0]\\n\\n # Add regularization to the loss and gradients.\\n loss += reg * np.sum(W * W)\\n dW += reg * 2 * W\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a120bba147dd6f3c9395f0a5b86e8202\",\n \"score\": \"0.56269467\",\n \"text\": \"def naiveSoftmaxLossAndGradient(centerWordVec, outsideWordIdx, outsideVectors, dataset):\\n\\n ### YOUR CODE HERE\\n ### This is the basic version\\n\\n # 在HW中, outsideVectors 是 U, 每一列就是一个 word 的vector\\n # 在代码中, 没必要 centerWordVec = centerWordVec.reshape(-1,1) # 初始化为列形式 = hw公式表示的初始形式\\n # 直接放在dot的右边, 计算时, 就以列向量的形式进行broadcasting\\n # 在代码中, outsideVectors 每一 row就是一个 word 的vector\\n # softmax 的input应该是一个行向量. 如果是列向量, 就无法计算了.\\n y_pred = softmax(np.dot(outsideVectors, centerWordVec)) # input 为一个1-array, numpy默认为行向量\\n # print(\\\"#\\\"*30, \\\"\\\\n y_pred shape:\\\", y_pred.shape,\\\"\\\\n\\\",y_pred)\\n\\n ### Based on the provement (c i), the simplified CE is Negative Summation of (y_true*log(y_predict)),\\n ### since y is one-hot vector, only the kth y is 1 and the other are 0.\\n ### the loss should be -1*log(softmax(input));\\n ### Therefore, we can just treat the outsideWordIdx of y_pred as y_hat\\n loss = -np.log(y_pred[outsideWordIdx]) # 如果这里只有一个outsideword的话, 那么这样没问题. 如果不止一个, 则外面需要求和\\n\\n ### According to provement (c i), the gradient of loss related to center word is (y_pred - y_true)*U.T (如果都是vector,那么dot不影响)\\n y_pred[outsideWordIdx] -= 1 # For grad_Vc of the outside words, the first part is y_pred - 1; 因为只有他们存在梯度,所有只有改变他们的必要\\n gradCenterVec = np.dot(y_pred, outsideVectors) # 1x5 * 5x3 = 1x3 ~ gradCenterVec\\n\\n ### According to (c ii), the gradient of loss related to the one outside word is (y_pred - y_true)*v_c.T\\n # gradOutsideVecs = np.dot(y_pred.reshape(y_pred.shape[0],1), centerWordVec.reshape(1, centerWordVec.shape[0]) )\\n gradOutsideVecs = np.outer(y_pred, centerWordVec) # 5x1 * 1x3 = 5x3 ~ gradOutsideVecs\\n # print(\\\"#\\\"*30,\\\"\\\\n\\\", gradOutsideVecs)\\n ##########################################################\\n\\n ### END YOUR CODE\\n\\n return loss, gradCenterVec, gradOutsideVecs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e4d35ef33ce5b78c213cf48928ccba\",\n \"score\": \"0.5607843\",\n \"text\": \"def mc_lda_model(D, S, V, K, w_obs):\\n\\n alpha = tf.zeros(K) + 0.01\\n\\n beta, phi = [None] * S, [None] * S\\n for s in range(S):\\n beta[s] = tf.zeros(V[s]) + 0.01\\n phi[s] = Dirichlet(concentration=beta[s],\\n sample_shape=K)\\n\\n theta = [None] * D\\n w = [[None] * S for d in range(D)]\\n z = [[None] * S for d in range(D)]\\n\\n for d in range(D):\\n theta[d] = Dirichlet(concentration=alpha)\\n\\n for s in range(S):\\n w[d][s] = ParamMixture(mixing_weights=theta[d],\\n component_params={'probs': phi[s]},\\n component_dist=Categorical,\\n sample_shape=len(w_obs[d][s]),\\n validate_args=True)\\n\\n z[d][s] = w[d][s].cat\\n\\n return alpha, beta, theta, phi, z, w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30c055b587b6d4a0ce1a39a19b4490cc\",\n \"score\": \"0.5603729\",\n \"text\": \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n \\n N=X.shape[0]\\n C=W.shape[1] # num of classes\\n D=W.shape[0] # D=3\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n\\n for i in range(N):\\n score=X[i,:] @ W\\n score-=score.max() # shift to <0\\n \\n partition=np.sum(np.exp(score))\\n\\n margin=-score[y[i]]+np.log(partition)\\n loss+=margin\\n\\n for j in range(C):\\n for k in range(D):\\n if j==y[i]:\\n dW[k,j]-=X[i,k]\\n dW[k,j]+=X[i,k]*np.exp(score[j])/partition\\n \\n loss/=N\\n dW/=N\\n\\n loss+=reg*np.sum(W*W)\\n dW+=2*reg*W\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8c597f569d3179544e25029a97afb59\",\n \"score\": \"0.5599227\",\n \"text\": \"def svm_loss(x, y):\\n N = x.shape[0]\\n x = x.reshape(-1)\\n y = 2 * y - 1\\n loss = np.zeros((len(x), 1))\\n loss = np.maximum(0, 1 - y * x)\\n loss = np.mean(loss)\\n dx = np.zeros((len(x), 1))\\n for i in range(len(x)):\\n if x[i] * y[i] < 1: \\n dx[i] = -y[i]\\n dx = dx/np.shape(x)[0]\\n dx = dx.reshape(-1)\\n return loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"466b9710fbfcf469cd66e2dde61e40c3\",\n \"score\": \"0.5578418\",\n \"text\": \"def nonlinearSVM(x, y, c, kernel='rbf', gamma='auto'):\\n model = sklearn.svm.SVC(C=c, kernel=kernel, gamma=gamma, decision_function_shape='ovr')\\n model.fit(x, y)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9af09f8007b5452f7c8771fa60ef4aa4\",\n \"score\": \"0.5543761\",\n \"text\": \"def svm_loss(x, y):\\n\\tN = x.shape[0]\\n\\tloss = 0\\n\\tdx = np.zeros(x.shape)\\n\\tfor i in range(0, N):\\n\\t\\tif(y[i] == 0):\\n\\t\\t\\ty[i] = -1\\n\\t\\ttemp = 1-x[i]*y[i]\\n\\t\\tif(temp > 0):\\n\\t\\t\\tloss += temp\\n\\t\\t\\tdx[i] = -y[i]\\n\\t\\t\\t\\n\\tloss /= x.shape[0]\\n\\tdx /= x.shape[0]\\n\\treturn loss, dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9c6c6863117c60414e81c12625d1bd4\",\n \"score\": \"0.55404276\",\n \"text\": \"def train_sgd(train_features,\\n train_labels,\\n penalty_weights,\\n skip_grid_search,\\n evaluation,\\n algorithm,\\n num_jobs,\\n penalty,\\n alpha,\\n l1_ratio):\\n if algorithm == 'sgd-logistic':\\n loss = 'log'\\n elif algorithm == 'sgd-svm':\\n loss = 'hinge'\\n else:\\n sys.exit('bad algorithm provided to sgd model.')\\n \\n # requested grid search. find best parameters, to achieve highest average score\\n if not skip_grid_search:\\n clf = grid_search.grid_search(evaluation,\\n train_features,\\n train_labels,\\n penalty_weights,\\n algorithm,\\n num_jobs)\\n \\n params = clf.best_params_\\n penalty = params['penalty']\\n alpha = params['alpha']\\n if 'l1_ratio' in params:\\n l1_ratio = params['l1_ratio']\\n # Now perform the training on full train data.\\n n_iter = numpy.ceil(5*(10**6) / train_features.shape[0])\\n model = SGDClassifier(loss=loss,\\n penalty=penalty,\\n alpha=alpha,\\n l1_ratio=l1_ratio,\\n class_weight=penalty_weights,\\n n_iter=n_iter,\\n n_jobs=num_jobs)\\n model = model.fit(train_features, train_labels)\\n \\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34f941f832a91d2d6a0012a755291f70\",\n \"score\": \"0.5536596\",\n \"text\": \"def compute_sigma_mles(train_data, train_labels):\\r\\n _, D = train_data.shape\\r\\n K = len(np.unique(train_labels))\\r\\n\\r\\n covariances = np.zeros((K, D, D))\\r\\n # Compute covariances\\r\\n\\r\\n for i in range(K):\\r\\n indices = train_data[train_labels == i]\\r\\n indices -= np.mean(indices, axis=0)\\r\\n covariances[i] = np.dot(np.transpose(indices), indices) / float(indices.shape[0]) + 0.01 * np.eye(D)\\r\\n\\r\\n return covariances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27748084996daa6bfb1fc3c2a03b3291\",\n \"score\": \"0.5532704\",\n \"text\": \"def loss_kd_class_weight(y_student, y_true, y_teacher, params):\\n T = params.temperature\\n class_num = torch.tensor([5000, 2997, 1796, 1077, 645, 387, 232, 139, 83, 50])\\n reversed_class_num = 1.0 / class_num\\n class_weight = reversed_class_num / reversed_class_num.sum()\\n\\n # take max confidence of student model as coefficient of CE loss_fn\\n # max_prob = torch.max(F.softmax(y_student, dim=1).detach(), dim=1).values\\n # take max confidence of teacher model as coefficient of CE loss_fn\\n\\n y_student_log_sm = F.log_softmax(y_student, dim=1) # N*C\\n onehot_target = utils.scalar2onehot(y_true, params.num_classes).cuda() # N*C\\n\\n weighted_y_student_log_sm = y_student_log_sm * class_weight.cuda() # N*C\\n loss_CE = - torch.sum(onehot_target * weighted_y_student_log_sm, dim=1) # N*1\\n\\n y_student_log_T_softmax = F.log_softmax(y_student / T, dim=1) # N*C\\n y_teacher_T_softmax = F.softmax(y_teacher / T, dim=1) # N*C\\n\\n # max_prob = torch.max(F.softmax(y_teacher / T, dim=1).detach(), dim=1).values\\n # max_prob = torch.max(y_teacher_T_softmax.detach(), dim=1).values\\n # print('Max confidence of teacher model:', torch.mean(max_prob))\\n # params.maxT = torch.mean(max_prob)\\n\\n kl_div_each = F.kl_div(y_student_log_T_softmax, y_teacher_T_softmax, reduction='none') * (T * T) # N*C\\n\\n loss_KLD = torch.mean(kl_div_each, dim=1) # N*1\\n\\n # print('\\\\nLoss_kd:\\\\nCE:{}\\\\nKLD:{}\\\\n'.format(loss_CE.detach()[0], loss_KLD.detach()[0]))\\n _loss = torch.mean((1. - params.alpha) * loss_CE + params.alpha * loss_KLD)\\n # loss = torch.mean(max_prob * loss_CE + (torch.ones_like(max_prob) - max_prob) * loss_KLD)\\n\\n return _loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb2e6f8177b0b3fdc9a5274644c3b357\",\n \"score\": \"0.552854\",\n \"text\": \"def loss(self, X, y=None, reg=0.0, verbose=False):\\n # Unpack variables from the params dictionary\\n W1, b1 = self.params['W1'], self.params['b1']\\n W2, b2 = self.params['W2'], self.params['b2']\\n N, D = X.shape\\n num_train = N\\n num_classes = D\\n\\n # Initialize intermediate values and local gradients for the stages\\n # of the computational graph implementation of SVM\\n \\n dW1 = np.zeros(W1.shape)\\n db1 = np.zeros(b1.shape)\\n dW2 = np.zeros(W2.shape)\\n db2 = np.zeros(b2.shape)\\n \\n scores = np.zeros((num_train,num_classes)) # intermediate value scores\\n dscores = np.zeros((num_train,num_classes)) # and its gradient\\n\\n norm_scores = np.zeros(scores.shape) # intermediate value margins\\n dnorm_scores = np.zeros(scores.shape) # and its gradient\\n dnorm_scores_1 = np.zeros(scores.shape) # and its gradient\\n\\n correct_scores = np.zeros(num_train) # intermediate value margins\\n dcorrect_scores = np.zeros(num_train) # and its gradient\\n \\n exp_scores = np.zeros(scores.shape) # intermediate value margins\\n dexp_scores = np.zeros(scores.shape) # and its gradient\\n lg_exp_scores = np.zeros(scores.shape) # its local gradient\\n\\n sums = np.zeros(num_train) # intermediate value margins\\n dsums = np.zeros(num_train) # and its gradient\\n\\n log_sums = np.zeros(num_train) # intermediate value margins\\n dlog_sums = np.zeros(num_train) # its gradient\\n lg_log_sums = np.zeros(num_train) # its local gradient\\n \\n losses = np.zeros(num_train) # loss and\\n dlosses = np.zeros(num_train) # its gradient\\n data_loss = 0\\n reg_loss = 0\\n \\n # Compute the forward pass\\n scores = None\\n #############################################################################\\n # TODO: Perform the forward pass, computing the class scores for the input. #\\n # Store the result in the scores variable, which should be an array of #\\n # shape (N, C). #\\n #############################################################################\\n\\n \\\"\\\"\\\"\\n We dealing with a 2-layer NN followed by a softmax classifier with the following \\n architecture:\\n\\n input - fully connected layer - ReLU - fully connected layer - softmax\\n \\n The loss and gradient computation will be performed in 2 passes:\\n - In the forward pass, the loss calculation is decomposed into 10 stages\\n At the end of the forward pass, loss is calculated\\n - In the backward pass, we apply chain rule to calculate the gradients\\n from Stage 10 back to Stage 1\\n At the end of the backward pass, the gradients dW1 and dW2 are calculated\\n \\n The input to the 2-layer NN is X, and the output is scores.\\n The scores is then fed into the softmax classifier, which is defined as:\\n \\n L(i) = -Score_Correct_Class + log(summation(exp(Scores_of_all_Classes)))\\n\\n \\\"\\\"\\\"\\n # 2-layer Neural Network Section:\\n # Input - X\\n # Output - scores\\n \\n # Stage 1 - X.dot(W1)+b1 --> o1 #[1] \\n o1= X.dot(W1) + b1\\n \\n # Stage 2 - ReLU(o1) --> h1 #[2]\\n h1 = np.maximum(o1, np.zeros(o1.shape))\\n lg_h1 = np.zeros(o1.shape)\\n lg_h1[np.nonzero(h1)] = 1 # local gradient of ReLU is 1\\n # for any element which is >0\\n\\n # Stage 3 - h1.dot(W2)+b2 --> scores #[3] \\n scores = h1.dot(W2) + b2\\n\\n if verbose:\\n print \\\"\\\\nNeural Network Layer 1:\\\"\\n print X.shape\\n print W1.shape\\n print o1\\n print h1\\n print lg_h1\\n print \\\"\\\\nNeural Network Layer 2:\\\"\\n print h1.shape\\n print W2.shape\\n print scores\\n \\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n \\n # If the targets are not given then jump out, we're done\\n if y is None:\\n return scores\\n\\n # Compute the loss\\n loss = None\\n #############################################################################\\n # TODO: Finish the forward pass, and compute the loss. This should include #\\n # both the data loss and L2 regularization for W1 and W2. Store the result #\\n # in the variable loss, which should be a scalar. Use the Softmax #\\n # classifier loss. So that your results match ours, multiply the #\\n # regularization loss by 0.5 #\\n #############################################################################\\n\\n # Softmax Classifier Section:\\n # Input - scores\\n # Output - data_loss\\n \\n # Stage 4 - scores - scale_const --> norm_scores #[4]\\n # The scaling constant is an arbitrary constant. In this case, we pick the\\n # maximum value in scores, so that elements of norm_scores are all 0 or \\n # less. This is to prevent overflow error in subsequent calculation.\\n # The local gradient in this gate is thus d(x-c)/dx = 1\\n scale_const = np.max(scores)\\n norm_scores = scores - scale_const\\n\\n # Stage 5 - score of correct class --> correct_scores #[5] \\n correct_scores = norm_scores[np.arange(num_train), y]\\n\\n # Stage 6 - exp(norm_scores) --> exp_scores #[6] \\n exp_scores = np.exp(norm_scores)\\n lg_exp_scores = np.exp(norm_scores) # local gradient of exp(x) = exp(x)\\n\\n # Stage 7 - sum(exp_scores) --> sums #[7]\\n sums = np.sum(exp_scores, axis=1)\\n \\n # Stage 8 - log(sums) --> log_sums #[8]\\n log_sums = np.log(sums)\\n lg_log_sums = 1.0/sums # local gradient of log(x) = 1/x\\n \\n # Stage 9 - log_sums - correct_scores --> losses #[9]\\n losses = log_sums - correct_scores\\n\\n # Stage 10 - sum(losses)/num_train --> loss #[10]\\n data_loss = np.sum(losses)/num_train\\n\\n # Reg Forward Pass \\n reg_loss = 0.5 * reg * (np.sum(W1*W1)+np.sum(W2*W2))\\n \\n loss = data_loss + reg_loss\\n \\n if verbose:\\n print \\\"Training data - Forward Pass\\\" \\n print \\\"\\\\n Scores:\\\"\\n print scores\\n print \\\"\\\\n Scaling Constant:\\\"\\n print scale_const\\n print \\\"\\\\n Normalized Scores:\\\"\\n print norm_scores\\n print \\\"\\\\n True Labels (y):\\\"\\n print y\\n print \\\"\\\\n Scores of correct classes:\\\" \\n print correct_scores\\n print \\\"\\\\n Exp(Normalized Scores):\\\" \\n print exp_scores\\n print \\\"\\\\n Sum(Exp(Normalized Scores)):\\\" \\n print sums\\n print \\\"\\\\n Log(Sum):\\\" \\n print log_sums\\n print \\\"\\\\n Losses:\\\" \\n print losses\\n print \\\"\\\\n Data Loss:\\\"\\n print data_loss\\n print \\\"\\\\n Reg Loss:\\\"\\n print reg_loss\\n print \\\"\\\\n Loss:\\\"\\n print loss\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n # Backward pass: compute gradients\\n grads = {}\\n #############################################################################\\n # TODO: Compute the backward pass, computing the derivatives of the weights #\\n # and biases. Store the results in the grads dictionary. For example, #\\n # grads['W1'] should store the gradient on W1, and be a matrix of same size #\\n #############################################################################\\n \\n # Softmax Segment \\n # Backward Pass - 8 Stages in Reverse \\n\\n # Stage 10 - Backprop sum(losses)/num_train --> loss #[10]\\n dlosses = np.zeros((num_train)) + 1.0/num_train\\n \\n # Stage 9 - Backprop log_sums - correct_scores --> losses #[9]\\n dlog_sums = 1 * dlosses\\n dcorrect_scores = -1 * dlosses\\n \\n # Stage 8 - Backprop log(sums) --> log_sums #[8]\\n dsums = lg_log_sums * dlog_sums\\n \\n # Stage 7 - Backprop sum(exp_scores) --> sums #[7]\\n dexp_scores = np.zeros(scores.shape) + dsums.reshape(-1,1) # Broadcast operation\\n\\n # Stage 6 - Backprop exp(scores) --> exp_scores #[6] \\n dnorm_scores = lg_exp_scores * dexp_scores\\n dnorm_scores_1 = np.copy(dnorm_scores) # Store d_norm_scores before adding\\n # back dcorrect_scores\\n \\n # Stage 5 - Backprop score of correct class --> correct_scores #[5] \\n dnorm_scores[np.arange(num_train), y] += dcorrect_scores\\n\\n # Stage 4 - Backprop scores - max(scores) --> norm_scores #[4] \\n dscores = dnorm_scores\\n\\n \\n # 2-layer Neural Network Section:\\n # Backward Pass - 3 Stages in Reverse\\n\\n # Stage 3 - Backprop h1.dot(W2)+b2 --> scores #[3] \\n dW2 += h1.T.dot(dscores)\\n dh1 = dscores.dot(W2.T)\\n db2 = np.sum(dscores, axis=0)\\n\\n # Stage 2 - Backprop ReLU(o1) --> h1 #[2]\\n do1 = lg_h1 * dh1\\n \\n # Stage 1 - Backprop X.dot(W1)+b1 --> o1 #[1] \\n dW1 += X.T.dot(do1)\\n db1 = np.sum(do1, axis=0)\\n\\n # Reg Backward Pass\\n dW1 += 2 * 0.5 * reg * W1\\n dW2 += 2 * 0.5 * reg * W2\\n \\n grads['W1'] = dW1\\n grads['W2'] = dW2\\n grads['b1'] = db1\\n grads['b2'] = db2\\n \\n if verbose:\\n print \\\"\\\\n Training data - Backward Pass\\\" \\n print \\\"\\\\n dloss:\\\"\\n print dlosses\\n print \\\"\\\\n dcorrect_scores:\\\"\\n print dcorrect_scores\\n print \\\"\\\\n dlog_sums:\\\" \\n print dlog_sums \\n print \\\"\\\\n lg_log_sums:\\\" \\n print lg_log_sums\\n print \\\"\\\\n sdums:\\\" \\n print dsums \\n print \\\"\\\\n dexp_scores:\\\" \\n print dexp_scores \\n print \\\"\\\\n lg_exp_scores:\\\" \\n print lg_exp_scores\\n print \\\"\\\\n dnorm_scores (before addback):\\\" \\n print dnorm_scores_1\\n print \\\"\\\\n dnorm_scores (after addback):\\\" \\n print dnorm_scores \\n print \\\"\\\\n dscores:\\\"\\n print dscores\\n print dscores.shape\\n \\n print \\\"\\\\n dW2:\\\"\\n print dW2\\n print dW2.shape\\n print \\\"\\\\n dh2:\\\"\\n print dh1\\n print dh1.shape\\n print \\\"\\\\n db2:\\\"\\n print db2\\n print db2.shape \\n print \\\"\\\\n do1:\\\"\\n print do1\\n print \\\"\\\\n dW1:\\\"\\n print dW1\\n print \\\"\\\\n db1:\\\"\\n print db1\\n print db1.shape\\n \\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, grads\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ea57dd8cef526d12c561c415810679c\",\n \"score\": \"0.551168\",\n \"text\": \"def svm_loss_vectorized(W, X, y, reg):\\n num_train = X.shape[0]\\n score = np.dot(X, W)\\n delta = 1\\n correct_class_score = score[np.arange(num_train), y]\\n margin = np.maximum(.0, score - correct_class_score[:, np.newaxis] + 1)\\n margin[np.arange(num_train), y] = .0\\n loss = np.sum(margin) / num_train + .5 * reg * np.sum(W * W)\\n\\n # https://zhuanlan.zhihu.com/p/21478575\\n margin[margin > 0] = 1.0\\n incorrect_cnt = np.sum(margin, axis=1)\\n margin[np.arange(num_train), y] = -incorrect_cnt\\n dW = np.dot(X.transpose(), margin) / num_train + reg * W\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d09b956ba2bd5dce0c8b5f18f9f55c9\",\n \"score\": \"0.5502266\",\n \"text\": \"def compute_cost(Y, AL, parameters, lambd, *args):\\n def cross_entropy_loss(Y, AL):\\n \\\"\\\"\\\"\\n Computes binary classification loss.\\n \\\"\\\"\\\"\\n return -np.multiply(Y, np.log(AL)) + np.multiply((1 - Y), np.log(1 - AL))\\n\\n def softmax_loss(Y):\\n \\\"\\\"\\\"\\n Computes multi-class classification loss L on all classes.\\n \\\"\\\"\\\"\\n Z = args[0]\\n b = np.max(Z, axis=0)\\n stable_loss = b + np.log(np.sum(np.exp(Z - b), axis=0))\\n # return -np.atleast_2d(np.sum(np.log(AL) * (Y), axis=0))\\n return np.atleast_2d(stable_loss)\\n\\n m = AL.shape[1]\\n C = Y.shape[0] # number of classes\\n\\n if C == 1:\\n # binary classification loss\\n loss = cross_entropy_loss(Y, AL)\\n else:\\n # multi-class classification loss\\n loss = softmax_loss(Y)\\n\\n J = np.mean(loss)\\n\\n # Adding regularization term if required\\n if lambd != 0:\\n # computing L2-regularization term\\n L = int(len(parameters) / 2)\\n reg_term = 0\\n for l in range(1, L + 1):\\n reg_term += np.nansum(np.square(parameters['W' + str(l)]))\\n\\n reg_term *= lambd / (2 * m)\\n\\n J += reg_term\\n\\n J = np.squeeze(J)\\n\\n assert (J.shape == ())\\n return (J)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d8d8011d039ba64d9f751080c5fdf0\",\n \"score\": \"0.54967153\",\n \"text\": \"def SGD(train, test, means, gamma=0.0005, num_features=10, lambda_user=0.1, lambda_item=0.1, max_it=10):\\n it = 0\\n # set seed\\n np.random.seed(988)\\n\\n train_lil = sp.lil_matrix(train)\\n train_dense = sp.lil_matrix.todense(train_lil)\\n\\n # init matrix\\n user_features, item_features = init_SVD(train_dense, num_features) #user Z N na K item W d na K\\n\\n # find the non-zero ratings indices \\n nz_row, nz_col = train.nonzero()\\n nz_train = list(zip(nz_row, nz_col))\\n nz_row, nz_col = test.nonzero()\\n nz_test = list(zip(nz_row, nz_col))\\n\\n nonzero_rows_train = np.where(train_dense!=0)[0]\\n nonzero_cols_train = np.where(train_dense!=0)[1]\\n nonzero_rows_test = np.where(test.todense()!=0)[0]\\n nonzero_cols_test = np.where(test.todense()!=0)[1]\\n\\n while (it<max_it): \\n \\n it += 1\\n np.random.shuffle(nz_train)\\n # decrease step size\\n gamma /= 1.2\\n nb = int(len(nz_train)/2)\\n \\n predict = np.dot(user_features, item_features)\\n for d, n in nz_train[:nb]: \\n # Calculate error on the prediction\\n predict_error=train[d,n]-predict[d,n]\\n # Update user and item features\\n item_features[:,n]+=gamma*(predict_error*user_features[d,:].T-lambda_item*item_features[:,n])\\n user_features[d,:]+=gamma*(predict_error*item_features[:,n].T-lambda_user*user_features[d,:])\\n\\n # Calculate again the predictino\\n predict = np.dot(user_features, item_features)\\n\\n # Get the RMSE errors for this prediction and go to the next iteration\\n rmse_train = compute_rmse(train, predict, nonzero_rows_train, nonzero_cols_train)\\n denormalized = denormalize(predict, means)\\n rmse_test = compute_rmse(test, denormalized , nonzero_rows_test, nonzero_cols_test)\\n predictions.append(predict)\\n print(\\\"It\\\", it, \\\"training error:\\\", rmse_train, \\\"testing error:\\\", rmse_test)\\n\\n rmse_test = compute_rmse(test, denormalize(predict, means), nonzero_rows_test, nonzero_cols_test)\\n print(\\\"Testing error:\\\", rmse_test)\\n\\n return predictions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99038f891d24d4c4bc2ff4c326091cc3\",\n \"score\": \"0.5490841\",\n \"text\": \"def lista_network(input_dim, D, n_layers=10, activation=\\\"st\\\", lmbd=.1,\\n weights=None):\\n\\n K, p = D.shape\\n assert input_dim == p\\n\\n f_cost = LossL2(np.zeros(shape=(1, p)), D)\\n Wz = np.eye(K, dtype=D.dtype) - D.dot(D.T) / f_cost.L\\n Wx = D.T / f_cost.L\\n if weights is None:\\n weights = [(Wx, Wz)] * n_layers\\n elif len(weights) < n_layers:\\n weights = list(weights) + [(Wx, Wz)] * (n_layers - len(weights))\\n\\n # Define some layers to build the neural network\\n x = Input(shape=(input_dim, ))\\n cost_layer = get_cost_lasso_layer(x, D, lmbd)\\n activation = get_soft_thresholding_layer(lmbd / f_cost.L)\\n\\n wx0, _ = weights[0]\\n z = Dense(K, use_bias=False, kernel_initializer=lambda s: wx0)(x)\\n z = activation(z)\\n for k in range(n_layers - 1):\\n wxk, wzk = weights[k + 1]\\n y1 = Dense(K, use_bias=False, kernel_initializer=lambda s: wzk)(z)\\n y2 = Dense(K, use_bias=False, kernel_initializer=lambda s: wxk)(x)\\n z = activation(Add()([y1, y2]))\\n\\n model = Model(inputs=x, outputs=z)\\n\\n def loss(y, yp):\\n return cost_layer(z)\\n\\n opt = keras.optimizers.rmsprop(lr=0.0001 / n_layers, decay=1e-6)\\n model.compile(loss=loss, optimizer=opt)\\n\\n def export_weights():\\n w = [(model.weights[0].eval(get_session()), None)]\\n for wz, wx in zip(model.weights[1::2], model.weights[2::2]):\\n w += [(wx.eval(get_session()), wz.eval(get_session()))]\\n return w\\n\\n model.export_weights = export_weights\\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0cd4134c40d11b5668366253494b34f\",\n \"score\": \"0.54867834\",\n \"text\": \"def linearSVM(x, y, c):\\n model = LinearSVC(C=c, multi_class='ovr', max_iter=10000)\\n model.fit(x, y)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03217e62420be2fe74f963618ee34054\",\n \"score\": \"0.54723626\",\n \"text\": \"def softmax_loss_vectorized(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n N=X.shape[0]\\n C=W.shape[1] # num of classes\\n D=W.shape[0] # D=3\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n score=X @ W\\n score-=score.max(axis=1).reshape(-1,1)\\n partition=np.sum(np.exp(score),axis=1).reshape(-1,1)\\n \\n loss=np.mean(-(score[np.arange(N),y])+np.log(partition))\\n \\n dW+=X.T @ (np.exp(score)/partition) \\n map_mtx=np.zeros((N,C))\\n map_mtx[np.arange(N),y]=1\\n dW-=X.T @ map_mtx\\n\\n dW/=N\\n\\n loss+=reg*np.sum(W*W)\\n dW+=2*reg*W\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1722dfc6a16eb443bde058da7ffd2d8\",\n \"score\": \"0.54702085\",\n \"text\": \"def update_plabels(V, Y, labeled_idx, unlabeled_idx,\\n nclasses, k=50, max_iter=30, alpha=0.99):\\n\\n # normalize_L2 eats only doubles aka float32\\n V = V.reshape(len(Y), -1).astype('float32')\\n alpha = 0.99\\n k = 50\\n labels = Y\\n # maxiter for Conjugate Gradient Solver\\n max_iter = 20\\n\\n # kNN search for the graph\\n d = V.shape[1]\\n res = faiss.StandardGpuResources()\\n flat_config = faiss.GpuIndexFlatConfig()\\n flat_config.device = int(torch.cuda.device_count()) - 1\\n # building the index takes some time\\n index = faiss.GpuIndexFlatIP(res, d, flat_config)\\n\\n normalize_L2(V)\\n index.add(V)\\n N = V.shape[0]\\n\\n # D = similarity matrix\\n # I = index matrix storing the indices of the k nn's per sample\\n D, I = index.search(V, k + 1)\\n\\n # Create the graph\\n # gamma parameter for manifold learning penalization as in eq 9\\n gamma = 3\\n D = D[:, 1:] ** gamma\\n I = I[:, 1:]\\n row_idx = np.arange(N)\\n row_idx_rep = np.tile(row_idx, (k, 1)).T\\n # create sparse matrix with 50 nns -> 50*V.shape[0] elements != 0\\n W = scipy.sparse.csr_matrix((D.flatten('F'),\\n (row_idx_rep.flatten('F'), I.flatten('F'))),\\n shape=(N, N))\\n W = W + W.T\\n\\n # Normalize the graph\\n W = W - scipy.sparse.diags(W.diagonal())\\n S = W.sum(axis=1)\\n S[S == 0] = 1\\n D = np.array(1. / np.sqrt(S))\\n D = scipy.sparse.diags(D.reshape(-1))\\n Wn = D * W * D\\n\\n # Initiliaze the y vector for each class (eq 5 from the paper,\\n # normalized with the class size) and apply label propagation\\n Z = np.zeros((N, nclasses))\\n A = scipy.sparse.eye(Wn.shape[0]) - alpha * Wn\\n for i in range(nclasses):\\n # a = np.where(labels[labeled_idx] == 0)[0]\\n # cur_idx = [labeled_idx[b] for b in a]\\n # CHECK WHAT HAPPENS HERE\\n cur_idx = labeled_idx[np.where(labels[labeled_idx] == i)]\\n y = np.zeros((N,))\\n y[cur_idx] = 1.0 / cur_idx.shape[0]\\n # conjugate gradient from linalg\\n f, _ = scipy.sparse.linalg.cg(A, y, tol=1e-6, maxiter=max_iter,\\n atol=0)\\n Z[:, i] = f\\n\\n # Handle numerical errors\\n Z[Z < 0] = 0\\n\\n # Compute the weight for each instance based on the entropy\\n # (eq 11 from the paper)\\n probs_l1 = F.normalize(torch.tensor(Z), 1).numpy()\\n probs_l1[probs_l1 < 0] = 0\\n entropy = scipy.stats.entropy(probs_l1.T)\\n weights = 1 - entropy / np.log(nclasses)\\n weights = weights / np.max(weights)\\n p_labels = np.argmax(probs_l1, 1)\\n\\n # Compute the accuracy of pseudolabels for statistical purposes\\n correct_idx = (p_labels[labels != -1] == labels[labels != -1])\\n print('pseudo label accuracy: {:0.5f}, mean entropy: {:0.5f}'.format(\\n correct_idx.mean(), entropy.mean()))\\n\\n # fix the pseudo labels for the labeled data to its original label\\n p_labels[labeled_idx] = labels[labeled_idx]\\n weights[labeled_idx] = 1.0\\n p_weights = weights.tolist()\\n\\n class_weights = []\\n # Compute the class weights\\n for i in range(nclasses):\\n cur_idx = np.where(np.asarray(p_labels) == i)[0]\\n class_weights.append(\\n (float(labels.shape[0]) / nclasses) / cur_idx.size)\\n\\n return (p_labels, p_weights, class_weights)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8aecedad7ce8d008c82b6f48a3ca861\",\n \"score\": \"0.5466806\",\n \"text\": \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n # loss_i = -(f_yi + stability_constant) + log(sum_j(exp(f_j + stability_constant))) \\n \\n num_samples = X.shape[0]\\n num_classes = W.shape[1]\\n \\n for i in range(num_samples):\\n # 1 x D * D x K -> 1 x K\\n # logits\\n Xi = X[i]\\n \\n # Scores 1 x K\\n scores = Xi.dot(W)\\n \\n # numerical stability\\n stability_constant = -np.max(scores)\\n \\n # exponentiate the scores for the sum\\n scores_exp = np.exp(scores + stability_constant)\\n scores_exp_sum = scores_exp.sum()\\n \\n # take the log of the sum\\n scores_sum = np.log(scores_exp_sum)\\n \\n loss += -scores[y[i]] - stability_constant + scores_sum\\n \\n ##### Gradient #####\\n \\n \\n for j in range(num_classes):\\n # calculate the fraction for the gradient\\n scores_exp_frac = scores_exp[j] / scores_exp_sum\\n\\n if j != y[i]:\\n dW[:, j] += Xi * scores_exp_frac\\n else:\\n dW[:, j] += Xi * (scores_exp_frac - 1)\\n \\n \\n loss /= num_samples\\n loss += reg * np.sum(W * W)\\n \\n dW /= num_samples\\n dW += 2 * reg * W\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7758a2f487966e594981f7d19c8f671\",\n \"score\": \"0.5464327\",\n \"text\": \"def kernel_regression_with_sgd(X,Y,kernel,mu,lmbda,num_iter):\\n\\n # Initialise alpha and errors\\n N = np.shape(X)[0]\\n D = np.shape(X)[1]\\n alpha = np.zeros((N,1))\\n errors = np.zeros((num_iter,1))\\n\\n K = np.zeros((N,N))\\n for i in range(N):\\n for j in range(N):\\n K[i,j] = kernel(X[i],X[j])\\n\\n for step in range(num_iter):\\n i = np.random.randint(N)\\n x = X[i:i+1]\\n y = Y[i]\\n errors[step] = loss(alpha,K,Y)\\n alpha = kernel_sgd_update_alpha(alpha,mu,y,i,K,lmbda)\\n\\n return K,alpha,errors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff95354b91b2e4e3578cf9822ec08727\",\n \"score\": \"0.5454151\",\n \"text\": \"def sgdClassifier(trainingData,trainingLabels):\\n from sklearn.linear_model import SGDClassifier\\n clf = SGDClassifier(loss=\\\"hinge\\\", penalty=\\\"l2\\\")\\n clf.fit(trainingData, trainingLabels)\\n \\n print \\\"Stochastic Gradient Descent Classifier generated with a training set size of\\\",len(trainingLabels)\\n return clf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a80db1b4eed380bfb78cc1ba8704d1a0\",\n \"score\": \"0.54514855\",\n \"text\": \"def train_softmax(learning_rate=0.2, n_epochs=20000, n_in=200, n_hid=100,\\r\\n datasets=None,\\r\\n batch_size=None, start_num=4):\\r\\n if batch_size == None:\\r\\n batch_size = int(datasets[0].shape[0])\\r\\n\\r\\n train_set_x = to_shared_data(datasets[0])\\r\\n train_set_y = to_shared_data(datasets[1], dtype='int32')\\r\\n # compute number of minibatches for training, validation and testing\\r\\n n_train_batches = int(train_set_x.get_value(borrow=True).shape[0] / batch_size)\\r\\n # n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n\\r\\n ######################\\r\\n # BUILD ACTUAL MODEL #\\r\\n ######################\\r\\n\\r\\n\\r\\n # allocate symbolic variables for the data\\r\\n index = T.lscalar() # index to a [mini]batch\\r\\n\\r\\n # generate symbolic variables for input (x and y represent a\\r\\n # minibatch)\\r\\n x = T.matrix('x') # data, presented as rasterized images\\r\\n y = T.ivector('y') # labels, presented as 1D vector of [int] labels\\r\\n classifiers = []\\r\\n outputs_ = []\\r\\n updates_ = []\\r\\n for i in range(0, start_num):\\r\\n classifier = SM(input=x, n_in=n_in, n_out=n_hid, n_out2=6)\\r\\n\\r\\n # the cost we minimize during training is the negative log likelihood of\\r\\n # the model in symbolic format\\r\\n cost = classifier.monitor_cost(y)\\r\\n\\r\\n\\r\\n # compute the gradient of cost with respect to theta = (W,b)\\r\\n g_W = T.grad(cost=cost, wrt=classifier.W)\\r\\n g_b = T.grad(cost=cost, wrt=classifier.b)\\r\\n g_W2 = T.grad(cost=cost, wrt=classifier.W2)\\r\\n g_b2 = T.grad(cost=cost, wrt=classifier.b2)\\r\\n g_rbW = T.grad(cost=cost, wrt=classifier.ae.rbm.W)\\r\\n g_rbh = T.grad(cost=cost, wrt=classifier.ae.rbm.hbias)\\r\\n g_aeW = T.grad(cost=cost, wrt=classifier.ae.W)\\r\\n g_aeb = T.grad(cost=cost, wrt=classifier.ae.b)\\r\\n\\r\\n\\r\\n\\r\\n # start-snippet-3\\r\\n # specify how to update the parameters of the model as a list of\\r\\n # (variable, update expression) pairs.\\r\\n updates = [(classifier.W, classifier.W - learning_rate * g_W),\\r\\n (classifier.b, classifier.b - learning_rate * g_b),\\r\\n (classifier.ae.rbm.W, classifier.ae.rbm.W - learning_rate * g_rbW),\\r\\n (classifier.ae.rbm.hbias, classifier.ae.rbm.hbias - learning_rate * g_rbh),\\r\\n (classifier.ae.W, classifier.ae.W - learning_rate * g_aeW),\\r\\n (classifier.ae.b, classifier.ae.b - learning_rate * g_aeb),\\r\\n (classifier.W2, classifier.W2 - learning_rate * g_W2),\\r\\n (classifier.b2, classifier.b2 - learning_rate * g_b2)\\r\\n ]\\r\\n # updates_ and outputs_ are used for multistart\\r\\n updates_.extend(updates)\\r\\n outputs_.append(classifier.errors(y))\\r\\n classifiers.append((classifier))\\r\\n\\r\\n train_model = theano.function(\\r\\n inputs=[index],\\r\\n outputs=outputs_,\\r\\n updates=updates_,\\r\\n givens={\\r\\n x: train_set_x[index * batch_size: (index + 1) * batch_size],\\r\\n y: train_set_y[index * batch_size: (index + 1) * batch_size]\\r\\n }\\r\\n )\\r\\n ###############\\r\\n # TRAIN MODEL #\\r\\n ###############\\r\\n\\r\\n\\r\\n\\r\\n start_time = timeit.default_timer()\\r\\n epoch = 0\\r\\n\\r\\n while (epoch < n_epochs):\\r\\n epoch = epoch + 1\\r\\n minibatch_avg_cost = []\\r\\n for minibatch_index in range(n_train_batches):\\r\\n minibatch_avg_cost.append(train_model(minibatch_index))\\r\\n print numpy.mean(minibatch_avg_cost, axis=0) * 100,'epoch ',epoch\\r\\n\\r\\n print 'best is ', numpy.argmin(numpy.mean(minibatch_avg_cost, axis=0))\\r\\n best = classifiers[numpy.argmin(minibatch_avg_cost)]\\r\\n\\r\\n\\r\\n # save the best model\\r\\n with open('best_model', 'wb') as f:\\r\\n pickle.dump(best, f)\\r\\n end_time = timeit.default_timer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8423b62e0e0012f01f7cbdb4e3dbed81\",\n \"score\": \"0.54391444\",\n \"text\": \"def perceptron(x, d, mean, centers, c = 1, iterations = 100):\\n inp = rbfActivation(x, mean)\\n w = np.random.rand(x.shape[1], centers)\\n t = Threshold()\\n for i in range(iterations):\\n for ind, point in enumerate(inp):\\n net = point.dot(w)\\n o = t.binary(net)\\n out = -1*np.ones(centers)\\n out[d[ind]] = 1\\n w = w + c*(out - o)*(point.reshape(point.shape[0], 1))\\n return w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c930e5c77cea78944bf5d0bd3abbc8e4\",\n \"score\": \"0.54283535\",\n \"text\": \"def cross_entropoy_loss_naive(W, X, y, reg):\\n\\n #return cross_entropoy_loss_vectorized(W, X, y, reg)\\n # pylint: disable=too-many-locals\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n ############################################################################\\n # TODO: Compute the cross-entropy loss and its gradient using explicit #\\n # loops. Store the loss in loss and the gradient in dW. If you are not #\\n # careful here, it is easy to run into numeric instability. Don't forget #\\n # the regularization! #\\n ############################################################################\\n\\n N = X.shape[0]\\n D = X.shape[1]\\n C = W.shape[1]\\n\\n reg_kind = \\\"L1\\\"\\n\\n outputs = np.zeros((C,))\\n\\n for num_sample, sample in enumerate(X):\\n\\n for num_class in range(C):\\n incoming = 0.\\n\\n for var in range(D):\\n incoming += sample[var]*W[var, num_class]\\n\\n outputs[num_class] = np.exp(incoming)\\n\\n outputs = outputs/sum(outputs)\\n\\n # print(outputs)\\n\\n loss += - np.log(outputs[y[num_sample]])\\n\\n loss += 0.5*reg*np.sum(W**2)\\n\\n for num_class in range(C):\\n dW[:,num_class] += np.reshape(outputs[num_class] * sample, dW[:, num_class].shape) + reg * W[:, num_class]\\n\\n if num_class == y[num_sample]:\\n dW[:,num_class] -= sample.reshape(dW[:, num_class].shape)\\n\\n dW = dW/N\\n loss = loss/N\\n\\n\\n\\n ############################################################################\\n # END OF YOUR CODE #\\n ############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b4cd88a782f8e0f8b0628e9ea648211\",\n \"score\": \"0.5426194\",\n \"text\": \"def loss_kd(y_student, y_true, y_teacher, params):\\n\\n T = params.temperature\\n\\n # take max confidence of student model as coefficient of CE loss_fn\\n # max_prob = torch.max(F.softmax(y_student, dim=1).detach(), dim=1).values\\n # take max confidence of teacher model as coefficient of CE loss_fn\\n\\n loss_CE = F.cross_entropy(y_student, y_true, reduction='none') # N*1\\n\\n y_student_log_T_softmax = F.log_softmax(y_student / T, dim=1) # N*C\\n y_teacher_T_softmax = F.softmax(y_teacher / T, dim=1) # N*C\\n\\n # max_prob = torch.max(F.softmax(y_teacher / T, dim=1).detach(), dim=1).values\\n # max_prob = torch.max(y_teacher_T_softmax.detach(), dim=1).values\\n # print('Max confidence of teacher model:', torch.mean(max_prob))\\n # params.maxT = torch.mean(max_prob)\\n\\n kl_div_each = F.kl_div(y_student_log_T_softmax, y_teacher_T_softmax, reduction='none') * (T * T) # N*C\\n\\n loss_KLD = torch.mean(kl_div_each, dim=1) # N*1\\n\\n # print('\\\\nLoss_kd:\\\\nCE:{}\\\\nKLD:{}\\\\n'.format(loss_CE.detach()[0], loss_KLD.detach()[0]))\\n _loss = torch.mean((1. - params.alpha) * loss_CE + params.alpha * loss_KLD)\\n # loss = torch.mean(max_prob * loss_CE + (torch.ones_like(max_prob) - max_prob) * loss_KLD)\\n\\n return _loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81713d7fdc8e922aad6ff3ae95d9f669\",\n \"score\": \"0.54250103\",\n \"text\": \"def msnerv_obj_fct(x, sigma_ij, N, n_components, p_h, t_h, n_perp, w_cf):\\r\\n X = np.reshape(a=x, newshape=(N, n_components))\\r\\n # Computing the pairwise squared euclidean distances in the LDS\\r\\n dsm_lds = pairwise_dist(X=X, metric='sqeuclidean', tomatrix=True)\\r\\n # LDS multi-scale similarities. np.zeros(shape=(N,N,N)) is too big to be stored.\\r\\n s_ij = np.zeros(shape=(N,N))\\r\\n arr_ones = np.ones(shape=N)\\r\\n # For each perplexity\\r\\n for h in range(n_perp):\\r\\n # Computing the corresponding LDS similarities and updating s_ij\\r\\n s_ij += sne_lds_similarities_fast(dsm_lds=dsm_lds, arr_den_s_i=t_h[h]*arr_ones, compute_log=False)[0]\\r\\n # Scaling s_ij\\r\\n s_ij /= n_perp\\r\\n # Computing the cost function value\\r\\n return (1-w_cf)*scipy.special.rel_entr(sigma_ij, np.maximum(np.finfo(dtype=np.float64).eps, s_ij)).sum() + w_cf*scipy.special.rel_entr(s_ij, np.maximum(np.finfo(dtype=np.float64).eps, sigma_ij)).sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04d3374d9ddf30125eac4687ff79af02\",\n \"score\": \"0.54176205\",\n \"text\": \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n D, C = W.shape\\n N = y.shape[0]\\n \\n scores = np.zeros((N, C))\\n for sample_idx in range(N):\\n for class_idx in range(C):\\n for coord_idx in range(D):\\n scores[sample_idx, class_idx] += X[sample_idx, coord_idx] * W[coord_idx, class_idx]\\n \\n probs = np.zeros_like(scores)\\n for sample_idx in range(N):\\n softmax_den = 0\\n for class_idx in range(C):\\n exponent = np.exp( scores[sample_idx, class_idx] )\\n probs[sample_idx, class_idx] = exponent\\n softmax_den += exponent\\n for class_idx in range(C):\\n probs[sample_idx, class_idx] /= softmax_den\\n \\n # Compute loss\\n log_probs = 0\\n for sample_idx in range(N):\\n true_class_idx = y[sample_idx]\\n log_probs += np.log( probs[sample_idx, true_class_idx] )\\n \\n data_loss = -(1 / N) * log_probs\\n \\n reg_loss = 0\\n if reg != 0:\\n for coord_idx in range(D):\\n for class_idx in range(C):\\n reg_loss += W[coord_idx, class_idx] ** 2\\n \\n reg_loss = 0.5 * reg * reg_loss\\n \\n loss = data_loss + reg_loss\\n\\n #Compute grad\\n dscores = probs\\n \\n for sample_idx in range(N):\\n true_class_idx = y[sample_idx]\\n dscores[sample_idx, true_class_idx] -= 1\\n \\n for sample_idx in range(N):\\n for class_idx in range(C):\\n dscores[sample_idx, class_idx] /= N\\n \\n for coord_idx in range(D):\\n for class_idx in range(C):\\n for sample_idx in range(N): \\n dW[coord_idx, class_idx] += X.T[coord_idx, sample_idx] * dscores[sample_idx, class_idx]\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1551e8d7cc0e649ab30e417fc92409ce\",\n \"score\": \"0.5417067\",\n \"text\": \"def fcn2min(params, ACD,L, d):\\n a0 = params['a0']\\n a1 = params['a1']\\n a2 = params['a2']\\n loss = (a0+ACD*a1+L*a2 -d )**2\\n return loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a63543e0ae94bb33fdb374e9e9809a16\",\n \"score\": \"0.5413414\",\n \"text\": \"def train_with_hard_negatives(d, dtest,cbwords, cbsamps, codebook, cls, pos_table, neg_table,feature_type,\\\\\\n iterations, kernel='chi2', L=2, \\\\\\n testsize = 'max',randomize=False):\\n # Featurize and pyramidize the input\\n e = Extractor()\\n M = codebook.shape[0]\\n pyr_size = M*1./3.*(4**(L+1)-1)\\n\\n print 'get pos train pyrs'\\n pos_pyrs = get_pyr(d,e,pos_table,pyr_size,L,codebook,feature_type,cls)\\n print 'get neg train pyrs'\\n neg_pyrs = get_pyr(d,e,neg_table,pyr_size,L,codebook,feature_type,cls)\\n \\n print 'built all positive pyramids'\\n \\n classification = np.asarray([1]*pos_table.arr.shape[0] + [-1]*neg_table.arr.shape[0])\\n \\n filename = config.data_dir + 'features/' + feature_type + '/svms/' + kernel + \\\\\\n '/'+ cls\\n ut.makedirs(config.data_dir + 'features/' + feature_type + '/svms/' + kernel)\\n \\n \\n # with that we now determined our whole dataset D \\n #D = np.concatenate((pos_pyrs, neg_pyrs, pos_test_pyr, neg_test_pyr))\\n #Y = np.concatenate((classification, test_classification))\\n #idx_train = np.arange(pos_pyrs.shape[0] + neg_pyrs.shape[0])\\n #idx_test = np.arange(pos_test_pyr.shape[0] + neg_test_pyr.shape[0]) + idx_train.size\\n train_pyrs = np.concatenate((pos_pyrs,neg_pyrs)) \\n for i in range(iterations):\\n # train new model - according to hard mining algorithm by Felszenswalb et al.\\n # \\\"Object Detection with Discriminatively Trained Part Based Modles\\\"\\n \\n [test_pyrs, test_classification] = get_test_windows(testsize,dtest,e,\\\\\\n pyr_size,L,codebook,feature_type,cls,\\\\\\n pos_table.cols, randomize=randomize)\\n print 'node',comm_rank,'training in round', i, 'with', np.sum(classification==1),'pos and',\\\\\\n np.sum(classification == -1),'negs'\\n print 'testing', test_pyrs.shape[0], 'new samples'\\n print time.strftime('%m-%d %H:%M')\\n \\n # get new test samples\\n \\n \\n # ----------1-------------\\n model = train_svm(train_pyrs, classification, kernel)\\n \\n testY = svm_predict(np.concatenate((train_pyrs,test_pyrs)), model)\\n result = testY\\n print result\\n \\n res_train = testY[:train_pyrs.shape[0]]\\n res_test = testY[train_pyrs.shape[0]:]\\n \\n # ----------3------------- \\n # remove easy samples from train-set\\n idx_tr_list = []\\n for s_ind in range(res_train.shape[0]):\\n if res_train[s_ind]*classification[s_ind] <= 1:\\n idx_tr_list.append(s_ind)\\n indices = np.matrix(idx_tr_list).reshape(1,len(idx_tr_list))\\n indices = np.array(indices.astype(Int))[0] \\n train_pyrs = train_pyrs[indices]\\n classification = classification[indices]\\n \\n # ----------4-------------\\n idx_hn_list = []\\n new_hards = False\\n for s_ind in range(res_test.shape[0]):\\n if res_test[s_ind]*test_classification[s_ind] < 1:\\n new_hards = True\\n idx_hn_list.append(s_ind) \\n\\n nu_train_idc = np.matrix(idx_hn_list).reshape(1,len(idx_hn_list))\\n nu_train_idc = np.array(nu_train_idc.astype(Int))[0] \\n train_pyrs = np.vstack((train_pyrs, test_pyrs[nu_train_idc]))\\n classification = np.concatenate((classification, test_classification[nu_train_idc]))\\n \\n test_result = result[-test_pyrs.shape[0]:]\\n fp = np.sum(np.multiply(test_result < 0, np.transpose(np.matrix(test_classification == 1))))\\n tp = np.sum(np.multiply(test_result > 0, np.transpose(np.matrix(test_classification == 1))))\\n fn = np.sum(np.multiply(test_result > 0, np.transpose(np.matrix(test_classification == -1))))\\n \\n # save these to the training file\\n prec = tp/float(tp+fp)\\n rec = tp/float(tp+fn)\\n print 'tp, fp:',tp,fp\\n print 'prec, rec:', prec,rec\\n with open(filename + '_train', \\\"a\\\") as myfile:\\n myfile.write(str(prec) + ' ' + str(rec)+'\\\\n') \\n \\n # ----------2-------------\\n if not new_hards:\\n # no new_hards from test set,we want to quit.\\n break\\n # save the trained svm. \\n #save_svm(model, filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f2059396a2b316414474f0a2a44260c\",\n \"score\": \"0.5402359\",\n \"text\": \"def __call__(self, ctx, dloss):\\n ls_dlogits = []\\n s = len(ctx)\\n for i in range(s):\\n if ctx[i][1]<0:\\n# dlogits_i = -ctx[i][0]+1/(1+np.e**(-ctx[i][1]))\\n dlogits_i =np.e**(ctx[i][1])/(1+np.e**(ctx[i][1])) -ctx[i][0]\\n ls_dlogits.append(dlogits_i)\\n else:\\n dlogits_i = 1 -ctx[i][0]-np.e**(-ctx[i][1])/(1+np.e**(-ctx[i][1]))\\n ls_dlogits.append(dlogits_i)\\n# try:\\n# dlogits_i = 1 -ctx[i][0]-1/(1+np.e**(ctx[i][1]))\\n# ls_dlogits.append(dlogits_i)\\n# except:\\n# # dlogits_i = 1 -ctx[i][0]-1/(1+np.e**(ctx[i][1]))\\n# dlogits_i =np.e**(ctx[i][1])/(1+np.e**(ctx[i][1])) -ctx[i][0]\\n# ls_dlogits.append(dlogits_i)\\n dlogits = np.array(ls_dlogits)/s\\n return dlogits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be4cf1448d0cce8c95d2a6ac8aef108b\",\n \"score\": \"0.5402058\",\n \"text\": \"def train_sgd(self, max_iter=100, learn_rate=0.01, lamb=0.1, delta=0.01):\\r\\n print '-' * 60\\r\\n print \\\"START TRAIN SGD:\\\"\\r\\n # y =[]\\r\\n m = len(self.sample_list)\\r\\n n = self.feat_dimension + 1\\r\\n k = len(self.label_set)\\r\\n\\r\\n J = 0.\\r\\n J_pre = 0.\\r\\n # grad = np.zeros(self.Theta.shape)\\r\\n rd = 0\\r\\n while rd < max_iter * m:\\r\\n if rd % m == 0 and rd != 0:\\r\\n loop = rd / m\\r\\n error = 0\\r\\n J = 0.\\r\\n prb = []\\r\\n for i in xrange(m):\\r\\n prb.append(self.predict(self.sample_list[i]))\\r\\n # the label of sample[i] not the index\\r\\n label = self.label_list[i]\\r\\n pred = prb[i].index(max(prb[i]))\\r\\n if label != self.label_set[pred]:\\r\\n error += 1\\r\\n J += math.log(prb[i][self.label_set.index(label)])\\r\\n\\r\\n J = -J / m + lamb * sum(sum(self.Theta * self.Theta)) / 2\\r\\n acc = 1 - error / float(m)\\r\\n print \\\"Iter %4d Cost:%4.4f Acc:%4.4f\\\" % (loop, J, acc)\\r\\n if loop != 1 and (J_pre - J) <= delta and J < J_pre:\\r\\n print \\\"\\\\n\\\\nReach the minimal cost value threshold!\\\"\\r\\n break\\r\\n J_pre = J\\r\\n\\r\\n i = random.randint(0, m - 1)\\r\\n label = self.label_list[i] # the label of sample[i] not the index\\r\\n pred_prb = self.predict(self.sample_list[i])\\r\\n pred = pred_prb.index(max(pred_prb))\\r\\n x = self.__getSampleVec(self.sample_list[i])\\r\\n y = self.label_set.index(label) # the index of sample[i]'s label\\r\\n for j in xrange(k):\\r\\n # grad[j] += ((1 if y==j else 0) - pred_prb[j]) * x[0]\\r\\n self.Theta[j] += learn_rate * \\\\\\r\\n (((1 if y == j else 0) -\\r\\n pred_prb[j]) * x[0] - lamb * self.Theta[j])\\r\\n\\r\\n # grad = -grad+lamb*self.Theta\\r\\n # self.Theta -= learn_rate * grad\\r\\n rd += 1\\r\\n\\r\\n if rd == max_iter * m:\\r\\n print \\\"Train loop has reached the maximum of iteration.\\\"\\r\\n\\r\\n print \\\"Training process finished.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76c4afda5213bd97e6bc88841a754011\",\n \"score\": \"0.54009956\",\n \"text\": \"def solve_lasso_sparse(lamb, X_m, y, N, d,init_w_vec):\\n w_tilde = copy(init_w_vec)\\n w_prev = zeros(d+1)\\n\\n \\\"\\\"\\\" Start with round 0 \\\"\\\"\\\"\\n round_robin = 0\\n\\n \\\"\\\"\\\" Choose w_0 \\\"\\\"\\\"\\n w_0 = init_w_vec[0]\\n\\n print(\\\"Lambda provided for lasso: \\\", lamb)\\n\\n # Precompute Xij^2\\n # Flatten X\\n X_arr = X_m.copy()\\n X_arr.data **= 2\\n\\n # This variable will be updated whenever we change w\\n Xw = X_m.dot(transpose(w_tilde[1:]))\\n while round_robin < 3000 :\\n # Make a copy of the old vector ..\\n w_prev = copy(w_tilde)\\n\\n for feature_j in range(0,d):\\n\\n # convert feature j column to an array\\n arr = array(X_m[:,feature_j].todense()).flatten()\\n c_j = 2 * arr.dot(y - Xw + arr * w_tilde[feature_j+1] - w_0)\\n w_tilde_j_old = w_tilde[feature_j+1]\\n if(c_j < (-1 * lamb)):\\n w_tilde[feature_j+1] = (c_j + lamb)/(2 *sum(X_m[:,feature_j].data))\\n elif(c_j > lamb):\\n w_tilde[feature_j+1] = (c_j - lamb)/(2 *sum(X_m[:,feature_j].data))\\n else:\\n w_tilde[feature_j+1] = 0\\n delta_w = w_tilde[feature_j+1] - w_tilde_j_old\\n Xw += delta_w * arr\\n\\n # Calculate the new w_0\\n w_0 = sum(y - X_m.dot(w_tilde[1:]))/N\\n # Update the new w_0\\n w_tilde[0] = w_0\\n # Create the diff vector which we'll use to compute c_next\\n diff_w = w_tilde - w_prev\\n # Compute whether we've reached a good enough precision to warrant any other iteration.\\n new_error = max(abs(diff_w))\\n #print \\\"Expected Error :\\\",new_error\\n round_robin += 1\\n if new_error < .001:\\n return w_tilde, w_prev, new_error,round_robin\\n print(\\\"Not done even after 3000 iterations - Exiting !\\\")\\n return w_tilde,new_error,round_robin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e578f1b1e61b7bd215cfc6e2b50c4a45\",\n \"score\": \"0.5398153\",\n \"text\": \"def classification_error(self, X_d, Y_d):\\n # first, convert labels to account for semi-supervised labeling\\n Y_mask = 1.0 * (Y_d != 0)\\n Y_d = Y_d - 1\\n # compute the main PeaNet's output for X_d\\n Y_p = self.get_pn_output(X_d)\\n # get the implied class labels\\n Y_c = np.argmax(Y_p, axis=1).reshape((Y_d.shape[0],1))\\n # compute the classification error for points with valid labels\\n err_rate = np.sum(((Y_d != Y_c) * Y_mask)) / np.sum(Y_mask)\\n return err_rate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98c9d13a0579f65830057c9cac106b3\",\n \"score\": \"0.53904605\",\n \"text\": \"def perceptron(vector_x, vector_y):\\n\\n X_train, X_test, y_train, y_test = train_test_split(vector_x, vector_y)\\n scaler = StandardScaler() ##standardizes the data for outliers\\n\\n scaler.fit(X_train) ## each column will have a mean of 0 and standard deviation = 1\\n X_train = scaler.transform(X_train) ##transforms the tests data so each feature is on the same scale\\n X_test = scaler.transform(X_test)\\n\\n mlp = MLPClassifier(hidden_layer_sizes=(31,31,31), max_iter=500)\\n mlp.fit(X_train, y_train)\\n predictions = mlp.predict(X_test)\\n\\n print(confusion_matrix(y_test,predictions))\\n print(classification_report(y_test, predictions))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993489,"cells":{"query_id":{"kind":"string","value":"9703f40fa9ce030515a496a9cc3d4b6d"},"query":{"kind":"string","value":"Create quiz providing basic fields with LIVE_STATUS and two categories."},"positive_passages":{"kind":"list like","value":[{"docid":"c578e483defc8bf217d15cee61fb9ec1","score":"0.6504106","text":"def testCreateQuizLiveWithCategories(self):\n status = Quiz.LIVE_STATUS\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\n quiz.save()\n c1 = Category.objects.create(title='Programming', slug='programming')\n c2 = Category.objects.create(title='Science', slug='science')\n quiz.categories.add(c1, c2)\n categories = Quiz.objects.get(slug=self.slug).categories.all()\n self.assertEqual(Quiz.objects.get(slug=self.slug).categories.count(), 2)\n self.assertEqual(categories[0], c1)\n self.assertEqual(categories[1], c2)","title":""}],"string":"[\n {\n \"docid\": \"c578e483defc8bf217d15cee61fb9ec1\",\n \"score\": \"0.6504106\",\n \"text\": \"def testCreateQuizLiveWithCategories(self):\\n status = Quiz.LIVE_STATUS\\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\\n quiz.save()\\n c1 = Category.objects.create(title='Programming', slug='programming')\\n c2 = Category.objects.create(title='Science', slug='science')\\n quiz.categories.add(c1, c2)\\n categories = Quiz.objects.get(slug=self.slug).categories.all()\\n self.assertEqual(Quiz.objects.get(slug=self.slug).categories.count(), 2)\\n self.assertEqual(categories[0], c1)\\n self.assertEqual(categories[1], c2)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"23ea909a4f70cd48b86b9e7215fd7a4e","score":"0.6298574","text":"def test_quiz_question_categorized(self):\n url = '/api/quizzes'\n previous_questions = []\n\n data = {\n 'previous_questions': previous_questions,\n 'quiz_category': self.science.id\n }\n\n # There is only 1 possible question in the science category\n resp = self.client.post(url, json=data)\n assert resp.status_code == 200\n resp_data = json.loads(resp.data)\n\n assert resp_data == {\n 'question': {\n 'answer': 'Nobody knows',\n 'category': 1,\n 'difficulty': 5,\n 'id': 1,\n 'question': 'What is science?'\n }\n }\n\n # Get depleted questions\n previous_questions.append(resp_data['question']['id'])\n data['previous_questions'] = previous_questions\n\n resp = self.client.post(url, json=data)\n assert resp.status_code == 200\n\n resp_data = json.loads(resp.data)\n assert resp_data['question'] is None","title":""},{"docid":"ad5a6900f35518472e2f3817823a4ff7","score":"0.6146127","text":"def testCreateQuestion(self):\n status = Quiz.LIVE_STATUS\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\n quiz.save()\n\n question_1 = Question(question='Choose your color', quiz=quiz)\n question_1.save()\n\n question_2 = Question(question='Choose your month', quiz=quiz)\n question_2.save()\n\n self.assertEqual(Quiz.objects.get(slug=self.slug).question_set.count(), 2)","title":""},{"docid":"7848f9f54470587527cb940daf908331","score":"0.61299694","text":"def sample_question(quiz, **kwargs):\n defaults = {'label': 'Sample Question Label'}\n defaults.update(kwargs)\n return models.Question.objects.create(quiz=quiz, **defaults)","title":""},{"docid":"b698ead979a3a0573b7e46f135390451","score":"0.60569924","text":"def create_free_quiz(quiz_name, quiz_language_id, quiz_short_desc):\n\n # Check for errors\n if not quiz_name or not quiz_language_id or not quiz_short_desc:\n raise ValueError(\"Property in body is empty\")\n\n # Check types\n if type(quiz_name) != str or type(quiz_language_id) != int or type(\n quiz_short_desc) != str:\n raise ValueError(\"Types in body are incorrect\")\n\n if len(quiz_name) > 20 or len(quiz_short_desc) > 20:\n raise ValueError(\"One field in body is too long\")\n\n # Also check that the language_id actually exists\n query = \"\"\"\n SELECT language_id\n FROM languages\n WHERE language_id = %s\n \"\"\"\n\n languages = db.query(query, (quiz_language_id))\n\n if not languages:\n raise ValueError(\"Language ID does not exist\")\n\n query = \"\"\"\n INSERT INTO quizzes (quiz_name, quiz_language_id, quiz_short_desc)\n VALUES (%s, %s, %s)\n \"\"\"\n\n db.insert_query(query, (quiz_name, quiz_language_id, quiz_short_desc))","title":""},{"docid":"43575613e7b6030e1680a9384ed7aa2c","score":"0.6027665","text":"def test_create_question_fields(self):\n url = '/api/questions'\n\n # Test missing fields\n data = {}\n resp = self.client.post(url, json=data)\n assert resp.status_code == 400\n assert json.loads(resp.data) == {\n 'error': '400: Bad Request',\n 'message': [\n {'question': 'Field is required'},\n {'answer': 'Field is required'},\n {'difficulty': 'Field is required'},\n {'category': 'Field is required'}\n ]\n }\n\n data = {\n 'question': 'Why is science?',\n 'answer': 'Only Bill Nye knows',\n 'category': 5000,\n 'difficulty': 0\n }\n\n # Test difficulty and cateory validation\n resp = self.client.post(url, json=data)\n assert resp.status_code == 400\n assert json.loads(resp.data) == {\n 'error': '400: Bad Request',\n 'message': [\n {'difficulty': 'Difficulty must be an integer between 1 and 5'},\n {'category': 'Category is not supported'}\n ]\n }","title":""},{"docid":"933461ce8f7604a63cf9960fbbccf11f","score":"0.6026407","text":"def test_create_quiz(self):\r\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\r\n pass","title":""},{"docid":"914f0294fc9feedfbbaa714c33daf854","score":"0.5942922","text":"def sample_quiz(supervisor, **kwargs):\n defaults = {'name': 'Sample Quiz'}\n defaults.update(kwargs)\n return models.Quiz.objects.create(supervisor=supervisor, **defaults)","title":""},{"docid":"f6dfe542ed5fa22b4aea537f88a460d1","score":"0.5934828","text":"def test_create(self):\n assert Question.query.count() == 2\n\n data = {\n 'question': 'Why is science?',\n 'answer': 'Only Bill Nye knows',\n 'category': self.science.id,\n 'difficulty': 3\n }\n resp = self.client.post('/api/questions', json=data)\n assert resp.status_code == 200\n resp_data = json.loads(resp.data)\n assert resp_data == {\n 'answer': 'Only Bill Nye knows',\n 'category': 1,\n 'difficulty': 3,\n 'id': 3,\n 'question': 'Why is science?'\n }\n\n # Ensure changes persist in DB\n assert Question.query.count() == 3\n question = Question.query.get(resp_data['id'])\n assert question is not None\n assert question.format() == resp_data","title":""},{"docid":"d44d2a0081595ae3f7682292ab367dd0","score":"0.58319896","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n self.new_question = {\n 'question': 'Question_test 1',\n 'answer': 'Answer_test 1',\n 'difficulty': 1,\n 'category': 1\n }\n\n self.category_id = {\n 'type':1\n }\n #SOURCE: https://knowledge.udacity.com/questions/583577\n # IT MUST MATCH THE JSON.STRINGIFY 'S BODY IN QUESTIONVIEW\n self.searchTerm = {\n 'searchTerm':'Question'\n }\n\n #SOURCE: https://knowledge.udacity.com/questions/583696\n # IT MUST MATCH THE JSON.STRINGIFY 'S BODY IN QUESTIONVIEW\n self.quizCategory = { \n 'previous_questions': [20,21],\n 'quiz_category':{'type':'Science', 'id':'1'}\n }\n\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"ca9f1fa62f868f935515cce5754827df","score":"0.58075905","text":"def create_question(self, question_form):\n return # osid.assessment.Question","title":""},{"docid":"69448e47c4bf02b0a627fd76339aa93d","score":"0.5796546","text":"def test_quiz_question_uncategorized(self):\n url = '/api/quizzes'\n previous_questions = []\n data = {'previous_questions': previous_questions}\n\n resp = self.client.post(url, json=data)\n assert resp.status_code == 200\n resp_data = json.loads(resp.data)\n\n # Check response for question that was returned\n assert 'question' in resp_data\n assert 'id' in resp_data['question']\n question1 = Question.query.get(resp_data['question']['id'])\n assert question1 is not None\n assert resp_data['question'] == question1.format()\n\n # Get next question\n previous_questions.append(question1.id)\n data['previous_questions'] = previous_questions\n\n resp = self.client.post(url, json=data)\n assert resp.status_code == 200\n\n resp_data = json.loads(resp.data)\n question2 = Question.query.get(resp_data['question']['id'])\n assert question2 is not None\n assert question2.id not in previous_questions\n\n # Get depleted questions\n previous_questions.append(question2.id)\n data['previous_questions'] = previous_questions\n\n resp = self.client.post(url, json=data)\n assert resp.status_code == 200\n\n resp_data = json.loads(resp.data)\n assert resp_data['question'] is None","title":""},{"docid":"fd5c835c0285e5600d6d427e044136a6","score":"0.5777712","text":"def test_play_quiz_1(self):\r\n res = self.client().post('/api/quizzes', json={'previous_questions':[], 'quiz_category': {'type':'Sports', 'id':'6'}})\r\n data = json.loads(res.data)\r\n self.assertEqual(data['success'], True)\r\n self.assertEqual(data['question']['category'], 6)","title":""},{"docid":"4a301d4a204263d96fc8244582e9e2ac","score":"0.5749012","text":"def create_exam():\n exam = Exam.objects.create(name='Test Exam',\n kind = ExamKind.CI,\n stage = ExamStage.DEV,\n description='an exam for testing')\n concept_type = ContentType.objects.get_for_model(Concept)\n concept = Concept.objects.get(name = \"Concept A\")\n FreeResponseQuestion.objects.create(exam=exam,\n question=\"What is the answer to this FR question?\",\n number=1,\n content_type=concept_type,\n object_id=concept.id)\n concept = Concept.objects.get(name = \"Concept B\")\n mcq = MultipleChoiceQuestion.objects.create(exam=exam,\n question=\"What is the answer to this MC question?\",\n number=2,\n content_type=concept_type,\n object_id=concept.id)\n MultipleChoiceOption.objects.create(question=mcq, text=\"choice 1\", index=1, is_correct=True)\n MultipleChoiceOption.objects.create(question=mcq, text=\"choice 2\", index=2)\n MultipleChoiceOption.objects.create(question=mcq, text=\"choice 3\", index=3)\n return exam","title":""},{"docid":"60d8f79821ed5c1c520d16430f08aaad","score":"0.57096446","text":"def testCreateQuizLive(self):\n status = Quiz.LIVE_STATUS\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\n quiz.save()\n self.assertEqual(Quiz.objects.filter(status=status).count(), 1)\n self.assertEqual(Quiz.objects.get(slug=self.slug).title, self.title)","title":""},{"docid":"77684eb79390efe1c869ca34064220ba","score":"0.5664846","text":"def create_question(controls, index, question_type, test, number, dbsession):\n if question_type == \"selectTrue\" or question_type == \"multipleChoice\":\n questionText = str(controls[index+1][1])\n question = Question(True, question_type, questionText, \n test, number)\n dbsession.add(question)\n dbsession.flush()\n return question\n if question_type == \"shortAnswer\":\n questionText = str(controls[index][1])\n question = Question(False, question_type, questionText,\n test, number)\n dbsession.add(question)\n dbsession.flush()\n return None\n return None","title":""},{"docid":"2b3ccb04e87f1e811108cc485d95e350","score":"0.56620574","text":"def create(info, context):\n q = context # context is the newly created question\n log.debug(\"[QUESTION CREATE] [%s] [%s]\" % (info, q))\n utils.setQuestionDefaults(info, q) # !+\n utils.setBungeniOwner(q)","title":""},{"docid":"c0c46e6a8f23aaaea758f6d7616cfd0c","score":"0.56395406","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_user = os.environ.get(\"PSQL_USER\")\n self.database_password = os.environ.get(\"PSQL_PWD\")\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}:{}@{}/{}\".format(self.database_user, self.database_password,'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n \n self.new_question = {\n 'question':'Test Question',\n 'answer': 'Test Answer',\n 'difficulty': 1,\n 'category':1\n }\n self.new_question_bad_category = {\n 'question':'Test Question',\n 'answer': 'Test Answer',\n 'difficulty': 1,\n 'category':9\n }\n \n self.new_search = {\n 'searchTerm': 'title'\n }\n \n self.quizz_post = {\n \"previous_questions\": [33],\n \"quiz_category\": {\n \"type\": \"Science\",\n \"id\": \"1\"}\n }\n self.quizz_post_bad = {\n \"previous_questions\": [],\n \"quiz_category\": {\n \"type\": \"Science\", \n \"id\": \"9\"\n }\n }\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"b21529def42e5a5cb6d9408d5fa44ff7","score":"0.5599331","text":"def _generate_quiz_form(questions):\n # reset quiz form\n for attr, value in QuizForm().__dict__.items():\n if attr[:-1] == \"question\":\n delattr(QuizForm, attr)\n \n for question in questions:\n if questions[question][\"type\"] == \"String\":\n setattr(QuizForm, question, StringField(questions[question][\"text\"], validators=[DataRequired(), Length(max=20)]))\n elif questions[question][\"type\"] == \"Radio\":\n setattr(QuizForm, question, RadioField(questions[question][\"text\"], choices=questions[question][\"choices\"], validators=[DataRequired()]))\n elif questions[question][\"type\"] == \"Integer\":\n setattr(QuizForm, question, IntegerField(questions[question][\"text\"], validators=[DataRequired(), NumberRange(min=0,max=999)]))\n elif questions[question][\"type\"] == \"Checkbox\":\n setattr(QuizForm, question, MultiCheckboxField(questions[question][\"text\"], choices=questions[question][\"choices\"])) \n return QuizForm()","title":""},{"docid":"840d35c7eff8c0af76b87bb90b916fc6","score":"0.55596423","text":"def get_quiz(quiz_type):\n\n # get the questions for the requested quiz and generate the corresponding quiz form\n quiz = Quiz.query.filter_by(name=quiz_type).first()\n questions = quiz.get_questions()\n form = _generate_quiz_form(questions)\n\n name = quiz.name\n completed = False\n results = None\n # retrieve user's attempt at the current quiz\n prev_attempt = UserAnswer.query.filter_by(quiz_id=quiz.id, user_id=current_user.id).first()\n if prev_attempt is not None:\n completed = True\n\n # populate the form with the user's answers\n user_answers = prev_attempt.get_user_answers()\n _populate_form(form, user_answers)\n\n # construct object to be used to display the correct answers\n solutions = quiz.get_solutions()\n results = _construct_solution(form, solutions)\n user_answers = _retrieve_answers(form)\n\n elif form.validate_on_submit():\n # get the user's submitted answers\n user_answers = _retrieve_answers(form)\n\n # get the quiz solution\n solutions = quiz.get_solutions()\n\n # calculate the user's score\n score = _calculate_quiz_score(user_answers, solutions)\n\n # add the user's answers to the db\n completed = True\n results = _construct_solution(form, solutions)\n attempt = UserAnswer(quiz_id=quiz.id, \n user_id=current_user.id,\n ans1=user_answers[\"answer1\"][\"value\"], \n ans2=user_answers[\"answer2\"][\"value\"], \n ans3=user_answers[\"answer3\"][\"value\"], \n ans4=user_answers[\"answer4\"][\"value\"],\n ans5=user_answers[\"answer5\"][\"value\"] if \"answer5\" in user_answers else None,\n ans6=user_answers[\"answer6\"][\"value\"] if \"answer6\" in user_answers else None,\n ans7=user_answers[\"answer7\"][\"value\"] if \"answer7\" in user_answers else None,\n ans8=user_answers[\"answer8\"][\"value\"] if \"answer8\" in user_answers else None,\n score=score\n )\n db.session.add(attempt)\n db.session.commit()\n return render_template('quiz/quiz_template.html', title=\"Quiz - \" + name, heading= name + \" Quiz\", form=form, completed=completed, results=results)","title":""},{"docid":"3effa6537a82fe68dd64d34113856e46","score":"0.55323803","text":"def test_new_quiz():\n\n with app.test_request_context():\n Quiz.max_questions = 2\n Quiz.max_time_in_seconds = 0.1\n\n client = app.test_client()\n data = dict(\n username=\"pietje\",\n action=\"creategame\"\n )\n rv = client.post(url_for('index'), data=data)\n\n with client.session_transaction() as sess:\n user_id = sess['user_id']\n\n quiz = store.get_quiz_by_user_id(user_id)\n question = store.get_question_by_id(quiz.questions[0])\n answer = store.get_answer_by_id(question.answers[0])\n\n assert quiz\n assert question\n assert answer\n assert user_id in quiz.users\n assert rv.status_code == 302","title":""},{"docid":"2386cd474308db8bbde68558023fba84","score":"0.5491061","text":"async def quiz(self, ctx):\n pass","title":""},{"docid":"f6028f5673ab7da0d560f685dd63213d","score":"0.5491058","text":"def test_quizz_get(self):\n res = self.client().post('/quizzes',json={\n 'previous_questions':[],\n 'quiz_category':{'type': 'Science', 'id': '1'}\n })\n # assert if the request status is successful\n self.assertEqual(res.status_code, 200)\n data = json.loads(res.data)\n # assert the payload of the response\n self.assertTrue(data['success'])\n self.assertTrue(data['question'])","title":""},{"docid":"acbbb07e60a32bd602c0402c7467220c","score":"0.5446392","text":"def test_create_a_new_question(self):\n res = self.client().post('/questions', json=self.new_question)\n data = json.loads(res.data)\n\n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertTrue(data['created'])\n self.assertTrue(data['questions'])\n self.assertTrue(data['total_questions'])\n self.assertTrue(data['current_category'])","title":""},{"docid":"f80aa743d59d6fb27d7fa0bb6033f2af","score":"0.5439644","text":"def test_play_quiz_game(self):\n\n # send post request with category and previous questions\n response = self.client().post('/quizzes',\n json={'previous_questions': [9, 5, 12],\n 'quiz_category': {\n 'type': 'History',\n 'id': '4'\n }\n })\n data = json.loads(response.data)\n\n # check response status code.\n self.assertEqual(response.status_code, 201)\n # check success value.\n self.assertEqual(data['success'], True)\n # check for the existance of the questions property in the response.\n self.assertTrue(data['question'])\n\n # Check that the question returned is in the correct category\n self.assertEqual(data['question']['category'], 4)\n\n # Question returned is not in the previous questions list\n self.assertNotEqual(data['question']['id'], 9)\n self.assertNotEqual(data['question']['id'], 5)\n self.assertNotEqual(data['question']['id'], 12)","title":""},{"docid":"156c238257fc9ddf41b052d88f22d773","score":"0.5439635","text":"def testCreateQuizHidden(self):\n\n status = Quiz.HIDDEN_STATUS\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\n quiz.save()\n self.assertEqual(Quiz.objects.filter(status=status).count(), 1)\n self.assertEqual(Quiz.objects.get(slug=self.slug).title, self.title)","title":""},{"docid":"ed57fd558f2f7cc4d9d44a223a344d89","score":"0.54300576","text":"def testCreateQuizDraft(self):\n\n status = Quiz.DRAFT_STATUS\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\n quiz.save()\n self.assertEqual(Quiz.objects.filter(status=status).count(), 1)\n self.assertEqual(Quiz.objects.get(slug=self.slug).title, self.title)","title":""},{"docid":"d12621c26a340eb8604a7bada56b064b","score":"0.5427113","text":"async def musicquiz(self, ctx, category_name=\"\"):\n await ctx.send_help(ctx.command)","title":""},{"docid":"d46aa7b0fac28183a77937c089843305","score":"0.5423655","text":"def post(self, request):\n quiz_id = request.data['quizID']\n quiz = Quiz.objects.get(author=request.user, id=quiz_id)\n\n debug(f'Quiz is: {quiz}')\n\n questions = Question.objects.filter(test=quiz)\n response = {}\n\n for question in questions:\n debug(f'For question: {question}')\n if question.type == 1: # short answer\n answers = Answer.objects.filter(question=question)\n debug(f'Answers are: {answers}')\n cluster_centers = cluster_answers(answers, question.id)\n response[question.id] = cluster_centers\n else:\n print('Skipping question of type MCQ')\n\n return APIResponse(response)","title":""},{"docid":"867ee56bbfe20d0a9b73615ee88d3601","score":"0.5414378","text":"def __init__(self):\n \n self.id = ''\n self.quiz_in_progress = Quiz\n self.attemps = 0\n self.quiz_answer = {}\n self.complete = False\n\n self.quest_set= {}","title":""},{"docid":"43b137833c3ae5de772039cfe547657e","score":"0.5356012","text":"def test_quiz(self):\n # get an intital question\n first_question_get_res = self.client().post('/questions/quiz', json={\n 'previous_questions': [],\n 'quiz_category': {\n 'type': 'click',\n 'id': 0\n }\n })\n\n first_question_data = json.loads(first_question_get_res.data)\n # ensure request was good\n self.assertEqual(first_question_get_res.status_code, 200)\n self.assertEqual(first_question_data['success'], True)\n first_question = first_question_data['question']\n first_answer = first_question['answer']\n\n # get a second question\n second_question_get_res = self.client().post('/questions/quiz', json={\n 'previous_questions': [5, 11],\n 'quiz_category': {\n 'type': 'Sports',\n 'id': 0\n }\n })\n second_question_data = json.loads(second_question_get_res.data)\n # ensure request was good\n self.assertEqual(second_question_get_res.status_code, 200)\n self.assertEqual(second_question_data['success'], True)\n second_question = second_question_data['question']\n second_answer = second_question['answer']\n\n # the first and second questions should be different\n self.assertNotEqual(first_question, second_question)\n # the first and second answers should be different\n self.assertNotEqual(first_answer, second_answer)","title":""},{"docid":"f18646e09fc11ad828884614b43a90e0","score":"0.53457","text":"def test_get_quiz_question_with_success_response(self):\n response = self.client().post(\n '/quizzes',\n content_type='application/json',\n data=json.dumps(\n {\n 'previous_questions': [],\n 'quiz_category': {'id': 0, 'type': 'all'}\n }\n )\n )\n data = json.loads(response.data)\n self.assertEqual(response.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertTrue(data['question'])","title":""},{"docid":"567e6b34c8cbe76d643442c34d440e57","score":"0.5337698","text":"def create(self, validated_data):\n \n objects=Questions.objects.create(question=validated_data.get('question'),types=validated_data.get('types'))\n # print >> sys.stderr, objects\n return objects","title":""},{"docid":"6feaaa0087054c3c7101ce88c2ce7e3f","score":"0.53255945","text":"def create_question_overview_df(self):\n\n questions = self.survey.questions\n\n # create an empty lists to append the columns to\n all_answer_columns = []\n all_question_names = []\n all_question_types = []\n all_question_indices = []\n\n # iterate over all questions, q holds the question ID in form of '1', '2', ...\n for q in questions:\n\n question_code = questions[q]['title']\n question_text = self.clean_text(questions[q]['question'])\n question_type = questions[q]['question_type']\n question_index = questions[q]['qid']\n\n # Process questions according to their type.\n if question_type == \"Multiple choice\" or question_type == \"Multiple choice with comments\" or question_type == \"Array\":\n # iterate over all subquestions (by the format of limepy, the subquestions section always has key '0')\n for s in questions[q]['subquestions']['0']:\n sub_question_code = (s['title'])\n sub_question_text = self.clean_text(s['question'])\n\n merged_code = question_code + \"[\" + sub_question_code + \"]\"\n merged_text = question_text + \" - \" + sub_question_text\n all_answer_columns.append(merged_code)\n all_question_names.append(merged_text)\n all_question_types.append(question_type)\n all_question_indices.append(question_index)\n\n # in Rankin the naming of the \"subquestions\" is slightly different and called \"answer\"\n elif question_type == \"Ranking\":\n # iterate over all possible answers (by the format of limepy,\n # the subquestions section always has key '0')\n for s in questions[q]['answers']['0']:\n sub_question_code = (s['sortorder'])\n sub_question_text = self.clean_text(s['answer'])\n\n merged_code = question_code + \"[\" + sub_question_code + \"]\"\n merged_text = question_text + \" - \" + sub_question_text\n all_answer_columns.append(merged_code)\n all_question_names.append(merged_text)\n all_question_types.append(question_type)\n all_question_indices.append(question_index)\n\n # there is no subtype to be handled, like in normal text\n # and the data can be appended directly\n else:\n all_answer_columns.append(question_code)\n all_question_names.append(question_text)\n all_question_types.append(question_type)\n all_question_indices.append(question_index)\n\n # Make the dataframe with the Question code as a header and text and type as rows\n survey_answers_df = pd.DataFrame(list(zip(all_question_names, all_question_types, all_question_indices)),\n index=all_answer_columns,\n columns=['Question', 'Question Type', 'Lime Index']).T\n\n return survey_answers_df","title":""},{"docid":"3ee0b733bca7e9436be3e900999a0926","score":"0.53237695","text":"def test_question_creation(self):\n response = self.client().post('/api/v1/questions',\n data=json.dumps(self.question),\n content_type='application/json')\n\n self.assertEqual(response.status_code, 201)\n self.assertIn('restful API', str(response.data))","title":""},{"docid":"3420874bf3668054a29b982efca80b9f","score":"0.53224343","text":"def sample_quizz(**params):\n defaults = {\n 'title': 'Boomer WW II Quizz',\n 'description': 'Are You A World War II Whiz?'\n }\n defaults.update(params)\n\n return Quizz.objects.create(**defaults)","title":""},{"docid":"d1141d6b678a7f0682ea16f78f420b59","score":"0.5297418","text":"def test_success_response_for_playing_quiz_game(self):\n response = self.client().post(\n '/api/v1/quizzes',\n content_type='application/json',\n data=json.dumps(\n {\n 'previous_questions': [],\n 'quiz_category': {'id': 0, 'type': 'all'}}\n )\n )\n data = json.loads(response.data)\n self.assertEqual(response.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertTrue(data['question'])","title":""},{"docid":"0c86b392d22faae6bc00314089b11aa2","score":"0.52733284","text":"def create_answer(self, answer_form):\n return # osid.assessment.Answer","title":""},{"docid":"1340c6471409ed03ab6e399234bbfe10","score":"0.5269123","text":"def create(cls, question_text):\n q = cls(\n question_text=question_text,\n question_type=cls.__name__,\n options=None\n )\n return q","title":""},{"docid":"a5aa8a1d746b54e163ca22085a4c8639","score":"0.52504885","text":"def setUp(self):\n self.question1 = Question(\n level=\"walking\",\n number=\"1\",\n question_title=\"Is it a good idea to walk?\",\n right_answer=\"Yes\",\n wrong_answer1=\"No\",\n wrong_answer2=\"No\",\n wrong_answer3=\"No\"\n )\n self.question1.save()\n\n self.question2 = Question(\n level=\"walking\",\n number=\"2\",\n question_title=\"Is it a good idea to run?\",\n right_answer=\"Yes\",\n wrong_answer1=\"No\",\n wrong_answer2=\"No\",\n wrong_answer3=\"No\"\n )\n self.question2.save()","title":""},{"docid":"fb0324383fd26fcebad4fd86c0fc56d4","score":"0.5235127","text":"def choice_quiz(self, word_list_name: str, quiz_strategy: str) -> (bool, Question, Flashcard):\r\n\r\n # Pick 5 expressions, get flashcards and alternatives\r\n learning_progress_dict: Dict[int, str] = _get_learning_progress(self._get_word_list(word_list_name))\r\n row_keys_new = pick_words(learning_progress_dict=learning_progress_dict,\r\n filter_by_progress=lambda p: p == Progress.NEW,\r\n order=PickOrder.ORIGINAL,\r\n max_count_from_size=lambda v: 5)\r\n\r\n row_keys_recent = pick_words(learning_progress_dict=learning_progress_dict,\r\n filter_by_progress=lambda p: p == Progress.RECENT,\r\n order=PickOrder.SHUFFLED,\r\n max_count_from_size=lambda v: 5)\r\n\r\n row_keys_learned = pick_words(learning_progress_dict=learning_progress_dict,\r\n filter_by_progress=lambda p: p == Progress.LEARNED,\r\n order=PickOrder.SHUFFLED,\r\n max_count_from_size=lambda size: 3 if size > 10 else 0)\r\n\r\n flashcards_only = [_build_quiz(word_list=self._get_word_list(word_list_name),\r\n row_key=row_key,\r\n alternatives_pool=None,\r\n flashcard_only=True) for row_key in row_keys_new]\r\n new_questions = [_build_quiz(word_list=self._get_word_list(word_list_name),\r\n row_key=row_key,\r\n alternatives_pool=self.word_pool_lang1,\r\n flashcard_only=False) for row_key in row_keys_new]\r\n random.shuffle(new_questions)\r\n\r\n recent_questions = [_build_quiz(word_list=self._get_word_list(word_list_name),\r\n row_key=row_key,\r\n alternatives_pool=self.word_pool_lang1,\r\n flashcard_only=False) for row_key in row_keys_recent]\r\n random.shuffle(recent_questions)\r\n\r\n learned_questions = [_build_quiz(word_list=self._get_word_list(word_list_name),\r\n row_key=row_key,\r\n alternatives_pool=self.word_pool_lang1,\r\n flashcard_only=False) for row_key in row_keys_learned]\r\n random.shuffle(learned_questions)\r\n\r\n quiz_packages = flashcards_only + new_questions + recent_questions + learned_questions\r\n\r\n return quiz_packages","title":""},{"docid":"fa46fd7262daef40b1c117bcf2ec823a","score":"0.5234181","text":"def test_one_form_valid(self):\n response_post = self.client.post(\n reverse('dash:create_question_choice', args=[self.quiz.pk]),\n {\n 'question_text': 'question text',\n 'form-TOTAL_FORMS': 4,\n 'form-INITIAL_FORMS': 0,\n 'form-MAX_NUM_FORMS': 4,\n 'form-MIN_NUM_FORMS': 0,\n 'finish': True,\n }\n )\n self.assertEqual(response_post.status_code, 200)\n self.assertTemplateUsed(response_post, 'dashboard/create_quiz.html')","title":""},{"docid":"6c6aa998d780e102d9b6ac422b87f362","score":"0.5226121","text":"def __init__(self, name):\n super(Quiz, self).__init__(name)","title":""},{"docid":"346151a5950b381268226648f9024e81","score":"0.5198816","text":"def test_create_new_question(self):\n\n # Total questions before posting a new question\n questions_before = Question.query.all()\n\n # create new question and load response data\n response = self.client().post('/questions', json=self.new_question)\n data = json.loads(response.data)\n\n # Total questions after posting a new question\n questions_after = Question.query.all()\n\n # check status code and success message\n self.assertEqual(response.status_code, 201)\n self.assertEqual(data['success'], True)\n\n # Check difference in total question after posting\n self.assertTrue(len(questions_after) - len(questions_before) == 1)\n\n # check that question is not None\n question = Question.query.get(data['question_id'])\n self.assertIsNotNone(question)","title":""},{"docid":"e7adc50cbddbb942a5f74677742abaef","score":"0.51843405","text":"def test_successfully_adding_a_question_to_database(self):\n response = self.client().post(\n '/api/v1/questions',\n content_type='application/json', data=json.dumps(\n {\n 'question': 'Is computer Science good?',\n 'answer': 'Yes its good',\n 'difficulty': 2,\n 'category': 2\n }\n )\n )\n data = json.loads(response.data)\n self.assertTrue(response.status_code, 201)\n self.assertEqual(data['success'], True)\n self.assertEqual(data['message'], 'Question successfully added')","title":""},{"docid":"6bc465780b2fc2962e44cda029687b38","score":"0.5180577","text":"def test_get_quiz(self):\n quiz = {'previous_questions': [], 'quiz_category': {'type': 'History', 'id': 5}}\n res = self.client().post('/quizzes', json=quiz)\n data = json.loads(res.data)\n\n \n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)","title":""},{"docid":"11d8ad95a4d2c705856bf79cd388cdca","score":"0.5169148","text":"def test_create_a_question_with_success_response(self):\n response = self.client().post(\n '/questions',\n content_type='application/json',\n data=json.dumps(\n {\n 'question': 'Test question',\n 'answer': 'Test answer',\n 'difficulty': 2,\n 'category': 2\n }\n )\n )\n data = json.loads(response.data)\n self.assertTrue(response.status_code, 201)\n self.assertEqual(data['success'], True)\n self.assertEqual(data['message'], 'Question successfully added')","title":""},{"docid":"8adc13d2fbc71dadd7c592327c9d1ef9","score":"0.5167067","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgresql://{}:{}@{}/{}\".format(\n 'postgres', 'root', 'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n self.new_question = {\n \"answer\": \"Apollo 10\",\n \"category\": 5,\n \"difficulty\": 3,\n \"id\": 8,\n \"question\": \"What movie earned Tom Hanks his third straight Oscar nomination, in 1896?\"\n }\n self.empty_question = {\n 'question': '',\n 'answer': '',\n 'difficulty': 0,\n 'category': 0\n }\n\n self.search_term = {\n \"searchTerm\": 'Who discovered penicillin?'\n }\n\n self.empty_search_term = {\n \"searchTerm\": ''\n }\n\n self.invalid_search_term = {\n \"searchTerm\": 'askdjasdkjdiqe871623876azd'\n }\n\n self.quiz_data = {\n 'previous_questions': [3, 7],\n 'quiz_category': {\n 'type': 'Science',\n 'id': 1\n }\n }\n\n self.empty_quiz_data = {\n 'previous_questions': '',\n 'quiz_category': ''\n }\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"c722087e8a75f007ff40c54c77458a37","score":"0.51610744","text":"def test_play_quiz_2(self):\r\n res = self.client().post('/api/quizzes', json={'previous_questions':[10], 'quiz_category': {'type':'Sports', 'id':'6'}})\r\n data = json.loads(res.data)\r\n self.assertEqual(data['success'], True)\r\n self.assertEqual(data['question']['id'], 11)","title":""},{"docid":"3b1a761e1515cdcc1aa12bdbb6e4509f","score":"0.51568455","text":"def test_question_by_category(self):\n assert Category.query.count() == 2\n cat = self.science\n resp = self.client.get(f'/api/categories/{cat.id}/questions')\n assert resp.status_code == 200\n\n data = json.loads(resp.data)\n\n assert 'current_category' in data\n assert data['current_category'] == cat.id\n\n assert 'questions' in data\n assert data['questions'][0]['category'] == cat.id\n\n # Check response shape\n assert data == {\n 'current_category': 1,\n 'questions': [\n {\n 'answer': 'Nobody knows',\n 'category': 1,\n 'difficulty': 5,\n 'id': 1,\n 'question': 'What is science?'\n }\n ],\n 'total_questions': 1\n }","title":""},{"docid":"b73032f2f582dce21fbc2f465e478b38","score":"0.5155747","text":"def create_question(cls, data, question_number):\n question = data['questions'][question_number]\n elements = '<div class=\"nhsuk-form-group\">'\n elements += '<fieldset class=\"nhsuk-fieldset\">'\n elements += '<legend class=\"nhsuk-fieldset__legend nhsuk-fieldset__legend--l\">'\n elements += '<h1 class=\"nhsuk-fieldset__heading\">'\n elements += question['body']\n elements += '</h1>'\n elements += '</legend>'\n if question['info_box']:\n elements += cls.create_info_box(data, question['info_box'])\n if question['type'] == 'single select':\n elements += cls.create_single_select(question)\n elif question['type'] == 'multiple select':\n elements += cls.create_multi_select(question)\n elements += '</fieldset>'\n elements += '</div>'\n return elements","title":""},{"docid":"14b094e4e02da416fb07bbc92c7e0cc9","score":"0.5146169","text":"def _ask_questions(**kwargs) -> Tuple[dict, dict]:\n\n choices = dict(\n import_data=\"Import data\",\n register_rule=\"Configure and register rule »\",\n add_procedure=\"Add procedure »\",\n preview=\"Generate preview\",\n check=\"Check for BIDS conformity\",\n cleanup=\"Cleanup\",\n\n rule_create=\"Create new rule\",\n rule_import=\"Import rule\",\n\n proc_change=\"Change active procedures\",\n proc_create=\"Create new procedure\",\n proc_import=\"Import procedure\",\n )\n\n questions = [\n {\n \"type\": \"select\",\n \"name\": \"step_select\",\n \"message\": \"What do you want to do?\",\n \"choices\": [\n # Sets up a datalad dataset and prepares the convesion\n choices[\"import_data\"],\n # Registers and applies datalad hirni rule\n choices[\"register_rule\"],\n # Create, import or register procedures\n choices[\"add_procedure\"],\n # Generates the BIDS converion\n choices[\"preview\"],\n choices[\"check\"],\n # Remove imported and converted\n choices[\"cleanup\"],\n questionary.Separator(),\n \"Help\",\n \"Exit\"\n ],\n \"use_shortcuts\": True,\n \"default\": \"Exit\",\n\n },\n {\n \"type\": \"path\",\n \"name\": \"data_path\",\n \"message\": \"Path to the data tar ball:\",\n \"when\": lambda x: x[\"step_select\"] == choices[\"import_data\"],\n },\n {\n \"type\": \"select\",\n \"name\": \"rule_select\",\n \"message\": \"What do you want to do?\",\n \"when\": lambda x: x[\"step_select\"] == choices[\"register_rule\"],\n \"choices\": [\n choices[\"rule_create\"],\n choices[\"rule_import\"],\n questionary.Separator(),\n \"Return\"\n ],\n \"use_shortcuts\": True,\n \"default\": \"Return\",\n },\n {\n \"type\": \"path\",\n \"name\": \"rule_file\",\n \"message\": \"Path to the rule:\",\n \"when\": (lambda x: x[\"step_select\"] == choices[\"register_rule\"]\n and x[\"rule_select\"] == choices[\"rule_import\"]),\n },\n {\n \"type\": \"select\",\n \"name\": \"procedure_select\",\n \"message\": \"What do you want to do?\",\n \"when\": lambda x: x[\"step_select\"] == choices[\"add_procedure\"],\n \"choices\": [\n choices[\"proc_change\"],\n choices[\"proc_create\"],\n choices[\"proc_import\"],\n questionary.Separator(),\n \"Return\",\n ],\n \"use_shortcuts\": True,\n \"default\": \"Return\",\n },\n {\n \"type\": \"select\",\n \"name\": \"procedure_type\",\n \"message\": \"What type of procedure do you want to create?\",\n \"when\": (lambda x: x[\"step_select\"] == choices[\"add_procedure\"]\n and x[\"procedure_select\"] == choices[\"proc_create\"]),\n \"choices\": [\n \"shell\",\n \"python\",\n questionary.Separator(),\n \"Return\",\n ],\n \"use_shortcuts\": True,\n \"default\": \"Return\",\n },\n {\n \"type\": \"text\",\n \"name\": \"procedure_name\",\n \"message\": \"How should the procedure be called?\",\n \"when\": (lambda x: x[\"step_select\"] == choices[\"add_procedure\"]\n and x[\"procedure_select\"] == choices[\"proc_create\"]\n and x[\"procedure_type\"] != \"Return\"),\n },\n {\n \"type\": \"path\",\n \"name\": \"procedure_file\",\n \"message\": \"Path to the procedure:\",\n \"when\": (lambda x: x[\"step_select\"] == choices[\"add_procedure\"]\n and x[\"procedure_select\"] == choices[\"proc_import\"]),\n },\n {\n \"type\": \"text\",\n \"name\": \"procedure_file_name\",\n \"message\": \"Name of the procedure:\",\n \"when\": (lambda x: x[\"step_select\"] == choices[\"add_procedure\"]\n and x[\"procedure_select\"] == choices[\"proc_import\"]\n and x[\"procedure_file\"]),\n \"default\": lambda x: Path(x[\"procedure_file\"]).stem\n },\n ]\n return questionary.prompt(questions, **kwargs), choices","title":""},{"docid":"9ce1fccb1fa8c8bafb62b4ff93a30f59","score":"0.5141362","text":"def test_create_answer(self):\n question_serializer = serializers.QuestionSerializer(self.question, context=serializer_context)\n payload = {\n 'question': question_serializer.data['url'],\n 'text': 'Test text 2',\n 'is_correct': True\n }\n\n res = self.client.post(ANSWER_URL, payload)\n\n answer = models.Answer.objects.get(id=res.data['id'])\n answer_serializer = serializers.AnswerSerializer(answer, context=serializer_context)\n\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n test_all_model_attributes(self, payload, answer, answer_serializer)","title":""},{"docid":"7f6ff8d2dcc58aea039dc73babfd8aff","score":"0.5140274","text":"def test_question_creation(self):\n self.register_user()\n user= self.login_user()\n Access_token=json.loads(user.data.decode())['access_token']\n res= self.client().post(\n '/questions/',\n headers=dict(Authorization=\"Bearer \"+Access_token),\n data= self.question\n )","title":""},{"docid":"0a9b41c76d0aee771876f3dcb4f13a52","score":"0.51293486","text":"def create(cls, question_text, options):\n q = cls(\n question_text=question_text,\n question_type=cls.__name__,\n options=options.__str__()\n )\n return q","title":""},{"docid":"0a9b41c76d0aee771876f3dcb4f13a52","score":"0.51293486","text":"def create(cls, question_text, options):\n q = cls(\n question_text=question_text,\n question_type=cls.__name__,\n options=options.__str__()\n )\n return q","title":""},{"docid":"0a9b41c76d0aee771876f3dcb4f13a52","score":"0.51293486","text":"def create(cls, question_text, options):\n q = cls(\n question_text=question_text,\n question_type=cls.__name__,\n options=options.__str__()\n )\n return q","title":""},{"docid":"af8be9f552699e1a54c294c91f256cef","score":"0.51272994","text":"def load_quizzes():\n quiz_list = [\n 'drum_fill_friday.json'\n ]\n\n for quiz in quiz_list:\n with open('www/assets/data/%s' % quiz, 'rb') as readfile:\n quiz_json = dict(json.loads(readfile.read()))\n\n quiz = {\n 'category': \"Drum Fill Friday\",\n 'title': quiz_json['title'],\n 'text': 'TKTK',\n 'photo': None,\n 'seamus_url': '',\n 'author': 'Bob Boilen'\n }\n\n # Create photo\n if quiz_json['photo']:\n quiz['photo'] = _create_photo(quiz_json['photo'])\n\n # Create quiz\n qz = models.Quiz(**quiz)\n qz.save()\n\n print \"Saved quiz: %s\" % qz\n\n for question_index, question_json in enumerate(quiz_json['questions']):\n question = {\n 'order': question_index,\n 'quiz': qz,\n 'text': question_json['text'],\n 'photo': None,\n 'audio': None\n }\n\n # Create photo\n if question_json['photo']:\n question['photo'] = _create_photo(question_json['photo'])\n\n # Create audio\n if question_json['audio']:\n question['audio'] = _create_audio(question_json['audio'])\n\n # Create question\n qn = models.Question(**question)\n qn.save()\n\n print \"Saved question: %s\" % qn\n\n for choice_index, choice_json in enumerate(question_json['choices']):\n choice = {\n 'order': choice_index,\n 'question': qn,\n 'text': choice_json['text'],\n 'correct_answer': False,\n 'photo': None,\n 'audio': None,\n }\n\n if choice_index == question_json['answer']:\n choice['correct_answer'] = True\n\n # Create photo\n if choice_json['photo']:\n choice['photo'] = _create_photo(choice_json['photo'])\n\n # Create audio\n if choice_json['audio']:\n choice['audio'] = _create_audio(choice_json['audio'])\n\n # Create choice\n ch = models.Choice(**choice)\n ch.save()\n\n print 'Saved choice: %s' % ch\n\n qz.deploy()\n print 'Deployed quiz: %s' % qz","title":""},{"docid":"6fcf0e9a4843e5c2997df0bbfaebcd91","score":"0.51129526","text":"def sample_answer(question, **kwargs):\n defaults = {'text': 'some text'}\n defaults.update(kwargs)\n return models.Answer.objects.create(question=question, **defaults)","title":""},{"docid":"8cc53fb1dda1386fb965a9fa321d8083","score":"0.5108282","text":"def test_endpoint_create_question_creates_new_question_and_returns_created_question(self):\n res = self.client.post('api/v1/questions',\n data = json.dumps({ \n \"createdBy\": 1,\n \"meetup\": 1,\n \"title\": 'Test title',\n \"body\": \"Swali langu ni je\",\n \"votes\": 0\n }),\n content_type='application/json'\n ) \n expected_output = {\n \"status\": 201,\n \"data\": [\n {\n \"user\": 1, # user who posted the question\n \"meetup\": 1, # meetup record primary key\n \"title\": 'Test title',\n \"body\": \"Swali langu ni je\"\n }\n ]\n }\n \n self.assertEqual(res.json['status'], expected_output['status'])\n self.assertTrue(all(item in res.json['data'][0].items() for item in expected_output['data'][0].items()))\n self.assertEqual(res.status_code, 201)","title":""},{"docid":"186a932951c622cafc720fd4b74cdfa9","score":"0.50935435","text":"def quiz():\n data = request.get_json()\n prev_qs = list(data[\"previous_questions\"])\n quiz_category = int(data[\"quiz_category\"][\"id\"])\n\n if quiz_category:\n\n if not Category.query.get(quiz_category):\n abort(404)\n get_questions = Question.query.filter(\n Question.category == quiz_category,\n Question.id.notin_(prev_qs)).all()\n else:\n get_questions = Question.query.filter(\n Question.id.notin_(prev_qs)).all()\n\n if len(get_questions) == 0:\n return jsonify(None)\n else:\n questions = list(map(Question.format, get_questions))\n question = random.choice(questions)\n return jsonify({\n \"success\": True,\n **question\n })","title":""},{"docid":"7c0f5049c46e4fea0871b4e46de078d4","score":"0.5093345","text":"def test_play_quiz_3(self):\r\n res = self.client().post('/api/quizzes', json={'previous_questions':[10, 11], 'quiz_category': {'type':'Sports', 'id':'6'}})\r\n data = json.loads(res.data)\r\n self.assertEqual(data['success'], True)\r\n self.assertEqual('question' not in data, True)","title":""},{"docid":"54639854db0fbff7c8567eb8668c3499","score":"0.507514","text":"def test_play_quiz_success(self):\n data = {\n \"quiz_category\": {\n \"id\": 1\n },\n \"previous_questions\": []\n }\n response = self.client().post(\n '/quizzes', json=data, headers=self.player_headers\n )\n json_data = response.get_json()\n self.assertEqual(response.status_code, StatusCode.HTTP_200_OK.value)\n self.assertTrue(json_data.get('success'))","title":""},{"docid":"3e370b2c08eb0f56e0e383723c18031d","score":"0.5072601","text":"def create(self, validated_data):\n # La validación de que si el formulario está activo debe hacerse fuera de este serializador\n form_participant = self.context['form_participant']\n question = self.context['question']\n validated_data.pop('queestion_id')\n answer = AnswerQuestion.objects.create(\n question=question,\n form_participant=form_participant,\n **validated_data\n )\n return answer","title":""},{"docid":"cade0012ab39295b2ce0664afe932077","score":"0.5064219","text":"def trivia(self, ctx, questions: int=10):\n if questions <= 2:\n # At least 3 questions\n yield from self.bot.say(\":warning: Too little questions!\")\n return\n # TODO: implement a scoreboard to keep track of the winner\n scoreboard = {}\n for i in range(questions):\n r = requests.get('https://opentdb.com/api.php?amount=1&type=multiple')\n q = r.json()\n q = q['results'][0]\n r.close()\n question, category, answer = (unescape(q['question']), unescape(q['category']), unescape(q['correct_answer']))\n print(question, answer)\n yield from self.bot.say(\"**Category: **%s\\n**Question: **%s\" % (category, question))\n\n def check(msg):\n if msg.content.lower() == answer.lower():\n return True\n return False\n\n response = yield from self.bot.wait_for_message(timeout=4.0, check=check)\n if response != None:\n yield from self.bot.say(\"%s is correct!\" % response.author.mention)\n continue\n\n clue = ''.join('?' if random.randint(0,3) and i!=' ' else i for i in answer)\n yield from self.bot.say(\"`Clue: %s`\" % clue)\n\n response = yield from self.bot.wait_for_message(timeout=20.0, check=check)\n if response != None:\n yield from self.bot.say(\"%s is correct!\" % response.author.mention)\n else:\n yield from self.bot.say(\"Time's up!\\n**Answer: **%s\" % answer)","title":""},{"docid":"eb1342660eb6b3571ff6d18a41c74719","score":"0.5054026","text":"def _create_qa_features(\n example,\n model_type,\n tokenizer,\n unique_id,\n is_training,\n max_question_length,\n max_seq_length,\n doc_stride,\n custom_tokenize=None,\n):\n\n # _QAFeatures is data structure representing features of an unique document\n # span-question-answer triplet.\n # Args:\n # unique_id (int): An unique id identifying the span-question-answer triplet.\n # qa_id (int or str): An unique id identifying the document-question-answer\n # sample in the original :class:`utils_nlp.dataset.pytorch.QADataset`\n # tokens (list): Concatenated question tokens and paragraph tokens.\n # token_to_orig_map (dict): A dictionary mapping token indices in the\n # document span back to the token indices in the original document\n # before document splitting.\n # This is needed during post-processing to generate the final\n # predicted answer.\n # token_is_max_context (list): List of booleans indicating whether a\n # token has the maximum context in teh current document span if it\n # appears in multiple document spans and gets multiple predicted\n # scores. We only want to consider the score with \"maximum context\".\n # \"Maximum context\" is defined as the *minimum* of its left and\n # right context.\n # For example:\n # Doc: the man went to the store and bought a gallon of milk\n # Span A: the man went to the\n # Span B: to the store and bought\n # Span C: and bought a gallon of\n\n # In the example the maximum context for 'bought' would be span C\n # since it has 1 left context and 3 right context, while span B\n # has 4 left context and 0 right context.\n # This is needed during post-processing to generate the final\n # predicted answer.\n # input_ids (list): List of numerical token indices corresponding to\n # the tokens.\n # input_mask (list): List of 1s and 0s indicating if a token is from\n # the input data or padded to conform to the maximum sequence\n # length. 1 for actual token and 0 for padded token.\n # segment_ids (list): List of 0s and 1s indicating if a token is from\n # the question text (0) or paragraph text (1).\n # start_position (int): Index of the starting token of the answer span.\n # end_position (int): Index of the ending token of the answer span.\n # cls_index (int): Index of the CLS token.\n # p_mask (list): Mask with 1 for token than cannot be in the answer,\n # 0 for token which can be in an answer.\n # paragraph_len(int): Number of tokens in the document span.\n _QAFeatures = collections.namedtuple(\n \"_QAFeatures\",\n [\n \"unique_id\",\n \"qa_id\",\n \"tokens\",\n \"token_to_orig_map\",\n \"token_is_max_context\",\n \"input_ids\",\n \"input_mask\",\n \"segment_ids\",\n \"start_position\",\n \"end_position\",\n \"cls_index\",\n \"p_mask\",\n \"paragraph_len\",\n ],\n )\n\n if custom_tokenize:\n tokenize_func = custom_tokenize\n else:\n tokenize_func = tokenizer.tokenize\n\n def _improve_answer_span(doc_tokens, input_start, input_end, orig_answer_text):\n \"\"\"Returns tokenized answer spans that better match the annotated answer.\"\"\"\n\n # We first project character-based annotations to\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\n # often find a \"better match\". For example:\n #\n # Question: What year was John Smith born?\n # Context: The leader was John Smith (1895-1943).\n # Answer: 1895\n #\n # The original whitespace-tokenized answer will be \"(1895-1943).\". However\n # after tokenization, our tokens will be \"( 1895 - 1943 ) .\". So we can match\n # the exact answer, 1895.\n #\n # However, this is not always possible. Consider the following:\n #\n # Question: What country is the top exporter of electornics?\n # Context: The Japanese electronics industry is the lagest in the world.\n # Answer: Japan\n #\n # In this case, the annotator chose \"Japan\" as a character sub-span of\n # the word \"Japanese\". Since our WordPiece tokenizer does not split\n # \"Japanese\", we just use \"Japanese\" as the annotation. This is fairly rare,\n # but does happen.\n tok_answer_text = \" \".join(tokenize_func(orig_answer_text))\n\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start : (new_end + 1)])\n if text_span == tok_answer_text:\n return (new_start, new_end)\n\n return (input_start, input_end)\n\n def _check_is_max_context(doc_spans, cur_span_index, position):\n \"\"\"Check if this is the 'max context' doc span for the token.\"\"\"\n\n # Because of the sliding window approach taken to scoring documents, a single\n # token can appear in multiple documents. E.g.\n # Doc: the man went to the store and bought a gallon of milk\n # Span A: the man went to the\n # Span B: to the store and bought\n # Span C: and bought a gallon of\n # ...\n #\n # Now the word 'bought' will have two scores from spans B and C. We only\n # want to consider the score with \"maximum context\", which we define as\n # the *minimum* of its left and right context (the *sum* of left and\n # right context will always be the same, of course).\n #\n # In the example the maximum context for 'bought' would be span C since\n # it has 1 left context and 3 right context, while span B has 4 left context\n # and 0 right context.\n best_score = None\n best_span_index = None\n for (span_index, doc_span) in enumerate(doc_spans):\n end = doc_span.start + doc_span.length - 1\n if position < doc_span.start:\n continue\n if position > end:\n continue\n num_left_context = position - doc_span.start\n num_right_context = end - position\n score = min(num_left_context, num_right_context) + 0.01 * doc_span.length\n if best_score is None or score > best_score:\n best_score = score\n best_span_index = span_index\n\n return cur_span_index == best_span_index\n\n cls_token = \"[CLS]\"\n sep_token = \"[SEP]\"\n pad_token = 0\n sequence_a_segment_id = 0\n sequence_b_segment_id = 1\n mask_padding_with_zero = True\n\n if model_type == \"xlnet\":\n cls_token_segment_id = 2\n # Should this be 4, or it doesn't matter?\n pad_token_segment_id = 3\n cls_token_at_end = True\n else:\n cls_token_segment_id = 0\n pad_token_segment_id = 0\n cls_token_at_end = False\n\n qa_features = []\n\n # unique_id identified unique feature/label pairs. It's different\n # from qa_id in that each qa_example can be broken down into\n # multiple feature samples if the paragraph length is longer than\n # maximum sequence length allowed\n query_tokens = tokenize_func(example.question_text)\n\n if len(query_tokens) > max_question_length:\n query_tokens = query_tokens[0:max_question_length]\n # map word-piece tokens to original tokens\n tok_to_orig_index = []\n # map original tokens to corresponding word-piece tokens\n orig_to_tok_index = []\n all_doc_tokens = []\n for (i, token) in enumerate(example.doc_tokens):\n orig_to_tok_index.append(len(all_doc_tokens))\n sub_tokens = tokenize_func(token)\n for sub_token in sub_tokens:\n tok_to_orig_index.append(i)\n all_doc_tokens.append(sub_token)\n\n tok_start_position = None\n tok_end_position = None\n if is_training and example.is_impossible:\n tok_start_position = -1\n tok_end_position = -1\n if is_training and not example.is_impossible:\n tok_start_position = orig_to_tok_index[example.start_position]\n if example.end_position < len(example.doc_tokens) - 1:\n # +1: move the the token after the ending token in\n # original tokens\n # -1, moves one step back\n # these two operations ensures word piece is covered\n # when it's part of the original ending token.\n tok_end_position = orig_to_tok_index[example.end_position + 1] - 1\n else:\n tok_end_position = len(all_doc_tokens) - 1\n (tok_start_position, tok_end_position) = _improve_answer_span(\n all_doc_tokens,\n tok_start_position,\n tok_end_position,\n example.orig_answer_text,\n )\n\n # The -3 accounts for [CLS], [SEP] and [SEP]\n max_tokens_for_doc = max_seq_length - len(query_tokens) - 3\n\n # We can have documents that are longer than the maximum sequence length.\n # To deal with this we do a sliding window approach, where we take chunks\n # of the up to our max length with a stride of `doc_stride`.\n _DocSpan = collections.namedtuple(\"DocSpan\", [\"start\", \"length\"])\n doc_spans = []\n start_offset = 0\n while start_offset < len(all_doc_tokens):\n length = len(all_doc_tokens) - start_offset\n if length > max_tokens_for_doc:\n length = max_tokens_for_doc\n doc_spans.append(_DocSpan(start=start_offset, length=length))\n if start_offset + length == len(all_doc_tokens):\n break\n start_offset += min(length, doc_stride)\n\n for (doc_span_index, doc_span) in enumerate(doc_spans):\n if is_training:\n unique_id += 1\n else:\n unique_id += 2\n\n tokens = []\n token_to_orig_map = {}\n token_is_max_context = {}\n segment_ids = []\n\n # p_mask: mask with 1 for token than cannot be in the answer\n # (0 for token which can be in an answer)\n # Original TF implem also keep the classification token (set to 0)\n # (not sure why...)\n # TODO: Should we set p_mask = 1 for cls token?\n p_mask = []\n\n # CLS token at the beginning\n if not cls_token_at_end:\n tokens.append(cls_token)\n segment_ids.append(cls_token_segment_id)\n p_mask.append(0)\n cls_index = 0\n\n # XLNet: P SEP Q SEP CLS\n # Others: CLS Q SEP P SEP\n if model_type != \"xlnet\":\n # Query\n tokens += query_tokens\n segment_ids += [sequence_a_segment_id] * len(query_tokens)\n p_mask += [1] * len(query_tokens)\n\n # SEP token\n tokens.append(sep_token)\n segment_ids.append(sequence_a_segment_id)\n p_mask.append(1)\n\n # Paragraph\n for i in range(doc_span.length):\n split_token_index = doc_span.start + i\n token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]\n\n # TODO: maybe this can be improved to compute\n # is_max_context for each token only once.\n is_max_context = _check_is_max_context(\n doc_spans, doc_span_index, split_token_index\n )\n token_is_max_context[len(tokens)] = is_max_context\n tokens.append(all_doc_tokens[split_token_index])\n if model_type == \"xlnet\":\n segment_ids.append(sequence_a_segment_id)\n else:\n segment_ids.append(sequence_b_segment_id)\n p_mask.append(0)\n paragraph_len = doc_span.length\n\n if model_type == \"xlnet\":\n # SEP token\n tokens.append(sep_token)\n segment_ids.append(sequence_a_segment_id)\n p_mask.append(1)\n\n tokens += query_tokens\n segment_ids += [sequence_b_segment_id] * len(query_tokens)\n p_mask += [1] * len(query_tokens)\n\n # SEP token\n tokens.append(sep_token)\n segment_ids.append(sequence_b_segment_id)\n p_mask.append(1)\n\n # CLS token at the end\n if cls_token_at_end:\n tokens.append(cls_token)\n segment_ids.append(cls_token_segment_id)\n p_mask.append(0)\n cls_index = len(tokens) - 1 # Index of classification token\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n if len(input_ids) < max_seq_length:\n pad_token_length = max_seq_length - len(input_ids)\n pad_mask = 0 if mask_padding_with_zero else 1\n input_ids += [pad_token] * pad_token_length\n input_mask += [pad_mask] * pad_token_length\n segment_ids += [pad_token_segment_id] * pad_token_length\n p_mask += [1] * pad_token_length\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n\n span_is_impossible = example.is_impossible\n start_position = None\n end_position = None\n if is_training and not span_is_impossible:\n # For training, if our document chunk does not contain an annotation\n # we throw it out, since there is nothing to predict.\n doc_start = doc_span.start\n doc_end = doc_span.start + doc_span.length - 1\n out_of_span = False\n if not (tok_start_position >= doc_start and tok_end_position <= doc_end):\n out_of_span = True\n if out_of_span:\n start_position = 0\n end_position = 0\n span_is_impossible = True\n else:\n # +1 for [CLS] token\n # +1 for [SEP] token\n if model_type == \"xlnet\":\n doc_offset = 0\n else:\n doc_offset = len(query_tokens) + 2\n start_position = tok_start_position - doc_start + doc_offset\n end_position = tok_end_position - doc_start + doc_offset\n\n if is_training and span_is_impossible:\n start_position = cls_index\n end_position = cls_index\n\n qa_features.append(\n _QAFeatures(\n unique_id=unique_id,\n qa_id=example.qa_id,\n tokens=tokens,\n token_to_orig_map=token_to_orig_map,\n token_is_max_context=token_is_max_context,\n input_ids=input_ids,\n input_mask=input_mask,\n segment_ids=segment_ids,\n start_position=start_position,\n end_position=end_position,\n cls_index=cls_index,\n p_mask=p_mask,\n paragraph_len=paragraph_len,\n )\n )\n\n return qa_features","title":""},{"docid":"9425ee916d9d40c4a3b4421df40a2a4b","score":"0.5049735","text":"def create_questionnaire():\n pre = request.json['pre']\n post = request.json['post']\n\n new_questionnaire = Questionnaire(pre=pre, post=post)\n\n db.session.add(new_questionnaire)\n db.session.commit()\n\n result = questionnaire_schema.dump(new_questionnaire).data\n\n return jsonify({'status': 'success', 'message': 'Created new questionnaire', 'data': result}), 201","title":""},{"docid":"0d4bf1b60646b32e23cec6c0af7aaea6","score":"0.5047603","text":"def create_poll(question, days):\n pass","title":""},{"docid":"21ae3a7f0dba382e237f5e43531ea06c","score":"0.5042114","text":"def test_create_quizes(self):\n\n driver = self.driver\n wait = self.wait\n\n # Test data:\n number = randint(100,1000)\n quiz_name = \"QA BASIC {}\".format(number)\n textual_question_1 = \"What is Software Testing?\"\n textual_question_2 = \"What is Software Quality Assurance?\"\n textual_question_3 = \"Explain SDLC methodology?\"\n\n\n # 1. Login with tichers role\n\n email_teacher = 'alina.korolevich@yopmail.com'\n password_teacher = 'internship'\n\n login_url = \"http://local.school.portnov.com:4520/#/login\"\n driver.get(login_url)\n driver.find_element_by_id(\"mat-input-0\").send_keys(email_teacher)\n driver.find_element_by_id(\"mat-input-1\").send_keys(password_teacher)\n driver.find_element_by_css_selector(\"button[type='submit']\").click()\n\n wait.until(EC.presence_of_element_located((By.XPATH, \"// div[@class = 'info']/p[contains(text(),'TEACHER')]\")))\n sleep(1)\n\n wait.until(EC.visibility_of_element_located((By.XPATH, \"// div[@class = 'info']/p[contains(text(),'TEACHER')]\")))\n driver.find_element(By.PARTIAL_LINK_TEXT, \"Quizzes\").click()\n wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, \"a[href='#/quiz-builder']\")))\n driver.find_element(By.PARTIAL_LINK_TEXT,\"Create New Quiz\").click()\n\n driver.find_element(By.TAG_NAME,\"input\").send_keys(quiz_name)\n\n driver.find_element(By.CSS_SELECTOR, \"div.controls.ng-star-inserted>button\").click()\n sleep(1)\n driver.find_element(By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Textual')]\").click()\n sleep(1)\n driver.find_element(By.CSS_SELECTOR, \"div.mat-input-infix.mat-form-field-infix textarea\").send_keys(textual_question_1)\n sleep(1)\n driver.find_element(By.CSS_SELECTOR, \"div.controls.ng-star-inserted>button\").click()\n sleep(1)\n driver.find_element(By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Textual')]\").click()\n sleep(1)\n driver.find_element(By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\").send_keys(textual_question_2)\n sleep(1)\n driver.find_element(By.CSS_SELECTOR, \"div.controls.ng-star-inserted>button\").click()\n sleep(1)\n driver.find_element(By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Textual')]\").click()\n sleep(1)\n driver.find_element(By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\").send_keys(textual_question_3)\n sleep(1)\n driver.find_element(By.CSS_SELECTOR, \"div.controls.ng-star-inserted>button\").click()\n sleep(1)\n\n # Single choice questions:\n single_choice_1 = \"What is a Defect?\"\n single_choice_1_opt_1 = \"Any flaw or imperfection in a software work product\"\n single_choice_1_opt_2 = \"without any issues\"\n single_choice_2 = \"What is Priority?\"\n single_choice_2_opt_1 = \"It indicates the importance or urgency of fixing a defect\"\n single_choice_2_opt_2 = \"anytime can fix this bug. No time limit\"\n single_choice_3 = \"What is the difference between static testing?\"\n single_choice_3_opt_1 = \"without code executing the program is called as Static Testing.\"\n single_choice_3_opt_2 = \"with code\"\n\n driver.find_element(By.XPATH, \"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Single-Choice')]\").click()\n sleep(1)\n driver.find_element(By.XPATH, \"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\").send_keys(single_choice_1)\n sleep(1)\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\".format(single_choice_1)).send_keys(single_choice_1_opt_1)\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']/../../../../..//mat-radio-button\".format(single_choice_1)).click()\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\".format(single_choice_1)).send_keys(single_choice_1_opt_2)\n sleep(1)\n driver.find_element(By.CSS_SELECTOR, \"div.controls.ng-star-inserted>button\").click()\n sleep(1)\n wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \"div.left.wide mat-slider\")))\n driver.find_element(By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Single-Choice')]\").click()\n wait.until(EC.visibility_of_element_located((By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\"))).send_keys(single_choice_2)\n wait.until(EC.visibility_of_element_located((By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\".format(single_choice_2)))).send_keys(single_choice_2_opt_1)\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']/../../../../..//mat-radio-button\".format(single_choice_2)).click()\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\".format(single_choice_2)).send_keys(single_choice_2_opt_2)\n sleep(1)\n driver.find_element(By.CSS_SELECTOR, \"div.controls.ng-star-inserted>button\").click()\n wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \"div.left.wide mat-slider\")))\n driver.find_element(By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Single-Choice')]\").click()\n wait.until(EC.visibility_of_element_located((By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\"))).send_keys(single_choice_3)\n wait.until(EC.visibility_of_element_located((By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\".format(single_choice_3)))).send_keys(single_choice_3_opt_1)\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']/../../../../..//mat-radio-button\".format(single_choice_3)).click()\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\".format(single_choice_3)).send_keys(single_choice_3_opt_2)\n\n\n # Multiple choice questions:\n multiple_choice_1 = \"What is a Bug?\"\n multiple_choice_1_opt_1 = \"Mismatch between actual and intended behaviors of the software\"\n multiple_choice_1_opt_2 = \"Some small insect that flies around\"\n multiple_choice_2 = \"Are Java and Javascript same languages?\"\n multiple_choice_2_opt_1 = \"Yes\"\n multiple_choice_2_opt_2 = \"No\"\n multiple_choice_3 = \"What is a prime objective of a bug tracking database?\"\n multiple_choice_3_opt_1 = \"Tracking the bugs\"\n multiple_choice_3_opt_2 = \"To get a bug fixed\"\n\n driver.find_element(By.CSS_SELECTOR, \"div.controls.ng-star-inserted>button\").click()\n driver.find_element(By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Multiple-Choice')]\").click()\n wait.until(EC.visibility_of_element_located((By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\"))).send_keys(multiple_choice_1)\n wait.until(EC.visibility_of_element_located((By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\".format(multiple_choice_1)))).send_keys(multiple_choice_1_opt_1)\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']/../../../../../mat-checkbox\".format(multiple_choice_1)).click()\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\".format(multiple_choice_1)).send_keys(multiple_choice_1_opt_2)\n sleep(1)\n driver.find_element(By.CSS_SELECTOR, \"div.controls.ng-star-inserted>button\").click()\n sleep(1)\n driver.find_element(By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Multiple-Choice')]\").click()\n wait.until(EC.visibility_of_element_located((By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\"))).send_keys(multiple_choice_2)\n wait.until(EC.visibility_of_element_located((By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\".format(multiple_choice_2)))).send_keys(multiple_choice_2_opt_1)\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\".format(multiple_choice_2)).send_keys(multiple_choice_2_opt_2)\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']/../../../../../mat-checkbox\".format(multiple_choice_2)).click()\n sleep(1)\n driver.find_element(By.CSS_SELECTOR, \"div.controls.ng-star-inserted>button\").click()\n sleep(1)\n driver.find_element(By.XPATH, \"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Multiple-Choice')]\").click()\n wait.until(EC.visibility_of_element_located((By.XPATH,\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\"))).send_keys(multiple_choice_3)\n wait.until(EC.visibility_of_element_located((By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\".format(multiple_choice_3)))).send_keys(multiple_choice_3_opt_1)\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\".format(multiple_choice_3)).send_keys(multiple_choice_3_opt_2)\n driver.find_element(By.XPATH, \"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']/../../../../../mat-checkbox\".format(multiple_choice_3)).click()\n\n driver.find_element(By.XPATH, \"//button/*[contains(text(),'Save')]\").click()\n quiz_locator = \"//ac-quizzes-list//div[@class = 'quizzes']//*[contains(text(),'{}')]\".format(quiz_name)\n wait.until(EC.visibility_of_element_located((By.XPATH, quiz_locator)))\n element = driver.find_element_by_xpath(quiz_locator)\n driver.execute_script(\"arguments[0].scrollIntoView();\", element)\n element.click()\n driver.get_screenshot_as_file('{} created.png'.format(quiz_name))\n driver.find_element_by_xpath(\"//div[@class='mat-list-item-content']//h5[contains(text(),'Log Out')]\").click()\n wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, \".mat-button.mat-warn\")))\n driver.find_element_by_css_selector(\".mat-button.mat-warn\").click()\n wait.until(EC.presence_of_element_located((By.CSS_SELECTOR,\"button[type='submit']\")))\n\n\n #Sign in with role TEACHER\n\n email = 'alina.korolevich@yopmail.com'\n\n password = 'internship'\n\n url = \"http://local.school.portnov.com:4520/api/v1/sign-in\"\n\n payload = {\n 'email': email,\n 'password': password\n }\n\n headers = {\n 'content-type': \"application/json\",\n 'Connection': \"keep-alive\"\n }\n\n response = requests.post(url, data=json.dumps(payload), headers=headers)\n\n parsed_json = json.loads(response.text)\n\n token = parsed_json[\"token\"]\n\n url = \"http://local.school.portnov.com:4520/api/v1/quizzes\"\n\n headers = {\n 'Authorization': \"Bearer {}\".format(token)\n }\n\n r = requests.get(url, headers=headers)\n parsed_json = json.loads(r.text)\n\n quiz_id = None\n\n for i in parsed_json:\n if i[\"name\"] == quiz_name:\n quiz_id = i[\"id\"]\n else:\n continue\n\n url = \"http://local.school.portnov.com:4520/api/v1/quiz/{}\".format(quiz_id)\n\n r = requests.delete(url, headers=headers)\n print(r.status_code)\n self.assertTrue(r.status_code == 200)\n print(\"Quiz: {} with id {} was permanently deleted\".format(quiz_name, quiz_id))","title":""},{"docid":"4f25a61269a0963f3ab1f7df025ad6c4","score":"0.50349176","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgresql://{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n \n # create all tables\n self.db.create_all()\n\n self.new_question = {\n \"question\": \"Whose autobiography is entitled 'Catcher in the Rye'?\",\n \"answer\": \"J.D Salinger\",\n \"category\": 2,\n \"difficulty\": 4\n }\n\n self.new_incomplete_question = {\n \"question\": \"Whose autobiography is entitled 'Catcher in the Rye'?\",\n \"category\": 2,\n \"difficulty\": 4\n }\n\n self.new_invalid_question = {\n \"question\": 22.66,\n \"answer\": \"J.D Salinger\",\n \"category\": 2,\n \"difficulty\": 4\n }","title":""},{"docid":"22f6b42de4f3c4f0575ae93c3467e235","score":"0.5027913","text":"def create_poll(self, request):\n bookclub = BookClub.objects.get(pk=request.data.get('BookClub_ID', {}))\n user = User.objects.get(pk=request.data.get('User_ID', {}))\n\n if CheckPermissions(user).hasPerm('Create', bookclub):\n time = datetime.strptime(request.data.get('Time', {}), \"%d-%m-%Y %H:%M:%S\")\n title = request.data.get('Title', {})\n choices = request.data.get('Choices', {})\n\n new_poll = Poll(End_Time=time, Title=title, BookClub_ID=bookclub)\n new_poll.save()\n new_poll = Poll.objects.get(pk=new_poll.Poll_ID)\n\n for choice in choices:\n new_choice = Choice(Description=choice, Poll_ID=new_poll)\n new_choice.save()\n\n serializer = PollSerializer(new_poll)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n else:\n return Response(status=status.HTTP_401_UNAUTHORIZED)","title":""},{"docid":"3153f58799aa821d86a30a0e13067f75","score":"0.5005962","text":"def visualQuiz():\n return render_template(\"Visual_Quiz.html\")","title":""},{"docid":"d423eb9c57fb29cee4f8724dac114681","score":"0.5001011","text":"def convert_questions_to_df(self):\n\n questions = self.survey.questions\n\n # collect all dataframes; for every (sub)question a 2-index dataframe is created\n # so that they can be merged at the end\n frames = []\n\n # iterate over all questions and treat them according to the question type\n for q in questions:\n question_code = questions[q]['title']\n question_text = self.clean_text(questions[q]['question'])\n question_type = questions[q]['question_type']\n\n # if there is only one element to select (List radio or (List dropdown)\n # create 3 columns: one for machine readable code (e.g. 1,..,n), one for answer code (e.g. A1,...An)\n # and one for long text answer\n if question_type == \"List radio\" or question_type == \"List dropdown\":\n\n second_level_index = [\"a_code\", \"a_text\", \"a\"]\n header = pd.MultiIndex.from_product([[question_code],\n second_level_index], names=['code', 'version'])\n\n df = pd.DataFrame(columns=header)\n\n # append the new cdataframe\n frames.append(df)\n\n # for Multiple choice questions append two columns per answer.\n # a machine readable one (0/1 for no or yes) and one with the text of this subquestion\n # the text field is not really necessary, however, it helps to have it in the row\n # so we can extract it more easily during analyses\n elif question_type == \"Multiple choice\":\n\n # iterate over all subquestions (by the format of limepy, the subquestions section always has key '0')\n for s in questions[q]['subquestions']['0']:\n sub_question_code = (s['title'])\n merged_code = question_code + \"[\" + sub_question_code + \"]\"\n\n second_level_index = [\"a_text\", \"a\"]\n header = pd.MultiIndex.from_product([[merged_code],\n second_level_index], names=['code', 'version'])\n df = pd.DataFrame(columns=header)\n\n # append the new cdataframe\n frames.append(df)\n\n # if there is only one element to select (List radio or (List dropdown) + a comment\n # create 4 columns: the 3 from list + an additional one for the comment\n elif question_type == \"List with comment\":\n\n second_level_index = [\"a_code\", \"a_text\", \"a\", question_code + \"[comment]\"]\n header = pd.MultiIndex.from_product([[question_code],\n second_level_index],\n names=['code', 'version'])\n\n df = pd.DataFrame(columns=header)\n\n # append the new cdataframe\n frames.append(df)\n\n # for Multiple choice questions append three columns per answer.\n # like Multiple choice + comment field\n elif questions[q]['question_type'] == \"Multiple choice with comments\":\n\n # iterate over all subquestions (by the format of limepy, the subquestions section always has key '0')\n for s in questions[q]['subquestions']['0']:\n sub_question_code = (s['title'])\n merged_code = question_code + \"[\" + sub_question_code + \"]\"\n\n second_level_index = [\"a_text\", \"a\", question_code + \"[\" + sub_question_code + \"comment]\"]\n header = pd.MultiIndex.from_product([[merged_code],\n second_level_index], names=['code', 'version'])\n df = pd.DataFrame(columns=header)\n\n # append the new cdataframe\n frames.append(df)\n\n # here we need 2 columns per possible answer (like multiple choice).\n # One that contains the text.\n # a second one that should be machine readable:\n # Results of the participants are entered with integers. 1 shows highest priority, n lowest,\n # 0 shows that it was not selected by the participant\n elif questions[q]['question_type'] == \"Ranking\":\n\n # iterate over all answers (by the format of limepy, the answers section always has key '0')\n for a in questions[q]['answers']['0']:\n answer_code = a['sortorder']\n merged_code = question_code + \"[\" + answer_code + \"]\"\n\n second_level_index = [\"a\", \"a_text\"]\n header = pd.MultiIndex.from_product([[merged_code],\n second_level_index],\n names=['code', 'version'])\n df = pd.DataFrame(columns=header)\n\n # append the new cdataframe\n frames.append(df)\n\n # each question in the array is to be treated like a normal list radio question and should therefore\n # receive 3 columns\n elif questions[q]['question_type'] == \"Array\":\n\n second_level_index = [\"a_code\", \"a_text\", \"a\"]\n\n # iterate over all subquestions (by the format of limepy, the subquestions section always has key '0')\n for s in questions[q]['subquestions']['0']:\n sub_question_code = (s['title']) # subquestion code\n merged_code = question_code + \"[\" + sub_question_code + \"]\" # bring the codes together\n\n header = pd.MultiIndex.from_product([[merged_code],\n second_level_index], names=['code', 'version'])\n\n df = pd.DataFrame(columns=header)\n\n # append the new cdataframe\n frames.append(df)\n\n # one column for the free text\n elif questions[q]['question_type'] == \"Long free text\" or questions[q][\n 'question_type'] == \"Short free text\":\n\n second_level_index = [\"a\"]\n header = pd.MultiIndex.from_product([[question_code],\n second_level_index], names=['code', 'version'])\n\n df = pd.DataFrame(columns=header)\n\n # append the new dataframe\n frames.append(df)\n\n # Case for questions of which the type is not listed\n else:\n print(\"error, type not known \", questions[q]['question_type'])\n\n # create an empty dataframe from all the individual ones\n result = pd.concat(frames)\n return result","title":""},{"docid":"0b00baa75b3c0a0678d41630c74e1c0c","score":"0.500017","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}/{}\".format(\n 'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n category = Category.query.filter(\n Category.type == \"science\").one_or_none()\n self.new_question = {\n 'question': 'How many miles away from earth is the moon?',\n 'answer': '238,900',\n 'category': category,\n 'difficulty': 5\n }\n\n self.new_question_missing_answer = {\n 'question': 'In what year was Mike Tyson the boxer?',\n 'category': 'sports',\n 'difficulty': '4'\n }\n\n self.search_term = {\n 'searchTerm': 'soccer'\n }\n\n self.previous_questions = {\n 'previous_questions': [],\n 'quiz_category': {\n 'type': 'Science',\n 'id': '1'\n }\n }\n\n \"\"\" restore question used in delete test after each test \"\"\"\n ali = Question.query.filter(\n Question.question ==\n \"What boxer's original name is Cassius Clay?\").one_or_none()\n if ali is None:\n question = Question(\n question=\"What boxer's original name is Cassius Clay?\",\n answer=\"Muhammad Ali\",\n category=4,\n difficulty=1)\n question.insert()\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"baa1038abb391cbe51fd945941926aba","score":"0.4996972","text":"def new_question():\n if not g.user:\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\")\n\n form = QuestionForm()\n if form.validate_on_submit():\n new_question = Question(\n user_id=session[CURR_USER_KEY],\n official_id=form.official.data,\n text=form.text.data\n )\n db.session.add(new_question)\n db.session.commit()\n flash(\"Your question has been asked!\", \"success\")\n return redirect('/')\n \n form.official.choices = [(o.id, f'{o.name} ({o.office})') for o in g.user.city.officials]\n return render_template('questions/new_question.html', form=form)","title":""},{"docid":"6bea32e391d6b03eee132c3df8aa5de2","score":"0.49908298","text":"def rebuild_qa_dictionary(self):\n self.question_answers.clear()\n\n self.question_answers = {\n 'What type of triangle is ': QA('What type of triangle is ', get_triangle_type),\n 'What type of quadrilateral is ': QA('What type of quadrilateral is ',\n get_quadrilateral_type),\n 'What time is it': QA('What time is it', time.asctime()),\n 'What is the n digit of fibonacci': QA('What is the n digit of fibonacci',\n get_fibonacci),\n 'What is the digit of pi': QA('What is the digit of pi', get_some_pi),\n 'Why did the chicken cross the road': QA('Why did the chicken cross the road', 'To '\n 'prove to the armadillo that it can be done'),\n 'What is n multiplied by m': QA('What is n multiplied by m', simple_multiply),\n 'What student really deserves an A': QA('What student really deserves an A',\n 'Kirt Stark does!'),\n 'What is the cube of ': QA('What is the cube of ', cube_of_number),\n 'How many times will divide completely into ': QA('How many times will divide complete'\n 'ly into ', divide_evenly),\n 'What is when it is multiplied by and then added to ': QA('What is when it is '\n 'multiplied by and then '\n 'added to ', paycheck),\n 'What is the mass of a measure of with a volume of cubic meters?':\n QA('What is the mass of a measure of with a volume of cubic meters?',\n self.send_for_density),\n 'Why are these labs such a struggle for me': QA('Why are these labs such a struggle for'\n ' me', struggle_reason),\n 'Is the <file name> in the repo': QA('Is the in the repo', is_file_in_repo),\n 'What is the status of <file name>': QA('What is the status of ', get_git_file_info),\n 'What is the deal with <file name>': QA('What is the deal with ', get_file_info),\n 'What branch is <file path>': QA('What branch is ', get_repo_branch),\n 'Where did <file path> come from': QA('Where did come from', get_repo_url),\n 'What is the repo root for <file path>': QA('What is the repo root for ', get_repo_root)\n }\n return \"done\"","title":""},{"docid":"3cfe0a2d8d6967d9e12fe077d5be821d","score":"0.49891895","text":"def quiz_example():\n\n quiz = Quiz(\"Pop Quiz\")\n \n quiz.add_question(\"How many houses in our cohort?\", \"Four\")\n quiz.add_question(\"Which is the best house?\", \"Gryffindor\")\n\n student = Student(\"Nicole\", \"Negri\", \"SF\")\n\n take_test(quiz, student)","title":""},{"docid":"094087e8e3185fc7fbbfef7a4f4804f5","score":"0.49800134","text":"def qAnswer(self):\n #get user input\n Body = input('Please answer question: ')\n #format the data\n #Set ID self-increment\n print(\"Posting answer...\")\n\n Id = self.get_id(\"Posts\")\n data={\"Id\": Id,\\\n \"PostTypeId\":\"2\",\\\n \"ParentId\":self.questionId,\\\n \"CreationDate\":datetime.datetime.now().isoformat(),\\\n \"Score\":0,\\\n \"Body\":Body,\\\n \"LastActivityDate\":datetime.datetime.now().isoformat(),\\\n \"CommentCount\":0,\\\n \"ContentLicense\":\"CC BY-SA 2.5\"}\n if self.userId != None:\n data[\"OwnerUserId\"] = self.userId\n #insert the data\n temp = [data]\n extraction_terms(temp)\n db.Posts.insert_one(data)\n self.create_index()\n #Success message\n\n print(\"Successfully posted the answer.\")","title":""},{"docid":"88cb98d9a4d1c901e2ea3a75bf562958","score":"0.4976668","text":"def add_a_new_question():\n try:\n body = request.get_json()\n category_id = body.get('category', None)\n new_question = Question(\n question=body.get('question', None),\n answer=body.get('answer', None),\n category=category_id,\n difficulty=body.get('difficulty', None)\n )\n new_question.insert()\n\n all_questions = Question.query.order_by(Question.id).all()\n category_type = Category.query.get(category_id).type\n\n return jsonify({\n 'success': True,\n 'created': new_question.id,\n 'current_category': category_type,\n 'total_questions': len(all_questions),\n })\n\n except Exception:\n abort(422)","title":""},{"docid":"b40b60db6a0fa73c9be0375c72e368ba","score":"0.49735948","text":"def create_answer(controls, index, question, answer_num, dbsession):\n answerText = str(controls[index+1][1])\n if controls[index+2][0] == 'correct': correct = True\n else: correct = False\n answer = Answer(question.id, answerText, \n correct)\n dbsession.add(answer)\n dbsession.flush\n return answer","title":""},{"docid":"8b1fe743ba493c9edb469d32e205e5bf","score":"0.49719405","text":"def test_mark_questions(self):\n\n # Short Quiz - No Questions Right -\n\n # Short Quiz - Half Questions Right -\n\n # Short Quiz - All Questions Right -\n\n # Long Quiz - No Questions Right -\n\n # Long Quiz - Half Questions Right -\n\n # Long Quiz - All Questions Right -","title":""},{"docid":"4ff082c2936fe37a319fb5ec4bb27d73","score":"0.4953064","text":"def test_question_list(self):\n third_question = Question('Why is science?', 'Only Bill Nye knows', self.science.id, 5)\n third_question.insert()\n\n # Test list with pagination only returns 2 result\n resp = self.client.get('/api/questions')\n assert resp.status_code == 200\n\n data = json.loads(resp.data)\n assert 'categories' in data\n assert 'questions' in data\n assert 'total_questions' in data\n\n assert data == {\n 'categories': {\n '1': 'Science',\n '2': 'Geography'\n },\n 'questions': [\n {\n 'answer': 'Nobody knows',\n 'category': 1,\n 'difficulty': 5,\n 'id': 1,\n 'question': 'What is science?'\n },\n {\n 'answer': 'Probably not',\n 'category': 2,\n 'difficulty': 5,\n 'id': 2, 'question':\n 'Is geography real?'\n }\n ],\n 'total_questions': 3\n }","title":""},{"docid":"4789c911a01e0326ceac748ae4f5feb1","score":"0.49438152","text":"def test_create_new_category():\n results = []\n category_obj = prism.Categories(api_client=_api())\n\n categories = [\n {'name': 'test_category', 'description': 'api test category'},\n {'name': 'test_category_2', 'description': 'api test category #2'},\n ]\n\n for category in categories:\n category_obj.set_category(**category)\n if category_obj.search_category(name=category.get('name'), refresh=True):\n results.append(True)\n category_cleanup.append(category.get('name'))\n else:\n results.append(False)\n\n assert all(results)","title":""},{"docid":"2814704fe1f943cc0e9c4475bb2e7e70","score":"0.49265248","text":"def test_answer_with_param_score_with_data(self):\n self.headers(academy=1)\n base = self.generate_models(authenticate=True,\n user=True,\n cohort=True,\n academy=True,\n event=True,\n profile_academy=True,\n capability='read_nps_answers',\n role='potato')\n\n for score in range(1, 10):\n self.remove_all_answer()\n\n answer_kwargs = {'score': score}\n model = self.generate_models(answer=True, answer_kwargs=answer_kwargs, models=base)\n db = self.model_to_dict(model, 'answer')\n params = {\n 'score': score,\n }\n base_url = reverse_lazy('feedback:answer')\n url = f'{base_url}?{urllib.parse.urlencode(params)}'\n response = self.client.get(url, headers={'Academy', model['academy'].id})\n json = response.json()\n\n json = [{**x, 'created_at': None} for x in json if self.assertDatetime(x['created_at'])]\n\n self.assertEqual(json, [{\n 'created_at': None,\n 'academy': {\n 'id': model['answer'].academy.id,\n 'name': model['answer'].academy.name,\n 'slug': model['answer'].academy.slug,\n },\n 'cohort': {\n 'id': model['cohort'].id,\n 'name': model['cohort'].name,\n 'slug': model['cohort'].slug,\n },\n 'comment': model['answer'].comment,\n 'event': {\n 'id': model['event'].id,\n 'description': model['event'].description,\n 'excerpt': model['event'].excerpt,\n 'lang': model['event'].lang,\n 'title': model['event'].title,\n },\n 'highest': model['answer'].highest,\n 'id': model['answer'].id,\n 'lang': model['answer'].lang,\n 'lowest': model['answer'].lowest,\n 'mentor': {\n 'first_name': model['user'].first_name,\n 'id': model['user'].id,\n 'last_name': model['user'].last_name,\n 'profile': None,\n },\n 'score': score,\n 'status': model['answer'].status,\n 'title': model['answer'].title,\n 'user': {\n 'first_name': model['user'].first_name,\n 'id': model['user'].id,\n 'last_name': model['user'].last_name,\n 'profile': None,\n },\n }])\n\n db['score'] = score\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(self.all_answer_dict(), [db])","title":""},{"docid":"c61697df150c9152a6fb7709142124a6","score":"0.4917346","text":"def test_create_categories(self):\n self.init_database()","title":""},{"docid":"6d7cfd2b050a4de59bb576c361be987b","score":"0.49121854","text":"def test_6_init(self):\n quizz = Quizz(self.user)\n self.assertEqual( quizz.user, self.user)\n self.assertEqual( quizz.userInfo, self.userInfo)\n self.assertEqual( quizz.user, self.user)\n self.assertEqual( quizz.scoreSheet, {1: 20, 2: 0})\n self.assertEqual( len(quizz.questions), 10)\n self.assertEqual( len(quizz.answers), 10)\n self.assertEqual( quizz.answers[1], {})\n self.assertEqual( quizz.level, 1)\n self.assertEqual( quizz.size, 10)\n self.assertEqual( quizz.categories, ['hiraganas'])\n self.assertEqual( quizz.currentScore, 0)\n self.assertEqual( quizz.completed, False)","title":""},{"docid":"cc7b410934ea039e0d06dc32b2aa7655","score":"0.4911111","text":"def create_category():\n return render_template(\"add-category-form.html\"), HTTPStatus.OK","title":""},{"docid":"dad260c6004ba66080a8047058431542","score":"0.4909211","text":"def SetStateQuestion(self):\n self.Timer.display(questiondata.question_time)\n self.Timer.setStyleSheet('color: blue')\n self.BtnEnd.setEnabled(True)\n category: questiondata.Category = self.state.category\n if self.state.question == len(self.state.category.questions) - 1:\n self.BtnNew.setEnabled(False)\n else:\n self.BtnNew.setEnabled(True)\n self.BtnTrue.setEnabled(False)\n self.BtnFalse.setEnabled(False)\n self.BtnNext.setEnabled(False)\n self.BtnTimer.setEnabled(True)\n self.BtnTimer.setText('Стоп')\n\n self.LblDscr.setText(\"%s: Вопрос № %i из %i. Стоимость %i \" %\n (category.name, self.state.question+1, len(category.questions),\n category.questions[self.state.question].points))\n self.TxtQstn.setHtml(\"<!DOCTYPE HTML PUBLIC \\\"-//W3C//DTD HTML 4.0//EN\\\" \"\n \"\\\"http://www.w3.org/TR/REC-html40/strict.dtd\\\">\\n\"\n \"<html><head><meta name=\\\"qrichtext\\\" content=\\\"1\\\" /><style type=\\\"text/css\\\">\\n\"\n \"p, li { white-space: pre-wrap; }\\n\"\n \"</style></head><body style=\\\" font-family:\\'MS Shell Dlg 2\\'; font-size:7.8pt; \"\n \"font-weight:400; font-style:normal;\\\">\\n\"\n \"<p style=\\\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px;\"\n \" -qt-block-indent:0; text-indent:0px;\\\">\"\n \"<span style=\\\" font-family:\\'Calibri\\'; font-size:20pt; \"\n \"font-weight:600; color:#000000;\\\">%s</span></p></body></html>\"\n % category.questions[self.state.question].description)\n self.LblAnswer.setText(category.questions[self.state.question].answer)\n self.category_form.question = question_opened.QuestionDialog(category, self.state.question)\n self.category_form.question.show()","title":""},{"docid":"fb58331c653dd0f0a90cc1ea0244dc0e","score":"0.4908329","text":"def __init__(self, name):\n self.name = name\n self.questions = []","title":""},{"docid":"c30bc471711ab66f7b511326be3e0b9e","score":"0.49033698","text":"def test_post_new_question(self):\r\n\r\n self.new_question = {\r\n 'question':'How much wood would a woodchuck chuck if a woodchuck could chuck wood?',\r\n 'answer':'About 700 pounds',\r\n 'category': 1, # Science\r\n 'difficulty': '4'\r\n }\r\n res = self.client().post('/api/questions', json=self.new_question)\r\n data = json.loads(res.data)\r\n new_question_id = data['created']\r\n\r\n self.assertEqual(data['success'], True)\r\n\r\n question_pull = Question.query.filter(Question.id == new_question_id).one_or_none()\r\n\r\n self.assertEqual(question_pull.answer, 'About 700 pounds')\r\n\r\n # Delete question to revert back to original database\r\n\r\n res = self.client().delete(f'/api/questions/{new_question_id}')\r\n data = json.loads(res.data)\r\n\r\n self.assertEqual(data['success'], True)\r\n self.assertEqual(data['deleted'], new_question_id)","title":""},{"docid":"d5c71de8569629a7dda2a1032ea2d1b0","score":"0.4898194","text":"def __init__(self, question_id=None, user_id=None, timestamp=time, date=date, title=None, question=None):\n self.question_id = question_id\n self.user_id = user_id\n self.question_title = title\n self.question_text = question\n self.question_timestamp = timestamp\n self.asked_date = date","title":""},{"docid":"44e8d2e1cb2dfc19e1ce4552efdc4247","score":"0.48858854","text":"def _load_all_categorical(self):\n\t\tfield_name = 'text_answer_fr' if self.lang == 'fr' else 'text_answer'\n\t\tquery = \"\"\"\n\t\t\tSELECT short_question, {0}, COUNT({0})\n\t\t\tFROM comments\n\t\t\tWHERE\n\t\t\t\tcourse_code = %s\n\t\t\t\tAND\n\t\t\t\tshort_question IN ('Reason to Participate', 'Technical Issues', 'OL Available', 'GCcampus Tools Used', 'Prep')\n\t\t\tGROUP BY short_question, {0}\n\t\t\tORDER BY 1 ASC;\n\t\t\"\"\".format(field_name)\n\t\tresults = query_mysql(query, (self.course_code,))\n\t\tresults = pd.DataFrame(results, columns=['short_question', 'text_answer', 'count'])\n\t\t# Return False if course has received no feedback\n\t\tself.categorical_data = False if results.empty else results","title":""},{"docid":"ebd7cef229322fe1f7617f62a1a07bfa","score":"0.488028","text":"def post_question(self):\n self.sign_up()\n login_response = self.login()\n result = self.create_meetup()\n meetup_id = result['data']['id']\n print(result)\n\n self.headers.update({\"x-access-token\":\n login_response['token']})\n question_payload = {\n \"title\": \"How to train your dragon?\",\n \"body\": \"How does one get to tame their dragons?\"\n \" Mine just ate my neighbors\",\n \"meetup\": meetup_id\n }\n with self.client as c:\n question_post_response = c.post('api/v2/questions',\n json=question_payload,\n headers=self.headers)\n result = json.loads(question_post_response.data.decode('utf-8'))\n\n return [question_post_response, login_response['token']]","title":""},{"docid":"189141700bc28334368738d007ecc017","score":"0.48762187","text":"def _create_qa_example(qa_input, is_training):\n\n # _QAExample is a data structure representing an unique document-question-answer\n # triplet.\n # Args:\n # qa_id (int): An unique id identifying the document-question pair.\n # This is used to map prediction results to ground truth answers\n # during evaluation, because the data order is not preserved\n # during pre-processing and post-processing.\n # doc_tokens (list): White-space tokenized tokens of the document\n # text. This is used to generate the final answer based on\n # predicted start and end token indices during post-processing.\n # question_text (str): Text of the question.\n # orig_answer_text (str): Text of the ground truth answer if available.\n # start_position (int): Index of the starting token of the answer\n # span, if available.\n # end_position (int): Index of the ending token of the answer span,\n # if available.\n # is_impossible (bool): If the question is impossible to answer based\n # on the given document.\n _QAExample = collections.namedtuple(\n \"_QAExample\",\n [\n \"qa_id\",\n \"doc_tokens\",\n \"question_text\",\n \"orig_answer_text\",\n \"start_position\",\n \"end_position\",\n \"is_impossible\",\n ],\n )\n\n def _is_whitespace(c):\n if c == \" \" or c == \"\\t\" or c == \"\\r\" or c == \"\\n\" or ord(c) == 0x202F:\n return True\n return False\n\n d_text = qa_input.doc_text\n q_text = qa_input.question_text\n a_start = qa_input.answer_start\n a_text = qa_input.answer_text\n q_id = qa_input.qa_id\n impossible = qa_input.is_impossible\n\n d_tokens = []\n char_to_word_offset = []\n prev_is_whitespace = True\n for c in d_text:\n if _is_whitespace(c):\n prev_is_whitespace = True\n else:\n if prev_is_whitespace:\n d_tokens.append(c)\n else:\n d_tokens[-1] += c\n prev_is_whitespace = False\n char_to_word_offset.append(len(d_tokens) - 1)\n\n if _is_iterable_but_not_string(a_start):\n if not _is_iterable_but_not_string(a_text):\n raise Exception(\n \"The answer text must be a list when answer start is a list.\"\n )\n if len(a_start) != 1 and is_training and not impossible:\n raise Exception(\"For training, each question should have exactly 1 answer.\")\n a_start = a_start[0]\n a_text = a_text[0]\n\n start_position = None\n end_position = None\n if is_training:\n if not impossible:\n answer_length = len(a_text)\n start_position = char_to_word_offset[a_start]\n end_position = char_to_word_offset[a_start + answer_length - 1]\n # Only add answers where the text can be exactly recovered from the\n # document. If this CAN'T happen it's likely due to weird Unicode\n # stuff so we will just skip the example.\n #\n # Note that this means for training mode, every example is NOT\n # guaranteed to be preserved.\n actual_text = \" \".join(d_tokens[start_position : (end_position + 1)])\n cleaned_answer_text = \" \".join(whitespace_tokenize(a_text))\n if actual_text.find(cleaned_answer_text) == -1:\n logger.warning(\n \"Could not find answer: '%s' vs. '%s'\",\n actual_text,\n cleaned_answer_text,\n )\n return\n else:\n start_position = -1\n end_position = -1\n\n return _QAExample(\n qa_id=q_id,\n doc_tokens=d_tokens,\n question_text=q_text,\n orig_answer_text=a_text,\n start_position=start_position,\n end_position=end_position,\n is_impossible=impossible,\n )","title":""},{"docid":"7d14ab8fab17882aecb3fb9e7d74a140","score":"0.48756945","text":"def create_question_list(self):\n\n question_list = pd.DataFrame()\n for qid, question in self.questions.items():\n question_list.loc[qid, 'question'] = question['question']\n question_list.loc[qid, 'question_type'] = question['question_type']\n question_list.loc[qid, 'help'] = question['help']\n question_list.loc[qid, 'position'] = question['position']\n start, nr_columns = question['columns']\n question_list.loc[qid, 'start'] = int(start)\n question_list.loc[qid, 'nr_columns'] = int(nr_columns)\n group_name = self.groups[question['gid']]['group_name']\n question_list.loc[qid, 'group'] = group_name\n question_list.loc[qid, 'mandatory'] = question['mandatory']\n question_list.loc[qid, 'other'] = question['other']\n question_list.loc[qid, 'title'] = question['title']\n return question_list.sort_values(by='position')","title":""},{"docid":"987f402dcd55766283638a33f2093f54","score":"0.4868873","text":"def test_post_question(self):\n # get toal number of question accessible via the API\n get_before = self.client().get('/questions/page/1')\n get_before_data = get_before.json\n init_question_num = get_before_data['total_questions']\n # post data to the test database\n post = self.client().post('/questions', json={\n 'question': 'test question one',\n 'answer': 'test answer one',\n 'category': 1,\n 'difficulty': 2\n })\n post_data = json.loads(post.data)\n # get total number of question accessible to API after POST\n get_after = self.client().get('/questions/page/1')\n get_after_data = get_after.json\n final_question_num = get_after_data['total_questions']\n # ensure request was good\n self.assertEqual(post.status_code, 200)\n self.assertEqual(post_data['success'], True)\n # make sure the initial number of questions is\n # greater than the number of question after the POST\n self.assertGreater(final_question_num, init_question_num)","title":""},{"docid":"1ea593ab85b89397ebe104bbd7a604db","score":"0.48682702","text":"def initialize(quiz):\n\n current_choices = 0\n most_choices = 0\n total_questions = 0\n new_question = True\n\n for line in quiz:\n line = line.strip()\n if line == '':\n if current_choices > most_choices:\n most_choices = current_choices\n current_choices = 0\n new_question = True\n continue\n elif new_question:\n total_questions += 1\n new_question = False\n else:\n current_choices += 1\n\n return [[None] * most_choices for i in range(total_questions)]","title":""},{"docid":"923486715403dc0b66b1c70ad7a8ef83","score":"0.48672304","text":"def test_future_questions(self):\n\n create_question(text=future_question_text(), days=+30)\n\n response = self.client.get(reverse(\"polls:index\"))\n\n self.assertQuerysetEqual(\n response.context[\"question_list\"],\n [],\n )\n self.assertContains(response, \"No polls are available.\")\n\n return","title":""},{"docid":"cd3997b0205da39a7b84acd62f2216dc","score":"0.4864154","text":"async def _start(self, ctx, id, score_type='static'):\n if self.in_play:\n return await ctx.send(\"A quiz is already in progress\")\n try:\n if int(id) not in self.ids:\n return await ctx.send(\"Quiz with this ID does not exist.\")\n except ValueError:\n return await ctx.send(\"Please enter a valid number for ID.\")\n if score_type.lower() not in ['static', 'dynamic']:\n return await ctx.send(\"Specify a valid scoring type (static/dynamic)\")\n\n quiz_data = await self._db.fetchrow(\"SELECT * FROM quiz_config WHERE id = $1\", id)\n name = quiz_data['name']\n desc = quiz_data['description'] or \"\\u200b\"\n self.questions = []\n ques_records = await self._db.fetch(\"SELECT * FROM questions WHERE quiz_id = $1\", id)\n for record in ques_records:\n self.questions.append({\n 'question' : record['question'],\n '1' : record['option1'],\n '2' : record['option2'],\n '3' : record['option3'],\n '4' : record['option4'],\n 'Correct' : record['correct']})\n random.shuffle(self.questions)\n await ctx.send(\"Questions loaded. Starting in 5 seconds\", embed=discord.Embed(title=name, description=desc, colour=discord.Colour.blurple()))\n self.in_play = True\n self._lb = dict()\n await asyncio.sleep(5)\n for num, question in enumerate(self.questions, 1):\n # If this loop is interrupted, big F\n reactions = []\n self._score = MAX_POINTS\n embed = discord.Embed(title=f\"Question {num}:\", description=question['question'], colour=discord.Colour.green())\n if question['1'] != '':\n embed.add_field(name=\"1\\N{variation selector-16}\\N{combining enclosing keycap}\", value=question['1'])\n reactions.append(\"1\\N{variation selector-16}\\N{combining enclosing keycap}\")\n if question['2'] != '':\n embed.add_field(name=\"2\\N{variation selector-16}\\N{combining enclosing keycap}\", value=question['2'])\n reactions.append(\"2\\N{variation selector-16}\\N{combining enclosing keycap}\")\n if question['3'] != '':\n embed.add_field(name=\"3\\N{variation selector-16}\\N{combining enclosing keycap}\", value=question['3'])\n reactions.append(\"3\\N{variation selector-16}\\N{combining enclosing keycap}\")\n if question['4'] != '':\n embed.add_field(name=\"4\\N{variation selector-16}\\N{combining enclosing keycap}\", value=question['4'])\n reactions.append(\"4\\N{variation selector-16}\\N{combining enclosing keycap}\")\n msg = await ctx.send(embed=embed)\n self._current = [msg.id, question['Correct'], []]\n for reaction in reactions:\n await msg.add_reaction(reaction)\n for _ in range(TIMEOUT):\n await asyncio.sleep(1)\n if score_type.lower() == 'dynamic':\n self._score -= MAX_POINTS / TIMEOUT\n # Time up here\n time_up_text = f\"\"\"**Time up!**\\nThe answers of the following members have been recorded:\n\n {chr(10).join([ctx.guild.get_member(member_id).mention for member_id in self._current[2]])}\"\"\"\n self._current = []\n await msg.edit(content=time_up_text, embed=discord.Embed(title=f\"Question {num}:\", description=f\"{question['question']}\\n\\n:white_check_mark: {question['correct']}\", colour=discord.Colour.red()))\n await msg.clear_reactions()\n\n await self._publish(ctx)","title":""},{"docid":"863ab7ce4c305b4c58fad4a225fc947d","score":"0.48587698","text":"def getQuiz():\n Total=0\n Quizes = session.query(Quiz).all()\n for quiz in Quizes:\n print(\"{} . {} \".format(quiz.id,quiz.name))\n\n x = 0\n while x == 0:\n try:\n ID= input(\"Enter the id of the quiz: \")\n ID = int(ID)\n x = 1\n except (ValueError):\n print(\"please enter int number: \")\n x = 0\n\n quiz=session.query(Quiz).filter(Quiz.id == int(ID))\n for row in quiz:\n print(f\"The Duration of the quiz is : {row.Duration} minutes\")\n print(\"Let's start the quiz!\")\n\n\n Mcq=session.query(MCQ).filter(MCQ.Quiz_id == int(ID))\n for q in Mcq :\n print(q.Question)\n print(q.first_Choice)\n print(q.second_Choice)\n print(q.third_Choice)\n print(q.fourth_Choice)\n answer = input(\"Answer: \")\n answer= str(answer)\n\n while (answer!=q.first_Choice and answer!=q.second_Choice and answer!=q.third_Choice and answer!=q.fourth_Choice) :\n print(\"You must choose from the choices\")\n answer = input(\"Answer: \")\n\n if(answer==q.answer):\n Total+=q.mark\n\n Trueorfalse = session.query(TrueOrFalse).filter(TrueOrFalse.Quiz_id == int(ID))\n for q in Trueorfalse:\n print(q.Question)\n answer = input(\"Answer: \")\n if (bool(answer) == q.answer):\n Total += q.mark\n\n\n Essay = session.query(Essay_Question).filter(Essay_Question.Quiz_id == int(ID))\n for q in Essay:\n print(q.Question)\n answer = input(\"Answer: \")\n if (answer == q.answer):\n Total += q.mark\n\n integer_qus = session.query(Integer_Question).filter(Integer_Question.Quiz_id == int(ID))\n for q in integer_qus:\n print(q.Question)\n x = 0\n while x == 0:\n try:\n answer = input(\"Answer: \")\n answer = int(answer)\n x = 1\n except (ValueError):\n print(\"please enter float number\")\n x = 0\n if (answer == q.answer):\n Total += q.mark\n\n float_qus = session.query(Float_Question).filter(Float_Question.Quiz_id == int(ID))\n for q in float_qus:\n print(q.Question)\n x=0\n while x==0:\n try:\n answer = input(\"Answer: \")\n answer=float(answer)\n x=1\n except (ValueError):\n print(\"please enter float number\")\n x=0\n if (answer == q.answer):\n Total += q.mark\n\n print(f\"Mark: {Total}\")","title":""},{"docid":"21ad5307bc30fc9524ccb729867d5c3b","score":"0.4858514","text":"def test_post_question(self):\n\n res = self.client.post('/api/v1/questions', json=self.question, headers=self.headers)\n data = res.get_json()\n\n self.assertEqual(res.status_code, 201)\n self.assertEqual(data['status'], 201)\n self.assertEqual(data['message'], 'Question posted successfully')","title":""}],"string":"[\n {\n \"docid\": \"23ea909a4f70cd48b86b9e7215fd7a4e\",\n \"score\": \"0.6298574\",\n \"text\": \"def test_quiz_question_categorized(self):\\n url = '/api/quizzes'\\n previous_questions = []\\n\\n data = {\\n 'previous_questions': previous_questions,\\n 'quiz_category': self.science.id\\n }\\n\\n # There is only 1 possible question in the science category\\n resp = self.client.post(url, json=data)\\n assert resp.status_code == 200\\n resp_data = json.loads(resp.data)\\n\\n assert resp_data == {\\n 'question': {\\n 'answer': 'Nobody knows',\\n 'category': 1,\\n 'difficulty': 5,\\n 'id': 1,\\n 'question': 'What is science?'\\n }\\n }\\n\\n # Get depleted questions\\n previous_questions.append(resp_data['question']['id'])\\n data['previous_questions'] = previous_questions\\n\\n resp = self.client.post(url, json=data)\\n assert resp.status_code == 200\\n\\n resp_data = json.loads(resp.data)\\n assert resp_data['question'] is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad5a6900f35518472e2f3817823a4ff7\",\n \"score\": \"0.6146127\",\n \"text\": \"def testCreateQuestion(self):\\n status = Quiz.LIVE_STATUS\\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\\n quiz.save()\\n\\n question_1 = Question(question='Choose your color', quiz=quiz)\\n question_1.save()\\n\\n question_2 = Question(question='Choose your month', quiz=quiz)\\n question_2.save()\\n\\n self.assertEqual(Quiz.objects.get(slug=self.slug).question_set.count(), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7848f9f54470587527cb940daf908331\",\n \"score\": \"0.61299694\",\n \"text\": \"def sample_question(quiz, **kwargs):\\n defaults = {'label': 'Sample Question Label'}\\n defaults.update(kwargs)\\n return models.Question.objects.create(quiz=quiz, **defaults)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b698ead979a3a0573b7e46f135390451\",\n \"score\": \"0.60569924\",\n \"text\": \"def create_free_quiz(quiz_name, quiz_language_id, quiz_short_desc):\\n\\n # Check for errors\\n if not quiz_name or not quiz_language_id or not quiz_short_desc:\\n raise ValueError(\\\"Property in body is empty\\\")\\n\\n # Check types\\n if type(quiz_name) != str or type(quiz_language_id) != int or type(\\n quiz_short_desc) != str:\\n raise ValueError(\\\"Types in body are incorrect\\\")\\n\\n if len(quiz_name) > 20 or len(quiz_short_desc) > 20:\\n raise ValueError(\\\"One field in body is too long\\\")\\n\\n # Also check that the language_id actually exists\\n query = \\\"\\\"\\\"\\n SELECT language_id\\n FROM languages\\n WHERE language_id = %s\\n \\\"\\\"\\\"\\n\\n languages = db.query(query, (quiz_language_id))\\n\\n if not languages:\\n raise ValueError(\\\"Language ID does not exist\\\")\\n\\n query = \\\"\\\"\\\"\\n INSERT INTO quizzes (quiz_name, quiz_language_id, quiz_short_desc)\\n VALUES (%s, %s, %s)\\n \\\"\\\"\\\"\\n\\n db.insert_query(query, (quiz_name, quiz_language_id, quiz_short_desc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43575613e7b6030e1680a9384ed7aa2c\",\n \"score\": \"0.6027665\",\n \"text\": \"def test_create_question_fields(self):\\n url = '/api/questions'\\n\\n # Test missing fields\\n data = {}\\n resp = self.client.post(url, json=data)\\n assert resp.status_code == 400\\n assert json.loads(resp.data) == {\\n 'error': '400: Bad Request',\\n 'message': [\\n {'question': 'Field is required'},\\n {'answer': 'Field is required'},\\n {'difficulty': 'Field is required'},\\n {'category': 'Field is required'}\\n ]\\n }\\n\\n data = {\\n 'question': 'Why is science?',\\n 'answer': 'Only Bill Nye knows',\\n 'category': 5000,\\n 'difficulty': 0\\n }\\n\\n # Test difficulty and cateory validation\\n resp = self.client.post(url, json=data)\\n assert resp.status_code == 400\\n assert json.loads(resp.data) == {\\n 'error': '400: Bad Request',\\n 'message': [\\n {'difficulty': 'Difficulty must be an integer between 1 and 5'},\\n {'category': 'Category is not supported'}\\n ]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933461ce8f7604a63cf9960fbbccf11f\",\n \"score\": \"0.6026407\",\n \"text\": \"def test_create_quiz(self):\\r\\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"914f0294fc9feedfbbaa714c33daf854\",\n \"score\": \"0.5942922\",\n \"text\": \"def sample_quiz(supervisor, **kwargs):\\n defaults = {'name': 'Sample Quiz'}\\n defaults.update(kwargs)\\n return models.Quiz.objects.create(supervisor=supervisor, **defaults)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6dfe542ed5fa22b4aea537f88a460d1\",\n \"score\": \"0.5934828\",\n \"text\": \"def test_create(self):\\n assert Question.query.count() == 2\\n\\n data = {\\n 'question': 'Why is science?',\\n 'answer': 'Only Bill Nye knows',\\n 'category': self.science.id,\\n 'difficulty': 3\\n }\\n resp = self.client.post('/api/questions', json=data)\\n assert resp.status_code == 200\\n resp_data = json.loads(resp.data)\\n assert resp_data == {\\n 'answer': 'Only Bill Nye knows',\\n 'category': 1,\\n 'difficulty': 3,\\n 'id': 3,\\n 'question': 'Why is science?'\\n }\\n\\n # Ensure changes persist in DB\\n assert Question.query.count() == 3\\n question = Question.query.get(resp_data['id'])\\n assert question is not None\\n assert question.format() == resp_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d44d2a0081595ae3f7682292ab367dd0\",\n \"score\": \"0.58319896\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n self.new_question = {\\n 'question': 'Question_test 1',\\n 'answer': 'Answer_test 1',\\n 'difficulty': 1,\\n 'category': 1\\n }\\n\\n self.category_id = {\\n 'type':1\\n }\\n #SOURCE: https://knowledge.udacity.com/questions/583577\\n # IT MUST MATCH THE JSON.STRINGIFY 'S BODY IN QUESTIONVIEW\\n self.searchTerm = {\\n 'searchTerm':'Question'\\n }\\n\\n #SOURCE: https://knowledge.udacity.com/questions/583696\\n # IT MUST MATCH THE JSON.STRINGIFY 'S BODY IN QUESTIONVIEW\\n self.quizCategory = { \\n 'previous_questions': [20,21],\\n 'quiz_category':{'type':'Science', 'id':'1'}\\n }\\n\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca9f1fa62f868f935515cce5754827df\",\n \"score\": \"0.58075905\",\n \"text\": \"def create_question(self, question_form):\\n return # osid.assessment.Question\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69448e47c4bf02b0a627fd76339aa93d\",\n \"score\": \"0.5796546\",\n \"text\": \"def test_quiz_question_uncategorized(self):\\n url = '/api/quizzes'\\n previous_questions = []\\n data = {'previous_questions': previous_questions}\\n\\n resp = self.client.post(url, json=data)\\n assert resp.status_code == 200\\n resp_data = json.loads(resp.data)\\n\\n # Check response for question that was returned\\n assert 'question' in resp_data\\n assert 'id' in resp_data['question']\\n question1 = Question.query.get(resp_data['question']['id'])\\n assert question1 is not None\\n assert resp_data['question'] == question1.format()\\n\\n # Get next question\\n previous_questions.append(question1.id)\\n data['previous_questions'] = previous_questions\\n\\n resp = self.client.post(url, json=data)\\n assert resp.status_code == 200\\n\\n resp_data = json.loads(resp.data)\\n question2 = Question.query.get(resp_data['question']['id'])\\n assert question2 is not None\\n assert question2.id not in previous_questions\\n\\n # Get depleted questions\\n previous_questions.append(question2.id)\\n data['previous_questions'] = previous_questions\\n\\n resp = self.client.post(url, json=data)\\n assert resp.status_code == 200\\n\\n resp_data = json.loads(resp.data)\\n assert resp_data['question'] is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd5c835c0285e5600d6d427e044136a6\",\n \"score\": \"0.5777712\",\n \"text\": \"def test_play_quiz_1(self):\\r\\n res = self.client().post('/api/quizzes', json={'previous_questions':[], 'quiz_category': {'type':'Sports', 'id':'6'}})\\r\\n data = json.loads(res.data)\\r\\n self.assertEqual(data['success'], True)\\r\\n self.assertEqual(data['question']['category'], 6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a301d4a204263d96fc8244582e9e2ac\",\n \"score\": \"0.5749012\",\n \"text\": \"def create_exam():\\n exam = Exam.objects.create(name='Test Exam',\\n kind = ExamKind.CI,\\n stage = ExamStage.DEV,\\n description='an exam for testing')\\n concept_type = ContentType.objects.get_for_model(Concept)\\n concept = Concept.objects.get(name = \\\"Concept A\\\")\\n FreeResponseQuestion.objects.create(exam=exam,\\n question=\\\"What is the answer to this FR question?\\\",\\n number=1,\\n content_type=concept_type,\\n object_id=concept.id)\\n concept = Concept.objects.get(name = \\\"Concept B\\\")\\n mcq = MultipleChoiceQuestion.objects.create(exam=exam,\\n question=\\\"What is the answer to this MC question?\\\",\\n number=2,\\n content_type=concept_type,\\n object_id=concept.id)\\n MultipleChoiceOption.objects.create(question=mcq, text=\\\"choice 1\\\", index=1, is_correct=True)\\n MultipleChoiceOption.objects.create(question=mcq, text=\\\"choice 2\\\", index=2)\\n MultipleChoiceOption.objects.create(question=mcq, text=\\\"choice 3\\\", index=3)\\n return exam\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60d8f79821ed5c1c520d16430f08aaad\",\n \"score\": \"0.57096446\",\n \"text\": \"def testCreateQuizLive(self):\\n status = Quiz.LIVE_STATUS\\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\\n quiz.save()\\n self.assertEqual(Quiz.objects.filter(status=status).count(), 1)\\n self.assertEqual(Quiz.objects.get(slug=self.slug).title, self.title)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77684eb79390efe1c869ca34064220ba\",\n \"score\": \"0.5664846\",\n \"text\": \"def create_question(controls, index, question_type, test, number, dbsession):\\n if question_type == \\\"selectTrue\\\" or question_type == \\\"multipleChoice\\\":\\n questionText = str(controls[index+1][1])\\n question = Question(True, question_type, questionText, \\n test, number)\\n dbsession.add(question)\\n dbsession.flush()\\n return question\\n if question_type == \\\"shortAnswer\\\":\\n questionText = str(controls[index][1])\\n question = Question(False, question_type, questionText,\\n test, number)\\n dbsession.add(question)\\n dbsession.flush()\\n return None\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b3ccb04e87f1e811108cc485d95e350\",\n \"score\": \"0.56620574\",\n \"text\": \"def create(info, context):\\n q = context # context is the newly created question\\n log.debug(\\\"[QUESTION CREATE] [%s] [%s]\\\" % (info, q))\\n utils.setQuestionDefaults(info, q) # !+\\n utils.setBungeniOwner(q)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c46e6a8f23aaaea758f6d7616cfd0c\",\n \"score\": \"0.56395406\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_user = os.environ.get(\\\"PSQL_USER\\\")\\n self.database_password = os.environ.get(\\\"PSQL_PWD\\\")\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format(self.database_user, self.database_password,'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n \\n self.new_question = {\\n 'question':'Test Question',\\n 'answer': 'Test Answer',\\n 'difficulty': 1,\\n 'category':1\\n }\\n self.new_question_bad_category = {\\n 'question':'Test Question',\\n 'answer': 'Test Answer',\\n 'difficulty': 1,\\n 'category':9\\n }\\n \\n self.new_search = {\\n 'searchTerm': 'title'\\n }\\n \\n self.quizz_post = {\\n \\\"previous_questions\\\": [33],\\n \\\"quiz_category\\\": {\\n \\\"type\\\": \\\"Science\\\",\\n \\\"id\\\": \\\"1\\\"}\\n }\\n self.quizz_post_bad = {\\n \\\"previous_questions\\\": [],\\n \\\"quiz_category\\\": {\\n \\\"type\\\": \\\"Science\\\", \\n \\\"id\\\": \\\"9\\\"\\n }\\n }\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21529def42e5a5cb6d9408d5fa44ff7\",\n \"score\": \"0.5599331\",\n \"text\": \"def _generate_quiz_form(questions):\\n # reset quiz form\\n for attr, value in QuizForm().__dict__.items():\\n if attr[:-1] == \\\"question\\\":\\n delattr(QuizForm, attr)\\n \\n for question in questions:\\n if questions[question][\\\"type\\\"] == \\\"String\\\":\\n setattr(QuizForm, question, StringField(questions[question][\\\"text\\\"], validators=[DataRequired(), Length(max=20)]))\\n elif questions[question][\\\"type\\\"] == \\\"Radio\\\":\\n setattr(QuizForm, question, RadioField(questions[question][\\\"text\\\"], choices=questions[question][\\\"choices\\\"], validators=[DataRequired()]))\\n elif questions[question][\\\"type\\\"] == \\\"Integer\\\":\\n setattr(QuizForm, question, IntegerField(questions[question][\\\"text\\\"], validators=[DataRequired(), NumberRange(min=0,max=999)]))\\n elif questions[question][\\\"type\\\"] == \\\"Checkbox\\\":\\n setattr(QuizForm, question, MultiCheckboxField(questions[question][\\\"text\\\"], choices=questions[question][\\\"choices\\\"])) \\n return QuizForm()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"840d35c7eff8c0af76b87bb90b916fc6\",\n \"score\": \"0.55596423\",\n \"text\": \"def get_quiz(quiz_type):\\n\\n # get the questions for the requested quiz and generate the corresponding quiz form\\n quiz = Quiz.query.filter_by(name=quiz_type).first()\\n questions = quiz.get_questions()\\n form = _generate_quiz_form(questions)\\n\\n name = quiz.name\\n completed = False\\n results = None\\n # retrieve user's attempt at the current quiz\\n prev_attempt = UserAnswer.query.filter_by(quiz_id=quiz.id, user_id=current_user.id).first()\\n if prev_attempt is not None:\\n completed = True\\n\\n # populate the form with the user's answers\\n user_answers = prev_attempt.get_user_answers()\\n _populate_form(form, user_answers)\\n\\n # construct object to be used to display the correct answers\\n solutions = quiz.get_solutions()\\n results = _construct_solution(form, solutions)\\n user_answers = _retrieve_answers(form)\\n\\n elif form.validate_on_submit():\\n # get the user's submitted answers\\n user_answers = _retrieve_answers(form)\\n\\n # get the quiz solution\\n solutions = quiz.get_solutions()\\n\\n # calculate the user's score\\n score = _calculate_quiz_score(user_answers, solutions)\\n\\n # add the user's answers to the db\\n completed = True\\n results = _construct_solution(form, solutions)\\n attempt = UserAnswer(quiz_id=quiz.id, \\n user_id=current_user.id,\\n ans1=user_answers[\\\"answer1\\\"][\\\"value\\\"], \\n ans2=user_answers[\\\"answer2\\\"][\\\"value\\\"], \\n ans3=user_answers[\\\"answer3\\\"][\\\"value\\\"], \\n ans4=user_answers[\\\"answer4\\\"][\\\"value\\\"],\\n ans5=user_answers[\\\"answer5\\\"][\\\"value\\\"] if \\\"answer5\\\" in user_answers else None,\\n ans6=user_answers[\\\"answer6\\\"][\\\"value\\\"] if \\\"answer6\\\" in user_answers else None,\\n ans7=user_answers[\\\"answer7\\\"][\\\"value\\\"] if \\\"answer7\\\" in user_answers else None,\\n ans8=user_answers[\\\"answer8\\\"][\\\"value\\\"] if \\\"answer8\\\" in user_answers else None,\\n score=score\\n )\\n db.session.add(attempt)\\n db.session.commit()\\n return render_template('quiz/quiz_template.html', title=\\\"Quiz - \\\" + name, heading= name + \\\" Quiz\\\", form=form, completed=completed, results=results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3effa6537a82fe68dd64d34113856e46\",\n \"score\": \"0.55323803\",\n \"text\": \"def test_new_quiz():\\n\\n with app.test_request_context():\\n Quiz.max_questions = 2\\n Quiz.max_time_in_seconds = 0.1\\n\\n client = app.test_client()\\n data = dict(\\n username=\\\"pietje\\\",\\n action=\\\"creategame\\\"\\n )\\n rv = client.post(url_for('index'), data=data)\\n\\n with client.session_transaction() as sess:\\n user_id = sess['user_id']\\n\\n quiz = store.get_quiz_by_user_id(user_id)\\n question = store.get_question_by_id(quiz.questions[0])\\n answer = store.get_answer_by_id(question.answers[0])\\n\\n assert quiz\\n assert question\\n assert answer\\n assert user_id in quiz.users\\n assert rv.status_code == 302\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2386cd474308db8bbde68558023fba84\",\n \"score\": \"0.5491061\",\n \"text\": \"async def quiz(self, ctx):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6028f5673ab7da0d560f685dd63213d\",\n \"score\": \"0.5491058\",\n \"text\": \"def test_quizz_get(self):\\n res = self.client().post('/quizzes',json={\\n 'previous_questions':[],\\n 'quiz_category':{'type': 'Science', 'id': '1'}\\n })\\n # assert if the request status is successful\\n self.assertEqual(res.status_code, 200)\\n data = json.loads(res.data)\\n # assert the payload of the response\\n self.assertTrue(data['success'])\\n self.assertTrue(data['question'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acbbb07e60a32bd602c0402c7467220c\",\n \"score\": \"0.5446392\",\n \"text\": \"def test_create_a_new_question(self):\\n res = self.client().post('/questions', json=self.new_question)\\n data = json.loads(res.data)\\n\\n self.assertEqual(res.status_code, 200)\\n self.assertEqual(data['success'], True)\\n self.assertTrue(data['created'])\\n self.assertTrue(data['questions'])\\n self.assertTrue(data['total_questions'])\\n self.assertTrue(data['current_category'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f80aa743d59d6fb27d7fa0bb6033f2af\",\n \"score\": \"0.5439644\",\n \"text\": \"def test_play_quiz_game(self):\\n\\n # send post request with category and previous questions\\n response = self.client().post('/quizzes',\\n json={'previous_questions': [9, 5, 12],\\n 'quiz_category': {\\n 'type': 'History',\\n 'id': '4'\\n }\\n })\\n data = json.loads(response.data)\\n\\n # check response status code.\\n self.assertEqual(response.status_code, 201)\\n # check success value.\\n self.assertEqual(data['success'], True)\\n # check for the existance of the questions property in the response.\\n self.assertTrue(data['question'])\\n\\n # Check that the question returned is in the correct category\\n self.assertEqual(data['question']['category'], 4)\\n\\n # Question returned is not in the previous questions list\\n self.assertNotEqual(data['question']['id'], 9)\\n self.assertNotEqual(data['question']['id'], 5)\\n self.assertNotEqual(data['question']['id'], 12)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"156c238257fc9ddf41b052d88f22d773\",\n \"score\": \"0.5439635\",\n \"text\": \"def testCreateQuizHidden(self):\\n\\n status = Quiz.HIDDEN_STATUS\\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\\n quiz.save()\\n self.assertEqual(Quiz.objects.filter(status=status).count(), 1)\\n self.assertEqual(Quiz.objects.get(slug=self.slug).title, self.title)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed57fd558f2f7cc4d9d44a223a344d89\",\n \"score\": \"0.54300576\",\n \"text\": \"def testCreateQuizDraft(self):\\n\\n status = Quiz.DRAFT_STATUS\\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\\n quiz.save()\\n self.assertEqual(Quiz.objects.filter(status=status).count(), 1)\\n self.assertEqual(Quiz.objects.get(slug=self.slug).title, self.title)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d12621c26a340eb8604a7bada56b064b\",\n \"score\": \"0.5427113\",\n \"text\": \"async def musicquiz(self, ctx, category_name=\\\"\\\"):\\n await ctx.send_help(ctx.command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46aa7b0fac28183a77937c089843305\",\n \"score\": \"0.5423655\",\n \"text\": \"def post(self, request):\\n quiz_id = request.data['quizID']\\n quiz = Quiz.objects.get(author=request.user, id=quiz_id)\\n\\n debug(f'Quiz is: {quiz}')\\n\\n questions = Question.objects.filter(test=quiz)\\n response = {}\\n\\n for question in questions:\\n debug(f'For question: {question}')\\n if question.type == 1: # short answer\\n answers = Answer.objects.filter(question=question)\\n debug(f'Answers are: {answers}')\\n cluster_centers = cluster_answers(answers, question.id)\\n response[question.id] = cluster_centers\\n else:\\n print('Skipping question of type MCQ')\\n\\n return APIResponse(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"867ee56bbfe20d0a9b73615ee88d3601\",\n \"score\": \"0.5414378\",\n \"text\": \"def __init__(self):\\n \\n self.id = ''\\n self.quiz_in_progress = Quiz\\n self.attemps = 0\\n self.quiz_answer = {}\\n self.complete = False\\n\\n self.quest_set= {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b137833c3ae5de772039cfe547657e\",\n \"score\": \"0.5356012\",\n \"text\": \"def test_quiz(self):\\n # get an intital question\\n first_question_get_res = self.client().post('/questions/quiz', json={\\n 'previous_questions': [],\\n 'quiz_category': {\\n 'type': 'click',\\n 'id': 0\\n }\\n })\\n\\n first_question_data = json.loads(first_question_get_res.data)\\n # ensure request was good\\n self.assertEqual(first_question_get_res.status_code, 200)\\n self.assertEqual(first_question_data['success'], True)\\n first_question = first_question_data['question']\\n first_answer = first_question['answer']\\n\\n # get a second question\\n second_question_get_res = self.client().post('/questions/quiz', json={\\n 'previous_questions': [5, 11],\\n 'quiz_category': {\\n 'type': 'Sports',\\n 'id': 0\\n }\\n })\\n second_question_data = json.loads(second_question_get_res.data)\\n # ensure request was good\\n self.assertEqual(second_question_get_res.status_code, 200)\\n self.assertEqual(second_question_data['success'], True)\\n second_question = second_question_data['question']\\n second_answer = second_question['answer']\\n\\n # the first and second questions should be different\\n self.assertNotEqual(first_question, second_question)\\n # the first and second answers should be different\\n self.assertNotEqual(first_answer, second_answer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f18646e09fc11ad828884614b43a90e0\",\n \"score\": \"0.53457\",\n \"text\": \"def test_get_quiz_question_with_success_response(self):\\n response = self.client().post(\\n '/quizzes',\\n content_type='application/json',\\n data=json.dumps(\\n {\\n 'previous_questions': [],\\n 'quiz_category': {'id': 0, 'type': 'all'}\\n }\\n )\\n )\\n data = json.loads(response.data)\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(data['success'], True)\\n self.assertTrue(data['question'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"567e6b34c8cbe76d643442c34d440e57\",\n \"score\": \"0.5337698\",\n \"text\": \"def create(self, validated_data):\\n \\n objects=Questions.objects.create(question=validated_data.get('question'),types=validated_data.get('types'))\\n # print >> sys.stderr, objects\\n return objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6feaaa0087054c3c7101ce88c2ce7e3f\",\n \"score\": \"0.53255945\",\n \"text\": \"def create_question_overview_df(self):\\n\\n questions = self.survey.questions\\n\\n # create an empty lists to append the columns to\\n all_answer_columns = []\\n all_question_names = []\\n all_question_types = []\\n all_question_indices = []\\n\\n # iterate over all questions, q holds the question ID in form of '1', '2', ...\\n for q in questions:\\n\\n question_code = questions[q]['title']\\n question_text = self.clean_text(questions[q]['question'])\\n question_type = questions[q]['question_type']\\n question_index = questions[q]['qid']\\n\\n # Process questions according to their type.\\n if question_type == \\\"Multiple choice\\\" or question_type == \\\"Multiple choice with comments\\\" or question_type == \\\"Array\\\":\\n # iterate over all subquestions (by the format of limepy, the subquestions section always has key '0')\\n for s in questions[q]['subquestions']['0']:\\n sub_question_code = (s['title'])\\n sub_question_text = self.clean_text(s['question'])\\n\\n merged_code = question_code + \\\"[\\\" + sub_question_code + \\\"]\\\"\\n merged_text = question_text + \\\" - \\\" + sub_question_text\\n all_answer_columns.append(merged_code)\\n all_question_names.append(merged_text)\\n all_question_types.append(question_type)\\n all_question_indices.append(question_index)\\n\\n # in Rankin the naming of the \\\"subquestions\\\" is slightly different and called \\\"answer\\\"\\n elif question_type == \\\"Ranking\\\":\\n # iterate over all possible answers (by the format of limepy,\\n # the subquestions section always has key '0')\\n for s in questions[q]['answers']['0']:\\n sub_question_code = (s['sortorder'])\\n sub_question_text = self.clean_text(s['answer'])\\n\\n merged_code = question_code + \\\"[\\\" + sub_question_code + \\\"]\\\"\\n merged_text = question_text + \\\" - \\\" + sub_question_text\\n all_answer_columns.append(merged_code)\\n all_question_names.append(merged_text)\\n all_question_types.append(question_type)\\n all_question_indices.append(question_index)\\n\\n # there is no subtype to be handled, like in normal text\\n # and the data can be appended directly\\n else:\\n all_answer_columns.append(question_code)\\n all_question_names.append(question_text)\\n all_question_types.append(question_type)\\n all_question_indices.append(question_index)\\n\\n # Make the dataframe with the Question code as a header and text and type as rows\\n survey_answers_df = pd.DataFrame(list(zip(all_question_names, all_question_types, all_question_indices)),\\n index=all_answer_columns,\\n columns=['Question', 'Question Type', 'Lime Index']).T\\n\\n return survey_answers_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee0b733bca7e9436be3e900999a0926\",\n \"score\": \"0.53237695\",\n \"text\": \"def test_question_creation(self):\\n response = self.client().post('/api/v1/questions',\\n data=json.dumps(self.question),\\n content_type='application/json')\\n\\n self.assertEqual(response.status_code, 201)\\n self.assertIn('restful API', str(response.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3420874bf3668054a29b982efca80b9f\",\n \"score\": \"0.53224343\",\n \"text\": \"def sample_quizz(**params):\\n defaults = {\\n 'title': 'Boomer WW II Quizz',\\n 'description': 'Are You A World War II Whiz?'\\n }\\n defaults.update(params)\\n\\n return Quizz.objects.create(**defaults)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1141d6b678a7f0682ea16f78f420b59\",\n \"score\": \"0.5297418\",\n \"text\": \"def test_success_response_for_playing_quiz_game(self):\\n response = self.client().post(\\n '/api/v1/quizzes',\\n content_type='application/json',\\n data=json.dumps(\\n {\\n 'previous_questions': [],\\n 'quiz_category': {'id': 0, 'type': 'all'}}\\n )\\n )\\n data = json.loads(response.data)\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(data['success'], True)\\n self.assertTrue(data['question'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c86b392d22faae6bc00314089b11aa2\",\n \"score\": \"0.52733284\",\n \"text\": \"def create_answer(self, answer_form):\\n return # osid.assessment.Answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1340c6471409ed03ab6e399234bbfe10\",\n \"score\": \"0.5269123\",\n \"text\": \"def create(cls, question_text):\\n q = cls(\\n question_text=question_text,\\n question_type=cls.__name__,\\n options=None\\n )\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5aa8a1d746b54e163ca22085a4c8639\",\n \"score\": \"0.52504885\",\n \"text\": \"def setUp(self):\\n self.question1 = Question(\\n level=\\\"walking\\\",\\n number=\\\"1\\\",\\n question_title=\\\"Is it a good idea to walk?\\\",\\n right_answer=\\\"Yes\\\",\\n wrong_answer1=\\\"No\\\",\\n wrong_answer2=\\\"No\\\",\\n wrong_answer3=\\\"No\\\"\\n )\\n self.question1.save()\\n\\n self.question2 = Question(\\n level=\\\"walking\\\",\\n number=\\\"2\\\",\\n question_title=\\\"Is it a good idea to run?\\\",\\n right_answer=\\\"Yes\\\",\\n wrong_answer1=\\\"No\\\",\\n wrong_answer2=\\\"No\\\",\\n wrong_answer3=\\\"No\\\"\\n )\\n self.question2.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb0324383fd26fcebad4fd86c0fc56d4\",\n \"score\": \"0.5235127\",\n \"text\": \"def choice_quiz(self, word_list_name: str, quiz_strategy: str) -> (bool, Question, Flashcard):\\r\\n\\r\\n # Pick 5 expressions, get flashcards and alternatives\\r\\n learning_progress_dict: Dict[int, str] = _get_learning_progress(self._get_word_list(word_list_name))\\r\\n row_keys_new = pick_words(learning_progress_dict=learning_progress_dict,\\r\\n filter_by_progress=lambda p: p == Progress.NEW,\\r\\n order=PickOrder.ORIGINAL,\\r\\n max_count_from_size=lambda v: 5)\\r\\n\\r\\n row_keys_recent = pick_words(learning_progress_dict=learning_progress_dict,\\r\\n filter_by_progress=lambda p: p == Progress.RECENT,\\r\\n order=PickOrder.SHUFFLED,\\r\\n max_count_from_size=lambda v: 5)\\r\\n\\r\\n row_keys_learned = pick_words(learning_progress_dict=learning_progress_dict,\\r\\n filter_by_progress=lambda p: p == Progress.LEARNED,\\r\\n order=PickOrder.SHUFFLED,\\r\\n max_count_from_size=lambda size: 3 if size > 10 else 0)\\r\\n\\r\\n flashcards_only = [_build_quiz(word_list=self._get_word_list(word_list_name),\\r\\n row_key=row_key,\\r\\n alternatives_pool=None,\\r\\n flashcard_only=True) for row_key in row_keys_new]\\r\\n new_questions = [_build_quiz(word_list=self._get_word_list(word_list_name),\\r\\n row_key=row_key,\\r\\n alternatives_pool=self.word_pool_lang1,\\r\\n flashcard_only=False) for row_key in row_keys_new]\\r\\n random.shuffle(new_questions)\\r\\n\\r\\n recent_questions = [_build_quiz(word_list=self._get_word_list(word_list_name),\\r\\n row_key=row_key,\\r\\n alternatives_pool=self.word_pool_lang1,\\r\\n flashcard_only=False) for row_key in row_keys_recent]\\r\\n random.shuffle(recent_questions)\\r\\n\\r\\n learned_questions = [_build_quiz(word_list=self._get_word_list(word_list_name),\\r\\n row_key=row_key,\\r\\n alternatives_pool=self.word_pool_lang1,\\r\\n flashcard_only=False) for row_key in row_keys_learned]\\r\\n random.shuffle(learned_questions)\\r\\n\\r\\n quiz_packages = flashcards_only + new_questions + recent_questions + learned_questions\\r\\n\\r\\n return quiz_packages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa46fd7262daef40b1c117bcf2ec823a\",\n \"score\": \"0.5234181\",\n \"text\": \"def test_one_form_valid(self):\\n response_post = self.client.post(\\n reverse('dash:create_question_choice', args=[self.quiz.pk]),\\n {\\n 'question_text': 'question text',\\n 'form-TOTAL_FORMS': 4,\\n 'form-INITIAL_FORMS': 0,\\n 'form-MAX_NUM_FORMS': 4,\\n 'form-MIN_NUM_FORMS': 0,\\n 'finish': True,\\n }\\n )\\n self.assertEqual(response_post.status_code, 200)\\n self.assertTemplateUsed(response_post, 'dashboard/create_quiz.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c6aa998d780e102d9b6ac422b87f362\",\n \"score\": \"0.5226121\",\n \"text\": \"def __init__(self, name):\\n super(Quiz, self).__init__(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"346151a5950b381268226648f9024e81\",\n \"score\": \"0.5198816\",\n \"text\": \"def test_create_new_question(self):\\n\\n # Total questions before posting a new question\\n questions_before = Question.query.all()\\n\\n # create new question and load response data\\n response = self.client().post('/questions', json=self.new_question)\\n data = json.loads(response.data)\\n\\n # Total questions after posting a new question\\n questions_after = Question.query.all()\\n\\n # check status code and success message\\n self.assertEqual(response.status_code, 201)\\n self.assertEqual(data['success'], True)\\n\\n # Check difference in total question after posting\\n self.assertTrue(len(questions_after) - len(questions_before) == 1)\\n\\n # check that question is not None\\n question = Question.query.get(data['question_id'])\\n self.assertIsNotNone(question)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7adc50cbddbb942a5f74677742abaef\",\n \"score\": \"0.51843405\",\n \"text\": \"def test_successfully_adding_a_question_to_database(self):\\n response = self.client().post(\\n '/api/v1/questions',\\n content_type='application/json', data=json.dumps(\\n {\\n 'question': 'Is computer Science good?',\\n 'answer': 'Yes its good',\\n 'difficulty': 2,\\n 'category': 2\\n }\\n )\\n )\\n data = json.loads(response.data)\\n self.assertTrue(response.status_code, 201)\\n self.assertEqual(data['success'], True)\\n self.assertEqual(data['message'], 'Question successfully added')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bc465780b2fc2962e44cda029687b38\",\n \"score\": \"0.5180577\",\n \"text\": \"def test_get_quiz(self):\\n quiz = {'previous_questions': [], 'quiz_category': {'type': 'History', 'id': 5}}\\n res = self.client().post('/quizzes', json=quiz)\\n data = json.loads(res.data)\\n\\n \\n self.assertEqual(res.status_code, 200)\\n self.assertEqual(data['success'], True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11d8ad95a4d2c705856bf79cd388cdca\",\n \"score\": \"0.5169148\",\n \"text\": \"def test_create_a_question_with_success_response(self):\\n response = self.client().post(\\n '/questions',\\n content_type='application/json',\\n data=json.dumps(\\n {\\n 'question': 'Test question',\\n 'answer': 'Test answer',\\n 'difficulty': 2,\\n 'category': 2\\n }\\n )\\n )\\n data = json.loads(response.data)\\n self.assertTrue(response.status_code, 201)\\n self.assertEqual(data['success'], True)\\n self.assertEqual(data['message'], 'Question successfully added')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8adc13d2fbc71dadd7c592327c9d1ef9\",\n \"score\": \"0.5167067\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgresql://{}:{}@{}/{}\\\".format(\\n 'postgres', 'root', 'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n self.new_question = {\\n \\\"answer\\\": \\\"Apollo 10\\\",\\n \\\"category\\\": 5,\\n \\\"difficulty\\\": 3,\\n \\\"id\\\": 8,\\n \\\"question\\\": \\\"What movie earned Tom Hanks his third straight Oscar nomination, in 1896?\\\"\\n }\\n self.empty_question = {\\n 'question': '',\\n 'answer': '',\\n 'difficulty': 0,\\n 'category': 0\\n }\\n\\n self.search_term = {\\n \\\"searchTerm\\\": 'Who discovered penicillin?'\\n }\\n\\n self.empty_search_term = {\\n \\\"searchTerm\\\": ''\\n }\\n\\n self.invalid_search_term = {\\n \\\"searchTerm\\\": 'askdjasdkjdiqe871623876azd'\\n }\\n\\n self.quiz_data = {\\n 'previous_questions': [3, 7],\\n 'quiz_category': {\\n 'type': 'Science',\\n 'id': 1\\n }\\n }\\n\\n self.empty_quiz_data = {\\n 'previous_questions': '',\\n 'quiz_category': ''\\n }\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c722087e8a75f007ff40c54c77458a37\",\n \"score\": \"0.51610744\",\n \"text\": \"def test_play_quiz_2(self):\\r\\n res = self.client().post('/api/quizzes', json={'previous_questions':[10], 'quiz_category': {'type':'Sports', 'id':'6'}})\\r\\n data = json.loads(res.data)\\r\\n self.assertEqual(data['success'], True)\\r\\n self.assertEqual(data['question']['id'], 11)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b1a761e1515cdcc1aa12bdbb6e4509f\",\n \"score\": \"0.51568455\",\n \"text\": \"def test_question_by_category(self):\\n assert Category.query.count() == 2\\n cat = self.science\\n resp = self.client.get(f'/api/categories/{cat.id}/questions')\\n assert resp.status_code == 200\\n\\n data = json.loads(resp.data)\\n\\n assert 'current_category' in data\\n assert data['current_category'] == cat.id\\n\\n assert 'questions' in data\\n assert data['questions'][0]['category'] == cat.id\\n\\n # Check response shape\\n assert data == {\\n 'current_category': 1,\\n 'questions': [\\n {\\n 'answer': 'Nobody knows',\\n 'category': 1,\\n 'difficulty': 5,\\n 'id': 1,\\n 'question': 'What is science?'\\n }\\n ],\\n 'total_questions': 1\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b73032f2f582dce21fbc2f465e478b38\",\n \"score\": \"0.5155747\",\n \"text\": \"def create_question(cls, data, question_number):\\n question = data['questions'][question_number]\\n elements = '<div class=\\\"nhsuk-form-group\\\">'\\n elements += '<fieldset class=\\\"nhsuk-fieldset\\\">'\\n elements += '<legend class=\\\"nhsuk-fieldset__legend nhsuk-fieldset__legend--l\\\">'\\n elements += '<h1 class=\\\"nhsuk-fieldset__heading\\\">'\\n elements += question['body']\\n elements += '</h1>'\\n elements += '</legend>'\\n if question['info_box']:\\n elements += cls.create_info_box(data, question['info_box'])\\n if question['type'] == 'single select':\\n elements += cls.create_single_select(question)\\n elif question['type'] == 'multiple select':\\n elements += cls.create_multi_select(question)\\n elements += '</fieldset>'\\n elements += '</div>'\\n return elements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14b094e4e02da416fb07bbc92c7e0cc9\",\n \"score\": \"0.5146169\",\n \"text\": \"def _ask_questions(**kwargs) -> Tuple[dict, dict]:\\n\\n choices = dict(\\n import_data=\\\"Import data\\\",\\n register_rule=\\\"Configure and register rule »\\\",\\n add_procedure=\\\"Add procedure »\\\",\\n preview=\\\"Generate preview\\\",\\n check=\\\"Check for BIDS conformity\\\",\\n cleanup=\\\"Cleanup\\\",\\n\\n rule_create=\\\"Create new rule\\\",\\n rule_import=\\\"Import rule\\\",\\n\\n proc_change=\\\"Change active procedures\\\",\\n proc_create=\\\"Create new procedure\\\",\\n proc_import=\\\"Import procedure\\\",\\n )\\n\\n questions = [\\n {\\n \\\"type\\\": \\\"select\\\",\\n \\\"name\\\": \\\"step_select\\\",\\n \\\"message\\\": \\\"What do you want to do?\\\",\\n \\\"choices\\\": [\\n # Sets up a datalad dataset and prepares the convesion\\n choices[\\\"import_data\\\"],\\n # Registers and applies datalad hirni rule\\n choices[\\\"register_rule\\\"],\\n # Create, import or register procedures\\n choices[\\\"add_procedure\\\"],\\n # Generates the BIDS converion\\n choices[\\\"preview\\\"],\\n choices[\\\"check\\\"],\\n # Remove imported and converted\\n choices[\\\"cleanup\\\"],\\n questionary.Separator(),\\n \\\"Help\\\",\\n \\\"Exit\\\"\\n ],\\n \\\"use_shortcuts\\\": True,\\n \\\"default\\\": \\\"Exit\\\",\\n\\n },\\n {\\n \\\"type\\\": \\\"path\\\",\\n \\\"name\\\": \\\"data_path\\\",\\n \\\"message\\\": \\\"Path to the data tar ball:\\\",\\n \\\"when\\\": lambda x: x[\\\"step_select\\\"] == choices[\\\"import_data\\\"],\\n },\\n {\\n \\\"type\\\": \\\"select\\\",\\n \\\"name\\\": \\\"rule_select\\\",\\n \\\"message\\\": \\\"What do you want to do?\\\",\\n \\\"when\\\": lambda x: x[\\\"step_select\\\"] == choices[\\\"register_rule\\\"],\\n \\\"choices\\\": [\\n choices[\\\"rule_create\\\"],\\n choices[\\\"rule_import\\\"],\\n questionary.Separator(),\\n \\\"Return\\\"\\n ],\\n \\\"use_shortcuts\\\": True,\\n \\\"default\\\": \\\"Return\\\",\\n },\\n {\\n \\\"type\\\": \\\"path\\\",\\n \\\"name\\\": \\\"rule_file\\\",\\n \\\"message\\\": \\\"Path to the rule:\\\",\\n \\\"when\\\": (lambda x: x[\\\"step_select\\\"] == choices[\\\"register_rule\\\"]\\n and x[\\\"rule_select\\\"] == choices[\\\"rule_import\\\"]),\\n },\\n {\\n \\\"type\\\": \\\"select\\\",\\n \\\"name\\\": \\\"procedure_select\\\",\\n \\\"message\\\": \\\"What do you want to do?\\\",\\n \\\"when\\\": lambda x: x[\\\"step_select\\\"] == choices[\\\"add_procedure\\\"],\\n \\\"choices\\\": [\\n choices[\\\"proc_change\\\"],\\n choices[\\\"proc_create\\\"],\\n choices[\\\"proc_import\\\"],\\n questionary.Separator(),\\n \\\"Return\\\",\\n ],\\n \\\"use_shortcuts\\\": True,\\n \\\"default\\\": \\\"Return\\\",\\n },\\n {\\n \\\"type\\\": \\\"select\\\",\\n \\\"name\\\": \\\"procedure_type\\\",\\n \\\"message\\\": \\\"What type of procedure do you want to create?\\\",\\n \\\"when\\\": (lambda x: x[\\\"step_select\\\"] == choices[\\\"add_procedure\\\"]\\n and x[\\\"procedure_select\\\"] == choices[\\\"proc_create\\\"]),\\n \\\"choices\\\": [\\n \\\"shell\\\",\\n \\\"python\\\",\\n questionary.Separator(),\\n \\\"Return\\\",\\n ],\\n \\\"use_shortcuts\\\": True,\\n \\\"default\\\": \\\"Return\\\",\\n },\\n {\\n \\\"type\\\": \\\"text\\\",\\n \\\"name\\\": \\\"procedure_name\\\",\\n \\\"message\\\": \\\"How should the procedure be called?\\\",\\n \\\"when\\\": (lambda x: x[\\\"step_select\\\"] == choices[\\\"add_procedure\\\"]\\n and x[\\\"procedure_select\\\"] == choices[\\\"proc_create\\\"]\\n and x[\\\"procedure_type\\\"] != \\\"Return\\\"),\\n },\\n {\\n \\\"type\\\": \\\"path\\\",\\n \\\"name\\\": \\\"procedure_file\\\",\\n \\\"message\\\": \\\"Path to the procedure:\\\",\\n \\\"when\\\": (lambda x: x[\\\"step_select\\\"] == choices[\\\"add_procedure\\\"]\\n and x[\\\"procedure_select\\\"] == choices[\\\"proc_import\\\"]),\\n },\\n {\\n \\\"type\\\": \\\"text\\\",\\n \\\"name\\\": \\\"procedure_file_name\\\",\\n \\\"message\\\": \\\"Name of the procedure:\\\",\\n \\\"when\\\": (lambda x: x[\\\"step_select\\\"] == choices[\\\"add_procedure\\\"]\\n and x[\\\"procedure_select\\\"] == choices[\\\"proc_import\\\"]\\n and x[\\\"procedure_file\\\"]),\\n \\\"default\\\": lambda x: Path(x[\\\"procedure_file\\\"]).stem\\n },\\n ]\\n return questionary.prompt(questions, **kwargs), choices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ce1fccb1fa8c8bafb62b4ff93a30f59\",\n \"score\": \"0.5141362\",\n \"text\": \"def test_create_answer(self):\\n question_serializer = serializers.QuestionSerializer(self.question, context=serializer_context)\\n payload = {\\n 'question': question_serializer.data['url'],\\n 'text': 'Test text 2',\\n 'is_correct': True\\n }\\n\\n res = self.client.post(ANSWER_URL, payload)\\n\\n answer = models.Answer.objects.get(id=res.data['id'])\\n answer_serializer = serializers.AnswerSerializer(answer, context=serializer_context)\\n\\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\\n test_all_model_attributes(self, payload, answer, answer_serializer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6ff8d2dcc58aea039dc73babfd8aff\",\n \"score\": \"0.5140274\",\n \"text\": \"def test_question_creation(self):\\n self.register_user()\\n user= self.login_user()\\n Access_token=json.loads(user.data.decode())['access_token']\\n res= self.client().post(\\n '/questions/',\\n headers=dict(Authorization=\\\"Bearer \\\"+Access_token),\\n data= self.question\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a9b41c76d0aee771876f3dcb4f13a52\",\n \"score\": \"0.51293486\",\n \"text\": \"def create(cls, question_text, options):\\n q = cls(\\n question_text=question_text,\\n question_type=cls.__name__,\\n options=options.__str__()\\n )\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a9b41c76d0aee771876f3dcb4f13a52\",\n \"score\": \"0.51293486\",\n \"text\": \"def create(cls, question_text, options):\\n q = cls(\\n question_text=question_text,\\n question_type=cls.__name__,\\n options=options.__str__()\\n )\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a9b41c76d0aee771876f3dcb4f13a52\",\n \"score\": \"0.51293486\",\n \"text\": \"def create(cls, question_text, options):\\n q = cls(\\n question_text=question_text,\\n question_type=cls.__name__,\\n options=options.__str__()\\n )\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af8be9f552699e1a54c294c91f256cef\",\n \"score\": \"0.51272994\",\n \"text\": \"def load_quizzes():\\n quiz_list = [\\n 'drum_fill_friday.json'\\n ]\\n\\n for quiz in quiz_list:\\n with open('www/assets/data/%s' % quiz, 'rb') as readfile:\\n quiz_json = dict(json.loads(readfile.read()))\\n\\n quiz = {\\n 'category': \\\"Drum Fill Friday\\\",\\n 'title': quiz_json['title'],\\n 'text': 'TKTK',\\n 'photo': None,\\n 'seamus_url': '',\\n 'author': 'Bob Boilen'\\n }\\n\\n # Create photo\\n if quiz_json['photo']:\\n quiz['photo'] = _create_photo(quiz_json['photo'])\\n\\n # Create quiz\\n qz = models.Quiz(**quiz)\\n qz.save()\\n\\n print \\\"Saved quiz: %s\\\" % qz\\n\\n for question_index, question_json in enumerate(quiz_json['questions']):\\n question = {\\n 'order': question_index,\\n 'quiz': qz,\\n 'text': question_json['text'],\\n 'photo': None,\\n 'audio': None\\n }\\n\\n # Create photo\\n if question_json['photo']:\\n question['photo'] = _create_photo(question_json['photo'])\\n\\n # Create audio\\n if question_json['audio']:\\n question['audio'] = _create_audio(question_json['audio'])\\n\\n # Create question\\n qn = models.Question(**question)\\n qn.save()\\n\\n print \\\"Saved question: %s\\\" % qn\\n\\n for choice_index, choice_json in enumerate(question_json['choices']):\\n choice = {\\n 'order': choice_index,\\n 'question': qn,\\n 'text': choice_json['text'],\\n 'correct_answer': False,\\n 'photo': None,\\n 'audio': None,\\n }\\n\\n if choice_index == question_json['answer']:\\n choice['correct_answer'] = True\\n\\n # Create photo\\n if choice_json['photo']:\\n choice['photo'] = _create_photo(choice_json['photo'])\\n\\n # Create audio\\n if choice_json['audio']:\\n choice['audio'] = _create_audio(choice_json['audio'])\\n\\n # Create choice\\n ch = models.Choice(**choice)\\n ch.save()\\n\\n print 'Saved choice: %s' % ch\\n\\n qz.deploy()\\n print 'Deployed quiz: %s' % qz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fcf0e9a4843e5c2997df0bbfaebcd91\",\n \"score\": \"0.51129526\",\n \"text\": \"def sample_answer(question, **kwargs):\\n defaults = {'text': 'some text'}\\n defaults.update(kwargs)\\n return models.Answer.objects.create(question=question, **defaults)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cc53fb1dda1386fb965a9fa321d8083\",\n \"score\": \"0.5108282\",\n \"text\": \"def test_endpoint_create_question_creates_new_question_and_returns_created_question(self):\\n res = self.client.post('api/v1/questions',\\n data = json.dumps({ \\n \\\"createdBy\\\": 1,\\n \\\"meetup\\\": 1,\\n \\\"title\\\": 'Test title',\\n \\\"body\\\": \\\"Swali langu ni je\\\",\\n \\\"votes\\\": 0\\n }),\\n content_type='application/json'\\n ) \\n expected_output = {\\n \\\"status\\\": 201,\\n \\\"data\\\": [\\n {\\n \\\"user\\\": 1, # user who posted the question\\n \\\"meetup\\\": 1, # meetup record primary key\\n \\\"title\\\": 'Test title',\\n \\\"body\\\": \\\"Swali langu ni je\\\"\\n }\\n ]\\n }\\n \\n self.assertEqual(res.json['status'], expected_output['status'])\\n self.assertTrue(all(item in res.json['data'][0].items() for item in expected_output['data'][0].items()))\\n self.assertEqual(res.status_code, 201)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"186a932951c622cafc720fd4b74cdfa9\",\n \"score\": \"0.50935435\",\n \"text\": \"def quiz():\\n data = request.get_json()\\n prev_qs = list(data[\\\"previous_questions\\\"])\\n quiz_category = int(data[\\\"quiz_category\\\"][\\\"id\\\"])\\n\\n if quiz_category:\\n\\n if not Category.query.get(quiz_category):\\n abort(404)\\n get_questions = Question.query.filter(\\n Question.category == quiz_category,\\n Question.id.notin_(prev_qs)).all()\\n else:\\n get_questions = Question.query.filter(\\n Question.id.notin_(prev_qs)).all()\\n\\n if len(get_questions) == 0:\\n return jsonify(None)\\n else:\\n questions = list(map(Question.format, get_questions))\\n question = random.choice(questions)\\n return jsonify({\\n \\\"success\\\": True,\\n **question\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c0f5049c46e4fea0871b4e46de078d4\",\n \"score\": \"0.5093345\",\n \"text\": \"def test_play_quiz_3(self):\\r\\n res = self.client().post('/api/quizzes', json={'previous_questions':[10, 11], 'quiz_category': {'type':'Sports', 'id':'6'}})\\r\\n data = json.loads(res.data)\\r\\n self.assertEqual(data['success'], True)\\r\\n self.assertEqual('question' not in data, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54639854db0fbff7c8567eb8668c3499\",\n \"score\": \"0.507514\",\n \"text\": \"def test_play_quiz_success(self):\\n data = {\\n \\\"quiz_category\\\": {\\n \\\"id\\\": 1\\n },\\n \\\"previous_questions\\\": []\\n }\\n response = self.client().post(\\n '/quizzes', json=data, headers=self.player_headers\\n )\\n json_data = response.get_json()\\n self.assertEqual(response.status_code, StatusCode.HTTP_200_OK.value)\\n self.assertTrue(json_data.get('success'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e370b2c08eb0f56e0e383723c18031d\",\n \"score\": \"0.5072601\",\n \"text\": \"def create(self, validated_data):\\n # La validación de que si el formulario está activo debe hacerse fuera de este serializador\\n form_participant = self.context['form_participant']\\n question = self.context['question']\\n validated_data.pop('queestion_id')\\n answer = AnswerQuestion.objects.create(\\n question=question,\\n form_participant=form_participant,\\n **validated_data\\n )\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cade0012ab39295b2ce0664afe932077\",\n \"score\": \"0.5064219\",\n \"text\": \"def trivia(self, ctx, questions: int=10):\\n if questions <= 2:\\n # At least 3 questions\\n yield from self.bot.say(\\\":warning: Too little questions!\\\")\\n return\\n # TODO: implement a scoreboard to keep track of the winner\\n scoreboard = {}\\n for i in range(questions):\\n r = requests.get('https://opentdb.com/api.php?amount=1&type=multiple')\\n q = r.json()\\n q = q['results'][0]\\n r.close()\\n question, category, answer = (unescape(q['question']), unescape(q['category']), unescape(q['correct_answer']))\\n print(question, answer)\\n yield from self.bot.say(\\\"**Category: **%s\\\\n**Question: **%s\\\" % (category, question))\\n\\n def check(msg):\\n if msg.content.lower() == answer.lower():\\n return True\\n return False\\n\\n response = yield from self.bot.wait_for_message(timeout=4.0, check=check)\\n if response != None:\\n yield from self.bot.say(\\\"%s is correct!\\\" % response.author.mention)\\n continue\\n\\n clue = ''.join('?' if random.randint(0,3) and i!=' ' else i for i in answer)\\n yield from self.bot.say(\\\"`Clue: %s`\\\" % clue)\\n\\n response = yield from self.bot.wait_for_message(timeout=20.0, check=check)\\n if response != None:\\n yield from self.bot.say(\\\"%s is correct!\\\" % response.author.mention)\\n else:\\n yield from self.bot.say(\\\"Time's up!\\\\n**Answer: **%s\\\" % answer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1342660eb6b3571ff6d18a41c74719\",\n \"score\": \"0.5054026\",\n \"text\": \"def _create_qa_features(\\n example,\\n model_type,\\n tokenizer,\\n unique_id,\\n is_training,\\n max_question_length,\\n max_seq_length,\\n doc_stride,\\n custom_tokenize=None,\\n):\\n\\n # _QAFeatures is data structure representing features of an unique document\\n # span-question-answer triplet.\\n # Args:\\n # unique_id (int): An unique id identifying the span-question-answer triplet.\\n # qa_id (int or str): An unique id identifying the document-question-answer\\n # sample in the original :class:`utils_nlp.dataset.pytorch.QADataset`\\n # tokens (list): Concatenated question tokens and paragraph tokens.\\n # token_to_orig_map (dict): A dictionary mapping token indices in the\\n # document span back to the token indices in the original document\\n # before document splitting.\\n # This is needed during post-processing to generate the final\\n # predicted answer.\\n # token_is_max_context (list): List of booleans indicating whether a\\n # token has the maximum context in teh current document span if it\\n # appears in multiple document spans and gets multiple predicted\\n # scores. We only want to consider the score with \\\"maximum context\\\".\\n # \\\"Maximum context\\\" is defined as the *minimum* of its left and\\n # right context.\\n # For example:\\n # Doc: the man went to the store and bought a gallon of milk\\n # Span A: the man went to the\\n # Span B: to the store and bought\\n # Span C: and bought a gallon of\\n\\n # In the example the maximum context for 'bought' would be span C\\n # since it has 1 left context and 3 right context, while span B\\n # has 4 left context and 0 right context.\\n # This is needed during post-processing to generate the final\\n # predicted answer.\\n # input_ids (list): List of numerical token indices corresponding to\\n # the tokens.\\n # input_mask (list): List of 1s and 0s indicating if a token is from\\n # the input data or padded to conform to the maximum sequence\\n # length. 1 for actual token and 0 for padded token.\\n # segment_ids (list): List of 0s and 1s indicating if a token is from\\n # the question text (0) or paragraph text (1).\\n # start_position (int): Index of the starting token of the answer span.\\n # end_position (int): Index of the ending token of the answer span.\\n # cls_index (int): Index of the CLS token.\\n # p_mask (list): Mask with 1 for token than cannot be in the answer,\\n # 0 for token which can be in an answer.\\n # paragraph_len(int): Number of tokens in the document span.\\n _QAFeatures = collections.namedtuple(\\n \\\"_QAFeatures\\\",\\n [\\n \\\"unique_id\\\",\\n \\\"qa_id\\\",\\n \\\"tokens\\\",\\n \\\"token_to_orig_map\\\",\\n \\\"token_is_max_context\\\",\\n \\\"input_ids\\\",\\n \\\"input_mask\\\",\\n \\\"segment_ids\\\",\\n \\\"start_position\\\",\\n \\\"end_position\\\",\\n \\\"cls_index\\\",\\n \\\"p_mask\\\",\\n \\\"paragraph_len\\\",\\n ],\\n )\\n\\n if custom_tokenize:\\n tokenize_func = custom_tokenize\\n else:\\n tokenize_func = tokenizer.tokenize\\n\\n def _improve_answer_span(doc_tokens, input_start, input_end, orig_answer_text):\\n \\\"\\\"\\\"Returns tokenized answer spans that better match the annotated answer.\\\"\\\"\\\"\\n\\n # We first project character-based annotations to\\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\\n # often find a \\\"better match\\\". For example:\\n #\\n # Question: What year was John Smith born?\\n # Context: The leader was John Smith (1895-1943).\\n # Answer: 1895\\n #\\n # The original whitespace-tokenized answer will be \\\"(1895-1943).\\\". However\\n # after tokenization, our tokens will be \\\"( 1895 - 1943 ) .\\\". So we can match\\n # the exact answer, 1895.\\n #\\n # However, this is not always possible. Consider the following:\\n #\\n # Question: What country is the top exporter of electornics?\\n # Context: The Japanese electronics industry is the lagest in the world.\\n # Answer: Japan\\n #\\n # In this case, the annotator chose \\\"Japan\\\" as a character sub-span of\\n # the word \\\"Japanese\\\". Since our WordPiece tokenizer does not split\\n # \\\"Japanese\\\", we just use \\\"Japanese\\\" as the annotation. This is fairly rare,\\n # but does happen.\\n tok_answer_text = \\\" \\\".join(tokenize_func(orig_answer_text))\\n\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start : (new_end + 1)])\\n if text_span == tok_answer_text:\\n return (new_start, new_end)\\n\\n return (input_start, input_end)\\n\\n def _check_is_max_context(doc_spans, cur_span_index, position):\\n \\\"\\\"\\\"Check if this is the 'max context' doc span for the token.\\\"\\\"\\\"\\n\\n # Because of the sliding window approach taken to scoring documents, a single\\n # token can appear in multiple documents. E.g.\\n # Doc: the man went to the store and bought a gallon of milk\\n # Span A: the man went to the\\n # Span B: to the store and bought\\n # Span C: and bought a gallon of\\n # ...\\n #\\n # Now the word 'bought' will have two scores from spans B and C. We only\\n # want to consider the score with \\\"maximum context\\\", which we define as\\n # the *minimum* of its left and right context (the *sum* of left and\\n # right context will always be the same, of course).\\n #\\n # In the example the maximum context for 'bought' would be span C since\\n # it has 1 left context and 3 right context, while span B has 4 left context\\n # and 0 right context.\\n best_score = None\\n best_span_index = None\\n for (span_index, doc_span) in enumerate(doc_spans):\\n end = doc_span.start + doc_span.length - 1\\n if position < doc_span.start:\\n continue\\n if position > end:\\n continue\\n num_left_context = position - doc_span.start\\n num_right_context = end - position\\n score = min(num_left_context, num_right_context) + 0.01 * doc_span.length\\n if best_score is None or score > best_score:\\n best_score = score\\n best_span_index = span_index\\n\\n return cur_span_index == best_span_index\\n\\n cls_token = \\\"[CLS]\\\"\\n sep_token = \\\"[SEP]\\\"\\n pad_token = 0\\n sequence_a_segment_id = 0\\n sequence_b_segment_id = 1\\n mask_padding_with_zero = True\\n\\n if model_type == \\\"xlnet\\\":\\n cls_token_segment_id = 2\\n # Should this be 4, or it doesn't matter?\\n pad_token_segment_id = 3\\n cls_token_at_end = True\\n else:\\n cls_token_segment_id = 0\\n pad_token_segment_id = 0\\n cls_token_at_end = False\\n\\n qa_features = []\\n\\n # unique_id identified unique feature/label pairs. It's different\\n # from qa_id in that each qa_example can be broken down into\\n # multiple feature samples if the paragraph length is longer than\\n # maximum sequence length allowed\\n query_tokens = tokenize_func(example.question_text)\\n\\n if len(query_tokens) > max_question_length:\\n query_tokens = query_tokens[0:max_question_length]\\n # map word-piece tokens to original tokens\\n tok_to_orig_index = []\\n # map original tokens to corresponding word-piece tokens\\n orig_to_tok_index = []\\n all_doc_tokens = []\\n for (i, token) in enumerate(example.doc_tokens):\\n orig_to_tok_index.append(len(all_doc_tokens))\\n sub_tokens = tokenize_func(token)\\n for sub_token in sub_tokens:\\n tok_to_orig_index.append(i)\\n all_doc_tokens.append(sub_token)\\n\\n tok_start_position = None\\n tok_end_position = None\\n if is_training and example.is_impossible:\\n tok_start_position = -1\\n tok_end_position = -1\\n if is_training and not example.is_impossible:\\n tok_start_position = orig_to_tok_index[example.start_position]\\n if example.end_position < len(example.doc_tokens) - 1:\\n # +1: move the the token after the ending token in\\n # original tokens\\n # -1, moves one step back\\n # these two operations ensures word piece is covered\\n # when it's part of the original ending token.\\n tok_end_position = orig_to_tok_index[example.end_position + 1] - 1\\n else:\\n tok_end_position = len(all_doc_tokens) - 1\\n (tok_start_position, tok_end_position) = _improve_answer_span(\\n all_doc_tokens,\\n tok_start_position,\\n tok_end_position,\\n example.orig_answer_text,\\n )\\n\\n # The -3 accounts for [CLS], [SEP] and [SEP]\\n max_tokens_for_doc = max_seq_length - len(query_tokens) - 3\\n\\n # We can have documents that are longer than the maximum sequence length.\\n # To deal with this we do a sliding window approach, where we take chunks\\n # of the up to our max length with a stride of `doc_stride`.\\n _DocSpan = collections.namedtuple(\\\"DocSpan\\\", [\\\"start\\\", \\\"length\\\"])\\n doc_spans = []\\n start_offset = 0\\n while start_offset < len(all_doc_tokens):\\n length = len(all_doc_tokens) - start_offset\\n if length > max_tokens_for_doc:\\n length = max_tokens_for_doc\\n doc_spans.append(_DocSpan(start=start_offset, length=length))\\n if start_offset + length == len(all_doc_tokens):\\n break\\n start_offset += min(length, doc_stride)\\n\\n for (doc_span_index, doc_span) in enumerate(doc_spans):\\n if is_training:\\n unique_id += 1\\n else:\\n unique_id += 2\\n\\n tokens = []\\n token_to_orig_map = {}\\n token_is_max_context = {}\\n segment_ids = []\\n\\n # p_mask: mask with 1 for token than cannot be in the answer\\n # (0 for token which can be in an answer)\\n # Original TF implem also keep the classification token (set to 0)\\n # (not sure why...)\\n # TODO: Should we set p_mask = 1 for cls token?\\n p_mask = []\\n\\n # CLS token at the beginning\\n if not cls_token_at_end:\\n tokens.append(cls_token)\\n segment_ids.append(cls_token_segment_id)\\n p_mask.append(0)\\n cls_index = 0\\n\\n # XLNet: P SEP Q SEP CLS\\n # Others: CLS Q SEP P SEP\\n if model_type != \\\"xlnet\\\":\\n # Query\\n tokens += query_tokens\\n segment_ids += [sequence_a_segment_id] * len(query_tokens)\\n p_mask += [1] * len(query_tokens)\\n\\n # SEP token\\n tokens.append(sep_token)\\n segment_ids.append(sequence_a_segment_id)\\n p_mask.append(1)\\n\\n # Paragraph\\n for i in range(doc_span.length):\\n split_token_index = doc_span.start + i\\n token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]\\n\\n # TODO: maybe this can be improved to compute\\n # is_max_context for each token only once.\\n is_max_context = _check_is_max_context(\\n doc_spans, doc_span_index, split_token_index\\n )\\n token_is_max_context[len(tokens)] = is_max_context\\n tokens.append(all_doc_tokens[split_token_index])\\n if model_type == \\\"xlnet\\\":\\n segment_ids.append(sequence_a_segment_id)\\n else:\\n segment_ids.append(sequence_b_segment_id)\\n p_mask.append(0)\\n paragraph_len = doc_span.length\\n\\n if model_type == \\\"xlnet\\\":\\n # SEP token\\n tokens.append(sep_token)\\n segment_ids.append(sequence_a_segment_id)\\n p_mask.append(1)\\n\\n tokens += query_tokens\\n segment_ids += [sequence_b_segment_id] * len(query_tokens)\\n p_mask += [1] * len(query_tokens)\\n\\n # SEP token\\n tokens.append(sep_token)\\n segment_ids.append(sequence_b_segment_id)\\n p_mask.append(1)\\n\\n # CLS token at the end\\n if cls_token_at_end:\\n tokens.append(cls_token)\\n segment_ids.append(cls_token_segment_id)\\n p_mask.append(0)\\n cls_index = len(tokens) - 1 # Index of classification token\\n\\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\\n\\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\\n # tokens are attended to.\\n input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\\n\\n # Zero-pad up to the sequence length.\\n if len(input_ids) < max_seq_length:\\n pad_token_length = max_seq_length - len(input_ids)\\n pad_mask = 0 if mask_padding_with_zero else 1\\n input_ids += [pad_token] * pad_token_length\\n input_mask += [pad_mask] * pad_token_length\\n segment_ids += [pad_token_segment_id] * pad_token_length\\n p_mask += [1] * pad_token_length\\n\\n assert len(input_ids) == max_seq_length\\n assert len(input_mask) == max_seq_length\\n assert len(segment_ids) == max_seq_length\\n\\n span_is_impossible = example.is_impossible\\n start_position = None\\n end_position = None\\n if is_training and not span_is_impossible:\\n # For training, if our document chunk does not contain an annotation\\n # we throw it out, since there is nothing to predict.\\n doc_start = doc_span.start\\n doc_end = doc_span.start + doc_span.length - 1\\n out_of_span = False\\n if not (tok_start_position >= doc_start and tok_end_position <= doc_end):\\n out_of_span = True\\n if out_of_span:\\n start_position = 0\\n end_position = 0\\n span_is_impossible = True\\n else:\\n # +1 for [CLS] token\\n # +1 for [SEP] token\\n if model_type == \\\"xlnet\\\":\\n doc_offset = 0\\n else:\\n doc_offset = len(query_tokens) + 2\\n start_position = tok_start_position - doc_start + doc_offset\\n end_position = tok_end_position - doc_start + doc_offset\\n\\n if is_training and span_is_impossible:\\n start_position = cls_index\\n end_position = cls_index\\n\\n qa_features.append(\\n _QAFeatures(\\n unique_id=unique_id,\\n qa_id=example.qa_id,\\n tokens=tokens,\\n token_to_orig_map=token_to_orig_map,\\n token_is_max_context=token_is_max_context,\\n input_ids=input_ids,\\n input_mask=input_mask,\\n segment_ids=segment_ids,\\n start_position=start_position,\\n end_position=end_position,\\n cls_index=cls_index,\\n p_mask=p_mask,\\n paragraph_len=paragraph_len,\\n )\\n )\\n\\n return qa_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9425ee916d9d40c4a3b4421df40a2a4b\",\n \"score\": \"0.5049735\",\n \"text\": \"def create_questionnaire():\\n pre = request.json['pre']\\n post = request.json['post']\\n\\n new_questionnaire = Questionnaire(pre=pre, post=post)\\n\\n db.session.add(new_questionnaire)\\n db.session.commit()\\n\\n result = questionnaire_schema.dump(new_questionnaire).data\\n\\n return jsonify({'status': 'success', 'message': 'Created new questionnaire', 'data': result}), 201\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d4bf1b60646b32e23cec6c0af7aaea6\",\n \"score\": \"0.5047603\",\n \"text\": \"def create_poll(question, days):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ae3a7f0dba382e237f5e43531ea06c\",\n \"score\": \"0.5042114\",\n \"text\": \"def test_create_quizes(self):\\n\\n driver = self.driver\\n wait = self.wait\\n\\n # Test data:\\n number = randint(100,1000)\\n quiz_name = \\\"QA BASIC {}\\\".format(number)\\n textual_question_1 = \\\"What is Software Testing?\\\"\\n textual_question_2 = \\\"What is Software Quality Assurance?\\\"\\n textual_question_3 = \\\"Explain SDLC methodology?\\\"\\n\\n\\n # 1. Login with tichers role\\n\\n email_teacher = 'alina.korolevich@yopmail.com'\\n password_teacher = 'internship'\\n\\n login_url = \\\"http://local.school.portnov.com:4520/#/login\\\"\\n driver.get(login_url)\\n driver.find_element_by_id(\\\"mat-input-0\\\").send_keys(email_teacher)\\n driver.find_element_by_id(\\\"mat-input-1\\\").send_keys(password_teacher)\\n driver.find_element_by_css_selector(\\\"button[type='submit']\\\").click()\\n\\n wait.until(EC.presence_of_element_located((By.XPATH, \\\"// div[@class = 'info']/p[contains(text(),'TEACHER')]\\\")))\\n sleep(1)\\n\\n wait.until(EC.visibility_of_element_located((By.XPATH, \\\"// div[@class = 'info']/p[contains(text(),'TEACHER')]\\\")))\\n driver.find_element(By.PARTIAL_LINK_TEXT, \\\"Quizzes\\\").click()\\n wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, \\\"a[href='#/quiz-builder']\\\")))\\n driver.find_element(By.PARTIAL_LINK_TEXT,\\\"Create New Quiz\\\").click()\\n\\n driver.find_element(By.TAG_NAME,\\\"input\\\").send_keys(quiz_name)\\n\\n driver.find_element(By.CSS_SELECTOR, \\\"div.controls.ng-star-inserted>button\\\").click()\\n sleep(1)\\n driver.find_element(By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Textual')]\\\").click()\\n sleep(1)\\n driver.find_element(By.CSS_SELECTOR, \\\"div.mat-input-infix.mat-form-field-infix textarea\\\").send_keys(textual_question_1)\\n sleep(1)\\n driver.find_element(By.CSS_SELECTOR, \\\"div.controls.ng-star-inserted>button\\\").click()\\n sleep(1)\\n driver.find_element(By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Textual')]\\\").click()\\n sleep(1)\\n driver.find_element(By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\\\").send_keys(textual_question_2)\\n sleep(1)\\n driver.find_element(By.CSS_SELECTOR, \\\"div.controls.ng-star-inserted>button\\\").click()\\n sleep(1)\\n driver.find_element(By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Textual')]\\\").click()\\n sleep(1)\\n driver.find_element(By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\\\").send_keys(textual_question_3)\\n sleep(1)\\n driver.find_element(By.CSS_SELECTOR, \\\"div.controls.ng-star-inserted>button\\\").click()\\n sleep(1)\\n\\n # Single choice questions:\\n single_choice_1 = \\\"What is a Defect?\\\"\\n single_choice_1_opt_1 = \\\"Any flaw or imperfection in a software work product\\\"\\n single_choice_1_opt_2 = \\\"without any issues\\\"\\n single_choice_2 = \\\"What is Priority?\\\"\\n single_choice_2_opt_1 = \\\"It indicates the importance or urgency of fixing a defect\\\"\\n single_choice_2_opt_2 = \\\"anytime can fix this bug. No time limit\\\"\\n single_choice_3 = \\\"What is the difference between static testing?\\\"\\n single_choice_3_opt_1 = \\\"without code executing the program is called as Static Testing.\\\"\\n single_choice_3_opt_2 = \\\"with code\\\"\\n\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Single-Choice')]\\\").click()\\n sleep(1)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\\\").send_keys(single_choice_1)\\n sleep(1)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\\\".format(single_choice_1)).send_keys(single_choice_1_opt_1)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']/../../../../..//mat-radio-button\\\".format(single_choice_1)).click()\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\\\".format(single_choice_1)).send_keys(single_choice_1_opt_2)\\n sleep(1)\\n driver.find_element(By.CSS_SELECTOR, \\\"div.controls.ng-star-inserted>button\\\").click()\\n sleep(1)\\n wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \\\"div.left.wide mat-slider\\\")))\\n driver.find_element(By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Single-Choice')]\\\").click()\\n wait.until(EC.visibility_of_element_located((By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\\\"))).send_keys(single_choice_2)\\n wait.until(EC.visibility_of_element_located((By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\\\".format(single_choice_2)))).send_keys(single_choice_2_opt_1)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']/../../../../..//mat-radio-button\\\".format(single_choice_2)).click()\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\\\".format(single_choice_2)).send_keys(single_choice_2_opt_2)\\n sleep(1)\\n driver.find_element(By.CSS_SELECTOR, \\\"div.controls.ng-star-inserted>button\\\").click()\\n wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \\\"div.left.wide mat-slider\\\")))\\n driver.find_element(By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Single-Choice')]\\\").click()\\n wait.until(EC.visibility_of_element_located((By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\\\"))).send_keys(single_choice_3)\\n wait.until(EC.visibility_of_element_located((By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\\\".format(single_choice_3)))).send_keys(single_choice_3_opt_1)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']/../../../../..//mat-radio-button\\\".format(single_choice_3)).click()\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\\\".format(single_choice_3)).send_keys(single_choice_3_opt_2)\\n\\n\\n # Multiple choice questions:\\n multiple_choice_1 = \\\"What is a Bug?\\\"\\n multiple_choice_1_opt_1 = \\\"Mismatch between actual and intended behaviors of the software\\\"\\n multiple_choice_1_opt_2 = \\\"Some small insect that flies around\\\"\\n multiple_choice_2 = \\\"Are Java and Javascript same languages?\\\"\\n multiple_choice_2_opt_1 = \\\"Yes\\\"\\n multiple_choice_2_opt_2 = \\\"No\\\"\\n multiple_choice_3 = \\\"What is a prime objective of a bug tracking database?\\\"\\n multiple_choice_3_opt_1 = \\\"Tracking the bugs\\\"\\n multiple_choice_3_opt_2 = \\\"To get a bug fixed\\\"\\n\\n driver.find_element(By.CSS_SELECTOR, \\\"div.controls.ng-star-inserted>button\\\").click()\\n driver.find_element(By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Multiple-Choice')]\\\").click()\\n wait.until(EC.visibility_of_element_located((By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\\\"))).send_keys(multiple_choice_1)\\n wait.until(EC.visibility_of_element_located((By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\\\".format(multiple_choice_1)))).send_keys(multiple_choice_1_opt_1)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']/../../../../../mat-checkbox\\\".format(multiple_choice_1)).click()\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\\\".format(multiple_choice_1)).send_keys(multiple_choice_1_opt_2)\\n sleep(1)\\n driver.find_element(By.CSS_SELECTOR, \\\"div.controls.ng-star-inserted>button\\\").click()\\n sleep(1)\\n driver.find_element(By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Multiple-Choice')]\\\").click()\\n wait.until(EC.visibility_of_element_located((By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\\\"))).send_keys(multiple_choice_2)\\n wait.until(EC.visibility_of_element_located((By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\\\".format(multiple_choice_2)))).send_keys(multiple_choice_2_opt_1)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\\\".format(multiple_choice_2)).send_keys(multiple_choice_2_opt_2)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']/../../../../../mat-checkbox\\\".format(multiple_choice_2)).click()\\n sleep(1)\\n driver.find_element(By.CSS_SELECTOR, \\\"div.controls.ng-star-inserted>button\\\").click()\\n sleep(1)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), 'new empty question')]/../../..//div[contains(text(), 'Multiple-Choice')]\\\").click()\\n wait.until(EC.visibility_of_element_located((By.XPATH,\\\"//*[contains(text(), 'new empty question')]/../../..//textarea[@placeholder='Question *']\\\"))).send_keys(multiple_choice_3)\\n wait.until(EC.visibility_of_element_located((By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 1*']\\\".format(multiple_choice_3)))).send_keys(multiple_choice_3_opt_1)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']\\\".format(multiple_choice_3)).send_keys(multiple_choice_3_opt_2)\\n driver.find_element(By.XPATH, \\\"//*[contains(text(), '{}')]/../../..//*[@placeholder='Option 2*']/../../../../../mat-checkbox\\\".format(multiple_choice_3)).click()\\n\\n driver.find_element(By.XPATH, \\\"//button/*[contains(text(),'Save')]\\\").click()\\n quiz_locator = \\\"//ac-quizzes-list//div[@class = 'quizzes']//*[contains(text(),'{}')]\\\".format(quiz_name)\\n wait.until(EC.visibility_of_element_located((By.XPATH, quiz_locator)))\\n element = driver.find_element_by_xpath(quiz_locator)\\n driver.execute_script(\\\"arguments[0].scrollIntoView();\\\", element)\\n element.click()\\n driver.get_screenshot_as_file('{} created.png'.format(quiz_name))\\n driver.find_element_by_xpath(\\\"//div[@class='mat-list-item-content']//h5[contains(text(),'Log Out')]\\\").click()\\n wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, \\\".mat-button.mat-warn\\\")))\\n driver.find_element_by_css_selector(\\\".mat-button.mat-warn\\\").click()\\n wait.until(EC.presence_of_element_located((By.CSS_SELECTOR,\\\"button[type='submit']\\\")))\\n\\n\\n #Sign in with role TEACHER\\n\\n email = 'alina.korolevich@yopmail.com'\\n\\n password = 'internship'\\n\\n url = \\\"http://local.school.portnov.com:4520/api/v1/sign-in\\\"\\n\\n payload = {\\n 'email': email,\\n 'password': password\\n }\\n\\n headers = {\\n 'content-type': \\\"application/json\\\",\\n 'Connection': \\\"keep-alive\\\"\\n }\\n\\n response = requests.post(url, data=json.dumps(payload), headers=headers)\\n\\n parsed_json = json.loads(response.text)\\n\\n token = parsed_json[\\\"token\\\"]\\n\\n url = \\\"http://local.school.portnov.com:4520/api/v1/quizzes\\\"\\n\\n headers = {\\n 'Authorization': \\\"Bearer {}\\\".format(token)\\n }\\n\\n r = requests.get(url, headers=headers)\\n parsed_json = json.loads(r.text)\\n\\n quiz_id = None\\n\\n for i in parsed_json:\\n if i[\\\"name\\\"] == quiz_name:\\n quiz_id = i[\\\"id\\\"]\\n else:\\n continue\\n\\n url = \\\"http://local.school.portnov.com:4520/api/v1/quiz/{}\\\".format(quiz_id)\\n\\n r = requests.delete(url, headers=headers)\\n print(r.status_code)\\n self.assertTrue(r.status_code == 200)\\n print(\\\"Quiz: {} with id {} was permanently deleted\\\".format(quiz_name, quiz_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f25a61269a0963f3ab1f7df025ad6c4\",\n \"score\": \"0.50349176\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgresql://{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n \\n # create all tables\\n self.db.create_all()\\n\\n self.new_question = {\\n \\\"question\\\": \\\"Whose autobiography is entitled 'Catcher in the Rye'?\\\",\\n \\\"answer\\\": \\\"J.D Salinger\\\",\\n \\\"category\\\": 2,\\n \\\"difficulty\\\": 4\\n }\\n\\n self.new_incomplete_question = {\\n \\\"question\\\": \\\"Whose autobiography is entitled 'Catcher in the Rye'?\\\",\\n \\\"category\\\": 2,\\n \\\"difficulty\\\": 4\\n }\\n\\n self.new_invalid_question = {\\n \\\"question\\\": 22.66,\\n \\\"answer\\\": \\\"J.D Salinger\\\",\\n \\\"category\\\": 2,\\n \\\"difficulty\\\": 4\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f6b42de4f3c4f0575ae93c3467e235\",\n \"score\": \"0.5027913\",\n \"text\": \"def create_poll(self, request):\\n bookclub = BookClub.objects.get(pk=request.data.get('BookClub_ID', {}))\\n user = User.objects.get(pk=request.data.get('User_ID', {}))\\n\\n if CheckPermissions(user).hasPerm('Create', bookclub):\\n time = datetime.strptime(request.data.get('Time', {}), \\\"%d-%m-%Y %H:%M:%S\\\")\\n title = request.data.get('Title', {})\\n choices = request.data.get('Choices', {})\\n\\n new_poll = Poll(End_Time=time, Title=title, BookClub_ID=bookclub)\\n new_poll.save()\\n new_poll = Poll.objects.get(pk=new_poll.Poll_ID)\\n\\n for choice in choices:\\n new_choice = Choice(Description=choice, Poll_ID=new_poll)\\n new_choice.save()\\n\\n serializer = PollSerializer(new_poll)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n\\n else:\\n return Response(status=status.HTTP_401_UNAUTHORIZED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3153f58799aa821d86a30a0e13067f75\",\n \"score\": \"0.5005962\",\n \"text\": \"def visualQuiz():\\n return render_template(\\\"Visual_Quiz.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d423eb9c57fb29cee4f8724dac114681\",\n \"score\": \"0.5001011\",\n \"text\": \"def convert_questions_to_df(self):\\n\\n questions = self.survey.questions\\n\\n # collect all dataframes; for every (sub)question a 2-index dataframe is created\\n # so that they can be merged at the end\\n frames = []\\n\\n # iterate over all questions and treat them according to the question type\\n for q in questions:\\n question_code = questions[q]['title']\\n question_text = self.clean_text(questions[q]['question'])\\n question_type = questions[q]['question_type']\\n\\n # if there is only one element to select (List radio or (List dropdown)\\n # create 3 columns: one for machine readable code (e.g. 1,..,n), one for answer code (e.g. A1,...An)\\n # and one for long text answer\\n if question_type == \\\"List radio\\\" or question_type == \\\"List dropdown\\\":\\n\\n second_level_index = [\\\"a_code\\\", \\\"a_text\\\", \\\"a\\\"]\\n header = pd.MultiIndex.from_product([[question_code],\\n second_level_index], names=['code', 'version'])\\n\\n df = pd.DataFrame(columns=header)\\n\\n # append the new cdataframe\\n frames.append(df)\\n\\n # for Multiple choice questions append two columns per answer.\\n # a machine readable one (0/1 for no or yes) and one with the text of this subquestion\\n # the text field is not really necessary, however, it helps to have it in the row\\n # so we can extract it more easily during analyses\\n elif question_type == \\\"Multiple choice\\\":\\n\\n # iterate over all subquestions (by the format of limepy, the subquestions section always has key '0')\\n for s in questions[q]['subquestions']['0']:\\n sub_question_code = (s['title'])\\n merged_code = question_code + \\\"[\\\" + sub_question_code + \\\"]\\\"\\n\\n second_level_index = [\\\"a_text\\\", \\\"a\\\"]\\n header = pd.MultiIndex.from_product([[merged_code],\\n second_level_index], names=['code', 'version'])\\n df = pd.DataFrame(columns=header)\\n\\n # append the new cdataframe\\n frames.append(df)\\n\\n # if there is only one element to select (List radio or (List dropdown) + a comment\\n # create 4 columns: the 3 from list + an additional one for the comment\\n elif question_type == \\\"List with comment\\\":\\n\\n second_level_index = [\\\"a_code\\\", \\\"a_text\\\", \\\"a\\\", question_code + \\\"[comment]\\\"]\\n header = pd.MultiIndex.from_product([[question_code],\\n second_level_index],\\n names=['code', 'version'])\\n\\n df = pd.DataFrame(columns=header)\\n\\n # append the new cdataframe\\n frames.append(df)\\n\\n # for Multiple choice questions append three columns per answer.\\n # like Multiple choice + comment field\\n elif questions[q]['question_type'] == \\\"Multiple choice with comments\\\":\\n\\n # iterate over all subquestions (by the format of limepy, the subquestions section always has key '0')\\n for s in questions[q]['subquestions']['0']:\\n sub_question_code = (s['title'])\\n merged_code = question_code + \\\"[\\\" + sub_question_code + \\\"]\\\"\\n\\n second_level_index = [\\\"a_text\\\", \\\"a\\\", question_code + \\\"[\\\" + sub_question_code + \\\"comment]\\\"]\\n header = pd.MultiIndex.from_product([[merged_code],\\n second_level_index], names=['code', 'version'])\\n df = pd.DataFrame(columns=header)\\n\\n # append the new cdataframe\\n frames.append(df)\\n\\n # here we need 2 columns per possible answer (like multiple choice).\\n # One that contains the text.\\n # a second one that should be machine readable:\\n # Results of the participants are entered with integers. 1 shows highest priority, n lowest,\\n # 0 shows that it was not selected by the participant\\n elif questions[q]['question_type'] == \\\"Ranking\\\":\\n\\n # iterate over all answers (by the format of limepy, the answers section always has key '0')\\n for a in questions[q]['answers']['0']:\\n answer_code = a['sortorder']\\n merged_code = question_code + \\\"[\\\" + answer_code + \\\"]\\\"\\n\\n second_level_index = [\\\"a\\\", \\\"a_text\\\"]\\n header = pd.MultiIndex.from_product([[merged_code],\\n second_level_index],\\n names=['code', 'version'])\\n df = pd.DataFrame(columns=header)\\n\\n # append the new cdataframe\\n frames.append(df)\\n\\n # each question in the array is to be treated like a normal list radio question and should therefore\\n # receive 3 columns\\n elif questions[q]['question_type'] == \\\"Array\\\":\\n\\n second_level_index = [\\\"a_code\\\", \\\"a_text\\\", \\\"a\\\"]\\n\\n # iterate over all subquestions (by the format of limepy, the subquestions section always has key '0')\\n for s in questions[q]['subquestions']['0']:\\n sub_question_code = (s['title']) # subquestion code\\n merged_code = question_code + \\\"[\\\" + sub_question_code + \\\"]\\\" # bring the codes together\\n\\n header = pd.MultiIndex.from_product([[merged_code],\\n second_level_index], names=['code', 'version'])\\n\\n df = pd.DataFrame(columns=header)\\n\\n # append the new cdataframe\\n frames.append(df)\\n\\n # one column for the free text\\n elif questions[q]['question_type'] == \\\"Long free text\\\" or questions[q][\\n 'question_type'] == \\\"Short free text\\\":\\n\\n second_level_index = [\\\"a\\\"]\\n header = pd.MultiIndex.from_product([[question_code],\\n second_level_index], names=['code', 'version'])\\n\\n df = pd.DataFrame(columns=header)\\n\\n # append the new dataframe\\n frames.append(df)\\n\\n # Case for questions of which the type is not listed\\n else:\\n print(\\\"error, type not known \\\", questions[q]['question_type'])\\n\\n # create an empty dataframe from all the individual ones\\n result = pd.concat(frames)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b00baa75b3c0a0678d41630c74e1c0c\",\n \"score\": \"0.500017\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format(\\n 'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n category = Category.query.filter(\\n Category.type == \\\"science\\\").one_or_none()\\n self.new_question = {\\n 'question': 'How many miles away from earth is the moon?',\\n 'answer': '238,900',\\n 'category': category,\\n 'difficulty': 5\\n }\\n\\n self.new_question_missing_answer = {\\n 'question': 'In what year was Mike Tyson the boxer?',\\n 'category': 'sports',\\n 'difficulty': '4'\\n }\\n\\n self.search_term = {\\n 'searchTerm': 'soccer'\\n }\\n\\n self.previous_questions = {\\n 'previous_questions': [],\\n 'quiz_category': {\\n 'type': 'Science',\\n 'id': '1'\\n }\\n }\\n\\n \\\"\\\"\\\" restore question used in delete test after each test \\\"\\\"\\\"\\n ali = Question.query.filter(\\n Question.question ==\\n \\\"What boxer's original name is Cassius Clay?\\\").one_or_none()\\n if ali is None:\\n question = Question(\\n question=\\\"What boxer's original name is Cassius Clay?\\\",\\n answer=\\\"Muhammad Ali\\\",\\n category=4,\\n difficulty=1)\\n question.insert()\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baa1038abb391cbe51fd945941926aba\",\n \"score\": \"0.4996972\",\n \"text\": \"def new_question():\\n if not g.user:\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n\\n form = QuestionForm()\\n if form.validate_on_submit():\\n new_question = Question(\\n user_id=session[CURR_USER_KEY],\\n official_id=form.official.data,\\n text=form.text.data\\n )\\n db.session.add(new_question)\\n db.session.commit()\\n flash(\\\"Your question has been asked!\\\", \\\"success\\\")\\n return redirect('/')\\n \\n form.official.choices = [(o.id, f'{o.name} ({o.office})') for o in g.user.city.officials]\\n return render_template('questions/new_question.html', form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bea32e391d6b03eee132c3df8aa5de2\",\n \"score\": \"0.49908298\",\n \"text\": \"def rebuild_qa_dictionary(self):\\n self.question_answers.clear()\\n\\n self.question_answers = {\\n 'What type of triangle is ': QA('What type of triangle is ', get_triangle_type),\\n 'What type of quadrilateral is ': QA('What type of quadrilateral is ',\\n get_quadrilateral_type),\\n 'What time is it': QA('What time is it', time.asctime()),\\n 'What is the n digit of fibonacci': QA('What is the n digit of fibonacci',\\n get_fibonacci),\\n 'What is the digit of pi': QA('What is the digit of pi', get_some_pi),\\n 'Why did the chicken cross the road': QA('Why did the chicken cross the road', 'To '\\n 'prove to the armadillo that it can be done'),\\n 'What is n multiplied by m': QA('What is n multiplied by m', simple_multiply),\\n 'What student really deserves an A': QA('What student really deserves an A',\\n 'Kirt Stark does!'),\\n 'What is the cube of ': QA('What is the cube of ', cube_of_number),\\n 'How many times will divide completely into ': QA('How many times will divide complete'\\n 'ly into ', divide_evenly),\\n 'What is when it is multiplied by and then added to ': QA('What is when it is '\\n 'multiplied by and then '\\n 'added to ', paycheck),\\n 'What is the mass of a measure of with a volume of cubic meters?':\\n QA('What is the mass of a measure of with a volume of cubic meters?',\\n self.send_for_density),\\n 'Why are these labs such a struggle for me': QA('Why are these labs such a struggle for'\\n ' me', struggle_reason),\\n 'Is the <file name> in the repo': QA('Is the in the repo', is_file_in_repo),\\n 'What is the status of <file name>': QA('What is the status of ', get_git_file_info),\\n 'What is the deal with <file name>': QA('What is the deal with ', get_file_info),\\n 'What branch is <file path>': QA('What branch is ', get_repo_branch),\\n 'Where did <file path> come from': QA('Where did come from', get_repo_url),\\n 'What is the repo root for <file path>': QA('What is the repo root for ', get_repo_root)\\n }\\n return \\\"done\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfe0a2d8d6967d9e12fe077d5be821d\",\n \"score\": \"0.49891895\",\n \"text\": \"def quiz_example():\\n\\n quiz = Quiz(\\\"Pop Quiz\\\")\\n \\n quiz.add_question(\\\"How many houses in our cohort?\\\", \\\"Four\\\")\\n quiz.add_question(\\\"Which is the best house?\\\", \\\"Gryffindor\\\")\\n\\n student = Student(\\\"Nicole\\\", \\\"Negri\\\", \\\"SF\\\")\\n\\n take_test(quiz, student)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"094087e8e3185fc7fbbfef7a4f4804f5\",\n \"score\": \"0.49800134\",\n \"text\": \"def qAnswer(self):\\n #get user input\\n Body = input('Please answer question: ')\\n #format the data\\n #Set ID self-increment\\n print(\\\"Posting answer...\\\")\\n\\n Id = self.get_id(\\\"Posts\\\")\\n data={\\\"Id\\\": Id,\\\\\\n \\\"PostTypeId\\\":\\\"2\\\",\\\\\\n \\\"ParentId\\\":self.questionId,\\\\\\n \\\"CreationDate\\\":datetime.datetime.now().isoformat(),\\\\\\n \\\"Score\\\":0,\\\\\\n \\\"Body\\\":Body,\\\\\\n \\\"LastActivityDate\\\":datetime.datetime.now().isoformat(),\\\\\\n \\\"CommentCount\\\":0,\\\\\\n \\\"ContentLicense\\\":\\\"CC BY-SA 2.5\\\"}\\n if self.userId != None:\\n data[\\\"OwnerUserId\\\"] = self.userId\\n #insert the data\\n temp = [data]\\n extraction_terms(temp)\\n db.Posts.insert_one(data)\\n self.create_index()\\n #Success message\\n\\n print(\\\"Successfully posted the answer.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88cb98d9a4d1c901e2ea3a75bf562958\",\n \"score\": \"0.4976668\",\n \"text\": \"def add_a_new_question():\\n try:\\n body = request.get_json()\\n category_id = body.get('category', None)\\n new_question = Question(\\n question=body.get('question', None),\\n answer=body.get('answer', None),\\n category=category_id,\\n difficulty=body.get('difficulty', None)\\n )\\n new_question.insert()\\n\\n all_questions = Question.query.order_by(Question.id).all()\\n category_type = Category.query.get(category_id).type\\n\\n return jsonify({\\n 'success': True,\\n 'created': new_question.id,\\n 'current_category': category_type,\\n 'total_questions': len(all_questions),\\n })\\n\\n except Exception:\\n abort(422)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b40b60db6a0fa73c9be0375c72e368ba\",\n \"score\": \"0.49735948\",\n \"text\": \"def create_answer(controls, index, question, answer_num, dbsession):\\n answerText = str(controls[index+1][1])\\n if controls[index+2][0] == 'correct': correct = True\\n else: correct = False\\n answer = Answer(question.id, answerText, \\n correct)\\n dbsession.add(answer)\\n dbsession.flush\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1fe743ba493c9edb469d32e205e5bf\",\n \"score\": \"0.49719405\",\n \"text\": \"def test_mark_questions(self):\\n\\n # Short Quiz - No Questions Right -\\n\\n # Short Quiz - Half Questions Right -\\n\\n # Short Quiz - All Questions Right -\\n\\n # Long Quiz - No Questions Right -\\n\\n # Long Quiz - Half Questions Right -\\n\\n # Long Quiz - All Questions Right -\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff082c2936fe37a319fb5ec4bb27d73\",\n \"score\": \"0.4953064\",\n \"text\": \"def test_question_list(self):\\n third_question = Question('Why is science?', 'Only Bill Nye knows', self.science.id, 5)\\n third_question.insert()\\n\\n # Test list with pagination only returns 2 result\\n resp = self.client.get('/api/questions')\\n assert resp.status_code == 200\\n\\n data = json.loads(resp.data)\\n assert 'categories' in data\\n assert 'questions' in data\\n assert 'total_questions' in data\\n\\n assert data == {\\n 'categories': {\\n '1': 'Science',\\n '2': 'Geography'\\n },\\n 'questions': [\\n {\\n 'answer': 'Nobody knows',\\n 'category': 1,\\n 'difficulty': 5,\\n 'id': 1,\\n 'question': 'What is science?'\\n },\\n {\\n 'answer': 'Probably not',\\n 'category': 2,\\n 'difficulty': 5,\\n 'id': 2, 'question':\\n 'Is geography real?'\\n }\\n ],\\n 'total_questions': 3\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4789c911a01e0326ceac748ae4f5feb1\",\n \"score\": \"0.49438152\",\n \"text\": \"def test_create_new_category():\\n results = []\\n category_obj = prism.Categories(api_client=_api())\\n\\n categories = [\\n {'name': 'test_category', 'description': 'api test category'},\\n {'name': 'test_category_2', 'description': 'api test category #2'},\\n ]\\n\\n for category in categories:\\n category_obj.set_category(**category)\\n if category_obj.search_category(name=category.get('name'), refresh=True):\\n results.append(True)\\n category_cleanup.append(category.get('name'))\\n else:\\n results.append(False)\\n\\n assert all(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2814704fe1f943cc0e9c4475bb2e7e70\",\n \"score\": \"0.49265248\",\n \"text\": \"def test_answer_with_param_score_with_data(self):\\n self.headers(academy=1)\\n base = self.generate_models(authenticate=True,\\n user=True,\\n cohort=True,\\n academy=True,\\n event=True,\\n profile_academy=True,\\n capability='read_nps_answers',\\n role='potato')\\n\\n for score in range(1, 10):\\n self.remove_all_answer()\\n\\n answer_kwargs = {'score': score}\\n model = self.generate_models(answer=True, answer_kwargs=answer_kwargs, models=base)\\n db = self.model_to_dict(model, 'answer')\\n params = {\\n 'score': score,\\n }\\n base_url = reverse_lazy('feedback:answer')\\n url = f'{base_url}?{urllib.parse.urlencode(params)}'\\n response = self.client.get(url, headers={'Academy', model['academy'].id})\\n json = response.json()\\n\\n json = [{**x, 'created_at': None} for x in json if self.assertDatetime(x['created_at'])]\\n\\n self.assertEqual(json, [{\\n 'created_at': None,\\n 'academy': {\\n 'id': model['answer'].academy.id,\\n 'name': model['answer'].academy.name,\\n 'slug': model['answer'].academy.slug,\\n },\\n 'cohort': {\\n 'id': model['cohort'].id,\\n 'name': model['cohort'].name,\\n 'slug': model['cohort'].slug,\\n },\\n 'comment': model['answer'].comment,\\n 'event': {\\n 'id': model['event'].id,\\n 'description': model['event'].description,\\n 'excerpt': model['event'].excerpt,\\n 'lang': model['event'].lang,\\n 'title': model['event'].title,\\n },\\n 'highest': model['answer'].highest,\\n 'id': model['answer'].id,\\n 'lang': model['answer'].lang,\\n 'lowest': model['answer'].lowest,\\n 'mentor': {\\n 'first_name': model['user'].first_name,\\n 'id': model['user'].id,\\n 'last_name': model['user'].last_name,\\n 'profile': None,\\n },\\n 'score': score,\\n 'status': model['answer'].status,\\n 'title': model['answer'].title,\\n 'user': {\\n 'first_name': model['user'].first_name,\\n 'id': model['user'].id,\\n 'last_name': model['user'].last_name,\\n 'profile': None,\\n },\\n }])\\n\\n db['score'] = score\\n\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(self.all_answer_dict(), [db])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61697df150c9152a6fb7709142124a6\",\n \"score\": \"0.4917346\",\n \"text\": \"def test_create_categories(self):\\n self.init_database()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d7cfd2b050a4de59bb576c361be987b\",\n \"score\": \"0.49121854\",\n \"text\": \"def test_6_init(self):\\n quizz = Quizz(self.user)\\n self.assertEqual( quizz.user, self.user)\\n self.assertEqual( quizz.userInfo, self.userInfo)\\n self.assertEqual( quizz.user, self.user)\\n self.assertEqual( quizz.scoreSheet, {1: 20, 2: 0})\\n self.assertEqual( len(quizz.questions), 10)\\n self.assertEqual( len(quizz.answers), 10)\\n self.assertEqual( quizz.answers[1], {})\\n self.assertEqual( quizz.level, 1)\\n self.assertEqual( quizz.size, 10)\\n self.assertEqual( quizz.categories, ['hiraganas'])\\n self.assertEqual( quizz.currentScore, 0)\\n self.assertEqual( quizz.completed, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc7b410934ea039e0d06dc32b2aa7655\",\n \"score\": \"0.4911111\",\n \"text\": \"def create_category():\\n return render_template(\\\"add-category-form.html\\\"), HTTPStatus.OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dad260c6004ba66080a8047058431542\",\n \"score\": \"0.4909211\",\n \"text\": \"def SetStateQuestion(self):\\n self.Timer.display(questiondata.question_time)\\n self.Timer.setStyleSheet('color: blue')\\n self.BtnEnd.setEnabled(True)\\n category: questiondata.Category = self.state.category\\n if self.state.question == len(self.state.category.questions) - 1:\\n self.BtnNew.setEnabled(False)\\n else:\\n self.BtnNew.setEnabled(True)\\n self.BtnTrue.setEnabled(False)\\n self.BtnFalse.setEnabled(False)\\n self.BtnNext.setEnabled(False)\\n self.BtnTimer.setEnabled(True)\\n self.BtnTimer.setText('Стоп')\\n\\n self.LblDscr.setText(\\\"%s: Вопрос № %i из %i. Стоимость %i \\\" %\\n (category.name, self.state.question+1, len(category.questions),\\n category.questions[self.state.question].points))\\n self.TxtQstn.setHtml(\\\"<!DOCTYPE HTML PUBLIC \\\\\\\"-//W3C//DTD HTML 4.0//EN\\\\\\\" \\\"\\n \\\"\\\\\\\"http://www.w3.org/TR/REC-html40/strict.dtd\\\\\\\">\\\\n\\\"\\n \\\"<html><head><meta name=\\\\\\\"qrichtext\\\\\\\" content=\\\\\\\"1\\\\\\\" /><style type=\\\\\\\"text/css\\\\\\\">\\\\n\\\"\\n \\\"p, li { white-space: pre-wrap; }\\\\n\\\"\\n \\\"</style></head><body style=\\\\\\\" font-family:\\\\'MS Shell Dlg 2\\\\'; font-size:7.8pt; \\\"\\n \\\"font-weight:400; font-style:normal;\\\\\\\">\\\\n\\\"\\n \\\"<p style=\\\\\\\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px;\\\"\\n \\\" -qt-block-indent:0; text-indent:0px;\\\\\\\">\\\"\\n \\\"<span style=\\\\\\\" font-family:\\\\'Calibri\\\\'; font-size:20pt; \\\"\\n \\\"font-weight:600; color:#000000;\\\\\\\">%s</span></p></body></html>\\\"\\n % category.questions[self.state.question].description)\\n self.LblAnswer.setText(category.questions[self.state.question].answer)\\n self.category_form.question = question_opened.QuestionDialog(category, self.state.question)\\n self.category_form.question.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb58331c653dd0f0a90cc1ea0244dc0e\",\n \"score\": \"0.4908329\",\n \"text\": \"def __init__(self, name):\\n self.name = name\\n self.questions = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c30bc471711ab66f7b511326be3e0b9e\",\n \"score\": \"0.49033698\",\n \"text\": \"def test_post_new_question(self):\\r\\n\\r\\n self.new_question = {\\r\\n 'question':'How much wood would a woodchuck chuck if a woodchuck could chuck wood?',\\r\\n 'answer':'About 700 pounds',\\r\\n 'category': 1, # Science\\r\\n 'difficulty': '4'\\r\\n }\\r\\n res = self.client().post('/api/questions', json=self.new_question)\\r\\n data = json.loads(res.data)\\r\\n new_question_id = data['created']\\r\\n\\r\\n self.assertEqual(data['success'], True)\\r\\n\\r\\n question_pull = Question.query.filter(Question.id == new_question_id).one_or_none()\\r\\n\\r\\n self.assertEqual(question_pull.answer, 'About 700 pounds')\\r\\n\\r\\n # Delete question to revert back to original database\\r\\n\\r\\n res = self.client().delete(f'/api/questions/{new_question_id}')\\r\\n data = json.loads(res.data)\\r\\n\\r\\n self.assertEqual(data['success'], True)\\r\\n self.assertEqual(data['deleted'], new_question_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5c71de8569629a7dda2a1032ea2d1b0\",\n \"score\": \"0.4898194\",\n \"text\": \"def __init__(self, question_id=None, user_id=None, timestamp=time, date=date, title=None, question=None):\\n self.question_id = question_id\\n self.user_id = user_id\\n self.question_title = title\\n self.question_text = question\\n self.question_timestamp = timestamp\\n self.asked_date = date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44e8d2e1cb2dfc19e1ce4552efdc4247\",\n \"score\": \"0.48858854\",\n \"text\": \"def _load_all_categorical(self):\\n\\t\\tfield_name = 'text_answer_fr' if self.lang == 'fr' else 'text_answer'\\n\\t\\tquery = \\\"\\\"\\\"\\n\\t\\t\\tSELECT short_question, {0}, COUNT({0})\\n\\t\\t\\tFROM comments\\n\\t\\t\\tWHERE\\n\\t\\t\\t\\tcourse_code = %s\\n\\t\\t\\t\\tAND\\n\\t\\t\\t\\tshort_question IN ('Reason to Participate', 'Technical Issues', 'OL Available', 'GCcampus Tools Used', 'Prep')\\n\\t\\t\\tGROUP BY short_question, {0}\\n\\t\\t\\tORDER BY 1 ASC;\\n\\t\\t\\\"\\\"\\\".format(field_name)\\n\\t\\tresults = query_mysql(query, (self.course_code,))\\n\\t\\tresults = pd.DataFrame(results, columns=['short_question', 'text_answer', 'count'])\\n\\t\\t# Return False if course has received no feedback\\n\\t\\tself.categorical_data = False if results.empty else results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd7cef229322fe1f7617f62a1a07bfa\",\n \"score\": \"0.488028\",\n \"text\": \"def post_question(self):\\n self.sign_up()\\n login_response = self.login()\\n result = self.create_meetup()\\n meetup_id = result['data']['id']\\n print(result)\\n\\n self.headers.update({\\\"x-access-token\\\":\\n login_response['token']})\\n question_payload = {\\n \\\"title\\\": \\\"How to train your dragon?\\\",\\n \\\"body\\\": \\\"How does one get to tame their dragons?\\\"\\n \\\" Mine just ate my neighbors\\\",\\n \\\"meetup\\\": meetup_id\\n }\\n with self.client as c:\\n question_post_response = c.post('api/v2/questions',\\n json=question_payload,\\n headers=self.headers)\\n result = json.loads(question_post_response.data.decode('utf-8'))\\n\\n return [question_post_response, login_response['token']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"189141700bc28334368738d007ecc017\",\n \"score\": \"0.48762187\",\n \"text\": \"def _create_qa_example(qa_input, is_training):\\n\\n # _QAExample is a data structure representing an unique document-question-answer\\n # triplet.\\n # Args:\\n # qa_id (int): An unique id identifying the document-question pair.\\n # This is used to map prediction results to ground truth answers\\n # during evaluation, because the data order is not preserved\\n # during pre-processing and post-processing.\\n # doc_tokens (list): White-space tokenized tokens of the document\\n # text. This is used to generate the final answer based on\\n # predicted start and end token indices during post-processing.\\n # question_text (str): Text of the question.\\n # orig_answer_text (str): Text of the ground truth answer if available.\\n # start_position (int): Index of the starting token of the answer\\n # span, if available.\\n # end_position (int): Index of the ending token of the answer span,\\n # if available.\\n # is_impossible (bool): If the question is impossible to answer based\\n # on the given document.\\n _QAExample = collections.namedtuple(\\n \\\"_QAExample\\\",\\n [\\n \\\"qa_id\\\",\\n \\\"doc_tokens\\\",\\n \\\"question_text\\\",\\n \\\"orig_answer_text\\\",\\n \\\"start_position\\\",\\n \\\"end_position\\\",\\n \\\"is_impossible\\\",\\n ],\\n )\\n\\n def _is_whitespace(c):\\n if c == \\\" \\\" or c == \\\"\\\\t\\\" or c == \\\"\\\\r\\\" or c == \\\"\\\\n\\\" or ord(c) == 0x202F:\\n return True\\n return False\\n\\n d_text = qa_input.doc_text\\n q_text = qa_input.question_text\\n a_start = qa_input.answer_start\\n a_text = qa_input.answer_text\\n q_id = qa_input.qa_id\\n impossible = qa_input.is_impossible\\n\\n d_tokens = []\\n char_to_word_offset = []\\n prev_is_whitespace = True\\n for c in d_text:\\n if _is_whitespace(c):\\n prev_is_whitespace = True\\n else:\\n if prev_is_whitespace:\\n d_tokens.append(c)\\n else:\\n d_tokens[-1] += c\\n prev_is_whitespace = False\\n char_to_word_offset.append(len(d_tokens) - 1)\\n\\n if _is_iterable_but_not_string(a_start):\\n if not _is_iterable_but_not_string(a_text):\\n raise Exception(\\n \\\"The answer text must be a list when answer start is a list.\\\"\\n )\\n if len(a_start) != 1 and is_training and not impossible:\\n raise Exception(\\\"For training, each question should have exactly 1 answer.\\\")\\n a_start = a_start[0]\\n a_text = a_text[0]\\n\\n start_position = None\\n end_position = None\\n if is_training:\\n if not impossible:\\n answer_length = len(a_text)\\n start_position = char_to_word_offset[a_start]\\n end_position = char_to_word_offset[a_start + answer_length - 1]\\n # Only add answers where the text can be exactly recovered from the\\n # document. If this CAN'T happen it's likely due to weird Unicode\\n # stuff so we will just skip the example.\\n #\\n # Note that this means for training mode, every example is NOT\\n # guaranteed to be preserved.\\n actual_text = \\\" \\\".join(d_tokens[start_position : (end_position + 1)])\\n cleaned_answer_text = \\\" \\\".join(whitespace_tokenize(a_text))\\n if actual_text.find(cleaned_answer_text) == -1:\\n logger.warning(\\n \\\"Could not find answer: '%s' vs. '%s'\\\",\\n actual_text,\\n cleaned_answer_text,\\n )\\n return\\n else:\\n start_position = -1\\n end_position = -1\\n\\n return _QAExample(\\n qa_id=q_id,\\n doc_tokens=d_tokens,\\n question_text=q_text,\\n orig_answer_text=a_text,\\n start_position=start_position,\\n end_position=end_position,\\n is_impossible=impossible,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d14ab8fab17882aecb3fb9e7d74a140\",\n \"score\": \"0.48756945\",\n \"text\": \"def create_question_list(self):\\n\\n question_list = pd.DataFrame()\\n for qid, question in self.questions.items():\\n question_list.loc[qid, 'question'] = question['question']\\n question_list.loc[qid, 'question_type'] = question['question_type']\\n question_list.loc[qid, 'help'] = question['help']\\n question_list.loc[qid, 'position'] = question['position']\\n start, nr_columns = question['columns']\\n question_list.loc[qid, 'start'] = int(start)\\n question_list.loc[qid, 'nr_columns'] = int(nr_columns)\\n group_name = self.groups[question['gid']]['group_name']\\n question_list.loc[qid, 'group'] = group_name\\n question_list.loc[qid, 'mandatory'] = question['mandatory']\\n question_list.loc[qid, 'other'] = question['other']\\n question_list.loc[qid, 'title'] = question['title']\\n return question_list.sort_values(by='position')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"987f402dcd55766283638a33f2093f54\",\n \"score\": \"0.4868873\",\n \"text\": \"def test_post_question(self):\\n # get toal number of question accessible via the API\\n get_before = self.client().get('/questions/page/1')\\n get_before_data = get_before.json\\n init_question_num = get_before_data['total_questions']\\n # post data to the test database\\n post = self.client().post('/questions', json={\\n 'question': 'test question one',\\n 'answer': 'test answer one',\\n 'category': 1,\\n 'difficulty': 2\\n })\\n post_data = json.loads(post.data)\\n # get total number of question accessible to API after POST\\n get_after = self.client().get('/questions/page/1')\\n get_after_data = get_after.json\\n final_question_num = get_after_data['total_questions']\\n # ensure request was good\\n self.assertEqual(post.status_code, 200)\\n self.assertEqual(post_data['success'], True)\\n # make sure the initial number of questions is\\n # greater than the number of question after the POST\\n self.assertGreater(final_question_num, init_question_num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea593ab85b89397ebe104bbd7a604db\",\n \"score\": \"0.48682702\",\n \"text\": \"def initialize(quiz):\\n\\n current_choices = 0\\n most_choices = 0\\n total_questions = 0\\n new_question = True\\n\\n for line in quiz:\\n line = line.strip()\\n if line == '':\\n if current_choices > most_choices:\\n most_choices = current_choices\\n current_choices = 0\\n new_question = True\\n continue\\n elif new_question:\\n total_questions += 1\\n new_question = False\\n else:\\n current_choices += 1\\n\\n return [[None] * most_choices for i in range(total_questions)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"923486715403dc0b66b1c70ad7a8ef83\",\n \"score\": \"0.48672304\",\n \"text\": \"def test_future_questions(self):\\n\\n create_question(text=future_question_text(), days=+30)\\n\\n response = self.client.get(reverse(\\\"polls:index\\\"))\\n\\n self.assertQuerysetEqual(\\n response.context[\\\"question_list\\\"],\\n [],\\n )\\n self.assertContains(response, \\\"No polls are available.\\\")\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd3997b0205da39a7b84acd62f2216dc\",\n \"score\": \"0.4864154\",\n \"text\": \"async def _start(self, ctx, id, score_type='static'):\\n if self.in_play:\\n return await ctx.send(\\\"A quiz is already in progress\\\")\\n try:\\n if int(id) not in self.ids:\\n return await ctx.send(\\\"Quiz with this ID does not exist.\\\")\\n except ValueError:\\n return await ctx.send(\\\"Please enter a valid number for ID.\\\")\\n if score_type.lower() not in ['static', 'dynamic']:\\n return await ctx.send(\\\"Specify a valid scoring type (static/dynamic)\\\")\\n\\n quiz_data = await self._db.fetchrow(\\\"SELECT * FROM quiz_config WHERE id = $1\\\", id)\\n name = quiz_data['name']\\n desc = quiz_data['description'] or \\\"\\\\u200b\\\"\\n self.questions = []\\n ques_records = await self._db.fetch(\\\"SELECT * FROM questions WHERE quiz_id = $1\\\", id)\\n for record in ques_records:\\n self.questions.append({\\n 'question' : record['question'],\\n '1' : record['option1'],\\n '2' : record['option2'],\\n '3' : record['option3'],\\n '4' : record['option4'],\\n 'Correct' : record['correct']})\\n random.shuffle(self.questions)\\n await ctx.send(\\\"Questions loaded. Starting in 5 seconds\\\", embed=discord.Embed(title=name, description=desc, colour=discord.Colour.blurple()))\\n self.in_play = True\\n self._lb = dict()\\n await asyncio.sleep(5)\\n for num, question in enumerate(self.questions, 1):\\n # If this loop is interrupted, big F\\n reactions = []\\n self._score = MAX_POINTS\\n embed = discord.Embed(title=f\\\"Question {num}:\\\", description=question['question'], colour=discord.Colour.green())\\n if question['1'] != '':\\n embed.add_field(name=\\\"1\\\\N{variation selector-16}\\\\N{combining enclosing keycap}\\\", value=question['1'])\\n reactions.append(\\\"1\\\\N{variation selector-16}\\\\N{combining enclosing keycap}\\\")\\n if question['2'] != '':\\n embed.add_field(name=\\\"2\\\\N{variation selector-16}\\\\N{combining enclosing keycap}\\\", value=question['2'])\\n reactions.append(\\\"2\\\\N{variation selector-16}\\\\N{combining enclosing keycap}\\\")\\n if question['3'] != '':\\n embed.add_field(name=\\\"3\\\\N{variation selector-16}\\\\N{combining enclosing keycap}\\\", value=question['3'])\\n reactions.append(\\\"3\\\\N{variation selector-16}\\\\N{combining enclosing keycap}\\\")\\n if question['4'] != '':\\n embed.add_field(name=\\\"4\\\\N{variation selector-16}\\\\N{combining enclosing keycap}\\\", value=question['4'])\\n reactions.append(\\\"4\\\\N{variation selector-16}\\\\N{combining enclosing keycap}\\\")\\n msg = await ctx.send(embed=embed)\\n self._current = [msg.id, question['Correct'], []]\\n for reaction in reactions:\\n await msg.add_reaction(reaction)\\n for _ in range(TIMEOUT):\\n await asyncio.sleep(1)\\n if score_type.lower() == 'dynamic':\\n self._score -= MAX_POINTS / TIMEOUT\\n # Time up here\\n time_up_text = f\\\"\\\"\\\"**Time up!**\\\\nThe answers of the following members have been recorded:\\n\\n {chr(10).join([ctx.guild.get_member(member_id).mention for member_id in self._current[2]])}\\\"\\\"\\\"\\n self._current = []\\n await msg.edit(content=time_up_text, embed=discord.Embed(title=f\\\"Question {num}:\\\", description=f\\\"{question['question']}\\\\n\\\\n:white_check_mark: {question['correct']}\\\", colour=discord.Colour.red()))\\n await msg.clear_reactions()\\n\\n await self._publish(ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863ab7ce4c305b4c58fad4a225fc947d\",\n \"score\": \"0.48587698\",\n \"text\": \"def getQuiz():\\n Total=0\\n Quizes = session.query(Quiz).all()\\n for quiz in Quizes:\\n print(\\\"{} . {} \\\".format(quiz.id,quiz.name))\\n\\n x = 0\\n while x == 0:\\n try:\\n ID= input(\\\"Enter the id of the quiz: \\\")\\n ID = int(ID)\\n x = 1\\n except (ValueError):\\n print(\\\"please enter int number: \\\")\\n x = 0\\n\\n quiz=session.query(Quiz).filter(Quiz.id == int(ID))\\n for row in quiz:\\n print(f\\\"The Duration of the quiz is : {row.Duration} minutes\\\")\\n print(\\\"Let's start the quiz!\\\")\\n\\n\\n Mcq=session.query(MCQ).filter(MCQ.Quiz_id == int(ID))\\n for q in Mcq :\\n print(q.Question)\\n print(q.first_Choice)\\n print(q.second_Choice)\\n print(q.third_Choice)\\n print(q.fourth_Choice)\\n answer = input(\\\"Answer: \\\")\\n answer= str(answer)\\n\\n while (answer!=q.first_Choice and answer!=q.second_Choice and answer!=q.third_Choice and answer!=q.fourth_Choice) :\\n print(\\\"You must choose from the choices\\\")\\n answer = input(\\\"Answer: \\\")\\n\\n if(answer==q.answer):\\n Total+=q.mark\\n\\n Trueorfalse = session.query(TrueOrFalse).filter(TrueOrFalse.Quiz_id == int(ID))\\n for q in Trueorfalse:\\n print(q.Question)\\n answer = input(\\\"Answer: \\\")\\n if (bool(answer) == q.answer):\\n Total += q.mark\\n\\n\\n Essay = session.query(Essay_Question).filter(Essay_Question.Quiz_id == int(ID))\\n for q in Essay:\\n print(q.Question)\\n answer = input(\\\"Answer: \\\")\\n if (answer == q.answer):\\n Total += q.mark\\n\\n integer_qus = session.query(Integer_Question).filter(Integer_Question.Quiz_id == int(ID))\\n for q in integer_qus:\\n print(q.Question)\\n x = 0\\n while x == 0:\\n try:\\n answer = input(\\\"Answer: \\\")\\n answer = int(answer)\\n x = 1\\n except (ValueError):\\n print(\\\"please enter float number\\\")\\n x = 0\\n if (answer == q.answer):\\n Total += q.mark\\n\\n float_qus = session.query(Float_Question).filter(Float_Question.Quiz_id == int(ID))\\n for q in float_qus:\\n print(q.Question)\\n x=0\\n while x==0:\\n try:\\n answer = input(\\\"Answer: \\\")\\n answer=float(answer)\\n x=1\\n except (ValueError):\\n print(\\\"please enter float number\\\")\\n x=0\\n if (answer == q.answer):\\n Total += q.mark\\n\\n print(f\\\"Mark: {Total}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ad5307bc30fc9524ccb729867d5c3b\",\n \"score\": \"0.4858514\",\n \"text\": \"def test_post_question(self):\\n\\n res = self.client.post('/api/v1/questions', json=self.question, headers=self.headers)\\n data = res.get_json()\\n\\n self.assertEqual(res.status_code, 201)\\n self.assertEqual(data['status'], 201)\\n self.assertEqual(data['message'], 'Question posted successfully')\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993490,"cells":{"query_id":{"kind":"string","value":"79266c27c0f35444c6f2b5e7f0b20869"},"query":{"kind":"string","value":"Reads a GeoJSON and gives the results in GeoJSON format Speed = 0.091ms/feature"},"positive_passages":{"kind":"list like","value":[{"docid":"8e4266a2a809483dca42fc8c9b0f2bea","score":"0.6681749","text":"def read_geojson(self, infile, **kwargs):\n\n logging.info('Reading GeoJSON: %s' % infile)\n if isinstance(infile, dict):\n geojson = infile\n else:\n with open(infile) as f:\n geojson = json.load(f)\n\n if 'type' not in geojson:\n raise ValueError('GeoJSON must contain a [type] \"FeatureCollection\" or \"Feature\"')\n if 'crs' not in geojson:\n logging.warning('Coordinate Reference System was not detected (default=EPSG:4326)')\n self.epsg = 'EPSG:4326'\n else:\n self.epsg = 'EPSG:{}'.format(extract_epsg(geojson['crs']))\n\n # Read GeoJSON Feature Collection\n if geojson['type'] == 'FeatureCollection':\n if not geojson['features']:\n raise ValueError('FeatureCollection has [0] features.')\n features = geojson['features']\n\n # Read Single GeoJSON Feature\n elif geojson['type'] == 'Feature':\n features = [geojson]\n\n properties = set()\n self.geometry = set()\n\n for feature in features:\n # Add unique attribute keys to properties\n if 'properties' in feature:\n properties.update(feature['properties'].keys())\n\n # Only add features with geometry\n if feature.get('geometry'):\n feature = confirm_geometry(feature)\n self.features.append(feature)\n self.geometry.update([feature['geometry']['type']])\n else:\n logging.warning('Could not find [geometry] in feature.')\n\n # Creating basic properties for attribute table when building a shapefile.\n self.properties = dict((key, 'str') for key in properties)","title":""}],"string":"[\n {\n \"docid\": \"8e4266a2a809483dca42fc8c9b0f2bea\",\n \"score\": \"0.6681749\",\n \"text\": \"def read_geojson(self, infile, **kwargs):\\n\\n logging.info('Reading GeoJSON: %s' % infile)\\n if isinstance(infile, dict):\\n geojson = infile\\n else:\\n with open(infile) as f:\\n geojson = json.load(f)\\n\\n if 'type' not in geojson:\\n raise ValueError('GeoJSON must contain a [type] \\\"FeatureCollection\\\" or \\\"Feature\\\"')\\n if 'crs' not in geojson:\\n logging.warning('Coordinate Reference System was not detected (default=EPSG:4326)')\\n self.epsg = 'EPSG:4326'\\n else:\\n self.epsg = 'EPSG:{}'.format(extract_epsg(geojson['crs']))\\n\\n # Read GeoJSON Feature Collection\\n if geojson['type'] == 'FeatureCollection':\\n if not geojson['features']:\\n raise ValueError('FeatureCollection has [0] features.')\\n features = geojson['features']\\n\\n # Read Single GeoJSON Feature\\n elif geojson['type'] == 'Feature':\\n features = [geojson]\\n\\n properties = set()\\n self.geometry = set()\\n\\n for feature in features:\\n # Add unique attribute keys to properties\\n if 'properties' in feature:\\n properties.update(feature['properties'].keys())\\n\\n # Only add features with geometry\\n if feature.get('geometry'):\\n feature = confirm_geometry(feature)\\n self.features.append(feature)\\n self.geometry.update([feature['geometry']['type']])\\n else:\\n logging.warning('Could not find [geometry] in feature.')\\n\\n # Creating basic properties for attribute table when building a shapefile.\\n self.properties = dict((key, 'str') for key in properties)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3038df70905acd2489ae82ffca63ed98","score":"0.6661169","text":"def read_geojson(geojson_file):\n # example in order to see the polygon_vertices_pixels\n #data['features'][0]['properties']['polygon_vertices_pixels']\n d = dict()\n \n # get all the solar panel coordinates in each picture and save it in a dictionary\n for i in range(len(data['features'])):\n if i%50==0: print(i)\n im_name = data['features'][i]['properties']['image_name']\n if not im_name in d.keys():\n d[im_name] = []\n d[im_name].append(i)\n return(d)","title":""},{"docid":"dfb868339faad755f062796bbcfa141b","score":"0.6360556","text":"def read_json():","title":""},{"docid":"2343895512d46373a0dbd12fb3656ee2","score":"0.63202417","text":"def node_geojson():\n nodes = request.json.get(\"nodes\", [])\n geojson_prop = request.json.get(\"geoJsonProp\")\n if not geojson_prop:\n return \"error: must provide a geoJsonProp field\", 400\n features = []\n geojson_by_node = fetch.property_values(nodes, geojson_prop)\n for node_id, json_text in geojson_by_node.items():\n if json_text:\n geo_feature = get_geojson_feature(node_id, node_id, json_text)\n if geo_feature:\n features.append(geo_feature)\n result = {\n \"type\": \"FeatureCollection\",\n \"features\": features,\n \"properties\": {\n \"currentGeo\": \"\"\n }\n }\n return Response(json.dumps(result), 200, mimetype='application/json')","title":""},{"docid":"2a93168a27eb926608b7ceebc3b557b3","score":"0.6300121","text":"def geojson():\n place_dcid = request.args.get(\"placeDcid\")\n if not place_dcid:\n return Response(json.dumps(\"error: must provide a placeDcid field\"),\n 400,\n mimetype='application/json')\n place_type = request.args.get(\"placeType\")\n if not place_type:\n place_dcid, place_type = get_choropleth_display_level(place_dcid)\n cached_geojson = current_app.config['CACHED_GEOJSONS'].get(\n place_dcid, {}).get(place_type, None)\n if cached_geojson:\n return lib_util.gzip_compress_response(cached_geojson, is_json=True)\n geos = []\n if place_dcid and place_type:\n geos = fetch.descendent_places([place_dcid], place_type).get(place_dcid, [])\n if not geos:\n return Response(json.dumps({}), 200, mimetype='application/json')\n geojson_prop = CHOROPLETH_GEOJSON_PROPERTY_MAP.get(place_type, \"\")\n # geoId/72 needs higher resolution geojson because otherwise, the map looks\n # too fragmented\n if place_dcid == 'geoId/72':\n geojson_prop = 'geoJsonCoordinatesDP1'\n names_by_geo = place_api.get_display_name(geos)\n features = []\n if geojson_prop:\n geojson_by_geo = fetch.property_values(geos, geojson_prop)\n # geoId/46102 is known to only have unsimplified geojson so need to use\n # geoJsonCoordinates as the prop for this one place\n if 'geoId/46102' in geojson_by_geo:\n geojson_by_geo['geoId/46102'] = fetch.property_values(\n ['geoId/46102'], 'geoJsonCoordinates').get('geoId/46102', '')\n for geo_id, json_text in geojson_by_geo.items():\n if json_text and geo_id in names_by_geo:\n geo_name = names_by_geo.get(geo_id, \"Unnamed Area\")\n geo_feature = get_geojson_feature(geo_id, geo_name, json_text)\n if geo_feature:\n features.append(geo_feature)\n result = {\n \"type\": \"FeatureCollection\",\n \"features\": features,\n \"properties\": {\n \"currentGeo\": place_dcid\n }\n }\n return lib_util.gzip_compress_response(result, is_json=True)","title":""},{"docid":"ce2dd990ddf626e0a89ad9e19ca81236","score":"0.6159015","text":"def ST_GeomFromGeoJSON(json):\n import pyarrow as pa\n geo = pa.array(json, type='string')\n geo = _to_arrow_array_list(geo)\n result = [arctern_core_.ST_GeomFromGeoJSON(g) for g in geo]\n return _to_pandas_series(result)","title":""},{"docid":"6744cc102add14fbd1dbdd413ea6fe7c","score":"0.61482495","text":"def process_geojson(poly_base_geojson):\n count_build_without_height = 0\n count_bug_linestring = 0\n count_deleted_points = 0\n count_deleted_multi_points = 0\n count_deleted_unused_fields = 0\n init_features_number = len(poly_base_geojson[\"features\"])\n try:\n for item in poly_base_geojson[\"features\"]:\n properties = item[\"properties\"]\n geom_type = item[\"geometry\"][\"type\"]\n if geom_type == \"Point\":\n poly_base_geojson[\"features\"].remove(item)\n count_deleted_points += 1\n else:\n #make dafault value of height if absent\n if \"height\" not in properties and \"building:levels\" not in properties:\n item[\"properties\"][\"height\"] = str(DEFAULT_VALUE_HEIGHT)\n count_build_without_height += 1\n elif \"height\" not in properties and len(re.sub(r\"[^0-9^.]\", \"\", item[\"properties\"][\"building:levels\"])) > 0:\n # remove all symbols except number from the building:levels field and calculate height field\n item[\"properties\"][\"height\"] = str(HEIGHT_OF_FLOOR * float(re.sub(r\"[^0-9^.]\", \"\", item[\"properties\"][\"building:levels\"])))\n count_build_without_height += 1\n\n # correct LineString geometry type to Polygon\n if geom_type == \"LineString\":\n geom_coordinates = item[\"geometry\"][\"coordinates\"]\n item[\"geometry\"][\"type\"] = \"Polygon\"\n item[\"geometry\"][\"coordinates\"] = [geom_coordinates]\n count_bug_linestring += 1\n\n # remove unnececery fields `roof:colour`, `building`, `roof:shape`, `opening_hours`, `fuel:lpg`, `brand`\n for field in UNUESED_FILEDS[1]:\n if field in item[\"properties\"]:\n del(item[\"properties\"][field])\n count_deleted_unused_fields += 1\n \n except TypeError as error:\n print(error)\n print(type(item))\n final_features_number = len(poly_base_geojson[\"features\"])\n #save corrected json in the file\n #poly_base_geojson[\"features\"] = poly_base_geojson[\"features\"]\n with open(PROCESSED_JSON_FILENAME, 'w') as outfile:\n json.dump(poly_base_geojson, outfile, indent=3)\n # print report \n print(REPORT_SUMMARY.format(init_features_number, final_features_number, count_build_without_height, count_bug_linestring, count_deleted_points, count_deleted_multi_points, count_deleted_unused_fields))","title":""},{"docid":"cd956f8f669e468102eafe1ec8a25ecb","score":"0.6102305","text":"def get_geojson_feature(geo_id: str, geo_name: str, json_text: List[str]):\n # Exclude geo if no renderings are present.\n if len(json_text) < 1:\n return None\n geojson = json.loads(json_text[0])\n geo_feature = {\n \"type\": \"Feature\",\n \"id\": geo_id,\n \"properties\": {\n \"name\": geo_name,\n \"geoDcid\": geo_id,\n }\n }\n geojson_type = geojson.get(\"type\", \"\")\n if geojson_type == MULTILINE_GEOJSON_TYPE:\n geo_feature['geometry'] = geojson\n elif geojson_type == POLYGON_GEOJSON_TYPE or geojson_type == MULTIPOLYGON_GEOJSON_TYPE:\n coordinates = get_multipolygon_geojson_coordinates(geojson)\n geo_feature['geometry'] = {\n \"type\": \"MultiPolygon\",\n \"coordinates\": coordinates\n }\n else:\n geo_feature = None\n return geo_feature","title":""},{"docid":"67c1de743dcfcddfa55e673da9fa929e","score":"0.609611","text":"def get_geojson_from_geoserver(user, shapefile_name):\n kml_links = []\n context = {}\n\n # Find the contents of GeoServer for the user and display them\n dataset_engine = get_spatial_dataset_engine(name='gsshaindex_geoserver', app_class=GSSHAIndex)\n overlay_result = gi_lib.get_layer_and_resource(dataset_engine, user, shapefile_name)\n\n if overlay_result['success'] == True:\n url = overlay_result['resource']['wfs']['geojson']\n r= requests.get(url)\n status = r.status_code\n if status == 200:\n result = r.json()\n geojson = result['features']\n srid = result['crs']\n return {'geojson':geojson, 'crs':srid, 'success':True}\n else:\n return {'success':False}","title":""},{"docid":"eed72a79ca70fb624be55b786ef1641c","score":"0.60737634","text":"def getCityGeoJSON(adress):\n if adress in adresses:\n return adresses[adress]\n else:\n from geopy.geocoders import Nominatim\n geolocator = Nominatim(user_agent=\"practica-abbdd\")\n location = geolocator.geocode(adress)\n sleep(5)\n geojson = json.dumps({'type': 'Point', 'coordinates' : [location.latitude, location.longitude]})\n adresses[adress] = geojson\n return geojson","title":""},{"docid":"f85cf4d27d8d79efdf5cb21ce18c81ee","score":"0.60265493","text":"def read_geojson_geograph(geojson_path):\n\n geojson = json.load(geojson_path)\n dict_getter = nested_dict_getter()\n\n # build GeoGraph from a plain-old networkx graph\n g = nx.Graph()\n\n # Only add crs if it's explicitly set in crs.properties.name\n crs = dict_getter(geojson, ['crs', 'properties', 'name'])\n if crs:\n try:\n prj.Proj(crs)\n except Exception as e:\n raise SpatialReferenceInvalidException(\n \"Spatial reference must comply with proj4, got {}\" % crs)\n\n # TODO: Apply geojson schema validation\n features = geojson['features']\n\n # Currently this separates nodes and coordinates due to structure of\n # GeoGraph. This may change to allow Nodes and Edges to have distinct \n # Geometry.\n nodes, coords = geojson_get_nodes(features)\n g.add_nodes_from(nodes)\n \n edges = geojson_get_edges(features)\n g.add_edges_from(edges)\n\n if crs is None and gm.is_in_lon_lat(coords.values()):\n crs = gm.PROJ4_LATLONG\n \n if crs is None:\n warnings.warn(\"Spatial Reference could not be set for {}\".\n format(geojson_path))\n\n # TODO: More srs/projection validation?\n return GeoGraph(srs=crs, coords=coords, data=g)","title":""},{"docid":"6122bff6b45ec16a52a5bca219bdfba0","score":"0.597878","text":"def read_shp_layer(input_file, layer_read = 0, max_feats_read = 100000):\n import ast\n from osgeo import ogr\n \n output_list = []\n input_data = ogr.Open(input_file)\n layer = input_data.GetLayer(layer_read)\n # Try to read first feature of layer, and throw error if not possible\n feature = layer.GetFeature(0)\n print(feature)\n \n # Loop through features\n for i_feat in range(max_feats_read):\n try:\n # Read feature\n feature = layer.GetFeature(i_feat)\n # Export to dict string\n dict_str = feature.ExportToJson()\n # Append to output\n output_list.append(ast.literal_eval(dict_str))\n except:\n # Last feature read, break loop\n break\n \n return output_list","title":""},{"docid":"881f50dee42cfc96fc91d69d609a6a74","score":"0.595223","text":"def load_data(json_path, gps_data_path, filter_exotics=True):\n # with open(pickle_path, 'rb') as f:\n # ucf_objects_mobilenet = pickle.load(f)\n\n # object_counts = {}\n # for key, value in ucf_objects_mobilenet.items():\n # object_counts[key] = len(value)\n\n with open(json_path, \"r\") as read_file:\n prediction_data = json.load(read_file)\n\n prediction_data = pd.DataFrame(prediction_data)\n\n prediction_data['scene'] = prediction_data['scene'].map(\n lambda x: x.split('_')[0])\n prediction_data['year'] = prediction_data['label'].map(\n lambda x: int(x.split(' ')[-1]))\n\n image_names = ['0'*(6-len(str(num))) + str(num) for num in range(1, 10344)]\n gps = np.squeeze(scipy.io.loadmat(gps_data_path)['GPS_Compass'])\n gps_df = pd.DataFrame(gps, index=image_names, columns=[\n 'lat', 'long', 'compass'])\n\n all_scene_data = prediction_data.merge(\n gps_df, how='outer', left_on='scene', right_index=True)\n\n # all_views_obj_count = {}\n # for key, value in object_counts.items():\n # if key[:6] in all_views_obj_count:\n # all_views_obj_count[key[:6]] += value\n # else:\n # all_views_obj_count[key[:6]] = value\n\n # gps_df['count'] = gps_df.index.map(all_views_obj_count)\n\n return all_scene_data","title":""},{"docid":"cc00b3930c10c57110940e859a8dc7d7","score":"0.5927192","text":"def all_geojson(request):\n\n try:\n programs = Program.objects.transform(4326).all().select_related()\n except Place.DoesNotExist:\n raise Http404\n\n features = []\n\n for program in programs:\n # truncatewords\n properties = dict(title=program.title, description=truncatewords(program.description,20), absolute_url=program.get_absolute_url(), map_icon='layer-0')\n if program.image:\n properties['image_url'] = program.image.url\n if program.icon:\n properties['map_icon'] = 'layer-%s' % program.icon.id\n geometry = simplejson.loads(program.geometry.geojson)\n feature = dict(type='Feature', geometry=geometry, properties=properties)\n features.append(feature)\n\n response = dict(type='FeatureCollection', features=features)\n return HttpResponse(simplejson.dumps(response), mimetype='application/json')","title":""},{"docid":"74eb238116857f61430293b17253b456","score":"0.5923925","text":"def convertTOGEOJSON(data, ftype): \n ftype = ftype.lower()\n \n if ftype == \"point\":\n result_feat =[]\n for i in range(0,len(data)):\n point = geojson.Point(data[i])\n result_feat.append(geojson.Feature(geometry=point))\n result_feat = geojson.FeatureCollection(result_feat)\n # pp.pprint(collection) # valid\n return result_feat\n\n if ftype == \"polygon\":\n polygon = geojson.Polygon(data)\n # print(polygon) # valid \n return polygon\n\n elif ftype == \"linestring\":\n linestring = geojson.LineString(data)\n # print(linestring) #valid\n return (linestring)\n\n elif ftype == \"multilinestring\":\n mls = geojson.MultiLineString(data)\n # print(mls) # valid\n return mls\n\n else:\n print(\"\\nwrong argument\\n\")","title":""},{"docid":"b0e43d397e7e632bb84aadff6c2cdbba","score":"0.588498","text":"def _geom_from_geojson(fpath):\n with open(fpath) as f:\n geojson = json.load(f)\n if geojson['type'] == 'Feature':\n geom = shape(geojson['geometry'])\n elif geojson['type'] == 'FeatureCollection':\n geom = shape(geojson['features'][0]['geometry'])\n else:\n raise click.exceptions.BadOptionUsage('No GeoJSON feature found.')\n return geom","title":""},{"docid":"f7ce3bdfe15c3a8536758f69ffb64f26","score":"0.58566725","text":"def test_geospatial_positive_query_with_geojson(self):\n records = []\n query = self.as_connection.query(\"test\", \"demo\")\n\n geo_object2 = aerospike.geojson(\n '{\"type\": \"Polygon\", \"coordinates\": [[[-122.500000, 37.000000], \\\n [-121.000000, 37.000000], [-121.000000, 38.080000],\\\n [-122.500000, 38.080000], [-122.500000, 37.000000]]]}'\n )\n\n query.where(p.geo_within_geojson_region(\"loc\", geo_object2.dumps()))\n\n def callback(input_tuple):\n _, _, record = input_tuple\n records.append(record)\n\n query.foreach(callback)\n\n assert len(records) == 3\n expected = [\n {\"coordinates\": [-122.0, 37.5], \"type\": \"Point\"},\n {\"coordinates\": [-121.8, 37.7], \"type\": \"Point\"},\n {\"coordinates\": [-121.6, 37.9], \"type\": \"Point\"},\n ]\n for r in records:\n assert r[\"loc\"].unwrap() in expected","title":""},{"docid":"4cfe277d56869251bb8c69eeab3e7697","score":"0.5830556","text":"def read_nhdplus_flowline_geojson_attribute(sFilename_geojson_in):\n\n iReturn_code = 1\n if os.path.isfile(sFilename_geojson_in):\n pass\n else:\n print('This geojson does not exist: ', sFilename_geojson_in )\n iReturn_code = 0\n return iReturn_code\n\n aNHDPlusID=list()\n pDriver_shapefile = ogr.GetDriverByName('GeoJSON')\n \n pDataset_shapefile = pDriver_shapefile.Open(sFilename_geojson_in, gdal.GA_ReadOnly)\n pLayer_shapefile = pDataset_shapefile.GetLayer(0)\n pSpatialRef_shapefile = pLayer_shapefile.GetSpatialRef() \n for pFeature_shapefile in pLayer_shapefile: \n pGeometry_in = pFeature_shapefile.GetGeometryRef()\n sGeometry_type = pGeometry_in.GetGeometryName() \n lNHDPlusID = int(pFeature_shapefile.GetField(\"NHDPlusID\"))\n aNHDPlusID.append(lNHDPlusID) \n \n #we also need to spatial reference\n #aFromNode, aToNode, \n return aNHDPlusID","title":""},{"docid":"cd9c9e9ac36d6e13bb32e92b5b319597","score":"0.573606","text":"def _load_geojson_as_list(fn):\n shapes = []\n areas = []\n with fiona.open(fn) as f:\n src_crs = f.crs\n for row in f:\n geom = row[\"geometry\"]\n\n area = get_area_from_geometry(geom, src_crs)\n areas.append(area)\n\n shape = shapely.geometry.shape(geom)\n shapes.append(shape)\n return shapes, areas, src_crs","title":""},{"docid":"966a81b5526c2ba1435c4e3dd764404c","score":"0.57322085","text":"def get_json(url):\n f = urllib2.urlopen(url)\n response_text = f.read()\n response_data = json.loads(response_text)\n \n\n return response_data[\"results\"][0][\"geometry\"][\"location\"]","title":""},{"docid":"2c681f03300e91fda7feb1bf55de320b","score":"0.57250303","text":"def loadGeoJsonFromOverpass(savePath, overpassQuery):\n\t\tapi = overpass.API(debug=False)\n\t\ttry:\n\t\t\tresponse = api.Get(overpassQuery, responseformat=\"geojson\")\n\t\texcept overpass.OverpassError:\n\t\t\t# if too many requests, wait and send again\n\t\t\tprint \"Too many overpass requests, trying again in 2 sec.\"\n\t\t\ttime.sleep(2)\n\t\t\tOsm.loadGeoJsonFromOverpass(savePath, overpassQuery)\n\t\t\treturn\n\n\t\t# save as geojson\n\t\tdeleteFile(savePath)\n\t\twith open(savePath, 'w') as fp:\n\t\t\tjson.dump(response, fp)","title":""},{"docid":"9a6be64272a121fc5f54eb2bb39b4c07","score":"0.5722722","text":"def read_geograph(filename, *args, **kwargs):\n\n # throw out arguments if we don't need 'em\n read_map = {\n '.json': lambda filename: read_json_geograph(filename),\n '.geojson': lambda filename: read_geojson_geograph(filename),\n '.shp': lambda filename: read_shp_geograph(filename, *args, **kwargs),\n '.csv': lambda filename: read_csv_geograph(filename, *args, **kwargs)\n }\n\n match = re.search(r'\\.[^\\.]*$', filename)\n if match is None or match.group() not in read_map:\n msg = \"input filename {} does not have extension of .shp, .csv, \"\\\n \".json or .geojson\".format(filename)\n raise NetworkerException(msg)\n else:\n return read_map[match.group()](filename)","title":""},{"docid":"11ae8a647ab2e05b71e755b7c5634447","score":"0.57142514","text":"def read_with_ogr (filename, fatal_errors=True):\n source = osgeo.ogr.Open(filename, False)\n if not source: raise Exception(\"Can't open %s\" % filename)\n\n layer = source.GetLayer(0)\n defn = layer.GetLayerDefn()\n fields = [defn.GetFieldDefn(i).GetName().lower() for i in range(defn.GetFieldCount())]\n\n layer.ResetReading()\n ogr_feature = layer.GetNextFeature()\n while ogr_feature:\n # try:\n # geometry = shapely.wkb.loads(ogr_feature.GetGeometryRef().ExportToWkb())\n # except Exception, e:\n # if fatal_errors:\n # raise\n # else:\n # print >>sys.stderr, \"Shapely error:\", e\n # ogr_feature.Destroy()\n # ogr_feature = layer.GetNextFeature()\n # continue\n geometry_ref = ogr_feature.GetGeometryRef()\n geometry = (geometry_ref.GetX(), geometry_ref.GetY())\n attrs = {}\n for n, name in enumerate(fields):\n value = ogr_feature.GetField(n)\n if isinstance(value, basestring):\n try:\n value = value.decode(\"utf-8\")\n except UnicodeDecodeError:\n value = value.decode(\"latin-1\") \n attrs[name] = value\n ogr_feature.Destroy()\n yield geometry, attrs\n ogr_feature = layer.GetNextFeature()\n source.Destroy()","title":""},{"docid":"4ead0c91aa8812dbe027f014ce0b9770","score":"0.5707251","text":"def get_geist_data(theURL, query):\n user_agent = 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.111 Safari/537.36'\n # req = urllib.request.Request(theURL+query, headers={'User-Agent':user_agent})\n # req = urllib.request.Request(theURL + query)\n opener = urllib.request.urlopen(theURL + query)\n html = opener.read().decode('utf-8')\n logger.debug('json size = %d' %(len(html)))\n jdata = json.loads(html)\n return jdata","title":""},{"docid":"fbff62ccd7e8b415ad24e3e7c2722bca","score":"0.5706733","text":"def getFeatures(gdf):\n import json\n return [json.loads(gdf.to_json())['features'][0]['geometry']]","title":""},{"docid":"fbff62ccd7e8b415ad24e3e7c2722bca","score":"0.5706733","text":"def getFeatures(gdf):\n import json\n return [json.loads(gdf.to_json())['features'][0]['geometry']]","title":""},{"docid":"45ef8bc6860596e6ebf81a95039d1ed6","score":"0.5698234","text":"def read_json(path2json_file):\n # parse data from the input file\n with open(path2json_file, \"r\") as json_file:\n json_data = json_file.read()\n json_loaded = json.loads(json_data)\n boundary_data = try_extraction(json_loaded, BOUNDARY_JSON_KEY)\n holes_data = try_extraction(json_loaded, HOLES_JSON_KEY)\n boundary_coordinates = convert2polygon(boundary_data)\n list_of_holes = [convert2polygon(hole_data) for hole_data in holes_data]\n return boundary_coordinates, list_of_holes","title":""},{"docid":"740fed0bd57ffc8eebfd8ff2b9d18103","score":"0.56888956","text":"def start_pipeline_to_geojson():\n # get xml files from osm\n for region in REGIONS_TEST:\n print(\"XML obtaining\\nprceeded: {}\".format(region))\n filename = region.replace(\" \", \"_\")\n filenames.append(filename)\n os.system('python3 getosm.py \"{0}\" > {2}/{1}.xml'.format(region, filename, RESULT_DIR))\n \n # make geojson files from xml\n os.chdir(RESULT_DIR)\n print(os.listdir(os.getcwd()))\n for filename in filenames:\n print('Generating of geojson: \\nosmtogeojson {0}.xml > {0}.geojson'.format(filename))\n os.system('node --max_old_space_size=4000 `which osmtogeojson` {0}.xml > {0}.geojson'.format(filename))\n print(os.listdir(os.getcwd()))\n \n # open base file\n print(filenames[0])\n with open('{}.geojson'.format(filenames[0])) as geojson1:\n poly_base_geojson = json.load(geojson1)\n\n # merdge features of the base file with other\n for file in filenames[1:]:\n print(\"Merging \\n\")\n print(file)\n with open('{}.geojson'.format(file)) as geojson_temp:\n poly_temp_geojson = json.load(geojson_temp)\n # add to features of the next \n poly_base_geojson['features'] += poly_temp_geojson['features']\n\n with open(MERGED_FILENAME, 'w') as outfile:\n json.dump(poly_base_geojson, outfile, indent=3)\n \n return poly_base_geojson","title":""},{"docid":"c6ce3f17552d421b077f008b692a2f69","score":"0.56849504","text":"def toGeoJson(data, dtype):\n dtype = dtype.lower()\n if dtype == \"point\":\n collection =[]\n for i in range(0,len(data)):\n point = geojson.Point(data[i])\n collection.append(geojson.Feature(geometry=point))\n collection = geojson.FeatureCollection(collection)\n # pp.pprint(collection) # valid\n return collection\n if dtype == \"polygon\":\n polygon = geojson.Polygon(data)\n # print(polygon) # valid \n return polygon\n elif dtype == \"linestring\":\n linestring = geojson.LineString(data)\n # print(linestring) #valid\n return (linestring)\n elif dtype == \"multilinestring\":\n mls = geojson.MultiLineString(data)\n # print(mls) # valid\n return mls\n else:\n print(\"\\nBAD ARGUMENT\\n\")","title":""},{"docid":"1a285a568c655a982cbc57d69b21daff","score":"0.5634496","text":"def test_geospatial_loads_positive_with_query(self):\n geo_object_loads = get_geo_object()\n\n geo_object_loads.loads(\n '{\"type\": \"Polygon\", \"coordinates\": [[[-122.500000, 37.000000],\\\n [-121.000000, 37.000000], [-121.000000, 38.080000],\\\n [-122.500000, 38.080000], [-122.500000, 37.000000]]]}'\n )\n\n assert geo_object_loads.unwrap() == {\n \"coordinates\": [[[-122.5, 37.0], [-121.0, 37.0], [-121.0, 38.08], [-122.5, 38.08], [-122.5, 37.0]]],\n \"type\": \"Polygon\",\n }\n\n records = []\n query = self.as_connection.query(\"test\", \"demo\")\n query.where(p.geo_within_geojson_region(\"loc\", geo_object_loads.dumps()))\n\n def callback(input_tuple):\n _, _, record = input_tuple\n records.append(record)\n\n query.foreach(callback)\n\n assert len(records) == 3\n expected = [\n {\"coordinates\": [-122.0, 37.5], \"type\": \"Point\"},\n {\"coordinates\": [-121.8, 37.7], \"type\": \"Point\"},\n {\"coordinates\": [-121.6, 37.9], \"type\": \"Point\"},\n ]\n for r in records:\n assert r[\"loc\"].unwrap() in expected","title":""},{"docid":"165beb48f17d2377303dc05e6d7636e1","score":"0.5633273","text":"def geojson(*forms: Union[\"Polygon\", \"Point\", \"LineString\"]):\n print(geopandas.GeoSeries([form.ftype(form.lonlat) for form in forms]).to_json())","title":""},{"docid":"e65f792b066be7777a19ad7a1c1132e2","score":"0.5610999","text":"def test_geospatial_loads_positive(self):\n geo_object = get_geo_object()\n geo_object.loads(\n '{\"type\": \"Polygon\", \"coordinates\": [[[-122.500000, 37.000000],\\\n [-121.000000, 37.000000], [-121.000000, 38.080000],\\\n [-122.500000, 38.080000], [-122.500000, 37.000000]]]}'\n )\n\n assert geo_object.unwrap() == {\n \"coordinates\": [[[-122.5, 37.0], [-121.0, 37.0], [-121.0, 38.08], [-122.5, 38.08], [-122.5, 37.0]]],\n \"type\": \"Polygon\",\n }","title":""},{"docid":"e3af6b9056f4030ba402805a014eac36","score":"0.55970067","text":"def parseLocs(dataPath):\n with open(dataPath) as f:\n data = json.load(f)\n f.close()\n # Flatten the json and convert to df\n df = pd.json_normalize(data, 'locations')\n # Read Timestamp as date format\n df['datetimeUTC'] = pd.to_datetime(df['timestampMs'], unit='ms')\n # Change to Swiss Timezone\n df['datetimeCH'] = df['datetimeUTC'] + pd.DateOffset(hours=1)\n # Extract the day\n df['date'] = df['datetimeUTC'].dt.date\n df = df.set_index('datetimeCH')\n # Read the coordinates as float numbers\n df['latitudeE7'] = df['latitudeE7'].astype(float)/10000000\n df['longitudeE7'] = df['longitudeE7'].astype(float)/10000000\n # Convert to geopandas\n gdf = gpd.GeoDataFrame(\n df, geometry=gpd.points_from_xy(df['longitudeE7'], df['latitudeE7']))\n return df, gdf","title":""},{"docid":"ac6b82f82b6e5ad6283f37c9bcdc8d3b","score":"0.5595491","text":"def test_read_json(self):\n\n result = read_json(\"NYC_Shapes.json\")\n neighborhood = [result.keys()]\n self.assertEqual(len(neighborhood), 195)","title":""},{"docid":"88e82f612a9b2cabd7ef4e6fdda092b8","score":"0.5592714","text":"def parse(json_filename, encoding=\"UTF-8\"):\r\n with codecs.open(json_filename, \"rU\", encoding=encoding) as fp:\r\n json_data = json.load(fp, encoding=encoding)\r\n json_processor = JsonParser()\r\n features = json_processor.parse_features(json_data)\r\n return features","title":""},{"docid":"74a9f1de2d513d351db32e5510aa19a4","score":"0.55665684","text":"def topojson_to_geojson(infile,outfile):\n cmd = os.path.join(PurePath(PARSER_PATH).parents[2],\"node_modules/topojson-client/bin/topo2geo\")\n \n st,r = sp.getstatusoutput(cmd + \" --version\")\n\n if st == 0:\n process = sp.Popen([cmd, infile, \"--out\", outfile])\n else:\n print(\"Couldn't find topo2geo at {}\".format(cmd))","title":""},{"docid":"d4a387f78673110a980080a5a8587c9e","score":"0.5545909","text":"def geojson2enu(features):\n if not isinstance(features, (geojson.FeatureCollection, dict)):\n if isinstance(features, io.IOBase):\n features = geojson.load(features)\n else:\n with open(features) as f:\n features = geojson.load(f)\n\n if not isinstance(features, EasyDict):\n features = EasyDict(features)\n\n ecef_xyz = []\n scores = []\n for feature in features.features:\n if feature.geometry.type == 'Point':\n llh = feature.geometry.coordinates\n if len(llh) == 2:\n llh.append(0) # altitude\n lon, lat, h = llh\n x, y, z = pymap3d.ecef.geodetic2ecef(lat, lon, h)\n score = feature.properties.get('detection-score', 0.5)\n ecef_xyz.append((x, y, z))\n scores.append(score)\n ecef_xyz = np.array(ecef_xyz)\n # Convert to ENU coordinates\n lat0, lon0, h0 = pymap3d.ecef.ecef2geodetic(*ecef_xyz[:, :3].mean(0))\n enu_xyz = pymap3d.ecef.ecef2enu(ecef_xyz[:, 0], ecef_xyz[:, 1], ecef_xyz[:, 2], lat0, lon0, h0)\n enu_xyz = np.column_stack(enu_xyz)\n assert len(enu_xyz) > 0\n assert len(enu_xyz[0]) >= 2\n assert len(scores) == len(enu_xyz)\n return enu_xyz, scores, (lat0, lon0, h0)","title":""},{"docid":"a4857b79e1ab5aa08cac5b5ef0363d5b","score":"0.5536641","text":"def get_stats(filenames, geom):\n stats = []\n origin = Proj(init='epsg:4326')\n _geojson = deepcopy(geom)\n\n origin_coords = geom['coordinates']\n\n total_count = None\n\n for i, f in enumerate(filenames):\n # convert polygon to image srs\n with rasterio.open(f) as src:\n dst = Proj(src.crs)\n _geojson['coordinates'] = convert_coordinates(origin_coords, origin, dst)\n\n # write to temporary geojson file\n pfilename = '/tmp/poly.geojson'\n with open(pfilename, 'w') as poly_f:\n poly_f.write(json.dumps(_geojson))\n\n if i == 0:\n total_count = float(zonal_stats(pfilename, f, stats=\"count\", nodata=-1)[0]['count'])\n\n # calculate stats\n st = zonal_stats(pfilename, f, stats=\"count mean min max\", nodata=999)[0]\n st['count'] = zonal_stats(pfilename, f, stats=\"count\", nodata=-9999)[0]['count']\n st['percent_flooded'] = st['count']/total_count * 100\n stats.append(st)\n return stats","title":""},{"docid":"a162dc6659718c048ebaca76e54f350a","score":"0.5536223","text":"def format_england_geojson(eng_file):\n with open(eng_file) as f:\n eng_geojson = geojson.load(f)\n\n for i, item in enumerate(eng_geojson[\"features\"]):\n # rename name and lsoa code properties.\n # discard everything else.\n item['properties'] = {\"name\": item['properties']['LSOA11NM'],\n \"ZoneID\": item['properties']['LSOA11CD']}\n return eng_geojson","title":""},{"docid":"1c509dc6a525694bb0b2d5dbc1fdf263","score":"0.55337656","text":"def aggregate_features(sp_path, osm_path):\n features = []\n with open(sp_path, \"r\") as sp_file:\n geojson = json.load(sp_file)\n features += geojson[\"features\"]\n with open(osm_path, \"r\") as osm_file:\n geojson = json.load(osm_file)\n features += geojson[\"features\"]\n return features","title":""},{"docid":"f53b403de9dfe26c0ab30b31b4d3681e","score":"0.5524737","text":"def readZip_psurge(self, add, name_dbf, name_prj, name_shp, name_shx):\n proxies = {\"http\":\"http://username:password@proxy_ip:proxy_port\"}\n session = requests.Session()\n retry = Retry(connect=3, backoff_factor=0.5)\n adapter = HTTPAdapter(max_retries=retry)\n session.mount('http://', adapter)\n session.mount('https://', adapter)\n \n county_file_url = 'https://www.nhc.noaa.gov/gis/' + add\n zipfile = ZipFile(io.BytesIO(session.get(county_file_url).content))\n sleep(5)\n \n end_dic = [name_dbf, name_prj, name_shp, name_shx]\n filenames = [y for y in sorted(zipfile.namelist()) for ending in end_dic if y.endswith(ending)] \n if filenames:\n dbf, prj, shp, shx = [io.BytesIO(zipfile.read(filename)) for filename in filenames]\n else:\n return None\n r = shapefile.Reader(shp=shp, shx=shx, dbf=dbf)\n attributes, geometry = [], []\n field_names = [field[0] for field in r.fields[1:]]\n for row in r.shapeRecords(): \n geometry.append(shape(row.shape.__geo_interface__)) \n attributes.append(dict(zip(field_names, row.record)))\n \n gdf = gpd.GeoDataFrame(data = attributes, geometry = geometry)\n gdf['timestamp'] = add.split('_')[3][0:10]\n \n return gdf","title":""},{"docid":"b5644d358a3e6af7fa54b2d01c9fd196","score":"0.5519732","text":"def load_countries():\n base_path = pathlib.Path(__file__).resolve().parent\n path = base_path.joinpath('countries', '50m', 'world.geo.json.gz')\n data = load_json_file(path)\n\n country_map = {}\n\n for item in data['features']:\n name = item['properties']['name']\n country_map[name] = item['id']\n return None, data, country_map","title":""},{"docid":"47aecc99467f1bf6f515240d29bc7155","score":"0.55083215","text":"def convert(arcgis):\n\n geojson = {}\n\n for iterable_name in [\"features\", \"results\"]:\n if iterable_name in arcgis and arcgis[iterable_name]:\n geojson[\"type\"] = \"FeatureCollection\"\n geojson[\"features\"] = []\n for feature in arcgis[iterable_name]:\n geojson[\"features\"].append(convert_feature(feature))\n return geojson","title":""},{"docid":"985a79f3fb4ebb99001190973c73c931","score":"0.5501992","text":"def find_realtime_coord(self):\n data = pd.read_csv(\"text_files\\Sat_coordinates.csv\")\n lat = data['LAT']\n lon = data['LON']\n\n point = Point([float(lon), float(lat)])\n feature = Feature(geometry=point)\n dump = geojson.dumps(feature, sort_keys=True)\n with open(\"static\\map.geojson\", 'w') as file_object:\n file_object.writelines(dump)","title":""},{"docid":"672dbb5448bd7592bd6dddcbe21110ab","score":"0.5483584","text":"def main(i, o, yes):\n input_geojson_file = i\n features_directory = o\n\n print \"Read input file path:\", input_geojson_file\n if features_directory == './':\n print 'OK, your features data will be stored in the current directory:', features_directory\n else:\n print 'OK, your features data will be stored in:', features_directory\n\n\n #fetch geojson from local path or remote path. Throw error if not found.\n print \"\\nfetching geojson...\"\n file_type = 'local' #set default to local anyway assuming not an http/https remote link\n if os.path.exists(input_geojson_file):\n file_type = 'local'\n else:\n if 'http' in input_geojson_file:\n file_type = 'remote'\n else:\n raise ValueError(\"Could not identify file type as remote or local.\")\n\n if file_type == 'local':\n js = get_json_local(input_geojson_file)\n elif file_type == 'remote':\n js = get_json_remote(input_geojson_file)\n if js == -1:\n print >> sys.stderr, \"No remote file found at\", input_geojson_file\n sys.exit(1)\n\n print \"\"\n\n\n\n # validate json for all geometries and correct labels:\n js, flag = validate_json(js)\n if flag == 1: # if flag is activated then do not execute rest\n sys.exit(1)\n\n #load CatalogImage given catalog id in geojson\n print \"\\nloading CatalogImage...\"\n image = load_cat_image(js['properties']['catalog_id'], pan=True)\n\n print \"current number of features:\", len(js['features']) # total features found in geojson\n\n # remove any Tomnod labels that are None\n print \"\\nremoving Tomnod_labels = None...\"\n filtered_feats = filter(lambda x: x['properties']['Tomnod_label'] != None, js['features'])\n js['features'] = filtered_feats\n\n print \"new number of features:\", len(js['features']) # total features after removing Tomnod labels that are None type\n\n # load the raster, mask it by the polygon segment\n print \"\\nmasking image...\"\n polys = geojson_to_polygons(js)\n image_aoi_segs, seg_masks = get_segment_masks(image, polys, invert=False) #toggle the inversion if necessary... remember this should be set to True for multiplying image to mask\n image_aoi_blobs = [ma.array(aoi, mask=np.dstack((seg_masks[i],)*8)) for i, aoi in enumerate(image_aoi_segs)] #image masks instead of just multiplying the image to mask. image_aoi_blobs will be a list of numpy masked arrays that mask out 0 (black) pixels\n\n\n # TODO: debug masked array in segments/pixels as features, make sure the masked numbers are not playing with the computation\n print \"\\ncreating features dataset...\"\n #creating segments as features -- use asynchronous polling to calculate features for every segment\n p = multiprocessing.Pool(processes=4)\n rsi_samples = [p.apply_async(segment_as_feature, args=(blob,), kwds={'include_gabors': True}) for blob in image_aoi_blobs]\n rsi_samples_output = [p.get() for p in rsi_samples]\n\n #create X and y dataset\n X_all, y_all = create_feature_dataset(rsi_samples_output, js)\n\n #save out the dataset\n print \"\\nsaving the features to\", features_directory, \"\\n\"\n if not os.path.exists(features_directory): #if path/subdirs do not exist then make them\n os.makedirs(features_directory)\n file_name = input_geojson_file.split('/')[-1].split('.')[0] #get filename without extensions\n path_X = features_directory+'/'+file_name+'_X_all' #new file path for X_all\n path_y = features_directory+'/'+file_name+'_y_all' # new file path for y_all\n if os.path.isfile(path_X): #check if X_all file already exists, then prompt for overwriting the file\n if yes:\n np.save(path_X, X_all)\n elif click.confirm(path_X.split('/')[-1] + \" already exists, are you sure you want to overwrite?\"):\n np.save(path_X, X_all)\n else:\n np.save(path_X, X_all)\n\n print \"\"\n\n if os.path.isfile(path_y): #check if y_all file already exists, then prompt for overwriting the file\n if yes:\n np.save(path_y, y_all)\n elif click.confirm(path_y.split('/')[-1] + \" already exists, are you sure you want to overwrite?\"):\n np.save(path_y, y_all)\n else:\n np.save(path_y, y_all)\n\n print \"\\ndone.\"","title":""},{"docid":"36584c764c69bbf69aad4786ab9d548b","score":"0.54740644","text":"def convert_shapefile_to_geojson(iFlag_type_in, sFilename_shapefile_in, sFilename_geojson_in):\n if os.path.isfile(sFilename_shapefile_in):\n pass\n else:\n print('This shapefile does not exist: ', sFilename_shapefile_in )\n iReturn_code = 0\n return iReturn_code\n\n if iFlag_type_in == 0:\n pass\n else:\n if iFlag_type_in == 1:\n aFlowline_basin, pSpatial_reference = read_flowline_shapefile( sFilename_shapefile_in ) \n #aFlowline_basin, pSpatial_reference = read_flowline_geojson( sFilename_shapefile_in ) \n #convert it\n\n iFlag_projected = 0\n pSpatial_reference_gcs = osr.SpatialReference()\n pSpatial_reference_gcs.ImportFromEPSG(4326)\n pSpatial_reference_gcs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)\n export_flowline_to_geojson(aFlowline_basin, sFilename_geojson_in)\n else:\n if iFlag_type_in == 2:\n #polygon\n pass\n\n\n return","title":""},{"docid":"5108e7f828dba9e1663329d9e317a435","score":"0.5462496","text":"def getCityGeoJSON(address):\n from geopy.geocoders import Nominatim # Importamos la libreria\n geolocator = Nominatim(user_agent=\"My-store\") # Establecemos un nombre de aplicacion\n location = geolocator.geocode(address) # Hacemos la llamada de la direccion\n return { # retorna las coordenadas en formato de GEOJSON\n \"type\": \"Point\",\n \"coordinates\": [location.latitude, location.longitude]\n }","title":""},{"docid":"e858d87dfbeb7f135e682f5eed10b863","score":"0.5453036","text":"def washed_out_roads_geojson():\n with sqlite3.connect('hikes.db') as con:\n cursor = con.cursor()\n results = cursor.execute('SELECT hike.name, alert.text, hike.th_lat, hike.th_long FROM hike INNER JOIN alert ON alert.hike_id = hike.id WHERE alert.type = \"red\" AND alert.text LIKE \"%road%\"').fetchall()\n return {\n 'type' : 'FeatureCollection',\n 'features' : [{\n 'type' : 'Feature',\n 'geometry' : {\n 'type' : 'Point',\n 'coordinates' : [result[3], result[2]]\n },\n 'properties' : {\n 'title' : result[0],\n 'description' : result[1]\n }\n } for result in results]\n }\n with open('washed_out_roads.json', 'w+') as file:\n file.write(json.dumps(washed_out_roads_geojson()))","title":""},{"docid":"420d724876e71616f7baef8730c42650","score":"0.5418384","text":"def parse_mapzen_response(txt):\n gdict = {} # just initialize a dict for now, with status of None\n data = json.loads(txt)\n if data['features']: # it has at least one feature...\n gdict['status'] = 'OK' \n feature = data['features'][0] # pick out the first one\n props = feature['properties'] # just for easier reference\n gdict['confidence'] = props['confidence']\n gdict['label'] = props['label']\n\n # now get the coordinates\n coords = feature['geometry']['coordinates']\n gdict['longitude'] = coords[0]\n gdict['latitude'] = coords[1]\n else:\n gdict['status'] = None\n return gdict","title":""},{"docid":"49855a75af51c7b1c94bee950c4546fc","score":"0.5402312","text":"def save_as_geojson():\n args = ['ogr2ogr', '-f', 'GeoJson', 'app/data/PSU_full_schedule.geojson', 'full_sched.vrt', '-s_srs', 'EPSG:2913', '-t_srs', 'EPSG:4326']\n call(args)","title":""},{"docid":"b361c1216b29ebb5e2284799d643fb2d","score":"0.53982383","text":"def geojson_builder(node):\n return {\n 'type': 'Feature',\n 'properties': {\n 'name': node['name'],\n 'population': node['pop']\n },\n 'geometry': {\n 'type': 'Point',\n 'coordinates': [node['lat'], node['lon']]\n }\n }","title":""},{"docid":"c4176e827f0274998a9565ba752420c2","score":"0.5392154","text":"def get_geo_json_for_connections(self):\n list_of_geo_json_objects = []\n\n for emulation_node, connected_emulation_nodes in singletons.network_manager.connection_store.get_connections_per_node().items():\n for connected_emulation_node in connected_emulation_nodes:\n list_of_geo_json_objects.append(\n self.__get_geo_json_object_for_connection(emulation_node.id - 1, connected_emulation_node.id - 1))\n\n feature_coll_roads = geojson.FeatureCollection(list_of_geo_json_objects)\n return geojson.dumps(feature_coll_roads)","title":""},{"docid":"73c3074e6929b506d37ec63808603dcc","score":"0.5371144","text":"def get_feature_json(self):\n feature = _osm_polygon_request(\n query=self.query, limit=1, polygon_geojson=1)\n return feature","title":""},{"docid":"15eaf32f1a449d099450edb205fd2e11","score":"0.5367315","text":"def by_geopoint(self, lat, long):\n\n header, content = self._http_request(self.BASE_URL, lat=lat, long=long)\t\t\n return json.loads(content)","title":""},{"docid":"7cf768652a44f22b56452d3f3a30d3dc","score":"0.5361886","text":"def create_map(data_file):\n\n # Define type of GeoJSON we're creating\n geo_map = {\"type\": \"FeatureCollection\"}\n\n # Define empty list to collect each point to graph\n item_list = []\n\n # Iterate over our data to create GeoJSOn document.\n # We're using enumerate() so we get the line, as well\n # the index, which is the line number.\n for index, line in enumerate(data_file):\n\n # Skip any zero coordinates as this will throw off\n # our map.\n if line['X'] == \"0\" or line['Y'] == \"0\":\n continue\n\n # Setup a new dictionary for each iteration.\n data = {}\n\n # Assigne line items to appropriate GeoJSON fields.\n data['type'] = 'Feature'\n data['id'] = index\n data['properties'] = {'title': line['Category'],\n 'description': line['Descript'],\n 'date': line['Date']}\n data['geometry'] = {'type': 'Point',\n 'coordinates': (line['X'], line['Y'])}\n\n # Add data dictionary to our item_list\n item_list.append(data)\n\n # For each point in our item_list, we add the point to our\n # dictionary. setdefault creates a key called 'features' that\n # has a value type of an empty list. With each iteration, we\n # are appending our point to that list.\n for point in item_list:\n geo_map.setdefault('features', []).append(point)\n\n # Now that all data is parsed in GeoJSON write to a file so we\n # can upload it to gist.github.com\n with open('file_sf.geojson', 'w') as f:\n f.write(geojson.dumps(geo_map))","title":""},{"docid":"70619b7a27031c13750595e6cf18bc0c","score":"0.53572655","text":"def scrape_rest_endpoint(urls, folder):\n arcpy.AddMessage(u\"Fetching GeoJSON files from Map Service\")\n layersToSync = {}\n\n # mapServiceUrl should be a list of urls pointing at map service or feature service layers.\n # each should end in \"MapServer/<some number>\" or \"FeatureServer/<some number>\"\n\n # load contents of feature service info page\n for mapServiceUrl in urls:\n mapServiceContents = requests.get(\"{}?f=json\".format(mapServiceUrl))\n mapServiceData = mapServiceContents.json()\n\n # deal with layer names that start with a number\n layerName = safeName(mapServiceData['name'])\n if not re.match(u'[a-zA-Z_]', layerName):\n layerName = u\"_{}\".format(layerName)\n\n # add the layer name as a key to the layersToSync dictionary with an empty list as the value\n layersToSync[layerName] = []\n count = 0\n\n # fetch esriFieldTypeOID name\n try:\n for field in mapServiceData['fields']:\n if field['type'] == 'esriFieldTypeOID':\n layerOID = field['name']\n except:\n continue\n\n # cycle through queries to scrape all data\n while True:\n downloadDataUrl = \"{}/query?where={}%3E%3D{}&outFields=*&f=json&returnGeometry=True&orderByFields={}\". \\\n format(mapServiceUrl, layerOID, str(count), layerOID)\n arcpy.AddMessage(downloadDataUrl)\n layerData = requests.get(downloadDataUrl)\n features = layerData.json()\n featurecount = len(features['features'])\n\n # if no features returned, you're done and move on to the next layer\n if featurecount == 0:\n break\n\n # otherwise, find the last objecit id you retrieved and pass that to your query next time\n nextoid = features['features'][featurecount - 1]['attributes'][layerOID] + 1\n\n # save the output json to the mirror directory\n if not os.path.exists(os.path.join(\"{}/{}\".format(folder, mapServiceData['id']))):\n os.makedirs(os.path.join(\"{}/{}\".format(folder, mapServiceData['id'])))\n layerGeojsonfile = os.path.join(\"{}/{}/{}{}.json\".format(\n folder, mapServiceData['id'], layerName, str(count)))\n\n layerGeojson = open(layerGeojsonfile, 'w+')\n json.dump(features, layerGeojson)\n\n # populate the value in layersToSync with the list of JSON fiels associated with the layer\n layersToSync[layerName].append(\"{}{}\".format(\n safeName(layerName), str(count)))\n layerGeojson.close()\n count = nextoid\n print(layersToSync)\n\n print('locating JSON data')\n downloadedJson = {}\n layers = []\n for dirpath, dirnames, filenames in os.walk(os.path.join(folder)):\n layers = []\n for layer in filenames:\n if layer[-5:] == \".json\":\n layers.append(layer)\n if layers != [] or None:\n downloadedJson[dirpath] = layers\n\n arcpy.AddMessage('Moving geojson files to merged file gdb')\n\n mergedGDBName = u'merged_layers.gdb'\n mergedGDB = os.path.join(folder, mergedGDBName)\n if not os.path.exists(mergedGDB):\n arcpy.CreateFileGDB_management(folder, mergedGDBName)\n\n for key in downloadedJson:\n\n # cycle through the files in each download directory\n for layer in downloadedJson[key]:\n\n # copy the json file into the mergedlayers.gdb\n if arcpy.Exists(os.path.join(mergedGDB, layer.split('.')[0])) == False:\n arcpy.JSONToFeatures_conversion(os.path.join(key, layer), os.path.join(mergedGDB, layer))\n\n # skip cases where the datatype will cause things to fail\n layertype = arcpy.Describe(os.path.join(mergedGDB, layer.split('.')[0]))\n if layertype.datasetType not in [\"FeatureClass\", \"FeatureLayer\"]:\n arcpy.AddMessage(\n \"Not syncing layer: {} because of datatype {}\".format(layer, layertype.datasetType))\n arcpy.Delete_management(os.path.join(mergedGDB, layer.split('.')[0]))\n continue\n\n # use the layersToSync dictionary to identify what layer the json file is supposed to be merged to\n for mergedLayer in layersToSync:\n if layer.split('.')[0] in layersToSync[mergedLayer]:\n if arcpy.Exists(os.path.join(mergedGDB, mergedLayer)):\n arcpy.Append_management(os.path.join(mergedGDB, layer.split('.')[0]), os.path.join\n (mergedGDB, mergedLayer))\n else:\n arcpy.FeatureClassToFeatureClass_conversion \\\n (os.path.join(mergedGDB, layer.split('.')[0]), mergedGDB, mergedLayer)\n arcpy.Delete_management(os.path.join(mergedGDB, layer.split('.')[0]))\n\n return","title":""},{"docid":"d5923ee50ae3902302a4710ef87754b0","score":"0.5352095","text":"def readJsonF(fileName):\n fileHandle = open(fileName,'r')\n try:\n data = json.load(fileHandle)\n return data\n except IOError:\n print(\"IO error\")\n finally:\n fileHandle.close()","title":""},{"docid":"be8d561241fe0151e73afd4561880ef3","score":"0.53411275","text":"def geojson_to_polygons(js_):\n\n polys = []\n for i, feat in enumerate(js_['features']):\n o = {\n \"coordinates\": feat['geometry']['coordinates'],\n \"type\": feat['geometry']['type']\n }\n s = json.dumps(o)\n\n # convert to geojson.geometry.Polygon\n g1 = geojson.loads(s)\n\n # covert to shapely.geometry.polygon.Polygon\n g2 = shape(g1)\n\n polys.append(g2)\n\n return polys","title":""},{"docid":"918fa6d4fc453bfa1076ce29628baebc","score":"0.5333225","text":"def get_geostore(geostore_id, format='esri'):\n\n config = {\n 'uri': '/geostore/{}?format={}'.format(geostore_id, format),\n 'method': 'GET',\n }\n\n return request_to_microservice(config)","title":""},{"docid":"42aa4c924dcda5ad993aec67233eb6b2","score":"0.53298545","text":"def arcgis2geojson(arcgis: Dict[str, Any], id_attr: Optional[str] = None) -> Dict[str, Any]:\n\n def convert(arcgis: Dict[str, Any], id_attr: Optional[str] = None) -> Dict[str, Any]:\n \"\"\"Convert an ArcGIS JSON object to a GeoJSON object.\"\"\"\n geojson: Dict[str, Any] = {}\n\n if \"features\" in arcgis and arcgis[\"features\"]:\n geojson[\"type\"] = \"FeatureCollection\"\n geojson[\"features\"] = [convert(feature, id_attr) for feature in arcgis[\"features\"]]\n\n if (\n \"x\" in arcgis\n and isinstance(arcgis[\"x\"], numbers.Number)\n and \"y\" in arcgis\n and isinstance(arcgis[\"y\"], numbers.Number)\n ):\n geojson[\"type\"] = \"Point\"\n geojson[\"coordinates\"] = [arcgis[\"x\"], arcgis[\"y\"]]\n if \"z\" in arcgis and isinstance(arcgis[\"z\"], numbers.Number):\n geojson[\"coordinates\"].append(arcgis[\"z\"])\n\n if \"points\" in arcgis:\n geojson[\"type\"] = \"MultiPoint\"\n geojson[\"coordinates\"] = arcgis[\"points\"]\n\n if \"paths\" in arcgis:\n if len(arcgis[\"paths\"]) == 1:\n geojson[\"type\"] = \"LineString\"\n geojson[\"coordinates\"] = arcgis[\"paths\"][0]\n else:\n geojson[\"type\"] = \"MultiLineString\"\n geojson[\"coordinates\"] = arcgis[\"paths\"]\n\n if \"rings\" in arcgis:\n geojson = rings2geojson(arcgis[\"rings\"])\n\n if (\n \"xmin\" in arcgis\n and isinstance(arcgis[\"xmin\"], numbers.Number)\n and \"ymin\" in arcgis\n and isinstance(arcgis[\"ymin\"], numbers.Number)\n and \"xmax\" in arcgis\n and isinstance(arcgis[\"xmax\"], numbers.Number)\n and \"ymax\" in arcgis\n and isinstance(arcgis[\"ymax\"], numbers.Number)\n ):\n geojson[\"type\"] = \"Polygon\"\n geojson[\"coordinates\"] = [\n [\n [arcgis[\"xmax\"], arcgis[\"ymax\"]],\n [arcgis[\"xmin\"], arcgis[\"ymax\"]],\n [arcgis[\"xmin\"], arcgis[\"ymin\"]],\n [arcgis[\"xmax\"], arcgis[\"ymin\"]],\n [arcgis[\"xmax\"], arcgis[\"ymax\"]],\n ]\n ]\n\n if \"geometry\" in arcgis or \"attributes\" in arcgis:\n geojson[\"type\"] = \"Feature\"\n if \"geometry\" in arcgis:\n geojson[\"geometry\"] = convert(arcgis[\"geometry\"])\n else:\n geojson[\"geometry\"] = None\n\n if \"attributes\" in arcgis:\n geojson[\"properties\"] = arcgis[\"attributes\"]\n try:\n attributes = arcgis[\"attributes\"]\n keys = [id_attr, \"OBJECTID\", \"FID\"] if id_attr else [\"OBJECTID\", \"FID\"]\n for key in keys:\n if key in attributes and (\n isinstance(attributes[key], (numbers.Number, str))\n ):\n geojson[\"id\"] = attributes[key]\n break\n except KeyError:\n warn(\"No valid id attribute found.\")\n else:\n geojson[\"properties\"] = None\n\n if \"geometry\" in geojson and not geojson[\"geometry\"]:\n geojson[\"geometry\"] = None\n\n return geojson\n\n def rings2geojson(rings: List[List[List[float]]]) -> Dict[str, Any]:\n \"\"\"Check for holes in the ring and fill them.\"\"\"\n outer_rings = []\n holes = []\n x = None # iterable\n outer_ring = None # current outer ring being evaluated\n hole = None # current hole being evaluated\n\n for ring in rings:\n if not np.all(np.isclose(ring[0], ring[-1])):\n ring.append(ring[0])\n\n if len(ring) < 4:\n continue\n\n total = sum(\n (pt2[0] - pt1[0]) * (pt2[1] + pt1[1]) for pt1, pt2 in zip(ring[:-1], ring[1:])\n )\n # Clock-wise check\n if total >= 0:\n outer_rings.append(\n [ring[::-1]]\n ) # wind outer rings counterclockwise for RFC 7946 compliance\n else:\n holes.append(ring[::-1]) # wind inner rings clockwise for RFC 7946 compliance\n\n uncontained_holes = []\n\n # while there are holes left...\n while holes:\n # pop a hole off out stack\n hole = holes.pop()\n\n # loop over all outer rings and see if they contain our hole.\n contained = False\n x = len(outer_rings) - 1\n while x >= 0:\n outer_ring = outer_rings[x][0]\n l1, l2 = LineString(outer_ring), LineString(hole)\n p2 = Point(hole[0])\n intersects = l1.intersects(l2)\n contains = l1.contains(p2)\n if not intersects and contains:\n # the hole is contained push it into our polygon\n outer_rings[x].append(hole)\n contained = True\n break\n x = x - 1\n\n # ring is not contained in any outer ring\n # sometimes this happens https://github.com/Esri/esri-leaflet/issues/320\n if not contained:\n uncontained_holes.append(hole)\n\n # if we couldn't match any holes using contains we can try intersects...\n while uncontained_holes:\n # pop a hole off out stack\n hole = uncontained_holes.pop()\n\n # loop over all outer rings and see if any intersect our hole.\n intersects = False\n x = len(outer_rings) - 1\n while x >= 0:\n outer_ring = outer_rings[x][0]\n l1, l2 = LineString(outer_ring), LineString(hole)\n intersects = l1.intersects(l2)\n if intersects:\n # the hole is contained push it into our polygon\n outer_rings[x].append(hole)\n intersects = True\n break\n x = x - 1\n\n if not intersects:\n outer_rings.append([hole[::-1]])\n\n if len(outer_rings) == 1:\n return {\"type\": \"Polygon\", \"coordinates\": outer_rings[0]}\n\n return {\"type\": \"MultiPolygon\", \"coordinates\": outer_rings}\n\n if isinstance(arcgis, str): # type: ignore\n return json.dumps(convert(json.loads(arcgis), id_attr)) # type: ignore\n\n return convert(arcgis, id_attr)","title":""},{"docid":"c3e4d01e050fc6cb2b1c31954eeed348","score":"0.53291166","text":"def _geojson_coordinates(geojsonurl):\n\n # load geojson file\n data = load(open(geojsonurl))\n\n # create the list of coordinates\n if data['features'][0]['geometry']['type'] == \"MultiPolygon\":\n list_coordinates = np.array(\n [component[0] for component in data['features'][0]['geometry']['coordinates']])\n\n else:\n list_coordinates = np.array(\n data['features'][0]['geometry']['coordinates'])\n\n return list_coordinates","title":""},{"docid":"36071f5a7e8328c8023aeb0c9958b012","score":"0.53189456","text":"def convert_flowline_to_geojson(iFlag_type_in, sFilename_geojson_in, sFilename_geojson_out):\n if os.path.isfile(sFilename_geojson_in):\n pass\n else:\n print('This shapefile does not exist: ', sFilename_geojson_in )\n iReturn_code = 0\n return iReturn_code\n\n if iFlag_type_in ==0:\n pass\n else:\n if iFlag_type_in == 1:\n \n aFlowline_basin, pSpatial_reference = read_flowline_geojson( sFilename_geojson_in ) \n #convert it\n\n iFlag_projected = 0\n #pSpatial_reference_gcs = osr.SpatialReference()\n #pSpatial_reference_gcs.ImportFromEPSG(4326)\n #pSpatial_reference_gcs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)\n export_flowline_to_geojson(aFlowline_basin, sFilename_geojson_out)\n else:\n if iFlag_type_in == 2:\n #polygon\n pass\n\n\n return","title":""},{"docid":"42ab7562c537f8d812a4bab49b3c8e1d","score":"0.53086746","text":"def test_geojson_response(self):\n response = self.post_boundary('bayarea_macosx.zip')\n uuid = response.data['uuid']\n url = '{}geojson/'.format(reverse('boundary-detail', args=[uuid]))\n\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['type'], 'FeatureCollection')\n self.assertEqual(len(response.data['features']), 3)","title":""},{"docid":"0a68239b47422a3973e4bcab556f2b01","score":"0.53048486","text":"def get_multipolygon_geojson_coordinates(geojson):\n # The geojson data for each place varies in whether it follows the\n # righthand rule or not. We want to ensure geojsons for all places\n # does follow the righthand rule.\n right_handed_geojson = rewind(geojson)\n geojson_type = right_handed_geojson['type']\n geojson_coords = right_handed_geojson['coordinates']\n if geojson_type == POLYGON_GEOJSON_TYPE:\n geojson_coords[0].reverse()\n return [geojson_coords]\n elif geojson_type == MULTIPOLYGON_GEOJSON_TYPE:\n for polygon in geojson_coords:\n polygon[0].reverse()\n return geojson_coords\n else:\n assert False, f\"Type {geojson_type} unknown!\"","title":""},{"docid":"972f7994c5f40cc40d921f65a126bf00","score":"0.5293927","text":"def getPOIs():\n\n points_of_interest = session.query(POI).all()\n\n coordinates = []\n\n for poi in points_of_interest:\n\n title = poi.title\n lng = poi.lng\n lat = poi.lat\n info = poi.info\n\n # Functions from the geojson library create geoson objects with the\n # details specified\n my_point = Feature(geometry=Point((lng, lat)), properties={\n \"title\": title, \"longitude\": lng, \"lat\": lat, \"info\": info, \"marker-color\": \"#D95929\"})\n coordinates.append(my_point)\n\n # We return all this geojson as a feature collection\n new_coords = FeatureCollection(coordinates)\n return new_coords","title":""},{"docid":"af743ecdac00dc7fc4d76659d0ec787f","score":"0.5284874","text":"def fetch_missing_geographical_data():\n \n URL = \"http://nominatim.openstreetmap.org/reverse\"\n with open('missing_records.json', 'r') as infile:\n records = json.load(infile)\n addresses = []\n faults = []\n for record in records:\n rec_id = record[0]\n lng = record[1]\n lat = record[2]\n print(lng, lat)\n payload = {\"format\":\"json\", \"lon\":lng, \"lat\":lat, \"email\":\"syrelyre@gmail.com\", \"accept-language\":\"en-us\", \"User-Agent\":\"Data Science App\"}\n while True:\n try:\n response = requests.get(URL, params=payload).text\n break\n except requests.exceptions.ConnectionError as e:\n print(\"Exception: {}\".format(str(e)))\n time.sleep(1)\n \n try:\n response = json.loads(response)\n except (Exception, ValueError) as e:\n print(\"ValueError: {}\".format(str(e)))\n faults.append((rec_id, lng, lat))\n \n if \"address\" in response:\n address = response[\"address\"]\n address[\"lat\"] = lat\n address[\"lng\"] = lng\n addresses.append(address)\n \n time.sleep(1)\n\n with open('missing_data.json', 'w') as outfile:\n json.dump(addresses, outfile)\n\n if faults:\n with open('faults.pickle', 'wb') as outfile:\n pickle.dump(faults, outfile)","title":""},{"docid":"4340e6a134f90106a0229bc73e45b22e","score":"0.5283664","text":"def download_geojson(self, scenario_id, layer_name):\n\n r = self._get(self.api_url + \"/scenario/\" + str(scenario_id) + \"/layer/\" + str(layer_name))\n if not r.status_code == 200:\n raise Exception(f\"Could not download result {r.status_code}, {r.json()}\")\n\n return r.text","title":""},{"docid":"596b9bc38c9d9f7b8ab1934d277f8c86","score":"0.5278918","text":"def load_feature_collection(feature_dict):\n\n feature_list = []\n \n for feat in feature_dict['features']:\n geos = GEOSGeometry(json.dumps(feat['geometry']))\n feature_list.append((geos, feat['properties']))\n \n return feature_list","title":""},{"docid":"48c0ac08cdd2e9d4e0fbe0b9cc6ce8ac","score":"0.52750343","text":"def as_geojson(self):\n point = geojson.Point((self.longitude, self.latitude))\n feature = geojson.Feature(geometry=point,\n properties={\n 'filename': self._path.split('/')[-1],\n 'path': self._path,\n 'timestamp': self.timestamp\n })\n return feature","title":""},{"docid":"a1f0a117c0eeb6fcba3427f2e519b3c3","score":"0.5273252","text":"def get_data_and_extract_best_coordinates(url):\n\n resp = requests.get(url)\n data = json.loads(resp.text)\n\n features = data['features']\n best_feature = features[0] # this will break if features is empty\n coords = best_feature['geometry']['coordinates']\n lng = coords[0]\n lat = coords[1]\n\n return {'latitude': lat, 'longitude': lng}","title":""},{"docid":"b80c95edceb310094229c1f3aca2b837","score":"0.5250691","text":"def geojson_builder(down_left,up_right,travels=0):\n p1 = [str(down_left[0]),str(down_left[1])]\n p2 = [str(up_right[0]),str(down_left[1])]\n p3 = [str(up_right[0]),str(up_right[1])]\n p4 = [str(down_left[0]),str(up_right[1])]\n \n geojson = {\"type\": \"FeatureCollection\",\n \"features\": [\n {\"type\": \"Feature\",\n \"properties\":{\n \"arrivals_outs\": travels\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [[p1,p2,p3,p4,p1]] }}]}\n\n return geojson","title":""},{"docid":"9f7c7fb8ae098ac3916b0f5631d0bbc5","score":"0.5248551","text":"def main():\r\n url = \"https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/1.0_month.geojson\"\r\n json_obj = get_json(url)\r\n entire_report_dictionary = json_loads_to_dictionary(json_obj)\r\n number_of_records, id_lon_lat = extract_records(entire_report_dictionary)[0], extract_records(entire_report_dictionary)[1]\r\n \r\n print(\"writing to file\\n\")\r\n with open('id_coordinates.csv', 'wt') as textiowrapper:\r\n textiowrapper.writelines('name,lon,lat\\n')\r\n for i in range(number_of_records):\r\n x = 0\r\n id_num = str(id_lon_lat[i][x])\r\n lon = str(id_lon_lat[i][x+1])\r\n lat = str(id_lon_lat[i][x+2])\r\n s = id_num + ',' + lon + ',' + lat\r\n textiowrapper.writelines(s + '\\n')\r\n del x\r\n textiowrapper.close()\r\n print(\"done writing to file\\n\")\r\n\r\n print(\"mapping the data\\n\")\r\n data = read_csv('id_coordinates.csv')\r\n geoplotlib.dot(data)\r\n geoplotlib.show()","title":""},{"docid":"c0fdb657db999ea7fbe55a6219281629","score":"0.52259755","text":"def convert_geometries_to_geojson(data: networkx.Graph) -> None:\n for node in data[\"nodes\"]:\n for key in node:\n # having a ``__geo_interface__``` property identifies the object\n # as being a ``shapely`` geometry object\n if hasattr(node[key], \"__geo_interface__\"):\n # The ``__geo_interface__`` property is essentially GeoJSON.\n # This is what :func:`geopandas.GeoSeries.to_json` uses under\n # the hood.\n node[key] = node[key].__geo_interface__","title":""},{"docid":"01d4ce826c7881b1192380e74345f9c0","score":"0.52009773","text":"def export2geojson(query_result):\n\n for row in db_rows:\n name = row[0]\n geom = row[1]\n geoj_geom = loads(geom)\n myfeat = Feature(geometry=geoj_geom, \n properties={'name': name})\n new_geom_collection.append(myfeat)\n\n # use the geojson module to create the final Feature\n # Collection of features created from for loop above\n my_geojson = FeatureCollection(new_geom_collection)\n\n # define the output folder and GeoJSon file name\n output_geojson_buf = \"../geodata/out_schools_in_100m.geojson\"\n\n # save geojson to a file in our geodata folder\n def write_geojson():\n fo = open(output_geojson_buf, \"w\")\n fo.write(json.dumps(my_geojson))\n fo.close()\n\n # run the write function to actually create the GeoJSON file\n write_geojson()","title":""},{"docid":"8e6caafa6adc65b37050115b71b5b2b3","score":"0.51936054","text":"def get_geometry_description(geometry):\n orm_geom = geometry\n geojson = json.loads(orm_geom.json)\n bbox = orm_geom.extent\n\n cache_entry = {\n \"bbox\": bbox,\n \"bbox_area\": get_area_bbox(bbox),\n \"geometry\": geojson,\n \"area\": get_area_geojson(geojson),\n }\n return cache_entry","title":""},{"docid":"b63f56376c6c6932b74c04e19a2862b9","score":"0.5191625","text":"def queen_geojson(gjobj):\n features = list(find_features(gjobj))[0]\n #features = list(gjobj['geojson']['features'])\n polys = []\n ids = []\n i = 0\n for feature in features:\n polys.append(ps.cg.asShape(feature['geometry']))\n ids.append(i)\n i += 1\n polygons = PolygonCollection(dict(zip(ids,polys)))\n neighbors = ps.weights.Contiguity.ContiguityWeightsPolygons(polygons).w\n w = ps.W(neighbors) \n return w","title":""},{"docid":"13c6a06805d293d66865fb9d51d000b7","score":"0.5188155","text":"def geo2topo(files, out, quantization, simplification):\n\n\tgeojson = dict()\n\n\tfor file in files:\n\t\twith open(file) as fp:\n\t\t\tgeojson[os.path.basename(file)] = json.load(fp)\n\n\ttopojson = topology(geojson, quantization, simplification)\n\n\tif out is None or out == '-':\n\t\tjson.dump(topojson, sys.stdout)\n\telse:\n\t\twith open(out, 'w') as fp:\n\t\t\tjson.dump(topojson, fp)","title":""},{"docid":"ad96a450b49dbc97b94a1095c0a0cc2d","score":"0.51817423","text":"def geometric_run_json(in_json_dict):\n\n # Default logger configuration (prevents extra newline from being printed)\n logIni = pkg_resources.resource_filename(geometric.optimize.__name__, 'config/logJson.ini')\n import logging.config\n logging.config.fileConfig(logIni,disable_existing_loggers=False)\n\n # Set a temporary logger to capture output\n log_stream = RawStreamHandler(stream=StringIO())\n logger.addHandler(log_stream)\n\n input_opts = parse_input_json_dict(in_json_dict)\n hess = input_opts.pop('hessian', None)\n M, engine = geometric.optimize.get_molecule_engine(**input_opts)\n\n # Get initial coordinates in bohr\n coords = M.xyzs[0].flatten() * geometric.nifty.ang2bohr\n\n # Read in the constraints\n constraints_dict = input_opts.get('constraints', {})\n if \"scan\" in constraints_dict:\n raise KeyError(\"The constraint 'scan' keyword is not yet supported by the JSON interface\")\n\n constraints_string = make_constraints_string(constraints_dict)\n Cons, CVals = None, None\n if constraints_string:\n Cons, CVals = geometric.prepare.parse_constraints(M, constraints_string)\n\n # set up the internal coordinate system\n coordsys = input_opts.get('coordsys', 'tric')\n CoordSysDict = {\n 'cart': (geometric.internal.CartesianCoordinates, False, False),\n 'prim': (geometric.internal.PrimitiveInternalCoordinates, True, False),\n 'dlc': (geometric.internal.DelocalizedInternalCoordinates, True, False),\n 'hdlc': (geometric.internal.DelocalizedInternalCoordinates, False, True),\n 'tric': (geometric.internal.DelocalizedInternalCoordinates, False, False)\n }\n\n CoordClass, connect, addcart = CoordSysDict[coordsys.lower()]\n IC = CoordClass(\n M,\n build=True,\n connect=connect,\n addcart=addcart,\n constraints=Cons,\n cvals=CVals[0] if CVals is not None else None)\n\n # Print out information about the coordinate system\n if isinstance(IC, geometric.internal.CartesianCoordinates):\n logger.info(\"%i Cartesian coordinates being used\\n\" % (3 * M.na))\n else:\n logger.info(\"%i internal coordinates being used (instead of %i Cartesians)\\n\" % (len(IC.Internals), 3 * M.na))\n logger.info(IC)\n logger.info(\"\\n\")\n\n params = geometric.optimize.OptParams(**input_opts)\n dirname = tempfile.mkdtemp()\n\n if hess is not None:\n n = len(coords)\n params.hess_data = np.array(hess).reshape(n, n)\n\n try:\n # Run the optimization\n if Cons is None:\n # Run a standard geometry optimization\n params.xyzout = \"qce_optim.xyz\"\n geometric.optimize.Optimize(coords, M, IC, engine, dirname, params)\n else:\n # Run a constrained geometry optimization\n if isinstance(IC, (geometric.internal.CartesianCoordinates,\n geometric.internal.PrimitiveInternalCoordinates)):\n raise RuntimeError(\"Constraints only work with delocalized internal coordinates\")\n for ic, CVal in enumerate(CVals):\n if len(CVals) > 1:\n logger.info(\"---=== Scan %i/%i : Constrained Optimization ===---\\n\" % (ic + 1, len(CVals)))\n IC = CoordClass(M, build=True, connect=connect, addcart=addcart, constraints=Cons, cvals=CVal)\n IC.printConstraints(coords, thre=-1)\n geometric.optimize.Optimize(coords, M, IC, engine, dirname, params, print_info = (ic==0))\n\n out_json_dict = get_output_json_dict(in_json_dict, engine.schema_traj)\n out_json_dict[\"success\"] = True\n\n except Exception as e:\n out_json_dict = get_output_json_dict(in_json_dict, engine.schema_traj)\n out_json_dict[\"success\"] = False\n out_json_dict[\"error\"] = {\n \"error_type\": \"unknown\",\n \"error_message\": \"geomeTRIC run_json error:\\n\" + traceback.format_exc()\n }\n\n # Grab logging and pop logger\n out_json_dict[\"stdout\"] = log_stream.stream.getvalue()\n log_stream.close()\n logger.handlers.remove(log_stream)\n\n return out_json_dict","title":""},{"docid":"edd717009f73bef47710bbbb75076ff8","score":"0.51729196","text":"def test_geospatial_positive_query_with_geodata(self):\n records = []\n query = self.as_connection.query(\"test\", \"demo\")\n\n geo_object2 = aerospike.geodata(\n {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [-122.500000, 37.000000],\n [-121.000000, 37.000000],\n [-121.000000, 38.080000],\n [-122.500000, 38.080000],\n [-122.500000, 37.000000],\n ]\n ],\n }\n )\n\n query.where(p.geo_within_geojson_region(\"loc\", geo_object2.dumps()))\n\n def callback(input_tuple):\n _, _, record = input_tuple\n records.append(record)\n\n query.foreach(callback)\n\n assert len(records) == 3\n expected = [\n {\"coordinates\": [-122.0, 37.5], \"type\": \"Point\"},\n {\"coordinates\": [-121.8, 37.7], \"type\": \"Point\"},\n {\"coordinates\": [-121.6, 37.9], \"type\": \"Point\"},\n ]\n for r in records:\n assert r[\"loc\"].unwrap() in expected","title":""},{"docid":"6facafd1c79e7ade4826bdfd23fbee69","score":"0.51716","text":"def get_geo_json_for_roads(self):\n return self.roads.get_geo_json()","title":""},{"docid":"8e245aed8062590fb3d6409e12a5ae62","score":"0.5171086","text":"def get_geolocation_data(address):\n url = 'http://maps.googleapis.com/maps/api/geocode/json'\n fields = {'address': address.encode('utf-8'), 'sensor': 'false', 'region': 'pe'}\n resp = http.request('GET', url, fields=fields)\n return json.loads(resp.data)","title":""},{"docid":"81c094f170db7ab375e6eec6c4af82cd","score":"0.5156938","text":"def count_geometries(geojson_paths):\n types = {}\n for geojson_path in geojson_paths:\n with open(geojson_path, \"r\") as geojson_file:\n geojson = json.load(geojson_file)\n for feature in geojson[\"features\"]:\n type = sg.shape(feature[\"geometry\"]).type\n if type not in types:\n types[type] = 1\n else:\n types[type] += 1\n return types","title":""},{"docid":"5b4e6e26c2e20a30a0c151249582a9e6","score":"0.51526296","text":"def get_region_data(buildings_plz_location, geofabrik_location, region_ags_dict, output_path):\n # Extract plz list\n plz_list = os.listdir(buildings_plz_location)\n plz_list = [x.split('.')[0].split('_')[1] for x in plz_list if 'buildings' in x]\n \n # full path pbf\n region_list_path = [os.path.join(path, name) for path, subdirs, files in os.walk(geofabrik_location) for name in files]\n # region pbf name\n pbf_list = [name for path, subdirs, files in os.walk(geofabrik_location) for name in files]\n \n # Start loop for all region\n i = 0\n while i < len(pbf_list):\n # Get target region\n target_region_path = region_list_path[i]\n target_region = pbf_list[i]\n # Get AGS code belong to the target region\n target_ags_list = region_ags_dict.get(target_region)\n # Length of AGS (2 or 3)\n ags_len=len(target_ags_list[0])\n \n logging.info(f'{i}/{len(pbf_list)} Reading info of region {target_region}')\n \n # Initialize the OSM parser object\n osm = OSM(target_region_path)\n # Get buildings in the region\n buildings = osm.get_buildings()\n \n logging.info(f'Total of {len(buildings)} in region {target_region}')\n \n # Extract info of all PLZ belong to that region\n region_plz_list = plz_place[(dp.left(plz_place.ags.str, ags_len).isin(target_ags_list))].plz.reset_index(drop=True)\n \n # Iterate through list of PLZ to enhance dataset\n enhance_area(region_plz_list, buildings, output_path)\n i = i + 1","title":""},{"docid":"976bad4c3a3ce87e2fab9b700a4d3d5b","score":"0.5141256","text":"def test_push_geojson_to_geonode(self):\n async_result = push_to_geonode.delay(geojson_layer_uri)\n result = async_result.get()\n self.assertEqual(\n result['status'], GEONODE_UPLOAD_SUCCESS, result['message'])","title":""},{"docid":"45aa3a9112cdce8fc7395449f992e2eb","score":"0.5136351","text":"def get_spatial_reference_from_json(geojson: str):\n yaml_geom = yaml.safe_load(geojson)\n current_geom = ogr.CreateGeometryFromJson(json.dumps(yaml_geom))\n\n if current_geom:\n return current_geom.GetSpatialReference()\n\n raise RuntimeError(\"Specified JSON does not have a valid sptial reference\")","title":""},{"docid":"260b5a28500a33db4c5a262672be1512","score":"0.5135094","text":"def convert2geojson(l, outfile=\"GeoJSON-out.json\", extra_properties=(),\n file_or_print=\"file\"):\n property_index=0\n #make sure the extra properties list and the shape list are the same size\n if extra_properties:\n if len(l)!=len(extra_properties):\n print \"FATAL ERROR: Extra properties list and shape list must be the same length!\"\n sys.exit(1)\n \n \"\"\"create a feature collection boilerplate\"\"\"\n feature_collection_boiler={\"type\": \"FeatureCollection\", \"features\":[]}\n \n featurelist=[]\n #iterate over our list of shapes\n for i in l:\n #create a new feature from our boilerplate\n f={}\n #print f\n #build the geometry\n f[\"geometry\"]={\"type\":i[\"geometry_type\"], \"coordinates\": i[\"geometry_coordinates\"]}\n # erase the geometry from out shape dict and stuff everything else\n # in the properties\n del i[\"geometry_type\"]\n del i[\"geometry_coordinates\"]\n f[\"properties\"]=i\n\n #Add extra properties if they exist.\n if extra_properties:\n f[\"properties\"].update(extra_properties[property_index])\n \n featurelist.append(f)\n\n property_index+=1\n \n feature_collection_boiler['features']=featurelist\n\n if file_or_print==\"file\":\n FILE = open(outfile,\"w\")\n # Write all the lines at once:\n FILE.writelines(json.dumps(feature_collection_boiler, indent=4))\n FILE.close()\n else:\n print json.dumps(feature_collection_boiler, indent=4)\n return feature_collection_boiler","title":""},{"docid":"d1f45bf8d2af17d0dbbee4e05bbd91a7","score":"0.5134756","text":"def json2geodf(\n content: Union[List[Dict[str, Any]], Dict[str, Any]],\n in_crs: str = DEF_CRS,\n crs: str = DEF_CRS,\n) -> gpd.GeoDataFrame:\n if not isinstance(content, (list, dict)):\n raise InvalidInputType(\"content\", \"list or list of dict ((geo)json)\")\n\n content = content if isinstance(content, list) else [content]\n try:\n geodf = gpd.GeoDataFrame.from_features(content[0], crs=in_crs)\n except AttributeError:\n geodf = gpd.GeoDataFrame.from_features(content[0])\n except TypeError:\n content = [arcgis2geojson(c) for c in content]\n try:\n geodf = gpd.GeoDataFrame.from_features(content[0], crs=in_crs)\n except AttributeError:\n geodf = gpd.GeoDataFrame.from_features(content[0])\n\n if len(content) > 1:\n for c in content[1:]:\n try:\n geodf = geodf.append(gpd.GeoDataFrame.from_features(c, crs=in_crs))\n except AttributeError:\n geodf = gpd.GeoDataFrame.from_features(c)\n\n if in_crs != crs and \"geometry\" in geodf:\n geodf = geodf.to_crs(crs)\n\n return geodf","title":""},{"docid":"40f4968bd59059ddbe97cb5b33b88af1","score":"0.5130631","text":"def read_json(filename_json):\n with open(filename_json) as ff:\n data = json.load(ff)\n ff.close()\n data = data[\"RESULTS\"][\"model\"]\n return data","title":""},{"docid":"7a0ca7276ec7c92ba0c83f5898e81962","score":"0.5130019","text":"def do_GEOHASHTOGEOJSON(self, geoh):\n geoh, with_neighbors = self._match_option(\"NEIGHBORS\", geoh)\n bbox = geohash.bbox(geoh)\n try:\n with_neighbors = int(with_neighbors)\n except TypeError:\n with_neighbors = 0\n\n def expand(bbox, geoh, depth):\n neighbors = geohash.neighbors(geoh)\n for neighbor in neighbors:\n other = geohash.bbox(neighbor)\n if with_neighbors > depth:\n expand(bbox, neighbor, depth + 1)\n else:\n if other[\"n\"] > bbox[\"n\"]:\n bbox[\"n\"] = other[\"n\"]\n if other[\"s\"] < bbox[\"s\"]:\n bbox[\"s\"] = other[\"s\"]\n if other[\"e\"] > bbox[\"e\"]:\n bbox[\"e\"] = other[\"e\"]\n if other[\"w\"] < bbox[\"w\"]:\n bbox[\"w\"] = other[\"w\"]\n\n if with_neighbors > 0:\n expand(bbox, geoh, 0)\n\n geojson = {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [bbox[\"w\"], bbox[\"n\"]],\n [bbox[\"e\"], bbox[\"n\"]],\n [bbox[\"e\"], bbox[\"s\"]],\n [bbox[\"w\"], bbox[\"s\"]],\n [bbox[\"w\"], bbox[\"n\"]],\n ]\n ],\n }\n print(white(json.dumps(geojson)))","title":""},{"docid":"5ffd60ec66f58bc519dd37c67daa1945","score":"0.51254004","text":"def get_address_data_OSM(address):#using OSM Nominatim API\r\n \r\n URL = \"https://nominatim.openstreetmap.org/search/\"\r\n \r\n address = address.replace(\"Boul.\", \"Boulevard\")\r\n PARAMS = {'q': address + \", Montréal\",\r\n 'format': \"json\"}\r\n \r\n resp = requests.get(url = URL, params = PARAMS) \r\n resp = resp.text\r\n #print (resp)\r\n \r\n geo_data={}\r\n \r\n if resp == '[]':\r\n print (address +\" was not found\")\r\n geo_data[0] = address #address saved\r\n geo_data[1] = 45.432397 #\"Not Found\"\r\n geo_data[2] = -73.532947 #\"Not Found\"\r\n geo_data[3] = \"OSM lacks DA return\" #OSM doesn't return a dissemination area. Either add that later or don't\r\n \r\n return geo_data\r\n \r\n else:\r\n resp_json = json.loads(resp)\r\n print (address + \" @ \" +resp_json[0][\"lat\"] +\", \"+ resp_json[0][\"lon\"])\r\n \r\n geo_data[0] = address #address saved\r\n geo_data[1] = resp_json[0][\"lat\"]\r\n geo_data[2] = resp_json[0][\"lon\"]\r\n geo_data[3] = \"OSM lacks DA return\" #OSM doesn't return a dissemination area. Either add that later or don't\r\n \r\n return geo_data","title":""},{"docid":"c2456b7f5baf99ed3bcc29f62396046d","score":"0.51214176","text":"def graphml_to_geojson(graphml_input_filepath, geojson_output_filepath):\n\n def geojson_builder(node):\n \"\"\"\n Takes a node from the graph as input and returns the data in valid\n GeojSON format.\n \"\"\"\n return {\n 'type': 'Feature',\n 'properties': {\n 'name': node['name'],\n 'population': node['pop']\n },\n 'geometry': {\n 'type': 'Point',\n 'coordinates': [node['lat'], node['lon']]\n }\n }\n\n G = nx.read_graphml(graphml_input_filepath)\n\n geojson_list = [geojson_builder(G.node[x]) for x in G.nodes_iter()]\n\n file_content = 'nodes = ' + json.dumps(geojson_list)\n\n with open(geojson_output_filepath, 'w') as f:\n f.write(file_content)","title":""},{"docid":"bed3305f38fde5db6e7b461864706485","score":"0.5118621","text":"def get_coordinates_info(lon, lat):\n\n path = r\"https://geocode.arcgis.com/arcgis/rest/services/World/GeocodeServer/reverseGeocode?\" \\\n \"location={}%2C{}&langCode=en&outSR=&forStorage=false&f=pjson\".format(lon, lat)\n\n x = requests.get(path)\n if x.ok:\n return json.loads(x.text)","title":""},{"docid":"d5672e15da1d1293c576fffa0a59d6d6","score":"0.51180065","text":"def get_borders():\n with open('./departements.geojson') as geojson:\n return json.load(geojson)","title":""},{"docid":"52abddcbc35a0ab78592537dc00d197a","score":"0.51171476","text":"def readZip(self, add, name_dbf, name_prj, name_shp, name_shx):\n # setting:\n proxies = {\"http\":\"http://username:password@proxy_ip:proxy_port\"} \n\n session = requests.Session()\n retry = Retry(connect=3, backoff_factor=0.5)\n adapter = HTTPAdapter(max_retries=retry)\n session.mount('http://', adapter)\n session.mount('https://', adapter)\n\n county_file_url = 'https://www.nhc.noaa.gov/gis/' + add\n zipfile = ZipFile(io.BytesIO(session.get(county_file_url).content))\n namelist = []\n sleep(5)\n\n for ele in sorted(zipfile.namelist()):\n if ('position') in ele:\n namelist.append(ele)\n\n if ('pts') in ele:\n namelist.append(ele)\n\n end_dic = [name_dbf, name_prj, name_shp, name_shx]\n filenames = [y for y in sorted(namelist) for ending in end_dic if y.endswith(ending)] \n # update: using set to remove duplicated files\n selected_filenames = sorted(filenames)[0:4]\n dbf, prj, shp, shx = [io.BytesIO(zipfile.read(filename)) for filename in selected_filenames]\n\n\n r = shapefile.Reader(shp=shp, shx=shx, dbf=dbf)\n attributes, geometry = [], []\n field_names = [field[0] for field in r.fields[1:]]\n\n for row in r.shapeRecords(): \n geometry.append(shape(row.shape.__geo_interface__)) \n attributes.append(dict(zip(field_names, row.record)))\n\n gdf = gpd.GeoDataFrame(data = attributes, geometry = geometry)\n\n return gdf","title":""},{"docid":"6c75e481a91571041a793922b2ccc462","score":"0.5116793","text":"def test_coordinates_with_altitude(empty_space):\n fp_geojson = (\n Path(__file__).parents[2] / \"xyzspaces\" / \"datasets\" / \"chicago_parks.geo.json\"\n )\n space = empty_space\n space.add_features_geojson(fp_geojson, encoding=\"utf-8-sig\")\n stats = space.get_statistics()\n assert stats[\"count\"][\"value\"] == 9\n assert stats[\"geometryTypes\"][\"value\"] == [\"Point\"]\n feature = space.get_feature(feature_id=\"LP\")\n assert feature.id == \"LP\"\n assert feature.geometry[\"coordinates\"] == [-87.637596, 41.940403, 4.0]\n tagging_rules = {\"large\": \"$.features[?(@.properties.area>=500)]\"}\n _ = space.update(tagging_rules=tagging_rules)\n large_parks = empty_space.search(tags=[\"large\"])\n for park in large_parks:\n assert park[\"id\"] in [\"LP\", \"BP\", \"JP\"]\n feature_iter = space.iter_feature()\n feature = next(feature_iter)\n assert feature[\"geometry\"][\"coordinates\"] == [-87.637596, 41.940403, 4]\n res = space.cluster(clustering=\"hexbin\")\n assert res[\"type\"] == \"FeatureCollection\"","title":""},{"docid":"d50b04c4bd72f409e243b62cd8eaf121","score":"0.51059777","text":"def read_topojson(self, infile, **kwargs):\n\n logging.info('Reading TopoJSON: %s' % infile)\n raise ValueError('Reading TopoJSON not implemented')","title":""},{"docid":"75d2534010ced2ef707c53de240af59b","score":"0.5105181","text":"def load_osm_document_cached(\n file_path,\n url_path,\n post_data=None,\n returns_json=True):\n elapsed_seconds = 0\n limit_seconds = 900 # 15 minutes\n file_time = time.time()\n updating_status = False\n if os.path.exists(file_path):\n current_time = time.time() # in unix epoch\n file_time = os.path.getmtime(file_path) # in unix epoch\n elapsed_seconds = current_time - file_time\n\n osm_data = {'elements': []}\n if elapsed_seconds > limit_seconds or not os.path.exists(file_path):\n if not os.path.exists(file_path):\n try:\n if post_data:\n fetch_osm_with_post(\n file_path,\n url_path,\n post_data,\n returns_format='json' if returns_json else 'xml')\n else:\n fetch_osm(file_path, url_path)\n except (OverpassBadRequestException, OverpassDoesNotReturnData):\n return osm_data, file_time, updating_status\n\n else:\n FetchOsmThread(file_path, url_path, post_data).start()\n updating_status = True\n\n file_handle = None\n if os.path.exists(file_path):\n file_handle = open(file_path, 'rb')\n\n if returns_json and file_handle:\n try:\n osm_data = json.loads(file_handle.read().decode('utf-8'))\n except ValueError:\n pass\n else:\n osm_data = file_handle\n\n return osm_data, file_time, updating_status","title":""},{"docid":"f391e0ad8efeb2400cb2df259621d399","score":"0.5104513","text":"def geos_geometry_can_be_created_from_geojson_string():\n from django.contrib.gis.geos import GEOSGeometry\n import json\n\n geojson_point = dict(type=\"Point\", coordinates=[100.0, 0.0])\n geojson_point_string = json.dumps(geojson_point)\n GEOSGeometry(geojson_point_string)","title":""},{"docid":"99bc376bd0b49e61e8e89b067c5d06a0","score":"0.50885135","text":"def test_call_endpoint(self):\n token = get_token()\n response = call_mapbox_endpoint(\"the white house\",token,GEOCODING_ENDPOINT_TEMPORARY )\n\n payload = json.loads(response.text) #convert json to dictionary\n features=payload[\"features\"] # get list of results\n first=(features[0]) # get first result\n properties = first[\"properties\"] # get properties of result (where address is)\n address = properties[\"address\"] # street address\n self.assertEqual(address,'1600 Pennsylvania Ave NW')\n context = first[\"context\"] # get context (where zipcode and state are)\n print(first)\n postcode = [y for y in context if y[\"id\"].startswith(\"postcode\")][0][\"text\"]\n region = [y for y in context if y[\"id\"].startswith(\"region\")][0][\"text\"]\n self.assertEqual(postcode,\"20006\")\n self.assertEqual(region,\"District of Columbia\")","title":""},{"docid":"29ae7f08922bb9adbf3c297a5d16ac1f","score":"0.50815785","text":"def main():\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"features_geojson\", help=\"Path to GeoJSON with features to be gridded.\")\n parser.add_argument(\"output_folder\", help=\"Folder to contain output grid GeoJSONs.\")\n args = parser.parse_args()\n\n with open(args.features_geojson, 'r') as fin:\n features_gj = json.load(fin)\n\n if not os.path.isdir(GRIDS_DIR):\n os.mkdir(GRIDS_DIR)\n\n count = 0\n for feature in features_gj['features']:\n try:\n feature['properties']['FIPS'] = \"{0}{1}\".format(feature['properties']['STATE'], feature['properties']['COUNTY'])\n except:\n pass\n count += 1\n boundary = shape(feature['geometry'])\n bb = boundary.bounds\n\n xmin = bb[0] # most western point\n xmax = bb[2] # most eastern point\n ymin = bb[1] # most southern point\n ymax = bb[3] # most northern point\n\n gridHeight = 0.001\n gridWidth = 0.001\n xmin = floor(xmin * 10**SCALE) / 10**SCALE\n ymax = ceil(ymax * 10**SCALE) / 10**SCALE\n\n grid(\"{0}.geojson\".format(os.path.join(args.output_folder, feature['properties']['FIPS'])),\n xmin, xmax, ymin, ymax, gridHeight, gridWidth, boundary)\n if count % 150 == 0:\n print(\"{0} counties complete.\".format(count))","title":""},{"docid":"8a844371878d1321a541f2f8d25e1215","score":"0.5073145","text":"def test_geospatial_dumps_positive(self):\n geo_object = get_geo_object()\n\n geojson_str = geo_object.dumps()\n assert isinstance(geojson_str, str)\n\n obj = aerospike.geojson(geojson_str)\n assert obj.unwrap() == geo_object.unwrap()","title":""}],"string":"[\n {\n \"docid\": \"3038df70905acd2489ae82ffca63ed98\",\n \"score\": \"0.6661169\",\n \"text\": \"def read_geojson(geojson_file):\\n # example in order to see the polygon_vertices_pixels\\n #data['features'][0]['properties']['polygon_vertices_pixels']\\n d = dict()\\n \\n # get all the solar panel coordinates in each picture and save it in a dictionary\\n for i in range(len(data['features'])):\\n if i%50==0: print(i)\\n im_name = data['features'][i]['properties']['image_name']\\n if not im_name in d.keys():\\n d[im_name] = []\\n d[im_name].append(i)\\n return(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb868339faad755f062796bbcfa141b\",\n \"score\": \"0.6360556\",\n \"text\": \"def read_json():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2343895512d46373a0dbd12fb3656ee2\",\n \"score\": \"0.63202417\",\n \"text\": \"def node_geojson():\\n nodes = request.json.get(\\\"nodes\\\", [])\\n geojson_prop = request.json.get(\\\"geoJsonProp\\\")\\n if not geojson_prop:\\n return \\\"error: must provide a geoJsonProp field\\\", 400\\n features = []\\n geojson_by_node = fetch.property_values(nodes, geojson_prop)\\n for node_id, json_text in geojson_by_node.items():\\n if json_text:\\n geo_feature = get_geojson_feature(node_id, node_id, json_text)\\n if geo_feature:\\n features.append(geo_feature)\\n result = {\\n \\\"type\\\": \\\"FeatureCollection\\\",\\n \\\"features\\\": features,\\n \\\"properties\\\": {\\n \\\"currentGeo\\\": \\\"\\\"\\n }\\n }\\n return Response(json.dumps(result), 200, mimetype='application/json')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a93168a27eb926608b7ceebc3b557b3\",\n \"score\": \"0.6300121\",\n \"text\": \"def geojson():\\n place_dcid = request.args.get(\\\"placeDcid\\\")\\n if not place_dcid:\\n return Response(json.dumps(\\\"error: must provide a placeDcid field\\\"),\\n 400,\\n mimetype='application/json')\\n place_type = request.args.get(\\\"placeType\\\")\\n if not place_type:\\n place_dcid, place_type = get_choropleth_display_level(place_dcid)\\n cached_geojson = current_app.config['CACHED_GEOJSONS'].get(\\n place_dcid, {}).get(place_type, None)\\n if cached_geojson:\\n return lib_util.gzip_compress_response(cached_geojson, is_json=True)\\n geos = []\\n if place_dcid and place_type:\\n geos = fetch.descendent_places([place_dcid], place_type).get(place_dcid, [])\\n if not geos:\\n return Response(json.dumps({}), 200, mimetype='application/json')\\n geojson_prop = CHOROPLETH_GEOJSON_PROPERTY_MAP.get(place_type, \\\"\\\")\\n # geoId/72 needs higher resolution geojson because otherwise, the map looks\\n # too fragmented\\n if place_dcid == 'geoId/72':\\n geojson_prop = 'geoJsonCoordinatesDP1'\\n names_by_geo = place_api.get_display_name(geos)\\n features = []\\n if geojson_prop:\\n geojson_by_geo = fetch.property_values(geos, geojson_prop)\\n # geoId/46102 is known to only have unsimplified geojson so need to use\\n # geoJsonCoordinates as the prop for this one place\\n if 'geoId/46102' in geojson_by_geo:\\n geojson_by_geo['geoId/46102'] = fetch.property_values(\\n ['geoId/46102'], 'geoJsonCoordinates').get('geoId/46102', '')\\n for geo_id, json_text in geojson_by_geo.items():\\n if json_text and geo_id in names_by_geo:\\n geo_name = names_by_geo.get(geo_id, \\\"Unnamed Area\\\")\\n geo_feature = get_geojson_feature(geo_id, geo_name, json_text)\\n if geo_feature:\\n features.append(geo_feature)\\n result = {\\n \\\"type\\\": \\\"FeatureCollection\\\",\\n \\\"features\\\": features,\\n \\\"properties\\\": {\\n \\\"currentGeo\\\": place_dcid\\n }\\n }\\n return lib_util.gzip_compress_response(result, is_json=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce2dd990ddf626e0a89ad9e19ca81236\",\n \"score\": \"0.6159015\",\n \"text\": \"def ST_GeomFromGeoJSON(json):\\n import pyarrow as pa\\n geo = pa.array(json, type='string')\\n geo = _to_arrow_array_list(geo)\\n result = [arctern_core_.ST_GeomFromGeoJSON(g) for g in geo]\\n return _to_pandas_series(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6744cc102add14fbd1dbdd413ea6fe7c\",\n \"score\": \"0.61482495\",\n \"text\": \"def process_geojson(poly_base_geojson):\\n count_build_without_height = 0\\n count_bug_linestring = 0\\n count_deleted_points = 0\\n count_deleted_multi_points = 0\\n count_deleted_unused_fields = 0\\n init_features_number = len(poly_base_geojson[\\\"features\\\"])\\n try:\\n for item in poly_base_geojson[\\\"features\\\"]:\\n properties = item[\\\"properties\\\"]\\n geom_type = item[\\\"geometry\\\"][\\\"type\\\"]\\n if geom_type == \\\"Point\\\":\\n poly_base_geojson[\\\"features\\\"].remove(item)\\n count_deleted_points += 1\\n else:\\n #make dafault value of height if absent\\n if \\\"height\\\" not in properties and \\\"building:levels\\\" not in properties:\\n item[\\\"properties\\\"][\\\"height\\\"] = str(DEFAULT_VALUE_HEIGHT)\\n count_build_without_height += 1\\n elif \\\"height\\\" not in properties and len(re.sub(r\\\"[^0-9^.]\\\", \\\"\\\", item[\\\"properties\\\"][\\\"building:levels\\\"])) > 0:\\n # remove all symbols except number from the building:levels field and calculate height field\\n item[\\\"properties\\\"][\\\"height\\\"] = str(HEIGHT_OF_FLOOR * float(re.sub(r\\\"[^0-9^.]\\\", \\\"\\\", item[\\\"properties\\\"][\\\"building:levels\\\"])))\\n count_build_without_height += 1\\n\\n # correct LineString geometry type to Polygon\\n if geom_type == \\\"LineString\\\":\\n geom_coordinates = item[\\\"geometry\\\"][\\\"coordinates\\\"]\\n item[\\\"geometry\\\"][\\\"type\\\"] = \\\"Polygon\\\"\\n item[\\\"geometry\\\"][\\\"coordinates\\\"] = [geom_coordinates]\\n count_bug_linestring += 1\\n\\n # remove unnececery fields `roof:colour`, `building`, `roof:shape`, `opening_hours`, `fuel:lpg`, `brand`\\n for field in UNUESED_FILEDS[1]:\\n if field in item[\\\"properties\\\"]:\\n del(item[\\\"properties\\\"][field])\\n count_deleted_unused_fields += 1\\n \\n except TypeError as error:\\n print(error)\\n print(type(item))\\n final_features_number = len(poly_base_geojson[\\\"features\\\"])\\n #save corrected json in the file\\n #poly_base_geojson[\\\"features\\\"] = poly_base_geojson[\\\"features\\\"]\\n with open(PROCESSED_JSON_FILENAME, 'w') as outfile:\\n json.dump(poly_base_geojson, outfile, indent=3)\\n # print report \\n print(REPORT_SUMMARY.format(init_features_number, final_features_number, count_build_without_height, count_bug_linestring, count_deleted_points, count_deleted_multi_points, count_deleted_unused_fields))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd956f8f669e468102eafe1ec8a25ecb\",\n \"score\": \"0.6102305\",\n \"text\": \"def get_geojson_feature(geo_id: str, geo_name: str, json_text: List[str]):\\n # Exclude geo if no renderings are present.\\n if len(json_text) < 1:\\n return None\\n geojson = json.loads(json_text[0])\\n geo_feature = {\\n \\\"type\\\": \\\"Feature\\\",\\n \\\"id\\\": geo_id,\\n \\\"properties\\\": {\\n \\\"name\\\": geo_name,\\n \\\"geoDcid\\\": geo_id,\\n }\\n }\\n geojson_type = geojson.get(\\\"type\\\", \\\"\\\")\\n if geojson_type == MULTILINE_GEOJSON_TYPE:\\n geo_feature['geometry'] = geojson\\n elif geojson_type == POLYGON_GEOJSON_TYPE or geojson_type == MULTIPOLYGON_GEOJSON_TYPE:\\n coordinates = get_multipolygon_geojson_coordinates(geojson)\\n geo_feature['geometry'] = {\\n \\\"type\\\": \\\"MultiPolygon\\\",\\n \\\"coordinates\\\": coordinates\\n }\\n else:\\n geo_feature = None\\n return geo_feature\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67c1de743dcfcddfa55e673da9fa929e\",\n \"score\": \"0.609611\",\n \"text\": \"def get_geojson_from_geoserver(user, shapefile_name):\\n kml_links = []\\n context = {}\\n\\n # Find the contents of GeoServer for the user and display them\\n dataset_engine = get_spatial_dataset_engine(name='gsshaindex_geoserver', app_class=GSSHAIndex)\\n overlay_result = gi_lib.get_layer_and_resource(dataset_engine, user, shapefile_name)\\n\\n if overlay_result['success'] == True:\\n url = overlay_result['resource']['wfs']['geojson']\\n r= requests.get(url)\\n status = r.status_code\\n if status == 200:\\n result = r.json()\\n geojson = result['features']\\n srid = result['crs']\\n return {'geojson':geojson, 'crs':srid, 'success':True}\\n else:\\n return {'success':False}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eed72a79ca70fb624be55b786ef1641c\",\n \"score\": \"0.60737634\",\n \"text\": \"def getCityGeoJSON(adress):\\n if adress in adresses:\\n return adresses[adress]\\n else:\\n from geopy.geocoders import Nominatim\\n geolocator = Nominatim(user_agent=\\\"practica-abbdd\\\")\\n location = geolocator.geocode(adress)\\n sleep(5)\\n geojson = json.dumps({'type': 'Point', 'coordinates' : [location.latitude, location.longitude]})\\n adresses[adress] = geojson\\n return geojson\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f85cf4d27d8d79efdf5cb21ce18c81ee\",\n \"score\": \"0.60265493\",\n \"text\": \"def read_geojson_geograph(geojson_path):\\n\\n geojson = json.load(geojson_path)\\n dict_getter = nested_dict_getter()\\n\\n # build GeoGraph from a plain-old networkx graph\\n g = nx.Graph()\\n\\n # Only add crs if it's explicitly set in crs.properties.name\\n crs = dict_getter(geojson, ['crs', 'properties', 'name'])\\n if crs:\\n try:\\n prj.Proj(crs)\\n except Exception as e:\\n raise SpatialReferenceInvalidException(\\n \\\"Spatial reference must comply with proj4, got {}\\\" % crs)\\n\\n # TODO: Apply geojson schema validation\\n features = geojson['features']\\n\\n # Currently this separates nodes and coordinates due to structure of\\n # GeoGraph. This may change to allow Nodes and Edges to have distinct \\n # Geometry.\\n nodes, coords = geojson_get_nodes(features)\\n g.add_nodes_from(nodes)\\n \\n edges = geojson_get_edges(features)\\n g.add_edges_from(edges)\\n\\n if crs is None and gm.is_in_lon_lat(coords.values()):\\n crs = gm.PROJ4_LATLONG\\n \\n if crs is None:\\n warnings.warn(\\\"Spatial Reference could not be set for {}\\\".\\n format(geojson_path))\\n\\n # TODO: More srs/projection validation?\\n return GeoGraph(srs=crs, coords=coords, data=g)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6122bff6b45ec16a52a5bca219bdfba0\",\n \"score\": \"0.597878\",\n \"text\": \"def read_shp_layer(input_file, layer_read = 0, max_feats_read = 100000):\\n import ast\\n from osgeo import ogr\\n \\n output_list = []\\n input_data = ogr.Open(input_file)\\n layer = input_data.GetLayer(layer_read)\\n # Try to read first feature of layer, and throw error if not possible\\n feature = layer.GetFeature(0)\\n print(feature)\\n \\n # Loop through features\\n for i_feat in range(max_feats_read):\\n try:\\n # Read feature\\n feature = layer.GetFeature(i_feat)\\n # Export to dict string\\n dict_str = feature.ExportToJson()\\n # Append to output\\n output_list.append(ast.literal_eval(dict_str))\\n except:\\n # Last feature read, break loop\\n break\\n \\n return output_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"881f50dee42cfc96fc91d69d609a6a74\",\n \"score\": \"0.595223\",\n \"text\": \"def load_data(json_path, gps_data_path, filter_exotics=True):\\n # with open(pickle_path, 'rb') as f:\\n # ucf_objects_mobilenet = pickle.load(f)\\n\\n # object_counts = {}\\n # for key, value in ucf_objects_mobilenet.items():\\n # object_counts[key] = len(value)\\n\\n with open(json_path, \\\"r\\\") as read_file:\\n prediction_data = json.load(read_file)\\n\\n prediction_data = pd.DataFrame(prediction_data)\\n\\n prediction_data['scene'] = prediction_data['scene'].map(\\n lambda x: x.split('_')[0])\\n prediction_data['year'] = prediction_data['label'].map(\\n lambda x: int(x.split(' ')[-1]))\\n\\n image_names = ['0'*(6-len(str(num))) + str(num) for num in range(1, 10344)]\\n gps = np.squeeze(scipy.io.loadmat(gps_data_path)['GPS_Compass'])\\n gps_df = pd.DataFrame(gps, index=image_names, columns=[\\n 'lat', 'long', 'compass'])\\n\\n all_scene_data = prediction_data.merge(\\n gps_df, how='outer', left_on='scene', right_index=True)\\n\\n # all_views_obj_count = {}\\n # for key, value in object_counts.items():\\n # if key[:6] in all_views_obj_count:\\n # all_views_obj_count[key[:6]] += value\\n # else:\\n # all_views_obj_count[key[:6]] = value\\n\\n # gps_df['count'] = gps_df.index.map(all_views_obj_count)\\n\\n return all_scene_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc00b3930c10c57110940e859a8dc7d7\",\n \"score\": \"0.5927192\",\n \"text\": \"def all_geojson(request):\\n\\n try:\\n programs = Program.objects.transform(4326).all().select_related()\\n except Place.DoesNotExist:\\n raise Http404\\n\\n features = []\\n\\n for program in programs:\\n # truncatewords\\n properties = dict(title=program.title, description=truncatewords(program.description,20), absolute_url=program.get_absolute_url(), map_icon='layer-0')\\n if program.image:\\n properties['image_url'] = program.image.url\\n if program.icon:\\n properties['map_icon'] = 'layer-%s' % program.icon.id\\n geometry = simplejson.loads(program.geometry.geojson)\\n feature = dict(type='Feature', geometry=geometry, properties=properties)\\n features.append(feature)\\n\\n response = dict(type='FeatureCollection', features=features)\\n return HttpResponse(simplejson.dumps(response), mimetype='application/json')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74eb238116857f61430293b17253b456\",\n \"score\": \"0.5923925\",\n \"text\": \"def convertTOGEOJSON(data, ftype): \\n ftype = ftype.lower()\\n \\n if ftype == \\\"point\\\":\\n result_feat =[]\\n for i in range(0,len(data)):\\n point = geojson.Point(data[i])\\n result_feat.append(geojson.Feature(geometry=point))\\n result_feat = geojson.FeatureCollection(result_feat)\\n # pp.pprint(collection) # valid\\n return result_feat\\n\\n if ftype == \\\"polygon\\\":\\n polygon = geojson.Polygon(data)\\n # print(polygon) # valid \\n return polygon\\n\\n elif ftype == \\\"linestring\\\":\\n linestring = geojson.LineString(data)\\n # print(linestring) #valid\\n return (linestring)\\n\\n elif ftype == \\\"multilinestring\\\":\\n mls = geojson.MultiLineString(data)\\n # print(mls) # valid\\n return mls\\n\\n else:\\n print(\\\"\\\\nwrong argument\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0e43d397e7e632bb84aadff6c2cdbba\",\n \"score\": \"0.588498\",\n \"text\": \"def _geom_from_geojson(fpath):\\n with open(fpath) as f:\\n geojson = json.load(f)\\n if geojson['type'] == 'Feature':\\n geom = shape(geojson['geometry'])\\n elif geojson['type'] == 'FeatureCollection':\\n geom = shape(geojson['features'][0]['geometry'])\\n else:\\n raise click.exceptions.BadOptionUsage('No GeoJSON feature found.')\\n return geom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7ce3bdfe15c3a8536758f69ffb64f26\",\n \"score\": \"0.58566725\",\n \"text\": \"def test_geospatial_positive_query_with_geojson(self):\\n records = []\\n query = self.as_connection.query(\\\"test\\\", \\\"demo\\\")\\n\\n geo_object2 = aerospike.geojson(\\n '{\\\"type\\\": \\\"Polygon\\\", \\\"coordinates\\\": [[[-122.500000, 37.000000], \\\\\\n [-121.000000, 37.000000], [-121.000000, 38.080000],\\\\\\n [-122.500000, 38.080000], [-122.500000, 37.000000]]]}'\\n )\\n\\n query.where(p.geo_within_geojson_region(\\\"loc\\\", geo_object2.dumps()))\\n\\n def callback(input_tuple):\\n _, _, record = input_tuple\\n records.append(record)\\n\\n query.foreach(callback)\\n\\n assert len(records) == 3\\n expected = [\\n {\\\"coordinates\\\": [-122.0, 37.5], \\\"type\\\": \\\"Point\\\"},\\n {\\\"coordinates\\\": [-121.8, 37.7], \\\"type\\\": \\\"Point\\\"},\\n {\\\"coordinates\\\": [-121.6, 37.9], \\\"type\\\": \\\"Point\\\"},\\n ]\\n for r in records:\\n assert r[\\\"loc\\\"].unwrap() in expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cfe277d56869251bb8c69eeab3e7697\",\n \"score\": \"0.5830556\",\n \"text\": \"def read_nhdplus_flowline_geojson_attribute(sFilename_geojson_in):\\n\\n iReturn_code = 1\\n if os.path.isfile(sFilename_geojson_in):\\n pass\\n else:\\n print('This geojson does not exist: ', sFilename_geojson_in )\\n iReturn_code = 0\\n return iReturn_code\\n\\n aNHDPlusID=list()\\n pDriver_shapefile = ogr.GetDriverByName('GeoJSON')\\n \\n pDataset_shapefile = pDriver_shapefile.Open(sFilename_geojson_in, gdal.GA_ReadOnly)\\n pLayer_shapefile = pDataset_shapefile.GetLayer(0)\\n pSpatialRef_shapefile = pLayer_shapefile.GetSpatialRef() \\n for pFeature_shapefile in pLayer_shapefile: \\n pGeometry_in = pFeature_shapefile.GetGeometryRef()\\n sGeometry_type = pGeometry_in.GetGeometryName() \\n lNHDPlusID = int(pFeature_shapefile.GetField(\\\"NHDPlusID\\\"))\\n aNHDPlusID.append(lNHDPlusID) \\n \\n #we also need to spatial reference\\n #aFromNode, aToNode, \\n return aNHDPlusID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd9c9e9ac36d6e13bb32e92b5b319597\",\n \"score\": \"0.573606\",\n \"text\": \"def _load_geojson_as_list(fn):\\n shapes = []\\n areas = []\\n with fiona.open(fn) as f:\\n src_crs = f.crs\\n for row in f:\\n geom = row[\\\"geometry\\\"]\\n\\n area = get_area_from_geometry(geom, src_crs)\\n areas.append(area)\\n\\n shape = shapely.geometry.shape(geom)\\n shapes.append(shape)\\n return shapes, areas, src_crs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966a81b5526c2ba1435c4e3dd764404c\",\n \"score\": \"0.57322085\",\n \"text\": \"def get_json(url):\\n f = urllib2.urlopen(url)\\n response_text = f.read()\\n response_data = json.loads(response_text)\\n \\n\\n return response_data[\\\"results\\\"][0][\\\"geometry\\\"][\\\"location\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c681f03300e91fda7feb1bf55de320b\",\n \"score\": \"0.57250303\",\n \"text\": \"def loadGeoJsonFromOverpass(savePath, overpassQuery):\\n\\t\\tapi = overpass.API(debug=False)\\n\\t\\ttry:\\n\\t\\t\\tresponse = api.Get(overpassQuery, responseformat=\\\"geojson\\\")\\n\\t\\texcept overpass.OverpassError:\\n\\t\\t\\t# if too many requests, wait and send again\\n\\t\\t\\tprint \\\"Too many overpass requests, trying again in 2 sec.\\\"\\n\\t\\t\\ttime.sleep(2)\\n\\t\\t\\tOsm.loadGeoJsonFromOverpass(savePath, overpassQuery)\\n\\t\\t\\treturn\\n\\n\\t\\t# save as geojson\\n\\t\\tdeleteFile(savePath)\\n\\t\\twith open(savePath, 'w') as fp:\\n\\t\\t\\tjson.dump(response, fp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a6be64272a121fc5f54eb2bb39b4c07\",\n \"score\": \"0.5722722\",\n \"text\": \"def read_geograph(filename, *args, **kwargs):\\n\\n # throw out arguments if we don't need 'em\\n read_map = {\\n '.json': lambda filename: read_json_geograph(filename),\\n '.geojson': lambda filename: read_geojson_geograph(filename),\\n '.shp': lambda filename: read_shp_geograph(filename, *args, **kwargs),\\n '.csv': lambda filename: read_csv_geograph(filename, *args, **kwargs)\\n }\\n\\n match = re.search(r'\\\\.[^\\\\.]*$', filename)\\n if match is None or match.group() not in read_map:\\n msg = \\\"input filename {} does not have extension of .shp, .csv, \\\"\\\\\\n \\\".json or .geojson\\\".format(filename)\\n raise NetworkerException(msg)\\n else:\\n return read_map[match.group()](filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11ae8a647ab2e05b71e755b7c5634447\",\n \"score\": \"0.57142514\",\n \"text\": \"def read_with_ogr (filename, fatal_errors=True):\\n source = osgeo.ogr.Open(filename, False)\\n if not source: raise Exception(\\\"Can't open %s\\\" % filename)\\n\\n layer = source.GetLayer(0)\\n defn = layer.GetLayerDefn()\\n fields = [defn.GetFieldDefn(i).GetName().lower() for i in range(defn.GetFieldCount())]\\n\\n layer.ResetReading()\\n ogr_feature = layer.GetNextFeature()\\n while ogr_feature:\\n # try:\\n # geometry = shapely.wkb.loads(ogr_feature.GetGeometryRef().ExportToWkb())\\n # except Exception, e:\\n # if fatal_errors:\\n # raise\\n # else:\\n # print >>sys.stderr, \\\"Shapely error:\\\", e\\n # ogr_feature.Destroy()\\n # ogr_feature = layer.GetNextFeature()\\n # continue\\n geometry_ref = ogr_feature.GetGeometryRef()\\n geometry = (geometry_ref.GetX(), geometry_ref.GetY())\\n attrs = {}\\n for n, name in enumerate(fields):\\n value = ogr_feature.GetField(n)\\n if isinstance(value, basestring):\\n try:\\n value = value.decode(\\\"utf-8\\\")\\n except UnicodeDecodeError:\\n value = value.decode(\\\"latin-1\\\") \\n attrs[name] = value\\n ogr_feature.Destroy()\\n yield geometry, attrs\\n ogr_feature = layer.GetNextFeature()\\n source.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ead0c91aa8812dbe027f014ce0b9770\",\n \"score\": \"0.5707251\",\n \"text\": \"def get_geist_data(theURL, query):\\n user_agent = 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.111 Safari/537.36'\\n # req = urllib.request.Request(theURL+query, headers={'User-Agent':user_agent})\\n # req = urllib.request.Request(theURL + query)\\n opener = urllib.request.urlopen(theURL + query)\\n html = opener.read().decode('utf-8')\\n logger.debug('json size = %d' %(len(html)))\\n jdata = json.loads(html)\\n return jdata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbff62ccd7e8b415ad24e3e7c2722bca\",\n \"score\": \"0.5706733\",\n \"text\": \"def getFeatures(gdf):\\n import json\\n return [json.loads(gdf.to_json())['features'][0]['geometry']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbff62ccd7e8b415ad24e3e7c2722bca\",\n \"score\": \"0.5706733\",\n \"text\": \"def getFeatures(gdf):\\n import json\\n return [json.loads(gdf.to_json())['features'][0]['geometry']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ef8bc6860596e6ebf81a95039d1ed6\",\n \"score\": \"0.5698234\",\n \"text\": \"def read_json(path2json_file):\\n # parse data from the input file\\n with open(path2json_file, \\\"r\\\") as json_file:\\n json_data = json_file.read()\\n json_loaded = json.loads(json_data)\\n boundary_data = try_extraction(json_loaded, BOUNDARY_JSON_KEY)\\n holes_data = try_extraction(json_loaded, HOLES_JSON_KEY)\\n boundary_coordinates = convert2polygon(boundary_data)\\n list_of_holes = [convert2polygon(hole_data) for hole_data in holes_data]\\n return boundary_coordinates, list_of_holes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"740fed0bd57ffc8eebfd8ff2b9d18103\",\n \"score\": \"0.56888956\",\n \"text\": \"def start_pipeline_to_geojson():\\n # get xml files from osm\\n for region in REGIONS_TEST:\\n print(\\\"XML obtaining\\\\nprceeded: {}\\\".format(region))\\n filename = region.replace(\\\" \\\", \\\"_\\\")\\n filenames.append(filename)\\n os.system('python3 getosm.py \\\"{0}\\\" > {2}/{1}.xml'.format(region, filename, RESULT_DIR))\\n \\n # make geojson files from xml\\n os.chdir(RESULT_DIR)\\n print(os.listdir(os.getcwd()))\\n for filename in filenames:\\n print('Generating of geojson: \\\\nosmtogeojson {0}.xml > {0}.geojson'.format(filename))\\n os.system('node --max_old_space_size=4000 `which osmtogeojson` {0}.xml > {0}.geojson'.format(filename))\\n print(os.listdir(os.getcwd()))\\n \\n # open base file\\n print(filenames[0])\\n with open('{}.geojson'.format(filenames[0])) as geojson1:\\n poly_base_geojson = json.load(geojson1)\\n\\n # merdge features of the base file with other\\n for file in filenames[1:]:\\n print(\\\"Merging \\\\n\\\")\\n print(file)\\n with open('{}.geojson'.format(file)) as geojson_temp:\\n poly_temp_geojson = json.load(geojson_temp)\\n # add to features of the next \\n poly_base_geojson['features'] += poly_temp_geojson['features']\\n\\n with open(MERGED_FILENAME, 'w') as outfile:\\n json.dump(poly_base_geojson, outfile, indent=3)\\n \\n return poly_base_geojson\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6ce3f17552d421b077f008b692a2f69\",\n \"score\": \"0.56849504\",\n \"text\": \"def toGeoJson(data, dtype):\\n dtype = dtype.lower()\\n if dtype == \\\"point\\\":\\n collection =[]\\n for i in range(0,len(data)):\\n point = geojson.Point(data[i])\\n collection.append(geojson.Feature(geometry=point))\\n collection = geojson.FeatureCollection(collection)\\n # pp.pprint(collection) # valid\\n return collection\\n if dtype == \\\"polygon\\\":\\n polygon = geojson.Polygon(data)\\n # print(polygon) # valid \\n return polygon\\n elif dtype == \\\"linestring\\\":\\n linestring = geojson.LineString(data)\\n # print(linestring) #valid\\n return (linestring)\\n elif dtype == \\\"multilinestring\\\":\\n mls = geojson.MultiLineString(data)\\n # print(mls) # valid\\n return mls\\n else:\\n print(\\\"\\\\nBAD ARGUMENT\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a285a568c655a982cbc57d69b21daff\",\n \"score\": \"0.5634496\",\n \"text\": \"def test_geospatial_loads_positive_with_query(self):\\n geo_object_loads = get_geo_object()\\n\\n geo_object_loads.loads(\\n '{\\\"type\\\": \\\"Polygon\\\", \\\"coordinates\\\": [[[-122.500000, 37.000000],\\\\\\n [-121.000000, 37.000000], [-121.000000, 38.080000],\\\\\\n [-122.500000, 38.080000], [-122.500000, 37.000000]]]}'\\n )\\n\\n assert geo_object_loads.unwrap() == {\\n \\\"coordinates\\\": [[[-122.5, 37.0], [-121.0, 37.0], [-121.0, 38.08], [-122.5, 38.08], [-122.5, 37.0]]],\\n \\\"type\\\": \\\"Polygon\\\",\\n }\\n\\n records = []\\n query = self.as_connection.query(\\\"test\\\", \\\"demo\\\")\\n query.where(p.geo_within_geojson_region(\\\"loc\\\", geo_object_loads.dumps()))\\n\\n def callback(input_tuple):\\n _, _, record = input_tuple\\n records.append(record)\\n\\n query.foreach(callback)\\n\\n assert len(records) == 3\\n expected = [\\n {\\\"coordinates\\\": [-122.0, 37.5], \\\"type\\\": \\\"Point\\\"},\\n {\\\"coordinates\\\": [-121.8, 37.7], \\\"type\\\": \\\"Point\\\"},\\n {\\\"coordinates\\\": [-121.6, 37.9], \\\"type\\\": \\\"Point\\\"},\\n ]\\n for r in records:\\n assert r[\\\"loc\\\"].unwrap() in expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"165beb48f17d2377303dc05e6d7636e1\",\n \"score\": \"0.5633273\",\n \"text\": \"def geojson(*forms: Union[\\\"Polygon\\\", \\\"Point\\\", \\\"LineString\\\"]):\\n print(geopandas.GeoSeries([form.ftype(form.lonlat) for form in forms]).to_json())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e65f792b066be7777a19ad7a1c1132e2\",\n \"score\": \"0.5610999\",\n \"text\": \"def test_geospatial_loads_positive(self):\\n geo_object = get_geo_object()\\n geo_object.loads(\\n '{\\\"type\\\": \\\"Polygon\\\", \\\"coordinates\\\": [[[-122.500000, 37.000000],\\\\\\n [-121.000000, 37.000000], [-121.000000, 38.080000],\\\\\\n [-122.500000, 38.080000], [-122.500000, 37.000000]]]}'\\n )\\n\\n assert geo_object.unwrap() == {\\n \\\"coordinates\\\": [[[-122.5, 37.0], [-121.0, 37.0], [-121.0, 38.08], [-122.5, 38.08], [-122.5, 37.0]]],\\n \\\"type\\\": \\\"Polygon\\\",\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3af6b9056f4030ba402805a014eac36\",\n \"score\": \"0.55970067\",\n \"text\": \"def parseLocs(dataPath):\\n with open(dataPath) as f:\\n data = json.load(f)\\n f.close()\\n # Flatten the json and convert to df\\n df = pd.json_normalize(data, 'locations')\\n # Read Timestamp as date format\\n df['datetimeUTC'] = pd.to_datetime(df['timestampMs'], unit='ms')\\n # Change to Swiss Timezone\\n df['datetimeCH'] = df['datetimeUTC'] + pd.DateOffset(hours=1)\\n # Extract the day\\n df['date'] = df['datetimeUTC'].dt.date\\n df = df.set_index('datetimeCH')\\n # Read the coordinates as float numbers\\n df['latitudeE7'] = df['latitudeE7'].astype(float)/10000000\\n df['longitudeE7'] = df['longitudeE7'].astype(float)/10000000\\n # Convert to geopandas\\n gdf = gpd.GeoDataFrame(\\n df, geometry=gpd.points_from_xy(df['longitudeE7'], df['latitudeE7']))\\n return df, gdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac6b82f82b6e5ad6283f37c9bcdc8d3b\",\n \"score\": \"0.5595491\",\n \"text\": \"def test_read_json(self):\\n\\n result = read_json(\\\"NYC_Shapes.json\\\")\\n neighborhood = [result.keys()]\\n self.assertEqual(len(neighborhood), 195)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e82f612a9b2cabd7ef4e6fdda092b8\",\n \"score\": \"0.5592714\",\n \"text\": \"def parse(json_filename, encoding=\\\"UTF-8\\\"):\\r\\n with codecs.open(json_filename, \\\"rU\\\", encoding=encoding) as fp:\\r\\n json_data = json.load(fp, encoding=encoding)\\r\\n json_processor = JsonParser()\\r\\n features = json_processor.parse_features(json_data)\\r\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74a9f1de2d513d351db32e5510aa19a4\",\n \"score\": \"0.55665684\",\n \"text\": \"def topojson_to_geojson(infile,outfile):\\n cmd = os.path.join(PurePath(PARSER_PATH).parents[2],\\\"node_modules/topojson-client/bin/topo2geo\\\")\\n \\n st,r = sp.getstatusoutput(cmd + \\\" --version\\\")\\n\\n if st == 0:\\n process = sp.Popen([cmd, infile, \\\"--out\\\", outfile])\\n else:\\n print(\\\"Couldn't find topo2geo at {}\\\".format(cmd))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a387f78673110a980080a5a8587c9e\",\n \"score\": \"0.5545909\",\n \"text\": \"def geojson2enu(features):\\n if not isinstance(features, (geojson.FeatureCollection, dict)):\\n if isinstance(features, io.IOBase):\\n features = geojson.load(features)\\n else:\\n with open(features) as f:\\n features = geojson.load(f)\\n\\n if not isinstance(features, EasyDict):\\n features = EasyDict(features)\\n\\n ecef_xyz = []\\n scores = []\\n for feature in features.features:\\n if feature.geometry.type == 'Point':\\n llh = feature.geometry.coordinates\\n if len(llh) == 2:\\n llh.append(0) # altitude\\n lon, lat, h = llh\\n x, y, z = pymap3d.ecef.geodetic2ecef(lat, lon, h)\\n score = feature.properties.get('detection-score', 0.5)\\n ecef_xyz.append((x, y, z))\\n scores.append(score)\\n ecef_xyz = np.array(ecef_xyz)\\n # Convert to ENU coordinates\\n lat0, lon0, h0 = pymap3d.ecef.ecef2geodetic(*ecef_xyz[:, :3].mean(0))\\n enu_xyz = pymap3d.ecef.ecef2enu(ecef_xyz[:, 0], ecef_xyz[:, 1], ecef_xyz[:, 2], lat0, lon0, h0)\\n enu_xyz = np.column_stack(enu_xyz)\\n assert len(enu_xyz) > 0\\n assert len(enu_xyz[0]) >= 2\\n assert len(scores) == len(enu_xyz)\\n return enu_xyz, scores, (lat0, lon0, h0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4857b79e1ab5aa08cac5b5ef0363d5b\",\n \"score\": \"0.5536641\",\n \"text\": \"def get_stats(filenames, geom):\\n stats = []\\n origin = Proj(init='epsg:4326')\\n _geojson = deepcopy(geom)\\n\\n origin_coords = geom['coordinates']\\n\\n total_count = None\\n\\n for i, f in enumerate(filenames):\\n # convert polygon to image srs\\n with rasterio.open(f) as src:\\n dst = Proj(src.crs)\\n _geojson['coordinates'] = convert_coordinates(origin_coords, origin, dst)\\n\\n # write to temporary geojson file\\n pfilename = '/tmp/poly.geojson'\\n with open(pfilename, 'w') as poly_f:\\n poly_f.write(json.dumps(_geojson))\\n\\n if i == 0:\\n total_count = float(zonal_stats(pfilename, f, stats=\\\"count\\\", nodata=-1)[0]['count'])\\n\\n # calculate stats\\n st = zonal_stats(pfilename, f, stats=\\\"count mean min max\\\", nodata=999)[0]\\n st['count'] = zonal_stats(pfilename, f, stats=\\\"count\\\", nodata=-9999)[0]['count']\\n st['percent_flooded'] = st['count']/total_count * 100\\n stats.append(st)\\n return stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a162dc6659718c048ebaca76e54f350a\",\n \"score\": \"0.5536223\",\n \"text\": \"def format_england_geojson(eng_file):\\n with open(eng_file) as f:\\n eng_geojson = geojson.load(f)\\n\\n for i, item in enumerate(eng_geojson[\\\"features\\\"]):\\n # rename name and lsoa code properties.\\n # discard everything else.\\n item['properties'] = {\\\"name\\\": item['properties']['LSOA11NM'],\\n \\\"ZoneID\\\": item['properties']['LSOA11CD']}\\n return eng_geojson\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c509dc6a525694bb0b2d5dbc1fdf263\",\n \"score\": \"0.55337656\",\n \"text\": \"def aggregate_features(sp_path, osm_path):\\n features = []\\n with open(sp_path, \\\"r\\\") as sp_file:\\n geojson = json.load(sp_file)\\n features += geojson[\\\"features\\\"]\\n with open(osm_path, \\\"r\\\") as osm_file:\\n geojson = json.load(osm_file)\\n features += geojson[\\\"features\\\"]\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f53b403de9dfe26c0ab30b31b4d3681e\",\n \"score\": \"0.5524737\",\n \"text\": \"def readZip_psurge(self, add, name_dbf, name_prj, name_shp, name_shx):\\n proxies = {\\\"http\\\":\\\"http://username:password@proxy_ip:proxy_port\\\"}\\n session = requests.Session()\\n retry = Retry(connect=3, backoff_factor=0.5)\\n adapter = HTTPAdapter(max_retries=retry)\\n session.mount('http://', adapter)\\n session.mount('https://', adapter)\\n \\n county_file_url = 'https://www.nhc.noaa.gov/gis/' + add\\n zipfile = ZipFile(io.BytesIO(session.get(county_file_url).content))\\n sleep(5)\\n \\n end_dic = [name_dbf, name_prj, name_shp, name_shx]\\n filenames = [y for y in sorted(zipfile.namelist()) for ending in end_dic if y.endswith(ending)] \\n if filenames:\\n dbf, prj, shp, shx = [io.BytesIO(zipfile.read(filename)) for filename in filenames]\\n else:\\n return None\\n r = shapefile.Reader(shp=shp, shx=shx, dbf=dbf)\\n attributes, geometry = [], []\\n field_names = [field[0] for field in r.fields[1:]]\\n for row in r.shapeRecords(): \\n geometry.append(shape(row.shape.__geo_interface__)) \\n attributes.append(dict(zip(field_names, row.record)))\\n \\n gdf = gpd.GeoDataFrame(data = attributes, geometry = geometry)\\n gdf['timestamp'] = add.split('_')[3][0:10]\\n \\n return gdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5644d358a3e6af7fa54b2d01c9fd196\",\n \"score\": \"0.5519732\",\n \"text\": \"def load_countries():\\n base_path = pathlib.Path(__file__).resolve().parent\\n path = base_path.joinpath('countries', '50m', 'world.geo.json.gz')\\n data = load_json_file(path)\\n\\n country_map = {}\\n\\n for item in data['features']:\\n name = item['properties']['name']\\n country_map[name] = item['id']\\n return None, data, country_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47aecc99467f1bf6f515240d29bc7155\",\n \"score\": \"0.55083215\",\n \"text\": \"def convert(arcgis):\\n\\n geojson = {}\\n\\n for iterable_name in [\\\"features\\\", \\\"results\\\"]:\\n if iterable_name in arcgis and arcgis[iterable_name]:\\n geojson[\\\"type\\\"] = \\\"FeatureCollection\\\"\\n geojson[\\\"features\\\"] = []\\n for feature in arcgis[iterable_name]:\\n geojson[\\\"features\\\"].append(convert_feature(feature))\\n return geojson\",\n \"title\": \"\"\n },\n {\n \"docid\": \"985a79f3fb4ebb99001190973c73c931\",\n \"score\": \"0.5501992\",\n \"text\": \"def find_realtime_coord(self):\\n data = pd.read_csv(\\\"text_files\\\\Sat_coordinates.csv\\\")\\n lat = data['LAT']\\n lon = data['LON']\\n\\n point = Point([float(lon), float(lat)])\\n feature = Feature(geometry=point)\\n dump = geojson.dumps(feature, sort_keys=True)\\n with open(\\\"static\\\\map.geojson\\\", 'w') as file_object:\\n file_object.writelines(dump)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"672dbb5448bd7592bd6dddcbe21110ab\",\n \"score\": \"0.5483584\",\n \"text\": \"def main(i, o, yes):\\n input_geojson_file = i\\n features_directory = o\\n\\n print \\\"Read input file path:\\\", input_geojson_file\\n if features_directory == './':\\n print 'OK, your features data will be stored in the current directory:', features_directory\\n else:\\n print 'OK, your features data will be stored in:', features_directory\\n\\n\\n #fetch geojson from local path or remote path. Throw error if not found.\\n print \\\"\\\\nfetching geojson...\\\"\\n file_type = 'local' #set default to local anyway assuming not an http/https remote link\\n if os.path.exists(input_geojson_file):\\n file_type = 'local'\\n else:\\n if 'http' in input_geojson_file:\\n file_type = 'remote'\\n else:\\n raise ValueError(\\\"Could not identify file type as remote or local.\\\")\\n\\n if file_type == 'local':\\n js = get_json_local(input_geojson_file)\\n elif file_type == 'remote':\\n js = get_json_remote(input_geojson_file)\\n if js == -1:\\n print >> sys.stderr, \\\"No remote file found at\\\", input_geojson_file\\n sys.exit(1)\\n\\n print \\\"\\\"\\n\\n\\n\\n # validate json for all geometries and correct labels:\\n js, flag = validate_json(js)\\n if flag == 1: # if flag is activated then do not execute rest\\n sys.exit(1)\\n\\n #load CatalogImage given catalog id in geojson\\n print \\\"\\\\nloading CatalogImage...\\\"\\n image = load_cat_image(js['properties']['catalog_id'], pan=True)\\n\\n print \\\"current number of features:\\\", len(js['features']) # total features found in geojson\\n\\n # remove any Tomnod labels that are None\\n print \\\"\\\\nremoving Tomnod_labels = None...\\\"\\n filtered_feats = filter(lambda x: x['properties']['Tomnod_label'] != None, js['features'])\\n js['features'] = filtered_feats\\n\\n print \\\"new number of features:\\\", len(js['features']) # total features after removing Tomnod labels that are None type\\n\\n # load the raster, mask it by the polygon segment\\n print \\\"\\\\nmasking image...\\\"\\n polys = geojson_to_polygons(js)\\n image_aoi_segs, seg_masks = get_segment_masks(image, polys, invert=False) #toggle the inversion if necessary... remember this should be set to True for multiplying image to mask\\n image_aoi_blobs = [ma.array(aoi, mask=np.dstack((seg_masks[i],)*8)) for i, aoi in enumerate(image_aoi_segs)] #image masks instead of just multiplying the image to mask. image_aoi_blobs will be a list of numpy masked arrays that mask out 0 (black) pixels\\n\\n\\n # TODO: debug masked array in segments/pixels as features, make sure the masked numbers are not playing with the computation\\n print \\\"\\\\ncreating features dataset...\\\"\\n #creating segments as features -- use asynchronous polling to calculate features for every segment\\n p = multiprocessing.Pool(processes=4)\\n rsi_samples = [p.apply_async(segment_as_feature, args=(blob,), kwds={'include_gabors': True}) for blob in image_aoi_blobs]\\n rsi_samples_output = [p.get() for p in rsi_samples]\\n\\n #create X and y dataset\\n X_all, y_all = create_feature_dataset(rsi_samples_output, js)\\n\\n #save out the dataset\\n print \\\"\\\\nsaving the features to\\\", features_directory, \\\"\\\\n\\\"\\n if not os.path.exists(features_directory): #if path/subdirs do not exist then make them\\n os.makedirs(features_directory)\\n file_name = input_geojson_file.split('/')[-1].split('.')[0] #get filename without extensions\\n path_X = features_directory+'/'+file_name+'_X_all' #new file path for X_all\\n path_y = features_directory+'/'+file_name+'_y_all' # new file path for y_all\\n if os.path.isfile(path_X): #check if X_all file already exists, then prompt for overwriting the file\\n if yes:\\n np.save(path_X, X_all)\\n elif click.confirm(path_X.split('/')[-1] + \\\" already exists, are you sure you want to overwrite?\\\"):\\n np.save(path_X, X_all)\\n else:\\n np.save(path_X, X_all)\\n\\n print \\\"\\\"\\n\\n if os.path.isfile(path_y): #check if y_all file already exists, then prompt for overwriting the file\\n if yes:\\n np.save(path_y, y_all)\\n elif click.confirm(path_y.split('/')[-1] + \\\" already exists, are you sure you want to overwrite?\\\"):\\n np.save(path_y, y_all)\\n else:\\n np.save(path_y, y_all)\\n\\n print \\\"\\\\ndone.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36584c764c69bbf69aad4786ab9d548b\",\n \"score\": \"0.54740644\",\n \"text\": \"def convert_shapefile_to_geojson(iFlag_type_in, sFilename_shapefile_in, sFilename_geojson_in):\\n if os.path.isfile(sFilename_shapefile_in):\\n pass\\n else:\\n print('This shapefile does not exist: ', sFilename_shapefile_in )\\n iReturn_code = 0\\n return iReturn_code\\n\\n if iFlag_type_in == 0:\\n pass\\n else:\\n if iFlag_type_in == 1:\\n aFlowline_basin, pSpatial_reference = read_flowline_shapefile( sFilename_shapefile_in ) \\n #aFlowline_basin, pSpatial_reference = read_flowline_geojson( sFilename_shapefile_in ) \\n #convert it\\n\\n iFlag_projected = 0\\n pSpatial_reference_gcs = osr.SpatialReference()\\n pSpatial_reference_gcs.ImportFromEPSG(4326)\\n pSpatial_reference_gcs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)\\n export_flowline_to_geojson(aFlowline_basin, sFilename_geojson_in)\\n else:\\n if iFlag_type_in == 2:\\n #polygon\\n pass\\n\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5108e7f828dba9e1663329d9e317a435\",\n \"score\": \"0.5462496\",\n \"text\": \"def getCityGeoJSON(address):\\n from geopy.geocoders import Nominatim # Importamos la libreria\\n geolocator = Nominatim(user_agent=\\\"My-store\\\") # Establecemos un nombre de aplicacion\\n location = geolocator.geocode(address) # Hacemos la llamada de la direccion\\n return { # retorna las coordenadas en formato de GEOJSON\\n \\\"type\\\": \\\"Point\\\",\\n \\\"coordinates\\\": [location.latitude, location.longitude]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e858d87dfbeb7f135e682f5eed10b863\",\n \"score\": \"0.5453036\",\n \"text\": \"def washed_out_roads_geojson():\\n with sqlite3.connect('hikes.db') as con:\\n cursor = con.cursor()\\n results = cursor.execute('SELECT hike.name, alert.text, hike.th_lat, hike.th_long FROM hike INNER JOIN alert ON alert.hike_id = hike.id WHERE alert.type = \\\"red\\\" AND alert.text LIKE \\\"%road%\\\"').fetchall()\\n return {\\n 'type' : 'FeatureCollection',\\n 'features' : [{\\n 'type' : 'Feature',\\n 'geometry' : {\\n 'type' : 'Point',\\n 'coordinates' : [result[3], result[2]]\\n },\\n 'properties' : {\\n 'title' : result[0],\\n 'description' : result[1]\\n }\\n } for result in results]\\n }\\n with open('washed_out_roads.json', 'w+') as file:\\n file.write(json.dumps(washed_out_roads_geojson()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"420d724876e71616f7baef8730c42650\",\n \"score\": \"0.5418384\",\n \"text\": \"def parse_mapzen_response(txt):\\n gdict = {} # just initialize a dict for now, with status of None\\n data = json.loads(txt)\\n if data['features']: # it has at least one feature...\\n gdict['status'] = 'OK' \\n feature = data['features'][0] # pick out the first one\\n props = feature['properties'] # just for easier reference\\n gdict['confidence'] = props['confidence']\\n gdict['label'] = props['label']\\n\\n # now get the coordinates\\n coords = feature['geometry']['coordinates']\\n gdict['longitude'] = coords[0]\\n gdict['latitude'] = coords[1]\\n else:\\n gdict['status'] = None\\n return gdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49855a75af51c7b1c94bee950c4546fc\",\n \"score\": \"0.5402312\",\n \"text\": \"def save_as_geojson():\\n args = ['ogr2ogr', '-f', 'GeoJson', 'app/data/PSU_full_schedule.geojson', 'full_sched.vrt', '-s_srs', 'EPSG:2913', '-t_srs', 'EPSG:4326']\\n call(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b361c1216b29ebb5e2284799d643fb2d\",\n \"score\": \"0.53982383\",\n \"text\": \"def geojson_builder(node):\\n return {\\n 'type': 'Feature',\\n 'properties': {\\n 'name': node['name'],\\n 'population': node['pop']\\n },\\n 'geometry': {\\n 'type': 'Point',\\n 'coordinates': [node['lat'], node['lon']]\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4176e827f0274998a9565ba752420c2\",\n \"score\": \"0.5392154\",\n \"text\": \"def get_geo_json_for_connections(self):\\n list_of_geo_json_objects = []\\n\\n for emulation_node, connected_emulation_nodes in singletons.network_manager.connection_store.get_connections_per_node().items():\\n for connected_emulation_node in connected_emulation_nodes:\\n list_of_geo_json_objects.append(\\n self.__get_geo_json_object_for_connection(emulation_node.id - 1, connected_emulation_node.id - 1))\\n\\n feature_coll_roads = geojson.FeatureCollection(list_of_geo_json_objects)\\n return geojson.dumps(feature_coll_roads)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73c3074e6929b506d37ec63808603dcc\",\n \"score\": \"0.5371144\",\n \"text\": \"def get_feature_json(self):\\n feature = _osm_polygon_request(\\n query=self.query, limit=1, polygon_geojson=1)\\n return feature\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15eaf32f1a449d099450edb205fd2e11\",\n \"score\": \"0.5367315\",\n \"text\": \"def by_geopoint(self, lat, long):\\n\\n header, content = self._http_request(self.BASE_URL, lat=lat, long=long)\\t\\t\\n return json.loads(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf768652a44f22b56452d3f3a30d3dc\",\n \"score\": \"0.5361886\",\n \"text\": \"def create_map(data_file):\\n\\n # Define type of GeoJSON we're creating\\n geo_map = {\\\"type\\\": \\\"FeatureCollection\\\"}\\n\\n # Define empty list to collect each point to graph\\n item_list = []\\n\\n # Iterate over our data to create GeoJSOn document.\\n # We're using enumerate() so we get the line, as well\\n # the index, which is the line number.\\n for index, line in enumerate(data_file):\\n\\n # Skip any zero coordinates as this will throw off\\n # our map.\\n if line['X'] == \\\"0\\\" or line['Y'] == \\\"0\\\":\\n continue\\n\\n # Setup a new dictionary for each iteration.\\n data = {}\\n\\n # Assigne line items to appropriate GeoJSON fields.\\n data['type'] = 'Feature'\\n data['id'] = index\\n data['properties'] = {'title': line['Category'],\\n 'description': line['Descript'],\\n 'date': line['Date']}\\n data['geometry'] = {'type': 'Point',\\n 'coordinates': (line['X'], line['Y'])}\\n\\n # Add data dictionary to our item_list\\n item_list.append(data)\\n\\n # For each point in our item_list, we add the point to our\\n # dictionary. setdefault creates a key called 'features' that\\n # has a value type of an empty list. With each iteration, we\\n # are appending our point to that list.\\n for point in item_list:\\n geo_map.setdefault('features', []).append(point)\\n\\n # Now that all data is parsed in GeoJSON write to a file so we\\n # can upload it to gist.github.com\\n with open('file_sf.geojson', 'w') as f:\\n f.write(geojson.dumps(geo_map))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70619b7a27031c13750595e6cf18bc0c\",\n \"score\": \"0.53572655\",\n \"text\": \"def scrape_rest_endpoint(urls, folder):\\n arcpy.AddMessage(u\\\"Fetching GeoJSON files from Map Service\\\")\\n layersToSync = {}\\n\\n # mapServiceUrl should be a list of urls pointing at map service or feature service layers.\\n # each should end in \\\"MapServer/<some number>\\\" or \\\"FeatureServer/<some number>\\\"\\n\\n # load contents of feature service info page\\n for mapServiceUrl in urls:\\n mapServiceContents = requests.get(\\\"{}?f=json\\\".format(mapServiceUrl))\\n mapServiceData = mapServiceContents.json()\\n\\n # deal with layer names that start with a number\\n layerName = safeName(mapServiceData['name'])\\n if not re.match(u'[a-zA-Z_]', layerName):\\n layerName = u\\\"_{}\\\".format(layerName)\\n\\n # add the layer name as a key to the layersToSync dictionary with an empty list as the value\\n layersToSync[layerName] = []\\n count = 0\\n\\n # fetch esriFieldTypeOID name\\n try:\\n for field in mapServiceData['fields']:\\n if field['type'] == 'esriFieldTypeOID':\\n layerOID = field['name']\\n except:\\n continue\\n\\n # cycle through queries to scrape all data\\n while True:\\n downloadDataUrl = \\\"{}/query?where={}%3E%3D{}&outFields=*&f=json&returnGeometry=True&orderByFields={}\\\". \\\\\\n format(mapServiceUrl, layerOID, str(count), layerOID)\\n arcpy.AddMessage(downloadDataUrl)\\n layerData = requests.get(downloadDataUrl)\\n features = layerData.json()\\n featurecount = len(features['features'])\\n\\n # if no features returned, you're done and move on to the next layer\\n if featurecount == 0:\\n break\\n\\n # otherwise, find the last objecit id you retrieved and pass that to your query next time\\n nextoid = features['features'][featurecount - 1]['attributes'][layerOID] + 1\\n\\n # save the output json to the mirror directory\\n if not os.path.exists(os.path.join(\\\"{}/{}\\\".format(folder, mapServiceData['id']))):\\n os.makedirs(os.path.join(\\\"{}/{}\\\".format(folder, mapServiceData['id'])))\\n layerGeojsonfile = os.path.join(\\\"{}/{}/{}{}.json\\\".format(\\n folder, mapServiceData['id'], layerName, str(count)))\\n\\n layerGeojson = open(layerGeojsonfile, 'w+')\\n json.dump(features, layerGeojson)\\n\\n # populate the value in layersToSync with the list of JSON fiels associated with the layer\\n layersToSync[layerName].append(\\\"{}{}\\\".format(\\n safeName(layerName), str(count)))\\n layerGeojson.close()\\n count = nextoid\\n print(layersToSync)\\n\\n print('locating JSON data')\\n downloadedJson = {}\\n layers = []\\n for dirpath, dirnames, filenames in os.walk(os.path.join(folder)):\\n layers = []\\n for layer in filenames:\\n if layer[-5:] == \\\".json\\\":\\n layers.append(layer)\\n if layers != [] or None:\\n downloadedJson[dirpath] = layers\\n\\n arcpy.AddMessage('Moving geojson files to merged file gdb')\\n\\n mergedGDBName = u'merged_layers.gdb'\\n mergedGDB = os.path.join(folder, mergedGDBName)\\n if not os.path.exists(mergedGDB):\\n arcpy.CreateFileGDB_management(folder, mergedGDBName)\\n\\n for key in downloadedJson:\\n\\n # cycle through the files in each download directory\\n for layer in downloadedJson[key]:\\n\\n # copy the json file into the mergedlayers.gdb\\n if arcpy.Exists(os.path.join(mergedGDB, layer.split('.')[0])) == False:\\n arcpy.JSONToFeatures_conversion(os.path.join(key, layer), os.path.join(mergedGDB, layer))\\n\\n # skip cases where the datatype will cause things to fail\\n layertype = arcpy.Describe(os.path.join(mergedGDB, layer.split('.')[0]))\\n if layertype.datasetType not in [\\\"FeatureClass\\\", \\\"FeatureLayer\\\"]:\\n arcpy.AddMessage(\\n \\\"Not syncing layer: {} because of datatype {}\\\".format(layer, layertype.datasetType))\\n arcpy.Delete_management(os.path.join(mergedGDB, layer.split('.')[0]))\\n continue\\n\\n # use the layersToSync dictionary to identify what layer the json file is supposed to be merged to\\n for mergedLayer in layersToSync:\\n if layer.split('.')[0] in layersToSync[mergedLayer]:\\n if arcpy.Exists(os.path.join(mergedGDB, mergedLayer)):\\n arcpy.Append_management(os.path.join(mergedGDB, layer.split('.')[0]), os.path.join\\n (mergedGDB, mergedLayer))\\n else:\\n arcpy.FeatureClassToFeatureClass_conversion \\\\\\n (os.path.join(mergedGDB, layer.split('.')[0]), mergedGDB, mergedLayer)\\n arcpy.Delete_management(os.path.join(mergedGDB, layer.split('.')[0]))\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5923ee50ae3902302a4710ef87754b0\",\n \"score\": \"0.5352095\",\n \"text\": \"def readJsonF(fileName):\\n fileHandle = open(fileName,'r')\\n try:\\n data = json.load(fileHandle)\\n return data\\n except IOError:\\n print(\\\"IO error\\\")\\n finally:\\n fileHandle.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8d561241fe0151e73afd4561880ef3\",\n \"score\": \"0.53411275\",\n \"text\": \"def geojson_to_polygons(js_):\\n\\n polys = []\\n for i, feat in enumerate(js_['features']):\\n o = {\\n \\\"coordinates\\\": feat['geometry']['coordinates'],\\n \\\"type\\\": feat['geometry']['type']\\n }\\n s = json.dumps(o)\\n\\n # convert to geojson.geometry.Polygon\\n g1 = geojson.loads(s)\\n\\n # covert to shapely.geometry.polygon.Polygon\\n g2 = shape(g1)\\n\\n polys.append(g2)\\n\\n return polys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"918fa6d4fc453bfa1076ce29628baebc\",\n \"score\": \"0.5333225\",\n \"text\": \"def get_geostore(geostore_id, format='esri'):\\n\\n config = {\\n 'uri': '/geostore/{}?format={}'.format(geostore_id, format),\\n 'method': 'GET',\\n }\\n\\n return request_to_microservice(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42aa4c924dcda5ad993aec67233eb6b2\",\n \"score\": \"0.53298545\",\n \"text\": \"def arcgis2geojson(arcgis: Dict[str, Any], id_attr: Optional[str] = None) -> Dict[str, Any]:\\n\\n def convert(arcgis: Dict[str, Any], id_attr: Optional[str] = None) -> Dict[str, Any]:\\n \\\"\\\"\\\"Convert an ArcGIS JSON object to a GeoJSON object.\\\"\\\"\\\"\\n geojson: Dict[str, Any] = {}\\n\\n if \\\"features\\\" in arcgis and arcgis[\\\"features\\\"]:\\n geojson[\\\"type\\\"] = \\\"FeatureCollection\\\"\\n geojson[\\\"features\\\"] = [convert(feature, id_attr) for feature in arcgis[\\\"features\\\"]]\\n\\n if (\\n \\\"x\\\" in arcgis\\n and isinstance(arcgis[\\\"x\\\"], numbers.Number)\\n and \\\"y\\\" in arcgis\\n and isinstance(arcgis[\\\"y\\\"], numbers.Number)\\n ):\\n geojson[\\\"type\\\"] = \\\"Point\\\"\\n geojson[\\\"coordinates\\\"] = [arcgis[\\\"x\\\"], arcgis[\\\"y\\\"]]\\n if \\\"z\\\" in arcgis and isinstance(arcgis[\\\"z\\\"], numbers.Number):\\n geojson[\\\"coordinates\\\"].append(arcgis[\\\"z\\\"])\\n\\n if \\\"points\\\" in arcgis:\\n geojson[\\\"type\\\"] = \\\"MultiPoint\\\"\\n geojson[\\\"coordinates\\\"] = arcgis[\\\"points\\\"]\\n\\n if \\\"paths\\\" in arcgis:\\n if len(arcgis[\\\"paths\\\"]) == 1:\\n geojson[\\\"type\\\"] = \\\"LineString\\\"\\n geojson[\\\"coordinates\\\"] = arcgis[\\\"paths\\\"][0]\\n else:\\n geojson[\\\"type\\\"] = \\\"MultiLineString\\\"\\n geojson[\\\"coordinates\\\"] = arcgis[\\\"paths\\\"]\\n\\n if \\\"rings\\\" in arcgis:\\n geojson = rings2geojson(arcgis[\\\"rings\\\"])\\n\\n if (\\n \\\"xmin\\\" in arcgis\\n and isinstance(arcgis[\\\"xmin\\\"], numbers.Number)\\n and \\\"ymin\\\" in arcgis\\n and isinstance(arcgis[\\\"ymin\\\"], numbers.Number)\\n and \\\"xmax\\\" in arcgis\\n and isinstance(arcgis[\\\"xmax\\\"], numbers.Number)\\n and \\\"ymax\\\" in arcgis\\n and isinstance(arcgis[\\\"ymax\\\"], numbers.Number)\\n ):\\n geojson[\\\"type\\\"] = \\\"Polygon\\\"\\n geojson[\\\"coordinates\\\"] = [\\n [\\n [arcgis[\\\"xmax\\\"], arcgis[\\\"ymax\\\"]],\\n [arcgis[\\\"xmin\\\"], arcgis[\\\"ymax\\\"]],\\n [arcgis[\\\"xmin\\\"], arcgis[\\\"ymin\\\"]],\\n [arcgis[\\\"xmax\\\"], arcgis[\\\"ymin\\\"]],\\n [arcgis[\\\"xmax\\\"], arcgis[\\\"ymax\\\"]],\\n ]\\n ]\\n\\n if \\\"geometry\\\" in arcgis or \\\"attributes\\\" in arcgis:\\n geojson[\\\"type\\\"] = \\\"Feature\\\"\\n if \\\"geometry\\\" in arcgis:\\n geojson[\\\"geometry\\\"] = convert(arcgis[\\\"geometry\\\"])\\n else:\\n geojson[\\\"geometry\\\"] = None\\n\\n if \\\"attributes\\\" in arcgis:\\n geojson[\\\"properties\\\"] = arcgis[\\\"attributes\\\"]\\n try:\\n attributes = arcgis[\\\"attributes\\\"]\\n keys = [id_attr, \\\"OBJECTID\\\", \\\"FID\\\"] if id_attr else [\\\"OBJECTID\\\", \\\"FID\\\"]\\n for key in keys:\\n if key in attributes and (\\n isinstance(attributes[key], (numbers.Number, str))\\n ):\\n geojson[\\\"id\\\"] = attributes[key]\\n break\\n except KeyError:\\n warn(\\\"No valid id attribute found.\\\")\\n else:\\n geojson[\\\"properties\\\"] = None\\n\\n if \\\"geometry\\\" in geojson and not geojson[\\\"geometry\\\"]:\\n geojson[\\\"geometry\\\"] = None\\n\\n return geojson\\n\\n def rings2geojson(rings: List[List[List[float]]]) -> Dict[str, Any]:\\n \\\"\\\"\\\"Check for holes in the ring and fill them.\\\"\\\"\\\"\\n outer_rings = []\\n holes = []\\n x = None # iterable\\n outer_ring = None # current outer ring being evaluated\\n hole = None # current hole being evaluated\\n\\n for ring in rings:\\n if not np.all(np.isclose(ring[0], ring[-1])):\\n ring.append(ring[0])\\n\\n if len(ring) < 4:\\n continue\\n\\n total = sum(\\n (pt2[0] - pt1[0]) * (pt2[1] + pt1[1]) for pt1, pt2 in zip(ring[:-1], ring[1:])\\n )\\n # Clock-wise check\\n if total >= 0:\\n outer_rings.append(\\n [ring[::-1]]\\n ) # wind outer rings counterclockwise for RFC 7946 compliance\\n else:\\n holes.append(ring[::-1]) # wind inner rings clockwise for RFC 7946 compliance\\n\\n uncontained_holes = []\\n\\n # while there are holes left...\\n while holes:\\n # pop a hole off out stack\\n hole = holes.pop()\\n\\n # loop over all outer rings and see if they contain our hole.\\n contained = False\\n x = len(outer_rings) - 1\\n while x >= 0:\\n outer_ring = outer_rings[x][0]\\n l1, l2 = LineString(outer_ring), LineString(hole)\\n p2 = Point(hole[0])\\n intersects = l1.intersects(l2)\\n contains = l1.contains(p2)\\n if not intersects and contains:\\n # the hole is contained push it into our polygon\\n outer_rings[x].append(hole)\\n contained = True\\n break\\n x = x - 1\\n\\n # ring is not contained in any outer ring\\n # sometimes this happens https://github.com/Esri/esri-leaflet/issues/320\\n if not contained:\\n uncontained_holes.append(hole)\\n\\n # if we couldn't match any holes using contains we can try intersects...\\n while uncontained_holes:\\n # pop a hole off out stack\\n hole = uncontained_holes.pop()\\n\\n # loop over all outer rings and see if any intersect our hole.\\n intersects = False\\n x = len(outer_rings) - 1\\n while x >= 0:\\n outer_ring = outer_rings[x][0]\\n l1, l2 = LineString(outer_ring), LineString(hole)\\n intersects = l1.intersects(l2)\\n if intersects:\\n # the hole is contained push it into our polygon\\n outer_rings[x].append(hole)\\n intersects = True\\n break\\n x = x - 1\\n\\n if not intersects:\\n outer_rings.append([hole[::-1]])\\n\\n if len(outer_rings) == 1:\\n return {\\\"type\\\": \\\"Polygon\\\", \\\"coordinates\\\": outer_rings[0]}\\n\\n return {\\\"type\\\": \\\"MultiPolygon\\\", \\\"coordinates\\\": outer_rings}\\n\\n if isinstance(arcgis, str): # type: ignore\\n return json.dumps(convert(json.loads(arcgis), id_attr)) # type: ignore\\n\\n return convert(arcgis, id_attr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3e4d01e050fc6cb2b1c31954eeed348\",\n \"score\": \"0.53291166\",\n \"text\": \"def _geojson_coordinates(geojsonurl):\\n\\n # load geojson file\\n data = load(open(geojsonurl))\\n\\n # create the list of coordinates\\n if data['features'][0]['geometry']['type'] == \\\"MultiPolygon\\\":\\n list_coordinates = np.array(\\n [component[0] for component in data['features'][0]['geometry']['coordinates']])\\n\\n else:\\n list_coordinates = np.array(\\n data['features'][0]['geometry']['coordinates'])\\n\\n return list_coordinates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36071f5a7e8328c8023aeb0c9958b012\",\n \"score\": \"0.53189456\",\n \"text\": \"def convert_flowline_to_geojson(iFlag_type_in, sFilename_geojson_in, sFilename_geojson_out):\\n if os.path.isfile(sFilename_geojson_in):\\n pass\\n else:\\n print('This shapefile does not exist: ', sFilename_geojson_in )\\n iReturn_code = 0\\n return iReturn_code\\n\\n if iFlag_type_in ==0:\\n pass\\n else:\\n if iFlag_type_in == 1:\\n \\n aFlowline_basin, pSpatial_reference = read_flowline_geojson( sFilename_geojson_in ) \\n #convert it\\n\\n iFlag_projected = 0\\n #pSpatial_reference_gcs = osr.SpatialReference()\\n #pSpatial_reference_gcs.ImportFromEPSG(4326)\\n #pSpatial_reference_gcs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)\\n export_flowline_to_geojson(aFlowline_basin, sFilename_geojson_out)\\n else:\\n if iFlag_type_in == 2:\\n #polygon\\n pass\\n\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42ab7562c537f8d812a4bab49b3c8e1d\",\n \"score\": \"0.53086746\",\n \"text\": \"def test_geojson_response(self):\\n response = self.post_boundary('bayarea_macosx.zip')\\n uuid = response.data['uuid']\\n url = '{}geojson/'.format(reverse('boundary-detail', args=[uuid]))\\n\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(response.data['type'], 'FeatureCollection')\\n self.assertEqual(len(response.data['features']), 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a68239b47422a3973e4bcab556f2b01\",\n \"score\": \"0.53048486\",\n \"text\": \"def get_multipolygon_geojson_coordinates(geojson):\\n # The geojson data for each place varies in whether it follows the\\n # righthand rule or not. We want to ensure geojsons for all places\\n # does follow the righthand rule.\\n right_handed_geojson = rewind(geojson)\\n geojson_type = right_handed_geojson['type']\\n geojson_coords = right_handed_geojson['coordinates']\\n if geojson_type == POLYGON_GEOJSON_TYPE:\\n geojson_coords[0].reverse()\\n return [geojson_coords]\\n elif geojson_type == MULTIPOLYGON_GEOJSON_TYPE:\\n for polygon in geojson_coords:\\n polygon[0].reverse()\\n return geojson_coords\\n else:\\n assert False, f\\\"Type {geojson_type} unknown!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972f7994c5f40cc40d921f65a126bf00\",\n \"score\": \"0.5293927\",\n \"text\": \"def getPOIs():\\n\\n points_of_interest = session.query(POI).all()\\n\\n coordinates = []\\n\\n for poi in points_of_interest:\\n\\n title = poi.title\\n lng = poi.lng\\n lat = poi.lat\\n info = poi.info\\n\\n # Functions from the geojson library create geoson objects with the\\n # details specified\\n my_point = Feature(geometry=Point((lng, lat)), properties={\\n \\\"title\\\": title, \\\"longitude\\\": lng, \\\"lat\\\": lat, \\\"info\\\": info, \\\"marker-color\\\": \\\"#D95929\\\"})\\n coordinates.append(my_point)\\n\\n # We return all this geojson as a feature collection\\n new_coords = FeatureCollection(coordinates)\\n return new_coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af743ecdac00dc7fc4d76659d0ec787f\",\n \"score\": \"0.5284874\",\n \"text\": \"def fetch_missing_geographical_data():\\n \\n URL = \\\"http://nominatim.openstreetmap.org/reverse\\\"\\n with open('missing_records.json', 'r') as infile:\\n records = json.load(infile)\\n addresses = []\\n faults = []\\n for record in records:\\n rec_id = record[0]\\n lng = record[1]\\n lat = record[2]\\n print(lng, lat)\\n payload = {\\\"format\\\":\\\"json\\\", \\\"lon\\\":lng, \\\"lat\\\":lat, \\\"email\\\":\\\"syrelyre@gmail.com\\\", \\\"accept-language\\\":\\\"en-us\\\", \\\"User-Agent\\\":\\\"Data Science App\\\"}\\n while True:\\n try:\\n response = requests.get(URL, params=payload).text\\n break\\n except requests.exceptions.ConnectionError as e:\\n print(\\\"Exception: {}\\\".format(str(e)))\\n time.sleep(1)\\n \\n try:\\n response = json.loads(response)\\n except (Exception, ValueError) as e:\\n print(\\\"ValueError: {}\\\".format(str(e)))\\n faults.append((rec_id, lng, lat))\\n \\n if \\\"address\\\" in response:\\n address = response[\\\"address\\\"]\\n address[\\\"lat\\\"] = lat\\n address[\\\"lng\\\"] = lng\\n addresses.append(address)\\n \\n time.sleep(1)\\n\\n with open('missing_data.json', 'w') as outfile:\\n json.dump(addresses, outfile)\\n\\n if faults:\\n with open('faults.pickle', 'wb') as outfile:\\n pickle.dump(faults, outfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4340e6a134f90106a0229bc73e45b22e\",\n \"score\": \"0.5283664\",\n \"text\": \"def download_geojson(self, scenario_id, layer_name):\\n\\n r = self._get(self.api_url + \\\"/scenario/\\\" + str(scenario_id) + \\\"/layer/\\\" + str(layer_name))\\n if not r.status_code == 200:\\n raise Exception(f\\\"Could not download result {r.status_code}, {r.json()}\\\")\\n\\n return r.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"596b9bc38c9d9f7b8ab1934d277f8c86\",\n \"score\": \"0.5278918\",\n \"text\": \"def load_feature_collection(feature_dict):\\n\\n feature_list = []\\n \\n for feat in feature_dict['features']:\\n geos = GEOSGeometry(json.dumps(feat['geometry']))\\n feature_list.append((geos, feat['properties']))\\n \\n return feature_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48c0ac08cdd2e9d4e0fbe0b9cc6ce8ac\",\n \"score\": \"0.52750343\",\n \"text\": \"def as_geojson(self):\\n point = geojson.Point((self.longitude, self.latitude))\\n feature = geojson.Feature(geometry=point,\\n properties={\\n 'filename': self._path.split('/')[-1],\\n 'path': self._path,\\n 'timestamp': self.timestamp\\n })\\n return feature\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1f0a117c0eeb6fcba3427f2e519b3c3\",\n \"score\": \"0.5273252\",\n \"text\": \"def get_data_and_extract_best_coordinates(url):\\n\\n resp = requests.get(url)\\n data = json.loads(resp.text)\\n\\n features = data['features']\\n best_feature = features[0] # this will break if features is empty\\n coords = best_feature['geometry']['coordinates']\\n lng = coords[0]\\n lat = coords[1]\\n\\n return {'latitude': lat, 'longitude': lng}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b80c95edceb310094229c1f3aca2b837\",\n \"score\": \"0.5250691\",\n \"text\": \"def geojson_builder(down_left,up_right,travels=0):\\n p1 = [str(down_left[0]),str(down_left[1])]\\n p2 = [str(up_right[0]),str(down_left[1])]\\n p3 = [str(up_right[0]),str(up_right[1])]\\n p4 = [str(down_left[0]),str(up_right[1])]\\n \\n geojson = {\\\"type\\\": \\\"FeatureCollection\\\",\\n \\\"features\\\": [\\n {\\\"type\\\": \\\"Feature\\\",\\n \\\"properties\\\":{\\n \\\"arrivals_outs\\\": travels\\n },\\n \\\"geometry\\\": {\\n \\\"type\\\": \\\"Polygon\\\",\\n \\\"coordinates\\\": [[p1,p2,p3,p4,p1]] }}]}\\n\\n return geojson\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f7c7fb8ae098ac3916b0f5631d0bbc5\",\n \"score\": \"0.5248551\",\n \"text\": \"def main():\\r\\n url = \\\"https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/1.0_month.geojson\\\"\\r\\n json_obj = get_json(url)\\r\\n entire_report_dictionary = json_loads_to_dictionary(json_obj)\\r\\n number_of_records, id_lon_lat = extract_records(entire_report_dictionary)[0], extract_records(entire_report_dictionary)[1]\\r\\n \\r\\n print(\\\"writing to file\\\\n\\\")\\r\\n with open('id_coordinates.csv', 'wt') as textiowrapper:\\r\\n textiowrapper.writelines('name,lon,lat\\\\n')\\r\\n for i in range(number_of_records):\\r\\n x = 0\\r\\n id_num = str(id_lon_lat[i][x])\\r\\n lon = str(id_lon_lat[i][x+1])\\r\\n lat = str(id_lon_lat[i][x+2])\\r\\n s = id_num + ',' + lon + ',' + lat\\r\\n textiowrapper.writelines(s + '\\\\n')\\r\\n del x\\r\\n textiowrapper.close()\\r\\n print(\\\"done writing to file\\\\n\\\")\\r\\n\\r\\n print(\\\"mapping the data\\\\n\\\")\\r\\n data = read_csv('id_coordinates.csv')\\r\\n geoplotlib.dot(data)\\r\\n geoplotlib.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0fdb657db999ea7fbe55a6219281629\",\n \"score\": \"0.52259755\",\n \"text\": \"def convert_geometries_to_geojson(data: networkx.Graph) -> None:\\n for node in data[\\\"nodes\\\"]:\\n for key in node:\\n # having a ``__geo_interface__``` property identifies the object\\n # as being a ``shapely`` geometry object\\n if hasattr(node[key], \\\"__geo_interface__\\\"):\\n # The ``__geo_interface__`` property is essentially GeoJSON.\\n # This is what :func:`geopandas.GeoSeries.to_json` uses under\\n # the hood.\\n node[key] = node[key].__geo_interface__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01d4ce826c7881b1192380e74345f9c0\",\n \"score\": \"0.52009773\",\n \"text\": \"def export2geojson(query_result):\\n\\n for row in db_rows:\\n name = row[0]\\n geom = row[1]\\n geoj_geom = loads(geom)\\n myfeat = Feature(geometry=geoj_geom, \\n properties={'name': name})\\n new_geom_collection.append(myfeat)\\n\\n # use the geojson module to create the final Feature\\n # Collection of features created from for loop above\\n my_geojson = FeatureCollection(new_geom_collection)\\n\\n # define the output folder and GeoJSon file name\\n output_geojson_buf = \\\"../geodata/out_schools_in_100m.geojson\\\"\\n\\n # save geojson to a file in our geodata folder\\n def write_geojson():\\n fo = open(output_geojson_buf, \\\"w\\\")\\n fo.write(json.dumps(my_geojson))\\n fo.close()\\n\\n # run the write function to actually create the GeoJSON file\\n write_geojson()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e6caafa6adc65b37050115b71b5b2b3\",\n \"score\": \"0.51936054\",\n \"text\": \"def get_geometry_description(geometry):\\n orm_geom = geometry\\n geojson = json.loads(orm_geom.json)\\n bbox = orm_geom.extent\\n\\n cache_entry = {\\n \\\"bbox\\\": bbox,\\n \\\"bbox_area\\\": get_area_bbox(bbox),\\n \\\"geometry\\\": geojson,\\n \\\"area\\\": get_area_geojson(geojson),\\n }\\n return cache_entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b63f56376c6c6932b74c04e19a2862b9\",\n \"score\": \"0.5191625\",\n \"text\": \"def queen_geojson(gjobj):\\n features = list(find_features(gjobj))[0]\\n #features = list(gjobj['geojson']['features'])\\n polys = []\\n ids = []\\n i = 0\\n for feature in features:\\n polys.append(ps.cg.asShape(feature['geometry']))\\n ids.append(i)\\n i += 1\\n polygons = PolygonCollection(dict(zip(ids,polys)))\\n neighbors = ps.weights.Contiguity.ContiguityWeightsPolygons(polygons).w\\n w = ps.W(neighbors) \\n return w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13c6a06805d293d66865fb9d51d000b7\",\n \"score\": \"0.5188155\",\n \"text\": \"def geo2topo(files, out, quantization, simplification):\\n\\n\\tgeojson = dict()\\n\\n\\tfor file in files:\\n\\t\\twith open(file) as fp:\\n\\t\\t\\tgeojson[os.path.basename(file)] = json.load(fp)\\n\\n\\ttopojson = topology(geojson, quantization, simplification)\\n\\n\\tif out is None or out == '-':\\n\\t\\tjson.dump(topojson, sys.stdout)\\n\\telse:\\n\\t\\twith open(out, 'w') as fp:\\n\\t\\t\\tjson.dump(topojson, fp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad96a450b49dbc97b94a1095c0a0cc2d\",\n \"score\": \"0.51817423\",\n \"text\": \"def geometric_run_json(in_json_dict):\\n\\n # Default logger configuration (prevents extra newline from being printed)\\n logIni = pkg_resources.resource_filename(geometric.optimize.__name__, 'config/logJson.ini')\\n import logging.config\\n logging.config.fileConfig(logIni,disable_existing_loggers=False)\\n\\n # Set a temporary logger to capture output\\n log_stream = RawStreamHandler(stream=StringIO())\\n logger.addHandler(log_stream)\\n\\n input_opts = parse_input_json_dict(in_json_dict)\\n hess = input_opts.pop('hessian', None)\\n M, engine = geometric.optimize.get_molecule_engine(**input_opts)\\n\\n # Get initial coordinates in bohr\\n coords = M.xyzs[0].flatten() * geometric.nifty.ang2bohr\\n\\n # Read in the constraints\\n constraints_dict = input_opts.get('constraints', {})\\n if \\\"scan\\\" in constraints_dict:\\n raise KeyError(\\\"The constraint 'scan' keyword is not yet supported by the JSON interface\\\")\\n\\n constraints_string = make_constraints_string(constraints_dict)\\n Cons, CVals = None, None\\n if constraints_string:\\n Cons, CVals = geometric.prepare.parse_constraints(M, constraints_string)\\n\\n # set up the internal coordinate system\\n coordsys = input_opts.get('coordsys', 'tric')\\n CoordSysDict = {\\n 'cart': (geometric.internal.CartesianCoordinates, False, False),\\n 'prim': (geometric.internal.PrimitiveInternalCoordinates, True, False),\\n 'dlc': (geometric.internal.DelocalizedInternalCoordinates, True, False),\\n 'hdlc': (geometric.internal.DelocalizedInternalCoordinates, False, True),\\n 'tric': (geometric.internal.DelocalizedInternalCoordinates, False, False)\\n }\\n\\n CoordClass, connect, addcart = CoordSysDict[coordsys.lower()]\\n IC = CoordClass(\\n M,\\n build=True,\\n connect=connect,\\n addcart=addcart,\\n constraints=Cons,\\n cvals=CVals[0] if CVals is not None else None)\\n\\n # Print out information about the coordinate system\\n if isinstance(IC, geometric.internal.CartesianCoordinates):\\n logger.info(\\\"%i Cartesian coordinates being used\\\\n\\\" % (3 * M.na))\\n else:\\n logger.info(\\\"%i internal coordinates being used (instead of %i Cartesians)\\\\n\\\" % (len(IC.Internals), 3 * M.na))\\n logger.info(IC)\\n logger.info(\\\"\\\\n\\\")\\n\\n params = geometric.optimize.OptParams(**input_opts)\\n dirname = tempfile.mkdtemp()\\n\\n if hess is not None:\\n n = len(coords)\\n params.hess_data = np.array(hess).reshape(n, n)\\n\\n try:\\n # Run the optimization\\n if Cons is None:\\n # Run a standard geometry optimization\\n params.xyzout = \\\"qce_optim.xyz\\\"\\n geometric.optimize.Optimize(coords, M, IC, engine, dirname, params)\\n else:\\n # Run a constrained geometry optimization\\n if isinstance(IC, (geometric.internal.CartesianCoordinates,\\n geometric.internal.PrimitiveInternalCoordinates)):\\n raise RuntimeError(\\\"Constraints only work with delocalized internal coordinates\\\")\\n for ic, CVal in enumerate(CVals):\\n if len(CVals) > 1:\\n logger.info(\\\"---=== Scan %i/%i : Constrained Optimization ===---\\\\n\\\" % (ic + 1, len(CVals)))\\n IC = CoordClass(M, build=True, connect=connect, addcart=addcart, constraints=Cons, cvals=CVal)\\n IC.printConstraints(coords, thre=-1)\\n geometric.optimize.Optimize(coords, M, IC, engine, dirname, params, print_info = (ic==0))\\n\\n out_json_dict = get_output_json_dict(in_json_dict, engine.schema_traj)\\n out_json_dict[\\\"success\\\"] = True\\n\\n except Exception as e:\\n out_json_dict = get_output_json_dict(in_json_dict, engine.schema_traj)\\n out_json_dict[\\\"success\\\"] = False\\n out_json_dict[\\\"error\\\"] = {\\n \\\"error_type\\\": \\\"unknown\\\",\\n \\\"error_message\\\": \\\"geomeTRIC run_json error:\\\\n\\\" + traceback.format_exc()\\n }\\n\\n # Grab logging and pop logger\\n out_json_dict[\\\"stdout\\\"] = log_stream.stream.getvalue()\\n log_stream.close()\\n logger.handlers.remove(log_stream)\\n\\n return out_json_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edd717009f73bef47710bbbb75076ff8\",\n \"score\": \"0.51729196\",\n \"text\": \"def test_geospatial_positive_query_with_geodata(self):\\n records = []\\n query = self.as_connection.query(\\\"test\\\", \\\"demo\\\")\\n\\n geo_object2 = aerospike.geodata(\\n {\\n \\\"type\\\": \\\"Polygon\\\",\\n \\\"coordinates\\\": [\\n [\\n [-122.500000, 37.000000],\\n [-121.000000, 37.000000],\\n [-121.000000, 38.080000],\\n [-122.500000, 38.080000],\\n [-122.500000, 37.000000],\\n ]\\n ],\\n }\\n )\\n\\n query.where(p.geo_within_geojson_region(\\\"loc\\\", geo_object2.dumps()))\\n\\n def callback(input_tuple):\\n _, _, record = input_tuple\\n records.append(record)\\n\\n query.foreach(callback)\\n\\n assert len(records) == 3\\n expected = [\\n {\\\"coordinates\\\": [-122.0, 37.5], \\\"type\\\": \\\"Point\\\"},\\n {\\\"coordinates\\\": [-121.8, 37.7], \\\"type\\\": \\\"Point\\\"},\\n {\\\"coordinates\\\": [-121.6, 37.9], \\\"type\\\": \\\"Point\\\"},\\n ]\\n for r in records:\\n assert r[\\\"loc\\\"].unwrap() in expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6facafd1c79e7ade4826bdfd23fbee69\",\n \"score\": \"0.51716\",\n \"text\": \"def get_geo_json_for_roads(self):\\n return self.roads.get_geo_json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e245aed8062590fb3d6409e12a5ae62\",\n \"score\": \"0.5171086\",\n \"text\": \"def get_geolocation_data(address):\\n url = 'http://maps.googleapis.com/maps/api/geocode/json'\\n fields = {'address': address.encode('utf-8'), 'sensor': 'false', 'region': 'pe'}\\n resp = http.request('GET', url, fields=fields)\\n return json.loads(resp.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c094f170db7ab375e6eec6c4af82cd\",\n \"score\": \"0.5156938\",\n \"text\": \"def count_geometries(geojson_paths):\\n types = {}\\n for geojson_path in geojson_paths:\\n with open(geojson_path, \\\"r\\\") as geojson_file:\\n geojson = json.load(geojson_file)\\n for feature in geojson[\\\"features\\\"]:\\n type = sg.shape(feature[\\\"geometry\\\"]).type\\n if type not in types:\\n types[type] = 1\\n else:\\n types[type] += 1\\n return types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b4e6e26c2e20a30a0c151249582a9e6\",\n \"score\": \"0.51526296\",\n \"text\": \"def get_region_data(buildings_plz_location, geofabrik_location, region_ags_dict, output_path):\\n # Extract plz list\\n plz_list = os.listdir(buildings_plz_location)\\n plz_list = [x.split('.')[0].split('_')[1] for x in plz_list if 'buildings' in x]\\n \\n # full path pbf\\n region_list_path = [os.path.join(path, name) for path, subdirs, files in os.walk(geofabrik_location) for name in files]\\n # region pbf name\\n pbf_list = [name for path, subdirs, files in os.walk(geofabrik_location) for name in files]\\n \\n # Start loop for all region\\n i = 0\\n while i < len(pbf_list):\\n # Get target region\\n target_region_path = region_list_path[i]\\n target_region = pbf_list[i]\\n # Get AGS code belong to the target region\\n target_ags_list = region_ags_dict.get(target_region)\\n # Length of AGS (2 or 3)\\n ags_len=len(target_ags_list[0])\\n \\n logging.info(f'{i}/{len(pbf_list)} Reading info of region {target_region}')\\n \\n # Initialize the OSM parser object\\n osm = OSM(target_region_path)\\n # Get buildings in the region\\n buildings = osm.get_buildings()\\n \\n logging.info(f'Total of {len(buildings)} in region {target_region}')\\n \\n # Extract info of all PLZ belong to that region\\n region_plz_list = plz_place[(dp.left(plz_place.ags.str, ags_len).isin(target_ags_list))].plz.reset_index(drop=True)\\n \\n # Iterate through list of PLZ to enhance dataset\\n enhance_area(region_plz_list, buildings, output_path)\\n i = i + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976bad4c3a3ce87e2fab9b700a4d3d5b\",\n \"score\": \"0.5141256\",\n \"text\": \"def test_push_geojson_to_geonode(self):\\n async_result = push_to_geonode.delay(geojson_layer_uri)\\n result = async_result.get()\\n self.assertEqual(\\n result['status'], GEONODE_UPLOAD_SUCCESS, result['message'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45aa3a9112cdce8fc7395449f992e2eb\",\n \"score\": \"0.5136351\",\n \"text\": \"def get_spatial_reference_from_json(geojson: str):\\n yaml_geom = yaml.safe_load(geojson)\\n current_geom = ogr.CreateGeometryFromJson(json.dumps(yaml_geom))\\n\\n if current_geom:\\n return current_geom.GetSpatialReference()\\n\\n raise RuntimeError(\\\"Specified JSON does not have a valid sptial reference\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260b5a28500a33db4c5a262672be1512\",\n \"score\": \"0.5135094\",\n \"text\": \"def convert2geojson(l, outfile=\\\"GeoJSON-out.json\\\", extra_properties=(),\\n file_or_print=\\\"file\\\"):\\n property_index=0\\n #make sure the extra properties list and the shape list are the same size\\n if extra_properties:\\n if len(l)!=len(extra_properties):\\n print \\\"FATAL ERROR: Extra properties list and shape list must be the same length!\\\"\\n sys.exit(1)\\n \\n \\\"\\\"\\\"create a feature collection boilerplate\\\"\\\"\\\"\\n feature_collection_boiler={\\\"type\\\": \\\"FeatureCollection\\\", \\\"features\\\":[]}\\n \\n featurelist=[]\\n #iterate over our list of shapes\\n for i in l:\\n #create a new feature from our boilerplate\\n f={}\\n #print f\\n #build the geometry\\n f[\\\"geometry\\\"]={\\\"type\\\":i[\\\"geometry_type\\\"], \\\"coordinates\\\": i[\\\"geometry_coordinates\\\"]}\\n # erase the geometry from out shape dict and stuff everything else\\n # in the properties\\n del i[\\\"geometry_type\\\"]\\n del i[\\\"geometry_coordinates\\\"]\\n f[\\\"properties\\\"]=i\\n\\n #Add extra properties if they exist.\\n if extra_properties:\\n f[\\\"properties\\\"].update(extra_properties[property_index])\\n \\n featurelist.append(f)\\n\\n property_index+=1\\n \\n feature_collection_boiler['features']=featurelist\\n\\n if file_or_print==\\\"file\\\":\\n FILE = open(outfile,\\\"w\\\")\\n # Write all the lines at once:\\n FILE.writelines(json.dumps(feature_collection_boiler, indent=4))\\n FILE.close()\\n else:\\n print json.dumps(feature_collection_boiler, indent=4)\\n return feature_collection_boiler\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1f45bf8d2af17d0dbbee4e05bbd91a7\",\n \"score\": \"0.5134756\",\n \"text\": \"def json2geodf(\\n content: Union[List[Dict[str, Any]], Dict[str, Any]],\\n in_crs: str = DEF_CRS,\\n crs: str = DEF_CRS,\\n) -> gpd.GeoDataFrame:\\n if not isinstance(content, (list, dict)):\\n raise InvalidInputType(\\\"content\\\", \\\"list or list of dict ((geo)json)\\\")\\n\\n content = content if isinstance(content, list) else [content]\\n try:\\n geodf = gpd.GeoDataFrame.from_features(content[0], crs=in_crs)\\n except AttributeError:\\n geodf = gpd.GeoDataFrame.from_features(content[0])\\n except TypeError:\\n content = [arcgis2geojson(c) for c in content]\\n try:\\n geodf = gpd.GeoDataFrame.from_features(content[0], crs=in_crs)\\n except AttributeError:\\n geodf = gpd.GeoDataFrame.from_features(content[0])\\n\\n if len(content) > 1:\\n for c in content[1:]:\\n try:\\n geodf = geodf.append(gpd.GeoDataFrame.from_features(c, crs=in_crs))\\n except AttributeError:\\n geodf = gpd.GeoDataFrame.from_features(c)\\n\\n if in_crs != crs and \\\"geometry\\\" in geodf:\\n geodf = geodf.to_crs(crs)\\n\\n return geodf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40f4968bd59059ddbe97cb5b33b88af1\",\n \"score\": \"0.5130631\",\n \"text\": \"def read_json(filename_json):\\n with open(filename_json) as ff:\\n data = json.load(ff)\\n ff.close()\\n data = data[\\\"RESULTS\\\"][\\\"model\\\"]\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a0ca7276ec7c92ba0c83f5898e81962\",\n \"score\": \"0.5130019\",\n \"text\": \"def do_GEOHASHTOGEOJSON(self, geoh):\\n geoh, with_neighbors = self._match_option(\\\"NEIGHBORS\\\", geoh)\\n bbox = geohash.bbox(geoh)\\n try:\\n with_neighbors = int(with_neighbors)\\n except TypeError:\\n with_neighbors = 0\\n\\n def expand(bbox, geoh, depth):\\n neighbors = geohash.neighbors(geoh)\\n for neighbor in neighbors:\\n other = geohash.bbox(neighbor)\\n if with_neighbors > depth:\\n expand(bbox, neighbor, depth + 1)\\n else:\\n if other[\\\"n\\\"] > bbox[\\\"n\\\"]:\\n bbox[\\\"n\\\"] = other[\\\"n\\\"]\\n if other[\\\"s\\\"] < bbox[\\\"s\\\"]:\\n bbox[\\\"s\\\"] = other[\\\"s\\\"]\\n if other[\\\"e\\\"] > bbox[\\\"e\\\"]:\\n bbox[\\\"e\\\"] = other[\\\"e\\\"]\\n if other[\\\"w\\\"] < bbox[\\\"w\\\"]:\\n bbox[\\\"w\\\"] = other[\\\"w\\\"]\\n\\n if with_neighbors > 0:\\n expand(bbox, geoh, 0)\\n\\n geojson = {\\n \\\"type\\\": \\\"Polygon\\\",\\n \\\"coordinates\\\": [\\n [\\n [bbox[\\\"w\\\"], bbox[\\\"n\\\"]],\\n [bbox[\\\"e\\\"], bbox[\\\"n\\\"]],\\n [bbox[\\\"e\\\"], bbox[\\\"s\\\"]],\\n [bbox[\\\"w\\\"], bbox[\\\"s\\\"]],\\n [bbox[\\\"w\\\"], bbox[\\\"n\\\"]],\\n ]\\n ],\\n }\\n print(white(json.dumps(geojson)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ffd60ec66f58bc519dd37c67daa1945\",\n \"score\": \"0.51254004\",\n \"text\": \"def get_address_data_OSM(address):#using OSM Nominatim API\\r\\n \\r\\n URL = \\\"https://nominatim.openstreetmap.org/search/\\\"\\r\\n \\r\\n address = address.replace(\\\"Boul.\\\", \\\"Boulevard\\\")\\r\\n PARAMS = {'q': address + \\\", Montréal\\\",\\r\\n 'format': \\\"json\\\"}\\r\\n \\r\\n resp = requests.get(url = URL, params = PARAMS) \\r\\n resp = resp.text\\r\\n #print (resp)\\r\\n \\r\\n geo_data={}\\r\\n \\r\\n if resp == '[]':\\r\\n print (address +\\\" was not found\\\")\\r\\n geo_data[0] = address #address saved\\r\\n geo_data[1] = 45.432397 #\\\"Not Found\\\"\\r\\n geo_data[2] = -73.532947 #\\\"Not Found\\\"\\r\\n geo_data[3] = \\\"OSM lacks DA return\\\" #OSM doesn't return a dissemination area. Either add that later or don't\\r\\n \\r\\n return geo_data\\r\\n \\r\\n else:\\r\\n resp_json = json.loads(resp)\\r\\n print (address + \\\" @ \\\" +resp_json[0][\\\"lat\\\"] +\\\", \\\"+ resp_json[0][\\\"lon\\\"])\\r\\n \\r\\n geo_data[0] = address #address saved\\r\\n geo_data[1] = resp_json[0][\\\"lat\\\"]\\r\\n geo_data[2] = resp_json[0][\\\"lon\\\"]\\r\\n geo_data[3] = \\\"OSM lacks DA return\\\" #OSM doesn't return a dissemination area. Either add that later or don't\\r\\n \\r\\n return geo_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2456b7f5baf99ed3bcc29f62396046d\",\n \"score\": \"0.51214176\",\n \"text\": \"def graphml_to_geojson(graphml_input_filepath, geojson_output_filepath):\\n\\n def geojson_builder(node):\\n \\\"\\\"\\\"\\n Takes a node from the graph as input and returns the data in valid\\n GeojSON format.\\n \\\"\\\"\\\"\\n return {\\n 'type': 'Feature',\\n 'properties': {\\n 'name': node['name'],\\n 'population': node['pop']\\n },\\n 'geometry': {\\n 'type': 'Point',\\n 'coordinates': [node['lat'], node['lon']]\\n }\\n }\\n\\n G = nx.read_graphml(graphml_input_filepath)\\n\\n geojson_list = [geojson_builder(G.node[x]) for x in G.nodes_iter()]\\n\\n file_content = 'nodes = ' + json.dumps(geojson_list)\\n\\n with open(geojson_output_filepath, 'w') as f:\\n f.write(file_content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed3305f38fde5db6e7b461864706485\",\n \"score\": \"0.5118621\",\n \"text\": \"def get_coordinates_info(lon, lat):\\n\\n path = r\\\"https://geocode.arcgis.com/arcgis/rest/services/World/GeocodeServer/reverseGeocode?\\\" \\\\\\n \\\"location={}%2C{}&langCode=en&outSR=&forStorage=false&f=pjson\\\".format(lon, lat)\\n\\n x = requests.get(path)\\n if x.ok:\\n return json.loads(x.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5672e15da1d1293c576fffa0a59d6d6\",\n \"score\": \"0.51180065\",\n \"text\": \"def get_borders():\\n with open('./departements.geojson') as geojson:\\n return json.load(geojson)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52abddcbc35a0ab78592537dc00d197a\",\n \"score\": \"0.51171476\",\n \"text\": \"def readZip(self, add, name_dbf, name_prj, name_shp, name_shx):\\n # setting:\\n proxies = {\\\"http\\\":\\\"http://username:password@proxy_ip:proxy_port\\\"} \\n\\n session = requests.Session()\\n retry = Retry(connect=3, backoff_factor=0.5)\\n adapter = HTTPAdapter(max_retries=retry)\\n session.mount('http://', adapter)\\n session.mount('https://', adapter)\\n\\n county_file_url = 'https://www.nhc.noaa.gov/gis/' + add\\n zipfile = ZipFile(io.BytesIO(session.get(county_file_url).content))\\n namelist = []\\n sleep(5)\\n\\n for ele in sorted(zipfile.namelist()):\\n if ('position') in ele:\\n namelist.append(ele)\\n\\n if ('pts') in ele:\\n namelist.append(ele)\\n\\n end_dic = [name_dbf, name_prj, name_shp, name_shx]\\n filenames = [y for y in sorted(namelist) for ending in end_dic if y.endswith(ending)] \\n # update: using set to remove duplicated files\\n selected_filenames = sorted(filenames)[0:4]\\n dbf, prj, shp, shx = [io.BytesIO(zipfile.read(filename)) for filename in selected_filenames]\\n\\n\\n r = shapefile.Reader(shp=shp, shx=shx, dbf=dbf)\\n attributes, geometry = [], []\\n field_names = [field[0] for field in r.fields[1:]]\\n\\n for row in r.shapeRecords(): \\n geometry.append(shape(row.shape.__geo_interface__)) \\n attributes.append(dict(zip(field_names, row.record)))\\n\\n gdf = gpd.GeoDataFrame(data = attributes, geometry = geometry)\\n\\n return gdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c75e481a91571041a793922b2ccc462\",\n \"score\": \"0.5116793\",\n \"text\": \"def test_coordinates_with_altitude(empty_space):\\n fp_geojson = (\\n Path(__file__).parents[2] / \\\"xyzspaces\\\" / \\\"datasets\\\" / \\\"chicago_parks.geo.json\\\"\\n )\\n space = empty_space\\n space.add_features_geojson(fp_geojson, encoding=\\\"utf-8-sig\\\")\\n stats = space.get_statistics()\\n assert stats[\\\"count\\\"][\\\"value\\\"] == 9\\n assert stats[\\\"geometryTypes\\\"][\\\"value\\\"] == [\\\"Point\\\"]\\n feature = space.get_feature(feature_id=\\\"LP\\\")\\n assert feature.id == \\\"LP\\\"\\n assert feature.geometry[\\\"coordinates\\\"] == [-87.637596, 41.940403, 4.0]\\n tagging_rules = {\\\"large\\\": \\\"$.features[?(@.properties.area>=500)]\\\"}\\n _ = space.update(tagging_rules=tagging_rules)\\n large_parks = empty_space.search(tags=[\\\"large\\\"])\\n for park in large_parks:\\n assert park[\\\"id\\\"] in [\\\"LP\\\", \\\"BP\\\", \\\"JP\\\"]\\n feature_iter = space.iter_feature()\\n feature = next(feature_iter)\\n assert feature[\\\"geometry\\\"][\\\"coordinates\\\"] == [-87.637596, 41.940403, 4]\\n res = space.cluster(clustering=\\\"hexbin\\\")\\n assert res[\\\"type\\\"] == \\\"FeatureCollection\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d50b04c4bd72f409e243b62cd8eaf121\",\n \"score\": \"0.51059777\",\n \"text\": \"def read_topojson(self, infile, **kwargs):\\n\\n logging.info('Reading TopoJSON: %s' % infile)\\n raise ValueError('Reading TopoJSON not implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75d2534010ced2ef707c53de240af59b\",\n \"score\": \"0.5105181\",\n \"text\": \"def load_osm_document_cached(\\n file_path,\\n url_path,\\n post_data=None,\\n returns_json=True):\\n elapsed_seconds = 0\\n limit_seconds = 900 # 15 minutes\\n file_time = time.time()\\n updating_status = False\\n if os.path.exists(file_path):\\n current_time = time.time() # in unix epoch\\n file_time = os.path.getmtime(file_path) # in unix epoch\\n elapsed_seconds = current_time - file_time\\n\\n osm_data = {'elements': []}\\n if elapsed_seconds > limit_seconds or not os.path.exists(file_path):\\n if not os.path.exists(file_path):\\n try:\\n if post_data:\\n fetch_osm_with_post(\\n file_path,\\n url_path,\\n post_data,\\n returns_format='json' if returns_json else 'xml')\\n else:\\n fetch_osm(file_path, url_path)\\n except (OverpassBadRequestException, OverpassDoesNotReturnData):\\n return osm_data, file_time, updating_status\\n\\n else:\\n FetchOsmThread(file_path, url_path, post_data).start()\\n updating_status = True\\n\\n file_handle = None\\n if os.path.exists(file_path):\\n file_handle = open(file_path, 'rb')\\n\\n if returns_json and file_handle:\\n try:\\n osm_data = json.loads(file_handle.read().decode('utf-8'))\\n except ValueError:\\n pass\\n else:\\n osm_data = file_handle\\n\\n return osm_data, file_time, updating_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f391e0ad8efeb2400cb2df259621d399\",\n \"score\": \"0.5104513\",\n \"text\": \"def geos_geometry_can_be_created_from_geojson_string():\\n from django.contrib.gis.geos import GEOSGeometry\\n import json\\n\\n geojson_point = dict(type=\\\"Point\\\", coordinates=[100.0, 0.0])\\n geojson_point_string = json.dumps(geojson_point)\\n GEOSGeometry(geojson_point_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99bc376bd0b49e61e8e89b067c5d06a0\",\n \"score\": \"0.50885135\",\n \"text\": \"def test_call_endpoint(self):\\n token = get_token()\\n response = call_mapbox_endpoint(\\\"the white house\\\",token,GEOCODING_ENDPOINT_TEMPORARY )\\n\\n payload = json.loads(response.text) #convert json to dictionary\\n features=payload[\\\"features\\\"] # get list of results\\n first=(features[0]) # get first result\\n properties = first[\\\"properties\\\"] # get properties of result (where address is)\\n address = properties[\\\"address\\\"] # street address\\n self.assertEqual(address,'1600 Pennsylvania Ave NW')\\n context = first[\\\"context\\\"] # get context (where zipcode and state are)\\n print(first)\\n postcode = [y for y in context if y[\\\"id\\\"].startswith(\\\"postcode\\\")][0][\\\"text\\\"]\\n region = [y for y in context if y[\\\"id\\\"].startswith(\\\"region\\\")][0][\\\"text\\\"]\\n self.assertEqual(postcode,\\\"20006\\\")\\n self.assertEqual(region,\\\"District of Columbia\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ae7f08922bb9adbf3c297a5d16ac1f\",\n \"score\": \"0.50815785\",\n \"text\": \"def main():\\n\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"features_geojson\\\", help=\\\"Path to GeoJSON with features to be gridded.\\\")\\n parser.add_argument(\\\"output_folder\\\", help=\\\"Folder to contain output grid GeoJSONs.\\\")\\n args = parser.parse_args()\\n\\n with open(args.features_geojson, 'r') as fin:\\n features_gj = json.load(fin)\\n\\n if not os.path.isdir(GRIDS_DIR):\\n os.mkdir(GRIDS_DIR)\\n\\n count = 0\\n for feature in features_gj['features']:\\n try:\\n feature['properties']['FIPS'] = \\\"{0}{1}\\\".format(feature['properties']['STATE'], feature['properties']['COUNTY'])\\n except:\\n pass\\n count += 1\\n boundary = shape(feature['geometry'])\\n bb = boundary.bounds\\n\\n xmin = bb[0] # most western point\\n xmax = bb[2] # most eastern point\\n ymin = bb[1] # most southern point\\n ymax = bb[3] # most northern point\\n\\n gridHeight = 0.001\\n gridWidth = 0.001\\n xmin = floor(xmin * 10**SCALE) / 10**SCALE\\n ymax = ceil(ymax * 10**SCALE) / 10**SCALE\\n\\n grid(\\\"{0}.geojson\\\".format(os.path.join(args.output_folder, feature['properties']['FIPS'])),\\n xmin, xmax, ymin, ymax, gridHeight, gridWidth, boundary)\\n if count % 150 == 0:\\n print(\\\"{0} counties complete.\\\".format(count))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a844371878d1321a541f2f8d25e1215\",\n \"score\": \"0.5073145\",\n \"text\": \"def test_geospatial_dumps_positive(self):\\n geo_object = get_geo_object()\\n\\n geojson_str = geo_object.dumps()\\n assert isinstance(geojson_str, str)\\n\\n obj = aerospike.geojson(geojson_str)\\n assert obj.unwrap() == geo_object.unwrap()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993491,"cells":{"query_id":{"kind":"string","value":"05bc8e2304577295887424b010ed7a87"},"query":{"kind":"string","value":"generated source for method getFuelLevel"},"positive_passages":{"kind":"list like","value":[{"docid":"9d9088df5fd780e9da59f78ed407a612","score":"0.8427119","text":"def getFuelLevel(self):\n return self.brewingStand.getProperty(1)","title":""}],"string":"[\n {\n \"docid\": \"9d9088df5fd780e9da59f78ed407a612\",\n \"score\": \"0.8427119\",\n \"text\": \"def getFuelLevel(self):\\n return self.brewingStand.getProperty(1)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"51fc8d466defad910e1ff2f03bb998fd","score":"0.7995947","text":"def fuel_level(self):\n return self.tank.fuel_level","title":""},{"docid":"508a0b2ef68dbfac6b1842802129002d","score":"0.6756143","text":"def load_fuel_base(self):\n values = self.values[:]\n if values[-1] == 0:\n values[-2] = MAX_FUEL\n else:\n return None\n return Desert(values)","title":""},{"docid":"e01df2b65a8d90fd08c9d8e39cd4e860","score":"0.6524199","text":"def get_fuel(self):\n return self.taxi_env.state[FUELS][self.taxi_index]","title":""},{"docid":"2b703e51fa1f487339e6a0dcc8dd36c8","score":"0.63960844","text":"def load_fuel_1(self):\n values = self.values[:]\n cur_pos = values[-1] - 1\n if cur_pos >= 0 and values[-2] + 1 <= MAX_FUEL and MAX_FUEL >= values[cur_pos] >= 1:\n values[cur_pos] -= 1\n values[-2] += 1\n else:\n return None\n return Desert(values)","title":""},{"docid":"f0f3ecedb800d37c5b5491984cc3b819","score":"0.63846433","text":"def update_fuel_level(self, new_level):\n if new_level <= self.fuel_capacity:\n self.fuel_level = new_level\n else:\n print(\"The tank can't hold that much!\")","title":""},{"docid":"8e34fbfc7256c762a0109cba74e42373","score":"0.63419837","text":"def setFuelLevel(self, level):\n self.brewingStand.setProperty(1, level)","title":""},{"docid":"18c4226342b1a4cf09c0121883244375","score":"0.6273936","text":"def get_level(self):\n return self.level","title":""},{"docid":"18c4226342b1a4cf09c0121883244375","score":"0.6273936","text":"def get_level(self):\n return self.level","title":""},{"docid":"ced15ea795be64fef2b1c3ec02c932fa","score":"0.62554574","text":"def get_damage(self) -> int:\r\n return round(self.level**2)*rarities[self.rarity][0]","title":""},{"docid":"775848ec5a0d32486c8702823c339d55","score":"0.6253093","text":"def get_level(self):\n return 0","title":""},{"docid":"278e0635f20ad9933dddd441eaa7e509","score":"0.6240365","text":"def get_gain(self):","title":""},{"docid":"9c9d5a539586e5163cfc4b8cde86191e","score":"0.62335825","text":"def nextlevelcost(self):\n return self.upgradecost","title":""},{"docid":"005398b2cf79deb60536679b18bf93da","score":"0.62234807","text":"def get_damage(self) -> int:\r\n base_damage = self.level**3\r\n if choice([0,1]):\r\n if choice([0,1]):\r\n base_damage += round(base_damage/3)\r\n return base_damage\r\n base_damage -= round(base_damage/2)\r\n return base_damage\r\n return base_damage","title":""},{"docid":"f6d02d5fc670846a996ee8add087211e","score":"0.6222376","text":"def getLevel(self):\n return","title":""},{"docid":"b8c7cae673e57ab5b6595f9d46a85205","score":"0.6216812","text":"def get_level() -> int:\n return level","title":""},{"docid":"32991146d49dd4483d23ba45ea143fe1","score":"0.6196392","text":"def fuel_consumption(self):\n return self._fuel_consumption","title":""},{"docid":"7e6a038ad3830b9192654be2e57db631","score":"0.6133731","text":"def get_gas(self):\n return(self.gas_level)","title":""},{"docid":"78ce16e9b66deace8424e5aa080cf762","score":"0.6112013","text":"def get_fuel_total_saved (self):\n #~ eff = self.cd[\"diesel generation efficiency\"]\n #~ proposed = self.electric_consumption/eff\n return self.heating_oil_saved #- proposed","title":""},{"docid":"489d47eac50fd1f22ba233b5dca5700a","score":"0.60368943","text":"def calculate_fuel(m):\r\n return (m // 3) - 2","title":""},{"docid":"51fa2a902effbac9589f52ed9333e0c7","score":"0.60259944","text":"def getBatteryVoltage(self) -> float:\n ...","title":""},{"docid":"64308e9ccde96a32f8b9743cf6fac2c5","score":"0.60111517","text":"def level(self) -> float:\n return float(self.__device.query(\":TRIG:EDG:LEV?\"))","title":""},{"docid":"821d461d9d39428aa5a30f2c7ff4772c","score":"0.59956646","text":"def SoftwareFeatureLevel(self) -> int:","title":""},{"docid":"35add8dbc7c20014dc973d721b07b5e3","score":"0.59893864","text":"def energy(self, level=0):\n assert isinstance(level, int) and level >= 0, \"need positive integer for vibrational level\"\n\n freq = self.frequency\n if freq < MIN_FREQUENCY:\n freq = MIN_FREQUENCY\n\n # 0.5 * h * c * frequency (c in cm/s bc wavenumbers)\n # 0.5 * (6.626 * 10**-34) * (3 * 10**10) * (6.026 * 10**23) / 4184) = 0.0014305 \n zpe = 0.0014305 * freq\n return zpe * (2 * level + 1)","title":""},{"docid":"5dc22179dbc13042d7ddeac5e56a51ff","score":"0.59770936","text":"def get_level(self, x, y):\r\n return self.basins[y][x]","title":""},{"docid":"98e52ca44dcef210b2bd0f296641e1e4","score":"0.5946241","text":"def getLevel(self):\r\n return self.levels[self.selectedIndex]","title":""},{"docid":"3203ea0d176e1a5573ca61569066bed7","score":"0.5942816","text":"def fuel_system(self):\n return self._fuel_system","title":""},{"docid":"7d763515eb6c4dd6befd6666950e3885","score":"0.5931404","text":"def get_field_strength() -> int: # type: ignore\n ...","title":""},{"docid":"3a2432348fe2c0bf309315d528f5c9b7","score":"0.5930113","text":"def fuel_type(self):\n return self._fuel_type","title":""},{"docid":"739ff1732eff42dc60e8e83fb5342230","score":"0.5915593","text":"def battery_level(self) -> int:\r\n return max(\r\n 0,\r\n min(\r\n 100,\r\n self.__get_mower_attributes()[\"battery\"][\"batteryPercent\"],\r\n ),\r\n )","title":""},{"docid":"a9b28abbd34eaf71d59d7233f0586b1c","score":"0.59134936","text":"def get_fuel_requirement(module_mass):\n return module_mass//3-2","title":""},{"docid":"6cf21b60752cdd03f0e337dcdfbaec34","score":"0.5905465","text":"def get_reorganization_energy(self):\r\n return self.convert_frequency_2_current_u(self.lamb)","title":""},{"docid":"88b32831f04043a1b14e4afc5e2be6cb","score":"0.58987826","text":"def battery_level(self):\n return self.device[\"battery\"][\"percent\"]","title":""},{"docid":"a5f02362b4ce118e550d20d9399e7770","score":"0.5892388","text":"def FeatureLevel(self) -> int:","title":""},{"docid":"dee30f3ff9ac0004dec3a07936d47ea4","score":"0.58696866","text":"def do_get_power(self) -> float:\n logging.info(__name__ + ' : Getting Power')\n self._handle = ctypes.c_void_p(self._dll.sc5511a_open_device(self._serial_number))\n self._dll.sc5511a_get_rf_parameters(self._handle, ctypes.byref(self._rf_params))\n rf_level = self._rf_params.rf_level\n self._dll.sc5511a_close_device(self._handle)\n return rf_level","title":""},{"docid":"b1fe77e4d50a5531c27897c70cdc7bfb","score":"0.5857879","text":"def getLevel(self):\n return self._level","title":""},{"docid":"b4dee99554e8c60ae122bcab97cb440b","score":"0.5830211","text":"def fuelMax(self):\r\n return self._sumNestedProperty('fuel_max', 'fuelMax')","title":""},{"docid":"d28f53111deee1cf22da556bb125b6c8","score":"0.5816274","text":"def get_battery_level(self):\n command = struct.pack('>BBBqq', 14, 1, 0, 0, 0)\n self._tx_q.put((command, True))","title":""},{"docid":"e1abfc79873829f6e290e50bb3072a00","score":"0.581591","text":"def get_energy(self):\n return self.energy","title":""},{"docid":"85743b50120166d9070b450f4ee05e64","score":"0.5813707","text":"def getInsurgency(self):\n return getMaxLevel((1,7,31,127,511,2047), self.strengthRatio)","title":""},{"docid":"c34610178e1c4cf623c9e58876cc9060","score":"0.5812951","text":"def fuel_calculator(module_mass):\n return int(module_mass / 3) - 2","title":""},{"docid":"ef534b7ae54ccd62ebd570703056a6a2","score":"0.579966","text":"def get_energy(self) -> float:\n return self.energy","title":""},{"docid":"22f2239c81cd8cd5d8ee052b2c5a6a48","score":"0.57764256","text":"def level(self) -> int: \n\n return self.__level","title":""},{"docid":"43104ceee9013535ec880c0fba8ea9d4","score":"0.5760691","text":"def fuel(fuel_flow, remaining_fuel):\n return int(remaining_fuel / fuel_flow * 100)","title":""},{"docid":"c0cf45b659adbd2d6d4555b2ee077af3","score":"0.5753099","text":"def getResistance(self):\n return float(self.query(\"CR?\"))","title":""},{"docid":"7eaf8205343f1af27bf25bc3d8c3cbae","score":"0.5747295","text":"def calc_gold(self):\n i = self.lvl\n value = 10 * math.pow(i, 2)\n return int(value)","title":""},{"docid":"7d4e90ae73853cd020120d26a1925e12","score":"0.5717051","text":"def getLevels(self):\n return","title":""},{"docid":"8c9725e4768fc871f4abab629fd0a710","score":"0.5716833","text":"def level(self):\n return LuaExpression(self.player_unit, Method('level'))","title":""},{"docid":"f444c4555bfc070e05c71991e69a9f1f","score":"0.57162786","text":"def battery_level(self):\n battery = self._dp_mgr.dp.get_battery()\n return battery[\"level\"]","title":""},{"docid":"192b1e6ae794e4e9a1a46dcfe145cef8","score":"0.57130486","text":"def coverage_weight(self):\n sel = self.viable_fields\n weight = self.hour_angle\n weight[~sel] = np.inf\n weight += 6. * 360. * self.fields['TILING'] # Was 6, 60\n weight += self.slew**3 # slew**2\n weight += 100. * (self.airmass - 1.)**3\n return weight","title":""},{"docid":"a05d67b644fd19ea52c8c02f50545d03","score":"0.5710315","text":"def define_fuel_blends(self):\n\n fuels_lhv = {\n \"petrol\": 42.4,\n \"bioethanol - wheat straw\": 26.8,\n \"bioethanol - maize starch\": 26.8,\n \"bioethanol - sugarbeet\": 26.8,\n \"bioethanol - forest residues\": 26.8,\n \"synthetic gasoline\": 42.4,\n \"diesel\": 42.8,\n \"biodiesel - cooking oil\": 31.7,\n \"biodiesel - algae\": 31.7,\n \"synthetic diesel\": 43.3,\n \"cng\": 55.5,\n \"biogas - sewage sludge\": 55.5,\n \"biogas - biowaste\": 55.5,\n \"syngas\": 55.5,\n }\n\n fuels_CO2 = {\n \"petrol\": 3.18,\n \"bioethanol - wheat straw\": 1.91,\n \"bioethanol - maize starch\": 1.91,\n \"bioethanol - sugarbeet\": 1.91,\n \"bioethanol - forest residues\": 1.91,\n \"synthetic gasoline\": 3.18,\n \"diesel\": 3.14,\n \"biodiesel - cooking oil\": 2.85,\n \"biodiesel - algae\": 2.85,\n \"synthetic diesel\": 3.16,\n \"cng\": 2.65,\n \"biogas - sewage sludge\": 2.65,\n \"biogas - biowaste\": 2.65,\n \"syngas\": 2.65,\n }\n\n if {\"ICEV-p\", \"HEV-p\", \"PHEV-p\"}.intersection(set(self.scope[\"powertrain\"])):\n fuel_type = \"petrol\"\n primary, secondary, primary_share, secondary_share, tertiary, tertiary_share = self.find_fuel_shares(\n fuel_type\n )\n self.create_fuel_markets(\n fuel_type, primary, secondary, tertiary, primary_share, secondary_share, tertiary_share\n )\n self.fuel_blends[fuel_type] = {\n \"primary\": {\n \"type\": primary,\n \"share\": primary_share,\n \"lhv\": fuels_lhv[primary],\n \"CO2\": fuels_CO2[primary],\n },\n \"secondary\": {\n \"type\": secondary,\n \"share\": secondary_share,\n \"lhv\": fuels_lhv[secondary],\n \"CO2\": fuels_CO2[secondary],\n }\n }\n\n if tertiary:\n self.fuel_blends[fuel_type][\"tertiary\"] = {\n \"type\": tertiary,\n \"share\": tertiary_share,\n \"lhv\": fuels_lhv[tertiary],\n \"CO2\": fuels_CO2[tertiary],\n }\n\n\n if {\"ICEV-d\", \"HEV-d\", \"PHEV-d\"}.intersection(set(self.scope[\"powertrain\"])):\n fuel_type = \"diesel\"\n primary, secondary, primary_share, secondary_share, tertiary, tertiary_share = self.find_fuel_shares(\n fuel_type\n )\n self.create_fuel_markets(\n fuel_type, primary, secondary, tertiary, primary_share, secondary_share, tertiary_share\n )\n self.fuel_blends[fuel_type] = {\n \"primary\": {\n \"type\": primary,\n \"share\": primary_share,\n \"lhv\": fuels_lhv[primary],\n \"CO2\": fuels_CO2[primary],\n },\n \"secondary\": {\n \"type\": secondary,\n \"share\": secondary_share,\n \"lhv\": fuels_lhv[secondary],\n \"CO2\": fuels_CO2[secondary],\n },\n }\n\n if tertiary:\n self.fuel_blends[fuel_type][\"tertiary\"] = {\n \"type\": tertiary,\n \"share\": tertiary_share,\n \"lhv\": fuels_lhv[tertiary],\n \"CO2\": fuels_CO2[tertiary],\n }\n\n if {\"ICEV-g\"}.intersection(set(self.scope[\"powertrain\"])):\n fuel_type = \"cng\"\n primary, secondary, primary_share, secondary_share, tertiary, tertiary_share = self.find_fuel_shares(\n fuel_type\n )\n self.create_fuel_markets(\n fuel_type, primary, secondary, tertiary, primary_share, secondary_share, tertiary_share\n )\n self.fuel_blends[fuel_type] = {\n \"primary\": {\n \"type\": primary,\n \"share\": primary_share,\n \"lhv\": fuels_lhv[primary],\n \"CO2\": fuels_CO2[primary],\n },\n \"secondary\": {\n \"type\": secondary,\n \"share\": secondary_share,\n \"lhv\": fuels_lhv[primary],\n \"CO2\": fuels_CO2[primary],\n },\n }\n\n if tertiary:\n self.fuel_blends[fuel_type][\"tertiary\"] = {\n \"type\": tertiary,\n \"share\": tertiary_share,\n \"lhv\": fuels_lhv[tertiary],\n \"CO2\": fuels_CO2[tertiary],\n }\n\n if {\"FCEV\"}.intersection(set(self.scope[\"powertrain\"])):\n fuel_type = \"hydrogen\"\n primary, secondary, primary_share, secondary_share, tertiary, tertiary_share = self.find_fuel_shares(\n fuel_type\n )\n self.create_fuel_markets(\n fuel_type, primary, secondary, tertiary, primary_share, secondary_share, tertiary_share\n )\n self.fuel_blends[fuel_type] = {\n \"primary\": {\"type\": primary, \"share\": primary_share},\n \"secondary\": {\"type\": secondary, \"share\": secondary_share},\n }\n\n if tertiary:\n self.fuel_blends[fuel_type][\"tertiary\"] = {\n \"type\": tertiary,\n \"share\": tertiary_share\n }\n\n if {\"BEV\", \"PHEV-p\", \"PHEV-d\"}.intersection(set(self.scope[\"powertrain\"])):\n fuel_type = \"electricity\"\n self.create_fuel_markets(fuel_type)","title":""},{"docid":"2401c18a6449cbb1bebbb93a53ab9ffc","score":"0.5707544","text":"def energy(self):\n return","title":""},{"docid":"c8c579f457b6f73db85e7d09ff14ec50","score":"0.5707229","text":"def get_max_level(self):\r\n return len(self.level_data)","title":""},{"docid":"43e90c128e7c363eaf0bcb96e4d2d7ad","score":"0.5701703","text":"def _unit_floz(self):\n return self.us_customary_base * 6.0","title":""},{"docid":"62df3d51d6ac96820f04267ab300d385","score":"0.56989706","text":"def waterLevel(self):\n if self.distance is None:\n self.retrieveDistance()\n\n return self.depth - self.distance","title":""},{"docid":"fdd4fddcdb0c19d7d46d6df031bb75a9","score":"0.5695319","text":"def update_fuel_level(self, P_gen_output_1, P_gen_output_2, delta_t):\n\n # get fuel rates gal/s\n fuel_rate_1 = self.get_fuel_flow_rate(P_gen_output_1)\n fuel_rate_2 = self.get_fuel_flow_rate(P_gen_output_2)\n\n self.fuel_flow_rate = fuel_rate_1;\n\n # Set engine loss\n P_gen_input_1 = P_gen_output_1 / (self.ENGINE_EFF_LUT(P_gen_output_1)*self.POWER_EFF_MOTOR )\n P_gen_input_2 = P_gen_output_2 / (self.ENGINE_EFF_LUT(P_gen_output_2)*self.POWER_EFF_MOTOR )\n self.Genset_loss = self.get_energy_loss(P_gen_output_1, P_gen_input_1, P_gen_output_2, P_gen_input_2, delta_t)\n\n # Reset fuel level\n current_level = self.fuel_level * self.MAX_TANK_CAPACITY\n self.fuel_level= (current_level - self.integrate_fuel_flow(fuel_rate_1, fuel_rate_2, delta_t)) / self.MAX_TANK_CAPACITY\n return self.fuel_level","title":""},{"docid":"687e768ee3324036f8158d9c0d73b328","score":"0.56951714","text":"def ReportLowLiquidLevel(self, lType, internalBottleLocation, kioskID):\n\t \n\t return liqPercentage","title":""},{"docid":"4dd31b98990d3076aee09b7321f059a1","score":"0.5694667","text":"def get_max_level(self):\n maxy = -1000\n max_level = None\n for lvl in self.level_set.all():\n if maxy < lvl.threshold:\n maxy = lvl.threshold\n max_level = lvl\n return max_level","title":""},{"docid":"ed5c20c587bd7b14dff153fcb7622cdc","score":"0.5682814","text":"def low_battery(self):\n return self._low_battery","title":""},{"docid":"27a6fd54fd7b3c2c35a5c94e1fe07479","score":"0.56775534","text":"def level(self):\n if not self._level_calculated:\n self._level_calculated = True\n self._extract_level()\n return self._level","title":""},{"docid":"1e8ad32ca94e678c148d24bde573ccab","score":"0.56766844","text":"def getLevel(self):\n JSON = self.JSON\n \n networkExp = JSON.get('networkExp', 0) \n networkLevel = JSON.get('networkLevel', 0)\n \n exp = leveling.getExperience(networkExp, networkLevel)\n myoutput = leveling.getExactLevel(exp)\n return myoutput","title":""},{"docid":"56f78095458e0ab24f222145bdd91a24","score":"0.56741554","text":"def crf_level(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"crf_level\")","title":""},{"docid":"56f78095458e0ab24f222145bdd91a24","score":"0.56741554","text":"def crf_level(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"crf_level\")","title":""},{"docid":"c7d209d9eae308c622222db093332254","score":"0.5668044","text":"def get_level(self):\n return Student.STR_LEVEL[self.level]","title":""},{"docid":"feb617acfa179753269528c75e7bb0f2","score":"0.56564945","text":"def get_level_display(self):\n for choice in self.FORTIFICATION_CHOICES:\n if self.level == choice[0]:\n return choice[1]\n # if level is too high, return the last element in choices\n return self.FORTIFICATION_CHOICES[-1][1]","title":""},{"docid":"77d8c42f71781e8e38f096a81ce53ac7","score":"0.5654729","text":"def native_value(self) -> int:\n return self.resource.power_state.battery_level","title":""},{"docid":"824b1c29009bb309a5d0e648e7b500e8","score":"0.56505066","text":"def battery_level():\n with open(\"/sys/class/power_supply/BAT0/capacity\", \"r\") as f:\n return int(f.read())","title":""},{"docid":"672ce56d46bd74de9ac54d0c5776d49c","score":"0.564116","text":"def KGEnergyLevel(cylindrical,Qnuc,qorb,morb,nr,lam,lzam,Bz):\n\t# spherical stability of an electronic atom requires Z <= 68\n\t# cylindrical stability requires lam>0\n\tif cylindrical:\n\t\tif lzam**2 - (Qnuc*qorb)**2 < 0.0:\n\t\t\treturn 0.0\n\t\te2i = 1.0 + (Qnuc*qorb)**2/(np.sqrt(lzam**2 - (Qnuc*qorb)**2) + nr + 0.5)**2\n\telse:\n\t\tif (lam+0.5)**2 - (Qnuc*qorb)**2 < 0.0:\n\t\t\treturn 0.0\n\t\te2i = 1.0 + (Qnuc*qorb)**2/(np.sqrt((lam+0.5)**2 - (Qnuc*qorb)**2) + nr + 0.5)**2\n\treturn morb*np.sqrt(1.0-lzam*qorb*Bz/morb**2)/np.sqrt(e2i)","title":""},{"docid":"31fd9abf20bd110b6839f3aa594fb83e","score":"0.56398284","text":"def fuel_requirement(m):\n return max(0, int(m / 3) - 2)","title":""},{"docid":"778e206f3d891fd46ac1cf8737867019","score":"0.5639819","text":"def get_gain(self):\n # v = self.vintage\n # gain = 0.0\n # for s in v:\n # lower = 0.0\n # for layer in s.stack:\n # lower += layer.unit['dz']\n # s.attributes(prm.DIGI, prm.LTRACE)\n # xtr, ztr, g = s.limittr(lower, 0.0)\n # gain += g / len(v)\n\n v = self.vintage\n gain = 0.0\n for s in v:\n s.attributes(prm.DIGI, prm.LTRACE)\n xtr, g = L.autogain(s.trace, s.dx / 2.0)\n gain += g / len(v)\n\n return gain","title":""},{"docid":"651469fb96a60da33ced8206879443be","score":"0.5603719","text":"def getPower(self):\n result = self.getTempPower()\n return result[2]","title":""},{"docid":"2950829d92a6b5ad2dd62e35c5f25c93","score":"0.5602412","text":"def get_Lo(self):\n return self.Lo","title":""},{"docid":"1a51e5624226d3aef056854520ffb26e","score":"0.5594745","text":"def level(self):\n return self._level","title":""},{"docid":"1a51e5624226d3aef056854520ffb26e","score":"0.5594745","text":"def level(self):\n return self._level","title":""},{"docid":"1a51e5624226d3aef056854520ffb26e","score":"0.5594745","text":"def level(self):\n return self._level","title":""},{"docid":"e693d94f15733d978a64595599d07576","score":"0.559003","text":"def battery(self): # pylint: disable=R0201\n\t\treturn None","title":""},{"docid":"d08fd9c25f9a3bf6aab502d726f79487","score":"0.5586803","text":"def battery_level(self) -> int | None:\n return self.tuya_device.status.get(DPCODE_BATTERY)","title":""},{"docid":"11b230005b7028895e2db6bf6cce18b0","score":"0.5582059","text":"def recursive_fuel_requirement(m):\n if m <= 0:\n return 0\n\n new_fuel = fuel_requirement(m)\n return new_fuel + recursive_fuel_requirement(new_fuel)","title":""},{"docid":"60a3adf92d44cb2eab3df8ba0eab61fa","score":"0.55751735","text":"def calculate_fuel(mass):\n return mass // 3 - 2","title":""},{"docid":"cb6f25d76d81889dfc721a631f92bf9b","score":"0.5573937","text":"def getMilPower(self):\n return getMaxLevel((20,40,100,200,500,1000,2000), self.milPower)","title":""},{"docid":"8b5f7cb50911f2830eb5e1c16d60a3ee","score":"0.55732524","text":"def get_range(self):\n if self.battery_size == 75:\n range = 260\n elif self.battery_size == 100:\n range = 315\n else:\n range = \"unknown\"\n print(f\"This car can go about {range} miles on a full charge.\")","title":""},{"docid":"eb20d613d8b4921f7d86fae7540d2f83","score":"0.55693936","text":"def drop_fuel(self):\n values = self.values[:]\n if values[-1] > 0 and values[-2] > 0:\n cur_pos = values[-1] - 1\n max_allowed, values[-2] = values[-2], 0\n values[cur_pos] += max_allowed\n else:\n return None\n return Desert(values)","title":""},{"docid":"24d62bb178be06fa83d6151a5bc231aa","score":"0.5568161","text":"def level_init(cls):\n resource_rice.quantity_property = 10\n resource_nori.quantity_property = 10\n resource_roe.quantity_property = 10\n resource_salmon.quantity_property = 5\n resource_shrimp.quantity_property = 5\n resource_unagi.quantity_property = 5\n\n # Meal instances init\n if cls.current_level == 1:\n meal_onigiri = Meal('Onigiri', 2, 1, 0, 0, 0, 0, 'onigiri{}')\n meal_california_roll = Meal('California Roll', 1, 1, 1, 0, 0, 0, 'california_roll{}')\n meal_gunkan_maki = Meal('Gunkan Maki', 1, 1, 2, 0, 0, 0, 'gunkan_maki{}')\n elif cls.current_level == 2:\n meal_salmon_roll = Meal('Salmon Roll', 1, 1, 0, 2, 0, 0, 'salmon_roll{}')\n elif cls.current_level == 3:\n meal_shrimp_sushi = Meal('Shrimp Sushi', 1, 1, 0, 0, 2, 0, 'shrimp_sushi{}')\n elif cls.current_level == 4:\n meal_unagi_roll = Meal('Unagi Roll', 1, 1, 0, 0, 0, 2, 'unagi_roll{}')\n elif cls.current_level == 5:\n meal_dragon_roll = Meal('Dragon Roll', 2, 1, 1, 0, 0, 2, 'dragon_roll{}')\n elif cls.current_level == 6:\n meal_combo_sushi = Meal('Combo Sushi', 2, 1, 1, 1, 1, 1, 'combo_sushi{}')\n for x in Meal.meals_list:\n print(x.name)\n print(\"-\"*30)","title":""},{"docid":"59e2989775d03b2660f09368a363b5f2","score":"0.55630827","text":"def update_fuel_level(self, amount):\n if (self.fuel_level + amount \n <= self.fuel_capacity):\n self.fuel_level += amount\n print(\"Added fuel.\")\n else:\n print(\"The tank won't hold that much.\")","title":""},{"docid":"e8c7ca885f301e23d893ad5a25249e6a","score":"0.5559175","text":"def fuel(mass: int) -> int:\n return math.floor(mass / 3) - 2","title":""},{"docid":"a62785814d396be1e3ff9e63a3c16140","score":"0.5551176","text":"def source_level(self):\n return float(self.ask(\":SOURce:LEVel?\"))","title":""},{"docid":"fe78bbe098ae9d37918d3372ab100296","score":"0.55421","text":"def fan_level(self) -> int:\n return self.details.get('level', 0)","title":""},{"docid":"bc6d7b99290c8a56c9a34d67de42d520","score":"0.55406314","text":"def fuel_cost(self, update=False):\n if update or self._dfs[\"fuel_cost\"] is None:\n self._dfs[\"fuel_cost\"] = pudl.analysis.mcoe.fuel_cost(self)\n return self._dfs[\"fuel_cost\"]","title":""},{"docid":"eafcd2f731305d5ae52b644450687074","score":"0.55402744","text":"def __float__(self):\n return self._altitude","title":""},{"docid":"1846edeebbeae0c0fd84045290a64c83","score":"0.5518733","text":"def ReportLiquidLevel(self, lType, internalBottleLocation, kioskID):\n \t\t\n\t return liqPercentage","title":""},{"docid":"4b50368c814c5be8fda7761cdf24e538","score":"0.55152506","text":"def GetLevelName(self):\r\n return self.levelname","title":""},{"docid":"f965ad750993c0abfeca679192e8f015","score":"0.55145913","text":"def get_max_damage(self):\n\t\t_ret = self.get_base_damage()\n\t\t\n\t\tif self.weapon: _ret+=self.weapon['damage']\n\t\t\n\t\treturn _ret","title":""},{"docid":"98f10d45240ee61abb490d9c76cf2c21","score":"0.55142164","text":"def get_crit(self, speed, level):\n crit_rate = speed * 100 / 512\n chance = random.uniform(1, 100)\n if chance <= crit_rate:\n return (2 * level + 5) / (level + 5)\n else:\n return 1.0","title":""},{"docid":"035d5f1b877b82948a7167a577ba5fc0","score":"0.55073607","text":"def voltage(self):\n return self._voltage","title":""},{"docid":"fa6c48e841a4a9cf34bbba0c1144fb61","score":"0.55048","text":"def getpower(self):\n raise NotImplementedError","title":""},{"docid":"f35d991acda4d0896e31b372012d0040","score":"0.54867935","text":"def specific_heat_capacity(self):\n return 2415.78 * units.joule / (units.kg * units.kelvin)","title":""},{"docid":"433162bffa5072437711104914ca8437","score":"0.5483245","text":"def Level(self) -> Bip44Levels:\n return Bip44Levels(self.m_bip32_obj.Depth().ToInt())","title":""},{"docid":"b6865e135edd036ff34c5579403c137d","score":"0.54733413","text":"def get_defense() -> int:\n return defense","title":""},{"docid":"d4754a409db05104d83fe27c4f83a183","score":"0.54705584","text":"def battery_soc(self) -> float:\n return self.battery_charge / self.battery_capacity","title":""},{"docid":"4a8523bebfa1d032ec2ab2e9a07ababf","score":"0.5464402","text":"def critical_damage(self):\n if self.owner and self.equipment:\n equipment = self.equipment.critical_damage_bonus\n else:\n equipment = 0\n\n return self.attributes.base_critical_damage + equipment","title":""},{"docid":"7cf5e7d46234a667f9aa6cf3951aff6d","score":"0.54614574","text":"def get_Lo(self):\n flow_area = self.a * self.W\n wetted_perimeter = 2.*self.a+2.*self.W\n return flow_area/wetted_perimeter","title":""}],"string":"[\n {\n \"docid\": \"51fc8d466defad910e1ff2f03bb998fd\",\n \"score\": \"0.7995947\",\n \"text\": \"def fuel_level(self):\\n return self.tank.fuel_level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"508a0b2ef68dbfac6b1842802129002d\",\n \"score\": \"0.6756143\",\n \"text\": \"def load_fuel_base(self):\\n values = self.values[:]\\n if values[-1] == 0:\\n values[-2] = MAX_FUEL\\n else:\\n return None\\n return Desert(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e01df2b65a8d90fd08c9d8e39cd4e860\",\n \"score\": \"0.6524199\",\n \"text\": \"def get_fuel(self):\\n return self.taxi_env.state[FUELS][self.taxi_index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b703e51fa1f487339e6a0dcc8dd36c8\",\n \"score\": \"0.63960844\",\n \"text\": \"def load_fuel_1(self):\\n values = self.values[:]\\n cur_pos = values[-1] - 1\\n if cur_pos >= 0 and values[-2] + 1 <= MAX_FUEL and MAX_FUEL >= values[cur_pos] >= 1:\\n values[cur_pos] -= 1\\n values[-2] += 1\\n else:\\n return None\\n return Desert(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0f3ecedb800d37c5b5491984cc3b819\",\n \"score\": \"0.63846433\",\n \"text\": \"def update_fuel_level(self, new_level):\\n if new_level <= self.fuel_capacity:\\n self.fuel_level = new_level\\n else:\\n print(\\\"The tank can't hold that much!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e34fbfc7256c762a0109cba74e42373\",\n \"score\": \"0.63419837\",\n \"text\": \"def setFuelLevel(self, level):\\n self.brewingStand.setProperty(1, level)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c4226342b1a4cf09c0121883244375\",\n \"score\": \"0.6273936\",\n \"text\": \"def get_level(self):\\n return self.level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c4226342b1a4cf09c0121883244375\",\n \"score\": \"0.6273936\",\n \"text\": \"def get_level(self):\\n return self.level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ced15ea795be64fef2b1c3ec02c932fa\",\n \"score\": \"0.62554574\",\n \"text\": \"def get_damage(self) -> int:\\r\\n return round(self.level**2)*rarities[self.rarity][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"775848ec5a0d32486c8702823c339d55\",\n \"score\": \"0.6253093\",\n \"text\": \"def get_level(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"278e0635f20ad9933dddd441eaa7e509\",\n \"score\": \"0.6240365\",\n \"text\": \"def get_gain(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c9d5a539586e5163cfc4b8cde86191e\",\n \"score\": \"0.62335825\",\n \"text\": \"def nextlevelcost(self):\\n return self.upgradecost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"005398b2cf79deb60536679b18bf93da\",\n \"score\": \"0.62234807\",\n \"text\": \"def get_damage(self) -> int:\\r\\n base_damage = self.level**3\\r\\n if choice([0,1]):\\r\\n if choice([0,1]):\\r\\n base_damage += round(base_damage/3)\\r\\n return base_damage\\r\\n base_damage -= round(base_damage/2)\\r\\n return base_damage\\r\\n return base_damage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d02d5fc670846a996ee8add087211e\",\n \"score\": \"0.6222376\",\n \"text\": \"def getLevel(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c7cae673e57ab5b6595f9d46a85205\",\n \"score\": \"0.6216812\",\n \"text\": \"def get_level() -> int:\\n return level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32991146d49dd4483d23ba45ea143fe1\",\n \"score\": \"0.6196392\",\n \"text\": \"def fuel_consumption(self):\\n return self._fuel_consumption\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e6a038ad3830b9192654be2e57db631\",\n \"score\": \"0.6133731\",\n \"text\": \"def get_gas(self):\\n return(self.gas_level)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ce16e9b66deace8424e5aa080cf762\",\n \"score\": \"0.6112013\",\n \"text\": \"def get_fuel_total_saved (self):\\n #~ eff = self.cd[\\\"diesel generation efficiency\\\"]\\n #~ proposed = self.electric_consumption/eff\\n return self.heating_oil_saved #- proposed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"489d47eac50fd1f22ba233b5dca5700a\",\n \"score\": \"0.60368943\",\n \"text\": \"def calculate_fuel(m):\\r\\n return (m // 3) - 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51fa2a902effbac9589f52ed9333e0c7\",\n \"score\": \"0.60259944\",\n \"text\": \"def getBatteryVoltage(self) -> float:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64308e9ccde96a32f8b9743cf6fac2c5\",\n \"score\": \"0.60111517\",\n \"text\": \"def level(self) -> float:\\n return float(self.__device.query(\\\":TRIG:EDG:LEV?\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"821d461d9d39428aa5a30f2c7ff4772c\",\n \"score\": \"0.59956646\",\n \"text\": \"def SoftwareFeatureLevel(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35add8dbc7c20014dc973d721b07b5e3\",\n \"score\": \"0.59893864\",\n \"text\": \"def energy(self, level=0):\\n assert isinstance(level, int) and level >= 0, \\\"need positive integer for vibrational level\\\"\\n\\n freq = self.frequency\\n if freq < MIN_FREQUENCY:\\n freq = MIN_FREQUENCY\\n\\n # 0.5 * h * c * frequency (c in cm/s bc wavenumbers)\\n # 0.5 * (6.626 * 10**-34) * (3 * 10**10) * (6.026 * 10**23) / 4184) = 0.0014305 \\n zpe = 0.0014305 * freq\\n return zpe * (2 * level + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dc22179dbc13042d7ddeac5e56a51ff\",\n \"score\": \"0.59770936\",\n \"text\": \"def get_level(self, x, y):\\r\\n return self.basins[y][x]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e52ca44dcef210b2bd0f296641e1e4\",\n \"score\": \"0.5946241\",\n \"text\": \"def getLevel(self):\\r\\n return self.levels[self.selectedIndex]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3203ea0d176e1a5573ca61569066bed7\",\n \"score\": \"0.5942816\",\n \"text\": \"def fuel_system(self):\\n return self._fuel_system\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d763515eb6c4dd6befd6666950e3885\",\n \"score\": \"0.5931404\",\n \"text\": \"def get_field_strength() -> int: # type: ignore\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2432348fe2c0bf309315d528f5c9b7\",\n \"score\": \"0.5930113\",\n \"text\": \"def fuel_type(self):\\n return self._fuel_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"739ff1732eff42dc60e8e83fb5342230\",\n \"score\": \"0.5915593\",\n \"text\": \"def battery_level(self) -> int:\\r\\n return max(\\r\\n 0,\\r\\n min(\\r\\n 100,\\r\\n self.__get_mower_attributes()[\\\"battery\\\"][\\\"batteryPercent\\\"],\\r\\n ),\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9b28abbd34eaf71d59d7233f0586b1c\",\n \"score\": \"0.59134936\",\n \"text\": \"def get_fuel_requirement(module_mass):\\n return module_mass//3-2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cf21b60752cdd03f0e337dcdfbaec34\",\n \"score\": \"0.5905465\",\n \"text\": \"def get_reorganization_energy(self):\\r\\n return self.convert_frequency_2_current_u(self.lamb)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88b32831f04043a1b14e4afc5e2be6cb\",\n \"score\": \"0.58987826\",\n \"text\": \"def battery_level(self):\\n return self.device[\\\"battery\\\"][\\\"percent\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f02362b4ce118e550d20d9399e7770\",\n \"score\": \"0.5892388\",\n \"text\": \"def FeatureLevel(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dee30f3ff9ac0004dec3a07936d47ea4\",\n \"score\": \"0.58696866\",\n \"text\": \"def do_get_power(self) -> float:\\n logging.info(__name__ + ' : Getting Power')\\n self._handle = ctypes.c_void_p(self._dll.sc5511a_open_device(self._serial_number))\\n self._dll.sc5511a_get_rf_parameters(self._handle, ctypes.byref(self._rf_params))\\n rf_level = self._rf_params.rf_level\\n self._dll.sc5511a_close_device(self._handle)\\n return rf_level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1fe77e4d50a5531c27897c70cdc7bfb\",\n \"score\": \"0.5857879\",\n \"text\": \"def getLevel(self):\\n return self._level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4dee99554e8c60ae122bcab97cb440b\",\n \"score\": \"0.5830211\",\n \"text\": \"def fuelMax(self):\\r\\n return self._sumNestedProperty('fuel_max', 'fuelMax')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d28f53111deee1cf22da556bb125b6c8\",\n \"score\": \"0.5816274\",\n \"text\": \"def get_battery_level(self):\\n command = struct.pack('>BBBqq', 14, 1, 0, 0, 0)\\n self._tx_q.put((command, True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1abfc79873829f6e290e50bb3072a00\",\n \"score\": \"0.581591\",\n \"text\": \"def get_energy(self):\\n return self.energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85743b50120166d9070b450f4ee05e64\",\n \"score\": \"0.5813707\",\n \"text\": \"def getInsurgency(self):\\n return getMaxLevel((1,7,31,127,511,2047), self.strengthRatio)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34610178e1c4cf623c9e58876cc9060\",\n \"score\": \"0.5812951\",\n \"text\": \"def fuel_calculator(module_mass):\\n return int(module_mass / 3) - 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef534b7ae54ccd62ebd570703056a6a2\",\n \"score\": \"0.579966\",\n \"text\": \"def get_energy(self) -> float:\\n return self.energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f2239c81cd8cd5d8ee052b2c5a6a48\",\n \"score\": \"0.57764256\",\n \"text\": \"def level(self) -> int: \\n\\n return self.__level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43104ceee9013535ec880c0fba8ea9d4\",\n \"score\": \"0.5760691\",\n \"text\": \"def fuel(fuel_flow, remaining_fuel):\\n return int(remaining_fuel / fuel_flow * 100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0cf45b659adbd2d6d4555b2ee077af3\",\n \"score\": \"0.5753099\",\n \"text\": \"def getResistance(self):\\n return float(self.query(\\\"CR?\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eaf8205343f1af27bf25bc3d8c3cbae\",\n \"score\": \"0.5747295\",\n \"text\": \"def calc_gold(self):\\n i = self.lvl\\n value = 10 * math.pow(i, 2)\\n return int(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d4e90ae73853cd020120d26a1925e12\",\n \"score\": \"0.5717051\",\n \"text\": \"def getLevels(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c9725e4768fc871f4abab629fd0a710\",\n \"score\": \"0.5716833\",\n \"text\": \"def level(self):\\n return LuaExpression(self.player_unit, Method('level'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f444c4555bfc070e05c71991e69a9f1f\",\n \"score\": \"0.57162786\",\n \"text\": \"def battery_level(self):\\n battery = self._dp_mgr.dp.get_battery()\\n return battery[\\\"level\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"192b1e6ae794e4e9a1a46dcfe145cef8\",\n \"score\": \"0.57130486\",\n \"text\": \"def coverage_weight(self):\\n sel = self.viable_fields\\n weight = self.hour_angle\\n weight[~sel] = np.inf\\n weight += 6. * 360. * self.fields['TILING'] # Was 6, 60\\n weight += self.slew**3 # slew**2\\n weight += 100. * (self.airmass - 1.)**3\\n return weight\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a05d67b644fd19ea52c8c02f50545d03\",\n \"score\": \"0.5710315\",\n \"text\": \"def define_fuel_blends(self):\\n\\n fuels_lhv = {\\n \\\"petrol\\\": 42.4,\\n \\\"bioethanol - wheat straw\\\": 26.8,\\n \\\"bioethanol - maize starch\\\": 26.8,\\n \\\"bioethanol - sugarbeet\\\": 26.8,\\n \\\"bioethanol - forest residues\\\": 26.8,\\n \\\"synthetic gasoline\\\": 42.4,\\n \\\"diesel\\\": 42.8,\\n \\\"biodiesel - cooking oil\\\": 31.7,\\n \\\"biodiesel - algae\\\": 31.7,\\n \\\"synthetic diesel\\\": 43.3,\\n \\\"cng\\\": 55.5,\\n \\\"biogas - sewage sludge\\\": 55.5,\\n \\\"biogas - biowaste\\\": 55.5,\\n \\\"syngas\\\": 55.5,\\n }\\n\\n fuels_CO2 = {\\n \\\"petrol\\\": 3.18,\\n \\\"bioethanol - wheat straw\\\": 1.91,\\n \\\"bioethanol - maize starch\\\": 1.91,\\n \\\"bioethanol - sugarbeet\\\": 1.91,\\n \\\"bioethanol - forest residues\\\": 1.91,\\n \\\"synthetic gasoline\\\": 3.18,\\n \\\"diesel\\\": 3.14,\\n \\\"biodiesel - cooking oil\\\": 2.85,\\n \\\"biodiesel - algae\\\": 2.85,\\n \\\"synthetic diesel\\\": 3.16,\\n \\\"cng\\\": 2.65,\\n \\\"biogas - sewage sludge\\\": 2.65,\\n \\\"biogas - biowaste\\\": 2.65,\\n \\\"syngas\\\": 2.65,\\n }\\n\\n if {\\\"ICEV-p\\\", \\\"HEV-p\\\", \\\"PHEV-p\\\"}.intersection(set(self.scope[\\\"powertrain\\\"])):\\n fuel_type = \\\"petrol\\\"\\n primary, secondary, primary_share, secondary_share, tertiary, tertiary_share = self.find_fuel_shares(\\n fuel_type\\n )\\n self.create_fuel_markets(\\n fuel_type, primary, secondary, tertiary, primary_share, secondary_share, tertiary_share\\n )\\n self.fuel_blends[fuel_type] = {\\n \\\"primary\\\": {\\n \\\"type\\\": primary,\\n \\\"share\\\": primary_share,\\n \\\"lhv\\\": fuels_lhv[primary],\\n \\\"CO2\\\": fuels_CO2[primary],\\n },\\n \\\"secondary\\\": {\\n \\\"type\\\": secondary,\\n \\\"share\\\": secondary_share,\\n \\\"lhv\\\": fuels_lhv[secondary],\\n \\\"CO2\\\": fuels_CO2[secondary],\\n }\\n }\\n\\n if tertiary:\\n self.fuel_blends[fuel_type][\\\"tertiary\\\"] = {\\n \\\"type\\\": tertiary,\\n \\\"share\\\": tertiary_share,\\n \\\"lhv\\\": fuels_lhv[tertiary],\\n \\\"CO2\\\": fuels_CO2[tertiary],\\n }\\n\\n\\n if {\\\"ICEV-d\\\", \\\"HEV-d\\\", \\\"PHEV-d\\\"}.intersection(set(self.scope[\\\"powertrain\\\"])):\\n fuel_type = \\\"diesel\\\"\\n primary, secondary, primary_share, secondary_share, tertiary, tertiary_share = self.find_fuel_shares(\\n fuel_type\\n )\\n self.create_fuel_markets(\\n fuel_type, primary, secondary, tertiary, primary_share, secondary_share, tertiary_share\\n )\\n self.fuel_blends[fuel_type] = {\\n \\\"primary\\\": {\\n \\\"type\\\": primary,\\n \\\"share\\\": primary_share,\\n \\\"lhv\\\": fuels_lhv[primary],\\n \\\"CO2\\\": fuels_CO2[primary],\\n },\\n \\\"secondary\\\": {\\n \\\"type\\\": secondary,\\n \\\"share\\\": secondary_share,\\n \\\"lhv\\\": fuels_lhv[secondary],\\n \\\"CO2\\\": fuels_CO2[secondary],\\n },\\n }\\n\\n if tertiary:\\n self.fuel_blends[fuel_type][\\\"tertiary\\\"] = {\\n \\\"type\\\": tertiary,\\n \\\"share\\\": tertiary_share,\\n \\\"lhv\\\": fuels_lhv[tertiary],\\n \\\"CO2\\\": fuels_CO2[tertiary],\\n }\\n\\n if {\\\"ICEV-g\\\"}.intersection(set(self.scope[\\\"powertrain\\\"])):\\n fuel_type = \\\"cng\\\"\\n primary, secondary, primary_share, secondary_share, tertiary, tertiary_share = self.find_fuel_shares(\\n fuel_type\\n )\\n self.create_fuel_markets(\\n fuel_type, primary, secondary, tertiary, primary_share, secondary_share, tertiary_share\\n )\\n self.fuel_blends[fuel_type] = {\\n \\\"primary\\\": {\\n \\\"type\\\": primary,\\n \\\"share\\\": primary_share,\\n \\\"lhv\\\": fuels_lhv[primary],\\n \\\"CO2\\\": fuels_CO2[primary],\\n },\\n \\\"secondary\\\": {\\n \\\"type\\\": secondary,\\n \\\"share\\\": secondary_share,\\n \\\"lhv\\\": fuels_lhv[primary],\\n \\\"CO2\\\": fuels_CO2[primary],\\n },\\n }\\n\\n if tertiary:\\n self.fuel_blends[fuel_type][\\\"tertiary\\\"] = {\\n \\\"type\\\": tertiary,\\n \\\"share\\\": tertiary_share,\\n \\\"lhv\\\": fuels_lhv[tertiary],\\n \\\"CO2\\\": fuels_CO2[tertiary],\\n }\\n\\n if {\\\"FCEV\\\"}.intersection(set(self.scope[\\\"powertrain\\\"])):\\n fuel_type = \\\"hydrogen\\\"\\n primary, secondary, primary_share, secondary_share, tertiary, tertiary_share = self.find_fuel_shares(\\n fuel_type\\n )\\n self.create_fuel_markets(\\n fuel_type, primary, secondary, tertiary, primary_share, secondary_share, tertiary_share\\n )\\n self.fuel_blends[fuel_type] = {\\n \\\"primary\\\": {\\\"type\\\": primary, \\\"share\\\": primary_share},\\n \\\"secondary\\\": {\\\"type\\\": secondary, \\\"share\\\": secondary_share},\\n }\\n\\n if tertiary:\\n self.fuel_blends[fuel_type][\\\"tertiary\\\"] = {\\n \\\"type\\\": tertiary,\\n \\\"share\\\": tertiary_share\\n }\\n\\n if {\\\"BEV\\\", \\\"PHEV-p\\\", \\\"PHEV-d\\\"}.intersection(set(self.scope[\\\"powertrain\\\"])):\\n fuel_type = \\\"electricity\\\"\\n self.create_fuel_markets(fuel_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2401c18a6449cbb1bebbb93a53ab9ffc\",\n \"score\": \"0.5707544\",\n \"text\": \"def energy(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c579f457b6f73db85e7d09ff14ec50\",\n \"score\": \"0.5707229\",\n \"text\": \"def get_max_level(self):\\r\\n return len(self.level_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43e90c128e7c363eaf0bcb96e4d2d7ad\",\n \"score\": \"0.5701703\",\n \"text\": \"def _unit_floz(self):\\n return self.us_customary_base * 6.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62df3d51d6ac96820f04267ab300d385\",\n \"score\": \"0.56989706\",\n \"text\": \"def waterLevel(self):\\n if self.distance is None:\\n self.retrieveDistance()\\n\\n return self.depth - self.distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd4fddcdb0c19d7d46d6df031bb75a9\",\n \"score\": \"0.5695319\",\n \"text\": \"def update_fuel_level(self, P_gen_output_1, P_gen_output_2, delta_t):\\n\\n # get fuel rates gal/s\\n fuel_rate_1 = self.get_fuel_flow_rate(P_gen_output_1)\\n fuel_rate_2 = self.get_fuel_flow_rate(P_gen_output_2)\\n\\n self.fuel_flow_rate = fuel_rate_1;\\n\\n # Set engine loss\\n P_gen_input_1 = P_gen_output_1 / (self.ENGINE_EFF_LUT(P_gen_output_1)*self.POWER_EFF_MOTOR )\\n P_gen_input_2 = P_gen_output_2 / (self.ENGINE_EFF_LUT(P_gen_output_2)*self.POWER_EFF_MOTOR )\\n self.Genset_loss = self.get_energy_loss(P_gen_output_1, P_gen_input_1, P_gen_output_2, P_gen_input_2, delta_t)\\n\\n # Reset fuel level\\n current_level = self.fuel_level * self.MAX_TANK_CAPACITY\\n self.fuel_level= (current_level - self.integrate_fuel_flow(fuel_rate_1, fuel_rate_2, delta_t)) / self.MAX_TANK_CAPACITY\\n return self.fuel_level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"687e768ee3324036f8158d9c0d73b328\",\n \"score\": \"0.56951714\",\n \"text\": \"def ReportLowLiquidLevel(self, lType, internalBottleLocation, kioskID):\\n\\t \\n\\t return liqPercentage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dd31b98990d3076aee09b7321f059a1\",\n \"score\": \"0.5694667\",\n \"text\": \"def get_max_level(self):\\n maxy = -1000\\n max_level = None\\n for lvl in self.level_set.all():\\n if maxy < lvl.threshold:\\n maxy = lvl.threshold\\n max_level = lvl\\n return max_level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed5c20c587bd7b14dff153fcb7622cdc\",\n \"score\": \"0.5682814\",\n \"text\": \"def low_battery(self):\\n return self._low_battery\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27a6fd54fd7b3c2c35a5c94e1fe07479\",\n \"score\": \"0.56775534\",\n \"text\": \"def level(self):\\n if not self._level_calculated:\\n self._level_calculated = True\\n self._extract_level()\\n return self._level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8ad32ca94e678c148d24bde573ccab\",\n \"score\": \"0.56766844\",\n \"text\": \"def getLevel(self):\\n JSON = self.JSON\\n \\n networkExp = JSON.get('networkExp', 0) \\n networkLevel = JSON.get('networkLevel', 0)\\n \\n exp = leveling.getExperience(networkExp, networkLevel)\\n myoutput = leveling.getExactLevel(exp)\\n return myoutput\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56f78095458e0ab24f222145bdd91a24\",\n \"score\": \"0.56741554\",\n \"text\": \"def crf_level(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"crf_level\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56f78095458e0ab24f222145bdd91a24\",\n \"score\": \"0.56741554\",\n \"text\": \"def crf_level(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"crf_level\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7d209d9eae308c622222db093332254\",\n \"score\": \"0.5668044\",\n \"text\": \"def get_level(self):\\n return Student.STR_LEVEL[self.level]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb617acfa179753269528c75e7bb0f2\",\n \"score\": \"0.56564945\",\n \"text\": \"def get_level_display(self):\\n for choice in self.FORTIFICATION_CHOICES:\\n if self.level == choice[0]:\\n return choice[1]\\n # if level is too high, return the last element in choices\\n return self.FORTIFICATION_CHOICES[-1][1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d8c42f71781e8e38f096a81ce53ac7\",\n \"score\": \"0.5654729\",\n \"text\": \"def native_value(self) -> int:\\n return self.resource.power_state.battery_level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"824b1c29009bb309a5d0e648e7b500e8\",\n \"score\": \"0.56505066\",\n \"text\": \"def battery_level():\\n with open(\\\"/sys/class/power_supply/BAT0/capacity\\\", \\\"r\\\") as f:\\n return int(f.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"672ce56d46bd74de9ac54d0c5776d49c\",\n \"score\": \"0.564116\",\n \"text\": \"def KGEnergyLevel(cylindrical,Qnuc,qorb,morb,nr,lam,lzam,Bz):\\n\\t# spherical stability of an electronic atom requires Z <= 68\\n\\t# cylindrical stability requires lam>0\\n\\tif cylindrical:\\n\\t\\tif lzam**2 - (Qnuc*qorb)**2 < 0.0:\\n\\t\\t\\treturn 0.0\\n\\t\\te2i = 1.0 + (Qnuc*qorb)**2/(np.sqrt(lzam**2 - (Qnuc*qorb)**2) + nr + 0.5)**2\\n\\telse:\\n\\t\\tif (lam+0.5)**2 - (Qnuc*qorb)**2 < 0.0:\\n\\t\\t\\treturn 0.0\\n\\t\\te2i = 1.0 + (Qnuc*qorb)**2/(np.sqrt((lam+0.5)**2 - (Qnuc*qorb)**2) + nr + 0.5)**2\\n\\treturn morb*np.sqrt(1.0-lzam*qorb*Bz/morb**2)/np.sqrt(e2i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31fd9abf20bd110b6839f3aa594fb83e\",\n \"score\": \"0.56398284\",\n \"text\": \"def fuel_requirement(m):\\n return max(0, int(m / 3) - 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"778e206f3d891fd46ac1cf8737867019\",\n \"score\": \"0.5639819\",\n \"text\": \"def get_gain(self):\\n # v = self.vintage\\n # gain = 0.0\\n # for s in v:\\n # lower = 0.0\\n # for layer in s.stack:\\n # lower += layer.unit['dz']\\n # s.attributes(prm.DIGI, prm.LTRACE)\\n # xtr, ztr, g = s.limittr(lower, 0.0)\\n # gain += g / len(v)\\n\\n v = self.vintage\\n gain = 0.0\\n for s in v:\\n s.attributes(prm.DIGI, prm.LTRACE)\\n xtr, g = L.autogain(s.trace, s.dx / 2.0)\\n gain += g / len(v)\\n\\n return gain\",\n \"title\": \"\"\n },\n {\n \"docid\": \"651469fb96a60da33ced8206879443be\",\n \"score\": \"0.5603719\",\n \"text\": \"def getPower(self):\\n result = self.getTempPower()\\n return result[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2950829d92a6b5ad2dd62e35c5f25c93\",\n \"score\": \"0.5602412\",\n \"text\": \"def get_Lo(self):\\n return self.Lo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a51e5624226d3aef056854520ffb26e\",\n \"score\": \"0.5594745\",\n \"text\": \"def level(self):\\n return self._level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a51e5624226d3aef056854520ffb26e\",\n \"score\": \"0.5594745\",\n \"text\": \"def level(self):\\n return self._level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a51e5624226d3aef056854520ffb26e\",\n \"score\": \"0.5594745\",\n \"text\": \"def level(self):\\n return self._level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e693d94f15733d978a64595599d07576\",\n \"score\": \"0.559003\",\n \"text\": \"def battery(self): # pylint: disable=R0201\\n\\t\\treturn None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08fd9c25f9a3bf6aab502d726f79487\",\n \"score\": \"0.5586803\",\n \"text\": \"def battery_level(self) -> int | None:\\n return self.tuya_device.status.get(DPCODE_BATTERY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11b230005b7028895e2db6bf6cce18b0\",\n \"score\": \"0.5582059\",\n \"text\": \"def recursive_fuel_requirement(m):\\n if m <= 0:\\n return 0\\n\\n new_fuel = fuel_requirement(m)\\n return new_fuel + recursive_fuel_requirement(new_fuel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a3adf92d44cb2eab3df8ba0eab61fa\",\n \"score\": \"0.55751735\",\n \"text\": \"def calculate_fuel(mass):\\n return mass // 3 - 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb6f25d76d81889dfc721a631f92bf9b\",\n \"score\": \"0.5573937\",\n \"text\": \"def getMilPower(self):\\n return getMaxLevel((20,40,100,200,500,1000,2000), self.milPower)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b5f7cb50911f2830eb5e1c16d60a3ee\",\n \"score\": \"0.55732524\",\n \"text\": \"def get_range(self):\\n if self.battery_size == 75:\\n range = 260\\n elif self.battery_size == 100:\\n range = 315\\n else:\\n range = \\\"unknown\\\"\\n print(f\\\"This car can go about {range} miles on a full charge.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb20d613d8b4921f7d86fae7540d2f83\",\n \"score\": \"0.55693936\",\n \"text\": \"def drop_fuel(self):\\n values = self.values[:]\\n if values[-1] > 0 and values[-2] > 0:\\n cur_pos = values[-1] - 1\\n max_allowed, values[-2] = values[-2], 0\\n values[cur_pos] += max_allowed\\n else:\\n return None\\n return Desert(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24d62bb178be06fa83d6151a5bc231aa\",\n \"score\": \"0.5568161\",\n \"text\": \"def level_init(cls):\\n resource_rice.quantity_property = 10\\n resource_nori.quantity_property = 10\\n resource_roe.quantity_property = 10\\n resource_salmon.quantity_property = 5\\n resource_shrimp.quantity_property = 5\\n resource_unagi.quantity_property = 5\\n\\n # Meal instances init\\n if cls.current_level == 1:\\n meal_onigiri = Meal('Onigiri', 2, 1, 0, 0, 0, 0, 'onigiri{}')\\n meal_california_roll = Meal('California Roll', 1, 1, 1, 0, 0, 0, 'california_roll{}')\\n meal_gunkan_maki = Meal('Gunkan Maki', 1, 1, 2, 0, 0, 0, 'gunkan_maki{}')\\n elif cls.current_level == 2:\\n meal_salmon_roll = Meal('Salmon Roll', 1, 1, 0, 2, 0, 0, 'salmon_roll{}')\\n elif cls.current_level == 3:\\n meal_shrimp_sushi = Meal('Shrimp Sushi', 1, 1, 0, 0, 2, 0, 'shrimp_sushi{}')\\n elif cls.current_level == 4:\\n meal_unagi_roll = Meal('Unagi Roll', 1, 1, 0, 0, 0, 2, 'unagi_roll{}')\\n elif cls.current_level == 5:\\n meal_dragon_roll = Meal('Dragon Roll', 2, 1, 1, 0, 0, 2, 'dragon_roll{}')\\n elif cls.current_level == 6:\\n meal_combo_sushi = Meal('Combo Sushi', 2, 1, 1, 1, 1, 1, 'combo_sushi{}')\\n for x in Meal.meals_list:\\n print(x.name)\\n print(\\\"-\\\"*30)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e2989775d03b2660f09368a363b5f2\",\n \"score\": \"0.55630827\",\n \"text\": \"def update_fuel_level(self, amount):\\n if (self.fuel_level + amount \\n <= self.fuel_capacity):\\n self.fuel_level += amount\\n print(\\\"Added fuel.\\\")\\n else:\\n print(\\\"The tank won't hold that much.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8c7ca885f301e23d893ad5a25249e6a\",\n \"score\": \"0.5559175\",\n \"text\": \"def fuel(mass: int) -> int:\\n return math.floor(mass / 3) - 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a62785814d396be1e3ff9e63a3c16140\",\n \"score\": \"0.5551176\",\n \"text\": \"def source_level(self):\\n return float(self.ask(\\\":SOURce:LEVel?\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe78bbe098ae9d37918d3372ab100296\",\n \"score\": \"0.55421\",\n \"text\": \"def fan_level(self) -> int:\\n return self.details.get('level', 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc6d7b99290c8a56c9a34d67de42d520\",\n \"score\": \"0.55406314\",\n \"text\": \"def fuel_cost(self, update=False):\\n if update or self._dfs[\\\"fuel_cost\\\"] is None:\\n self._dfs[\\\"fuel_cost\\\"] = pudl.analysis.mcoe.fuel_cost(self)\\n return self._dfs[\\\"fuel_cost\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eafcd2f731305d5ae52b644450687074\",\n \"score\": \"0.55402744\",\n \"text\": \"def __float__(self):\\n return self._altitude\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1846edeebbeae0c0fd84045290a64c83\",\n \"score\": \"0.5518733\",\n \"text\": \"def ReportLiquidLevel(self, lType, internalBottleLocation, kioskID):\\n \\t\\t\\n\\t return liqPercentage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b50368c814c5be8fda7761cdf24e538\",\n \"score\": \"0.55152506\",\n \"text\": \"def GetLevelName(self):\\r\\n return self.levelname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f965ad750993c0abfeca679192e8f015\",\n \"score\": \"0.55145913\",\n \"text\": \"def get_max_damage(self):\\n\\t\\t_ret = self.get_base_damage()\\n\\t\\t\\n\\t\\tif self.weapon: _ret+=self.weapon['damage']\\n\\t\\t\\n\\t\\treturn _ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98f10d45240ee61abb490d9c76cf2c21\",\n \"score\": \"0.55142164\",\n \"text\": \"def get_crit(self, speed, level):\\n crit_rate = speed * 100 / 512\\n chance = random.uniform(1, 100)\\n if chance <= crit_rate:\\n return (2 * level + 5) / (level + 5)\\n else:\\n return 1.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"035d5f1b877b82948a7167a577ba5fc0\",\n \"score\": \"0.55073607\",\n \"text\": \"def voltage(self):\\n return self._voltage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa6c48e841a4a9cf34bbba0c1144fb61\",\n \"score\": \"0.55048\",\n \"text\": \"def getpower(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f35d991acda4d0896e31b372012d0040\",\n \"score\": \"0.54867935\",\n \"text\": \"def specific_heat_capacity(self):\\n return 2415.78 * units.joule / (units.kg * units.kelvin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"433162bffa5072437711104914ca8437\",\n \"score\": \"0.5483245\",\n \"text\": \"def Level(self) -> Bip44Levels:\\n return Bip44Levels(self.m_bip32_obj.Depth().ToInt())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6865e135edd036ff34c5579403c137d\",\n \"score\": \"0.54733413\",\n \"text\": \"def get_defense() -> int:\\n return defense\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4754a409db05104d83fe27c4f83a183\",\n \"score\": \"0.54705584\",\n \"text\": \"def battery_soc(self) -> float:\\n return self.battery_charge / self.battery_capacity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a8523bebfa1d032ec2ab2e9a07ababf\",\n \"score\": \"0.5464402\",\n \"text\": \"def critical_damage(self):\\n if self.owner and self.equipment:\\n equipment = self.equipment.critical_damage_bonus\\n else:\\n equipment = 0\\n\\n return self.attributes.base_critical_damage + equipment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf5e7d46234a667f9aa6cf3951aff6d\",\n \"score\": \"0.54614574\",\n \"text\": \"def get_Lo(self):\\n flow_area = self.a * self.W\\n wetted_perimeter = 2.*self.a+2.*self.W\\n return flow_area/wetted_perimeter\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993492,"cells":{"query_id":{"kind":"string","value":"d4cd93f5d748fce6278f88010ec3b46b"},"query":{"kind":"string","value":"SetConfidenceImage(itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2 self, itkImageF2 _arg)"},"positive_passages":{"kind":"list like","value":[{"docid":"cca36d4a0abdb64086ef7638f38fbaf0","score":"0.91615874","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetConfidenceImage(self, _arg)","title":""}],"string":"[\n {\n \"docid\": \"cca36d4a0abdb64086ef7638f38fbaf0\",\n \"score\": \"0.91615874\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"415a1a1b5d335a108c6dd621714243da","score":"0.9181978","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetConfidenceImage(self, _arg)","title":""},{"docid":"55792555db62c30cda001a0e5a88df40","score":"0.91630626","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetConfidenceImage(self, _arg)","title":""},{"docid":"62d79142dd4de3f5ad959d64b1430976","score":"0.91626525","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetConfidenceImage(self, _arg)","title":""},{"docid":"06eb03a159cf0e3a80915dcdc171d44a","score":"0.91439426","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetConfidenceImage(self, _arg)","title":""},{"docid":"80065499f8eb2c242eb48f0a16d17a6e","score":"0.90899944","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IF2_SetConfidenceImage(self, _arg)","title":""},{"docid":"af08be735b84cede864c44d3fc1f8930","score":"0.9084473","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IF2_SetConfidenceImage(self, _arg)","title":""},{"docid":"5c72ba269277ce18a17c1dd859c0fe89","score":"0.9073548","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUC2_SetConfidenceImage(self, _arg)","title":""},{"docid":"c2fa67537462abe49a024c38bf8ea50e","score":"0.9061937","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUS2_SetConfidenceImage(self, _arg)","title":""},{"docid":"3352257dbb3b896108915b3c1fdbe3d9","score":"0.9043429","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2ISS2_SetConfidenceImage(self, _arg)","title":""},{"docid":"6ae083b8fae84386a8260c9102cec704","score":"0.90386087","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUC2_SetConfidenceImage(self, _arg)","title":""},{"docid":"8aaa236dcee1bc378274b2d7003fa19c","score":"0.89890593","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IF2_SetConfidenceImage(self, _arg)","title":""},{"docid":"e1a63a9d12b21b4aea364ef93379592b","score":"0.8982227","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUS2_SetConfidenceImage(self, _arg)","title":""},{"docid":"6bf940df4b317ac174082bb762fe42d9","score":"0.8980655","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUS2_SetConfidenceImage(self, _arg)","title":""},{"docid":"8ef8604aade1f0918d0dd043b0c609c8","score":"0.8959206","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUC2_SetConfidenceImage(self, _arg)","title":""},{"docid":"0b22d2af4fdf25248e40b272db67b47b","score":"0.88225126","text":"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUS2_SetConfidenceImage(self, _arg)","title":""},{"docid":"a4bfc07dd0c4ad41ee57153411f0bdd4","score":"0.7824099","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS3IUC3IUC3_SetConfidenceImage(self, _arg)","title":""},{"docid":"4515eb26096d366c6f59234d1f03e2fe","score":"0.78033787","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS3IUC3ISS3_SetConfidenceImage(self, _arg)","title":""},{"docid":"0f8a8f04e69bdfd7b005a833676e5716","score":"0.77884185","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC3IUC3ISS3_SetConfidenceImage(self, _arg)","title":""},{"docid":"462c52f37b500a94fb6f618ace3386f7","score":"0.77218884","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS3IUC3ISS3_SetConfidenceImage(self, _arg)","title":""},{"docid":"6d7d2a003398452b882b61cb919e2969","score":"0.7688832","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS3IUC3IUS3_SetConfidenceImage(self, _arg)","title":""},{"docid":"e15b54cc56b322448a66f2eeb5fc37fc","score":"0.7686909","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS3IUC3IF3_SetConfidenceImage(self, _arg)","title":""},{"docid":"ee47d4c08d2598d73b153bb500d2fc96","score":"0.7683617","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF3IUC3ISS3_SetConfidenceImage(self, _arg)","title":""},{"docid":"3d482ab3f6b9fbf77da9d5bd42b52a48","score":"0.76709074","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC3IUC3IUC3_SetConfidenceImage(self, _arg)","title":""},{"docid":"8210cc21a93c578b7c894fc48df48f0a","score":"0.76175123","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS3IUC3IUC3_SetConfidenceImage(self, _arg)","title":""},{"docid":"a7d583fd957a07314f3a1fb903293f3b","score":"0.75803554","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF3IUC3IUC3_SetConfidenceImage(self, _arg)","title":""},{"docid":"0e2f97648b80bca04cf6f49a8e0ccca7","score":"0.75453186","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC3IUC3IUS3_SetConfidenceImage(self, _arg)","title":""},{"docid":"97b2964a8254610be380fa09a5c3b97e","score":"0.7542602","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC3IUC3IF3_SetConfidenceImage(self, _arg)","title":""},{"docid":"9c7b96763badbfee5c4e2580a95a9f43","score":"0.75074124","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_GetConfidenceImage(self)","title":""},{"docid":"68f07ea06bb5183b102f2db8b560825d","score":"0.75049967","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS3IUC3IF3_SetConfidenceImage(self, _arg)","title":""},{"docid":"fade97b753ed5878a648a3570903d784","score":"0.7504326","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_GetConfidenceImage(self)","title":""},{"docid":"e7ee528569bc5150235c390d7cd759ef","score":"0.7495035","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_GetConfidenceImage(self)","title":""},{"docid":"e65644c2f1453fda66417fc7afd10bfe","score":"0.7479861","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_GetConfidenceImage(self)","title":""},{"docid":"8e62916e33170d6cafb683f96106ac03","score":"0.7467486","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF3IUC3IUS3_SetConfidenceImage(self, _arg)","title":""},{"docid":"f53469f82304bcfefff1b374cafc3084","score":"0.7463805","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_GetConfidenceImage(self)","title":""},{"docid":"79d54bcb7a987d45ee9cc9d5e62ef33e","score":"0.74322873","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS3IUC3IUS3_SetConfidenceImage(self, _arg)","title":""},{"docid":"58e01763a3899cc5c7fbff1add0144e9","score":"0.7419779","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUS2_GetConfidenceImage(self)","title":""},{"docid":"6a3ebb53807e2c9e5b73f36f1621f94a","score":"0.74160707","text":"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF3IUC3IF3_SetConfidenceImage(self, _arg)","title":""},{"docid":"9c674c452fb2c6392806aaf9ad953d7f","score":"0.73960936","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IF2_GetConfidenceImage(self)","title":""},{"docid":"147200f1c03f2e82c15ace77fb7e0568","score":"0.73917013","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2ISS2_GetConfidenceImage(self)","title":""},{"docid":"a00dbe1732e5ea6e78bb8afaab42f6db","score":"0.7378125","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IF2_GetConfidenceImage(self)","title":""},{"docid":"d83b8414e72fd0f3ecf20185322c4feb","score":"0.7370967","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUC2_GetConfidenceImage(self)","title":""},{"docid":"be0e8ce6bf282c64a6de9f513cc9a6c5","score":"0.7330723","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUS2_GetConfidenceImage(self)","title":""},{"docid":"af18f67c905cae8abc7c267d054a3429","score":"0.7320884","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUS2_GetConfidenceImage(self)","title":""},{"docid":"2b0683716c499e9a87acceac84519bbf","score":"0.7317759","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IF2_GetConfidenceImage(self)","title":""},{"docid":"19eb1942fa9efdb5b8ccb29ec403e737","score":"0.7311853","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUC2_GetConfidenceImage(self)","title":""},{"docid":"5b0b3d4790042a25708bf2ea349868f1","score":"0.7285493","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2ISS2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"54dd9af69a8077e338f7dd2f8d185bdd","score":"0.72586465","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUC2_GetConfidenceImage(self)","title":""},{"docid":"94b9ac0d6fba2317ae7b2d3d6a4ad2eb","score":"0.72319764","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUS2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"9b4af17236569614bd145b921dd44d2b","score":"0.7230286","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"457acd6e65b0f09226386773a3e58407","score":"0.7229331","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"f1c9ce12939fb2c2ad535eeaa9045a30","score":"0.7182653","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"33ce8f810a3163c62afe0947820ffd55","score":"0.7159211","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IF2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"1b46eb9ed8848e43ad092d8b6186439a","score":"0.71557325","text":"def GetConfidenceImage(self) -> \"itkImageF2 const *\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUS2_GetConfidenceImage(self)","title":""},{"docid":"5fdb3797d84dedf9257a41a347fd6857","score":"0.7152297","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"589077f3a7a60e75fdb1b8462958879d","score":"0.71318626","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"ee09c396244c4b20bf8412e42b3a81b3","score":"0.70904136","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUS2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"f0cd340daf2c7cc0135f53d434b02c56","score":"0.70436764","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUS2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"f8cf514bc59492ab69652907aee4773c","score":"0.7027294","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IF2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"987d89b9211462f1a0e5a2d1a5f8af2d","score":"0.70244837","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUC2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"4fb1616271a74a5dc41efce272ad4852","score":"0.69579166","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IF2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"8929a60a5adee31c8147bad6ceba10d8","score":"0.69567245","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUC2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"3a718f8304b32dfc21cf397816698196","score":"0.6951047","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUS2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"a961368e67e25410a24022c657f5dd44","score":"0.68309015","text":"def SetInput1(self, image: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetInput1(self, image)","title":""},{"docid":"f59ec5e669773d2988cda296300558a0","score":"0.68243355","text":"def SetConvergenceThreshold(self, _arg: 'float const') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUC2_SetConvergenceThreshold(self, _arg)","title":""},{"docid":"d3074d28682e85b85c45c322a1e74a33","score":"0.6774732","text":"def SetInput1(self, image: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IF2_SetInput1(self, image)","title":""},{"docid":"72d400a6642525c9f5d0e1af4a1d9e11","score":"0.6731396","text":"def SetInput1(self, image: 'itkImageSS2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetInput1(self, image)","title":""},{"docid":"d11963c200aed781306d0fa8f119ccb7","score":"0.6714523","text":"def SetInput1(self, image: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetInput1(self, image)","title":""},{"docid":"e9de7aabf0e26f547cac9d1164f3774c","score":"0.6705237","text":"def SetInput1(self, image: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUC2_SetInput1(self, image)","title":""},{"docid":"368d1c5db3bb09144edfcbb7b2dd7f4e","score":"0.6629424","text":"def SetInput1(self, image: 'itkImageSS2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetInput1(self, image)","title":""},{"docid":"fb9b2999ec30eac067a83830dbc3b129","score":"0.66117585","text":"def SetInput1(self, image: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IF2_SetInput1(self, image)","title":""},{"docid":"dc45f09cfb9c6d0e5889a49cb327b230","score":"0.6596073","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetMaskImage(self, _arg)","title":""},{"docid":"458d60d8b36f6d117b91ed482ca6211d","score":"0.65922886","text":"def SetInput3(self, image: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetInput3(self, image)","title":""},{"docid":"18c28fff349ef635ae4c2c42a322d187","score":"0.6590845","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetMaskImage(self, _arg)","title":""},{"docid":"fd32d7a66ce9835a748894d4f121c888","score":"0.6586954","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetMaskImage(self, _arg)","title":""},{"docid":"4bc3bb8de189af70db6a8ef200c7978b","score":"0.6585312","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetMaskImage(self, _arg)","title":""},{"docid":"509a9ed6bf255515103f07aabd0c7168","score":"0.65666896","text":"def SetInput1(self, image: 'itkImageSS2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetInput1(self, image)","title":""},{"docid":"4c5ef7390a33f86917397f48519ae020","score":"0.6554358","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetMaskImage(self, _arg)","title":""},{"docid":"2c647164aa8f3b1b47b71d60efa73ba2","score":"0.6520484","text":"def SetInput1(self, image: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUS2_SetInput1(self, image)","title":""},{"docid":"eb8b2c2c7efbff348e6ae84b6bb3bac3","score":"0.64996845","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IF2_SetMaskImage(self, _arg)","title":""},{"docid":"77f6dae854442981deaf907b0b94bd9d","score":"0.64971185","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IF2_SetMaskImage(self, _arg)","title":""},{"docid":"2f6ebb02a6e8223dacb928e99b0ff56b","score":"0.64884895","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUS2_SetMaskImage(self, _arg)","title":""},{"docid":"33469e6910c0bc87437e79e1579c42d1","score":"0.64774334","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUC2_SetMaskImage(self, _arg)","title":""},{"docid":"075dbb17d2804885a10cc84420f0aa93","score":"0.64711446","text":"def SetInput3(self, image: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetInput3(self, image)","title":""},{"docid":"9ea6f778cd245d57a9dbb00686d87520","score":"0.6469047","text":"def SetInput1(self, image: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUC2_SetInput1(self, image)","title":""},{"docid":"d9075c1bb484d62dbd253dc6bbb9020b","score":"0.64646626","text":"def SetInput1(self, image: 'itkImageSS2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUS2_SetInput1(self, image)","title":""},{"docid":"19eb2f0f3bc7e96730db4c9c85d6e2c1","score":"0.6460571","text":"def SetInput2(self, mask: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetInput2(self, mask)","title":""},{"docid":"103c14314600f25308d180e9466f03da","score":"0.6459785","text":"def SetInput3(self, image: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetInput3(self, image)","title":""},{"docid":"7058ddee9eb3aa8faf9e4cfea1071c64","score":"0.6458641","text":"def SetInput2(self, mask: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetInput2(self, mask)","title":""},{"docid":"b549ed60e277f3f688f52d15fa0803d4","score":"0.6457873","text":"def SetInput2(self, mask: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetInput2(self, mask)","title":""},{"docid":"277ff244f5dd7ed1d1c08d0f15a0f25e","score":"0.6457664","text":"def SetInput3(self, image: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetInput3(self, image)","title":""},{"docid":"592e177617aaadbda3857190e89f015e","score":"0.6449479","text":"def SetInput3(self, image: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetInput3(self, image)","title":""},{"docid":"3d2d7de0239b192fc9ec6aacaad71452","score":"0.64413214","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUC2_SetMaskImage(self, _arg)","title":""},{"docid":"5db84f5e8c384c4fae58298043ca86c1","score":"0.6438869","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUS2_SetMaskImage(self, _arg)","title":""},{"docid":"ee3fc2c61f9577440eeb9f2dc04af818","score":"0.64234126","text":"def SetReplaceValue(self, *args):\n return _itkConnectedThresholdImageFilterPython.itkConnectedThresholdImageFilterIUC2IUC2_SetReplaceValue(self, *args)","title":""},{"docid":"ad0d9faaa8c5cd8b94985d557321d0b2","score":"0.6420199","text":"def SetInput1(self, image: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUS2_SetInput1(self, image)","title":""},{"docid":"c5d69b443bb98e695a8efcbf4d6ea5d0","score":"0.64196706","text":"def SetInput2(self, mask: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetInput2(self, mask)","title":""},{"docid":"280d5422169c4bbbd321217db56bc0c7","score":"0.6417673","text":"def SetMaskImage(self, _arg: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUS2_SetMaskImage(self, _arg)","title":""},{"docid":"1b6d855c85a2923db2335f8e875ea573","score":"0.6397154","text":"def SetInput2(self, mask: 'itkImageUC2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetInput2(self, mask)","title":""},{"docid":"8c796c82d40078c3f34a6b81192e03cd","score":"0.6386173","text":"def SetInput1(self, image: 'itkImageUS2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2ISS2_SetInput1(self, image)","title":""},{"docid":"b4dd589a224a9a44e6d55f4423e494be","score":"0.6383905","text":"def SetReplaceValue(self, _arg: 'float const') -> \"void\":\n return _itkConnectedThresholdImageFilterPython.itkConnectedThresholdImageFilterIF2IF2_SetReplaceValue(self, _arg)","title":""}],"string":"[\n {\n \"docid\": \"415a1a1b5d335a108c6dd621714243da\",\n \"score\": \"0.9181978\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55792555db62c30cda001a0e5a88df40\",\n \"score\": \"0.91630626\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62d79142dd4de3f5ad959d64b1430976\",\n \"score\": \"0.91626525\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06eb03a159cf0e3a80915dcdc171d44a\",\n \"score\": \"0.91439426\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80065499f8eb2c242eb48f0a16d17a6e\",\n \"score\": \"0.90899944\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IF2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af08be735b84cede864c44d3fc1f8930\",\n \"score\": \"0.9084473\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IF2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c72ba269277ce18a17c1dd859c0fe89\",\n \"score\": \"0.9073548\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUC2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2fa67537462abe49a024c38bf8ea50e\",\n \"score\": \"0.9061937\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUS2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3352257dbb3b896108915b3c1fdbe3d9\",\n \"score\": \"0.9043429\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2ISS2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae083b8fae84386a8260c9102cec704\",\n \"score\": \"0.90386087\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUC2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aaa236dcee1bc378274b2d7003fa19c\",\n \"score\": \"0.89890593\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IF2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1a63a9d12b21b4aea364ef93379592b\",\n \"score\": \"0.8982227\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUS2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bf940df4b317ac174082bb762fe42d9\",\n \"score\": \"0.8980655\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUS2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef8604aade1f0918d0dd043b0c609c8\",\n \"score\": \"0.8959206\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUC2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b22d2af4fdf25248e40b272db67b47b\",\n \"score\": \"0.88225126\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUS2_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4bfc07dd0c4ad41ee57153411f0bdd4\",\n \"score\": \"0.7824099\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS3IUC3IUC3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4515eb26096d366c6f59234d1f03e2fe\",\n \"score\": \"0.78033787\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS3IUC3ISS3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f8a8f04e69bdfd7b005a833676e5716\",\n \"score\": \"0.77884185\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC3IUC3ISS3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462c52f37b500a94fb6f618ace3386f7\",\n \"score\": \"0.77218884\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS3IUC3ISS3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d7d2a003398452b882b61cb919e2969\",\n \"score\": \"0.7688832\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS3IUC3IUS3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e15b54cc56b322448a66f2eeb5fc37fc\",\n \"score\": \"0.7686909\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS3IUC3IF3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee47d4c08d2598d73b153bb500d2fc96\",\n \"score\": \"0.7683617\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF3IUC3ISS3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d482ab3f6b9fbf77da9d5bd42b52a48\",\n \"score\": \"0.76709074\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC3IUC3IUC3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8210cc21a93c578b7c894fc48df48f0a\",\n \"score\": \"0.76175123\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS3IUC3IUC3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7d583fd957a07314f3a1fb903293f3b\",\n \"score\": \"0.75803554\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF3IUC3IUC3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e2f97648b80bca04cf6f49a8e0ccca7\",\n \"score\": \"0.75453186\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC3IUC3IUS3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b2964a8254610be380fa09a5c3b97e\",\n \"score\": \"0.7542602\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC3IUC3IF3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c7b96763badbfee5c4e2580a95a9f43\",\n \"score\": \"0.75074124\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68f07ea06bb5183b102f2db8b560825d\",\n \"score\": \"0.75049967\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS3IUC3IF3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fade97b753ed5878a648a3570903d784\",\n \"score\": \"0.7504326\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7ee528569bc5150235c390d7cd759ef\",\n \"score\": \"0.7495035\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e65644c2f1453fda66417fc7afd10bfe\",\n \"score\": \"0.7479861\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e62916e33170d6cafb683f96106ac03\",\n \"score\": \"0.7467486\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF3IUC3IUS3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f53469f82304bcfefff1b374cafc3084\",\n \"score\": \"0.7463805\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79d54bcb7a987d45ee9cc9d5e62ef33e\",\n \"score\": \"0.74322873\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS3IUC3IUS3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58e01763a3899cc5c7fbff1add0144e9\",\n \"score\": \"0.7419779\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUS2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a3ebb53807e2c9e5b73f36f1621f94a\",\n \"score\": \"0.74160707\",\n \"text\": \"def SetConfidenceImage(self, _arg: 'itkImageF3') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF3IUC3IF3_SetConfidenceImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c674c452fb2c6392806aaf9ad953d7f\",\n \"score\": \"0.73960936\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IF2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147200f1c03f2e82c15ace77fb7e0568\",\n \"score\": \"0.73917013\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2ISS2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a00dbe1732e5ea6e78bb8afaab42f6db\",\n \"score\": \"0.7378125\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IF2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d83b8414e72fd0f3ecf20185322c4feb\",\n \"score\": \"0.7370967\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUC2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be0e8ce6bf282c64a6de9f513cc9a6c5\",\n \"score\": \"0.7330723\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUS2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af18f67c905cae8abc7c267d054a3429\",\n \"score\": \"0.7320884\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUS2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b0683716c499e9a87acceac84519bbf\",\n \"score\": \"0.7317759\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IF2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19eb1942fa9efdb5b8ccb29ec403e737\",\n \"score\": \"0.7311853\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUC2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b0b3d4790042a25708bf2ea349868f1\",\n \"score\": \"0.7285493\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2ISS2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54dd9af69a8077e338f7dd2f8d185bdd\",\n \"score\": \"0.72586465\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUC2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b9ac0d6fba2317ae7b2d3d6a4ad2eb\",\n \"score\": \"0.72319764\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUS2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b4af17236569614bd145b921dd44d2b\",\n \"score\": \"0.7230286\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"457acd6e65b0f09226386773a3e58407\",\n \"score\": \"0.7229331\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c9ce12939fb2c2ad535eeaa9045a30\",\n \"score\": \"0.7182653\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33ce8f810a3163c62afe0947820ffd55\",\n \"score\": \"0.7159211\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IF2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b46eb9ed8848e43ad092d8b6186439a\",\n \"score\": \"0.71557325\",\n \"text\": \"def GetConfidenceImage(self) -> \\\"itkImageF2 const *\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUS2_GetConfidenceImage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fdb3797d84dedf9257a41a347fd6857\",\n \"score\": \"0.7152297\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"589077f3a7a60e75fdb1b8462958879d\",\n \"score\": \"0.71318626\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee09c396244c4b20bf8412e42b3a81b3\",\n \"score\": \"0.70904136\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUS2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0cd340daf2c7cc0135f53d434b02c56\",\n \"score\": \"0.70436764\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUS2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8cf514bc59492ab69652907aee4773c\",\n \"score\": \"0.7027294\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IF2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"987d89b9211462f1a0e5a2d1a5f8af2d\",\n \"score\": \"0.70244837\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUC2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb1616271a74a5dc41efce272ad4852\",\n \"score\": \"0.69579166\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IF2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8929a60a5adee31c8147bad6ceba10d8\",\n \"score\": \"0.69567245\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUC2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a718f8304b32dfc21cf397816698196\",\n \"score\": \"0.6951047\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2IUS2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a961368e67e25410a24022c657f5dd44\",\n \"score\": \"0.68309015\",\n \"text\": \"def SetInput1(self, image: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f59ec5e669773d2988cda296300558a0\",\n \"score\": \"0.68243355\",\n \"text\": \"def SetConvergenceThreshold(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUC2_SetConvergenceThreshold(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3074d28682e85b85c45c322a1e74a33\",\n \"score\": \"0.6774732\",\n \"text\": \"def SetInput1(self, image: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IF2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d400a6642525c9f5d0e1af4a1d9e11\",\n \"score\": \"0.6731396\",\n \"text\": \"def SetInput1(self, image: 'itkImageSS2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11963c200aed781306d0fa8f119ccb7\",\n \"score\": \"0.6714523\",\n \"text\": \"def SetInput1(self, image: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9de7aabf0e26f547cac9d1164f3774c\",\n \"score\": \"0.6705237\",\n \"text\": \"def SetInput1(self, image: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUC2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"368d1c5db3bb09144edfcbb7b2dd7f4e\",\n \"score\": \"0.6629424\",\n \"text\": \"def SetInput1(self, image: 'itkImageSS2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9b2999ec30eac067a83830dbc3b129\",\n \"score\": \"0.66117585\",\n \"text\": \"def SetInput1(self, image: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IF2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc45f09cfb9c6d0e5889a49cb327b230\",\n \"score\": \"0.6596073\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458d60d8b36f6d117b91ed482ca6211d\",\n \"score\": \"0.65922886\",\n \"text\": \"def SetInput3(self, image: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetInput3(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c28fff349ef635ae4c2c42a322d187\",\n \"score\": \"0.6590845\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd32d7a66ce9835a748894d4f121c888\",\n \"score\": \"0.6586954\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc3bb8de189af70db6a8ef200c7978b\",\n \"score\": \"0.6585312\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509a9ed6bf255515103f07aabd0c7168\",\n \"score\": \"0.65666896\",\n \"text\": \"def SetInput1(self, image: 'itkImageSS2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c5ef7390a33f86917397f48519ae020\",\n \"score\": \"0.6554358\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c647164aa8f3b1b47b71d60efa73ba2\",\n \"score\": \"0.6520484\",\n \"text\": \"def SetInput1(self, image: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUS2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb8b2c2c7efbff348e6ae84b6bb3bac3\",\n \"score\": \"0.64996845\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IF2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f6dae854442981deaf907b0b94bd9d\",\n \"score\": \"0.64971185\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IF2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f6ebb02a6e8223dacb928e99b0ff56b\",\n \"score\": \"0.64884895\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUS2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33469e6910c0bc87437e79e1579c42d1\",\n \"score\": \"0.64774334\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUC2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"075dbb17d2804885a10cc84420f0aa93\",\n \"score\": \"0.64711446\",\n \"text\": \"def SetInput3(self, image: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetInput3(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ea6f778cd245d57a9dbb00686d87520\",\n \"score\": \"0.6469047\",\n \"text\": \"def SetInput1(self, image: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUC2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9075c1bb484d62dbd253dc6bbb9020b\",\n \"score\": \"0.64646626\",\n \"text\": \"def SetInput1(self, image: 'itkImageSS2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUS2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19eb2f0f3bc7e96730db4c9c85d6e2c1\",\n \"score\": \"0.6460571\",\n \"text\": \"def SetInput2(self, mask: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetInput2(self, mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"103c14314600f25308d180e9466f03da\",\n \"score\": \"0.6459785\",\n \"text\": \"def SetInput3(self, image: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2ISS2_SetInput3(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7058ddee9eb3aa8faf9e4cfea1071c64\",\n \"score\": \"0.6458641\",\n \"text\": \"def SetInput2(self, mask: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetInput2(self, mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b549ed60e277f3f688f52d15fa0803d4\",\n \"score\": \"0.6457873\",\n \"text\": \"def SetInput2(self, mask: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetInput2(self, mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"277ff244f5dd7ed1d1c08d0f15a0f25e\",\n \"score\": \"0.6457664\",\n \"text\": \"def SetInput3(self, image: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetInput3(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"592e177617aaadbda3857190e89f015e\",\n \"score\": \"0.6449479\",\n \"text\": \"def SetInput3(self, image: 'itkImageF2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetInput3(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d2d7de0239b192fc9ec6aacaad71452\",\n \"score\": \"0.64413214\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUC2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5db84f5e8c384c4fae58298043ca86c1\",\n \"score\": \"0.6438869\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUS2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee3fc2c61f9577440eeb9f2dc04af818\",\n \"score\": \"0.64234126\",\n \"text\": \"def SetReplaceValue(self, *args):\\n return _itkConnectedThresholdImageFilterPython.itkConnectedThresholdImageFilterIUC2IUC2_SetReplaceValue(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad0d9faaa8c5cd8b94985d557321d0b2\",\n \"score\": \"0.6420199\",\n \"text\": \"def SetInput1(self, image: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2IUS2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5d69b443bb98e695a8efcbf4d6ea5d0\",\n \"score\": \"0.64196706\",\n \"text\": \"def SetInput2(self, mask: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IUC2_SetInput2(self, mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"280d5422169c4bbbd321217db56bc0c7\",\n \"score\": \"0.6417673\",\n \"text\": \"def SetMaskImage(self, _arg: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIF2IUC2IUS2_SetMaskImage(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b6d855c85a2923db2335f8e875ea573\",\n \"score\": \"0.6397154\",\n \"text\": \"def SetInput2(self, mask: 'itkImageUC2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2ISS2_SetInput2(self, mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c796c82d40078c3f34a6b81192e03cd\",\n \"score\": \"0.6386173\",\n \"text\": \"def SetInput1(self, image: 'itkImageUS2') -> \\\"void\\\":\\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUS2IUC2ISS2_SetInput1(self, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4dd589a224a9a44e6d55f4423e494be\",\n \"score\": \"0.6383905\",\n \"text\": \"def SetReplaceValue(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkConnectedThresholdImageFilterPython.itkConnectedThresholdImageFilterIF2IF2_SetReplaceValue(self, _arg)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993493,"cells":{"query_id":{"kind":"string","value":"4c403227b17588c8a00c52a5dbe76c27"},"query":{"kind":"string","value":"A context manager that will lock on the device given, if configured to do so."},"positive_passages":{"kind":"list like","value":[{"docid":"4769ef3c47052963d0e455f3bd3b1be3","score":"0.62667316","text":"def replication_lock(self, device):\r\n if self.replication_one_per_device:\r\n dev_path = self.get_dev_path(device)\r\n with lock_path(\r\n dev_path,\r\n timeout=self.replication_lock_timeout,\r\n timeout_class=ReplicationLockTimeout):\r\n yield True\r\n else:\r\n yield True","title":""}],"string":"[\n {\n \"docid\": \"4769ef3c47052963d0e455f3bd3b1be3\",\n \"score\": \"0.62667316\",\n \"text\": \"def replication_lock(self, device):\\r\\n if self.replication_one_per_device:\\r\\n dev_path = self.get_dev_path(device)\\r\\n with lock_path(\\r\\n dev_path,\\r\\n timeout=self.replication_lock_timeout,\\r\\n timeout_class=ReplicationLockTimeout):\\r\\n yield True\\r\\n else:\\r\\n yield True\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"dccae90a973f8f2e76e03f36ca6969a1","score":"0.69568276","text":"def lock_context(self, timeout='default', requested_key='exclusive'):\n if requested_key == 'exclusive':\n self.lock_excl(timeout)\n access_key = None\n else:\n access_key = self.lock(timeout, requested_key)\n try:\n yield access_key\n finally:\n self.unlock()","title":""},{"docid":"2a9ebe91afc8ef299370fd3fe4f999b8","score":"0.65677816","text":"def test_context_manager_locking(self):\n bluetooth_if = BluetoothInterface(MockBackend)\n self.assertFalse(bluetooth_if.is_connected())\n\n with bluetooth_if.connect(\"abc\"): # as connection:\n self.assertTrue(bluetooth_if.is_connected())\n\n self.assertFalse(bluetooth_if.is_connected())","title":""},{"docid":"9e22df8617ae243b7011dc79f53e110d","score":"0.6399493","text":"def is_device_locked(self):\n raise NotImplementedError()","title":""},{"docid":"de7c45c822c8a22872c8125a1ba9ecb8","score":"0.63030064","text":"def lock(self):\n raise NotImplementedError(\"Lock not implemented\")","title":""},{"docid":"cf7f7eb2d4325818c3ce20b6c64be36f","score":"0.6302039","text":"def lock(self):\n \n pass","title":""},{"docid":"6fc561f88959ee17f74ae5988acda628","score":"0.62960786","text":"def lock_to_device(self, device):\n self._RemoteSL__effect_controller.lock_to_device(device)","title":""},{"docid":"a967a097b1beee488844b2cedf127a6f","score":"0.62846076","text":"def createLock(self) -> None:\n self.lock = TerminalHandler.sharedlock","title":""},{"docid":"bf679c81ca024c14654302dbbe58e5a1","score":"0.6231577","text":"def openLock():\n opener.open()\n # opener.pi_open() ","title":""},{"docid":"afb6d76e3a6ae3ed440195a43a416be0","score":"0.6212352","text":"def can_lock_to_devices(self):\n return True","title":""},{"docid":"976b7c1de75e2814b8f3caefa1a823ad","score":"0.6190904","text":"def __enter__(self):\n raise RuntimeError(\n 'Device class does not support runtime context using `with` '\n 'statement. Use chainer.using_device instead.')","title":""},{"docid":"cdf865271a980f9cd28677f6fb37c408","score":"0.6182173","text":"def lock(self):\n raise NotImplementedError","title":""},{"docid":"8a18ad2f58422cf3692fbd641fbb13bb","score":"0.61740434","text":"def lock(self):\n self._lock.acquire()\n self._lock_owner = threading.current_thread()","title":""},{"docid":"8b3ce352b4665f0958e38bcc0cb300ba","score":"0.6138324","text":"def _lock(self):\n if not self.locked:\n try:\n self.device.cu.lock()\n self.locked = True\n except JnprLockError as jle:\n raise LockError(str(jle))","title":""},{"docid":"1b137d2637e047dfb4b8563850cc7245","score":"0.61174935","text":"def acquire_lock():\n pass","title":""},{"docid":"26dd0f5469353cb3f3250eb585e977e0","score":"0.6090395","text":"def lock(self, Verbose=False):\n\n if self.lockfile != None and self.lockfile != \"\":\n if Verbose:\n print(\"Acquiring lock file \" + self.lockfile)\n while True:\n try:\n lockfd = os.open(self.lockfile, os.O_CREAT | os.O_EXCL | os.O_RDWR)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise ZEBUError(\"Unable to write lockfile \" + self.lockfile)\n time.sleep(0.1) # Wait 0.1 seconds, then retry the lockfile\n else:\n break\n if Verbose:\n print(\"Locked \" + self.lockfile)\n else:\n lockfd = None\n\n return lockfd","title":""},{"docid":"4b8bc5547b993c6a472fdd7faf06720a","score":"0.6071644","text":"def CreateLock():\r\n if MultiprocessingIsAvailable()[0]:\r\n return manager.Lock()\r\n else:\r\n return threading.Lock()","title":""},{"docid":"b31776bfdcccf4c14b11e2c7c9aaa8af","score":"0.60390854","text":"def __get_lock(self):\n self.core.get_lock()\n self.locked = True","title":""},{"docid":"3509fde471e0a2f31159c313abbfcac8","score":"0.6012302","text":"def lock_configuration(dev):\n #Lock device configuration \n print(\"Locking the configuration\")\n try: \n dev.cu.lock()\n locked = True\n except LockError as err:\n print(\"Unable to lock configuration: \\n {0}\".format(err))\n dev.close()\n locked = False\n return locked","title":""},{"docid":"63306e0b8ff1c0e48fb9bea320a968ff","score":"0.5992754","text":"def create_lock(self):","title":""},{"docid":"6d22650041ffd9c666022c51f5eace7f","score":"0.5957061","text":"def _lock(self):\n return _lock","title":""},{"docid":"73b1c2043dfef8f5abe73ecb041b8e47","score":"0.58978397","text":"def lock(self, timeout='default', requested_key=None):\n timeout = self.timeout if timeout == 'default' else timeout\n timeout = self._cleanup_timeout(timeout)\n return self.visalib.lock(self.session, constants.AccessModes.shared_lock, timeout, requested_key)[0]","title":""},{"docid":"7d7105ea6b4f8c8b519c50d64abbf960","score":"0.58613545","text":"def with_lock(lock):\n\n def decorator(fn):\n @contextmanager\n @functools.wraps(fn)\n def wrapper(*args, **kwargs):\n with lock:\n with fn(*args, **kwargs):\n yield\n\n return wrapper\n\n return decorator","title":""},{"docid":"6d291d1c7f1894d76541928e8f2475ae","score":"0.58487815","text":"def lock_for_update(self):\n if hasattr(self._base_manager, 'select_for_update'):\n return self._native_lock()\n\n return self._raw_lock()","title":""},{"docid":"d27551ecbe8da778d06d14c620d5e90f","score":"0.5843628","text":"def active_and_locked(self):\n self.lock.acquire()\n egl.eglMakeCurrent(self.dpy, self.surface, self.surface, self.context)\n try:\n yield\n finally:\n egl.eglMakeCurrent(\n self.dpy, egl.EGL_NO_SURFACE, egl.EGL_NO_SURFACE, egl.EGL_NO_CONTEXT\n )\n self.lock.release()","title":""},{"docid":"166a308a1d9a398b542056de3b8f8069","score":"0.5833352","text":"def lock(self):\n #print \"lock\"\n o = self.instance\n while not o.db.setnx(o.key('_lock'), self.lock_timeout):\n lock = o.db.get(o.key('_lock'))\n if not lock:\n continue\n if not self.lock_has_expired(lock):\n time.sleep(0.5)\n continue\n lock = o.db.getset(o.key('_lock'), self.lock_timeout)\n if not lock:\n break\n if self.lock_has_expired(lock):\n break","title":""},{"docid":"10b0a0db2193c9acfb24a91ccf697292","score":"0.58251214","text":"def lock(self):\n with self._meta.workspace.lock(self.get_id()):\n yield","title":""},{"docid":"e56703c3ba7c8999d5853045585e476a","score":"0.581702","text":"def lock(self):\n return self.Gtk_Lock(self)","title":""},{"docid":"7345597d8e366b18fa9beca164602140","score":"0.58147186","text":"def __call__(self):\n thread_id = threading.get_ident()\n this_id = id(self)\n if DEBUG_PRINTS:\n if random.random() > 0.5:\n print('[{}.{}.v1] aquire outer lock1'.format(thread_id, this_id))\n with self.lock1:\n print('[{}.{}.v1] aquired outer lock1'.format(thread_id, this_id))\n print('[{}.{}.v1] aquire inner lock2'.format(thread_id, this_id))\n with self.lock2:\n print('[{}.{}.v1] aquired inner lock2'.format(thread_id, this_id))\n print('[{}.{}.v1] releasing inner lock2'.format(thread_id, this_id))\n print('[{}.{}.v1] released inner lock2'.format(thread_id, this_id))\n print('[{}.{}.v1] releasing outer lock1'.format(thread_id, this_id))\n print('[{}.{}.v1] released outer lock1'.format(thread_id, this_id))\n else:\n print('[{}.{}.v2] aquire outer lock2'.format(thread_id, this_id))\n with self.lock2:\n print('[{}.{}.v2] aquired outer lock2'.format(thread_id, this_id))\n print('[{}.{}.v2] aquire inner lock1'.format(thread_id, this_id))\n with self.lock1:\n print('[{}.{}.v2] aquired inner lock1'.format(thread_id, this_id))\n print('[{}.{}.v2] releasing inner lock1'.format(thread_id, this_id))\n print('[{}.{}.v2] released inner lock1'.format(thread_id, this_id))\n print('[{}.{}.v2] releasing outer lock2'.format(thread_id, this_id))\n print('[{}.{}.v2] released outer lock2'.format(thread_id, this_id))\n else:\n if random.random() > 0.5:\n with self.lock1:\n time.sleep(0.03)\n with self.lock2:\n ...\n else:\n with self.lock2:\n time.sleep(0.03)\n with self.lock1:\n ...","title":""},{"docid":"f6c179b2f8275e429b7f2c1a9fe6adbb","score":"0.581453","text":"def lock(self):\n pass","title":""},{"docid":"3c97cee6508d982a558f8f63a958ead0","score":"0.5812277","text":"def lock_it(lock=g_lock):\n\n def func_wrapper(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n with lock:\n return func(*args, **kwargs)\n\n return wrapper\n\n return func_wrapper","title":""},{"docid":"da821e44d9cef1efbc701bb8c3067da6","score":"0.5809921","text":"def lock(self):\n if not self.use_lock_file:\n return\n locked = False\n while not locked:\n try:\n self.lock_file = open(self.db_uri + \".handle\", \"w\")\n fcntl.lockf(self.lock_file, fcntl.LOCK_EX | fcntl.LOCK_NB)\n locked = True\n except Exception:\n time.sleep(0.1)","title":""},{"docid":"ac42a8cf921b1940e7284adb7bbdc8e0","score":"0.5798407","text":"def lock(self):\n self.__consumer__.lock()","title":""},{"docid":"c822e8cddb94d3c2557df1c92c66a209","score":"0.5797938","text":"def create_lock(self, context, lock_key):\n raise NotImplementedError()","title":""},{"docid":"4d4d25ecc6ff72ee9111bf78bf798d22","score":"0.5792462","text":"def example_global_lock(task):\n lock = task.run(netconf_lock, datastore=\"candidate\", operation=\"lock\")\n # Retrieve the Manager(agent) from lock operation and store for further\n # operations.\n task.host[\"manager\"] = lock.result[\"manager\"]","title":""},{"docid":"d6165a7e23dab0fd357a89e514a93bed","score":"0.57902485","text":"def lock(self):\n self.__locked__ = True","title":""},{"docid":"68eaaafc6230082d3950ad44a5976788","score":"0.57829446","text":"def create_lock(self):\n return Lock()","title":""},{"docid":"f3a5c834f477c1c9874f3ebc9aa64201","score":"0.57777643","text":"def fetch_locking(self):\n return None","title":""},{"docid":"bafd70793969b911e5a8fef68185cf0f","score":"0.5768873","text":"def lock(self, timeout=None):\n\t\tlockfile = self.path[0] + '/.__dblock.lock'\n\n\t\treturn locking.lock(lockfile, timeout)","title":""},{"docid":"2ba0f431b8e3cc0db0b1af2e11ce0e12","score":"0.5766459","text":"def test_lock(self):\n self.s.lock()\n self.s.unlock()","title":""},{"docid":"13daf3b900879e9bcb8b05f5ede24a72","score":"0.5759565","text":"def setiflock(self):\r\n command = TTiBase.format_tx(self, \"IFLOCK\")\r\n TTiBase.tx(self, command)","title":""},{"docid":"36d9144cce87b3f699e7d6cba68d40a9","score":"0.5744884","text":"def _T_lock(self, s : Storage):\n if self.trace:\n self.telemetry.trace.record('lock', self.clock, s.root_id)","title":""},{"docid":"6692e195b7fe3e348db0f9eef872a12a","score":"0.57381415","text":"def CMD_Lock_Func(args, ser):\n print (\"Locking Module\")\n ser.write(BL_LOCK)","title":""},{"docid":"ffaf806f287645fa64496fdd7ae5a115","score":"0.57317543","text":"async def lock_context(\n self,\n lock_key: str,\n lock_value: bytes | str | None = None,\n *,\n blocking: bool = False,\n blocking_timeout_s: NonNegativeFloat = 5,\n ) -> AsyncIterator[Lock]:\n\n total_lock_duration: datetime.timedelta = _DEFAULT_LOCK_TTL\n lock_unique_id = f\"lock_extender_{lock_key}_{uuid4()}\"\n\n ttl_lock: Lock = self._client.lock(\n name=lock_key,\n timeout=total_lock_duration.total_seconds(),\n blocking=blocking,\n blocking_timeout=blocking_timeout_s,\n )\n\n if not await ttl_lock.acquire(token=lock_value):\n raise CouldNotAcquireLockError(lock=ttl_lock)\n\n async def _extend_lock(lock: Lock) -> None:\n with log_context(\n logger, logging.DEBUG, f\"Extending lock {lock_unique_id}\"\n ), log_catch(logger, reraise=False):\n await lock.reacquire()\n\n try:\n async with periodic_task(\n _extend_lock,\n interval=total_lock_duration / 2,\n task_name=lock_unique_id,\n lock=ttl_lock,\n stop_timeout=0.1,\n ):\n # lock is in use now\n yield ttl_lock\n finally:\n # NOTE Why is this error suppressed? Given the following situation:\n # - 250 locks are acquire in parallel with the option `blocking=True`,\n # meaning: it will wait for the lock to be free before acquiring it\n # - when the lock is acquired the `_extend_lock` task is started\n # in the background, extending the lock at a fixed interval of time,\n # which is half of the duration of the lock's TTL\n # - before the task is released the lock extension task is cancelled\n # Here is where the issue occurs:\n # - some time passes between the task's cancellation and\n # the call to release the lock\n # - if the TTL is too small, 1/2 of the TTL might be just shorter than\n # the time it passes to between the task is canceled and the task lock is released\n # - this means that the lock will expire and be considered as not owned any longer\n # For example: in one of the failing tests the TTL is set to `0.25` seconds,\n # and half of that is `0.125` seconds.\n\n # Above implies that only one \"task\" `owns` and `extends` the lock at a time.\n # The issue appears to be related some timings (being too low).\n try:\n await ttl_lock.release()\n except redis.exceptions.LockNotOwnedError:\n # if this appears outside tests it can cause issues since something might be happening\n logger.warning(\n \"Attention: lock is no longer owned. This is unexpected and requires investigation\"\n )","title":""},{"docid":"b757cf6d6a97d9221ec47fb72cf90cdf","score":"0.57275677","text":"def __enter__(self):\n if not self.is_locked:\n self.acquire()\n return self","title":""},{"docid":"b757cf6d6a97d9221ec47fb72cf90cdf","score":"0.57275677","text":"def __enter__(self):\n if not self.is_locked:\n self.acquire()\n return self","title":""},{"docid":"df875d7c6e34616d178f19f3673ff957","score":"0.570744","text":"def _lockit(func):\n\n def wrapper(self, *args, **kwargs):\n global locks\n if '__lock_{}__'.format(lock_name) in locks.keys():\n mylock = locks['__lock_{}__'.format(lock_name)]\n else:\n mylock = threading.RLock()\n locks['__lock_{}__'.format(lock_name)] = mylock\n is_acquired = mylock.acquire(timeout=timeout)\n if is_acquired:\n result = func(self, *args, **kwargs)\n else:\n raise LockException('Lock named {} could not be acquired in the given time'.format(lock_name))\n mylock.release()\n return result\n\n wrapper._original = func\n wrapper.thread_safe = True\n wrapper.__name__ = func.__name__\n return wrapper","title":""},{"docid":"23538226e11df19d6c1ba0c8beb6ce2f","score":"0.57052356","text":"def lock(self):","title":""},{"docid":"85e95f4b88226d894a764a684575d7cd","score":"0.56965125","text":"def handle_lock(self, obj):\n return self._handle_generic_op(obj, 'server_lock',\n 'lock')","title":""},{"docid":"04e1659dd72124cd19516fbb8937ef4b","score":"0.5695853","text":"def lock(self):\n self._locked = True","title":""},{"docid":"7c4fa22d00ceea04b1a5f8b178a5e508","score":"0.5676614","text":"def set_lock(self, lock):\n self.lock = lock","title":""},{"docid":"7c4fa22d00ceea04b1a5f8b178a5e508","score":"0.5676614","text":"def set_lock(self, lock):\n self.lock = lock","title":""},{"docid":"3e0a56c073d3799866e3c3d76e433f15","score":"0.5661893","text":"def acquire_lock(self):\n with self._lock:\n yield","title":""},{"docid":"bef38772b78638c4e5973ed72fcb2b53","score":"0.5647262","text":"def lock(self, lockMetadata={}):\n osaka.utils.LOGGER.debug(\"Locking {0} with {1}\".format(self.ouri, self.luri))\n tmp = {\"pid\": PID, \"hostname\": HOST_FQDN, \"osaka-lock-secret\": self.secret}\n tmp.update(lockMetadata)\n # tmp.update(self.lockExtras)\n stream = io.StringIO(str(json.dumps(tmp)))\n with PermTemp(self.luri, self.handle, self.params) as handle:\n # Not perfect exception\n if handle.exists(self.luri):\n raise osaka.utils.OsakaException(\"Lock file already locked\")\n handle.put(stream, self.luri)\n self.locked = True","title":""},{"docid":"d4044768b2099b5319de1135333e245f","score":"0.56425756","text":"def lock(self):\n if self.Lock:\n self.Lock.acquire()","title":""},{"docid":"6e3c8f847dd472b3971e09779877ac22","score":"0.5632444","text":"def locking(meth):\n @wraps(meth)\n async def wrapper(oself, *args, **kwargs):\n async with oself.lock:\n return await meth(oself, *args, **kwargs)\n\n return wrapper","title":""},{"docid":"f3ab4d64a483ab77bfa1ba8eec7d2fe2","score":"0.5619851","text":"def use_lock(fn):\n def wrapper(self, *arg):\n with self.lock:\n res = fn(self, *arg)\n return res\n return wrapper","title":""},{"docid":"79386689acaf7090d970716d583bed02","score":"0.5618229","text":"def control(self):\n if self.busy:\n raise DeviceBusyException\n self.busy = True\n self.time_left = self.lock_time # add time for control\n self.locked_mode = \"\"\n self.locked_range = None","title":""},{"docid":"043e237cda3fd96690f1136b8ddb0fa6","score":"0.56019586","text":"def lock_marker(self):\n return self.Gtk_Lock(self, mark=True)","title":""},{"docid":"e171bf51d5d72e89efe59c105974b49e","score":"0.55840635","text":"def lockable(func):\n\n def wrapper(this, *args, **kwargs):\n \"\"\"\n Wrapper method takes object and all args/kwargs and forwards to the func(tion) that is being decorated\n :param this: Object reference (self), object MUST have a .lock attribute (ideally RLock) and .debug method\n :param args: arbitrary arguments passed to \"func\"\n :param kwargs: arbitrary key-word arguments passed to \"func\"\n :return: whatever \"func(*args, **kwargs)\" returns normally\n \"\"\"\n this.debug(f'{func} using lock')\n with this.lock:\n return func(this, *args, **kwargs)\n\n return wrapper","title":""},{"docid":"db7494ff968095b07d42bf9d8e7926d6","score":"0.558287","text":"def lock(self):\n self.__lock = True","title":""},{"docid":"79f28afa2a2313635de33525a2f966c2","score":"0.5577199","text":"def hardlock(self):\n def override():\n orig_create_session = self.create_session\n\n def new_create_session(username: str, raw_password: str):\n \"\"\"\n Alternate function if hardlocked.\n :param username: The account username.\n :param raw_password: The account password.\n \"\"\"\n raise TooManyAttempts\n\n self.create_session = new_create_session\n self.session_hardlock = True\n\n sleep(self.hardlock_delay)\n self.hardlock_delay += SessionManager.HARDLOCK_DELAY_INCR\n\n self.create_session = orig_create_session\n self.session_hardlock = False\n\n Thread(target=override, daemon=True).start()","title":""},{"docid":"8a6bb997ce12c320f6c6f054c4cdb48f","score":"0.5571389","text":"def lock():\n\n import subprocess\n from ..configuration.cppfilerunner import CPP_FILE_RUNNER\n\n wrapperFileName = CPP_FILE_RUNNER().filename(WRAPPER_LOCATION)\n command = ['flock','--nonblock',MANUAL_LOCK_PATH,'-c','\"/bin/bash --rcfile {}\"'.format(wrapperFileName)]\n command = \" \".join(command)\n #print(WRAPPER_LOCATION,wrapperFileName)\n #print(command)\n try:\n subprocess.check_call(command,shell=True)\n try:\n os.remove(MANUAL_LOCK_PATH)\n except:\n pass\n except subprocess.CalledProcessError as e:\n print(\"Error getting lock: Somebody else probably has the lock already.\\n Run lslocks and identify the user locking {}\".format(MANUAL_LOCK_PATH))\n sys.exit(1)","title":""},{"docid":"e805a63aa48c050e56eeee69dee0f899","score":"0.55569214","text":"def lock(self, code):\n payload = {\"userId\": 1, \"password\": code, \"sessionId\": self._session_id}\n response = self._session.post(self._router.lock, data=payload)\n response.raise_for_status()\n\n self._lock.acquire()\n yield self\n self.unlock()","title":""},{"docid":"346703cc6893cfbc6c9f78805b28c0ae","score":"0.5548119","text":"def get_lock(self, pool, host):","title":""},{"docid":"cba8789ecc082a93db7ff77088a0ce7d","score":"0.5541703","text":"def lock(self):\n headers = {\n 'Content-Type': 'text/xml; charset=\"utf-8\"',\n 'Timeout': WEBDAV_TIMEOUT,\n 'Depth': '0',\n }\n\n body = (\n '<?xml version=\"1.0\" encoding=\"utf-8\"?>\\n'\n '<D:lockinfo xmlns:D=\"DAV:\">\\n'\n ' <D:lockscope><D:exclusive/></D:lockscope>\\n'\n ' <D:locktype><D:write/></D:locktype>\\n'\n ' <D:owner>\\n'\n ' <D:href>Office Connector</D:href>\\n'\n ' </D:owner>\\n'\n '</D:lockinfo>'\n )\n\n response = self._request('LOCK', body=body, headers=headers)\n self.lock_token = self._extract_lock_token(response.text)","title":""},{"docid":"ee2a8fe1e23c2ce6b7965c769e9eaefc","score":"0.55357414","text":"def acquire(self):\n start_time = time.time()\n while True:\n try:\n #Create enclosing folder (if necessary)\n if not os.path.exists(os.path.dirname(self.lockfile)):\n os.makedirs(os.path.dirname(self.lockfile))\n self.fd = os.open(self.lockfile, os.O_CREAT|os.O_EXCL|os.O_RDWR)\n break;\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n if (time.time() - start_time) >= self.timeout:\n raise FileLockException(\"Timeout occured.\")\n time.sleep(self.delay)\n self.is_locked = True","title":""},{"docid":"0fe4600dd5fa72508c9ce565f25d9a16","score":"0.5526624","text":"def _get_tqdm_lock() -> Lock:\n return TqdmManager.LOCK","title":""},{"docid":"6698c299601fc1616462a1006c9a2a44","score":"0.5526298","text":"def acquire_lock(cls, obj):\n try:\n mutex = obj.__lock\n except AttributeError:\n obj.__lock = mutex = multiprocessing.Lock()\n mutex.acquire()","title":""},{"docid":"91e5024ab64faa53baa852f8216ac5f5","score":"0.5522653","text":"def with_lock(lock):\n def decorator(func):\n @functools.wraps(func)\n def _with_lock(*args, **kwargs):\n with lock:\n return func(*args, **kwargs)\n return _with_lock\n return decorator","title":""},{"docid":"e28c72a97d26292d7105e15fe7d6b829","score":"0.5518522","text":"def acquire(path, onwait=None):\n touch(path)\n lock_fd = lock_file(path, blocking=True)\n lock_fd.truncate(0)\n lock_fd.write('%d\\n' % os.getpid())\n lock_fd.flush()\n return Lock(lock_fd)","title":""},{"docid":"38445582b28a94e060ad9e65e292b16e","score":"0.55143034","text":"def lock_device(self, device_id):\r\n request_body = {\"deviceId\": u\"{}\".format(device_id)}\r\n\r\n try:\r\n response = self.execute_with_retry(\"post\", LOCK_DEVICE_URL, CON_TYPE_FORM_ENCODED, data=request_body)\r\n results = response.json()\r\n except IntegrationError as err:\r\n raise IntegrationError(\"Unable to execute call Lock Device: {}\".format(err))\r\n\r\n action_response = results.get(\"actionResponse\")\r\n if action_response is None:\r\n return None\r\n\r\n action_status = action_response.get(\"actionStatus\") # 0:success; 1:error\r\n if action_status == 1:\r\n raise IntegrationError(\r\n u\"Unable to execute call Lock Device: {}\".format(action_response.get(\"description\")))\r\n\r\n return action_response","title":""},{"docid":"856bd738a1ea5bf669d322f7c2e14ef7","score":"0.5506381","text":"def flock(fd, op):\n return None","title":""},{"docid":"5ecddd99a087ce01fb5ebfde709a9f77","score":"0.55053294","text":"def test_default_creation(self):\n with WriteLock(self._sess_path) as write_lock:\n self.assertIsNone(write_lock.try_lock())\n out_pid = write_lock.try_lock(self._orig_pid)\n self.assertEqual(self._orig_pid, out_pid)\n with WriteLock(self._sess_path) as other_lock:\n out_pid = other_lock.try_lock(self._other_pid)\n self.assertEqual(self._orig_pid, out_pid)\n other_lock.unlock(self._orig_pid)\n out_pid = other_lock.try_lock(self._other_pid)\n self.assertEqual(self._other_pid, out_pid)\n other_lock.unlock(self._other_pid)\n self.assertIsNone(other_lock.try_lock())","title":""},{"docid":"48f1c56003a63ec29211e0e975673ca2","score":"0.55027235","text":"def unlocked():\n return Lock(None)","title":""},{"docid":"43cd3d477ee59788bc4b4f55ba5017db","score":"0.54913896","text":"def allocate_lock():\n return LockType()","title":""},{"docid":"34a06afc802e542b65ea8a910c1e40f0","score":"0.54905576","text":"def on_locked(self):\n return True","title":""},{"docid":"2071a2a6c316382d1c64397ace50515f","score":"0.5490056","text":"async def async_set_child_lock_on(self):\n if self._device_features & FEATURE_SET_CHILD_LOCK == 0:\n return\n\n await self._try_command(\n \"Turning the child lock of the miio device on failed.\",\n self._device.set_child_lock,\n True,\n )","title":""},{"docid":"78dd3eb68444c5d9c75bfade4db5fd73","score":"0.5484056","text":"def _synchronized(func):\n @wraps(func)\n def call_with_lock(self, *args, **kwargs):\n with self.lock:\n return func(self, *args, **kwargs)\n return call_with_lock","title":""},{"docid":"154c6b2d7853801c547fa5c0885118dc","score":"0.54803085","text":"def _obtain_lock(self):\n\t\treturn self._obtain_lock_or_raise()","title":""},{"docid":"c1a85fe6b5845bf555f121d7428bdf31","score":"0.5473694","text":"def lock(self):\n return self._lock","title":""},{"docid":"f17d52e2f26db067b936d3b6f17ac4c2","score":"0.5470077","text":"def locked(*args, **kwargs):\n\n def decorator(f):\n attr_name = kwargs.get('lock', '_lock')\n\n @six.wraps(f)\n def wrapper(self, *args, **kwargs):\n lock = getattr(self, attr_name)\n if isinstance(lock, (tuple, list)):\n lock = MultiLock(locks=list(lock))\n with lock:\n return f(self, *args, **kwargs)\n\n return wrapper\n\n # This is needed to handle when the decorator has args or the decorator\n # doesn't have args, python is rather weird here...\n if kwargs or not args:\n return decorator\n else:\n if len(args) == 1:\n return decorator(args[0])\n else:\n return decorator","title":""},{"docid":"c3d0f5ed9063075e3464d9b0f14bc2e0","score":"0.5459922","text":"def lockwrapper_woo(lock):\n def decorator(func):\n @woo.wraponlyonce(func,\"lock id = \"+ str(id(lock)) ) # imprints on the id of the lock\n @functools.wraps(func)\n def foo(*args,**kwargs):\n lock.acquire()\n try:\n output = func(*args,**kwargs)\n finally:\n lock.release()\n return output \n return foo\n return decorator","title":""},{"docid":"70c6cf5025b7168fa715c6d00ad46306","score":"0.54565805","text":"def request_mutex(self, *args, **kwargs) -> None:\n pass","title":""},{"docid":"5fcd95eb12b84c4da7a80c743cf14cb1","score":"0.54484427","text":"def acquire(self):\n start_time = time.time()\n while True:\n try:\n self.fd = os.open(self.lockfile,\n os.O_CREAT | os.O_EXCL | os.O_RDWR)\n break\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise e\n if (time.time() - start_time) >= self.timeout:\n raise FileLockException(\"Timeout occurred.\")\n time.sleep(self.delay)\n self.is_locked = True","title":""},{"docid":"01aa03121039353a2b46acad88722bd1","score":"0.544546","text":"def acquire(self):\n if not self.lock.trylock():\n util.SMlog(\"Failed to lock %s on first attempt, \" % self.lockpath\n + \"blocked by PID %d\" % self.lock.test())\n self.lock.lock()\n if VERBOSE:\n util.SMlog(\"lock: acquired %s\" % self.lockpath)","title":""},{"docid":"15b36c6c0dccc0eb06dcc829c840c926","score":"0.544241","text":"def lock(self):\n try:\n val = os.environ[\"FEMB_MANUAL_LOCK_OVERRIDE\"] # KeyError if variable not defined\n except KeyError:\n try:\n userid = os.stat(MANUAL_LOCK_PATH).st_uid\n username = pwd.getpwuid(userid).pw_name\n print(\"Error: The manual lock file exists, {} created by user: '{}'. Exiting. You may set the env var FEMB_MANUAL_LOCK_OVERRIDE to override\".format(MANUAL_LOCK_PATH,username))\n sys.exit(1)\n except FileNotFoundError:\n pass\n try:\n val = os.environ[\"FEMB_AUTOMATIC_LOCK_OVERRIDE\"] # KeyError if variable not defined\n except KeyError:\n if not (self.fp is None):\n self.unlock()\n # checking pid and username in case somebody else has the lock\n pid = None\n username = None\n try:\n with open(AUTOMATIC_LOCK_PATH) as pidf:\n pid = str(pidf.read(100))\n userid = os.stat(AUTOMATIC_LOCK_PATH).st_uid\n username = pwd.getpwuid(userid).pw_name\n except FileNotFoundError:\n pass\n # now actually locking\n self.fp = open(AUTOMATIC_LOCK_PATH,\"a+\")\n try:\n fcntl.flock(self.fp, fcntl.LOCK_EX|fcntl.LOCK_NB)\n self.fp.truncate(0) # delete content if somehow already exists\n self.fp.write(str(os.getpid()))\n self.fp.flush()\n os.fsync(self.fp.fileno())\n except BlockingIOError as e:\n print(\"Error: FEMB automatic lock user: '{}' pid: {}. Exiting. You may set the env var FEMB_AUTOMATIC_LOCK_OVERRIDE to override\".format(username,pid))\n sys.exit(1)","title":""},{"docid":"b9763b077d4bc784dcc19159c49b6ac2","score":"0.5422665","text":"def shield():\n with (\n anyio.CancelScope(shield=True)\n if get_running_loop()\n else contextlib.nullcontext()\n ):\n with _get_thread_shield(threading.current_thread()):\n yield","title":""},{"docid":"028dfe3f7fe50eb313936f972f0036de","score":"0.54206777","text":"def acquire_lock(self, app_id, txid, entity_key = GLOBAL_LOCK_KEY):\n return True","title":""},{"docid":"ba5f1ada3e5e3b8c84b163cfefe6e5f0","score":"0.5400609","text":"def __init__(self, lock=None):\n self.lock = lock","title":""},{"docid":"323365c0d0c39e04b6a591ca7a56135f","score":"0.5392851","text":"def set_lock(client, data):\n pin_id = data.payload.pinID\n try:\n if data.payload.locked:\n gpio_controller.get_pin(pin_id).lock_pin()\n else:\n gpio_controller.get_pin(pin_id).unlock_pin()\n client.send(GET_STATUS(status=gpio_controller.get_pins_status()))\n except Exception as exception:\n client.send(GET_STATUS(status=gpio_controller.get_pins_status(), errors=[exception]))","title":""},{"docid":"c04fb841b63f10fe81f292ee6b6d6418","score":"0.5390253","text":"def _acquire_lock(self):\r\n self._lock.acquire()","title":""},{"docid":"01ca6684ea197de435463cf578d8abcb","score":"0.53897846","text":"def _obtain_lock_or_raise(self):\n\t\tif self._has_lock():\n\t\t\treturn \n\t\tlock_file = self._lock_file_path()\n\t\tif os.path.isfile(lock_file):\n\t\t\traise IOError(\"Lock for file %r did already exist, delete %r in case the lock is illegal\" % (self._file_path, lock_file))\n\t\t\t\n\t\ttry:\n\t\t\tfd = os.open(lock_file, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0)\n\t\t\tos.close(fd)\n\t\texcept OSError,e:\n\t\t\traise IOError(str(e))\n\t\t\n\t\tself._owns_lock = True","title":""},{"docid":"6c62e373f780d9f77eb760d7d1280b04","score":"0.5387927","text":"def lock_sut(self, res_query, props={}):\n return self.lock_resource(res_query, props)","title":""},{"docid":"38c709cc05f75aec10493652d6291070","score":"0.5383082","text":"def set_thread_safety(self, threadsafe=True):\n if threadsafe:\n self._lock = threading.RLock()\n else:\n self._lock = DummyContextHandler()","title":""},{"docid":"10bfb1949b9aab270f342e176a45a790","score":"0.53797674","text":"def acquire(self, context, lock_key):\n if lock_key is not None and len(lock_key) < self.MAX_LOCK_LENGTH:\n lock_id = self.lock_api.create_lock(context, lock_key)\n LOG.info(_LI(\"Lock %(lock_id)s acquired for lock_key \"\n \"%(lock_key)s\"),\n {'lock_id': lock_id, 'lock_key': lock_key})\n else:\n lock_id = None\n LOG.info(_LI(\"No lock for lock_key %s\"), lock_key)\n\n return Lock(context, lock_id, lock_key, self.release)","title":""},{"docid":"0502dab8c132577e6ef181bf557693a5","score":"0.53731763","text":"def get_lock(consul_local, datacenter):\n lock_key = 'lock/{}'.format(datacenter)\n sid = consul_local.session.create(ttl='30s')\n while not consul_local.kv.acquire_lock(lock_key, sid):\n print('waiting for lock on {}...'.format(datacenter))\n sleep(0.2)\n print('lock acquired on {}'.format(datacenter))\n return sid","title":""},{"docid":"ac08442b04c539e4e39929008f578a85","score":"0.5370247","text":"def use_lock(lock_file):\n def _decorate_lock(function):\n def wait_for_lock(self):\n if self.check_and_cleanup_lockfiles():\n self.log.info(\"Cleaned up unused lockfiles before start\")\n for second in range(DEFAULT_TIMEOUT):\n try:\n self.log.info('Waiting for lock %s...', lock_file)\n lock = lock_file + '.lock'\n pid = str(os.getpid())\n cmd = ['sudo', 'flock', '--timeout', '1',\n '--conflict-exit-code', '16', lock_file, '-c',\n '\"( test ! -e ' + lock + ' || exit 17 ) ' +\n '&& echo ' + pid + ' | sudo ' + ' tee ' + lock + '\"']\n result = self.shell.cmd_val(cmd)\n if result == 16:\n self.log.info('Another conversion has the lock.')\n elif result == 17:\n self.log.info('Another conversion is holding the lock.')\n elif result == 0:\n break\n time.sleep(1)\n except subprocess.CalledProcessError as err:\n time.sleep(1)\n self.log.info('Error waiting for lock: %s', str(err))\n else:\n raise RuntimeError('Unable to acquire lock ' + lock_file)\n try:\n return function(self)\n finally:\n try:\n lock = lock_file + '.lock'\n result = self.shell.cmd_out(['sudo', 'rm', '-f', lock])\n self.log.debug('Released lock: %s', result)\n except subprocess.CalledProcessError as err:\n self.log.error('Error releasing lock: %s', str(err))\n\n return wait_for_lock\n return _decorate_lock","title":""},{"docid":"1aee0c86746e3593f9ce69492bbce06c","score":"0.53701395","text":"def begin_lock_door(token, vin):\n ns = 'DL'\n root = Commands.__get_root()\n Commands.__add_common(root, token, vin)\n Commands.__add_config(root, ns, '1')\n Commands.__add_fake_position(root)\n return Command(root,\n \"/remote_control.aspx\",\n rp.BasicCommandResponseParser,\n namespace = ns,\n query={\n 'command': 'DoorLock',\n 'VIN': vin\n },\n headers={\n 'Authorization': 'Bearer ' + token\n })","title":""},{"docid":"567cbb3f227ee29203ef008589a1c8a9","score":"0.5369068","text":"def acquire_lock(self):\r\n self.locked = True\r\n self.locks.setdefault(self.prefix+self.id, threading.RLock()).acquire()","title":""},{"docid":"a98fe32d3a3f5e3a8864aa51ff50a0d8","score":"0.5368354","text":"def isLocked(self):\n \n pass","title":""}],"string":"[\n {\n \"docid\": \"dccae90a973f8f2e76e03f36ca6969a1\",\n \"score\": \"0.69568276\",\n \"text\": \"def lock_context(self, timeout='default', requested_key='exclusive'):\\n if requested_key == 'exclusive':\\n self.lock_excl(timeout)\\n access_key = None\\n else:\\n access_key = self.lock(timeout, requested_key)\\n try:\\n yield access_key\\n finally:\\n self.unlock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a9ebe91afc8ef299370fd3fe4f999b8\",\n \"score\": \"0.65677816\",\n \"text\": \"def test_context_manager_locking(self):\\n bluetooth_if = BluetoothInterface(MockBackend)\\n self.assertFalse(bluetooth_if.is_connected())\\n\\n with bluetooth_if.connect(\\\"abc\\\"): # as connection:\\n self.assertTrue(bluetooth_if.is_connected())\\n\\n self.assertFalse(bluetooth_if.is_connected())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e22df8617ae243b7011dc79f53e110d\",\n \"score\": \"0.6399493\",\n \"text\": \"def is_device_locked(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de7c45c822c8a22872c8125a1ba9ecb8\",\n \"score\": \"0.63030064\",\n \"text\": \"def lock(self):\\n raise NotImplementedError(\\\"Lock not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf7f7eb2d4325818c3ce20b6c64be36f\",\n \"score\": \"0.6302039\",\n \"text\": \"def lock(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc561f88959ee17f74ae5988acda628\",\n \"score\": \"0.62960786\",\n \"text\": \"def lock_to_device(self, device):\\n self._RemoteSL__effect_controller.lock_to_device(device)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a967a097b1beee488844b2cedf127a6f\",\n \"score\": \"0.62846076\",\n \"text\": \"def createLock(self) -> None:\\n self.lock = TerminalHandler.sharedlock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf679c81ca024c14654302dbbe58e5a1\",\n \"score\": \"0.6231577\",\n \"text\": \"def openLock():\\n opener.open()\\n # opener.pi_open() \",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb6d76e3a6ae3ed440195a43a416be0\",\n \"score\": \"0.6212352\",\n \"text\": \"def can_lock_to_devices(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976b7c1de75e2814b8f3caefa1a823ad\",\n \"score\": \"0.6190904\",\n \"text\": \"def __enter__(self):\\n raise RuntimeError(\\n 'Device class does not support runtime context using `with` '\\n 'statement. Use chainer.using_device instead.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdf865271a980f9cd28677f6fb37c408\",\n \"score\": \"0.6182173\",\n \"text\": \"def lock(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a18ad2f58422cf3692fbd641fbb13bb\",\n \"score\": \"0.61740434\",\n \"text\": \"def lock(self):\\n self._lock.acquire()\\n self._lock_owner = threading.current_thread()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b3ce352b4665f0958e38bcc0cb300ba\",\n \"score\": \"0.6138324\",\n \"text\": \"def _lock(self):\\n if not self.locked:\\n try:\\n self.device.cu.lock()\\n self.locked = True\\n except JnprLockError as jle:\\n raise LockError(str(jle))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b137d2637e047dfb4b8563850cc7245\",\n \"score\": \"0.61174935\",\n \"text\": \"def acquire_lock():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26dd0f5469353cb3f3250eb585e977e0\",\n \"score\": \"0.6090395\",\n \"text\": \"def lock(self, Verbose=False):\\n\\n if self.lockfile != None and self.lockfile != \\\"\\\":\\n if Verbose:\\n print(\\\"Acquiring lock file \\\" + self.lockfile)\\n while True:\\n try:\\n lockfd = os.open(self.lockfile, os.O_CREAT | os.O_EXCL | os.O_RDWR)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise ZEBUError(\\\"Unable to write lockfile \\\" + self.lockfile)\\n time.sleep(0.1) # Wait 0.1 seconds, then retry the lockfile\\n else:\\n break\\n if Verbose:\\n print(\\\"Locked \\\" + self.lockfile)\\n else:\\n lockfd = None\\n\\n return lockfd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8bc5547b993c6a472fdd7faf06720a\",\n \"score\": \"0.6071644\",\n \"text\": \"def CreateLock():\\r\\n if MultiprocessingIsAvailable()[0]:\\r\\n return manager.Lock()\\r\\n else:\\r\\n return threading.Lock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b31776bfdcccf4c14b11e2c7c9aaa8af\",\n \"score\": \"0.60390854\",\n \"text\": \"def __get_lock(self):\\n self.core.get_lock()\\n self.locked = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3509fde471e0a2f31159c313abbfcac8\",\n \"score\": \"0.6012302\",\n \"text\": \"def lock_configuration(dev):\\n #Lock device configuration \\n print(\\\"Locking the configuration\\\")\\n try: \\n dev.cu.lock()\\n locked = True\\n except LockError as err:\\n print(\\\"Unable to lock configuration: \\\\n {0}\\\".format(err))\\n dev.close()\\n locked = False\\n return locked\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63306e0b8ff1c0e48fb9bea320a968ff\",\n \"score\": \"0.5992754\",\n \"text\": \"def create_lock(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d22650041ffd9c666022c51f5eace7f\",\n \"score\": \"0.5957061\",\n \"text\": \"def _lock(self):\\n return _lock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73b1c2043dfef8f5abe73ecb041b8e47\",\n \"score\": \"0.58978397\",\n \"text\": \"def lock(self, timeout='default', requested_key=None):\\n timeout = self.timeout if timeout == 'default' else timeout\\n timeout = self._cleanup_timeout(timeout)\\n return self.visalib.lock(self.session, constants.AccessModes.shared_lock, timeout, requested_key)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7105ea6b4f8c8b519c50d64abbf960\",\n \"score\": \"0.58613545\",\n \"text\": \"def with_lock(lock):\\n\\n def decorator(fn):\\n @contextmanager\\n @functools.wraps(fn)\\n def wrapper(*args, **kwargs):\\n with lock:\\n with fn(*args, **kwargs):\\n yield\\n\\n return wrapper\\n\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d291d1c7f1894d76541928e8f2475ae\",\n \"score\": \"0.58487815\",\n \"text\": \"def lock_for_update(self):\\n if hasattr(self._base_manager, 'select_for_update'):\\n return self._native_lock()\\n\\n return self._raw_lock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27551ecbe8da778d06d14c620d5e90f\",\n \"score\": \"0.5843628\",\n \"text\": \"def active_and_locked(self):\\n self.lock.acquire()\\n egl.eglMakeCurrent(self.dpy, self.surface, self.surface, self.context)\\n try:\\n yield\\n finally:\\n egl.eglMakeCurrent(\\n self.dpy, egl.EGL_NO_SURFACE, egl.EGL_NO_SURFACE, egl.EGL_NO_CONTEXT\\n )\\n self.lock.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166a308a1d9a398b542056de3b8f8069\",\n \"score\": \"0.5833352\",\n \"text\": \"def lock(self):\\n #print \\\"lock\\\"\\n o = self.instance\\n while not o.db.setnx(o.key('_lock'), self.lock_timeout):\\n lock = o.db.get(o.key('_lock'))\\n if not lock:\\n continue\\n if not self.lock_has_expired(lock):\\n time.sleep(0.5)\\n continue\\n lock = o.db.getset(o.key('_lock'), self.lock_timeout)\\n if not lock:\\n break\\n if self.lock_has_expired(lock):\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b0a0db2193c9acfb24a91ccf697292\",\n \"score\": \"0.58251214\",\n \"text\": \"def lock(self):\\n with self._meta.workspace.lock(self.get_id()):\\n yield\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e56703c3ba7c8999d5853045585e476a\",\n \"score\": \"0.581702\",\n \"text\": \"def lock(self):\\n return self.Gtk_Lock(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7345597d8e366b18fa9beca164602140\",\n \"score\": \"0.58147186\",\n \"text\": \"def __call__(self):\\n thread_id = threading.get_ident()\\n this_id = id(self)\\n if DEBUG_PRINTS:\\n if random.random() > 0.5:\\n print('[{}.{}.v1] aquire outer lock1'.format(thread_id, this_id))\\n with self.lock1:\\n print('[{}.{}.v1] aquired outer lock1'.format(thread_id, this_id))\\n print('[{}.{}.v1] aquire inner lock2'.format(thread_id, this_id))\\n with self.lock2:\\n print('[{}.{}.v1] aquired inner lock2'.format(thread_id, this_id))\\n print('[{}.{}.v1] releasing inner lock2'.format(thread_id, this_id))\\n print('[{}.{}.v1] released inner lock2'.format(thread_id, this_id))\\n print('[{}.{}.v1] releasing outer lock1'.format(thread_id, this_id))\\n print('[{}.{}.v1] released outer lock1'.format(thread_id, this_id))\\n else:\\n print('[{}.{}.v2] aquire outer lock2'.format(thread_id, this_id))\\n with self.lock2:\\n print('[{}.{}.v2] aquired outer lock2'.format(thread_id, this_id))\\n print('[{}.{}.v2] aquire inner lock1'.format(thread_id, this_id))\\n with self.lock1:\\n print('[{}.{}.v2] aquired inner lock1'.format(thread_id, this_id))\\n print('[{}.{}.v2] releasing inner lock1'.format(thread_id, this_id))\\n print('[{}.{}.v2] released inner lock1'.format(thread_id, this_id))\\n print('[{}.{}.v2] releasing outer lock2'.format(thread_id, this_id))\\n print('[{}.{}.v2] released outer lock2'.format(thread_id, this_id))\\n else:\\n if random.random() > 0.5:\\n with self.lock1:\\n time.sleep(0.03)\\n with self.lock2:\\n ...\\n else:\\n with self.lock2:\\n time.sleep(0.03)\\n with self.lock1:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c179b2f8275e429b7f2c1a9fe6adbb\",\n \"score\": \"0.581453\",\n \"text\": \"def lock(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c97cee6508d982a558f8f63a958ead0\",\n \"score\": \"0.5812277\",\n \"text\": \"def lock_it(lock=g_lock):\\n\\n def func_wrapper(func):\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n with lock:\\n return func(*args, **kwargs)\\n\\n return wrapper\\n\\n return func_wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da821e44d9cef1efbc701bb8c3067da6\",\n \"score\": \"0.5809921\",\n \"text\": \"def lock(self):\\n if not self.use_lock_file:\\n return\\n locked = False\\n while not locked:\\n try:\\n self.lock_file = open(self.db_uri + \\\".handle\\\", \\\"w\\\")\\n fcntl.lockf(self.lock_file, fcntl.LOCK_EX | fcntl.LOCK_NB)\\n locked = True\\n except Exception:\\n time.sleep(0.1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac42a8cf921b1940e7284adb7bbdc8e0\",\n \"score\": \"0.5798407\",\n \"text\": \"def lock(self):\\n self.__consumer__.lock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c822e8cddb94d3c2557df1c92c66a209\",\n \"score\": \"0.5797938\",\n \"text\": \"def create_lock(self, context, lock_key):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d4d25ecc6ff72ee9111bf78bf798d22\",\n \"score\": \"0.5792462\",\n \"text\": \"def example_global_lock(task):\\n lock = task.run(netconf_lock, datastore=\\\"candidate\\\", operation=\\\"lock\\\")\\n # Retrieve the Manager(agent) from lock operation and store for further\\n # operations.\\n task.host[\\\"manager\\\"] = lock.result[\\\"manager\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6165a7e23dab0fd357a89e514a93bed\",\n \"score\": \"0.57902485\",\n \"text\": \"def lock(self):\\n self.__locked__ = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68eaaafc6230082d3950ad44a5976788\",\n \"score\": \"0.57829446\",\n \"text\": \"def create_lock(self):\\n return Lock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a5c834f477c1c9874f3ebc9aa64201\",\n \"score\": \"0.57777643\",\n \"text\": \"def fetch_locking(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bafd70793969b911e5a8fef68185cf0f\",\n \"score\": \"0.5768873\",\n \"text\": \"def lock(self, timeout=None):\\n\\t\\tlockfile = self.path[0] + '/.__dblock.lock'\\n\\n\\t\\treturn locking.lock(lockfile, timeout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ba0f431b8e3cc0db0b1af2e11ce0e12\",\n \"score\": \"0.5766459\",\n \"text\": \"def test_lock(self):\\n self.s.lock()\\n self.s.unlock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13daf3b900879e9bcb8b05f5ede24a72\",\n \"score\": \"0.5759565\",\n \"text\": \"def setiflock(self):\\r\\n command = TTiBase.format_tx(self, \\\"IFLOCK\\\")\\r\\n TTiBase.tx(self, command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d9144cce87b3f699e7d6cba68d40a9\",\n \"score\": \"0.5744884\",\n \"text\": \"def _T_lock(self, s : Storage):\\n if self.trace:\\n self.telemetry.trace.record('lock', self.clock, s.root_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6692e195b7fe3e348db0f9eef872a12a\",\n \"score\": \"0.57381415\",\n \"text\": \"def CMD_Lock_Func(args, ser):\\n print (\\\"Locking Module\\\")\\n ser.write(BL_LOCK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffaf806f287645fa64496fdd7ae5a115\",\n \"score\": \"0.57317543\",\n \"text\": \"async def lock_context(\\n self,\\n lock_key: str,\\n lock_value: bytes | str | None = None,\\n *,\\n blocking: bool = False,\\n blocking_timeout_s: NonNegativeFloat = 5,\\n ) -> AsyncIterator[Lock]:\\n\\n total_lock_duration: datetime.timedelta = _DEFAULT_LOCK_TTL\\n lock_unique_id = f\\\"lock_extender_{lock_key}_{uuid4()}\\\"\\n\\n ttl_lock: Lock = self._client.lock(\\n name=lock_key,\\n timeout=total_lock_duration.total_seconds(),\\n blocking=blocking,\\n blocking_timeout=blocking_timeout_s,\\n )\\n\\n if not await ttl_lock.acquire(token=lock_value):\\n raise CouldNotAcquireLockError(lock=ttl_lock)\\n\\n async def _extend_lock(lock: Lock) -> None:\\n with log_context(\\n logger, logging.DEBUG, f\\\"Extending lock {lock_unique_id}\\\"\\n ), log_catch(logger, reraise=False):\\n await lock.reacquire()\\n\\n try:\\n async with periodic_task(\\n _extend_lock,\\n interval=total_lock_duration / 2,\\n task_name=lock_unique_id,\\n lock=ttl_lock,\\n stop_timeout=0.1,\\n ):\\n # lock is in use now\\n yield ttl_lock\\n finally:\\n # NOTE Why is this error suppressed? Given the following situation:\\n # - 250 locks are acquire in parallel with the option `blocking=True`,\\n # meaning: it will wait for the lock to be free before acquiring it\\n # - when the lock is acquired the `_extend_lock` task is started\\n # in the background, extending the lock at a fixed interval of time,\\n # which is half of the duration of the lock's TTL\\n # - before the task is released the lock extension task is cancelled\\n # Here is where the issue occurs:\\n # - some time passes between the task's cancellation and\\n # the call to release the lock\\n # - if the TTL is too small, 1/2 of the TTL might be just shorter than\\n # the time it passes to between the task is canceled and the task lock is released\\n # - this means that the lock will expire and be considered as not owned any longer\\n # For example: in one of the failing tests the TTL is set to `0.25` seconds,\\n # and half of that is `0.125` seconds.\\n\\n # Above implies that only one \\\"task\\\" `owns` and `extends` the lock at a time.\\n # The issue appears to be related some timings (being too low).\\n try:\\n await ttl_lock.release()\\n except redis.exceptions.LockNotOwnedError:\\n # if this appears outside tests it can cause issues since something might be happening\\n logger.warning(\\n \\\"Attention: lock is no longer owned. This is unexpected and requires investigation\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b757cf6d6a97d9221ec47fb72cf90cdf\",\n \"score\": \"0.57275677\",\n \"text\": \"def __enter__(self):\\n if not self.is_locked:\\n self.acquire()\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b757cf6d6a97d9221ec47fb72cf90cdf\",\n \"score\": \"0.57275677\",\n \"text\": \"def __enter__(self):\\n if not self.is_locked:\\n self.acquire()\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df875d7c6e34616d178f19f3673ff957\",\n \"score\": \"0.570744\",\n \"text\": \"def _lockit(func):\\n\\n def wrapper(self, *args, **kwargs):\\n global locks\\n if '__lock_{}__'.format(lock_name) in locks.keys():\\n mylock = locks['__lock_{}__'.format(lock_name)]\\n else:\\n mylock = threading.RLock()\\n locks['__lock_{}__'.format(lock_name)] = mylock\\n is_acquired = mylock.acquire(timeout=timeout)\\n if is_acquired:\\n result = func(self, *args, **kwargs)\\n else:\\n raise LockException('Lock named {} could not be acquired in the given time'.format(lock_name))\\n mylock.release()\\n return result\\n\\n wrapper._original = func\\n wrapper.thread_safe = True\\n wrapper.__name__ = func.__name__\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23538226e11df19d6c1ba0c8beb6ce2f\",\n \"score\": \"0.57052356\",\n \"text\": \"def lock(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85e95f4b88226d894a764a684575d7cd\",\n \"score\": \"0.56965125\",\n \"text\": \"def handle_lock(self, obj):\\n return self._handle_generic_op(obj, 'server_lock',\\n 'lock')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04e1659dd72124cd19516fbb8937ef4b\",\n \"score\": \"0.5695853\",\n \"text\": \"def lock(self):\\n self._locked = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c4fa22d00ceea04b1a5f8b178a5e508\",\n \"score\": \"0.5676614\",\n \"text\": \"def set_lock(self, lock):\\n self.lock = lock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c4fa22d00ceea04b1a5f8b178a5e508\",\n \"score\": \"0.5676614\",\n \"text\": \"def set_lock(self, lock):\\n self.lock = lock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e0a56c073d3799866e3c3d76e433f15\",\n \"score\": \"0.5661893\",\n \"text\": \"def acquire_lock(self):\\n with self._lock:\\n yield\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef38772b78638c4e5973ed72fcb2b53\",\n \"score\": \"0.5647262\",\n \"text\": \"def lock(self, lockMetadata={}):\\n osaka.utils.LOGGER.debug(\\\"Locking {0} with {1}\\\".format(self.ouri, self.luri))\\n tmp = {\\\"pid\\\": PID, \\\"hostname\\\": HOST_FQDN, \\\"osaka-lock-secret\\\": self.secret}\\n tmp.update(lockMetadata)\\n # tmp.update(self.lockExtras)\\n stream = io.StringIO(str(json.dumps(tmp)))\\n with PermTemp(self.luri, self.handle, self.params) as handle:\\n # Not perfect exception\\n if handle.exists(self.luri):\\n raise osaka.utils.OsakaException(\\\"Lock file already locked\\\")\\n handle.put(stream, self.luri)\\n self.locked = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4044768b2099b5319de1135333e245f\",\n \"score\": \"0.56425756\",\n \"text\": \"def lock(self):\\n if self.Lock:\\n self.Lock.acquire()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3c8f847dd472b3971e09779877ac22\",\n \"score\": \"0.5632444\",\n \"text\": \"def locking(meth):\\n @wraps(meth)\\n async def wrapper(oself, *args, **kwargs):\\n async with oself.lock:\\n return await meth(oself, *args, **kwargs)\\n\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ab4d64a483ab77bfa1ba8eec7d2fe2\",\n \"score\": \"0.5619851\",\n \"text\": \"def use_lock(fn):\\n def wrapper(self, *arg):\\n with self.lock:\\n res = fn(self, *arg)\\n return res\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79386689acaf7090d970716d583bed02\",\n \"score\": \"0.5618229\",\n \"text\": \"def control(self):\\n if self.busy:\\n raise DeviceBusyException\\n self.busy = True\\n self.time_left = self.lock_time # add time for control\\n self.locked_mode = \\\"\\\"\\n self.locked_range = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"043e237cda3fd96690f1136b8ddb0fa6\",\n \"score\": \"0.56019586\",\n \"text\": \"def lock_marker(self):\\n return self.Gtk_Lock(self, mark=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e171bf51d5d72e89efe59c105974b49e\",\n \"score\": \"0.55840635\",\n \"text\": \"def lockable(func):\\n\\n def wrapper(this, *args, **kwargs):\\n \\\"\\\"\\\"\\n Wrapper method takes object and all args/kwargs and forwards to the func(tion) that is being decorated\\n :param this: Object reference (self), object MUST have a .lock attribute (ideally RLock) and .debug method\\n :param args: arbitrary arguments passed to \\\"func\\\"\\n :param kwargs: arbitrary key-word arguments passed to \\\"func\\\"\\n :return: whatever \\\"func(*args, **kwargs)\\\" returns normally\\n \\\"\\\"\\\"\\n this.debug(f'{func} using lock')\\n with this.lock:\\n return func(this, *args, **kwargs)\\n\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db7494ff968095b07d42bf9d8e7926d6\",\n \"score\": \"0.558287\",\n \"text\": \"def lock(self):\\n self.__lock = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f28afa2a2313635de33525a2f966c2\",\n \"score\": \"0.5577199\",\n \"text\": \"def hardlock(self):\\n def override():\\n orig_create_session = self.create_session\\n\\n def new_create_session(username: str, raw_password: str):\\n \\\"\\\"\\\"\\n Alternate function if hardlocked.\\n :param username: The account username.\\n :param raw_password: The account password.\\n \\\"\\\"\\\"\\n raise TooManyAttempts\\n\\n self.create_session = new_create_session\\n self.session_hardlock = True\\n\\n sleep(self.hardlock_delay)\\n self.hardlock_delay += SessionManager.HARDLOCK_DELAY_INCR\\n\\n self.create_session = orig_create_session\\n self.session_hardlock = False\\n\\n Thread(target=override, daemon=True).start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6bb997ce12c320f6c6f054c4cdb48f\",\n \"score\": \"0.5571389\",\n \"text\": \"def lock():\\n\\n import subprocess\\n from ..configuration.cppfilerunner import CPP_FILE_RUNNER\\n\\n wrapperFileName = CPP_FILE_RUNNER().filename(WRAPPER_LOCATION)\\n command = ['flock','--nonblock',MANUAL_LOCK_PATH,'-c','\\\"/bin/bash --rcfile {}\\\"'.format(wrapperFileName)]\\n command = \\\" \\\".join(command)\\n #print(WRAPPER_LOCATION,wrapperFileName)\\n #print(command)\\n try:\\n subprocess.check_call(command,shell=True)\\n try:\\n os.remove(MANUAL_LOCK_PATH)\\n except:\\n pass\\n except subprocess.CalledProcessError as e:\\n print(\\\"Error getting lock: Somebody else probably has the lock already.\\\\n Run lslocks and identify the user locking {}\\\".format(MANUAL_LOCK_PATH))\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e805a63aa48c050e56eeee69dee0f899\",\n \"score\": \"0.55569214\",\n \"text\": \"def lock(self, code):\\n payload = {\\\"userId\\\": 1, \\\"password\\\": code, \\\"sessionId\\\": self._session_id}\\n response = self._session.post(self._router.lock, data=payload)\\n response.raise_for_status()\\n\\n self._lock.acquire()\\n yield self\\n self.unlock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"346703cc6893cfbc6c9f78805b28c0ae\",\n \"score\": \"0.5548119\",\n \"text\": \"def get_lock(self, pool, host):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cba8789ecc082a93db7ff77088a0ce7d\",\n \"score\": \"0.5541703\",\n \"text\": \"def lock(self):\\n headers = {\\n 'Content-Type': 'text/xml; charset=\\\"utf-8\\\"',\\n 'Timeout': WEBDAV_TIMEOUT,\\n 'Depth': '0',\\n }\\n\\n body = (\\n '<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\\\"?>\\\\n'\\n '<D:lockinfo xmlns:D=\\\"DAV:\\\">\\\\n'\\n ' <D:lockscope><D:exclusive/></D:lockscope>\\\\n'\\n ' <D:locktype><D:write/></D:locktype>\\\\n'\\n ' <D:owner>\\\\n'\\n ' <D:href>Office Connector</D:href>\\\\n'\\n ' </D:owner>\\\\n'\\n '</D:lockinfo>'\\n )\\n\\n response = self._request('LOCK', body=body, headers=headers)\\n self.lock_token = self._extract_lock_token(response.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee2a8fe1e23c2ce6b7965c769e9eaefc\",\n \"score\": \"0.55357414\",\n \"text\": \"def acquire(self):\\n start_time = time.time()\\n while True:\\n try:\\n #Create enclosing folder (if necessary)\\n if not os.path.exists(os.path.dirname(self.lockfile)):\\n os.makedirs(os.path.dirname(self.lockfile))\\n self.fd = os.open(self.lockfile, os.O_CREAT|os.O_EXCL|os.O_RDWR)\\n break;\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n if (time.time() - start_time) >= self.timeout:\\n raise FileLockException(\\\"Timeout occured.\\\")\\n time.sleep(self.delay)\\n self.is_locked = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fe4600dd5fa72508c9ce565f25d9a16\",\n \"score\": \"0.5526624\",\n \"text\": \"def _get_tqdm_lock() -> Lock:\\n return TqdmManager.LOCK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6698c299601fc1616462a1006c9a2a44\",\n \"score\": \"0.5526298\",\n \"text\": \"def acquire_lock(cls, obj):\\n try:\\n mutex = obj.__lock\\n except AttributeError:\\n obj.__lock = mutex = multiprocessing.Lock()\\n mutex.acquire()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e5024ab64faa53baa852f8216ac5f5\",\n \"score\": \"0.5522653\",\n \"text\": \"def with_lock(lock):\\n def decorator(func):\\n @functools.wraps(func)\\n def _with_lock(*args, **kwargs):\\n with lock:\\n return func(*args, **kwargs)\\n return _with_lock\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e28c72a97d26292d7105e15fe7d6b829\",\n \"score\": \"0.5518522\",\n \"text\": \"def acquire(path, onwait=None):\\n touch(path)\\n lock_fd = lock_file(path, blocking=True)\\n lock_fd.truncate(0)\\n lock_fd.write('%d\\\\n' % os.getpid())\\n lock_fd.flush()\\n return Lock(lock_fd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38445582b28a94e060ad9e65e292b16e\",\n \"score\": \"0.55143034\",\n \"text\": \"def lock_device(self, device_id):\\r\\n request_body = {\\\"deviceId\\\": u\\\"{}\\\".format(device_id)}\\r\\n\\r\\n try:\\r\\n response = self.execute_with_retry(\\\"post\\\", LOCK_DEVICE_URL, CON_TYPE_FORM_ENCODED, data=request_body)\\r\\n results = response.json()\\r\\n except IntegrationError as err:\\r\\n raise IntegrationError(\\\"Unable to execute call Lock Device: {}\\\".format(err))\\r\\n\\r\\n action_response = results.get(\\\"actionResponse\\\")\\r\\n if action_response is None:\\r\\n return None\\r\\n\\r\\n action_status = action_response.get(\\\"actionStatus\\\") # 0:success; 1:error\\r\\n if action_status == 1:\\r\\n raise IntegrationError(\\r\\n u\\\"Unable to execute call Lock Device: {}\\\".format(action_response.get(\\\"description\\\")))\\r\\n\\r\\n return action_response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856bd738a1ea5bf669d322f7c2e14ef7\",\n \"score\": \"0.5506381\",\n \"text\": \"def flock(fd, op):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ecddd99a087ce01fb5ebfde709a9f77\",\n \"score\": \"0.55053294\",\n \"text\": \"def test_default_creation(self):\\n with WriteLock(self._sess_path) as write_lock:\\n self.assertIsNone(write_lock.try_lock())\\n out_pid = write_lock.try_lock(self._orig_pid)\\n self.assertEqual(self._orig_pid, out_pid)\\n with WriteLock(self._sess_path) as other_lock:\\n out_pid = other_lock.try_lock(self._other_pid)\\n self.assertEqual(self._orig_pid, out_pid)\\n other_lock.unlock(self._orig_pid)\\n out_pid = other_lock.try_lock(self._other_pid)\\n self.assertEqual(self._other_pid, out_pid)\\n other_lock.unlock(self._other_pid)\\n self.assertIsNone(other_lock.try_lock())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48f1c56003a63ec29211e0e975673ca2\",\n \"score\": \"0.55027235\",\n \"text\": \"def unlocked():\\n return Lock(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43cd3d477ee59788bc4b4f55ba5017db\",\n \"score\": \"0.54913896\",\n \"text\": \"def allocate_lock():\\n return LockType()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34a06afc802e542b65ea8a910c1e40f0\",\n \"score\": \"0.54905576\",\n \"text\": \"def on_locked(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2071a2a6c316382d1c64397ace50515f\",\n \"score\": \"0.5490056\",\n \"text\": \"async def async_set_child_lock_on(self):\\n if self._device_features & FEATURE_SET_CHILD_LOCK == 0:\\n return\\n\\n await self._try_command(\\n \\\"Turning the child lock of the miio device on failed.\\\",\\n self._device.set_child_lock,\\n True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78dd3eb68444c5d9c75bfade4db5fd73\",\n \"score\": \"0.5484056\",\n \"text\": \"def _synchronized(func):\\n @wraps(func)\\n def call_with_lock(self, *args, **kwargs):\\n with self.lock:\\n return func(self, *args, **kwargs)\\n return call_with_lock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"154c6b2d7853801c547fa5c0885118dc\",\n \"score\": \"0.54803085\",\n \"text\": \"def _obtain_lock(self):\\n\\t\\treturn self._obtain_lock_or_raise()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a85fe6b5845bf555f121d7428bdf31\",\n \"score\": \"0.5473694\",\n \"text\": \"def lock(self):\\n return self._lock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17d52e2f26db067b936d3b6f17ac4c2\",\n \"score\": \"0.5470077\",\n \"text\": \"def locked(*args, **kwargs):\\n\\n def decorator(f):\\n attr_name = kwargs.get('lock', '_lock')\\n\\n @six.wraps(f)\\n def wrapper(self, *args, **kwargs):\\n lock = getattr(self, attr_name)\\n if isinstance(lock, (tuple, list)):\\n lock = MultiLock(locks=list(lock))\\n with lock:\\n return f(self, *args, **kwargs)\\n\\n return wrapper\\n\\n # This is needed to handle when the decorator has args or the decorator\\n # doesn't have args, python is rather weird here...\\n if kwargs or not args:\\n return decorator\\n else:\\n if len(args) == 1:\\n return decorator(args[0])\\n else:\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3d0f5ed9063075e3464d9b0f14bc2e0\",\n \"score\": \"0.5459922\",\n \"text\": \"def lockwrapper_woo(lock):\\n def decorator(func):\\n @woo.wraponlyonce(func,\\\"lock id = \\\"+ str(id(lock)) ) # imprints on the id of the lock\\n @functools.wraps(func)\\n def foo(*args,**kwargs):\\n lock.acquire()\\n try:\\n output = func(*args,**kwargs)\\n finally:\\n lock.release()\\n return output \\n return foo\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70c6cf5025b7168fa715c6d00ad46306\",\n \"score\": \"0.54565805\",\n \"text\": \"def request_mutex(self, *args, **kwargs) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fcd95eb12b84c4da7a80c743cf14cb1\",\n \"score\": \"0.54484427\",\n \"text\": \"def acquire(self):\\n start_time = time.time()\\n while True:\\n try:\\n self.fd = os.open(self.lockfile,\\n os.O_CREAT | os.O_EXCL | os.O_RDWR)\\n break\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise e\\n if (time.time() - start_time) >= self.timeout:\\n raise FileLockException(\\\"Timeout occurred.\\\")\\n time.sleep(self.delay)\\n self.is_locked = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01aa03121039353a2b46acad88722bd1\",\n \"score\": \"0.544546\",\n \"text\": \"def acquire(self):\\n if not self.lock.trylock():\\n util.SMlog(\\\"Failed to lock %s on first attempt, \\\" % self.lockpath\\n + \\\"blocked by PID %d\\\" % self.lock.test())\\n self.lock.lock()\\n if VERBOSE:\\n util.SMlog(\\\"lock: acquired %s\\\" % self.lockpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b36c6c0dccc0eb06dcc829c840c926\",\n \"score\": \"0.544241\",\n \"text\": \"def lock(self):\\n try:\\n val = os.environ[\\\"FEMB_MANUAL_LOCK_OVERRIDE\\\"] # KeyError if variable not defined\\n except KeyError:\\n try:\\n userid = os.stat(MANUAL_LOCK_PATH).st_uid\\n username = pwd.getpwuid(userid).pw_name\\n print(\\\"Error: The manual lock file exists, {} created by user: '{}'. Exiting. You may set the env var FEMB_MANUAL_LOCK_OVERRIDE to override\\\".format(MANUAL_LOCK_PATH,username))\\n sys.exit(1)\\n except FileNotFoundError:\\n pass\\n try:\\n val = os.environ[\\\"FEMB_AUTOMATIC_LOCK_OVERRIDE\\\"] # KeyError if variable not defined\\n except KeyError:\\n if not (self.fp is None):\\n self.unlock()\\n # checking pid and username in case somebody else has the lock\\n pid = None\\n username = None\\n try:\\n with open(AUTOMATIC_LOCK_PATH) as pidf:\\n pid = str(pidf.read(100))\\n userid = os.stat(AUTOMATIC_LOCK_PATH).st_uid\\n username = pwd.getpwuid(userid).pw_name\\n except FileNotFoundError:\\n pass\\n # now actually locking\\n self.fp = open(AUTOMATIC_LOCK_PATH,\\\"a+\\\")\\n try:\\n fcntl.flock(self.fp, fcntl.LOCK_EX|fcntl.LOCK_NB)\\n self.fp.truncate(0) # delete content if somehow already exists\\n self.fp.write(str(os.getpid()))\\n self.fp.flush()\\n os.fsync(self.fp.fileno())\\n except BlockingIOError as e:\\n print(\\\"Error: FEMB automatic lock user: '{}' pid: {}. Exiting. You may set the env var FEMB_AUTOMATIC_LOCK_OVERRIDE to override\\\".format(username,pid))\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9763b077d4bc784dcc19159c49b6ac2\",\n \"score\": \"0.5422665\",\n \"text\": \"def shield():\\n with (\\n anyio.CancelScope(shield=True)\\n if get_running_loop()\\n else contextlib.nullcontext()\\n ):\\n with _get_thread_shield(threading.current_thread()):\\n yield\",\n \"title\": \"\"\n },\n {\n \"docid\": \"028dfe3f7fe50eb313936f972f0036de\",\n \"score\": \"0.54206777\",\n \"text\": \"def acquire_lock(self, app_id, txid, entity_key = GLOBAL_LOCK_KEY):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba5f1ada3e5e3b8c84b163cfefe6e5f0\",\n \"score\": \"0.5400609\",\n \"text\": \"def __init__(self, lock=None):\\n self.lock = lock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"323365c0d0c39e04b6a591ca7a56135f\",\n \"score\": \"0.5392851\",\n \"text\": \"def set_lock(client, data):\\n pin_id = data.payload.pinID\\n try:\\n if data.payload.locked:\\n gpio_controller.get_pin(pin_id).lock_pin()\\n else:\\n gpio_controller.get_pin(pin_id).unlock_pin()\\n client.send(GET_STATUS(status=gpio_controller.get_pins_status()))\\n except Exception as exception:\\n client.send(GET_STATUS(status=gpio_controller.get_pins_status(), errors=[exception]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c04fb841b63f10fe81f292ee6b6d6418\",\n \"score\": \"0.5390253\",\n \"text\": \"def _acquire_lock(self):\\r\\n self._lock.acquire()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01ca6684ea197de435463cf578d8abcb\",\n \"score\": \"0.53897846\",\n \"text\": \"def _obtain_lock_or_raise(self):\\n\\t\\tif self._has_lock():\\n\\t\\t\\treturn \\n\\t\\tlock_file = self._lock_file_path()\\n\\t\\tif os.path.isfile(lock_file):\\n\\t\\t\\traise IOError(\\\"Lock for file %r did already exist, delete %r in case the lock is illegal\\\" % (self._file_path, lock_file))\\n\\t\\t\\t\\n\\t\\ttry:\\n\\t\\t\\tfd = os.open(lock_file, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0)\\n\\t\\t\\tos.close(fd)\\n\\t\\texcept OSError,e:\\n\\t\\t\\traise IOError(str(e))\\n\\t\\t\\n\\t\\tself._owns_lock = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c62e373f780d9f77eb760d7d1280b04\",\n \"score\": \"0.5387927\",\n \"text\": \"def lock_sut(self, res_query, props={}):\\n return self.lock_resource(res_query, props)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c709cc05f75aec10493652d6291070\",\n \"score\": \"0.5383082\",\n \"text\": \"def set_thread_safety(self, threadsafe=True):\\n if threadsafe:\\n self._lock = threading.RLock()\\n else:\\n self._lock = DummyContextHandler()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10bfb1949b9aab270f342e176a45a790\",\n \"score\": \"0.53797674\",\n \"text\": \"def acquire(self, context, lock_key):\\n if lock_key is not None and len(lock_key) < self.MAX_LOCK_LENGTH:\\n lock_id = self.lock_api.create_lock(context, lock_key)\\n LOG.info(_LI(\\\"Lock %(lock_id)s acquired for lock_key \\\"\\n \\\"%(lock_key)s\\\"),\\n {'lock_id': lock_id, 'lock_key': lock_key})\\n else:\\n lock_id = None\\n LOG.info(_LI(\\\"No lock for lock_key %s\\\"), lock_key)\\n\\n return Lock(context, lock_id, lock_key, self.release)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0502dab8c132577e6ef181bf557693a5\",\n \"score\": \"0.53731763\",\n \"text\": \"def get_lock(consul_local, datacenter):\\n lock_key = 'lock/{}'.format(datacenter)\\n sid = consul_local.session.create(ttl='30s')\\n while not consul_local.kv.acquire_lock(lock_key, sid):\\n print('waiting for lock on {}...'.format(datacenter))\\n sleep(0.2)\\n print('lock acquired on {}'.format(datacenter))\\n return sid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac08442b04c539e4e39929008f578a85\",\n \"score\": \"0.5370247\",\n \"text\": \"def use_lock(lock_file):\\n def _decorate_lock(function):\\n def wait_for_lock(self):\\n if self.check_and_cleanup_lockfiles():\\n self.log.info(\\\"Cleaned up unused lockfiles before start\\\")\\n for second in range(DEFAULT_TIMEOUT):\\n try:\\n self.log.info('Waiting for lock %s...', lock_file)\\n lock = lock_file + '.lock'\\n pid = str(os.getpid())\\n cmd = ['sudo', 'flock', '--timeout', '1',\\n '--conflict-exit-code', '16', lock_file, '-c',\\n '\\\"( test ! -e ' + lock + ' || exit 17 ) ' +\\n '&& echo ' + pid + ' | sudo ' + ' tee ' + lock + '\\\"']\\n result = self.shell.cmd_val(cmd)\\n if result == 16:\\n self.log.info('Another conversion has the lock.')\\n elif result == 17:\\n self.log.info('Another conversion is holding the lock.')\\n elif result == 0:\\n break\\n time.sleep(1)\\n except subprocess.CalledProcessError as err:\\n time.sleep(1)\\n self.log.info('Error waiting for lock: %s', str(err))\\n else:\\n raise RuntimeError('Unable to acquire lock ' + lock_file)\\n try:\\n return function(self)\\n finally:\\n try:\\n lock = lock_file + '.lock'\\n result = self.shell.cmd_out(['sudo', 'rm', '-f', lock])\\n self.log.debug('Released lock: %s', result)\\n except subprocess.CalledProcessError as err:\\n self.log.error('Error releasing lock: %s', str(err))\\n\\n return wait_for_lock\\n return _decorate_lock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aee0c86746e3593f9ce69492bbce06c\",\n \"score\": \"0.53701395\",\n \"text\": \"def begin_lock_door(token, vin):\\n ns = 'DL'\\n root = Commands.__get_root()\\n Commands.__add_common(root, token, vin)\\n Commands.__add_config(root, ns, '1')\\n Commands.__add_fake_position(root)\\n return Command(root,\\n \\\"/remote_control.aspx\\\",\\n rp.BasicCommandResponseParser,\\n namespace = ns,\\n query={\\n 'command': 'DoorLock',\\n 'VIN': vin\\n },\\n headers={\\n 'Authorization': 'Bearer ' + token\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"567cbb3f227ee29203ef008589a1c8a9\",\n \"score\": \"0.5369068\",\n \"text\": \"def acquire_lock(self):\\r\\n self.locked = True\\r\\n self.locks.setdefault(self.prefix+self.id, threading.RLock()).acquire()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a98fe32d3a3f5e3a8864aa51ff50a0d8\",\n \"score\": \"0.5368354\",\n \"text\": \"def isLocked(self):\\n \\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993494,"cells":{"query_id":{"kind":"string","value":"cd1077eda04f8edc8caf84074f701a3e"},"query":{"kind":"string","value":"Provide customable on number of hidden units in layer 1/2 As well as drop out size in connection between layer 1 and 2"},"positive_passages":{"kind":"list like","value":[{"docid":"15ea1ce1012f8e94460b7fe955c465df","score":"0.0","text":"def nn_3layers(nb_input, nb_output,hn1=210,hn2=100,hn3=50,dp1=0.4,dp2=0.2, **kwargs):\n # create model\n model = Sequential()\n model.add(Dense(hn1, input_dim= nb_input, init='glorot_uniform', activation='relu'))\n model.add(Dropout(dp1))\n model.add(Dense(hn2, init='normal', activation='tanh'))\n model.add(Dropout(dp2))\n model.add(Dense(hn3, init='glorot_uniform', activation='linear'))\n model.add(Dense(nb_output, init='normal', activation='softmax'))\n # Compile model\n sgd = SGD(lr=0.08, decay =0.0001)\n model.compile(loss='categorical_crossentropy', optimizer='adadelta') #logloss\n return model","title":""}],"string":"[\n {\n \"docid\": \"15ea1ce1012f8e94460b7fe955c465df\",\n \"score\": \"0.0\",\n \"text\": \"def nn_3layers(nb_input, nb_output,hn1=210,hn2=100,hn3=50,dp1=0.4,dp2=0.2, **kwargs):\\n # create model\\n model = Sequential()\\n model.add(Dense(hn1, input_dim= nb_input, init='glorot_uniform', activation='relu'))\\n model.add(Dropout(dp1))\\n model.add(Dense(hn2, init='normal', activation='tanh'))\\n model.add(Dropout(dp2))\\n model.add(Dense(hn3, init='glorot_uniform', activation='linear'))\\n model.add(Dense(nb_output, init='normal', activation='softmax'))\\n # Compile model\\n sgd = SGD(lr=0.08, decay =0.0001)\\n model.compile(loss='categorical_crossentropy', optimizer='adadelta') #logloss\\n return model\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b9350cd5058fd91107fba622e285d446","score":"0.7422406","text":"def hidden_unit(Layer): \n\tfan_in = Layer.weight.data.size()[0]\n\tlim = 1. / np.sqrt(fan_in)\n\treturn (-lim, lim)","title":""},{"docid":"6d850db14618078b77e68af232c24387","score":"0.6618541","text":"def define_hidden_layers(self):\n self.model.hidden_layer_sizes = self.hidden_layers","title":""},{"docid":"3774caf8ae4661264b5319166bf717c2","score":"0.65002656","text":"def __init__(self, input_size, output_size, hidden_layers, drop_p=0.5):\n super().__init__()\n #input to hidden layer\n self.hidden_layers = nn.ModuleList([nn.Linear(input_size, hidden_layers[0])])\n\n #add a variable no of more hidden layers\n layer_sizes = zip(hidden_layers[:-1], hidden_layers[1:])\n self.hidden_layers.extend([nn.Linear(h1, h2) for h1, h2 in layer_sizes])\n\n self.output = nn.Linear(hidden_layers[-1], output_size)\n\n self.dropout = nn.Dropout(p=drop_p)","title":""},{"docid":"64694a0acdb7724103a3bfcf0c0b1b34","score":"0.6280762","text":"def hidden_init(layer):\n fan_in = layer.weight.data.size()[0]\n lim = 1. / np.sqrt(fan_in)\n return (-lim, lim)","title":""},{"docid":"1541ff5cca577ea5278712a53364ea44","score":"0.6189127","text":"def hidden_sizes(self) -> List[int]:\n raise NotImplementedError","title":""},{"docid":"572b4752338802031c8ba18abe84c2c1","score":"0.61134386","text":"def __init__(self, input, output, hidden_layers, drop_out=0.5):\n super().__init__()\n self.hidden_layers = nn.ModuleList([nn.Linear(input, hidden_layers[0])])\n layer_sizes = zip(hidden_layers[:-1], hidden_layers[1:])\n self.hidden_layers.extend([nn.Linear(hidden1, hidden2) for hidden1, hidden2 in layer_sizes])\n self.output = nn.Linear(hidden_layers[-1], output)\n self.dropout = nn.Dropout(p=drop_out)","title":""},{"docid":"b5beb765ac144a907609179b6112f548","score":"0.6017871","text":"def get_num_hidden(self) -> int:\n return self.config.model_size","title":""},{"docid":"e3b0fe867f91a8cb5f8870c859ebc77c","score":"0.60013855","text":"def Dimfrac(self) -> int:","title":""},{"docid":"e3b0fe867f91a8cb5f8870c859ebc77c","score":"0.60013855","text":"def Dimfrac(self) -> int:","title":""},{"docid":"e3b0fe867f91a8cb5f8870c859ebc77c","score":"0.60013855","text":"def Dimfrac(self) -> int:","title":""},{"docid":"c1d3153483a584dab7b6a210d50729be","score":"0.59481287","text":"def Dimdli(self) -> float:","title":""},{"docid":"c1d3153483a584dab7b6a210d50729be","score":"0.59481287","text":"def Dimdli(self) -> float:","title":""},{"docid":"c1d3153483a584dab7b6a210d50729be","score":"0.59481287","text":"def Dimdli(self) -> float:","title":""},{"docid":"08cd0f1b061e416326ab17ed21ca4f7b","score":"0.59405166","text":"def show_size(brain):","title":""},{"docid":"016630648c91ac1704f677f5ab88649a","score":"0.591925","text":"def test_dropouts():\n hidden_layers = [{'units': 64, 'name': 'relu1', 'dropout': 0.1},\n {'units': 64, 'name': 'relu2', 'dropout': 0.1}]\n layers = HiddenLayers(hidden_layers)\n\n assert len(layers) == 4","title":""},{"docid":"191dee7e7ca2a9877ad168cb916231ef","score":"0.591023","text":"def recurrent_hidden_state_size(\n self,\n ) -> Union[int, Dict[str, Tuple[Sequence[Tuple[str, Optional[int]]], torch.dtype]]]:\n return self.hidden_size","title":""},{"docid":"1adbc0ce502e8eef8aa8ef930a982768","score":"0.58989924","text":"def def_layer(self):\n self.inp_drop = tf.keras.layers.Dropout(rate=self.config.input_dropout)\n self.hidden_dropout1 = tf.keras.layers.Dropout(rate=self.config.hidden_dropout1)\n self.hidden_dropout2 = tf.keras.layers.Dropout(rate=self.config.hidden_dropout2)\n\n self.bn0 = tf.keras.layers.BatchNormalization(trainable=True)\n self.bn1 = tf.keras.layers.BatchNormalization(trainable=True)","title":""},{"docid":"271516be40fe02f2c764c8e1b0a68db5","score":"0.58838993","text":"def recalculate_container_layer_thickness(self):","title":""},{"docid":"5da7be0ae3a1f8636472191c8d4b9640","score":"0.587329","text":"def IncludingHidden(self) -> LayerTable:","title":""},{"docid":"519822c93380a16c319b8ec0b08a5712","score":"0.58562195","text":"def size(self):\n if self.layers > 1:\n return self.scope[0] * self.scope[1] * self.scope[2]\n else:\n return self.scope[0] * self.scope[1]","title":""},{"docid":"3c4e977f690994d5487396977240ad1f","score":"0.58517355","text":"def Dimatfit(self) -> int:","title":""},{"docid":"3c4e977f690994d5487396977240ad1f","score":"0.58517355","text":"def Dimatfit(self) -> int:","title":""},{"docid":"3c4e977f690994d5487396977240ad1f","score":"0.58517355","text":"def Dimatfit(self) -> int:","title":""},{"docid":"ae317d8785c584bc3a434d2330da7685","score":"0.5849485","text":"def BreakSize(self) -> float:","title":""},{"docid":"6b4cd2cb163430b1c39cce0481b7b77e","score":"0.5825524","text":"def __init__(self, hidden_size, eps=1e-12):\n super(LayerNorm, self).__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.bias = nn.Parameter(torch.zeros(hidden_size))\n self.variance_epsilon = eps","title":""},{"docid":"5b47af42acfd9539faa08e82f158e562","score":"0.581501","text":"def layer_size(self):\n return self.scope[0] * self.scope[1]","title":""},{"docid":"0928ac7323e35c703bd23fc8ffad7271","score":"0.5805787","text":"def test_units(self):\r\n layout = Layout()\r\n layout.units = None\r\n layout.layers.append(Layer())\r\n design = Design()\r\n design.layout = layout\r\n writer = Writer()\r\n writer.write(design)","title":""},{"docid":"2a6b9966699907b19b92fea046ef69b8","score":"0.57984674","text":"def Dimfxlen(self) -> float:","title":""},{"docid":"2a6b9966699907b19b92fea046ef69b8","score":"0.57984674","text":"def Dimfxlen(self) -> float:","title":""},{"docid":"2a6b9966699907b19b92fea046ef69b8","score":"0.57984674","text":"def Dimfxlen(self) -> float:","title":""},{"docid":"4153e93ca0fb64b2014f7d0d0b19782c","score":"0.57983804","text":"def test_complex_layers():\n hidden_layers = [{'units': 64, 'activation': 'relu', 'dropout': 0.01},\n {'units': 64},\n {'batch_normalization': {'axis': -1}},\n {'activation': 'relu'},\n {'dropout': 0.01}]\n layers = HiddenLayers(hidden_layers)\n\n assert len(layers) == 7","title":""},{"docid":"d72b9411dab0f8c0b9df8432c4672bec","score":"0.5786331","text":"def __init__(self, dropout_value = 0.1):\n super(DenseDepthDNN2, self).__init__()\n # Input Block\n self.dropout_value = dropout_value # This is to use if it overfits, but not yet used.\n self.x2 = nn.Sequential( #input 6*W*H, output=W*H*64, RF=2\n nn.Conv2d(in_channels=6, out_channels=64, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(64)\n #nn.Dropout(dropout_value)\n )\n #x3 = Conv(x1 + x2) , no x1\n self.x3 = nn.Sequential( #input W*H*64, output=W*H*64, RF=4 \n nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(64)\n #nn.Dropout(dropout_value)\n )\n \n #x4 maxpool (x2+x3) \n self.x4 = nn.MaxPool2d(2, 2) #input W*H*128, output=W/2*H/2*128, RF=5\n \n #x5 = Conv(x4)\n self.x5 = nn.Sequential( #input W/2*H/2*128, output=W/2*H/2*128, RF=10\n nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(128)\n #nn.Dropout(dropout_value)\n )\n \n #x6 = Conv(x4 + x5)\n self.x6 = nn.Sequential( #input W/2*H/2*256, output=W/2*H/2*128, RF=12\n nn.Conv2d(in_channels=256, out_channels=128, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(128)\n #nn.Dropout(dropout_value)\n )\n\n #x7 = Conv(x4 + x5 + x6)\n self.x7 = nn.Sequential( #input W/2*H/2*384, output=W/2*H/2*128, RF=14\n nn.Conv2d(in_channels=384, out_channels=128, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(128)\n #nn.Dropout(dropout_value)\n )\n \n #x8 = MaxPooling(x5 + x6 + x7)\n self.x8 = nn.MaxPool2d(2, 2) #input W/2*H/2*384, output_size = (W/4*H/4*384), RF=15\n \n #x9 = Conv(x8)\n self.x9 = nn.Sequential( #input W/4*W/4*384, output_size = (W/4*H/4*256), RF=30\n nn.Conv2d(in_channels=384, out_channels=256, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(256)\n #nn.Dropout(dropout_value)\n )\n #x10 = Conv (x8 + x9)\n self.x10 = nn.Sequential( #input W/4*H/4*640, output_size = (W/4*H/4*256), RF=32\n nn.Conv2d(in_channels=640, out_channels=256, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(256)\n #nn.Dropout(dropout_value)\n )\n \n #x11 = Conv (x8 + x9 + x10)\n self.x11 = nn.Sequential( #input W/4*H/4*896, output_size = (W/4*H/4*256), RF=34\n nn.Conv2d(in_channels=896, out_channels=256, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(256)\n #nn.Dropout(dropout_value)\n )\n\n #x12 = Conv (x9 + x10 + x11)\n self.x12 = nn.MaxPool2d(2, 2) #input W/4*H/4*768, output_size = (W/8*H/8*768), RF=35\n \n #x13 = Conv(x12)\n self.x13 = nn.Sequential( #input W/4*H/4*768, output_size = (W/4*H/4*512), RF=70\n nn.Conv2d(in_channels=768, out_channels=512, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(512)\n #nn.Dropout(dropout_value)\n )\n\n #x14 = Conv(x12 + x13)\n self.x14 = nn.Sequential( #input W/8*H/8*1280, output_size = (W/8*H/8*512), RF=72\n nn.Conv2d(in_channels=1280, out_channels=512, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(512)\n #nn.Dropout(dropout_value)\n )\n\n #x15 = Conv(x12 + x13 + x14)\n self.x15 = nn.Sequential( #input W/8*H/8*1792, output_size = (W/8*H/8*512), RF=74\n nn.Conv2d(in_channels=1792, out_channels=512, kernel_size=3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(512)\n #nn.Dropout(dropout_value)\n )\n\n\n \"\"\"\n # OUTPUT BLOCK -> Add GAP Layer\n self.x12_gap = nn.Sequential( # input 8*8*64, output_size = (1*1*64), Receptive FieldF=42\n nn.AvgPool2d(kernel_size=8)\n )\n\n self.x13_fc = nn.Sequential(\n nn.Linear(in_features=64, out_features=10)\n # nn.BatchNorm2d(10),\n # nn.ReLU(),\n # nn.Dropout(dropout_value)\n )\n \"\"\"\n # We do not use Fully connected layers. Removed gap layer and added additional conv layers.\n \n self.xf1_invconv_512_256 = nn.Sequential( #input W/4*H/4*512, output_size = (W/4*H/4*256), RF=78\n nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1,padding=0),\n nn.BatchNorm2d(256),\n nn.ReLU(),\n #nn.Dropout(dropout_value)\n )\n self.xf1_inconv_256 = nn.Sequential( #input W/4*W/4*256, output_size = (W/4*W/4*256), RF=80\n nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3,padding=1),\n nn.BatchNorm2d(256),\n nn.ReLU(),\n #nn.Dropout(dropout_value)\n )\n \n self.xf2_invconv_256_128 = nn.Sequential( #input W/2*H/2*256, output_size = (W/2*H/2*128), RF=82\n nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1,padding=0),\n nn.BatchNorm2d(128),\n nn.ReLU(),\n #nn.Dropout(dropout_value)\n )\n\n self.xf2_inconv_128 = nn.Sequential( #input W/2*H/2*128, output_size = (W/2*H/2*128), RF=84\n nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3,padding=1),\n nn.BatchNorm2d(128),\n nn.ReLU(),\n #nn.Dropout(dropout_value)\n )\n \n self.xf3_invconv_128_64 = nn.Sequential( #input W*H*128, output_size = (W*H*64), RF=86\n nn.Conv2d(in_channels=128, out_channels=64, kernel_size=1,padding=0),\n nn.BatchNorm2d(64),\n nn.ReLU(),\n #nn.Dropout(dropout_value)\n )\n\n self.xf3_inconv_64 = nn.Sequential( #input W*H*64, output_size = (W*H*64), RF=88\n nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3,padding=1),\n nn.BatchNorm2d(64),\n nn.ReLU(),\n #nn.Dropout(dropout_value)\n )\n\n self.x64_inconv_32 = nn.Sequential( #input 64*64*64, output_size = (64*64*32)\n nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3,padding=1),\n nn.BatchNorm2d(32),\n nn.ReLU(),\n #nn.Dropout(dropout_value)\n )\n\n #one for depth and one for mask\n self.xdepth_conv_1_1 = nn.Sequential( #input 64*64*32, output_size = (64*64*3)\n nn.Conv2d(in_channels=32, out_channels=1, kernel_size=1)\n #nn.Dropout(dropout_value)\n )\n self.predict_mask = nn.Conv2d(32, 1, kernel_size=3, padding=1)","title":""},{"docid":"62bf53b7de7292281b120b67bea5f476","score":"0.5774521","text":"def recurrent_hidden_state_size(self) -> int:\n return self.hidden_size","title":""},{"docid":"35f11e410c0917c7b85a0fa2fd6cd172","score":"0.57668483","text":"def Lunits(self) -> int:","title":""},{"docid":"9bc5ac6986b3adad00e3e8da64f0a105","score":"0.5764619","text":"def DrawingUnits(self) -> float:","title":""},{"docid":"dc3fa560c346ac9d5e97bba9b9122149","score":"0.5758371","text":"def Dimensions(self) -> RenderGlobal.DimensionsParameter:","title":""},{"docid":"69b226a6c1906d6217f7aee261d36b22","score":"0.5745153","text":"def __init__(self, hidden_size, eps=1e-06):\n super().__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.variance_epsilon = eps","title":""},{"docid":"3a0da5a65fd5b8e6aada0043c0418d9c","score":"0.5741508","text":"def LightingUnits(self) -> _n_6_t_19:","title":""},{"docid":"dc4cd4a103b82d44d94bdb0cc11dd1f5","score":"0.57290715","text":"def __init__(self, hidden_size, eps=1e-6):\n super().__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.variance_epsilon = eps","title":""},{"docid":"404951695473bfd341670c6a0d6b4ac4","score":"0.5728463","text":"def Dimcen(self) -> float:","title":""},{"docid":"404951695473bfd341670c6a0d6b4ac4","score":"0.5728463","text":"def Dimcen(self) -> float:","title":""},{"docid":"404951695473bfd341670c6a0d6b4ac4","score":"0.5728463","text":"def Dimcen(self) -> float:","title":""},{"docid":"9b07c2e6ab22014f8e5c9f9a6109f667","score":"0.5713198","text":"def getLayerSizes(self) -> retval:\n ...","title":""},{"docid":"70ffff54fcf79918a36f2fc6c369e0de","score":"0.56853294","text":"def standardizedDimension(self):\n return 0","title":""},{"docid":"562295d6d0c346cba660cf1daeeef032","score":"0.5667133","text":"def Dimscale(self) -> float:","title":""},{"docid":"562295d6d0c346cba660cf1daeeef032","score":"0.5667133","text":"def Dimscale(self) -> float:","title":""},{"docid":"562295d6d0c346cba660cf1daeeef032","score":"0.5667133","text":"def Dimscale(self) -> float:","title":""},{"docid":"562295d6d0c346cba660cf1daeeef032","score":"0.5667133","text":"def Dimscale(self) -> float:","title":""},{"docid":"562295d6d0c346cba660cf1daeeef032","score":"0.5667133","text":"def Dimscale(self) -> float:","title":""},{"docid":"9247bf3fe70ad723afefa53d4af6fbd6","score":"0.56331897","text":"def Dimtp(self) -> float:","title":""},{"docid":"9247bf3fe70ad723afefa53d4af6fbd6","score":"0.56331897","text":"def Dimtp(self) -> float:","title":""},{"docid":"9247bf3fe70ad723afefa53d4af6fbd6","score":"0.56331897","text":"def Dimtp(self) -> float:","title":""},{"docid":"ae7ddb2436d18222e941c2d70284d31e","score":"0.56325835","text":"def __init__(self, hidden_state_size=10, output_size=1, *args, **kwargs):\n super(ReadoutLocalLayer, self).__init__(*args, **kwargs)\n self.output_size = output_size","title":""},{"docid":"3cd630720b26b355e183d7366a022753","score":"0.5628463","text":"def __init__(self, input_dim: int, hidden_layer: bool) -> None:\n\n # Number of hidden units if hidden_layer = True.\n self.hidden_units = 25\n\n self.lr = 1e-2\n\n self.epochs = 400\n\n self.x_train, self.y_train = None, None\n self.x_test, self.y_test = None, None\n\n\n self.input_dim = input_dim\n self.hidden_layer = hidden_layer\n self.layers=[]","title":""},{"docid":"05420e034acd6665b7a81f8210012358","score":"0.5628366","text":"def StepSize(self) -> float:","title":""},{"docid":"5e298d559bc2716b6d7faf311d140cf4","score":"0.5625653","text":"def __init__(self, in_size, fc_units, out_size):\n super(Actor, self).__init__()\n units = [in_size] + fc_units + [out_size]\n layers = []\n for i in range(len(units)-2):\n layer = nn.Linear(units[i], units[i+1])\n layer.weight.data.uniform_(*hidden_init(layer))\n layers.append(layer)\n layers.append(nn.ReLU())\n # last layer: different activation and initialization\n layer = nn.Linear(units[-2], units[-1])\n layer.weight.data.uniform_(-3e-3, 3e-3)\n layers.append(layer)\n layers.append(nn.Tanh())\n self.net = nn.ModuleList(layers)","title":""},{"docid":"ece1613aed202c3eb46837f2eb37ba4e","score":"0.56133837","text":"def n_dimension(self):","title":""},{"docid":"0ba8b85ee8348cadd97aa25a8309af3e","score":"0.5590752","text":"def Dimzin(self) -> int:","title":""},{"docid":"0ba8b85ee8348cadd97aa25a8309af3e","score":"0.5590752","text":"def Dimzin(self) -> int:","title":""},{"docid":"0ba8b85ee8348cadd97aa25a8309af3e","score":"0.5590752","text":"def Dimzin(self) -> int:","title":""},{"docid":"486b4fa042fff6e701dc429355ba5871","score":"0.5580775","text":"def FilterHeight(self) -> float:","title":""},{"docid":"eee0eb2308d7b0668de133285980e8f8","score":"0.5579714","text":"def __init__(self, spatial_size, #mlp_hidden_size,\n epsilon=1e-8):\n # super().__init__()\n # self.num_slots = num_slots\n self.slot_spatial_size = spatial_size\n self.slot_size = spatial_size * spatial_size\n # self.mlp_hidden_size = mlp_hidden_size\n self.epsilon = epsilon\n\n self.norm_inputs = tfkl.LayerNormalization(dtype=tf.float32) #layers.LayerNormalization()\n # self.norm_slots = layers.LayerNormalization()\n # self.norm_mlp = layers.LayerNormalization()\n\n # Linear maps for the attention module.\n self.project_q = tfkl.Dense(self.slot_size, use_bias=False, kernel_initializer=tf.keras.initializers.Identity()) #layers.Dense(self.slot_size, use_bias=False, name=\"q\")\n self.project_k = tfkl.Dense(self.slot_size, use_bias=False, kernel_initializer=tf.keras.initializers.Identity()) #layers.Dense(self.slot_size, use_bias=False, name=\"k\")\n self.project_v = tfkl.Dense(self.slot_size, use_bias=False, kernel_initializer=tf.keras.initializers.Identity()) #layers.Dense(self.slot_size, use_bias=False, name=\"v\")\n\n # Slot update functions.\n # self.mlp = tf.keras.Sequential([\n # layers.Dense(self.mlp_hidden_size, activation=\"relu\"),\n # layers.Dense(self.slot_size)\n # ], name=\"mlp\")","title":""},{"docid":"25518a7e10bd686771c0e2e5b69eb8ae","score":"0.5579462","text":"def PriorSize(self) -> float:","title":""},{"docid":"54ba75ac95028e4b161fc5171fe23ece","score":"0.5575941","text":"def Aunits(self) -> int:","title":""},{"docid":"eca6c32a2f94d9981228547fd6bbadd9","score":"0.55757153","text":"def Dimaltu(self) -> int:","title":""},{"docid":"eca6c32a2f94d9981228547fd6bbadd9","score":"0.55757153","text":"def Dimaltu(self) -> int:","title":""},{"docid":"eca6c32a2f94d9981228547fd6bbadd9","score":"0.55757153","text":"def Dimaltu(self) -> int:","title":""},{"docid":"14e6f742903ee2629c5d7b61f0371eec","score":"0.55751354","text":"def __init__(self, rng, input, n_in, n_hiddens, dropout_rates, activation=None, n_slack=0, init_ignore_out=False, init_account_dropout=False):\n self.params = []\n self.W = []\n self.b = []\n\n self.W_actual = []\n self.b_actual = []\n\n # keep track of model input\n self.input = input\n\n # Multiple hidden layers\n print >> sys.stderr, dropout_rates\n last_layer_out = self.input\n last_layer_dropout = _dropout_from_layer(rng, self.input, p=dropout_rates[0])\n last_layer_size = n_in\n\n slacks = numpy.append(numpy.asarray([n_slack], dtype='int32'), numpy.zeros((len(n_hiddens)-1,), dtype='int32')) if len(n_hiddens) > 0 else None\n for i in range(0, len(n_hiddens)):\n # dropped-out path: for training\n dropoutLayer = DropoutHiddenLayer(rng=rng,\n input=last_layer_dropout, activation=activation,\n n_in=last_layer_size, n_out=n_hiddens[i],\n dropout_rate=dropout_rates[i+1],\n n_slack=slacks[i] + (n_hiddens[i] if init_ignore_out else 0),\n dilate_factor = (1./(1. - dropout_rates[i]) if init_account_dropout else 1.)\n )\n last_layer_dropout = dropoutLayer.output\n\n self.params += dropoutLayer.params\n self.W += [dropoutLayer.W]\n self.b += [dropoutLayer.b]\n \n # original (untouched) path: for testing\n hiddenLayer = HiddenLayer(rng=rng,\n input=last_layer_out, activation=activation,\n n_in=last_layer_size, n_out=n_hiddens[i],\n W=dropoutLayer.W * (1. - dropout_rates[i]),\n b=dropoutLayer.b,\n n_slack=slacks[i]\n )\n last_layer_out = hiddenLayer.output\n last_layer_size = n_hiddens[i]\n\n self.W_actual += [hiddenLayer.W]\n self.b_actual += [hiddenLayer.b]\n\n # The logistic regression layer gets as input the hidden units\n # of the hidden layer\n # Dropped-out path: for training\n self.dropoutLogRegressionLayer = LogisticRegression(\n rng=rng, input=last_layer_dropout,\n n_in=(n_hiddens[-1] if len(n_hiddens) > 0 else n_in)\n )\n self.params += self.dropoutLogRegressionLayer.params\n\n # original (untouched) path: for testing\n self.logRegressionLayer = LogisticRegression(\n rng=rng, input=last_layer_out,\n n_in=(n_hiddens[-1] if len(n_hiddens) > 0 else n_in),\n W=self.dropoutLogRegressionLayer.W * (1. - dropout_rates[-1]),\n b=self.dropoutLogRegressionLayer.b\n )\n\n # prediction of the MLP is given by the prediction of the output of the\n # model, computed in the logistic regression layer\n self.dropout_errors = self.dropoutLogRegressionLayer.errors\n self.dropout_cross_entropy = self.dropoutLogRegressionLayer.cross_entropy\n\n self.y_pred = self.logRegressionLayer.y_pred\n self.errors = self.logRegressionLayer.errors\n self.cross_entropy = self.logRegressionLayer.cross_entropy\n\n self.true_positives = self.logRegressionLayer.true_positives\n self.true_negatives = self.logRegressionLayer.true_negatives\n self.false_positives = self.logRegressionLayer.false_positives\n self.false_negatives = self.logRegressionLayer.false_negatives","title":""},{"docid":"f4ebc1a0d572941145b3c710af27dcde","score":"0.557238","text":"def Dimdle(self) -> float:","title":""},{"docid":"f4ebc1a0d572941145b3c710af27dcde","score":"0.557238","text":"def Dimdle(self) -> float:","title":""},{"docid":"f4ebc1a0d572941145b3c710af27dcde","score":"0.557238","text":"def Dimdle(self) -> float:","title":""},{"docid":"da3b4ec401fa42296f6fbbf30cc27c1e","score":"0.5571596","text":"def Dimtm(self) -> float:","title":""},{"docid":"da3b4ec401fa42296f6fbbf30cc27c1e","score":"0.5570935","text":"def Dimtm(self) -> float:","title":""},{"docid":"da3b4ec401fa42296f6fbbf30cc27c1e","score":"0.5570935","text":"def Dimtm(self) -> float:","title":""},{"docid":"5a91e4210853ca6517ed2f6699baaac3","score":"0.5570743","text":"def recurrent_hidden_state_size(self) -> int:\n return self._hidden_size","title":""},{"docid":"5a91e4210853ca6517ed2f6699baaac3","score":"0.5570743","text":"def recurrent_hidden_state_size(self) -> int:\n return self._hidden_size","title":""},{"docid":"43fee013a5446820edd174e61300e3c4","score":"0.5569468","text":"def Diameter(self) -> float:","title":""},{"docid":"1cc1b6cb50b7d4f041bad2635974c913","score":"0.55670696","text":"def Dimazin(self) -> int:","title":""},{"docid":"1cc1b6cb50b7d4f041bad2635974c913","score":"0.55670696","text":"def Dimazin(self) -> int:","title":""},{"docid":"1cc1b6cb50b7d4f041bad2635974c913","score":"0.55670696","text":"def Dimazin(self) -> int:","title":""},{"docid":"2f914e39f139bbcddb4780b27c43de2e","score":"0.55670553","text":"def get_n_neural_parameters(self):","title":""},{"docid":"4f2ef493a18adb42f6d8a45e9a5a65fe","score":"0.55659014","text":"def BlockScale(self) -> _n_2_t_11:","title":""},{"docid":"4f2ef493a18adb42f6d8a45e9a5a65fe","score":"0.55654293","text":"def BlockScale(self) -> _n_2_t_11:","title":""},{"docid":"51d69cb6c817542026b3abfa15efd89b","score":"0.5562829","text":"def __init__(self, hidden_state_size=10, output_size=1, *args, **kwargs):\n super(ReadoutGlobalLayer, self).__init__(*args, **kwargs)\n self.output_size = output_size","title":""},{"docid":"81f01e3aebf5c23822cd27e6f149d070","score":"0.5561108","text":"def __init__(self, inWidth, outWidth, layers):\r\n super(DeepNeuralNet, self).__init__()\r\n\r\n self.linears = nn.ModuleList()\r\n\r\n #setting up hidden layer size through arg parse in our initialization\r\n commaSplit = layers.split(\",\")\r\n firstChangeSize = inWidth\r\n\r\n # set up relu activation in init\r\n self.relu = nn.ReLU()\r\n\r\n #set up softmax post act\r\n self.softmax = nn.Softmax()\r\n\r\n for z in commaSplit:\r\n splitString = z.split(\"x\") #\"32x4,64x2\" --> {32x4 , 64x2}\r\n hiddenLayerSize = int(splitString[0] ) #grabbing size\r\n numUnits = int(splitString[1]) #grabbing numUnits\r\n\r\n #now define hidden layer sizes\r\n flag1 = 0\r\n\r\n #hidden layers\r\n for i in range(numUnits):\r\n if (flag1 == 0):\r\n flag1 = 1\r\n self.linears.append(nn.Linear(firstChangeSize, hiddenLayerSize)) #creates layer\r\n firstChangeSize = hiddenLayerSize #update next change size\r\n else:\r\n self.linears.append(nn.Linear(hiddenLayerSize, hiddenLayerSize))\r\n\r\n #output dimensions\r\n self.linears.append((nn.Linear(firstChangeSize, outWidth)))\r\n\r\n #print(\"b4 params\")\r\n #print params\r\n for name, param in self.named_parameters():\r\n print(name, param.data.shape)\r\n #print(\"after params for loop\")\r","title":""},{"docid":"9dfdf58f603c283b64235a5d32205b27","score":"0.55560976","text":"def OwningViewScale(self) -> float:","title":""},{"docid":"187b4925f72e5962f7ddf7300e693bc7","score":"0.5548241","text":"def get_hidden_size(self):\n\n return self.encoder_output_size","title":""},{"docid":"1fb936f72c3047da0c8cfd9c5f58529b","score":"0.55391115","text":"def calc_numdim(image_sidel_use,n_h_layers,nodes_per_h_layer,n_classes ):\n\n n_nodes_lowerlayer, n_nodes_thislayer = image_sidel_use**2, nodes_per_h_layer # first hidden layer\n numdim = (1 + n_nodes_lowerlayer) * n_nodes_thislayer\n n_nodes_lowerlayer, n_nodes_thislayer = nodes_per_h_layer, nodes_per_h_layer # other hidden layers\n numdim += ( n_h_layers - 1 ) * (1 + n_nodes_lowerlayer) * n_nodes_thislayer\n n_nodes_lowerlayer, n_nodes_thislayer = nodes_per_h_layer, n_classes # output layer\n numdim += (1 + n_nodes_lowerlayer) * n_nodes_thislayer\n\n return numdim","title":""},{"docid":"0c4275fc949cd9cf48211abd9a9da5ab","score":"0.55383253","text":"def Dimtvp(self) -> float:","title":""},{"docid":"0c4275fc949cd9cf48211abd9a9da5ab","score":"0.5538277","text":"def Dimtvp(self) -> float:","title":""},{"docid":"0c4275fc949cd9cf48211abd9a9da5ab","score":"0.5538277","text":"def Dimtvp(self) -> float:","title":""},{"docid":"a3c92ca35e59205ad2a742ee5d22d3ce","score":"0.55342156","text":"def Dimalttd(self) -> int:","title":""},{"docid":"a3c92ca35e59205ad2a742ee5d22d3ce","score":"0.55342156","text":"def Dimalttd(self) -> int:","title":""},{"docid":"a3c92ca35e59205ad2a742ee5d22d3ce","score":"0.55342156","text":"def Dimalttd(self) -> int:","title":""},{"docid":"e99fe108d63408c53f63672fd8d8c818","score":"0.5533856","text":"def __init__(self, input_size, hidden_size, name=\"\", weight_init=HeUniform(1.0)):\n\n # input to hidden\n self.W_xh = parameter(init_array(weight_init, (input_size, hidden_size)),\n name=name+\".W_xh\")\n\n # hidden to hidden\n self.W_hh = parameter(init_array(weight_init, (hidden_size, hidden_size)),\n name=name+\".W_hh\")\n\n # hidden to output\n self.W_ho = parameter(init_array(weight_init, (hidden_size, hidden_size)),\n name=name+\".W_ho\")","title":""},{"docid":"0e391abbec615b801fa3968d9d32a1cb","score":"0.5516211","text":"def __init__(self, hidden_size: int, resolution: List[int]):\n super().__init__()\n self.hidden_size = hidden_size\n self.resolution = resolution\n self.dense = L.Dense(hidden_size, use_bias=True)\n self.grid = self.build_grid(resolution)","title":""},{"docid":"0fa7677b324e9f9c537867bcb8c58fba","score":"0.5511988","text":"def ThicknessDepth(self) -> float:","title":""},{"docid":"eb44ddbb6808e30b3129307418642b93","score":"0.55110234","text":"def AnnoHeight(self) -> float:","title":""},{"docid":"7222df74e4b1c29a44244a9d77a1b5d2","score":"0.55087787","text":"def load_network(size_value, dropout_rate: float = 0.1, dropout_strategy: str = \"all\",\n layers_filters: tuple = (16, 16, 24, 32), expansions: tuple = (1, 6, 6), print_summary=True,\n use_resnet=False, use_mobile_net=False, use_additional_output=False):\n height, width = size_value\n\n ####################################\n # Custome Backbone\n ####################################\n\n squares = []\n prediction_shapes = []\n sizes = [6, 10, 15, 24, 42, 78] # optimised for curve signs\n sizes = [int(s / 220 * height) for s in sizes] # for smaller input size (than 220, 400)\n alpha = 1.0\n\n if not use_additional_output:\n sizes = sizes[:5]\n\n dropout_all = dropout_rate if dropout_strategy == \"all\" else None\n dropout_end = dropout_rate if dropout_strategy != \"all\" else None\n\n first_layer_kernel = 3\n\n f1, f2, f3, f4 = layers_filters\n e1, e2, e3 = expansions\n\n input_layer = Input(shape=(height, width, 3), name='input')\n x = tf.keras.layers.ZeroPadding2D(padding=correct_pad(tf.keras.backend, input_layer, first_layer_kernel),\n name='Conv1_pad')(input_layer)\n x = Conv2D(filters=f1, kernel_size=first_layer_kernel, strides=2, activation=None, padding='valid',\n kernel_regularizer=l2(0.01),\n use_bias=False, name=\"Conv1\")(x)\n x = BatchNormalization(epsilon=1e-3, momentum=0.99, name=\"Conv1_BN\")(x)\n x = tf.keras.layers.ReLU(6., name=\"Conv1_relu\")(x)\n if dropout_all is not None:\n x = Dropout(dropout_all, name='Conv1_dropout')(x)\n x = _inverted_res_block(x, filters=f2, alpha=alpha, stride=1, expansion=e1, block_id=0, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\n x = _inverted_res_block(x, filters=f3, alpha=alpha, stride=2, expansion=e2, block_id=1, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\n x = _inverted_res_block(x, filters=f3, alpha=alpha, stride=1, expansion=e2, block_id=2, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\n x = _inverted_res_block(x, filters=f3, alpha=alpha, stride=1, expansion=e2, block_id=3, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\n if dropout_end is not None:\n x = Dropout(dropout_end, name='part1_dropout')(x)\n\n if use_additional_output:\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=10,\n # force_output_filter_count=True)\n out = Conv2D(filters=class_count + 1,\n kernel_size=3,\n strides=1,\n padding='same',\n activation='linear',\n use_bias=True,\n kernel_regularizer=l2(0.01),\n name=\"output_0\")(x)\n out = Softmax(axis=3, name=\"output_0_softmax\")(out)\n squares.append(out)\n prediction_shapes.append(np.array(out.shape[1:3]))\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=10,\n # force_output_filter_count=True)\n out = Conv2D(filters=class_count + 1,\n kernel_size=3,\n strides=1,\n padding='same',\n activation='linear',\n use_bias=True,\n kernel_regularizer=l2(0.01),\n name=\"output_1\")(x)\n out = Softmax(axis=3, name=\"output_1_softmax\")(out)\n squares.append(out)\n prediction_shapes.append(np.array(out.shape[1:3]))\n\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=11,\n # force_output_filter_count=True)\n out = Conv2D(filters=class_count + 1,\n kernel_size=3,\n strides=1,\n padding='same',\n activation='linear',\n use_bias=True,\n kernel_regularizer=l2(0.01),\n name=\"output_2\")(x)\n out = Softmax(axis=3, name=\"output_2_softmax\")(out)\n squares.append(out)\n prediction_shapes.append(np.array(out.shape[1:3]))\n\n x = _inverted_res_block(x, filters=f4, alpha=alpha, stride=2, expansion=e3, block_id=4, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\n x = _inverted_res_block(x, filters=f4, alpha=alpha, stride=1, expansion=e3, block_id=5, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\n x = _inverted_res_block(x, filters=f4, alpha=alpha, stride=1, expansion=e3, block_id=6, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\n if dropout_end is not None:\n x = Dropout(dropout_end, name='part2_dropout')(x)\n\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=12,\n # force_output_filter_count=True)\n out = Conv2D(filters=class_count + 1,\n kernel_size=3,\n strides=1,\n padding='same',\n activation='linear',\n use_bias=True,\n kernel_regularizer=l2(0.01),\n name=\"output_3\")(x)\n out = Softmax(axis=3, name=\"output_3_softmax\")(out)\n squares.append(out)\n prediction_shapes.append(np.array(out.shape[1:3]))\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=13,\n # force_output_filter_count=True)\n out = Conv2D(filters=class_count + 1,\n kernel_size=3,\n strides=1,\n padding='same',\n activation='linear',\n use_bias=True,\n kernel_regularizer=l2(0.01),\n name=\"output_4\")(x)\n out = Softmax(axis=3, name=\"output_4_softmax\")(out)\n squares.append(out)\n prediction_shapes.append(np.array(out.shape[1:3]))\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=14,\n # force_output_filter_count=True)\n out = Conv2D(filters=class_count + 1,\n kernel_size=3,\n strides=1,\n padding='same',\n activation='linear',\n use_bias=True,\n kernel_regularizer=l2(0.01),\n name=\"output_5\")(x)\n out = Softmax(axis=3, name=\"output_5_softmax\")(out)\n squares.append(out)\n prediction_shapes.append(np.array(out.shape[1:3]))\n\n print(\"Pyramid setup to track sizes: {}\".format(sizes))\n print(\"Sizes on a 1080p video: {}\".format([int(s / height * 1080) for s in sizes]))\n print(\"Pyramid prediction shapes are: {}\".format(prediction_shapes))\n\n flatten_squares = [Reshape(target_shape=(x.shape[1] * x.shape[2], x.shape[3]), name=\"Flatten{}\".format(i))(x)\n for i, x in enumerate(squares)]\n\n prediction = Concatenate(axis=1, name=\"concatenate_final\")(flatten_squares)\n\n model = Model(inputs=input_layer, outputs=prediction)\n\n if print_summary:\n model.summary()\n return model, sizes, prediction_shapes","title":""},{"docid":"e11fc79795585e014c0f967aa7355151","score":"0.5503511","text":"def Scale(self) -> _n_2_t_23:","title":""},{"docid":"6fc1c6f0d2a48922959ea3523fed72ca","score":"0.54935366","text":"def Dimaltf(self) -> float:","title":""}],"string":"[\n {\n \"docid\": \"b9350cd5058fd91107fba622e285d446\",\n \"score\": \"0.7422406\",\n \"text\": \"def hidden_unit(Layer): \\n\\tfan_in = Layer.weight.data.size()[0]\\n\\tlim = 1. / np.sqrt(fan_in)\\n\\treturn (-lim, lim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d850db14618078b77e68af232c24387\",\n \"score\": \"0.6618541\",\n \"text\": \"def define_hidden_layers(self):\\n self.model.hidden_layer_sizes = self.hidden_layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3774caf8ae4661264b5319166bf717c2\",\n \"score\": \"0.65002656\",\n \"text\": \"def __init__(self, input_size, output_size, hidden_layers, drop_p=0.5):\\n super().__init__()\\n #input to hidden layer\\n self.hidden_layers = nn.ModuleList([nn.Linear(input_size, hidden_layers[0])])\\n\\n #add a variable no of more hidden layers\\n layer_sizes = zip(hidden_layers[:-1], hidden_layers[1:])\\n self.hidden_layers.extend([nn.Linear(h1, h2) for h1, h2 in layer_sizes])\\n\\n self.output = nn.Linear(hidden_layers[-1], output_size)\\n\\n self.dropout = nn.Dropout(p=drop_p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64694a0acdb7724103a3bfcf0c0b1b34\",\n \"score\": \"0.6280762\",\n \"text\": \"def hidden_init(layer):\\n fan_in = layer.weight.data.size()[0]\\n lim = 1. / np.sqrt(fan_in)\\n return (-lim, lim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1541ff5cca577ea5278712a53364ea44\",\n \"score\": \"0.6189127\",\n \"text\": \"def hidden_sizes(self) -> List[int]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"572b4752338802031c8ba18abe84c2c1\",\n \"score\": \"0.61134386\",\n \"text\": \"def __init__(self, input, output, hidden_layers, drop_out=0.5):\\n super().__init__()\\n self.hidden_layers = nn.ModuleList([nn.Linear(input, hidden_layers[0])])\\n layer_sizes = zip(hidden_layers[:-1], hidden_layers[1:])\\n self.hidden_layers.extend([nn.Linear(hidden1, hidden2) for hidden1, hidden2 in layer_sizes])\\n self.output = nn.Linear(hidden_layers[-1], output)\\n self.dropout = nn.Dropout(p=drop_out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5beb765ac144a907609179b6112f548\",\n \"score\": \"0.6017871\",\n \"text\": \"def get_num_hidden(self) -> int:\\n return self.config.model_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b0fe867f91a8cb5f8870c859ebc77c\",\n \"score\": \"0.60013855\",\n \"text\": \"def Dimfrac(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b0fe867f91a8cb5f8870c859ebc77c\",\n \"score\": \"0.60013855\",\n \"text\": \"def Dimfrac(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b0fe867f91a8cb5f8870c859ebc77c\",\n \"score\": \"0.60013855\",\n \"text\": \"def Dimfrac(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1d3153483a584dab7b6a210d50729be\",\n \"score\": \"0.59481287\",\n \"text\": \"def Dimdli(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1d3153483a584dab7b6a210d50729be\",\n \"score\": \"0.59481287\",\n \"text\": \"def Dimdli(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1d3153483a584dab7b6a210d50729be\",\n \"score\": \"0.59481287\",\n \"text\": \"def Dimdli(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08cd0f1b061e416326ab17ed21ca4f7b\",\n \"score\": \"0.59405166\",\n \"text\": \"def show_size(brain):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"016630648c91ac1704f677f5ab88649a\",\n \"score\": \"0.591925\",\n \"text\": \"def test_dropouts():\\n hidden_layers = [{'units': 64, 'name': 'relu1', 'dropout': 0.1},\\n {'units': 64, 'name': 'relu2', 'dropout': 0.1}]\\n layers = HiddenLayers(hidden_layers)\\n\\n assert len(layers) == 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"191dee7e7ca2a9877ad168cb916231ef\",\n \"score\": \"0.591023\",\n \"text\": \"def recurrent_hidden_state_size(\\n self,\\n ) -> Union[int, Dict[str, Tuple[Sequence[Tuple[str, Optional[int]]], torch.dtype]]]:\\n return self.hidden_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1adbc0ce502e8eef8aa8ef930a982768\",\n \"score\": \"0.58989924\",\n \"text\": \"def def_layer(self):\\n self.inp_drop = tf.keras.layers.Dropout(rate=self.config.input_dropout)\\n self.hidden_dropout1 = tf.keras.layers.Dropout(rate=self.config.hidden_dropout1)\\n self.hidden_dropout2 = tf.keras.layers.Dropout(rate=self.config.hidden_dropout2)\\n\\n self.bn0 = tf.keras.layers.BatchNormalization(trainable=True)\\n self.bn1 = tf.keras.layers.BatchNormalization(trainable=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"271516be40fe02f2c764c8e1b0a68db5\",\n \"score\": \"0.58838993\",\n \"text\": \"def recalculate_container_layer_thickness(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da7be0ae3a1f8636472191c8d4b9640\",\n \"score\": \"0.587329\",\n \"text\": \"def IncludingHidden(self) -> LayerTable:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"519822c93380a16c319b8ec0b08a5712\",\n \"score\": \"0.58562195\",\n \"text\": \"def size(self):\\n if self.layers > 1:\\n return self.scope[0] * self.scope[1] * self.scope[2]\\n else:\\n return self.scope[0] * self.scope[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c4e977f690994d5487396977240ad1f\",\n \"score\": \"0.58517355\",\n \"text\": \"def Dimatfit(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c4e977f690994d5487396977240ad1f\",\n \"score\": \"0.58517355\",\n \"text\": \"def Dimatfit(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c4e977f690994d5487396977240ad1f\",\n \"score\": \"0.58517355\",\n \"text\": \"def Dimatfit(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae317d8785c584bc3a434d2330da7685\",\n \"score\": \"0.5849485\",\n \"text\": \"def BreakSize(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b4cd2cb163430b1c39cce0481b7b77e\",\n \"score\": \"0.5825524\",\n \"text\": \"def __init__(self, hidden_size, eps=1e-12):\\n super(LayerNorm, self).__init__()\\n self.weight = nn.Parameter(torch.ones(hidden_size))\\n self.bias = nn.Parameter(torch.zeros(hidden_size))\\n self.variance_epsilon = eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b47af42acfd9539faa08e82f158e562\",\n \"score\": \"0.581501\",\n \"text\": \"def layer_size(self):\\n return self.scope[0] * self.scope[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0928ac7323e35c703bd23fc8ffad7271\",\n \"score\": \"0.5805787\",\n \"text\": \"def test_units(self):\\r\\n layout = Layout()\\r\\n layout.units = None\\r\\n layout.layers.append(Layer())\\r\\n design = Design()\\r\\n design.layout = layout\\r\\n writer = Writer()\\r\\n writer.write(design)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a6b9966699907b19b92fea046ef69b8\",\n \"score\": \"0.57984674\",\n \"text\": \"def Dimfxlen(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a6b9966699907b19b92fea046ef69b8\",\n \"score\": \"0.57984674\",\n \"text\": \"def Dimfxlen(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a6b9966699907b19b92fea046ef69b8\",\n \"score\": \"0.57984674\",\n \"text\": \"def Dimfxlen(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4153e93ca0fb64b2014f7d0d0b19782c\",\n \"score\": \"0.57983804\",\n \"text\": \"def test_complex_layers():\\n hidden_layers = [{'units': 64, 'activation': 'relu', 'dropout': 0.01},\\n {'units': 64},\\n {'batch_normalization': {'axis': -1}},\\n {'activation': 'relu'},\\n {'dropout': 0.01}]\\n layers = HiddenLayers(hidden_layers)\\n\\n assert len(layers) == 7\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72b9411dab0f8c0b9df8432c4672bec\",\n \"score\": \"0.5786331\",\n \"text\": \"def __init__(self, dropout_value = 0.1):\\n super(DenseDepthDNN2, self).__init__()\\n # Input Block\\n self.dropout_value = dropout_value # This is to use if it overfits, but not yet used.\\n self.x2 = nn.Sequential( #input 6*W*H, output=W*H*64, RF=2\\n nn.Conv2d(in_channels=6, out_channels=64, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(64)\\n #nn.Dropout(dropout_value)\\n )\\n #x3 = Conv(x1 + x2) , no x1\\n self.x3 = nn.Sequential( #input W*H*64, output=W*H*64, RF=4 \\n nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(64)\\n #nn.Dropout(dropout_value)\\n )\\n \\n #x4 maxpool (x2+x3) \\n self.x4 = nn.MaxPool2d(2, 2) #input W*H*128, output=W/2*H/2*128, RF=5\\n \\n #x5 = Conv(x4)\\n self.x5 = nn.Sequential( #input W/2*H/2*128, output=W/2*H/2*128, RF=10\\n nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(128)\\n #nn.Dropout(dropout_value)\\n )\\n \\n #x6 = Conv(x4 + x5)\\n self.x6 = nn.Sequential( #input W/2*H/2*256, output=W/2*H/2*128, RF=12\\n nn.Conv2d(in_channels=256, out_channels=128, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(128)\\n #nn.Dropout(dropout_value)\\n )\\n\\n #x7 = Conv(x4 + x5 + x6)\\n self.x7 = nn.Sequential( #input W/2*H/2*384, output=W/2*H/2*128, RF=14\\n nn.Conv2d(in_channels=384, out_channels=128, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(128)\\n #nn.Dropout(dropout_value)\\n )\\n \\n #x8 = MaxPooling(x5 + x6 + x7)\\n self.x8 = nn.MaxPool2d(2, 2) #input W/2*H/2*384, output_size = (W/4*H/4*384), RF=15\\n \\n #x9 = Conv(x8)\\n self.x9 = nn.Sequential( #input W/4*W/4*384, output_size = (W/4*H/4*256), RF=30\\n nn.Conv2d(in_channels=384, out_channels=256, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(256)\\n #nn.Dropout(dropout_value)\\n )\\n #x10 = Conv (x8 + x9)\\n self.x10 = nn.Sequential( #input W/4*H/4*640, output_size = (W/4*H/4*256), RF=32\\n nn.Conv2d(in_channels=640, out_channels=256, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(256)\\n #nn.Dropout(dropout_value)\\n )\\n \\n #x11 = Conv (x8 + x9 + x10)\\n self.x11 = nn.Sequential( #input W/4*H/4*896, output_size = (W/4*H/4*256), RF=34\\n nn.Conv2d(in_channels=896, out_channels=256, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(256)\\n #nn.Dropout(dropout_value)\\n )\\n\\n #x12 = Conv (x9 + x10 + x11)\\n self.x12 = nn.MaxPool2d(2, 2) #input W/4*H/4*768, output_size = (W/8*H/8*768), RF=35\\n \\n #x13 = Conv(x12)\\n self.x13 = nn.Sequential( #input W/4*H/4*768, output_size = (W/4*H/4*512), RF=70\\n nn.Conv2d(in_channels=768, out_channels=512, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(512)\\n #nn.Dropout(dropout_value)\\n )\\n\\n #x14 = Conv(x12 + x13)\\n self.x14 = nn.Sequential( #input W/8*H/8*1280, output_size = (W/8*H/8*512), RF=72\\n nn.Conv2d(in_channels=1280, out_channels=512, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(512)\\n #nn.Dropout(dropout_value)\\n )\\n\\n #x15 = Conv(x12 + x13 + x14)\\n self.x15 = nn.Sequential( #input W/8*H/8*1792, output_size = (W/8*H/8*512), RF=74\\n nn.Conv2d(in_channels=1792, out_channels=512, kernel_size=3, padding=1),\\n nn.ReLU(),\\n nn.BatchNorm2d(512)\\n #nn.Dropout(dropout_value)\\n )\\n\\n\\n \\\"\\\"\\\"\\n # OUTPUT BLOCK -> Add GAP Layer\\n self.x12_gap = nn.Sequential( # input 8*8*64, output_size = (1*1*64), Receptive FieldF=42\\n nn.AvgPool2d(kernel_size=8)\\n )\\n\\n self.x13_fc = nn.Sequential(\\n nn.Linear(in_features=64, out_features=10)\\n # nn.BatchNorm2d(10),\\n # nn.ReLU(),\\n # nn.Dropout(dropout_value)\\n )\\n \\\"\\\"\\\"\\n # We do not use Fully connected layers. Removed gap layer and added additional conv layers.\\n \\n self.xf1_invconv_512_256 = nn.Sequential( #input W/4*H/4*512, output_size = (W/4*H/4*256), RF=78\\n nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1,padding=0),\\n nn.BatchNorm2d(256),\\n nn.ReLU(),\\n #nn.Dropout(dropout_value)\\n )\\n self.xf1_inconv_256 = nn.Sequential( #input W/4*W/4*256, output_size = (W/4*W/4*256), RF=80\\n nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3,padding=1),\\n nn.BatchNorm2d(256),\\n nn.ReLU(),\\n #nn.Dropout(dropout_value)\\n )\\n \\n self.xf2_invconv_256_128 = nn.Sequential( #input W/2*H/2*256, output_size = (W/2*H/2*128), RF=82\\n nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1,padding=0),\\n nn.BatchNorm2d(128),\\n nn.ReLU(),\\n #nn.Dropout(dropout_value)\\n )\\n\\n self.xf2_inconv_128 = nn.Sequential( #input W/2*H/2*128, output_size = (W/2*H/2*128), RF=84\\n nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3,padding=1),\\n nn.BatchNorm2d(128),\\n nn.ReLU(),\\n #nn.Dropout(dropout_value)\\n )\\n \\n self.xf3_invconv_128_64 = nn.Sequential( #input W*H*128, output_size = (W*H*64), RF=86\\n nn.Conv2d(in_channels=128, out_channels=64, kernel_size=1,padding=0),\\n nn.BatchNorm2d(64),\\n nn.ReLU(),\\n #nn.Dropout(dropout_value)\\n )\\n\\n self.xf3_inconv_64 = nn.Sequential( #input W*H*64, output_size = (W*H*64), RF=88\\n nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3,padding=1),\\n nn.BatchNorm2d(64),\\n nn.ReLU(),\\n #nn.Dropout(dropout_value)\\n )\\n\\n self.x64_inconv_32 = nn.Sequential( #input 64*64*64, output_size = (64*64*32)\\n nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3,padding=1),\\n nn.BatchNorm2d(32),\\n nn.ReLU(),\\n #nn.Dropout(dropout_value)\\n )\\n\\n #one for depth and one for mask\\n self.xdepth_conv_1_1 = nn.Sequential( #input 64*64*32, output_size = (64*64*3)\\n nn.Conv2d(in_channels=32, out_channels=1, kernel_size=1)\\n #nn.Dropout(dropout_value)\\n )\\n self.predict_mask = nn.Conv2d(32, 1, kernel_size=3, padding=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62bf53b7de7292281b120b67bea5f476\",\n \"score\": \"0.5774521\",\n \"text\": \"def recurrent_hidden_state_size(self) -> int:\\n return self.hidden_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f11e410c0917c7b85a0fa2fd6cd172\",\n \"score\": \"0.57668483\",\n \"text\": \"def Lunits(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc5ac6986b3adad00e3e8da64f0a105\",\n \"score\": \"0.5764619\",\n \"text\": \"def DrawingUnits(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc3fa560c346ac9d5e97bba9b9122149\",\n \"score\": \"0.5758371\",\n \"text\": \"def Dimensions(self) -> RenderGlobal.DimensionsParameter:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b226a6c1906d6217f7aee261d36b22\",\n \"score\": \"0.5745153\",\n \"text\": \"def __init__(self, hidden_size, eps=1e-06):\\n super().__init__()\\n self.weight = nn.Parameter(torch.ones(hidden_size))\\n self.variance_epsilon = eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a0da5a65fd5b8e6aada0043c0418d9c\",\n \"score\": \"0.5741508\",\n \"text\": \"def LightingUnits(self) -> _n_6_t_19:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc4cd4a103b82d44d94bdb0cc11dd1f5\",\n \"score\": \"0.57290715\",\n \"text\": \"def __init__(self, hidden_size, eps=1e-6):\\n super().__init__()\\n self.weight = nn.Parameter(torch.ones(hidden_size))\\n self.variance_epsilon = eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"404951695473bfd341670c6a0d6b4ac4\",\n \"score\": \"0.5728463\",\n \"text\": \"def Dimcen(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"404951695473bfd341670c6a0d6b4ac4\",\n \"score\": \"0.5728463\",\n \"text\": \"def Dimcen(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"404951695473bfd341670c6a0d6b4ac4\",\n \"score\": \"0.5728463\",\n \"text\": \"def Dimcen(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b07c2e6ab22014f8e5c9f9a6109f667\",\n \"score\": \"0.5713198\",\n \"text\": \"def getLayerSizes(self) -> retval:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70ffff54fcf79918a36f2fc6c369e0de\",\n \"score\": \"0.56853294\",\n \"text\": \"def standardizedDimension(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"562295d6d0c346cba660cf1daeeef032\",\n \"score\": \"0.5667133\",\n \"text\": \"def Dimscale(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"562295d6d0c346cba660cf1daeeef032\",\n \"score\": \"0.5667133\",\n \"text\": \"def Dimscale(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"562295d6d0c346cba660cf1daeeef032\",\n \"score\": \"0.5667133\",\n \"text\": \"def Dimscale(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"562295d6d0c346cba660cf1daeeef032\",\n \"score\": \"0.5667133\",\n \"text\": \"def Dimscale(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"562295d6d0c346cba660cf1daeeef032\",\n \"score\": \"0.5667133\",\n \"text\": \"def Dimscale(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9247bf3fe70ad723afefa53d4af6fbd6\",\n \"score\": \"0.56331897\",\n \"text\": \"def Dimtp(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9247bf3fe70ad723afefa53d4af6fbd6\",\n \"score\": \"0.56331897\",\n \"text\": \"def Dimtp(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9247bf3fe70ad723afefa53d4af6fbd6\",\n \"score\": \"0.56331897\",\n \"text\": \"def Dimtp(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7ddb2436d18222e941c2d70284d31e\",\n \"score\": \"0.56325835\",\n \"text\": \"def __init__(self, hidden_state_size=10, output_size=1, *args, **kwargs):\\n super(ReadoutLocalLayer, self).__init__(*args, **kwargs)\\n self.output_size = output_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cd630720b26b355e183d7366a022753\",\n \"score\": \"0.5628463\",\n \"text\": \"def __init__(self, input_dim: int, hidden_layer: bool) -> None:\\n\\n # Number of hidden units if hidden_layer = True.\\n self.hidden_units = 25\\n\\n self.lr = 1e-2\\n\\n self.epochs = 400\\n\\n self.x_train, self.y_train = None, None\\n self.x_test, self.y_test = None, None\\n\\n\\n self.input_dim = input_dim\\n self.hidden_layer = hidden_layer\\n self.layers=[]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05420e034acd6665b7a81f8210012358\",\n \"score\": \"0.5628366\",\n \"text\": \"def StepSize(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e298d559bc2716b6d7faf311d140cf4\",\n \"score\": \"0.5625653\",\n \"text\": \"def __init__(self, in_size, fc_units, out_size):\\n super(Actor, self).__init__()\\n units = [in_size] + fc_units + [out_size]\\n layers = []\\n for i in range(len(units)-2):\\n layer = nn.Linear(units[i], units[i+1])\\n layer.weight.data.uniform_(*hidden_init(layer))\\n layers.append(layer)\\n layers.append(nn.ReLU())\\n # last layer: different activation and initialization\\n layer = nn.Linear(units[-2], units[-1])\\n layer.weight.data.uniform_(-3e-3, 3e-3)\\n layers.append(layer)\\n layers.append(nn.Tanh())\\n self.net = nn.ModuleList(layers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ece1613aed202c3eb46837f2eb37ba4e\",\n \"score\": \"0.56133837\",\n \"text\": \"def n_dimension(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba8b85ee8348cadd97aa25a8309af3e\",\n \"score\": \"0.5590752\",\n \"text\": \"def Dimzin(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba8b85ee8348cadd97aa25a8309af3e\",\n \"score\": \"0.5590752\",\n \"text\": \"def Dimzin(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba8b85ee8348cadd97aa25a8309af3e\",\n \"score\": \"0.5590752\",\n \"text\": \"def Dimzin(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"486b4fa042fff6e701dc429355ba5871\",\n \"score\": \"0.5580775\",\n \"text\": \"def FilterHeight(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eee0eb2308d7b0668de133285980e8f8\",\n \"score\": \"0.5579714\",\n \"text\": \"def __init__(self, spatial_size, #mlp_hidden_size,\\n epsilon=1e-8):\\n # super().__init__()\\n # self.num_slots = num_slots\\n self.slot_spatial_size = spatial_size\\n self.slot_size = spatial_size * spatial_size\\n # self.mlp_hidden_size = mlp_hidden_size\\n self.epsilon = epsilon\\n\\n self.norm_inputs = tfkl.LayerNormalization(dtype=tf.float32) #layers.LayerNormalization()\\n # self.norm_slots = layers.LayerNormalization()\\n # self.norm_mlp = layers.LayerNormalization()\\n\\n # Linear maps for the attention module.\\n self.project_q = tfkl.Dense(self.slot_size, use_bias=False, kernel_initializer=tf.keras.initializers.Identity()) #layers.Dense(self.slot_size, use_bias=False, name=\\\"q\\\")\\n self.project_k = tfkl.Dense(self.slot_size, use_bias=False, kernel_initializer=tf.keras.initializers.Identity()) #layers.Dense(self.slot_size, use_bias=False, name=\\\"k\\\")\\n self.project_v = tfkl.Dense(self.slot_size, use_bias=False, kernel_initializer=tf.keras.initializers.Identity()) #layers.Dense(self.slot_size, use_bias=False, name=\\\"v\\\")\\n\\n # Slot update functions.\\n # self.mlp = tf.keras.Sequential([\\n # layers.Dense(self.mlp_hidden_size, activation=\\\"relu\\\"),\\n # layers.Dense(self.slot_size)\\n # ], name=\\\"mlp\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25518a7e10bd686771c0e2e5b69eb8ae\",\n \"score\": \"0.5579462\",\n \"text\": \"def PriorSize(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54ba75ac95028e4b161fc5171fe23ece\",\n \"score\": \"0.5575941\",\n \"text\": \"def Aunits(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca6c32a2f94d9981228547fd6bbadd9\",\n \"score\": \"0.55757153\",\n \"text\": \"def Dimaltu(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca6c32a2f94d9981228547fd6bbadd9\",\n \"score\": \"0.55757153\",\n \"text\": \"def Dimaltu(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca6c32a2f94d9981228547fd6bbadd9\",\n \"score\": \"0.55757153\",\n \"text\": \"def Dimaltu(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e6f742903ee2629c5d7b61f0371eec\",\n \"score\": \"0.55751354\",\n \"text\": \"def __init__(self, rng, input, n_in, n_hiddens, dropout_rates, activation=None, n_slack=0, init_ignore_out=False, init_account_dropout=False):\\n self.params = []\\n self.W = []\\n self.b = []\\n\\n self.W_actual = []\\n self.b_actual = []\\n\\n # keep track of model input\\n self.input = input\\n\\n # Multiple hidden layers\\n print >> sys.stderr, dropout_rates\\n last_layer_out = self.input\\n last_layer_dropout = _dropout_from_layer(rng, self.input, p=dropout_rates[0])\\n last_layer_size = n_in\\n\\n slacks = numpy.append(numpy.asarray([n_slack], dtype='int32'), numpy.zeros((len(n_hiddens)-1,), dtype='int32')) if len(n_hiddens) > 0 else None\\n for i in range(0, len(n_hiddens)):\\n # dropped-out path: for training\\n dropoutLayer = DropoutHiddenLayer(rng=rng,\\n input=last_layer_dropout, activation=activation,\\n n_in=last_layer_size, n_out=n_hiddens[i],\\n dropout_rate=dropout_rates[i+1],\\n n_slack=slacks[i] + (n_hiddens[i] if init_ignore_out else 0),\\n dilate_factor = (1./(1. - dropout_rates[i]) if init_account_dropout else 1.)\\n )\\n last_layer_dropout = dropoutLayer.output\\n\\n self.params += dropoutLayer.params\\n self.W += [dropoutLayer.W]\\n self.b += [dropoutLayer.b]\\n \\n # original (untouched) path: for testing\\n hiddenLayer = HiddenLayer(rng=rng,\\n input=last_layer_out, activation=activation,\\n n_in=last_layer_size, n_out=n_hiddens[i],\\n W=dropoutLayer.W * (1. - dropout_rates[i]),\\n b=dropoutLayer.b,\\n n_slack=slacks[i]\\n )\\n last_layer_out = hiddenLayer.output\\n last_layer_size = n_hiddens[i]\\n\\n self.W_actual += [hiddenLayer.W]\\n self.b_actual += [hiddenLayer.b]\\n\\n # The logistic regression layer gets as input the hidden units\\n # of the hidden layer\\n # Dropped-out path: for training\\n self.dropoutLogRegressionLayer = LogisticRegression(\\n rng=rng, input=last_layer_dropout,\\n n_in=(n_hiddens[-1] if len(n_hiddens) > 0 else n_in)\\n )\\n self.params += self.dropoutLogRegressionLayer.params\\n\\n # original (untouched) path: for testing\\n self.logRegressionLayer = LogisticRegression(\\n rng=rng, input=last_layer_out,\\n n_in=(n_hiddens[-1] if len(n_hiddens) > 0 else n_in),\\n W=self.dropoutLogRegressionLayer.W * (1. - dropout_rates[-1]),\\n b=self.dropoutLogRegressionLayer.b\\n )\\n\\n # prediction of the MLP is given by the prediction of the output of the\\n # model, computed in the logistic regression layer\\n self.dropout_errors = self.dropoutLogRegressionLayer.errors\\n self.dropout_cross_entropy = self.dropoutLogRegressionLayer.cross_entropy\\n\\n self.y_pred = self.logRegressionLayer.y_pred\\n self.errors = self.logRegressionLayer.errors\\n self.cross_entropy = self.logRegressionLayer.cross_entropy\\n\\n self.true_positives = self.logRegressionLayer.true_positives\\n self.true_negatives = self.logRegressionLayer.true_negatives\\n self.false_positives = self.logRegressionLayer.false_positives\\n self.false_negatives = self.logRegressionLayer.false_negatives\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ebc1a0d572941145b3c710af27dcde\",\n \"score\": \"0.557238\",\n \"text\": \"def Dimdle(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ebc1a0d572941145b3c710af27dcde\",\n \"score\": \"0.557238\",\n \"text\": \"def Dimdle(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ebc1a0d572941145b3c710af27dcde\",\n \"score\": \"0.557238\",\n \"text\": \"def Dimdle(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da3b4ec401fa42296f6fbbf30cc27c1e\",\n \"score\": \"0.5571596\",\n \"text\": \"def Dimtm(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da3b4ec401fa42296f6fbbf30cc27c1e\",\n \"score\": \"0.5570935\",\n \"text\": \"def Dimtm(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da3b4ec401fa42296f6fbbf30cc27c1e\",\n \"score\": \"0.5570935\",\n \"text\": \"def Dimtm(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a91e4210853ca6517ed2f6699baaac3\",\n \"score\": \"0.5570743\",\n \"text\": \"def recurrent_hidden_state_size(self) -> int:\\n return self._hidden_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a91e4210853ca6517ed2f6699baaac3\",\n \"score\": \"0.5570743\",\n \"text\": \"def recurrent_hidden_state_size(self) -> int:\\n return self._hidden_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43fee013a5446820edd174e61300e3c4\",\n \"score\": \"0.5569468\",\n \"text\": \"def Diameter(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cc1b6cb50b7d4f041bad2635974c913\",\n \"score\": \"0.55670696\",\n \"text\": \"def Dimazin(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cc1b6cb50b7d4f041bad2635974c913\",\n \"score\": \"0.55670696\",\n \"text\": \"def Dimazin(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cc1b6cb50b7d4f041bad2635974c913\",\n \"score\": \"0.55670696\",\n \"text\": \"def Dimazin(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f914e39f139bbcddb4780b27c43de2e\",\n \"score\": \"0.55670553\",\n \"text\": \"def get_n_neural_parameters(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2ef493a18adb42f6d8a45e9a5a65fe\",\n \"score\": \"0.55659014\",\n \"text\": \"def BlockScale(self) -> _n_2_t_11:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2ef493a18adb42f6d8a45e9a5a65fe\",\n \"score\": \"0.55654293\",\n \"text\": \"def BlockScale(self) -> _n_2_t_11:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51d69cb6c817542026b3abfa15efd89b\",\n \"score\": \"0.5562829\",\n \"text\": \"def __init__(self, hidden_state_size=10, output_size=1, *args, **kwargs):\\n super(ReadoutGlobalLayer, self).__init__(*args, **kwargs)\\n self.output_size = output_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f01e3aebf5c23822cd27e6f149d070\",\n \"score\": \"0.5561108\",\n \"text\": \"def __init__(self, inWidth, outWidth, layers):\\r\\n super(DeepNeuralNet, self).__init__()\\r\\n\\r\\n self.linears = nn.ModuleList()\\r\\n\\r\\n #setting up hidden layer size through arg parse in our initialization\\r\\n commaSplit = layers.split(\\\",\\\")\\r\\n firstChangeSize = inWidth\\r\\n\\r\\n # set up relu activation in init\\r\\n self.relu = nn.ReLU()\\r\\n\\r\\n #set up softmax post act\\r\\n self.softmax = nn.Softmax()\\r\\n\\r\\n for z in commaSplit:\\r\\n splitString = z.split(\\\"x\\\") #\\\"32x4,64x2\\\" --> {32x4 , 64x2}\\r\\n hiddenLayerSize = int(splitString[0] ) #grabbing size\\r\\n numUnits = int(splitString[1]) #grabbing numUnits\\r\\n\\r\\n #now define hidden layer sizes\\r\\n flag1 = 0\\r\\n\\r\\n #hidden layers\\r\\n for i in range(numUnits):\\r\\n if (flag1 == 0):\\r\\n flag1 = 1\\r\\n self.linears.append(nn.Linear(firstChangeSize, hiddenLayerSize)) #creates layer\\r\\n firstChangeSize = hiddenLayerSize #update next change size\\r\\n else:\\r\\n self.linears.append(nn.Linear(hiddenLayerSize, hiddenLayerSize))\\r\\n\\r\\n #output dimensions\\r\\n self.linears.append((nn.Linear(firstChangeSize, outWidth)))\\r\\n\\r\\n #print(\\\"b4 params\\\")\\r\\n #print params\\r\\n for name, param in self.named_parameters():\\r\\n print(name, param.data.shape)\\r\\n #print(\\\"after params for loop\\\")\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dfdf58f603c283b64235a5d32205b27\",\n \"score\": \"0.55560976\",\n \"text\": \"def OwningViewScale(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"187b4925f72e5962f7ddf7300e693bc7\",\n \"score\": \"0.5548241\",\n \"text\": \"def get_hidden_size(self):\\n\\n return self.encoder_output_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb936f72c3047da0c8cfd9c5f58529b\",\n \"score\": \"0.55391115\",\n \"text\": \"def calc_numdim(image_sidel_use,n_h_layers,nodes_per_h_layer,n_classes ):\\n\\n n_nodes_lowerlayer, n_nodes_thislayer = image_sidel_use**2, nodes_per_h_layer # first hidden layer\\n numdim = (1 + n_nodes_lowerlayer) * n_nodes_thislayer\\n n_nodes_lowerlayer, n_nodes_thislayer = nodes_per_h_layer, nodes_per_h_layer # other hidden layers\\n numdim += ( n_h_layers - 1 ) * (1 + n_nodes_lowerlayer) * n_nodes_thislayer\\n n_nodes_lowerlayer, n_nodes_thislayer = nodes_per_h_layer, n_classes # output layer\\n numdim += (1 + n_nodes_lowerlayer) * n_nodes_thislayer\\n\\n return numdim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4275fc949cd9cf48211abd9a9da5ab\",\n \"score\": \"0.55383253\",\n \"text\": \"def Dimtvp(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4275fc949cd9cf48211abd9a9da5ab\",\n \"score\": \"0.5538277\",\n \"text\": \"def Dimtvp(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4275fc949cd9cf48211abd9a9da5ab\",\n \"score\": \"0.5538277\",\n \"text\": \"def Dimtvp(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c92ca35e59205ad2a742ee5d22d3ce\",\n \"score\": \"0.55342156\",\n \"text\": \"def Dimalttd(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c92ca35e59205ad2a742ee5d22d3ce\",\n \"score\": \"0.55342156\",\n \"text\": \"def Dimalttd(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c92ca35e59205ad2a742ee5d22d3ce\",\n \"score\": \"0.55342156\",\n \"text\": \"def Dimalttd(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e99fe108d63408c53f63672fd8d8c818\",\n \"score\": \"0.5533856\",\n \"text\": \"def __init__(self, input_size, hidden_size, name=\\\"\\\", weight_init=HeUniform(1.0)):\\n\\n # input to hidden\\n self.W_xh = parameter(init_array(weight_init, (input_size, hidden_size)),\\n name=name+\\\".W_xh\\\")\\n\\n # hidden to hidden\\n self.W_hh = parameter(init_array(weight_init, (hidden_size, hidden_size)),\\n name=name+\\\".W_hh\\\")\\n\\n # hidden to output\\n self.W_ho = parameter(init_array(weight_init, (hidden_size, hidden_size)),\\n name=name+\\\".W_ho\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e391abbec615b801fa3968d9d32a1cb\",\n \"score\": \"0.5516211\",\n \"text\": \"def __init__(self, hidden_size: int, resolution: List[int]):\\n super().__init__()\\n self.hidden_size = hidden_size\\n self.resolution = resolution\\n self.dense = L.Dense(hidden_size, use_bias=True)\\n self.grid = self.build_grid(resolution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa7677b324e9f9c537867bcb8c58fba\",\n \"score\": \"0.5511988\",\n \"text\": \"def ThicknessDepth(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb44ddbb6808e30b3129307418642b93\",\n \"score\": \"0.55110234\",\n \"text\": \"def AnnoHeight(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7222df74e4b1c29a44244a9d77a1b5d2\",\n \"score\": \"0.55087787\",\n \"text\": \"def load_network(size_value, dropout_rate: float = 0.1, dropout_strategy: str = \\\"all\\\",\\n layers_filters: tuple = (16, 16, 24, 32), expansions: tuple = (1, 6, 6), print_summary=True,\\n use_resnet=False, use_mobile_net=False, use_additional_output=False):\\n height, width = size_value\\n\\n ####################################\\n # Custome Backbone\\n ####################################\\n\\n squares = []\\n prediction_shapes = []\\n sizes = [6, 10, 15, 24, 42, 78] # optimised for curve signs\\n sizes = [int(s / 220 * height) for s in sizes] # for smaller input size (than 220, 400)\\n alpha = 1.0\\n\\n if not use_additional_output:\\n sizes = sizes[:5]\\n\\n dropout_all = dropout_rate if dropout_strategy == \\\"all\\\" else None\\n dropout_end = dropout_rate if dropout_strategy != \\\"all\\\" else None\\n\\n first_layer_kernel = 3\\n\\n f1, f2, f3, f4 = layers_filters\\n e1, e2, e3 = expansions\\n\\n input_layer = Input(shape=(height, width, 3), name='input')\\n x = tf.keras.layers.ZeroPadding2D(padding=correct_pad(tf.keras.backend, input_layer, first_layer_kernel),\\n name='Conv1_pad')(input_layer)\\n x = Conv2D(filters=f1, kernel_size=first_layer_kernel, strides=2, activation=None, padding='valid',\\n kernel_regularizer=l2(0.01),\\n use_bias=False, name=\\\"Conv1\\\")(x)\\n x = BatchNormalization(epsilon=1e-3, momentum=0.99, name=\\\"Conv1_BN\\\")(x)\\n x = tf.keras.layers.ReLU(6., name=\\\"Conv1_relu\\\")(x)\\n if dropout_all is not None:\\n x = Dropout(dropout_all, name='Conv1_dropout')(x)\\n x = _inverted_res_block(x, filters=f2, alpha=alpha, stride=1, expansion=e1, block_id=0, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\\n x = _inverted_res_block(x, filters=f3, alpha=alpha, stride=2, expansion=e2, block_id=1, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\\n x = _inverted_res_block(x, filters=f3, alpha=alpha, stride=1, expansion=e2, block_id=2, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\\n x = _inverted_res_block(x, filters=f3, alpha=alpha, stride=1, expansion=e2, block_id=3, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\\n if dropout_end is not None:\\n x = Dropout(dropout_end, name='part1_dropout')(x)\\n\\n if use_additional_output:\\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=10,\\n # force_output_filter_count=True)\\n out = Conv2D(filters=class_count + 1,\\n kernel_size=3,\\n strides=1,\\n padding='same',\\n activation='linear',\\n use_bias=True,\\n kernel_regularizer=l2(0.01),\\n name=\\\"output_0\\\")(x)\\n out = Softmax(axis=3, name=\\\"output_0_softmax\\\")(out)\\n squares.append(out)\\n prediction_shapes.append(np.array(out.shape[1:3]))\\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=10,\\n # force_output_filter_count=True)\\n out = Conv2D(filters=class_count + 1,\\n kernel_size=3,\\n strides=1,\\n padding='same',\\n activation='linear',\\n use_bias=True,\\n kernel_regularizer=l2(0.01),\\n name=\\\"output_1\\\")(x)\\n out = Softmax(axis=3, name=\\\"output_1_softmax\\\")(out)\\n squares.append(out)\\n prediction_shapes.append(np.array(out.shape[1:3]))\\n\\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=11,\\n # force_output_filter_count=True)\\n out = Conv2D(filters=class_count + 1,\\n kernel_size=3,\\n strides=1,\\n padding='same',\\n activation='linear',\\n use_bias=True,\\n kernel_regularizer=l2(0.01),\\n name=\\\"output_2\\\")(x)\\n out = Softmax(axis=3, name=\\\"output_2_softmax\\\")(out)\\n squares.append(out)\\n prediction_shapes.append(np.array(out.shape[1:3]))\\n\\n x = _inverted_res_block(x, filters=f4, alpha=alpha, stride=2, expansion=e3, block_id=4, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\\n x = _inverted_res_block(x, filters=f4, alpha=alpha, stride=1, expansion=e3, block_id=5, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\\n x = _inverted_res_block(x, filters=f4, alpha=alpha, stride=1, expansion=e3, block_id=6, dropout_rate=dropout_all, use_resnet=use_resnet, inverted_bottle_neck=use_mobile_net)\\n if dropout_end is not None:\\n x = Dropout(dropout_end, name='part2_dropout')(x)\\n\\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=12,\\n # force_output_filter_count=True)\\n out = Conv2D(filters=class_count + 1,\\n kernel_size=3,\\n strides=1,\\n padding='same',\\n activation='linear',\\n use_bias=True,\\n kernel_regularizer=l2(0.01),\\n name=\\\"output_3\\\")(x)\\n out = Softmax(axis=3, name=\\\"output_3_softmax\\\")(out)\\n squares.append(out)\\n prediction_shapes.append(np.array(out.shape[1:3]))\\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=13,\\n # force_output_filter_count=True)\\n out = Conv2D(filters=class_count + 1,\\n kernel_size=3,\\n strides=1,\\n padding='same',\\n activation='linear',\\n use_bias=True,\\n kernel_regularizer=l2(0.01),\\n name=\\\"output_4\\\")(x)\\n out = Softmax(axis=3, name=\\\"output_4_softmax\\\")(out)\\n squares.append(out)\\n prediction_shapes.append(np.array(out.shape[1:3]))\\n # out = _inverted_res_block(x, filters=class_count + 1, alpha=alpha, stride=1, expansion=1, block_id=14,\\n # force_output_filter_count=True)\\n out = Conv2D(filters=class_count + 1,\\n kernel_size=3,\\n strides=1,\\n padding='same',\\n activation='linear',\\n use_bias=True,\\n kernel_regularizer=l2(0.01),\\n name=\\\"output_5\\\")(x)\\n out = Softmax(axis=3, name=\\\"output_5_softmax\\\")(out)\\n squares.append(out)\\n prediction_shapes.append(np.array(out.shape[1:3]))\\n\\n print(\\\"Pyramid setup to track sizes: {}\\\".format(sizes))\\n print(\\\"Sizes on a 1080p video: {}\\\".format([int(s / height * 1080) for s in sizes]))\\n print(\\\"Pyramid prediction shapes are: {}\\\".format(prediction_shapes))\\n\\n flatten_squares = [Reshape(target_shape=(x.shape[1] * x.shape[2], x.shape[3]), name=\\\"Flatten{}\\\".format(i))(x)\\n for i, x in enumerate(squares)]\\n\\n prediction = Concatenate(axis=1, name=\\\"concatenate_final\\\")(flatten_squares)\\n\\n model = Model(inputs=input_layer, outputs=prediction)\\n\\n if print_summary:\\n model.summary()\\n return model, sizes, prediction_shapes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e11fc79795585e014c0f967aa7355151\",\n \"score\": \"0.5503511\",\n \"text\": \"def Scale(self) -> _n_2_t_23:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc1c6f0d2a48922959ea3523fed72ca\",\n \"score\": \"0.54935366\",\n \"text\": \"def Dimaltf(self) -> float:\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993495,"cells":{"query_id":{"kind":"string","value":"b12647070c6dcd54d4825e5d189045c2"},"query":{"kind":"string","value":"Waits for a resource to be deleted."},"positive_passages":{"kind":"list like","value":[{"docid":"e40d0d8b09095126878bc7e9ee57c02a","score":"0.7592842","text":"def wait_for_resource_deletion(self, resource_type, id, client=None):\n start_time = int(time.time())\n while True:\n if self.is_resource_deleted(resource_type, id, client=client):\n return\n if int(time.time()) - start_time >= self.build_timeout:\n raise exceptions.TimeoutException\n time.sleep(self.build_interval)","title":""}],"string":"[\n {\n \"docid\": \"e40d0d8b09095126878bc7e9ee57c02a\",\n \"score\": \"0.7592842\",\n \"text\": \"def wait_for_resource_deletion(self, resource_type, id, client=None):\\n start_time = int(time.time())\\n while True:\\n if self.is_resource_deleted(resource_type, id, client=client):\\n return\\n if int(time.time()) - start_time >= self.build_timeout:\\n raise exceptions.TimeoutException\\n time.sleep(self.build_interval)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0c6e8cc1c7e01252177fd073373b8eb1","score":"0.7432982","text":"def wait_for_delete(self, res, interval=2, wait=120):\n return resource.wait_for_delete(self, res, interval, wait)","title":""},{"docid":"761c304c769b4e71ef4f2a3648ec7efa","score":"0.7096719","text":"def wait_for_delete(resource, update_resource=None, timeout=60,\n check_interval=1):\n start = time.time()\n while True:\n try:\n resource = update_resource(resource)\n except exceptions.GetResourceNotFound:\n break\n time.sleep(check_interval)\n if time.time() - start > timeout:\n raise exceptions.TimeoutException()","title":""},{"docid":"794f489d32c3284ed434256c21c6517b","score":"0.6764941","text":"async def _delete_resource(self, resource_id, timeout=None):\n if timeout is None:\n timeout = self.timeout\n response = await self._session.delete(\n os.path.join(self._resource_api_url, resource_id), timeout=timeout\n )\n response.raise_for_status()\n return True","title":""},{"docid":"058ce945804f0ff27d81ad4eed59c66c","score":"0.67576593","text":"def wait_for_delete(\n self, id, timeout_secs=1200,\n ):\n self.wait_for_state(\n id=id, timeout_secs=timeout_secs,\n )","title":""},{"docid":"c9abeb979fe009b1e016e0a4916a7476","score":"0.629175","text":"async def delete(self):\n raise EphemeralDeletion()","title":""},{"docid":"c9abeb979fe009b1e016e0a4916a7476","score":"0.629175","text":"async def delete(self):\n raise EphemeralDeletion()","title":""},{"docid":"83ec06f610a6b686ff16b5885766aaf1","score":"0.6258565","text":"def delete(self):\n tk = self.mor.Destroy_Task()\n tk = self._dvs._server.wait_for_task(tk)\n return tk","title":""},{"docid":"82e74a6996e930dd12cf01404d5c45bd","score":"0.619539","text":"def waitForDeleteAu(self, au, timeout=DEF_TIMEOUT, sleep=DEF_SLEEP):\n return self.wait( lambda: not self.hasAu( au ), timeout, sleep )","title":""},{"docid":"4a40c142f262a11b81661bcfd4fe62d5","score":"0.6161981","text":"async def delete(self, resource, **params):\n\n return await self.client.delete(\n resource,\n service_name=self.SERVICE_NAME,\n params=params,\n signature=self.token,\n )","title":""},{"docid":"50a80071dbfd94745e070dcc631a1a25","score":"0.59850085","text":"def instance_delete_wait(self):\n self.test_runner.run_instance_delete_wait()","title":""},{"docid":"c814da482d87c564b0f82da65049fb93","score":"0.5935675","text":"async def delete_async(self) -> bool:\n return await self._version.delete_async(\n method=\"DELETE\",\n uri=self._uri,\n )","title":""},{"docid":"c814da482d87c564b0f82da65049fb93","score":"0.5935675","text":"async def delete_async(self) -> bool:\n return await self._version.delete_async(\n method=\"DELETE\",\n uri=self._uri,\n )","title":""},{"docid":"c814da482d87c564b0f82da65049fb93","score":"0.5935675","text":"async def delete_async(self) -> bool:\n return await self._version.delete_async(\n method=\"DELETE\",\n uri=self._uri,\n )","title":""},{"docid":"19c6760f3be29661ad26228c9a2be233","score":"0.59097993","text":"def delete():\n return 'not ready'","title":""},{"docid":"14c00482263c04f41c4f8997c0992748","score":"0.5881431","text":"def delete(self, resource, *args, **kwargs):\n return self.make_request(self.session.delete, resource, *args, **kwargs)","title":""},{"docid":"a430e9541c8f9933de058a617813b979","score":"0.58598536","text":"def storage_delete(self, name, timeout=10):\n result = Result(*self.perform_request(**{\n 'method': 'DELETE',\n 'url': '/storage/{0}.json'.format(name),\n 'params': {\n 'request_timeout': timeout\n }\n }))\n return result","title":""},{"docid":"607233c48571f660c8d5be04e02bace6","score":"0.5785337","text":"def file_delete_async( reply, gateway, volume, update ):\n storagetypes.deferred.defer( file_delete, reply.SerializeToString(), gateway, volume, update.SerializeToString(), async=True )\n return 0","title":""},{"docid":"b13dc7654218072c66f00b4ec57bb76c","score":"0.5781464","text":"def remove(self, resource, force=False, timeout=-1):\n return self._client.delete(resource, force=force, timeout=timeout)","title":""},{"docid":"61e8b1f4173c682c34f198fc185e4e66","score":"0.5768489","text":"async def delete_async(self) -> bool:\n return await self._proxy.delete_async()","title":""},{"docid":"61e8b1f4173c682c34f198fc185e4e66","score":"0.5768489","text":"async def delete_async(self) -> bool:\n return await self._proxy.delete_async()","title":""},{"docid":"61e8b1f4173c682c34f198fc185e4e66","score":"0.5768489","text":"async def delete_async(self) -> bool:\n return await self._proxy.delete_async()","title":""},{"docid":"255ccbd2df17795374a2bbfd20ac7e42","score":"0.57471544","text":"def delete_async(self, path: str, callback, context=None, params: dict = None, error_message: str = None, allow_404: bool = False) -> threading.Thread:\n # requests.Session is not thread safe: see https://github.com/psf/requests/issues/2766\n # so we create a new session object\n t = threading.Thread(target=_get_delete_callback, args=(\n None, self.url, path, callback, context, params, error_message, allow_404, True))\n t.daemon = True\n t.start()\n return t","title":""},{"docid":"3f0c0042456403016d11a1e20600cc52","score":"0.57441705","text":"def delete_resource(self, path, permanently=False, wait_for_finish=True, sleep=None):\n logger.info('Delete resource from {!r}'.format(path))\n params_string = urllib.parse.urlencode(\n {\n 'path': path,\n 'permanently': permanently,\n },\n doseq=False,\n )\n self._waiting_for_finish(\n self._requester.delete(\n url='disk/resources?{}'.format(params_string)\n ),\n wait_for_finish=wait_for_finish,\n sleep=sleep,\n )\n return True","title":""},{"docid":"62a3188346456764f0fb309a08e420ad","score":"0.57320744","text":"def delete(self, resource, **params):\n\n return self.client.delete(\n resource,\n service_name=self.SERVICE_NAME,\n params=params,\n signature=self.token,\n )","title":""},{"docid":"1424643ba4a4dfbf056774a964a9569f","score":"0.5718569","text":"async def async_delete(self, device_id: int) -> None:\n await self._client.async_request(\"delete\", f\"devices/{device_id}\")","title":""},{"docid":"8386ca0daeb6487a9b27b1cab884972d","score":"0.5716009","text":"def delete(self):\r\n self.acquire_lock()\r\n try:\r\n return self.locked_delete()\r\n finally:\r\n self.release_lock()","title":""},{"docid":"a8a7633bdbfa4c800456a72a17a8ad5a","score":"0.5706511","text":"def delete_resource(url):\n response = requests.delete(url, auth=get_auth())\n if response.status_code == 429:\n print('Rate limited! Please wait.')\n time.sleep(int(response.headers['retry-after']))\n response = requests.delete(url, auth=get_auth())\n if response.status_code != 204:\n print('Failed to delete record with error {}'.format(response.status_code))\n print(response.text)\n return False\n return None","title":""},{"docid":"f53cf2cd6295b2c0b121878d782175a5","score":"0.57018095","text":"def delete(path):\n baseutils.retry(client.delete, path=path)","title":""},{"docid":"49c03fb5c16b68aa3bb55520bd0b55f1","score":"0.5669405","text":"def do_DELETE(self):\n resource_parts = self.get_resource_parts()\n if len(resource_parts) != 2:\n self.write_invalid_api_uri_format_response()\n else:\n resource = resource_parts[0]\n resource_id = resource_parts[1]\n resource_path = os.path.join(API_DATA_PATH, resource, resource_id)\n if not os.path.exists(resource_path):\n self.write_not_found_response(resource, resource_id)\n else:\n try:\n os.remove(resource_path)\n self.write_response({}, 204)\n except IOError as e:\n if e.errno == errno.EACCES:\n self.write_no_access_permission_to_file_response(resource_path)\n else: # Not a permission error.\n raise","title":""},{"docid":"5e91d64a355c7076cf914fcadaeea54f","score":"0.56686676","text":"def delete(id: int = typer.Argument(..., min=1)):\n response: requests.Response = rest.delete(f'{ENDPOINT}/{id}')\n if response.status_code != 200:\n util.panic('Failure to delete resource')\n typer.echo('Resource deleted successfully.')","title":""},{"docid":"5e91d64a355c7076cf914fcadaeea54f","score":"0.56686676","text":"def delete(id: int = typer.Argument(..., min=1)):\n response: requests.Response = rest.delete(f'{ENDPOINT}/{id}')\n if response.status_code != 200:\n util.panic('Failure to delete resource')\n typer.echo('Resource deleted successfully.')","title":""},{"docid":"ce2f8ddbf0066ebd0b500cbe1b1ef1e7","score":"0.5663604","text":"def delete(self):\n if 'delete' in self._URL and hasattr(self, 'id'):\n extra = {'resource': type(self).__name__, 'query': {\n 'id': self.id}}\n logger.info(\"Deleting %s resource.\", self, extra=extra)\n self._api.delete(url=self._URL['delete'].format(id=self.id))\n else:\n raise SbgError('Resource can not be deleted!')","title":""},{"docid":"a7550cec50f526256f63ed867a771f24","score":"0.5595567","text":"def submit_delete(self) -> bool:\n try:\n self.delete()\n return True\n except google.api_core.exceptions.NotFound:\n # resource is gone, so we're good\n return True\n except google.api_core.exceptions.BadRequest:\n # Resource is tied up, so indicate that\n # the user will need to try again later\n return False","title":""},{"docid":"87ac4507a80ea0d4785286917bcba7c2","score":"0.5588551","text":"async def delete(self, *args, **kwargs):\n kwargs['method'] = 'DELETE'\n return await self.request(*args, **kwargs)","title":""},{"docid":"d1d366c031433c68cace23bbdd2d975e","score":"0.5577933","text":"def delete(self):\n self.client.delete(self.url, expect=204)","title":""},{"docid":"350f57cb9aecf38ffc4b74dc6447aad5","score":"0.5577463","text":"def wait_for_volume_attachment_remove(client, volume_id, attachment_id):\n start = int(time.time())\n attachments = client.show_volume(volume_id)['volume']['attachments']\n while any(attachment_id == a['attachment_id'] for a in attachments):\n time.sleep(client.build_interval)\n if int(time.time()) - start >= client.build_timeout:\n message = ('Failed to remove attachment %s from volume %s '\n 'within the required time (%s s).' %\n (attachment_id, volume_id, client.build_timeout))\n raise lib_exc.TimeoutException(message)\n attachments = client.show_volume(volume_id)['volume']['attachments']\n LOG.info('Attachment %s removed from volume %s after waiting for %f '\n 'seconds', attachment_id, volume_id, time.time() - start)","title":""},{"docid":"00b01e1a4eff029525c5a8f06e5282d6","score":"0.5560546","text":"def delete(openstack_resource):\n # Get the details for the created server instance\n try:\n server = openstack_resource.get()\n except exceptions.ResourceNotFound:\n msg = 'Server {0} is not found'.format(openstack_resource.resource_id)\n if SERVER_TASK_DELETE not in ctx.instance.runtime_properties:\n ctx.logger.error(msg)\n raise NonRecoverableError(msg)\n\n ctx.logger.info('Server {0} is deleted successfully'\n .format(openstack_resource.resource_id))\n return\n\n # Check if delete operation triggered or not before\n if SERVER_TASK_DELETE not in ctx.instance.runtime_properties:\n openstack_resource.delete()\n ctx.instance.runtime_properties[SERVER_TASK_DELETE] = True\n\n ctx.logger.info('Waiting for server \"{0}\" to be deleted.'\n ' current status: {1}'.format(server.id, server.status))\n\n raise OperationRetry(message='Server has {0} state.'.format(server.status))","title":""},{"docid":"1e6d7b1794d62e3a1bc86a4035913307","score":"0.55464095","text":"def delete(self) -> Awaitable[int]:\n\n return self.get_connection().delete(self._key)","title":""},{"docid":"cef7c5aa538d40eeedf4202f93d92dd9","score":"0.55459017","text":"def delete(iface, resource_config, **_):\n # Add the required QueueUrl parameter.\n if QUEUE_URL not in resource_config:\n resource_config.update({QUEUE_URL: iface.resource_id})\n\n # Actually delete the resource\n iface.delete(resource_config)","title":""},{"docid":"cb950d83b96a7faaed1d1e07d90961cf","score":"0.55288565","text":"def delete(self):\n self.repo.prepareFuture()\n task = self.repo.getCurrentTask()\n self.repo.deleteTask(task)","title":""},{"docid":"9d38fe2e4b9283b493669fe9bbf8225e","score":"0.5516576","text":"def DELETE(self, resource_id):\n connection = self._redis.rcon\n response = {\"meta\": {\"status\": True, \"verbose\": \"Ok\"}, \"content\": None}\n\n connection.delete(resource_id)\n return response","title":""},{"docid":"1759a6ff16225cda606dd2df0d0fefe1","score":"0.55057037","text":"async def delete(self, path, callback=None, timeout=None, **kwargs): # pragma: no cover\n request = self.helper.mk_request(self._address, defines.Code.DELETE, path)\n request.token = utils.generate_random_hex(2)\n\n for k, v in kwargs.items():\n if hasattr(request, k):\n setattr(request, k, v)\n\n return await self.helper.delete(request, callback, timeout)","title":""},{"docid":"efcd1dc7026ef1deb65101b3a272a248","score":"0.5486964","text":"async def delete(self, **kwargs):\n return await self._client.delete(\n self._path,\n **kwargs,\n )","title":""},{"docid":"efcd1dc7026ef1deb65101b3a272a248","score":"0.5486964","text":"async def delete(self, **kwargs):\n return await self._client.delete(\n self._path,\n **kwargs,\n )","title":""},{"docid":"ebaa02904053856b980808d45e04c91c","score":"0.5479539","text":"def test_resource_delete(self):\n pass","title":""},{"docid":"be1e74276def7d01db0a7e63186a0ceb","score":"0.5474309","text":"def delete(self, name):, name\n tries = 0\n while True:\n try:\n self.container.delete_object(name)\n break\n except NoSuchObject:\n # It doesn't exist and that's ok\n break\n except (HTTPException, SSLError, ServiceResponseFailure) as e:\n if tries >= self.max_retries:\n raise\n tries += 1\n logger.warning('Failed to delete %s: %r (attempt %d/%d)' % (\n name, e, tries, self.max_retries))","title":""},{"docid":"e6585da1a63183319c0b52c53dfb92b8","score":"0.54519564","text":"def get_async(self, path: str, callback, context=None, params: dict = None, error_message: str = None, allow_404: bool = False) -> threading.Thread:\n # requests.Session is not thread safe: see https://github.com/psf/requests/issues/2766\n # so we create a new session object\n t = threading.Thread(target=_get_delete_callback, args=(\n None, self.url, path, callback, context, params, error_message, allow_404, False))\n t.daemon = True\n t.start()\n return t","title":""},{"docid":"6aa638f607a233fe3c98835d84de2cdd","score":"0.5447188","text":"def _wait_for_cluster_delete(self, cluster_id, timeout=300,\n check_interval=1):\n start_time = time.time()\n while True:\n try:\n self._get_cluster(cluster_id)\n except ks_exceptions.NotFound:\n break\n if time.time() - start_time > timeout:\n raise exceptions.Exception(\"Delete cluster timed out\")\n time.sleep(check_interval)","title":""},{"docid":"f8dce748253cfadb37f4b7b4391d05f9","score":"0.54213893","text":"async def wait_for_reaction_remove(self):\n return await self.bot.wait_for(\n \"reaction_remove\", check=self.check, timeout=self.timeout\n )","title":""},{"docid":"3aa8aa5e3ce3e81a58d16dd331dc78f9","score":"0.5419876","text":"def _delete_queue(self, queue):\n self.log.info('Deleting Queue: %s...' % queue.url)\n ok = yield self.thread(self.sqs_conn.delete_queue, queue)\n\n # Raise an exception if the tasks failed\n if not ok:\n raise QueueDeletionFailed('Failed to delete \"%s\"' % queue.url)\n\n raise gen.Return(ok)","title":""},{"docid":"b9c2873a015c89c4e54d304e265c5f20","score":"0.5401388","text":"def _try_delete_resource(cls, delete_callable, *args, **kwargs):\n try:\n delete_callable(*args, **kwargs)\n # if resource is not found, this means it was deleted in the test\n except exceptions.NotFound:\n pass","title":""},{"docid":"f85102faddc080d3206a7f238f5c04fd","score":"0.5381764","text":"async def delete(self, *, delay: Optional[float] = None) -> None:\n\n if delay is not None:\n\n async def inner_call(delay: float = delay):\n await asyncio.sleep(delay)\n try:\n await self._state._webhook.delete_message(self.id, thread=self._state._thread)\n except HTTPException:\n pass\n\n asyncio.create_task(inner_call())\n else:\n await self._state._webhook.delete_message(self.id, thread=self._state._thread)","title":""},{"docid":"04c6f3cface2d43c53e6f9494a7d9fbb","score":"0.5372078","text":"def delete_resource(self, resource, file, allow_deletion=False):\n uri = resource.uri\n if (allow_deletion):\n if (self.dryrun):\n self.logger.info(\"dryrun: would delete %s -> %s\" % (uri,file))\n else:\n try:\n os.unlink(file)\n except OSError as e:\n msg = \"Failed to DELETE %s -> %s : %s\" % (uri,file,str(e))\n if (self.ignore_failures):\n self.logger.warning(msg)\n return\n else:\n raise ClientFatalError(msg)\n self.logger.info(\"deleted: %s -> %s\" % (uri,file))\n self.log_event(Resource(resource=resource, change=\"deleted\"))\n else:\n self.logger.info(\"nodelete: would delete %s (--delete to enable)\" % uri)","title":""},{"docid":"45094ccc8278888765bb13b4522df0a5","score":"0.53628486","text":"async def delete_message(ctx, time: float):\n\n try:\n await ctx.message.delete(delay=time)\n except discord.Forbidden:\n pass\n return","title":""},{"docid":"ecdc83587eb1ca9ab6e42ffbc68e72d1","score":"0.5356544","text":"def _cinder_delete_volume(self, volume, timeout=300, force=False, wait=False):\n self._debug('delete volume {0}'.format(volume.id))\n self._get_cinder_client()\n try:\n volume = self._cinder_get_volume_by_id(volume.id)\n except cinder_client_exceptions.NotFound:\n self._debug('Volume {0} not found, already deleted'.format(volume.id))\n return\n\n if volume.status == 'in-use':\n raise RuntimeError('Cannot delete volume {0} while it is in use'.format(volume.id))\n\n if not force:\n self._cinder_wait_until_volume_state(volume.id, status='available')\n self._debug('volume is now available')\n volume = self._cinder_get_volume_by_id(volume.id)\n if force:\n self.cinder_client.volumes.force_delete(volume)\n else:\n self.cinder_client.volumes.delete(volume)\n if wait:\n self._cinder_wait_until_volume_not_found(volume.id, timeout)\n self._debug('deleted volume {0}'.format(volume.id))","title":""},{"docid":"a0e12836d6be5d4297229bc7e5c98189","score":"0.5336774","text":"def _terminate(self):\n return self.client.bucket(bucket_name=self.bucket_name).delete()","title":""},{"docid":"e2c2b44e231bfb163e01fae77025fc6d","score":"0.5333866","text":"def check_delete(self):\n return self._destroy","title":""},{"docid":"d28fa18eda6475ccef47e90d7bad6878","score":"0.5328587","text":"def delete(self):\n if(self._can_delete):\n self._active = False\n self._connect.delete(self._resource)\n else:\n raise SkytapException(\"delete is not supported \"\n \"on this object.\")","title":""},{"docid":"1793cc459c44c3a2526adcc2a0e8e101","score":"0.53132915","text":"def delete_response(self):\n thread = self.get_random_thread(page=1, page_size=10)\n self.wait()\n if not thread:\n self.stop()\n response = self.create_response(thread_id=thread[\"id\"])\n self.wait()\n if not response:\n self.stop()\n self.delete_comment(comment_id=response[\"id\"])\n self.wait_then_stop()","title":""},{"docid":"975dff7ebd1c89018eda9254d836f75d","score":"0.53070325","text":"def delete(self) -> None:\n url = f\"{self.base_url}/{self._version}/{self.resource_url}/{self.__getattribute__('slug')}\"\n self.request(url, \"DELETE\", {})","title":""},{"docid":"c05e725ea38098bd90e8891037cdffd5","score":"0.5257113","text":"def wait_for_server_delete(self, server, timeout=None):\n if timeout is None:\n timeout = cfg.CONF.default_nova_timeout\n\n server_obj = self.conn.compute.find_server(server, True)\n if server_obj:\n self.conn.compute.wait_for_delete(server_obj, wait=timeout)\n\n return","title":""},{"docid":"351b4709659e506e5cc7701f1e78e59f","score":"0.5256657","text":"def remove_resource(self, tenant_id, resource_path, parse_response=True):\n if self.cleanup_mode:\n return True\n else:\n return self._resource_operation('DELETE', tenant_id, resource_path)","title":""},{"docid":"cb9171213399fe080ce27ab34a2c7adc","score":"0.5255185","text":"async def delete(self, id):\r\n await self.delete_by_id(id)","title":""},{"docid":"91f00d6dfad1e86eb685b593509191b3","score":"0.52511203","text":"def delete(self, *args):\n self._check_ended()\n self._check_read_only()\n return self._delete(False, args)","title":""},{"docid":"e6e920f7611ebec6aa94f60da5e1434a","score":"0.5235588","text":"def delete(self, resource_id: str):\n\n self.logger.debug(\"Deleting resource through UMA Handler\")\n mongo_resource= self.mongo.get_from_mongo(\"resource_id\", resource_id)\n res_id=None\n try:\n res_id= mongo_resource[\"_id\"]\n except Exception as e:\n self.logger.debug(\"Resource for ID \"+resource_id+\" does not exist\")\n try:\n scope_list= mongo_resource[\"scopes\"]\n except Exception as e:\n self.logger.debug(\"No scopes for ID \"+resource_id+\". Update the PEP version and clean the old resources\")\n\n if not 'open' in mongo_resource.get(\"scopes\"):\n resource_registration_endpoint = self.wkh.get(TYPE_UMA_V2, KEY_UMA_V2_RESOURCE_REGISTRATION_ENDPOINT)\n if resource_registration_endpoint[-1] is not \"/\":\n resource_registration_endpoint += \"/\"\n pat = self.oidch.get_new_pat()\n try:\n n = resource.delete(pat, resource_registration_endpoint, resource_id, secure = self.verify)\n self.logger.info(\"Deleted resource in IDP... \" + str(n))\n except Exception as e:\n self.logger.debug(\"Failed to delete resource. \" + str(e))\n\n try:\n resp = self.mongo.delete_in_mongo(\"resource_id\", resource_id)\n if resp:\n self.logger.debug('Resource deleted from DB succesfully')\n else:\n self.logger.debug('Failed to delete resource from DB')\n except Exception as e:\n self.logger.debug(\"Error while deleting resource: \"+str(e))","title":""},{"docid":"fca258f87f41ef745e90c1aec706a15e","score":"0.523438","text":"def is_deleted(self) -> bool:\n try:\n response = self.get(timeout=5)\n except ValueError as e:\n if str(e) != f\"Couldn't get resource {self.name}\":\n raise\n # couldn't find the resource, so assume\n # the deletion went off swimmingly\n return True\n if response.status > 5:\n self._raise_bad_status(response)\n return False","title":""},{"docid":"48c30d87aab300cecd2e674673c76019","score":"0.5224237","text":"def wait_for_pods_to_be_deleted(\n client,\n namespace,\n pod_selector,\n timeout=datetime.timedelta(minutes=10),\n polling_interval=datetime.timedelta(seconds=30)):\n end_time = datetime.datetime.now() + timeout\n while True:\n pods = list_pods(client, namespace, pod_selector)\n\n logging.info(\"%s pods matched %s pods\", len(pods.items), pod_selector)\n\n if not pods.items:\n return\n\n if datetime.datetime.now() + polling_interval > end_time:\n raise util.TimeoutError(\"Timeout waiting for pods to be deleted.\")\n\n time.sleep(polling_interval.seconds)","title":""},{"docid":"3c850d8f226a84ebeecda4bad2fb9f70","score":"0.5207129","text":"def delete(self, id):\n res = ReconResource.query.get_or_404(id)\n res.deep_delete()\n db.session.commit()\n\n return 'Resource successfully deleted.', 204","title":""},{"docid":"733b59754792479e5783034b02778426","score":"0.5195656","text":"def shutdown(self):\n self._current_task.cancel()\n\n if self.state == 'failed':\n return\n\n try:\n yield from self.context.request(\n Message(code=DELETE, uri=self.location)\n ).response_raising\n except Exception as e:\n self.log.error(\"Error deregistering from the RD\")\n self.log.exception(e)","title":""},{"docid":"42072d11482b77e21ed0fbbf51d607dc","score":"0.51952004","text":"def _reconcile_delete(resource: Resource) -> None:\n\n try:\n resource_name = kubectl.delete(resource)\n resource_kind = resource['kind']\n display_name = f'{resource_kind}/{resource_name}'.lower()\n print(display_name, 'deleted')\n\n except RuntimeError as err:\n print('Unable to delete resource:', err, file=sys.stderr)","title":""},{"docid":"335261497290aa0b407e5166d3b67303","score":"0.51945424","text":"def delete(self):\n self._info_cache = rest_request('DELETE', self._api_uri)","title":""},{"docid":"dc8057e8708e7f2e6c578c3732a92a0c","score":"0.51885533","text":"def test_delete_a_resource_that_exists():\n pass","title":""},{"docid":"c63727bbf79952eb46bcb88f072cc2ac","score":"0.5185573","text":"def no_op(event, context):\n logger.info('No action required when deleting this resource')","title":""},{"docid":"3e6a29f8da5333e10343be4d193db07a","score":"0.5179238","text":"def wait_delete_complete(stackname, timeout=600):\n cfn = getcfn()\n ret = 0\n now = None\n response = {}\n done = False \n now = None\n start_time = int(time.time())\n print \"Checking stack status: %s\" % stackname\n while not done:\n if now:\n time.sleep(10)\n stacks = [] \n next_token = None\n try:\n response = cfn.list_stacks()\n except Exception as e:\n print e\n sys.exit(1)\n stacks.extend(response[\"StackSummaries\"])\n found = False \n done = False\n failed = False\n for stack in stacks:\n if stack[\"StackName\"] != stackname:\n continue\n found = True\n completed = True\n falied = False \n info(\"Stack status: %s\" % stack[\"StackStatus\"])\n if stack[\"StackStatus\"] == \"DELETE_FAILED\":\n print \"Error: Delete stack failed\"\n failed = True \n break\n if stack[\"StackStatus\"] != \"DELETE_COMPLETE\":\n completed = False \n break\n if completed:\n done = True\n break\n \n if not found:\n print \"stack not found\"\n ret = 0\n break\n if failed == True:\n ret = 2\n break \n if done:\n continue\n\n now = int(time.time())\n if (now - start_time) > timeout:\n print \"CloudFormation status checking timeout\"\n ret = 2\n break\n return ret","title":""},{"docid":"3980c571e2457f29a26e2bcd835a3980","score":"0.51773417","text":"def delete(self, *args, **kwargs):\n return self.torreq('DELETE', *args, **kwargs)","title":""},{"docid":"6543eb8fa21b0d83e44b5e4ac9204e6c","score":"0.5167933","text":"def delete(self, resource, user_id, record_id):\n raise NotImplementedError","title":""},{"docid":"640dfc3692b5027696ca497eeb89d97f","score":"0.51672333","text":"def test_resource_delete(self):\n data = {\n \"urn\": (\"urn:%s\" % uuid.uuid1())[:30],\n \"description\": \"resource to delete\",\n }\n model = db_actions.crud(\n model=\"Resource\",\n api_model=Resource,\n data=data,\n action=\"create\"\n )\n response = self.client.open(\n '/api/v1/resources/{resource_id}'.format(resource_id=model.id),\n method='DELETE', headers=self.headers)\n\n with self.assertRaises(werkzeug.exceptions.NotFound):\n db_actions.crud(\n model=\"Resource\",\n api_model=Resource,\n action=\"read\",\n query={\"id\": model.id}\n )","title":""},{"docid":"0c6d6d3cfea74e87a4d81da41482ee5f","score":"0.51654536","text":"def delete(self):\n resource = self.RESOURCE.format(account_id=self.account.id, id=self.id)\n response = Request(self.account.client, 'delete', resource).perform()\n self.from_response(response.body['data'])","title":""},{"docid":"e4962763810be507b2ce14c2cfa4d325","score":"0.51647455","text":"def delete(self, *args, **kwargs):\n return self.make_request(self.client.delete, *args, **kwargs)","title":""},{"docid":"292b52f832f3565a59fcc5d88d3f8dc4","score":"0.5161566","text":"def queue_delete(self, *args, **kwargs):\n wrapped = self.__wrap_channel_method('queue_delete')\n queue_name = kwargs['queue']\n\n d = wrapped(*args, **kwargs)\n return d.addCallback(self.__clear_consumer, queue_name)","title":""},{"docid":"d4ba642321a4c8389135e70b63f4103c","score":"0.5159102","text":"async def async_delete(self, msgid: str) -> bool:\n raise NotImplementedError()","title":""},{"docid":"e2ba1a9df9991f0d949451f9174a70b8","score":"0.51565135","text":"def delete():","title":""},{"docid":"e2ba1a9df9991f0d949451f9174a70b8","score":"0.51565135","text":"def delete():","title":""},{"docid":"e2ba1a9df9991f0d949451f9174a70b8","score":"0.51565135","text":"def delete():","title":""},{"docid":"540275ed2bb3a0916193c35683c43ba8","score":"0.5154845","text":"def _delete_resources(self):\n connection = pika.BlockingConnection(self.pikaparams)\n channel = connection.channel()\n channel.queue_delete(queue=self.queue)\n channel.close()\n connection.close()","title":""},{"docid":"9a2e1125aa6bee8af5c8c60313c32335","score":"0.51544356","text":"def delete(self) -> None:\n self._user.request('DELETE', self.url)","title":""},{"docid":"c34a06766398e608330c678b2a3b9770","score":"0.51535714","text":"def step_see_named_query_deleted(context):\n wrappers.expect_exact(context, 'foo: Deleted', timeout=2)","title":""},{"docid":"9d25307aac935204154fe785497a95c5","score":"0.5149476","text":"def delete(self, wait=True):\n self.table.delete()\n if wait:\n self.table.meta.client.get_waiter('table_not_exists').wait(TableName=self.name)\n return True","title":""},{"docid":"64d78c452e8d07c1e0be5cc70a6e297d","score":"0.51321757","text":"def cancelWait(self):\n self.cleanup()","title":""},{"docid":"ce630fe187cf7b65970180cd4fae2f13","score":"0.51276094","text":"def delete_resource(self, resource, id):\n if not id or id in reserved_ids:\n # TODO: update using filters and reserved_ids features is not yet implemented\n raise ValueError(f\"Missing id in URI, or invalid id format: '{id}'\")\n\n query = self.session.query(self.model[resource])\n query = query.filter_by(id=id)\n self.session.delete(query.one())\n self.session.commit()","title":""},{"docid":"e7b0893e52fe1ae603f40ce09c3c27ad","score":"0.512589","text":"def handle_resource_delete(resource, request, parent_resource):\n if parent_resource is None:\n raise NotFoundException\n path = fp.prop('consumed_path', request)\n name = fp.last(path)\n if not hasattr(parent_resource, 'delete'):\n raise MethodNotAllowedException\n\n parent_resource.delete(name)\n\n return make_empty_response()","title":""},{"docid":"a073b390db3c0521a85c8bad66972a2e","score":"0.5115125","text":"def wait_for_deregistration(self, device_id, timeout=30):\n for _ in range(timeout):\n deregistration = self.check_deregistration(device_id)\n if deregistration:\n return deregistration\n sleep(1)\n return False","title":""},{"docid":"953144be08a0f9b58a86969a860797de","score":"0.5114396","text":"async def async_delete(self, msgid: str) -> bool:\n data: AsteriskData = self.hass.data[ASTERISK_DOMAIN]\n client = data.client\n _LOGGER.info(\"Deleting: %s\", msgid)\n await self.hass.async_add_executor_job(client.delete, msgid)\n return True","title":""},{"docid":"392d76dff791d683ba20a38e90f6dfe6","score":"0.51105034","text":"def delete(self):\n try:\n url = BASE_URL +\\\n '/file/delete/{0}/{1}'.format(self.metadata['fileId'],\n self.metadata['deleteToken'])\n\n request = requests.get(url)\n\n if not request.ok:\n request.raise_for_status()\n\n json = request.json()\n if json['error'] == u'':\n return\n else:\n raise FileDeleteException(json['error'])\n except:\n raise FileDeleteException(sys.exc_info()[0])","title":""},{"docid":"932e6c0d6ceba874f4df2b3cb2740515","score":"0.5094411","text":"def delete_uma_resource(self, **kwargs: Any):\n dialog = self.app.get_confirm_dialog(_(\"Are you sure want to delete UMA resoucres with id:\")+QUESTION_TEMP.format(kwargs ['selected'][0]))\n async def coroutine():\n focused_before = self.app.layout.current_window\n result = await self.app.show_dialog_as_float(dialog)\n try:\n self.app.layout.focus(focused_before)\n except Exception:\n self.app.layout.focus(self.edit_client_dialog)\n\n if result.lower() == 'yes':\n result = self.app.cli_object.process_command_by_id(\n operation_id='delete-oauth-uma-resources-by-id',\n url_suffix='id:{}'.format(kwargs['selected'][0]),\n endpoint_args='',\n data_fn=None,\n data={}\n )\n self.edit_client_dialog.oauth_get_uma_resources()\n\n return result\n\n asyncio.ensure_future(coroutine())","title":""},{"docid":"06d3f98f524e609a73df68578e2d813a","score":"0.5092728","text":"def delete(self, resource, rid=None): # note that rid can't be 0 - problem?\n if rid:\n if resource[-1] != '/':\n resource += '/'\n resource += str(rid)\n response = self._run_method('DELETE', resource)\n return self._handle_response(resource, response, suppress_empty=True)","title":""},{"docid":"cd9e9db23879ebc70ec49d6354869d78","score":"0.50896037","text":"def _destroy(self, wait=False):\n raise NotImplementedError()","title":""},{"docid":"e0002df820a6b2fdf398d6cf8889e131","score":"0.5088437","text":"def remove(self, timeout=None, finished_cb=None):\n #Complete request\n self.status = Status.removed\n #Run subscriptions to device state\n self._run_subs(obj=self, sub_type=self._default_sub)\n #Run subscriptions to mps state\n if self.mps:\n self.mps._run_subs(obj=self, sub_type=self.mps._default_sub)\n #Return complete status object\n return DeviceStatus(self, done=True, success=True)","title":""},{"docid":"eac0adf9c5053e336604eeb9bf3fc86a","score":"0.50867337","text":"async def test_delete(self):\n rsps = respx.delete(f'{PROVISIONING_API_URL}/users/current/accounts/id') \\\n .mock(return_value=Response(204))\n await account_client.delete_account('id')\n assert rsps.calls[0].request.url == f'{PROVISIONING_API_URL}/users/current/accounts/id'\n assert rsps.calls[0].request.method == 'DELETE'\n assert rsps.calls[0].request.headers['auth-token'] == 'header.payload.sign'","title":""},{"docid":"0ecdf7226c2e4c5945ef12d71d48dfaa","score":"0.5073351","text":"def delete(self):\n return self._finalize()","title":""}],"string":"[\n {\n \"docid\": \"0c6e8cc1c7e01252177fd073373b8eb1\",\n \"score\": \"0.7432982\",\n \"text\": \"def wait_for_delete(self, res, interval=2, wait=120):\\n return resource.wait_for_delete(self, res, interval, wait)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"761c304c769b4e71ef4f2a3648ec7efa\",\n \"score\": \"0.7096719\",\n \"text\": \"def wait_for_delete(resource, update_resource=None, timeout=60,\\n check_interval=1):\\n start = time.time()\\n while True:\\n try:\\n resource = update_resource(resource)\\n except exceptions.GetResourceNotFound:\\n break\\n time.sleep(check_interval)\\n if time.time() - start > timeout:\\n raise exceptions.TimeoutException()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"794f489d32c3284ed434256c21c6517b\",\n \"score\": \"0.6764941\",\n \"text\": \"async def _delete_resource(self, resource_id, timeout=None):\\n if timeout is None:\\n timeout = self.timeout\\n response = await self._session.delete(\\n os.path.join(self._resource_api_url, resource_id), timeout=timeout\\n )\\n response.raise_for_status()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"058ce945804f0ff27d81ad4eed59c66c\",\n \"score\": \"0.67576593\",\n \"text\": \"def wait_for_delete(\\n self, id, timeout_secs=1200,\\n ):\\n self.wait_for_state(\\n id=id, timeout_secs=timeout_secs,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9abeb979fe009b1e016e0a4916a7476\",\n \"score\": \"0.629175\",\n \"text\": \"async def delete(self):\\n raise EphemeralDeletion()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9abeb979fe009b1e016e0a4916a7476\",\n \"score\": \"0.629175\",\n \"text\": \"async def delete(self):\\n raise EphemeralDeletion()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83ec06f610a6b686ff16b5885766aaf1\",\n \"score\": \"0.6258565\",\n \"text\": \"def delete(self):\\n tk = self.mor.Destroy_Task()\\n tk = self._dvs._server.wait_for_task(tk)\\n return tk\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e74a6996e930dd12cf01404d5c45bd\",\n \"score\": \"0.619539\",\n \"text\": \"def waitForDeleteAu(self, au, timeout=DEF_TIMEOUT, sleep=DEF_SLEEP):\\n return self.wait( lambda: not self.hasAu( au ), timeout, sleep )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a40c142f262a11b81661bcfd4fe62d5\",\n \"score\": \"0.6161981\",\n \"text\": \"async def delete(self, resource, **params):\\n\\n return await self.client.delete(\\n resource,\\n service_name=self.SERVICE_NAME,\\n params=params,\\n signature=self.token,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50a80071dbfd94745e070dcc631a1a25\",\n \"score\": \"0.59850085\",\n \"text\": \"def instance_delete_wait(self):\\n self.test_runner.run_instance_delete_wait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c814da482d87c564b0f82da65049fb93\",\n \"score\": \"0.5935675\",\n \"text\": \"async def delete_async(self) -> bool:\\n return await self._version.delete_async(\\n method=\\\"DELETE\\\",\\n uri=self._uri,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c814da482d87c564b0f82da65049fb93\",\n \"score\": \"0.5935675\",\n \"text\": \"async def delete_async(self) -> bool:\\n return await self._version.delete_async(\\n method=\\\"DELETE\\\",\\n uri=self._uri,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c814da482d87c564b0f82da65049fb93\",\n \"score\": \"0.5935675\",\n \"text\": \"async def delete_async(self) -> bool:\\n return await self._version.delete_async(\\n method=\\\"DELETE\\\",\\n uri=self._uri,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c6760f3be29661ad26228c9a2be233\",\n \"score\": \"0.59097993\",\n \"text\": \"def delete():\\n return 'not ready'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14c00482263c04f41c4f8997c0992748\",\n \"score\": \"0.5881431\",\n \"text\": \"def delete(self, resource, *args, **kwargs):\\n return self.make_request(self.session.delete, resource, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a430e9541c8f9933de058a617813b979\",\n \"score\": \"0.58598536\",\n \"text\": \"def storage_delete(self, name, timeout=10):\\n result = Result(*self.perform_request(**{\\n 'method': 'DELETE',\\n 'url': '/storage/{0}.json'.format(name),\\n 'params': {\\n 'request_timeout': timeout\\n }\\n }))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607233c48571f660c8d5be04e02bace6\",\n \"score\": \"0.5785337\",\n \"text\": \"def file_delete_async( reply, gateway, volume, update ):\\n storagetypes.deferred.defer( file_delete, reply.SerializeToString(), gateway, volume, update.SerializeToString(), async=True )\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b13dc7654218072c66f00b4ec57bb76c\",\n \"score\": \"0.5781464\",\n \"text\": \"def remove(self, resource, force=False, timeout=-1):\\n return self._client.delete(resource, force=force, timeout=timeout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61e8b1f4173c682c34f198fc185e4e66\",\n \"score\": \"0.5768489\",\n \"text\": \"async def delete_async(self) -> bool:\\n return await self._proxy.delete_async()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61e8b1f4173c682c34f198fc185e4e66\",\n \"score\": \"0.5768489\",\n \"text\": \"async def delete_async(self) -> bool:\\n return await self._proxy.delete_async()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61e8b1f4173c682c34f198fc185e4e66\",\n \"score\": \"0.5768489\",\n \"text\": \"async def delete_async(self) -> bool:\\n return await self._proxy.delete_async()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"255ccbd2df17795374a2bbfd20ac7e42\",\n \"score\": \"0.57471544\",\n \"text\": \"def delete_async(self, path: str, callback, context=None, params: dict = None, error_message: str = None, allow_404: bool = False) -> threading.Thread:\\n # requests.Session is not thread safe: see https://github.com/psf/requests/issues/2766\\n # so we create a new session object\\n t = threading.Thread(target=_get_delete_callback, args=(\\n None, self.url, path, callback, context, params, error_message, allow_404, True))\\n t.daemon = True\\n t.start()\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f0c0042456403016d11a1e20600cc52\",\n \"score\": \"0.57441705\",\n \"text\": \"def delete_resource(self, path, permanently=False, wait_for_finish=True, sleep=None):\\n logger.info('Delete resource from {!r}'.format(path))\\n params_string = urllib.parse.urlencode(\\n {\\n 'path': path,\\n 'permanently': permanently,\\n },\\n doseq=False,\\n )\\n self._waiting_for_finish(\\n self._requester.delete(\\n url='disk/resources?{}'.format(params_string)\\n ),\\n wait_for_finish=wait_for_finish,\\n sleep=sleep,\\n )\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62a3188346456764f0fb309a08e420ad\",\n \"score\": \"0.57320744\",\n \"text\": \"def delete(self, resource, **params):\\n\\n return self.client.delete(\\n resource,\\n service_name=self.SERVICE_NAME,\\n params=params,\\n signature=self.token,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1424643ba4a4dfbf056774a964a9569f\",\n \"score\": \"0.5718569\",\n \"text\": \"async def async_delete(self, device_id: int) -> None:\\n await self._client.async_request(\\\"delete\\\", f\\\"devices/{device_id}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8386ca0daeb6487a9b27b1cab884972d\",\n \"score\": \"0.5716009\",\n \"text\": \"def delete(self):\\r\\n self.acquire_lock()\\r\\n try:\\r\\n return self.locked_delete()\\r\\n finally:\\r\\n self.release_lock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8a7633bdbfa4c800456a72a17a8ad5a\",\n \"score\": \"0.5706511\",\n \"text\": \"def delete_resource(url):\\n response = requests.delete(url, auth=get_auth())\\n if response.status_code == 429:\\n print('Rate limited! Please wait.')\\n time.sleep(int(response.headers['retry-after']))\\n response = requests.delete(url, auth=get_auth())\\n if response.status_code != 204:\\n print('Failed to delete record with error {}'.format(response.status_code))\\n print(response.text)\\n return False\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f53cf2cd6295b2c0b121878d782175a5\",\n \"score\": \"0.57018095\",\n \"text\": \"def delete(path):\\n baseutils.retry(client.delete, path=path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49c03fb5c16b68aa3bb55520bd0b55f1\",\n \"score\": \"0.5669405\",\n \"text\": \"def do_DELETE(self):\\n resource_parts = self.get_resource_parts()\\n if len(resource_parts) != 2:\\n self.write_invalid_api_uri_format_response()\\n else:\\n resource = resource_parts[0]\\n resource_id = resource_parts[1]\\n resource_path = os.path.join(API_DATA_PATH, resource, resource_id)\\n if not os.path.exists(resource_path):\\n self.write_not_found_response(resource, resource_id)\\n else:\\n try:\\n os.remove(resource_path)\\n self.write_response({}, 204)\\n except IOError as e:\\n if e.errno == errno.EACCES:\\n self.write_no_access_permission_to_file_response(resource_path)\\n else: # Not a permission error.\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e91d64a355c7076cf914fcadaeea54f\",\n \"score\": \"0.56686676\",\n \"text\": \"def delete(id: int = typer.Argument(..., min=1)):\\n response: requests.Response = rest.delete(f'{ENDPOINT}/{id}')\\n if response.status_code != 200:\\n util.panic('Failure to delete resource')\\n typer.echo('Resource deleted successfully.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e91d64a355c7076cf914fcadaeea54f\",\n \"score\": \"0.56686676\",\n \"text\": \"def delete(id: int = typer.Argument(..., min=1)):\\n response: requests.Response = rest.delete(f'{ENDPOINT}/{id}')\\n if response.status_code != 200:\\n util.panic('Failure to delete resource')\\n typer.echo('Resource deleted successfully.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce2f8ddbf0066ebd0b500cbe1b1ef1e7\",\n \"score\": \"0.5663604\",\n \"text\": \"def delete(self):\\n if 'delete' in self._URL and hasattr(self, 'id'):\\n extra = {'resource': type(self).__name__, 'query': {\\n 'id': self.id}}\\n logger.info(\\\"Deleting %s resource.\\\", self, extra=extra)\\n self._api.delete(url=self._URL['delete'].format(id=self.id))\\n else:\\n raise SbgError('Resource can not be deleted!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7550cec50f526256f63ed867a771f24\",\n \"score\": \"0.5595567\",\n \"text\": \"def submit_delete(self) -> bool:\\n try:\\n self.delete()\\n return True\\n except google.api_core.exceptions.NotFound:\\n # resource is gone, so we're good\\n return True\\n except google.api_core.exceptions.BadRequest:\\n # Resource is tied up, so indicate that\\n # the user will need to try again later\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87ac4507a80ea0d4785286917bcba7c2\",\n \"score\": \"0.5588551\",\n \"text\": \"async def delete(self, *args, **kwargs):\\n kwargs['method'] = 'DELETE'\\n return await self.request(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1d366c031433c68cace23bbdd2d975e\",\n \"score\": \"0.5577933\",\n \"text\": \"def delete(self):\\n self.client.delete(self.url, expect=204)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"350f57cb9aecf38ffc4b74dc6447aad5\",\n \"score\": \"0.5577463\",\n \"text\": \"def wait_for_volume_attachment_remove(client, volume_id, attachment_id):\\n start = int(time.time())\\n attachments = client.show_volume(volume_id)['volume']['attachments']\\n while any(attachment_id == a['attachment_id'] for a in attachments):\\n time.sleep(client.build_interval)\\n if int(time.time()) - start >= client.build_timeout:\\n message = ('Failed to remove attachment %s from volume %s '\\n 'within the required time (%s s).' %\\n (attachment_id, volume_id, client.build_timeout))\\n raise lib_exc.TimeoutException(message)\\n attachments = client.show_volume(volume_id)['volume']['attachments']\\n LOG.info('Attachment %s removed from volume %s after waiting for %f '\\n 'seconds', attachment_id, volume_id, time.time() - start)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00b01e1a4eff029525c5a8f06e5282d6\",\n \"score\": \"0.5560546\",\n \"text\": \"def delete(openstack_resource):\\n # Get the details for the created server instance\\n try:\\n server = openstack_resource.get()\\n except exceptions.ResourceNotFound:\\n msg = 'Server {0} is not found'.format(openstack_resource.resource_id)\\n if SERVER_TASK_DELETE not in ctx.instance.runtime_properties:\\n ctx.logger.error(msg)\\n raise NonRecoverableError(msg)\\n\\n ctx.logger.info('Server {0} is deleted successfully'\\n .format(openstack_resource.resource_id))\\n return\\n\\n # Check if delete operation triggered or not before\\n if SERVER_TASK_DELETE not in ctx.instance.runtime_properties:\\n openstack_resource.delete()\\n ctx.instance.runtime_properties[SERVER_TASK_DELETE] = True\\n\\n ctx.logger.info('Waiting for server \\\"{0}\\\" to be deleted.'\\n ' current status: {1}'.format(server.id, server.status))\\n\\n raise OperationRetry(message='Server has {0} state.'.format(server.status))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6d7b1794d62e3a1bc86a4035913307\",\n \"score\": \"0.55464095\",\n \"text\": \"def delete(self) -> Awaitable[int]:\\n\\n return self.get_connection().delete(self._key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cef7c5aa538d40eeedf4202f93d92dd9\",\n \"score\": \"0.55459017\",\n \"text\": \"def delete(iface, resource_config, **_):\\n # Add the required QueueUrl parameter.\\n if QUEUE_URL not in resource_config:\\n resource_config.update({QUEUE_URL: iface.resource_id})\\n\\n # Actually delete the resource\\n iface.delete(resource_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb950d83b96a7faaed1d1e07d90961cf\",\n \"score\": \"0.55288565\",\n \"text\": \"def delete(self):\\n self.repo.prepareFuture()\\n task = self.repo.getCurrentTask()\\n self.repo.deleteTask(task)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d38fe2e4b9283b493669fe9bbf8225e\",\n \"score\": \"0.5516576\",\n \"text\": \"def DELETE(self, resource_id):\\n connection = self._redis.rcon\\n response = {\\\"meta\\\": {\\\"status\\\": True, \\\"verbose\\\": \\\"Ok\\\"}, \\\"content\\\": None}\\n\\n connection.delete(resource_id)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1759a6ff16225cda606dd2df0d0fefe1\",\n \"score\": \"0.55057037\",\n \"text\": \"async def delete(self, path, callback=None, timeout=None, **kwargs): # pragma: no cover\\n request = self.helper.mk_request(self._address, defines.Code.DELETE, path)\\n request.token = utils.generate_random_hex(2)\\n\\n for k, v in kwargs.items():\\n if hasattr(request, k):\\n setattr(request, k, v)\\n\\n return await self.helper.delete(request, callback, timeout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efcd1dc7026ef1deb65101b3a272a248\",\n \"score\": \"0.5486964\",\n \"text\": \"async def delete(self, **kwargs):\\n return await self._client.delete(\\n self._path,\\n **kwargs,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efcd1dc7026ef1deb65101b3a272a248\",\n \"score\": \"0.5486964\",\n \"text\": \"async def delete(self, **kwargs):\\n return await self._client.delete(\\n self._path,\\n **kwargs,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebaa02904053856b980808d45e04c91c\",\n \"score\": \"0.5479539\",\n \"text\": \"def test_resource_delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be1e74276def7d01db0a7e63186a0ceb\",\n \"score\": \"0.5474309\",\n \"text\": \"def delete(self, name):, name\\n tries = 0\\n while True:\\n try:\\n self.container.delete_object(name)\\n break\\n except NoSuchObject:\\n # It doesn't exist and that's ok\\n break\\n except (HTTPException, SSLError, ServiceResponseFailure) as e:\\n if tries >= self.max_retries:\\n raise\\n tries += 1\\n logger.warning('Failed to delete %s: %r (attempt %d/%d)' % (\\n name, e, tries, self.max_retries))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6585da1a63183319c0b52c53dfb92b8\",\n \"score\": \"0.54519564\",\n \"text\": \"def get_async(self, path: str, callback, context=None, params: dict = None, error_message: str = None, allow_404: bool = False) -> threading.Thread:\\n # requests.Session is not thread safe: see https://github.com/psf/requests/issues/2766\\n # so we create a new session object\\n t = threading.Thread(target=_get_delete_callback, args=(\\n None, self.url, path, callback, context, params, error_message, allow_404, False))\\n t.daemon = True\\n t.start()\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aa638f607a233fe3c98835d84de2cdd\",\n \"score\": \"0.5447188\",\n \"text\": \"def _wait_for_cluster_delete(self, cluster_id, timeout=300,\\n check_interval=1):\\n start_time = time.time()\\n while True:\\n try:\\n self._get_cluster(cluster_id)\\n except ks_exceptions.NotFound:\\n break\\n if time.time() - start_time > timeout:\\n raise exceptions.Exception(\\\"Delete cluster timed out\\\")\\n time.sleep(check_interval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8dce748253cfadb37f4b7b4391d05f9\",\n \"score\": \"0.54213893\",\n \"text\": \"async def wait_for_reaction_remove(self):\\n return await self.bot.wait_for(\\n \\\"reaction_remove\\\", check=self.check, timeout=self.timeout\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa8aa5e3ce3e81a58d16dd331dc78f9\",\n \"score\": \"0.5419876\",\n \"text\": \"def _delete_queue(self, queue):\\n self.log.info('Deleting Queue: %s...' % queue.url)\\n ok = yield self.thread(self.sqs_conn.delete_queue, queue)\\n\\n # Raise an exception if the tasks failed\\n if not ok:\\n raise QueueDeletionFailed('Failed to delete \\\"%s\\\"' % queue.url)\\n\\n raise gen.Return(ok)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9c2873a015c89c4e54d304e265c5f20\",\n \"score\": \"0.5401388\",\n \"text\": \"def _try_delete_resource(cls, delete_callable, *args, **kwargs):\\n try:\\n delete_callable(*args, **kwargs)\\n # if resource is not found, this means it was deleted in the test\\n except exceptions.NotFound:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f85102faddc080d3206a7f238f5c04fd\",\n \"score\": \"0.5381764\",\n \"text\": \"async def delete(self, *, delay: Optional[float] = None) -> None:\\n\\n if delay is not None:\\n\\n async def inner_call(delay: float = delay):\\n await asyncio.sleep(delay)\\n try:\\n await self._state._webhook.delete_message(self.id, thread=self._state._thread)\\n except HTTPException:\\n pass\\n\\n asyncio.create_task(inner_call())\\n else:\\n await self._state._webhook.delete_message(self.id, thread=self._state._thread)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c6f3cface2d43c53e6f9494a7d9fbb\",\n \"score\": \"0.5372078\",\n \"text\": \"def delete_resource(self, resource, file, allow_deletion=False):\\n uri = resource.uri\\n if (allow_deletion):\\n if (self.dryrun):\\n self.logger.info(\\\"dryrun: would delete %s -> %s\\\" % (uri,file))\\n else:\\n try:\\n os.unlink(file)\\n except OSError as e:\\n msg = \\\"Failed to DELETE %s -> %s : %s\\\" % (uri,file,str(e))\\n if (self.ignore_failures):\\n self.logger.warning(msg)\\n return\\n else:\\n raise ClientFatalError(msg)\\n self.logger.info(\\\"deleted: %s -> %s\\\" % (uri,file))\\n self.log_event(Resource(resource=resource, change=\\\"deleted\\\"))\\n else:\\n self.logger.info(\\\"nodelete: would delete %s (--delete to enable)\\\" % uri)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45094ccc8278888765bb13b4522df0a5\",\n \"score\": \"0.53628486\",\n \"text\": \"async def delete_message(ctx, time: float):\\n\\n try:\\n await ctx.message.delete(delay=time)\\n except discord.Forbidden:\\n pass\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecdc83587eb1ca9ab6e42ffbc68e72d1\",\n \"score\": \"0.5356544\",\n \"text\": \"def _cinder_delete_volume(self, volume, timeout=300, force=False, wait=False):\\n self._debug('delete volume {0}'.format(volume.id))\\n self._get_cinder_client()\\n try:\\n volume = self._cinder_get_volume_by_id(volume.id)\\n except cinder_client_exceptions.NotFound:\\n self._debug('Volume {0} not found, already deleted'.format(volume.id))\\n return\\n\\n if volume.status == 'in-use':\\n raise RuntimeError('Cannot delete volume {0} while it is in use'.format(volume.id))\\n\\n if not force:\\n self._cinder_wait_until_volume_state(volume.id, status='available')\\n self._debug('volume is now available')\\n volume = self._cinder_get_volume_by_id(volume.id)\\n if force:\\n self.cinder_client.volumes.force_delete(volume)\\n else:\\n self.cinder_client.volumes.delete(volume)\\n if wait:\\n self._cinder_wait_until_volume_not_found(volume.id, timeout)\\n self._debug('deleted volume {0}'.format(volume.id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0e12836d6be5d4297229bc7e5c98189\",\n \"score\": \"0.5336774\",\n \"text\": \"def _terminate(self):\\n return self.client.bucket(bucket_name=self.bucket_name).delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c2b44e231bfb163e01fae77025fc6d\",\n \"score\": \"0.5333866\",\n \"text\": \"def check_delete(self):\\n return self._destroy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d28fa18eda6475ccef47e90d7bad6878\",\n \"score\": \"0.5328587\",\n \"text\": \"def delete(self):\\n if(self._can_delete):\\n self._active = False\\n self._connect.delete(self._resource)\\n else:\\n raise SkytapException(\\\"delete is not supported \\\"\\n \\\"on this object.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1793cc459c44c3a2526adcc2a0e8e101\",\n \"score\": \"0.53132915\",\n \"text\": \"def delete_response(self):\\n thread = self.get_random_thread(page=1, page_size=10)\\n self.wait()\\n if not thread:\\n self.stop()\\n response = self.create_response(thread_id=thread[\\\"id\\\"])\\n self.wait()\\n if not response:\\n self.stop()\\n self.delete_comment(comment_id=response[\\\"id\\\"])\\n self.wait_then_stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"975dff7ebd1c89018eda9254d836f75d\",\n \"score\": \"0.53070325\",\n \"text\": \"def delete(self) -> None:\\n url = f\\\"{self.base_url}/{self._version}/{self.resource_url}/{self.__getattribute__('slug')}\\\"\\n self.request(url, \\\"DELETE\\\", {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c05e725ea38098bd90e8891037cdffd5\",\n \"score\": \"0.5257113\",\n \"text\": \"def wait_for_server_delete(self, server, timeout=None):\\n if timeout is None:\\n timeout = cfg.CONF.default_nova_timeout\\n\\n server_obj = self.conn.compute.find_server(server, True)\\n if server_obj:\\n self.conn.compute.wait_for_delete(server_obj, wait=timeout)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"351b4709659e506e5cc7701f1e78e59f\",\n \"score\": \"0.5256657\",\n \"text\": \"def remove_resource(self, tenant_id, resource_path, parse_response=True):\\n if self.cleanup_mode:\\n return True\\n else:\\n return self._resource_operation('DELETE', tenant_id, resource_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb9171213399fe080ce27ab34a2c7adc\",\n \"score\": \"0.5255185\",\n \"text\": \"async def delete(self, id):\\r\\n await self.delete_by_id(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91f00d6dfad1e86eb685b593509191b3\",\n \"score\": \"0.52511203\",\n \"text\": \"def delete(self, *args):\\n self._check_ended()\\n self._check_read_only()\\n return self._delete(False, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6e920f7611ebec6aa94f60da5e1434a\",\n \"score\": \"0.5235588\",\n \"text\": \"def delete(self, resource_id: str):\\n\\n self.logger.debug(\\\"Deleting resource through UMA Handler\\\")\\n mongo_resource= self.mongo.get_from_mongo(\\\"resource_id\\\", resource_id)\\n res_id=None\\n try:\\n res_id= mongo_resource[\\\"_id\\\"]\\n except Exception as e:\\n self.logger.debug(\\\"Resource for ID \\\"+resource_id+\\\" does not exist\\\")\\n try:\\n scope_list= mongo_resource[\\\"scopes\\\"]\\n except Exception as e:\\n self.logger.debug(\\\"No scopes for ID \\\"+resource_id+\\\". Update the PEP version and clean the old resources\\\")\\n\\n if not 'open' in mongo_resource.get(\\\"scopes\\\"):\\n resource_registration_endpoint = self.wkh.get(TYPE_UMA_V2, KEY_UMA_V2_RESOURCE_REGISTRATION_ENDPOINT)\\n if resource_registration_endpoint[-1] is not \\\"/\\\":\\n resource_registration_endpoint += \\\"/\\\"\\n pat = self.oidch.get_new_pat()\\n try:\\n n = resource.delete(pat, resource_registration_endpoint, resource_id, secure = self.verify)\\n self.logger.info(\\\"Deleted resource in IDP... \\\" + str(n))\\n except Exception as e:\\n self.logger.debug(\\\"Failed to delete resource. \\\" + str(e))\\n\\n try:\\n resp = self.mongo.delete_in_mongo(\\\"resource_id\\\", resource_id)\\n if resp:\\n self.logger.debug('Resource deleted from DB succesfully')\\n else:\\n self.logger.debug('Failed to delete resource from DB')\\n except Exception as e:\\n self.logger.debug(\\\"Error while deleting resource: \\\"+str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca258f87f41ef745e90c1aec706a15e\",\n \"score\": \"0.523438\",\n \"text\": \"def is_deleted(self) -> bool:\\n try:\\n response = self.get(timeout=5)\\n except ValueError as e:\\n if str(e) != f\\\"Couldn't get resource {self.name}\\\":\\n raise\\n # couldn't find the resource, so assume\\n # the deletion went off swimmingly\\n return True\\n if response.status > 5:\\n self._raise_bad_status(response)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48c30d87aab300cecd2e674673c76019\",\n \"score\": \"0.5224237\",\n \"text\": \"def wait_for_pods_to_be_deleted(\\n client,\\n namespace,\\n pod_selector,\\n timeout=datetime.timedelta(minutes=10),\\n polling_interval=datetime.timedelta(seconds=30)):\\n end_time = datetime.datetime.now() + timeout\\n while True:\\n pods = list_pods(client, namespace, pod_selector)\\n\\n logging.info(\\\"%s pods matched %s pods\\\", len(pods.items), pod_selector)\\n\\n if not pods.items:\\n return\\n\\n if datetime.datetime.now() + polling_interval > end_time:\\n raise util.TimeoutError(\\\"Timeout waiting for pods to be deleted.\\\")\\n\\n time.sleep(polling_interval.seconds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c850d8f226a84ebeecda4bad2fb9f70\",\n \"score\": \"0.5207129\",\n \"text\": \"def delete(self, id):\\n res = ReconResource.query.get_or_404(id)\\n res.deep_delete()\\n db.session.commit()\\n\\n return 'Resource successfully deleted.', 204\",\n \"title\": \"\"\n },\n {\n \"docid\": \"733b59754792479e5783034b02778426\",\n \"score\": \"0.5195656\",\n \"text\": \"def shutdown(self):\\n self._current_task.cancel()\\n\\n if self.state == 'failed':\\n return\\n\\n try:\\n yield from self.context.request(\\n Message(code=DELETE, uri=self.location)\\n ).response_raising\\n except Exception as e:\\n self.log.error(\\\"Error deregistering from the RD\\\")\\n self.log.exception(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42072d11482b77e21ed0fbbf51d607dc\",\n \"score\": \"0.51952004\",\n \"text\": \"def _reconcile_delete(resource: Resource) -> None:\\n\\n try:\\n resource_name = kubectl.delete(resource)\\n resource_kind = resource['kind']\\n display_name = f'{resource_kind}/{resource_name}'.lower()\\n print(display_name, 'deleted')\\n\\n except RuntimeError as err:\\n print('Unable to delete resource:', err, file=sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335261497290aa0b407e5166d3b67303\",\n \"score\": \"0.51945424\",\n \"text\": \"def delete(self):\\n self._info_cache = rest_request('DELETE', self._api_uri)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc8057e8708e7f2e6c578c3732a92a0c\",\n \"score\": \"0.51885533\",\n \"text\": \"def test_delete_a_resource_that_exists():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c63727bbf79952eb46bcb88f072cc2ac\",\n \"score\": \"0.5185573\",\n \"text\": \"def no_op(event, context):\\n logger.info('No action required when deleting this resource')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6a29f8da5333e10343be4d193db07a\",\n \"score\": \"0.5179238\",\n \"text\": \"def wait_delete_complete(stackname, timeout=600):\\n cfn = getcfn()\\n ret = 0\\n now = None\\n response = {}\\n done = False \\n now = None\\n start_time = int(time.time())\\n print \\\"Checking stack status: %s\\\" % stackname\\n while not done:\\n if now:\\n time.sleep(10)\\n stacks = [] \\n next_token = None\\n try:\\n response = cfn.list_stacks()\\n except Exception as e:\\n print e\\n sys.exit(1)\\n stacks.extend(response[\\\"StackSummaries\\\"])\\n found = False \\n done = False\\n failed = False\\n for stack in stacks:\\n if stack[\\\"StackName\\\"] != stackname:\\n continue\\n found = True\\n completed = True\\n falied = False \\n info(\\\"Stack status: %s\\\" % stack[\\\"StackStatus\\\"])\\n if stack[\\\"StackStatus\\\"] == \\\"DELETE_FAILED\\\":\\n print \\\"Error: Delete stack failed\\\"\\n failed = True \\n break\\n if stack[\\\"StackStatus\\\"] != \\\"DELETE_COMPLETE\\\":\\n completed = False \\n break\\n if completed:\\n done = True\\n break\\n \\n if not found:\\n print \\\"stack not found\\\"\\n ret = 0\\n break\\n if failed == True:\\n ret = 2\\n break \\n if done:\\n continue\\n\\n now = int(time.time())\\n if (now - start_time) > timeout:\\n print \\\"CloudFormation status checking timeout\\\"\\n ret = 2\\n break\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3980c571e2457f29a26e2bcd835a3980\",\n \"score\": \"0.51773417\",\n \"text\": \"def delete(self, *args, **kwargs):\\n return self.torreq('DELETE', *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6543eb8fa21b0d83e44b5e4ac9204e6c\",\n \"score\": \"0.5167933\",\n \"text\": \"def delete(self, resource, user_id, record_id):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"640dfc3692b5027696ca497eeb89d97f\",\n \"score\": \"0.51672333\",\n \"text\": \"def test_resource_delete(self):\\n data = {\\n \\\"urn\\\": (\\\"urn:%s\\\" % uuid.uuid1())[:30],\\n \\\"description\\\": \\\"resource to delete\\\",\\n }\\n model = db_actions.crud(\\n model=\\\"Resource\\\",\\n api_model=Resource,\\n data=data,\\n action=\\\"create\\\"\\n )\\n response = self.client.open(\\n '/api/v1/resources/{resource_id}'.format(resource_id=model.id),\\n method='DELETE', headers=self.headers)\\n\\n with self.assertRaises(werkzeug.exceptions.NotFound):\\n db_actions.crud(\\n model=\\\"Resource\\\",\\n api_model=Resource,\\n action=\\\"read\\\",\\n query={\\\"id\\\": model.id}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c6d6d3cfea74e87a4d81da41482ee5f\",\n \"score\": \"0.51654536\",\n \"text\": \"def delete(self):\\n resource = self.RESOURCE.format(account_id=self.account.id, id=self.id)\\n response = Request(self.account.client, 'delete', resource).perform()\\n self.from_response(response.body['data'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4962763810be507b2ce14c2cfa4d325\",\n \"score\": \"0.51647455\",\n \"text\": \"def delete(self, *args, **kwargs):\\n return self.make_request(self.client.delete, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"292b52f832f3565a59fcc5d88d3f8dc4\",\n \"score\": \"0.5161566\",\n \"text\": \"def queue_delete(self, *args, **kwargs):\\n wrapped = self.__wrap_channel_method('queue_delete')\\n queue_name = kwargs['queue']\\n\\n d = wrapped(*args, **kwargs)\\n return d.addCallback(self.__clear_consumer, queue_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4ba642321a4c8389135e70b63f4103c\",\n \"score\": \"0.5159102\",\n \"text\": \"async def async_delete(self, msgid: str) -> bool:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ba1a9df9991f0d949451f9174a70b8\",\n \"score\": \"0.51565135\",\n \"text\": \"def delete():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ba1a9df9991f0d949451f9174a70b8\",\n \"score\": \"0.51565135\",\n \"text\": \"def delete():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ba1a9df9991f0d949451f9174a70b8\",\n \"score\": \"0.51565135\",\n \"text\": \"def delete():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"540275ed2bb3a0916193c35683c43ba8\",\n \"score\": \"0.5154845\",\n \"text\": \"def _delete_resources(self):\\n connection = pika.BlockingConnection(self.pikaparams)\\n channel = connection.channel()\\n channel.queue_delete(queue=self.queue)\\n channel.close()\\n connection.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a2e1125aa6bee8af5c8c60313c32335\",\n \"score\": \"0.51544356\",\n \"text\": \"def delete(self) -> None:\\n self._user.request('DELETE', self.url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34a06766398e608330c678b2a3b9770\",\n \"score\": \"0.51535714\",\n \"text\": \"def step_see_named_query_deleted(context):\\n wrappers.expect_exact(context, 'foo: Deleted', timeout=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d25307aac935204154fe785497a95c5\",\n \"score\": \"0.5149476\",\n \"text\": \"def delete(self, wait=True):\\n self.table.delete()\\n if wait:\\n self.table.meta.client.get_waiter('table_not_exists').wait(TableName=self.name)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64d78c452e8d07c1e0be5cc70a6e297d\",\n \"score\": \"0.51321757\",\n \"text\": \"def cancelWait(self):\\n self.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce630fe187cf7b65970180cd4fae2f13\",\n \"score\": \"0.51276094\",\n \"text\": \"def delete_resource(self, resource, id):\\n if not id or id in reserved_ids:\\n # TODO: update using filters and reserved_ids features is not yet implemented\\n raise ValueError(f\\\"Missing id in URI, or invalid id format: '{id}'\\\")\\n\\n query = self.session.query(self.model[resource])\\n query = query.filter_by(id=id)\\n self.session.delete(query.one())\\n self.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7b0893e52fe1ae603f40ce09c3c27ad\",\n \"score\": \"0.512589\",\n \"text\": \"def handle_resource_delete(resource, request, parent_resource):\\n if parent_resource is None:\\n raise NotFoundException\\n path = fp.prop('consumed_path', request)\\n name = fp.last(path)\\n if not hasattr(parent_resource, 'delete'):\\n raise MethodNotAllowedException\\n\\n parent_resource.delete(name)\\n\\n return make_empty_response()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a073b390db3c0521a85c8bad66972a2e\",\n \"score\": \"0.5115125\",\n \"text\": \"def wait_for_deregistration(self, device_id, timeout=30):\\n for _ in range(timeout):\\n deregistration = self.check_deregistration(device_id)\\n if deregistration:\\n return deregistration\\n sleep(1)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"953144be08a0f9b58a86969a860797de\",\n \"score\": \"0.5114396\",\n \"text\": \"async def async_delete(self, msgid: str) -> bool:\\n data: AsteriskData = self.hass.data[ASTERISK_DOMAIN]\\n client = data.client\\n _LOGGER.info(\\\"Deleting: %s\\\", msgid)\\n await self.hass.async_add_executor_job(client.delete, msgid)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"392d76dff791d683ba20a38e90f6dfe6\",\n \"score\": \"0.51105034\",\n \"text\": \"def delete(self):\\n try:\\n url = BASE_URL +\\\\\\n '/file/delete/{0}/{1}'.format(self.metadata['fileId'],\\n self.metadata['deleteToken'])\\n\\n request = requests.get(url)\\n\\n if not request.ok:\\n request.raise_for_status()\\n\\n json = request.json()\\n if json['error'] == u'':\\n return\\n else:\\n raise FileDeleteException(json['error'])\\n except:\\n raise FileDeleteException(sys.exc_info()[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"932e6c0d6ceba874f4df2b3cb2740515\",\n \"score\": \"0.5094411\",\n \"text\": \"def delete_uma_resource(self, **kwargs: Any):\\n dialog = self.app.get_confirm_dialog(_(\\\"Are you sure want to delete UMA resoucres with id:\\\")+QUESTION_TEMP.format(kwargs ['selected'][0]))\\n async def coroutine():\\n focused_before = self.app.layout.current_window\\n result = await self.app.show_dialog_as_float(dialog)\\n try:\\n self.app.layout.focus(focused_before)\\n except Exception:\\n self.app.layout.focus(self.edit_client_dialog)\\n\\n if result.lower() == 'yes':\\n result = self.app.cli_object.process_command_by_id(\\n operation_id='delete-oauth-uma-resources-by-id',\\n url_suffix='id:{}'.format(kwargs['selected'][0]),\\n endpoint_args='',\\n data_fn=None,\\n data={}\\n )\\n self.edit_client_dialog.oauth_get_uma_resources()\\n\\n return result\\n\\n asyncio.ensure_future(coroutine())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06d3f98f524e609a73df68578e2d813a\",\n \"score\": \"0.5092728\",\n \"text\": \"def delete(self, resource, rid=None): # note that rid can't be 0 - problem?\\n if rid:\\n if resource[-1] != '/':\\n resource += '/'\\n resource += str(rid)\\n response = self._run_method('DELETE', resource)\\n return self._handle_response(resource, response, suppress_empty=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd9e9db23879ebc70ec49d6354869d78\",\n \"score\": \"0.50896037\",\n \"text\": \"def _destroy(self, wait=False):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0002df820a6b2fdf398d6cf8889e131\",\n \"score\": \"0.5088437\",\n \"text\": \"def remove(self, timeout=None, finished_cb=None):\\n #Complete request\\n self.status = Status.removed\\n #Run subscriptions to device state\\n self._run_subs(obj=self, sub_type=self._default_sub)\\n #Run subscriptions to mps state\\n if self.mps:\\n self.mps._run_subs(obj=self, sub_type=self.mps._default_sub)\\n #Return complete status object\\n return DeviceStatus(self, done=True, success=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eac0adf9c5053e336604eeb9bf3fc86a\",\n \"score\": \"0.50867337\",\n \"text\": \"async def test_delete(self):\\n rsps = respx.delete(f'{PROVISIONING_API_URL}/users/current/accounts/id') \\\\\\n .mock(return_value=Response(204))\\n await account_client.delete_account('id')\\n assert rsps.calls[0].request.url == f'{PROVISIONING_API_URL}/users/current/accounts/id'\\n assert rsps.calls[0].request.method == 'DELETE'\\n assert rsps.calls[0].request.headers['auth-token'] == 'header.payload.sign'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ecdf7226c2e4c5945ef12d71d48dfaa\",\n \"score\": \"0.5073351\",\n \"text\": \"def delete(self):\\n return self._finalize()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993496,"cells":{"query_id":{"kind":"string","value":"f69d765571a2ea175f00782c38791dc6"},"query":{"kind":"string","value":"Function for the creation of a double ring neighborhood structure."},"positive_passages":{"kind":"list like","value":[{"docid":"9a2858696789e36b454db246991d9721","score":"0.6618666","text":"def create_neighborhood_structure_ring(alist):\n for i in range(len(alist)):\n if i == 0:\n alist[i].neighborhood.append(alist[-1])\n alist[i].neighborhood.append(alist[-2])\n alist[-1].neighborhood.append(alist[i])\n alist[-2].neighborhood.append(alist[i])\n elif i == 1:\n alist[i].neighborhood.append(alist[i-1])\n alist[i].neighborhood.append(alist[-1])\n alist[i-1].neighborhood.append(alist[i])\n alist[-1].neighborhood.append(alist[i])\n else:\n alist[i].neighborhood.append(alist[i-1])\n alist[i].neighborhood.append(alist[i-2])\n alist[i-1].neighborhood.append(alist[i])\n alist[i-2].neighborhood.append(alist[i])","title":""}],"string":"[\n {\n \"docid\": \"9a2858696789e36b454db246991d9721\",\n \"score\": \"0.6618666\",\n \"text\": \"def create_neighborhood_structure_ring(alist):\\n for i in range(len(alist)):\\n if i == 0:\\n alist[i].neighborhood.append(alist[-1])\\n alist[i].neighborhood.append(alist[-2])\\n alist[-1].neighborhood.append(alist[i])\\n alist[-2].neighborhood.append(alist[i])\\n elif i == 1:\\n alist[i].neighborhood.append(alist[i-1])\\n alist[i].neighborhood.append(alist[-1])\\n alist[i-1].neighborhood.append(alist[i])\\n alist[-1].neighborhood.append(alist[i])\\n else:\\n alist[i].neighborhood.append(alist[i-1])\\n alist[i].neighborhood.append(alist[i-2])\\n alist[i-1].neighborhood.append(alist[i])\\n alist[i-2].neighborhood.append(alist[i])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"09f8ba82c9d6d9128d3ee2916ff6e64a","score":"0.66190475","text":"def _create_neighbourhood(self):\n pass","title":""},{"docid":"307bffa20031a4c7d3f4fbe0f06371e7","score":"0.61556005","text":"def make_neighbor(self, n1, n2):\n pass","title":""},{"docid":"788959f7f0c9b8f53778c7153b240b32","score":"0.5827049","text":"def create_neighbours():\r\n \r\n #for every node in the network assign left and right neighbours\r\n \r\n network[0].neighbours = ['B','C']\r\n network[1].neighbours = ['A','D','E']\r\n network[2].neighbours = ['A','F','G']\r\n network[3].neighbours = ['B','H']\r\n network[4].neighbours = ['B','F','J']\r\n network[5].neighbours = ['C','E','K']\r\n\r\n network[6].neighbours = ['C','K']\r\n network[7].neighbours = ['D','I']\r\n network[8].neighbours = ['H','J','O']\r\n network[9].neighbours = ['I','E']\r\n network[10].neighbours = ['F','G','L']\r\n network[11].neighbours = ['K','M','N']\r\n\r\n network[12].neighbours = ['L','N']\r\n network[13].neighbours = ['O','L','M']\r\n network[14].neighbours = ['I','N']\r\n\r\n \r\n #initialise data flags for each neighbour as 0 (able to write)\r\n for i in range(len(network)) :\r\n \r\n for j in range(len(network[i].neighbours)) :\r\n network[i].w.append(0)","title":""},{"docid":"07e00ef715c50b894234b216b403201b","score":"0.5728528","text":"def new_neighbor(index):\n _unimplemented(__name__)","title":""},{"docid":"86f006746636c099b50e92318be36436","score":"0.56127805","text":"def moore_neighborhood(\n cell_ndindex: jnp.ndarray,\n d: int = 1) -> jnp.ndarray:\n with jax.ensure_compile_time_eval():\n sides_length = 1 + 2 * d\n dim = jnp.full(cell_ndindex.shape, sides_length)\n indices = jnp.flip(jnp.indices(dim), axis=0)\n n = cell_ndindex.shape[0]\n indices = jnp.transpose(indices.reshape(n, -1)) - d\n return cell_ndindex + indices","title":""},{"docid":"e3fe1f42ce98b5d7bd548f04462c74e5","score":"0.55598956","text":"def mk_dgnodes(self):\n dgn = np.zeros((self.master.nb, self.mesh.nElm))\n master_nodal_pts = np.squeeze(self.master.nodal_pts)\n for elm, elm_verts in enumerate(self.mesh.connectivity):\n elm_vert_pts = self.mesh.verts[elm_verts]\n elm_width = elm_vert_pts[1] - elm_vert_pts[0]\n mapped_pts = elm_vert_pts[0] + (1+master_nodal_pts)/2.*(elm_width)\n dgn[:, elm] = mapped_pts\n return dgn","title":""},{"docid":"b5cb37da91264d3060519db0526421b4","score":"0.5556699","text":"def build_rdn(coords, r, **kwargs):\n \n tree = BallTree(coords, **kwargs)\n ind = tree.query_radius(coords, r=r)\n # clean arrays of neighbors from self referencing neighbors\n # and aggregate at the same time\n source_nodes = []\n target_nodes = []\n for i, arr in enumerate(ind):\n neigh = arr[arr != i]\n source_nodes.append([i]*(neigh.size))\n target_nodes.append(neigh)\n # flatten arrays of arrays\n source_nodes = np.fromiter(itertools.chain.from_iterable(source_nodes), int).reshape(-1,1)\n target_nodes = np.fromiter(itertools.chain.from_iterable(target_nodes), int).reshape(-1,1)\n # remove duplicate pairs\n pairs = np.hstack((source_nodes, target_nodes))\n pairs = np.sort(pairs, axis=1)\n pairs = np.unique(pairs, axis=0)\n return pairs","title":""},{"docid":"90af4cf0e236bb2aa0877d2b2d760e96","score":"0.5525252","text":"def create_network(self):\n return Star(max_size=2)","title":""},{"docid":"c35af6d565a8ec39660e42c4c5a16fc3","score":"0.5488955","text":"def create_neighbors(self):\n for row in self._gridA:\n for cell in row:\n #\n # There are some nine situations that we have to account for:\n #\n # 1. upper left corner\n # 2. rest of the top row\n # 3. upper right corner\n # 4. far left side\n # 5. normal cells\n # 6. far right side\n # 7. lower left corner\n # 8. rest of bottom row\n # 9. lower right corner\n #\n row = cell.get_row()\n column = cell.get_column()\n #print(f'({row},{column})')\n # top row\n if row == 0:\n # 1. upper left corner\n if column == 0:\n #print('upper left')\n cell.add_neighbor(self._gridA[row][column + 1])\n cell.add_neighbor(self._gridA[row + 1][column])\n cell.add_neighbor(self._gridA[row + 1][column + 1])\n cell.add_neighbor(self._gridA[row][self._columns - 1])\n cell.add_neighbor(self._gridA[row + 1][self._columns - 1])\n cell.add_neighbor(self._gridA[self._rows - 1][column])\n cell.add_neighbor(self._gridA[self._rows - 1][column + 1])\n cell.add_neighbor(self._gridA[self._rows - 1][self._columns - 1])\n # 2. rest of the top row\n elif column < (self._columns - 1):\n #print('upper')\n cell.add_neighbor(self._gridA[row][column - 1])\n cell.add_neighbor(self._gridA[row][column + 1])\n cell.add_neighbor(self._gridA[row + 1][column - 1])\n cell.add_neighbor(self._gridA[row + 1][column])\n cell.add_neighbor(self._gridA[row + 1][column + 1])\n cell.add_neighbor(self._gridA[self._rows - 1][column - 1])\n cell.add_neighbor(self._gridA[self._rows - 1][column])\n cell.add_neighbor(self._gridA[self._rows - 1][column + 1])\n # upper right corner\n else:\n #print('upper right')\n cell.add_neighbor(self._gridA[row][column - 1])\n cell.add_neighbor(self._gridA[row + 1][column - 1])\n cell.add_neighbor(self._gridA[row + 1][column])\n cell.add_neighbor(self._gridA[0][0])\n cell.add_neighbor(self._gridA[1][0])\n cell.add_neighbor(self._gridA[self._rows - 1][self._columns - 1])\n cell.add_neighbor(self._gridA[self._rows - 1][self._columns - 2])\n cell.add_neighbor(self._gridA[0][self._columns - 1])\n # middle row\n elif row < (self._rows - 1):\n #1. middle left\n if column == 0:\n #print('middle left')\n cell.add_neighbor(self._gridA[row - 1][column])\n cell.add_neighbor(self._gridA[row - 1][column + 1])\n cell.add_neighbor(self._gridA[row][column + 1])\n cell.add_neighbor(self._gridA[row + 1][column])\n cell.add_neighbor(self._gridA[row + 1][column + 1])\n cell.add_neighbor(self._gridA[row][self._columns - 1])\n cell.add_neighbor(self._gridA[row - 1][self._columns - 1])\n cell.add_neighbor(self._gridA[row + 1][self._columns - 1])\n #2. the rest of the middle row (8 neighbors)\n elif column < (self._columns - 1):\n #print('middle')\n cell.add_neighbor(self._gridA[row - 1][column - 1])\n cell.add_neighbor(self._gridA[row - 1][column])\n cell.add_neighbor(self._gridA[row - 1][column + 1])\n cell.add_neighbor(self._gridA[row][column - 1])\n cell.add_neighbor(self._gridA[row][column + 1])\n cell.add_neighbor(self._gridA[row + 1][column - 1])\n cell.add_neighbor(self._gridA[row + 1][column])\n cell.add_neighbor(self._gridA[row + 1][column + 1])\n #3. middle right\n else:\n #print('middle right')\n cell.add_neighbor(self._gridA[row - 1][column])\n cell.add_neighbor(self._gridA[row - 1][column - 1])\n cell.add_neighbor(self._gridA[row][column - 1])\n cell.add_neighbor(self._gridA[row + 1][column])\n cell.add_neighbor(self._gridA[row + 1][column - 1])\n cell.add_neighbor(self._gridA[row][0])\n cell.add_neighbor(self._gridA[row - 1][0])\n cell.add_neighbor(self._gridA[row + 1][0])\n # bottom row\n else:\n # 1. lower left corner\n if column == 0:\n #print('lower left')\n cell.add_neighbor(self._gridA[row][column + 1])\n cell.add_neighbor(self._gridA[row - 1][column])\n cell.add_neighbor(self._gridA[row - 1][column + 1])\n cell.add_neighbor(self._gridA[self._rows - 2][self._columns - 1])\n cell.add_neighbor(self._gridA[self._rows - 1][self._columns - 1])\n cell.add_neighbor(self._gridA[0][0])\n cell.add_neighbor(self._gridA[0][1])\n cell.add_neighbor(self._gridA[0][self._columns - 1])\n # 2. rest of the bottom row\n elif column < (self._columns - 1):\n #print('lower')\n cell.add_neighbor(self._gridA[row][column - 1])\n cell.add_neighbor(self._gridA[row][column + 1])\n cell.add_neighbor(self._gridA[row - 1][column - 1])\n cell.add_neighbor(self._gridA[row - 1][column])\n cell.add_neighbor(self._gridA[row - 1][column + 1])\n cell.add_neighbor(self._gridA[0][column - 1])\n cell.add_neighbor(self._gridA[0][column])\n cell.add_neighbor(self._gridA[0][column + 1])\n # lower right corner\n else:\n #print('lower right')\n cell.add_neighbor(self._gridA[row][column - 1])\n cell.add_neighbor(self._gridA[row - 1][column - 1])\n cell.add_neighbor(self._gridA[row - 1][column])\n cell.add_neighbor(self._gridA[0][0])\n cell.add_neighbor(self._gridA[self._rows - 1][0])\n cell.add_neighbor(self._gridA[self._rows - 2][0])\n cell.add_neighbor(self._gridA[0][self._columns - 2])\n cell.add_neighbor(self._gridA[0][self._columns - 1])","title":""},{"docid":"135de9b64c38176dad29c8d46391433c","score":"0.54305255","text":"def ggpl_suburban_neighborhood():\n\tbase = CUBOID([62, 47, -5])\n\tbase = MATERIAL([.05,.05,.05,.05, .4,.2,0,1, 0,0,0,0, 0,0,0,1, 100])(base)\n\tgrass = CUBOID([60,45,0.1])\n\tgrass = MATERIAL([0,0,0,1, 0,.1,0,1, 0,.1,0,1, 0,0,0,1, 0])(grass)\n\tstreetsHeight = .5\n\tstreetsWidth = 4\n\tcurveStreetsWidth = streetsWidth - 0.7\n\n\t\n\ttennisField = generate_tennis_field(8, 6, 5)\n\ttennisField = T([1,2])([28,22.5])(tennisField)\n\t\n\tstraightStreet_1 = generate_straight_street(15.5, streetsWidth, streetsHeight, -PI/3.5, -3, 26)\n\tstraightStreet_2 = generate_straight_street(15, streetsWidth, streetsHeight, -PI/8, 47, 7)\n\tstraightStreet_3 = generate_straight_street(15, streetsWidth, streetsHeight, 0, 22, 30)\n\tstraightStreet_4 = generate_straight_street(8, streetsWidth -1, streetsHeight, PI/4, 9.5, 20)\n\n\tcurveStreet_1 = MAP(BEZIERCURVE([[9.5, 16], [15, 13], [27, 10], [35,11], [41, 12], [45,11], [50, 9]]))(INTERVALS(1)(32))\n\tcurveStreet_1 = OFFSET([curveStreetsWidth, curveStreetsWidth])(curveStreet_1)\n\tcurveStreet_1 = T([1,2])([-3.1,-1.9])(curveStreet_1)\n\tcurveStreet_1 = PROD([curveStreet_1, Q(streetsHeight)])\n\n\tcurveStreet_2 = MAP(BEZIERCURVE([[49,9], [47, 12], [49,18], [47,19], \n\t\t[47,20],[46,22], [45,24], [44,26],[43,28], [41,31], [39,32.5], [36,32.6]]))(INTERVALS(1)(32))\n\tcurveStreet_2 = OFFSET([curveStreetsWidth, curveStreetsWidth])(curveStreet_2)\n\tcurveStreet_2 = T([1,2])([-2,-2])(curveStreet_2)\n\tcurveStreet_2 = PROD([curveStreet_2, Q(streetsHeight)])\n\n\tcurveStreet_3 = MAP(BEZIERCURVE([[22,32.25],[21,31.5], [20,31], [19.5,30.5]]))(INTERVALS(1)(32))\n\tcurveStreet_3 = OFFSET([curveStreetsWidth, curveStreetsWidth])(curveStreet_3)\n\tcurveStreet_3 = T([1,2])([-2,-1.5])(curveStreet_3)\n\tcurveStreet_3 = PROD([curveStreet_3, Q(streetsHeight)])\n\n\trotary = CYLINDER([5, streetsHeight])(100)\n\trotary_sidewalker = CYLINDER([5.75, streetsHeight/2])(100)\n\trotary_center = CYLINDER([1, streetsHeight])(100)\n\trotary = DIFFERENCE([rotary, rotary_center])\n\trotary_sidewalker = DIFFERENCE([rotary_sidewalker, rotary_center])\n\trotary = T([1,2])([16,29])(rotary)\n\trotary_sidewalker = T([1,2])([16,29])(rotary_sidewalker)\n\n\t#generating houses\n\thouse_1 = generate_house(1, 5,6,5, PI/4, 7,22.5)\n\thouse_2 = generate_house(2, 8,6.75,8, PI/2.3, 24,14)\n\thouse_3 = generate_house(2, 8,14,9, PI/2, 43,13)\n\thouse_4 = generate_house(2, 8,13,8, PI/22, 51,9)\n\thouse_5 = generate_house(1, 8,8,6, PI/6, 48, 24)\n\thouse_6 = generate_house(1, 8,8,6, -PI/6, 36.5, 36.5)\t\t\t\n\thouse_7 = generate_house(2, 8,14,8, PI/2, 37, 35)\n\thouse_8 = generate_house(2, 8,8,8, PI/1.35, 15, 37)\n\n\t#adding walkways\n\twalkwayHeight = 0.15\n\thouse_1_walkway = generate_walkway(3.9,0.75,walkwayHeight, -PI/4, 5,25.6)\n\thouse_2_walkway = generate_walkway(1.25, 1.25,walkwayHeight, PI/2.3, 20.55,14.5)\n\thouse_3_walkway = generate_walkway(0.85, 2.2,walkwayHeight, PI/2, 35.65,13)\n\thouse_4_walkway = generate_walkway(1.5, 2,walkwayHeight, PI/22, 49.5,15.65)\n\thouse_5_walkway = MAP(BEZIERCURVE([[46, 24], [48.5, 24.5], [48, 26], [46.8, 28]]))(INTERVALS(1)(32))\n\thouse_5_walkway = OFFSET([1,1])(house_5_walkway)\n\thouse_5_walkway = PROD([house_5_walkway, Q(walkwayHeight)])\n\thouse_6_walkway = generate_walkway(5.5,1.2,walkwayHeight, PI/2.9, 37.95,34)\n\thouse_7_walkway = generate_walkway(1.25,2.3,walkwayHeight, PI/2, 29.7,34.6)\n\thouse_8_walkway = generate_walkway(1.7,1.5,walkwayHeight, PI/1.35, 12.6,33.6)\n\n\t#adding sidewalkers\n\tstraightStreet_1_sidewalker = generate_sidewalker(straightStreet_1)\n\tstraightStreet_2_sidewalker = generate_sidewalker(straightStreet_2)\n\tstraightStreet_3_sidewalker = generate_sidewalker(straightStreet_3)\n\tstraightStreet_4_sidewalker = generate_sidewalker(straightStreet_4)\n\tcurveStreet_1_sidewalker = generate_sidewalker(curveStreet_1)\n\tcurveStreet_2_sidewalker = generate_sidewalker(curveStreet_2)\n\tcurveStreet_3_sidewalker = generate_sidewalker(curveStreet_3)\n\n\t#cleaning borders\n\tcleanerLeftBorder = CUBOID([-5, 30, streetsHeight])\n\tcleanerRightBorder = CUBOID([5, 10, streetsHeight])\n\tcleanerRightBorder = T([1])([60])(cleanerRightBorder)\n\tstraightStreet_1 = DIFFERENCE([straightStreet_1, cleanerLeftBorder])\n\tstraightStreet_1_sidewalker = DIFFERENCE([straightStreet_1_sidewalker, cleanerLeftBorder])\n\tstraightStreet_2 = DIFFERENCE([straightStreet_2, cleanerRightBorder])\n\tstraightStreet_2_sidewalker = DIFFERENCE([straightStreet_2_sidewalker, cleanerRightBorder])\n\n\t#assembling\n\tstreets = STRUCT([straightStreet_1, straightStreet_2, straightStreet_3, straightStreet_4, curveStreet_1, curveStreet_2, curveStreet_3, rotary])\n\tsidewalkers = STRUCT([straightStreet_1_sidewalker, straightStreet_2_sidewalker, straightStreet_3_sidewalker, straightStreet_4_sidewalker, curveStreet_1_sidewalker, curveStreet_2_sidewalker, curveStreet_3_sidewalker, rotary_sidewalker])\n\tsidewalkers = T([3])([0.15])(sidewalkers)\n\twalkways = STRUCT([house_1_walkway, house_2_walkway, house_3_walkway, house_4_walkway, house_5_walkway, house_6_walkway, house_7_walkway, house_8_walkway])\n\tstreets = MATERIAL([.1,.1,.1,.2, 0,0,0,1, 0,0,0,1, 0,0,0,1, 10])(streets)\n\thouses = STRUCT([house_1, house_2, house_3, house_4, house_5, house_6, house_7, house_8])\n\tmodel = STRUCT([streets, grass, sidewalkers, houses, tennisField, walkways])\n\tmodel = T([1,2])([1,1])(model)\n\tVIEW(STRUCT([model, base]))","title":""},{"docid":"d55f309aa6638daf57be6cd2433290af","score":"0.542803","text":"def createNeighbours(self, electronTravelRange):\n\t\tself.neighbours = dict()\n\t\tfor index in range(self.N):\n\t\t\tindexNeighbours = list()\n\t\t\tpos1 = self.getPosition(index)\n\t\t\tfor i in range(self.N):\n\t\t\t\tpos2 = self.getPosition(i)\n\t\t\t\tdist = np.linalg.norm(pos1 - pos2)\n\t\t\t\tif dist <= electronTravelRange:\n\t\t\t\t\tindexNeighbours.append(i)\n\t\t\tself.neighbours[index] = np.array(indexNeighbours)","title":""},{"docid":"cd6c81f389b99425fd15cdce8eb82dd1","score":"0.540951","text":"def double2D(self):\n # First find the 2D direction\n for dim_index in range(3):\n if self.dims[dim_index] == 2:\n # Increase the size of the 2D dimension\n new_dimensions = self.dims[:]\n new_dimensions[dim_index] = new_dimensions[dim_index] + 1\n newCoords = np.zeros((new_dimensions[0], new_dimensions[1], new_dimensions[2], 3))\n\n if dim_index == 0:\n for i in range(self.dims[1]):\n for j in range(self.dims[2]):\n newCoords[0, i, j, :] = self.coords[0, i, j, :]\n newCoords[2, i, j, :] = self.coords[1, i, j, :]\n newCoords[1, i, j, :] = 0.5 * (newCoords[0, i, j, :] + newCoords[2, i, j, :])\n elif dim_index == 1:\n for i in range(self.dims[0]):\n for j in range(self.dims[2]):\n newCoords[i, 0, j, :] = self.coords[i, 0, j, :]\n newCoords[i, 2, j, :] = self.coords[i, 1, j, :]\n newCoords[i, 1, j, :] = 0.5 * (newCoords[i, 0, j, :] + newCoords[i, 2, j, :])\n elif dim_index == 2:\n for i in range(self.dims[0]):\n for j in range(self.dims[1]):\n newCoords[i, j, 0, :] = self.coords[i, j, 0, :]\n newCoords[i, j, 2, :] = self.coords[i, j, 1, :]\n newCoords[i, j, 1, :] = 0.5 * (newCoords[i, j, 0, :] + newCoords[i, j, 2, :])\n\n for boco in self.bocos:\n for j in range(2):\n if boco.ptRange[dim_index, j] == 2:\n boco.ptRange[dim_index, j] = 3\n for b2b in self.B2Bs:\n for j in range(2):\n if b2b.ptRange[dim_index, j] == 2:\n b2b.ptRange[dim_index, j] = 3\n if b2b.donorRange[dim_index, j] == 2:\n b2b.donorRange[dim_index, j] = 3\n\n # Replace previous coordinates\n self.coords = newCoords\n self.dims = new_dimensions[:]","title":""},{"docid":"0267bb606288ebd62f73628d7260228b","score":"0.5401753","text":"def add_bed_neighbors(bed, n_neighbor=40, sample_res=25):\n nrange = n_neighbor * sample_res\n neighbors = np.arange(-nrange, nrange + 1, sample_res)\n rs_tag = np.array([f';{x}' for x in neighbors])\n\n chr_n = np.repeat(bed['chr'].values[:, None], len(neighbors), axis=1)\n pos_n = bed['pos'].values[:, None] + neighbors[None, :]\n pos_end_n = pos_n + 1\n rs_n = bed['rs'].values[:, None] + rs_tag[None, :]\n\n neigh_bed = pd.DataFrame({'chr': chr_n.flatten(),\n 'pos': pos_n.flatten(),\n 'pos_end': pos_end_n.flatten(),\n 'rs': rs_n.flatten()})\n return neigh_bed","title":""},{"docid":"987772845ad741387018026239d1d8f7","score":"0.5394306","text":"def bond_neighborhoods(gra):\n bnd_keys = list(bond_keys(gra))\n\n def _neighborhood(bnd_key):\n nbh_bnd_keys = set(filter(lambda x: bnd_key & x, bnd_keys))\n return bond_induced_subgraph(gra, nbh_bnd_keys)\n\n bnd_nbh_dct = dict(zip(bnd_keys, map(_neighborhood, bnd_keys)))\n return bnd_nbh_dct","title":""},{"docid":"08754cdc9cfe6e920870008308d7a511","score":"0.53427255","text":"def w2dg(w):\n\n w_l = [(i,j) for i in w.neighbors for j in w[i]]\n G = nx.DiGraph()\n G.add_edges_from(w_l)\n return G","title":""},{"docid":"38b09026668fb412adfa7a05545fafbc","score":"0.5316365","text":"def create_init_wedge_if(self):\n coex_dens = self._cal_coex_dens()\n r = []\n for coex_i in coex_dens:\n startShape = self._size[1:]\n start = np.full(startShape, coex_i[0])\n stop = np.full(startShape, coex_i[1])\n r_i = np.linspace(start, stop, self._size[0], axis=0)\n r.append(r_i)\n r[1]=r[2]=np.full(self._size, 0)\n self.set_r(r)","title":""},{"docid":"df1a44e645455bf333c37d68a99a749b","score":"0.5285604","text":"def __init__(self, obj_in, sepsqr_in):\n this = _spilltree2D.new__NeighborInfo(obj_in, sepsqr_in)\n try:\n self.this.append(this)\n except Exception:\n self.this = this","title":""},{"docid":"6710863aa867507b85837288b8b6798a","score":"0.5273732","text":"def create_neighbours(self, orthogonal_only = 0):\n for i in xrange(self._n_cols):\n for j in xrange(self._n_rows):\n if not self.on_board(i,j):\n continue\n for k in (-1, 0, 1):\n for l in (-1, 0, 1):\n if k==l==0 or not self.on_board(i+k,j+l):\n continue\n if k != l and k != -l:\n self.grid[i][j].add_neighbour(self.grid[i+k][j+l])\n elif not orthogonal_only and k != 0 and l != 0:\n self.grid[i][j].add_neighbour(self.grid[i+k][j+l])","title":""},{"docid":"9201c6bcd1e33dbd134f038c21f44f09","score":"0.52707744","text":"def construct_four_nodes(self):\n self.four_nodes.make_neighbor(\"node1\", \"node2\")\n self.four_nodes.make_neighbor(\"node2\", \"node1\")\n self.four_nodes.make_neighbor(\"node1\", \"node3\")\n self.four_nodes.make_neighbor(\"node3\", \"node1\")\n\n self.four_nodes.make_neighbor(\"node1\", \"node4\")\n self.four_nodes.make_neighbor(\"node4\", \"node1\")\n self.four_nodes.make_neighbor(\"node2\", \"node4\")\n self.four_nodes.make_neighbor(\"node4\", \"node2\")\n self.four_nodes.make_neighbor(\"node3\", \"node4\")\n self.four_nodes.make_neighbor(\"node4\", \"node3\")","title":""},{"docid":"8763a72c2e7e03e7b6628136e9705f69","score":"0.5267199","text":"def __get_neighs(self, img: GrayImg, x: int, y: int, d: int, pad=False) -> Neighs:\n # Get new bounds and padding.\n x1, x2, padding_x = self.__check_bounds(x, d, img.shape[1])\n y1, y2, padding_y = self.__check_bounds(y, d, img.shape[0])\n\n # Create neighs.\n neighs = img[y1:y2, x1:x2]\n\n if pad:\n # Padding given the x, y values.\n padding = [padding_y, padding_x]\n # Pad neighs.\n val = img[y][x]\n neighs = np.pad(\n neighs, padding, mode=\"constant\", constant_values=(val, val)\n )\n\n return neighs","title":""},{"docid":"23001a1e990367a6373599be050264fd","score":"0.52653444","text":"def _nodal_gradient_stencil(self):\n # Compute divergence operator on faces\n if self.dim == 1:\n G = self._nodal_gradient_x_stencil\n elif self.dim == 2:\n G = sp.vstack(\n (self._nodal_gradient_x_stencil, self._nodal_gradient_y_stencil), format=\"csr\"\n )\n elif self.dim == 3:\n G = sp.vstack(\n (\n self._nodal_gradient_x_stencil,\n self._nodal_gradient_y_stencil,\n self._nodal_gradient_z_stencil,\n ),\n format=\"csr\",\n )\n return G","title":""},{"docid":"9639527171157286824846e6852fe115","score":"0.5261359","text":"def _nodal_gradient_stencil(self):\n # Compute divergence operator on faces\n if self.dim == 1:\n G = self._nodal_gradient_x_stencil\n elif self.dim == 2:\n G = sp.vstack(\n (self._nodal_gradient_x_stencil, self._nodal_gradient_y_stencil),\n format=\"csr\",\n )\n elif self.dim == 3:\n G = sp.vstack(\n (\n self._nodal_gradient_x_stencil,\n self._nodal_gradient_y_stencil,\n self._nodal_gradient_z_stencil,\n ),\n format=\"csr\",\n )\n return G","title":""},{"docid":"79d49375dbdcb16b523385dd8c2d23c4","score":"0.52481633","text":"def get_neighborhood(self, node_id):\n pass","title":""},{"docid":"2cc5cf1919ae7f576430bdeb112ae2af","score":"0.52372116","text":"def _nodal_gradient_z_stencil(self):\n if self.dim == 1 or self.dim == 2:\n return None\n else:\n Gz = kron3(\n ddx(self.shape_cells[2]),\n speye(self.shape_nodes[1]),\n speye(self.shape_nodes[0]),\n )\n return Gz","title":""},{"docid":"2cc5cf1919ae7f576430bdeb112ae2af","score":"0.52372116","text":"def _nodal_gradient_z_stencil(self):\n if self.dim == 1 or self.dim == 2:\n return None\n else:\n Gz = kron3(\n ddx(self.shape_cells[2]),\n speye(self.shape_nodes[1]),\n speye(self.shape_nodes[0]),\n )\n return Gz","title":""},{"docid":"ce621322559297ac70936d44d92a5f50","score":"0.52186394","text":"def _create_dbn(self):\n # Nodes in slice 1\n hidden_1 = NodeFactory.new_discrete_node(self.size_h, \"h1\") \n mus = numpy.zeros((self.size_h, 2))\n kappas = numpy.ones((self.size_h, 3)) * 0.5 \n dihedral_angles = NodeFactory.new_vonmises2d_node(\"d\", mus, kappas) \n amino_acids = NodeFactory.new_discrete_node(self.size_aa, \"a\")\n secondary_structure = NodeFactory.new_discrete_node(self.size_ss, \"s\")\n cis = NodeFactory.new_discrete_node(self.size_cis, \"c\")\n \n # Nodes in slice 2\n hidden_2 = NodeFactory.new_discrete_node(self.size_h, \"h2\")\n\n dbn = DBN() \n dbn.set_slices(\n [hidden_1, dihedral_angles, amino_acids, secondary_structure, cis],\n [hidden_2, dihedral_angles, amino_acids, secondary_structure, cis],\n ) \n dbn.add_inter(\"h1\", \"h2\")\n dbn.add_intra(\"h1\", \"d\")\n dbn.add_intra(\"h1\", \"a\")\n dbn.add_intra(\"h1\", \"s\")\n dbn.add_intra(\"h1\", \"c\")\n\n dbn.construct()\n return dbn","title":""},{"docid":"b091a1c1e219526a9437bd1f8af61259","score":"0.52170664","text":"def nd_new_game(dims, bombs):\n\n # fills boards\n b = nd_mkboard(dims, 0, 0)\n m = nd_mkboard(dims, False, 0)\n\n for coords in bombs:\n nd_set(b, coords, '.')\n neighbors = nd_neighbors({\"dimensions\": dims, \"board\" : b, \"mask\" : m, \"state\": \"ongoing\"}, coords)\n for n in neighbors:\n item = nd_get(b, n, 0)\n if item != '.':\n nd_set(b, n, item + 1)\n\n return {\"dimensions\": dims, \"board\" : b, \"mask\" : m, \"state\": \"ongoing\"}","title":""},{"docid":"ad8736bbc31ee424eaaa3104fdf42fb5","score":"0.52059615","text":"def __init__(self):\n self.starting_h = House(\"Skywalker\", self.skywalker_room())\n self.simpson_h = House(\"Simpson\", self.simpson_room())\n self.mccallister_h = House(\"McCallister\", self.mccallister_room())\n self.szalinski_h = House(\"Szalinski\", self.szalinski_room())\n self.addams_h = House(\"Addams\", self.addams_room())\n self.tanner_h = House(\"Tanner\", self.tanner_room())\n self.taylor_h = House(\"Taylor\", self.taylor_room())\n self.griswald_h = House(\"Griswald\", self.griswald_room())\n self.player_h = House(\"Player\", self.player_room())\n\n self.starting_h.add_neighbor(\"east\", self.simpson_h)\n self.simpson_h.add_neighbor(\"west\", self.starting_h)\n self.simpson_h.add_neighbor(\"south\", self.mccallister_h)\n self.mccallister_h.add_neighbor(\"north\", self.simpson_h)\n self.mccallister_h.add_neighbor(\"west\", self.szalinski_h)\n self.mccallister_h.add_neighbor(\"east\", self.addams_h)\n self.mccallister_h.add_neighbor(\"south\", self.tanner_h)\n self.szalinski_h.add_neighbor(\"east\", self.mccallister_h)\n self.addams_h.add_neighbor(\"west\", self.mccallister_h)\n self.tanner_h.add_neighbor(\"north\", self.mccallister_h)\n self.addams_h.add_neighbor(\"south\", self.taylor_h)\n self.taylor_h.add_neighbor(\"north\", self.addams_h)\n self.taylor_h.add_neighbor(\"west\", self.tanner_h)\n self.tanner_h.add_neighbor(\"east\", self.taylor_h)\n self.tanner_h.add_neighbor(\"south\", self.griswald_h)\n self.griswald_h.add_neighbor(\"north\", self.tanner_h)\n self.griswald_h.add_neighbor(\"west\", self.player_h)\n self.player_h.add_neighbor(\"east\", self.griswald_h)","title":""},{"docid":"be8726d35d93e70ba885b49084d294f8","score":"0.5192772","text":"def __init__(self, n, w, h, r, gs, nn):\n\n self.numCells = n\n self.width = w\n self.height = h\n self.avgRadius = r\n self.radiusDistIndex = gbl.radiusDistIndex\n self.radiiVec = np.random.normal(r, self.radiusDistIndex*r, n)\n self.centresVec = np.zeros((n*2))\n self.gradientsVec = np.zeros((n*2))\n self.numNeighbours = nn\n\n # Not sure if its necessary to initialise these pointers\n # here as they will be assigned to data by functions later\n self.KDTree = None\n self.nearestNeighboursVec = np.zeros((n, nn))\n self.nearestNeighbourDistanceVec = np.zeros((n, nn))\n self.nearestNeighbourGapVec = np.zeros((n, nn))\n\n # The following matrix is used by functions such as errorFunction\n # to keep x, y co-ordinates (self.centresVec) within the cell array\n # area defined by w, h\n # Only needs calculating once therefore do it at initialisation\n self.m = np.concatenate((np.repeat(w, n), np.repeat(h, n)))\n\n # set up a regular grid to be used for cell density\n # calculation\n self.gridSpacing = gs\n self.gridPoints = self.sequenceOfXYRegularGrid(self.gridSpacing)\n\n # Set radius of the circular area around each grid point to\n # ensure that all space is covered (circle edges touch when\n # self.areaRadiusFactor = 0.5*np.sqrt(2.0)\n self.areaRadiusFactor = 0.70710678118654757\n\n # Note: for good results ensure that grid spacing is large enough\n # ensure that:\n # gridSpacing * areaRadiusFactor > n * cellRadius\n # where n is at least 2\n\n self.cellDensitiesAtGridPoints = np.zeros((w/gs, h/gs))","title":""},{"docid":"bdc40a1a6f6d558c1299d239ccc58c2f","score":"0.5192499","text":"def build_neighbourhood_graph(edges):\n graph = networkx.Graph()\n edges = map( lambda e: [e[0],e[1],{'weight':e[2]}], edges)\n graph.add_edges_from(edges)\n return graph","title":""},{"docid":"259f023f4684e37128bb336a81c38333","score":"0.5181584","text":"def neighborhood(self):\n if self.DATA_FLAG:\n val_data = self.DATA.values.tolist()\n else:\n val_data = self.SAMPLE_DATA # for sample sets\n lrds = []\n reach_distances = []\n read_index1 = 0\n neighbors_dict = {}\n reduced_points = []\n for reading1 in val_data:\n # self.REDUCED_POINTS.append(visuals.dimension_reduction(reading1))\n self.REDUCED_POINTS.append(visuals.PCA_reduction(reading1))\n neighbors = {}\n neighbors_dict[read_index1] = []\n read_index2 = 0\n for reading2 in val_data:\n if read_index1 != read_index2:\n print(\"Reading indices: \" + str(read_index1) + \" \" + str(read_index2))\n distance = sum(abs(np.array(list(reading1)) - np.array(list(reading2))))\n distance = round(distance, ndigits=2)\n neighbors[read_index2] = distance\n read_index2 = read_index2 + 1\n sorted_temp = sorted(neighbors.items(), key=lambda kv: kv[1])\n neighbors = OrderedDict(sorted_temp)\n neighbors = list(itertools.islice(neighbors.items(), 0, self.K))\n # print(neighbors)\n for n in neighbors:\n neighbors_dict[read_index1].append(n)\n lrds.append(self.LRD(neighbors, self.K))\n read_index1 = read_index1 + 1\n\n return [lrds, neighbors_dict]","title":""},{"docid":"731951082d4326ba88e73490d6b0b3c1","score":"0.51571393","text":"def build_new_rings(self):\n\n self.other_complex = self.find_complex(self.unselected_partner)\n self.occupied_other = self.find_occupied(self.other_complex)\n self.possible_other = self.find_possible(self.other_complex)\n self.available_other = self.find_available(self.possible_other,\n self.occupied_other)\n self.possible_ring = []\n for site in self.available_other:\n self.possible_ring.append(site)\n # If the rule is already a ring closure, self.available_sites and\n # self.available_other will contain the same elements (but in a\n # different order). If the rule is not a ring closure,\n # self.available_sites and self.available_other will be completely\n # disjoint.\n # So, if I find at least one common element between\n # self.available_sites and self.available_other, it means that the\n # rule is already a ring closure.\n self.is_closure = False\n for site in self.available_sites:\n if site not in self.available_other:\n self.possible_ring.append(site)\n else:\n self.is_closure = True\n # No site from the chosen_partner should serve to close a ring.\n # It would form an intramolecular bond.\n auto_sites = []\n for site in self.possible_ring:\n if site[0] == self.selected_site[0]:\n auto_sites.append(site)\n for site in auto_sites:\n self.possible_ring.remove(site)\n # Find all possible rings.\n self.ring_matches = []\n for candidate in self.possible_ring:\n candidate_type = candidate[1]\n if (candidate[1].lower() == self.selected_site[3] and\n candidate[3] == self.selected_site[1].lower()):\n self.ring_matches.append(candidate)\n # Create a rule for each possible ring.\n rule_num = 3 # rule_free and rule_bound are numbers 1 and 2.\n self.rules_ring = []\n self.uni_rules_ring = []\n for ring_site in self.ring_matches:\n new_closure = False\n if self.is_closure == False and ring_site in self.available_other:\n new_closure = True\n ring_index = self.find_index(ring_site)\n agents_ring = self.new_agents()\n agents_ring = self.add_new_ring(ring_index, ring_site, agents_ring)\n name_ring = self.create_new_name(self.rule_name, rule_num)\n uni_name_ring = self.create_new_name(self.unary_name, rule_num)\n if new_closure == True:\n space = name_ring.rfind(\" \")\n name_ring = \"{} cls{}\".format(name_ring[:space],\n name_ring[space:])\n curl_open = self.unary_rate.index(\"{\")\n curl_close = self.unary_rate.index(\"}\")\n rate = self.unary_rate[curl_open+1:curl_close]\n else:\n rate = self.rule_rate\n rule_ring = self.create_new_rule(agents_ring, name_ring,\n rate)\n uni_rule_ring = self.create_new_rule(agents_ring, uni_name_ring,\n self.unary_rate)\n if new_closure == True:\n uni_rule_ring = \"//{}\".format(uni_rule_ring)\n rule_num += 1\n self.rules_ring.append(rule_ring)\n self.uni_rules_ring.append(uni_rule_ring)","title":""},{"docid":"d0fd6a3064793a3203e298d1b7195ba8","score":"0.5124115","text":"def get_double_grgrid_address(address, D_diag, PS=None):\n\n address_double = np.zeros(3, dtype='int_', order='C')\n if PS is None:\n _PS = np.zeros(3, dtype='int_')\n else:\n _PS = np.array(PS, dtype='int_')\n ksp.double_grgrid_address(address_double,\n np.array(address, dtype='int_'),\n np.array(D_diag, dtype='int_'),\n _PS)\n return address_double","title":""},{"docid":"1c7f57f1049325ac7d7e9150391c94a8","score":"0.5123845","text":"def suburban_neighborhood():\n\n\tpiano = CUBOID([540,180,3])\n\t#piano = MATERIAL([0,0.2,0,1, 0,0.2,0,1, 0,0.1,0,1, 0,0,0,1, 0])(piano)\n\t#TEXTURE([<string:nome file immagine>, <bool:boh?-Metto sempre a True>, <bool:boh?-Metto sempre a False>, <intero:boh?-Metto sempre 1>, <intero:boh?-Metto sempre 1>, <float: rotazione in gradi. Di solito 0 oppure -PI/2>, <numero ripetizioni su un asse>, <numero ripetizioni su altro asse>])\n\tpiano = TEXTURE([\"texture/grass.jpg\",True,False,1,1,0,30,15])(piano)\n\n\tstreets = []\n\tstreets = street1(streets)\n\tstreets = street2(streets)\n\n\tstreets = street3(streets)\n\tstreets = street4(streets)\n\n\tstreets = STRUCT(streets)\n\n\thouse1 = insertHouse(265,10,3,0,3,\"lines/house1/\",[1,2,3,1,3,1])\n\thouse2 = insertHouse(310,10,3,0,3,\"lines/house1/\",[1,2,3,1,3,1])\n\thouse3 = insertHouse(355,10,3,0,3,\"lines/house1/\",[1,2,3,1,3,1])\n\thouse4 = insertHouse(400,10,3,0,3,\"lines/house1/\",[1,2,3,1,3,1])\n\n\thouse5 = insertHouse(281,140,3,-PI/2,3,\"lines/house2/\",[1,2,1,2,4,3,3,1])\n\thouse6 = insertHouse(362,129,3,PI,3,\"lines/house1/\",[1,2,3,1,3,1])\n\thouse7 = insertHouse(400,122,3,PI,4,\"lines/house1/\",[1,2,3,1,3,1])\n\thouse8 = insertHouse(438,120,3,PI,4,\"lines/house1/\",[1,2,3,1,3,1])\n\n\thouse9 = insertHouse(241.5,62,3,0,3,\"lines/house2/\",[1,2,1,2,4,3,3,1])\n\n\thouses = STRUCT([house1,house2,house3,house4,house5,house6,house7,house8,house9])\n\n\t#trees = insertTrees()\n\ttrees1 = insertTrees2(60,5,54,20,105)\n\ttrees2 = insertTrees2(60,56,108,35,110)\n\ttrees3 = insertTrees2(40,110,162,45,110)\n\ttrees4 = insertTrees2(20,164,216,68,100)\n\n\ttrees5 = insertTrees2(17,270,300,67,77)\n\ttrees6 = insertTrees2(40,350,442,59,73)\n\ttrees7 = insertTrees2(4,324,334,66,72)\n\n\ttrees8 = insertTrees2(180,5,265,115,177)\n\n\ttrees = STRUCT([trees1,trees2,trees3,trees4,trees5,trees6,trees7,trees8])\n\n\tstreets = SOLIDIFY(streets)\n\tstreets = T([3])([3])(streets)\n\t#streets = TEXTURE([\"texture/streets.jpg\",True,False,1,1,0,100,100])(streets)\n\t#streets = MATERIAL([0.1,0.1,0.1,0.1, 0,0,0,1, 0,0,0,1, 0,0,0,1, 0])(streets)\n\tstreets = COLOR(Color4f([64/255., 64/255., 64/255., 1]))(streets)\n\n\tsuburban = STRUCT([piano,houses,trees,streets])\n\t#suburban = STRUCT([piano,streets])\n\treturn suburban","title":""},{"docid":"918b07b8677a71a29cfea81994a25ee3","score":"0.5119282","text":"def createGraph(pond, rowSize, columnSize):\n graph = Graph()\n for x in range(0, rowSize):\n for y in range(0, columnSize):\n if pond[x][y] != \"*\": # vertex for cell with a rock is not required\n src = makeVertexName(x, y)\n graph.addVertex(src)\n\n #possible path towards the right side of the cell\n if y != columnSize - 1:\n # go right\n rockIndex = -1\n for k in range(y + 1, columnSize):\n if pond[x][k] == \"*\":\n rockIndex = k\n break\n # if rock is found in the path, then connect the vertex\n # with cell prior to the rock otherwise, to the end of\n # path\n if rockIndex != -1:\n dest = makeVertexName(x, rockIndex - 1)\n else:\n dest = makeVertexName(x, columnSize - 1)\n graph.addEdge(src, dest) # make connection\n\n #possible path towards the left side of the cell\n if y != 0:\n # go left\n rockIndex = -1\n for k in range(y - 1, -1, -1):\n if pond[x][k] == \"*\":\n rockIndex = k\n break\n if rockIndex != -1:\n dest = makeVertexName(x, rockIndex + 1)\n else:\n dest = makeVertexName(x, 0)\n graph.addEdge(src, dest)\n\n #possible path going below the current cell\n if x != rowSize - 1:\n # go down\n rockIndex = -1\n for k in range(x + 1, rowSize):\n if pond[k][y] == \"*\":\n rockIndex = k\n break\n if rockIndex != -1:\n dest = makeVertexName(rockIndex - 1, y)\n else:\n dest = makeVertexName(columnSize - 1, y)\n graph.addEdge(src, dest)\n\n #possible path going above the current cell\n if x != 0:\n # go up\n rockIndex = -1\n for k in range(x - 1, -1, -1):\n if pond[k][y] == \"*\":\n rockIndex = k\n break\n if rockIndex != -1:\n dest = makeVertexName(rockIndex + 1, y)\n else:\n dest = makeVertexName(0, y)\n graph.addEdge(src, dest)\n return graph","title":""},{"docid":"048c767ccfc022c28f412f4e7305577a","score":"0.5111581","text":"def _nodal_gradient_y_stencil(self):\n if self.dim == 1:\n return None\n elif self.dim == 2:\n Gy = sp.kron(ddx(self.shape_cells[1]), speye(self.shape_nodes[0]))\n elif self.dim == 3:\n Gy = kron3(\n speye(self.shape_nodes[2]),\n ddx(self.shape_cells[1]),\n speye(self.shape_nodes[0]),\n )\n return Gy","title":""},{"docid":"048c767ccfc022c28f412f4e7305577a","score":"0.5111581","text":"def _nodal_gradient_y_stencil(self):\n if self.dim == 1:\n return None\n elif self.dim == 2:\n Gy = sp.kron(ddx(self.shape_cells[1]), speye(self.shape_nodes[0]))\n elif self.dim == 3:\n Gy = kron3(\n speye(self.shape_nodes[2]),\n ddx(self.shape_cells[1]),\n speye(self.shape_nodes[0]),\n )\n return Gy","title":""},{"docid":"cba368c2beff54262700ac46196339dd","score":"0.51020604","text":"def fill_neibor(self):\n for node in self.nodes.values():\n for coord in ((node.x + 1, node.y), (node.x, node.y + 1)):\n neibor = self.nodes.get(coord)\n if neibor:\n neibor.add_neibor(node)\n node.add_neibor(neibor)\n return self","title":""},{"docid":"05f3ca29e257ed48f2ead4d9548c1f83","score":"0.5074569","text":"def __init__(self, structure, bonding_strategy='MinimumOKeeffeNN',\n bonding_strategy_kwargs=None,\n color_scheme=\"VESTA\", color_scale=None,\n radius_strategy=\"ionic\",\n coordination_polyhedra=False,\n draw_image_atoms=True,\n repeat_of_atoms_on_boundaries=True,\n bonded_atoms_outside_unit_cell=True,\n scale=None):\n\n # draw periodic repeats if requested\n if scale:\n structure = structure * scale\n\n # we assume most uses of this class will give a structure as an input argument,\n # meaning we have to calculate the graph for bonding information, however if\n # the graph is already known and supplied, we will use that\n if not isinstance(structure, StructureGraph):\n if bonding_strategy not in self.allowed_bonding_strategies.keys():\n raise ValueError(\"Bonding strategy not supported. Please supply a name \"\n \"of a NearNeighbor subclass, choose from: {}\"\n .format(\", \".join(self.allowed_bonding_strategies.keys())))\n else:\n bonding_strategy_kwargs = bonding_strategy_kwargs or {}\n bonding_strategy = self.allowed_bonding_strategies[bonding_strategy](**bonding_strategy_kwargs)\n self.structure_graph = StructureGraph.with_local_env_strategy(structure,\n bonding_strategy,\n decorate=False)\n self.lattice = structure.lattice\n else:\n self.structure_graph = structure\n self.lattice = self.structure_graph.structure.lattice\n\n if coordination_polyhedra:\n # TODO: coming soon\n raise NotImplementedError\n\n self.color_scheme = color_scheme\n self.color_scale = color_scale\n self.radius_strategy = radius_strategy\n self.coordination_polyhedra = coordination_polyhedra\n self.draw_image_atoms = draw_image_atoms\n self.repeat_of_atoms_on_boundaries = repeat_of_atoms_on_boundaries\n self.bonded_atoms_outside_unit_cell = bonded_atoms_outside_unit_cell","title":""},{"docid":"41057d26da1d8737a25f2953e2510d0a","score":"0.50729245","text":"def get_double_grgrid_index(address_double, D_diag, PS=None):\n if PS is None:\n _PS = np.zeros(3, dtype='int_')\n else:\n _PS = np.array(PS, dtype='int_')\n return ksp.double_grgrid_index(np.array(address_double, dtype='int_'),\n np.array(D_diag, dtype='int_'),\n _PS)","title":""},{"docid":"75f6b10d9e50c56fe9142b12379bf488","score":"0.50646824","text":"def fill_2d_holes(slide):\n # Create a cross structural element\n structure = ndimage.morphology.generate_binary_structure(2, 1)\n \n # Fill holes\n filled_slide = ndimage.binary_fill_holes(slide, structure).astype(\n slide.dtype)\n\n return filled_slide","title":""},{"docid":"ed8f62aecd47e763b62c1e4d4fa463ad","score":"0.50445634","text":"def calc_double_connections(self, former_layer, index):\r\n layer = []\r\n new_bond_num = int(self.bonds[-1][0]) + 1\r\n self.molecule_depth += 1\r\n self.curr_layer_num += 1\r\n first_atom = former_layer[0]\r\n second_atom = former_layer[1]\r\n nearest_atom = self.levels_atoms[index]\r\n self.make_new_bond(new_bond_num, first_atom[0], nearest_atom[0][0], 2)\r\n new_bond_num += 1\r\n self.make_new_bond(new_bond_num, second_atom[0], nearest_atom[0][0], 2)\r\n layer.append(nearest_atom[0])\r\n self.connected_atoms.append(nearest_atom[0])\r\n index += 1\r\n return layer, index","title":""},{"docid":"dee6bd55ca3cf2f7ff3dc0a92d208b55","score":"0.5040204","text":"def get_radius_2d(binary_image, skeleton_image, boundary_image, pix_size=None):\n skeleton_image_copy = copy.deepcopy(skeleton_image)\n skeleton_image_copy[skeleton_image == 0] = 255\n skeleton_image_copy[boundary_image == 1] = 0\n eucledian_radius_image = ndimage.distance_transform_bf(skeleton_image_copy, metric='taxicab', sampling=pix_size)\n list_nzi = map(tuple, np.transpose(np.nonzero(skeleton_image)))\n dict_nodes_radius = {item: eucledian_radius_image[item] for item in list_nzi}\n return dict_nodes_radius","title":""},{"docid":"71858bd78a5c800a921cc9d80911f37e","score":"0.5033043","text":"def door2D (Ylist,Zlist,occupancy):\n def door2D0(dy,dz):\n \"\"\"\n Args:\n dy (numero reale): dimensione massima su y del solido\n dz (numero reale): dimensione massima su z del solido\n \n Returns:\n structure (oggetto hpc): porta o finestra riprodotta\n \"\"\"\n \n Y = adaptListToDimension(Ylist,dy)\n Z = adaptListToDimension(Zlist,dz)\n \n occ = occupancy\n \n structure = STRUCT([CUBOID([0,0,0])])\n\n sumZ = 0\n i = 0\n \n for row in occ :\n tmpY = []\n j=0\n for val in row:\n \n if val: tmpY.append(Y[j])\n else: tmpY.append(-Y[j])\n j+=1\n \n qy = QUOTE(tmpY)\n qz = QUOTE([Z[i]])\n pxy = PROD([QUOTE([0]),qy])\n pyz = PROD([pxy,qz])\n \n structure = STRUCT([structure,T([3])([sumZ]),pyz])\n sumZ += Z[i]\n i += 1\n \n return structure\n return door2D0","title":""},{"docid":"6bcc685828d0495ad910b79c7e0b60a3","score":"0.5010403","text":"def get_neighboring_zone_generic(zone_number: int, zone_letter: str, border: str) -> dict:\n\n if (zone_letter == \"X\" and \"N\" in border) or (zone_letter == \"C\" and \"S\" in border):\n return {}\n\n neighbor_borders = []\n neighbor_number = zone_number\n neighbor_letter = zone_letter\n\n if \"N\" in border:\n neighbor_letter = ZONE_LETTERS[ZONE_LETTERS.index(zone_letter.upper())+1]\n neighbor_borders.append(\"S\")\n\n if \"S\" in border:\n neighbor_letter = ZONE_LETTERS[ZONE_LETTERS.index(zone_letter.upper())-1]\n neighbor_borders.append(\"N\")\n\n if \"W\" in border:\n neighbor_number = 60 if zone_number == 1 else zone_number - 1\n neighbor_borders.append(\"E\")\n\n if \"E\" in border:\n neighbor_number = (zone_number + 1) % 60\n neighbor_borders.append(\"W\")\n\n return {(neighbor_number, neighbor_letter): neighbor_borders}","title":""},{"docid":"b1daae0035d848bc0d1b294f1d0f9420","score":"0.50021625","text":"def generateNeighborCoordinates(backbone, atoms, bonds, coordinates, ringSystems):\n\n for i in range(len(backbone)):\n atom0 = backbone[i]\n index0 = atoms.index(atom0)\n\n # Determine bond angles of all previously-determined bond locations for\n # this atom\n bondAngles = []\n for atom1 in bonds[atom0]:\n index1 = atoms.index(atom1)\n if atom1 in backbone:\n vector = coordinates[index1,:] - coordinates[index0,:]\n angle = math.atan2(vector[1], vector[0])\n bondAngles.append(angle)\n bondAngles.sort()\n \n bestAngle = 2 * math.pi / len(bonds[atom0])\n regular = True\n for angle1, angle2 in zip(bondAngles[0:-1], bondAngles[1:]):\n if all([abs(angle2 - angle1 - (i+1) * bestAngle) > 1e-4 for i in range(len(bonds[atom0]))]):\n regular = False\n\n if regular:\n # All the bonds around each atom are equally spaced\n # We just need to fill in the missing bond locations\n\n # Determine rotation angle and matrix\n rot = numpy.array([[math.cos(bestAngle), -math.sin(bestAngle)], [math.sin(bestAngle), math.cos(bestAngle)]], numpy.float64)\n # Determine the vector of any currently-existing bond from this atom\n vector = None\n for atom1 in bonds[atom0]:\n index1 = atoms.index(atom1)\n if atom1 in backbone or numpy.linalg.norm(coordinates[index1,:]) > 1e-4:\n vector = coordinates[index1,:] - coordinates[index0,:]\n\n # Iterate through each neighboring atom to this backbone atom\n # If the neighbor is not in the backbone and does not yet have\n # coordinates, then we need to determine coordinates for it\n for atom1 in bonds[atom0]:\n if atom1 not in backbone and numpy.linalg.norm(coordinates[atoms.index(atom1),:]) < 1e-4:\n occupied = True; count = 0\n # Rotate vector until we find an unoccupied location\n while occupied and count < len(bonds[atom0]):\n count += 1; occupied = False\n vector = numpy.dot(rot, vector)\n for atom2 in bonds[atom0]:\n index2 = atoms.index(atom2)\n if numpy.linalg.norm(coordinates[index2,:] - coordinates[index0,:] - vector) < 1e-4:\n occupied = True\n coordinates[atoms.index(atom1),:] = coordinates[index0,:] + vector\n generateFunctionalGroupCoordinates(atom0, atom1, atoms, bonds, coordinates, ringSystems)\n\n else:\n\n # The bonds are not evenly spaced (e.g. due to a ring)\n # We place all of the remaining bonds evenly over the reflex angle\n startAngle = max(bondAngles)\n endAngle = min(bondAngles)\n if 0.0 < endAngle - startAngle < math.pi: endAngle += 2 * math.pi\n elif 0.0 > endAngle - startAngle > -math.pi: startAngle -= 2 * math.pi\n dAngle = (endAngle - startAngle) / (len(bonds[atom0]) - len(bondAngles) + 1)\n \n index = 1\n for atom1 in bonds[atom0]:\n if atom1 not in backbone and numpy.linalg.norm(coordinates[atoms.index(atom1),:]) < 1e-4:\n angle = startAngle + index * dAngle\n index += 1\n vector = numpy.array([math.cos(angle), math.sin(angle)], numpy.float64)\n vector /= numpy.linalg.norm(vector)\n coordinates[atoms.index(atom1),:] = coordinates[index0,:] + vector\n generateFunctionalGroupCoordinates(atom0, atom1, atoms, bonds, coordinates, ringSystems)","title":""},{"docid":"6613052e6955c5df37ed89f5640e89e6","score":"0.49973366","text":"def make_new_bond(self, new_bond_num, source, dest, bond_pair_num):\r\n if bond_pair_num > 0:\r\n bond_type = 2\r\n else:\r\n bond_type = 1\r\n self.bonds.append([new_bond_num, source, dest, bond_type])\r\n new_bond_num += 1\r\n self.connected_atoms.append(source)\r\n self.connected_atoms.append(dest)\r\n return new_bond_num","title":""},{"docid":"ced57f2aed6284d7c242c17869327f7f","score":"0.49895075","text":"def _get_neighborhood(self, point_index, num_neighbors):\n if 0 <= point_index < self.get_size():\n neighborhood_index_increments = range(-num_neighbors, 0, 1) + range(1, num_neighbors + 1, 1)\n neighborhood_indices = [(point_index + increment) % self.get_size() for increment in\n neighborhood_index_increments]\n return np.array([self._data[index] for index in neighborhood_indices])\n return np.array([])","title":""},{"docid":"b165ecb5f935ce5b1420e1ced3296d3c","score":"0.49825335","text":"def get_neighborhood(self, x, d):\n assert d == 1, \"Sum(x) supports neighbourhood with diameter = 1 only\"\n nd = []\n for i in np.arange(self.n):\n if x[i] > self.a[i]:\n xx = x.copy()\n xx[i] -= 1\n nd.append(xx)\n if x[i] < self.b[i]:\n xx = x.copy()\n xx[i] += 1\n nd.append(xx)\n return nd","title":""},{"docid":"f695cb26daa5ef7c3117211764f61298","score":"0.49770507","text":"def __init__(self, *args, **kwargs):\n _fe.ND_QuadrilateralElement_swiginit(self, _fe.new_ND_QuadrilateralElement(*args, **kwargs))","title":""},{"docid":"163db620fc2b60cc331c44f430f6e055","score":"0.49753782","text":"def generate_sidewalker(street):\n\tleftSidewalker = OFFSET([0.75,0.75])(street)\n\tleftSidewalker = S([3])([SIZE([3])(street)[0]/2])(leftSidewalker)\n\trightSidewalker = OFFSET([-0.75,-0.75])(street)\n\trightSidewalker = S([3])([SIZE([3])(street)[0]/2])(rightSidewalker)\n\treturn STRUCT([leftSidewalker, rightSidewalker])","title":""},{"docid":"f907c17bac8be8aa59da8a23943994f2","score":"0.49295393","text":"def _nodal_gradient_x_stencil(self):\n if self.dim == 1:\n Gx = ddx(self.shape_cells[0])\n elif self.dim == 2:\n Gx = sp.kron(speye(self.shape_nodes[1]), ddx(self.shape_cells[0]))\n elif self.dim == 3:\n Gx = kron3(\n speye(self.shape_nodes[2]),\n speye(self.shape_nodes[1]),\n ddx(self.shape_cells[0]),\n )\n return Gx","title":""},{"docid":"f907c17bac8be8aa59da8a23943994f2","score":"0.49295393","text":"def _nodal_gradient_x_stencil(self):\n if self.dim == 1:\n Gx = ddx(self.shape_cells[0])\n elif self.dim == 2:\n Gx = sp.kron(speye(self.shape_nodes[1]), ddx(self.shape_cells[0]))\n elif self.dim == 3:\n Gx = kron3(\n speye(self.shape_nodes[2]),\n speye(self.shape_nodes[1]),\n ddx(self.shape_cells[0]),\n )\n return Gx","title":""},{"docid":"451b118d31f0438ba331a913f4b0e57e","score":"0.49265325","text":"def create_neighbor_lookup(nearest_neighbors):\n nn_lookup = {}\n for i in range(nearest_neighbors.shape[0]):\n nn_lookup[i] = nearest_neighbors[i, :]\n # print \"NN Lookup :\",nn_lookup\n return nn_lookup","title":""},{"docid":"eb0a51f5c492528e021733fcb77ca2e7","score":"0.4921538","text":"def count_neighbors_donut(self):\n for row in self.cells:\n for cell in row:\n #\n # Creates a list of locations for possible neighbors\n #\n neighbors = [(-1, -1), (-1, 0), (-1, 1),\n (0, -1), (0, 1),\n (1, -1), (1, 0), (1, 1)]\n cell.neighbors = []\n for neighbor in neighbors:\n #\n # Sees if location is a normal neighbor\n # If not it wraps around, like a torus/donut, and creates a neighbor from there\n #\n try:\n x = (cell.row + neighbor[0])\n y = (cell.column + neighbor[1])\n cell.neighbors.append(self.cells[x][y])\n except:\n x = (cell.row + neighbor[0]) % self.rows\n y = (cell.column + neighbor[1]) % self.columns\n cell.neighbors.append(self.cells[x][y])","title":""},{"docid":"4e91efcaa14f93293733f72328e86523","score":"0.49212787","text":"def neighbor_factory(xs, φ, k=5, slice_index=None, size=None):\n import torch\n from tqdm import trange\n from ml_logger import logger\n\n with torch.no_grad():\n if Args.load_neighbors:\n _ = logger.load_pkl(Args.load_neighbors)\n top_ds = _['top']\n inds = _['inds']\n elif Args.load_pairwise_ds:\n with logger.PrefixContext(Args.load_pairwise_ds):\n _ds = np.concatenate([logger.load_pkl(f\"chunk_{k:02d}.pkl\")[0] for k in trange(20, desc=\"load\")])\n ds = torch.tensor(_ds, dtype=torch.float32)[:size, :size]\n ds[torch.eye(len(ds), dtype=torch.uint8)] = float('inf')\n for k in range(1, 3): # add true neighbors\n diag = torch.diagflat(torch.ones(len(ds) - k, dtype=torch.uint8), k)\n ds[diag] = 0.5\n diag = torch.diagflat(torch.ones(len(ds) - k, dtype=torch.uint8), -k)\n ds[diag] = 0.5\n # with torch.no_grad():\n # _ds = torch.tensor(ds)\n # _ds[torch.eye(ds.shape[0], dtype=torch.uint8)] = float('inf')\n # _ = torch.topk(_ds, k=24, dim=1, largest=False, sorted=True)\n # top_ds = _[0].numpy()\n # inds = _[1].numpy()\n\n # # add threshold connection instead.\n full_range = torch.arange(len(ds))\n inds = [None] * len(ds)\n for idx, row in enumerate(ds):\n visited = []\n inds[idx] = full_range[row <= Args.term_r].numpy()\n if len(inds[idx]) == 0:\n raise Exception('term_r too small')\n break\n for i in range(1):\n new_neighbors = [full_range[ds[id] <= Args.term_r].numpy() for id in inds[idx] if id not in visited]\n if len(new_neighbors) > 0:\n neighbors = np.concatenate(new_neighbors)\n else:\n neighbors = []\n visited += list(inds[idx])\n inds[idx] = np.unique(np.concatenate([inds[idx], neighbors]))\n inds[idx] = inds[idx][inds[idx] != idx] # remove identity\n inds = np.array(inds)\n top_ds = np.array([row[_] for row, _ in zip(ds.numpy(), inds)])\n logger.log_data(data=dict(top=top_ds, inds=inds), path=\"top_ds.npy\")\n elif Args.load_top_k:\n # _ = logger.load_pkl(Args.load_top_k)[0]\n # top_ds = _['top']\n # inds = _['inds']\n k = 24\n with logger.PrefixContext(Args.load_top_k):\n _ds = np.concatenate([logger.load_pkl(f\"chunk_{k:02d}.pkl\")[0] for k in trange(20, desc=\"load\")])\n ds = torch.tensor(_ds, dtype=torch.float32)[:size, :size]\n ds[torch.eye(len(ds), dtype=torch.uint8)] = float('inf')\n with torch.no_grad():\n _ds = torch.tensor(ds)[:size, :size]\n _ds[torch.eye(ds.shape[0], dtype=torch.uint8)] = float('inf')\n top_ds, inds = torch.topk(_ds, k=k, dim=1, largest=False, sorted=True)\n else:\n raise Exception(\"Need to specify pre-computed pairwise distance matrix, or top-k\")\n\n\n def N(x_indices, visited=None):\n \"\"\"Query the neighbors using the indices\n\n :param x_indices: Size(m,)\n :return: x_next, dstance, indices\n with shape: Size(B, k, 2), Size(B\n \"\"\"\n nonlocal xs, top_ds, inds\n\n if visited is not None:\n curr_inds = [[ind for ind in inds[_] if ind not in visited[idx]] for idx, _ in enumerate(x_indices)]\n else:\n curr_inds = [inds[_] for _ in x_indices]\n return zip(*[(xs[curr_inds[idx]], top_ds[_], curr_inds[idx]) for idx, _ in enumerate(x_indices)])\n\n return N","title":""},{"docid":"ac2eb0099a4bf8dd5dd575b2bd88e9c7","score":"0.4917294","text":"def build2DHP(Window, Dim, Diameter=0, Outer=False, Cont=False):\n return np.ones(Dim)-build2DLP(Window, Dim, Diameter, Outer, Cont)","title":""},{"docid":"50a46d990d92334805bfd77f03d91249","score":"0.49166223","text":"def nxn(\n west: int = 1,\n east: int = 4,\n north: int = 0,\n south: int = 0,\n xsize: float = 8.0,\n ysize: float = 8.0,\n wg_width: float = 0.5,\n layer=pp.LAYER.WG,\n wg_margin: float = 1.0,\n):\n c = pp.Component()\n c << pp.c.rectangle(size=(xsize, ysize), layer=layer)\n\n for i in [west, north, south, east]:\n if not isinstance(i, int):\n raise ValueError(\n f\"west={west}, east={east}, north={north}, south={south} needs to be integers\"\n )\n\n if west > 0:\n x = 0\n y = (\n [ysize / 2]\n if west == 1\n else np.linspace(\n wg_margin + wg_width / 2, ysize - wg_margin - wg_width / 2, west\n )\n )\n y = pp.drc.snap_to_1nm_grid(y)\n orientation = 180\n\n for i, y in enumerate(y):\n c.add_port(\n f\"W{i}\",\n midpoint=(x, y),\n width=wg_width,\n orientation=orientation,\n layer=layer,\n )\n\n if east > 0:\n x = xsize\n y = (\n [ysize / 2]\n if east == 1\n else np.linspace(\n wg_margin + wg_width / 2, ysize - wg_margin - wg_width / 2, east\n )\n )\n y = pp.drc.snap_to_1nm_grid(y)\n orientation = 0\n\n for i, y in enumerate(y):\n c.add_port(\n f\"E{i}\",\n midpoint=(x, y),\n width=wg_width,\n orientation=orientation,\n layer=layer,\n )\n\n if north > 0:\n y = ysize\n x = (\n [xsize / 2]\n if north == 1\n else np.linspace(\n wg_margin + wg_width / 2, xsize - wg_margin - wg_width / 2, north\n )\n )\n x = pp.drc.snap_to_1nm_grid(x)\n orientation = 90\n\n for i, x in enumerate(x):\n c.add_port(\n f\"N{i}\",\n midpoint=(x, y),\n width=wg_width,\n orientation=orientation,\n layer=layer,\n )\n if south > 0:\n y = 0\n x = (\n [xsize / 2]\n if south == 1\n else np.linspace(\n wg_margin + wg_width / 2, xsize - wg_margin - wg_width / 2, south\n )\n )\n x = pp.drc.snap_to_1nm_grid(x)\n orientation = 270\n\n for i, x in enumerate(x):\n c.add_port(\n f\"S{i}\",\n midpoint=(x, y),\n width=wg_width,\n orientation=orientation,\n layer=layer,\n )\n\n return c","title":""},{"docid":"21026c8e3cad8d570ac19ac2f45f2c92","score":"0.4913781","text":"def make_ring(inner_radius: int, outer_radius: int,\n dtype: str='uint8'\n) -> np.ndarray:\n # if the radius is even, make it odd\n length = 2 * outer_radius + 1\n # create an RGBA box to house the circle in\n box = np.zeros((length, length), dtype=dtype)\n # get the coordinates for the outer circle\n x_pixels, y_pixels = draw_circle(outer_radius, outer_radius, outer_radius)\n # set the coordinates of the circle in the box\n box[x_pixels, y_pixels] = 1\n # get the coordinates for the inner circle\n x_pixels, y_pixels = draw_circle(outer_radius, outer_radius, inner_radius)\n # set the coordinates of the circle in the box\n box[x_pixels, y_pixels] = 0\n\n return box","title":""},{"docid":"26aeaa01a8208d34fdaa4f1ce3856e9d","score":"0.48986673","text":"def nd_neighbors(game, coords):\n list = []\n for n in range(len(coords)):\n t = ()\n for i in range(coords[n]-1, coords[n]+2):\n if 0 <= i < game[\"dimensions\"][n]:\n t = t + (i,)\n list.append(t)\n return nd_product(list)","title":""},{"docid":"af5d9065cb25602c30cba34b8b06cfb1","score":"0.48982462","text":"def __MakeDdoubleStar2DstarPi__(self):\n \n from GaudiConfUtils.ConfigurableGenerators import CombineParticles\n from PhysSelPython.Wrappers import Selection, DataOnDemand\n from StandardParticles import StdLoosePions\n \n \n CombDdoubleStar2DstarPi = CombineParticles( \n DecayDescriptors = [\"[D_1(2420)0 -> D*(2010)+ pi-]cc\", \"[D*_2(2460)0 -> D*(2010)+ pi-]cc\" ],\n DaughtersCuts = { \"pi-\" : self.D1_PionCut },\n MotherCut = self.D1Cut,\n )\n \n SelDdoubleStar2DstarPi = Selection(\"SelDdoubleStar2DstarPi\", Algorithm=CombDdoubleStar2DstarPi,\n RequiredSelections = [self.DstarSel,StdLoosePions])\n \n self.DdoubleStar2DstarPiSel=SelDdoubleStar2DstarPi","title":""},{"docid":"476717d1195eab8107f085b535db39cb","score":"0.48957065","text":"def __init__(self, nnodes: int,\n starting_neighbor_d: int = 5,\n neighborhood_decay_fn: Callable[[int, int, int], int] =\n lambda x: x - 1):\n\n self._nnodes = nnodes\n self._d0 = starting_neighbor_d\n self._d = starting_neighbor_d\n self._decay_fn = neighborhood_decay_fn","title":""},{"docid":"aaf4ba033df73fbcfe609ec7fbb23b9e","score":"0.48877","text":"def make_ws_graph(num_nodes, clockwise_neighbours, rewiring_prob):\n #initialize empty graph\n ws_graph = {}\n for vertex in range(num_nodes): ws_graph[vertex] = []\n #add each vertex to clockwise neighbours\n for vertex in range(num_nodes):\n for neighbour in range(vertex + 1, vertex + clockwise_neighbours + 1):\n neighbour = neighbour % num_nodes\n ws_graph[vertex] += [neighbour]\n ws_graph[neighbour] += [vertex]\n for vertex in range(num_nodes):\n for neighbour in ws_graph[vertex]:\n if random.random() < rewiring_prob:\n ws_graph[vertex].remove(neighbour)\n ws_graph[neighbour].remove(vertex)\n randNode = random.randint(0, num_nodes-1)\n while(vertex == randNode):\n randNode = random.randint(0, num_nodes - 1)\n ws_graph[vertex] += [randNode]\n ws_graph[randNode] += [vertex]","title":""},{"docid":"0e87e04138f71aed9e208aff6b5b2f2a","score":"0.48779523","text":"def obj_get_boundary_method1(labelled_array, fill_holes=True):\n\n dims = labelled_array.shape\n rows = dims[0]\n cols = dims[1]\n\n if fill_holes:\n orig_binary = (labelled_array > 0).astype('uint8')\n fill = obj_fill_holes(labelled_array)\n s = [[1,1,1],[1,1,1],[1,1,1]]\n labelled_array, nlabels = ndimage.label(fill, structure=s)\n\n # We'll opt for the perimeter co-ordinates to be ordered in a clockwise fashion. GIS convention???\n pix_directions = numpy.array([[ 0, 1],\n [ 1, 1],\n [ 1, 0],\n [ 1,-1],\n [ 0,-1],\n [-1,-1],\n [-1, 0],\n [-1, 1]])\n\n # Set up the distances as we traverse across a pixel\n diag = numpy.sqrt(2.0) # NumPy will return a float64, but just in case future versions change....\n pix_distances = {0 : 1.0,\n 1 : diag,\n 2 : 1.0,\n 3 : diag,\n 4 : 1.0,\n 5 : diag,\n 6 : 1.0,\n 7, diag\n }\n\n # Determine the co-ordinates (indices) of each segement\n # The first index of each segment will be used to define the start and end of a boundary/perimeter\n h = histogram(labelled_array.flatten(), Min=1, reverse_indices='ri')\n hist = h['histogram']\n ri = h['ri']\n nlabels = hist.shape[0]\n seg_start_idxs = numpy.zeros(nlabels, dtype='int')\n\n # Boundary or perimeter ?? Will go with perimeter, akin to a method implement earlier which uses a\n # convolution operator to determine perimeter length.\n\n # Obtain the start indices of each segment/object\n for i in numpy.arange(nlabels):\n #if (hist[i] == 0): # The labeled array should be consecutive\n # continue\n seg_start_idxs[i] = ri[ri[i]:ri[i+1]][0] # Return the first index\n\n # Convert the 1D indices to 2D indices used by NumPy\n seg_start_idxs = array_indices(dims, seg_start_idxs, dimensions=True)\n\n # Lots to figure out here. Dealing with 'from' and 'too' directions can make things confusing\n # Keep track of the direction we last travelled, that way we can start at the next clockwise direction\n # For single pixel objects or 'islands' use the histogram value to skip the follow boundary/search routine\n\n \"\"\"\n The memory of the order of the array should be 'C-Style': column, column, column, then next row.\n\n eg a 2x5 array\n\n 0, 1, 2, 3, 4\n 5, 6, 7, 8, 9\n\n Therefore the first index will only have labels to the right and below (below left, below right).\n\n eg an object with a label ID of 6\n\n 0, 0, 6, 6, 6\n 6, 6, 6, 6, 6\n\n As such, the first direction travelled will be to the right '0' in the freeman chain, and the first index will be the\n first boundary co-ordinate and will be the final co-ordinate to close the boundary thereby creating a polygon.\n As for linear features...\n \"\"\"\n\n # Probably deal with these inside the boundary tracking routine\n # Let the tracking routine handle to/from and just return the final result\n to_ = 0\n from_ = 4\n\n perimeter_info = {}\n\n for i in range(hist.shape[0]):\n if hist[i] == 0:\n continue\n if hist[i] == 1:\n # What is the perimeter of a single pixel, 0.0, 4.0???\n #perimeter_co_ords[i] = seg_start_idxs[i[0],i[1]] # Still need to design the function and how to return the result\n continue\n idx = (seg_start_idxs[0][i], seg_start_idxs[1][i])\n label = i + 1\n perimeter_info[i] = track_object_boundary(labelled_array, start_index=idx, label_id=label)\n\n # Might need to format the perimeter_info dictionary before returning, ie turn the co-ords into numpy arrays.\n # Or even into a polygon object using the shapely library???\n # Using shapely might be easier to report geometrical attributes\n\n # Still need to deal with holes within an object as ENVI does. They will increase an objects perimeter length.\n # SciPy have a binary_fill_holes function. label the filled array, then get the indices, and then retrive only those indices\n # for each object that are 0 in the original array.\n # That might be one way to do it, which means re-writing the above function....ughhh :)\n\n #!!!!This isn't the correct place for the handling of object holes, but just get the rough structure out!!!\n if fill_holes:\n for i in range(hist.shape[0]):\n if hist[i] == 0:\n continue\n if hist[i] == 1:\n # What is the perimeter of a single pixel, 0.0, 4.0???\n continue\n idx = (seg_start_idxs[0][i], seg_start_idxs[1][i])\n label = i + 1\n perimeter_info[i] = track_object_boundary(labelled_array, start_index=idx, label_id=label)\n # Can we trust that the labelling of the filled and unfilled arrays will give the same object index??\n # If we can we could use the area differences to determine if there are holes and only go through the \n # hole perimeter tracking if needed.\n single_object = numpy.zeros((rows*cols), dtype='uint8')\n single_object[ri[ri[i]:ri[i+1]]] = 1\n holes = numexpr.evaluate(\"(single_object - orig_binary) == 1\")\n labs, nlabs = ndimage.label(holes, s)\n h_holes = histogram(labs, Min=1, reverse_indices='ri')\n hist_holes = h_holes['histogram']\n ri_h = h_holes['ri']\n seg_holes_start_idxs = numpy.zeros(nlabs, dtype='int')\n for j in numpy.arange(nlabels):\n #if (hist[i] == 0): # The labeled array should be consecutive\n # continue\n seg_holes_start_idxs[j] = ri_h[ri_h[j]:ri_h[j+1]][0] # Return the first index\n\n # Convert the 1D indices to 2D indices used by NumPy\n seg_holes_start_idxs = array_indices(dims, seg_holes_start_idxs, dimensions=True)\n for k in range(hist_holes.shape):\n if hist_holes[k] == 0:\n continue\n if hist[i] == 1:\n # What is the perimeter of a single pixel, 0.0, 4.0???\n continue\n idx = (seg_holes_start_idxs[0][k], seg_holes_start_idxs[1][k])\n holes_label = k + 1\n holes_result = track_object_boundary(labs, start_index=idx, label_id=holes_label)\n perimeter_info[i]['Holes'] = holes_result['Vertices']\n perimeter_info[i]['Perimeter_Length'] += perimeter_info['Perimeter_Length']\n\n return perimeter_info","title":""},{"docid":"f2fd627e6b2bf9b02ac54deaf09a280c","score":"0.4845612","text":"def create_graph(w, h):\n graph = {}\n for i in range(h):\n for j in range(w):\n neighbors = []\n # Top left\n if i - 1 >= 0 and j - 1 >= 0:\n neighbors.append((i - 1, j - 1))\n # Top\n if i - 1 >= 0:\n neighbors.append((i - 1, j))\n # Top Right\n if i - 1 >= 0 and j + 1 < w:\n neighbors.append((i - 1, j + 1))\n # Right\n if j + 1 < w:\n neighbors.append((i, j + 1))\n # Bottom Right\n if i + 1 < h and j + 1 < w:\n neighbors.append((i + 1, j + 1))\n # Bottom\n if i + 1 < h:\n neighbors.append((i + 1, j))\n # Bottom Left\n if i + 1 < h and j - 1 >= 0:\n neighbors.append((i + 1, j - 1))\n # Left\n if j - 1 >= 0:\n neighbors.append((i, j - 1))\n graph[(i, j)] = neighbors\n return graph","title":""},{"docid":"0e52082b7c1becc8de17db95d64a734d","score":"0.4841775","text":"def neighbours(i,j,M,N,size=4):\n if size == 4:\n if (i == 0 and j == 0):\n n = [(0, 1), (1, 0)]\n elif i == 0 and j == N - 1:\n n = [(0, N - 2), (1, N - 1)]\n elif i == M - 1 and j == 0:\n n = [(M - 1, 1), (M - 2, 0)]\n elif i == M - 1 and j == N - 1:\n n = [(M - 1, N - 2), (M - 2, N - 1)]\n elif i == 0:\n n = [(0, j - 1), (0, j + 1), (1, j)]\n elif i == M - 1:\n n = [(M - 1, j - 1), (M - 1, j + 1), (M - 2, j)]\n elif j == 0:\n n = [(i - 1, 0), (i + 1, 0), (i, 1)]\n elif j == N - 1:\n n = [(i - 1, N - 1), (i + 1, N - 1), (i, N - 2)]\n else:\n n = [(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)]\n return n\n if size == 8:\n #print('Not yet implemented\\n')\n # top left\n if (i == 0 and j == 0):\n n = [(0, 1), (1, 0), (1, 1)]\n # bottom left\n elif i == 0 and j == N - 1:\n n = [(0, N - 2), (1, N - 1), (1, N - 2)]\n # top right\n elif i == M - 1 and j == 0:\n n = [(M - 1, 1), (M - 2, 0), (M - 2, 1)]\n # bottom right\n elif i == M - 1 and j == N - 1:\n n = [(M - 1, N - 2), (M - 2, N - 1), (M - 2, N - 2)]\n # left\n elif i == 0:\n n = [(0, j - 1), (0, j + 1), (1, j), (1, j - 1), (1, j + 1)]\n # right\n elif i == M - 1:\n n = [(M - 1, j - 1), (M - 1, j + 1), (M - 2, j), (M - 2, j + 1), (M - 2, j - 1)]\n # top\n elif j == 0:\n n = [(i - 1, 0), (i + 1, 0), (i, 1), (i - 1, 1), (i + 1, 1)]\n # bottom\n elif j == N - 1:\n n = [(i - 1, N - 1), (i + 1, N - 1), (i, N - 2), (i + 1, N - 2), (i - 1, N - 2)]\n # middle \n else:\n n = [(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1), (i - 1, j - 1), (i - 1, j + 1), (i + 1, j - 1), (i + 1, j + 1)]\n return n","title":""},{"docid":"7bf168daaea61b02d9cf09a9073ad030","score":"0.48404124","text":"def _neighbors(self, index):\n\n if index == 0:\n neighbor = [index + 1]\n elif 0 < index < self.data_amount-1:\n neighbor = [index - 1, index + 1]\n elif index == self.data_amount-1:\n neighbor = [index - 1]\n\n return neighbor","title":""},{"docid":"c762b287eeee92d878c23df5a4353301","score":"0.4838816","text":"def get_neighbours(w, h):\n def get_local_nbrs(x, y):\n return [(x, (y - 1) % h),\n ((x + 1) % w, y),\n (x, (y + 1) % h),\n ((x - 1) % w, y)]\n\n nbrs = {(x, y): get_local_nbrs(x, y)\n for x in range(w) for y in range(h)}\n\n return nbrs","title":""},{"docid":"3918c746c0253a867b07d952e3bd8cb2","score":"0.48302403","text":"def createNodeWAttr(self):\n count = 0\n r=0\n g=1\n b=0\n for node in self.nodes:\n for edge in self.edges:\n if edge[0] == node:\n count+=1\n self.G.add_node(node, size=count, r=r, g=g, b=b)\n\n if g>=0.2:\n g-=0.2\n elif r<=0.8:\n r+=0.2\n elif b<=0.8:\n b+=0.2\n else:\n g = 0.5\n b=0.5\n count=0","title":""},{"docid":"596185ba6919c86fc6bb35d00073ef48","score":"0.4827401","text":"def createGrayworldWB(): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"b481529f12c039c79dc18d720bfd8285","score":"0.4820118","text":"def getNeighbours(self):\r\n\r\n iBlank, jBlank = self.getBlankIndex()\r\n\r\n upNeighbour = self.getUpNeighbour(iBlank, jBlank)\r\n if upNeighbour != -1:\r\n self.neighbours.append(Node(upNeighbour, self.depth + 1, self))\r\n\r\n downNeighbour = self.getDownNeighbour(iBlank, jBlank)\r\n if downNeighbour != -1:\r\n self.neighbours.append(Node(downNeighbour, self.depth + 1, self))\r\n\r\n leftNeighbour = self.getLeftNeighbour(iBlank, jBlank)\r\n if leftNeighbour != -1:\r\n self.neighbours.append(Node(leftNeighbour, self.depth + 1, self))\r\n\r\n rightNeighbour = self.getRightNeighbour(iBlank, jBlank)\r\n if rightNeighbour != -1:\r\n self.neighbours.append(Node(rightNeighbour, self.depth + 1, self))\r\n\r\n return self.neighbours","title":""},{"docid":"c98388842549726034195e6b830206e7","score":"0.48194784","text":"def create_binding_holes(self, edge, ysize, xsize, ntimes):\n\n # reset\n self.foreground = []\n\n template_size = template_size_ori = 40\n # scale template size based on image size\n # 1000 * 800 is normal image size for template size = 40\n # use max to prevent small template and min to prevent large template\n template_size = int(\n max(template_size_ori / 2, 40 * ((ysize * xsize) / (1000 * 800))),\n )\n template_size = int(min(template_size, template_size_ori * 2))\n\n binding_effect = random.randint(0, 8)\n offset_direction = random.choice([1, -1])\n\n for i in range(ntimes):\n random_scale = random.uniform(1, 2)\n current_template_size = random.randint(int(template_size * 0.75), int(template_size * 1.25))\n\n if self.width_range == \"random\":\n template_size_x = current_template_size\n else:\n template_size_x = random.randint(self.width_range[0], self.width_range[1])\n\n if self.height_range == \"random\":\n template_size_y = int(current_template_size / 3)\n else:\n template_size_y = random.randint(self.height_range[0], self.height_range[1])\n\n # draw rectangle\n offset = int(min(template_size_x, template_size_y) / random.uniform(7, 8))\n image_rectangle = np.full(\n (template_size_y, int(template_size_x / random_scale)),\n fill_value=255,\n dtype=\"uint8\",\n )\n image_rectangle[offset:-offset:, offset:-offset] = 0\n\n new_offset = offset + random.randint(3, 6)\n image_rectangle_complement = np.full(\n (template_size_y, int(template_size_x / random_scale)),\n fill_value=0,\n dtype=\"uint8\",\n )\n\n if binding_effect == 0:\n x1 = new_offset + (offset_direction * random.randint(1, 3))\n x2 = image_rectangle_complement.shape[1]\n y1 = new_offset + (offset_direction * random.randint(1, 3))\n y2 = image_rectangle_complement.shape[1]\n elif binding_effect == 1:\n x1 = new_offset + (offset_direction * random.randint(1, 3))\n x2 = image_rectangle_complement.shape[1]\n y1 = 0\n y2 = -new_offset + (offset_direction * random.randint(1, 3))\n elif binding_effect == 2:\n x1 = 0\n x2 = -new_offset + (offset_direction * random.randint(1, 3))\n y1 = new_offset + (offset_direction * random.randint(1, 3))\n y2 = image_rectangle_complement.shape[1]\n elif binding_effect == 3:\n x1 = 0\n x2 = -new_offset + (offset_direction * random.randint(1, 3))\n y1 = 0\n y2 = -new_offset + (offset_direction * random.randint(1, 3))\n elif binding_effect == 4:\n x1 = new_offset + (offset_direction * random.randint(1, 3))\n x2 = -new_offset + (offset_direction * random.randint(1, 3))\n y1 = new_offset + (offset_direction * random.randint(1, 3))\n y2 = -new_offset + (offset_direction * random.randint(1, 3))\n elif binding_effect == 5:\n x1 = new_offset\n x2 = -new_offset\n y1 = new_offset\n y2 = -new_offset\n else:\n x1 = 0\n x2 = 0\n y1 = 0\n y2 = 0\n image_rectangle_complement[y1:y2:, x1:x2] = 255\n\n self.add_noise(\n image_rectangle,\n random.uniform(0.01, 0.21),\n (0, 255),\n )\n\n # merge 2 image to create offset effect\n image_rectangle = np.maximum(\n image_rectangle,\n image_rectangle_complement,\n )\n\n # add noise and apply blur\n self.add_noise(\n image_rectangle,\n random.uniform(0.01, 0.21),\n (0, 255),\n )\n image_rectangle = cv2.GaussianBlur(\n image_rectangle.astype(\"uint8\"),\n (3, 3),\n cv2.BORDER_DEFAULT,\n )\n\n # rotate rectangle to create a more proper binding holes\n if edge == \"left\":\n image_rectangle = np.rot90(image_rectangle, 1)\n elif edge == \"right\":\n image_rectangle = np.rot90(image_rectangle, 1)\n\n # convert to bgr\n image_rectangle_bgr = cv2.cvtColor(image_rectangle, cv2.COLOR_GRAY2BGR)\n\n self.foreground.append(image_rectangle_bgr)","title":""},{"docid":"019988e1be1719acdd2ee94698b10593","score":"0.48115528","text":"def nodes_and_weights_2d(nq):\n\n if nq == 1:\n z = np.array([[1./3, 1./3, 1./3]])\n w = np.array([1.])\n return z, w\n\n elif nq == 3:\n z = np.array([[1./2, 1./2, 0],\n [1./2, 0, 1./2],\n [0, 1./2, 1./2]])\n w = np.array([1./3, 1./3, 1./3])\n return z, w\n\n elif nq == 4:\n z = np.array([[1./3, 1./3, 1./3],\n [3./5, 1./5, 1./5],\n [1./5, 3./5, 1./5],\n [1./5, 1./5, 3./5]])\n w = np.array([-9./16, 25./48, 25./48, 25./48])\n return z, w\n\n else:\n print \"Please use a valid number of nodes (1, 3, or 4).\"\n return 1","title":""},{"docid":"6b5c31b383e7a635c1fbb1be7fff7dfd","score":"0.47986537","text":"def create_paired_geometry_rep(self):\n point1 = self.vhelix.end_coordinates[0]\n point2 = self.vhelix.end_coordinates[1]\n dna_structure = self.dna_structure\n radius = 1.15\n num_sides = 16\n angle_offset = 45.0\n helix = self.vhelix \n # Get the indexes into helix_axis_coords of dsDNA regions.\n boundary_points = self.get_boundaries()\n self.representations[VisHelixRepType.PAIRED_GEOMETRY] = []\n for i,points in enumerate(boundary_points): \n pt1 = points[0]\n pt2 = points[1]\n #self._logger.info(\"Pt1 %s pt2 %s\" % (str(pt1), str(pt2))) \n name = \"HelixGeometry:%d_%d\" % (helix.id,i+1) \n geom = VisGeometryCylinder(name, radius, pt1, pt2, num_sides)\n geom.selected_callback = self.select_paired_geometry\n geom.color = self.color\n geom.color[3] = 1.0 \n self.graphics.add_render_geometry(geom)\n self.representations[VisHelixRepType.PAIRED_GEOMETRY].append(geom)\n #__for pos in boundary_pos ","title":""},{"docid":"277091581948acab19328cb8fbba10ce","score":"0.47932747","text":"def ring_initial(spacing=0.01):\n nx, ny = int(1 / spacing), int(1 / spacing)\n xx, yy = np.meshgrid(np.linspace(0., 1., nx, dtype=np.float32),\n np.linspace(0., 1., ny, dtype=np.float32))\n ui = np.zeros((nx, ny), dtype=np.float32)\n r = (xx - .5)**2. + (yy - .5)**2.\n ui[np.logical_and(.05 <= r, r <= .1)] = 1.\n return ui","title":""},{"docid":"bd061680b45bfd4172b683407113a70b","score":"0.4793038","text":"def watershedding_2D(track, field_in, **kwargs):\n kwargs.pop(\"method\", None)\n return segmentation_2D(track, field_in, method=\"watershed\", **kwargs)","title":""},{"docid":"bf0969674bb6452223ff4961448aa2b6","score":"0.47901848","text":"def get_neighbors(self, n1):\n pass","title":""},{"docid":"f5c0f2dfe12420630a0a89a2a17198b5","score":"0.4789576","text":"def __neighbors(self, cell):\n return {\n (cell[0]-1, cell[1]-1), (cell[0]-1, cell[1]), (cell[0]-1, cell[1]+1),\n (cell[0], cell[1]-1), (cell[0], cell[1]+1),\n (cell[0]+1, cell[1]-1), (cell[0]+1, cell[1]), (cell[0]+1, cell[1]+1),\n }","title":""},{"docid":"7153f0645c9b1836a97e6b0491034a5a","score":"0.47862005","text":"def newneighbor(self, position):\n if self.flag not in LeafFlags:\n raise ValueError(\"cannot make leaf neighbor for non-leaf.\")\n neighbor = self.clone(position)\n size = self.size\n indices = self.indices\n neighbor.indices[size] = indices[size]\n indices[size] = position\n return neighbor","title":""},{"docid":"69baccdfa64e132e863935f7711aafdf","score":"0.4785181","text":"def atom_neighborhoods(gra):\n bnd_keys = bond_keys(gra)\n\n def _neighborhood(atm_key):\n nbh_bnd_keys = set(filter(lambda x: atm_key in x, bnd_keys))\n return bond_induced_subgraph(gra, nbh_bnd_keys)\n\n atm_keys = list(atom_keys(gra))\n atm_nbh_dct = dict(zip(atm_keys, map(_neighborhood, atm_keys)))\n return atm_nbh_dct","title":""},{"docid":"0607eac58f9ba2fc7255d18c88cc1ccf","score":"0.47850254","text":"def create_ring_network(number_of_cliques, nodes_per_clique):\r\n \r\n number_of_nodes = number_of_cliques * nodes_per_clique\r\n network = UndirectedNetwork(number_of_nodes)\r\n \r\n # for each clique\r\n for i in range(0, number_of_cliques):\r\n \r\n offset = i*nodes_per_clique\r\n \r\n # for each node in each clique\r\n for j in range(0, nodes_per_clique):\r\n \r\n # connect the node to all the other nodes in the clique\r\n for k in range(0,j): \r\n network.add_edge(offset+j,offset+k)\r\n \r\n # connect the clique to the previous one\r\n if not (offset == 0):\r\n network.add_edge(offset, offset-1)\r\n \r\n # we are done so connect first and last...\r\n network.add_edge(0,number_of_nodes-1)\r\n \r\n return network","title":""},{"docid":"1c76a8ee9d8b2087214be9f05881eb4b","score":"0.4782238","text":"def createEdgeWAttr(self):\n r=0\n g=0.8\n b=0\n prev_edge = self.edges[0][0]\n self.G.add_edge(prev_edge, self.edges[0][1], r=r, g=g, b=b)\n for e in range(1, len(self.edges)):\n if e == len(self.edges)-2:\n if prev_edge != self.edges[e+1][0]:\n if g>=0.2:\n g-=0.2\n elif r<=0.8:\n r+=0.2\n elif b<=0.8:\n b+=0.2\n else:\n g = 0.5\n b=0.5\n self.G.add_edge(self.edges[e+1][0], self.edges[e+1][1], r=r, g=g, b=b) \n self.G.add_edge(self.edges[e][0], self.edges[e][1], r=r, g=g, b=b) \n else:\n if prev_edge != self.edges[e][0]:\n if g>=0.2:\n g-=0.1\n elif r<=0.8:\n r+=0.1\n elif b<=0.8:\n b+=0.1\n else:\n g = 0.5\n b=0.5\n prev_edge = self.edges[e][0]\n self.G.add_edge(self.edges[e][0], self.edges[e][1], r=r, g=g, b=b)","title":""},{"docid":"2943380ca22aecef2b95ed07cb387e0d","score":"0.4771245","text":"def __init__(self):\n self.neighborhood = [] \n self.tec = None","title":""},{"docid":"c4291a8baa57fe2aa624c6043fe80042","score":"0.4769537","text":"def get_neighbor(x, n, data): # type: (float, int, np.ndarray) -> np.ndarray\n pad_width = np.ceil(n / 2).astype(int)\n padded = np.pad(data, pad_width, mode='edge')\n x += pad_width\n\n idxes = get_neighbor_idxes(x, n, len(padded))\n ret = padded[idxes]\n return idxes - pad_width, ret","title":""},{"docid":"8675de73188ecf657e46a9c864465d89","score":"0.47621432","text":"def test_spatial_neighbors_non_visium(non_visium_adata: AnnData):\n correct_knn_graph = np.array(\n [\n [0.0, 1.0, 1.0, 1.0],\n [1.0, 0.0, 1.0, 1.0],\n [1.0, 1.0, 0.0, 1.0],\n [1.0, 1.0, 1.0, 0.0],\n ]\n )\n\n correct_radius_graph = np.array(\n [\n [0.0, 1.0, 0.0, 1.0],\n [1.0, 0.0, 0.0, 1.0],\n [0.0, 0.0, 0.0, 0.0],\n [1.0, 1.0, 0.0, 0.0],\n ]\n )\n\n correct_delaunay_graph = np.array(\n [[0.0, 1.0, 0.0, 1.0], [1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 1.0], [1.0, 1.0, 1.0, 0.0]]\n )\n\n correct_delaunay_dist = np.array(\n [\n [0.0, 2.0, 0.0, 4.12310563],\n [2.0, 0.0, 6.32455532, 5.0],\n [0.0, 6.32455532, 0.0, 5.38516481],\n [4.12310563, 5.0, 5.38516481, 0.0],\n ]\n )\n\n spatial_neighbors(non_visium_adata, n_neigh=3, coord_type=None)\n spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()\n assert np.array_equal(spatial_graph, correct_knn_graph)\n\n spatial_neighbors(non_visium_adata, radius=5.0, coord_type=None)\n spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()\n assert np.array_equal(spatial_graph, correct_radius_graph)\n\n spatial_neighbors(non_visium_adata, delaunay=True, coord_type=None)\n spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()\n spatial_dist = non_visium_adata.obsp[Key.obsp.spatial_dist()].toarray()\n assert np.array_equal(spatial_graph, correct_delaunay_graph)\n np.testing.assert_allclose(spatial_dist, correct_delaunay_dist)","title":""},{"docid":"4d03859b3a53c5dd59003b37ad467ccd","score":"0.47570217","text":"def rebunch(self, spacing, extraCells, nStar):\n from pyspline import Curve\n\n # ********* WARNING THIS HARD CODED TO K-MIN PLANES *********\n self.dims[2] += extraCells\n if nStar == -1:\n nStar = self.dims[2]\n\n newNodes = np.zeros((self.dims[0], self.dims[1], self.dims[2], 3))\n for i in range(self.dims[0]):\n for j in range(self.dims[1]):\n xx = self.coords[i, j, :, :]\n c = Curve(X=xx, localInterp=True)\n # First get the distance off-wall:\n d = np.linalg.norm(self.coords[i, j, 0, :] - self.coords[i, j, 1, :])\n\n # This is the segment of S we are dealing with:\n sSegment = c.s[0:nStar]\n\n # Compute the new S0\n s0 = (spacing / d) * c.s[1]\n # Get the newS.\n newS = getS(len(sSegment) + extraCells + 1, s0, sSegment[-1])\n # The final 's' for evaluation\n newS = np.hstack([newS, c.s[nStar + 1 :]])\n newNodes[i, j, :, :] = c(newS)\n\n self.coords = newNodes","title":""},{"docid":"b2e8ed130c0f078bc1b7578545253d72","score":"0.47471","text":"def __init__(self, nrows, ncols, neighborhood, reproductionneighborhood):\n\t\tself.nrows, self.ncols = nrows, ncols\n\t\tself.neighborhood = neighborhood # list of tuples of x,y-offsets (relative to any grid location).\n\t\tself.reproductionneighborhood = reproductionneighborhood # list of tuples of x,y-offsets (relative to any grid location).\n\t\tself.agentMatrix = [[None]*ncols for i in range(nrows)] # ncols x nrows matrix that has agent or None at each location\n\t\t\n\t\tself.emptySites = [(i,j) for i in range(nrows) for j in range(ncols)]\n\t\tself.allSites = [(i,j) for i in range(nrows) for j in range(ncols)]\n\n\t\t# for each location (x,y) key will hold neighboring locations as list\n\t\tself.neighborLocs = {}\n\t\tfor i in range(self.nrows):\n\t\t\tfor j in range(self.ncols):\n\t\t\t\tneighLocs = list()\n\t\t\t\tfor offset in self.neighborhood:\n\t\t\t\t\tdc, dr = offset\t\t #note: x,y neighborhood\n\t\t\t\t\tr = (i + dr) % nrows\n\t\t\t\t\tc = (j + dc) % ncols\n\t\t\t\t\tneighLocs.append((r,c))\n\t\t\t\tself.neighborLocs[(i,j)] = neighLocs\n\n\t\t# for each location (x,y) key will hold reproduction neighborhoods as list\n\t\tself.reproductionneighborLocs = {}\n\t\tfor i in range(self.nrows):\n\t\t\tfor j in range(self.ncols):\n\t\t\t\tneighLocs = list()\n\t\t\t\tfor offset in self.neighborhood:\n\t\t\t\t\tdc, dr = offset\t\t #note: x,y neighborhood\n\t\t\t\t\tr = (i + dr) % nrows\n\t\t\t\t\tc = (j + dc) % ncols\n\t\t\t\t\tneighLocs.append((r,c))\n\t\t\t\tself.reproductionneighborLocs[(i,j)] = neighLocs","title":""},{"docid":"80b470a6b13299c3154d86f096a818dc","score":"0.47412646","text":"def make_neighborhood_column(df):\n neighborhood_col = []\n for i in range(len(df['Latitude'])):\n neighborhood_col.append(get_neighborhood_for_point(df['Latitude'][i], df['Longitude'][i]))\n df['neighborhood'] = neighborhood_col\n return df","title":""},{"docid":"7eda0e382af8fccb4e52aeeaae0519d1","score":"0.47386295","text":"def get_bprop_resize_nearest_neighbor_v2(self):\n align_corners = self.align_corners\n half_pixel_centers = self.half_pixel_centers\n grad_op = G.ResizeNearestNeighborV2Grad(align_corners, half_pixel_centers)\n\n def bprop(x, size, output, dout):\n x_shape = P.Shape()(x)\n grad_in_size = x_shape[2:4]\n\n if F.is_sequence_value_unknown(P.Shape()(x)):\n dx = grad_op(dout, grad_in_size)\n return dx, zeros_like(grad_in_size)\n\n dx = grad_op(dout, _create_tensor(grad_in_size, mstype.int32))\n return dx, zeros_like(grad_in_size)\n\n return bprop","title":""},{"docid":"6b182aa0a661d9f7dc9fb018d727864d","score":"0.4737028","text":"def neighbs(p):\n x,y=p\n for dx in [1,-1]: yield (x+dx,y)\n for dy in [1,-1]: yield (x,y+dy)","title":""},{"docid":"a1c0d1f96847bf21ade29b632bebd17a","score":"0.47366253","text":"def __init__(self, x_, nnz_, DoTriples_=False, DoPerms_=False, ele_=None, alg_ = None, sort_ = False):\n self.nlist = []\n self.nmol = x_.shape[0]\n self.maxnatom = x_.shape[1]\n self.alg = 0 if self.maxnatom < 2000000 else 1\n #self.alg = 0 if self.maxnatom < 20000 else 1\n if (alg_ != None):\n self.alg = alg_\n # alg=0 naive quadratic.\n # alg=1 linear scaling\n # alg=2 PairProvider\n self.x = x_.copy()\n self.nnz = nnz_.copy()\n self.nreal = nnz_.copy() # Over-ridden in the WithImages child of this class.\n self.ele = ele_\n self.sort = sort_\n self.pairs = None\n self.DoTriples = DoTriples_\n self.DoPerms = DoPerms_\n self.triples = None\n self.UpdateInterval = 1\n self.UpdateCounter = 0\n self.PairMaker=None\n if (self.alg<2):\n if self.ele is None:\n for i in range(self.nmol):\n self.nlist.append(NeighborList(x_[i,:nnz_[i]],DoTriples_,DoPerms_, None, self.alg, self.sort))\n else:\n for i in range(self.nmol):\n self.nlist.append(NeighborList(x_[i,:nnz_[i]],DoTriples_,DoPerms_, self.ele[i,:nnz_[i]], self.alg, self.sort))\n else:\n raise Exception(\"Using fucked up old code.\")\n self.PairMaker = PairProvider(self.nmol,self.maxnatom)\n return","title":""},{"docid":"8452695b383d5df26c9ca79f3bc725bd","score":"0.47275728","text":"def build2DBS(Window, Dim, Diameter=0, Width=3, Cont=False):\n return np.ones(Dim)-build2DBP(Window, Dim, Diameter, Width, Cont)","title":""},{"docid":"6334b163b423d8275348f5fbc4a7cff7","score":"0.4722107","text":"def neighborhood_kernel(kernel_size: int,\n groups: int,\n dtype: torch.dtype = torch.float32,\n device: torch.device = torch.device(\"cpu\")):\n\n neighbors = kernel_size * kernel_size\n weights = torch.zeros(neighbors, 1, kernel_size, kernel_size, dtype=dtype, device=device)\n idx = 1\n for i in range(kernel_size):\n for j in range(kernel_size):\n if i == j and i == kernel_size // 2:\n weights[0, :, i, j] = 1.0\n else:\n weights[idx, :, i, j] = 1.0\n idx += 1\n\n weights_neighborhood = weights\n weights_neighbors = weights.unsqueeze(0).expand(groups, neighbors, 1, kernel_size, kernel_size).reshape(\n groups * neighbors, 1,\n kernel_size, kernel_size)\n\n return weights_neighborhood, weights_neighbors","title":""},{"docid":"b363d0cc70904b3d4ee53c91c6e31665","score":"0.47184327","text":"def createNatGateWay(SubnetId, AllocationId, **kwargs):\n pass","title":""},{"docid":"22b52340625e866e6293fd034471b867","score":"0.47151592","text":"def buffer2d_(border, neighbors):\n y_border, x_border = border\n n_border = len(y_border)\n \n y_neighbors, x_neighbors = neighbors\n n_neighbors = len(y_neighbors)\n \n if n_border == 1:\n return y_neighbors + y_border[0], x_neighbors + x_border[0]\n else:\n assert len(y_neighbors) >= 3\n assert (y_neighbors[0] != y_neighbors[-2] or\n x_neighbors[0] != x_neighbors[-2])\n \n # `direction` should correspond to the previous cell\n direction = 0\n y = y_border[0] + y_neighbors[direction]\n x = x_border[0] + x_neighbors[direction]\n \n while y != y_border[-2] or x != x_border[-2]:\n direction = _decrement_direction(direction, n_neighbors)\n y = y_border[0] + y_neighbors[direction]\n x = x_border[0] + x_neighbors[direction]\n \n # direction points from neighbors[-2] to neighbors[0]\n direction = _increment_direction(direction, n_neighbors)\n \n y_buffer = np.zeros(64, y_border.dtype)\n x_buffer = np.zeros(64, y_border.dtype)\n n_buffer = 0\n \n for i in range(n_border - 1):\n \n y = y_border[i]\n x = x_border[i]\n \n y_next = y_border[i + 1]\n x_next = x_border[i + 1]\n \n y_test = y + y_neighbors[direction]\n x_test = x + x_neighbors[direction]\n \n while y_test != y_next or x_test != x_next:\n \n # Write the last ones, expanding the buffer if necessary\n if n_buffer >= y_buffer.size:\n y_buffer = enlarge_array(y_buffer)\n x_buffer = enlarge_array(x_buffer)\n \n y_buffer[n_buffer] = y_test\n x_buffer[n_buffer] = x_test\n n_buffer += 1\n \n # Point to the next neighbor\n direction = _increment_direction(direction, n_neighbors)\n \n y_test = y + y_neighbors[direction]\n x_test = x + x_neighbors[direction]\n \n # One more increment and inversion brings us back to the\n # next border pixel pointing back at this one\n direction = _invert_direction(direction, n_neighbors)\n \n # Start off not pointing back at the last neighbor\n direction = _increment_direction(direction, n_neighbors)\n \n return y_buffer[:n_buffer], x_buffer[:n_buffer]","title":""},{"docid":"6a05e7ec239db2afe87cbc6bd4078c26","score":"0.4714184","text":"def create_graph(image, wall_distance, outermost_coords=None):\n map_graph = {}\n # img_tmp = np.copy(img)\n\n # Go over once to identify nodes\n h, w = image.shape # HEIGHT - WIDTH\n\n min_x, min_y, max_x, max_y = outermost_coords if outermost_coords is not None else (0, 0, w, h)\n print(image.shape)\n for row in range(min_y, max_y):\n for col in range(min_x, max_x):\n # print(w)\n # watch for out of range\n # The -1 and +1 in _check_surrounding_px are because otherwise it stops short\n if min_x < col < max_x + 1 and min_y < row < max_y + 1 and image[row, col] != WALL_COLOR:\n dist = MapHandler._get_dist_heuristic(image, wall_distance, row, col)\n # if (row, col) == (53, 2):\n # print(\"Aaaaa\")\n new_node = Node(row, col)\n # TODO When we go in here there's a chance not all neighbors have been generated\n # neighbors = MapHandler.get_neighbor_coords(new_node, img)\n # new_node.add_neighbors(MapHandler.get_neighbors_from_img(new_node, img, graph))\n new_node.dist_from_wall = dist\n map_graph[(row, col)] = new_node\n\n # get neighbors: requires going back over after it's been generated\n for coords, node in map_graph.iteritems():\n # node = graph[(row, col)]\n node.add_neighbors(MapHandler.get_neighbors_from_img(node, image, map_graph, CLEAR_TERRAIN_MAP_COLOR))\n\n return map_graph","title":""},{"docid":"53465458698fbdad4840e6e1290392f0","score":"0.47090194","text":"def __init__(self, nelements):\n this = _structural.new_doubleArray(nelements)\n try:\n self.this.append(this)\n except __builtin__.Exception:\n self.this = this","title":""},{"docid":"d933863641161dada5b9331591fd708a","score":"0.47028866","text":"def _build_graph_representation(self):\n\n g = nx.Graph()\n for idx, elem in enumerate(self.elements):\n g.add_node(idx, element=elem)\n\n for b in self.bonds:\n a1, a2 = b\n g.add_edge(a1, a2)\n\n self._g = g","title":""},{"docid":"0121bf8989814017e8697c43181d766e","score":"0.47013953","text":"def gridLayout(nx,xSpacing,nz,zSpacing=None,hub_height=0,mirror=False):\n if mirror:\n ny=2\n else:\n ny=1\n nWT = nz * nx * ny\n xWT = np.zeros((nWT))\n yWT = np.zeros((nWT))\n zWT = np.zeros((nWT))\n k = 0\n for i in np.arange(1,nz+1).reshape(-1):\n for j in np.arange(1,nx+1).reshape(-1):\n k = k + 1\n xWT[k-1] = (j - int(np.floor(nx / 2)) - 1) * xSpacing\n zWT[k-1] = (i - 1) * zSpacing\n yWT[k-1] = hub_height\n if mirror:\n for i in np.arange(1,nz+1).reshape(-1):\n for j in np.arange(1,nx+1).reshape(-1):\n k = k + 1\n xWT[k-1] = (j - int(np.floor(nx / 2)) - 1) * xSpacing\n zWT[k-1] = (i - 1) * zSpacing\n yWT[k-1] = - hub_height\n return xWT,yWT,zWT","title":""},{"docid":"15cb74974c97c54dfd63e65f220412e6","score":"0.4690115","text":"def createB1mapInHanti():\n #railWay marker\n\n #blockWay marker\n global blockWay_59_Hanti_B1\n # elevator marker\n global elevator_1_Hanti_B1, elevator_2_Hanti_B1\n\n # blockWay marker\n blockWay_59_Hanti_B1 = markerNode(\"Hanti_59_B1\", 1, 59)\n\n # elevator marker\n elevator_1_Hanti_B1 = markerNode(\"Hanti_EV_1_B1\", 1, 7)\n elevator_2_Hanti_B1 = markerNode(\"Hanti_EV_2_B1\", 1, 8)\n\n #update\n blockWay_59_Hanti_B1.update(south = elevator_2_Hanti_B1, east = elevator_1_Hanti_B1)\n\n elevator_1_Hanti_B1.update(west = blockWay_59_Hanti_B1)\n elevator_2_Hanti_B1.update(north = blockWay_59_Hanti_B1)","title":""}],"string":"[\n {\n \"docid\": \"09f8ba82c9d6d9128d3ee2916ff6e64a\",\n \"score\": \"0.66190475\",\n \"text\": \"def _create_neighbourhood(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307bffa20031a4c7d3f4fbe0f06371e7\",\n \"score\": \"0.61556005\",\n \"text\": \"def make_neighbor(self, n1, n2):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"788959f7f0c9b8f53778c7153b240b32\",\n \"score\": \"0.5827049\",\n \"text\": \"def create_neighbours():\\r\\n \\r\\n #for every node in the network assign left and right neighbours\\r\\n \\r\\n network[0].neighbours = ['B','C']\\r\\n network[1].neighbours = ['A','D','E']\\r\\n network[2].neighbours = ['A','F','G']\\r\\n network[3].neighbours = ['B','H']\\r\\n network[4].neighbours = ['B','F','J']\\r\\n network[5].neighbours = ['C','E','K']\\r\\n\\r\\n network[6].neighbours = ['C','K']\\r\\n network[7].neighbours = ['D','I']\\r\\n network[8].neighbours = ['H','J','O']\\r\\n network[9].neighbours = ['I','E']\\r\\n network[10].neighbours = ['F','G','L']\\r\\n network[11].neighbours = ['K','M','N']\\r\\n\\r\\n network[12].neighbours = ['L','N']\\r\\n network[13].neighbours = ['O','L','M']\\r\\n network[14].neighbours = ['I','N']\\r\\n\\r\\n \\r\\n #initialise data flags for each neighbour as 0 (able to write)\\r\\n for i in range(len(network)) :\\r\\n \\r\\n for j in range(len(network[i].neighbours)) :\\r\\n network[i].w.append(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07e00ef715c50b894234b216b403201b\",\n \"score\": \"0.5728528\",\n \"text\": \"def new_neighbor(index):\\n _unimplemented(__name__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86f006746636c099b50e92318be36436\",\n \"score\": \"0.56127805\",\n \"text\": \"def moore_neighborhood(\\n cell_ndindex: jnp.ndarray,\\n d: int = 1) -> jnp.ndarray:\\n with jax.ensure_compile_time_eval():\\n sides_length = 1 + 2 * d\\n dim = jnp.full(cell_ndindex.shape, sides_length)\\n indices = jnp.flip(jnp.indices(dim), axis=0)\\n n = cell_ndindex.shape[0]\\n indices = jnp.transpose(indices.reshape(n, -1)) - d\\n return cell_ndindex + indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3fe1f42ce98b5d7bd548f04462c74e5\",\n \"score\": \"0.55598956\",\n \"text\": \"def mk_dgnodes(self):\\n dgn = np.zeros((self.master.nb, self.mesh.nElm))\\n master_nodal_pts = np.squeeze(self.master.nodal_pts)\\n for elm, elm_verts in enumerate(self.mesh.connectivity):\\n elm_vert_pts = self.mesh.verts[elm_verts]\\n elm_width = elm_vert_pts[1] - elm_vert_pts[0]\\n mapped_pts = elm_vert_pts[0] + (1+master_nodal_pts)/2.*(elm_width)\\n dgn[:, elm] = mapped_pts\\n return dgn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5cb37da91264d3060519db0526421b4\",\n \"score\": \"0.5556699\",\n \"text\": \"def build_rdn(coords, r, **kwargs):\\n \\n tree = BallTree(coords, **kwargs)\\n ind = tree.query_radius(coords, r=r)\\n # clean arrays of neighbors from self referencing neighbors\\n # and aggregate at the same time\\n source_nodes = []\\n target_nodes = []\\n for i, arr in enumerate(ind):\\n neigh = arr[arr != i]\\n source_nodes.append([i]*(neigh.size))\\n target_nodes.append(neigh)\\n # flatten arrays of arrays\\n source_nodes = np.fromiter(itertools.chain.from_iterable(source_nodes), int).reshape(-1,1)\\n target_nodes = np.fromiter(itertools.chain.from_iterable(target_nodes), int).reshape(-1,1)\\n # remove duplicate pairs\\n pairs = np.hstack((source_nodes, target_nodes))\\n pairs = np.sort(pairs, axis=1)\\n pairs = np.unique(pairs, axis=0)\\n return pairs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90af4cf0e236bb2aa0877d2b2d760e96\",\n \"score\": \"0.5525252\",\n \"text\": \"def create_network(self):\\n return Star(max_size=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c35af6d565a8ec39660e42c4c5a16fc3\",\n \"score\": \"0.5488955\",\n \"text\": \"def create_neighbors(self):\\n for row in self._gridA:\\n for cell in row:\\n #\\n # There are some nine situations that we have to account for:\\n #\\n # 1. upper left corner\\n # 2. rest of the top row\\n # 3. upper right corner\\n # 4. far left side\\n # 5. normal cells\\n # 6. far right side\\n # 7. lower left corner\\n # 8. rest of bottom row\\n # 9. lower right corner\\n #\\n row = cell.get_row()\\n column = cell.get_column()\\n #print(f'({row},{column})')\\n # top row\\n if row == 0:\\n # 1. upper left corner\\n if column == 0:\\n #print('upper left')\\n cell.add_neighbor(self._gridA[row][column + 1])\\n cell.add_neighbor(self._gridA[row + 1][column])\\n cell.add_neighbor(self._gridA[row + 1][column + 1])\\n cell.add_neighbor(self._gridA[row][self._columns - 1])\\n cell.add_neighbor(self._gridA[row + 1][self._columns - 1])\\n cell.add_neighbor(self._gridA[self._rows - 1][column])\\n cell.add_neighbor(self._gridA[self._rows - 1][column + 1])\\n cell.add_neighbor(self._gridA[self._rows - 1][self._columns - 1])\\n # 2. rest of the top row\\n elif column < (self._columns - 1):\\n #print('upper')\\n cell.add_neighbor(self._gridA[row][column - 1])\\n cell.add_neighbor(self._gridA[row][column + 1])\\n cell.add_neighbor(self._gridA[row + 1][column - 1])\\n cell.add_neighbor(self._gridA[row + 1][column])\\n cell.add_neighbor(self._gridA[row + 1][column + 1])\\n cell.add_neighbor(self._gridA[self._rows - 1][column - 1])\\n cell.add_neighbor(self._gridA[self._rows - 1][column])\\n cell.add_neighbor(self._gridA[self._rows - 1][column + 1])\\n # upper right corner\\n else:\\n #print('upper right')\\n cell.add_neighbor(self._gridA[row][column - 1])\\n cell.add_neighbor(self._gridA[row + 1][column - 1])\\n cell.add_neighbor(self._gridA[row + 1][column])\\n cell.add_neighbor(self._gridA[0][0])\\n cell.add_neighbor(self._gridA[1][0])\\n cell.add_neighbor(self._gridA[self._rows - 1][self._columns - 1])\\n cell.add_neighbor(self._gridA[self._rows - 1][self._columns - 2])\\n cell.add_neighbor(self._gridA[0][self._columns - 1])\\n # middle row\\n elif row < (self._rows - 1):\\n #1. middle left\\n if column == 0:\\n #print('middle left')\\n cell.add_neighbor(self._gridA[row - 1][column])\\n cell.add_neighbor(self._gridA[row - 1][column + 1])\\n cell.add_neighbor(self._gridA[row][column + 1])\\n cell.add_neighbor(self._gridA[row + 1][column])\\n cell.add_neighbor(self._gridA[row + 1][column + 1])\\n cell.add_neighbor(self._gridA[row][self._columns - 1])\\n cell.add_neighbor(self._gridA[row - 1][self._columns - 1])\\n cell.add_neighbor(self._gridA[row + 1][self._columns - 1])\\n #2. the rest of the middle row (8 neighbors)\\n elif column < (self._columns - 1):\\n #print('middle')\\n cell.add_neighbor(self._gridA[row - 1][column - 1])\\n cell.add_neighbor(self._gridA[row - 1][column])\\n cell.add_neighbor(self._gridA[row - 1][column + 1])\\n cell.add_neighbor(self._gridA[row][column - 1])\\n cell.add_neighbor(self._gridA[row][column + 1])\\n cell.add_neighbor(self._gridA[row + 1][column - 1])\\n cell.add_neighbor(self._gridA[row + 1][column])\\n cell.add_neighbor(self._gridA[row + 1][column + 1])\\n #3. middle right\\n else:\\n #print('middle right')\\n cell.add_neighbor(self._gridA[row - 1][column])\\n cell.add_neighbor(self._gridA[row - 1][column - 1])\\n cell.add_neighbor(self._gridA[row][column - 1])\\n cell.add_neighbor(self._gridA[row + 1][column])\\n cell.add_neighbor(self._gridA[row + 1][column - 1])\\n cell.add_neighbor(self._gridA[row][0])\\n cell.add_neighbor(self._gridA[row - 1][0])\\n cell.add_neighbor(self._gridA[row + 1][0])\\n # bottom row\\n else:\\n # 1. lower left corner\\n if column == 0:\\n #print('lower left')\\n cell.add_neighbor(self._gridA[row][column + 1])\\n cell.add_neighbor(self._gridA[row - 1][column])\\n cell.add_neighbor(self._gridA[row - 1][column + 1])\\n cell.add_neighbor(self._gridA[self._rows - 2][self._columns - 1])\\n cell.add_neighbor(self._gridA[self._rows - 1][self._columns - 1])\\n cell.add_neighbor(self._gridA[0][0])\\n cell.add_neighbor(self._gridA[0][1])\\n cell.add_neighbor(self._gridA[0][self._columns - 1])\\n # 2. rest of the bottom row\\n elif column < (self._columns - 1):\\n #print('lower')\\n cell.add_neighbor(self._gridA[row][column - 1])\\n cell.add_neighbor(self._gridA[row][column + 1])\\n cell.add_neighbor(self._gridA[row - 1][column - 1])\\n cell.add_neighbor(self._gridA[row - 1][column])\\n cell.add_neighbor(self._gridA[row - 1][column + 1])\\n cell.add_neighbor(self._gridA[0][column - 1])\\n cell.add_neighbor(self._gridA[0][column])\\n cell.add_neighbor(self._gridA[0][column + 1])\\n # lower right corner\\n else:\\n #print('lower right')\\n cell.add_neighbor(self._gridA[row][column - 1])\\n cell.add_neighbor(self._gridA[row - 1][column - 1])\\n cell.add_neighbor(self._gridA[row - 1][column])\\n cell.add_neighbor(self._gridA[0][0])\\n cell.add_neighbor(self._gridA[self._rows - 1][0])\\n cell.add_neighbor(self._gridA[self._rows - 2][0])\\n cell.add_neighbor(self._gridA[0][self._columns - 2])\\n cell.add_neighbor(self._gridA[0][self._columns - 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"135de9b64c38176dad29c8d46391433c\",\n \"score\": \"0.54305255\",\n \"text\": \"def ggpl_suburban_neighborhood():\\n\\tbase = CUBOID([62, 47, -5])\\n\\tbase = MATERIAL([.05,.05,.05,.05, .4,.2,0,1, 0,0,0,0, 0,0,0,1, 100])(base)\\n\\tgrass = CUBOID([60,45,0.1])\\n\\tgrass = MATERIAL([0,0,0,1, 0,.1,0,1, 0,.1,0,1, 0,0,0,1, 0])(grass)\\n\\tstreetsHeight = .5\\n\\tstreetsWidth = 4\\n\\tcurveStreetsWidth = streetsWidth - 0.7\\n\\n\\t\\n\\ttennisField = generate_tennis_field(8, 6, 5)\\n\\ttennisField = T([1,2])([28,22.5])(tennisField)\\n\\t\\n\\tstraightStreet_1 = generate_straight_street(15.5, streetsWidth, streetsHeight, -PI/3.5, -3, 26)\\n\\tstraightStreet_2 = generate_straight_street(15, streetsWidth, streetsHeight, -PI/8, 47, 7)\\n\\tstraightStreet_3 = generate_straight_street(15, streetsWidth, streetsHeight, 0, 22, 30)\\n\\tstraightStreet_4 = generate_straight_street(8, streetsWidth -1, streetsHeight, PI/4, 9.5, 20)\\n\\n\\tcurveStreet_1 = MAP(BEZIERCURVE([[9.5, 16], [15, 13], [27, 10], [35,11], [41, 12], [45,11], [50, 9]]))(INTERVALS(1)(32))\\n\\tcurveStreet_1 = OFFSET([curveStreetsWidth, curveStreetsWidth])(curveStreet_1)\\n\\tcurveStreet_1 = T([1,2])([-3.1,-1.9])(curveStreet_1)\\n\\tcurveStreet_1 = PROD([curveStreet_1, Q(streetsHeight)])\\n\\n\\tcurveStreet_2 = MAP(BEZIERCURVE([[49,9], [47, 12], [49,18], [47,19], \\n\\t\\t[47,20],[46,22], [45,24], [44,26],[43,28], [41,31], [39,32.5], [36,32.6]]))(INTERVALS(1)(32))\\n\\tcurveStreet_2 = OFFSET([curveStreetsWidth, curveStreetsWidth])(curveStreet_2)\\n\\tcurveStreet_2 = T([1,2])([-2,-2])(curveStreet_2)\\n\\tcurveStreet_2 = PROD([curveStreet_2, Q(streetsHeight)])\\n\\n\\tcurveStreet_3 = MAP(BEZIERCURVE([[22,32.25],[21,31.5], [20,31], [19.5,30.5]]))(INTERVALS(1)(32))\\n\\tcurveStreet_3 = OFFSET([curveStreetsWidth, curveStreetsWidth])(curveStreet_3)\\n\\tcurveStreet_3 = T([1,2])([-2,-1.5])(curveStreet_3)\\n\\tcurveStreet_3 = PROD([curveStreet_3, Q(streetsHeight)])\\n\\n\\trotary = CYLINDER([5, streetsHeight])(100)\\n\\trotary_sidewalker = CYLINDER([5.75, streetsHeight/2])(100)\\n\\trotary_center = CYLINDER([1, streetsHeight])(100)\\n\\trotary = DIFFERENCE([rotary, rotary_center])\\n\\trotary_sidewalker = DIFFERENCE([rotary_sidewalker, rotary_center])\\n\\trotary = T([1,2])([16,29])(rotary)\\n\\trotary_sidewalker = T([1,2])([16,29])(rotary_sidewalker)\\n\\n\\t#generating houses\\n\\thouse_1 = generate_house(1, 5,6,5, PI/4, 7,22.5)\\n\\thouse_2 = generate_house(2, 8,6.75,8, PI/2.3, 24,14)\\n\\thouse_3 = generate_house(2, 8,14,9, PI/2, 43,13)\\n\\thouse_4 = generate_house(2, 8,13,8, PI/22, 51,9)\\n\\thouse_5 = generate_house(1, 8,8,6, PI/6, 48, 24)\\n\\thouse_6 = generate_house(1, 8,8,6, -PI/6, 36.5, 36.5)\\t\\t\\t\\n\\thouse_7 = generate_house(2, 8,14,8, PI/2, 37, 35)\\n\\thouse_8 = generate_house(2, 8,8,8, PI/1.35, 15, 37)\\n\\n\\t#adding walkways\\n\\twalkwayHeight = 0.15\\n\\thouse_1_walkway = generate_walkway(3.9,0.75,walkwayHeight, -PI/4, 5,25.6)\\n\\thouse_2_walkway = generate_walkway(1.25, 1.25,walkwayHeight, PI/2.3, 20.55,14.5)\\n\\thouse_3_walkway = generate_walkway(0.85, 2.2,walkwayHeight, PI/2, 35.65,13)\\n\\thouse_4_walkway = generate_walkway(1.5, 2,walkwayHeight, PI/22, 49.5,15.65)\\n\\thouse_5_walkway = MAP(BEZIERCURVE([[46, 24], [48.5, 24.5], [48, 26], [46.8, 28]]))(INTERVALS(1)(32))\\n\\thouse_5_walkway = OFFSET([1,1])(house_5_walkway)\\n\\thouse_5_walkway = PROD([house_5_walkway, Q(walkwayHeight)])\\n\\thouse_6_walkway = generate_walkway(5.5,1.2,walkwayHeight, PI/2.9, 37.95,34)\\n\\thouse_7_walkway = generate_walkway(1.25,2.3,walkwayHeight, PI/2, 29.7,34.6)\\n\\thouse_8_walkway = generate_walkway(1.7,1.5,walkwayHeight, PI/1.35, 12.6,33.6)\\n\\n\\t#adding sidewalkers\\n\\tstraightStreet_1_sidewalker = generate_sidewalker(straightStreet_1)\\n\\tstraightStreet_2_sidewalker = generate_sidewalker(straightStreet_2)\\n\\tstraightStreet_3_sidewalker = generate_sidewalker(straightStreet_3)\\n\\tstraightStreet_4_sidewalker = generate_sidewalker(straightStreet_4)\\n\\tcurveStreet_1_sidewalker = generate_sidewalker(curveStreet_1)\\n\\tcurveStreet_2_sidewalker = generate_sidewalker(curveStreet_2)\\n\\tcurveStreet_3_sidewalker = generate_sidewalker(curveStreet_3)\\n\\n\\t#cleaning borders\\n\\tcleanerLeftBorder = CUBOID([-5, 30, streetsHeight])\\n\\tcleanerRightBorder = CUBOID([5, 10, streetsHeight])\\n\\tcleanerRightBorder = T([1])([60])(cleanerRightBorder)\\n\\tstraightStreet_1 = DIFFERENCE([straightStreet_1, cleanerLeftBorder])\\n\\tstraightStreet_1_sidewalker = DIFFERENCE([straightStreet_1_sidewalker, cleanerLeftBorder])\\n\\tstraightStreet_2 = DIFFERENCE([straightStreet_2, cleanerRightBorder])\\n\\tstraightStreet_2_sidewalker = DIFFERENCE([straightStreet_2_sidewalker, cleanerRightBorder])\\n\\n\\t#assembling\\n\\tstreets = STRUCT([straightStreet_1, straightStreet_2, straightStreet_3, straightStreet_4, curveStreet_1, curveStreet_2, curveStreet_3, rotary])\\n\\tsidewalkers = STRUCT([straightStreet_1_sidewalker, straightStreet_2_sidewalker, straightStreet_3_sidewalker, straightStreet_4_sidewalker, curveStreet_1_sidewalker, curveStreet_2_sidewalker, curveStreet_3_sidewalker, rotary_sidewalker])\\n\\tsidewalkers = T([3])([0.15])(sidewalkers)\\n\\twalkways = STRUCT([house_1_walkway, house_2_walkway, house_3_walkway, house_4_walkway, house_5_walkway, house_6_walkway, house_7_walkway, house_8_walkway])\\n\\tstreets = MATERIAL([.1,.1,.1,.2, 0,0,0,1, 0,0,0,1, 0,0,0,1, 10])(streets)\\n\\thouses = STRUCT([house_1, house_2, house_3, house_4, house_5, house_6, house_7, house_8])\\n\\tmodel = STRUCT([streets, grass, sidewalkers, houses, tennisField, walkways])\\n\\tmodel = T([1,2])([1,1])(model)\\n\\tVIEW(STRUCT([model, base]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d55f309aa6638daf57be6cd2433290af\",\n \"score\": \"0.542803\",\n \"text\": \"def createNeighbours(self, electronTravelRange):\\n\\t\\tself.neighbours = dict()\\n\\t\\tfor index in range(self.N):\\n\\t\\t\\tindexNeighbours = list()\\n\\t\\t\\tpos1 = self.getPosition(index)\\n\\t\\t\\tfor i in range(self.N):\\n\\t\\t\\t\\tpos2 = self.getPosition(i)\\n\\t\\t\\t\\tdist = np.linalg.norm(pos1 - pos2)\\n\\t\\t\\t\\tif dist <= electronTravelRange:\\n\\t\\t\\t\\t\\tindexNeighbours.append(i)\\n\\t\\t\\tself.neighbours[index] = np.array(indexNeighbours)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd6c81f389b99425fd15cdce8eb82dd1\",\n \"score\": \"0.540951\",\n \"text\": \"def double2D(self):\\n # First find the 2D direction\\n for dim_index in range(3):\\n if self.dims[dim_index] == 2:\\n # Increase the size of the 2D dimension\\n new_dimensions = self.dims[:]\\n new_dimensions[dim_index] = new_dimensions[dim_index] + 1\\n newCoords = np.zeros((new_dimensions[0], new_dimensions[1], new_dimensions[2], 3))\\n\\n if dim_index == 0:\\n for i in range(self.dims[1]):\\n for j in range(self.dims[2]):\\n newCoords[0, i, j, :] = self.coords[0, i, j, :]\\n newCoords[2, i, j, :] = self.coords[1, i, j, :]\\n newCoords[1, i, j, :] = 0.5 * (newCoords[0, i, j, :] + newCoords[2, i, j, :])\\n elif dim_index == 1:\\n for i in range(self.dims[0]):\\n for j in range(self.dims[2]):\\n newCoords[i, 0, j, :] = self.coords[i, 0, j, :]\\n newCoords[i, 2, j, :] = self.coords[i, 1, j, :]\\n newCoords[i, 1, j, :] = 0.5 * (newCoords[i, 0, j, :] + newCoords[i, 2, j, :])\\n elif dim_index == 2:\\n for i in range(self.dims[0]):\\n for j in range(self.dims[1]):\\n newCoords[i, j, 0, :] = self.coords[i, j, 0, :]\\n newCoords[i, j, 2, :] = self.coords[i, j, 1, :]\\n newCoords[i, j, 1, :] = 0.5 * (newCoords[i, j, 0, :] + newCoords[i, j, 2, :])\\n\\n for boco in self.bocos:\\n for j in range(2):\\n if boco.ptRange[dim_index, j] == 2:\\n boco.ptRange[dim_index, j] = 3\\n for b2b in self.B2Bs:\\n for j in range(2):\\n if b2b.ptRange[dim_index, j] == 2:\\n b2b.ptRange[dim_index, j] = 3\\n if b2b.donorRange[dim_index, j] == 2:\\n b2b.donorRange[dim_index, j] = 3\\n\\n # Replace previous coordinates\\n self.coords = newCoords\\n self.dims = new_dimensions[:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0267bb606288ebd62f73628d7260228b\",\n \"score\": \"0.5401753\",\n \"text\": \"def add_bed_neighbors(bed, n_neighbor=40, sample_res=25):\\n nrange = n_neighbor * sample_res\\n neighbors = np.arange(-nrange, nrange + 1, sample_res)\\n rs_tag = np.array([f';{x}' for x in neighbors])\\n\\n chr_n = np.repeat(bed['chr'].values[:, None], len(neighbors), axis=1)\\n pos_n = bed['pos'].values[:, None] + neighbors[None, :]\\n pos_end_n = pos_n + 1\\n rs_n = bed['rs'].values[:, None] + rs_tag[None, :]\\n\\n neigh_bed = pd.DataFrame({'chr': chr_n.flatten(),\\n 'pos': pos_n.flatten(),\\n 'pos_end': pos_end_n.flatten(),\\n 'rs': rs_n.flatten()})\\n return neigh_bed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"987772845ad741387018026239d1d8f7\",\n \"score\": \"0.5394306\",\n \"text\": \"def bond_neighborhoods(gra):\\n bnd_keys = list(bond_keys(gra))\\n\\n def _neighborhood(bnd_key):\\n nbh_bnd_keys = set(filter(lambda x: bnd_key & x, bnd_keys))\\n return bond_induced_subgraph(gra, nbh_bnd_keys)\\n\\n bnd_nbh_dct = dict(zip(bnd_keys, map(_neighborhood, bnd_keys)))\\n return bnd_nbh_dct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08754cdc9cfe6e920870008308d7a511\",\n \"score\": \"0.53427255\",\n \"text\": \"def w2dg(w):\\n\\n w_l = [(i,j) for i in w.neighbors for j in w[i]]\\n G = nx.DiGraph()\\n G.add_edges_from(w_l)\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b09026668fb412adfa7a05545fafbc\",\n \"score\": \"0.5316365\",\n \"text\": \"def create_init_wedge_if(self):\\n coex_dens = self._cal_coex_dens()\\n r = []\\n for coex_i in coex_dens:\\n startShape = self._size[1:]\\n start = np.full(startShape, coex_i[0])\\n stop = np.full(startShape, coex_i[1])\\n r_i = np.linspace(start, stop, self._size[0], axis=0)\\n r.append(r_i)\\n r[1]=r[2]=np.full(self._size, 0)\\n self.set_r(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1a44e645455bf333c37d68a99a749b\",\n \"score\": \"0.5285604\",\n \"text\": \"def __init__(self, obj_in, sepsqr_in):\\n this = _spilltree2D.new__NeighborInfo(obj_in, sepsqr_in)\\n try:\\n self.this.append(this)\\n except Exception:\\n self.this = this\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6710863aa867507b85837288b8b6798a\",\n \"score\": \"0.5273732\",\n \"text\": \"def create_neighbours(self, orthogonal_only = 0):\\n for i in xrange(self._n_cols):\\n for j in xrange(self._n_rows):\\n if not self.on_board(i,j):\\n continue\\n for k in (-1, 0, 1):\\n for l in (-1, 0, 1):\\n if k==l==0 or not self.on_board(i+k,j+l):\\n continue\\n if k != l and k != -l:\\n self.grid[i][j].add_neighbour(self.grid[i+k][j+l])\\n elif not orthogonal_only and k != 0 and l != 0:\\n self.grid[i][j].add_neighbour(self.grid[i+k][j+l])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9201c6bcd1e33dbd134f038c21f44f09\",\n \"score\": \"0.52707744\",\n \"text\": \"def construct_four_nodes(self):\\n self.four_nodes.make_neighbor(\\\"node1\\\", \\\"node2\\\")\\n self.four_nodes.make_neighbor(\\\"node2\\\", \\\"node1\\\")\\n self.four_nodes.make_neighbor(\\\"node1\\\", \\\"node3\\\")\\n self.four_nodes.make_neighbor(\\\"node3\\\", \\\"node1\\\")\\n\\n self.four_nodes.make_neighbor(\\\"node1\\\", \\\"node4\\\")\\n self.four_nodes.make_neighbor(\\\"node4\\\", \\\"node1\\\")\\n self.four_nodes.make_neighbor(\\\"node2\\\", \\\"node4\\\")\\n self.four_nodes.make_neighbor(\\\"node4\\\", \\\"node2\\\")\\n self.four_nodes.make_neighbor(\\\"node3\\\", \\\"node4\\\")\\n self.four_nodes.make_neighbor(\\\"node4\\\", \\\"node3\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8763a72c2e7e03e7b6628136e9705f69\",\n \"score\": \"0.5267199\",\n \"text\": \"def __get_neighs(self, img: GrayImg, x: int, y: int, d: int, pad=False) -> Neighs:\\n # Get new bounds and padding.\\n x1, x2, padding_x = self.__check_bounds(x, d, img.shape[1])\\n y1, y2, padding_y = self.__check_bounds(y, d, img.shape[0])\\n\\n # Create neighs.\\n neighs = img[y1:y2, x1:x2]\\n\\n if pad:\\n # Padding given the x, y values.\\n padding = [padding_y, padding_x]\\n # Pad neighs.\\n val = img[y][x]\\n neighs = np.pad(\\n neighs, padding, mode=\\\"constant\\\", constant_values=(val, val)\\n )\\n\\n return neighs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23001a1e990367a6373599be050264fd\",\n \"score\": \"0.52653444\",\n \"text\": \"def _nodal_gradient_stencil(self):\\n # Compute divergence operator on faces\\n if self.dim == 1:\\n G = self._nodal_gradient_x_stencil\\n elif self.dim == 2:\\n G = sp.vstack(\\n (self._nodal_gradient_x_stencil, self._nodal_gradient_y_stencil), format=\\\"csr\\\"\\n )\\n elif self.dim == 3:\\n G = sp.vstack(\\n (\\n self._nodal_gradient_x_stencil,\\n self._nodal_gradient_y_stencil,\\n self._nodal_gradient_z_stencil,\\n ),\\n format=\\\"csr\\\",\\n )\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9639527171157286824846e6852fe115\",\n \"score\": \"0.5261359\",\n \"text\": \"def _nodal_gradient_stencil(self):\\n # Compute divergence operator on faces\\n if self.dim == 1:\\n G = self._nodal_gradient_x_stencil\\n elif self.dim == 2:\\n G = sp.vstack(\\n (self._nodal_gradient_x_stencil, self._nodal_gradient_y_stencil),\\n format=\\\"csr\\\",\\n )\\n elif self.dim == 3:\\n G = sp.vstack(\\n (\\n self._nodal_gradient_x_stencil,\\n self._nodal_gradient_y_stencil,\\n self._nodal_gradient_z_stencil,\\n ),\\n format=\\\"csr\\\",\\n )\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79d49375dbdcb16b523385dd8c2d23c4\",\n \"score\": \"0.52481633\",\n \"text\": \"def get_neighborhood(self, node_id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc5cf1919ae7f576430bdeb112ae2af\",\n \"score\": \"0.52372116\",\n \"text\": \"def _nodal_gradient_z_stencil(self):\\n if self.dim == 1 or self.dim == 2:\\n return None\\n else:\\n Gz = kron3(\\n ddx(self.shape_cells[2]),\\n speye(self.shape_nodes[1]),\\n speye(self.shape_nodes[0]),\\n )\\n return Gz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc5cf1919ae7f576430bdeb112ae2af\",\n \"score\": \"0.52372116\",\n \"text\": \"def _nodal_gradient_z_stencil(self):\\n if self.dim == 1 or self.dim == 2:\\n return None\\n else:\\n Gz = kron3(\\n ddx(self.shape_cells[2]),\\n speye(self.shape_nodes[1]),\\n speye(self.shape_nodes[0]),\\n )\\n return Gz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce621322559297ac70936d44d92a5f50\",\n \"score\": \"0.52186394\",\n \"text\": \"def _create_dbn(self):\\n # Nodes in slice 1\\n hidden_1 = NodeFactory.new_discrete_node(self.size_h, \\\"h1\\\") \\n mus = numpy.zeros((self.size_h, 2))\\n kappas = numpy.ones((self.size_h, 3)) * 0.5 \\n dihedral_angles = NodeFactory.new_vonmises2d_node(\\\"d\\\", mus, kappas) \\n amino_acids = NodeFactory.new_discrete_node(self.size_aa, \\\"a\\\")\\n secondary_structure = NodeFactory.new_discrete_node(self.size_ss, \\\"s\\\")\\n cis = NodeFactory.new_discrete_node(self.size_cis, \\\"c\\\")\\n \\n # Nodes in slice 2\\n hidden_2 = NodeFactory.new_discrete_node(self.size_h, \\\"h2\\\")\\n\\n dbn = DBN() \\n dbn.set_slices(\\n [hidden_1, dihedral_angles, amino_acids, secondary_structure, cis],\\n [hidden_2, dihedral_angles, amino_acids, secondary_structure, cis],\\n ) \\n dbn.add_inter(\\\"h1\\\", \\\"h2\\\")\\n dbn.add_intra(\\\"h1\\\", \\\"d\\\")\\n dbn.add_intra(\\\"h1\\\", \\\"a\\\")\\n dbn.add_intra(\\\"h1\\\", \\\"s\\\")\\n dbn.add_intra(\\\"h1\\\", \\\"c\\\")\\n\\n dbn.construct()\\n return dbn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b091a1c1e219526a9437bd1f8af61259\",\n \"score\": \"0.52170664\",\n \"text\": \"def nd_new_game(dims, bombs):\\n\\n # fills boards\\n b = nd_mkboard(dims, 0, 0)\\n m = nd_mkboard(dims, False, 0)\\n\\n for coords in bombs:\\n nd_set(b, coords, '.')\\n neighbors = nd_neighbors({\\\"dimensions\\\": dims, \\\"board\\\" : b, \\\"mask\\\" : m, \\\"state\\\": \\\"ongoing\\\"}, coords)\\n for n in neighbors:\\n item = nd_get(b, n, 0)\\n if item != '.':\\n nd_set(b, n, item + 1)\\n\\n return {\\\"dimensions\\\": dims, \\\"board\\\" : b, \\\"mask\\\" : m, \\\"state\\\": \\\"ongoing\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad8736bbc31ee424eaaa3104fdf42fb5\",\n \"score\": \"0.52059615\",\n \"text\": \"def __init__(self):\\n self.starting_h = House(\\\"Skywalker\\\", self.skywalker_room())\\n self.simpson_h = House(\\\"Simpson\\\", self.simpson_room())\\n self.mccallister_h = House(\\\"McCallister\\\", self.mccallister_room())\\n self.szalinski_h = House(\\\"Szalinski\\\", self.szalinski_room())\\n self.addams_h = House(\\\"Addams\\\", self.addams_room())\\n self.tanner_h = House(\\\"Tanner\\\", self.tanner_room())\\n self.taylor_h = House(\\\"Taylor\\\", self.taylor_room())\\n self.griswald_h = House(\\\"Griswald\\\", self.griswald_room())\\n self.player_h = House(\\\"Player\\\", self.player_room())\\n\\n self.starting_h.add_neighbor(\\\"east\\\", self.simpson_h)\\n self.simpson_h.add_neighbor(\\\"west\\\", self.starting_h)\\n self.simpson_h.add_neighbor(\\\"south\\\", self.mccallister_h)\\n self.mccallister_h.add_neighbor(\\\"north\\\", self.simpson_h)\\n self.mccallister_h.add_neighbor(\\\"west\\\", self.szalinski_h)\\n self.mccallister_h.add_neighbor(\\\"east\\\", self.addams_h)\\n self.mccallister_h.add_neighbor(\\\"south\\\", self.tanner_h)\\n self.szalinski_h.add_neighbor(\\\"east\\\", self.mccallister_h)\\n self.addams_h.add_neighbor(\\\"west\\\", self.mccallister_h)\\n self.tanner_h.add_neighbor(\\\"north\\\", self.mccallister_h)\\n self.addams_h.add_neighbor(\\\"south\\\", self.taylor_h)\\n self.taylor_h.add_neighbor(\\\"north\\\", self.addams_h)\\n self.taylor_h.add_neighbor(\\\"west\\\", self.tanner_h)\\n self.tanner_h.add_neighbor(\\\"east\\\", self.taylor_h)\\n self.tanner_h.add_neighbor(\\\"south\\\", self.griswald_h)\\n self.griswald_h.add_neighbor(\\\"north\\\", self.tanner_h)\\n self.griswald_h.add_neighbor(\\\"west\\\", self.player_h)\\n self.player_h.add_neighbor(\\\"east\\\", self.griswald_h)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8726d35d93e70ba885b49084d294f8\",\n \"score\": \"0.5192772\",\n \"text\": \"def __init__(self, n, w, h, r, gs, nn):\\n\\n self.numCells = n\\n self.width = w\\n self.height = h\\n self.avgRadius = r\\n self.radiusDistIndex = gbl.radiusDistIndex\\n self.radiiVec = np.random.normal(r, self.radiusDistIndex*r, n)\\n self.centresVec = np.zeros((n*2))\\n self.gradientsVec = np.zeros((n*2))\\n self.numNeighbours = nn\\n\\n # Not sure if its necessary to initialise these pointers\\n # here as they will be assigned to data by functions later\\n self.KDTree = None\\n self.nearestNeighboursVec = np.zeros((n, nn))\\n self.nearestNeighbourDistanceVec = np.zeros((n, nn))\\n self.nearestNeighbourGapVec = np.zeros((n, nn))\\n\\n # The following matrix is used by functions such as errorFunction\\n # to keep x, y co-ordinates (self.centresVec) within the cell array\\n # area defined by w, h\\n # Only needs calculating once therefore do it at initialisation\\n self.m = np.concatenate((np.repeat(w, n), np.repeat(h, n)))\\n\\n # set up a regular grid to be used for cell density\\n # calculation\\n self.gridSpacing = gs\\n self.gridPoints = self.sequenceOfXYRegularGrid(self.gridSpacing)\\n\\n # Set radius of the circular area around each grid point to\\n # ensure that all space is covered (circle edges touch when\\n # self.areaRadiusFactor = 0.5*np.sqrt(2.0)\\n self.areaRadiusFactor = 0.70710678118654757\\n\\n # Note: for good results ensure that grid spacing is large enough\\n # ensure that:\\n # gridSpacing * areaRadiusFactor > n * cellRadius\\n # where n is at least 2\\n\\n self.cellDensitiesAtGridPoints = np.zeros((w/gs, h/gs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc40a1a6f6d558c1299d239ccc58c2f\",\n \"score\": \"0.5192499\",\n \"text\": \"def build_neighbourhood_graph(edges):\\n graph = networkx.Graph()\\n edges = map( lambda e: [e[0],e[1],{'weight':e[2]}], edges)\\n graph.add_edges_from(edges)\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"259f023f4684e37128bb336a81c38333\",\n \"score\": \"0.5181584\",\n \"text\": \"def neighborhood(self):\\n if self.DATA_FLAG:\\n val_data = self.DATA.values.tolist()\\n else:\\n val_data = self.SAMPLE_DATA # for sample sets\\n lrds = []\\n reach_distances = []\\n read_index1 = 0\\n neighbors_dict = {}\\n reduced_points = []\\n for reading1 in val_data:\\n # self.REDUCED_POINTS.append(visuals.dimension_reduction(reading1))\\n self.REDUCED_POINTS.append(visuals.PCA_reduction(reading1))\\n neighbors = {}\\n neighbors_dict[read_index1] = []\\n read_index2 = 0\\n for reading2 in val_data:\\n if read_index1 != read_index2:\\n print(\\\"Reading indices: \\\" + str(read_index1) + \\\" \\\" + str(read_index2))\\n distance = sum(abs(np.array(list(reading1)) - np.array(list(reading2))))\\n distance = round(distance, ndigits=2)\\n neighbors[read_index2] = distance\\n read_index2 = read_index2 + 1\\n sorted_temp = sorted(neighbors.items(), key=lambda kv: kv[1])\\n neighbors = OrderedDict(sorted_temp)\\n neighbors = list(itertools.islice(neighbors.items(), 0, self.K))\\n # print(neighbors)\\n for n in neighbors:\\n neighbors_dict[read_index1].append(n)\\n lrds.append(self.LRD(neighbors, self.K))\\n read_index1 = read_index1 + 1\\n\\n return [lrds, neighbors_dict]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"731951082d4326ba88e73490d6b0b3c1\",\n \"score\": \"0.51571393\",\n \"text\": \"def build_new_rings(self):\\n\\n self.other_complex = self.find_complex(self.unselected_partner)\\n self.occupied_other = self.find_occupied(self.other_complex)\\n self.possible_other = self.find_possible(self.other_complex)\\n self.available_other = self.find_available(self.possible_other,\\n self.occupied_other)\\n self.possible_ring = []\\n for site in self.available_other:\\n self.possible_ring.append(site)\\n # If the rule is already a ring closure, self.available_sites and\\n # self.available_other will contain the same elements (but in a\\n # different order). If the rule is not a ring closure,\\n # self.available_sites and self.available_other will be completely\\n # disjoint.\\n # So, if I find at least one common element between\\n # self.available_sites and self.available_other, it means that the\\n # rule is already a ring closure.\\n self.is_closure = False\\n for site in self.available_sites:\\n if site not in self.available_other:\\n self.possible_ring.append(site)\\n else:\\n self.is_closure = True\\n # No site from the chosen_partner should serve to close a ring.\\n # It would form an intramolecular bond.\\n auto_sites = []\\n for site in self.possible_ring:\\n if site[0] == self.selected_site[0]:\\n auto_sites.append(site)\\n for site in auto_sites:\\n self.possible_ring.remove(site)\\n # Find all possible rings.\\n self.ring_matches = []\\n for candidate in self.possible_ring:\\n candidate_type = candidate[1]\\n if (candidate[1].lower() == self.selected_site[3] and\\n candidate[3] == self.selected_site[1].lower()):\\n self.ring_matches.append(candidate)\\n # Create a rule for each possible ring.\\n rule_num = 3 # rule_free and rule_bound are numbers 1 and 2.\\n self.rules_ring = []\\n self.uni_rules_ring = []\\n for ring_site in self.ring_matches:\\n new_closure = False\\n if self.is_closure == False and ring_site in self.available_other:\\n new_closure = True\\n ring_index = self.find_index(ring_site)\\n agents_ring = self.new_agents()\\n agents_ring = self.add_new_ring(ring_index, ring_site, agents_ring)\\n name_ring = self.create_new_name(self.rule_name, rule_num)\\n uni_name_ring = self.create_new_name(self.unary_name, rule_num)\\n if new_closure == True:\\n space = name_ring.rfind(\\\" \\\")\\n name_ring = \\\"{} cls{}\\\".format(name_ring[:space],\\n name_ring[space:])\\n curl_open = self.unary_rate.index(\\\"{\\\")\\n curl_close = self.unary_rate.index(\\\"}\\\")\\n rate = self.unary_rate[curl_open+1:curl_close]\\n else:\\n rate = self.rule_rate\\n rule_ring = self.create_new_rule(agents_ring, name_ring,\\n rate)\\n uni_rule_ring = self.create_new_rule(agents_ring, uni_name_ring,\\n self.unary_rate)\\n if new_closure == True:\\n uni_rule_ring = \\\"//{}\\\".format(uni_rule_ring)\\n rule_num += 1\\n self.rules_ring.append(rule_ring)\\n self.uni_rules_ring.append(uni_rule_ring)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0fd6a3064793a3203e298d1b7195ba8\",\n \"score\": \"0.5124115\",\n \"text\": \"def get_double_grgrid_address(address, D_diag, PS=None):\\n\\n address_double = np.zeros(3, dtype='int_', order='C')\\n if PS is None:\\n _PS = np.zeros(3, dtype='int_')\\n else:\\n _PS = np.array(PS, dtype='int_')\\n ksp.double_grgrid_address(address_double,\\n np.array(address, dtype='int_'),\\n np.array(D_diag, dtype='int_'),\\n _PS)\\n return address_double\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7f57f1049325ac7d7e9150391c94a8\",\n \"score\": \"0.5123845\",\n \"text\": \"def suburban_neighborhood():\\n\\n\\tpiano = CUBOID([540,180,3])\\n\\t#piano = MATERIAL([0,0.2,0,1, 0,0.2,0,1, 0,0.1,0,1, 0,0,0,1, 0])(piano)\\n\\t#TEXTURE([<string:nome file immagine>, <bool:boh?-Metto sempre a True>, <bool:boh?-Metto sempre a False>, <intero:boh?-Metto sempre 1>, <intero:boh?-Metto sempre 1>, <float: rotazione in gradi. Di solito 0 oppure -PI/2>, <numero ripetizioni su un asse>, <numero ripetizioni su altro asse>])\\n\\tpiano = TEXTURE([\\\"texture/grass.jpg\\\",True,False,1,1,0,30,15])(piano)\\n\\n\\tstreets = []\\n\\tstreets = street1(streets)\\n\\tstreets = street2(streets)\\n\\n\\tstreets = street3(streets)\\n\\tstreets = street4(streets)\\n\\n\\tstreets = STRUCT(streets)\\n\\n\\thouse1 = insertHouse(265,10,3,0,3,\\\"lines/house1/\\\",[1,2,3,1,3,1])\\n\\thouse2 = insertHouse(310,10,3,0,3,\\\"lines/house1/\\\",[1,2,3,1,3,1])\\n\\thouse3 = insertHouse(355,10,3,0,3,\\\"lines/house1/\\\",[1,2,3,1,3,1])\\n\\thouse4 = insertHouse(400,10,3,0,3,\\\"lines/house1/\\\",[1,2,3,1,3,1])\\n\\n\\thouse5 = insertHouse(281,140,3,-PI/2,3,\\\"lines/house2/\\\",[1,2,1,2,4,3,3,1])\\n\\thouse6 = insertHouse(362,129,3,PI,3,\\\"lines/house1/\\\",[1,2,3,1,3,1])\\n\\thouse7 = insertHouse(400,122,3,PI,4,\\\"lines/house1/\\\",[1,2,3,1,3,1])\\n\\thouse8 = insertHouse(438,120,3,PI,4,\\\"lines/house1/\\\",[1,2,3,1,3,1])\\n\\n\\thouse9 = insertHouse(241.5,62,3,0,3,\\\"lines/house2/\\\",[1,2,1,2,4,3,3,1])\\n\\n\\thouses = STRUCT([house1,house2,house3,house4,house5,house6,house7,house8,house9])\\n\\n\\t#trees = insertTrees()\\n\\ttrees1 = insertTrees2(60,5,54,20,105)\\n\\ttrees2 = insertTrees2(60,56,108,35,110)\\n\\ttrees3 = insertTrees2(40,110,162,45,110)\\n\\ttrees4 = insertTrees2(20,164,216,68,100)\\n\\n\\ttrees5 = insertTrees2(17,270,300,67,77)\\n\\ttrees6 = insertTrees2(40,350,442,59,73)\\n\\ttrees7 = insertTrees2(4,324,334,66,72)\\n\\n\\ttrees8 = insertTrees2(180,5,265,115,177)\\n\\n\\ttrees = STRUCT([trees1,trees2,trees3,trees4,trees5,trees6,trees7,trees8])\\n\\n\\tstreets = SOLIDIFY(streets)\\n\\tstreets = T([3])([3])(streets)\\n\\t#streets = TEXTURE([\\\"texture/streets.jpg\\\",True,False,1,1,0,100,100])(streets)\\n\\t#streets = MATERIAL([0.1,0.1,0.1,0.1, 0,0,0,1, 0,0,0,1, 0,0,0,1, 0])(streets)\\n\\tstreets = COLOR(Color4f([64/255., 64/255., 64/255., 1]))(streets)\\n\\n\\tsuburban = STRUCT([piano,houses,trees,streets])\\n\\t#suburban = STRUCT([piano,streets])\\n\\treturn suburban\",\n \"title\": \"\"\n },\n {\n \"docid\": \"918b07b8677a71a29cfea81994a25ee3\",\n \"score\": \"0.5119282\",\n \"text\": \"def createGraph(pond, rowSize, columnSize):\\n graph = Graph()\\n for x in range(0, rowSize):\\n for y in range(0, columnSize):\\n if pond[x][y] != \\\"*\\\": # vertex for cell with a rock is not required\\n src = makeVertexName(x, y)\\n graph.addVertex(src)\\n\\n #possible path towards the right side of the cell\\n if y != columnSize - 1:\\n # go right\\n rockIndex = -1\\n for k in range(y + 1, columnSize):\\n if pond[x][k] == \\\"*\\\":\\n rockIndex = k\\n break\\n # if rock is found in the path, then connect the vertex\\n # with cell prior to the rock otherwise, to the end of\\n # path\\n if rockIndex != -1:\\n dest = makeVertexName(x, rockIndex - 1)\\n else:\\n dest = makeVertexName(x, columnSize - 1)\\n graph.addEdge(src, dest) # make connection\\n\\n #possible path towards the left side of the cell\\n if y != 0:\\n # go left\\n rockIndex = -1\\n for k in range(y - 1, -1, -1):\\n if pond[x][k] == \\\"*\\\":\\n rockIndex = k\\n break\\n if rockIndex != -1:\\n dest = makeVertexName(x, rockIndex + 1)\\n else:\\n dest = makeVertexName(x, 0)\\n graph.addEdge(src, dest)\\n\\n #possible path going below the current cell\\n if x != rowSize - 1:\\n # go down\\n rockIndex = -1\\n for k in range(x + 1, rowSize):\\n if pond[k][y] == \\\"*\\\":\\n rockIndex = k\\n break\\n if rockIndex != -1:\\n dest = makeVertexName(rockIndex - 1, y)\\n else:\\n dest = makeVertexName(columnSize - 1, y)\\n graph.addEdge(src, dest)\\n\\n #possible path going above the current cell\\n if x != 0:\\n # go up\\n rockIndex = -1\\n for k in range(x - 1, -1, -1):\\n if pond[k][y] == \\\"*\\\":\\n rockIndex = k\\n break\\n if rockIndex != -1:\\n dest = makeVertexName(rockIndex + 1, y)\\n else:\\n dest = makeVertexName(0, y)\\n graph.addEdge(src, dest)\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"048c767ccfc022c28f412f4e7305577a\",\n \"score\": \"0.5111581\",\n \"text\": \"def _nodal_gradient_y_stencil(self):\\n if self.dim == 1:\\n return None\\n elif self.dim == 2:\\n Gy = sp.kron(ddx(self.shape_cells[1]), speye(self.shape_nodes[0]))\\n elif self.dim == 3:\\n Gy = kron3(\\n speye(self.shape_nodes[2]),\\n ddx(self.shape_cells[1]),\\n speye(self.shape_nodes[0]),\\n )\\n return Gy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"048c767ccfc022c28f412f4e7305577a\",\n \"score\": \"0.5111581\",\n \"text\": \"def _nodal_gradient_y_stencil(self):\\n if self.dim == 1:\\n return None\\n elif self.dim == 2:\\n Gy = sp.kron(ddx(self.shape_cells[1]), speye(self.shape_nodes[0]))\\n elif self.dim == 3:\\n Gy = kron3(\\n speye(self.shape_nodes[2]),\\n ddx(self.shape_cells[1]),\\n speye(self.shape_nodes[0]),\\n )\\n return Gy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cba368c2beff54262700ac46196339dd\",\n \"score\": \"0.51020604\",\n \"text\": \"def fill_neibor(self):\\n for node in self.nodes.values():\\n for coord in ((node.x + 1, node.y), (node.x, node.y + 1)):\\n neibor = self.nodes.get(coord)\\n if neibor:\\n neibor.add_neibor(node)\\n node.add_neibor(neibor)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05f3ca29e257ed48f2ead4d9548c1f83\",\n \"score\": \"0.5074569\",\n \"text\": \"def __init__(self, structure, bonding_strategy='MinimumOKeeffeNN',\\n bonding_strategy_kwargs=None,\\n color_scheme=\\\"VESTA\\\", color_scale=None,\\n radius_strategy=\\\"ionic\\\",\\n coordination_polyhedra=False,\\n draw_image_atoms=True,\\n repeat_of_atoms_on_boundaries=True,\\n bonded_atoms_outside_unit_cell=True,\\n scale=None):\\n\\n # draw periodic repeats if requested\\n if scale:\\n structure = structure * scale\\n\\n # we assume most uses of this class will give a structure as an input argument,\\n # meaning we have to calculate the graph for bonding information, however if\\n # the graph is already known and supplied, we will use that\\n if not isinstance(structure, StructureGraph):\\n if bonding_strategy not in self.allowed_bonding_strategies.keys():\\n raise ValueError(\\\"Bonding strategy not supported. Please supply a name \\\"\\n \\\"of a NearNeighbor subclass, choose from: {}\\\"\\n .format(\\\", \\\".join(self.allowed_bonding_strategies.keys())))\\n else:\\n bonding_strategy_kwargs = bonding_strategy_kwargs or {}\\n bonding_strategy = self.allowed_bonding_strategies[bonding_strategy](**bonding_strategy_kwargs)\\n self.structure_graph = StructureGraph.with_local_env_strategy(structure,\\n bonding_strategy,\\n decorate=False)\\n self.lattice = structure.lattice\\n else:\\n self.structure_graph = structure\\n self.lattice = self.structure_graph.structure.lattice\\n\\n if coordination_polyhedra:\\n # TODO: coming soon\\n raise NotImplementedError\\n\\n self.color_scheme = color_scheme\\n self.color_scale = color_scale\\n self.radius_strategy = radius_strategy\\n self.coordination_polyhedra = coordination_polyhedra\\n self.draw_image_atoms = draw_image_atoms\\n self.repeat_of_atoms_on_boundaries = repeat_of_atoms_on_boundaries\\n self.bonded_atoms_outside_unit_cell = bonded_atoms_outside_unit_cell\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41057d26da1d8737a25f2953e2510d0a\",\n \"score\": \"0.50729245\",\n \"text\": \"def get_double_grgrid_index(address_double, D_diag, PS=None):\\n if PS is None:\\n _PS = np.zeros(3, dtype='int_')\\n else:\\n _PS = np.array(PS, dtype='int_')\\n return ksp.double_grgrid_index(np.array(address_double, dtype='int_'),\\n np.array(D_diag, dtype='int_'),\\n _PS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f6b10d9e50c56fe9142b12379bf488\",\n \"score\": \"0.50646824\",\n \"text\": \"def fill_2d_holes(slide):\\n # Create a cross structural element\\n structure = ndimage.morphology.generate_binary_structure(2, 1)\\n \\n # Fill holes\\n filled_slide = ndimage.binary_fill_holes(slide, structure).astype(\\n slide.dtype)\\n\\n return filled_slide\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed8f62aecd47e763b62c1e4d4fa463ad\",\n \"score\": \"0.50445634\",\n \"text\": \"def calc_double_connections(self, former_layer, index):\\r\\n layer = []\\r\\n new_bond_num = int(self.bonds[-1][0]) + 1\\r\\n self.molecule_depth += 1\\r\\n self.curr_layer_num += 1\\r\\n first_atom = former_layer[0]\\r\\n second_atom = former_layer[1]\\r\\n nearest_atom = self.levels_atoms[index]\\r\\n self.make_new_bond(new_bond_num, first_atom[0], nearest_atom[0][0], 2)\\r\\n new_bond_num += 1\\r\\n self.make_new_bond(new_bond_num, second_atom[0], nearest_atom[0][0], 2)\\r\\n layer.append(nearest_atom[0])\\r\\n self.connected_atoms.append(nearest_atom[0])\\r\\n index += 1\\r\\n return layer, index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dee6bd55ca3cf2f7ff3dc0a92d208b55\",\n \"score\": \"0.5040204\",\n \"text\": \"def get_radius_2d(binary_image, skeleton_image, boundary_image, pix_size=None):\\n skeleton_image_copy = copy.deepcopy(skeleton_image)\\n skeleton_image_copy[skeleton_image == 0] = 255\\n skeleton_image_copy[boundary_image == 1] = 0\\n eucledian_radius_image = ndimage.distance_transform_bf(skeleton_image_copy, metric='taxicab', sampling=pix_size)\\n list_nzi = map(tuple, np.transpose(np.nonzero(skeleton_image)))\\n dict_nodes_radius = {item: eucledian_radius_image[item] for item in list_nzi}\\n return dict_nodes_radius\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71858bd78a5c800a921cc9d80911f37e\",\n \"score\": \"0.5033043\",\n \"text\": \"def door2D (Ylist,Zlist,occupancy):\\n def door2D0(dy,dz):\\n \\\"\\\"\\\"\\n Args:\\n dy (numero reale): dimensione massima su y del solido\\n dz (numero reale): dimensione massima su z del solido\\n \\n Returns:\\n structure (oggetto hpc): porta o finestra riprodotta\\n \\\"\\\"\\\"\\n \\n Y = adaptListToDimension(Ylist,dy)\\n Z = adaptListToDimension(Zlist,dz)\\n \\n occ = occupancy\\n \\n structure = STRUCT([CUBOID([0,0,0])])\\n\\n sumZ = 0\\n i = 0\\n \\n for row in occ :\\n tmpY = []\\n j=0\\n for val in row:\\n \\n if val: tmpY.append(Y[j])\\n else: tmpY.append(-Y[j])\\n j+=1\\n \\n qy = QUOTE(tmpY)\\n qz = QUOTE([Z[i]])\\n pxy = PROD([QUOTE([0]),qy])\\n pyz = PROD([pxy,qz])\\n \\n structure = STRUCT([structure,T([3])([sumZ]),pyz])\\n sumZ += Z[i]\\n i += 1\\n \\n return structure\\n return door2D0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bcc685828d0495ad910b79c7e0b60a3\",\n \"score\": \"0.5010403\",\n \"text\": \"def get_neighboring_zone_generic(zone_number: int, zone_letter: str, border: str) -> dict:\\n\\n if (zone_letter == \\\"X\\\" and \\\"N\\\" in border) or (zone_letter == \\\"C\\\" and \\\"S\\\" in border):\\n return {}\\n\\n neighbor_borders = []\\n neighbor_number = zone_number\\n neighbor_letter = zone_letter\\n\\n if \\\"N\\\" in border:\\n neighbor_letter = ZONE_LETTERS[ZONE_LETTERS.index(zone_letter.upper())+1]\\n neighbor_borders.append(\\\"S\\\")\\n\\n if \\\"S\\\" in border:\\n neighbor_letter = ZONE_LETTERS[ZONE_LETTERS.index(zone_letter.upper())-1]\\n neighbor_borders.append(\\\"N\\\")\\n\\n if \\\"W\\\" in border:\\n neighbor_number = 60 if zone_number == 1 else zone_number - 1\\n neighbor_borders.append(\\\"E\\\")\\n\\n if \\\"E\\\" in border:\\n neighbor_number = (zone_number + 1) % 60\\n neighbor_borders.append(\\\"W\\\")\\n\\n return {(neighbor_number, neighbor_letter): neighbor_borders}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1daae0035d848bc0d1b294f1d0f9420\",\n \"score\": \"0.50021625\",\n \"text\": \"def generateNeighborCoordinates(backbone, atoms, bonds, coordinates, ringSystems):\\n\\n for i in range(len(backbone)):\\n atom0 = backbone[i]\\n index0 = atoms.index(atom0)\\n\\n # Determine bond angles of all previously-determined bond locations for\\n # this atom\\n bondAngles = []\\n for atom1 in bonds[atom0]:\\n index1 = atoms.index(atom1)\\n if atom1 in backbone:\\n vector = coordinates[index1,:] - coordinates[index0,:]\\n angle = math.atan2(vector[1], vector[0])\\n bondAngles.append(angle)\\n bondAngles.sort()\\n \\n bestAngle = 2 * math.pi / len(bonds[atom0])\\n regular = True\\n for angle1, angle2 in zip(bondAngles[0:-1], bondAngles[1:]):\\n if all([abs(angle2 - angle1 - (i+1) * bestAngle) > 1e-4 for i in range(len(bonds[atom0]))]):\\n regular = False\\n\\n if regular:\\n # All the bonds around each atom are equally spaced\\n # We just need to fill in the missing bond locations\\n\\n # Determine rotation angle and matrix\\n rot = numpy.array([[math.cos(bestAngle), -math.sin(bestAngle)], [math.sin(bestAngle), math.cos(bestAngle)]], numpy.float64)\\n # Determine the vector of any currently-existing bond from this atom\\n vector = None\\n for atom1 in bonds[atom0]:\\n index1 = atoms.index(atom1)\\n if atom1 in backbone or numpy.linalg.norm(coordinates[index1,:]) > 1e-4:\\n vector = coordinates[index1,:] - coordinates[index0,:]\\n\\n # Iterate through each neighboring atom to this backbone atom\\n # If the neighbor is not in the backbone and does not yet have\\n # coordinates, then we need to determine coordinates for it\\n for atom1 in bonds[atom0]:\\n if atom1 not in backbone and numpy.linalg.norm(coordinates[atoms.index(atom1),:]) < 1e-4:\\n occupied = True; count = 0\\n # Rotate vector until we find an unoccupied location\\n while occupied and count < len(bonds[atom0]):\\n count += 1; occupied = False\\n vector = numpy.dot(rot, vector)\\n for atom2 in bonds[atom0]:\\n index2 = atoms.index(atom2)\\n if numpy.linalg.norm(coordinates[index2,:] - coordinates[index0,:] - vector) < 1e-4:\\n occupied = True\\n coordinates[atoms.index(atom1),:] = coordinates[index0,:] + vector\\n generateFunctionalGroupCoordinates(atom0, atom1, atoms, bonds, coordinates, ringSystems)\\n\\n else:\\n\\n # The bonds are not evenly spaced (e.g. due to a ring)\\n # We place all of the remaining bonds evenly over the reflex angle\\n startAngle = max(bondAngles)\\n endAngle = min(bondAngles)\\n if 0.0 < endAngle - startAngle < math.pi: endAngle += 2 * math.pi\\n elif 0.0 > endAngle - startAngle > -math.pi: startAngle -= 2 * math.pi\\n dAngle = (endAngle - startAngle) / (len(bonds[atom0]) - len(bondAngles) + 1)\\n \\n index = 1\\n for atom1 in bonds[atom0]:\\n if atom1 not in backbone and numpy.linalg.norm(coordinates[atoms.index(atom1),:]) < 1e-4:\\n angle = startAngle + index * dAngle\\n index += 1\\n vector = numpy.array([math.cos(angle), math.sin(angle)], numpy.float64)\\n vector /= numpy.linalg.norm(vector)\\n coordinates[atoms.index(atom1),:] = coordinates[index0,:] + vector\\n generateFunctionalGroupCoordinates(atom0, atom1, atoms, bonds, coordinates, ringSystems)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6613052e6955c5df37ed89f5640e89e6\",\n \"score\": \"0.49973366\",\n \"text\": \"def make_new_bond(self, new_bond_num, source, dest, bond_pair_num):\\r\\n if bond_pair_num > 0:\\r\\n bond_type = 2\\r\\n else:\\r\\n bond_type = 1\\r\\n self.bonds.append([new_bond_num, source, dest, bond_type])\\r\\n new_bond_num += 1\\r\\n self.connected_atoms.append(source)\\r\\n self.connected_atoms.append(dest)\\r\\n return new_bond_num\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ced57f2aed6284d7c242c17869327f7f\",\n \"score\": \"0.49895075\",\n \"text\": \"def _get_neighborhood(self, point_index, num_neighbors):\\n if 0 <= point_index < self.get_size():\\n neighborhood_index_increments = range(-num_neighbors, 0, 1) + range(1, num_neighbors + 1, 1)\\n neighborhood_indices = [(point_index + increment) % self.get_size() for increment in\\n neighborhood_index_increments]\\n return np.array([self._data[index] for index in neighborhood_indices])\\n return np.array([])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b165ecb5f935ce5b1420e1ced3296d3c\",\n \"score\": \"0.49825335\",\n \"text\": \"def get_neighborhood(self, x, d):\\n assert d == 1, \\\"Sum(x) supports neighbourhood with diameter = 1 only\\\"\\n nd = []\\n for i in np.arange(self.n):\\n if x[i] > self.a[i]:\\n xx = x.copy()\\n xx[i] -= 1\\n nd.append(xx)\\n if x[i] < self.b[i]:\\n xx = x.copy()\\n xx[i] += 1\\n nd.append(xx)\\n return nd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f695cb26daa5ef7c3117211764f61298\",\n \"score\": \"0.49770507\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n _fe.ND_QuadrilateralElement_swiginit(self, _fe.new_ND_QuadrilateralElement(*args, **kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"163db620fc2b60cc331c44f430f6e055\",\n \"score\": \"0.49753782\",\n \"text\": \"def generate_sidewalker(street):\\n\\tleftSidewalker = OFFSET([0.75,0.75])(street)\\n\\tleftSidewalker = S([3])([SIZE([3])(street)[0]/2])(leftSidewalker)\\n\\trightSidewalker = OFFSET([-0.75,-0.75])(street)\\n\\trightSidewalker = S([3])([SIZE([3])(street)[0]/2])(rightSidewalker)\\n\\treturn STRUCT([leftSidewalker, rightSidewalker])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f907c17bac8be8aa59da8a23943994f2\",\n \"score\": \"0.49295393\",\n \"text\": \"def _nodal_gradient_x_stencil(self):\\n if self.dim == 1:\\n Gx = ddx(self.shape_cells[0])\\n elif self.dim == 2:\\n Gx = sp.kron(speye(self.shape_nodes[1]), ddx(self.shape_cells[0]))\\n elif self.dim == 3:\\n Gx = kron3(\\n speye(self.shape_nodes[2]),\\n speye(self.shape_nodes[1]),\\n ddx(self.shape_cells[0]),\\n )\\n return Gx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f907c17bac8be8aa59da8a23943994f2\",\n \"score\": \"0.49295393\",\n \"text\": \"def _nodal_gradient_x_stencil(self):\\n if self.dim == 1:\\n Gx = ddx(self.shape_cells[0])\\n elif self.dim == 2:\\n Gx = sp.kron(speye(self.shape_nodes[1]), ddx(self.shape_cells[0]))\\n elif self.dim == 3:\\n Gx = kron3(\\n speye(self.shape_nodes[2]),\\n speye(self.shape_nodes[1]),\\n ddx(self.shape_cells[0]),\\n )\\n return Gx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"451b118d31f0438ba331a913f4b0e57e\",\n \"score\": \"0.49265325\",\n \"text\": \"def create_neighbor_lookup(nearest_neighbors):\\n nn_lookup = {}\\n for i in range(nearest_neighbors.shape[0]):\\n nn_lookup[i] = nearest_neighbors[i, :]\\n # print \\\"NN Lookup :\\\",nn_lookup\\n return nn_lookup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb0a51f5c492528e021733fcb77ca2e7\",\n \"score\": \"0.4921538\",\n \"text\": \"def count_neighbors_donut(self):\\n for row in self.cells:\\n for cell in row:\\n #\\n # Creates a list of locations for possible neighbors\\n #\\n neighbors = [(-1, -1), (-1, 0), (-1, 1),\\n (0, -1), (0, 1),\\n (1, -1), (1, 0), (1, 1)]\\n cell.neighbors = []\\n for neighbor in neighbors:\\n #\\n # Sees if location is a normal neighbor\\n # If not it wraps around, like a torus/donut, and creates a neighbor from there\\n #\\n try:\\n x = (cell.row + neighbor[0])\\n y = (cell.column + neighbor[1])\\n cell.neighbors.append(self.cells[x][y])\\n except:\\n x = (cell.row + neighbor[0]) % self.rows\\n y = (cell.column + neighbor[1]) % self.columns\\n cell.neighbors.append(self.cells[x][y])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e91efcaa14f93293733f72328e86523\",\n \"score\": \"0.49212787\",\n \"text\": \"def neighbor_factory(xs, φ, k=5, slice_index=None, size=None):\\n import torch\\n from tqdm import trange\\n from ml_logger import logger\\n\\n with torch.no_grad():\\n if Args.load_neighbors:\\n _ = logger.load_pkl(Args.load_neighbors)\\n top_ds = _['top']\\n inds = _['inds']\\n elif Args.load_pairwise_ds:\\n with logger.PrefixContext(Args.load_pairwise_ds):\\n _ds = np.concatenate([logger.load_pkl(f\\\"chunk_{k:02d}.pkl\\\")[0] for k in trange(20, desc=\\\"load\\\")])\\n ds = torch.tensor(_ds, dtype=torch.float32)[:size, :size]\\n ds[torch.eye(len(ds), dtype=torch.uint8)] = float('inf')\\n for k in range(1, 3): # add true neighbors\\n diag = torch.diagflat(torch.ones(len(ds) - k, dtype=torch.uint8), k)\\n ds[diag] = 0.5\\n diag = torch.diagflat(torch.ones(len(ds) - k, dtype=torch.uint8), -k)\\n ds[diag] = 0.5\\n # with torch.no_grad():\\n # _ds = torch.tensor(ds)\\n # _ds[torch.eye(ds.shape[0], dtype=torch.uint8)] = float('inf')\\n # _ = torch.topk(_ds, k=24, dim=1, largest=False, sorted=True)\\n # top_ds = _[0].numpy()\\n # inds = _[1].numpy()\\n\\n # # add threshold connection instead.\\n full_range = torch.arange(len(ds))\\n inds = [None] * len(ds)\\n for idx, row in enumerate(ds):\\n visited = []\\n inds[idx] = full_range[row <= Args.term_r].numpy()\\n if len(inds[idx]) == 0:\\n raise Exception('term_r too small')\\n break\\n for i in range(1):\\n new_neighbors = [full_range[ds[id] <= Args.term_r].numpy() for id in inds[idx] if id not in visited]\\n if len(new_neighbors) > 0:\\n neighbors = np.concatenate(new_neighbors)\\n else:\\n neighbors = []\\n visited += list(inds[idx])\\n inds[idx] = np.unique(np.concatenate([inds[idx], neighbors]))\\n inds[idx] = inds[idx][inds[idx] != idx] # remove identity\\n inds = np.array(inds)\\n top_ds = np.array([row[_] for row, _ in zip(ds.numpy(), inds)])\\n logger.log_data(data=dict(top=top_ds, inds=inds), path=\\\"top_ds.npy\\\")\\n elif Args.load_top_k:\\n # _ = logger.load_pkl(Args.load_top_k)[0]\\n # top_ds = _['top']\\n # inds = _['inds']\\n k = 24\\n with logger.PrefixContext(Args.load_top_k):\\n _ds = np.concatenate([logger.load_pkl(f\\\"chunk_{k:02d}.pkl\\\")[0] for k in trange(20, desc=\\\"load\\\")])\\n ds = torch.tensor(_ds, dtype=torch.float32)[:size, :size]\\n ds[torch.eye(len(ds), dtype=torch.uint8)] = float('inf')\\n with torch.no_grad():\\n _ds = torch.tensor(ds)[:size, :size]\\n _ds[torch.eye(ds.shape[0], dtype=torch.uint8)] = float('inf')\\n top_ds, inds = torch.topk(_ds, k=k, dim=1, largest=False, sorted=True)\\n else:\\n raise Exception(\\\"Need to specify pre-computed pairwise distance matrix, or top-k\\\")\\n\\n\\n def N(x_indices, visited=None):\\n \\\"\\\"\\\"Query the neighbors using the indices\\n\\n :param x_indices: Size(m,)\\n :return: x_next, dstance, indices\\n with shape: Size(B, k, 2), Size(B\\n \\\"\\\"\\\"\\n nonlocal xs, top_ds, inds\\n\\n if visited is not None:\\n curr_inds = [[ind for ind in inds[_] if ind not in visited[idx]] for idx, _ in enumerate(x_indices)]\\n else:\\n curr_inds = [inds[_] for _ in x_indices]\\n return zip(*[(xs[curr_inds[idx]], top_ds[_], curr_inds[idx]) for idx, _ in enumerate(x_indices)])\\n\\n return N\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac2eb0099a4bf8dd5dd575b2bd88e9c7\",\n \"score\": \"0.4917294\",\n \"text\": \"def build2DHP(Window, Dim, Diameter=0, Outer=False, Cont=False):\\n return np.ones(Dim)-build2DLP(Window, Dim, Diameter, Outer, Cont)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50a46d990d92334805bfd77f03d91249\",\n \"score\": \"0.49166223\",\n \"text\": \"def nxn(\\n west: int = 1,\\n east: int = 4,\\n north: int = 0,\\n south: int = 0,\\n xsize: float = 8.0,\\n ysize: float = 8.0,\\n wg_width: float = 0.5,\\n layer=pp.LAYER.WG,\\n wg_margin: float = 1.0,\\n):\\n c = pp.Component()\\n c << pp.c.rectangle(size=(xsize, ysize), layer=layer)\\n\\n for i in [west, north, south, east]:\\n if not isinstance(i, int):\\n raise ValueError(\\n f\\\"west={west}, east={east}, north={north}, south={south} needs to be integers\\\"\\n )\\n\\n if west > 0:\\n x = 0\\n y = (\\n [ysize / 2]\\n if west == 1\\n else np.linspace(\\n wg_margin + wg_width / 2, ysize - wg_margin - wg_width / 2, west\\n )\\n )\\n y = pp.drc.snap_to_1nm_grid(y)\\n orientation = 180\\n\\n for i, y in enumerate(y):\\n c.add_port(\\n f\\\"W{i}\\\",\\n midpoint=(x, y),\\n width=wg_width,\\n orientation=orientation,\\n layer=layer,\\n )\\n\\n if east > 0:\\n x = xsize\\n y = (\\n [ysize / 2]\\n if east == 1\\n else np.linspace(\\n wg_margin + wg_width / 2, ysize - wg_margin - wg_width / 2, east\\n )\\n )\\n y = pp.drc.snap_to_1nm_grid(y)\\n orientation = 0\\n\\n for i, y in enumerate(y):\\n c.add_port(\\n f\\\"E{i}\\\",\\n midpoint=(x, y),\\n width=wg_width,\\n orientation=orientation,\\n layer=layer,\\n )\\n\\n if north > 0:\\n y = ysize\\n x = (\\n [xsize / 2]\\n if north == 1\\n else np.linspace(\\n wg_margin + wg_width / 2, xsize - wg_margin - wg_width / 2, north\\n )\\n )\\n x = pp.drc.snap_to_1nm_grid(x)\\n orientation = 90\\n\\n for i, x in enumerate(x):\\n c.add_port(\\n f\\\"N{i}\\\",\\n midpoint=(x, y),\\n width=wg_width,\\n orientation=orientation,\\n layer=layer,\\n )\\n if south > 0:\\n y = 0\\n x = (\\n [xsize / 2]\\n if south == 1\\n else np.linspace(\\n wg_margin + wg_width / 2, xsize - wg_margin - wg_width / 2, south\\n )\\n )\\n x = pp.drc.snap_to_1nm_grid(x)\\n orientation = 270\\n\\n for i, x in enumerate(x):\\n c.add_port(\\n f\\\"S{i}\\\",\\n midpoint=(x, y),\\n width=wg_width,\\n orientation=orientation,\\n layer=layer,\\n )\\n\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21026c8e3cad8d570ac19ac2f45f2c92\",\n \"score\": \"0.4913781\",\n \"text\": \"def make_ring(inner_radius: int, outer_radius: int,\\n dtype: str='uint8'\\n) -> np.ndarray:\\n # if the radius is even, make it odd\\n length = 2 * outer_radius + 1\\n # create an RGBA box to house the circle in\\n box = np.zeros((length, length), dtype=dtype)\\n # get the coordinates for the outer circle\\n x_pixels, y_pixels = draw_circle(outer_radius, outer_radius, outer_radius)\\n # set the coordinates of the circle in the box\\n box[x_pixels, y_pixels] = 1\\n # get the coordinates for the inner circle\\n x_pixels, y_pixels = draw_circle(outer_radius, outer_radius, inner_radius)\\n # set the coordinates of the circle in the box\\n box[x_pixels, y_pixels] = 0\\n\\n return box\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26aeaa01a8208d34fdaa4f1ce3856e9d\",\n \"score\": \"0.48986673\",\n \"text\": \"def nd_neighbors(game, coords):\\n list = []\\n for n in range(len(coords)):\\n t = ()\\n for i in range(coords[n]-1, coords[n]+2):\\n if 0 <= i < game[\\\"dimensions\\\"][n]:\\n t = t + (i,)\\n list.append(t)\\n return nd_product(list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af5d9065cb25602c30cba34b8b06cfb1\",\n \"score\": \"0.48982462\",\n \"text\": \"def __MakeDdoubleStar2DstarPi__(self):\\n \\n from GaudiConfUtils.ConfigurableGenerators import CombineParticles\\n from PhysSelPython.Wrappers import Selection, DataOnDemand\\n from StandardParticles import StdLoosePions\\n \\n \\n CombDdoubleStar2DstarPi = CombineParticles( \\n DecayDescriptors = [\\\"[D_1(2420)0 -> D*(2010)+ pi-]cc\\\", \\\"[D*_2(2460)0 -> D*(2010)+ pi-]cc\\\" ],\\n DaughtersCuts = { \\\"pi-\\\" : self.D1_PionCut },\\n MotherCut = self.D1Cut,\\n )\\n \\n SelDdoubleStar2DstarPi = Selection(\\\"SelDdoubleStar2DstarPi\\\", Algorithm=CombDdoubleStar2DstarPi,\\n RequiredSelections = [self.DstarSel,StdLoosePions])\\n \\n self.DdoubleStar2DstarPiSel=SelDdoubleStar2DstarPi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"476717d1195eab8107f085b535db39cb\",\n \"score\": \"0.48957065\",\n \"text\": \"def __init__(self, nnodes: int,\\n starting_neighbor_d: int = 5,\\n neighborhood_decay_fn: Callable[[int, int, int], int] =\\n lambda x: x - 1):\\n\\n self._nnodes = nnodes\\n self._d0 = starting_neighbor_d\\n self._d = starting_neighbor_d\\n self._decay_fn = neighborhood_decay_fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf4ba033df73fbcfe609ec7fbb23b9e\",\n \"score\": \"0.48877\",\n \"text\": \"def make_ws_graph(num_nodes, clockwise_neighbours, rewiring_prob):\\n #initialize empty graph\\n ws_graph = {}\\n for vertex in range(num_nodes): ws_graph[vertex] = []\\n #add each vertex to clockwise neighbours\\n for vertex in range(num_nodes):\\n for neighbour in range(vertex + 1, vertex + clockwise_neighbours + 1):\\n neighbour = neighbour % num_nodes\\n ws_graph[vertex] += [neighbour]\\n ws_graph[neighbour] += [vertex]\\n for vertex in range(num_nodes):\\n for neighbour in ws_graph[vertex]:\\n if random.random() < rewiring_prob:\\n ws_graph[vertex].remove(neighbour)\\n ws_graph[neighbour].remove(vertex)\\n randNode = random.randint(0, num_nodes-1)\\n while(vertex == randNode):\\n randNode = random.randint(0, num_nodes - 1)\\n ws_graph[vertex] += [randNode]\\n ws_graph[randNode] += [vertex]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e87e04138f71aed9e208aff6b5b2f2a\",\n \"score\": \"0.48779523\",\n \"text\": \"def obj_get_boundary_method1(labelled_array, fill_holes=True):\\n\\n dims = labelled_array.shape\\n rows = dims[0]\\n cols = dims[1]\\n\\n if fill_holes:\\n orig_binary = (labelled_array > 0).astype('uint8')\\n fill = obj_fill_holes(labelled_array)\\n s = [[1,1,1],[1,1,1],[1,1,1]]\\n labelled_array, nlabels = ndimage.label(fill, structure=s)\\n\\n # We'll opt for the perimeter co-ordinates to be ordered in a clockwise fashion. GIS convention???\\n pix_directions = numpy.array([[ 0, 1],\\n [ 1, 1],\\n [ 1, 0],\\n [ 1,-1],\\n [ 0,-1],\\n [-1,-1],\\n [-1, 0],\\n [-1, 1]])\\n\\n # Set up the distances as we traverse across a pixel\\n diag = numpy.sqrt(2.0) # NumPy will return a float64, but just in case future versions change....\\n pix_distances = {0 : 1.0,\\n 1 : diag,\\n 2 : 1.0,\\n 3 : diag,\\n 4 : 1.0,\\n 5 : diag,\\n 6 : 1.0,\\n 7, diag\\n }\\n\\n # Determine the co-ordinates (indices) of each segement\\n # The first index of each segment will be used to define the start and end of a boundary/perimeter\\n h = histogram(labelled_array.flatten(), Min=1, reverse_indices='ri')\\n hist = h['histogram']\\n ri = h['ri']\\n nlabels = hist.shape[0]\\n seg_start_idxs = numpy.zeros(nlabels, dtype='int')\\n\\n # Boundary or perimeter ?? Will go with perimeter, akin to a method implement earlier which uses a\\n # convolution operator to determine perimeter length.\\n\\n # Obtain the start indices of each segment/object\\n for i in numpy.arange(nlabels):\\n #if (hist[i] == 0): # The labeled array should be consecutive\\n # continue\\n seg_start_idxs[i] = ri[ri[i]:ri[i+1]][0] # Return the first index\\n\\n # Convert the 1D indices to 2D indices used by NumPy\\n seg_start_idxs = array_indices(dims, seg_start_idxs, dimensions=True)\\n\\n # Lots to figure out here. Dealing with 'from' and 'too' directions can make things confusing\\n # Keep track of the direction we last travelled, that way we can start at the next clockwise direction\\n # For single pixel objects or 'islands' use the histogram value to skip the follow boundary/search routine\\n\\n \\\"\\\"\\\"\\n The memory of the order of the array should be 'C-Style': column, column, column, then next row.\\n\\n eg a 2x5 array\\n\\n 0, 1, 2, 3, 4\\n 5, 6, 7, 8, 9\\n\\n Therefore the first index will only have labels to the right and below (below left, below right).\\n\\n eg an object with a label ID of 6\\n\\n 0, 0, 6, 6, 6\\n 6, 6, 6, 6, 6\\n\\n As such, the first direction travelled will be to the right '0' in the freeman chain, and the first index will be the\\n first boundary co-ordinate and will be the final co-ordinate to close the boundary thereby creating a polygon.\\n As for linear features...\\n \\\"\\\"\\\"\\n\\n # Probably deal with these inside the boundary tracking routine\\n # Let the tracking routine handle to/from and just return the final result\\n to_ = 0\\n from_ = 4\\n\\n perimeter_info = {}\\n\\n for i in range(hist.shape[0]):\\n if hist[i] == 0:\\n continue\\n if hist[i] == 1:\\n # What is the perimeter of a single pixel, 0.0, 4.0???\\n #perimeter_co_ords[i] = seg_start_idxs[i[0],i[1]] # Still need to design the function and how to return the result\\n continue\\n idx = (seg_start_idxs[0][i], seg_start_idxs[1][i])\\n label = i + 1\\n perimeter_info[i] = track_object_boundary(labelled_array, start_index=idx, label_id=label)\\n\\n # Might need to format the perimeter_info dictionary before returning, ie turn the co-ords into numpy arrays.\\n # Or even into a polygon object using the shapely library???\\n # Using shapely might be easier to report geometrical attributes\\n\\n # Still need to deal with holes within an object as ENVI does. They will increase an objects perimeter length.\\n # SciPy have a binary_fill_holes function. label the filled array, then get the indices, and then retrive only those indices\\n # for each object that are 0 in the original array.\\n # That might be one way to do it, which means re-writing the above function....ughhh :)\\n\\n #!!!!This isn't the correct place for the handling of object holes, but just get the rough structure out!!!\\n if fill_holes:\\n for i in range(hist.shape[0]):\\n if hist[i] == 0:\\n continue\\n if hist[i] == 1:\\n # What is the perimeter of a single pixel, 0.0, 4.0???\\n continue\\n idx = (seg_start_idxs[0][i], seg_start_idxs[1][i])\\n label = i + 1\\n perimeter_info[i] = track_object_boundary(labelled_array, start_index=idx, label_id=label)\\n # Can we trust that the labelling of the filled and unfilled arrays will give the same object index??\\n # If we can we could use the area differences to determine if there are holes and only go through the \\n # hole perimeter tracking if needed.\\n single_object = numpy.zeros((rows*cols), dtype='uint8')\\n single_object[ri[ri[i]:ri[i+1]]] = 1\\n holes = numexpr.evaluate(\\\"(single_object - orig_binary) == 1\\\")\\n labs, nlabs = ndimage.label(holes, s)\\n h_holes = histogram(labs, Min=1, reverse_indices='ri')\\n hist_holes = h_holes['histogram']\\n ri_h = h_holes['ri']\\n seg_holes_start_idxs = numpy.zeros(nlabs, dtype='int')\\n for j in numpy.arange(nlabels):\\n #if (hist[i] == 0): # The labeled array should be consecutive\\n # continue\\n seg_holes_start_idxs[j] = ri_h[ri_h[j]:ri_h[j+1]][0] # Return the first index\\n\\n # Convert the 1D indices to 2D indices used by NumPy\\n seg_holes_start_idxs = array_indices(dims, seg_holes_start_idxs, dimensions=True)\\n for k in range(hist_holes.shape):\\n if hist_holes[k] == 0:\\n continue\\n if hist[i] == 1:\\n # What is the perimeter of a single pixel, 0.0, 4.0???\\n continue\\n idx = (seg_holes_start_idxs[0][k], seg_holes_start_idxs[1][k])\\n holes_label = k + 1\\n holes_result = track_object_boundary(labs, start_index=idx, label_id=holes_label)\\n perimeter_info[i]['Holes'] = holes_result['Vertices']\\n perimeter_info[i]['Perimeter_Length'] += perimeter_info['Perimeter_Length']\\n\\n return perimeter_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2fd627e6b2bf9b02ac54deaf09a280c\",\n \"score\": \"0.4845612\",\n \"text\": \"def create_graph(w, h):\\n graph = {}\\n for i in range(h):\\n for j in range(w):\\n neighbors = []\\n # Top left\\n if i - 1 >= 0 and j - 1 >= 0:\\n neighbors.append((i - 1, j - 1))\\n # Top\\n if i - 1 >= 0:\\n neighbors.append((i - 1, j))\\n # Top Right\\n if i - 1 >= 0 and j + 1 < w:\\n neighbors.append((i - 1, j + 1))\\n # Right\\n if j + 1 < w:\\n neighbors.append((i, j + 1))\\n # Bottom Right\\n if i + 1 < h and j + 1 < w:\\n neighbors.append((i + 1, j + 1))\\n # Bottom\\n if i + 1 < h:\\n neighbors.append((i + 1, j))\\n # Bottom Left\\n if i + 1 < h and j - 1 >= 0:\\n neighbors.append((i + 1, j - 1))\\n # Left\\n if j - 1 >= 0:\\n neighbors.append((i, j - 1))\\n graph[(i, j)] = neighbors\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e52082b7c1becc8de17db95d64a734d\",\n \"score\": \"0.4841775\",\n \"text\": \"def neighbours(i,j,M,N,size=4):\\n if size == 4:\\n if (i == 0 and j == 0):\\n n = [(0, 1), (1, 0)]\\n elif i == 0 and j == N - 1:\\n n = [(0, N - 2), (1, N - 1)]\\n elif i == M - 1 and j == 0:\\n n = [(M - 1, 1), (M - 2, 0)]\\n elif i == M - 1 and j == N - 1:\\n n = [(M - 1, N - 2), (M - 2, N - 1)]\\n elif i == 0:\\n n = [(0, j - 1), (0, j + 1), (1, j)]\\n elif i == M - 1:\\n n = [(M - 1, j - 1), (M - 1, j + 1), (M - 2, j)]\\n elif j == 0:\\n n = [(i - 1, 0), (i + 1, 0), (i, 1)]\\n elif j == N - 1:\\n n = [(i - 1, N - 1), (i + 1, N - 1), (i, N - 2)]\\n else:\\n n = [(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)]\\n return n\\n if size == 8:\\n #print('Not yet implemented\\\\n')\\n # top left\\n if (i == 0 and j == 0):\\n n = [(0, 1), (1, 0), (1, 1)]\\n # bottom left\\n elif i == 0 and j == N - 1:\\n n = [(0, N - 2), (1, N - 1), (1, N - 2)]\\n # top right\\n elif i == M - 1 and j == 0:\\n n = [(M - 1, 1), (M - 2, 0), (M - 2, 1)]\\n # bottom right\\n elif i == M - 1 and j == N - 1:\\n n = [(M - 1, N - 2), (M - 2, N - 1), (M - 2, N - 2)]\\n # left\\n elif i == 0:\\n n = [(0, j - 1), (0, j + 1), (1, j), (1, j - 1), (1, j + 1)]\\n # right\\n elif i == M - 1:\\n n = [(M - 1, j - 1), (M - 1, j + 1), (M - 2, j), (M - 2, j + 1), (M - 2, j - 1)]\\n # top\\n elif j == 0:\\n n = [(i - 1, 0), (i + 1, 0), (i, 1), (i - 1, 1), (i + 1, 1)]\\n # bottom\\n elif j == N - 1:\\n n = [(i - 1, N - 1), (i + 1, N - 1), (i, N - 2), (i + 1, N - 2), (i - 1, N - 2)]\\n # middle \\n else:\\n n = [(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1), (i - 1, j - 1), (i - 1, j + 1), (i + 1, j - 1), (i + 1, j + 1)]\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf168daaea61b02d9cf09a9073ad030\",\n \"score\": \"0.48404124\",\n \"text\": \"def _neighbors(self, index):\\n\\n if index == 0:\\n neighbor = [index + 1]\\n elif 0 < index < self.data_amount-1:\\n neighbor = [index - 1, index + 1]\\n elif index == self.data_amount-1:\\n neighbor = [index - 1]\\n\\n return neighbor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c762b287eeee92d878c23df5a4353301\",\n \"score\": \"0.4838816\",\n \"text\": \"def get_neighbours(w, h):\\n def get_local_nbrs(x, y):\\n return [(x, (y - 1) % h),\\n ((x + 1) % w, y),\\n (x, (y + 1) % h),\\n ((x - 1) % w, y)]\\n\\n nbrs = {(x, y): get_local_nbrs(x, y)\\n for x in range(w) for y in range(h)}\\n\\n return nbrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3918c746c0253a867b07d952e3bd8cb2\",\n \"score\": \"0.48302403\",\n \"text\": \"def createNodeWAttr(self):\\n count = 0\\n r=0\\n g=1\\n b=0\\n for node in self.nodes:\\n for edge in self.edges:\\n if edge[0] == node:\\n count+=1\\n self.G.add_node(node, size=count, r=r, g=g, b=b)\\n\\n if g>=0.2:\\n g-=0.2\\n elif r<=0.8:\\n r+=0.2\\n elif b<=0.8:\\n b+=0.2\\n else:\\n g = 0.5\\n b=0.5\\n count=0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"596185ba6919c86fc6bb35d00073ef48\",\n \"score\": \"0.4827401\",\n \"text\": \"def createGrayworldWB(): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b481529f12c039c79dc18d720bfd8285\",\n \"score\": \"0.4820118\",\n \"text\": \"def getNeighbours(self):\\r\\n\\r\\n iBlank, jBlank = self.getBlankIndex()\\r\\n\\r\\n upNeighbour = self.getUpNeighbour(iBlank, jBlank)\\r\\n if upNeighbour != -1:\\r\\n self.neighbours.append(Node(upNeighbour, self.depth + 1, self))\\r\\n\\r\\n downNeighbour = self.getDownNeighbour(iBlank, jBlank)\\r\\n if downNeighbour != -1:\\r\\n self.neighbours.append(Node(downNeighbour, self.depth + 1, self))\\r\\n\\r\\n leftNeighbour = self.getLeftNeighbour(iBlank, jBlank)\\r\\n if leftNeighbour != -1:\\r\\n self.neighbours.append(Node(leftNeighbour, self.depth + 1, self))\\r\\n\\r\\n rightNeighbour = self.getRightNeighbour(iBlank, jBlank)\\r\\n if rightNeighbour != -1:\\r\\n self.neighbours.append(Node(rightNeighbour, self.depth + 1, self))\\r\\n\\r\\n return self.neighbours\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98388842549726034195e6b830206e7\",\n \"score\": \"0.48194784\",\n \"text\": \"def create_binding_holes(self, edge, ysize, xsize, ntimes):\\n\\n # reset\\n self.foreground = []\\n\\n template_size = template_size_ori = 40\\n # scale template size based on image size\\n # 1000 * 800 is normal image size for template size = 40\\n # use max to prevent small template and min to prevent large template\\n template_size = int(\\n max(template_size_ori / 2, 40 * ((ysize * xsize) / (1000 * 800))),\\n )\\n template_size = int(min(template_size, template_size_ori * 2))\\n\\n binding_effect = random.randint(0, 8)\\n offset_direction = random.choice([1, -1])\\n\\n for i in range(ntimes):\\n random_scale = random.uniform(1, 2)\\n current_template_size = random.randint(int(template_size * 0.75), int(template_size * 1.25))\\n\\n if self.width_range == \\\"random\\\":\\n template_size_x = current_template_size\\n else:\\n template_size_x = random.randint(self.width_range[0], self.width_range[1])\\n\\n if self.height_range == \\\"random\\\":\\n template_size_y = int(current_template_size / 3)\\n else:\\n template_size_y = random.randint(self.height_range[0], self.height_range[1])\\n\\n # draw rectangle\\n offset = int(min(template_size_x, template_size_y) / random.uniform(7, 8))\\n image_rectangle = np.full(\\n (template_size_y, int(template_size_x / random_scale)),\\n fill_value=255,\\n dtype=\\\"uint8\\\",\\n )\\n image_rectangle[offset:-offset:, offset:-offset] = 0\\n\\n new_offset = offset + random.randint(3, 6)\\n image_rectangle_complement = np.full(\\n (template_size_y, int(template_size_x / random_scale)),\\n fill_value=0,\\n dtype=\\\"uint8\\\",\\n )\\n\\n if binding_effect == 0:\\n x1 = new_offset + (offset_direction * random.randint(1, 3))\\n x2 = image_rectangle_complement.shape[1]\\n y1 = new_offset + (offset_direction * random.randint(1, 3))\\n y2 = image_rectangle_complement.shape[1]\\n elif binding_effect == 1:\\n x1 = new_offset + (offset_direction * random.randint(1, 3))\\n x2 = image_rectangle_complement.shape[1]\\n y1 = 0\\n y2 = -new_offset + (offset_direction * random.randint(1, 3))\\n elif binding_effect == 2:\\n x1 = 0\\n x2 = -new_offset + (offset_direction * random.randint(1, 3))\\n y1 = new_offset + (offset_direction * random.randint(1, 3))\\n y2 = image_rectangle_complement.shape[1]\\n elif binding_effect == 3:\\n x1 = 0\\n x2 = -new_offset + (offset_direction * random.randint(1, 3))\\n y1 = 0\\n y2 = -new_offset + (offset_direction * random.randint(1, 3))\\n elif binding_effect == 4:\\n x1 = new_offset + (offset_direction * random.randint(1, 3))\\n x2 = -new_offset + (offset_direction * random.randint(1, 3))\\n y1 = new_offset + (offset_direction * random.randint(1, 3))\\n y2 = -new_offset + (offset_direction * random.randint(1, 3))\\n elif binding_effect == 5:\\n x1 = new_offset\\n x2 = -new_offset\\n y1 = new_offset\\n y2 = -new_offset\\n else:\\n x1 = 0\\n x2 = 0\\n y1 = 0\\n y2 = 0\\n image_rectangle_complement[y1:y2:, x1:x2] = 255\\n\\n self.add_noise(\\n image_rectangle,\\n random.uniform(0.01, 0.21),\\n (0, 255),\\n )\\n\\n # merge 2 image to create offset effect\\n image_rectangle = np.maximum(\\n image_rectangle,\\n image_rectangle_complement,\\n )\\n\\n # add noise and apply blur\\n self.add_noise(\\n image_rectangle,\\n random.uniform(0.01, 0.21),\\n (0, 255),\\n )\\n image_rectangle = cv2.GaussianBlur(\\n image_rectangle.astype(\\\"uint8\\\"),\\n (3, 3),\\n cv2.BORDER_DEFAULT,\\n )\\n\\n # rotate rectangle to create a more proper binding holes\\n if edge == \\\"left\\\":\\n image_rectangle = np.rot90(image_rectangle, 1)\\n elif edge == \\\"right\\\":\\n image_rectangle = np.rot90(image_rectangle, 1)\\n\\n # convert to bgr\\n image_rectangle_bgr = cv2.cvtColor(image_rectangle, cv2.COLOR_GRAY2BGR)\\n\\n self.foreground.append(image_rectangle_bgr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019988e1be1719acdd2ee94698b10593\",\n \"score\": \"0.48115528\",\n \"text\": \"def nodes_and_weights_2d(nq):\\n\\n if nq == 1:\\n z = np.array([[1./3, 1./3, 1./3]])\\n w = np.array([1.])\\n return z, w\\n\\n elif nq == 3:\\n z = np.array([[1./2, 1./2, 0],\\n [1./2, 0, 1./2],\\n [0, 1./2, 1./2]])\\n w = np.array([1./3, 1./3, 1./3])\\n return z, w\\n\\n elif nq == 4:\\n z = np.array([[1./3, 1./3, 1./3],\\n [3./5, 1./5, 1./5],\\n [1./5, 3./5, 1./5],\\n [1./5, 1./5, 3./5]])\\n w = np.array([-9./16, 25./48, 25./48, 25./48])\\n return z, w\\n\\n else:\\n print \\\"Please use a valid number of nodes (1, 3, or 4).\\\"\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b5c31b383e7a635c1fbb1be7fff7dfd\",\n \"score\": \"0.47986537\",\n \"text\": \"def create_paired_geometry_rep(self):\\n point1 = self.vhelix.end_coordinates[0]\\n point2 = self.vhelix.end_coordinates[1]\\n dna_structure = self.dna_structure\\n radius = 1.15\\n num_sides = 16\\n angle_offset = 45.0\\n helix = self.vhelix \\n # Get the indexes into helix_axis_coords of dsDNA regions.\\n boundary_points = self.get_boundaries()\\n self.representations[VisHelixRepType.PAIRED_GEOMETRY] = []\\n for i,points in enumerate(boundary_points): \\n pt1 = points[0]\\n pt2 = points[1]\\n #self._logger.info(\\\"Pt1 %s pt2 %s\\\" % (str(pt1), str(pt2))) \\n name = \\\"HelixGeometry:%d_%d\\\" % (helix.id,i+1) \\n geom = VisGeometryCylinder(name, radius, pt1, pt2, num_sides)\\n geom.selected_callback = self.select_paired_geometry\\n geom.color = self.color\\n geom.color[3] = 1.0 \\n self.graphics.add_render_geometry(geom)\\n self.representations[VisHelixRepType.PAIRED_GEOMETRY].append(geom)\\n #__for pos in boundary_pos \",\n \"title\": \"\"\n },\n {\n \"docid\": \"277091581948acab19328cb8fbba10ce\",\n \"score\": \"0.47932747\",\n \"text\": \"def ring_initial(spacing=0.01):\\n nx, ny = int(1 / spacing), int(1 / spacing)\\n xx, yy = np.meshgrid(np.linspace(0., 1., nx, dtype=np.float32),\\n np.linspace(0., 1., ny, dtype=np.float32))\\n ui = np.zeros((nx, ny), dtype=np.float32)\\n r = (xx - .5)**2. + (yy - .5)**2.\\n ui[np.logical_and(.05 <= r, r <= .1)] = 1.\\n return ui\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd061680b45bfd4172b683407113a70b\",\n \"score\": \"0.4793038\",\n \"text\": \"def watershedding_2D(track, field_in, **kwargs):\\n kwargs.pop(\\\"method\\\", None)\\n return segmentation_2D(track, field_in, method=\\\"watershed\\\", **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0969674bb6452223ff4961448aa2b6\",\n \"score\": \"0.47901848\",\n \"text\": \"def get_neighbors(self, n1):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c0f2dfe12420630a0a89a2a17198b5\",\n \"score\": \"0.4789576\",\n \"text\": \"def __neighbors(self, cell):\\n return {\\n (cell[0]-1, cell[1]-1), (cell[0]-1, cell[1]), (cell[0]-1, cell[1]+1),\\n (cell[0], cell[1]-1), (cell[0], cell[1]+1),\\n (cell[0]+1, cell[1]-1), (cell[0]+1, cell[1]), (cell[0]+1, cell[1]+1),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7153f0645c9b1836a97e6b0491034a5a\",\n \"score\": \"0.47862005\",\n \"text\": \"def newneighbor(self, position):\\n if self.flag not in LeafFlags:\\n raise ValueError(\\\"cannot make leaf neighbor for non-leaf.\\\")\\n neighbor = self.clone(position)\\n size = self.size\\n indices = self.indices\\n neighbor.indices[size] = indices[size]\\n indices[size] = position\\n return neighbor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69baccdfa64e132e863935f7711aafdf\",\n \"score\": \"0.4785181\",\n \"text\": \"def atom_neighborhoods(gra):\\n bnd_keys = bond_keys(gra)\\n\\n def _neighborhood(atm_key):\\n nbh_bnd_keys = set(filter(lambda x: atm_key in x, bnd_keys))\\n return bond_induced_subgraph(gra, nbh_bnd_keys)\\n\\n atm_keys = list(atom_keys(gra))\\n atm_nbh_dct = dict(zip(atm_keys, map(_neighborhood, atm_keys)))\\n return atm_nbh_dct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0607eac58f9ba2fc7255d18c88cc1ccf\",\n \"score\": \"0.47850254\",\n \"text\": \"def create_ring_network(number_of_cliques, nodes_per_clique):\\r\\n \\r\\n number_of_nodes = number_of_cliques * nodes_per_clique\\r\\n network = UndirectedNetwork(number_of_nodes)\\r\\n \\r\\n # for each clique\\r\\n for i in range(0, number_of_cliques):\\r\\n \\r\\n offset = i*nodes_per_clique\\r\\n \\r\\n # for each node in each clique\\r\\n for j in range(0, nodes_per_clique):\\r\\n \\r\\n # connect the node to all the other nodes in the clique\\r\\n for k in range(0,j): \\r\\n network.add_edge(offset+j,offset+k)\\r\\n \\r\\n # connect the clique to the previous one\\r\\n if not (offset == 0):\\r\\n network.add_edge(offset, offset-1)\\r\\n \\r\\n # we are done so connect first and last...\\r\\n network.add_edge(0,number_of_nodes-1)\\r\\n \\r\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c76a8ee9d8b2087214be9f05881eb4b\",\n \"score\": \"0.4782238\",\n \"text\": \"def createEdgeWAttr(self):\\n r=0\\n g=0.8\\n b=0\\n prev_edge = self.edges[0][0]\\n self.G.add_edge(prev_edge, self.edges[0][1], r=r, g=g, b=b)\\n for e in range(1, len(self.edges)):\\n if e == len(self.edges)-2:\\n if prev_edge != self.edges[e+1][0]:\\n if g>=0.2:\\n g-=0.2\\n elif r<=0.8:\\n r+=0.2\\n elif b<=0.8:\\n b+=0.2\\n else:\\n g = 0.5\\n b=0.5\\n self.G.add_edge(self.edges[e+1][0], self.edges[e+1][1], r=r, g=g, b=b) \\n self.G.add_edge(self.edges[e][0], self.edges[e][1], r=r, g=g, b=b) \\n else:\\n if prev_edge != self.edges[e][0]:\\n if g>=0.2:\\n g-=0.1\\n elif r<=0.8:\\n r+=0.1\\n elif b<=0.8:\\n b+=0.1\\n else:\\n g = 0.5\\n b=0.5\\n prev_edge = self.edges[e][0]\\n self.G.add_edge(self.edges[e][0], self.edges[e][1], r=r, g=g, b=b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2943380ca22aecef2b95ed07cb387e0d\",\n \"score\": \"0.4771245\",\n \"text\": \"def __init__(self):\\n self.neighborhood = [] \\n self.tec = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4291a8baa57fe2aa624c6043fe80042\",\n \"score\": \"0.4769537\",\n \"text\": \"def get_neighbor(x, n, data): # type: (float, int, np.ndarray) -> np.ndarray\\n pad_width = np.ceil(n / 2).astype(int)\\n padded = np.pad(data, pad_width, mode='edge')\\n x += pad_width\\n\\n idxes = get_neighbor_idxes(x, n, len(padded))\\n ret = padded[idxes]\\n return idxes - pad_width, ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8675de73188ecf657e46a9c864465d89\",\n \"score\": \"0.47621432\",\n \"text\": \"def test_spatial_neighbors_non_visium(non_visium_adata: AnnData):\\n correct_knn_graph = np.array(\\n [\\n [0.0, 1.0, 1.0, 1.0],\\n [1.0, 0.0, 1.0, 1.0],\\n [1.0, 1.0, 0.0, 1.0],\\n [1.0, 1.0, 1.0, 0.0],\\n ]\\n )\\n\\n correct_radius_graph = np.array(\\n [\\n [0.0, 1.0, 0.0, 1.0],\\n [1.0, 0.0, 0.0, 1.0],\\n [0.0, 0.0, 0.0, 0.0],\\n [1.0, 1.0, 0.0, 0.0],\\n ]\\n )\\n\\n correct_delaunay_graph = np.array(\\n [[0.0, 1.0, 0.0, 1.0], [1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 1.0], [1.0, 1.0, 1.0, 0.0]]\\n )\\n\\n correct_delaunay_dist = np.array(\\n [\\n [0.0, 2.0, 0.0, 4.12310563],\\n [2.0, 0.0, 6.32455532, 5.0],\\n [0.0, 6.32455532, 0.0, 5.38516481],\\n [4.12310563, 5.0, 5.38516481, 0.0],\\n ]\\n )\\n\\n spatial_neighbors(non_visium_adata, n_neigh=3, coord_type=None)\\n spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()\\n assert np.array_equal(spatial_graph, correct_knn_graph)\\n\\n spatial_neighbors(non_visium_adata, radius=5.0, coord_type=None)\\n spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()\\n assert np.array_equal(spatial_graph, correct_radius_graph)\\n\\n spatial_neighbors(non_visium_adata, delaunay=True, coord_type=None)\\n spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()\\n spatial_dist = non_visium_adata.obsp[Key.obsp.spatial_dist()].toarray()\\n assert np.array_equal(spatial_graph, correct_delaunay_graph)\\n np.testing.assert_allclose(spatial_dist, correct_delaunay_dist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d03859b3a53c5dd59003b37ad467ccd\",\n \"score\": \"0.47570217\",\n \"text\": \"def rebunch(self, spacing, extraCells, nStar):\\n from pyspline import Curve\\n\\n # ********* WARNING THIS HARD CODED TO K-MIN PLANES *********\\n self.dims[2] += extraCells\\n if nStar == -1:\\n nStar = self.dims[2]\\n\\n newNodes = np.zeros((self.dims[0], self.dims[1], self.dims[2], 3))\\n for i in range(self.dims[0]):\\n for j in range(self.dims[1]):\\n xx = self.coords[i, j, :, :]\\n c = Curve(X=xx, localInterp=True)\\n # First get the distance off-wall:\\n d = np.linalg.norm(self.coords[i, j, 0, :] - self.coords[i, j, 1, :])\\n\\n # This is the segment of S we are dealing with:\\n sSegment = c.s[0:nStar]\\n\\n # Compute the new S0\\n s0 = (spacing / d) * c.s[1]\\n # Get the newS.\\n newS = getS(len(sSegment) + extraCells + 1, s0, sSegment[-1])\\n # The final 's' for evaluation\\n newS = np.hstack([newS, c.s[nStar + 1 :]])\\n newNodes[i, j, :, :] = c(newS)\\n\\n self.coords = newNodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2e8ed130c0f078bc1b7578545253d72\",\n \"score\": \"0.47471\",\n \"text\": \"def __init__(self, nrows, ncols, neighborhood, reproductionneighborhood):\\n\\t\\tself.nrows, self.ncols = nrows, ncols\\n\\t\\tself.neighborhood = neighborhood # list of tuples of x,y-offsets (relative to any grid location).\\n\\t\\tself.reproductionneighborhood = reproductionneighborhood # list of tuples of x,y-offsets (relative to any grid location).\\n\\t\\tself.agentMatrix = [[None]*ncols for i in range(nrows)] # ncols x nrows matrix that has agent or None at each location\\n\\t\\t\\n\\t\\tself.emptySites = [(i,j) for i in range(nrows) for j in range(ncols)]\\n\\t\\tself.allSites = [(i,j) for i in range(nrows) for j in range(ncols)]\\n\\n\\t\\t# for each location (x,y) key will hold neighboring locations as list\\n\\t\\tself.neighborLocs = {}\\n\\t\\tfor i in range(self.nrows):\\n\\t\\t\\tfor j in range(self.ncols):\\n\\t\\t\\t\\tneighLocs = list()\\n\\t\\t\\t\\tfor offset in self.neighborhood:\\n\\t\\t\\t\\t\\tdc, dr = offset\\t\\t #note: x,y neighborhood\\n\\t\\t\\t\\t\\tr = (i + dr) % nrows\\n\\t\\t\\t\\t\\tc = (j + dc) % ncols\\n\\t\\t\\t\\t\\tneighLocs.append((r,c))\\n\\t\\t\\t\\tself.neighborLocs[(i,j)] = neighLocs\\n\\n\\t\\t# for each location (x,y) key will hold reproduction neighborhoods as list\\n\\t\\tself.reproductionneighborLocs = {}\\n\\t\\tfor i in range(self.nrows):\\n\\t\\t\\tfor j in range(self.ncols):\\n\\t\\t\\t\\tneighLocs = list()\\n\\t\\t\\t\\tfor offset in self.neighborhood:\\n\\t\\t\\t\\t\\tdc, dr = offset\\t\\t #note: x,y neighborhood\\n\\t\\t\\t\\t\\tr = (i + dr) % nrows\\n\\t\\t\\t\\t\\tc = (j + dc) % ncols\\n\\t\\t\\t\\t\\tneighLocs.append((r,c))\\n\\t\\t\\t\\tself.reproductionneighborLocs[(i,j)] = neighLocs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80b470a6b13299c3154d86f096a818dc\",\n \"score\": \"0.47412646\",\n \"text\": \"def make_neighborhood_column(df):\\n neighborhood_col = []\\n for i in range(len(df['Latitude'])):\\n neighborhood_col.append(get_neighborhood_for_point(df['Latitude'][i], df['Longitude'][i]))\\n df['neighborhood'] = neighborhood_col\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eda0e382af8fccb4e52aeeaae0519d1\",\n \"score\": \"0.47386295\",\n \"text\": \"def get_bprop_resize_nearest_neighbor_v2(self):\\n align_corners = self.align_corners\\n half_pixel_centers = self.half_pixel_centers\\n grad_op = G.ResizeNearestNeighborV2Grad(align_corners, half_pixel_centers)\\n\\n def bprop(x, size, output, dout):\\n x_shape = P.Shape()(x)\\n grad_in_size = x_shape[2:4]\\n\\n if F.is_sequence_value_unknown(P.Shape()(x)):\\n dx = grad_op(dout, grad_in_size)\\n return dx, zeros_like(grad_in_size)\\n\\n dx = grad_op(dout, _create_tensor(grad_in_size, mstype.int32))\\n return dx, zeros_like(grad_in_size)\\n\\n return bprop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b182aa0a661d9f7dc9fb018d727864d\",\n \"score\": \"0.4737028\",\n \"text\": \"def neighbs(p):\\n x,y=p\\n for dx in [1,-1]: yield (x+dx,y)\\n for dy in [1,-1]: yield (x,y+dy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1c0d1f96847bf21ade29b632bebd17a\",\n \"score\": \"0.47366253\",\n \"text\": \"def __init__(self, x_, nnz_, DoTriples_=False, DoPerms_=False, ele_=None, alg_ = None, sort_ = False):\\n self.nlist = []\\n self.nmol = x_.shape[0]\\n self.maxnatom = x_.shape[1]\\n self.alg = 0 if self.maxnatom < 2000000 else 1\\n #self.alg = 0 if self.maxnatom < 20000 else 1\\n if (alg_ != None):\\n self.alg = alg_\\n # alg=0 naive quadratic.\\n # alg=1 linear scaling\\n # alg=2 PairProvider\\n self.x = x_.copy()\\n self.nnz = nnz_.copy()\\n self.nreal = nnz_.copy() # Over-ridden in the WithImages child of this class.\\n self.ele = ele_\\n self.sort = sort_\\n self.pairs = None\\n self.DoTriples = DoTriples_\\n self.DoPerms = DoPerms_\\n self.triples = None\\n self.UpdateInterval = 1\\n self.UpdateCounter = 0\\n self.PairMaker=None\\n if (self.alg<2):\\n if self.ele is None:\\n for i in range(self.nmol):\\n self.nlist.append(NeighborList(x_[i,:nnz_[i]],DoTriples_,DoPerms_, None, self.alg, self.sort))\\n else:\\n for i in range(self.nmol):\\n self.nlist.append(NeighborList(x_[i,:nnz_[i]],DoTriples_,DoPerms_, self.ele[i,:nnz_[i]], self.alg, self.sort))\\n else:\\n raise Exception(\\\"Using fucked up old code.\\\")\\n self.PairMaker = PairProvider(self.nmol,self.maxnatom)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8452695b383d5df26c9ca79f3bc725bd\",\n \"score\": \"0.47275728\",\n \"text\": \"def build2DBS(Window, Dim, Diameter=0, Width=3, Cont=False):\\n return np.ones(Dim)-build2DBP(Window, Dim, Diameter, Width, Cont)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6334b163b423d8275348f5fbc4a7cff7\",\n \"score\": \"0.4722107\",\n \"text\": \"def neighborhood_kernel(kernel_size: int,\\n groups: int,\\n dtype: torch.dtype = torch.float32,\\n device: torch.device = torch.device(\\\"cpu\\\")):\\n\\n neighbors = kernel_size * kernel_size\\n weights = torch.zeros(neighbors, 1, kernel_size, kernel_size, dtype=dtype, device=device)\\n idx = 1\\n for i in range(kernel_size):\\n for j in range(kernel_size):\\n if i == j and i == kernel_size // 2:\\n weights[0, :, i, j] = 1.0\\n else:\\n weights[idx, :, i, j] = 1.0\\n idx += 1\\n\\n weights_neighborhood = weights\\n weights_neighbors = weights.unsqueeze(0).expand(groups, neighbors, 1, kernel_size, kernel_size).reshape(\\n groups * neighbors, 1,\\n kernel_size, kernel_size)\\n\\n return weights_neighborhood, weights_neighbors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b363d0cc70904b3d4ee53c91c6e31665\",\n \"score\": \"0.47184327\",\n \"text\": \"def createNatGateWay(SubnetId, AllocationId, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22b52340625e866e6293fd034471b867\",\n \"score\": \"0.47151592\",\n \"text\": \"def buffer2d_(border, neighbors):\\n y_border, x_border = border\\n n_border = len(y_border)\\n \\n y_neighbors, x_neighbors = neighbors\\n n_neighbors = len(y_neighbors)\\n \\n if n_border == 1:\\n return y_neighbors + y_border[0], x_neighbors + x_border[0]\\n else:\\n assert len(y_neighbors) >= 3\\n assert (y_neighbors[0] != y_neighbors[-2] or\\n x_neighbors[0] != x_neighbors[-2])\\n \\n # `direction` should correspond to the previous cell\\n direction = 0\\n y = y_border[0] + y_neighbors[direction]\\n x = x_border[0] + x_neighbors[direction]\\n \\n while y != y_border[-2] or x != x_border[-2]:\\n direction = _decrement_direction(direction, n_neighbors)\\n y = y_border[0] + y_neighbors[direction]\\n x = x_border[0] + x_neighbors[direction]\\n \\n # direction points from neighbors[-2] to neighbors[0]\\n direction = _increment_direction(direction, n_neighbors)\\n \\n y_buffer = np.zeros(64, y_border.dtype)\\n x_buffer = np.zeros(64, y_border.dtype)\\n n_buffer = 0\\n \\n for i in range(n_border - 1):\\n \\n y = y_border[i]\\n x = x_border[i]\\n \\n y_next = y_border[i + 1]\\n x_next = x_border[i + 1]\\n \\n y_test = y + y_neighbors[direction]\\n x_test = x + x_neighbors[direction]\\n \\n while y_test != y_next or x_test != x_next:\\n \\n # Write the last ones, expanding the buffer if necessary\\n if n_buffer >= y_buffer.size:\\n y_buffer = enlarge_array(y_buffer)\\n x_buffer = enlarge_array(x_buffer)\\n \\n y_buffer[n_buffer] = y_test\\n x_buffer[n_buffer] = x_test\\n n_buffer += 1\\n \\n # Point to the next neighbor\\n direction = _increment_direction(direction, n_neighbors)\\n \\n y_test = y + y_neighbors[direction]\\n x_test = x + x_neighbors[direction]\\n \\n # One more increment and inversion brings us back to the\\n # next border pixel pointing back at this one\\n direction = _invert_direction(direction, n_neighbors)\\n \\n # Start off not pointing back at the last neighbor\\n direction = _increment_direction(direction, n_neighbors)\\n \\n return y_buffer[:n_buffer], x_buffer[:n_buffer]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a05e7ec239db2afe87cbc6bd4078c26\",\n \"score\": \"0.4714184\",\n \"text\": \"def create_graph(image, wall_distance, outermost_coords=None):\\n map_graph = {}\\n # img_tmp = np.copy(img)\\n\\n # Go over once to identify nodes\\n h, w = image.shape # HEIGHT - WIDTH\\n\\n min_x, min_y, max_x, max_y = outermost_coords if outermost_coords is not None else (0, 0, w, h)\\n print(image.shape)\\n for row in range(min_y, max_y):\\n for col in range(min_x, max_x):\\n # print(w)\\n # watch for out of range\\n # The -1 and +1 in _check_surrounding_px are because otherwise it stops short\\n if min_x < col < max_x + 1 and min_y < row < max_y + 1 and image[row, col] != WALL_COLOR:\\n dist = MapHandler._get_dist_heuristic(image, wall_distance, row, col)\\n # if (row, col) == (53, 2):\\n # print(\\\"Aaaaa\\\")\\n new_node = Node(row, col)\\n # TODO When we go in here there's a chance not all neighbors have been generated\\n # neighbors = MapHandler.get_neighbor_coords(new_node, img)\\n # new_node.add_neighbors(MapHandler.get_neighbors_from_img(new_node, img, graph))\\n new_node.dist_from_wall = dist\\n map_graph[(row, col)] = new_node\\n\\n # get neighbors: requires going back over after it's been generated\\n for coords, node in map_graph.iteritems():\\n # node = graph[(row, col)]\\n node.add_neighbors(MapHandler.get_neighbors_from_img(node, image, map_graph, CLEAR_TERRAIN_MAP_COLOR))\\n\\n return map_graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53465458698fbdad4840e6e1290392f0\",\n \"score\": \"0.47090194\",\n \"text\": \"def __init__(self, nelements):\\n this = _structural.new_doubleArray(nelements)\\n try:\\n self.this.append(this)\\n except __builtin__.Exception:\\n self.this = this\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d933863641161dada5b9331591fd708a\",\n \"score\": \"0.47028866\",\n \"text\": \"def _build_graph_representation(self):\\n\\n g = nx.Graph()\\n for idx, elem in enumerate(self.elements):\\n g.add_node(idx, element=elem)\\n\\n for b in self.bonds:\\n a1, a2 = b\\n g.add_edge(a1, a2)\\n\\n self._g = g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0121bf8989814017e8697c43181d766e\",\n \"score\": \"0.47013953\",\n \"text\": \"def gridLayout(nx,xSpacing,nz,zSpacing=None,hub_height=0,mirror=False):\\n if mirror:\\n ny=2\\n else:\\n ny=1\\n nWT = nz * nx * ny\\n xWT = np.zeros((nWT))\\n yWT = np.zeros((nWT))\\n zWT = np.zeros((nWT))\\n k = 0\\n for i in np.arange(1,nz+1).reshape(-1):\\n for j in np.arange(1,nx+1).reshape(-1):\\n k = k + 1\\n xWT[k-1] = (j - int(np.floor(nx / 2)) - 1) * xSpacing\\n zWT[k-1] = (i - 1) * zSpacing\\n yWT[k-1] = hub_height\\n if mirror:\\n for i in np.arange(1,nz+1).reshape(-1):\\n for j in np.arange(1,nx+1).reshape(-1):\\n k = k + 1\\n xWT[k-1] = (j - int(np.floor(nx / 2)) - 1) * xSpacing\\n zWT[k-1] = (i - 1) * zSpacing\\n yWT[k-1] = - hub_height\\n return xWT,yWT,zWT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15cb74974c97c54dfd63e65f220412e6\",\n \"score\": \"0.4690115\",\n \"text\": \"def createB1mapInHanti():\\n #railWay marker\\n\\n #blockWay marker\\n global blockWay_59_Hanti_B1\\n # elevator marker\\n global elevator_1_Hanti_B1, elevator_2_Hanti_B1\\n\\n # blockWay marker\\n blockWay_59_Hanti_B1 = markerNode(\\\"Hanti_59_B1\\\", 1, 59)\\n\\n # elevator marker\\n elevator_1_Hanti_B1 = markerNode(\\\"Hanti_EV_1_B1\\\", 1, 7)\\n elevator_2_Hanti_B1 = markerNode(\\\"Hanti_EV_2_B1\\\", 1, 8)\\n\\n #update\\n blockWay_59_Hanti_B1.update(south = elevator_2_Hanti_B1, east = elevator_1_Hanti_B1)\\n\\n elevator_1_Hanti_B1.update(west = blockWay_59_Hanti_B1)\\n elevator_2_Hanti_B1.update(north = blockWay_59_Hanti_B1)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993497,"cells":{"query_id":{"kind":"string","value":"1ec8569229ebed58baaf8b1c58f72297"},"query":{"kind":"string","value":"The show action, which shows details for a particular DocumentFile."},"positive_passages":{"kind":"list like","value":[{"docid":"c792bc572b8fe5fd7484420aadc30173","score":"0.7743916","text":"def document_show(project_id, document_file_id):\n #TODO: good spot for a helper\n #document = helpers.get_object_or_exception(Unit,\n # Unit.id == document_id, exceptions.DocumentNotFoundException)\n try:\n document_file = DocumentFile.query.get(document_file_id)\n except TypeError:\n return app.login_manager.unauthorized()\n\n access_granted = current_user.has_document_file(document_file)\n\n # Test if this user can see it\n if not access_granted:\n return app.login_manager.unauthorized()\n\n filename = os.path.split(document_file.path)[1]\n #TODO: move to objects\n\n project = Project.query.join(User).filter(User.id == current_user.id).\\\n filter(DocumentFile.id == document_file_id).one()\n # NOTE: do you mean projects? why do you only load one project when\n # documents can be part of more than one project? This should likely\n # just be document.projects\n\n return render_template(\"document_show.html\",\n document_file=document_file,\n project=project,\n filename=filename)","title":""}],"string":"[\n {\n \"docid\": \"c792bc572b8fe5fd7484420aadc30173\",\n \"score\": \"0.7743916\",\n \"text\": \"def document_show(project_id, document_file_id):\\n #TODO: good spot for a helper\\n #document = helpers.get_object_or_exception(Unit,\\n # Unit.id == document_id, exceptions.DocumentNotFoundException)\\n try:\\n document_file = DocumentFile.query.get(document_file_id)\\n except TypeError:\\n return app.login_manager.unauthorized()\\n\\n access_granted = current_user.has_document_file(document_file)\\n\\n # Test if this user can see it\\n if not access_granted:\\n return app.login_manager.unauthorized()\\n\\n filename = os.path.split(document_file.path)[1]\\n #TODO: move to objects\\n\\n project = Project.query.join(User).filter(User.id == current_user.id).\\\\\\n filter(DocumentFile.id == document_file_id).one()\\n # NOTE: do you mean projects? why do you only load one project when\\n # documents can be part of more than one project? This should likely\\n # just be document.projects\\n\\n return render_template(\\\"document_show.html\\\",\\n document_file=document_file,\\n project=project,\\n filename=filename)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7c868d3aaf579912e3baafb242d610b6","score":"0.67580366","text":"def show(self, name, docid=None, **options):\n path = _path_from_name(name, '_show')\n if docid:\n path.append(docid)\n status, headers, body = self.resource(*path).get(**options)\n return headers, body","title":""},{"docid":"94a86d475c3f393766537f7b2636f9ab","score":"0.65389746","text":"def show_file(request, file_id):\n file = m.File.objects.get(id=file_id)\n if not p.can_view_file(request.user, file):\n return HttpResponseForbidden()\n content = h.read_file_by_line(file.file)\n return HttpResponse(content_type=\"text/plain\", charset=\"utf-8\", content=content)","title":""},{"docid":"bb5aa5d38e30c9de1ca9dbb037222189","score":"0.65235895","text":"def files_show(file_id):\n file_id = files.find_one({'_id': ObjectId(file_id)})\n return render_template('files_show.html', stl=file_id)","title":""},{"docid":"2e005e333914c26479d63bf7d689df17","score":"0.63078725","text":"def view_file(self, itemClass) :\n\n # Show the table of specified item\n # self.show_table(itemClass)\n\n # Ask User for the id of the item they want to view\n id = self.id_input()\n\n # * View File\n # Connecting to the database\n conn = create_connection_db(itemClass.databaseFile)\n # Get the filepath\n filepath = get_item_attribute(conn, itemClass,\"filepath\",id)\n\n os.system(\"evince \" + filepath)","title":""},{"docid":"96c750eaa79b19ce78da7eddf642929e","score":"0.62104774","text":"def document_detail(request, slug):\n document = None\n try:\n document = _resolve_document(\n request,\n slug,\n 'base.view_resourcebase',\n _PERMISSION_MSG_VIEW)\n\n except Http404:\n return HttpResponse(\n loader.render_to_string(\n '404.html', render(\n request, {\n })), status=404)\n\n except PermissionDenied:\n return HttpResponse(\n loader.render_to_string(\n '401.html', render(\n request, {\n 'error_message': _(\"You are not allowed to view this document.\")})), status=403)\n\n if document is None:\n return HttpResponse(\n 'An unknown error has occured.',\n mimetype=\"text/plain\",\n status=401\n )\n\n else:\n related = ''\n # Update count for popularity ranking,\n # but do not includes admins or resource owners\n if request.user != document.owner and not request.user.is_superuser:\n WFPDocument.objects.filter(id=document.id).update(popular_count=F('popular_count') + 1)\n\n metadata = document.link_set.metadata().filter(\n name__in=settings.DOWNLOAD_FORMATS_METADATA)\n\n\n # TODO handle permissions here\n context_dict = {\n 'perms_list': get_perms(request.user, document.get_self_resource()),\n 'permissions_json': _perms_info_json(document),\n 'resource': document,\n 'metadata': metadata,\n 'imgtypes': IMGTYPES,\n 'related': related}\n\n if settings.SOCIAL_ORIGINS:\n context_dict[\"social_links\"] = build_social_links(request, document)\n\n return render(\n request,\n \"wfpdocs/document_detail.html\",\n context=context_dict)","title":""},{"docid":"7204386b89fffc06c5034f8ec20f4e90","score":"0.6000393","text":"def open_document(self, fileURL):\n # Convert the fileURL to a file path.\n fileURL = fileURL.rstrip(\"/\")\n path = unquote(urlparse(fileURL).path)\n extension = os.path.splitext(path)[1][1:]\n\n # Create the document instance\n DocType = self.interface.document_types[extension]\n document = DocType(fileURL, self.interface)\n self.interface._documents.append(document)\n\n document.show()","title":""},{"docid":"a1b3191e3623d1de04bed1e442c11d43","score":"0.58621496","text":"def show_file(hashid):\n fupload = FileUpload.get_by_hashid(hashid)\n\n if not fupload:\n flash(_('Could not find file'), 'error')\n\n return redirect(url_for('admin.file_index'))\n\n return render_template('admin/files/show.html', fupload=fupload)","title":""},{"docid":"7b619feae3d7e3b8a741515053939c08","score":"0.5822344","text":"def show(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"show\"), kwargs)","title":""},{"docid":"7b619feae3d7e3b8a741515053939c08","score":"0.5822344","text":"def show(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"show\"), kwargs)","title":""},{"docid":"7b619feae3d7e3b8a741515053939c08","score":"0.5822344","text":"def show(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"show\"), kwargs)","title":""},{"docid":"7b619feae3d7e3b8a741515053939c08","score":"0.5822344","text":"def show(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"show\"), kwargs)","title":""},{"docid":"7b619feae3d7e3b8a741515053939c08","score":"0.5822344","text":"def show(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"show\"), kwargs)","title":""},{"docid":"7b619feae3d7e3b8a741515053939c08","score":"0.5822344","text":"def show(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"show\"), kwargs)","title":""},{"docid":"5c49a01a1ae98c62924b73b1b535c7f9","score":"0.5735376","text":"def info_about_file(self,file_id):\n if self.current:\n try:\n self.current.info_about_file(int(file_id))\n except (KeyError, UnboundLocalError):\n print_info('No such file id')\n else:\n print_error('No dataset selected. Use -s option.')","title":""},{"docid":"5cc8b4758d5c41f08e9ff77047a740d3","score":"0.5726483","text":"def show(self, req, id):\n ctxt = req.environ['delfin.context']\n access_info = db.access_info_get(ctxt, id)\n return self._view_builder.show(access_info)","title":""},{"docid":"5e595ab02fb8fe7838b0c7fa93b45aa4","score":"0.5686247","text":"async def catalog_display(args: Namespace) -> ExitCode:\n # if the user specified a path\n if args.path:\n # ask the file catalog to retrieve the record of the file\n catalog_record = await _catalog_get(args.di[\"fc_rc\"], args.path)\n\n # if the user specified a uuid\n if args.uuid:\n try:\n catalog_record = await args.di[\"fc_rc\"].request(\"GET\", f\"/api/files/{args.uuid}\")\n except Exception:\n catalog_record = None\n\n # display the record to the caller\n if catalog_record:\n print_dict_as_pretty_json(catalog_record)\n else:\n print_dict_as_pretty_json({})\n\n return EXIT_OK","title":""},{"docid":"b58b51d29ff35d9b575701bc1a4ecb41","score":"0.5663755","text":"def ev_showDocs(self, event):\n raise NotImplementedError(\"Not implemented yet\")","title":""},{"docid":"ee6b053ece62b0303759830a646ace77","score":"0.56484455","text":"def retrieve(self):\n if not self.ok:\n self.initialize()\n if not hasattr(self, 'number'):\n raise Exception(\"Cannot retrieve a file without a file number.\")\n docassemble.base.functions.this_thread.open_files.add(self)\n #logmessage(\"Retrieve: calling file finder\")\n if self.has_specific_filename:\n self.file_info = server.file_number_finder(self.number, filename=self.filename)\n else:\n self.file_info = server.file_number_finder(self.number)\n self.extension = self.file_info.get('extension', None)\n self.mimetype = self.file_info.get('mimetype', None)\n self.persistent = self.file_info['persistent']\n self.private = self.file_info['private']","title":""},{"docid":"71f927360ec0382b781877821976ee51","score":"0.5622365","text":"def do_show(self, arg):\n line = arg.split()\n if not self.verify_class(line): # if dis returns anything not True\n return\n if not self.verify_id(line): # if dis returns anything not True\n return\n key = '{}.{}'.format(line[0], line[1]) # gets the action and id\n objects = models.storage.all()\n print(objects[key]) # prints id objects","title":""},{"docid":"288e00fee7871357f2f2bd3bd3f88bc5","score":"0.5586529","text":"def show(self, req, id):\n context = req.environ['manila.context']\n\n try:\n share = self.share_api.get(context, id)\n except exception.NotFound:\n raise exc.HTTPNotFound()\n\n return self._view_builder.detail(req, share)","title":""},{"docid":"692335c3fcfbdbc80faa2433f486dce2","score":"0.5560865","text":"def show(self, width=None):\n if docassemble.base.functions.this_thread.evaluation_context == 'docx':\n if self.mimetype == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':\n return docassemble.base.file_docx.include_docx_template(self)\n else:\n return docassemble.base.file_docx.image_for_docx(self.number, docassemble.base.functions.this_thread.current_question, docassemble.base.functions.this_thread.docx_template, width=width)\n else:\n if width is not None:\n return('[FILE ' + str(self.filename) + ', ' + str(width) + ']')\n else:\n return('[FILE ' + str(self.filename) + ']')","title":""},{"docid":"506d97a540d204bd40e05d0849d9386f","score":"0.55373466","text":"def document_view(request, scan_id):\n scan = ScanDocument.objects.get(id=scan_id)\n response = HttpResponse()\n response.content = scan.file.read()\n response[\"Content-Type\"] = \"application/pdf\"\n response[\"Content-Disposition\"] = \"inline; filename={0}\".format(\n scan.file.name\n )\n return response","title":""},{"docid":"5c6d8541987a251cc2c6bd59f282f8dd","score":"0.55270445","text":"def get(self, request, *args, **kwargs):\n\n doc_or_revision = self.get_object()\n field_name = self.kwargs.get('field_name')\n return serve_model_file_field(doc_or_revision, field_name)","title":""},{"docid":"941bf6ab0918891b771d3ced92b2cc93","score":"0.54384476","text":"def __str__(self):\n return self.file","title":""},{"docid":"51c28b92faf93a8554dfdc45eb8bf4f2","score":"0.5431468","text":"def documentFile(self):\r\n return self._currDocFile","title":""},{"docid":"dc0e4d1d08b780074faf616180d8799a","score":"0.54226613","text":"def info(client,docid, history):\n if history:\n click.echo(client.get('document/history/' + docid).text)\n else:\n click.echo(client.get('document/details/' + docid).text)","title":""},{"docid":"7f9ca330da4dc1621b5c65cc38c5645f","score":"0.54005593","text":"def file_get(self, file_id, **options):\n catalog_file = self.pycga.files.search(study=self.study_id, id=file_id, **options).get()\n if not catalog_file:\n raise BerthaOpenCGAException(\"File {} not found in OpenCGA\".format(file_id))\n else:\n return catalog_file[0]","title":""},{"docid":"54a3d28c68a31a2e7dc31009f1d7350f","score":"0.5374304","text":"def file_details(self) -> Sequence['outputs.FileIODetailsResponse']:\n return pulumi.get(self, \"file_details\")","title":""},{"docid":"23d5c0436385e83fbf7ce05e23659ce4","score":"0.53531086","text":"def files_info(self, *, file: str, **kwargs) -> SlackResponse:\n kwargs.update({\"file\": file})\n return self.api_call(\"files.info\", http_verb=\"GET\", params=kwargs)","title":""},{"docid":"b93ca29e264624273d44d7599b8e23af","score":"0.53442836","text":"def file_info(self, repo_key, file_path, **kwargs):\n api_method = self._category + \"File Info\"\n target = self._prefix + repo_key\n target = self._add_forward_slash_if_not_empty(target, file_path)\n return self._request(\"GET\", target, api_method, kwargs)","title":""},{"docid":"dd6a28f7e392dd915ea811ccc5c7d1bd","score":"0.53402716","text":"def viewdoc(request):\r\n parts = request.path.split('files/')\r\n return render(request, MEDIA_ROOT + parts[1], {})","title":""},{"docid":"fb310513846a0b7552a227dbd82bdf20","score":"0.53121144","text":"def documentView(self, path, docType, view='normal', has_zip=\"1\", root='app_folder'):\n url = 'http://conv.kuaipan.cn/1/fileops/documentView'\n param = {'type':docType,'view':view,'zip':has_zip,'path':path,'root':root, 'oauth_token': self._oauth_token}\n link = self.signature(url, param, self._oauth_token_secret)\n return self._getResponseWithCookie(link)","title":""},{"docid":"f985fbac4e4ca50152764535b5178064","score":"0.53057706","text":"def do_show(self,args):\n \n lks= len(problems[self.doc][1][0])\n args=self.parse(args)\n print args\n if not len(args)>=1:\n print \"error: enough arguments\"\n return\n try:\n exec(\"f=docread.{0}\".format(args[0]))\n except:\n print \"error: not representation available\"\n return\n if len(args)==1:\n args=range(lks+1)\n else:\n args=args[1:]\n\n\n for i in args:\n try:\n i=int(i)\n except ValueError:\n print \"error: invalid index has to be number\",i\n return\n \n if i+1 > lks:\n print \"===> Unknown document ({0})\".format(i)\n try:\n text=problems[self.doc][1][1][i-lks][1][1]\n doc=problems[self.doc][1][1][i-lks][1][0]\n rep=f(doc,text,sw=stopwords)\n printrep(rep,self.max)\n except IndexError:\n print \"error: no document with that index\",i\n return\n else:\n print \"===> known document ({0})\".format(i)\n text=problems[self.doc][1][0][i][1][1]\n doc=problems[self.doc][1][0][i][1][0]\n rep=f(doc,text,sw=stopwords)\n printrep(rep,self.max)\n print \"Done.\"","title":""},{"docid":"62f08823919e93f9829195eb7d79a0bf","score":"0.5286278","text":"def show(directory, revision):\n _show(directory, revision)","title":""},{"docid":"c68873966881b8e30b1950d75c264630","score":"0.52816325","text":"def documents (request, page):\n title = page.capitalize ()\n titlename = page+\"_h2\"\n titledesc = page+\"_minidesc\"\n pagetext = page+\"_text\"\n image = \"portfolio_head\"\n docs = UploadFile.objects.all ()\n return render_to_response ( \"documents.html\", locals() )","title":""},{"docid":"efb719059cc326cd468921aed0dea56c","score":"0.52813584","text":"def viewFile(self, itemHandle):\n \n if itemHandle in self.editPages.keys():\n logger.verbose(\"User: Showing file with handle %s\" % itemHandle)\n tabNum = self.editPages[itemHandle][\"index\"]\n self.nbContent.set_current_page(tabNum)\n return\n \n return","title":""},{"docid":"c05b355c61431c571b29e051066d6e85","score":"0.52714574","text":"def get_info_from_file(file_name):\n with open(file_name) as outfile:\n docs = load(outfile)\n return docs","title":""},{"docid":"cde23e5ec702d061b3400968d2323e7c","score":"0.52643245","text":"def show(self, **kwargs):\n the_files = [getattr(self, ext).show(**kwargs) for ext in self._extension_list() if hasattr(self, ext)]\n for the_file in the_files:\n if isinstance(the_file, InlineImage) or isinstance(the_file, Subdoc):\n return the_file\n return u' '.join(the_files)","title":""},{"docid":"e513f6eb7d8ea9b95a4ff7541081a1ff","score":"0.5259857","text":"def document(request, document_slug):\n # This may change depending on how we decide to structure\n # the url and handle locales.\n doc = get_object_or_404(Document, title=document_slug.replace('+', ' '))\n return jingo.render(request, 'wiki/document.html',\n {'document': doc})","title":""},{"docid":"52832b6ed3f5bb50533f57b070952f0c","score":"0.5253258","text":"def _view_file(f):\n\n subprocess.call([config.opts[\"viewer\"], f])","title":""},{"docid":"db7dd61bf1ddfc95d20f7a823dc8f2c5","score":"0.5246631","text":"def getDocInfo(self, docno):\n url = DOCDB_SHOW_DOC_TEMPLATE % docno\n info = {}\n data = urllib.urlopen(url).read()\n m = re.search('<div[^>]*id *= *\"DocTitle\"[^>]*>.*<h1>([^<]*)</h1>',\n data, re.S)\n if m:\n info['title'] = m.groups()[0]\n else:\n info['title'] = \"\"\n print \"No title found for %s\" % docno\n m = re.search('<div[^>]*id *= *\"Authors\"[^>]*>.*<ul>(.*)</ul>',\n data, re.S)\n if m:\n info['authors'] = m.groups()[0]\n else:\n info['authors'] = ''\n print \"No authors found for %s\" % docno\n return info","title":""},{"docid":"bc929fcf3fb9c8cd76eef6694158bb35","score":"0.52370554","text":"def viewDocumentPath(self, path, description=None, raiseOn404=True,\n category='', tab='', subtab=None, outcome=None):\n fl = self.fl\n if not description:\n description = \"View document path:\" + path\n ok_codes = [200, 301, 302, 303, 307]\n if not raiseOn404:\n ok_codes.append(404)\n if not outcome:\n outcome = \"view_documents\"\n resp = fl.get(fl.server_url + \"/nxpath/default/default-domain/\" +\n quote(path) + \"@\" + outcome + '?conversationId=0NXMAIN1' +\n getTabParams(tab, subtab=subtab, category=category),\n description=description, ok_codes=ok_codes)\n if resp.code == 404:\n fl.logi('Document ' + path + ' does not exists.')\n return None\n return self","title":""},{"docid":"bf0c415603446a15910edae87a0303d7","score":"0.5223739","text":"def show(self):\n pass","title":""},{"docid":"bf0c415603446a15910edae87a0303d7","score":"0.5223739","text":"def show(self):\n pass","title":""},{"docid":"bf0c415603446a15910edae87a0303d7","score":"0.5223739","text":"def show(self):\n pass","title":""},{"docid":"bf0c415603446a15910edae87a0303d7","score":"0.5223739","text":"def show(self):\n pass","title":""},{"docid":"54a01065784796f4814db235c664dc78","score":"0.52114683","text":"def get(self, request, *args, **kwargs):\n source_file = self.get_object()\n return FileResponse(source_file.fname)","title":""},{"docid":"6a6735d194bafc7f4de31aa451866c45","score":"0.5207133","text":"def show_product_documents(self, **kw):\n attachment = request.env['ir.attachment'].sudo().browse(int(kw.get('att_id')))\n values = {'attachment': attachment}\n return request.env['ir.ui.view'].render_template(\"skit_vendor_portal.show_product_attached_files\", values)","title":""},{"docid":"bd561a45b2835112009dd9df8ed7e311","score":"0.5204127","text":"def get_file_name(self):\n return self.view.file()","title":""},{"docid":"f6fb460985289e9a8122c7c66fa8ccc7","score":"0.5180643","text":"def _show_documentation(self):\n documents = []\n for key, document in self.app.documents.get().items():\n if key != \"main\":\n documents.append((key, document))\n documents = sorted(documents, key=lambda x: x[0])\n main = self.app.documents.get(\"main\")\n if main is not None:\n documents.insert(0, (main.name, main))\n\n user_answer = \"\"\n index = 0\n while user_answer != \"X\":\n if index < 0:\n index = 0\n if index > len(documents) - 1:\n index = len(documents) - 1\n document = documents[index][1]\n\n os.system('cls' if os.name == 'nt' else 'clear')\n echo(Environment().from_string(document.content).render(app=self.app, plugin=document.plugin))\n\n source = \"This document is registered by '%s' under the name '%s'\" % (document.plugin.name, document.name)\n echo(\"-\" * len(source))\n echo(source)\n echo(\"-\" * len(source))\n commands = \"Actions: \"\n if index < len(documents) - 1:\n commands += \"[N]ext, \"\n if index > 0:\n commands += \"[P]revious, \"\n commands += \"E[x]it\"\n echo(commands)\n\n if index < len(documents) - 1:\n default = \"N\"\n elif index > 0:\n default = \"P\"\n else:\n default = \"X\"\n user_answer = prompt(\"Select your action\", default=default).upper()\n\n if user_answer == \"N\":\n index += 1\n elif user_answer == \"P\":\n index -= 1","title":""},{"docid":"51718b1518658fa27b960de240f0778f","score":"0.516441","text":"def details(self, file_hash):\n return jsonify(File.get_file_details(file_hash))","title":""},{"docid":"3e8879cf031d0e0d39ede581bb62ce8c","score":"0.51614755","text":"def get_file(self, Id):\n # print('in get file')\n f = self.db_files.find_one({'_id': ObjectId(Id)})\n if f:\n return f\n else:\n raise my_errors.not_found","title":""},{"docid":"61236e36de2bb643f2961fc7d8b9848a","score":"0.51425976","text":"def show(self, req, id):\n\n LOG.debug('Get trigger(%s) start', id)\n\n context = req.environ['karbor.context']\n trigger = self._get_trigger_by_id(context, id)\n\n check_policy(context, 'get', trigger)\n return self._view_builder.detail(req, trigger)","title":""},{"docid":"72d0f432384627690d22cdff1057f686","score":"0.5127289","text":"def show(self):\n ...","title":""},{"docid":"ec6feb2f0edbce10c7d2b8470942954a","score":"0.5126294","text":"def get_document(self, document_id: int) -> Document:\n pass","title":""},{"docid":"a2bdf58277a1b036206067e6959f4b0c","score":"0.5093053","text":"def get_file(file_id):\n\n document_file = DocumentFile.query.get(file_id)\n try:\n access_granted = current_user.has_document_file(document_file)\n except TypeError:\n return app.login_manager.unauthorized()\n # TODO: clearer error handling\n\n # Test if this user can see it\n if not access_granted:\n return app.login_manager.unauthorized()\n directory, filename = os.path.split(document_file.path)\n\n return send_from_directory(directory, filename)","title":""},{"docid":"604f164d3ebf35d440b3011a6af35d0d","score":"0.50911343","text":"def details_of_a_food_prod(self):\n self.__view.details_of_a_food_prod_page(\n self.memory,\n self.event_handler)","title":""},{"docid":"61bf33539cb021b4d16c635ef4125c6a","score":"0.50874794","text":"def show_file(filename):\n\t\n\treturn send_from_directory(MEDIA_DIR, filename, as_attachment=True)","title":""},{"docid":"33f65cf138cf51198ae11b827faa99a7","score":"0.5087051","text":"def show_detail(request, show_pk): # TODO: adding notes feature goes here\n show = get_object_or_404(Show, pk=show_pk)\n notes = Note.objects.filter(show=show_pk)\n return render(request, 'lmn/shows/show_detail.html', {'show': show, 'notes': notes})","title":""},{"docid":"10924140e9095825d83a8491f8e51ec4","score":"0.507393","text":"def show(self):\n pass","title":""},{"docid":"84c851a8438d65f3232f3325b2697192","score":"0.50717926","text":"def do_show(self, args):\n args = shlex.split(args)\n if len(args) == 0:\n print(\"** class name missing **\")\n return\n elif args[0] not in model_names:\n print(\"** class doesn't exist **\")\n return\n elif len(args) != 2:\n print(\"** instance id missing **\")\n return\n cls, idx = args[0], args[1]\n # try:\n # eval(args[0])\n # except NameError:\n # print(\"** class doesn't exist **\")\n # return\n\n storage = FileStorage()\n storage.reload()\n obj_dict = storage.all()\n key = cls + '.' + idx\n\n try:\n obj = obj_dict[key]\n print(obj)\n except KeyError:\n print(\"** no instance found **\")","title":""},{"docid":"7f988219b5f0fb59f3db4f0533c4f6cc","score":"0.50641924","text":"def show(self, id=None, **kwargs):\n return self._get(item=id, **kwargs)","title":""},{"docid":"b1b5a58be4780bec8cc898fb38191457","score":"0.5048951","text":"def list_remote_action(self):\n response = self.api.list_documents(self.project_id)\n if response.status_code == 204:\n print(\"No documents to report\")\n return\n elif response.status_code != 200:\n if response.json():\n raise_error(response.json(), \"Failed to get status of documents\", True)\n else:\n raise_error(\"\", \"Failed to get status of documents\", True)\n else:\n print ('Remote documents: id, document name')\n for entry in response.json()['entities']:\n title = entry['entities'][1]['properties']['title'].replace(\"Status of \", \"\")\n id = entry['entities'][1]['properties']['id']\n info = '{id} \\t {title}'.format(id=id, title=title)\n print (info)\n return","title":""},{"docid":"de872cd9621657973a63bf228f672692","score":"0.5038279","text":"def get_document(self, document_id):\n return self.document_service.get_document(document_id)","title":""},{"docid":"5c26ad662c2f13821450cc0f099521bf","score":"0.5033167","text":"def getShowDetails(id):","title":""},{"docid":"2ed184f4d7f65a1be40b937a6a917461","score":"0.5027212","text":"def render_file(request, file_id):\n file = m.File.objects.get(id=file_id)\n if not p.can_view_file(request.user, file):\n return HttpResponseForbidden()\n content = h.read_file_by_line(file.file)\n detail = {\n \"content\": pyghi(content, pyglex.PythonLexer(), pygform.HtmlFormatter(linenos='table'))\n }\n return render(request, 'student/pretty_file.html', detail)","title":""},{"docid":"4710dd192a683c1f040a608f00182d9b","score":"0.50185657","text":"def GetFileDetails(self, args, context=None):\n\n raise NotImplementedError()","title":""},{"docid":"bd1c5845e8ef71c1b41ff0750dcad0f2","score":"0.5013779","text":"def details(partno):\n try:\n pn = PartNumber(partno)\n except ValueError:\n abort(404)\n\n # redirect to the revisionless URL if the user cannot view outdated revisions\n if pn.revision is not None and not current_user.has_role('component_edit'):\n return redirect(url_for('components.details', partno=pn.base_number))\n\n # ensure the object exists and the revision is valid\n obj = current_app.mongo.db.components.find_one_or_404(pn.base_number)\n\n # ensure the desired revision exists\n num_revisions = len(obj.get('revisions', list()))\n if pn.revision_number is not None and pn.revision_number >= num_revisions:\n abort(404)\n pn.set_num_revisions(num_revisions)\n\n files = _get_files(pn.id)\n\n preview_file = None\n for file in files:\n if file.startswith('preview.'):\n preview_file = file\n break\n\n return render_template('components/details.html', data=obj,\n partno=pn, files=files, preview_file=preview_file)","title":""},{"docid":"0fff94d66a10b418d1acc27548ddd5c9","score":"0.50009507","text":"def get_document(DocumentId=None):\n pass","title":""},{"docid":"dd4955b264955c6d13ca5af30a4250f0","score":"0.4999773","text":"def query(self, document_number):\n assert (\n self.session is not None\n ), \"session attribute of DocketReport cannot be None.\"\n # coerce the fourth digit of the document number to 1 to ensure we get\n # the attachment page.\n document_number = f\"{document_number[:3]}0{document_number[4:]}\"\n url = self.url + document_number\n logger.info(\"Querying the attachment page endpoint at URL: %s\", url)\n self.response = self.session.get(url)\n self.parse()","title":""},{"docid":"e3f76cc3b64c043bd3afede04d76f52e","score":"0.49956954","text":"def get_doc(_, file_id):\n\n filename = \"%02d.pdf\" % (int(file_id) % 10)\n file_path = join(STATIC_STORAGE_PATH, filename)\n if exists(file_path):\n return FileResponse(open(file_path, 'rb'), as_attachment=True)\n raise Http404","title":""},{"docid":"bf5303e01541f899179b9e4d07ed5233","score":"0.49928683","text":"def viewFile(self, checkForUpdates):\n logger.debug(util.funcName('begin'))\n if checkForUpdates:\n newSelectedItem = self.dlgCtrls.listboxFiles.getSelectedItem()\n logger.debug(\"newSelectedItem '%s'\", newSelectedItem)\n self.updateFile(False)\n if newSelectedItem:\n self.dlgCtrls.listboxFiles.selectItem(newSelectedItem, True)\n try:\n self.selectedIndex = dutil.get_selected_index(\n self.dlgCtrls.listboxFiles, \"a file\")\n logger.debug(\"self.selectedIndex %d\", self.selectedIndex)\n except exceptions.ChoiceProblem:\n return\n fileItem = self.fileItems[self.selectedIndex]\n logger.debug(\"Filepath %s\", fileItem.filepath)\n self.dlgCtrls.txtPrefix.setText(fileItem.prefix)\n self.dlgCtrls.chkDontUseSegnum.setState(not fileItem.use_segnum)\n logger.debug(util.funcName('end'))","title":""},{"docid":"37031483f1f5c9fe3fee2d901d39ce29","score":"0.49814904","text":"def show_view_field_scan_dialog(self):\n try:\n field_scan_list = []\n fs = _data.measurement.FieldScan(\n database_name=self.database_name,\n mongo=self.mongo, server=self.server)\n for idn in self.field_scan_id_list:\n fs.db_read(idn)\n field_scan_list.append(fs.copy())\n\n self.view_scan_dialog.show(\n field_scan_list, 'field')\n \n except Exception:\n _traceback.print_exc(file=_sys.stdout)","title":""},{"docid":"37031483f1f5c9fe3fee2d901d39ce29","score":"0.49814904","text":"def show_view_field_scan_dialog(self):\n try:\n field_scan_list = []\n fs = _data.measurement.FieldScan(\n database_name=self.database_name,\n mongo=self.mongo, server=self.server)\n for idn in self.field_scan_id_list:\n fs.db_read(idn)\n field_scan_list.append(fs.copy())\n\n self.view_scan_dialog.show(\n field_scan_list, 'field')\n \n except Exception:\n _traceback.print_exc(file=_sys.stdout)","title":""},{"docid":"a61025e13ea62ab33bcfb420169cf7ff","score":"0.49763122","text":"def showdoc(self):\n # abstract method does nothing. It simply has a return statement.\n return","title":""},{"docid":"f8e8ef7585bcdc54cd20ea49c993797d","score":"0.4973397","text":"def read_file_view(self, request, hash):\n raise NotImplementedError","title":""},{"docid":"f7af7fabfe16607c55d6706bc49da099","score":"0.4964308","text":"def _getDoc(self, f):\n\n parse = os.path.split(self._datas['Path'])[0]\n parsing = model_parser(parse)\n index = None\n\n for j in range(0,len(parsing)):\n if parsing[j].name == self._datas['Model name']:\n index = j\n break\n\n if index is None:\n f.close()\n with self._out:\n raise Exception('Critical error : model not found.')\n \n return docGenerator.DocGenerator(parsing[index])","title":""},{"docid":"ac4701d3459f6a5abcd566c13869005b","score":"0.49582866","text":"def __unicode__(self):\n return self.getFileReport()","title":""},{"docid":"9ab7b8ce4efd6361ce6c27f741248245","score":"0.49548638","text":"def fp(self):\n return self.collection.store.get(self.filename)","title":""},{"docid":"ff88a31d478f9e6e33730389e8bbf72a","score":"0.49527317","text":"async def sxm_show(self, ctx: SlashContext, show_id: Optional[str] = None) -> None:\n\n await self._play_archive_file(ctx, show_id, False)","title":""},{"docid":"f24280adcb1a22c258db8dd3b0ab93bf","score":"0.49426746","text":"def show_file_system(self):\n self._show_file_system_helper(self)","title":""},{"docid":"ad682aa4b980a391614a36dbce3c20ee","score":"0.49358463","text":"def do_show(self, line):\n args = line.split()\n if not self.verify_class(args):\n return\n if not self.verify_id(args):\n return\n string_key = str(args[0]) + '.' + str(args[1])\n objects = models.storage.all()\n print(objects[string_key])","title":""},{"docid":"83dcb03e4c02680ee0c05d15c1c227f7","score":"0.49295127","text":"def get_document(id):","title":""},{"docid":"f470fb5e66d4c24714de0ffada20f115","score":"0.49281743","text":"def view_design_doc(self):\n if self.viewer_var.get() == \"firefox\":\n # Browser mode: use HREFs with suffix #<anchortype>=<value>.\n if self.ref_mode_var.get() == \"page\":\n ref_in_doc = f\"#page={self.page_num_var.get().rstrip()}\"\n elif self.ref_mode_var.get() == \"section\":\n ref_in_doc = f\"#nameddest=section.{self.section_num_var.get().rstrip()}\"\n else:\n ref_in_doc = \"\"\n # Assume the location is a valid URL or an absolute path.\n doc_location = self.text1.get(0.0, tk.END).rstrip()\n # If no scheme (http://, file:// etc.) is given, treat it\n # as an absolute file path.\n if doc_location[0] == '/':\n doc_location = \"file://\" + doc_location\n command = [ \"firefox\", doc_location + ref_in_doc ]\n elif self.viewer_var.get() == \"evince\":\n # PDF viewer mode: use appropriate cmdline option.\n if self.ref_mode_var.get() == \"page\":\n ref_in_doc = [\"-i\", f\"{self.page_num_var.get().rstrip()}\" ]\n elif self.ref_mode_var.get() == \"section\":\n ref_in_doc = [\"-n\", f\"section.{self.section_num_var.get().rstrip()}\"]\n else:\n ref_in_doc = []\n # The requirement location should be a valid path to a PDF file.\n command = [\"evince\"] + ref_in_doc + [self.text1.get(0.0, tk.END).rstrip()]\n print(f\"### ==> command = {command}\")\n subprocess.Popen(command)","title":""},{"docid":"ac3b015d6cf7f360ff53ec0403f1e7ec","score":"0.4921231","text":"def get_document(self, project_name, file_name, output='rs3'):\n # only proceed if the project and file exist (for the user 'local')\n documents = self.get_project(project_name).get('documents')\n if documents is None or file_name not in documents:\n raise cherrypy.HTTPError(\n 404, (\"File '{0}' not available in project '{1}' for the current user. \"\n \"Available files: {2}\").format(file_name, project_name, documents))\n\n if output == 'rs3':\n return get_rs3_file(file_name, project_name, 'local')\n elif output == 'png':\n return get_screenshot(file_name, project_name, 'local', output_format='png')\n elif output == 'png-base64':\n return get_screenshot(file_name, project_name, 'local', output_format='png-base64')\n elif output == 'editor':\n # This will raise a cherrypy.HTTPRedirect to rstWeb's Structure Editor\n # opened with the given document.\n edit_document(file_name, project_name)\n else:\n raise cherrypy.HTTPError(\n 400, 'Unknown output format: {0}'.format(output))","title":""},{"docid":"f737ccd1a504a8321c64c11677358326","score":"0.49014312","text":"def file(self):\n return self._search_file()","title":""},{"docid":"9db3f3770a4ff92b08b6c933f3c0d0d8","score":"0.49002478","text":"def show(self):\n if self.project and self.project.isOpen():\n self.projectCheckBox.setEnabled(True)\n self.projectCheckBox.setChecked(True)\n else:\n self.projectCheckBox.setEnabled(False)\n self.projectCheckBox.setChecked(False)\n \n self.fileNameEdit.selectAll()\n self.fileNameEdit.setFocus()\n \n super(FindFileNameDialog, self).show()","title":""},{"docid":"6bf19ea96ff8892bcc2b84e1cb35a224","score":"0.48777357","text":"def show_request(self):\n try:\n # Get the selected row index\n selected_index = self.loan_request_table.selectedIndexes()[0].row()\n # If a request has been selected, show it\n [loan_request, _] = self.loan_requests[selected_index]\n self.mainwindow.fiplr2_controller.setup_view(loan_request.id)\n self.mainwindow.navigation.switch_to_fiplr2()\n except IndexError:\n self.mainwindow.show_dialog(\"Select request\", 'No loan requests have been selected.')","title":""},{"docid":"6efc91a260cc52060bd9e91c0fa05909","score":"0.48750532","text":"def view_records(self):\n print('Hi {}! Ask your managers if you need to see sales records'.format(self.fullname))","title":""},{"docid":"aba995fae8b5dde7b61a4d207826a482","score":"0.48749405","text":"def detail(self, req):\n return self.index(req)","title":""},{"docid":"ab380652d592f2a67c577fdd4482a3ca","score":"0.48691365","text":"def file(self, folder, file_):\n return self.get('file', params={'folder': folder,\n 'file': file_})","title":""},{"docid":"8358dedae105d73dc14c3c1dfb89ce99","score":"0.48623356","text":"def show():\n\tif not ('uri' in session):\n\t\treturn index(error=True)\n\n\turi = session['uri']\n\tsession.clear()\n\treturn render_template('show.html', captured_img=uri)","title":""},{"docid":"7b3171b13e08687f71b3f9774d3933d3","score":"0.48551252","text":"def document(self, document):\n super(RedirectHelper, self).document(document)\n self._url_name = 'show_gsoc_document'\n return self","title":""},{"docid":"46dbc3c2cfcfe6db756a453d7ec66536","score":"0.4853151","text":"def show():\n table = request.args.get('table')\n fieldNames = TableOperation.getFieldNames(table)\n rows = TableOperation.showTable(table)\n rows.insert(0, fieldNames)\n session['result'] = [rows]\n return render_template('result.html')","title":""},{"docid":"932c4ee98a96b26efd52768355eea90a","score":"0.48403206","text":"def get(self, file_key):\r\n # If the file exists in the blobstore, let the user save the data to their computer\r\n if blobstore.get(file_key):\r\n self.send_blob(blobstore.BlobInfo.get(file_key), save_as=True)\r\n # If the file doesn't exist in the blobstore, then 404 the user\r\n else:\r\n self.error(404)","title":""},{"docid":"2cb7b57c7c7455161047577a27f56c07","score":"0.48352572","text":"def show(revisions):\n base.show(revisions)","title":""},{"docid":"ff4d86c8d543d2320ab5dedbc367bf10","score":"0.48270407","text":"def edit_document(file_name, project_name):\n kwargs = {\n 'current_doc': file_name,\n 'current_guidelines': u'**current_guidelines**',\n 'current_project': project_name,\n 'serve_mode': u'local'\n }\n url_params = kwargs2urlparams(kwargs)\n raise cherrypy.HTTPRedirect('/structure?{0}'.format(url_params))","title":""},{"docid":"69f810decb2afef11be2454ccc24b71c","score":"0.4825802","text":"def show(self, server_id):\n\n _cmd = 'show'\n _response_type = responses.ServerResponse\n return self._process_command()","title":""},{"docid":"8573360b15ce0068dd270188acdfb83e","score":"0.4817358","text":"def show(self, req, vsa_id, id):\n context = req.environ['nova.context']\n authorize(context)\n\n LOG.audit(_(\"Show. vsa_id=%(vsa_id)s, id=%(id)s\"), locals())\n\n try:\n self._check_volume_ownership(context, vsa_id, id)\n except exception.NotFound:\n raise exc.HTTPNotFound()\n except exception.Invalid:\n raise exc.HTTPBadRequest()\n\n return super(VsaVolumeDriveController, self).show(req, id)","title":""},{"docid":"a77a3038b465c80d7992bf8affd437f6","score":"0.48166698","text":"def do_show(self, arg):\n models.storage.reload()\n if len(arg) < 1:\n print(\"** class name missing **\")\n pass\n else:\n arg = arg.split(' ')\n if arg[0] not in HBNBCommand.classes:\n print(\"** class doesn't exist **\")\n elif arg[0] in HBNBCommand.classes:\n if len(arg) < 2:\n print(\"** instance id missing **\")\n return\n key = arg[0] + '.' + arg[1]\n if key in FileStorage._FileStorage__objects:\n print(FileStorage._FileStorage__objects[key])\n else:\n print(\"** no instance found **\")","title":""}],"string":"[\n {\n \"docid\": \"7c868d3aaf579912e3baafb242d610b6\",\n \"score\": \"0.67580366\",\n \"text\": \"def show(self, name, docid=None, **options):\\n path = _path_from_name(name, '_show')\\n if docid:\\n path.append(docid)\\n status, headers, body = self.resource(*path).get(**options)\\n return headers, body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a86d475c3f393766537f7b2636f9ab\",\n \"score\": \"0.65389746\",\n \"text\": \"def show_file(request, file_id):\\n file = m.File.objects.get(id=file_id)\\n if not p.can_view_file(request.user, file):\\n return HttpResponseForbidden()\\n content = h.read_file_by_line(file.file)\\n return HttpResponse(content_type=\\\"text/plain\\\", charset=\\\"utf-8\\\", content=content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb5aa5d38e30c9de1ca9dbb037222189\",\n \"score\": \"0.65235895\",\n \"text\": \"def files_show(file_id):\\n file_id = files.find_one({'_id': ObjectId(file_id)})\\n return render_template('files_show.html', stl=file_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e005e333914c26479d63bf7d689df17\",\n \"score\": \"0.63078725\",\n \"text\": \"def view_file(self, itemClass) :\\n\\n # Show the table of specified item\\n # self.show_table(itemClass)\\n\\n # Ask User for the id of the item they want to view\\n id = self.id_input()\\n\\n # * View File\\n # Connecting to the database\\n conn = create_connection_db(itemClass.databaseFile)\\n # Get the filepath\\n filepath = get_item_attribute(conn, itemClass,\\\"filepath\\\",id)\\n\\n os.system(\\\"evince \\\" + filepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c750eaa79b19ce78da7eddf642929e\",\n \"score\": \"0.62104774\",\n \"text\": \"def document_detail(request, slug):\\n document = None\\n try:\\n document = _resolve_document(\\n request,\\n slug,\\n 'base.view_resourcebase',\\n _PERMISSION_MSG_VIEW)\\n\\n except Http404:\\n return HttpResponse(\\n loader.render_to_string(\\n '404.html', render(\\n request, {\\n })), status=404)\\n\\n except PermissionDenied:\\n return HttpResponse(\\n loader.render_to_string(\\n '401.html', render(\\n request, {\\n 'error_message': _(\\\"You are not allowed to view this document.\\\")})), status=403)\\n\\n if document is None:\\n return HttpResponse(\\n 'An unknown error has occured.',\\n mimetype=\\\"text/plain\\\",\\n status=401\\n )\\n\\n else:\\n related = ''\\n # Update count for popularity ranking,\\n # but do not includes admins or resource owners\\n if request.user != document.owner and not request.user.is_superuser:\\n WFPDocument.objects.filter(id=document.id).update(popular_count=F('popular_count') + 1)\\n\\n metadata = document.link_set.metadata().filter(\\n name__in=settings.DOWNLOAD_FORMATS_METADATA)\\n\\n\\n # TODO handle permissions here\\n context_dict = {\\n 'perms_list': get_perms(request.user, document.get_self_resource()),\\n 'permissions_json': _perms_info_json(document),\\n 'resource': document,\\n 'metadata': metadata,\\n 'imgtypes': IMGTYPES,\\n 'related': related}\\n\\n if settings.SOCIAL_ORIGINS:\\n context_dict[\\\"social_links\\\"] = build_social_links(request, document)\\n\\n return render(\\n request,\\n \\\"wfpdocs/document_detail.html\\\",\\n context=context_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7204386b89fffc06c5034f8ec20f4e90\",\n \"score\": \"0.6000393\",\n \"text\": \"def open_document(self, fileURL):\\n # Convert the fileURL to a file path.\\n fileURL = fileURL.rstrip(\\\"/\\\")\\n path = unquote(urlparse(fileURL).path)\\n extension = os.path.splitext(path)[1][1:]\\n\\n # Create the document instance\\n DocType = self.interface.document_types[extension]\\n document = DocType(fileURL, self.interface)\\n self.interface._documents.append(document)\\n\\n document.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1b3191e3623d1de04bed1e442c11d43\",\n \"score\": \"0.58621496\",\n \"text\": \"def show_file(hashid):\\n fupload = FileUpload.get_by_hashid(hashid)\\n\\n if not fupload:\\n flash(_('Could not find file'), 'error')\\n\\n return redirect(url_for('admin.file_index'))\\n\\n return render_template('admin/files/show.html', fupload=fupload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b619feae3d7e3b8a741515053939c08\",\n \"score\": \"0.5822344\",\n \"text\": \"def show(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"show\\\"), kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b619feae3d7e3b8a741515053939c08\",\n \"score\": \"0.5822344\",\n \"text\": \"def show(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"show\\\"), kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b619feae3d7e3b8a741515053939c08\",\n \"score\": \"0.5822344\",\n \"text\": \"def show(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"show\\\"), kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b619feae3d7e3b8a741515053939c08\",\n \"score\": \"0.5822344\",\n \"text\": \"def show(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"show\\\"), kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b619feae3d7e3b8a741515053939c08\",\n \"score\": \"0.5822344\",\n \"text\": \"def show(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"show\\\"), kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b619feae3d7e3b8a741515053939c08\",\n \"score\": \"0.5822344\",\n \"text\": \"def show(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"show\\\"), kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c49a01a1ae98c62924b73b1b535c7f9\",\n \"score\": \"0.5735376\",\n \"text\": \"def info_about_file(self,file_id):\\n if self.current:\\n try:\\n self.current.info_about_file(int(file_id))\\n except (KeyError, UnboundLocalError):\\n print_info('No such file id')\\n else:\\n print_error('No dataset selected. Use -s option.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc8b4758d5c41f08e9ff77047a740d3\",\n \"score\": \"0.5726483\",\n \"text\": \"def show(self, req, id):\\n ctxt = req.environ['delfin.context']\\n access_info = db.access_info_get(ctxt, id)\\n return self._view_builder.show(access_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e595ab02fb8fe7838b0c7fa93b45aa4\",\n \"score\": \"0.5686247\",\n \"text\": \"async def catalog_display(args: Namespace) -> ExitCode:\\n # if the user specified a path\\n if args.path:\\n # ask the file catalog to retrieve the record of the file\\n catalog_record = await _catalog_get(args.di[\\\"fc_rc\\\"], args.path)\\n\\n # if the user specified a uuid\\n if args.uuid:\\n try:\\n catalog_record = await args.di[\\\"fc_rc\\\"].request(\\\"GET\\\", f\\\"/api/files/{args.uuid}\\\")\\n except Exception:\\n catalog_record = None\\n\\n # display the record to the caller\\n if catalog_record:\\n print_dict_as_pretty_json(catalog_record)\\n else:\\n print_dict_as_pretty_json({})\\n\\n return EXIT_OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b58b51d29ff35d9b575701bc1a4ecb41\",\n \"score\": \"0.5663755\",\n \"text\": \"def ev_showDocs(self, event):\\n raise NotImplementedError(\\\"Not implemented yet\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee6b053ece62b0303759830a646ace77\",\n \"score\": \"0.56484455\",\n \"text\": \"def retrieve(self):\\n if not self.ok:\\n self.initialize()\\n if not hasattr(self, 'number'):\\n raise Exception(\\\"Cannot retrieve a file without a file number.\\\")\\n docassemble.base.functions.this_thread.open_files.add(self)\\n #logmessage(\\\"Retrieve: calling file finder\\\")\\n if self.has_specific_filename:\\n self.file_info = server.file_number_finder(self.number, filename=self.filename)\\n else:\\n self.file_info = server.file_number_finder(self.number)\\n self.extension = self.file_info.get('extension', None)\\n self.mimetype = self.file_info.get('mimetype', None)\\n self.persistent = self.file_info['persistent']\\n self.private = self.file_info['private']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71f927360ec0382b781877821976ee51\",\n \"score\": \"0.5622365\",\n \"text\": \"def do_show(self, arg):\\n line = arg.split()\\n if not self.verify_class(line): # if dis returns anything not True\\n return\\n if not self.verify_id(line): # if dis returns anything not True\\n return\\n key = '{}.{}'.format(line[0], line[1]) # gets the action and id\\n objects = models.storage.all()\\n print(objects[key]) # prints id objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"288e00fee7871357f2f2bd3bd3f88bc5\",\n \"score\": \"0.5586529\",\n \"text\": \"def show(self, req, id):\\n context = req.environ['manila.context']\\n\\n try:\\n share = self.share_api.get(context, id)\\n except exception.NotFound:\\n raise exc.HTTPNotFound()\\n\\n return self._view_builder.detail(req, share)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"692335c3fcfbdbc80faa2433f486dce2\",\n \"score\": \"0.5560865\",\n \"text\": \"def show(self, width=None):\\n if docassemble.base.functions.this_thread.evaluation_context == 'docx':\\n if self.mimetype == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':\\n return docassemble.base.file_docx.include_docx_template(self)\\n else:\\n return docassemble.base.file_docx.image_for_docx(self.number, docassemble.base.functions.this_thread.current_question, docassemble.base.functions.this_thread.docx_template, width=width)\\n else:\\n if width is not None:\\n return('[FILE ' + str(self.filename) + ', ' + str(width) + ']')\\n else:\\n return('[FILE ' + str(self.filename) + ']')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"506d97a540d204bd40e05d0849d9386f\",\n \"score\": \"0.55373466\",\n \"text\": \"def document_view(request, scan_id):\\n scan = ScanDocument.objects.get(id=scan_id)\\n response = HttpResponse()\\n response.content = scan.file.read()\\n response[\\\"Content-Type\\\"] = \\\"application/pdf\\\"\\n response[\\\"Content-Disposition\\\"] = \\\"inline; filename={0}\\\".format(\\n scan.file.name\\n )\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c6d8541987a251cc2c6bd59f282f8dd\",\n \"score\": \"0.55270445\",\n \"text\": \"def get(self, request, *args, **kwargs):\\n\\n doc_or_revision = self.get_object()\\n field_name = self.kwargs.get('field_name')\\n return serve_model_file_field(doc_or_revision, field_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"941bf6ab0918891b771d3ced92b2cc93\",\n \"score\": \"0.54384476\",\n \"text\": \"def __str__(self):\\n return self.file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c28b92faf93a8554dfdc45eb8bf4f2\",\n \"score\": \"0.5431468\",\n \"text\": \"def documentFile(self):\\r\\n return self._currDocFile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc0e4d1d08b780074faf616180d8799a\",\n \"score\": \"0.54226613\",\n \"text\": \"def info(client,docid, history):\\n if history:\\n click.echo(client.get('document/history/' + docid).text)\\n else:\\n click.echo(client.get('document/details/' + docid).text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f9ca330da4dc1621b5c65cc38c5645f\",\n \"score\": \"0.54005593\",\n \"text\": \"def file_get(self, file_id, **options):\\n catalog_file = self.pycga.files.search(study=self.study_id, id=file_id, **options).get()\\n if not catalog_file:\\n raise BerthaOpenCGAException(\\\"File {} not found in OpenCGA\\\".format(file_id))\\n else:\\n return catalog_file[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54a3d28c68a31a2e7dc31009f1d7350f\",\n \"score\": \"0.5374304\",\n \"text\": \"def file_details(self) -> Sequence['outputs.FileIODetailsResponse']:\\n return pulumi.get(self, \\\"file_details\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d5c0436385e83fbf7ce05e23659ce4\",\n \"score\": \"0.53531086\",\n \"text\": \"def files_info(self, *, file: str, **kwargs) -> SlackResponse:\\n kwargs.update({\\\"file\\\": file})\\n return self.api_call(\\\"files.info\\\", http_verb=\\\"GET\\\", params=kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b93ca29e264624273d44d7599b8e23af\",\n \"score\": \"0.53442836\",\n \"text\": \"def file_info(self, repo_key, file_path, **kwargs):\\n api_method = self._category + \\\"File Info\\\"\\n target = self._prefix + repo_key\\n target = self._add_forward_slash_if_not_empty(target, file_path)\\n return self._request(\\\"GET\\\", target, api_method, kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd6a28f7e392dd915ea811ccc5c7d1bd\",\n \"score\": \"0.53402716\",\n \"text\": \"def viewdoc(request):\\r\\n parts = request.path.split('files/')\\r\\n return render(request, MEDIA_ROOT + parts[1], {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb310513846a0b7552a227dbd82bdf20\",\n \"score\": \"0.53121144\",\n \"text\": \"def documentView(self, path, docType, view='normal', has_zip=\\\"1\\\", root='app_folder'):\\n url = 'http://conv.kuaipan.cn/1/fileops/documentView'\\n param = {'type':docType,'view':view,'zip':has_zip,'path':path,'root':root, 'oauth_token': self._oauth_token}\\n link = self.signature(url, param, self._oauth_token_secret)\\n return self._getResponseWithCookie(link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f985fbac4e4ca50152764535b5178064\",\n \"score\": \"0.53057706\",\n \"text\": \"def do_show(self,args):\\n \\n lks= len(problems[self.doc][1][0])\\n args=self.parse(args)\\n print args\\n if not len(args)>=1:\\n print \\\"error: enough arguments\\\"\\n return\\n try:\\n exec(\\\"f=docread.{0}\\\".format(args[0]))\\n except:\\n print \\\"error: not representation available\\\"\\n return\\n if len(args)==1:\\n args=range(lks+1)\\n else:\\n args=args[1:]\\n\\n\\n for i in args:\\n try:\\n i=int(i)\\n except ValueError:\\n print \\\"error: invalid index has to be number\\\",i\\n return\\n \\n if i+1 > lks:\\n print \\\"===> Unknown document ({0})\\\".format(i)\\n try:\\n text=problems[self.doc][1][1][i-lks][1][1]\\n doc=problems[self.doc][1][1][i-lks][1][0]\\n rep=f(doc,text,sw=stopwords)\\n printrep(rep,self.max)\\n except IndexError:\\n print \\\"error: no document with that index\\\",i\\n return\\n else:\\n print \\\"===> known document ({0})\\\".format(i)\\n text=problems[self.doc][1][0][i][1][1]\\n doc=problems[self.doc][1][0][i][1][0]\\n rep=f(doc,text,sw=stopwords)\\n printrep(rep,self.max)\\n print \\\"Done.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62f08823919e93f9829195eb7d79a0bf\",\n \"score\": \"0.5286278\",\n \"text\": \"def show(directory, revision):\\n _show(directory, revision)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c68873966881b8e30b1950d75c264630\",\n \"score\": \"0.52816325\",\n \"text\": \"def documents (request, page):\\n title = page.capitalize ()\\n titlename = page+\\\"_h2\\\"\\n titledesc = page+\\\"_minidesc\\\"\\n pagetext = page+\\\"_text\\\"\\n image = \\\"portfolio_head\\\"\\n docs = UploadFile.objects.all ()\\n return render_to_response ( \\\"documents.html\\\", locals() )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb719059cc326cd468921aed0dea56c\",\n \"score\": \"0.52813584\",\n \"text\": \"def viewFile(self, itemHandle):\\n \\n if itemHandle in self.editPages.keys():\\n logger.verbose(\\\"User: Showing file with handle %s\\\" % itemHandle)\\n tabNum = self.editPages[itemHandle][\\\"index\\\"]\\n self.nbContent.set_current_page(tabNum)\\n return\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c05b355c61431c571b29e051066d6e85\",\n \"score\": \"0.52714574\",\n \"text\": \"def get_info_from_file(file_name):\\n with open(file_name) as outfile:\\n docs = load(outfile)\\n return docs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde23e5ec702d061b3400968d2323e7c\",\n \"score\": \"0.52643245\",\n \"text\": \"def show(self, **kwargs):\\n the_files = [getattr(self, ext).show(**kwargs) for ext in self._extension_list() if hasattr(self, ext)]\\n for the_file in the_files:\\n if isinstance(the_file, InlineImage) or isinstance(the_file, Subdoc):\\n return the_file\\n return u' '.join(the_files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e513f6eb7d8ea9b95a4ff7541081a1ff\",\n \"score\": \"0.5259857\",\n \"text\": \"def document(request, document_slug):\\n # This may change depending on how we decide to structure\\n # the url and handle locales.\\n doc = get_object_or_404(Document, title=document_slug.replace('+', ' '))\\n return jingo.render(request, 'wiki/document.html',\\n {'document': doc})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52832b6ed3f5bb50533f57b070952f0c\",\n \"score\": \"0.5253258\",\n \"text\": \"def _view_file(f):\\n\\n subprocess.call([config.opts[\\\"viewer\\\"], f])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db7dd61bf1ddfc95d20f7a823dc8f2c5\",\n \"score\": \"0.5246631\",\n \"text\": \"def getDocInfo(self, docno):\\n url = DOCDB_SHOW_DOC_TEMPLATE % docno\\n info = {}\\n data = urllib.urlopen(url).read()\\n m = re.search('<div[^>]*id *= *\\\"DocTitle\\\"[^>]*>.*<h1>([^<]*)</h1>',\\n data, re.S)\\n if m:\\n info['title'] = m.groups()[0]\\n else:\\n info['title'] = \\\"\\\"\\n print \\\"No title found for %s\\\" % docno\\n m = re.search('<div[^>]*id *= *\\\"Authors\\\"[^>]*>.*<ul>(.*)</ul>',\\n data, re.S)\\n if m:\\n info['authors'] = m.groups()[0]\\n else:\\n info['authors'] = ''\\n print \\\"No authors found for %s\\\" % docno\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc929fcf3fb9c8cd76eef6694158bb35\",\n \"score\": \"0.52370554\",\n \"text\": \"def viewDocumentPath(self, path, description=None, raiseOn404=True,\\n category='', tab='', subtab=None, outcome=None):\\n fl = self.fl\\n if not description:\\n description = \\\"View document path:\\\" + path\\n ok_codes = [200, 301, 302, 303, 307]\\n if not raiseOn404:\\n ok_codes.append(404)\\n if not outcome:\\n outcome = \\\"view_documents\\\"\\n resp = fl.get(fl.server_url + \\\"/nxpath/default/default-domain/\\\" +\\n quote(path) + \\\"@\\\" + outcome + '?conversationId=0NXMAIN1' +\\n getTabParams(tab, subtab=subtab, category=category),\\n description=description, ok_codes=ok_codes)\\n if resp.code == 404:\\n fl.logi('Document ' + path + ' does not exists.')\\n return None\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0c415603446a15910edae87a0303d7\",\n \"score\": \"0.5223739\",\n \"text\": \"def show(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0c415603446a15910edae87a0303d7\",\n \"score\": \"0.5223739\",\n \"text\": \"def show(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0c415603446a15910edae87a0303d7\",\n \"score\": \"0.5223739\",\n \"text\": \"def show(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0c415603446a15910edae87a0303d7\",\n \"score\": \"0.5223739\",\n \"text\": \"def show(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54a01065784796f4814db235c664dc78\",\n \"score\": \"0.52114683\",\n \"text\": \"def get(self, request, *args, **kwargs):\\n source_file = self.get_object()\\n return FileResponse(source_file.fname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a6735d194bafc7f4de31aa451866c45\",\n \"score\": \"0.5207133\",\n \"text\": \"def show_product_documents(self, **kw):\\n attachment = request.env['ir.attachment'].sudo().browse(int(kw.get('att_id')))\\n values = {'attachment': attachment}\\n return request.env['ir.ui.view'].render_template(\\\"skit_vendor_portal.show_product_attached_files\\\", values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd561a45b2835112009dd9df8ed7e311\",\n \"score\": \"0.5204127\",\n \"text\": \"def get_file_name(self):\\n return self.view.file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6fb460985289e9a8122c7c66fa8ccc7\",\n \"score\": \"0.5180643\",\n \"text\": \"def _show_documentation(self):\\n documents = []\\n for key, document in self.app.documents.get().items():\\n if key != \\\"main\\\":\\n documents.append((key, document))\\n documents = sorted(documents, key=lambda x: x[0])\\n main = self.app.documents.get(\\\"main\\\")\\n if main is not None:\\n documents.insert(0, (main.name, main))\\n\\n user_answer = \\\"\\\"\\n index = 0\\n while user_answer != \\\"X\\\":\\n if index < 0:\\n index = 0\\n if index > len(documents) - 1:\\n index = len(documents) - 1\\n document = documents[index][1]\\n\\n os.system('cls' if os.name == 'nt' else 'clear')\\n echo(Environment().from_string(document.content).render(app=self.app, plugin=document.plugin))\\n\\n source = \\\"This document is registered by '%s' under the name '%s'\\\" % (document.plugin.name, document.name)\\n echo(\\\"-\\\" * len(source))\\n echo(source)\\n echo(\\\"-\\\" * len(source))\\n commands = \\\"Actions: \\\"\\n if index < len(documents) - 1:\\n commands += \\\"[N]ext, \\\"\\n if index > 0:\\n commands += \\\"[P]revious, \\\"\\n commands += \\\"E[x]it\\\"\\n echo(commands)\\n\\n if index < len(documents) - 1:\\n default = \\\"N\\\"\\n elif index > 0:\\n default = \\\"P\\\"\\n else:\\n default = \\\"X\\\"\\n user_answer = prompt(\\\"Select your action\\\", default=default).upper()\\n\\n if user_answer == \\\"N\\\":\\n index += 1\\n elif user_answer == \\\"P\\\":\\n index -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51718b1518658fa27b960de240f0778f\",\n \"score\": \"0.516441\",\n \"text\": \"def details(self, file_hash):\\n return jsonify(File.get_file_details(file_hash))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e8879cf031d0e0d39ede581bb62ce8c\",\n \"score\": \"0.51614755\",\n \"text\": \"def get_file(self, Id):\\n # print('in get file')\\n f = self.db_files.find_one({'_id': ObjectId(Id)})\\n if f:\\n return f\\n else:\\n raise my_errors.not_found\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61236e36de2bb643f2961fc7d8b9848a\",\n \"score\": \"0.51425976\",\n \"text\": \"def show(self, req, id):\\n\\n LOG.debug('Get trigger(%s) start', id)\\n\\n context = req.environ['karbor.context']\\n trigger = self._get_trigger_by_id(context, id)\\n\\n check_policy(context, 'get', trigger)\\n return self._view_builder.detail(req, trigger)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d0f432384627690d22cdff1057f686\",\n \"score\": \"0.5127289\",\n \"text\": \"def show(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec6feb2f0edbce10c7d2b8470942954a\",\n \"score\": \"0.5126294\",\n \"text\": \"def get_document(self, document_id: int) -> Document:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2bdf58277a1b036206067e6959f4b0c\",\n \"score\": \"0.5093053\",\n \"text\": \"def get_file(file_id):\\n\\n document_file = DocumentFile.query.get(file_id)\\n try:\\n access_granted = current_user.has_document_file(document_file)\\n except TypeError:\\n return app.login_manager.unauthorized()\\n # TODO: clearer error handling\\n\\n # Test if this user can see it\\n if not access_granted:\\n return app.login_manager.unauthorized()\\n directory, filename = os.path.split(document_file.path)\\n\\n return send_from_directory(directory, filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"604f164d3ebf35d440b3011a6af35d0d\",\n \"score\": \"0.50911343\",\n \"text\": \"def details_of_a_food_prod(self):\\n self.__view.details_of_a_food_prod_page(\\n self.memory,\\n self.event_handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61bf33539cb021b4d16c635ef4125c6a\",\n \"score\": \"0.50874794\",\n \"text\": \"def show_file(filename):\\n\\t\\n\\treturn send_from_directory(MEDIA_DIR, filename, as_attachment=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f65cf138cf51198ae11b827faa99a7\",\n \"score\": \"0.5087051\",\n \"text\": \"def show_detail(request, show_pk): # TODO: adding notes feature goes here\\n show = get_object_or_404(Show, pk=show_pk)\\n notes = Note.objects.filter(show=show_pk)\\n return render(request, 'lmn/shows/show_detail.html', {'show': show, 'notes': notes})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10924140e9095825d83a8491f8e51ec4\",\n \"score\": \"0.507393\",\n \"text\": \"def show(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84c851a8438d65f3232f3325b2697192\",\n \"score\": \"0.50717926\",\n \"text\": \"def do_show(self, args):\\n args = shlex.split(args)\\n if len(args) == 0:\\n print(\\\"** class name missing **\\\")\\n return\\n elif args[0] not in model_names:\\n print(\\\"** class doesn't exist **\\\")\\n return\\n elif len(args) != 2:\\n print(\\\"** instance id missing **\\\")\\n return\\n cls, idx = args[0], args[1]\\n # try:\\n # eval(args[0])\\n # except NameError:\\n # print(\\\"** class doesn't exist **\\\")\\n # return\\n\\n storage = FileStorage()\\n storage.reload()\\n obj_dict = storage.all()\\n key = cls + '.' + idx\\n\\n try:\\n obj = obj_dict[key]\\n print(obj)\\n except KeyError:\\n print(\\\"** no instance found **\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f988219b5f0fb59f3db4f0533c4f6cc\",\n \"score\": \"0.50641924\",\n \"text\": \"def show(self, id=None, **kwargs):\\n return self._get(item=id, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1b5a58be4780bec8cc898fb38191457\",\n \"score\": \"0.5048951\",\n \"text\": \"def list_remote_action(self):\\n response = self.api.list_documents(self.project_id)\\n if response.status_code == 204:\\n print(\\\"No documents to report\\\")\\n return\\n elif response.status_code != 200:\\n if response.json():\\n raise_error(response.json(), \\\"Failed to get status of documents\\\", True)\\n else:\\n raise_error(\\\"\\\", \\\"Failed to get status of documents\\\", True)\\n else:\\n print ('Remote documents: id, document name')\\n for entry in response.json()['entities']:\\n title = entry['entities'][1]['properties']['title'].replace(\\\"Status of \\\", \\\"\\\")\\n id = entry['entities'][1]['properties']['id']\\n info = '{id} \\\\t {title}'.format(id=id, title=title)\\n print (info)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de872cd9621657973a63bf228f672692\",\n \"score\": \"0.5038279\",\n \"text\": \"def get_document(self, document_id):\\n return self.document_service.get_document(document_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c26ad662c2f13821450cc0f099521bf\",\n \"score\": \"0.5033167\",\n \"text\": \"def getShowDetails(id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ed184f4d7f65a1be40b937a6a917461\",\n \"score\": \"0.5027212\",\n \"text\": \"def render_file(request, file_id):\\n file = m.File.objects.get(id=file_id)\\n if not p.can_view_file(request.user, file):\\n return HttpResponseForbidden()\\n content = h.read_file_by_line(file.file)\\n detail = {\\n \\\"content\\\": pyghi(content, pyglex.PythonLexer(), pygform.HtmlFormatter(linenos='table'))\\n }\\n return render(request, 'student/pretty_file.html', detail)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4710dd192a683c1f040a608f00182d9b\",\n \"score\": \"0.50185657\",\n \"text\": \"def GetFileDetails(self, args, context=None):\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1c5845e8ef71c1b41ff0750dcad0f2\",\n \"score\": \"0.5013779\",\n \"text\": \"def details(partno):\\n try:\\n pn = PartNumber(partno)\\n except ValueError:\\n abort(404)\\n\\n # redirect to the revisionless URL if the user cannot view outdated revisions\\n if pn.revision is not None and not current_user.has_role('component_edit'):\\n return redirect(url_for('components.details', partno=pn.base_number))\\n\\n # ensure the object exists and the revision is valid\\n obj = current_app.mongo.db.components.find_one_or_404(pn.base_number)\\n\\n # ensure the desired revision exists\\n num_revisions = len(obj.get('revisions', list()))\\n if pn.revision_number is not None and pn.revision_number >= num_revisions:\\n abort(404)\\n pn.set_num_revisions(num_revisions)\\n\\n files = _get_files(pn.id)\\n\\n preview_file = None\\n for file in files:\\n if file.startswith('preview.'):\\n preview_file = file\\n break\\n\\n return render_template('components/details.html', data=obj,\\n partno=pn, files=files, preview_file=preview_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fff94d66a10b418d1acc27548ddd5c9\",\n \"score\": \"0.50009507\",\n \"text\": \"def get_document(DocumentId=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd4955b264955c6d13ca5af30a4250f0\",\n \"score\": \"0.4999773\",\n \"text\": \"def query(self, document_number):\\n assert (\\n self.session is not None\\n ), \\\"session attribute of DocketReport cannot be None.\\\"\\n # coerce the fourth digit of the document number to 1 to ensure we get\\n # the attachment page.\\n document_number = f\\\"{document_number[:3]}0{document_number[4:]}\\\"\\n url = self.url + document_number\\n logger.info(\\\"Querying the attachment page endpoint at URL: %s\\\", url)\\n self.response = self.session.get(url)\\n self.parse()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f76cc3b64c043bd3afede04d76f52e\",\n \"score\": \"0.49956954\",\n \"text\": \"def get_doc(_, file_id):\\n\\n filename = \\\"%02d.pdf\\\" % (int(file_id) % 10)\\n file_path = join(STATIC_STORAGE_PATH, filename)\\n if exists(file_path):\\n return FileResponse(open(file_path, 'rb'), as_attachment=True)\\n raise Http404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf5303e01541f899179b9e4d07ed5233\",\n \"score\": \"0.49928683\",\n \"text\": \"def viewFile(self, checkForUpdates):\\n logger.debug(util.funcName('begin'))\\n if checkForUpdates:\\n newSelectedItem = self.dlgCtrls.listboxFiles.getSelectedItem()\\n logger.debug(\\\"newSelectedItem '%s'\\\", newSelectedItem)\\n self.updateFile(False)\\n if newSelectedItem:\\n self.dlgCtrls.listboxFiles.selectItem(newSelectedItem, True)\\n try:\\n self.selectedIndex = dutil.get_selected_index(\\n self.dlgCtrls.listboxFiles, \\\"a file\\\")\\n logger.debug(\\\"self.selectedIndex %d\\\", self.selectedIndex)\\n except exceptions.ChoiceProblem:\\n return\\n fileItem = self.fileItems[self.selectedIndex]\\n logger.debug(\\\"Filepath %s\\\", fileItem.filepath)\\n self.dlgCtrls.txtPrefix.setText(fileItem.prefix)\\n self.dlgCtrls.chkDontUseSegnum.setState(not fileItem.use_segnum)\\n logger.debug(util.funcName('end'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37031483f1f5c9fe3fee2d901d39ce29\",\n \"score\": \"0.49814904\",\n \"text\": \"def show_view_field_scan_dialog(self):\\n try:\\n field_scan_list = []\\n fs = _data.measurement.FieldScan(\\n database_name=self.database_name,\\n mongo=self.mongo, server=self.server)\\n for idn in self.field_scan_id_list:\\n fs.db_read(idn)\\n field_scan_list.append(fs.copy())\\n\\n self.view_scan_dialog.show(\\n field_scan_list, 'field')\\n \\n except Exception:\\n _traceback.print_exc(file=_sys.stdout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37031483f1f5c9fe3fee2d901d39ce29\",\n \"score\": \"0.49814904\",\n \"text\": \"def show_view_field_scan_dialog(self):\\n try:\\n field_scan_list = []\\n fs = _data.measurement.FieldScan(\\n database_name=self.database_name,\\n mongo=self.mongo, server=self.server)\\n for idn in self.field_scan_id_list:\\n fs.db_read(idn)\\n field_scan_list.append(fs.copy())\\n\\n self.view_scan_dialog.show(\\n field_scan_list, 'field')\\n \\n except Exception:\\n _traceback.print_exc(file=_sys.stdout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a61025e13ea62ab33bcfb420169cf7ff\",\n \"score\": \"0.49763122\",\n \"text\": \"def showdoc(self):\\n # abstract method does nothing. It simply has a return statement.\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8e8ef7585bcdc54cd20ea49c993797d\",\n \"score\": \"0.4973397\",\n \"text\": \"def read_file_view(self, request, hash):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7af7fabfe16607c55d6706bc49da099\",\n \"score\": \"0.4964308\",\n \"text\": \"def _getDoc(self, f):\\n\\n parse = os.path.split(self._datas['Path'])[0]\\n parsing = model_parser(parse)\\n index = None\\n\\n for j in range(0,len(parsing)):\\n if parsing[j].name == self._datas['Model name']:\\n index = j\\n break\\n\\n if index is None:\\n f.close()\\n with self._out:\\n raise Exception('Critical error : model not found.')\\n \\n return docGenerator.DocGenerator(parsing[index])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac4701d3459f6a5abcd566c13869005b\",\n \"score\": \"0.49582866\",\n \"text\": \"def __unicode__(self):\\n return self.getFileReport()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ab7b8ce4efd6361ce6c27f741248245\",\n \"score\": \"0.49548638\",\n \"text\": \"def fp(self):\\n return self.collection.store.get(self.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff88a31d478f9e6e33730389e8bbf72a\",\n \"score\": \"0.49527317\",\n \"text\": \"async def sxm_show(self, ctx: SlashContext, show_id: Optional[str] = None) -> None:\\n\\n await self._play_archive_file(ctx, show_id, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24280adcb1a22c258db8dd3b0ab93bf\",\n \"score\": \"0.49426746\",\n \"text\": \"def show_file_system(self):\\n self._show_file_system_helper(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad682aa4b980a391614a36dbce3c20ee\",\n \"score\": \"0.49358463\",\n \"text\": \"def do_show(self, line):\\n args = line.split()\\n if not self.verify_class(args):\\n return\\n if not self.verify_id(args):\\n return\\n string_key = str(args[0]) + '.' + str(args[1])\\n objects = models.storage.all()\\n print(objects[string_key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83dcb03e4c02680ee0c05d15c1c227f7\",\n \"score\": \"0.49295127\",\n \"text\": \"def get_document(id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f470fb5e66d4c24714de0ffada20f115\",\n \"score\": \"0.49281743\",\n \"text\": \"def view_design_doc(self):\\n if self.viewer_var.get() == \\\"firefox\\\":\\n # Browser mode: use HREFs with suffix #<anchortype>=<value>.\\n if self.ref_mode_var.get() == \\\"page\\\":\\n ref_in_doc = f\\\"#page={self.page_num_var.get().rstrip()}\\\"\\n elif self.ref_mode_var.get() == \\\"section\\\":\\n ref_in_doc = f\\\"#nameddest=section.{self.section_num_var.get().rstrip()}\\\"\\n else:\\n ref_in_doc = \\\"\\\"\\n # Assume the location is a valid URL or an absolute path.\\n doc_location = self.text1.get(0.0, tk.END).rstrip()\\n # If no scheme (http://, file:// etc.) is given, treat it\\n # as an absolute file path.\\n if doc_location[0] == '/':\\n doc_location = \\\"file://\\\" + doc_location\\n command = [ \\\"firefox\\\", doc_location + ref_in_doc ]\\n elif self.viewer_var.get() == \\\"evince\\\":\\n # PDF viewer mode: use appropriate cmdline option.\\n if self.ref_mode_var.get() == \\\"page\\\":\\n ref_in_doc = [\\\"-i\\\", f\\\"{self.page_num_var.get().rstrip()}\\\" ]\\n elif self.ref_mode_var.get() == \\\"section\\\":\\n ref_in_doc = [\\\"-n\\\", f\\\"section.{self.section_num_var.get().rstrip()}\\\"]\\n else:\\n ref_in_doc = []\\n # The requirement location should be a valid path to a PDF file.\\n command = [\\\"evince\\\"] + ref_in_doc + [self.text1.get(0.0, tk.END).rstrip()]\\n print(f\\\"### ==> command = {command}\\\")\\n subprocess.Popen(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac3b015d6cf7f360ff53ec0403f1e7ec\",\n \"score\": \"0.4921231\",\n \"text\": \"def get_document(self, project_name, file_name, output='rs3'):\\n # only proceed if the project and file exist (for the user 'local')\\n documents = self.get_project(project_name).get('documents')\\n if documents is None or file_name not in documents:\\n raise cherrypy.HTTPError(\\n 404, (\\\"File '{0}' not available in project '{1}' for the current user. \\\"\\n \\\"Available files: {2}\\\").format(file_name, project_name, documents))\\n\\n if output == 'rs3':\\n return get_rs3_file(file_name, project_name, 'local')\\n elif output == 'png':\\n return get_screenshot(file_name, project_name, 'local', output_format='png')\\n elif output == 'png-base64':\\n return get_screenshot(file_name, project_name, 'local', output_format='png-base64')\\n elif output == 'editor':\\n # This will raise a cherrypy.HTTPRedirect to rstWeb's Structure Editor\\n # opened with the given document.\\n edit_document(file_name, project_name)\\n else:\\n raise cherrypy.HTTPError(\\n 400, 'Unknown output format: {0}'.format(output))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f737ccd1a504a8321c64c11677358326\",\n \"score\": \"0.49014312\",\n \"text\": \"def file(self):\\n return self._search_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db3f3770a4ff92b08b6c933f3c0d0d8\",\n \"score\": \"0.49002478\",\n \"text\": \"def show(self):\\n if self.project and self.project.isOpen():\\n self.projectCheckBox.setEnabled(True)\\n self.projectCheckBox.setChecked(True)\\n else:\\n self.projectCheckBox.setEnabled(False)\\n self.projectCheckBox.setChecked(False)\\n \\n self.fileNameEdit.selectAll()\\n self.fileNameEdit.setFocus()\\n \\n super(FindFileNameDialog, self).show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bf19ea96ff8892bcc2b84e1cb35a224\",\n \"score\": \"0.48777357\",\n \"text\": \"def show_request(self):\\n try:\\n # Get the selected row index\\n selected_index = self.loan_request_table.selectedIndexes()[0].row()\\n # If a request has been selected, show it\\n [loan_request, _] = self.loan_requests[selected_index]\\n self.mainwindow.fiplr2_controller.setup_view(loan_request.id)\\n self.mainwindow.navigation.switch_to_fiplr2()\\n except IndexError:\\n self.mainwindow.show_dialog(\\\"Select request\\\", 'No loan requests have been selected.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6efc91a260cc52060bd9e91c0fa05909\",\n \"score\": \"0.48750532\",\n \"text\": \"def view_records(self):\\n print('Hi {}! Ask your managers if you need to see sales records'.format(self.fullname))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba995fae8b5dde7b61a4d207826a482\",\n \"score\": \"0.48749405\",\n \"text\": \"def detail(self, req):\\n return self.index(req)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab380652d592f2a67c577fdd4482a3ca\",\n \"score\": \"0.48691365\",\n \"text\": \"def file(self, folder, file_):\\n return self.get('file', params={'folder': folder,\\n 'file': file_})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8358dedae105d73dc14c3c1dfb89ce99\",\n \"score\": \"0.48623356\",\n \"text\": \"def show():\\n\\tif not ('uri' in session):\\n\\t\\treturn index(error=True)\\n\\n\\turi = session['uri']\\n\\tsession.clear()\\n\\treturn render_template('show.html', captured_img=uri)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3171b13e08687f71b3f9774d3933d3\",\n \"score\": \"0.48551252\",\n \"text\": \"def document(self, document):\\n super(RedirectHelper, self).document(document)\\n self._url_name = 'show_gsoc_document'\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46dbc3c2cfcfe6db756a453d7ec66536\",\n \"score\": \"0.4853151\",\n \"text\": \"def show():\\n table = request.args.get('table')\\n fieldNames = TableOperation.getFieldNames(table)\\n rows = TableOperation.showTable(table)\\n rows.insert(0, fieldNames)\\n session['result'] = [rows]\\n return render_template('result.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"932c4ee98a96b26efd52768355eea90a\",\n \"score\": \"0.48403206\",\n \"text\": \"def get(self, file_key):\\r\\n # If the file exists in the blobstore, let the user save the data to their computer\\r\\n if blobstore.get(file_key):\\r\\n self.send_blob(blobstore.BlobInfo.get(file_key), save_as=True)\\r\\n # If the file doesn't exist in the blobstore, then 404 the user\\r\\n else:\\r\\n self.error(404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cb7b57c7c7455161047577a27f56c07\",\n \"score\": \"0.48352572\",\n \"text\": \"def show(revisions):\\n base.show(revisions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff4d86c8d543d2320ab5dedbc367bf10\",\n \"score\": \"0.48270407\",\n \"text\": \"def edit_document(file_name, project_name):\\n kwargs = {\\n 'current_doc': file_name,\\n 'current_guidelines': u'**current_guidelines**',\\n 'current_project': project_name,\\n 'serve_mode': u'local'\\n }\\n url_params = kwargs2urlparams(kwargs)\\n raise cherrypy.HTTPRedirect('/structure?{0}'.format(url_params))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69f810decb2afef11be2454ccc24b71c\",\n \"score\": \"0.4825802\",\n \"text\": \"def show(self, server_id):\\n\\n _cmd = 'show'\\n _response_type = responses.ServerResponse\\n return self._process_command()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8573360b15ce0068dd270188acdfb83e\",\n \"score\": \"0.4817358\",\n \"text\": \"def show(self, req, vsa_id, id):\\n context = req.environ['nova.context']\\n authorize(context)\\n\\n LOG.audit(_(\\\"Show. vsa_id=%(vsa_id)s, id=%(id)s\\\"), locals())\\n\\n try:\\n self._check_volume_ownership(context, vsa_id, id)\\n except exception.NotFound:\\n raise exc.HTTPNotFound()\\n except exception.Invalid:\\n raise exc.HTTPBadRequest()\\n\\n return super(VsaVolumeDriveController, self).show(req, id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a77a3038b465c80d7992bf8affd437f6\",\n \"score\": \"0.48166698\",\n \"text\": \"def do_show(self, arg):\\n models.storage.reload()\\n if len(arg) < 1:\\n print(\\\"** class name missing **\\\")\\n pass\\n else:\\n arg = arg.split(' ')\\n if arg[0] not in HBNBCommand.classes:\\n print(\\\"** class doesn't exist **\\\")\\n elif arg[0] in HBNBCommand.classes:\\n if len(arg) < 2:\\n print(\\\"** instance id missing **\\\")\\n return\\n key = arg[0] + '.' + arg[1]\\n if key in FileStorage._FileStorage__objects:\\n print(FileStorage._FileStorage__objects[key])\\n else:\\n print(\\\"** no instance found **\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993498,"cells":{"query_id":{"kind":"string","value":"ccec0f53b7fad21155536c7de925e1d0"},"query":{"kind":"string","value":"initialize the weights for the neural network"},"positive_passages":{"kind":"list like","value":[{"docid":"5e9a76e48f7387150da17591d768fe14","score":"0.0","text":"def neural_net_weights(num_inputs, num_hidden_layers, num_nodes, num_outputs):\n \n network = []\n output_layer = []\n \n # no hidden layer\n if num_hidden_layers == 0: \n for n in range(num_outputs):\n weight_dict = {}\n weight_dict['weights'] = [random.uniform(-1, 1) for i in range(num_inputs + 1)]\n output_layer.append(weight_dict)\n network.append(output_layer)\n else:\n # neural network with hidden layers\n \n counter = 0\n \n for l in range(num_hidden_layers):\n if counter == 0:\n # for the first hidden layer\n hidden_layer = []\n for n in range(num_nodes):\n weight_dict = {}\n weight_dict['weights'] = [random.uniform(-1, 1) for i in range(num_inputs + 1)]\n hidden_layer.append(weight_dict)\n network.append(hidden_layer)\n counter += 1\n else:\n hidden_layer = []\n for n in range(num_nodes):\n weight_dict = {}\n weight_dict['weights'] = [random.uniform(-1, 1) for i in range(num_nodes + 1)]\n hidden_layer.append(weight_dict)\n network.append(hidden_layer)\n \n for n in range(num_outputs):\n weight_dict = {}\n weight_dict['weights'] = [random.uniform(-1, 1) for i in range(num_nodes + 1)]\n output_layer.append(weight_dict)\n network.append(output_layer)\n \n return network","title":""}],"string":"[\n {\n \"docid\": \"5e9a76e48f7387150da17591d768fe14\",\n \"score\": \"0.0\",\n \"text\": \"def neural_net_weights(num_inputs, num_hidden_layers, num_nodes, num_outputs):\\n \\n network = []\\n output_layer = []\\n \\n # no hidden layer\\n if num_hidden_layers == 0: \\n for n in range(num_outputs):\\n weight_dict = {}\\n weight_dict['weights'] = [random.uniform(-1, 1) for i in range(num_inputs + 1)]\\n output_layer.append(weight_dict)\\n network.append(output_layer)\\n else:\\n # neural network with hidden layers\\n \\n counter = 0\\n \\n for l in range(num_hidden_layers):\\n if counter == 0:\\n # for the first hidden layer\\n hidden_layer = []\\n for n in range(num_nodes):\\n weight_dict = {}\\n weight_dict['weights'] = [random.uniform(-1, 1) for i in range(num_inputs + 1)]\\n hidden_layer.append(weight_dict)\\n network.append(hidden_layer)\\n counter += 1\\n else:\\n hidden_layer = []\\n for n in range(num_nodes):\\n weight_dict = {}\\n weight_dict['weights'] = [random.uniform(-1, 1) for i in range(num_nodes + 1)]\\n hidden_layer.append(weight_dict)\\n network.append(hidden_layer)\\n \\n for n in range(num_outputs):\\n weight_dict = {}\\n weight_dict['weights'] = [random.uniform(-1, 1) for i in range(num_nodes + 1)]\\n output_layer.append(weight_dict)\\n network.append(output_layer)\\n \\n return network\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2f539308a4530e0d61a67321a3fd828a","score":"0.86598444","text":"def init_weights(self):\n pass","title":""},{"docid":"be8dec4d41250f35e3f156604e893851","score":"0.84111565","text":"def weight_init(self):\n self.weights = [np.random.randn(y, x)/np.sqrt(x) for x,y in zip(self.network_topology[:-1], self.network_topology[1:])]\n \n self.biases = [np.random.randn(y, 1) for y in self.network_topology[1:]]","title":""},{"docid":"044130279cb0d9aa8a2dd6bb79601a3c","score":"0.8381277","text":"def _initialize_weights(self, layer_units):","title":""},{"docid":"e15c068f4b8d89aa1ced602debf83940","score":"0.8347222","text":"def _initialize_weights(self):\r\n self.weights = np.random.normal(size= [self.number_of_classes,self.input_dimensions+1])","title":""},{"docid":"dc9bb1473c83794d505f216b3a17bef4","score":"0.8345059","text":"def init_weights(self):\n self.transformer.init_weights()","title":""},{"docid":"d6797d00e1caea91c8da94e513775ef2","score":"0.8295717","text":"def init_weights(self):\n # Initialize weights\n self.apply(self._init_weights)","title":""},{"docid":"d6797d00e1caea91c8da94e513775ef2","score":"0.8295717","text":"def init_weights(self):\n # Initialize weights\n self.apply(self._init_weights)","title":""},{"docid":"b1d20a49d83dd99d4ee64feef6ac256f","score":"0.82177496","text":"def initialize_weights(self):\n for layer in self.layers:\n WeightsInitializer.initialize_layer_or_model(layer)","title":""},{"docid":"d6be279a12700008d0a9a8201964fd57","score":"0.8206177","text":"def weights_init(neural_net):\n classname = neural_net.__class__.__name__\n if classname.find('Conv') != -1:\n neural_net.weight.data.normal_(0, 2e-2)\n elif classname.find('BatchNorm') != -1:\n neural_net.weight.data.normal_(1, 2e-2)\n neural_net.bias.data.fill_(0)","title":""},{"docid":"81a8e21621c675daf874c762f4aa1930","score":"0.81082976","text":"def set_weights(self, W):\r\n self.weights = np.zeros((self.number_of_nodes,self.input_dimensions+1))\r\n print(self.weights)","title":""},{"docid":"ff247ab8d4aaea28132c1ff3f33fcee4","score":"0.8081319","text":"def init_weights(self):\n \n # Can we optimize this initialization further?\n stdev = cp.sqrt(2) / cp.sqrt(self.output_size)\n # stdev = cp.sqrt(2) / cp.sqrt(self.input_size * self.output_size)\n self.weights = cp.random.normal(loc=0., scale=stdev, \n size=(self.input_size, self.output_size),\n dtype=cp.float32)\n self.bias = cp.zeros((1, self.output_size), dtype=cp.float32)","title":""},{"docid":"4a53b095720b6732875f572d21f7d3db","score":"0.7993603","text":"def weights_init(self):\r\n self.apply(weights_init_1D)","title":""},{"docid":"5552813b1d7657166f72a3a9a4a9e065","score":"0.79828733","text":"def init_weights(self):\n \n # Keep track of dimensions\n a, b, c, d = (self.input_size[2],) + self.filter_size + (self.nr_filters,)\n \n # Set weight scaling parameter.\n stdev = cp.sqrt(2) / cp.sqrt(a * b * c * d)\n \n # Initialize weights.\n self.weights = cp.random.normal(loc=0., scale=stdev, size=(a, b, c, d), dtype=cp.float32)\n self.bias = cp.zeros((1, 1, 1, d), dtype=cp.float32)","title":""},{"docid":"a119d3eb2c9813dab22f4c14c626f0bc","score":"0.79796815","text":"def initialize_all_weights_to_zeros(self):\r\n self.weights = np.zeros([self.number_of_classes,self.input_dimensions+1])","title":""},{"docid":"b5cbfca6403090173cbb1c28b2184938","score":"0.79690295","text":"def initialize_weights(self):\n BiLSTMModel.initialize_weights(self)\n LSTMBasedImportanceWeighting.initialize_weights(self)","title":""},{"docid":"017ebdbfabae6714eb1c6e3dd4dbaab1","score":"0.7949052","text":"def _initialize_weights(net: torch.nn.Module) -> None:\n \n if type(net) == torch.nn.Linear:\n torch.nn.init.xavier_uniform_(net.weight)\n net.bias.data.fill_(1e-2)","title":""},{"docid":"b6e64613d03f666d00698c33d79f45e8","score":"0.79345065","text":"def initialize_weights(self, initial_weights):\n\n #shapes for the weight vectors\n hidtup = (self.hidden_layer_widths[0], self.num_in + 1)\n outtup = (self.num_out, self.hidden_layer_widths[0] + 1) if self.num_out > 2 else (1, self.hidden_layer_widths[0] + 1)\n\n if not initial_weights:\n #print(\"inited weights!\")\n self.hid = np.zeros(hidtup)\n self.out = np.zeros(outtup)\n else:\n np.random.seed(seed=5)\n self.hid = np.random.normal(loc=0, scale=1.0, size=hidtup)\n self.out = np.random.normal(loc=0, scale=1.0, size=outtup)\n \n return","title":""},{"docid":"7fe162eb762faedc5e79d709c9761d65","score":"0.792823","text":"def init_weights(self):\n # Initialize weights\n self.apply(self._init_weights)\n\n # Prune heads if needed\n if self.config.pruned_heads:\n self.prune_heads(self.config.pruned_heads)\n\n # Tie weights if needed\n self.tie_weights()","title":""},{"docid":"878f3c2a94b6979b7b7063ebd09c3bea","score":"0.78852886","text":"def init_weights(self):\n init.xavier_uniform_(self.w_x[1].weight)\n init.xavier_uniform_(self.w_h[1].weight)","title":""},{"docid":"3239987ba5d5063eba153d05e5b7ceac","score":"0.78528994","text":"def _initialize_weights(self):\n self.theta = create_structure_for_ann(self)","title":""},{"docid":"39e64b18fb813bff634cda294eaab41f","score":"0.7845597","text":"def initialize_weights(self,seed=None):\r\n if seed != None:\r\n np.random.seed(seed)\r\n # W = np.random.rand((input_dimensions,number_of_nodes))*np.sqrt(1/(ni+no))\r\n self.weights = np.random.randn(self.number_of_nodes,self.input_dimensions+1)\r\n print(self.weights)\r\n print(\"No of classes:\",number_of_nodes)","title":""},{"docid":"f604207bcd7577012421ae1e5c6a670d","score":"0.7834929","text":"def init_weights(self):\n self.embedding.weight.data.uniform_(-0.1, 0.1)\n self.fc.bias.data.fill_(0)\n self.fc.weight.data.uniform_(-0.1, 0.1)","title":""},{"docid":"0afb91185a4a8bba885f44a12ef0dd97","score":"0.7824508","text":"def init_weights(self):\n for m in [self.conv_first, self.conv_hr, self.conv_last]:\n default_init_weights(m, 0.1)","title":""},{"docid":"810c9cab2ac4d30dcd01831834d81768","score":"0.78023416","text":"def init_weights(self):\n for name, param in self.enc_lstm.named_parameters():\n if 'bias' in name:\n nn.init.normal_(param, 0.0)\n elif 'weight' in name:\n nn.init.xavier_normal_(param)\n for name, param in self.dec_lstm.named_parameters():\n if 'bias' in name:\n nn.init.normal_(param, 0.0)\n elif 'weight' in name:\n nn.init.xavier_normal_(param)","title":""},{"docid":"c2683bee9bbddb373702628d53e4c047","score":"0.7777124","text":"def init_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv3d):\n nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n elif isinstance(m, nn.Linear):\n nn.init.normal_(m.weight, mean = 0, std = 1)\n elif isinstance(m, (nn.BatchNorm3d, nn.BatchNorm2d)):\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)","title":""},{"docid":"6d06e3b325fb2493483c210555342235","score":"0.7764375","text":"def _initialize_weights(self, size: int):\n\n # uses He Normal or He-at-el initialization become famous by a paper 2015 by He-et-al\n self.weights = np.random.randn(size, self.neurons) * np.sqrt((2/size)) * 0.1\n self.bias = np.zeros((1, self.neurons))","title":""},{"docid":"9422a1f347e9d8b670a396d124eae9f8","score":"0.7690929","text":"def init_weights(self):\n\t\tr = np.sqrt(6.) / np.sqrt(self.fc.in_features +\n\t\t self.fc.out_features)\n\t\tself.fc.weight.data.uniform_(-r, r)\n\t\tself.fc.bias.data.fill_(0)","title":""},{"docid":"2a32a5a36163fd9b028651294aa84daf","score":"0.7672449","text":"def init_parameters(self):\n\n self.weight.data.normal_()\n self.weight.data.mul_(1.0/pow(self.feat_dim*self.out_dim, 0.5))\n self.bias.data.normal_()","title":""},{"docid":"0389d48c46317e97c06c7b0e35997446","score":"0.7640888","text":"def init_weights(self):\n r = np.sqrt(6.0) / np.sqrt(self.fc.in_features + self.fc.out_features)\n self.fc.weight.data.uniform_(-r, r)\n self.fc.bias.data.fill_(0)","title":""},{"docid":"ba8ada5ec98e5bd26725e46dde873755","score":"0.76374394","text":"def init_weights(self):\n import numpy as np\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\n self.fc.out_features)\n self.fc.weight.data.uniform_(-r, r)\n self.fc.bias.data.fill_(0)","title":""},{"docid":"198c8f7f8a7b3148ee10e302fc67eda1","score":"0.7630416","text":"def create_weights(self):\n self._weight_matrix = self._random_weight_matrix()\n self._weight_matrix_diff = np.zeros_like(self._weight_matrix)\n \n self._bias_units = self._random_bias_units()\n self._bias_units_diff = np.zeros_like(self._bias_units)\n \n \n self.params = [self._weight_matrix, self._bias_units]\n self.gradients = [self._weight_matrix_diff, self._bias_units_diff]\n \n if self.tensor:\n self._weight_tensor = self._random_weight_tensor()\n self._weight_tensor_diff = np.zeros_like(self._weight_tensor)\n self.params.append(self._weight_tensor)\n self.gradients.append(self._weight_tensor_diff)","title":""},{"docid":"4ed0fecf0f3da5f7f0fa5385c1f6a080","score":"0.7616112","text":"def init_weights(self):\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\n self.fc.out_features)\n self.fc.weight.data.uniform_(-r, r)\n self.fc.bias.data.fill_(0)","title":""},{"docid":"4ed0fecf0f3da5f7f0fa5385c1f6a080","score":"0.7616112","text":"def init_weights(self):\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\n self.fc.out_features)\n self.fc.weight.data.uniform_(-r, r)\n self.fc.bias.data.fill_(0)","title":""},{"docid":"4ed0fecf0f3da5f7f0fa5385c1f6a080","score":"0.7616112","text":"def init_weights(self):\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\n self.fc.out_features)\n self.fc.weight.data.uniform_(-r, r)\n self.fc.bias.data.fill_(0)","title":""},{"docid":"4ed0fecf0f3da5f7f0fa5385c1f6a080","score":"0.7616112","text":"def init_weights(self):\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\n self.fc.out_features)\n self.fc.weight.data.uniform_(-r, r)\n self.fc.bias.data.fill_(0)","title":""},{"docid":"4ed0fecf0f3da5f7f0fa5385c1f6a080","score":"0.7616112","text":"def init_weights(self):\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\n self.fc.out_features)\n self.fc.weight.data.uniform_(-r, r)\n self.fc.bias.data.fill_(0)","title":""},{"docid":"7d9b1f8b125b3e0645e9a95baf96f8ca","score":"0.7612289","text":"def init_weights(self):\n\t\tfor m in self.modules():\n\t\t\tif isinstance(m, nn.Linear):\n\t\t\t\tnn.init.xavier_uniform_(m.weight.data)\n\t\t\t\tnn.init.constant_(m.bias.data, 0.0)\n\t\t\t\n\t\t\telif isinstance(m, nn.LSTM):\n\t\t\t\tnn.init.xavier_uniform_(m.weight_ih_l0.data)\n\t\t\t\tnn.init.orthogonal_(m.weight_hh_l0.data)\n\t\t\t\tnn.init.constant_(m.bias_ih_l0.data, 0.0)\n\t\t\t\tnn.init.constant_(m.bias_hh_l0.data, 0.0)\n\t\t\t\thidden_size = m.bias_hh_l0.data.shape[0] // 4\n\t\t\t\tm.bias_hh_l0.data[hidden_size:(2 * hidden_size)] = 1.0\n\t\t\t\t\n\t\t\t\tif (m.bidirectional):\n\t\t\t\t\tnn.init.xavier_uniform_(m.weight_ih_l0_reverse.data)\n\t\t\t\t\tnn.init.orthogonal_(m.weight_hh_l0_reverse.data)\n\t\t\t\t\tnn.init.constant_(m.bias_ih_l0_reverse.data, 0.0)\n\t\t\t\t\tnn.init.constant_(m.bias_hh_l0_reverse.data, 0.0)\n\t\t\t\t\tm.bias_hh_l0_reverse.data[hidden_size:(2 * hidden_size)] = 1.0","title":""},{"docid":"c2ecf58da166683eba2a0ec4026cdfa5","score":"0.75822425","text":"def init_weights(self):\n # Prune heads if needed\n if self.config.pruned_heads:\n self.prune_heads(self.config.pruned_heads)\n\n if _init_weights:\n # Initialize weights\n self.apply(self._initialize_weights)\n\n # Tie weights should be skipped when not initializing all weights\n # since from_pretrained(...) calls tie weights anyways\n self.tie_weights()","title":""},{"docid":"f857c077d5910fcdff78d8051ad5c357","score":"0.7567569","text":"def reset_weights(self):\n init.uniform_(self.linear.weight, -3e-3, 3e-3)\n init.zeros_(self.linear.bias)","title":""},{"docid":"1e7177899ac63f86ff84d2ab1e2b0156","score":"0.7567175","text":"def init_weights(self):\n self.conv1.weight.data.normal_(0, 0.01)\n self.conv2.weight.data.normal_(0, 0.01)\n if self.downsample is not None:\n self.downsample.weight.data.normal_(0, 0.01)","title":""},{"docid":"1e7177899ac63f86ff84d2ab1e2b0156","score":"0.7567175","text":"def init_weights(self):\n self.conv1.weight.data.normal_(0, 0.01)\n self.conv2.weight.data.normal_(0, 0.01)\n if self.downsample is not None:\n self.downsample.weight.data.normal_(0, 0.01)","title":""},{"docid":"7056f930e03a9383bc61c3eb35379659","score":"0.75577235","text":"def initialize_weights(self):\n self.initialize_blobs()\n\n for b in self.blobs[:-1]:\n b.set_weights(2 * np.random.random(b.shape) - 1)","title":""},{"docid":"d0544f81cec422c1f3497c70311d4a25","score":"0.75380516","text":"def init_weights(self):\n initrange = 0.1 #\n self.encoder.weight.data.uniform_(-initrange, initrange)\n #self.encoder.weight.data.normal_(mean=0.0, std=initrange)\n self.decoder.bias.data.zero_()\n self.decoder.weight.data.uniform_(-initrange, initrange)\n #self.decoder.weight.data.normal_(mean=0.0, std=initrange)","title":""},{"docid":"abb8ee6664f30ce930304f834fdb7c1f","score":"0.75330013","text":"def init_weights(self):\n init_value = 0.1\n self.linear1.weight.data.uniform_(-init_value, init_value)\n self.linear2.weight.data.uniform_(-init_value, init_value)","title":""},{"docid":"abb8ee6664f30ce930304f834fdb7c1f","score":"0.75330013","text":"def init_weights(self):\n init_value = 0.1\n self.linear1.weight.data.uniform_(-init_value, init_value)\n self.linear2.weight.data.uniform_(-init_value, init_value)","title":""},{"docid":"6c644dbf3dafaa9b920e19d7ea10b2d7","score":"0.7524491","text":"def init_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\n xavier_uniform_(m.weight)\n if m.bias is not None:\n zeros_(m.bias)","title":""},{"docid":"3bc746b8fc540f886655f2bc54c8cbae","score":"0.7520993","text":"def _initialize_weights(self, m):\n self.w_ = np.zeros(1 + m)\n self.w_init = True","title":""},{"docid":"ccaab33e0155554fdd8653c18e864ff5","score":"0.7520197","text":"def __init__(self, input_dimensions=2,number_of_nodes=4):\r\n\r\n self.input_dimensions = input_dimensions\r\n self.number_of_nodes=number_of_nodes\r\n self.initialize_weights()","title":""},{"docid":"2657e83d8428751781b0e4f7e721fd43","score":"0.7516299","text":"def initialize_weights(self):\n raise NotImplementedError(f'{type(self)} does not implement '\n 'initialize_weights()')","title":""},{"docid":"a85f48168c3c7c6b59dbce1289217a1a","score":"0.7512061","text":"def init_weights(self) -> None:\n\n generation_init_weights(\n self, init_type=self.init_type, init_gain=self.init_gain)\n\n self._is_init = True","title":""},{"docid":"6334e13fcd4eb564243c74dc6650f797","score":"0.75035286","text":"def init_weights(net):\n for m in net.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.xavier_uniform_(m.weight)\n #nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n\n elif isinstance(m, nn.Linear):\n nn.init.xavier_uniform_(m.weight)\n\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n\n return net","title":""},{"docid":"619f201dbd64f18777779af37798cc99","score":"0.75032514","text":"def weights_init(m):\n\tclassname = m.__class__.__name__\n\tif classname.find('Conv') != -1:\n\t\tnn.init.normal_(m.weight.data, 0.0, 0.02)\n\telif classname.find(\"BatchNorm\") != -1:\n\t\tnn.init.normal_(m.weight.data, 0.0, 0.02)\n\t\tnn.init.constant_(m.bias.data, 0)\n\telif classname.find(\"Linear\") != -1:\n\t\tnn.init.normal_(m.weight.data, 0.0, 0.02)","title":""},{"docid":"6e72add8be14af4fb8f043dbecb7e62a","score":"0.7496546","text":"def _initialize_weights(self, m):\n self.w_ = np.zeros(1 + m)\n self.w_initialized = True","title":""},{"docid":"a6e92d7ae27f946f92921dcb35a00ee2","score":"0.7484646","text":"def _init_weights(self, module):\n pass","title":""},{"docid":"3a65927f4476ae242d59badc2b972a7c","score":"0.74637544","text":"def init_weights(self):\n initrange = 0.1\n if self.W is not None:\n self.embed_1.weight = nn.Parameter(torch.from_numpy(self.W),\n requires_grad=self.require_W_grad)\n else:\n self.embed_1.weight.data.uniform_(-initrange, initrange)\n # self.logit.bias.data.fill_(0)\n # self.logit.weight.data.uniform_(-initrange, initrange)","title":""},{"docid":"b473b250fc6a969ddd65e91965dbae0d","score":"0.7460329","text":"def _weight_initializer(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1.0)\n nn.init.constant_(m.bias, 0.0)","title":""},{"docid":"bf691c59e29e6b1c9b4a283f211dcde6","score":"0.74594855","text":"def _init_weight_(self):\n torch.nn.init.normal_(self.embed_user_GMF.weight, std=0.01)\n torch.nn.init.normal_(self.embed_user_MLP.weight, std=0.01)\n torch.nn.init.normal_(self.embed_item_GMF.weight, std=0.01)\n torch.nn.init.normal_(self.embed_item_MLP.weight, std=0.01)\n\n for m in self.MLP_layers:\n if isinstance(m, torch.nn.Linear):\n torch.nn.init.xavier_uniform_(m.weight)\n torch.nn.init.kaiming_uniform_(self.predict_layer.weight, a=1, nonlinearity='sigmoid')","title":""},{"docid":"3c22c1b28f2727b49129a693641c64a9","score":"0.74581134","text":"def computeWeights(self):\n# if type(self.inputLayer) == np.ndarray: # input layer is array of inputs\n# innodes = len(self.inputLayer)\n# else: # input layer is class Layer (a hidden layer)\n# innodes = len(self.inputLayer.activation) # number of nodes in input\n# weights = np.zeros((innodes,self.nodes)) # weight matrix\n# for i in range(innodes): # rows are input nodes\n# for j in range(self.nodes): # columns are current layer nodes\n# \n# # for testing, stop use random seed\n# np.random.seed(3)\n# weights[i][j] = np.random.normal(0,0.01**2) \n # For weights associated with the previous layer\n if self.nextLayerNodes == None:\n weights = None # output layer has no weights\n else: # current layer is not the output (i.e it is the input or hidden layer)\n nextnodes = self.nextLayerNodes\n weights = np.zeros((self.nodes,nextnodes)) # weight matrix\n for i in range(self.nodes): # rows are input nodes\n for j in range(nextnodes): # columns are next layer nodes\n # for testing, stop use random seed\n #np.random.seed(3)\n weights[i][j] = np.random.normal(0,0.01**2) \n self.weights = weights\n return weights","title":""},{"docid":"eb6ababf871b9293874eae205c783e6c","score":"0.7453489","text":"def init_weights(self):\n for m in self.cls_convs:\n normal_init(m.conv, std=0.01)\n for m in self.reg_convs:\n normal_init(m.conv, std=0.01)\n bias_cls = bias_init_with_prob(0.01)\n normal_init(self.retina_cls, std=0.01, bias=bias_cls)\n normal_init(self.retina_reg, std=0.01)","title":""},{"docid":"3d0d84bdca551e9a56b2650e13ebce2d","score":"0.7451753","text":"def initialize_weights(self):\n all_weights = dict()\n all_weights['weights'] = {}\n all_weights['biases'] = {}\n\n n_layer = len(self.network_architecture['hidden'])\n units = [self.ae_hyper['z']] + self.network_architecture['hidden']\n print(units)\n\n for i in range(len(units)-1):\n print(units[i])\n all_weights['weights']['w' + str(i+1)] = tf.Variable(tf.random_normal([units[i], units[i+1]], stddev=2.0 / np.sqrt(units[i] + units[i+1])))\n all_weights['biases']['b' + str(i+1)] = tf.Variable(tf.zeros([units[i+1]]))\n\n all_weights['weights']['out'] = tf.Variable(tf.random_normal([units[-1], self.network_architecture['n_output']], stddev=2.0 / np.sqrt(units[-1] + self.network_architecture['n_output'])))\n all_weights['biases']['out'] = tf.Variable(tf.zeros([self.network_architecture['n_output']]))\n\n return all_weights","title":""},{"docid":"d9912df51ae0377a2326ca37f46ea20a","score":"0.74350744","text":"def default_weights_initializer(self):\r\n np.random.seed(1)\r\n # the first layer (i.e. input layer) does not have biases\r\n biases = [np.random.randn(num, 1) for num in self.sizes[1:]]\r\n # for each matrix in weights: row - layer to, column - layer from\r\n weights = [np.random.randn(x, y) / np.sqrt(y)\r\n for x, y in zip(self.sizes[1:], self.sizes[:-1])]\r\n return weights, biases","title":""},{"docid":"cd2dbab27c3d199d473d343eeda82c20","score":"0.7412681","text":"def __init__(self, num_units, num_weights):\n\t\tself._num_weights = num_weights\n\t\tself._num_units = num_units","title":""},{"docid":"b977b8f1e79b466ff015efbed72959c0","score":"0.7409157","text":"def _weight_initializer(self):\n for m in self.modules():\n if isinstance(m, nn.ConvTranspose2d):\n nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1.0)\n nn.init.constant_(m.bias, 0.0)","title":""},{"docid":"b977b8f1e79b466ff015efbed72959c0","score":"0.7409157","text":"def _weight_initializer(self):\n for m in self.modules():\n if isinstance(m, nn.ConvTranspose2d):\n nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1.0)\n nn.init.constant_(m.bias, 0.0)","title":""},{"docid":"b977b8f1e79b466ff015efbed72959c0","score":"0.7409157","text":"def _weight_initializer(self):\n for m in self.modules():\n if isinstance(m, nn.ConvTranspose2d):\n nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1.0)\n nn.init.constant_(m.bias, 0.0)","title":""},{"docid":"88ddce3df71c149e745e0eb491fb842f","score":"0.7404257","text":"def init_weight(self):\n nn.init.xavier_uniform_(self.rnn.weight_hh_l0, gain=1)\n nn.init.xavier_uniform_(self.rnn.weight_ih_l0, gain=1)\n if self.bidirect:\n nn.init.xavier_uniform_(self.rnn.weight_hh_l0_reverse, gain=1)\n nn.init.xavier_uniform_(self.rnn.weight_ih_l0_reverse, gain=1)\n nn.init.constant_(self.rnn.bias_hh_l0, 0)\n nn.init.constant_(self.rnn.bias_ih_l0, 0)\n if self.bidirect:\n nn.init.constant_(self.rnn.bias_hh_l0_reverse, 0)\n nn.init.constant_(self.rnn.bias_ih_l0_reverse, 0)","title":""},{"docid":"88ddce3df71c149e745e0eb491fb842f","score":"0.7404257","text":"def init_weight(self):\n nn.init.xavier_uniform_(self.rnn.weight_hh_l0, gain=1)\n nn.init.xavier_uniform_(self.rnn.weight_ih_l0, gain=1)\n if self.bidirect:\n nn.init.xavier_uniform_(self.rnn.weight_hh_l0_reverse, gain=1)\n nn.init.xavier_uniform_(self.rnn.weight_ih_l0_reverse, gain=1)\n nn.init.constant_(self.rnn.bias_hh_l0, 0)\n nn.init.constant_(self.rnn.bias_ih_l0, 0)\n if self.bidirect:\n nn.init.constant_(self.rnn.bias_hh_l0_reverse, 0)\n nn.init.constant_(self.rnn.bias_ih_l0_reverse, 0)","title":""},{"docid":"0dea40fb888a901cddc559e1a0f6adef","score":"0.73905605","text":"def init_weights():\n global w1, w2, w3, w4, w5, w6, b1, b2, b3, b4, b5, b6\n mu = 0\n sigma = 0.1\n w1 = tf.Variable(tf.truncated_normal([3, 3, 1, 6], mean = mu, stddev = sigma))\n b1 = tf.Variable(tf.zeros([6]))\n\n w2 = tf.Variable(tf.truncated_normal([3, 3, 6, 16], mean = mu, stddev = sigma))\n b2 = tf.Variable(tf.zeros([16]))\n\n w3 = tf.Variable(tf.truncated_normal([1, 1, 16, 32], mean = mu, stddev = sigma))\n b3 = tf.Variable(tf.zeros([32]))\n\n w4 = tf.Variable(tf.truncated_normal([6*6*32, 120], mean = mu, stddev = sigma))\n b4 = tf.Variable(tf.zeros([120]))\n\n w5 = tf.Variable(tf.truncated_normal([120, 84], mean = mu, stddev = sigma))\n b5 = tf.Variable(tf.zeros([84]))\n\n w6 = tf.Variable(tf.truncated_normal([84, 43], mean = mu, stddev = sigma))\n b6 = tf.Variable(tf.zeros([43]))","title":""},{"docid":"7e121987d082f29f76198636121f4323","score":"0.7388561","text":"def default_weight_initializer(self):\n self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]\n self.weights = [np.random.randn(y, x)/np.sqrt(x)\n for x, y in zip(self.sizes[:-1], self.sizes[1:])]","title":""},{"docid":"7e121987d082f29f76198636121f4323","score":"0.7388561","text":"def default_weight_initializer(self):\n self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]\n self.weights = [np.random.randn(y, x)/np.sqrt(x)\n for x, y in zip(self.sizes[:-1], self.sizes[1:])]","title":""},{"docid":"2e6d75b5699b382dd2f3d4297b1c6749","score":"0.7385814","text":"def __init__(self, weights):\n\n self.weights = weights","title":""},{"docid":"e55f873b253882bfab21bcc999dff207","score":"0.7380988","text":"def init_weights(net):\n for m in net.modules():\n if isinstance(m, nn.Conv2d):\n m.weight.data.normal_(0, 0.02)\n m.bias.data.zero_()\n elif isinstance(m, nn.ConvTranspose2d):\n m.weight.data.normal_(0, 0.02)\n m.bias.data.zero_()\n elif isinstance(m, nn.Linear):\n m.weight.data.normal_(0, 0.02)\n m.bias.data.zero_()","title":""},{"docid":"7017d7fc5e68845a0fc72cc2b609c36c","score":"0.73788613","text":"def init_weights(self):\n return full(self.dim, self.w_n), full(self.dim, self.w_f)","title":""},{"docid":"daacc62cdbe214b427c88fd833b22df4","score":"0.7375114","text":"def init_weights(self):\n if not self.clip_embed:\n self.embedding.weight.data.uniform_(-0.1, 0.1)\n self.fc.bias.data.fill_(0)\n self.fc.weight.data.uniform_(-0.1, 0.1)","title":""},{"docid":"ce46384090b721704747e310f4a354c1","score":"0.7371002","text":"def weights_init(m):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1:\n m.weight.data.normal_(0.0, 0.02)\n elif classname.find('BatchNorm') != -1:\n m.weight.data.normal_(1.0, 0.02)\n m.bias.data.fill_(0)","title":""},{"docid":"f03acbe41ce7dda704b1bf5929f1e56e","score":"0.7365421","text":"def init_weights(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n for m in self.modules():\n if isinstance(m, MultiScaleDeformableAttention):\n m.init_weight()\n if not self.as_two_stage:\n xavier_init(self.reference_points, distribution='uniform', bias=0.)\n normal_(self.level_embeds)","title":""},{"docid":"52132063f480a15bcdbb0c67b321e4af","score":"0.7350548","text":"def init_weight(self):\n for m in network_utils.iterate_sublayers(self):\n if isinstance(m, nn.Conv2d):\n torch.nn.init.xavier_uniform(m.weight)\n torch.nn.init.xavier_uniform(m.bias)","title":""},{"docid":"b82d45f15b131c7880c2ecc6482ef05d","score":"0.7348955","text":"def weights_init(m):\n m.weight.data.normal_(0.0, 0.01)","title":""},{"docid":"e335df6e1ea60d28d792075c7f3c731a","score":"0.7345488","text":"def init_weights(self):\n for m in self.modules():# .modules()返回模型里的组成元素 即所有层\n if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d): #如果是卷积层和反卷积层\n nn.init.xavier_normal(m.weight.data) # xavier_normal 初始化\n # torch.nn.init.xavier_normal_(tensor, gain=1)\n # tensor([[-0.1777, 0.6740, 0.1139],\n # [ 0.3018, -0.2443, 0.6824]])","title":""},{"docid":"1ff80bf26ada42bed094504fe7b32f3d","score":"0.73269033","text":"def init_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv1d):\n nn.init.xavier_normal(m.weight.data)","title":""},{"docid":"084ab973ce1aae0642df8f0e10ffb58b","score":"0.7326888","text":"def init_weights(self):\n self.init_cb_weights()\n for m in self.cb_modules:\n m.init_weights()","title":""},{"docid":"151af46cb185e428339941c0add8c464","score":"0.7321385","text":"def _create_weights(self):\n\n # c is a normalizing constant for creating weight matrices\n c = 1 / np.sqrt(self._num_hidden_layers)\n\n self._W, self._b = {}, {}\n\n if self._weight_type == 'rand':\n\n if self._fix_weights:\n np.random.seed(5)\n\n for i in range(self._num_hidden_layers + 1):\n self._W[i] = c * np.random.randn(self._dim_list[i+1], self._dim_list[i])\n self._b[i] = c * np.random.randn(self._dim_list[i+1], 1)\n\n elif self._weight_type == 'ones':\n\n for i in range(self._num_hidden_layers + 1):\n self._W[i] = c * np.ones(shape=(self._dim_list[i+1], self._dim_list[i]))\n self._b[i] = c * np.ones(shape=(self._dim_list[i+1], 1))\n\n else:\n raise ValueError('Please use weight_type = \"rand\" | \"ones\"')","title":""},{"docid":"fb3c13db099f9fb08cb6df4f9327ffef","score":"0.7304773","text":"def __init__(self, n_inputs, n_neurons):\n np.random.seed(0)\n self.weights = 0.01 * np.random.randn(n_inputs, n_neurons)\n self.biases = np.zeros((1,n_neurons))","title":""},{"docid":"6c4fcbc24fae2791d69746051818d122","score":"0.7298154","text":"def setWeights(self):\n pass","title":""},{"docid":"502f27d0d7937511d5d2c5b9fa606350","score":"0.7295881","text":"def init_weights(self):\n # constant_init(self.cb_linears, 0)\n if self.cb_zero_init:\n for ls in self.cb_linears:\n for m in ls:\n constant_init(m, 0)\n\n for m in self.cb_modules:\n m.init_weights()","title":""},{"docid":"0d698a4a9e7c547e4a66d3f8aa83f650","score":"0.7274645","text":"def weights_init(m):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1:\n m.weight.data.normal_(0.0, 0.02)\n elif classname.find('BatchNorm') != -1:\n m.weight.data.normal_(1.0, 0.02)\n m.bias.data.fill_(0)","title":""},{"docid":"9d9788a39bafa029dc464a3ee8e07f31","score":"0.7268157","text":"def InitializeWeights(nodes):\n layers, weights = len(nodes), []\n for i in range(1, layers):\n w = [[np.random.uniform(-1, 1) for k in range(nodes[i-1] + 1)]\n for j in range(nodes[i])]\n weights.append(np.matrix(w))\n return weights","title":""},{"docid":"069fdf95a9450f488353c6214741965f","score":"0.7266745","text":"def weights_init(m):\n if isinstance(m, nn.Conv3d):\n xavier(m.weight.data)\n m.bias.data.zero_()\n elif isinstance(m, nn.Linear):\n size = m.weight.size()\n fan_out = size[0] # number of rows\n fan_in = size[1] # number of columns\n variance = np.sqrt(2.0 / (fan_in + fan_out))\n m.weight.data.normal_(0.0, variance)","title":""},{"docid":"13ac9cb9875ed5c46bb90f5b70f84581","score":"0.72656286","text":"def weights_init(m):\n if type(m) == nn.Linear:\n m.weight.data.normal_(0.0, 0.01)","title":""},{"docid":"f6cb33a5452c54a42f8413b7807dedb9","score":"0.72555137","text":"def init_paramters(self):\n\t\tself.weights = 2*np.random.normal(size=(self.hidden_neurons.shape[0],self.visible_neurons.shape[0]))\n\t\tself.tresholds_hidden = np.zeros((self.hidden_neurons.shape[0], 1))\n\t\tself.tresholds_visible = np.zeros((self.visible_neurons.shape[0], 1))","title":""},{"docid":"d28b30aaff489a4c3e9351d7a1c26715","score":"0.72539806","text":"def init_weights(m):\n if isinstance(m, nn.Conv2d):\n # Note that there is no bias due to BN\n fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n m.weight.data.normal_(mean=0.0, std=math.sqrt(2.0 / fan_out))\n elif isinstance(m, nn.BatchNorm2d):\n zero_init_gamma = (\n hasattr(m, \"final_bn\") and m.final_bn and False\n )\n m.weight.data.fill_(0.0 if zero_init_gamma else 1.0)\n m.bias.data.zero_()\n elif isinstance(m, nn.Linear):\n m.weight.data.normal_(mean=0.0, std=0.01)\n m.bias.data.zero_()","title":""},{"docid":"ba0444bae4b5ac11b1018e01f7da2561","score":"0.7245869","text":"def init(self, neurons_before, optimizer):\n self.biases = np.random.randn(1, self.neurons).astype(dtype=\"float32\")\n self.weights = np.random.randn(int(neurons_before), self.neurons).astype(dtype=\"float32\") / np.sqrt(int(neurons_before)).astype(dtype=\"float32\")\n self.optimizer = optimizer\n return self.neurons","title":""},{"docid":"9bb9f1fe5527b6fdb3de4548b5828a11","score":"0.7245617","text":"def init_weights(self, n):\r\n self.neuro_weights = []\r\n for i in range(n):\r\n self.neuro_weights.append(random.random() * 2 - 1)","title":""},{"docid":"3c7b5dae3e2fc0855c4fe357745f46f3","score":"0.7239348","text":"def weights_init(m):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1:\n nn.init.normal_(m.weight.data, 0.0, 0.02)\n elif classname.find('BatchNorm') != -1:\n nn.init.normal_(m.weight.data, 1.0, 0.02)\n nn.init.constant_(m.bias.data, 0)","title":""},{"docid":"06d80adb4fae464cea971d81ef416a5d","score":"0.7236633","text":"def init_weights(self, n_features):\n self.rgen = np.random.RandomState(self.random_state)\n self.w_ = self.rgen.normal(loc=0.0, scale=0.01, size=1 + n_features)\n self.w_initialized = True","title":""},{"docid":"77476fc38094bbc38652f6e5c1d02f1d","score":"0.7235151","text":"def set_weights(self, data):\r\n previous_neurons = 0\r\n index_layer = 0\r\n index_weights = 0\r\n self.layers = []\r\n for i in data['network']:\r\n layer = Layer()\r\n layer.init_neurons(i, previous_neurons)\r\n for j in range(len(layer.neurons)):\r\n for k in range(len(layer.neurons[j].neuro_weights)):\r\n layer.neurons[j].neuro_weights[k] = data['weights'][index_weights]\r\n index_weights += 1\r\n previous_neurons = i\r\n index_layer += 1\r\n self.layers.append(layer)","title":""},{"docid":"8e3819ef989e201011823f3c14615b3b","score":"0.72335804","text":"def initialize_NN(layers):\n weights = []\n biases = []\n num_layers = len(layers) \n for l in range(0,num_layers-1):\n W = xavier_initialisation(size=[layers[l], layers[l+1]])\n b = tf.Variable(tf.zeros([1,layers[l+1]]), dtype=tf.float32)\n weights.append(W)\n biases.append(b) \n return weights, biases","title":""},{"docid":"922c9ede71c2110763fb5dfd54949e63","score":"0.72293776","text":"def weights_init(m):\n if isinstance(m, (torch.nn.modules.conv._ConvNd, torch.nn.Linear)):\n torch.nn.init.xavier_uniform_(m.weight)\n if m.bias is not None:\n torch.nn.init.constant_(m.bias, 0.0)\n elif isinstance(m, torch.nn.modules.batchnorm._BatchNorm):\n torch.nn.init.constant_(m.bias, 0.0)\n torch.nn.init.constant_(m.weight, 1.0)","title":""},{"docid":"4e10732228d50fa309852b864ca2e3c9","score":"0.7226845","text":"def construct_weights(self):\n assert len(self.w_inits) == 1\n w_tiled = tile_sparse_weights(self.w_inits[0], max_cols=self.latent_dim, tile_cols=self.n_channels)\n self.w_inits = [0.01 * w_tiled, 100. * w_tiled.T]\n self.b_inits = [None, None]\n\n return super().construct_weights()","title":""},{"docid":"6448d56ce36e2177a82fcdc770a2cb9b","score":"0.72242814","text":"def weights_init(m):\n class_name = m.__class__.__name__\n if class_name.find('Conv') != -1:\n nn.init.xavier_uniform_(m.weight.data)\n \n elif class_name.find('Linear') != -1:\n nn.init.xavier_uniform_(m.weight.data)\n \n elif class_name.find('BatchNorm') != -1:\n nn.init.constant_(m.weight.data,1)\n nn.init.constant_(m.bias.data,0)","title":""},{"docid":"bcfa12a5d84c577ffbbe1572b8281af0","score":"0.7220487","text":"def init_weights(self):\n bias_init = bias_init_with_prob(0.1)\n self.heatmap_head[-1].bias.data.fill_(bias_init)\n for head in [self.wh_head, self.offset_head]:\n for m in head.modules():\n if isinstance(m, nn.Conv2d):\n normal_init(m, std=0.001)","title":""}],"string":"[\n {\n \"docid\": \"2f539308a4530e0d61a67321a3fd828a\",\n \"score\": \"0.86598444\",\n \"text\": \"def init_weights(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8dec4d41250f35e3f156604e893851\",\n \"score\": \"0.84111565\",\n \"text\": \"def weight_init(self):\\n self.weights = [np.random.randn(y, x)/np.sqrt(x) for x,y in zip(self.network_topology[:-1], self.network_topology[1:])]\\n \\n self.biases = [np.random.randn(y, 1) for y in self.network_topology[1:]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"044130279cb0d9aa8a2dd6bb79601a3c\",\n \"score\": \"0.8381277\",\n \"text\": \"def _initialize_weights(self, layer_units):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e15c068f4b8d89aa1ced602debf83940\",\n \"score\": \"0.8347222\",\n \"text\": \"def _initialize_weights(self):\\r\\n self.weights = np.random.normal(size= [self.number_of_classes,self.input_dimensions+1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9bb1473c83794d505f216b3a17bef4\",\n \"score\": \"0.8345059\",\n \"text\": \"def init_weights(self):\\n self.transformer.init_weights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6797d00e1caea91c8da94e513775ef2\",\n \"score\": \"0.8295717\",\n \"text\": \"def init_weights(self):\\n # Initialize weights\\n self.apply(self._init_weights)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6797d00e1caea91c8da94e513775ef2\",\n \"score\": \"0.8295717\",\n \"text\": \"def init_weights(self):\\n # Initialize weights\\n self.apply(self._init_weights)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d20a49d83dd99d4ee64feef6ac256f\",\n \"score\": \"0.82177496\",\n \"text\": \"def initialize_weights(self):\\n for layer in self.layers:\\n WeightsInitializer.initialize_layer_or_model(layer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6be279a12700008d0a9a8201964fd57\",\n \"score\": \"0.8206177\",\n \"text\": \"def weights_init(neural_net):\\n classname = neural_net.__class__.__name__\\n if classname.find('Conv') != -1:\\n neural_net.weight.data.normal_(0, 2e-2)\\n elif classname.find('BatchNorm') != -1:\\n neural_net.weight.data.normal_(1, 2e-2)\\n neural_net.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a8e21621c675daf874c762f4aa1930\",\n \"score\": \"0.81082976\",\n \"text\": \"def set_weights(self, W):\\r\\n self.weights = np.zeros((self.number_of_nodes,self.input_dimensions+1))\\r\\n print(self.weights)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff247ab8d4aaea28132c1ff3f33fcee4\",\n \"score\": \"0.8081319\",\n \"text\": \"def init_weights(self):\\n \\n # Can we optimize this initialization further?\\n stdev = cp.sqrt(2) / cp.sqrt(self.output_size)\\n # stdev = cp.sqrt(2) / cp.sqrt(self.input_size * self.output_size)\\n self.weights = cp.random.normal(loc=0., scale=stdev, \\n size=(self.input_size, self.output_size),\\n dtype=cp.float32)\\n self.bias = cp.zeros((1, self.output_size), dtype=cp.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a53b095720b6732875f572d21f7d3db\",\n \"score\": \"0.7993603\",\n \"text\": \"def weights_init(self):\\r\\n self.apply(weights_init_1D)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5552813b1d7657166f72a3a9a4a9e065\",\n \"score\": \"0.79828733\",\n \"text\": \"def init_weights(self):\\n \\n # Keep track of dimensions\\n a, b, c, d = (self.input_size[2],) + self.filter_size + (self.nr_filters,)\\n \\n # Set weight scaling parameter.\\n stdev = cp.sqrt(2) / cp.sqrt(a * b * c * d)\\n \\n # Initialize weights.\\n self.weights = cp.random.normal(loc=0., scale=stdev, size=(a, b, c, d), dtype=cp.float32)\\n self.bias = cp.zeros((1, 1, 1, d), dtype=cp.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a119d3eb2c9813dab22f4c14c626f0bc\",\n \"score\": \"0.79796815\",\n \"text\": \"def initialize_all_weights_to_zeros(self):\\r\\n self.weights = np.zeros([self.number_of_classes,self.input_dimensions+1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5cbfca6403090173cbb1c28b2184938\",\n \"score\": \"0.79690295\",\n \"text\": \"def initialize_weights(self):\\n BiLSTMModel.initialize_weights(self)\\n LSTMBasedImportanceWeighting.initialize_weights(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"017ebdbfabae6714eb1c6e3dd4dbaab1\",\n \"score\": \"0.7949052\",\n \"text\": \"def _initialize_weights(net: torch.nn.Module) -> None:\\n \\n if type(net) == torch.nn.Linear:\\n torch.nn.init.xavier_uniform_(net.weight)\\n net.bias.data.fill_(1e-2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6e64613d03f666d00698c33d79f45e8\",\n \"score\": \"0.79345065\",\n \"text\": \"def initialize_weights(self, initial_weights):\\n\\n #shapes for the weight vectors\\n hidtup = (self.hidden_layer_widths[0], self.num_in + 1)\\n outtup = (self.num_out, self.hidden_layer_widths[0] + 1) if self.num_out > 2 else (1, self.hidden_layer_widths[0] + 1)\\n\\n if not initial_weights:\\n #print(\\\"inited weights!\\\")\\n self.hid = np.zeros(hidtup)\\n self.out = np.zeros(outtup)\\n else:\\n np.random.seed(seed=5)\\n self.hid = np.random.normal(loc=0, scale=1.0, size=hidtup)\\n self.out = np.random.normal(loc=0, scale=1.0, size=outtup)\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe162eb762faedc5e79d709c9761d65\",\n \"score\": \"0.792823\",\n \"text\": \"def init_weights(self):\\n # Initialize weights\\n self.apply(self._init_weights)\\n\\n # Prune heads if needed\\n if self.config.pruned_heads:\\n self.prune_heads(self.config.pruned_heads)\\n\\n # Tie weights if needed\\n self.tie_weights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"878f3c2a94b6979b7b7063ebd09c3bea\",\n \"score\": \"0.78852886\",\n \"text\": \"def init_weights(self):\\n init.xavier_uniform_(self.w_x[1].weight)\\n init.xavier_uniform_(self.w_h[1].weight)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3239987ba5d5063eba153d05e5b7ceac\",\n \"score\": \"0.78528994\",\n \"text\": \"def _initialize_weights(self):\\n self.theta = create_structure_for_ann(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39e64b18fb813bff634cda294eaab41f\",\n \"score\": \"0.7845597\",\n \"text\": \"def initialize_weights(self,seed=None):\\r\\n if seed != None:\\r\\n np.random.seed(seed)\\r\\n # W = np.random.rand((input_dimensions,number_of_nodes))*np.sqrt(1/(ni+no))\\r\\n self.weights = np.random.randn(self.number_of_nodes,self.input_dimensions+1)\\r\\n print(self.weights)\\r\\n print(\\\"No of classes:\\\",number_of_nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f604207bcd7577012421ae1e5c6a670d\",\n \"score\": \"0.7834929\",\n \"text\": \"def init_weights(self):\\n self.embedding.weight.data.uniform_(-0.1, 0.1)\\n self.fc.bias.data.fill_(0)\\n self.fc.weight.data.uniform_(-0.1, 0.1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0afb91185a4a8bba885f44a12ef0dd97\",\n \"score\": \"0.7824508\",\n \"text\": \"def init_weights(self):\\n for m in [self.conv_first, self.conv_hr, self.conv_last]:\\n default_init_weights(m, 0.1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"810c9cab2ac4d30dcd01831834d81768\",\n \"score\": \"0.78023416\",\n \"text\": \"def init_weights(self):\\n for name, param in self.enc_lstm.named_parameters():\\n if 'bias' in name:\\n nn.init.normal_(param, 0.0)\\n elif 'weight' in name:\\n nn.init.xavier_normal_(param)\\n for name, param in self.dec_lstm.named_parameters():\\n if 'bias' in name:\\n nn.init.normal_(param, 0.0)\\n elif 'weight' in name:\\n nn.init.xavier_normal_(param)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2683bee9bbddb373702628d53e4c047\",\n \"score\": \"0.7777124\",\n \"text\": \"def init_weights(self):\\n for m in self.modules():\\n if isinstance(m, nn.Conv3d):\\n nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\\n elif isinstance(m, nn.Linear):\\n nn.init.normal_(m.weight, mean = 0, std = 1)\\n elif isinstance(m, (nn.BatchNorm3d, nn.BatchNorm2d)):\\n nn.init.constant_(m.weight, 1)\\n nn.init.constant_(m.bias, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d06e3b325fb2493483c210555342235\",\n \"score\": \"0.7764375\",\n \"text\": \"def _initialize_weights(self, size: int):\\n\\n # uses He Normal or He-at-el initialization become famous by a paper 2015 by He-et-al\\n self.weights = np.random.randn(size, self.neurons) * np.sqrt((2/size)) * 0.1\\n self.bias = np.zeros((1, self.neurons))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9422a1f347e9d8b670a396d124eae9f8\",\n \"score\": \"0.7690929\",\n \"text\": \"def init_weights(self):\\n\\t\\tr = np.sqrt(6.) / np.sqrt(self.fc.in_features +\\n\\t\\t self.fc.out_features)\\n\\t\\tself.fc.weight.data.uniform_(-r, r)\\n\\t\\tself.fc.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a32a5a36163fd9b028651294aa84daf\",\n \"score\": \"0.7672449\",\n \"text\": \"def init_parameters(self):\\n\\n self.weight.data.normal_()\\n self.weight.data.mul_(1.0/pow(self.feat_dim*self.out_dim, 0.5))\\n self.bias.data.normal_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0389d48c46317e97c06c7b0e35997446\",\n \"score\": \"0.7640888\",\n \"text\": \"def init_weights(self):\\n r = np.sqrt(6.0) / np.sqrt(self.fc.in_features + self.fc.out_features)\\n self.fc.weight.data.uniform_(-r, r)\\n self.fc.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8ada5ec98e5bd26725e46dde873755\",\n \"score\": \"0.76374394\",\n \"text\": \"def init_weights(self):\\n import numpy as np\\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\\n self.fc.out_features)\\n self.fc.weight.data.uniform_(-r, r)\\n self.fc.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"198c8f7f8a7b3148ee10e302fc67eda1\",\n \"score\": \"0.7630416\",\n \"text\": \"def create_weights(self):\\n self._weight_matrix = self._random_weight_matrix()\\n self._weight_matrix_diff = np.zeros_like(self._weight_matrix)\\n \\n self._bias_units = self._random_bias_units()\\n self._bias_units_diff = np.zeros_like(self._bias_units)\\n \\n \\n self.params = [self._weight_matrix, self._bias_units]\\n self.gradients = [self._weight_matrix_diff, self._bias_units_diff]\\n \\n if self.tensor:\\n self._weight_tensor = self._random_weight_tensor()\\n self._weight_tensor_diff = np.zeros_like(self._weight_tensor)\\n self.params.append(self._weight_tensor)\\n self.gradients.append(self._weight_tensor_diff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed0fecf0f3da5f7f0fa5385c1f6a080\",\n \"score\": \"0.7616112\",\n \"text\": \"def init_weights(self):\\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\\n self.fc.out_features)\\n self.fc.weight.data.uniform_(-r, r)\\n self.fc.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed0fecf0f3da5f7f0fa5385c1f6a080\",\n \"score\": \"0.7616112\",\n \"text\": \"def init_weights(self):\\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\\n self.fc.out_features)\\n self.fc.weight.data.uniform_(-r, r)\\n self.fc.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed0fecf0f3da5f7f0fa5385c1f6a080\",\n \"score\": \"0.7616112\",\n \"text\": \"def init_weights(self):\\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\\n self.fc.out_features)\\n self.fc.weight.data.uniform_(-r, r)\\n self.fc.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed0fecf0f3da5f7f0fa5385c1f6a080\",\n \"score\": \"0.7616112\",\n \"text\": \"def init_weights(self):\\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\\n self.fc.out_features)\\n self.fc.weight.data.uniform_(-r, r)\\n self.fc.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed0fecf0f3da5f7f0fa5385c1f6a080\",\n \"score\": \"0.7616112\",\n \"text\": \"def init_weights(self):\\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\\n self.fc.out_features)\\n self.fc.weight.data.uniform_(-r, r)\\n self.fc.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d9b1f8b125b3e0645e9a95baf96f8ca\",\n \"score\": \"0.7612289\",\n \"text\": \"def init_weights(self):\\n\\t\\tfor m in self.modules():\\n\\t\\t\\tif isinstance(m, nn.Linear):\\n\\t\\t\\t\\tnn.init.xavier_uniform_(m.weight.data)\\n\\t\\t\\t\\tnn.init.constant_(m.bias.data, 0.0)\\n\\t\\t\\t\\n\\t\\t\\telif isinstance(m, nn.LSTM):\\n\\t\\t\\t\\tnn.init.xavier_uniform_(m.weight_ih_l0.data)\\n\\t\\t\\t\\tnn.init.orthogonal_(m.weight_hh_l0.data)\\n\\t\\t\\t\\tnn.init.constant_(m.bias_ih_l0.data, 0.0)\\n\\t\\t\\t\\tnn.init.constant_(m.bias_hh_l0.data, 0.0)\\n\\t\\t\\t\\thidden_size = m.bias_hh_l0.data.shape[0] // 4\\n\\t\\t\\t\\tm.bias_hh_l0.data[hidden_size:(2 * hidden_size)] = 1.0\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif (m.bidirectional):\\n\\t\\t\\t\\t\\tnn.init.xavier_uniform_(m.weight_ih_l0_reverse.data)\\n\\t\\t\\t\\t\\tnn.init.orthogonal_(m.weight_hh_l0_reverse.data)\\n\\t\\t\\t\\t\\tnn.init.constant_(m.bias_ih_l0_reverse.data, 0.0)\\n\\t\\t\\t\\t\\tnn.init.constant_(m.bias_hh_l0_reverse.data, 0.0)\\n\\t\\t\\t\\t\\tm.bias_hh_l0_reverse.data[hidden_size:(2 * hidden_size)] = 1.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2ecf58da166683eba2a0ec4026cdfa5\",\n \"score\": \"0.75822425\",\n \"text\": \"def init_weights(self):\\n # Prune heads if needed\\n if self.config.pruned_heads:\\n self.prune_heads(self.config.pruned_heads)\\n\\n if _init_weights:\\n # Initialize weights\\n self.apply(self._initialize_weights)\\n\\n # Tie weights should be skipped when not initializing all weights\\n # since from_pretrained(...) calls tie weights anyways\\n self.tie_weights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f857c077d5910fcdff78d8051ad5c357\",\n \"score\": \"0.7567569\",\n \"text\": \"def reset_weights(self):\\n init.uniform_(self.linear.weight, -3e-3, 3e-3)\\n init.zeros_(self.linear.bias)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7177899ac63f86ff84d2ab1e2b0156\",\n \"score\": \"0.7567175\",\n \"text\": \"def init_weights(self):\\n self.conv1.weight.data.normal_(0, 0.01)\\n self.conv2.weight.data.normal_(0, 0.01)\\n if self.downsample is not None:\\n self.downsample.weight.data.normal_(0, 0.01)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7177899ac63f86ff84d2ab1e2b0156\",\n \"score\": \"0.7567175\",\n \"text\": \"def init_weights(self):\\n self.conv1.weight.data.normal_(0, 0.01)\\n self.conv2.weight.data.normal_(0, 0.01)\\n if self.downsample is not None:\\n self.downsample.weight.data.normal_(0, 0.01)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7056f930e03a9383bc61c3eb35379659\",\n \"score\": \"0.75577235\",\n \"text\": \"def initialize_weights(self):\\n self.initialize_blobs()\\n\\n for b in self.blobs[:-1]:\\n b.set_weights(2 * np.random.random(b.shape) - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0544f81cec422c1f3497c70311d4a25\",\n \"score\": \"0.75380516\",\n \"text\": \"def init_weights(self):\\n initrange = 0.1 #\\n self.encoder.weight.data.uniform_(-initrange, initrange)\\n #self.encoder.weight.data.normal_(mean=0.0, std=initrange)\\n self.decoder.bias.data.zero_()\\n self.decoder.weight.data.uniform_(-initrange, initrange)\\n #self.decoder.weight.data.normal_(mean=0.0, std=initrange)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abb8ee6664f30ce930304f834fdb7c1f\",\n \"score\": \"0.75330013\",\n \"text\": \"def init_weights(self):\\n init_value = 0.1\\n self.linear1.weight.data.uniform_(-init_value, init_value)\\n self.linear2.weight.data.uniform_(-init_value, init_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abb8ee6664f30ce930304f834fdb7c1f\",\n \"score\": \"0.75330013\",\n \"text\": \"def init_weights(self):\\n init_value = 0.1\\n self.linear1.weight.data.uniform_(-init_value, init_value)\\n self.linear2.weight.data.uniform_(-init_value, init_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c644dbf3dafaa9b920e19d7ea10b2d7\",\n \"score\": \"0.7524491\",\n \"text\": \"def init_weights(self):\\n for m in self.modules():\\n if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\\n xavier_uniform_(m.weight)\\n if m.bias is not None:\\n zeros_(m.bias)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bc746b8fc540f886655f2bc54c8cbae\",\n \"score\": \"0.7520993\",\n \"text\": \"def _initialize_weights(self, m):\\n self.w_ = np.zeros(1 + m)\\n self.w_init = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccaab33e0155554fdd8653c18e864ff5\",\n \"score\": \"0.7520197\",\n \"text\": \"def __init__(self, input_dimensions=2,number_of_nodes=4):\\r\\n\\r\\n self.input_dimensions = input_dimensions\\r\\n self.number_of_nodes=number_of_nodes\\r\\n self.initialize_weights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2657e83d8428751781b0e4f7e721fd43\",\n \"score\": \"0.7516299\",\n \"text\": \"def initialize_weights(self):\\n raise NotImplementedError(f'{type(self)} does not implement '\\n 'initialize_weights()')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a85f48168c3c7c6b59dbce1289217a1a\",\n \"score\": \"0.7512061\",\n \"text\": \"def init_weights(self) -> None:\\n\\n generation_init_weights(\\n self, init_type=self.init_type, init_gain=self.init_gain)\\n\\n self._is_init = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6334e13fcd4eb564243c74dc6650f797\",\n \"score\": \"0.75035286\",\n \"text\": \"def init_weights(net):\\n for m in net.modules():\\n if isinstance(m, nn.Conv2d):\\n nn.init.xavier_uniform_(m.weight)\\n #nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\\n if m.bias is not None:\\n nn.init.constant_(m.bias, 0)\\n\\n elif isinstance(m, nn.BatchNorm2d):\\n nn.init.constant_(m.weight, 1)\\n nn.init.constant_(m.bias, 0)\\n\\n elif isinstance(m, nn.Linear):\\n nn.init.xavier_uniform_(m.weight)\\n\\n if m.bias is not None:\\n nn.init.constant_(m.bias, 0)\\n\\n return net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"619f201dbd64f18777779af37798cc99\",\n \"score\": \"0.75032514\",\n \"text\": \"def weights_init(m):\\n\\tclassname = m.__class__.__name__\\n\\tif classname.find('Conv') != -1:\\n\\t\\tnn.init.normal_(m.weight.data, 0.0, 0.02)\\n\\telif classname.find(\\\"BatchNorm\\\") != -1:\\n\\t\\tnn.init.normal_(m.weight.data, 0.0, 0.02)\\n\\t\\tnn.init.constant_(m.bias.data, 0)\\n\\telif classname.find(\\\"Linear\\\") != -1:\\n\\t\\tnn.init.normal_(m.weight.data, 0.0, 0.02)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e72add8be14af4fb8f043dbecb7e62a\",\n \"score\": \"0.7496546\",\n \"text\": \"def _initialize_weights(self, m):\\n self.w_ = np.zeros(1 + m)\\n self.w_initialized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6e92d7ae27f946f92921dcb35a00ee2\",\n \"score\": \"0.7484646\",\n \"text\": \"def _init_weights(self, module):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a65927f4476ae242d59badc2b972a7c\",\n \"score\": \"0.74637544\",\n \"text\": \"def init_weights(self):\\n initrange = 0.1\\n if self.W is not None:\\n self.embed_1.weight = nn.Parameter(torch.from_numpy(self.W),\\n requires_grad=self.require_W_grad)\\n else:\\n self.embed_1.weight.data.uniform_(-initrange, initrange)\\n # self.logit.bias.data.fill_(0)\\n # self.logit.weight.data.uniform_(-initrange, initrange)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b473b250fc6a969ddd65e91965dbae0d\",\n \"score\": \"0.7460329\",\n \"text\": \"def _weight_initializer(self):\\n for m in self.modules():\\n if isinstance(m, nn.Conv2d):\\n nn.init.kaiming_normal_(m.weight)\\n elif isinstance(m, nn.BatchNorm2d):\\n nn.init.constant_(m.weight, 1.0)\\n nn.init.constant_(m.bias, 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf691c59e29e6b1c9b4a283f211dcde6\",\n \"score\": \"0.74594855\",\n \"text\": \"def _init_weight_(self):\\n torch.nn.init.normal_(self.embed_user_GMF.weight, std=0.01)\\n torch.nn.init.normal_(self.embed_user_MLP.weight, std=0.01)\\n torch.nn.init.normal_(self.embed_item_GMF.weight, std=0.01)\\n torch.nn.init.normal_(self.embed_item_MLP.weight, std=0.01)\\n\\n for m in self.MLP_layers:\\n if isinstance(m, torch.nn.Linear):\\n torch.nn.init.xavier_uniform_(m.weight)\\n torch.nn.init.kaiming_uniform_(self.predict_layer.weight, a=1, nonlinearity='sigmoid')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c22c1b28f2727b49129a693641c64a9\",\n \"score\": \"0.74581134\",\n \"text\": \"def computeWeights(self):\\n# if type(self.inputLayer) == np.ndarray: # input layer is array of inputs\\n# innodes = len(self.inputLayer)\\n# else: # input layer is class Layer (a hidden layer)\\n# innodes = len(self.inputLayer.activation) # number of nodes in input\\n# weights = np.zeros((innodes,self.nodes)) # weight matrix\\n# for i in range(innodes): # rows are input nodes\\n# for j in range(self.nodes): # columns are current layer nodes\\n# \\n# # for testing, stop use random seed\\n# np.random.seed(3)\\n# weights[i][j] = np.random.normal(0,0.01**2) \\n # For weights associated with the previous layer\\n if self.nextLayerNodes == None:\\n weights = None # output layer has no weights\\n else: # current layer is not the output (i.e it is the input or hidden layer)\\n nextnodes = self.nextLayerNodes\\n weights = np.zeros((self.nodes,nextnodes)) # weight matrix\\n for i in range(self.nodes): # rows are input nodes\\n for j in range(nextnodes): # columns are next layer nodes\\n # for testing, stop use random seed\\n #np.random.seed(3)\\n weights[i][j] = np.random.normal(0,0.01**2) \\n self.weights = weights\\n return weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb6ababf871b9293874eae205c783e6c\",\n \"score\": \"0.7453489\",\n \"text\": \"def init_weights(self):\\n for m in self.cls_convs:\\n normal_init(m.conv, std=0.01)\\n for m in self.reg_convs:\\n normal_init(m.conv, std=0.01)\\n bias_cls = bias_init_with_prob(0.01)\\n normal_init(self.retina_cls, std=0.01, bias=bias_cls)\\n normal_init(self.retina_reg, std=0.01)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d0d84bdca551e9a56b2650e13ebce2d\",\n \"score\": \"0.7451753\",\n \"text\": \"def initialize_weights(self):\\n all_weights = dict()\\n all_weights['weights'] = {}\\n all_weights['biases'] = {}\\n\\n n_layer = len(self.network_architecture['hidden'])\\n units = [self.ae_hyper['z']] + self.network_architecture['hidden']\\n print(units)\\n\\n for i in range(len(units)-1):\\n print(units[i])\\n all_weights['weights']['w' + str(i+1)] = tf.Variable(tf.random_normal([units[i], units[i+1]], stddev=2.0 / np.sqrt(units[i] + units[i+1])))\\n all_weights['biases']['b' + str(i+1)] = tf.Variable(tf.zeros([units[i+1]]))\\n\\n all_weights['weights']['out'] = tf.Variable(tf.random_normal([units[-1], self.network_architecture['n_output']], stddev=2.0 / np.sqrt(units[-1] + self.network_architecture['n_output'])))\\n all_weights['biases']['out'] = tf.Variable(tf.zeros([self.network_architecture['n_output']]))\\n\\n return all_weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9912df51ae0377a2326ca37f46ea20a\",\n \"score\": \"0.74350744\",\n \"text\": \"def default_weights_initializer(self):\\r\\n np.random.seed(1)\\r\\n # the first layer (i.e. input layer) does not have biases\\r\\n biases = [np.random.randn(num, 1) for num in self.sizes[1:]]\\r\\n # for each matrix in weights: row - layer to, column - layer from\\r\\n weights = [np.random.randn(x, y) / np.sqrt(y)\\r\\n for x, y in zip(self.sizes[1:], self.sizes[:-1])]\\r\\n return weights, biases\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd2dbab27c3d199d473d343eeda82c20\",\n \"score\": \"0.7412681\",\n \"text\": \"def __init__(self, num_units, num_weights):\\n\\t\\tself._num_weights = num_weights\\n\\t\\tself._num_units = num_units\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b977b8f1e79b466ff015efbed72959c0\",\n \"score\": \"0.7409157\",\n \"text\": \"def _weight_initializer(self):\\n for m in self.modules():\\n if isinstance(m, nn.ConvTranspose2d):\\n nn.init.kaiming_normal_(m.weight)\\n elif isinstance(m, nn.BatchNorm2d):\\n nn.init.constant_(m.weight, 1.0)\\n nn.init.constant_(m.bias, 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b977b8f1e79b466ff015efbed72959c0\",\n \"score\": \"0.7409157\",\n \"text\": \"def _weight_initializer(self):\\n for m in self.modules():\\n if isinstance(m, nn.ConvTranspose2d):\\n nn.init.kaiming_normal_(m.weight)\\n elif isinstance(m, nn.BatchNorm2d):\\n nn.init.constant_(m.weight, 1.0)\\n nn.init.constant_(m.bias, 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b977b8f1e79b466ff015efbed72959c0\",\n \"score\": \"0.7409157\",\n \"text\": \"def _weight_initializer(self):\\n for m in self.modules():\\n if isinstance(m, nn.ConvTranspose2d):\\n nn.init.kaiming_normal_(m.weight)\\n elif isinstance(m, nn.BatchNorm2d):\\n nn.init.constant_(m.weight, 1.0)\\n nn.init.constant_(m.bias, 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88ddce3df71c149e745e0eb491fb842f\",\n \"score\": \"0.7404257\",\n \"text\": \"def init_weight(self):\\n nn.init.xavier_uniform_(self.rnn.weight_hh_l0, gain=1)\\n nn.init.xavier_uniform_(self.rnn.weight_ih_l0, gain=1)\\n if self.bidirect:\\n nn.init.xavier_uniform_(self.rnn.weight_hh_l0_reverse, gain=1)\\n nn.init.xavier_uniform_(self.rnn.weight_ih_l0_reverse, gain=1)\\n nn.init.constant_(self.rnn.bias_hh_l0, 0)\\n nn.init.constant_(self.rnn.bias_ih_l0, 0)\\n if self.bidirect:\\n nn.init.constant_(self.rnn.bias_hh_l0_reverse, 0)\\n nn.init.constant_(self.rnn.bias_ih_l0_reverse, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88ddce3df71c149e745e0eb491fb842f\",\n \"score\": \"0.7404257\",\n \"text\": \"def init_weight(self):\\n nn.init.xavier_uniform_(self.rnn.weight_hh_l0, gain=1)\\n nn.init.xavier_uniform_(self.rnn.weight_ih_l0, gain=1)\\n if self.bidirect:\\n nn.init.xavier_uniform_(self.rnn.weight_hh_l0_reverse, gain=1)\\n nn.init.xavier_uniform_(self.rnn.weight_ih_l0_reverse, gain=1)\\n nn.init.constant_(self.rnn.bias_hh_l0, 0)\\n nn.init.constant_(self.rnn.bias_ih_l0, 0)\\n if self.bidirect:\\n nn.init.constant_(self.rnn.bias_hh_l0_reverse, 0)\\n nn.init.constant_(self.rnn.bias_ih_l0_reverse, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dea40fb888a901cddc559e1a0f6adef\",\n \"score\": \"0.73905605\",\n \"text\": \"def init_weights():\\n global w1, w2, w3, w4, w5, w6, b1, b2, b3, b4, b5, b6\\n mu = 0\\n sigma = 0.1\\n w1 = tf.Variable(tf.truncated_normal([3, 3, 1, 6], mean = mu, stddev = sigma))\\n b1 = tf.Variable(tf.zeros([6]))\\n\\n w2 = tf.Variable(tf.truncated_normal([3, 3, 6, 16], mean = mu, stddev = sigma))\\n b2 = tf.Variable(tf.zeros([16]))\\n\\n w3 = tf.Variable(tf.truncated_normal([1, 1, 16, 32], mean = mu, stddev = sigma))\\n b3 = tf.Variable(tf.zeros([32]))\\n\\n w4 = tf.Variable(tf.truncated_normal([6*6*32, 120], mean = mu, stddev = sigma))\\n b4 = tf.Variable(tf.zeros([120]))\\n\\n w5 = tf.Variable(tf.truncated_normal([120, 84], mean = mu, stddev = sigma))\\n b5 = tf.Variable(tf.zeros([84]))\\n\\n w6 = tf.Variable(tf.truncated_normal([84, 43], mean = mu, stddev = sigma))\\n b6 = tf.Variable(tf.zeros([43]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e121987d082f29f76198636121f4323\",\n \"score\": \"0.7388561\",\n \"text\": \"def default_weight_initializer(self):\\n self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]\\n self.weights = [np.random.randn(y, x)/np.sqrt(x)\\n for x, y in zip(self.sizes[:-1], self.sizes[1:])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e121987d082f29f76198636121f4323\",\n \"score\": \"0.7388561\",\n \"text\": \"def default_weight_initializer(self):\\n self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]\\n self.weights = [np.random.randn(y, x)/np.sqrt(x)\\n for x, y in zip(self.sizes[:-1], self.sizes[1:])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e6d75b5699b382dd2f3d4297b1c6749\",\n \"score\": \"0.7385814\",\n \"text\": \"def __init__(self, weights):\\n\\n self.weights = weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55f873b253882bfab21bcc999dff207\",\n \"score\": \"0.7380988\",\n \"text\": \"def init_weights(net):\\n for m in net.modules():\\n if isinstance(m, nn.Conv2d):\\n m.weight.data.normal_(0, 0.02)\\n m.bias.data.zero_()\\n elif isinstance(m, nn.ConvTranspose2d):\\n m.weight.data.normal_(0, 0.02)\\n m.bias.data.zero_()\\n elif isinstance(m, nn.Linear):\\n m.weight.data.normal_(0, 0.02)\\n m.bias.data.zero_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7017d7fc5e68845a0fc72cc2b609c36c\",\n \"score\": \"0.73788613\",\n \"text\": \"def init_weights(self):\\n return full(self.dim, self.w_n), full(self.dim, self.w_f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daacc62cdbe214b427c88fd833b22df4\",\n \"score\": \"0.7375114\",\n \"text\": \"def init_weights(self):\\n if not self.clip_embed:\\n self.embedding.weight.data.uniform_(-0.1, 0.1)\\n self.fc.bias.data.fill_(0)\\n self.fc.weight.data.uniform_(-0.1, 0.1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce46384090b721704747e310f4a354c1\",\n \"score\": \"0.7371002\",\n \"text\": \"def weights_init(m):\\n classname = m.__class__.__name__\\n if classname.find('Conv') != -1:\\n m.weight.data.normal_(0.0, 0.02)\\n elif classname.find('BatchNorm') != -1:\\n m.weight.data.normal_(1.0, 0.02)\\n m.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f03acbe41ce7dda704b1bf5929f1e56e\",\n \"score\": \"0.7365421\",\n \"text\": \"def init_weights(self):\\n for p in self.parameters():\\n if p.dim() > 1:\\n nn.init.xavier_uniform_(p)\\n for m in self.modules():\\n if isinstance(m, MultiScaleDeformableAttention):\\n m.init_weight()\\n if not self.as_two_stage:\\n xavier_init(self.reference_points, distribution='uniform', bias=0.)\\n normal_(self.level_embeds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52132063f480a15bcdbb0c67b321e4af\",\n \"score\": \"0.7350548\",\n \"text\": \"def init_weight(self):\\n for m in network_utils.iterate_sublayers(self):\\n if isinstance(m, nn.Conv2d):\\n torch.nn.init.xavier_uniform(m.weight)\\n torch.nn.init.xavier_uniform(m.bias)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b82d45f15b131c7880c2ecc6482ef05d\",\n \"score\": \"0.7348955\",\n \"text\": \"def weights_init(m):\\n m.weight.data.normal_(0.0, 0.01)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e335df6e1ea60d28d792075c7f3c731a\",\n \"score\": \"0.7345488\",\n \"text\": \"def init_weights(self):\\n for m in self.modules():# .modules()返回模型里的组成元素 即所有层\\n if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d): #如果是卷积层和反卷积层\\n nn.init.xavier_normal(m.weight.data) # xavier_normal 初始化\\n # torch.nn.init.xavier_normal_(tensor, gain=1)\\n # tensor([[-0.1777, 0.6740, 0.1139],\\n # [ 0.3018, -0.2443, 0.6824]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ff80bf26ada42bed094504fe7b32f3d\",\n \"score\": \"0.73269033\",\n \"text\": \"def init_weights(self):\\n for m in self.modules():\\n if isinstance(m, nn.Conv1d):\\n nn.init.xavier_normal(m.weight.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"084ab973ce1aae0642df8f0e10ffb58b\",\n \"score\": \"0.7326888\",\n \"text\": \"def init_weights(self):\\n self.init_cb_weights()\\n for m in self.cb_modules:\\n m.init_weights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"151af46cb185e428339941c0add8c464\",\n \"score\": \"0.7321385\",\n \"text\": \"def _create_weights(self):\\n\\n # c is a normalizing constant for creating weight matrices\\n c = 1 / np.sqrt(self._num_hidden_layers)\\n\\n self._W, self._b = {}, {}\\n\\n if self._weight_type == 'rand':\\n\\n if self._fix_weights:\\n np.random.seed(5)\\n\\n for i in range(self._num_hidden_layers + 1):\\n self._W[i] = c * np.random.randn(self._dim_list[i+1], self._dim_list[i])\\n self._b[i] = c * np.random.randn(self._dim_list[i+1], 1)\\n\\n elif self._weight_type == 'ones':\\n\\n for i in range(self._num_hidden_layers + 1):\\n self._W[i] = c * np.ones(shape=(self._dim_list[i+1], self._dim_list[i]))\\n self._b[i] = c * np.ones(shape=(self._dim_list[i+1], 1))\\n\\n else:\\n raise ValueError('Please use weight_type = \\\"rand\\\" | \\\"ones\\\"')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb3c13db099f9fb08cb6df4f9327ffef\",\n \"score\": \"0.7304773\",\n \"text\": \"def __init__(self, n_inputs, n_neurons):\\n np.random.seed(0)\\n self.weights = 0.01 * np.random.randn(n_inputs, n_neurons)\\n self.biases = np.zeros((1,n_neurons))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c4fcbc24fae2791d69746051818d122\",\n \"score\": \"0.7298154\",\n \"text\": \"def setWeights(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"502f27d0d7937511d5d2c5b9fa606350\",\n \"score\": \"0.7295881\",\n \"text\": \"def init_weights(self):\\n # constant_init(self.cb_linears, 0)\\n if self.cb_zero_init:\\n for ls in self.cb_linears:\\n for m in ls:\\n constant_init(m, 0)\\n\\n for m in self.cb_modules:\\n m.init_weights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d698a4a9e7c547e4a66d3f8aa83f650\",\n \"score\": \"0.7274645\",\n \"text\": \"def weights_init(m):\\n classname = m.__class__.__name__\\n if classname.find('Conv') != -1:\\n m.weight.data.normal_(0.0, 0.02)\\n elif classname.find('BatchNorm') != -1:\\n m.weight.data.normal_(1.0, 0.02)\\n m.bias.data.fill_(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d9788a39bafa029dc464a3ee8e07f31\",\n \"score\": \"0.7268157\",\n \"text\": \"def InitializeWeights(nodes):\\n layers, weights = len(nodes), []\\n for i in range(1, layers):\\n w = [[np.random.uniform(-1, 1) for k in range(nodes[i-1] + 1)]\\n for j in range(nodes[i])]\\n weights.append(np.matrix(w))\\n return weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069fdf95a9450f488353c6214741965f\",\n \"score\": \"0.7266745\",\n \"text\": \"def weights_init(m):\\n if isinstance(m, nn.Conv3d):\\n xavier(m.weight.data)\\n m.bias.data.zero_()\\n elif isinstance(m, nn.Linear):\\n size = m.weight.size()\\n fan_out = size[0] # number of rows\\n fan_in = size[1] # number of columns\\n variance = np.sqrt(2.0 / (fan_in + fan_out))\\n m.weight.data.normal_(0.0, variance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ac9cb9875ed5c46bb90f5b70f84581\",\n \"score\": \"0.72656286\",\n \"text\": \"def weights_init(m):\\n if type(m) == nn.Linear:\\n m.weight.data.normal_(0.0, 0.01)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6cb33a5452c54a42f8413b7807dedb9\",\n \"score\": \"0.72555137\",\n \"text\": \"def init_paramters(self):\\n\\t\\tself.weights = 2*np.random.normal(size=(self.hidden_neurons.shape[0],self.visible_neurons.shape[0]))\\n\\t\\tself.tresholds_hidden = np.zeros((self.hidden_neurons.shape[0], 1))\\n\\t\\tself.tresholds_visible = np.zeros((self.visible_neurons.shape[0], 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d28b30aaff489a4c3e9351d7a1c26715\",\n \"score\": \"0.72539806\",\n \"text\": \"def init_weights(m):\\n if isinstance(m, nn.Conv2d):\\n # Note that there is no bias due to BN\\n fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\\n m.weight.data.normal_(mean=0.0, std=math.sqrt(2.0 / fan_out))\\n elif isinstance(m, nn.BatchNorm2d):\\n zero_init_gamma = (\\n hasattr(m, \\\"final_bn\\\") and m.final_bn and False\\n )\\n m.weight.data.fill_(0.0 if zero_init_gamma else 1.0)\\n m.bias.data.zero_()\\n elif isinstance(m, nn.Linear):\\n m.weight.data.normal_(mean=0.0, std=0.01)\\n m.bias.data.zero_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba0444bae4b5ac11b1018e01f7da2561\",\n \"score\": \"0.7245869\",\n \"text\": \"def init(self, neurons_before, optimizer):\\n self.biases = np.random.randn(1, self.neurons).astype(dtype=\\\"float32\\\")\\n self.weights = np.random.randn(int(neurons_before), self.neurons).astype(dtype=\\\"float32\\\") / np.sqrt(int(neurons_before)).astype(dtype=\\\"float32\\\")\\n self.optimizer = optimizer\\n return self.neurons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb9f1fe5527b6fdb3de4548b5828a11\",\n \"score\": \"0.7245617\",\n \"text\": \"def init_weights(self, n):\\r\\n self.neuro_weights = []\\r\\n for i in range(n):\\r\\n self.neuro_weights.append(random.random() * 2 - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c7b5dae3e2fc0855c4fe357745f46f3\",\n \"score\": \"0.7239348\",\n \"text\": \"def weights_init(m):\\n classname = m.__class__.__name__\\n if classname.find('Conv') != -1:\\n nn.init.normal_(m.weight.data, 0.0, 0.02)\\n elif classname.find('BatchNorm') != -1:\\n nn.init.normal_(m.weight.data, 1.0, 0.02)\\n nn.init.constant_(m.bias.data, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06d80adb4fae464cea971d81ef416a5d\",\n \"score\": \"0.7236633\",\n \"text\": \"def init_weights(self, n_features):\\n self.rgen = np.random.RandomState(self.random_state)\\n self.w_ = self.rgen.normal(loc=0.0, scale=0.01, size=1 + n_features)\\n self.w_initialized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77476fc38094bbc38652f6e5c1d02f1d\",\n \"score\": \"0.7235151\",\n \"text\": \"def set_weights(self, data):\\r\\n previous_neurons = 0\\r\\n index_layer = 0\\r\\n index_weights = 0\\r\\n self.layers = []\\r\\n for i in data['network']:\\r\\n layer = Layer()\\r\\n layer.init_neurons(i, previous_neurons)\\r\\n for j in range(len(layer.neurons)):\\r\\n for k in range(len(layer.neurons[j].neuro_weights)):\\r\\n layer.neurons[j].neuro_weights[k] = data['weights'][index_weights]\\r\\n index_weights += 1\\r\\n previous_neurons = i\\r\\n index_layer += 1\\r\\n self.layers.append(layer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3819ef989e201011823f3c14615b3b\",\n \"score\": \"0.72335804\",\n \"text\": \"def initialize_NN(layers):\\n weights = []\\n biases = []\\n num_layers = len(layers) \\n for l in range(0,num_layers-1):\\n W = xavier_initialisation(size=[layers[l], layers[l+1]])\\n b = tf.Variable(tf.zeros([1,layers[l+1]]), dtype=tf.float32)\\n weights.append(W)\\n biases.append(b) \\n return weights, biases\",\n \"title\": \"\"\n },\n {\n \"docid\": \"922c9ede71c2110763fb5dfd54949e63\",\n \"score\": \"0.72293776\",\n \"text\": \"def weights_init(m):\\n if isinstance(m, (torch.nn.modules.conv._ConvNd, torch.nn.Linear)):\\n torch.nn.init.xavier_uniform_(m.weight)\\n if m.bias is not None:\\n torch.nn.init.constant_(m.bias, 0.0)\\n elif isinstance(m, torch.nn.modules.batchnorm._BatchNorm):\\n torch.nn.init.constant_(m.bias, 0.0)\\n torch.nn.init.constant_(m.weight, 1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e10732228d50fa309852b864ca2e3c9\",\n \"score\": \"0.7226845\",\n \"text\": \"def construct_weights(self):\\n assert len(self.w_inits) == 1\\n w_tiled = tile_sparse_weights(self.w_inits[0], max_cols=self.latent_dim, tile_cols=self.n_channels)\\n self.w_inits = [0.01 * w_tiled, 100. * w_tiled.T]\\n self.b_inits = [None, None]\\n\\n return super().construct_weights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6448d56ce36e2177a82fcdc770a2cb9b\",\n \"score\": \"0.72242814\",\n \"text\": \"def weights_init(m):\\n class_name = m.__class__.__name__\\n if class_name.find('Conv') != -1:\\n nn.init.xavier_uniform_(m.weight.data)\\n \\n elif class_name.find('Linear') != -1:\\n nn.init.xavier_uniform_(m.weight.data)\\n \\n elif class_name.find('BatchNorm') != -1:\\n nn.init.constant_(m.weight.data,1)\\n nn.init.constant_(m.bias.data,0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcfa12a5d84c577ffbbe1572b8281af0\",\n \"score\": \"0.7220487\",\n \"text\": \"def init_weights(self):\\n bias_init = bias_init_with_prob(0.1)\\n self.heatmap_head[-1].bias.data.fill_(bias_init)\\n for head in [self.wh_head, self.offset_head]:\\n for m in head.modules():\\n if isinstance(m, nn.Conv2d):\\n normal_init(m, std=0.001)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993499,"cells":{"query_id":{"kind":"string","value":"0cc1dc4e58cdb02cae12456159fa0e11"},"query":{"kind":"string","value":"Returns the width of the given text."},"positive_passages":{"kind":"list like","value":[{"docid":"53c08eaa07f61457d07ef1b8774b6149","score":"0.75393206","text":"def textwidth(txt, **kwargs):\n if not isinstance(txt, Text) or len(kwargs) > 0:\n kwargs[\"draw\"] = False\n txt = text(txt, 0, 0, **kwargs)\n return txt.metrics[0]","title":""}],"string":"[\n {\n \"docid\": \"53c08eaa07f61457d07ef1b8774b6149\",\n \"score\": \"0.75393206\",\n \"text\": \"def textwidth(txt, **kwargs):\\n if not isinstance(txt, Text) or len(kwargs) > 0:\\n kwargs[\\\"draw\\\"] = False\\n txt = text(txt, 0, 0, **kwargs)\\n return txt.metrics[0]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1a5cd4da9031018ff6c5e964e1cc0038","score":"0.8688428","text":"def TextCalcWidth(Text) -> int:\n pass","title":""},{"docid":"35e93582b8c7ca034278968e8c51c1e6","score":"0.8660079","text":"def _get_text_width(self, text: str) -> int:\n return self._buffer_draw.textsize(text, font=self.FONT)[0]","title":""},{"docid":"783dc1f69ad27eeed22a6b8e5aef133a","score":"0.8637835","text":"def text_width(txt):\n return calc_width(txt, 0, len(txt))","title":""},{"docid":"6694fb5de9678850ddd25f23bd66b62a","score":"0.79394925","text":"def length(self, txt):\n return self.fontMetrics().width(self.__surrounding.format(txt))","title":""},{"docid":"102d525bcba1c7ec15a213348a3ad838","score":"0.7590983","text":"def text_length(text: str) -> int:\n return len(text) - text.count(\" \")","title":""},{"docid":"4dcbed9f838af766119695037e5acca6","score":"0.73216677","text":"def box_width(self, text):\n if self.markup:\n text = re.sub(\"<[^<]+?>\", \"\", text)\n width, _ = self.drawer.max_layout_size([text], self.font, self.fontsize)\n width = width + 2 * (self.padding_x + self.borderwidth)\n return width","title":""},{"docid":"51c01f0c7c2dd038d88a3a12f41038d9","score":"0.7286721","text":"def text_length(self, text, fontsize=11, wmode=0):\n return fontsize * sum([self.glyph_advance(ord(c), wmode=wmode) for c in text])","title":""},{"docid":"64e3917a6e8261e97ef1c867b7a582af","score":"0.7141145","text":"def GetWidth(*args):\n return _richtext.TextAttrBorder_GetWidth(*args)","title":""},{"docid":"594ae9ac2837bb83e69698e68b064368","score":"0.7036297","text":"def size(self, text):\n return self.font.size(text)","title":""},{"docid":"b772ebfd5427c2620755f054cac5ec2c","score":"0.69869506","text":"def get_text_len_in_px(self):\n test_label = tk.Label(None, text=self.text, font=self.font)\n test_label.place(x=100000, y=100000) # this is not nice but it works\n self.CLabel.update()\n text_len = test_label.winfo_width()\n test_label.destroy()\n\n return text_len","title":""},{"docid":"bb4b8e8c880385398645bfd22188aa88","score":"0.696879","text":"def GetWidth(*args):\n return _richtext.TextBoxAttr_GetWidth(*args)","title":""},{"docid":"ceda05f1f9e9231f88c5cb087cd63fe0","score":"0.6844141","text":"def get_char_width(self):\n try:\n view = self.window.get_active_view()\n # Get font description (code from \"text size\" plugin)\n context = view.get_style_context()\n description = context.get_font(context.get_state()).copy()\n # Get font metrics\n pango_context = view.get_pango_context()\n lang = Pango.Language.get_default()\n metrics = pango_context.get_metrics(description, lang)\n # Calculate char width in pixels\n width = metrics.get_approximate_char_width() / Pango.SCALE\n return width\n except:\n return 8 # If it didn't work, just use some appropriate value","title":""},{"docid":"cbf9005f7bfc89d68ce6f656a8db8a93","score":"0.678849","text":"def get_width_in_text_space(self) -> Decimal:\n w: Decimal = Decimal(0)\n for g in self._glyphs:\n glyph_width_in_text_space = g.get_width() * self._font_size * Decimal(0.001)\n\n # add word spacing where applicable\n if len(g.get_unicode_str()) == 1 and isspace(g.get_unicode_str()):\n glyph_width_in_text_space += self._word_spacing\n\n # horizontal scaling\n glyph_width_in_text_space *= self._horizontal_scaling / Decimal(100)\n\n # add character spacing to character_width\n glyph_width_in_text_space += self._character_spacing\n\n # add character width to total\n w += glyph_width_in_text_space\n\n # subtract character spacing once (there are only N-1 spacings in a string of N characters)\n w -= self._character_spacing\n\n # return\n return w","title":""},{"docid":"ca7df375ad6dae42ae5e6777652cb621","score":"0.6784089","text":"def measure_text(self, text, spacing=1):\n length = 0\n for letter in text:\n # Get index of letter\n letter_ord = ord(letter) - self.start_letter\n offset = letter_ord * self.bytes_per_letter\n # Add length of letter and spacing\n length += self.letters[offset] + spacing\n return length","title":""},{"docid":"325947274f798a1d6ba862e3c56742c3","score":"0.6744756","text":"def _calcWidthByCharacter(self, character_count, font=None):\n return int(character_count * 6)","title":""},{"docid":"0d910e0bdf9655600524a32b83935c54","score":"0.67381513","text":"def textsize(self, text, font=None, *args, **kwargs):\n if self._multiline_check(text):\n return self.multiline_textsize(text, font, *args, **kwargs)\n if font is None:\n font = self.getfont()\n return font.getsize(text)","title":""},{"docid":"ecf2112c4b3a938154a0159154a10145","score":"0.67141116","text":"def get_lexical_size(self, text: str) -> int:\n tokenizer = RegexpTokenizer(r'\\w+')\n words = tokenizer.tokenize(text)\n return len(set(words))","title":""},{"docid":"ea52455e964ddc54e311242c61940fc1","score":"0.6633261","text":"def write_width(self, font, string):\n width = 0\n for character in string:\n try:\n char_index = font.MAP.index(character)\n width += font.WIDTHS[char_index]\n\n except ValueError:\n pass\n\n return width","title":""},{"docid":"d153e69decae7f87f2b2051785ca412c","score":"0.6628564","text":"def getTextLength(self) -> int:\n ...","title":""},{"docid":"690c192a4ef49e8a6c92041f65f2239a","score":"0.66014194","text":"def calculate_string_width(string, font=None, letter_spacing=1, monospaced=False):\n width = 0\n for char in string:\n width += calculate_char_width(\n char,\n font=font,\n monospaced=monospaced)\n\n width += 1 + letter_spacing\n\n return width","title":""},{"docid":"3b65a3211eacc72f1b5590b071242f8c","score":"0.6588091","text":"def word_count(text):\n return len(text.split())","title":""},{"docid":"f4e7a574247e7c3c03bd48925581d54e","score":"0.6550759","text":"def get_length(self) -> int:\n return sum([text.get_length() for text in self._texts])","title":""},{"docid":"40ddc530376226a18574e48f1bdb7d38","score":"0.6539332","text":"def char_count(text):\n return len(text)","title":""},{"docid":"6969add6d91d418f9f886d6303c7d792","score":"0.652261","text":"def getFontMeasure( self, text ):\r\n #x,y,w,h = self.canvas.bbox( self.handler )\r\n #print 'BBOX', x,y, w-x, h-y\r\n #print 'COORDS', self.canvas.coords( self.handler )\r\n #print 'Metric', self.fontObject.metrics(\"linespace\")\r\n #print 'Actual', self.fontObject.actual() \r\n #self.canvas.create_rectangle( x,y,x+(w-x), y-(h-y) )\r\n \r\n if( self.fontObject ):\r\n # The following lines let you debug the fudge factor\r\n #x,y = self.canvas.bbox( self.handler )[:2]\r\n #w = self.fontObject.measure( text ) * 1.15\r\n #self.canvas.create_rectangle( x,y,x+w,y-28 ) \r\n return self.fontObject.measure( text )","title":""},{"docid":"cf22117fb12fcbc5a17f869ba06a31f6","score":"0.6500957","text":"def GetCharWidth(*args, **kwargs):\n return _core_.Window_GetCharWidth(*args, **kwargs)","title":""},{"docid":"810a6076d10a2cc7685a42ffa766d213","score":"0.6496057","text":"def size(self, text):\n #raise ValueError, \"Size is not supported in HDText (yet)\"\n return sdl2_DisplayManager.inst().font_get_size( text, self.name, self.font_size)","title":""},{"docid":"d605cab4ff13e7ae4a73cf9c507b5b35","score":"0.6486234","text":"def getDimensions(self):\n return _graphicsManager.executeFunction(('get text size', self._text, self._size))","title":""},{"docid":"0cb19c2e51f587187e9c695cd4d13dcf","score":"0.64432824","text":"def _count_words(text):\r\n return len(text.split()) if text else 0","title":""},{"docid":"2ad939404354cdd01232afa31cca456b","score":"0.64261883","text":"def text_length(text, tokenizer):\r\n text = open(text, 'r')\r\n if tokenizer == 'nltk':\r\n return len(word_tokenize(text.read()))\r\n elif tokenizer == 'simple':\r\n tokens = 0\r\n for line in text:\r\n for word in line.split():\r\n tokens += 1\r\n return tokens","title":""},{"docid":"e7e821f2303298e5fc0e91eb23829e62","score":"0.6413309","text":"def getTextlength(text, fontname=\"helv\", fontsize=11, encoding=0):\n fontname = fontname.lower()\n basename = Base14_fontdict.get(fontname, None)\n\n glyphs = None\n if basename == \"Symbol\":\n glyphs = symbol_glyphs\n if basename == \"ZapfDingbats\":\n glyphs = zapf_glyphs\n if glyphs is not None:\n w = sum([glyphs[ord(c)][1] if ord(c) < 256 else glyphs[183][1] for c in text])\n return w * fontsize\n\n if fontname in Base14_fontdict.keys():\n return TOOLS._measure_string(\n text, Base14_fontdict[fontname], fontsize, encoding\n )\n\n if fontname in (\n \"china-t\",\n \"china-s\",\n \"china-ts\",\n \"china-ss\",\n \"japan\",\n \"japan-s\",\n \"korea\",\n \"korea-s\",\n ):\n return len(text) * fontsize\n\n raise ValueError(\"Font '%s' is unsupported\" % fontname)","title":""},{"docid":"2f4b2ebfef786dc0b45b8462237a2ffc","score":"0.64131904","text":"def get_words_count(self, text):\n\t\traise NotImplementedError","title":""},{"docid":"dd15bf94c5bea7267c7e7dcd4409240f","score":"0.64108706","text":"def message_length(self, text):\n tokenized = tokenize(text)\n if tokenized:\n return len(tokenized)\n else:\n return 0","title":""},{"docid":"68c714b5d7b5392c9fea82ac446896bd","score":"0.63865507","text":"def count_chars_in_width(line_str: str, max_width: int) -> int:\n total_width = 0\n for i, char in enumerate(line_str):\n width = char_width(char)\n if width + total_width > max_width:\n return i\n total_width += width\n return len(line_str)","title":""},{"docid":"2f36754a1a5dc125edad5b906d92a561","score":"0.63759416","text":"def _get_text_height(self, text: str) -> int:\n return self._buffer_draw.textsize(text, font=self.FONT)[1]","title":""},{"docid":"02316ba97375b8584d55ae658b309b6d","score":"0.6358214","text":"def get_width_from_char_name(self, name):\n return self._metrics_by_name[name].width","title":""},{"docid":"6698133c8279acd06ac831b4291ba128","score":"0.63526183","text":"def size_of_word(word):\n return len(word)","title":""},{"docid":"9617b1ff4922532a986baf9fe36d427a","score":"0.62951577","text":"def text_length (self,data):\r\n\r\n total_alphabet = 0 #Variable to count total alphabet in the string\r\n space = 0 #Variable to count number of spaces in the string\r\n\r\n #Determine total number of strings with and without spaces.\r\n for alphabet in (str(self.data[\"text\"])):\r\n if alphabet != \" \":\r\n total_alphabet +=1\r\n else:\r\n total_alphabet +=1\r\n space +=1\r\n\r\n withSpaces = total_alphabet #Total number of alphabet with space\r\n withoutSpaces = total_alphabet-space #Total number of alphabet without space\r\n\r\n return withSpaces,withoutSpaces #Return the values\r","title":""},{"docid":"57d759e0d7b9687cb3ec471079d6e1ed","score":"0.62803316","text":"def get_width(self) -> int:\n if not self.label is None:\n return self.label.get_width()\n \n return 0","title":""},{"docid":"5378c52380a9470f0c2f27c75cd70478","score":"0.6277994","text":"def GetLabelWidth(label):\n assert type(label) is str, \"Incorrect type\"\n\n length = 0\n for c in label:\n length += glutBitmapWidth(GLUT_BITMAP_HELVETICA_18, ord(c))\n\n assert type(length) is int\n assert length >= 0\n\n return length","title":""},{"docid":"eec1f444b24b1b05f8f68d9b316ff9bc","score":"0.62596536","text":"def SimpleTextMetrics(text):\n\n #count the number of paragraphs/lines\n line_list = text.split(\"\\n\")\n num_lines = len(line_list)\n\n #count total number of words per paragraph/line\n num_words = 0\n num_word_per_p = []\n for l in line_list:\n l_word_list = l.split()\n l_num_words = len(l_word_list)\n \n num_words += l_num_words\n\n #average number of words per paragraph/line\n avg_line_length = num_words/num_lines\n\n return (num_words, num_lines, avg_line_length)","title":""},{"docid":"359c3e5168789b5e1abffcad41f319ca","score":"0.62593967","text":"def str_width(line_str: str) -> int:\n if line_str.isascii():\n # Fast path for a line consisting of only ASCII characters\n return len(line_str)\n return sum(map(char_width, line_str))","title":""},{"docid":"8ee6a030a8def9781caec6bf96c94e95","score":"0.6235574","text":"def get_word_lengths(self, text):\n return list(map(lambda x: len(x), text.split(' ')))","title":""},{"docid":"4d6e7a3988ec3655db441bb1bfdf6c2a","score":"0.6232222","text":"def headWidth(self,p=None,s=''):\n\n if p: s = p.headString()\n\n return self.font.measure(s)/self.font.measure('0')+1","title":""},{"docid":"180a8e97229347ba796d79cda084443a","score":"0.62223864","text":"def string_width_height(self, s):\n if not len(s):\n return 0, 0\n total_width = 0\n namelast = None\n miny = 1e9\n maxy = 0\n for c in s:\n if c == '\\n':\n continue\n wx, name, bbox = self._metrics[ord(c)]\n\n total_width += wx + self._kern.get((namelast, name), 0)\n l, b, w, h = bbox\n miny = min(miny, b)\n maxy = max(maxy, b + h)\n\n namelast = name\n\n return total_width, maxy - miny","title":""},{"docid":"f2dc5dc154c66dcce31d8dd1b67bdd85","score":"0.62138253","text":"def lecturename_len(self, text):\n length = 0\n for c in text:\n if unicodedata.east_asian_width(c) in 'FWA':\n length += 2\n else:\n length += 1\n return length","title":""},{"docid":"06ba2d241c601522edef598827d3ffad","score":"0.6211052","text":"def char_count(self, text, ignore_spaces=True):\n if ignore_spaces:\n text = text.replace(\" \", \"\")\n return len(text)","title":""},{"docid":"5bea06f12a9256cd25916ffa0092ad4f","score":"0.6208475","text":"def visual_width(line):\n\n return len(re.sub(colorama.ansitowin32.AnsiToWin32.ANSI_CSI_RE, '', line))","title":""},{"docid":"586711eb5f0357b5a0c714a0650cba15","score":"0.6208343","text":"def get_width(self, c) -> int:","title":""},{"docid":"012db36af843fa7f968fc5cb73fe9e51","score":"0.61926806","text":"def _visual_width(line):\n\n return len(re.sub(colorama.ansitowin32.AnsiToWin32.ANSI_CSI_RE, \"\", line))","title":""},{"docid":"04cf8adb99a0e629221ae9df566df0fe","score":"0.6191938","text":"def word_counter(text: str) -> int:\n\n word_pattern = re.compile(r'\\s')\n\n splitoid = word_pattern.split(text)\n\n words = len(splitoid)\n\n return words","title":""},{"docid":"0d0ea5c74fc33b8215afb08208de6ae6","score":"0.61783624","text":"def textheight(txt, width=None, **kwargs):\n if not isinstance(txt, Text) or len(kwargs) > 0 or width != txt.width:\n kwargs[\"draw\"] = False\n txt = text(txt, 0, 0, width=width, **kwargs)\n return txt.metrics[1]","title":""},{"docid":"82b0143a3a5ef9838713c7d93e11eebf","score":"0.6154242","text":"def text_size(self, text, size, *, bold=False):\n if not text:\n return (0, 0)\n return self.font(size, bold).size(text)","title":""},{"docid":"031372291fd7c33f0e723044fad391c7","score":"0.6112074","text":"def approx_string_em_width(string):\n\n return 0.6 * len(string) + 0.4 * sum(string.count(x) for x in [\"w\", \"m\", \"~\"])","title":""},{"docid":"a63941184ce2c1971cb01e18b932e704","score":"0.6078273","text":"def get_text_width_height_descent(self, s, prop, ismath):\n font = self.get_font(prop)\n w, h = self._canvas.measure_text(s, font)\n return w, h, 1","title":""},{"docid":"71e2869378fb6bb36c720e3c8e5515fa","score":"0.60734004","text":"def swidth(s):\n\n return max( [wcswidth(row) for row in s.strip().split('\\n')] )","title":""},{"docid":"0a3cfd5ccbf1f43dc97b5d2efeb37b89","score":"0.60592115","text":"def get_avg_word_len(text):\n # remove links, hashtags, @ and punctuation marks\n text = re.sub(r'https?://\\S+', '', text)\n text = re.sub(r'#\\S+', '', text)\n text = re.sub(r'@\\S+', '', text)\n text = re.sub(r'[,.)(!?:\\[\\]\\\"\\']', '', text)\n\n # split to words\n words = re.findall(r'\\S+', text)\n if len(words) == 0:\n return 0\n words_lens = [len(word) for word in words]\n\n # calc len of longest word\n return sum(words_lens) / len(words)","title":""},{"docid":"386e29dc2ff49409bb0ef547d737de63","score":"0.6057461","text":"def get_corpus_spaces(text):\n space_count = 0\n for key, value in text.items():\n space_count += (value[0] + value[1]).count(\" \")\n return space_count","title":""},{"docid":"798e42737bbad02c76b06143e4a29ed0","score":"0.60538","text":"def rev_wcwidth(text, width):\n if width == 0:\n return -1\n\n w = 0\n i = -1\n # loop over to check for double width chars\n for i, c in enumerate(text):\n w += wcwidth(c)\n if w >= width:\n break\n if w >= width:\n return i\n else:\n return i + width - w","title":""},{"docid":"5cb5b0bf7af3970417ee6f437dbcbaf9","score":"0.6042492","text":"def measureText(self, text, units=False):\n\n\t\tprop = text._fontproperties\n\t\twidth, height, descent, svg_elements, used_chars = self.renderer.mathtext_parser.parse(text.get_text(), 72, prop)\n\n\t\tif units:\n\t\t\twidth *= self.UNITS_PER_PT_x\n\t\t\theight *= self.UNITS_PER_PT_y\n\t\t\tdescent *= self.UNITS_PER_PT_y\n\n\t\treturn (width, height, descent)","title":""},{"docid":"a6b879ec377f7cfba19c2b8c0ae6ead3","score":"0.6024573","text":"def _visible_width(s):\n if isinstance(s, _text_type) or isinstance(s, _binary_type):\n return len(_strip_invisible(s))\n else:\n return len(_text_type(s))","title":""},{"docid":"a6b879ec377f7cfba19c2b8c0ae6ead3","score":"0.6024573","text":"def _visible_width(s):\n if isinstance(s, _text_type) or isinstance(s, _binary_type):\n return len(_strip_invisible(s))\n else:\n return len(_text_type(s))","title":""},{"docid":"eb60cea96b2ac699596513e3b9344e4f","score":"0.59930545","text":"def line_count(text):\n return len(text.splitlines())","title":""},{"docid":"679e436bd477326deefa1bbc9e03b6ab","score":"0.5977051","text":"def get_words_per_sentence(self, text: str) -> float:\n return np.mean(list(map(len, self._text_2_list_of_list_of_strings(text))))","title":""},{"docid":"821c426da1f50c13243deaf7d6b6df47","score":"0.5975071","text":"def word_length_ft(string):\n return len(string)","title":""},{"docid":"7f2c7e5a9a9c6adf0d14f1c8be742055","score":"0.5961074","text":"def text_dpi(text: str, fontsize: int):\n fig, ax = plt.subplots()\n text_plot = ax.text(0, 0, text, fontsize=fontsize)\n renderer = fig.canvas.get_renderer()\n bb = text_plot.get_window_extent(renderer=renderer)\n plt.close(fig)\n return bb.width, bb.height","title":""},{"docid":"f716b858fdaf85984df357d6c68d5c9a","score":"0.5960385","text":"def getWidth(self):\n util.raiseNotDefined()","title":""},{"docid":"fce553afb30e5d909ba883cf8dd4e56b","score":"0.5937174","text":"def calcByteLen(text):\n return len(text.encode('utf-8'))","title":""},{"docid":"677cff37c4587085b73e9d0680fff75f","score":"0.59327734","text":"def word_count(paragraph):\n word_count = len(paragraph.split(\" \"))\n return word_count","title":""},{"docid":"af0ab685dd1dee2dc8d09241e5bfaacf","score":"0.59266496","text":"def nr_cols_of(text):\n if not text:\n return 0\n return max((len(line) for line in text.splitlines()), default=0)","title":""},{"docid":"5125e7b0f48c32ead3505d244d5bba7f","score":"0.5908896","text":"def nr_rows_of(text):\n if not text:\n return 1\n return text.count(\"\\n\") + 1","title":""},{"docid":"2d4e77fa3f41311a8de03c3d7e76cd1c","score":"0.59072506","text":"def textmetrics(txt, width=None, **kwargs):\n if not isinstance(txt, Text) or len(kwargs) > 0 or width != txt.width:\n kwargs[\"draw\"] = False\n txt = text(txt, 0, 0, width=width, **kwargs)\n return txt.metrics","title":""},{"docid":"cd09371a1914d73ad68a09afe00f6685","score":"0.5894771","text":"def text_columns(self, text):\n tab_count = text.count(\"\\t\")\n newline_count = text.count(\"\\n\")\n return len(text) - tab_count - newline_count + (self.tab_size * tab_count)","title":""},{"docid":"49019dee4c986dbe9c8da3285cefc2be","score":"0.58930284","text":"def width(self) -> int:\n return self._width","title":""},{"docid":"49019dee4c986dbe9c8da3285cefc2be","score":"0.58930284","text":"def width(self) -> int:\n return self._width","title":""},{"docid":"3bea4df0a3bfe9a2526ba65d63e27b77","score":"0.5877223","text":"def test_text_dimension():\n string = 'This is a text for test. This is a test for text.py'\n _, _, _, width_1, height_1, _ = make_text(string, 200, font_size=12)\n\n _, _, _, width_2, height_2, _ = make_text(string, 200, font_size=20)\n assert width_1 * height_1 < width_2 * height_2","title":""},{"docid":"32f7cef4b98c70cc05b8846cf89220a9","score":"0.58640873","text":"def charCount(paragraph):\n char_count = len(paragraph)\n return char_count","title":""},{"docid":"32f7cef4b98c70cc05b8846cf89220a9","score":"0.58640873","text":"def charCount(paragraph):\n char_count = len(paragraph)\n return char_count","title":""},{"docid":"bf7ae977f7cfa2a5230a674bdc1bd522","score":"0.58520615","text":"def width(self):\n return self['width']","title":""},{"docid":"46bf25ba2fe5ea869339c6b29967c331","score":"0.58442056","text":"def get_text_count(self):\n return len(self._texts)","title":""},{"docid":"6d4e2b5311015a430675904c0d20218e","score":"0.5831076","text":"def size(self):\n return self._attributedString.size()","title":""},{"docid":"6d4e2b5311015a430675904c0d20218e","score":"0.5831076","text":"def size(self):\n return self._attributedString.size()","title":""},{"docid":"d72dc88ca68f2bab043e46cd26a97316","score":"0.5829908","text":"def count_indents(text: str) -> int:\r\n return len(text) - len(text.lstrip(\" \"))","title":""},{"docid":"a4ed51932802543a9f228bffd0c33dc2","score":"0.5828893","text":"def width(self):\n return self._width","title":""},{"docid":"a4ed51932802543a9f228bffd0c33dc2","score":"0.5828893","text":"def width(self):\n return self._width","title":""},{"docid":"a4ed51932802543a9f228bffd0c33dc2","score":"0.5828893","text":"def width(self):\n return self._width","title":""},{"docid":"a4ed51932802543a9f228bffd0c33dc2","score":"0.5828893","text":"def width(self):\n return self._width","title":""},{"docid":"a4ed51932802543a9f228bffd0c33dc2","score":"0.5828893","text":"def width(self):\n return self._width","title":""},{"docid":"a4ed51932802543a9f228bffd0c33dc2","score":"0.5828893","text":"def width(self):\n return self._width","title":""},{"docid":"a4ed51932802543a9f228bffd0c33dc2","score":"0.5828893","text":"def width(self):\n return self._width","title":""},{"docid":"a4ed51932802543a9f228bffd0c33dc2","score":"0.5828893","text":"def width(self):\n return self._width","title":""},{"docid":"a4ed51932802543a9f228bffd0c33dc2","score":"0.5828893","text":"def width(self):\n return self._width","title":""},{"docid":"8756f32cf124d01666234d1d0377f4fe","score":"0.5822983","text":"def source_wordcount(self):\n\t\treturn wordcount(self.text)","title":""},{"docid":"1afa940d581faf9d1e94c03d3cd5739a","score":"0.5812748","text":"def get_width(self):\n return self._width","title":""},{"docid":"1afa940d581faf9d1e94c03d3cd5739a","score":"0.5812748","text":"def get_width(self):\n return self._width","title":""},{"docid":"1afa940d581faf9d1e94c03d3cd5739a","score":"0.5812748","text":"def get_width(self):\n return self._width","title":""},{"docid":"1afa940d581faf9d1e94c03d3cd5739a","score":"0.5812748","text":"def get_width(self):\n return self._width","title":""},{"docid":"1afa940d581faf9d1e94c03d3cd5739a","score":"0.5812748","text":"def get_width(self):\n return self._width","title":""},{"docid":"9cee1e022b0dbb92b0a239f4777ca63a","score":"0.5810409","text":"def get_width(self):\n bbox = self.get_bounding_box()\n return bbox[1]-bbox[0]","title":""},{"docid":"c48318d12166c504a63b787068dfa14a","score":"0.5808792","text":"def width(self):\n return self.__width__","title":""},{"docid":"f3dfbab38f2285cd92c3958ae70ddba9","score":"0.5806985","text":"def num_tweets_required(text: str) -> int:\n return math.ceil(len(text)/MAX_TWEET_LENGTH)","title":""}],"string":"[\n {\n \"docid\": \"1a5cd4da9031018ff6c5e964e1cc0038\",\n \"score\": \"0.8688428\",\n \"text\": \"def TextCalcWidth(Text) -> int:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e93582b8c7ca034278968e8c51c1e6\",\n \"score\": \"0.8660079\",\n \"text\": \"def _get_text_width(self, text: str) -> int:\\n return self._buffer_draw.textsize(text, font=self.FONT)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"783dc1f69ad27eeed22a6b8e5aef133a\",\n \"score\": \"0.8637835\",\n \"text\": \"def text_width(txt):\\n return calc_width(txt, 0, len(txt))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6694fb5de9678850ddd25f23bd66b62a\",\n \"score\": \"0.79394925\",\n \"text\": \"def length(self, txt):\\n return self.fontMetrics().width(self.__surrounding.format(txt))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"102d525bcba1c7ec15a213348a3ad838\",\n \"score\": \"0.7590983\",\n \"text\": \"def text_length(text: str) -> int:\\n return len(text) - text.count(\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dcbed9f838af766119695037e5acca6\",\n \"score\": \"0.73216677\",\n \"text\": \"def box_width(self, text):\\n if self.markup:\\n text = re.sub(\\\"<[^<]+?>\\\", \\\"\\\", text)\\n width, _ = self.drawer.max_layout_size([text], self.font, self.fontsize)\\n width = width + 2 * (self.padding_x + self.borderwidth)\\n return width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c01f0c7c2dd038d88a3a12f41038d9\",\n \"score\": \"0.7286721\",\n \"text\": \"def text_length(self, text, fontsize=11, wmode=0):\\n return fontsize * sum([self.glyph_advance(ord(c), wmode=wmode) for c in text])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64e3917a6e8261e97ef1c867b7a582af\",\n \"score\": \"0.7141145\",\n \"text\": \"def GetWidth(*args):\\n return _richtext.TextAttrBorder_GetWidth(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"594ae9ac2837bb83e69698e68b064368\",\n \"score\": \"0.7036297\",\n \"text\": \"def size(self, text):\\n return self.font.size(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b772ebfd5427c2620755f054cac5ec2c\",\n \"score\": \"0.69869506\",\n \"text\": \"def get_text_len_in_px(self):\\n test_label = tk.Label(None, text=self.text, font=self.font)\\n test_label.place(x=100000, y=100000) # this is not nice but it works\\n self.CLabel.update()\\n text_len = test_label.winfo_width()\\n test_label.destroy()\\n\\n return text_len\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4b8e8c880385398645bfd22188aa88\",\n \"score\": \"0.696879\",\n \"text\": \"def GetWidth(*args):\\n return _richtext.TextBoxAttr_GetWidth(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceda05f1f9e9231f88c5cb087cd63fe0\",\n \"score\": \"0.6844141\",\n \"text\": \"def get_char_width(self):\\n try:\\n view = self.window.get_active_view()\\n # Get font description (code from \\\"text size\\\" plugin)\\n context = view.get_style_context()\\n description = context.get_font(context.get_state()).copy()\\n # Get font metrics\\n pango_context = view.get_pango_context()\\n lang = Pango.Language.get_default()\\n metrics = pango_context.get_metrics(description, lang)\\n # Calculate char width in pixels\\n width = metrics.get_approximate_char_width() / Pango.SCALE\\n return width\\n except:\\n return 8 # If it didn't work, just use some appropriate value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbf9005f7bfc89d68ce6f656a8db8a93\",\n \"score\": \"0.678849\",\n \"text\": \"def get_width_in_text_space(self) -> Decimal:\\n w: Decimal = Decimal(0)\\n for g in self._glyphs:\\n glyph_width_in_text_space = g.get_width() * self._font_size * Decimal(0.001)\\n\\n # add word spacing where applicable\\n if len(g.get_unicode_str()) == 1 and isspace(g.get_unicode_str()):\\n glyph_width_in_text_space += self._word_spacing\\n\\n # horizontal scaling\\n glyph_width_in_text_space *= self._horizontal_scaling / Decimal(100)\\n\\n # add character spacing to character_width\\n glyph_width_in_text_space += self._character_spacing\\n\\n # add character width to total\\n w += glyph_width_in_text_space\\n\\n # subtract character spacing once (there are only N-1 spacings in a string of N characters)\\n w -= self._character_spacing\\n\\n # return\\n return w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca7df375ad6dae42ae5e6777652cb621\",\n \"score\": \"0.6784089\",\n \"text\": \"def measure_text(self, text, spacing=1):\\n length = 0\\n for letter in text:\\n # Get index of letter\\n letter_ord = ord(letter) - self.start_letter\\n offset = letter_ord * self.bytes_per_letter\\n # Add length of letter and spacing\\n length += self.letters[offset] + spacing\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"325947274f798a1d6ba862e3c56742c3\",\n \"score\": \"0.6744756\",\n \"text\": \"def _calcWidthByCharacter(self, character_count, font=None):\\n return int(character_count * 6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d910e0bdf9655600524a32b83935c54\",\n \"score\": \"0.67381513\",\n \"text\": \"def textsize(self, text, font=None, *args, **kwargs):\\n if self._multiline_check(text):\\n return self.multiline_textsize(text, font, *args, **kwargs)\\n if font is None:\\n font = self.getfont()\\n return font.getsize(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecf2112c4b3a938154a0159154a10145\",\n \"score\": \"0.67141116\",\n \"text\": \"def get_lexical_size(self, text: str) -> int:\\n tokenizer = RegexpTokenizer(r'\\\\w+')\\n words = tokenizer.tokenize(text)\\n return len(set(words))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea52455e964ddc54e311242c61940fc1\",\n \"score\": \"0.6633261\",\n \"text\": \"def write_width(self, font, string):\\n width = 0\\n for character in string:\\n try:\\n char_index = font.MAP.index(character)\\n width += font.WIDTHS[char_index]\\n\\n except ValueError:\\n pass\\n\\n return width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d153e69decae7f87f2b2051785ca412c\",\n \"score\": \"0.6628564\",\n \"text\": \"def getTextLength(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"690c192a4ef49e8a6c92041f65f2239a\",\n \"score\": \"0.66014194\",\n \"text\": \"def calculate_string_width(string, font=None, letter_spacing=1, monospaced=False):\\n width = 0\\n for char in string:\\n width += calculate_char_width(\\n char,\\n font=font,\\n monospaced=monospaced)\\n\\n width += 1 + letter_spacing\\n\\n return width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b65a3211eacc72f1b5590b071242f8c\",\n \"score\": \"0.6588091\",\n \"text\": \"def word_count(text):\\n return len(text.split())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e7a574247e7c3c03bd48925581d54e\",\n \"score\": \"0.6550759\",\n \"text\": \"def get_length(self) -> int:\\n return sum([text.get_length() for text in self._texts])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40ddc530376226a18574e48f1bdb7d38\",\n \"score\": \"0.6539332\",\n \"text\": \"def char_count(text):\\n return len(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6969add6d91d418f9f886d6303c7d792\",\n \"score\": \"0.652261\",\n \"text\": \"def getFontMeasure( self, text ):\\r\\n #x,y,w,h = self.canvas.bbox( self.handler )\\r\\n #print 'BBOX', x,y, w-x, h-y\\r\\n #print 'COORDS', self.canvas.coords( self.handler )\\r\\n #print 'Metric', self.fontObject.metrics(\\\"linespace\\\")\\r\\n #print 'Actual', self.fontObject.actual() \\r\\n #self.canvas.create_rectangle( x,y,x+(w-x), y-(h-y) )\\r\\n \\r\\n if( self.fontObject ):\\r\\n # The following lines let you debug the fudge factor\\r\\n #x,y = self.canvas.bbox( self.handler )[:2]\\r\\n #w = self.fontObject.measure( text ) * 1.15\\r\\n #self.canvas.create_rectangle( x,y,x+w,y-28 ) \\r\\n return self.fontObject.measure( text )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf22117fb12fcbc5a17f869ba06a31f6\",\n \"score\": \"0.6500957\",\n \"text\": \"def GetCharWidth(*args, **kwargs):\\n return _core_.Window_GetCharWidth(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"810a6076d10a2cc7685a42ffa766d213\",\n \"score\": \"0.6496057\",\n \"text\": \"def size(self, text):\\n #raise ValueError, \\\"Size is not supported in HDText (yet)\\\"\\n return sdl2_DisplayManager.inst().font_get_size( text, self.name, self.font_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d605cab4ff13e7ae4a73cf9c507b5b35\",\n \"score\": \"0.6486234\",\n \"text\": \"def getDimensions(self):\\n return _graphicsManager.executeFunction(('get text size', self._text, self._size))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb19c2e51f587187e9c695cd4d13dcf\",\n \"score\": \"0.64432824\",\n \"text\": \"def _count_words(text):\\r\\n return len(text.split()) if text else 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad939404354cdd01232afa31cca456b\",\n \"score\": \"0.64261883\",\n \"text\": \"def text_length(text, tokenizer):\\r\\n text = open(text, 'r')\\r\\n if tokenizer == 'nltk':\\r\\n return len(word_tokenize(text.read()))\\r\\n elif tokenizer == 'simple':\\r\\n tokens = 0\\r\\n for line in text:\\r\\n for word in line.split():\\r\\n tokens += 1\\r\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7e821f2303298e5fc0e91eb23829e62\",\n \"score\": \"0.6413309\",\n \"text\": \"def getTextlength(text, fontname=\\\"helv\\\", fontsize=11, encoding=0):\\n fontname = fontname.lower()\\n basename = Base14_fontdict.get(fontname, None)\\n\\n glyphs = None\\n if basename == \\\"Symbol\\\":\\n glyphs = symbol_glyphs\\n if basename == \\\"ZapfDingbats\\\":\\n glyphs = zapf_glyphs\\n if glyphs is not None:\\n w = sum([glyphs[ord(c)][1] if ord(c) < 256 else glyphs[183][1] for c in text])\\n return w * fontsize\\n\\n if fontname in Base14_fontdict.keys():\\n return TOOLS._measure_string(\\n text, Base14_fontdict[fontname], fontsize, encoding\\n )\\n\\n if fontname in (\\n \\\"china-t\\\",\\n \\\"china-s\\\",\\n \\\"china-ts\\\",\\n \\\"china-ss\\\",\\n \\\"japan\\\",\\n \\\"japan-s\\\",\\n \\\"korea\\\",\\n \\\"korea-s\\\",\\n ):\\n return len(text) * fontsize\\n\\n raise ValueError(\\\"Font '%s' is unsupported\\\" % fontname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f4b2ebfef786dc0b45b8462237a2ffc\",\n \"score\": \"0.64131904\",\n \"text\": \"def get_words_count(self, text):\\n\\t\\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd15bf94c5bea7267c7e7dcd4409240f\",\n \"score\": \"0.64108706\",\n \"text\": \"def message_length(self, text):\\n tokenized = tokenize(text)\\n if tokenized:\\n return len(tokenized)\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68c714b5d7b5392c9fea82ac446896bd\",\n \"score\": \"0.63865507\",\n \"text\": \"def count_chars_in_width(line_str: str, max_width: int) -> int:\\n total_width = 0\\n for i, char in enumerate(line_str):\\n width = char_width(char)\\n if width + total_width > max_width:\\n return i\\n total_width += width\\n return len(line_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f36754a1a5dc125edad5b906d92a561\",\n \"score\": \"0.63759416\",\n \"text\": \"def _get_text_height(self, text: str) -> int:\\n return self._buffer_draw.textsize(text, font=self.FONT)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02316ba97375b8584d55ae658b309b6d\",\n \"score\": \"0.6358214\",\n \"text\": \"def get_width_from_char_name(self, name):\\n return self._metrics_by_name[name].width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6698133c8279acd06ac831b4291ba128\",\n \"score\": \"0.63526183\",\n \"text\": \"def size_of_word(word):\\n return len(word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9617b1ff4922532a986baf9fe36d427a\",\n \"score\": \"0.62951577\",\n \"text\": \"def text_length (self,data):\\r\\n\\r\\n total_alphabet = 0 #Variable to count total alphabet in the string\\r\\n space = 0 #Variable to count number of spaces in the string\\r\\n\\r\\n #Determine total number of strings with and without spaces.\\r\\n for alphabet in (str(self.data[\\\"text\\\"])):\\r\\n if alphabet != \\\" \\\":\\r\\n total_alphabet +=1\\r\\n else:\\r\\n total_alphabet +=1\\r\\n space +=1\\r\\n\\r\\n withSpaces = total_alphabet #Total number of alphabet with space\\r\\n withoutSpaces = total_alphabet-space #Total number of alphabet without space\\r\\n\\r\\n return withSpaces,withoutSpaces #Return the values\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57d759e0d7b9687cb3ec471079d6e1ed\",\n \"score\": \"0.62803316\",\n \"text\": \"def get_width(self) -> int:\\n if not self.label is None:\\n return self.label.get_width()\\n \\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5378c52380a9470f0c2f27c75cd70478\",\n \"score\": \"0.6277994\",\n \"text\": \"def GetLabelWidth(label):\\n assert type(label) is str, \\\"Incorrect type\\\"\\n\\n length = 0\\n for c in label:\\n length += glutBitmapWidth(GLUT_BITMAP_HELVETICA_18, ord(c))\\n\\n assert type(length) is int\\n assert length >= 0\\n\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eec1f444b24b1b05f8f68d9b316ff9bc\",\n \"score\": \"0.62596536\",\n \"text\": \"def SimpleTextMetrics(text):\\n\\n #count the number of paragraphs/lines\\n line_list = text.split(\\\"\\\\n\\\")\\n num_lines = len(line_list)\\n\\n #count total number of words per paragraph/line\\n num_words = 0\\n num_word_per_p = []\\n for l in line_list:\\n l_word_list = l.split()\\n l_num_words = len(l_word_list)\\n \\n num_words += l_num_words\\n\\n #average number of words per paragraph/line\\n avg_line_length = num_words/num_lines\\n\\n return (num_words, num_lines, avg_line_length)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"359c3e5168789b5e1abffcad41f319ca\",\n \"score\": \"0.62593967\",\n \"text\": \"def str_width(line_str: str) -> int:\\n if line_str.isascii():\\n # Fast path for a line consisting of only ASCII characters\\n return len(line_str)\\n return sum(map(char_width, line_str))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ee6a030a8def9781caec6bf96c94e95\",\n \"score\": \"0.6235574\",\n \"text\": \"def get_word_lengths(self, text):\\n return list(map(lambda x: len(x), text.split(' ')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d6e7a3988ec3655db441bb1bfdf6c2a\",\n \"score\": \"0.6232222\",\n \"text\": \"def headWidth(self,p=None,s=''):\\n\\n if p: s = p.headString()\\n\\n return self.font.measure(s)/self.font.measure('0')+1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"180a8e97229347ba796d79cda084443a\",\n \"score\": \"0.62223864\",\n \"text\": \"def string_width_height(self, s):\\n if not len(s):\\n return 0, 0\\n total_width = 0\\n namelast = None\\n miny = 1e9\\n maxy = 0\\n for c in s:\\n if c == '\\\\n':\\n continue\\n wx, name, bbox = self._metrics[ord(c)]\\n\\n total_width += wx + self._kern.get((namelast, name), 0)\\n l, b, w, h = bbox\\n miny = min(miny, b)\\n maxy = max(maxy, b + h)\\n\\n namelast = name\\n\\n return total_width, maxy - miny\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2dc5dc154c66dcce31d8dd1b67bdd85\",\n \"score\": \"0.62138253\",\n \"text\": \"def lecturename_len(self, text):\\n length = 0\\n for c in text:\\n if unicodedata.east_asian_width(c) in 'FWA':\\n length += 2\\n else:\\n length += 1\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ba2d241c601522edef598827d3ffad\",\n \"score\": \"0.6211052\",\n \"text\": \"def char_count(self, text, ignore_spaces=True):\\n if ignore_spaces:\\n text = text.replace(\\\" \\\", \\\"\\\")\\n return len(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bea06f12a9256cd25916ffa0092ad4f\",\n \"score\": \"0.6208475\",\n \"text\": \"def visual_width(line):\\n\\n return len(re.sub(colorama.ansitowin32.AnsiToWin32.ANSI_CSI_RE, '', line))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"586711eb5f0357b5a0c714a0650cba15\",\n \"score\": \"0.6208343\",\n \"text\": \"def get_width(self, c) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"012db36af843fa7f968fc5cb73fe9e51\",\n \"score\": \"0.61926806\",\n \"text\": \"def _visual_width(line):\\n\\n return len(re.sub(colorama.ansitowin32.AnsiToWin32.ANSI_CSI_RE, \\\"\\\", line))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04cf8adb99a0e629221ae9df566df0fe\",\n \"score\": \"0.6191938\",\n \"text\": \"def word_counter(text: str) -> int:\\n\\n word_pattern = re.compile(r'\\\\s')\\n\\n splitoid = word_pattern.split(text)\\n\\n words = len(splitoid)\\n\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d0ea5c74fc33b8215afb08208de6ae6\",\n \"score\": \"0.61783624\",\n \"text\": \"def textheight(txt, width=None, **kwargs):\\n if not isinstance(txt, Text) or len(kwargs) > 0 or width != txt.width:\\n kwargs[\\\"draw\\\"] = False\\n txt = text(txt, 0, 0, width=width, **kwargs)\\n return txt.metrics[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82b0143a3a5ef9838713c7d93e11eebf\",\n \"score\": \"0.6154242\",\n \"text\": \"def text_size(self, text, size, *, bold=False):\\n if not text:\\n return (0, 0)\\n return self.font(size, bold).size(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"031372291fd7c33f0e723044fad391c7\",\n \"score\": \"0.6112074\",\n \"text\": \"def approx_string_em_width(string):\\n\\n return 0.6 * len(string) + 0.4 * sum(string.count(x) for x in [\\\"w\\\", \\\"m\\\", \\\"~\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a63941184ce2c1971cb01e18b932e704\",\n \"score\": \"0.6078273\",\n \"text\": \"def get_text_width_height_descent(self, s, prop, ismath):\\n font = self.get_font(prop)\\n w, h = self._canvas.measure_text(s, font)\\n return w, h, 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71e2869378fb6bb36c720e3c8e5515fa\",\n \"score\": \"0.60734004\",\n \"text\": \"def swidth(s):\\n\\n return max( [wcswidth(row) for row in s.strip().split('\\\\n')] )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a3cfd5ccbf1f43dc97b5d2efeb37b89\",\n \"score\": \"0.60592115\",\n \"text\": \"def get_avg_word_len(text):\\n # remove links, hashtags, @ and punctuation marks\\n text = re.sub(r'https?://\\\\S+', '', text)\\n text = re.sub(r'#\\\\S+', '', text)\\n text = re.sub(r'@\\\\S+', '', text)\\n text = re.sub(r'[,.)(!?:\\\\[\\\\]\\\\\\\"\\\\']', '', text)\\n\\n # split to words\\n words = re.findall(r'\\\\S+', text)\\n if len(words) == 0:\\n return 0\\n words_lens = [len(word) for word in words]\\n\\n # calc len of longest word\\n return sum(words_lens) / len(words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"386e29dc2ff49409bb0ef547d737de63\",\n \"score\": \"0.6057461\",\n \"text\": \"def get_corpus_spaces(text):\\n space_count = 0\\n for key, value in text.items():\\n space_count += (value[0] + value[1]).count(\\\" \\\")\\n return space_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"798e42737bbad02c76b06143e4a29ed0\",\n \"score\": \"0.60538\",\n \"text\": \"def rev_wcwidth(text, width):\\n if width == 0:\\n return -1\\n\\n w = 0\\n i = -1\\n # loop over to check for double width chars\\n for i, c in enumerate(text):\\n w += wcwidth(c)\\n if w >= width:\\n break\\n if w >= width:\\n return i\\n else:\\n return i + width - w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cb5b0bf7af3970417ee6f437dbcbaf9\",\n \"score\": \"0.6042492\",\n \"text\": \"def measureText(self, text, units=False):\\n\\n\\t\\tprop = text._fontproperties\\n\\t\\twidth, height, descent, svg_elements, used_chars = self.renderer.mathtext_parser.parse(text.get_text(), 72, prop)\\n\\n\\t\\tif units:\\n\\t\\t\\twidth *= self.UNITS_PER_PT_x\\n\\t\\t\\theight *= self.UNITS_PER_PT_y\\n\\t\\t\\tdescent *= self.UNITS_PER_PT_y\\n\\n\\t\\treturn (width, height, descent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b879ec377f7cfba19c2b8c0ae6ead3\",\n \"score\": \"0.6024573\",\n \"text\": \"def _visible_width(s):\\n if isinstance(s, _text_type) or isinstance(s, _binary_type):\\n return len(_strip_invisible(s))\\n else:\\n return len(_text_type(s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b879ec377f7cfba19c2b8c0ae6ead3\",\n \"score\": \"0.6024573\",\n \"text\": \"def _visible_width(s):\\n if isinstance(s, _text_type) or isinstance(s, _binary_type):\\n return len(_strip_invisible(s))\\n else:\\n return len(_text_type(s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb60cea96b2ac699596513e3b9344e4f\",\n \"score\": \"0.59930545\",\n \"text\": \"def line_count(text):\\n return len(text.splitlines())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679e436bd477326deefa1bbc9e03b6ab\",\n \"score\": \"0.5977051\",\n \"text\": \"def get_words_per_sentence(self, text: str) -> float:\\n return np.mean(list(map(len, self._text_2_list_of_list_of_strings(text))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"821c426da1f50c13243deaf7d6b6df47\",\n \"score\": \"0.5975071\",\n \"text\": \"def word_length_ft(string):\\n return len(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f2c7e5a9a9c6adf0d14f1c8be742055\",\n \"score\": \"0.5961074\",\n \"text\": \"def text_dpi(text: str, fontsize: int):\\n fig, ax = plt.subplots()\\n text_plot = ax.text(0, 0, text, fontsize=fontsize)\\n renderer = fig.canvas.get_renderer()\\n bb = text_plot.get_window_extent(renderer=renderer)\\n plt.close(fig)\\n return bb.width, bb.height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f716b858fdaf85984df357d6c68d5c9a\",\n \"score\": \"0.5960385\",\n \"text\": \"def getWidth(self):\\n util.raiseNotDefined()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fce553afb30e5d909ba883cf8dd4e56b\",\n \"score\": \"0.5937174\",\n \"text\": \"def calcByteLen(text):\\n return len(text.encode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"677cff37c4587085b73e9d0680fff75f\",\n \"score\": \"0.59327734\",\n \"text\": \"def word_count(paragraph):\\n word_count = len(paragraph.split(\\\" \\\"))\\n return word_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af0ab685dd1dee2dc8d09241e5bfaacf\",\n \"score\": \"0.59266496\",\n \"text\": \"def nr_cols_of(text):\\n if not text:\\n return 0\\n return max((len(line) for line in text.splitlines()), default=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5125e7b0f48c32ead3505d244d5bba7f\",\n \"score\": \"0.5908896\",\n \"text\": \"def nr_rows_of(text):\\n if not text:\\n return 1\\n return text.count(\\\"\\\\n\\\") + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d4e77fa3f41311a8de03c3d7e76cd1c\",\n \"score\": \"0.59072506\",\n \"text\": \"def textmetrics(txt, width=None, **kwargs):\\n if not isinstance(txt, Text) or len(kwargs) > 0 or width != txt.width:\\n kwargs[\\\"draw\\\"] = False\\n txt = text(txt, 0, 0, width=width, **kwargs)\\n return txt.metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd09371a1914d73ad68a09afe00f6685\",\n \"score\": \"0.5894771\",\n \"text\": \"def text_columns(self, text):\\n tab_count = text.count(\\\"\\\\t\\\")\\n newline_count = text.count(\\\"\\\\n\\\")\\n return len(text) - tab_count - newline_count + (self.tab_size * tab_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49019dee4c986dbe9c8da3285cefc2be\",\n \"score\": \"0.58930284\",\n \"text\": \"def width(self) -> int:\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49019dee4c986dbe9c8da3285cefc2be\",\n \"score\": \"0.58930284\",\n \"text\": \"def width(self) -> int:\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bea4df0a3bfe9a2526ba65d63e27b77\",\n \"score\": \"0.5877223\",\n \"text\": \"def test_text_dimension():\\n string = 'This is a text for test. This is a test for text.py'\\n _, _, _, width_1, height_1, _ = make_text(string, 200, font_size=12)\\n\\n _, _, _, width_2, height_2, _ = make_text(string, 200, font_size=20)\\n assert width_1 * height_1 < width_2 * height_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f7cef4b98c70cc05b8846cf89220a9\",\n \"score\": \"0.58640873\",\n \"text\": \"def charCount(paragraph):\\n char_count = len(paragraph)\\n return char_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f7cef4b98c70cc05b8846cf89220a9\",\n \"score\": \"0.58640873\",\n \"text\": \"def charCount(paragraph):\\n char_count = len(paragraph)\\n return char_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf7ae977f7cfa2a5230a674bdc1bd522\",\n \"score\": \"0.58520615\",\n \"text\": \"def width(self):\\n return self['width']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46bf25ba2fe5ea869339c6b29967c331\",\n \"score\": \"0.58442056\",\n \"text\": \"def get_text_count(self):\\n return len(self._texts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4e2b5311015a430675904c0d20218e\",\n \"score\": \"0.5831076\",\n \"text\": \"def size(self):\\n return self._attributedString.size()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4e2b5311015a430675904c0d20218e\",\n \"score\": \"0.5831076\",\n \"text\": \"def size(self):\\n return self._attributedString.size()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72dc88ca68f2bab043e46cd26a97316\",\n \"score\": \"0.5829908\",\n \"text\": \"def count_indents(text: str) -> int:\\r\\n return len(text) - len(text.lstrip(\\\" \\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ed51932802543a9f228bffd0c33dc2\",\n \"score\": \"0.5828893\",\n \"text\": \"def width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ed51932802543a9f228bffd0c33dc2\",\n \"score\": \"0.5828893\",\n \"text\": \"def width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ed51932802543a9f228bffd0c33dc2\",\n \"score\": \"0.5828893\",\n \"text\": \"def width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ed51932802543a9f228bffd0c33dc2\",\n \"score\": \"0.5828893\",\n \"text\": \"def width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ed51932802543a9f228bffd0c33dc2\",\n \"score\": \"0.5828893\",\n \"text\": \"def width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ed51932802543a9f228bffd0c33dc2\",\n \"score\": \"0.5828893\",\n \"text\": \"def width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ed51932802543a9f228bffd0c33dc2\",\n \"score\": \"0.5828893\",\n \"text\": \"def width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ed51932802543a9f228bffd0c33dc2\",\n \"score\": \"0.5828893\",\n \"text\": \"def width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ed51932802543a9f228bffd0c33dc2\",\n \"score\": \"0.5828893\",\n \"text\": \"def width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8756f32cf124d01666234d1d0377f4fe\",\n \"score\": \"0.5822983\",\n \"text\": \"def source_wordcount(self):\\n\\t\\treturn wordcount(self.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1afa940d581faf9d1e94c03d3cd5739a\",\n \"score\": \"0.5812748\",\n \"text\": \"def get_width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1afa940d581faf9d1e94c03d3cd5739a\",\n \"score\": \"0.5812748\",\n \"text\": \"def get_width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1afa940d581faf9d1e94c03d3cd5739a\",\n \"score\": \"0.5812748\",\n \"text\": \"def get_width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1afa940d581faf9d1e94c03d3cd5739a\",\n \"score\": \"0.5812748\",\n \"text\": \"def get_width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1afa940d581faf9d1e94c03d3cd5739a\",\n \"score\": \"0.5812748\",\n \"text\": \"def get_width(self):\\n return self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cee1e022b0dbb92b0a239f4777ca63a\",\n \"score\": \"0.5810409\",\n \"text\": \"def get_width(self):\\n bbox = self.get_bounding_box()\\n return bbox[1]-bbox[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c48318d12166c504a63b787068dfa14a\",\n \"score\": \"0.5808792\",\n \"text\": \"def width(self):\\n return self.__width__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3dfbab38f2285cd92c3958ae70ddba9\",\n \"score\": \"0.5806985\",\n \"text\": \"def num_tweets_required(text: str) -> int:\\n return math.ceil(len(text)/MAX_TWEET_LENGTH)\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":9934,"numItemsPerPage":100,"numTotalItems":994482,"offset":993400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NDkwMjQ5NywianRpIjoiYWMzNWY2YTQtY2Y5My00MWViLTg3YTgtNmUyNjEzMDFmYjdkIiwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc4NDkwNjA5NywiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.sbIiJHYgXd-OE4yAVisia6sUtkpSaXcBJ3K6TtXjF9D51by-lDmSYcKMVjnZe4w_9chqecAQvmC2qOXTsIKbDg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
9695fbdbc8e399815b3b3229b643c2c6 | Echo a warning message to console. | [
{
"docid": "3086f7df6260724218b9470d875f0a2c",
"score": "0.6666118",
"text": "def echo_warn(msg: t.Optional[t.Union[str, t.List[str]]] = None, **kwargs):\n kwargs.setdefault(\"style_args\", WARN_ARGS)\n kwargs.setdefault(\"style_tmpl\", WARN_TMPL)\n kwargs.setdefault(\"log_level\", \"warning\")... | [
{
"docid": "a5d77f60dd51d6d5f2614e0427ef364a",
"score": "0.7996388",
"text": "def warning(self, msg):\n print(\"WARNING: {}\".format(msg), file=sys.stderr)",
"title": ""
},
{
"docid": "aa237f783b470fa0c85b9f1c127fc144",
"score": "0.78742915",
"text": "def warning(self, message... |
43251de341669b93f4748b5d4d414d28 | Make synchronous request to graphQL server. | [
{
"docid": "9bfef96901988038a1ec1964b8ca7aaf",
"score": "0.0",
"text": "def exec(\n self,\n query: str,\n variables: dict = None,\n operation_name: str = None,\n headers: dict = None,\n proxy: str = ''\n ):\n request_body = self.__request_body(\n ... | [
{
"docid": "c002e9f4a2cbc4b0f05a0ca421db354a",
"score": "0.60619205",
"text": "def graphql(url, query, session, variables):\n return session.post(\n url,\n data={\n \"query\": query,\n \"variables\": json.dumps(variables),\n },\n ).json()",
"title": "... |
5d3691362c706b7919a98494168f5373 | Test case for api_v1_games_game_id_put | [
{
"docid": "7a000586ecc17af94dae1c8fc9058699",
"score": "0.87574315",
"text": "def test_api_v1_games_game_id_put(self):\n game = Game()\n response = self.client.open('/api/v1/games/{game_id}'.format(game_id='game_id_example'),\n method='PUT',\n ... | [
{
"docid": "6fc6582b4af8cbd983f3b5585752b17b",
"score": "0.7226943",
"text": "def test_teams_id_put(self):\n pass",
"title": ""
},
{
"docid": "b1b184fe62f4c5dd12b0d1812187e229",
"score": "0.71786773",
"text": "def test_api_v1_games_game_id_get(self):\n response = self.c... |
7a51eecc211c4c3bde919d0bc29977a2 | Returns values for parameters of disease model sampled from prior distributions based off Radev et al. Note that we discard delta = number of days for transmission to have full effect from parameters, because the effect is negligible in long term data. | [
{
"docid": "c5323bb9f922c86f73ee4dcf2ed90dd6",
"score": "0.5451721",
"text": "def prior(T: int, num_interventions: int = 15):\n alpha_f = (0.7**2) * ((1-0.7)/(0.17**2) - (1-0.7))\n beta_f = alpha_f * (1/0.7 - 1)\n \n # Bounds for transmission rates\n value_bounds_low = [1.] + [0.05] * num... | [
{
"docid": "b496ffd475ff0661029e75c618a6b249",
"score": "0.60731524",
"text": "def __find_d_parameter(self, D):\n\n # set the starting number of differences\n d = 0\n differentiated_data = self.series_train_data.copy()\n\n # if model has a seasonal component then apply season... |
a0ef83a662c764c3ae8780a45abc7b04 | Returns an OAuth2 access token for a service account. If 'service_account_key' is specified, will use it to generate access token for corresponding .gserviceaccount.com account. Otherwise will invoke app_identity.get_access_token(...) to use app's .gserviceaccount.com account. If 'act_as' is specified, will return an a... | [
{
"docid": "213a9e2f9ae8d5f5c15993037ac6cfb4",
"score": "0.717205",
"text": "def get_access_token_async(\n scopes, service_account_key=None, act_as=None, min_lifetime_sec=5*60):\n # Limit min_lifetime_sec, since requesting very long-lived tokens reduces\n # efficiency of the cache (we need to const... | [
{
"docid": "c796434c994a21c46d076506c4394c99",
"score": "0.64293814",
"text": "def get_access_token():\n\n scopes = [\n 'https://www.googleapis.com/auth/cloud-platform', 'email', 'profile'\n ]\n\n credentials, _ = default()\n credentials = auth.delegated_credentials(credentials, scope... |
47938fdbbb52b9e9ed3ce0fc6a2e1c14 | Enable Confidential Nodes for this cluster. | [
{
"docid": "8b7f9b5071a1577ec845863ad1fdb178",
"score": "0.0",
"text": "def enabled(self) -> bool:\n return pulumi.get(self, \"enabled\")",
"title": ""
}
] | [
{
"docid": "6af95cf4b3c6da616e84c68dfa6b027f",
"score": "0.5698958",
"text": "def enable(node):\n node.ensure_valid()\n reconfigure(node)\n node.autoregister()\n sys.exit(node.spawnctl('enable'))",
"title": ""
},
{
"docid": "02f15ba10f094e441c684e379a07d35f",
"score": "0.5350... |
272fa3322cb2b944b3932f297b6c9695 | Test we can handle a basic task execute | [
{
"docid": "dc626c2fdb080e2c2066de99eba4bad3",
"score": "0.7398577",
"text": "def test_task_initial(self):\n parser = build_hivemind_parser()\n additional_args = [\n 'create_node',\n 'FooBarTask',\n '-c',\n 'hivemind_tasks.TaskNode'\n ]\n ... | [
{
"docid": "0b1e6b183c55ea68720fda01b0477366",
"score": "0.79583627",
"text": "def task_test(options):\n print 'testing'",
"title": ""
},
{
"docid": "09b9b1a565d8789da8b1fd858eb0854b",
"score": "0.7589941",
"text": "def test_submit_clue_task(self):\n pass",
"title": ""
... |
8cb9bcdcfb0648969fb60e199dcb314d | Gets Regon API status code and status message Returns tuple | [
{
"docid": "c3ac2c2e439e5f41d25cf2325878991b",
"score": "0.5941965",
"text": "def get_service_status(self):\n response = self.service.GetValue(pNazwaParametru=\"StatusUslugi\")\n ret = response, t(\"ServiceStatus\", response)\n logger.debug(ret)\n return ret",
"title": ""... | [
{
"docid": "5c447ec9bd7647138977e65b63224df6",
"score": "0.7345084",
"text": "def get_status():\n return give_message(\"get_status\", {})",
"title": ""
},
{
"docid": "3ef01c7c66b66e558e3a2927a533b094",
"score": "0.727843",
"text": "def status():\n return ({\"status\": \"OK\"})"... |
55bbb3282cc129d6b333433c05f2ef4d | Initialize an instance of the MultinomialPosterior class. | [
{
"docid": "a50fa2719f6abad531e78fa0f89c800e",
"score": "0.0",
"text": "def __init__(self, alphabet):\n self.alphabet = [str(i) for i in alphabet]\n self._distribution_parameter_names = \\\n [str('p_{}'.format(i)) for i in self.alphabet]\n\n self._distribution_parameter_s... | [
{
"docid": "9d43f3dfba63014cb0fd268ef1abe2c6",
"score": "0.63679075",
"text": "def __init__(self, bandits: List[Bandit]):\n self.bandits = bandits\n # mu_pri and var_pri are hypermaterers for prior distribution\n self.mu_pri = np.zeros(len(self.bandits)) # 5 for each\n self.v... |
da3b9c3395ab2a3ec3f17f245f69b02f | Test DefenderDefender include_option is a boolean, when False only required params are included, when True both required and optional params are included | [
{
"docid": "175125a75b1118352d5b9d46c4c57fef",
"score": "0.47491163",
"text": "def make_instance(self, include_optional):\n # model = openapi_client.models.defender_defender.DefenderDefender() # noqa: E501\n if include_optional :\n return DefenderDefender(\n cate... | [
{
"docid": "a1354cd9d9f53450d8f17d2703c20bc6",
"score": "0.5683065",
"text": "def include_optional(self, include_optional):\n\n self._include_optional = include_optional",
"title": ""
},
{
"docid": "e465f0884e4fcd99f1402e3c2871b177",
"score": "0.5449698",
"text": "def _addMand... |
802e3d7d8c053c8fe055125220b1786a | Get the parameters of the posterior over the topics, also referred to as "the topics". Returns numpy.ndarray Parameters of the posterior probability over topics. | [
{
"docid": "eba56bd03dd280cf9da315a7d9576a2b",
"score": "0.0",
"text": "def get_lambda(self):\n return self.eta + self.sstats",
"title": ""
}
] | [
{
"docid": "b65b17dc191c50755b93d7e438d3b499",
"score": "0.67156535",
"text": "def posterior_parameters(self, suff_stats):\n # extract sufficient statistics\n n, mu, ssq = suff_stats[0:3]\n\n # update kappa and nu\n kappaN = self.kappa0 + n\n nuN = self.nu0 + n\n\n ... |
2c60c6e92708fac14140e0a6fef77bbe | Gas FVF calculated in other units | [
{
"docid": "287d5c1de9370cafcceac1555b27f4a9",
"score": "0.7354404",
"text": "def gas_fvf2(unit='unit1', z=0.8, temp=186, pressure=2000):\n if unit == 'unit1':\n return(0.00503676 * z * temp / pressure) \n if unit == 'unit2':\n return(0.350958 * z * temp / pressure)",
"title": ""
}
] | [
{
"docid": "74b43d41f419db9fdc40a87b2793ccb9",
"score": "0.73810804",
"text": "def gas_fvf(P, T, grav):\n #P pressure, psia\n #T temperature,°F\n #grav gas specific gravity\n Tr = (T + 460) / Tc(grav)\n Pr = P / Pc(grav)\n Z = zfact(Tr, Pr)\n return 0.0283 *... |
4135b2da3e6d74d23591ea7edced30cf | \ returns the gravityScore as an integer from this node | [
{
"docid": "1056d67a461f21b4a8dbe7c4856bd6b1",
"score": "0.778533",
"text": "def get_score(self, node):\r\n return self.get_node_gravity_score(node) or 0",
"title": ""
}
] | [
{
"docid": "636be2e9557c4e6f80f0662453d13234",
"score": "0.77913153",
"text": "def getScore(self, node):\n return self.getGravityScoreFromNode(node) or 0",
"title": ""
},
{
"docid": "a6224c4bfafa946e2eea6a7dc93c2972",
"score": "0.775516",
"text": "def get_score(self, node):\n ... |
6b7647cc0c2eecff9d84e8a27d2dbf91 | Test the log_config_updated event. | [
{
"docid": "2d763b5fd3eb27d366ddf189d0497646",
"score": "0.7828317",
"text": "async def test_log_config_updated(driver):\n # Modify current log config in an update and assert that it changed\n assert driver.log_config.level != LogLevel.SILLY\n log_config = driver.log_config.to_dict()\n log_c... | [
{
"docid": "5b7cbefe3a8ddd524299235e84151231",
"score": "0.71186805",
"text": "def _on_config_changed(self, _):\n # TODO\n # Set debug logging and restart services\n pass",
"title": ""
},
{
"docid": "e5ad8a1f2e6d7b1bc8e23a42e09f7ce3",
"score": "0.67582893",
"text... |
de106e8dacf765b6f9e7044c768bb008 | Retorna a matriz A concatenada da B como em [ A ] [ B ] O numero de colunas em ambas matrizes deve ser o mesmo. | [
{
"docid": "4ca1bca658aae9a0748fae236b830524",
"score": "0.6976433",
"text": "def concat(self, other):\n\n if isinstance(other, Vetor):\n other = type(self)([other])\n\n m, n = self.m, self.n\n om, on = other.m, other.n\n\n assert n == on, \"O numero de colunas das... | [
{
"docid": "2548b7bb8a093e50673889355a5d2ba5",
"score": "0.67496264",
"text": "def matrixes_add(a, b):\r\n assert len(a) == len(b) and len(a[0]) == len(b[0])\r\n \r\n c = []\r\n \r\n for i in range(len(a)):\r\n c.append([])\r\n for j in range(len(a[0])):\r\n c[i].... |
767623d589babdd9fd09b815f28477a9 | Generate jsonl data for object detection or instance segmentation | [
{
"docid": "6581e9f6b04a6adab7723595de73898e",
"score": "0.6267898",
"text": "def convert(self):\n json_line_sample = {\n \"image_url\": self.base_url,\n \"image_details\": {\"format\": None, \"width\": None, \"height\": None},\n \"label\": [],\n }\n\n ... | [
{
"docid": "b4a9e52d334e1fd4871151b0110743ee",
"score": "0.64919776",
"text": "def field_to_dict(image_dir,gt_dir):\n from shapely.geometry import Polygon\n\n dataset_dicts=[]\n image_id=0\n for image_file in glob.glob(os.path.join(image_dir, \"*.jpg\")):\n suffix=\".jpg\"\n as... |
83d61973588145278732e26cb54762d4 | Return environment name fixture. | [
{
"docid": "d81b0095fef1ad5649a1d50fc3733288",
"score": "0.0",
"text": "def expected_common_name():\n return \"certbot-runner-test.presidio.pxsys.net\"",
"title": ""
}
] | [
{
"docid": "46c8af039122b93997bdc21543e6fa0a",
"score": "0.7394099",
"text": "def get_environment_name(self):\n name = osp.basename(self._prefix)\n\n if not (name and self.api.environment_exists(name=name)):\n name = self._prefix\n\n return name",
"title": ""
},
{... |
e4979486f795e0cf241b484d1c49873d | Takes a binary file and converts it into a hexencoded string, in the format used by PostreSQL. | [
{
"docid": "0772bbd7961c0f3d29c70ce5fa85028e",
"score": "0.6617502",
"text": "def encode_hex(f):\n output = \"\\\\x\"\n while (byte := f.read(1)) :\n hex_byte = hex(ord(byte))[2:]\n if len(hex_byte) % 2 == 1:\n hex_byte = \"0\" + hex_byte\n output += hex_byte\n r... | [
{
"docid": "783eafa7858619d192940238eee0992f",
"score": "0.679857",
"text": "def filename_to_hex(filename):\r\n # grab the last (up to) two path components\r\n names = filename.rsplit(os.path.sep, 2)[-2:]\r\n errmsg = \"Invalid object filename: %s\" % filename\r\n assert len(names) == 2, err... |
f17a1e40c277e482307136c58e241695 | Clicks last 30 days button in the calendar | [
{
"docid": "9681229c12c4c20959536ef97139a864",
"score": "0.8381066",
"text": "def click_last_30days_button(self):\n return self",
"title": ""
}
] | [
{
"docid": "7b70502d5997aeb3947fa67f0bdb1ff4",
"score": "0.7712211",
"text": "def click_last_30days_button(self):\n self._wait.until(ec.element_to_be_clickable(self._last_30days_button)).click()\n self._wait.until(ec.visibility_of_element_located(self._clear_all_filters_button))",
"tit... |
f473ac7bff7ce367c01061c42a13555b | Reads a json file passed in as a parameter, fills in defaults where appropriate, then returns a standard dictionary structure that can be used further to run various operations on EMR clusters, like launch the cluster, add steps to it, etc. | [
{
"docid": "bb1720fbae26cc2b732e5dcf3354dcbe",
"score": "0.57953733",
"text": "def get_emr_cluster_settings(user_inputs: dict, config_file: str, metadata_file: str, role: str, cluster_name: str):\n\n # Read emr config file\n with open(config_file, 'r') as emr_conf_file:\n emr_config = json.... | [
{
"docid": "ece144246f5fe9c50c8a6b8b202701b1",
"score": "0.61768204",
"text": "def read_data(file_path: str = None) -> dict:\n if file_path is None:\n return {}\n\n with open(file_path, \"r+\", encoding=\"utf-8\") as json_file:\n req_data = json.load(json_file)\n return req_da... |
66b42be79b45a97d6ef9deacbb25a8f1 | Test if the constraint is b >= 0 or [a, b] >= 0 with b < 0. | [
{
"docid": "8370bd94fed33d3cd1bf87b2f85e9fc8",
"score": "0.0",
"text": "def elina_lincons0_is_unsat(lincons):\n\n result = None\n try:\n elina_lincons0_is_unsat_c = elina_auxiliary_api.elina_lincons0_is_unsat\n elina_lincons0_is_unsat_c.restype = c_bool\n elina_lincons0_is_uns... | [
{
"docid": "3eab992995f27e55f8bee358da6fb0b5",
"score": "0.6964691",
"text": "def isFeasible(self,b):\n for i in b.flatten():\n if i<0.:\n return False\n return True",
"title": ""
},
{
"docid": "cee142fa01d544489ce059baa5f9a0f7",
"score": "0.676206... |
686553203bc1a24a8b272cf752b900f7 | loads .pts file. returns distance limit and points. | [
{
"docid": "c7a70498a5e5d3bcfa2f60215b415ddb",
"score": "0.6102229",
"text": "def load_instance(filename):\n with open(filename, \"r\") as instance_file:\n lines = iter(instance_file)\n distance = float(next(lines))\n points = [Point([float(f) for f in l.split(\",\")]) for l in l... | [
{
"docid": "bfb7c0c405a7229afb374c62a2b4ea63",
"score": "0.71540225",
"text": "def test_load_pts_file(self):\n\t\tpath = \"sample_point_cloud.pts\"\n\t\tpoints = load(path)\n\t\texpected_y_coord = 302.270092\n\t\tself.assertEqual(expected_y_coord, points[3][1])",
"title": ""
},
{
"docid": "0... |
0d84a0e8375d99b0127d85b2f42b86fd | Retrieves CanonicalIdentity objects by associated service identities. | [
{
"docid": "b2db611b7e36ead979ba8e96b70c05b4",
"score": "0.46572497",
"text": "def filter_by_identity(self,identity):\n return self.filter(pk__in=[claim.pk for claim in IdentityClaim.objects.filter(identity=identity)])",
"title": ""
}
] | [
{
"docid": "4b58deec283ab80627d8781d4937d878",
"score": "0.5722831",
"text": "def identities(self):\n return self.properties.get('identities', ClientValueCollection(ObjectIdentity))",
"title": ""
},
{
"docid": "f870637eef42b5d88052bd186720dee0",
"score": "0.5511099",
"text": "... |
e54da0d186f71a917c51092434bf9fb1 | Attempts to log the user in | [
{
"docid": "ce79c4cd3598cd71cbd6ab30d9ef18d4",
"score": "0.6492791",
"text": "def login():\r\n\r\n from werkzeug.security import check_password_hash\r\n\r\n username = request.form['user']\r\n password = request.form['password']\r\n\r\n if not is_valid_username(username):\r\n return r... | [
{
"docid": "52c0e168e036eb9e06587f81ac96ad7e",
"score": "0.750958",
"text": "def log_in(self):\n\n value = db.loginUser(self.user.get(), self.password.get())\n if value == \"yes\":\n self.password = \"\"\n self.passwordStorageWindow()\n elif value == \"no\":\n ... |
65a76de0264cae1f25c9b706ab55b9b7 | Secondprice auction on each good; see which is most preferred by the seller. | [
{
"docid": "679cc35bd5a768765131cf59576d2f76",
"score": "0.82362276",
"text": "def best_second_price( self, iteration_count, seller, bids ):\n\n seller_planet = self.galaxy.planets[seller.planet]\n \n preferred_bids = []\n for g, order_book in bids.items():\n order... | [
{
"docid": "cba748335e08191669fa02af05ed040d",
"score": "0.6125903",
"text": "def get_best_bid_ask(self, ticker):\n if ticker in self.tickers:\n bid = self.tickers[ticker][\"bid\"]\n ask = self.tickers[ticker][\"ask\"]\n return bid, ask\n else:\n ... |
1f21579c3c9ae1936c4d09e8e8556a58 | Implements the forward pass of the Descriptor Decoder RNN. | [
{
"docid": "befccd849a6dcdada4906fb0e0147b8e",
"score": "0.0",
"text": "def forward(self, token_idx, hidden=None, image_embeddings=None):\n batch_size = token_idx.size(0)\n # init the hidden state (and cell state) with\n # the latent representation of the input image\n if ima... | [
{
"docid": "d6c68107782e7d4e687103268efb5af0",
"score": "0.6384827",
"text": "def forward_pass(data_original, exp, model_name, in_channel=1, inputFeatDim=36): \n data_feat_unpack = data_original\n len_in = data_feat_unpack.shape[0]\n f=data_feat_unpack.shape[0]//800\n trim=800*f \n\n untrim = num... |
8f8906b4c60ff3d6915cc0b9526a2385 | Set the development mode. Defaults to True. | [
{
"docid": "dd5cab1426ef4e29d809c13c1aea4e4e",
"score": "0.74898833",
"text": "def enable_development_mode(self, is_enabled=True):\n self.is_enabled = is_enabled",
"title": ""
}
] | [
{
"docid": "8973c1a2bc819beee9cfd35fa79ab348",
"score": "0.7660416",
"text": "def isDebugMode(self):\n return DevelopmentMode",
"title": ""
},
{
"docid": "1d28ff785bcb0834a516b72372bbb1c1",
"score": "0.70030004",
"text": "def dev():\n env.COMPOSE_FILE = 'dev.yml'\n env.D... |
edc8851b312ebabd948303a98b07a44c | return the number of seats associated with the b2b order | [
{
"docid": "495d9db558a6ed71174bcd85261792d1",
"score": "0.5926627",
"text": "def get_quantity(self, instance):\n return instance.num_seats",
"title": ""
}
] | [
{
"docid": "90672be88a1a022057578c01e489cde7",
"score": "0.61112374",
"text": "def buy_count(trades: pandas.DataFrame) -> int:\n return len(buys(trades))",
"title": ""
},
{
"docid": "0ba6e8a504d9701cb55d20b739dde904",
"score": "0.5931142",
"text": "def number_of_reserved_seats(sel... |
710e5beef62a74d3ef797a5ca570b756 | Select devices that match at least one of the given selectors. >>> or_(tag('sports'), tag('business')) | [
{
"docid": "09a3da9328549ac3e5a84190635b3304",
"score": "0.5255297",
"text": "def or_(*children):\n return {'or': [child for child in children]}",
"title": ""
}
] | [
{
"docid": "ea0aa4b6243b9d20bff21f4b0886de7d",
"score": "0.60431856",
"text": "def _match_or(self):\n for search_op in self.args['search_key']:\n if search_op.match(self.ctx):\n return True\n return False",
"title": ""
},
{
"docid": "27efed65212ce90a50... |
1da3dcd89d143eed7218501446902874 | return function dictionary with all the feature extractions functions | [
{
"docid": "b7d556094034007c5d85ba5a1e4b9af1",
"score": "0.6962744",
"text": "def get_functions_dictionary():\n return {\n 'clustering_coefficient': extract_clustering_coefficient,\n 'degree': extract_degree_centrality,\n 'betweenness': extract_betweenness_centrality,\n 'c... | [
{
"docid": "be9dd55a76cf5fe60f0825b4368f458b",
"score": "0.6833436",
"text": "def get_feature_functions(token, ref, feature_id_list):\n pass",
"title": ""
},
{
"docid": "e7f8f9288d40667f609375e7a9d489d6",
"score": "0.66487414",
"text": "def derive_features(self):\n dict_fea... |
06c2f2fca317803d4318970d88c6b90d | Adds a word into the data structure. | [
{
"docid": "a3614399f8b6535aa9e6ebcd7fed7e12",
"score": "0.7730911",
"text": "def addWord(self, word: str) -> None:\n curr_node = self.root\n for i, c in enumerate(word):\n if not curr_node.get(c):\n curr_node[c] = {}\n if i == len(word) - 1:\n ... | [
{
"docid": "a8dd03c84492a17cf9569ad442eaa85e",
"score": "0.87670594",
"text": "def add_word(self, word):\n self.words.append(word)",
"title": ""
},
{
"docid": "ea47150b3ebb2169b73059650a7b839d",
"score": "0.8736371",
"text": "def addWord(self, word: str):\n self.WordDic... |
3f21ec6ebc308a88c3d49a9b601aeef6 | Given a date trajectory, find the corresponding day name. | [
{
"docid": "3b933e2f045a14e59fa151c1745bce25",
"score": "0.52409637",
"text": "def day_add(string,t1):\n \n d = (day_num(string) + t1) % 7\n return day_name(d)",
"title": ""
}
] | [
{
"docid": "8022b2787df79af9c4e2fb29a39d59ec",
"score": "0.69907445",
"text": "def getWeekdayName(day):\n pass",
"title": ""
},
{
"docid": "7b8c2d2a979a57c728ba77beaac18668",
"score": "0.6698994",
"text": "def _get_dayname(day):\n try:\n dt = datetime.datetime.strptime(day... |
c18c8abddaa3a536879cce89b7b799cf | This function creates a GTK menu. | [
{
"docid": "456a1c4c5ef19b9e4b2f42f382c7a66a",
"score": "0.7486232",
"text": "def create_menu(indi,*args):\n \n menu = gtk.Menu()\n \n menu_item = gtk.MenuItem(\"Refresh\")\n menu_item.connect(\"activate\",create_menu,ind)\n menu.append(menu_item)\n menu_item.show()\n \n # IPv4 public\n... | [
{
"docid": "a395384f896b349094e16ef48aad532f",
"score": "0.78669643",
"text": "def create_menu(self):\n self.menu = gtk.Menu()\n\n self.women_menu_item = gtk.MenuItem(self.women_toilet.to_string())\n self.menu.append(self.women_menu_item)\n self.women_menu_item.show()\n\n ... |
42e947d61d10964bcd61de12b6bbfd96 | Constructs a new decodable object from input features. Input can be just a feature matrix or a tuple of a feature matrix and an ivector or a tuple of a feature matrix and an online ivector matrix. | [
{
"docid": "5e3a72a21bf44b71b60a7bb76cdb0140",
"score": "0.7723934",
"text": "def _make_decodable(self, features):\n ivector, online_ivectors = None, None\n if isinstance(features, tuple):\n features, ivector_features = features\n if isinstance(ivector_features, _kald... | [
{
"docid": "1bc67112f8b6351b0c34d8ed961c31cf",
"score": "0.61783695",
"text": "def _make_decodable(self, features):\n if features.num_rows == 0:\n raise ValueError(\"Empty feature matrix.\")\n\n nnet_computer = _nnet3.DecodableNnetSimple(\n self.decodable_opts, self.m... |
ac488bbec4b8ead2299cc50e51f4eb31 | Scrape the contents of a set of pdf documents and assemble the data into a global dataframe. | [
{
"docid": "819dc3887982018261c8a924a3707097",
"score": "0.62664145",
"text": "def scrape(self):\n\n\t\tself.getFilesFromPath()\n\t\tfilename = str(self.filenames[1])\n\t\tdocument = self.openFileFromPath(filename)\n\t\tfields = self.resolveFields(document)\n\t\tdf = self.getColumnsFromFields(fields)\n\... | [
{
"docid": "d9bfb74d7492bb83d4c22aff7ddcc40e",
"score": "0.77720034",
"text": "def build_pdf_df(file_name): \n fp = open(file_name, 'rb')\n parser = PDFParser(fp)\n document = PDFDocument(parser)\n print('\\n\\n\\tProcessing file ', file_name)\n annot_dict, content_dict, data_d... |
bff2986f680be72654afa106971ff567 | The instance state to which you want to attach the lifecycle hook. | [
{
"docid": "e583747e42afc4f7501ae9250badbc1e",
"score": "0.0",
"text": "def lifecycle_transition(self) -> Optional[str]:\n return pulumi.get(self, \"lifecycle_transition\")",
"title": ""
}
] | [
{
"docid": "b9f1055d0e51fee5201378b8a0ed8005",
"score": "0.71542156",
"text": "def trunkLifecycleState():",
"title": ""
},
{
"docid": "b9f1055d0e51fee5201378b8a0ed8005",
"score": "0.71542156",
"text": "def trunkLifecycleState():",
"title": ""
},
{
"docid": "4aea713b456d63... |
76624e4fcdefa2c604d5dd412474c225 | It should raise an error if try to aspirate more than possible. | [
{
"docid": "0ec4a8e347e69392467b786dfac1f2e7",
"score": "0.0",
"text": "def test_aspirate_too_much(subject: AbstractInstrument,\n labware: AbstractLabware) -> None:\n subject.home()\n subject.pick_up_tip(well=labware.get_wells()[0], tip_length=1, presses=None,\n ... | [
{
"docid": "ce1a49b2db26c5a0618505fa4a5238eb",
"score": "0.6180764",
"text": "def fail_D209():",
"title": ""
},
{
"docid": "4eb6b8851688d9643ee3689a24c96196",
"score": "0.6035638",
"text": "def mayRaiseExceptionOperation(self):",
"title": ""
},
{
"docid": "4eb6b8851688d96... |
c6296565db19fa8b486e93b3d28f479d | corrects plural noun grammatical errors | [
{
"docid": "73f957c2531f17306684bf2e66b6f408",
"score": "0.5533731",
"text": "def plural_to_singular_edit(canonical, resources=None):\n\n for resource in resources:\n rname = resource.name\n if resource.resource_type != SINGLETON:\n continue\n\n canonical = canonical.r... | [
{
"docid": "ce2d6f46ea07360a0e00d535f6b417f8",
"score": "0.67823726",
"text": "def plural_name(noun, language='en'):\n for applyRule in rules(language):\n result = applyRule(noun)\n if result:\n return result",
"title": ""
},
{
"docid": "8a5c15a505e60d24280ebbde76... |
bbbcfca8d52218b6ff75189d3d90b947 | Add two infinite streams (elementwise). | [
{
"docid": "012fa013abe99eec963e3ecb6563715b",
"score": "0.61956424",
"text": "def adds(s1, s2):\n return map(add, s1, s2)",
"title": ""
}
] | [
{
"docid": "f98753baa0059155ab60bef1783dfa0e",
"score": "0.6734333",
"text": "def __add__(self,otherstream):\r\n overlapdates = self.overlap([otherstream])\r\n stream1returns = self.datereturns(overlapdates)\r\n stream2returns = otherstream.datereturns(overlapdates)\r\n retur... |
17eb931e7fb56669dd79d009ee54d0ed | Perform a BFGS update. | [
{
"docid": "e7f1390146b0007321e5dbb5a5decec4",
"score": "0.6545723",
"text": "def _bfgs_update(value_and_gradients_function,\n current_position,\n current_objective,\n current_gradient,\n search_direction,\n current_inverse_... | [
{
"docid": "7863bb3e6ecd9e84edde23b0f1d1cad2",
"score": "0.643692",
"text": "def update(self):\n status = self.gso.GRBupdatemodel(self.model_c)\n if status != 0:\n raise Exception(\"Error updating model: {}\".format(status))",
"title": ""
},
{
"docid": "2097834a5b5da57492bb... |
dfb458409f0030dfa26c64cb2c04218d | Requesting a nonexisting child branch results in a KeyError. | [
{
"docid": "2eac68145caff9195c4dadd1603c010f",
"score": "0.7184823",
"text": "def test_keyerror_for_nonexisting_branch(root):\n with pytest.raises(KeyError):\n root['nonexistant']",
"title": ""
}
] | [
{
"docid": "b724bd5b7e5b671ca87f89c2c92ffa49",
"score": "0.68065286",
"text": "def test_branch_load_nonexisting(root):\n with pytest.raises(KeyError):\n root['people']['zenu']",
"title": ""
},
{
"docid": "7cc1465b4a31462fd2dce25048790396",
"score": "0.6238675",
"text": "def... |
3529c3e38866a2a5eaf4be4f5ed19e0d | Make alias for computed column, which results in ``fname_col_wname_by_suffix``. If any of these elements is empty or ``None``, this empty element would be ignored in alias. | [
{
"docid": "966dea93a2d21da8f99243ac34b668b7",
"score": "0.75428605",
"text": "def _make_alias(self, fname, col, wname, suffix):\n\n alias = DELIMITER.join(\n [item for item in [fname, col, wname, 'by', suffix] if len(item) > 0]\n )\n\n return alias",
"title":... | [
{
"docid": "936754c4d40d08c509a015180f6fac45",
"score": "0.58686614",
"text": "def add_alias(column_name, table_alias):\n\tcolumn_name = column_name.replace('\"', '')\n\tif '.' in column_name:\n\t\ttable_alias, separator, column_name = column_name.partition('.')\n\tcolumn_name = f'{q(table_alias)}.{q(co... |
74839b5ce2276c213bf01e0f8966499f | If True packets also contain conntrack information Defaults to false. | [
{
"docid": "e36f5089f37010d0dd7374ad0089fda4",
"score": "0.70113397",
"text": "def conntrack(self) -> bool:\n return self._get_flag(lib.NFQA_CFG_F_CONNTRACK)",
"title": ""
}
] | [
{
"docid": "7be3401a587cb213f29122056c8d5d92",
"score": "0.6801527",
"text": "def packets_may_arrive(self):\n return True",
"title": ""
},
{
"docid": "e26d7fbe12f439d05a8ac29838c574dd",
"score": "0.5779524",
"text": "def read_packet(self) -> bool:\n return True",
"t... |
38d7c5c357227a74bb32d86d32782579 | Create an array of TECHNICIAN Objects from a row in the dataframe that represent the READABLECSV created by the key.py file | [
{
"docid": "42b18881adbb3d82091aaaff726c629f",
"score": "0.0",
"text": "def create_technicians(row, propertyToHeader_technician, schema):\n charsplit = '/'\n skills = []\n try:\n for skill in row[propertyToHeader_technician['technician']['skills']].split(charsplit):\n ... | [
{
"docid": "6af07d40462c1251df72594e5a040efa",
"score": "0.580087",
"text": "def getDataSet(self): \n\t\twith open(self.filename, 'rb') as f:\n\t\t\treader = unicodecsv.DictReader(f)\n\t\t\tdataSet = list(reader)\n\t\t\treturn dataSet",
"title": ""
},
{
"docid": "4371f47f9c31acf94e3b532d850b... |
a174bdfcc14d96ef67c152526b233a01 | Generates token to prevent CSRF attacks, used in forms that submit using POST | [
{
"docid": "1a46cea94aead35ce22a57c06ac8e387",
"score": "0.7956658",
"text": "def generate_csrf_token():\n if \"_csrf_token\" not in login_session:\n login_session[\"_csrf_token\"] = hashlib.sha256(\n os.urandom(1024)).hexdigest()\n return login_session[\"_csrf_token\"]",
"ti... | [
{
"docid": "f95065617a3e57d02dc86ca527ff6875",
"score": "0.82124776",
"text": "def generate_form_token():\n if '_form_token' not in session:\n form_token = ''.join(\n [random.choice(ascii_letters+digits) for i in range(32)])\n session['_form_token'] = form_token\n return s... |
b8203ddb3eeecbd313b08f6eed9a130d | Creates a new deny list or allow list entry for a scanner | [
{
"docid": "9fc33b5c8e27c4139bc02d90e5d811c2",
"score": "0.63732636",
"text": "def add_new_deny_or_allow_list_entry(self, channel_type_id, channel_type_name, channel_vulnerability_id, channel_vulnerability_name):\n params = {}\n if channel_type_id:\n params['channelTypeId'] = ch... | [
{
"docid": "6c4223725ccd82eef2b30dfde8cbb03a",
"score": "0.5305605",
"text": "def create(self, trans, tool_id, **kwd):\n if tool_id not in trans.app.config.sanitize_allowlist:\n trans.app.config.sanitize_allowlist.append(tool_id)\n self._save_allowlist(trans)\n return... |
1834ad470086af37862763dbdc8e5c80 | Invoke tcServer's instance creation script. | [
{
"docid": "bb2172206a4a0e837dc0f672cdeb3097",
"score": "0.8158045",
"text": "def create_instance(tcserver_dir, instance_name=\"instance1\"):\n print(\"Creating an instance of tcServer in %s\" % tcserver_dir)\n\n pushdir(tcserver_dir)\n subprocess.call([\"./tcruntime-instance.sh\", \"create\", ... | [
{
"docid": "ea2e8f58873e76d4aa31f973dd7f3219",
"score": "0.7507731",
"text": "def start_instance(tcserver_dir, instance_name=\"instance1\"):\n print(\"Starting up a tcServer instance...\")\n\n pushdir(tcserver_dir)\n subprocess.call([\"./tcruntime-ctl.sh\", instance_name, \"start\"])\n popdir()"... |
c889c77138fb01685639d1498b34157e | Parse cmdline args. It expects 's '. Otherwise it'll exit out with a help message. | [
{
"docid": "bb25518ae1d596a3846b13cca6abd86d",
"score": "0.68446225",
"text": "def parse_command_line(self, argv: list):\n function_name = inspect.currentframe().f_code.co_name\n message = f'In {function_name}(), ARGV: {argv}'\n if self.debug > 1:\n print(message)\n\n ... | [
{
"docid": "4fe6828e709c35c7bdf68f69351e88c1",
"score": "0.6996857",
"text": "def parse_args():\n description = \"\"\"This Script creates a list of n random cluster coordinates and\n the the indexes of surrounding coordinates with distances 1-3 from a HiSeq 4000 or HiSeqX s.locs file.\n \"\"\"\... |
b029b2f122b634fee3ae831f92d95c34 | Convert UTM to lat, lon (WGS84) Defaults to Zone 18N | [
{
"docid": "c2f5da1e73c91853b19152f0818fdc87",
"score": "0.67119277",
"text": "def UTM2LatLon(easting, northing, initepsg='epsg:26918'):\n outProj = Proj(init='epsg:4326')\n inProj = Proj(init=initepsg)\n lon,lat = transform(inProj,outProj,easting,northing)\n return lon, lat",
"title": "... | [
{
"docid": "9f3380bfd5e2484b491601d5e9098437",
"score": "0.73258734",
"text": "def LatLon2UTM(lat,lon,init_epsg='epsg:26918'):\n inProj = Proj(init='epsg:4326')\n outProj = Proj(init=init_epsg)\n outx,outy = transform(inProj,outProj,lon,lat)\n return outx, outy",
"title": ""
},
{
... |
8375bf56525d3b5d735606cfc348ae45 | Extracts the diagram ID from a URI >>> tests = [ \ | [
{
"docid": "b23375993c317a3c20aea36d235d3a02",
"score": "0.607165",
"text": "def extractIdFromURI(uri):\n for p in _patterns:\n g = p.search( uri )\n try: id = g.group(1)\n except: id = None\n if (id is not None):\n return id\n return None",
"title": "... | [
{
"docid": "9a04ac5c1cb8df59ee05816432fd625a",
"score": "0.6347979",
"text": "def uri_id(url):\n ident = urlparse(url)\n uid = base64.urlsafe_b64encode(ident.path.lstrip('/'))\n return uid.rstrip('=')",
"title": ""
},
{
"docid": "59a63ec55b8d9fadcafea01195d170d5",
"score": "0.63... |
7020ebfce2286932ab8964d2ac60aaec | Method to change the name of the variable | [
{
"docid": "f90bcba57bf918eb66035999bdf78192",
"score": "0.7124402",
"text": "def change_name(self, new_name):\n self.name = new_name",
"title": ""
}
] | [
{
"docid": "3cc5c1c3aca821275ae6e61806040383",
"score": "0.7880457",
"text": "def __set_name__(self, owner: Prefixable, name: str):\n self.name = name\n self.var_name = f\"{owner.prefix.upper()}_{name.upper()}\"",
"title": ""
},
{
"docid": "8f8a26d1f0afc5c5a4a2e734b38b78d1",
... |
e95f56338dde5e3568ef3f643a17ada2 | Jv = jacobev(q) is the spatial velocity Jacobian, at joint configuration q, which relates the velocity in the base frame to the velocity in the endeffector frame. Jv = jacobev() as above except uses the stored q value of the robot object. | [
{
"docid": "ec2bb05532592538a77ff47d902b411f",
"score": "0.71177065",
"text": "def jacobev(\n self, q=None, from_link=None, to_link=None,\n offset=None, T=None):\n\n if from_link is None:\n from_link = self.base_link\n\n if to_link is None:\n to_... | [
{
"docid": "b39bf65c589be61960371809848e61e1",
"score": "0.7463",
"text": "def jacob0v(self, q=None):\n\n r = (self.base.inv() * self.fkine(q)).R\n\n Jv = np.zeros((6, 6))\n Jv[:3, :3] = r\n Jv[3:, 3:] = r\n\n return Jv",
"title": ""
},
{
"docid": "8cd0d839... |
319a93db64c358f3b8f4d59091a0bbb1 | for nurbs curves only. get the parameter length. | [
{
"docid": "decccc0b33a2317b0a3c73a3bdb6f94c",
"score": "0.72856337",
"text": "def get_param_length(curve_name):\n shape_fn = object_utils.get_shape_fn(curve_name)[0]\n return shape_fn.findParamFromLength(shape_fn.length())",
"title": ""
}
] | [
{
"docid": "6ecc3b99369b3877d9768b6a75ecaa74",
"score": "0.7094655",
"text": "def length(self) -> float:\r\n ...",
"title": ""
},
{
"docid": "84008ccadc6e930ac45f2670648e4539",
"score": "0.6656993",
"text": "def num_params(self):\n return len(self.data)",
"title": "... |
53bf06816ad671070b6bb4e78ee2b1c2 | critical_point_current_density should return astropy.units.Quantity | [
{
"docid": "03693be428578f65e1627168749e09f0",
"score": "0.84548855",
"text": "def test_critical_point_current_density(self):\n self.assertIsInstance(self.t.critical_point_current_density(), units.Quantity)",
"title": ""
}
] | [
{
"docid": "b2586560530a8ba0c8f4e63a1980e072",
"score": "0.8172111",
"text": "def test_critical_point_current_density(self):\n self.assertEqual(self.t.critical_point_current_density().unit, units.Unit(\"A cm-2\"))",
"title": ""
},
{
"docid": "c6e08590b2b80ce3db30145faa3fff51",
"sc... |
24ef81b3baf8549e0629e2880eb810c5 | Parse `dynamic_args` and generate valid `qsrs_for`. It parses the `dynamic_args` to see if the user has specified `qsrs_for` and then validates them, or uses default `qsrs_for` generation if user has not specified anything. | [
{
"docid": "fe6199ab3ec603391f7080e8ea5691b0",
"score": "0.5931462",
"text": "def _process_qsrs_for(self, objects_names_of_world_state, dynamic_args, **kwargs):\n if isinstance(objects_names_of_world_state[0], str):\n try:\n return self.__check_qsrs_for_data_exist_at_wor... | [
{
"docid": "7ec957fd86e117f5729ed622076bfd85",
"score": "0.6195913",
"text": "def EvaluateDynamicPipelineOptions(dynamic_options):\n filledOptions = []\n for optionDescriptor in dynamic_options:\n fillType = optionDescriptor['type']\n optionName = optionDescriptor['name']\n valueFormat = opti... |
8c6913812ad017e7f64cd2b898399714 | Renders the form to add a tool. | [
{
"docid": "c57b72713de60feb1fcde0fce5325a9d",
"score": "0.6707833",
"text": "def addToolForm(request, id=None):\n if request.user.is_authenticated():\n first_shed_list = Shed.objects.filter(zipcode=request.user.zipcode)\n shed_list = []\n for shed in first_shed_list:\n ... | [
{
"docid": "439ba179417374cef7f5a9dddd4a5cc0",
"score": "0.6686587",
"text": "def display(self, tool):\n self._tools.append(tool)\n self._justDisplay(tool)",
"title": ""
},
{
"docid": "b84d46d196222f1913aa79b6494c94a8",
"score": "0.64594257",
"text": "def addTool(reques... |
b6338c1cc000ae18792c67b88490d6cb | factory function of mock handler | [
{
"docid": "131d4224dac18dff4f7cb24dbc64339a",
"score": "0.77851194",
"text": "def mock_handler_function_wrapper(ret):\n def mock_handler_function(http_response, response):\n \"\"\"\n mock handler\n :param http_response:\n :param response:\n :return:\n \"\"\"... | [
{
"docid": "22fdcea395e63509c0ca1f516f60a4d9",
"score": "0.7107924",
"text": "def test_dispatch(self):\n\n content = '<b>hi sup</b>'\n\n\n class SubHandler(handler.Handler):\n\n \"\"\" I am an example handler \"\"\"\n\n get_was_called = False\n\n def GET(self):\n\n \"\"\" I a... |
886c077299e596df809ddb5260a537cb | This function call the showFram function which means whenever this function is called the edit page will be visible to the user as this function will be called by the "Edit Page" button. And secondly this function reinitalizes the edit shapes frame so everything looks realtime. | [
{
"docid": "9a29ffa078e68dbbebcf6c363ba44ec2",
"score": "0.69142205",
"text": "def editButtomCommand(self):\n self.showFrame(self.frameEditShapes)\n self.frameEditShape()",
"title": ""
}
] | [
{
"docid": "4bfa662221ec5604523614d4b3b8abd8",
"score": "0.6618589",
"text": "def frameEditShape(self):\n self.refresh_board()\n for widgets in self.frameEditShapes.winfo_children():\n widgets.destroy()\n self.canvas.unbind(\"<Button-1>\")\n self.canvas.bind('<Butt... |
69b2d8b9a0a9d6030dcaa35d8a5275b9 | Retrieving the continuum window Returns _ The collection of continuum points | [
{
"docid": "6aa363c5771e9c175fb8e600710a5f3c",
"score": "0.7036397",
"text": "def getcontwindow(self) -> Optional[Tuple[List[u.Quantity], List[u.Quantity]]]:\n if not self.__checkcontpoints__():\n return None\n return [self.c1, self.c2], [self.c3, self.c4]",
"title": ""
}
... | [
{
"docid": "87ae90f6b39e4fedf2ca05405912bbc6",
"score": "0.69538367",
"text": "def get_window(self):\n return np.ones(self.stop_idx - self.start_idx)",
"title": ""
},
{
"docid": "258efb027830d85e0f513fe7360b1184",
"score": "0.6198811",
"text": "def window(self):\r\n cur... |
ef99b902f2326d3c76911f48982a1025 | Values for the dependent variable | [
{
"docid": "17b3d0717db4b6d80ac1777faff4f743",
"score": "0.7510663",
"text": "def dependent(self):\n return self.y",
"title": ""
}
] | [
{
"docid": "ef8a66714a96635ac01f85a3163b6f30",
"score": "0.6807894",
"text": "def independent_variables(self):\n return self._independent_variables",
"title": ""
},
{
"docid": "2579622b8647521182270d7b8101874e",
"score": "0.66313666",
"text": "def get_variables(self) -> np.arr... |
caafc7f534987283953d04b1b210fa36 | Sequence mustMatch should return True when no possible mismatches | [
{
"docid": "c69f2cbb842d84641d4840f3ce6849b1",
"score": "0.5766759",
"text": "def test_mustMatch(self):\n assert self.RNA('').mustMatch('')\n assert not self.RNA('N').mustMatch('N')\n assert not self.RNA('R').mustMatch('R')\n assert not self.RNA('N').mustMatch('r')\n a... | [
{
"docid": "12dc9dea663d8ed3fbc32a8402baaaf6",
"score": "0.65546477",
"text": "def test_invalid_match():\r\n match1 = fr.match_input_with_pickle(linhPickleData, alexInputEncodings)\r\n match2 = fr.match_input_with_pickle(alexPickleData, linhInputEncodings)\r\n\r\n assert match1 == False\r\n ... |
42fcac8dbf57218509a51c02ac3ba4e4 | Given a mapping from each sample to its set of annotated terms and a target term, return the set of all samples annoated with the target term and those that are not. | [
{
"docid": "1e73b5bfc995ef65b66f7856bfabb4ca",
"score": "0.5485493",
"text": "def term_to_samples(sample_to_terms, term_id_to_name, term):\n # If the user supplied a term ID, then map it back to a term\n # name\n if 'UBERON:' in term or 'CL:' in term or 'CVCL:' in term \\\n or 'DOID:' in... | [
{
"docid": "c51be798d75d1eb7cde223be15f9ba21",
"score": "0.5797579",
"text": "def null_intersection(x_train, y_train, target_words, word_index):\n x_train_new, y_train_new = [], []\n\n for i, x_t in enumerate(x_train):\n intersection = len(set(x_t).intersection([word_index[word] for word in... |
5e635a0f138de6481944d8bf1025acec | Wprawia stworzenie w ruch w kierunku danego pola. | [
{
"docid": "0503fd08951da7b4917877d1c9a70007",
"score": "0.0",
"text": "def move(self, _vec):\n\n\t\t_dest = self._grid[0] + _vec[0], self._grid[1] + _vec[1], self._grid[2] + _vec[2]\n\t\tif _vec[2]: # jeśli ruszamy się w z to znaczy że wchodzimy po schodach\n\t\t\tself._map.getLayer('Fields').get(self.... | [
{
"docid": "303b090737f11b28a9174d4b53496fe9",
"score": "0.68418884",
"text": "def mezclar_bolsa(self):",
"title": ""
},
{
"docid": "991e0245df134584bbc6b3bcd6de3c7b",
"score": "0.67294735",
"text": "def preberi_pot(ukazi):",
"title": ""
},
{
"docid": "991e0245df134584bbc... |
8b2c117b9c1f0790cbd981625802d8cc | Test SSP computation on epochs. | [
{
"docid": "73736b819cee7ab83c86312c7c11d916",
"score": "0.0",
"text": "def test_compute_proj_epochs(tmp_path):\n event_id, tmin, tmax = 1, -0.2, 0.3\n\n raw = read_raw_fif(raw_fname, preload=True)\n events = read_events(event_fname)\n bad_ch = \"MEG 2443\"\n picks = pick_types(raw.info, ... | [
{
"docid": "bb77ae2848be3b277eb1f6ebc241cbbb",
"score": "0.6580616",
"text": "def _run_ensemble_funcs(self, ens, xpts):\n\n pdfs = ens.pdf(xpts)\n cdfs = ens.cdf(xpts)\n logpdfs = ens.logpdf(xpts)\n logcdfs = ens.logcdf(xpts)\n\n if hasattr(ens.gen_obj, 'npdf'):\n ... |
1fa7188ccf9d129ff7c6b38b4bbb5a62 | New values for the corrections have been submitted. A new call to update suffieces to redo the calculations, since _accurate_E always gets called by update() because this ensures that any changes the user makes to thresholds or plotoptions will not undo the accurate fret. | [
{
"docid": "533318194971572b788ef4c724048f56",
"score": "0.69895715",
"text": "def apply_corrections(self):\n self.update()",
"title": ""
}
] | [
{
"docid": "a0781ceb4905da8bd4e566aecf6f1310",
"score": "0.7248283",
"text": "def _accurate_E(self):\n print 'Calculating accurate FRET'\n # First we must correct the signal for background. The 1000 is because bkg_XX is in counts /ms\n DA_bkg_corr = self.data['F_Dexc_Aem'] - self.co... |
e1d7405469a47e95c9cd2d77a13c4c28 | show chart of answers per day | [
{
"docid": "df19d3268f88a2afa60ff6a29bb17c4a",
"score": "0.0",
"text": "def changelist_view(self, request, extra_context=None):\n if request.POST.get(\"run_generate_daily_stats_script\", False):\n management.call_command(\"generate_daily_stats\")\n\n # custom form\n curre... | [
{
"docid": "c4a83b66b494e8cb1bb15e0aa0cb3030",
"score": "0.6323107",
"text": "def daily_diagram(login):\n height, bars, date = day(login)\n barWidth = 0.25\n y_pos = np.arange(len(bars))\n plt.bar(y_pos, height, color='#EB984E', width=barWidth, edgecolor='white', label='var1')\n plt.xtick... |
af894739e47bd0295ba02752666c07f3 | Convert df to dictionary. | [
{
"docid": "7d071e87558c9c26afe02ab55a83a8bb",
"score": "0.0",
"text": "def data_dict(self):\n mydict = lambda: defaultdict(mydict)\n meta_data_dict = mydict()\n\n for cord_uid, abstract, title, sha in zip(self.meta_data['cord_uid'], self.meta_data['abstract'], self.meta_data['title... | [
{
"docid": "f6f44705836dfa968ae5c7c879794576",
"score": "0.8157039",
"text": "def _data_df2dict(df):\n data = {\"A\": np.array(df.A),\n \"DA\": np.array(df.DA),\n \"B\": np.array(df.B),\n \"DB\": np.array(df.DB),\n \"R\": np.array(df.R)}\n return data",
... |
d7928fa7fbf48603cb94dd9d10f89bde | Load the model from the checkpoint by filtering out the unnecessary parameters | [
{
"docid": "7693094ad117d96666c397a863239660",
"score": "0.7148552",
"text": "def load_model_from_dict(self, checkpoint):\n model_dict = self.state_dict()\n # filter out unnecessary keys in the imported model\n pretrained_dict = {k:v for k, v in checkpoint['model_state_dict'].items(... | [
{
"docid": "c44e217a0849ba1cdb31e90246b6a6cc",
"score": "0.73850775",
"text": "def load_model(self):\n self.actor.load_checkpoint()\n self.critic.load_checkpoint()\n self.target_actor.load_checkpoint()\n self.target_critic.load_checkpoint()",
"title": ""
},
{
"doc... |
5e7197bb410052648fb4f548a1adef3d | Define forward propagation for the attention network. | [
{
"docid": "fa7ffdba1e9919bf82541570ac2f87cc",
"score": "0.0",
"text": "def forward(self, decoder_states, encoder_output, x_padding_masks):\n # Concatenate h and c to get s_t and expand the dim of s_t.\n h_dec, c_dec = decoder_states\n # (1, batch_size, 2*hidden_units)\n s_t ... | [
{
"docid": "fe8251443026d7dea3f5b693314535c9",
"score": "0.7427378",
"text": "def set_forward_propagation(self):\n self.fw_propagating = True",
"title": ""
},
{
"docid": "70b74ff3d86423e5c86d527b473b1985",
"score": "0.7089231",
"text": "def forward_propagation(self):\n ... |
34d4fbb727c589de5b4b74852239253a | Set up a dictionary of detector locations and responses. Either put indigo in bangalore or a different site | [
{
"docid": "5f4cf003357f387e7e0eec0ecd1bf6f3",
"score": "0.6552748",
"text": "def detectors(ifos, india=\"bangalore\", south_africa=\"sutherland\", orientation_angle_1=0, orientation_angle_2=0):\n location = {}\n response = {}\n # Use cached detectors for known sites:\n lho = lal.lal.CachedDetectors... | [
{
"docid": "0c9b2ddd67a5f8406f04a3fad9f26998",
"score": "0.5627128",
"text": "def main(self):\n question = Parser(self.question)\n latitude = 0\n longitude = 0\n address = \"Bonjour, je vais bien merci !\"\n history = self._get_papy_sentence()\n article_title = ... |
e19e19a14bfc966eae94ad450fc44e4f | Converts a 2bytes (16 bits) string to an unsigned integer. | [
{
"docid": "83afc3af95d13c0bedfb515508a19cb9",
"score": "0.0",
"text": "def i16le(c, o=0):\n return unpack_from(\"<H\", c, o)[0]",
"title": ""
}
] | [
{
"docid": "9f859d9adcda3009a3cdc34da345ba49",
"score": "0.7961217",
"text": "def decode_unsigned_int(string):\n return int(string,16)",
"title": ""
},
{
"docid": "2aabe2c3ca74d37f7e02ec543618cbfe",
"score": "0.69533163",
"text": "def bytesToUShortInt(bytestring):\n return _doC... |
3263d45e1d156fba8277ca49663bad50 | Method to accept cash payments. | [
{
"docid": "c818168a2a06ac72d9d073384c352350",
"score": "0.56980604",
"text": "def receive(self, amount: float):\n self.cash += amount\n self.profits_losses += amount",
"title": ""
}
] | [
{
"docid": "8023b7cee48abb4260b3576db1ce9337",
"score": "0.6500569",
"text": "def pay(self, order: OnlineOrder):\n total = order.get_total()\n if self.cards:\n for card in self.cards:\n if card.ballance >= total:\n card.charge(total)\n ... |
08afafda08458bd52dae1eb95fd8d3e9 | Function creates dictionary that holds all the unique phone numbers and tallies the seconds | [
{
"docid": "952fee6521258ba7d8eefb1c5f272bea",
"score": "0.6745791",
"text": "def phone_durations(num_list):\n # create empty defaultdict object\n num_dict = defaultdict(int)\n\n for num in num_list:\n num_dict[num[0]] += int(num[3])\n num_dict[num[1]] += int(num[3])\n\n return... | [
{
"docid": "55c94d4782fdf07b3ac0226e249ef640",
"score": "0.614255",
"text": "def get_phonemes(num_phones):\n phonemes = {}\n\n if num_phones == int(48):\n logging.debug(\"Using 48 phones for modeling!\")\n # This dictionary is used to convert the 60 phoneme set into the 48 one.\n ... |
53a853bf9167e8ae9890c42544faa1e5 | User provides details about game. | [
{
"docid": "2c75fbee56d835a92c806c6821d72d1f",
"score": "0.0",
"text": "def handle_multiplication_tables_response(self, message):\n self.remove_context('InitTablesContext')\n self.handle_utterance(message, True)",
"title": ""
}
] | [
{
"docid": "5312250a42ecce58372cf20831568a8d",
"score": "0.6623642",
"text": "def game_info(self):\n return self._game_info",
"title": ""
},
{
"docid": "909feeb00eaf9d733de3ec54844ff715",
"score": "0.6586223",
"text": "def game_detailed(request, game):\n try:\n g = Game.... |
6d17a86977aa5e07fc3bf30cc70cec9c | Callback for /twitter command. | [
{
"docid": "9a39d4f42305095043641d4083530278",
"score": "0.71408504",
"text": "def twitter_cmd(data, buffer, args):\n if args != \"\":\n twitter_get(args)\n else:\n twitter_get(\"last\")\n return w.WEECHAT_RC_OK",
"title": ""
}
] | [
{
"docid": "7455b2d3bd5471f490b37626590d06ad",
"score": "0.72913617",
"text": "def do_twitter(self, s):\n if \"login\" in s:\n try:\n driver = twitter_login(self)\n twitter_end(self, driver)\n except ConnectionError:\n print(CONNE... |
b840234fa9d8f1681256cc2189f82323 | r"""Call the update phrase matcher method over HTTP. | [
{
"docid": "fcdc30237ae20d3245c69c5b7c8cd8ff",
"score": "0.62303835",
"text": "def __call__(\n self,\n request: contact_center_insights.UpdatePhraseMatcherRequest,\n *,\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\n timeout: Optional[float] = N... | [
{
"docid": "2e2b4ff5679148e0a86d475b255aa123",
"score": "0.70168084",
"text": "def post_update_phrase_matcher(\n self, response: resources.PhraseMatcher\n ) -> resources.PhraseMatcher:\n return response",
"title": ""
},
{
"docid": "c4882d357b086afe3205dc8133d5987a",
"sco... |
1776f4905487abaf8b63ef81db24fc00 | Returns a wrapper that enforces the presence of certain fields in a JSON body, if a body is present. Passes the JSON body as the first argument to the inner, or None if there is no body. If a field is missing or has the wrong type, returns 400. | [
{
"docid": "27c1831cfc5dd7f74980b5cf54a01a5e",
"score": "0.5529812",
"text": "def maybe_json(**jkwargs):\n\n def wrapper(func):\n @functools.wraps(func)\n def inner(*args, **kwargs):\n if request.content_length == 0:\n return func(None, *args, **kwargs)\n ... | [
{
"docid": "1f56a068edabb5c94c2214a769dcb306",
"score": "0.7296733",
"text": "def expect_json(*, strict=False, optional=None, **fields):\n if optional is None:\n optional = set()\n\n def wrapper(func):\n @functools.wraps(func)\n def inner(*args, **kwargs):\n # ensur... |
70e692868094504e23935c6af3f3608a | Getter method for state, mapped from YANG variable /qos/classifiers/classifier/terms/term/state (container) | [
{
"docid": "f22d788da20f7c4a9213740db7ac7129",
"score": "0.0",
"text": "def _get_state(self):\n return self.__state",
"title": ""
}
] | [
{
"docid": "f7f34e144872a5afcae6730f3513d2d6",
"score": "0.7400615",
"text": "def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_classifiers_classifier_terms_term_state, is_container... |
495b060afd563a3b0a6691f036e37e95 | Takes a feature and plots a primitive's time for completion (assumed to be successful) with respect to initial feature condition | [
{
"docid": "dba72e07488e66f6febbedec16134cdc",
"score": "0.7870494",
"text": "def analyze_feature_to_time(self, feature, primitive):\n time = np.array(self.time[primitive])\n s = self.success_start[primitive]\n s = s[:, features[feature]].ravel()\n \n plt.scatter(s, time)\... | [
{
"docid": "0d4c94955098393f2b1123cd9107e53a",
"score": "0.590543",
"text": "def plotting_time_feat(df, features, start, stop, scaler=False):\n \n df1 = df[features]\n df2 = df1[start:stop]\n \n if scaler:\n from sklearn.preprocessing import StandardScaler\n scaler = Standar... |
acd20aee05a7edf794a69b3f3aa3bf3c | decompose the vector field (u,v) into potential (up,vp) and solenoidal (us,vs) fields using 2D FT a la Smith JPO 2008 | [
{
"docid": "f7f32f5f034ff0911e688bdd88cc3cd3",
"score": "0.64122146",
"text": "def ps(u,v,dx,dy):\n\n ix,jx,kx = u.shape\n dl = 1./(ix*dy)\n dk = 1./(jx*dx)\n kNy = 1./(2*dx)\n lNy = 1./(2*dy)\n k = np.arange(-kNy,kNy,dk)\n k = np.fft.fftshift(k)\n l = np.arange(-lNy,lNy,dl)\n ... | [
{
"docid": "3e69e3b72302484e63106a940fe1501a",
"score": "0.68711233",
"text": "def vectorfield(w, t, p):\n x1, y1, x2, y2 = w\n t1, t2, m, l = p\n\n # Create f = (x1',y1',x2',y2'):\n f = [y1,\n ((2./3.)*((t1/(0.5*m*l**2)+(np.sin(x2)*y2*(2*y1+y2))))-(((2./3.)+(np.cos(x2))*((t2/0.5*m*l... |
53a18a58bf82ca82cde3df84f4b10779 | This method unbinds the skin for all the meshes in the given list, if any. | [
{
"docid": "57e74bb8b7ccaf11d9be63c75b70cbbf",
"score": "0.69904643",
"text": "def detachSkinFromMesh(meshes):\n\n pass",
"title": ""
}
] | [
{
"docid": "ab9be50819a072e20e9f8437cb740c4d",
"score": "0.63012815",
"text": "def unregister_layers(self, names, out):\n skinstool = getToolByName(self, 'portal_skins')\n\n # remove this layer from all known skins\n for skinName in skinstool.getSkinSelections():\n path = skinstool.getSk... |
675c230b3f4344b7d3447c81c8c65558 | Send valid get request to github_callback and receive no access_token. | [
{
"docid": "a69334d39c6abec677166da4651c9076",
"score": "0.7379556",
"text": "def test_github_callback_incomplete_get(self, mock_post, mock_g, mock_user_model):\n mock_post.return_value.json.return_value = {}\n\n with self.app.test_client() as client:\n response = client.get(\"/... | [
{
"docid": "bc74d9d65ec4b98ac2716efa2f8a1873",
"score": "0.6934215",
"text": "def test_github_callback_empty_get(self, mock_post):\n with self.app.test_client() as client:\n response = client.get(\"/account/github_callback\")\n\n self.assertEqual(response.status_code, 400)\n ... |
6f9db51ad2eab65a0c229c057a3eef2d | Add gaussian heatmap for character bbox to the image | [
{
"docid": "507a93ec78ea3d50b2bc339483425447",
"score": "0.6403565",
"text": "def add_character(image, bbox):\n\n\tif not Polygon(bbox.reshape([4, 2]).astype(np.int32)).is_valid:\n\t\treturn image\n\n\ttop_left = np.array([np.min(bbox[:, 0]), np.min(bbox[:, 1])]).astype(np.int32)\n\ttop_right = np.array... | [
{
"docid": "f243def6ba73436b53aeb3f0168fde06",
"score": "0.63595766",
"text": "def process_image(img):\n avg_heat = np.zeros_like(image[:, :, 0]).astype(np.float)\n out_img, out_boxes, heat_map = find_cars(img, scale)\n\n # -----\n heat = np.zeros_like(image[:, :, 0]).astype(np.float)\n #... |
adc3c746496e5167389997c4151ae2f6 | Convenience method for making a table | [
{
"docid": "81e610a25bbd00c09f72427cdfeb2245",
"score": "0.698341",
"text": "def make_table(self):\n hash_key = DynamoKey('id')\n self.dynamo.create_table('foobar', hash_key=hash_key)",
"title": ""
}
] | [
{
"docid": "917e9f127e58b7bf071b3275c62bd88c",
"score": "0.8010181",
"text": "def makeTable(self):\n\tcols = [Column(name=k, **v) for k,v in self.col_dict.items()]\n\treturn Table(cols)",
"title": ""
},
{
"docid": "0f41e7f18a334779321cd1b0d029b69d",
"score": "0.7826398",
"text": "def... |
ab89c7636396ac096d9f684b45b0325c | Check atom types for matched atoms are the same in both molecules. If not the same report to user and ask if they wish to continue and ends if not. | [
{
"docid": "3b8703a5cc93018b33404a9fa433f656",
"score": "0.72817165",
"text": "def check_atom_type_match(matched_idx_map, initial_mol_info, final_mol_info):\n\n init_names = initial_mol_info.struct.name()\n final_names = final_mol_info.struct.name()\n\n type_match_error = False\n\n for init_... | [
{
"docid": "acc86706fa2928bf1f4e37aa6956f931",
"score": "0.6576755",
"text": "def match_molecules(molecule1, molecule2, logger, onlyH):\r\n\r\n if logger.use_groups: # If group charges are to be used\r\n\r\n chg1, chg2 = molecule1.chg_grp, molecule2.chg_grp\r\n\r\n else:\r\n\r\n chg... |
2e0aef6d6ccd975462cef39574e85761 | Helper function to compute our content and style feature representations. This function will simply load and preprocess both the content and style images from their path. Then it will feed them through the network to obtain the outputs of the intermediate layers. | [
{
"docid": "d4208e32afe57cbe7499df2e38a92d0c",
"score": "0.69074523",
"text": "def feature_representations(\n self, content_img: np.array, style_img: np.array\n ) -> Tuple[tf.Tensor, tf.Tensor]:\n\n content_img = self._process_img(content_img)\n style_img = self._process_img(styl... | [
{
"docid": "f3f5bf2fac38a5d95359f7beb6529362",
"score": "0.64880633",
"text": "def stylize(network, initial, content, styles, iterations,\n content_weight, style_weight, style_blend_weights, tv_weight,\n learning_rate, print_iterations=None, checkpoint_iterations=None):\n shape ... |
b7db9c4c6c9fb6dd1d332e25a176ebb7 | Lookup quote starting position using google diff_match_patch (levenshtein) algorithm | [
{
"docid": "d44958809665a563a50dc4b7074aa5b5",
"score": "0.7858059",
"text": "def quote_start_position(self):\n\n quote_locate = diff_match_patch()\n quote_locate.Diff_Timeout = 5.0 \t# Tweak this?\n quote_locate.Match_Threshold = 0.5 # Tweak this?\n\n # Guess a big distance... | [
{
"docid": "6199483675f4357c9c9a305fcdc5b449",
"score": "0.65813494",
"text": "def findClosestMatchingQuote(self, quote, threshold=.9):\n quotelen = len(quote.split(\" \"))\n chaptersubstrings = []\n lst_of_substrings = []\n for chapter in self.chapter_lists:\n for i in range(... |
2fd056d69913ba40d9b0321433b46e94 | Check package identifier is valid before querying results. Check package identifier is valid before querying results. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. | [
{
"docid": "384efe157aa2adbca036f6da249728a2",
"score": "0.0",
"text": "def vulnerabilities_list1_with_http_info(self, owner, repo, package, **kwargs):\n\n all_params = ['owner', 'repo', 'package', 'page', 'page_size']\n all_params.append('callback')\n all_params.append('_return_htt... | [
{
"docid": "aec3eda9c5ef29ed467555c6c8f7127c",
"score": "0.6192965",
"text": "def is_package_id_valid(package_id: str) -> None:\n pattern = re.compile(r\"^pkg_[1-9a-zA-Z]+$\")\n\n if not package_id.startswith(\"pkg_\"):\n raise ValidationError(\n message=f\"[{package_id[0:4]}] is... |
ca766eae32d3138b488dc464fd2fc166 | Structured SVM loss function, naive implementation (with loops). Inputs have dimension D, there are C classes, and we operate on minibatches of N examples. | [
{
"docid": "f73b08783021763c99cd131751eff8fa",
"score": "0.63592327",
"text": "def svm_loss_naive(W, X, y, reg):\n dW = np.zeros(W.shape) # initialize the gradient as zero\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n \n # Iterate throu... | [
{
"docid": "67adf20f38500772f8f93f199913c491",
"score": "0.6719385",
"text": "def svm_loss_vectorized(W, X, y, reg):\n #############################################################################\n # TODO: #\n # Implement a vectoriz... |
9703f40fa9ce030515a496a9cc3d4b6d | Create quiz providing basic fields with LIVE_STATUS and two categories. | [
{
"docid": "c578e483defc8bf217d15cee61fb9ec1",
"score": "0.6504106",
"text": "def testCreateQuizLiveWithCategories(self):\n status = Quiz.LIVE_STATUS\n quiz = Quiz(title=self.title, body=self.body, slug=self.slug, author=self.editor, status=status)\n quiz.save()\n c1 = Catego... | [
{
"docid": "23ea909a4f70cd48b86b9e7215fd7a4e",
"score": "0.6298574",
"text": "def test_quiz_question_categorized(self):\n url = '/api/quizzes'\n previous_questions = []\n\n data = {\n 'previous_questions': previous_questions,\n 'quiz_category': self.science.id\... |
79266c27c0f35444c6f2b5e7f0b20869 | Reads a GeoJSON and gives the results in GeoJSON format Speed = 0.091ms/feature | [
{
"docid": "8e4266a2a809483dca42fc8c9b0f2bea",
"score": "0.6681749",
"text": "def read_geojson(self, infile, **kwargs):\n\n logging.info('Reading GeoJSON: %s' % infile)\n if isinstance(infile, dict):\n geojson = infile\n else:\n with open(infile) as f:\n ... | [
{
"docid": "3038df70905acd2489ae82ffca63ed98",
"score": "0.6661169",
"text": "def read_geojson(geojson_file):\n # example in order to see the polygon_vertices_pixels\n #data['features'][0]['properties']['polygon_vertices_pixels']\n d = dict()\n \n # get all the solar panel coordinates in ... |
05bc8e2304577295887424b010ed7a87 | generated source for method getFuelLevel | [
{
"docid": "9d9088df5fd780e9da59f78ed407a612",
"score": "0.8427119",
"text": "def getFuelLevel(self):\n return self.brewingStand.getProperty(1)",
"title": ""
}
] | [
{
"docid": "51fc8d466defad910e1ff2f03bb998fd",
"score": "0.7995947",
"text": "def fuel_level(self):\n return self.tank.fuel_level",
"title": ""
},
{
"docid": "508a0b2ef68dbfac6b1842802129002d",
"score": "0.6756143",
"text": "def load_fuel_base(self):\n values = self.val... |
d4cd93f5d748fce6278f88010ec3b46b | SetConfidenceImage(itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2 self, itkImageF2 _arg) | [
{
"docid": "cca36d4a0abdb64086ef7638f38fbaf0",
"score": "0.91615874",
"text": "def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterISS2IUC2IF2_SetConfidenceImage(self, _arg)",
"title": ""
}
] | [
{
"docid": "415a1a1b5d335a108c6dd621714243da",
"score": "0.9181978",
"text": "def SetConfidenceImage(self, _arg: 'itkImageF2') -> \"void\":\n return _itkN4BiasFieldCorrectionImageFilterPython.itkN4BiasFieldCorrectionImageFilterIUC2IUC2ISS2_SetConfidenceImage(self, _arg)",
"title": ""
},
{... |
4c403227b17588c8a00c52a5dbe76c27 | A context manager that will lock on the device given, if configured to do so. | [
{
"docid": "4769ef3c47052963d0e455f3bd3b1be3",
"score": "0.62667316",
"text": "def replication_lock(self, device):\r\n if self.replication_one_per_device:\r\n dev_path = self.get_dev_path(device)\r\n with lock_path(\r\n dev_path,\r\n tim... | [
{
"docid": "dccae90a973f8f2e76e03f36ca6969a1",
"score": "0.69568276",
"text": "def lock_context(self, timeout='default', requested_key='exclusive'):\n if requested_key == 'exclusive':\n self.lock_excl(timeout)\n access_key = None\n else:\n access_key = self... |
cd1077eda04f8edc8caf84074f701a3e | Provide customable on number of hidden units in layer 1/2 As well as drop out size in connection between layer 1 and 2 | [
{
"docid": "15ea1ce1012f8e94460b7fe955c465df",
"score": "0.0",
"text": "def nn_3layers(nb_input, nb_output,hn1=210,hn2=100,hn3=50,dp1=0.4,dp2=0.2, **kwargs):\n # create model\n model = Sequential()\n model.add(Dense(hn1, input_dim= nb_input, init='glorot_uniform', activation='relu')... | [
{
"docid": "b9350cd5058fd91107fba622e285d446",
"score": "0.7422406",
"text": "def hidden_unit(Layer): \n\tfan_in = Layer.weight.data.size()[0]\n\tlim = 1. / np.sqrt(fan_in)\n\treturn (-lim, lim)",
"title": ""
},
{
"docid": "6d850db14618078b77e68af232c24387",
"score": "0.6618541",
"te... |
b12647070c6dcd54d4825e5d189045c2 | Waits for a resource to be deleted. | [
{
"docid": "e40d0d8b09095126878bc7e9ee57c02a",
"score": "0.7592842",
"text": "def wait_for_resource_deletion(self, resource_type, id, client=None):\n start_time = int(time.time())\n while True:\n if self.is_resource_deleted(resource_type, id, client=client):\n ret... | [
{
"docid": "0c6e8cc1c7e01252177fd073373b8eb1",
"score": "0.7432982",
"text": "def wait_for_delete(self, res, interval=2, wait=120):\n return resource.wait_for_delete(self, res, interval, wait)",
"title": ""
},
{
"docid": "761c304c769b4e71ef4f2a3648ec7efa",
"score": "0.7096719",
... |
f69d765571a2ea175f00782c38791dc6 | Function for the creation of a double ring neighborhood structure. | [
{
"docid": "9a2858696789e36b454db246991d9721",
"score": "0.6618666",
"text": "def create_neighborhood_structure_ring(alist):\n for i in range(len(alist)):\n if i == 0:\n alist[i].neighborhood.append(alist[-1])\n alist[i].neighborhood.append(alist[-2])\n alist[-... | [
{
"docid": "09f8ba82c9d6d9128d3ee2916ff6e64a",
"score": "0.66190475",
"text": "def _create_neighbourhood(self):\n pass",
"title": ""
},
{
"docid": "307bffa20031a4c7d3f4fbe0f06371e7",
"score": "0.61556005",
"text": "def make_neighbor(self, n1, n2):\n pass",
"title": ... |
1ec8569229ebed58baaf8b1c58f72297 | The show action, which shows details for a particular DocumentFile. | [
{
"docid": "c792bc572b8fe5fd7484420aadc30173",
"score": "0.7743916",
"text": "def document_show(project_id, document_file_id):\n #TODO: good spot for a helper\n #document = helpers.get_object_or_exception(Unit,\n # Unit.id == document_id, exceptions.DocumentNotFoundException)\n try:\n ... | [
{
"docid": "7c868d3aaf579912e3baafb242d610b6",
"score": "0.67580366",
"text": "def show(self, name, docid=None, **options):\n path = _path_from_name(name, '_show')\n if docid:\n path.append(docid)\n status, headers, body = self.resource(*path).get(**options)\n retu... |
ccec0f53b7fad21155536c7de925e1d0 | initialize the weights for the neural network | [
{
"docid": "5e9a76e48f7387150da17591d768fe14",
"score": "0.0",
"text": "def neural_net_weights(num_inputs, num_hidden_layers, num_nodes, num_outputs):\n \n network = []\n output_layer = []\n \n # no hidden layer\n if num_hidden_layers == 0: \n for n in range(num_outputs):\n ... | [
{
"docid": "2f539308a4530e0d61a67321a3fd828a",
"score": "0.86598444",
"text": "def init_weights(self):\n pass",
"title": ""
},
{
"docid": "be8dec4d41250f35e3f156604e893851",
"score": "0.84111565",
"text": "def weight_init(self):\n self.weights = [np.random.randn(y, x)/n... |
0cc1dc4e58cdb02cae12456159fa0e11 | Returns the width of the given text. | [
{
"docid": "53c08eaa07f61457d07ef1b8774b6149",
"score": "0.75393206",
"text": "def textwidth(txt, **kwargs):\n if not isinstance(txt, Text) or len(kwargs) > 0:\n kwargs[\"draw\"] = False\n txt = text(txt, 0, 0, **kwargs)\n return txt.metrics[0]",
"title": ""
}
] | [
{
"docid": "1a5cd4da9031018ff6c5e964e1cc0038",
"score": "0.8688428",
"text": "def TextCalcWidth(Text) -> int:\n pass",
"title": ""
},
{
"docid": "35e93582b8c7ca034278968e8c51c1e6",
"score": "0.8660079",
"text": "def _get_text_width(self, text: str) -> int:\n return self._bu... |